From 8a9ff04b4d369c227645c5b361ec1f0b65c94e27 Mon Sep 17 00:00:00 2001 From: milan Date: Thu, 23 Jul 2026 20:48:01 +0000 Subject: [PATCH 001/225] fix(rag): forward retrieval_filter from retrieval_config to Bedrock KB search Co-Authored-By: Devin AI <158243242+devin-ai-integration[bot]@users.noreply.github.com> --- litellm/rag/main.py | 2 + litellm/types/rag.py | 1 + tests/test_litellm/rag/test_main.py | 77 ++++++++++++++++++++++++++++- 3 files changed, 79 insertions(+), 1 deletion(-) diff --git a/litellm/rag/main.py b/litellm/rag/main.py index 29891ccfd24..a1a5bbd38e9 100644 --- a/litellm/rag/main.py +++ b/litellm/rag/main.py @@ -233,10 +233,12 @@ async def _execute_query_pipeline( raise ValueError("No query found in messages for RAG query") # 2. Search vector store + filters = retrieval_config.get("retrieval_filter") or retrieval_config.get("filters") with _suppressed_sub_call_billing(): search_response = await litellm.vector_stores.asearch( vector_store_id=retrieval_config["vector_store_id"], query=query_text, + filters=filters, max_num_results=retrieval_config.get("top_k", 10), custom_llm_provider=retrieval_config.get("custom_llm_provider", "openai"), **kwargs, diff --git a/litellm/types/rag.py b/litellm/types/rag.py index 802bf7a6e9e..216c5a611ca 100644 --- a/litellm/types/rag.py +++ b/litellm/types/rag.py @@ -244,6 +244,7 @@ class RAGRetrievalConfig(TypedDict, total=False): custom_llm_provider: str top_k: int # max results from vector store filters: Optional[Dict[str, Any]] # optional - vector store filters + retrieval_filter: Optional[Dict[str, Any]] # optional - alias forwarded as vector store filters class RAGRerankConfig(TypedDict, total=False): diff --git a/tests/test_litellm/rag/test_main.py b/tests/test_litellm/rag/test_main.py index 584124ba06a..30ff0b76cd8 100644 --- a/tests/test_litellm/rag/test_main.py +++ b/tests/test_litellm/rag/test_main.py @@ -11,7 +11,7 @@ aquery carries the completion response with real usage and cost. """ import asyncio -from unittest.mock import patch +from unittest.mock import AsyncMock, patch import pytest @@ -254,6 +254,81 @@ async def test_aquery_streaming_bills_sub_call_costs_into_final_event(): assert standard_logging_object["response_cost"] >= 0.003 +@pytest.mark.asyncio +@pytest.mark.parametrize("filter_key", ["retrieval_filter", "filters"]) +async def test_aquery_forwards_retrieval_filter_to_vector_store_search(filter_key): + """ + The retrieval_config filter (AWS Bedrock KB metadata filter) must reach the + vector store search call. Before the fix it was dropped, so Bedrock ran an + unfiltered Retrieve and returned documents from the wrong metadata partition. + Both the customer-facing `retrieval_filter` key and the typed `filters` alias + must be forwarded as the search `filters` argument. + """ + from litellm.types.vector_stores import VectorStoreSearchResponse + + retrieval_filter = { + "andAll": [ + {"equals": {"key": "Technology", "value": "Blade"}}, + {"equals": {"key": "Parameter", "value": "Nicotine"}}, + ] + } + + fake_search = AsyncMock( + return_value=VectorStoreSearchResponse( + object="vector_store.search_results.page", + search_query="q", + data=[], + ) + ) + + with patch("litellm.vector_stores.asearch", new=fake_search): + response = await litellm.aquery( + model="gpt-4o-mini", + messages=[{"role": "user", "content": "most frequent causes of low nicotine"}], + retrieval_config={ + "vector_store_id": "CBVFYF3MYF", + "custom_llm_provider": "bedrock", + "top_k": 50, + filter_key: retrieval_filter, + }, + mock_response="answer", + ) + + assert isinstance(response, ModelResponse) + fake_search.assert_awaited_once() + assert fake_search.await_args.kwargs["filters"] == retrieval_filter + assert fake_search.await_args.kwargs["vector_store_id"] == "CBVFYF3MYF" + assert fake_search.await_args.kwargs["max_num_results"] == 50 + + +@pytest.mark.asyncio +async def test_aquery_without_filter_forwards_none(): + """ + When no filter is provided, the search call must receive filters=None rather + than a truthy default that would silently constrain an unfiltered query. + """ + from litellm.types.vector_stores import VectorStoreSearchResponse + + fake_search = AsyncMock( + return_value=VectorStoreSearchResponse( + object="vector_store.search_results.page", + search_query="q", + data=[], + ) + ) + + with patch("litellm.vector_stores.asearch", new=fake_search): + await litellm.aquery( + model="gpt-4o-mini", + messages=[{"role": "user", "content": "hello"}], + retrieval_config={"vector_store_id": "vs_test_123", "custom_llm_provider": "openai"}, + mock_response="hi", + ) + + fake_search.assert_awaited_once() + assert fake_search.await_args.kwargs["filters"] is None + + def test_rag_call_types_are_registered(): """ query/aquery/ingest/aingest are @client-decorated entry points, so their From 232b9e8e78ff5b7d14376bf7d72173cb469a05f6 Mon Sep 17 00:00:00 2001 From: milan Date: Thu, 23 Jul 2026 21:04:42 +0000 Subject: [PATCH 002/225] fix(rag): consume top-level filters kwarg to avoid duplicate keyword in search Co-Authored-By: Devin AI <158243242+devin-ai-integration[bot]@users.noreply.github.com> --- litellm/rag/main.py | 3 ++- tests/test_litellm/rag/test_main.py | 35 +++++++++++++++++++++++++++++ 2 files changed, 37 insertions(+), 1 deletion(-) diff --git a/litellm/rag/main.py b/litellm/rag/main.py index a1a5bbd38e9..628680e8a62 100644 --- a/litellm/rag/main.py +++ b/litellm/rag/main.py @@ -233,7 +233,8 @@ async def _execute_query_pipeline( raise ValueError("No query found in messages for RAG query") # 2. Search vector store - filters = retrieval_config.get("retrieval_filter") or retrieval_config.get("filters") + kwargs_filters = kwargs.pop("filters", None) + filters = retrieval_config.get("retrieval_filter") or retrieval_config.get("filters") or kwargs_filters with _suppressed_sub_call_billing(): search_response = await litellm.vector_stores.asearch( vector_store_id=retrieval_config["vector_store_id"], diff --git a/tests/test_litellm/rag/test_main.py b/tests/test_litellm/rag/test_main.py index 30ff0b76cd8..796f8c38750 100644 --- a/tests/test_litellm/rag/test_main.py +++ b/tests/test_litellm/rag/test_main.py @@ -301,6 +301,41 @@ async def test_aquery_forwards_retrieval_filter_to_vector_store_search(filter_ke assert fake_search.await_args.kwargs["max_num_results"] == 50 +@pytest.mark.asyncio +async def test_aquery_top_level_filters_kwarg_does_not_collide(): + """ + An SDK caller may pass a top-level `filters` kwarg (it used to flow to the + search via **kwargs). Now that the pipeline passes `filters` explicitly, the + top-level kwarg must be consumed rather than forwarded twice, otherwise + asearch raises TypeError for a duplicate keyword before any search runs. + """ + from litellm.types.vector_stores import VectorStoreSearchResponse + + top_level_filter = {"equals": {"key": "tenant", "value": "a"}} + + fake_search = AsyncMock( + return_value=VectorStoreSearchResponse( + object="vector_store.search_results.page", + search_query="q", + data=[], + ) + ) + + with patch("litellm.vector_stores.asearch", new=fake_search): + response = await litellm.aquery( + model="gpt-4o-mini", + messages=[{"role": "user", "content": "hello"}], + retrieval_config={"vector_store_id": "vs_test_123", "custom_llm_provider": "openai"}, + filters=top_level_filter, + mock_response="hi", + ) + + assert isinstance(response, ModelResponse) + fake_search.assert_awaited_once() + assert fake_search.await_args.kwargs["filters"] == top_level_filter + assert "filters" not in fake_search.await_args.kwargs.get("kwargs", {}) + + @pytest.mark.asyncio async def test_aquery_without_filter_forwards_none(): """ From c6a5aab74c9a2c11b2d55ba289659a4ff3942ad5 Mon Sep 17 00:00:00 2001 From: milan Date: Thu, 23 Jul 2026 21:27:25 +0000 Subject: [PATCH 003/225] ci: run orphaned tests/test_litellm/rag suite in misc shard Co-Authored-By: Devin AI <158243242+devin-ai-integration[bot]@users.noreply.github.com> --- .github/workflows/test-unit-misc.yml | 1 + 1 file changed, 1 insertion(+) diff --git a/.github/workflows/test-unit-misc.yml b/.github/workflows/test-unit-misc.yml index 7c3b195f0ad..34d5eef6caa 100644 --- a/.github/workflows/test-unit-misc.yml +++ b/.github/workflows/test-unit-misc.yml @@ -35,6 +35,7 @@ jobs: tests/test_litellm/interactions tests/test_litellm/ocr tests/test_litellm/passthrough + tests/test_litellm/rag tests/test_litellm/sandbox tests/test_litellm/vector_stores tests/test_litellm/videos From 11225b7f13e28dcf5e7f438b71edc0fb803319f4 Mon Sep 17 00:00:00 2001 From: milan Date: Fri, 31 Jul 2026 18:48:31 +0000 Subject: [PATCH 004/225] fix(spend): sum multi-round session duration in logs UI Co-Authored-By: Devin AI <158243242+devin-ai-integration[bot]@users.noreply.github.com> --- .../spend_management_endpoints.py | 8 +++ .../test_spend_management_endpoints.py | 72 +++++++++++++++++++ .../RequestLogsTableColumns.test.tsx | 31 ++++++++ .../view_logs/RequestLogsTableColumns.tsx | 18 +++-- .../src/components/view_logs/columns.tsx | 1 + 5 files changed, 125 insertions(+), 5 deletions(-) diff --git a/litellm/proxy/spend_tracking/spend_management_endpoints.py b/litellm/proxy/spend_tracking/spend_management_endpoints.py index 42788227acc..d49bd6eb912 100644 --- a/litellm/proxy/spend_tracking/spend_management_endpoints.py +++ b/litellm/proxy/spend_tracking/spend_management_endpoints.py @@ -3461,6 +3461,12 @@ async def _build_ui_spend_logs_response( """ SELECT session_id, COALESCE(SUM(spend), 0)::double precision AS session_total_spend, + COALESCE(SUM( + COALESCE( + request_duration_ms, + (EXTRACT(EPOCH FROM ("endTime" - "startTime")) * 1000)::INTEGER + ) + ), 0)::double precision AS session_total_duration_ms, COUNT(*) FILTER ( WHERE call_type IN ('call_mcp_tool', 'list_mcp_tools') )::int AS mcp_tool_call_count, @@ -3478,6 +3484,7 @@ async def _build_ui_spend_logs_response( session_spend_map = { row["session_id"]: { "session_total_spend": float(row.get("session_total_spend") or 0.0), + "session_total_duration_ms": int(row.get("session_total_duration_ms") or 0), "mcp_tool_call_count": int(row.get("mcp_tool_call_count") or 0), "mcp_tool_call_spend": float(row.get("mcp_tool_call_spend") or 0.0), } @@ -3499,6 +3506,7 @@ async def _build_ui_spend_logs_response( session_stats = session_spend_map.get(sid) if sid else None if session_stats: row_dict["session_total_spend"] = session_stats["session_total_spend"] + row_dict["session_total_duration_ms"] = session_stats["session_total_duration_ms"] if session_stats["mcp_tool_call_count"]: row_dict["mcp_tool_call_count"] = session_stats["mcp_tool_call_count"] row_dict["mcp_tool_call_spend"] = session_stats["mcp_tool_call_spend"] diff --git a/tests/test_litellm/proxy/spend_tracking/test_spend_management_endpoints.py b/tests/test_litellm/proxy/spend_tracking/test_spend_management_endpoints.py index aa20c3f6ed4..c93810bdce0 100644 --- a/tests/test_litellm/proxy/spend_tracking/test_spend_management_endpoints.py +++ b/tests/test_litellm/proxy/spend_tracking/test_spend_management_endpoints.py @@ -3516,6 +3516,78 @@ async def test_build_ui_spend_logs_response_sums_multi_round_session_spend(): assert call_args[2] == [api_key] +@pytest.mark.asyncio +async def test_build_ui_spend_logs_response_sums_multi_round_session_duration(): + """ + Regression test: a multi-round session collapses into a single UI row, so that row + must carry the duration of every round summed, not just the representative call's. + Rows written before request_duration_ms existed are NULL, so the aggregate falls back + to endTime - startTime for them. + """ + from litellm.proxy.spend_tracking.spend_management_endpoints import ( + _build_ui_spend_logs_response, + ) + + session_id = "sess-multi-round-duration" + api_key = "hashed-key-xyz" + dict_rows = [ + { + "request_id": "req-1", + "session_id": session_id, + "call_type": "completion", + "api_key": api_key, + "spend": 0.01, + "request_duration_ms": 1200, + }, + { + "request_id": "req-2", + "session_id": session_id, + "call_type": "completion", + "api_key": api_key, + "spend": 0.02, + "request_duration_ms": 4200, + }, + ] + + mock_prisma = MagicMock() + mock_prisma.db.litellm_spendlogs.group_by = AsyncMock( + return_value=[{"session_id": session_id, "_count": {"session_id": 2}}] + ) + mock_prisma.db.query_raw = AsyncMock( + return_value=[ + { + "session_id": session_id, + "session_total_spend": 0.03, + "session_total_duration_ms": 5400.0, + "mcp_tool_call_count": 0, + "mcp_tool_call_spend": 0.0, + } + ] + ) + + result = await _build_ui_spend_logs_response( + prisma_client=mock_prisma, + data=dict_rows, + total_records=2, + page=1, + page_size=50, + total_pages=1, + enrich_session_counts=True, + ) + + rows = result["data"] + assert [row["session_total_duration_ms"] for row in rows] == [5400, 5400] + assert all(isinstance(row["session_total_duration_ms"], int) for row in rows) + assert [row["request_duration_ms"] for row in rows] == [1200, 4200] + + _, call_args, _ = mock_prisma.db.query_raw.mock_calls[0] + sql = " ".join(call_args[0].split()) + assert ( + 'SUM( COALESCE( request_duration_ms, (EXTRACT(EPOCH FROM ("endTime" - "startTime")) * 1000)::INTEGER ) )' + in sql + ) + + # --------------------------------------------------------------------------- # Tests for /spend/logs team-member permission # --------------------------------------------------------------------------- diff --git a/ui/litellm-dashboard/src/components/view_logs/RequestLogsTableColumns.test.tsx b/ui/litellm-dashboard/src/components/view_logs/RequestLogsTableColumns.test.tsx index 2fdc8455ca1..f099edd249a 100644 --- a/ui/litellm-dashboard/src/components/view_logs/RequestLogsTableColumns.test.tsx +++ b/ui/litellm-dashboard/src/components/view_logs/RequestLogsTableColumns.test.tsx @@ -75,6 +75,37 @@ describe("Cost column", () => { }); }); +describe("Duration column", () => { + it("shows the summed session duration, not the representative call's duration, for a multi-round session", () => { + renderRows([ + logEntry({ + request_id: "req-session-duration", + request_duration_ms: 1200, + session_id: "sess-1", + session_total_count: 3, + session_total_duration_ms: 5400, + }), + ]); + + expect(screen.getByText("5.40")).toBeInTheDocument(); + expect(screen.queryByText("1.20")).not.toBeInTheDocument(); + }); + + it("shows the call's own duration for a single-call session", () => { + renderRows([ + logEntry({ + request_id: "req-single-duration", + request_duration_ms: 1200, + session_id: "sess-2", + session_total_count: 1, + session_total_duration_ms: 1200, + }), + ]); + + expect(screen.getByText("1.20")).toBeInTheDocument(); + }); +}); + describe("row action cells", () => { it("reports the key hash through the injected dependency rather than a row field", async () => { const user = userEvent.setup(); diff --git a/ui/litellm-dashboard/src/components/view_logs/RequestLogsTableColumns.tsx b/ui/litellm-dashboard/src/components/view_logs/RequestLogsTableColumns.tsx index 4e5a83ac7dd..ffdd16e26a9 100644 --- a/ui/litellm-dashboard/src/components/view_logs/RequestLogsTableColumns.tsx +++ b/ui/litellm-dashboard/src/components/view_logs/RequestLogsTableColumns.tsx @@ -160,13 +160,21 @@ export const getRequestLogsTableColumns = ({ enableSorting: true, meta: { numeric: true }, cell: ({ row }) => { - const ms = row.original.request_duration_ms; + const log = row.original; + const isMultiCallSession = (log.session_total_count || 1) > 1; + const ms = + isMultiCallSession && log.session_total_duration_ms != null + ? log.session_total_duration_ms + : log.request_duration_ms; if (ms == null) return -; return ( - {(ms / 1000).toFixed(2)}} - /> +
+ {(ms / 1000).toFixed(2)}} + /> + {isMultiCallSession && session total} +
); }, }, diff --git a/ui/litellm-dashboard/src/components/view_logs/columns.tsx b/ui/litellm-dashboard/src/components/view_logs/columns.tsx index d4f784bf165..5b89c6f282c 100644 --- a/ui/litellm-dashboard/src/components/view_logs/columns.tsx +++ b/ui/litellm-dashboard/src/components/view_logs/columns.tsx @@ -42,6 +42,7 @@ export type LogEntry = { request_duration_ms?: number; session_total_count?: number; session_total_spend?: number; + session_total_duration_ms?: number; mcp_tool_call_count?: number; mcp_tool_call_spend?: number; session_llm_count?: number; From c4ab243fa63e76423d44ce3efa91c641c170a6e3 Mon Sep 17 00:00:00 2001 From: milan Date: Fri, 31 Jul 2026 19:25:57 +0000 Subject: [PATCH 005/225] fix(ui): only label logs cost and duration a session total when the aggregate exists Co-Authored-By: Devin AI <158243242+devin-ai-integration[bot]@users.noreply.github.com> --- .../RequestLogsTableColumns.test.tsx | 29 +++++++++++++++++++ .../view_logs/RequestLogsTableColumns.tsx | 13 ++++----- 2 files changed, 35 insertions(+), 7 deletions(-) diff --git a/ui/litellm-dashboard/src/components/view_logs/RequestLogsTableColumns.test.tsx b/ui/litellm-dashboard/src/components/view_logs/RequestLogsTableColumns.test.tsx index f099edd249a..0b54f78736d 100644 --- a/ui/litellm-dashboard/src/components/view_logs/RequestLogsTableColumns.test.tsx +++ b/ui/litellm-dashboard/src/components/view_logs/RequestLogsTableColumns.test.tsx @@ -73,6 +73,20 @@ describe("Cost column", () => { expect(screen.queryByText("$0.010000")).not.toBeInTheDocument(); expect(screen.getByText("session total")).toBeInTheDocument(); }); + + it("does not label the per-call spend a session total when the aggregate is unavailable", () => { + renderRows([ + logEntry({ + request_id: "req-session-no-aggregate", + spend: 0.01, + session_id: "sess-1", + session_total_count: 3, + }), + ]); + + expect(screen.getByText("$0.010000")).toBeInTheDocument(); + expect(screen.queryByText("session total")).not.toBeInTheDocument(); + }); }); describe("Duration column", () => { @@ -89,6 +103,21 @@ describe("Duration column", () => { expect(screen.getByText("5.40")).toBeInTheDocument(); expect(screen.queryByText("1.20")).not.toBeInTheDocument(); + expect(screen.getByText("session total")).toBeInTheDocument(); + }); + + it("does not label the per-call duration a session total when the aggregate is unavailable", () => { + renderRows([ + logEntry({ + request_id: "req-no-aggregate", + request_duration_ms: 1200, + session_id: "sess-3", + session_total_count: 3, + }), + ]); + + expect(screen.getByText("1.20")).toBeInTheDocument(); + expect(screen.queryByText("session total")).not.toBeInTheDocument(); }); it("shows the call's own duration for a single-call session", () => { diff --git a/ui/litellm-dashboard/src/components/view_logs/RequestLogsTableColumns.tsx b/ui/litellm-dashboard/src/components/view_logs/RequestLogsTableColumns.tsx index ffdd16e26a9..1bd7c5c8dba 100644 --- a/ui/litellm-dashboard/src/components/view_logs/RequestLogsTableColumns.tsx +++ b/ui/litellm-dashboard/src/components/view_logs/RequestLogsTableColumns.tsx @@ -133,7 +133,8 @@ export const getRequestLogsTableColumns = ({ const mcpCount = log.mcp_tool_call_count || 0; const mcpSpend = log.mcp_tool_call_spend || 0; const isMultiCallSession = (log.session_total_count || 1) > 1; - const spend = isMultiCallSession && log.session_total_spend != null ? log.session_total_spend : log.spend; + const sessionTotalSpend = isMultiCallSession ? log.session_total_spend : undefined; + const spend = sessionTotalSpend ?? log.spend; const money = ( @@ -143,7 +144,7 @@ export const getRequestLogsTableColumns = ({ return (
{spend ? : money} - {isMultiCallSession && session total} + {sessionTotalSpend != null && session total} {mcpCount > 0 && mcpSpend > 0 && ( incl. {getSpendString(mcpSpend)} from {mcpCount} MCP @@ -162,10 +163,8 @@ export const getRequestLogsTableColumns = ({ cell: ({ row }) => { const log = row.original; const isMultiCallSession = (log.session_total_count || 1) > 1; - const ms = - isMultiCallSession && log.session_total_duration_ms != null - ? log.session_total_duration_ms - : log.request_duration_ms; + const sessionTotalMs = isMultiCallSession ? log.session_total_duration_ms : undefined; + const ms = sessionTotalMs ?? log.request_duration_ms; if (ms == null) return -; return (
@@ -173,7 +172,7 @@ export const getRequestLogsTableColumns = ({ content={`${ms}ms`} trigger={{(ms / 1000).toFixed(2)}} /> - {isMultiCallSession && session total} + {sessionTotalMs != null && session total}
); }, From d2c574608ad430bcc5f2f9bd00fd603a0c3aacbd Mon Sep 17 00:00:00 2001 From: milan Date: Tue, 25 Aug 2026 14:56:22 +0000 Subject: [PATCH 006/225] test(rag): validate retrieval filters at HTTP boundary Co-Authored-By: Devin AI <158243242+devin-ai-integration[bot]@users.noreply.github.com> --- litellm/types/rag.py | 13 +- tests/test_litellm/rag/test_main.py | 176 ++++++++++++---------------- 2 files changed, 82 insertions(+), 107 deletions(-) diff --git a/litellm/types/rag.py b/litellm/types/rag.py index bed3fdfa7e9..629979afde9 100644 --- a/litellm/types/rag.py +++ b/litellm/types/rag.py @@ -2,10 +2,11 @@ Type definitions for RAG (Retrieval Augmented Generation) Ingest API. """ +from collections.abc import Mapping from typing import Any, Literal from pydantic import BaseModel, ConfigDict -from typing_extensions import TypedDict +from typing_extensions import ReadOnly, TypedDict from litellm.types.utils import ModelResponse @@ -237,11 +238,11 @@ class RAGIngestRequest(BaseModel): class RAGRetrievalConfig(TypedDict, total=False): """Configuration for vector store retrieval.""" - vector_store_id: str - custom_llm_provider: str - top_k: int # max results from vector store - filters: dict[str, Any] | None # optional - vector store filters - retrieval_filter: dict[str, Any] | None # optional - alias forwarded as vector store filters + vector_store_id: ReadOnly[str] + custom_llm_provider: ReadOnly[str] + top_k: ReadOnly[int] + filters: ReadOnly[Mapping[str, object] | None] + retrieval_filter: ReadOnly[Mapping[str, object] | None] class RAGRerankConfig(TypedDict, total=False): diff --git a/tests/test_litellm/rag/test_main.py b/tests/test_litellm/rag/test_main.py index 796f8c38750..ab0a9c0f002 100644 --- a/tests/test_litellm/rag/test_main.py +++ b/tests/test_litellm/rag/test_main.py @@ -11,9 +11,13 @@ aquery carries the completion response with real usage and cost. """ import asyncio -from unittest.mock import AsyncMock, patch +import json +from typing import Final +from unittest.mock import patch +import httpx import pytest +import respx import litellm from litellm._internal_context import is_internal_call @@ -255,113 +259,83 @@ async def test_aquery_streaming_bills_sub_call_costs_into_final_event(): @pytest.mark.asyncio -@pytest.mark.parametrize("filter_key", ["retrieval_filter", "filters"]) -async def test_aquery_forwards_retrieval_filter_to_vector_store_search(filter_key): - """ - The retrieval_config filter (AWS Bedrock KB metadata filter) must reach the - vector store search call. Before the fix it was dropped, so Bedrock ran an - unfiltered Retrieve and returned documents from the wrong metadata partition. - Both the customer-facing `retrieval_filter` key and the typed `filters` alias - must be forwarded as the search `filters` argument. - """ - from litellm.types.vector_stores import VectorStoreSearchResponse +@pytest.mark.parametrize( + ("retrieval_config_json", "top_level_filter_json", "expected_filter_json"), + ( + ( + '{"vector_store_id":"vs_test_123","custom_llm_provider":"openai","top_k":50,' + '"retrieval_filter":{"equals":{"key":"tenant","value":"retrieval"}}}', + None, + '{"equals":{"key":"tenant","value":"retrieval"}}', + ), + ( + '{"vector_store_id":"vs_test_123","custom_llm_provider":"openai","top_k":50,' + '"filters":{"equals":{"key":"tenant","value":"alias"}}}', + None, + '{"equals":{"key":"tenant","value":"alias"}}', + ), + ( + '{"vector_store_id":"vs_test_123","custom_llm_provider":"openai","top_k":50}', + '{"equals":{"key":"tenant","value":"top-level"}}', + '{"equals":{"key":"tenant","value":"top-level"}}', + ), + ( + '{"vector_store_id":"vs_test_123","custom_llm_provider":"openai","top_k":50,' + '"retrieval_filter":{"equals":{"key":"tenant","value":"retrieval"}},' + '"filters":{"equals":{"key":"tenant","value":"alias"}}}', + '{"equals":{"key":"tenant","value":"top-level"}}', + '{"equals":{"key":"tenant","value":"retrieval"}}', + ), + ( + '{"vector_store_id":"vs_test_123","custom_llm_provider":"openai","top_k":50}', + None, + None, + ), + ), +) +async def test_aquery_forwards_filters_to_vector_store_search( + retrieval_config_json: str, + top_level_filter_json: str | None, + expected_filter_json: str | None, + monkeypatch, +): + monkeypatch.setattr(litellm, "disable_aiohttp_transport", True) + retrieval_config: Final = json.loads(retrieval_config_json) + top_level_filter: Final = json.loads(top_level_filter_json) if top_level_filter_json is not None else None + expected_filter: Final = json.loads(expected_filter_json) if expected_filter_json is not None else None - retrieval_filter = { - "andAll": [ - {"equals": {"key": "Technology", "value": "Blade"}}, - {"equals": {"key": "Parameter", "value": "Nicotine"}}, - ] - } - - fake_search = AsyncMock( - return_value=VectorStoreSearchResponse( - object="vector_store.search_results.page", - search_query="q", - data=[], + with respx.mock(assert_all_called=True) as respx_mock: + search_route: Final = respx_mock.post("https://example.com/v1/vector_stores/vs_test_123/search").mock( + return_value=httpx.Response( + 200, + content='{"object":"vector_store.search_results.page","search_query":"q","data":[]}', + ) ) - ) - - with patch("litellm.vector_stores.asearch", new=fake_search): - response = await litellm.aquery( - model="gpt-4o-mini", - messages=[{"role": "user", "content": "most frequent causes of low nicotine"}], - retrieval_config={ - "vector_store_id": "CBVFYF3MYF", - "custom_llm_provider": "bedrock", - "top_k": 50, - filter_key: retrieval_filter, - }, - mock_response="answer", + respx_mock.post("https://example.com/v1/chat/completions").mock( + return_value=httpx.Response( + 200, + content=( + '{"id":"chatcmpl-test","object":"chat.completion","created":1,"model":"gpt-4o-mini",' + '"choices":[{"index":0,"message":{"role":"assistant","content":"answer"},"finish_reason":"stop"}],' + '"usage":{"prompt_tokens":1,"completion_tokens":1,"total_tokens":2}}' + ), + ) ) - - assert isinstance(response, ModelResponse) - fake_search.assert_awaited_once() - assert fake_search.await_args.kwargs["filters"] == retrieval_filter - assert fake_search.await_args.kwargs["vector_store_id"] == "CBVFYF3MYF" - assert fake_search.await_args.kwargs["max_num_results"] == 50 - - -@pytest.mark.asyncio -async def test_aquery_top_level_filters_kwarg_does_not_collide(): - """ - An SDK caller may pass a top-level `filters` kwarg (it used to flow to the - search via **kwargs). Now that the pipeline passes `filters` explicitly, the - top-level kwarg must be consumed rather than forwarded twice, otherwise - asearch raises TypeError for a duplicate keyword before any search runs. - """ - from litellm.types.vector_stores import VectorStoreSearchResponse - - top_level_filter = {"equals": {"key": "tenant", "value": "a"}} - - fake_search = AsyncMock( - return_value=VectorStoreSearchResponse( - object="vector_store.search_results.page", - search_query="q", - data=[], - ) - ) - - with patch("litellm.vector_stores.asearch", new=fake_search): - response = await litellm.aquery( - model="gpt-4o-mini", - messages=[{"role": "user", "content": "hello"}], - retrieval_config={"vector_store_id": "vs_test_123", "custom_llm_provider": "openai"}, + response: Final = await litellm.aquery( + model="openai/gpt-4o-mini", + messages=json.loads('[{"role":"user","content":"most frequent causes of low nicotine"}]'), + retrieval_config=retrieval_config, filters=top_level_filter, - mock_response="hi", + api_key="sk-test", + api_base="https://example.com/v1", ) + request_body: Final = json.loads(search_route.calls.last.request.content) assert isinstance(response, ModelResponse) - fake_search.assert_awaited_once() - assert fake_search.await_args.kwargs["filters"] == top_level_filter - assert "filters" not in fake_search.await_args.kwargs.get("kwargs", {}) - - -@pytest.mark.asyncio -async def test_aquery_without_filter_forwards_none(): - """ - When no filter is provided, the search call must receive filters=None rather - than a truthy default that would silently constrain an unfiltered query. - """ - from litellm.types.vector_stores import VectorStoreSearchResponse - - fake_search = AsyncMock( - return_value=VectorStoreSearchResponse( - object="vector_store.search_results.page", - search_query="q", - data=[], - ) - ) - - with patch("litellm.vector_stores.asearch", new=fake_search): - await litellm.aquery( - model="gpt-4o-mini", - messages=[{"role": "user", "content": "hello"}], - retrieval_config={"vector_store_id": "vs_test_123", "custom_llm_provider": "openai"}, - mock_response="hi", - ) - - fake_search.assert_awaited_once() - assert fake_search.await_args.kwargs["filters"] is None + assert response.choices[0].message.content == "answer" + assert request_body["query"] == "most frequent causes of low nicotine" + assert request_body["filters"] == expected_filter + assert request_body["max_num_results"] == 50 def test_rag_call_types_are_registered(): From dd504fd7efe0ba0809243d5ba2440516d9cd5141 Mon Sep 17 00:00:00 2001 From: Yucheng Zhu Date: Tue, 25 Aug 2026 01:28:38 -0700 Subject: [PATCH 007/225] feat(guardrails): add Microsoft Agent 365 MCP tool-call guardrail --- .../guardrail_hooks/agent_365/__init__.py | 61 ++ .../guardrail_hooks/agent_365/agent_365.py | 546 +++++++++++++ litellm/types/guardrails.py | 7 +- .../guardrails/guardrail_hooks/agent_365.py | 66 ++ .../guardrail_hooks/test_agent_365.py | 717 ++++++++++++++++++ .../_components/guardrail_garden_configs.ts | 7 + .../_components/guardrail_garden_data.test.ts | 1 + .../_components/guardrail_garden_data.ts | 10 + .../_components/guardrail_info_helpers.tsx | 1 + 9 files changed, 1415 insertions(+), 1 deletion(-) create mode 100644 litellm/proxy/guardrails/guardrail_hooks/agent_365/__init__.py create mode 100644 litellm/proxy/guardrails/guardrail_hooks/agent_365/agent_365.py create mode 100644 litellm/types/proxy/guardrails/guardrail_hooks/agent_365.py create mode 100644 tests/test_litellm/proxy/guardrails/guardrail_hooks/test_agent_365.py diff --git a/litellm/proxy/guardrails/guardrail_hooks/agent_365/__init__.py b/litellm/proxy/guardrails/guardrail_hooks/agent_365/__init__.py new file mode 100644 index 00000000000..839bf5ba888 --- /dev/null +++ b/litellm/proxy/guardrails/guardrail_hooks/agent_365/__init__.py @@ -0,0 +1,61 @@ +from typing import TYPE_CHECKING, Final + +from litellm.types.guardrails import SupportedGuardrailIntegrations +from litellm.types.proxy.guardrails.guardrail_hooks.agent_365 import ( + AGENT_365_PROD_API_BASE, + AGENT_365_PROD_RESOURCE_APP_ID, +) + +from .agent_365 import Agent365Guardrail + +if TYPE_CHECKING: + from litellm.types.guardrails import Guardrail, LitellmParams + + +def initialize_guardrail(litellm_params: "LitellmParams", guardrail: "Guardrail") -> Agent365Guardrail: + import litellm + from litellm.secret_managers.main import get_secret_str + + tenant_id: Final = litellm_params.tenant_id or get_secret_str("AGENT365_TENANT_ID") + client_id: Final = litellm_params.client_id or get_secret_str("AGENT365_CLIENT_ID") + client_secret: Final = ( + litellm_params.client_secret or litellm_params.api_key or get_secret_str("AGENT365_CLIENT_SECRET") + ) + api_base: Final = litellm_params.api_base or get_secret_str("AGENT365_API_BASE") + resource_app_id: Final = litellm_params.resource_app_id or get_secret_str("AGENT365_RESOURCE_APP_ID") + + if not tenant_id: + raise ValueError("Microsoft Agent 365: tenant_id is required") + if not client_id: + raise ValueError("Microsoft Agent 365: client_id is required") + if not client_secret: + raise ValueError("Microsoft Agent 365: client_secret (or api_key) is required") + + guardrail_name: Final = guardrail.get("guardrail_name") + if not guardrail_name: + raise ValueError("Microsoft Agent 365: guardrail_name is required") + + agent_365_guardrail: Final = Agent365Guardrail( + guardrail_name=guardrail_name, + tenant_id=tenant_id, + client_id=client_id, + client_secret=client_secret, + api_base=api_base or AGENT_365_PROD_API_BASE, + resource_app_id=resource_app_id or AGENT_365_PROD_RESOURCE_APP_ID, + agent_id=litellm_params.agent_id, + request_timeout=litellm_params.timeout if litellm_params.timeout is not None else 10.0, + unreachable_fallback=litellm_params.unreachable_fallback, + event_hook=litellm_params.mode, + default_on=litellm_params.default_on, + ) + litellm.logging_callback_manager.add_litellm_callback(agent_365_guardrail) + return agent_365_guardrail + + +guardrail_initializer_registry: Final = { # mutable-ok: registry auto-discovery requires a dict instance + SupportedGuardrailIntegrations.AGENT_365.value: initialize_guardrail, +} + +guardrail_class_registry: Final = { # mutable-ok: registry auto-discovery requires a dict instance + SupportedGuardrailIntegrations.AGENT_365.value: Agent365Guardrail, +} diff --git a/litellm/proxy/guardrails/guardrail_hooks/agent_365/agent_365.py b/litellm/proxy/guardrails/guardrail_hooks/agent_365/agent_365.py new file mode 100644 index 00000000000..5d17a87e640 --- /dev/null +++ b/litellm/proxy/guardrails/guardrail_hooks/agent_365/agent_365.py @@ -0,0 +1,546 @@ +"""Microsoft Agent 365 governance guardrail for MCP tool calls. + +Before the gateway executes an MCP tool, the pending call is sent to the +Agent 365 tool-evaluation endpoint, where Microsoft Defender scores it and +Agent 365 records it for observability. The returned allow/block verdict is +enforced here. Authentication is the Entra On-Behalf-Of flow: the caller's +incoming bearer token (audienced to this gateway's app registration) is +exchanged for a delegated Agent 365 token, so Defender evaluates and audits +as the signed-in user. +""" + +import hashlib +import threading +import time +import uuid +from collections import OrderedDict +from collections.abc import Mapping +from typing import TYPE_CHECKING, ClassVar, Final, Literal, NoReturn + +import httpx +from fastapi import HTTPException +from typing_extensions import ReadOnly, TypedDict + +from litellm._logging import verbose_proxy_logger +from litellm.exceptions import Timeout as LitellmTimeout +from litellm.integrations.custom_guardrail import ( + CustomGuardrail, + log_guardrail_information, +) +from litellm.llms.custom_httpx.http_handler import ( + AsyncHTTPHandler, + get_async_httpx_client, + httpxSpecialProvider, +) +from litellm.types.guardrails import GuardrailEventHooks +from litellm.types.proxy.guardrails.guardrail_hooks.agent_365 import ( + AGENT_365_PROD_API_BASE, + AGENT_365_PROD_RESOURCE_APP_ID, + AGENT_365_SCOPE_NAME, + Agent365GuardrailConfigModel, +) + +if TYPE_CHECKING: + from litellm.caching.caching import DualCache + from litellm.proxy._types import UserAPIKeyAuth + from litellm.types.proxy.guardrails.guardrail_hooks.base import GuardrailConfigModel + from litellm.types.utils import GuardrailStatus + +TOKEN_ENDPOINT_TEMPLATE: Final = "https://login.microsoftonline.com/{tenant_id}/oauth2/v2.0/token" +EVALUATE_PATH: Final = "/agents/tool-evaluation/evaluate" +MCP_SESSION_ID_HEADER: Final = "mcp-session-id" +_MCP_CALL_TYPES: Final[tuple[str, ...]] = ("mcp_call", "call_mcp_tool") +_OBO_CACHE_MAX_ENTRIES: Final = 1000 +_DEFAULT_TOKEN_TTL_SECONDS: Final = 3599.0 +_TOKEN_EXPIRY_SLACK_SECONDS: Final = 60.0 + + +def _parse_expires_in(raw: object) -> float: + if not isinstance(raw, (int, float, str)): + return _DEFAULT_TOKEN_TTL_SECONDS + try: + return float(raw) + except ValueError: + return _DEFAULT_TOKEN_TTL_SECONDS + + +class _DefenderResult(TypedDict, total=False): + status: ReadOnly[str] + verdict: ReadOnly[str | None] + message: ReadOnly[str | None] + + +class _EvaluateResponse(TypedDict, total=False): + allowed: ReadOnly[bool] + defender: ReadOnly[_DefenderResult] + correlationId: ReadOnly[str] + + +class _ToolReference(TypedDict): + name: ReadOnly[str] + + +class _UnavailableDetail(TypedDict): + error: ReadOnly[str] + message: ReadOnly[str] + tool: ReadOnly[str] + + +class _BlockedDetail(TypedDict): + error: ReadOnly[str] + message: ReadOnly[str] + tool: ReadOnly[str] + correlation_id: ReadOnly[str | None] + + +class Agent365TokenExchangeError(Exception): + def __init__(self, status_code: int, error_code: str, description: str) -> None: + super().__init__(f"{error_code}: {description}") + self.status_code = status_code + self.error_code = error_code + self.description = description + + +class Agent365MalformedResponseError(Exception): + pass + + +class Agent365Guardrail(CustomGuardrail): + """Pre-MCP-call guardrail enforcing Microsoft Agent 365 tool-evaluation verdicts.""" + + records_own_guardrail_information: ClassVar[bool] = True + + def __init__( + self, + guardrail_name: str, + tenant_id: str, + client_id: str, + client_secret: str, + api_base: str = AGENT_365_PROD_API_BASE, + resource_app_id: str = AGENT_365_PROD_RESOURCE_APP_ID, + agent_id: str | None = None, + request_timeout: float = 10.0, + unreachable_fallback: Literal["fail_closed", "fail_open"] = "fail_closed", + async_handler: AsyncHTTPHandler | None = None, + **kwargs, # noqa: ANN003 # kwargs-ok: forwarded verbatim to CustomGuardrail (event_hook, default_on) + ) -> None: + super().__init__( + guardrail_name=guardrail_name, + supported_event_hooks=self.get_supported_event_hooks(), + **kwargs, + ) + self.guardrail_provider = "agent_365" + self.tenant_id = tenant_id + self.client_id = client_id + self.client_secret = client_secret + self.api_base = api_base.rstrip("/") + self.resource_app_id = resource_app_id + self.agent_id = agent_id + self.request_timeout = request_timeout + self.unreachable_fallback: Literal["fail_closed", "fail_open"] = ( + "fail_open" if unreachable_fallback == "fail_open" else "fail_closed" + ) + self.async_handler = async_handler or get_async_httpx_client( + llm_provider=httpxSpecialProvider.GuardrailCallback + ) + self._obo_token_cache: OrderedDict[str, tuple[str, float]] = OrderedDict() # mutable-ok: lock-guarded LRU + self._obo_cache_lock = threading.Lock() + verbose_proxy_logger.info("Initialized Microsoft Agent 365 guardrail: %s", guardrail_name) + + @staticmethod + def get_config_model() -> "type[GuardrailConfigModel] | None": + return Agent365GuardrailConfigModel + + @classmethod + def get_supported_event_hooks(cls) -> list[GuardrailEventHooks]: # mutable-ok: CustomGuardrail contract + return [GuardrailEventHooks.pre_mcp_call] # mutable-ok: CustomGuardrail contract expects a list + + @log_guardrail_information + async def async_pre_call_hook( + self, + user_api_key_dict: "UserAPIKeyAuth", + cache: "DualCache", + data: dict, # mutable-ok: hook contract; guardrail logging appends into the request metadata in place + call_type: str, + ) -> Exception | str | dict | None: # mutable-ok: CustomGuardrail.async_pre_call_hook contract + if call_type not in _MCP_CALL_TYPES: + return data + if "mcp_tool_name" not in data: + return data + if self.should_run_guardrail(data=data, event_type=GuardrailEventHooks.pre_mcp_call) is not True: + return data + + tool_name: Final = str(data.get("mcp_tool_name") or "") + assertion: Final = data.get("incoming_bearer_token") + if not isinstance(assertion, str) or assertion.count(".") != 2: + self._handle_caller_fault( + data=data, + tool_name=tool_name, + status_code=401, + reason=( + "the caller did not present an Entra bearer token; the Agent 365 guardrail " + "authorizes tool calls On-Behalf-Of the signed-in user" + ), + ) + + try: + obo_token: Final = await self._get_obo_token(assertion) + except Agent365TokenExchangeError as exc: + self._handle_caller_fault( + data=data, + tool_name=tool_name, + status_code=401, + reason=f"the Entra On-Behalf-Of token exchange was rejected ({exc.error_code})", + ) + except (httpx.HTTPError, LitellmTimeout, TimeoutError) as exc: + return self._handle_unavailable( + data=data, + tool_name=tool_name, + reason=f"the Entra token endpoint could not be reached ({type(exc).__name__})", + ) + except Agent365MalformedResponseError as exc: + return self._handle_unavailable( + data=data, + tool_name=tool_name, + reason=str(exc), + ) + + start: Final = time.perf_counter() + try: + response: Final = await self._post_allowing_error_status( + url=f"{self.api_base}{EVALUATE_PATH}", + json=self._build_evaluate_payload(data=data, user_api_key_dict=user_api_key_dict), + headers={"Authorization": f"Bearer {obo_token}"}, # mutable-ok: httpx header dict + ) + except (httpx.HTTPError, LitellmTimeout, TimeoutError) as exc: + return self._handle_unavailable( + data=data, + tool_name=tool_name, + reason=f"the Agent 365 endpoint could not be reached ({type(exc).__name__})", + ) + latency_ms: Final = (time.perf_counter() - start) * 1000.0 + fallback: Final = self._handle_evaluate_error( + data=data, tool_name=tool_name, assertion=assertion, response=response, latency_ms=latency_ms + ) + if fallback is not None: + return fallback + return self._enforce_verdict(data=data, tool_name=tool_name, response=response, latency_ms=latency_ms) + + def _handle_evaluate_error( + self, + data: dict, # mutable-ok: guardrail logging appends into the request metadata in place + tool_name: str, + assertion: str, + response: httpx.Response, + latency_ms: float, + ) -> dict | None: # mutable-ok: returns the request data dict per hook contract on fail_open + if response.status_code in (408, 429): + self._record_verdict( + data=data, + verdict="Throttled", + guardrail_status="guardrail_failed_to_respond", + defender_status=None, + correlation_id=None, + latency_ms=latency_ms, + reason=f"the Agent 365 endpoint returned HTTP {response.status_code}", + ) + throttled_detail: Final[_UnavailableDetail] = { + "error": "Agent 365 guardrail could not authorize the tool call", + "message": f"Tool call '{tool_name}' was blocked because the Agent 365 endpoint returned " + f"HTTP {response.status_code}; throttled evaluations block regardless of unreachable_fallback.", + "tool": tool_name, + } + raise HTTPException(status_code=503, detail=throttled_detail) + if 400 <= response.status_code < 500: + if response.status_code == 401: + self._evict_obo_token(assertion) + self._record_verdict( + data=data, + verdict="Rejected", + guardrail_status="guardrail_intervened", + defender_status=None, + correlation_id=None, + latency_ms=latency_ms, + reason=f"HTTP {response.status_code}: {response.text[:512]}", + ) + rejected_detail: Final[_UnavailableDetail] = { + "error": "Agent 365 rejected the tool evaluation request", + "message": response.text[:512] + if response.status_code == 400 + else f"the Agent 365 evaluation request failed with HTTP {response.status_code}", + "tool": tool_name, + } + raise HTTPException(status_code=400, detail=rejected_detail) + if response.status_code != 200: + return self._handle_unavailable( + data=data, + tool_name=tool_name, + reason=f"the Agent 365 endpoint returned HTTP {response.status_code}", + ) + return None + + def _enforce_verdict( + self, + data: dict, # mutable-ok: guardrail logging appends into the request metadata in place + tool_name: str, + response: httpx.Response, + latency_ms: float, + ) -> dict: # mutable-ok: returns the request data dict per hook contract + try: + parsed_verdict: Final = response.json() + except ValueError: + return self._handle_unavailable( + data=data, + tool_name=tool_name, + reason="the Agent 365 endpoint returned a non-JSON body", + ) + if not isinstance(parsed_verdict, dict): + return self._handle_unavailable( + data=data, + tool_name=tool_name, + reason="the Agent 365 endpoint returned a non-object JSON body", + ) + verdict: Final[_EvaluateResponse] = parsed_verdict + raw_defender: Final = verdict.get("defender") + defender: Final = raw_defender if isinstance(raw_defender, dict) else _DefenderResult() + raw_correlation_id: Final = verdict.get("correlationId") + correlation_id: Final = raw_correlation_id if isinstance(raw_correlation_id, str) else None + allowed: Final = verdict.get("allowed") is True + self._record_verdict( + data=data, + verdict="Allow" if allowed else "Block", + guardrail_status="success" if allowed else "guardrail_intervened", + defender_status=defender.get("status"), + correlation_id=correlation_id, + latency_ms=latency_ms, + ) + if not allowed: + blocked_detail: Final[_BlockedDetail] = { + "error": "Blocked by Microsoft Defender", + "message": ( + defender.get("message") + or f"Invocation of '{tool_name}' is blocked by Microsoft Threat Detection policies " + "configured by your administrator." + ), + "tool": tool_name, + "correlation_id": correlation_id, + } + raise HTTPException(status_code=400, detail=blocked_detail) + return data + + def _build_evaluate_payload( + self, + data: Mapping[str, object], + user_api_key_dict: "UserAPIKeyAuth", + ) -> dict[str, object]: # mutable-ok: JSON body for AsyncHTTPHandler.post, which requires dict + tool_name: Final = str(data.get("mcp_tool_name") or "") + arguments: Final = data.get("mcp_arguments") + server_name: Final = str(data.get("mcp_server_name") or "litellm") + agent_id: Final = self.agent_id or getattr(user_api_key_dict, "key_alias", None) + tool_reference: Final[_ToolReference] = {"name": tool_name} + payload: Final[dict[str, object]] = { # mutable-ok: JSON body with optional fields added below + "tool": tool_reference, + "serverName": server_name, + "conversationId": self._resolve_conversation_id(data), + } + if isinstance(arguments, dict): + payload["arguments"] = arguments + if agent_id: + payload["agentId"] = str(agent_id) + return payload + + @staticmethod + def _resolve_conversation_id(data: Mapping[str, object]) -> str: + metadata: Final = next( + (m for m in (data.get("metadata"), data.get("litellm_metadata")) if isinstance(m, Mapping)), + None, + ) + headers: Final = metadata.get("headers") if isinstance(metadata, Mapping) else None + if isinstance(headers, Mapping): + session_id: Final = next( + (value for name, value in headers.items() if str(name).lower() == MCP_SESSION_ID_HEADER), + None, + ) + if isinstance(session_id, str) and session_id: + return session_id + logging_obj: Final = data.get("litellm_logging_obj") + call_details: Final = getattr(logging_obj, "model_call_details", None) + if isinstance(call_details, Mapping): + tool_call_metadata: Final = call_details.get("mcp_tool_call_metadata") + session_from_logging: Final = ( + tool_call_metadata.get("mcp_session_id") if isinstance(tool_call_metadata, Mapping) else None + ) + if isinstance(session_from_logging, str) and session_from_logging: + return session_from_logging + call_id: Final = data.get("litellm_call_id") or getattr(logging_obj, "litellm_call_id", None) + if isinstance(call_id, str) and call_id: + return call_id + return str(uuid.uuid4()) + + async def _get_obo_token(self, assertion: str) -> str: + cache_key: Final = hashlib.sha256(assertion.encode("utf-8")).hexdigest() + now: Final = time.time() + with self._obo_cache_lock: + cached: Final = self._obo_token_cache.get(cache_key) + if cached and cached[1] > now + _TOKEN_EXPIRY_SLACK_SECONDS: + self._obo_token_cache.move_to_end(cache_key) + return cached[0] + + response: Final = await self._post_allowing_error_status( + url=TOKEN_ENDPOINT_TEMPLATE.format(tenant_id=self.tenant_id), + data={ # mutable-ok: OAuth form body; AsyncHTTPHandler.post requires dict + "grant_type": "urn:ietf:params:oauth:grant-type:jwt-bearer", + "client_id": self.client_id, + "client_secret": self.client_secret, + "assertion": assertion, + "scope": f"{self.resource_app_id}/{AGENT_365_SCOPE_NAME}", + "requested_token_use": "on_behalf_of", + }, + headers={"Content-Type": "application/x-www-form-urlencoded"}, # mutable-ok: httpx header dict + ) + if response.status_code >= 500: + raise httpx.HTTPStatusError( + f"Entra token endpoint returned {response.status_code}", + request=response.request, + response=response, + ) + try: + parsed_body: Final = response.json() + except ValueError as exc: + raise Agent365MalformedResponseError("the Entra token endpoint returned a non-JSON body") from exc + if not isinstance(parsed_body, dict): + raise Agent365MalformedResponseError("the Entra token endpoint returned a non-object JSON body") + body: Final = parsed_body + if response.status_code >= 400: + raise Agent365TokenExchangeError( + status_code=response.status_code, + error_code=str(body.get("error", "invalid_grant")), + description=str(body.get("error_description", ""))[:512], + ) + if "access_token" not in body: + raise Agent365MalformedResponseError("the Entra token endpoint returned no access_token") + raw_access_token: Final = body.get("access_token") + if not isinstance(raw_access_token, str) or not raw_access_token: + raise Agent365MalformedResponseError("the Entra token endpoint returned a non-string access_token") + access_token: Final = raw_access_token + expires_at: Final = time.time() + _parse_expires_in(body.get("expires_in", 3599)) + with self._obo_cache_lock: + self._obo_token_cache[cache_key] = (access_token, expires_at) + self._obo_token_cache.move_to_end(cache_key) + while len(self._obo_token_cache) > _OBO_CACHE_MAX_ENTRIES: + self._obo_token_cache.popitem(last=False) + return access_token + + async def _post_allowing_error_status( + self, + url: str, + headers: dict[str, str], # mutable-ok: AsyncHTTPHandler.post requires dict + data: dict[str, str] | None = None, # mutable-ok: AsyncHTTPHandler.post requires dict + json: dict[str, object] | None = None, # mutable-ok: AsyncHTTPHandler.post requires dict + ) -> httpx.Response: + try: + return await self.async_handler.post( + url=url, + data=data, + json=json, + headers=headers, + timeout=self.request_timeout, + ) + except httpx.HTTPStatusError as exc: + return exc.response + + def _handle_caller_fault( + self, + data: dict, # mutable-ok: guardrail logging appends into the request metadata in place + tool_name: str, + status_code: int, + reason: str, + ) -> NoReturn: + self._record_verdict( + data=data, + verdict="Rejected", + guardrail_status="guardrail_intervened", + defender_status=None, + correlation_id=None, + latency_ms=None, + reason=reason, + ) + caller_fault_detail: Final[_UnavailableDetail] = { + "error": "Agent 365 guardrail rejected the tool call", + "message": f"Tool call '{tool_name}' was blocked because {reason}.", + "tool": tool_name, + } + raise HTTPException(status_code=status_code, detail=caller_fault_detail) + + def _evict_obo_token(self, assertion: str) -> None: + cache_key: Final = hashlib.sha256(assertion.encode("utf-8")).hexdigest() + with self._obo_cache_lock: + self._obo_token_cache.pop(cache_key, None) + + def _handle_unavailable( + self, + data: dict, # mutable-ok: guardrail logging appends into the request metadata in place + tool_name: str, + reason: str, + ) -> dict: # mutable-ok: returns the request data dict per hook contract + if self.unreachable_fallback == "fail_open": + verbose_proxy_logger.warning( + "Agent 365 guardrail (%s): %s; unreachable_fallback='fail_open', allowing tool call '%s' unscanned", + self.guardrail_name, + reason, + tool_name, + ) + self._record_verdict( + data=data, + verdict="Unscanned", + guardrail_status="guardrail_failed_to_respond", + defender_status=None, + correlation_id=None, + latency_ms=None, + reason=reason, + ) + return data + self._record_verdict( + data=data, + verdict="Unavailable", + guardrail_status="guardrail_failed_to_respond", + defender_status=None, + correlation_id=None, + latency_ms=None, + reason=reason, + ) + unavailable_detail: Final[_UnavailableDetail] = { + "error": "Agent 365 guardrail could not authorize the tool call", + "message": f"Tool call '{tool_name}' was blocked because {reason} and unreachable_fallback is " + "'fail_closed'.", + "tool": tool_name, + } + raise HTTPException(status_code=503, detail=unavailable_detail) + + def _record_verdict( + self, + data: dict[str, object], # mutable-ok: standard guardrail logging appends into the request metadata in place + verdict: str, + guardrail_status: "GuardrailStatus", + defender_status: str | None, + correlation_id: str | None, + latency_ms: float | None, + reason: str | None = None, + ) -> None: + payload: Final[dict[str, object]] = {"verdict": verdict} # mutable-ok: optional fields added below + if defender_status: + payload["defender_status"] = defender_status + if correlation_id: + payload["correlation_id"] = correlation_id + if latency_ms is not None: + payload["latency_ms"] = round(latency_ms, 1) + if reason: + payload["reason"] = reason + self.add_standard_logging_guardrail_information_to_request_data( + guardrail_json_response=payload, + request_data=data, + guardrail_status=guardrail_status, + duration=(latency_ms / 1000.0) if latency_ms is not None else None, + guardrail_provider=self.guardrail_provider, + event_type=GuardrailEventHooks.pre_mcp_call, + ) diff --git a/litellm/types/guardrails.py b/litellm/types/guardrails.py index c7cdfaad780..88bd7f70435 100644 --- a/litellm/types/guardrails.py +++ b/litellm/types/guardrails.py @@ -6,6 +6,9 @@ from pydantic import BaseModel, ConfigDict, Field, field_validator, model_valida from typing_extensions import Required, TypedDict from litellm.constants import BEDROCK_APPLY_GUARDRAIL_CHUNK_BUDGET_CHARS +from litellm.types.proxy.guardrails.guardrail_hooks.agent_365 import ( + Agent365GuardrailConfigModel, +) from litellm.types.proxy.guardrails.guardrail_hooks.akto import ( AktoConfigModel, ) @@ -134,6 +137,7 @@ class SupportedGuardrailIntegrations(Enum): HEADROOM = "headroom" COMPRESR = "compresr" STRAIKER = "straiker" + AGENT_365 = "agent_365" class Role(Enum): @@ -872,7 +876,7 @@ class BaseLitellmParams(ContentFilterConfigModel): # works for new and patch up default="fail_closed", description=( "Behavior when a guardrail endpoint is unreachable due to network errors. " - "Implemented by guardrail='generic_guardrail_api', 'akto', 'vigil_guard', 'repelloai', 'headroom', and 'compresr'. " + "Implemented by guardrail='generic_guardrail_api', 'agent_365', 'akto', 'vigil_guard', 'repelloai', 'headroom', and 'compresr'. " "'fail_closed' raises an error (default). 'fail_open' logs a critical error and allows the request to proceed." ), ) @@ -999,6 +1003,7 @@ class LitellmParams( QostodianNexusConfigModel, VigilGuardGuardrailConfigModel, SingulrGuardrailConfigModel, + Agent365GuardrailConfigModel, ): guardrail: str = Field(description="The type of guardrail integration to use") mode: str | list[str] | Mode = Field( diff --git a/litellm/types/proxy/guardrails/guardrail_hooks/agent_365.py b/litellm/types/proxy/guardrails/guardrail_hooks/agent_365.py new file mode 100644 index 00000000000..dd3d7fe5f74 --- /dev/null +++ b/litellm/types/proxy/guardrails/guardrail_hooks/agent_365.py @@ -0,0 +1,66 @@ +from typing import Final + +from pydantic import Field + +from .base import GuardrailConfigModel + +AGENT_365_PROD_API_BASE: Final = "https://agent365.svc.cloud.microsoft" +AGENT_365_PROD_RESOURCE_APP_ID: Final = "ea9ffc3e-8a23-4a7d-836d-234d7c7565c1" +AGENT_365_SCOPE_NAME: Final = "ThreatProtection.Evaluate.All" + + +class Agent365GuardrailConfigModel(GuardrailConfigModel): + tenant_id: str | None = Field( + default=None, + description=( + "Entra tenant id used for the On-Behalf-Of token exchange. " + "Falls back to the AGENT365_TENANT_ID environment variable." + ), + ) + + client_id: str | None = Field( + default=None, + description=( + "Client id of the gateway's Entra app registration (a confidential client). " + "Falls back to the AGENT365_CLIENT_ID environment variable." + ), + ) + + client_secret: str | None = Field( + default=None, + description=( + "Client secret of the gateway's Entra app registration, used to perform the " + "On-Behalf-Of exchange. Falls back to the AGENT365_CLIENT_SECRET environment variable." + ), + ) + + api_base: str | None = Field( + default=None, + description=( + "Base URL of the Microsoft Agent 365 tool-evaluation endpoint. " + f"Defaults to the production endpoint {AGENT_365_PROD_API_BASE}. " + "Falls back to the AGENT365_API_BASE environment variable." + ), + ) + + resource_app_id: str | None = Field( + default=None, + description=( + "Application id of the Agent 365 resource the OBO token is minted for. " + f"Defaults to the production resource {AGENT_365_PROD_RESOURCE_APP_ID}; " + "the Test and PreProd environments use a different id. " + "Falls back to the AGENT365_RESOURCE_APP_ID environment variable." + ), + ) + + agent_id: str | None = Field( + default=None, + description=( + "Agent identity reported to Agent 365 with every tool evaluation. " + "When unset, the caller's key alias is used." + ), + ) + + @staticmethod + def ui_friendly_name() -> str: + return "Microsoft Agent 365" diff --git a/tests/test_litellm/proxy/guardrails/guardrail_hooks/test_agent_365.py b/tests/test_litellm/proxy/guardrails/guardrail_hooks/test_agent_365.py new file mode 100644 index 00000000000..14dbfd21aa7 --- /dev/null +++ b/tests/test_litellm/proxy/guardrails/guardrail_hooks/test_agent_365.py @@ -0,0 +1,717 @@ +from types import SimpleNamespace +from typing import Any, Final + +import httpx +import time + +import pytest +from fastapi import HTTPException + +from litellm.exceptions import Timeout as LitellmTimeout +from litellm.proxy._types import UserAPIKeyAuth +from litellm.proxy.guardrails.guardrail_hooks.agent_365 import ( + Agent365Guardrail, + guardrail_class_registry, + guardrail_initializer_registry, + initialize_guardrail, +) +from litellm.types.guardrails import ( + GuardrailEventHooks, + LitellmParams, + SupportedGuardrailIntegrations, +) +from litellm.types.proxy.guardrails.guardrail_hooks.agent_365 import ( + AGENT_365_PROD_API_BASE, + AGENT_365_PROD_RESOURCE_APP_ID, + Agent365GuardrailConfigModel, +) + +FAKE_ASSERTION: Final = "eyJhbGciOi.eyJhdWQiOi.c2lnbmF0dXJl" +TOKEN_URL: Final = "https://login.microsoftonline.com/tenant-abc/oauth2/v2.0/token" +EVALUATE_URL: Final = f"{AGENT_365_PROD_API_BASE}/agents/tool-evaluation/evaluate" + + +def _response(status_code: int, payload: Any = None, text: str | None = None) -> httpx.Response: + request: Final = httpx.Request("POST", "https://example.test") + if payload is not None: + return httpx.Response(status_code=status_code, json=payload, request=request) + return httpx.Response(status_code=status_code, text=text or "", request=request) + + +def _token_response(access_token: str = "obo-access-token", expires_in: int = 3599) -> httpx.Response: + return _response(200, {"access_token": access_token, "expires_in": expires_in}) + + +def _allow_response(correlation_id: str = "corr-1") -> httpx.Response: + return _response( + 200, + { + "allowed": True, + "defender": {"status": "Evaluated", "verdict": "Allow", "message": None}, + "observability": {"status": "Recorded"}, + "correlationId": correlation_id, + }, + ) + + +def _block_response(message: str = "Blocked by policy", correlation_id: str = "corr-2") -> httpx.Response: + return _response( + 200, + { + "allowed": False, + "defender": {"status": "Evaluated", "verdict": "Block", "message": message}, + "correlationId": correlation_id, + }, + ) + + +class FakeHandler: + def __init__(self, items: list[Any]): + self._items = list(items) + self.calls: list[SimpleNamespace] = [] + + async def post(self, *, url, headers=None, data=None, json=None, timeout=None): + self.calls.append(SimpleNamespace(url=url, headers=headers, data=data, json=json, timeout=timeout)) + if not self._items: + raise AssertionError("FakeHandler ran out of programmed responses") + item = self._items.pop(0) + if isinstance(item, BaseException): + raise item + if item.status_code >= 400: + raise httpx.HTTPStatusError("error status", request=item.request, response=item) + return item + + +def _make_guardrail( + handler: FakeHandler, + *, + unreachable_fallback: str = "fail_closed", + agent_id: str | None = None, + api_base: str = AGENT_365_PROD_API_BASE, +) -> Agent365Guardrail: + return Agent365Guardrail( + guardrail_name="agent-365-guard", + tenant_id="tenant-abc", + client_id="client-xyz", + client_secret="secret-123", + api_base=api_base, + agent_id=agent_id, + unreachable_fallback=unreachable_fallback, + async_handler=handler, + event_hook="pre_mcp_call", + default_on=True, + ) + + +def _mcp_data(**overrides: Any) -> dict: + data: Final[dict] = { + "mcp_tool_name": "send_email", + "mcp_arguments": {"to": "user@example.com", "body": "hello"}, + "mcp_server_name": "outlook_mcp", + "incoming_bearer_token": FAKE_ASSERTION, + "metadata": {"headers": {"mcp-session-id": "sess-123"}}, + } + data.update(overrides) + return data + + +def _user() -> UserAPIKeyAuth: + return UserAPIKeyAuth(api_key="hashed-key", key_alias="my-agent-key") + + +async def _run(guardrail: Agent365Guardrail, data: dict, call_type: str = "call_mcp_tool"): + return await guardrail.async_pre_call_hook( + user_api_key_dict=_user(), + cache=None, + data=data, + call_type=call_type, + ) + + +class TestRegistryWiring: + def test_enum_member_exists(self): + assert SupportedGuardrailIntegrations.AGENT_365.value == "agent_365" + + def test_initializer_registry(self): + assert guardrail_initializer_registry["agent_365"] is initialize_guardrail + + def test_class_registry(self): + assert guardrail_class_registry["agent_365"] is Agent365Guardrail + + def test_config_model_wired(self): + assert Agent365Guardrail.get_config_model() is Agent365GuardrailConfigModel + assert Agent365GuardrailConfigModel.ui_friendly_name() == "Microsoft Agent 365" + + def test_supported_event_hooks(self): + assert Agent365Guardrail.get_supported_event_hooks() == [GuardrailEventHooks.pre_mcp_call] + + +class TestInitializeGuardrail: + def test_requires_tenant_id(self, monkeypatch): + monkeypatch.delenv("AGENT365_TENANT_ID", raising=False) + params: Final = LitellmParams( + guardrail="agent_365", + mode="pre_mcp_call", + client_id="client-xyz", + api_key="secret-123", + ) + with pytest.raises(ValueError, match="tenant_id is required"): + initialize_guardrail(params, {"guardrail_name": "a365"}) + + def test_requires_client_secret(self, monkeypatch): + monkeypatch.delenv("AGENT365_CLIENT_SECRET", raising=False) + params: Final = LitellmParams( + guardrail="agent_365", + mode="pre_mcp_call", + tenant_id="tenant-abc", + client_id="client-xyz", + ) + with pytest.raises(ValueError, match="client_secret"): + initialize_guardrail(params, {"guardrail_name": "a365"}) + + def test_env_var_fallbacks(self, monkeypatch): + monkeypatch.delenv("AGENT365_RESOURCE_APP_ID", raising=False) + monkeypatch.setenv("AGENT365_TENANT_ID", "env-tenant") + monkeypatch.setenv("AGENT365_CLIENT_ID", "env-client") + monkeypatch.setenv("AGENT365_CLIENT_SECRET", "env-secret") + monkeypatch.setenv("AGENT365_API_BASE", "https://env.example.test") + params: Final = LitellmParams(guardrail="agent_365", mode="pre_mcp_call") + guardrail: Final = initialize_guardrail(params, {"guardrail_name": "a365-env"}) + assert guardrail.tenant_id == "env-tenant" + assert guardrail.client_id == "env-client" + assert guardrail.client_secret == "env-secret" + assert guardrail.api_base == "https://env.example.test" + assert guardrail.resource_app_id == AGENT_365_PROD_RESOURCE_APP_ID + assert guardrail.unreachable_fallback == "fail_closed" + + def test_explicit_params_win(self, monkeypatch): + monkeypatch.setenv("AGENT365_TENANT_ID", "env-tenant") + params: Final = LitellmParams( + guardrail="agent_365", + mode="pre_mcp_call", + tenant_id="param-tenant", + client_id="client-xyz", + client_secret="param-secret", + agent_id="agent-007", + unreachable_fallback="fail_open", + timeout=5, + ) + guardrail: Final = initialize_guardrail(params, {"guardrail_name": "a365-params"}) + assert guardrail.tenant_id == "param-tenant" + assert guardrail.client_secret == "param-secret" + assert guardrail.agent_id == "agent-007" + assert guardrail.unreachable_fallback == "fail_open" + assert guardrail.request_timeout == 5.0 + + def test_wrong_mode_rejected(self): + params: Final = LitellmParams( + guardrail="agent_365", + mode="post_call", + tenant_id="tenant-abc", + client_id="client-xyz", + api_key="secret-123", + ) + with pytest.raises(Exception, match="post_call"): + initialize_guardrail(params, {"guardrail_name": "a365-badmode"}) + + +def _guardrail_info(data: dict) -> dict: + entries: Final = data["metadata"]["standard_logging_guardrail_information"] + return entries[-1] + + +class TestAllowFlow: + @pytest.mark.asyncio + async def test_allowed_call_passes_through(self): + handler: Final = FakeHandler([_token_response(), _allow_response()]) + guardrail: Final = _make_guardrail(handler) + data: Final = _mcp_data() + result: Final = await _run(guardrail, data) + assert result is data + info: Final = _guardrail_info(data) + assert info["guardrail_status"] == "success" + assert info["guardrail_provider"] == "agent_365" + assert info["guardrail_response"]["verdict"] == "Allow" + assert info["guardrail_response"]["defender_status"] == "Evaluated" + assert info["guardrail_response"]["correlation_id"] == "corr-1" + assert info["guardrail_response"]["latency_ms"] >= 0 + + @pytest.mark.asyncio + async def test_obo_exchange_form(self): + handler: Final = FakeHandler([_token_response(), _allow_response()]) + guardrail: Final = _make_guardrail(handler) + await _run(guardrail, _mcp_data()) + token_call: Final = handler.calls[0] + assert token_call.url == TOKEN_URL + assert token_call.data["grant_type"] == "urn:ietf:params:oauth:grant-type:jwt-bearer" + assert token_call.data["requested_token_use"] == "on_behalf_of" + assert token_call.data["assertion"] == FAKE_ASSERTION + assert token_call.data["client_id"] == "client-xyz" + assert token_call.data["client_secret"] == "secret-123" + assert token_call.data["scope"] == f"{AGENT_365_PROD_RESOURCE_APP_ID}/ThreatProtection.Evaluate.All" + + @pytest.mark.asyncio + async def test_evaluate_payload(self): + handler: Final = FakeHandler([_token_response(), _allow_response()]) + guardrail: Final = _make_guardrail(handler, agent_id="agent-007") + await _run(guardrail, _mcp_data()) + evaluate_call: Final = handler.calls[1] + assert evaluate_call.url == EVALUATE_URL + assert evaluate_call.headers["Authorization"] == "Bearer obo-access-token" + assert evaluate_call.json["tool"] == {"name": "send_email"} + assert evaluate_call.json["serverName"] == "outlook_mcp" + assert evaluate_call.json["arguments"] == {"to": "user@example.com", "body": "hello"} + assert evaluate_call.json["conversationId"] == "sess-123" + assert evaluate_call.json["agentId"] == "agent-007" + + @pytest.mark.asyncio + async def test_agent_id_falls_back_to_key_alias(self): + handler: Final = FakeHandler([_token_response(), _allow_response()]) + guardrail: Final = _make_guardrail(handler) + await _run(guardrail, _mcp_data()) + assert handler.calls[1].json["agentId"] == "my-agent-key" + + @pytest.mark.asyncio + async def test_non_mcp_call_type_skipped(self): + handler: Final = FakeHandler([]) + guardrail: Final = _make_guardrail(handler) + data: Final = _mcp_data() + result: Final = await _run(guardrail, data, call_type="completion") + assert result is data + assert handler.calls == [] + + +class TestConversationId: + @pytest.mark.asyncio + async def test_session_id_header_case_insensitive(self): + handler: Final = FakeHandler([_token_response(), _allow_response()]) + guardrail: Final = _make_guardrail(handler) + data: Final = _mcp_data(metadata={"headers": {"Mcp-Session-Id": "sess-CASED"}}) + await _run(guardrail, data) + assert handler.calls[1].json["conversationId"] == "sess-CASED" + + @pytest.mark.asyncio + async def test_falls_back_to_logging_obj_session_id(self): + handler: Final = FakeHandler([_token_response(), _allow_response()]) + guardrail: Final = _make_guardrail(handler) + logging_obj: Final = SimpleNamespace( + model_call_details={"mcp_tool_call_metadata": {"mcp_session_id": "sess-from-logging"}}, + litellm_call_id="call-id-1", + ) + data: Final = _mcp_data(metadata={"headers": {}}, litellm_logging_obj=logging_obj) + await _run(guardrail, data) + assert handler.calls[1].json["conversationId"] == "sess-from-logging" + + @pytest.mark.asyncio + async def test_falls_back_to_litellm_call_id(self): + handler: Final = FakeHandler([_token_response(), _allow_response()]) + guardrail: Final = _make_guardrail(handler) + data: Final = _mcp_data(metadata={"headers": {}}, litellm_call_id="call-id-2") + await _run(guardrail, data) + assert handler.calls[1].json["conversationId"] == "call-id-2" + + +class TestBlockFlow: + @pytest.mark.asyncio + async def test_blocked_call_raises_400(self): + handler: Final = FakeHandler([_token_response(), _block_response(message="Injection detected")]) + guardrail: Final = _make_guardrail(handler) + data: Final = _mcp_data() + with pytest.raises(HTTPException) as exc_info: + await _run(guardrail, data) + assert exc_info.value.status_code == 400 + assert exc_info.value.detail["error"] == "Blocked by Microsoft Defender" + assert exc_info.value.detail["message"] == "Injection detected" + assert exc_info.value.detail["tool"] == "send_email" + assert exc_info.value.detail["correlation_id"] == "corr-2" + info: Final = _guardrail_info(data) + assert info["guardrail_status"] == "guardrail_intervened" + assert info["guardrail_response"]["verdict"] == "Block" + + @pytest.mark.asyncio + async def test_blocked_even_with_fail_open(self): + handler: Final = FakeHandler([_token_response(), _block_response()]) + guardrail: Final = _make_guardrail(handler, unreachable_fallback="fail_open") + with pytest.raises(HTTPException) as exc_info: + await _run(guardrail, _mcp_data()) + assert exc_info.value.status_code == 400 + + @pytest.mark.asyncio + async def test_http_400_always_blocks_even_fail_open(self): + handler: Final = FakeHandler([_token_response(), _response(400, text="Bad request: serverName missing")]) + guardrail: Final = _make_guardrail(handler, unreachable_fallback="fail_open") + with pytest.raises(HTTPException) as exc_info: + await _run(guardrail, _mcp_data()) + assert exc_info.value.status_code == 400 + assert "rejected" in exc_info.value.detail["error"] + + +class TestUnreachableFallback: + @pytest.mark.asyncio + async def test_evaluate_litellm_timeout_fail_closed(self): + handler: Final = FakeHandler( + [ + _token_response(), + LitellmTimeout(message="Connection timed out", model="default-model-name", llm_provider="httpx"), + ] + ) + guardrail: Final = _make_guardrail(handler) + with pytest.raises(HTTPException) as exc_info: + await _run(guardrail, _mcp_data()) + assert exc_info.value.status_code == 503 + + @pytest.mark.asyncio + async def test_evaluate_timeout_fail_closed(self): + handler: Final = FakeHandler([_token_response(), httpx.ReadTimeout("timed out")]) + guardrail: Final = _make_guardrail(handler) + with pytest.raises(HTTPException) as exc_info: + await _run(guardrail, _mcp_data()) + assert exc_info.value.status_code == 503 + assert "fail_closed" in exc_info.value.detail["message"] + + @pytest.mark.asyncio + async def test_evaluate_timeout_fail_open(self): + handler: Final = FakeHandler([_token_response(), httpx.ReadTimeout("timed out")]) + guardrail: Final = _make_guardrail(handler, unreachable_fallback="fail_open") + data: Final = _mcp_data() + result: Final = await _run(guardrail, data) + assert result is data + info: Final = _guardrail_info(data) + assert info["guardrail_status"] == "guardrail_failed_to_respond" + assert info["guardrail_response"]["verdict"] == "Unscanned" + + @pytest.mark.asyncio + async def test_evaluate_5xx_fail_closed(self): + handler: Final = FakeHandler([_token_response(), _response(502, text="bad gateway")]) + guardrail: Final = _make_guardrail(handler) + with pytest.raises(HTTPException) as exc_info: + await _run(guardrail, _mcp_data()) + assert exc_info.value.status_code == 503 + assert "502" in exc_info.value.detail["message"] + + @pytest.mark.asyncio + async def test_missing_bearer_token_fail_closed(self): + handler: Final = FakeHandler([]) + guardrail: Final = _make_guardrail(handler) + with pytest.raises(HTTPException) as exc_info: + await _run(guardrail, _mcp_data(incoming_bearer_token=None)) + assert exc_info.value.status_code == 401 + assert handler.calls == [] + + @pytest.mark.asyncio + async def test_non_jwt_bearer_token_fail_closed(self): + handler: Final = FakeHandler([]) + guardrail: Final = _make_guardrail(handler) + with pytest.raises(HTTPException) as exc_info: + await _run(guardrail, _mcp_data(incoming_bearer_token="sk-litellm-virtual-key")) + assert exc_info.value.status_code == 401 + + @pytest.mark.asyncio + async def test_missing_bearer_token_blocks_even_fail_open(self): + handler: Final = FakeHandler([]) + guardrail: Final = _make_guardrail(handler, unreachable_fallback="fail_open") + data: Final = _mcp_data(incoming_bearer_token=None) + with pytest.raises(HTTPException) as exc_info: + await _run(guardrail, data) + assert exc_info.value.status_code == 401 + assert handler.calls == [] + info: Final = _guardrail_info(data) + assert info["guardrail_status"] == "guardrail_intervened" + assert info["guardrail_response"]["verdict"] == "Rejected" + + @pytest.mark.asyncio + async def test_obo_rejected_blocks_even_fail_open(self): + handler: Final = FakeHandler( + [_response(400, {"error": "invalid_grant", "error_description": "AADSTS50013: bad assertion"})] + ) + guardrail: Final = _make_guardrail(handler, unreachable_fallback="fail_open") + with pytest.raises(HTTPException) as exc_info: + await _run(guardrail, _mcp_data()) + assert exc_info.value.status_code == 401 + assert "invalid_grant" in exc_info.value.detail["message"] + + @pytest.mark.asyncio + async def test_evaluate_4xx_blocks_even_fail_open(self): + handler: Final = FakeHandler([_token_response(), _response(403, text="obo token lacks the scope")]) + guardrail: Final = _make_guardrail(handler, unreachable_fallback="fail_open") + data: Final = _mcp_data() + with pytest.raises(HTTPException) as exc_info: + await _run(guardrail, data) + assert exc_info.value.status_code == 400 + assert "403" in exc_info.value.detail["message"] + assert "lacks the scope" not in exc_info.value.detail["message"] + info: Final = _guardrail_info(data) + assert info["guardrail_status"] == "guardrail_intervened" + assert info["guardrail_response"]["reason"] == "HTTP 403: obo token lacks the scope" + + @pytest.mark.asyncio + async def test_obo_rejected_fail_closed(self): + handler: Final = FakeHandler( + [_response(400, {"error": "invalid_grant", "error_description": "AADSTS50013: bad assertion"})] + ) + guardrail: Final = _make_guardrail(handler) + with pytest.raises(HTTPException) as exc_info: + await _run(guardrail, _mcp_data()) + assert exc_info.value.status_code == 401 + assert "invalid_grant" in exc_info.value.detail["message"] + + @pytest.mark.asyncio + async def test_obo_endpoint_5xx_fail_open(self): + handler: Final = FakeHandler([_response(503, text="entra down")]) + guardrail: Final = _make_guardrail(handler, unreachable_fallback="fail_open") + data: Final = _mcp_data() + result: Final = await _run(guardrail, data) + assert result is data + info: Final = _guardrail_info(data) + assert info["guardrail_status"] == "guardrail_failed_to_respond" + assert info["guardrail_response"]["verdict"] == "Unscanned" + + +class TestOboTokenCache: + @pytest.mark.asyncio + async def test_same_assertion_reuses_token(self): + handler: Final = FakeHandler([_token_response(), _allow_response(), _allow_response()]) + guardrail: Final = _make_guardrail(handler) + await _run(guardrail, _mcp_data()) + await _run(guardrail, _mcp_data()) + token_calls: Final = [c for c in handler.calls if c.url == TOKEN_URL] + assert len(token_calls) == 1 + + @pytest.mark.asyncio + async def test_different_assertions_get_distinct_tokens(self): + other_assertion: Final = "eyJhbGciOi.eyJvdGhlciI.b3RoZXJzaWc" + handler: Final = FakeHandler( + [ + _token_response(access_token="token-a"), + _allow_response(), + _token_response(access_token="token-b"), + _allow_response(), + ] + ) + guardrail: Final = _make_guardrail(handler) + await _run(guardrail, _mcp_data()) + await _run(guardrail, _mcp_data(incoming_bearer_token=other_assertion)) + token_calls: Final = [c for c in handler.calls if c.url == TOKEN_URL] + assert len(token_calls) == 2 + assert handler.calls[3].headers["Authorization"] == "Bearer token-b" + + @pytest.mark.asyncio + async def test_expired_token_refreshed(self): + handler: Final = FakeHandler( + [ + _token_response(access_token="short-lived", expires_in=1), + _allow_response(), + _token_response(access_token="fresh"), + _allow_response(), + ] + ) + guardrail: Final = _make_guardrail(handler) + await _run(guardrail, _mcp_data()) + await _run(guardrail, _mcp_data()) + token_calls: Final = [c for c in handler.calls if c.url == TOKEN_URL] + assert len(token_calls) == 2 + assert handler.calls[3].headers["Authorization"] == "Bearer fresh" + + +class TestEarlyPhasePassthrough: + @pytest.mark.asyncio + async def test_rest_body_shape_without_mcp_fields_skipped(self): + handler: Final = FakeHandler([]) + guardrail: Final = _make_guardrail(handler) + data: Final = { + "server_id": "266024044f9612bf481c78f6cfef1ff0", + "name": "deepwiki-read_wiki_structure", + "arguments": {"repoName": "BerriAI/litellm"}, + "metadata": {"headers": {"mcp-session-id": "sess-123"}}, + } + result: Final = await _run(guardrail, data) + assert result is data + assert handler.calls == [] + assert "standard_logging_guardrail_information" not in data["metadata"] + + +class TestRegistryDiscovery: + def test_auto_discovery_finds_agent_365(self): + from litellm.proxy.guardrails.guardrail_registry import ( + get_guardrail_class_from_hooks, + get_guardrail_initializer_from_hooks, + ) + + assert "agent_365" in get_guardrail_initializer_from_hooks() + assert get_guardrail_class_from_hooks()["agent_365"] is Agent365Guardrail + + +class TestMalformedResponses: + @pytest.mark.asyncio + async def test_obo_html_body_fail_open(self): + handler: Final = FakeHandler([_response(200, text="blocked by egress proxy")]) + guardrail: Final = _make_guardrail(handler, unreachable_fallback="fail_open") + data: Final = _mcp_data() + result: Final = await _run(guardrail, data) + assert result is data + assert _guardrail_info(data)["guardrail_response"]["verdict"] == "Unscanned" + + @pytest.mark.asyncio + async def test_obo_html_body_fail_closed(self): + handler: Final = FakeHandler([_response(200, text="outage")]) + guardrail: Final = _make_guardrail(handler) + with pytest.raises(HTTPException) as exc_info: + await _run(guardrail, _mcp_data()) + assert exc_info.value.status_code == 503 + assert "non-JSON" in exc_info.value.detail["message"] + + @pytest.mark.asyncio + async def test_obo_non_object_json_fail_closed(self): + handler: Final = FakeHandler([_response(200, ["not", "a", "dict"])]) + guardrail: Final = _make_guardrail(handler) + with pytest.raises(HTTPException) as exc_info: + await _run(guardrail, _mcp_data()) + assert exc_info.value.status_code == 503 + + @pytest.mark.asyncio + async def test_evaluate_html_body_fail_open(self): + handler: Final = FakeHandler([_token_response(), _response(200, text="waf page")]) + guardrail: Final = _make_guardrail(handler, unreachable_fallback="fail_open") + data: Final = _mcp_data() + result: Final = await _run(guardrail, data) + assert result is data + assert _guardrail_info(data)["guardrail_response"]["verdict"] == "Unscanned" + + @pytest.mark.asyncio + async def test_evaluate_html_body_fail_closed(self): + handler: Final = FakeHandler([_token_response(), _response(200, text="waf page")]) + guardrail: Final = _make_guardrail(handler) + with pytest.raises(HTTPException) as exc_info: + await _run(guardrail, _mcp_data()) + assert exc_info.value.status_code == 503 + + @pytest.mark.asyncio + async def test_evaluate_non_object_json_fail_closed(self): + handler: Final = FakeHandler([_token_response(), _response(200, "allowed")]) + guardrail: Final = _make_guardrail(handler) + with pytest.raises(HTTPException) as exc_info: + await _run(guardrail, _mcp_data()) + assert exc_info.value.status_code == 503 + + @pytest.mark.asyncio + async def test_bad_expires_in_still_allows(self): + handler: Final = FakeHandler([_response(200, {"access_token": "tok-1", "expires_in": "soon"}), _allow_response()]) + guardrail: Final = _make_guardrail(handler) + data: Final = _mcp_data() + result: Final = await _run(guardrail, data) + assert result is data + + @pytest.mark.asyncio + async def test_obo_litellm_timeout_fail_open(self): + handler: Final = FakeHandler( + [LitellmTimeout(message="Connection timed out", model="default-model-name", llm_provider="httpx")] + ) + guardrail: Final = _make_guardrail(handler, unreachable_fallback="fail_open") + data: Final = _mcp_data() + result: Final = await _run(guardrail, data) + assert result is data + assert _guardrail_info(data)["guardrail_status"] == "guardrail_failed_to_respond" + + +class TestDeltaHardening: + @pytest.mark.asyncio + async def test_non_string_access_token_fail_closed(self): + handler: Final = FakeHandler([_response(200, {"access_token": None, "expires_in": 3599})]) + guardrail: Final = _make_guardrail(handler) + with pytest.raises(HTTPException) as exc_info: + await _run(guardrail, _mcp_data()) + assert exc_info.value.status_code == 503 + assert "access_token" in exc_info.value.detail["message"] + + @pytest.mark.asyncio + async def test_numeric_string_expires_in_honored(self): + handler: Final = FakeHandler([_response(200, {"access_token": "tok-9", "expires_in": "120"}), _allow_response()]) + guardrail: Final = _make_guardrail(handler) + await _run(guardrail, _mcp_data()) + entries: Final = list(guardrail._obo_token_cache.values()) + assert len(entries) == 1 + assert entries[0][1] - time.time() < 200 + + @pytest.mark.asyncio + async def test_evaluate_400_records_intervention(self): + handler: Final = FakeHandler([_token_response(), _response(400, text="bad request shape")]) + guardrail: Final = _make_guardrail(handler) + data: Final = _mcp_data() + with pytest.raises(HTTPException) as exc_info: + await _run(guardrail, data) + assert exc_info.value.status_code == 400 + info: Final = _guardrail_info(data) + assert info["guardrail_status"] == "guardrail_intervened" + assert info["guardrail_response"]["verdict"] == "Rejected" + + +class TestVeriaHardening: + @pytest.mark.asyncio + async def test_evaluate_401_evicts_cached_obo_token(self): + handler: Final = FakeHandler( + [ + _token_response(), + _response(401, text="token expired"), + _token_response(access_token="tok-2"), + _allow_response(), + ] + ) + guardrail: Final = _make_guardrail(handler) + with pytest.raises(HTTPException): + await _run(guardrail, _mcp_data()) + result: Final = await _run(guardrail, _mcp_data()) + assert result is not None + token_calls: Final = [c for c in handler.calls if c.url == TOKEN_URL] + assert len(token_calls) == 2 + + @pytest.mark.asyncio + async def test_evaluate_429_blocks_even_fail_open_as_throttled(self): + handler: Final = FakeHandler([_token_response(), _response(429, text="slow down")]) + guardrail: Final = _make_guardrail(handler, unreachable_fallback="fail_open") + data: Final = _mcp_data() + with pytest.raises(HTTPException) as exc_info: + await _run(guardrail, data) + assert exc_info.value.status_code == 503 + assert "429" in exc_info.value.detail["message"] + info: Final = _guardrail_info(data) + assert info["guardrail_status"] == "guardrail_failed_to_respond" + assert info["guardrail_response"]["verdict"] == "Throttled" + + @pytest.mark.asyncio + async def test_evaluate_500_is_unavailable(self): + handler: Final = FakeHandler([_token_response(), _response(500, text="oops")]) + guardrail: Final = _make_guardrail(handler) + with pytest.raises(HTTPException) as exc_info: + await _run(guardrail, _mcp_data()) + assert exc_info.value.status_code == 503 + assert "500" in exc_info.value.detail["message"] + + @pytest.mark.asyncio + async def test_token_endpoint_4xx_html_stays_infra_fail_open(self): + handler: Final = FakeHandler([_response(403, text="waf block page")]) + guardrail: Final = _make_guardrail(handler, unreachable_fallback="fail_open") + data: Final = _mcp_data() + result: Final = await _run(guardrail, data) + assert result is data + assert _guardrail_info(data)["guardrail_response"]["verdict"] == "Unscanned" + + @pytest.mark.asyncio + async def test_entra_200_missing_access_token_is_malformed(self): + handler: Final = FakeHandler([_response(200, {"token_type": "Bearer"})]) + guardrail: Final = _make_guardrail(handler) + with pytest.raises(HTTPException) as exc_info: + await _run(guardrail, _mcp_data()) + assert exc_info.value.status_code == 503 + assert "access_token" in exc_info.value.detail["message"] + + @pytest.mark.asyncio + async def test_evaluate_5xx_fail_open_allows_unscanned_once(self): + handler: Final = FakeHandler([_token_response(), _response(502, text='{"error": "bad gateway"}')]) + guardrail: Final = _make_guardrail(handler, unreachable_fallback="fail_open") + data: Final = _mcp_data() + result: Final = await _run(guardrail, data) + assert result is data + records: Final = data["metadata"]["standard_logging_guardrail_information"] + assert len(records) == 1 + assert records[0]["guardrail_response"]["verdict"] == "Unscanned" + assert records[0]["guardrail_status"] == "guardrail_failed_to_respond" diff --git a/ui/litellm-dashboard/src/app/(dashboard)/guardrails/_components/guardrail_garden_configs.ts b/ui/litellm-dashboard/src/app/(dashboard)/guardrails/_components/guardrail_garden_configs.ts index 03cfeed42ff..ed5953cdbae 100644 --- a/ui/litellm-dashboard/src/app/(dashboard)/guardrails/_components/guardrail_garden_configs.ts +++ b/ui/litellm-dashboard/src/app/(dashboard)/guardrails/_components/guardrail_garden_configs.ts @@ -312,4 +312,11 @@ export const GUARDRAIL_PRESETS: Record = { mode: "pre_call", defaultOn: false, }, + agent_365: { + provider: "Agent365", + guardrailNameSuggestion: "Microsoft Agent 365 Guardrail", + mode: "pre_mcp_call", + // MCP-only: default_on is the only activation path on the MCP hook + defaultOn: true, + }, }; diff --git a/ui/litellm-dashboard/src/app/(dashboard)/guardrails/_components/guardrail_garden_data.test.ts b/ui/litellm-dashboard/src/app/(dashboard)/guardrails/_components/guardrail_garden_data.test.ts index 13909e48185..cf77811a9dd 100644 --- a/ui/litellm-dashboard/src/app/(dashboard)/guardrails/_components/guardrail_garden_data.test.ts +++ b/ui/litellm-dashboard/src/app/(dashboard)/guardrails/_components/guardrail_garden_data.test.ts @@ -27,6 +27,7 @@ const EXPECTED_PARTNER_LOGO_FILES: Record = { deepkeep: "deepkeep.svg", repelloai: "repelloai.png", straiker: "straiker.svg", + agent_365: "microsoft_azure.svg", }; describe("guardrail_garden_data logos", () => { diff --git a/ui/litellm-dashboard/src/app/(dashboard)/guardrails/_components/guardrail_garden_data.ts b/ui/litellm-dashboard/src/app/(dashboard)/guardrails/_components/guardrail_garden_data.ts index 744af89a357..ab1ada8d2b3 100644 --- a/ui/litellm-dashboard/src/app/(dashboard)/guardrails/_components/guardrail_garden_data.ts +++ b/ui/litellm-dashboard/src/app/(dashboard)/guardrails/_components/guardrail_garden_data.ts @@ -464,6 +464,16 @@ export const PARTNER_GUARDRAIL_CARDS: GuardrailCardInfo[] = [ tags: ["Agentic", "Prompt Injection", "Tool Misuse", "MCP", "Skills"], providerKey: "Straiker", }, + { + id: "agent_365", + name: "Microsoft Agent 365", + description: + "Microsoft Agent 365 tool-call governance: Defender threat evaluation and observability for MCP tool calls, acting on behalf of the signed-in user", + category: "partner", + logo: guardrailLogoMap["Microsoft Agent 365"], + tags: ["Agentic", "MCP", "Tool Misuse", "Observability"], + providerKey: "Agent365", + }, ]; export const ALL_CARDS = [...LITELLM_CONTENT_FILTER_CARDS, ...PARTNER_GUARDRAIL_CARDS]; diff --git a/ui/litellm-dashboard/src/app/(dashboard)/guardrails/_components/guardrail_info_helpers.tsx b/ui/litellm-dashboard/src/app/(dashboard)/guardrails/_components/guardrail_info_helpers.tsx index 12aaba0d696..9ed7b406239 100644 --- a/ui/litellm-dashboard/src/app/(dashboard)/guardrails/_components/guardrail_info_helpers.tsx +++ b/ui/litellm-dashboard/src/app/(dashboard)/guardrails/_components/guardrail_info_helpers.tsx @@ -193,6 +193,7 @@ export const guardrailLogoMap = { "Qostodian Nexus": qohashLogo.src, "RepelloAI Argus": repelloAiLogo.src, Straiker: straikerLogo.src, + "Microsoft Agent 365": microsoftAzureLogo.src, } satisfies Record; export const getGuardrailLogo = (displayName: string): string | undefined => From 28467d404a9a3b53122fa0c14e16099cd43c31e9 Mon Sep 17 00:00:00 2001 From: Yucheng Zhu Date: Tue, 25 Aug 2026 16:48:17 -0700 Subject: [PATCH 008/225] fix(agent_365): classify Entra token-endpoint 408/429 as throttled, not caller fault --- .../guardrail_hooks/agent_365/agent_365.py | 55 ++++++++++++++----- .../guardrail_hooks/test_agent_365.py | 25 +++++++++ 2 files changed, 67 insertions(+), 13 deletions(-) diff --git a/litellm/proxy/guardrails/guardrail_hooks/agent_365/agent_365.py b/litellm/proxy/guardrails/guardrail_hooks/agent_365/agent_365.py index 5d17a87e640..0d45b5ff238 100644 --- a/litellm/proxy/guardrails/guardrail_hooks/agent_365/agent_365.py +++ b/litellm/proxy/guardrails/guardrail_hooks/agent_365/agent_365.py @@ -105,6 +105,12 @@ class Agent365MalformedResponseError(Exception): pass +class Agent365ThrottledError(Exception): + def __init__(self, status_code: int) -> None: + super().__init__(f"HTTP {status_code}") + self.status_code = status_code + + class Agent365Guardrail(CustomGuardrail): """Pre-MCP-call guardrail enforcing Microsoft Agent 365 tool-evaluation verdicts.""" @@ -192,6 +198,13 @@ class Agent365Guardrail(CustomGuardrail): status_code=401, reason=f"the Entra On-Behalf-Of token exchange was rejected ({exc.error_code})", ) + except Agent365ThrottledError as exc: + self._handle_throttled( + data=data, + tool_name=tool_name, + reason=f"the Entra token endpoint returned HTTP {exc.status_code}", + latency_ms=None, + ) except (httpx.HTTPError, LitellmTimeout, TimeoutError) as exc: return self._handle_unavailable( data=data, @@ -235,22 +248,12 @@ class Agent365Guardrail(CustomGuardrail): latency_ms: float, ) -> dict | None: # mutable-ok: returns the request data dict per hook contract on fail_open if response.status_code in (408, 429): - self._record_verdict( + self._handle_throttled( data=data, - verdict="Throttled", - guardrail_status="guardrail_failed_to_respond", - defender_status=None, - correlation_id=None, - latency_ms=latency_ms, + tool_name=tool_name, reason=f"the Agent 365 endpoint returned HTTP {response.status_code}", + latency_ms=latency_ms, ) - throttled_detail: Final[_UnavailableDetail] = { - "error": "Agent 365 guardrail could not authorize the tool call", - "message": f"Tool call '{tool_name}' was blocked because the Agent 365 endpoint returned " - f"HTTP {response.status_code}; throttled evaluations block regardless of unreachable_fallback.", - "tool": tool_name, - } - raise HTTPException(status_code=503, detail=throttled_detail) if 400 <= response.status_code < 500: if response.status_code == 401: self._evict_obo_token(assertion) @@ -398,6 +401,8 @@ class Agent365Guardrail(CustomGuardrail): }, headers={"Content-Type": "application/x-www-form-urlencoded"}, # mutable-ok: httpx header dict ) + if response.status_code in (408, 429): + raise Agent365ThrottledError(status_code=response.status_code) if response.status_code >= 500: raise httpx.HTTPStatusError( f"Entra token endpoint returned {response.status_code}", @@ -472,6 +477,30 @@ class Agent365Guardrail(CustomGuardrail): } raise HTTPException(status_code=status_code, detail=caller_fault_detail) + def _handle_throttled( + self, + data: dict, # mutable-ok: guardrail logging appends into the request metadata in place + tool_name: str, + reason: str, + latency_ms: float | None, + ) -> NoReturn: + self._record_verdict( + data=data, + verdict="Throttled", + guardrail_status="guardrail_failed_to_respond", + defender_status=None, + correlation_id=None, + latency_ms=latency_ms, + reason=reason, + ) + throttled_detail: Final[_UnavailableDetail] = { + "error": "Agent 365 guardrail could not authorize the tool call", + "message": f"Tool call '{tool_name}' was blocked because {reason}; " + "throttled evaluations block regardless of unreachable_fallback.", + "tool": tool_name, + } + raise HTTPException(status_code=503, detail=throttled_detail) + def _evict_obo_token(self, assertion: str) -> None: cache_key: Final = hashlib.sha256(assertion.encode("utf-8")).hexdigest() with self._obo_cache_lock: diff --git a/tests/test_litellm/proxy/guardrails/guardrail_hooks/test_agent_365.py b/tests/test_litellm/proxy/guardrails/guardrail_hooks/test_agent_365.py index 14dbfd21aa7..152674aab7f 100644 --- a/tests/test_litellm/proxy/guardrails/guardrail_hooks/test_agent_365.py +++ b/tests/test_litellm/proxy/guardrails/guardrail_hooks/test_agent_365.py @@ -686,6 +686,31 @@ class TestVeriaHardening: assert exc_info.value.status_code == 503 assert "500" in exc_info.value.detail["message"] + @pytest.mark.asyncio + async def test_token_endpoint_429_blocks_even_fail_open_as_throttled(self): + handler: Final = FakeHandler( + [_response(429, {"error": "temporarily_throttled", "error_description": "AADSTS90056"})] + ) + guardrail: Final = _make_guardrail(handler, unreachable_fallback="fail_open") + data: Final = _mcp_data() + with pytest.raises(HTTPException) as exc_info: + await _run(guardrail, data) + assert exc_info.value.status_code == 503 + assert "429" in exc_info.value.detail["message"] + info: Final = _guardrail_info(data) + assert info["guardrail_status"] == "guardrail_failed_to_respond" + assert info["guardrail_response"]["verdict"] == "Throttled" + + @pytest.mark.asyncio + async def test_token_endpoint_408_non_json_blocks_as_throttled(self): + handler: Final = FakeHandler([_response(408, text="Request Timeout")]) + guardrail: Final = _make_guardrail(handler, unreachable_fallback="fail_open") + data: Final = _mcp_data() + with pytest.raises(HTTPException) as exc_info: + await _run(guardrail, data) + 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T.createElement(T.Fragment,null,T.createElement(hx,{legendPayload:(n=t.dataKey,i=t.name,a=t.stroke,o=t.fill,l=t.legendType,[{inactive:t.hide,dataKey:n,type:l,color:vo(a,o),value:nX(i,n),payload:t}])}),T.createElement(vl,{dataKey:t.dataKey,data:t.data,stroke:t.stroke,strokeWidth:t.strokeWidth,fill:t.fill,name:t.name,hide:t.hide,unit:t.unit,formatter:t.formatter,tooltipType:t.tooltipType,id:e}),T.createElement(ye,{type:"area",id:e,data:t.data,dataKey:t.dataKey,xAxisId:t.xAxisId,yAxisId:t.yAxisId,zAxisId:0,stackId:nU(t.stackId),hide:t.hide,barSize:void 0,baseValue:t.baseValue,isPanorama:r,connectNulls:t.connectNulls}),T.createElement(vh,vt({},t,{id:e})))})},yg);vy.displayName="Area";var vv=(e,t)=>{if(t&&Array.isArray(e)){var r=Number.parseInt(t,10);if(!ee(r))return e[r]}},vm=rB({name:"options",initialState:{chartName:"",tooltipPayloadSearcher:()=>void 0,eventEmitter:void 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this._client.getAPIList("/v1/sessions?beta=true",sL,{query:r,...t,headers:sY([{"anthropic-beta":[...s??[],"managed-agents-2026-04-01"].toString()},t?.headers])})}delete(e,t={},s){let{betas:r}=t??{};return this._client.delete(s0`/v1/sessions/${e}?beta=true`,{...s,headers:sY([{"anthropic-beta":[...r??[],"managed-agents-2026-04-01"].toString()},s?.headers])})}archive(e,t={},s){let{betas:r}=t??{};return this._client.post(s0`/v1/sessions/${e}/archive?beta=true`,{...s,headers:sY([{"anthropic-beta":[...r??[],"managed-agents-2026-04-01"].toString()},s?.headers])})}}rC.Events=rS,rC.Resources=rk;class rT extends sK{create(e,t={},s){let{betas:r,...a}=t??{};return this._client.post(s0`/v1/skills/${e}/versions?beta=true`,sq({body:a,...s,headers:sY([{"anthropic-beta":[...r??[],"skills-2025-10-02"].toString()},s?.headers])},this._client))}retrieve(e,t,s){let{skill_id:r,betas:a}=t;return 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this._client.get(s0`/v1/skills/${e}?beta=true`,{...s,headers:sY([{"anthropic-beta":[...r??[],"skills-2025-10-02"].toString()},s?.headers])})}list(e={},t){let{betas:s,...r}=e??{};return this._client.getAPIList("/v1/skills?beta=true",sL,{query:r,...t,headers:sY([{"anthropic-beta":[...s??[],"skills-2025-10-02"].toString()},t?.headers])})}delete(e,t={},s){let{betas:r}=t??{};return this._client.delete(s0`/v1/skills/${e}?beta=true`,{...s,headers:sY([{"anthropic-beta":[...r??[],"skills-2025-10-02"].toString()},s?.headers])})}}rE.Versions=rT;class rA extends sK{create(e,t,s){let{betas:r,...a}=t;return this._client.post(s0`/v1/vaults/${e}/credentials?beta=true`,{body:a,...s,headers:sY([{"anthropic-beta":[...r??[],"managed-agents-2026-04-01"].toString()},s?.headers])})}retrieve(e,t,s){let{vault_id:r,betas:a}=t;return this._client.get(s0`/v1/vaults/${r}/credentials/${e}?beta=true`,{...s,headers:sY([{"anthropic-beta":[...a??[],"managed-agents-2026-04-01"].toString()},s?.headers])})}update(e,t,s){let{vault_id:r,betas:a,...n}=t;return this._client.post(s0`/v1/vaults/${r}/credentials/${e}?beta=true`,{body:n,...s,headers:sY([{"anthropic-beta":[...a??[],"managed-agents-2026-04-01"].toString()},s?.headers])})}list(e,t={},s){let{betas:r,...a}=t??{};return this._client.getAPIList(s0`/v1/vaults/${e}/credentials?beta=true`,sL,{query:a,...s,headers:sY([{"anthropic-beta":[...r??[],"managed-agents-2026-04-01"].toString()},s?.headers])})}delete(e,t,s){let{vault_id:r,betas:a}=t;return this._client.delete(s0`/v1/vaults/${r}/credentials/${e}?beta=true`,{...s,headers:sY([{"anthropic-beta":[...a??[],"managed-agents-2026-04-01"].toString()},s?.headers])})}archive(e,t,s){let{vault_id:r,betas:a}=t;return this._client.post(s0`/v1/vaults/${r}/credentials/${e}/archive?beta=true`,{...s,headers:sY([{"anthropic-beta":[...a??[],"managed-agents-2026-04-01"].toString()},s?.headers])})}}class rP extends sK{constructor(){super(...arguments),this.credentials=new rA(this._client)}create(e,t){let{betas:s,...r}=e;return this._client.post("/v1/vaults?beta=true",{body:r,...t,headers:sY([{"anthropic-beta":[...s??[],"managed-agents-2026-04-01"].toString()},t?.headers])})}retrieve(e,t={},s){let{betas:r}=t??{};return this._client.get(s0`/v1/vaults/${e}?beta=true`,{...s,headers:sY([{"anthropic-beta":[...r??[],"managed-agents-2026-04-01"].toString()},s?.headers])})}update(e,t,s){let{betas:r,...a}=t;return this._client.post(s0`/v1/vaults/${e}?beta=true`,{body:a,...s,headers:sY([{"anthropic-beta":[...r??[],"managed-agents-2026-04-01"].toString()},s?.headers])})}list(e={},t){let{betas:s,...r}=e??{};return this._client.getAPIList("/v1/vaults?beta=true",sL,{query:r,...t,headers:sY([{"anthropic-beta":[...s??[],"managed-agents-2026-04-01"].toString()},t?.headers])})}delete(e,t={},s){let{betas:r}=t??{};return this._client.delete(s0`/v1/vaults/${e}?beta=true`,{...s,headers:sY([{"anthropic-beta":[...r??[],"managed-agents-2026-04-01"].toString()},s?.headers])})}archive(e,t={},s){let{betas:r}=t??{};return this._client.post(s0`/v1/vaults/${e}/archive?beta=true`,{...s,headers:sY([{"anthropic-beta":[...r??[],"managed-agents-2026-04-01"].toString()},s?.headers])})}}rP.Credentials=rA;class rI extends sK{constructor(){super(...arguments),this.models=new s8(this._client),this.messages=new r_(this._client),this.agents=new re(this._client),this.environments=new s1(this._client),this.sessions=new rC(this._client),this.vaults=new rP(this._client),this.memoryStores=new rr(this._client),this.files=new s6(this._client),this.skills=new rE(this._client),this.userProfiles=new s9(this._client)}}function rM(e){return e?.output_config?.format}function rR(e,t,s){let r=rM(t);return t&&"parse"in(r??{})?r$(e,t,s):{...e,content:e.content.map(e=>"text"===e.type?Object.defineProperty({...e},"parsed_output",{value:null,enumerable:!1}):e),parsed_output:null}}function r$(e,t,s){let r=null,a=e.content.map(e=>{if("text"===e.type){let s=function(e,t){let s=rM(e);if(s?.type!=="json_schema")return null;try{if("parse"in s)return s.parse(t);return JSON.parse(t)}catch(e){throw new tZ(`Failed to parse structured output: ${e}`)}}(t,e.text);return null===r&&(r=s),Object.defineProperty({...e},"parsed_output",{value:s,enumerable:!1})}return e});return{...e,content:a,parsed_output:r}}rI.Models=s8,rI.Messages=r_,rI.Agents=re,rI.Environments=s1,rI.Sessions=rC,rI.Vaults=rP,rI.MemoryStores=rr,rI.Files=s6,rI.Skills=rE,rI.UserProfiles=s9;let rO="__json_buf";function rL(e){return"tool_use"===e.type||"server_tool_use"===e.type}class rU{constructor(e,t){W.add(this),this.messages=[],this.receivedMessages=[],V.set(this,void 0),H.set(this,null),this.controller=new AbortController,G.set(this,void 0),J.set(this,()=>{}),K.set(this,()=>{}),X.set(this,void 0),Y.set(this,()=>{}),Q.set(this,()=>{}),Z.set(this,{}),ee.set(this,!1),et.set(this,!1),es.set(this,!1),er.set(this,!1),ea.set(this,void 0),en.set(this,void 0),ei.set(this,void 0),ed.set(this,e=>{if(tJ(this,et,!0,"f"),tY(e)&&(e=new t1),e instanceof t1)return tJ(this,es,!0,"f"),this._emit("abort",e);if(e instanceof tZ)return this._emit("error",e);if(e instanceof Error){let t=new tZ(e.message);return t.cause=e,this._emit("error",t)}return this._emit("error",new tZ(String(e)))}),tJ(this,G,new Promise((e,t)=>{tJ(this,J,e,"f"),tJ(this,K,t,"f")}),"f"),tJ(this,X,new Promise((e,t)=>{tJ(this,Y,e,"f"),tJ(this,Q,t,"f")}),"f"),tK(this,G,"f").catch(()=>{}),tK(this,X,"f").catch(()=>{}),tJ(this,H,e,"f"),tJ(this,ei,t?.logger??console,"f")}get response(){return tK(this,ea,"f")}get 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s=t.content.at(-1);switch(e.delta.type){case"text_delta":"text"===s.type&&this._emit("text",e.delta.text,s.text||"");break;case"citations_delta":"text"===s.type&&this._emit("citation",e.delta.citation,s.citations??[]);break;case"input_json_delta":rL(s)&&s.input&&this._emit("inputJson",e.delta.partial_json,s.input);break;case"thinking_delta":"thinking"===s.type&&this._emit("thinking",e.delta.thinking,s.thinking);break;case"signature_delta":"thinking"===s.type&&this._emit("signature",s.signature);break;default:rD(e.delta)}break}case"message_stop":this._addMessageParam(t),this._addMessage(rR(t,tK(this,H,"f"),{logger:tK(this,ei,"f")}),!0);break;case"content_block_stop":this._emit("contentBlock",t.content.at(-1));break;case"message_start":tJ(this,V,t,"f")}},em=function(){if(this.ended)throw new tZ("stream has ended, this shouldn't happen");let e=tK(this,V,"f");if(!e)throw new tZ("request ended without sending any chunks");return tJ(this,V,void 0,"f"),rR(e,tK(this,H,"f"),{logger:tK(this,ei,"f")})},eh=function(e){let t=tK(this,V,"f");if("message_start"===e.type){if(t)throw new tZ(`Unexpected event order, got ${e.type} before receiving "message_stop"`);return e.message}if(!t)throw new tZ(`Unexpected event order, got ${e.type} before "message_start"`);switch(e.type){case"message_stop":case"content_block_stop":return t;case"message_delta":return t.stop_reason=e.delta.stop_reason,t.stop_sequence=e.delta.stop_sequence,t.usage.output_tokens=e.usage.output_tokens,null!=e.usage.input_tokens&&(t.usage.input_tokens=e.usage.input_tokens),null!=e.usage.cache_creation_input_tokens&&(t.usage.cache_creation_input_tokens=e.usage.cache_creation_input_tokens),null!=e.usage.cache_read_input_tokens&&(t.usage.cache_read_input_tokens=e.usage.cache_read_input_tokens),null!=e.usage.server_tool_use&&(t.usage.server_tool_use=e.usage.server_tool_use),t;case"content_block_start":return t.content.push({...e.content_block}),t;case"content_block_delta":{let s=t.content.at(e.index);switch(e.delta.type){case"text_delta":s?.type==="text"&&(t.content[e.index]={...s,text:(s.text||"")+e.delta.text});break;case"citations_delta":s?.type==="text"&&(t.content[e.index]={...s,citations:[...s.citations??[],e.delta.citation]});break;case"input_json_delta":if(s&&rL(s)){let r=s[rO]||"";r+=e.delta.partial_json;let a={...s};Object.defineProperty(a,rO,{value:r,enumerable:!1,writable:!0}),r&&(a.input=ru(r)),t.content[e.index]=a}break;case"thinking_delta":s?.type==="thinking"&&(t.content[e.index]={...s,thinking:s.thinking+e.delta.thinking});break;case"signature_delta":s?.type==="thinking"&&(t.content[e.index]={...s,signature:e.delta.signature});break;default:rD(e.delta)}return t}}},Symbol.asyncIterator)](){let e=[],t=[],s=!1;return this.on("streamEvent",s=>{let r=t.shift();r?r.resolve(s):e.push(s)}),this.on("end",()=>{for(let e of(s=!0,t))e.resolve(void 0);t.length=0}),this.on("abort",e=>{for(let r of(s=!0,t))r.reject(e);t.length=0}),this.on("error",e=>{for(let r of(s=!0,t))r.reject(e);t.length=0}),{next:async()=>e.length?{value:e.shift(),done:!1}:s?{value:void 0,done:!0}:new Promise((e,s)=>t.push({resolve:e,reject:s})).then(e=>e?{value:e,done:!1}:{value:void 0,done:!0}),return:async()=>(this.abort(),{value:void 0,done:!0})}}toReadableStream(){return new sC(this[Symbol.asyncIterator].bind(this),this.controller).toReadableStream()}}function rD(e){}class rz extends sK{create(e,t){return this._client.post("/v1/messages/batches",{body:e,...t})}retrieve(e,t){return this._client.get(s0`/v1/messages/batches/${e}`,t)}list(e={},t){return this._client.getAPIList("/v1/messages/batches",sO,{query:e,...t})}delete(e,t){return this._client.delete(s0`/v1/messages/batches/${e}`,t)}cancel(e,t){return this._client.post(s0`/v1/messages/batches/${e}/cancel`,t)}async results(e,t){let s=await this.retrieve(e);if(!s.results_url)throw new tZ(`No batch \`results_url\`; Has it finished processing? ${s.processing_status} - ${s.id}`);return this._client.get(s.results_url,{...t,headers:sY([{Accept:"application/binary"},t?.headers]),stream:!0,__binaryResponse:!0})._thenUnwrap((e,t)=>ra.fromResponse(t.response,t.controller))}}class rB extends sK{constructor(){super(...arguments),this.batches=new rz(this._client)}create(e,t){e.model in rq&&console.warn(`The model '${e.model}' is deprecated and will reach end-of-life on ${rq[e.model]} -Please migrate to a newer model. Visit https://docs.anthropic.com/en/docs/resources/model-deprecations for more information.`),rF.includes(e.model)&&e.thinking&&"enabled"===e.thinking.type&&console.warn(`Using Claude with ${e.model} and 'thinking.type=enabled' is deprecated. 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Expected either apiKey or authToken to be set. Or for one of the "X-Api-Key" or "Authorization" headers to be explicitly omitted')}async authHeaders(e){return sY([await this.apiKeyAuth(e),await this.bearerAuth(e)])}async apiKeyAuth(e){if(null!=this.apiKey)return sY([{"X-Api-Key":this.apiKey}])}async bearerAuth(e){if(null!=this.authToken)return sY([{Authorization:`Bearer ${this.authToken}`}])}stringifyQuery(e){return Object.entries(e).filter(([e,t])=>void 0!==t).map(([e,t])=>{if("string"==typeof t||"number"==typeof t||"boolean"==typeof t)return`${encodeURIComponent(e)}=${encodeURIComponent(t)}`;if(null===t)return`${encodeURIComponent(e)}=`;throw new tZ(`Cannot stringify type ${typeof t}; Expected string, number, boolean, or null. 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See https://github.com/anthropics/anthropic-sdk-typescript#streaming-responses for more details");return 6e5}async prepareOptions(e){}async prepareRequest(e,{url:t,options:s}){}get(e,t){return this.methodRequest("get",e,t)}post(e,t){return this.methodRequest("post",e,t)}patch(e,t){return this.methodRequest("patch",e,t)}put(e,t){return this.methodRequest("put",e,t)}delete(e,t){return this.methodRequest("delete",e,t)}methodRequest(e,t,s){return this.request(Promise.resolve(s).then(s=>({method:e,path:t,...s})))}request(e,t=null){return new sM(this,this.makeRequest(e,t,void 0))}async makeRequest(e,t,s){let r=await e,a=r.maxRetries??this.maxRetries;null==t&&(t=a),await this.prepareOptions(r);let{req:n,url:i,timeout:o}=await this.buildRequest(r,{retryCount:a-t});await this.prepareRequest(n,{url:i,options:r});let l="log_"+(0x1000000*Math.random()|0).toString(16).padStart(6,"0"),d=void 0===s?"":`, retryOf: ${s}`,c=Date.now();if(sS(this).debug(`[${l}] sending request`,sk({retryOfRequestLogID:s,method:r.method,url:i,options:r,headers:n.headers})),r.signal?.aborted)throw new t1;let u=new AbortController,m=await this.fetchWithTimeout(i,n,o,u).catch(tQ),h=Date.now();if(m instanceof globalThis.Error){let e=`retrying, ${t} attempts remaining`;if(r.signal?.aborted)throw new t1;let a=tY(m)||/timed? ?out/i.test(String(m)+("cause"in m?String(m.cause):""));if(t)return sS(this).info(`[${l}] connection ${a?"timed out":"failed"} - ${e}`),sS(this).debug(`[${l}] connection ${a?"timed out":"failed"} (${e})`,sk({retryOfRequestLogID:s,url:i,durationMs:h-c,message:m.message})),this.retryRequest(r,t,s??l);if(sS(this).info(`[${l}] connection ${a?"timed out":"failed"} - error; no more retries left`),sS(this).debug(`[${l}] connection ${a?"timed out":"failed"} (error; no more retries left)`,sk({retryOfRequestLogID:s,url:i,durationMs:h-c,message:m.message})),a)throw new t5;throw new t2({cause:m})}let p=[...m.headers.entries()].filter(([e])=>"request-id"===e).map(([e,t])=>", "+e+": "+JSON.stringify(t)).join(""),f=`[${l}${d}${p}] ${n.method} ${i} ${m.ok?"succeeded":"failed"} with status ${m.status} in ${h-c}ms`;if(!m.ok){let e=await this.shouldRetry(m);if(t&&e){let e=`retrying, ${t} attempts remaining`;return await sp(m.body),sS(this).info(`${f} - ${e}`),sS(this).debug(`[${l}] response error (${e})`,sk({retryOfRequestLogID:s,url:m.url,status:m.status,headers:m.headers,durationMs:h-c})),this.retryRequest(r,t,s??l,m.headers)}let a=e?"error; no more retries left":"error; not retryable";sS(this).info(`${f} - ${a}`);let n=await m.text().catch(e=>tQ(e).message),i=so(n),o=i?void 0:n;throw sS(this).debug(`[${l}] response error (${a})`,sk({retryOfRequestLogID:s,url:m.url,status:m.status,headers:m.headers,message:o,durationMs:Date.now()-c})),this.makeStatusError(m.status,i,o,m.headers)}return sS(this).info(f),sS(this).debug(`[${l}] response start`,sk({retryOfRequestLogID:s,url:m.url,status:m.status,headers:m.headers,durationMs:h-c})),{response:m,options:r,controller:u,requestLogID:l,retryOfRequestLogID:s,startTime:c}}getAPIList(e,t,s){return this.requestAPIList(t,s&&"then"in s?s.then(t=>({method:"get",path:e,...t})):{method:"get",path:e,...s})}requestAPIList(e,t){return new s$(this,this.makeRequest(t,null,void 0),e)}async fetchWithTimeout(e,t,s,r){let{signal:a,method:n,...i}=t||{},o=this._makeAbort(r);a&&a.addEventListener("abort",o,{once:!0});let l=setTimeout(o,s),d=globalThis.ReadableStream&&i.body instanceof globalThis.ReadableStream||"object"==typeof i.body&&null!==i.body&&Symbol.asyncIterator in i.body,c={signal:r.signal,...d?{duplex:"half"}:{},method:"GET",...i};n&&(c.method=n.toUpperCase());try{return await this.fetch.call(void 0,e,c)}finally{clearTimeout(l)}}async shouldRetry(e){let t=e.headers.get("x-should-retry");return"true"===t||"false"!==t&&(408===e.status||409===e.status||429===e.status||!!(e.status>=500))}async retryRequest(e,t,s,r){let a,n,i=r?.get("retry-after-ms");if(i){let e=parseFloat(i);Number.isNaN(e)||(a=e)}let o=r?.get("retry-after");if(o&&!a){let e=parseFloat(o);a=Number.isNaN(e)?Date.parse(o)-Date.now():1e3*e}if(void 0===a){let s=e.maxRetries??this.maxRetries;a=this.calculateDefaultRetryTimeoutMillis(t,s)}return await (n=a,new Promise(e=>setTimeout(e,n))),this.makeRequest(e,t-1,s)}calculateDefaultRetryTimeoutMillis(e,t){return Math.min(.5*Math.pow(2,t-e),8)*(1-.25*Math.random())*1e3}calculateNonstreamingTimeout(e,t){if(36e5*e/128e3>6e5||null!=t&&e>t)throw new tZ("Streaming is required for operations that may take longer than 10 minutes. 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n={};this.idempotencyHeader&&"get"!==s&&(e.idempotencyKey||(e.idempotencyKey=this.defaultIdempotencyKey()),n[this.idempotencyHeader]=e.idempotencyKey);let i=sY([n,{Accept:"application/json","User-Agent":this.getUserAgent(),"X-Stainless-Retry-Count":String(a),...e.timeout?{"X-Stainless-Timeout":String(Math.trunc(e.timeout/1e3))}:{},...t??(t=(()=>{let e="u">typeof Deno&&null!=Deno.build?"deno":"u">typeof EdgeRuntime?"edge":"[object process]"===Object.prototype.toString.call(void 0!==globalThis.process?globalThis.process:0)?"node":"unknown";if("deno"===e)return{"X-Stainless-Lang":"js","X-Stainless-Package-Version":sl,"X-Stainless-OS":sc(Deno.build.os),"X-Stainless-Arch":sd(Deno.build.arch),"X-Stainless-Runtime":"deno","X-Stainless-Runtime-Version":"string"==typeof Deno.version?Deno.version:Deno.version?.deno??"unknown"};if("u">typeof EdgeRuntime)return{"X-Stainless-Lang":"js","X-Stainless-Package-Version":sl,"X-Stainless-OS":"Unknown","X-Stainless-Arch":`other:${EdgeRuntime}`,"X-Stainless-Runtime":"edge","X-Stainless-Runtime-Version":globalThis.process.version};if("node"===e)return{"X-Stainless-Lang":"js","X-Stainless-Package-Version":sl,"X-Stainless-OS":sc(globalThis.process.platform??"unknown"),"X-Stainless-Arch":sd(globalThis.process.arch??"unknown"),"X-Stainless-Runtime":"node","X-Stainless-Runtime-Version":globalThis.process.version??"unknown"};let t=function(){if("u"e.abort()}buildBody({options:{body:e,headers:t}}){if(!e)return{bodyHeaders:void 0,body:void 0};let s=sY([t]);return ArrayBuffer.isView(e)||e instanceof ArrayBuffer||e instanceof DataView||"string"==typeof e&&s.values.has("content-type")||globalThis.Blob&&e instanceof globalThis.Blob||e instanceof FormData||e instanceof URLSearchParams||globalThis.ReadableStream&&e instanceof globalThis.ReadableStream?{bodyHeaders:void 0,body:e}:"object"==typeof e&&(Symbol.asyncIterator in e||Symbol.iterator in e&&"next"in e&&"function"==typeof e.next)?{bodyHeaders:void 0,body:sm(e)}:"object"==typeof e&&"application/x-www-form-urlencoded"===s.values.get("content-type")?{bodyHeaders:{"content-type":"application/x-www-form-urlencoded"},body:this.stringifyQuery(e)}:tK(this,eg,"f").call(this,{body:e,headers:s})}}ef=rG,eg=new WeakMap,ep=new WeakSet,ex=function(){return"https://api.anthropic.com"!==this.baseURL},rG.Anthropic=ef,rG.HUMAN_PROMPT="\\n\\nHuman:",rG.AI_PROMPT="\\n\\nAssistant:",rG.DEFAULT_TIMEOUT=6e5,rG.AnthropicError=tZ,rG.APIError=t0,rG.APIConnectionError=t2,rG.APIConnectionTimeoutError=t5,rG.APIUserAbortError=t1,rG.NotFoundError=t8,rG.ConflictError=t9,rG.RateLimitError=se,rG.BadRequestError=t4,rG.AuthenticationError=t3,rG.InternalServerError=st,rG.PermissionDeniedError=t6,rG.UnprocessableEntityError=t7,rG.toFile=sG;class rJ extends rG{constructor(){super(...arguments),this.completions=new rV(this),this.messages=new rB(this),this.models=new rW(this),this.beta=new rI(this)}}rJ.Completions=rV,rJ.Messages=rB,rJ.Models=rW,rJ.Beta=rI;let rK="toolset:";async function rX(e,t,s,r,a=[],n,i,o,l,d,c,u,m,h,p,f,g,x){if(!r)throw Error("Virtual Key is required");console.log=function(){};let b=p||(0,eU.getProxyBaseUrl)(),y={};a&&a.length>0&&(y["x-litellm-tags"]=a.join(","));let v=new rJ({apiKey:r,baseURL:b,dangerouslyAllowBrowser:!0,defaultHeaders:y});try{let r=Date.now(),a=!1,p={model:s,messages:e.map(e=>({role:e.role,content:e.content})),stream:!0,max_tokens:1024,litellm_trace_id:d},b=function({selectedMCPServers:e,mcpServers:t,mcpToolsets:s,mcpServerToolRestrictions:r}){return e&&0!==e.length?e.includes("__all__")?[{type:"mcp",server_label:"litellm",server_url:"litellm_proxy/mcp",require_approval:"never"}]:e.map(e=>{if(e.startsWith(rK)){let t=e.slice(rK.length),r=s?.find(e=>e.toolset_id===t),a=r?.toolset_name||t;return{type:"mcp",server_label:a,server_url:`litellm_proxy/mcp/${a}`,require_approval:"never"}}let a=t?.find(t=>t.server_id===e),n=a?.server_name||e,i=r?.[e]||[];return{type:"mcp",server_label:n,server_url:`litellm_proxy/mcp/${n}`,require_approval:"never",...i.length>0?{allowed_tools:i}:{}}}):[]}({selectedMCPServers:h,mcpServers:f,mcpToolsets:x,mcpServerToolRestrictions:g});for await(let e of(b.length>0&&(p.tools=b),c&&(p.vector_store_ids=c),u&&(p.guardrails=u),m&&(p.policies=m),v.messages.stream(p,{signal:n}))){if("content_block_delta"===e.type){let n=e.delta;if(!a){a=!0;let e=Date.now()-r;o&&o(e)}"text_delta"===n.type?t("assistant",n.text,s):"reasoning_delta"===n.type&&i&&i(n.text)}if("message_delta"===e.type&&e.usage&&l){let t=e.usage,s={completionTokens:t.output_tokens,promptTokens:t.input_tokens,totalTokens:t.input_tokens+t.output_tokens,...(0,eH.extractPromptCacheTokens)(t)};l(s)}}}catch(e){throw n?.aborted||eL.toast.fromError(`Error occurred while generating model response. 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Error: ${e}`),e}}async function rQ(e,t,s,r,a,n,i,o,l,d,c){console.log=function(){};let u=c||(0,eU.getProxyBaseUrl)(),m=new eV.default.OpenAI({apiKey:r,baseURL:u,dangerouslyAllowBrowser:!0,defaultHeaders:a&&a.length>0?{"x-litellm-tags":a.join(",")}:void 0});try{let r=await m.audio.transcriptions.create({model:s,file:e,...i?{language:i}:{},...o?{prompt:o}:{},...l?{response_format:l}:{},...void 0!==d?{temperature:d}:{}},{signal:n});if(r&&r.text)t(r.text,s),eL.toast.success("Audio transcribed successfully");else throw Error("No transcription text in response")}catch(e){if(console.error("Error making audio transcription request:",e),n?.aborted);else{let t="Failed to transcribe audio";e?.error?.message?t=e.error.message:e?.message&&(t=e.message),eL.toast.fromError(`Audio transcription failed: ${t}`)}throw e}}async function rZ(e,t,s,r,a,n){if(!r)throw Error("Virtual Key is required");console.log=function(){};let i=n||(0,eU.getProxyBaseUrl)(),o={};a&&a.length>0&&(o["x-litellm-tags"]=a.join(","));try{let a=i.endsWith("/")?i.slice(0,-1):i,n=`${a}/embeddings`,l=await fetch(n,{method:"POST",headers:{"Content-Type":"application/json",[(0,eU.getGlobalLitellmHeaderName)()]:`Bearer ${r}`,...o},body:JSON.stringify({model:s,input:e})});if(!l.ok){let e=await l.text();throw Error(e||`Request failed with status ${l.status}`)}let d=await l.json(),c=d?.data?.[0]?.embedding;if(!c)throw Error("No embedding returned from server");t(JSON.stringify(c),d?.model??s)}catch(e){throw eL.toast.fromError(`Error occurred while making embeddings request. Please try again. 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shadow-xs",style:{maxHeight:"500px"}})})]}),"assistant"===e.role&&(e.timeToFirstToken||e.totalLatency||e.usage)&&!e.a2aMetadata&&(0,eb.jsx)(aQ.default,{timeToFirstToken:e.timeToFirstToken,totalLatency:e.totalLatency,usage:e.usage,toolName:e.toolName}),"assistant"===e.role&&e.a2aMetadata&&(0,eb.jsx)(aq,{a2aMetadata:e.a2aMetadata,timeToFirstToken:e.timeToFirstToken,totalLatency:e.totalLatency})]})]})})},a4=({responsesUploadedImage:e,onImageUpload:t,disabled:s=!1})=>{let r=(0,ey.useRef)(null),a=(0,ey.useId)();return e?null:(0,eb.jsxs)(eb.Fragment,{children:[(0,eb.jsx)("input",{id:a,ref:r,type:"file",accept:al,className:"sr-only",tabIndex:-1,disabled:s,onChange:e=>{let s=e.target.files?.[0];if(e.target.value="",!s)return;let r=ab(s);r.ok?t(s):eL.toast.error(r.error)}}),(0,eb.jsxs)(t$.Tooltip,{children:[(0,eb.jsx)(t$.TooltipTrigger,{render:(0,eb.jsx)(eT.Button,{type:"button",variant:"ghost",size:"icon-sm",disabled:s,"aria-label":"Attach image or PDF",className:"text-muted-foreground 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${r}...`})()]}),t&&(0,eb.jsxs)(t$.Tooltip,{children:[(0,eb.jsx)(t$.TooltipTrigger,{render:(0,eb.jsx)(eT.Button,{type:"button",variant:"ghost",size:"icon-xs",onClick:a,"aria-label":"Copy response ID",className:"ml-2 hover:bg-success/15"}),children:(0,eb.jsx)(aL.Copy,{className:"size-3"})}),(0,eb.jsx)(t$.TooltipContent,{className:"max-w-lg",children:(0,eb.jsxs)("div",{className:"text-xs",children:[(0,eb.jsx)("div",{className:"mb-1",children:"Copy response ID to continue session:"}),(0,eb.jsx)("div",{className:"bg-gray-800 text-gray-100 p-2 rounded-sm font-mono text-xs whitespace-pre-wrap",children:`curl -X POST "your-proxy-url/v1/responses" \\ +Please migrate to a newer model. Visit https://docs.anthropic.com/en/docs/resources/model-deprecations for more information.`),rF.includes(e.model)&&e.thinking&&"enabled"===e.thinking.type&&console.warn(`Using Claude with ${e.model} and 'thinking.type=enabled' is deprecated. Use 'thinking.type=adaptive' instead which results in better model performance in our testing: https://platform.claude.com/docs/en/build-with-claude/adaptive-thinking`);let s=this._client._options.timeout;if(!e.stream&&null==s){let t=ri[e.model]??void 0;s=this._client.calculateNonstreamingTimeout(e.max_tokens,t)}let r=s3(e.tools,e.messages);return this._client.post("/v1/messages",{body:e,timeout:s??6e5,...t,headers:sY([r,t?.headers]),stream:e.stream??!1})}parse(e,t){return this.create(e,t).then(t=>r$(t,e,{logger:this._client.logger??console}))}stream(e,t){return rU.createMessage(this,e,t,{logger:this._client.logger??console})}countTokens(e,t){return this._client.post("/v1/messages/count_tokens",{body:e,...t})}}let rq={"claude-1.3":"November 6th, 2024","claude-1.3-100k":"November 6th, 2024","claude-instant-1.1":"November 6th, 2024","claude-instant-1.1-100k":"November 6th, 2024","claude-instant-1.2":"November 6th, 2024","claude-3-sonnet-20240229":"July 21st, 2025","claude-3-opus-20240229":"January 5th, 2026","claude-2.1":"July 21st, 2025","claude-2.0":"July 21st, 2025","claude-3-7-sonnet-latest":"February 19th, 2026","claude-3-7-sonnet-20250219":"February 19th, 2026","claude-3-5-haiku-latest":"February 19th, 2026","claude-3-5-haiku-20241022":"February 19th, 2026","claude-opus-4-0":"June 15th, 2026","claude-opus-4-20250514":"June 15th, 2026","claude-sonnet-4-0":"June 15th, 2026","claude-sonnet-4-20250514":"June 15th, 2026"},rF=["claude-mythos-preview","claude-opus-4-6"];rB.Batches=rz;class rW extends sK{retrieve(e,t={},s){let{betas:r}=t??{};return this._client.get(s0`/v1/models/${e}`,{...s,headers:sY([{...r?.toString()!=null?{"anthropic-beta":r?.toString()}:void 0},s?.headers])})}list(e={},t){let{betas:s,...r}=e??{};return this._client.getAPIList("/v1/models",sO,{query:r,...t,headers:sY([{...s?.toString()!=null?{"anthropic-beta":s?.toString()}:void 0},t?.headers])})}}class rV extends sK{create(e,t){let{betas:s,...r}=e;return this._client.post("/v1/complete",{body:r,timeout:this._client._options.timeout??6e5,...t,headers:sY([{...s?.toString()!=null?{"anthropic-beta":s?.toString()}:void 0},t?.headers]),stream:e.stream??!1})}}let rH=e=>void 0!==globalThis.process?globalThis.process.env?.[e]?.trim()||void 0:void 0!==globalThis.Deno&&globalThis.Deno.env?.get?.(e)?.trim()||void 0;class rG{constructor({baseURL:e=rH("ANTHROPIC_BASE_URL"),apiKey:t=rH("ANTHROPIC_API_KEY")??null,authToken:s=rH("ANTHROPIC_AUTH_TOKEN")??null,...r}={}){ep.add(this),eg.set(this,void 0);const a={apiKey:t,authToken:s,...r,baseURL:e||"https://api.anthropic.com"};if(!a.dangerouslyAllowBrowser&&"u">typeof window&&void 0!==window.document&&"u">typeof navigator)throw new tZ("It looks like you're running in a browser-like environment.\n\nThis is disabled by default, as it risks exposing your secret API credentials to attackers.\nIf you understand the risks and have appropriate mitigations in place,\nyou can set the `dangerouslyAllowBrowser` option to `true`, e.g.,\n\nnew Anthropic({ apiKey, dangerouslyAllowBrowser: true });\n");this.baseURL=a.baseURL,this.timeout=a.timeout??ef.DEFAULT_TIMEOUT,this.logger=a.logger??console;const n="warn";this.logLevel=n,this.logLevel=sv(a.logLevel,"ClientOptions.logLevel",this)??sv(rH("ANTHROPIC_LOG"),"process.env['ANTHROPIC_LOG']",this)??n,this.fetchOptions=a.fetchOptions,this.maxRetries=a.maxRetries??2,this.fetch=a.fetch??function(){if("u">typeof fetch)return fetch;throw Error("`fetch` is not defined as a global; Either pass `fetch` to the client, `new Anthropic({ fetch })` or polyfill the global, `globalThis.fetch = fetch`")}(),tJ(this,eg,sf,"f");const i=rH("ANTHROPIC_CUSTOM_HEADERS");if(i){const e={};for(const t of i.split("\n")){const s=t.indexOf(":");s>=0&&(e[t.substring(0,s).trim()]=t.substring(s+1).trim())}a.defaultHeaders={...e,...a.defaultHeaders}}this._options=a,this.apiKey="string"==typeof t?t:null,this.authToken=s}withOptions(e){return new this.constructor({...this._options,baseURL:this.baseURL,maxRetries:this.maxRetries,timeout:this.timeout,logger:this.logger,logLevel:this.logLevel,fetch:this.fetch,fetchOptions:this.fetchOptions,apiKey:this.apiKey,authToken:this.authToken,...e})}defaultQuery(){return this._options.defaultQuery}validateHeaders({values:e,nulls:t}){if(!(e.get("x-api-key")||e.get("authorization")||this.apiKey&&e.get("x-api-key")||t.has("x-api-key")||this.authToken&&e.get("authorization"))&&!t.has("authorization"))throw Error('Could not resolve authentication method. 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n={};this.idempotencyHeader&&"get"!==s&&(e.idempotencyKey||(e.idempotencyKey=this.defaultIdempotencyKey()),n[this.idempotencyHeader]=e.idempotencyKey);let i=sY([n,{Accept:"application/json","User-Agent":this.getUserAgent(),"X-Stainless-Retry-Count":String(a),...e.timeout?{"X-Stainless-Timeout":String(Math.trunc(e.timeout/1e3))}:{},...t??(t=(()=>{let e="u">typeof Deno&&null!=Deno.build?"deno":"u">typeof EdgeRuntime?"edge":"[object process]"===Object.prototype.toString.call(void 0!==globalThis.process?globalThis.process:0)?"node":"unknown";if("deno"===e)return{"X-Stainless-Lang":"js","X-Stainless-Package-Version":sl,"X-Stainless-OS":sc(Deno.build.os),"X-Stainless-Arch":sd(Deno.build.arch),"X-Stainless-Runtime":"deno","X-Stainless-Runtime-Version":"string"==typeof Deno.version?Deno.version:Deno.version?.deno??"unknown"};if("u">typeof EdgeRuntime)return{"X-Stainless-Lang":"js","X-Stainless-Package-Version":sl,"X-Stainless-OS":"Unknown","X-Stainless-Arch":`other:${EdgeRuntime}`,"X-Stainless-Runtime":"edge","X-Stainless-Runtime-Version":globalThis.process.version};if("node"===e)return{"X-Stainless-Lang":"js","X-Stainless-Package-Version":sl,"X-Stainless-OS":sc(globalThis.process.platform??"unknown"),"X-Stainless-Arch":sd(globalThis.process.arch??"unknown"),"X-Stainless-Runtime":"node","X-Stainless-Runtime-Version":globalThis.process.version??"unknown"};let t=function(){if("u"e.abort()}buildBody({options:{body:e,headers:t}}){if(!e)return{bodyHeaders:void 0,body:void 0};let s=sY([t]);return ArrayBuffer.isView(e)||e instanceof ArrayBuffer||e instanceof DataView||"string"==typeof e&&s.values.has("content-type")||globalThis.Blob&&e instanceof globalThis.Blob||e instanceof FormData||e instanceof URLSearchParams||globalThis.ReadableStream&&e instanceof globalThis.ReadableStream?{bodyHeaders:void 0,body:e}:"object"==typeof 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n={image_generation:"image",video_generation:"video",chat:"chat",completion:"chat",responses:"responses",image_edit:"image_edits",anthropic_messages:"anthropic_messages",audio_speech:"speech",audio_transcription:"transcription",embedding:"embeddings",realtime:"realtime"};e.s(["EndpointType",()=>o,"ModelMode",()=>a,"getEndpointType",0,e=>Object.values(a).includes(e)?n[e]:"chat"],865361),e.s(["generateCodeSnippet",0,e=>{let t,{apiKeySource:i,accessToken:a,apiKey:n,inputMessage:r,chatHistory:s,selectedTags:l,selectedVectorStores:p,selectedGuardrails:d,selectedPolicies:m,selectedVoice:g,endpointType:u,selectedModel:c,selectedSdk:f,proxySettings:h}=e,_="session"===i?a:n,b=window.location.origin,x=h?.LITELLM_UI_API_DOC_BASE_URL;x&&x.trim()?b=x:h?.PROXY_BASE_URL&&(b=h.PROXY_BASE_URL);let y=r||"Your prompt here",j=y.replace(/\\/g,"\\\\").replace(/"/g,'\\"').replace(/\n/g,"\\n"),S=s.filter(e=>!e.isImage).map(({role:e,content:t})=>({role:e,content:t})),v={};l.length>0&&(v.tags=l),p.length>0&&(v.vector_stores=p),d.length>0&&(v.guardrails=d),m.length>0&&(v.policies=m);let k=c||"your-model-name",w="azure"===f?`import openai - -client = openai.AzureOpenAI( - api_key="${_||"YOUR_LITELLM_API_KEY"}", - azure_endpoint="${b}", - api_version="2024-02-01" -)`:`import openai - -client = openai.OpenAI( - api_key="${_||"YOUR_LITELLM_API_KEY"}", - base_url="${b}" -)`;switch(u){case o.CHAT:{let e=Object.keys(v).length>0,i="";if(e){let e=JSON.stringify({metadata:v},null,2).split("\n").map(e=>" ".repeat(4)+e).join("\n").trim();i=`, - extra_body=${e}`}let a=S.length>0?S:[{role:"user",content:y}];t=` -import base64 - -# Helper function to encode images to base64 -def encode_image(image_path): - with open(image_path, "rb") as image_file: - return base64.b64encode(image_file.read()).decode('utf-8') - -# Example with text only -response = client.chat.completions.create( - model="${k}", - messages=${JSON.stringify(a,null,4)}${i} -) - -print(response) - -# Example with image or PDF (uncomment and provide file path to use) -# base64_file = encode_image("path/to/your/file.jpg") # or .pdf -# response_with_file = client.chat.completions.create( -# model="${k}", -# messages=[ -# { -# "role": "user", -# "content": [ -# { -# "type": "text", -# "text": "${j}" -# }, -# { -# "type": "image_url", -# "image_url": { -# "url": f"data:image/jpeg;base64,{base64_file}" # or data:application/pdf;base64,{base64_file} -# } -# } -# ] -# } -# ]${i} -# ) -# print(response_with_file) -`;break}case o.RESPONSES:{let e=Object.keys(v).length>0,i="";if(e){let e=JSON.stringify({metadata:v},null,2).split("\n").map(e=>" ".repeat(4)+e).join("\n").trim();i=`, - extra_body=${e}`}let a=S.length>0?S:[{role:"user",content:y}];t=` -import base64 - -# Helper function to encode images to base64 -def encode_image(image_path): - with open(image_path, "rb") as image_file: - return base64.b64encode(image_file.read()).decode('utf-8') - -# Example with text only -response = client.responses.create( - model="${k}", - input=${JSON.stringify(a,null,4)}${i} -) - -print(response.output_text) - -# Example with image or PDF (uncomment and provide file path to use) -# base64_file = encode_image("path/to/your/file.jpg") # or .pdf -# response_with_file = client.responses.create( -# model="${k}", -# input=[ -# { -# "role": "user", -# "content": [ -# {"type": "input_text", "text": "${j}"}, -# { -# "type": "input_image", -# "image_url": f"data:image/jpeg;base64,{base64_file}", # or data:application/pdf;base64,{base64_file} -# }, -# ], -# } -# ]${i} -# ) -# print(response_with_file.output_text) -`;break}case o.IMAGE:t="azure"===f?` -# NOTE: The Azure SDK does not have a direct equivalent to the multi-modal 'responses.create' method shown for OpenAI. -# This snippet uses 'client.images.generate' and will create a new image based on your prompt. -# It does not use the uploaded image, as 'client.images.generate' does not support image inputs in this context. -import os -import requests -import json -import time -from PIL import Image - -result = client.images.generate( - model="${k}", - prompt="${r}", - n=1 -) - -json_response = json.loads(result.model_dump_json()) - -# Set the directory for the stored image -image_dir = os.path.join(os.curdir, 'images') - -# If the directory doesn't exist, create it -if not os.path.isdir(image_dir): - os.mkdir(image_dir) - -# Initialize the image path -image_filename = f"generated_image_{int(time.time())}.png" -image_path = os.path.join(image_dir, image_filename) - -try: - # Retrieve the generated image - if json_response.get("data") && len(json_response["data"]) > 0 && json_response["data"][0].get("url"): - image_url = json_response["data"][0]["url"] - generated_image = requests.get(image_url).content - with open(image_path, "wb") as image_file: - image_file.write(generated_image) - - print(f"Image saved to {image_path}") - # Display the image - image = Image.open(image_path) - image.show() - else: - print("Could not find image URL in response.") - print("Full response:", json_response) -except Exception as e: - print(f"An error occurred: {e}") - print("Full response:", json_response) -`:` -import base64 -import os -import time -import json -from PIL import Image -import requests - -# Helper function to encode images to base64 -def encode_image(image_path): - with open(image_path, "rb") as image_file: - return base64.b64encode(image_file.read()).decode('utf-8') - -# Helper function to create a file (simplified for this example) -def create_file(image_path): - # In a real implementation, this would upload the file to OpenAI - # For this example, we'll just return a placeholder ID - return f"file_{os.path.basename(image_path).replace('.', '_')}" - -# The prompt entered by the user -prompt = "${j}" - -# Encode images to base64 -base64_image1 = encode_image("body-lotion.png") -base64_image2 = encode_image("soap.png") - -# Create file IDs -file_id1 = create_file("body-lotion.png") -file_id2 = create_file("incense-kit.png") - -response = client.responses.create( - model="${k}", - input=[ - { - "role": "user", - "content": [ - {"type": "input_text", "text": prompt}, - { - "type": "input_image", - "image_url": f"data:image/jpeg;base64,{base64_image1}", - }, - { - "type": "input_image", - "image_url": f"data:image/jpeg;base64,{base64_image2}", - }, - { - "type": "input_image", - "file_id": file_id1, - }, - { - "type": "input_image", - "file_id": file_id2, - } - ], - } - ], - tools=[{"type": "image_generation"}], -) - -# Process the response -image_generation_calls = [ - output - for output in response.output - if output.type == "image_generation_call" -] - -image_data = [output.result for output in image_generation_calls] - -if image_data: - image_base64 = image_data[0] - image_filename = f"edited_image_{int(time.time())}.png" - with open(image_filename, "wb") as f: - f.write(base64.b64decode(image_base64)) - print(f"Image saved to {image_filename}") -else: - # If no image is generated, there might be a text response with an explanation - text_response = [output.text for output in response.output if hasattr(output, 'text')] - if text_response: - print("No image generated. Model response:") - print("\\n".join(text_response)) - else: - print("No image data found in response.") - print("Full response for debugging:") - print(response) -`;break;case o.IMAGE_EDITS:t="azure"===f?` -import base64 -import os -import time -import json -from PIL import Image -import requests - -# Helper function to encode images to base64 -def encode_image(image_path): - with open(image_path, "rb") as image_file: - return base64.b64encode(image_file.read()).decode('utf-8') - -# The prompt entered by the user -prompt = "${j}" - -# Encode images to base64 -base64_image1 = encode_image("body-lotion.png") -base64_image2 = encode_image("soap.png") - -# Create file IDs -file_id1 = create_file("body-lotion.png") -file_id2 = create_file("incense-kit.png") - -response = client.responses.create( - model="${k}", - input=[ - { - "role": "user", - "content": [ - {"type": "input_text", "text": prompt}, - { - "type": "input_image", - "image_url": f"data:image/jpeg;base64,{base64_image1}", - }, - { - "type": "input_image", - "image_url": f"data:image/jpeg;base64,{base64_image2}", - }, - { - "type": "input_image", - "file_id": file_id1, - }, - { - "type": "input_image", - "file_id": file_id2, - } - ], - } - ], - tools=[{"type": "image_generation"}], -) - -# Process the response -image_generation_calls = [ - output - for output in response.output - if output.type == "image_generation_call" -] - -image_data = [output.result for output in image_generation_calls] - -if image_data: - image_base64 = image_data[0] - image_filename = f"edited_image_{int(time.time())}.png" - with open(image_filename, "wb") as f: - f.write(base64.b64decode(image_base64)) - print(f"Image saved to {image_filename}") -else: - # If no image is generated, there might be a text response with an explanation - text_response = [output.text for output in response.output if hasattr(output, 'text')] - if text_response: - print("No image generated. Model response:") - print("\\n".join(text_response)) - else: - print("No image data found in response.") - print("Full response for debugging:") - print(response) -`:` -import base64 -import os -import time - -# Helper function to encode images to base64 -def encode_image(image_path): - with open(image_path, "rb") as image_file: - return base64.b64encode(image_file.read()).decode('utf-8') - -# Helper function to create a file (simplified for this example) -def create_file(image_path): - # In a real implementation, this would upload the file to OpenAI - # For this example, we'll just return a placeholder ID - return f"file_{os.path.basename(image_path).replace('.', '_')}" - -# The prompt entered by the user -prompt = "${j}" - -# Encode images to base64 -base64_image1 = encode_image("body-lotion.png") -base64_image2 = encode_image("soap.png") - -# Create file IDs -file_id1 = create_file("body-lotion.png") -file_id2 = create_file("incense-kit.png") - -response = client.responses.create( - model="${k}", - input=[ - { - "role": "user", - "content": [ - {"type": "input_text", "text": prompt}, - { - "type": "input_image", - "image_url": f"data:image/jpeg;base64,{base64_image1}", - }, - { - "type": "input_image", - "image_url": f"data:image/jpeg;base64,{base64_image2}", - }, - { - "type": "input_image", - "file_id": file_id1, - }, - { - "type": "input_image", - "file_id": file_id2, - } - ], - } - ], - tools=[{"type": "image_generation"}], -) - -# Process the response -image_generation_calls = [ - output - for output in response.output - if output.type == "image_generation_call" -] - -image_data = [output.result for output in image_generation_calls] - -if image_data: - image_base64 = image_data[0] - image_filename = f"edited_image_{int(time.time())}.png" - with open(image_filename, "wb") as f: - f.write(base64.b64decode(image_base64)) - print(f"Image saved to {image_filename}") -else: - # If no image is generated, there might be a text response with an explanation - text_response = [output.text for output in response.output if hasattr(output, 'text')] - if text_response: - print("No image generated. 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with text only +response = client.chat.completions.create( + model="${v}", + messages=${JSON.stringify(a,null,4)}${i} +) + +print(response) + +# Example with image or PDF (uncomment and provide file path to use) +# base64_file = encode_image("path/to/your/file.jpg") # or .pdf +# response_with_file = client.chat.completions.create( +# model="${v}", +# messages=[ +# { +# "role": "user", +# "content": [ +# { +# "type": "text", +# "text": "${j}" +# }, +# { +# "type": "image_url", +# "image_url": { +# "url": f"data:image/jpeg;base64,{base64_file}" # or data:application/pdf;base64,{base64_file} +# } +# } +# ] +# } +# ]${i} +# ) +# print(response_with_file) +`;break}case o.RESPONSES:{let e=Object.keys(S).length>0,i="";if(e){let e=JSON.stringify({metadata:S},null,2).split("\n").map(e=>" ".repeat(4)+e).join("\n").trim();i=`, + extra_body=${e}`}let a=k.length>0?k:[{role:"user",content:y}];t=` +import base64 + +# Helper function to encode images to base64 +def encode_image(image_path): + with open(image_path, "rb") as image_file: + return base64.b64encode(image_file.read()).decode('utf-8') + +# Example with text only +response = client.responses.create( + model="${v}", + input=${JSON.stringify(a,null,4)}${i} +) + +print(response.output_text) + +# Example with image or PDF (uncomment and provide file path to use) +# base64_file = encode_image("path/to/your/file.jpg") # or .pdf +# response_with_file = client.responses.create( +# model="${v}", +# input=[ +# { +# "role": "user", +# "content": [ +# {"type": "input_text", "text": "${j}"}, +# { +# "type": "input_image", +# "image_url": f"data:image/jpeg;base64,{base64_file}", # or data:application/pdf;base64,{base64_file} +# }, +# ], +# } +# ]${i} +# ) +# print(response_with_file.output_text) +`;break}case o.IMAGE:t="azure"===f?` +# NOTE: The Azure SDK does not have a direct equivalent to the multi-modal 'responses.create' method shown for OpenAI. +# This snippet uses 'client.images.generate' and will create a new image based on your prompt. +# It does not use the uploaded image, as 'client.images.generate' does not support image inputs in this context. +import os +import requests +import json +import time +from PIL import Image + +result = client.images.generate( + model="${v}", + prompt="${r}", + n=1 +) + +json_response = json.loads(result.model_dump_json()) + +# Set the directory for the stored image +image_dir = os.path.join(os.curdir, 'images') + +# If the directory doesn't exist, create it +if not os.path.isdir(image_dir): + os.mkdir(image_dir) + +# Initialize the image path +image_filename = f"generated_image_{int(time.time())}.png" +image_path = os.path.join(image_dir, image_filename) + +try: + # Retrieve the generated image + if json_response.get("data") && len(json_response["data"]) > 0 && json_response["data"][0].get("url"): + image_url = json_response["data"][0]["url"] + generated_image = requests.get(image_url).content + with open(image_path, "wb") as image_file: + image_file.write(generated_image) + + print(f"Image saved to {image_path}") + # Display the image + image = Image.open(image_path) + image.show() + else: + print("Could not find image URL in response.") + print("Full response:", json_response) +except Exception as e: + print(f"An error occurred: {e}") + print("Full response:", json_response) +`:` +import base64 +import os +import time +import json +from PIL import Image +import requests + +# Helper function to encode images to base64 +def encode_image(image_path): + with open(image_path, "rb") as image_file: + return base64.b64encode(image_file.read()).decode('utf-8') + +# Helper function to create a file (simplified for this example) +def create_file(image_path): + # In a real implementation, this would upload the file to OpenAI + # For this example, we'll just return a placeholder ID + return f"file_{os.path.basename(image_path).replace('.', '_')}" + +# The prompt entered by the user +prompt = "${j}" + +# Encode images to base64 +base64_image1 = encode_image("body-lotion.png") +base64_image2 = encode_image("soap.png") + +# Create file IDs +file_id1 = create_file("body-lotion.png") +file_id2 = create_file("incense-kit.png") + +response = client.responses.create( + model="${v}", + input=[ + { + "role": "user", + "content": [ + {"type": "input_text", "text": prompt}, + { + "type": "input_image", + "image_url": f"data:image/jpeg;base64,{base64_image1}", + }, + { + "type": "input_image", + "image_url": f"data:image/jpeg;base64,{base64_image2}", + }, + { + "type": "input_image", + "file_id": file_id1, + }, + { + "type": "input_image", + "file_id": file_id2, + } + ], + } + ], + tools=[{"type": "image_generation"}], +) + +# Process the response +image_generation_calls = [ + output + for output in response.output + if output.type == "image_generation_call" +] + +image_data = [output.result for output in image_generation_calls] + +if image_data: + image_base64 = image_data[0] + image_filename = f"edited_image_{int(time.time())}.png" + with open(image_filename, "wb") as f: + f.write(base64.b64decode(image_base64)) + print(f"Image saved to {image_filename}") +else: + # If no image is generated, there might be a text response with an explanation + text_response = [output.text for output in response.output if hasattr(output, 'text')] + if text_response: + print("No image generated. Model response:") + print("\\n".join(text_response)) + else: + print("No image data found in response.") + print("Full response for debugging:") + print(response) +`;break;case o.IMAGE_EDITS:t="azure"===f?` +import base64 +import os +import time +import json +from PIL import Image +import requests + +# Helper function to encode images to base64 +def encode_image(image_path): + with open(image_path, "rb") as image_file: + return base64.b64encode(image_file.read()).decode('utf-8') + +# The prompt entered by the user +prompt = "${j}" + +# Encode images to base64 +base64_image1 = encode_image("body-lotion.png") +base64_image2 = encode_image("soap.png") + +# Create file IDs +file_id1 = create_file("body-lotion.png") +file_id2 = create_file("incense-kit.png") + +response = client.responses.create( + model="${v}", + input=[ + { + "role": "user", + "content": [ + {"type": "input_text", "text": prompt}, + { + "type": "input_image", + "image_url": f"data:image/jpeg;base64,{base64_image1}", + }, + { + "type": "input_image", + "image_url": f"data:image/jpeg;base64,{base64_image2}", + }, + { + "type": "input_image", + "file_id": file_id1, + }, + { + "type": "input_image", + "file_id": file_id2, + } + ], + } + ], + tools=[{"type": "image_generation"}], +) + +# Process the response +image_generation_calls = [ + output + for output in response.output + if output.type == "image_generation_call" +] + +image_data = [output.result for output in image_generation_calls] + +if image_data: + image_base64 = image_data[0] + image_filename = f"edited_image_{int(time.time())}.png" + with open(image_filename, "wb") as f: + f.write(base64.b64decode(image_base64)) + print(f"Image saved to {image_filename}") +else: + # If no image is generated, there might be a text response with an explanation + text_response = [output.text for output in response.output if hasattr(output, 'text')] + if text_response: + print("No image generated. Model response:") + print("\\n".join(text_response)) + else: + print("No image data found in response.") + print("Full response for debugging:") + print(response) +`:` +import base64 +import os +import time + +# Helper function to encode images to base64 +def encode_image(image_path): + with open(image_path, "rb") as image_file: + return base64.b64encode(image_file.read()).decode('utf-8') + +# Helper function to create a file (simplified for this example) +def create_file(image_path): + # In a real implementation, this would upload the file to OpenAI + # For this example, we'll just return a placeholder ID + return f"file_{os.path.basename(image_path).replace('.', '_')}" + +# The prompt entered by the user +prompt = "${j}" + +# Encode images to base64 +base64_image1 = encode_image("body-lotion.png") +base64_image2 = encode_image("soap.png") + +# Create file IDs +file_id1 = create_file("body-lotion.png") +file_id2 = create_file("incense-kit.png") + +response = client.responses.create( + model="${v}", + input=[ + { + "role": "user", + "content": [ + {"type": "input_text", "text": prompt}, + { + "type": "input_image", + "image_url": f"data:image/jpeg;base64,{base64_image1}", + }, + { + "type": "input_image", + "image_url": f"data:image/jpeg;base64,{base64_image2}", + }, + { + "type": "input_image", + "file_id": file_id1, + }, + { + "type": "input_image", + "file_id": file_id2, + } + ], + } + ], + tools=[{"type": "image_generation"}], +) + +# Process the response +image_generation_calls = [ + output + for output in response.output + if output.type == "image_generation_call" +] + +image_data = [output.result for output in image_generation_calls] + +if image_data: + image_base64 = image_data[0] + image_filename = f"edited_image_{int(time.time())}.png" + with open(image_filename, "wb") as f: + f.write(base64.b64decode(image_base64)) + print(f"Image saved to {image_filename}") +else: + # If no image is generated, there might be a text response with an explanation + text_response = [output.text for output in response.output if hasattr(output, 'text')] + if text_response: + print("No image generated. 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🚅 LiteLLM
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\ No newline at end of file +LiteLLM Dashboard
🚅 LiteLLM
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T.createElement(T.Fragment,null,T.createElement(hx,{legendPayload:(n=t.dataKey,i=t.name,a=t.stroke,o=t.fill,l=t.legendType,[{inactive:t.hide,dataKey:n,type:l,color:vo(a,o),value:nX(i,n),payload:t}])}),T.createElement(vl,{dataKey:t.dataKey,data:t.data,stroke:t.stroke,strokeWidth:t.strokeWidth,fill:t.fill,name:t.name,hide:t.hide,unit:t.unit,formatter:t.formatter,tooltipType:t.tooltipType,id:e}),T.createElement(ye,{type:"area",id:e,data:t.data,dataKey:t.dataKey,xAxisId:t.xAxisId,yAxisId:t.yAxisId,zAxisId:0,stackId:nU(t.stackId),hide:t.hide,barSize:void 0,baseValue:t.baseValue,isPanorama:r,connectNulls:t.connectNulls}),T.createElement(vh,vt({},t,{id:e})))})},yg);vy.displayName="Area";var vv=(e,t)=>{if(t&&Array.isArray(e)){var r=Number.parseInt(t,10);if(!ee(r))return e[r]}},vm=rB({name:"options",initialState:{chartName:"",tooltipPayloadSearcher:()=>void 0,eventEmitter:void 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u=e,c=t,s=n,f=i,d=a,p=o,h=l;if(u&&s&&f&&d&&(y=u.relativeX,v=u.relativeY,y>=h.left&&y<=h.left+h.width&&v>=h.top&&v<=h.top+h.height)){var y,v,m=p9("horizontal"===c?u.relativeX:"vertical"===c?u.relativeY:void 0,p,d,s,f),g=((e,t,r,n)=>{var i=t.find(e=>e&&e.index===r);if(i){if("horizontal"===e)return{x:i.coordinate,y:n.relativeY};if("vertical"===e)return{x:n.relativeX,y:i.coordinate}}return{x:0,y:0}})(c,d,m,u);return{activeIndex:String(m),activeCoordinate:g}}return}if(e&&n&&i&&a&&r){var b=((e,t)=>{var r,n,i,a,o=((e,t)=>{var r,n,i,a,o=e.x,l=e.y,u=t.cx,c=t.cy,s=(r={x:o,y:l},n={x:u,y:c},i=r.x,a=r.y,Math.sqrt((i-n.x)**2+(a-n.y)**2));if(s<=0)return{radius:s,angle:0};var f=Math.acos((o-u)/s);return l>c&&(f=2*Math.PI-f),{radius:s,angle:180*f/Math.PI,angleInRadian:f}})({x:e.relativeX,y:e.relativeY},t),l=o.radius,u=o.angle,c=t.innerRadius,s=t.outerRadius;if(ls||0===l)return null;var 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t.container=e.delta.container,t.stop_reason=e.delta.stop_reason,t.stop_sequence=e.delta.stop_sequence,t.usage.output_tokens=e.usage.output_tokens,t.context_management=e.context_management,null!=e.usage.input_tokens&&(t.usage.input_tokens=e.usage.input_tokens),null!=e.usage.cache_creation_input_tokens&&(t.usage.cache_creation_input_tokens=e.usage.cache_creation_input_tokens),null!=e.usage.cache_read_input_tokens&&(t.usage.cache_read_input_tokens=e.usage.cache_read_input_tokens),null!=e.usage.server_tool_use&&(t.usage.server_tool_use=e.usage.server_tool_use),null!=e.usage.iterations&&(t.usage.iterations=e.usage.iterations),t;case"content_block_start":return t.content.push(e.content_block),t;case"content_block_delta":{let s=t.content.at(e.index);switch(e.delta.type){case"text_delta":s?.type==="text"&&(t.content[e.index]={...s,text:(s.text||"")+e.delta.text});break;case"citations_delta":s?.type==="text"&&(t.content[e.index]={...s,citations:[...s.citations??[],e.delta.citation]});break;case"input_json_delta":if(s&&rh(s)){let r=s[rm]||"";r+=e.delta.partial_json;let a={...s};if(Object.defineProperty(a,rm,{value:r,enumerable:!1,writable:!0}),r)try{a.input=ru(r)}catch(t){let e=new tZ(`Unable to parse tool parameter JSON from model. 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Please use only output_config.format (output_format is deprecated).");let{output_format:t,...s}=e;return{...s,output_config:{...e.output_config,format:t}}}r_.Batches=rn,r_.BetaToolRunner=ry,r_.ToolError=rg;class rS extends sK{list(e,t={},s){let{betas:r,...a}=t??{};return this._client.getAPIList(s0`/v1/sessions/${e}/events?beta=true`,sL,{query:a,...s,headers:sY([{"anthropic-beta":[...r??[],"managed-agents-2026-04-01"].toString()},s?.headers])})}send(e,t,s){let{betas:r,...a}=t;return this._client.post(s0`/v1/sessions/${e}/events?beta=true`,{body:a,...s,headers:sY([{"anthropic-beta":[...r??[],"managed-agents-2026-04-01"].toString()},s?.headers])})}stream(e,t={},s){let{betas:r}=t??{};return this._client.get(s0`/v1/sessions/${e}/events/stream?beta=true`,{...s,headers:sY([{"anthropic-beta":[...r??[],"managed-agents-2026-04-01"].toString()},s?.headers]),stream:!0})}}class rk extends sK{retrieve(e,t,s){let{session_id:r,betas:a}=t;return this._client.get(s0`/v1/sessions/${r}/resources/${e}?beta=true`,{...s,headers:sY([{"anthropic-beta":[...a??[],"managed-agents-2026-04-01"].toString()},s?.headers])})}update(e,t,s){let{session_id:r,betas:a,...n}=t;return this._client.post(s0`/v1/sessions/${r}/resources/${e}?beta=true`,{body:n,...s,headers:sY([{"anthropic-beta":[...a??[],"managed-agents-2026-04-01"].toString()},s?.headers])})}list(e,t={},s){let{betas:r,...a}=t??{};return this._client.getAPIList(s0`/v1/sessions/${e}/resources?beta=true`,sL,{query:a,...s,headers:sY([{"anthropic-beta":[...r??[],"managed-agents-2026-04-01"].toString()},s?.headers])})}delete(e,t,s){let{session_id:r,betas:a}=t;return this._client.delete(s0`/v1/sessions/${r}/resources/${e}?beta=true`,{...s,headers:sY([{"anthropic-beta":[...a??[],"managed-agents-2026-04-01"].toString()},s?.headers])})}add(e,t,s){let{betas:r,...a}=t;return this._client.post(s0`/v1/sessions/${e}/resources?beta=true`,{body:a,...s,headers:sY([{"anthropic-beta":[...r??[],"managed-agents-2026-04-01"].toString()},s?.headers])})}}class rC extends sK{constructor(){super(...arguments),this.events=new rS(this._client),this.resources=new rk(this._client)}create(e,t){let{betas:s,...r}=e;return this._client.post("/v1/sessions?beta=true",{body:r,...t,headers:sY([{"anthropic-beta":[...s??[],"managed-agents-2026-04-01"].toString()},t?.headers])})}retrieve(e,t={},s){let{betas:r}=t??{};return this._client.get(s0`/v1/sessions/${e}?beta=true`,{...s,headers:sY([{"anthropic-beta":[...r??[],"managed-agents-2026-04-01"].toString()},s?.headers])})}update(e,t,s){let{betas:r,...a}=t;return this._client.post(s0`/v1/sessions/${e}?beta=true`,{body:a,...s,headers:sY([{"anthropic-beta":[...r??[],"managed-agents-2026-04-01"].toString()},s?.headers])})}list(e={},t){let{betas:s,...r}=e??{};return this._client.getAPIList("/v1/sessions?beta=true",sL,{query:r,...t,headers:sY([{"anthropic-beta":[...s??[],"managed-agents-2026-04-01"].toString()},t?.headers])})}delete(e,t={},s){let{betas:r}=t??{};return this._client.delete(s0`/v1/sessions/${e}?beta=true`,{...s,headers:sY([{"anthropic-beta":[...r??[],"managed-agents-2026-04-01"].toString()},s?.headers])})}archive(e,t={},s){let{betas:r}=t??{};return this._client.post(s0`/v1/sessions/${e}/archive?beta=true`,{...s,headers:sY([{"anthropic-beta":[...r??[],"managed-agents-2026-04-01"].toString()},s?.headers])})}}rC.Events=rS,rC.Resources=rk;class rT extends sK{create(e,t={},s){let{betas:r,...a}=t??{};return this._client.post(s0`/v1/skills/${e}/versions?beta=true`,sq({body:a,...s,headers:sY([{"anthropic-beta":[...r??[],"skills-2025-10-02"].toString()},s?.headers])},this._client))}retrieve(e,t,s){let{skill_id:r,betas:a}=t;return this._client.get(s0`/v1/skills/${r}/versions/${e}?beta=true`,{...s,headers:sY([{"anthropic-beta":[...a??[],"skills-2025-10-02"].toString()},s?.headers])})}list(e,t={},s){let{betas:r,...a}=t??{};return this._client.getAPIList(s0`/v1/skills/${e}/versions?beta=true`,sL,{query:a,...s,headers:sY([{"anthropic-beta":[...r??[],"skills-2025-10-02"].toString()},s?.headers])})}delete(e,t,s){let{skill_id:r,betas:a}=t;return this._client.delete(s0`/v1/skills/${r}/versions/${e}?beta=true`,{...s,headers:sY([{"anthropic-beta":[...a??[],"skills-2025-10-02"].toString()},s?.headers])})}}class rE extends sK{constructor(){super(...arguments),this.versions=new rT(this._client)}create(e={},t){let{betas:s,...r}=e??{};return this._client.post("/v1/skills?beta=true",sq({body:r,...t,headers:sY([{"anthropic-beta":[...s??[],"skills-2025-10-02"].toString()},t?.headers])},this._client,!1))}retrieve(e,t={},s){let{betas:r}=t??{};return this._client.get(s0`/v1/skills/${e}?beta=true`,{...s,headers:sY([{"anthropic-beta":[...r??[],"skills-2025-10-02"].toString()},s?.headers])})}list(e={},t){let{betas:s,...r}=e??{};return this._client.getAPIList("/v1/skills?beta=true",sL,{query:r,...t,headers:sY([{"anthropic-beta":[...s??[],"skills-2025-10-02"].toString()},t?.headers])})}delete(e,t={},s){let{betas:r}=t??{};return this._client.delete(s0`/v1/skills/${e}?beta=true`,{...s,headers:sY([{"anthropic-beta":[...r??[],"skills-2025-10-02"].toString()},s?.headers])})}}rE.Versions=rT;class rA extends sK{create(e,t,s){let{betas:r,...a}=t;return this._client.post(s0`/v1/vaults/${e}/credentials?beta=true`,{body:a,...s,headers:sY([{"anthropic-beta":[...r??[],"managed-agents-2026-04-01"].toString()},s?.headers])})}retrieve(e,t,s){let{vault_id:r,betas:a}=t;return this._client.get(s0`/v1/vaults/${r}/credentials/${e}?beta=true`,{...s,headers:sY([{"anthropic-beta":[...a??[],"managed-agents-2026-04-01"].toString()},s?.headers])})}update(e,t,s){let{vault_id:r,betas:a,...n}=t;return this._client.post(s0`/v1/vaults/${r}/credentials/${e}?beta=true`,{body:n,...s,headers:sY([{"anthropic-beta":[...a??[],"managed-agents-2026-04-01"].toString()},s?.headers])})}list(e,t={},s){let{betas:r,...a}=t??{};return this._client.getAPIList(s0`/v1/vaults/${e}/credentials?beta=true`,sL,{query:a,...s,headers:sY([{"anthropic-beta":[...r??[],"managed-agents-2026-04-01"].toString()},s?.headers])})}delete(e,t,s){let{vault_id:r,betas:a}=t;return this._client.delete(s0`/v1/vaults/${r}/credentials/${e}?beta=true`,{...s,headers:sY([{"anthropic-beta":[...a??[],"managed-agents-2026-04-01"].toString()},s?.headers])})}archive(e,t,s){let{vault_id:r,betas:a}=t;return this._client.post(s0`/v1/vaults/${r}/credentials/${e}/archive?beta=true`,{...s,headers:sY([{"anthropic-beta":[...a??[],"managed-agents-2026-04-01"].toString()},s?.headers])})}}class rP extends sK{constructor(){super(...arguments),this.credentials=new rA(this._client)}create(e,t){let{betas:s,...r}=e;return this._client.post("/v1/vaults?beta=true",{body:r,...t,headers:sY([{"anthropic-beta":[...s??[],"managed-agents-2026-04-01"].toString()},t?.headers])})}retrieve(e,t={},s){let{betas:r}=t??{};return this._client.get(s0`/v1/vaults/${e}?beta=true`,{...s,headers:sY([{"anthropic-beta":[...r??[],"managed-agents-2026-04-01"].toString()},s?.headers])})}update(e,t,s){let{betas:r,...a}=t;return this._client.post(s0`/v1/vaults/${e}?beta=true`,{body:a,...s,headers:sY([{"anthropic-beta":[...r??[],"managed-agents-2026-04-01"].toString()},s?.headers])})}list(e={},t){let{betas:s,...r}=e??{};return 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rM(e){return e?.output_config?.format}function rR(e,t,s){let r=rM(t);return t&&"parse"in(r??{})?r$(e,t,s):{...e,content:e.content.map(e=>"text"===e.type?Object.defineProperty({...e},"parsed_output",{value:null,enumerable:!1}):e),parsed_output:null}}function r$(e,t,s){let r=null,a=e.content.map(e=>{if("text"===e.type){let s=function(e,t){let s=rM(e);if(s?.type!=="json_schema")return null;try{if("parse"in s)return s.parse(t);return JSON.parse(t)}catch(e){throw new tZ(`Failed to parse structured output: ${e}`)}}(t,e.text);return null===r&&(r=s),Object.defineProperty({...e},"parsed_output",{value:s,enumerable:!1})}return e});return{...e,content:a,parsed_output:r}}rI.Models=s8,rI.Messages=r_,rI.Agents=re,rI.Environments=s1,rI.Sessions=rC,rI.Vaults=rP,rI.MemoryStores=rr,rI.Files=s6,rI.Skills=rE,rI.UserProfiles=s9;let rO="__json_buf";function rL(e){return"tool_use"===e.type||"server_tool_use"===e.type}class rU{constructor(e,t){W.add(this),this.messages=[],this.receivedMessages=[],V.set(this,void 0),H.set(this,null),this.controller=new AbortController,G.set(this,void 0),J.set(this,()=>{}),K.set(this,()=>{}),X.set(this,void 0),Y.set(this,()=>{}),Q.set(this,()=>{}),Z.set(this,{}),ee.set(this,!1),et.set(this,!1),es.set(this,!1),er.set(this,!1),ea.set(this,void 0),en.set(this,void 0),ei.set(this,void 0),ed.set(this,e=>{if(tJ(this,et,!0,"f"),tY(e)&&(e=new t1),e instanceof t1)return tJ(this,es,!0,"f"),this._emit("abort",e);if(e instanceof tZ)return this._emit("error",e);if(e instanceof Error){let t=new tZ(e.message);return t.cause=e,this._emit("error",t)}return this._emit("error",new tZ(String(e)))}),tJ(this,G,new Promise((e,t)=>{tJ(this,J,e,"f"),tJ(this,K,t,"f")}),"f"),tJ(this,X,new Promise((e,t)=>{tJ(this,Y,e,"f"),tJ(this,Q,t,"f")}),"f"),tK(this,G,"f").catch(()=>{}),tK(this,X,"f").catch(()=>{}),tJ(this,H,e,"f"),tJ(this,ei,t?.logger??console,"f")}get response(){return tK(this,ea,"f")}get 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0,"f"),rR(e,tK(this,H,"f"),{logger:tK(this,ei,"f")})},eh=function(e){let t=tK(this,V,"f");if("message_start"===e.type){if(t)throw new tZ(`Unexpected event order, got ${e.type} before receiving "message_stop"`);return e.message}if(!t)throw new tZ(`Unexpected event order, got ${e.type} before "message_start"`);switch(e.type){case"message_stop":case"content_block_stop":return t;case"message_delta":return t.stop_reason=e.delta.stop_reason,t.stop_sequence=e.delta.stop_sequence,t.usage.output_tokens=e.usage.output_tokens,null!=e.usage.input_tokens&&(t.usage.input_tokens=e.usage.input_tokens),null!=e.usage.cache_creation_input_tokens&&(t.usage.cache_creation_input_tokens=e.usage.cache_creation_input_tokens),null!=e.usage.cache_read_input_tokens&&(t.usage.cache_read_input_tokens=e.usage.cache_read_input_tokens),null!=e.usage.server_tool_use&&(t.usage.server_tool_use=e.usage.server_tool_use),t;case"content_block_start":return t.content.push({...e.content_block}),t;case"content_block_delta":{let s=t.content.at(e.index);switch(e.delta.type){case"text_delta":s?.type==="text"&&(t.content[e.index]={...s,text:(s.text||"")+e.delta.text});break;case"citations_delta":s?.type==="text"&&(t.content[e.index]={...s,citations:[...s.citations??[],e.delta.citation]});break;case"input_json_delta":if(s&&rL(s)){let r=s[rO]||"";r+=e.delta.partial_json;let a={...s};Object.defineProperty(a,rO,{value:r,enumerable:!1,writable:!0}),r&&(a.input=ru(r)),t.content[e.index]=a}break;case"thinking_delta":s?.type==="thinking"&&(t.content[e.index]={...s,thinking:s.thinking+e.delta.thinking});break;case"signature_delta":s?.type==="thinking"&&(t.content[e.index]={...s,signature:e.delta.signature});break;default:rD(e.delta)}return t}}},Symbol.asyncIterator)](){let e=[],t=[],s=!1;return this.on("streamEvent",s=>{let r=t.shift();r?r.resolve(s):e.push(s)}),this.on("end",()=>{for(let e of(s=!0,t))e.resolve(void 0);t.length=0}),this.on("abort",e=>{for(let r of(s=!0,t))r.reject(e);t.length=0}),this.on("error",e=>{for(let r of(s=!0,t))r.reject(e);t.length=0}),{next:async()=>e.length?{value:e.shift(),done:!1}:s?{value:void 0,done:!0}:new Promise((e,s)=>t.push({resolve:e,reject:s})).then(e=>e?{value:e,done:!1}:{value:void 0,done:!0}),return:async()=>(this.abort(),{value:void 0,done:!0})}}toReadableStream(){return new sC(this[Symbol.asyncIterator].bind(this),this.controller).toReadableStream()}}function rD(e){}class rz extends sK{create(e,t){return this._client.post("/v1/messages/batches",{body:e,...t})}retrieve(e,t){return this._client.get(s0`/v1/messages/batches/${e}`,t)}list(e={},t){return this._client.getAPIList("/v1/messages/batches",sO,{query:e,...t})}delete(e,t){return this._client.delete(s0`/v1/messages/batches/${e}`,t)}cancel(e,t){return this._client.post(s0`/v1/messages/batches/${e}/cancel`,t)}async results(e,t){let s=await this.retrieve(e);if(!s.results_url)throw new tZ(`No batch \`results_url\`; Has it finished processing? ${s.processing_status} - ${s.id}`);return this._client.get(s.results_url,{...t,headers:sY([{Accept:"application/binary"},t?.headers]),stream:!0,__binaryResponse:!0})._thenUnwrap((e,t)=>ra.fromResponse(t.response,t.controller))}}class rB extends sK{constructor(){super(...arguments),this.batches=new rz(this._client)}create(e,t){e.model in rq&&console.warn(`The model '${e.model}' is deprecated and will reach end-of-life on ${rq[e.model]} -Please migrate to a newer model. Visit https://docs.anthropic.com/en/docs/resources/model-deprecations for more information.`),rF.includes(e.model)&&e.thinking&&"enabled"===e.thinking.type&&console.warn(`Using Claude with ${e.model} and 'thinking.type=enabled' is deprecated. 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Expected either apiKey or authToken to be set. Or for one of the "X-Api-Key" or "Authorization" headers to be explicitly omitted')}async authHeaders(e){return sY([await this.apiKeyAuth(e),await this.bearerAuth(e)])}async apiKeyAuth(e){if(null!=this.apiKey)return sY([{"X-Api-Key":this.apiKey}])}async bearerAuth(e){if(null!=this.authToken)return sY([{Authorization:`Bearer ${this.authToken}`}])}stringifyQuery(e){return Object.entries(e).filter(([e,t])=>void 0!==t).map(([e,t])=>{if("string"==typeof t||"number"==typeof t||"boolean"==typeof t)return`${encodeURIComponent(e)}=${encodeURIComponent(t)}`;if(null===t)return`${encodeURIComponent(e)}=`;throw new tZ(`Cannot stringify type ${typeof t}; Expected string, number, boolean, or null. 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See https://github.com/anthropics/anthropic-sdk-typescript#streaming-responses for more details");return 6e5}async prepareOptions(e){}async prepareRequest(e,{url:t,options:s}){}get(e,t){return this.methodRequest("get",e,t)}post(e,t){return this.methodRequest("post",e,t)}patch(e,t){return this.methodRequest("patch",e,t)}put(e,t){return this.methodRequest("put",e,t)}delete(e,t){return this.methodRequest("delete",e,t)}methodRequest(e,t,s){return this.request(Promise.resolve(s).then(s=>({method:e,path:t,...s})))}request(e,t=null){return new sM(this,this.makeRequest(e,t,void 0))}async makeRequest(e,t,s){let r=await e,a=r.maxRetries??this.maxRetries;null==t&&(t=a),await this.prepareOptions(r);let{req:n,url:i,timeout:o}=await this.buildRequest(r,{retryCount:a-t});await this.prepareRequest(n,{url:i,options:r});let l="log_"+(0x1000000*Math.random()|0).toString(16).padStart(6,"0"),d=void 0===s?"":`, retryOf: ${s}`,c=Date.now();if(sS(this).debug(`[${l}] sending request`,sk({retryOfRequestLogID:s,method:r.method,url:i,options:r,headers:n.headers})),r.signal?.aborted)throw new t1;let u=new AbortController,m=await this.fetchWithTimeout(i,n,o,u).catch(tQ),h=Date.now();if(m instanceof globalThis.Error){let e=`retrying, ${t} attempts remaining`;if(r.signal?.aborted)throw new t1;let a=tY(m)||/timed? ?out/i.test(String(m)+("cause"in m?String(m.cause):""));if(t)return sS(this).info(`[${l}] connection ${a?"timed out":"failed"} - ${e}`),sS(this).debug(`[${l}] connection ${a?"timed out":"failed"} (${e})`,sk({retryOfRequestLogID:s,url:i,durationMs:h-c,message:m.message})),this.retryRequest(r,t,s??l);if(sS(this).info(`[${l}] connection ${a?"timed out":"failed"} - error; no more retries left`),sS(this).debug(`[${l}] connection ${a?"timed out":"failed"} (error; no more retries left)`,sk({retryOfRequestLogID:s,url:i,durationMs:h-c,message:m.message})),a)throw new t4;throw new t2({cause:m})}let p=[...m.headers.entries()].filter(([e])=>"request-id"===e).map(([e,t])=>", "+e+": "+JSON.stringify(t)).join(""),f=`[${l}${d}${p}] ${n.method} ${i} ${m.ok?"succeeded":"failed"} with status ${m.status} in ${h-c}ms`;if(!m.ok){let e=await this.shouldRetry(m);if(t&&e){let e=`retrying, ${t} attempts remaining`;return await sp(m.body),sS(this).info(`${f} - ${e}`),sS(this).debug(`[${l}] response error (${e})`,sk({retryOfRequestLogID:s,url:m.url,status:m.status,headers:m.headers,durationMs:h-c})),this.retryRequest(r,t,s??l,m.headers)}let a=e?"error; no more retries left":"error; not retryable";sS(this).info(`${f} - ${a}`);let n=await m.text().catch(e=>tQ(e).message),i=so(n),o=i?void 0:n;throw sS(this).debug(`[${l}] response error (${a})`,sk({retryOfRequestLogID:s,url:m.url,status:m.status,headers:m.headers,message:o,durationMs:Date.now()-c})),this.makeStatusError(m.status,i,o,m.headers)}return sS(this).info(f),sS(this).debug(`[${l}] response start`,sk({retryOfRequestLogID:s,url:m.url,status:m.status,headers:m.headers,durationMs:h-c})),{response:m,options:r,controller:u,requestLogID:l,retryOfRequestLogID:s,startTime:c}}getAPIList(e,t,s){return this.requestAPIList(t,s&&"then"in s?s.then(t=>({method:"get",path:e,...t})):{method:"get",path:e,...s})}requestAPIList(e,t){return new s$(this,this.makeRequest(t,null,void 0),e)}async fetchWithTimeout(e,t,s,r){let{signal:a,method:n,...i}=t||{},o=this._makeAbort(r);a&&a.addEventListener("abort",o,{once:!0});let l=setTimeout(o,s),d=globalThis.ReadableStream&&i.body instanceof globalThis.ReadableStream||"object"==typeof i.body&&null!==i.body&&Symbol.asyncIterator in i.body,c={signal:r.signal,...d?{duplex:"half"}:{},method:"GET",...i};n&&(c.method=n.toUpperCase());try{return await this.fetch.call(void 0,e,c)}finally{clearTimeout(l)}}async shouldRetry(e){let t=e.headers.get("x-should-retry");return"true"===t||"false"!==t&&(408===e.status||409===e.status||429===e.status||!!(e.status>=500))}async retryRequest(e,t,s,r){let a,n,i=r?.get("retry-after-ms");if(i){let e=parseFloat(i);Number.isNaN(e)||(a=e)}let o=r?.get("retry-after");if(o&&!a){let e=parseFloat(o);a=Number.isNaN(e)?Date.parse(o)-Date.now():1e3*e}if(void 0===a){let s=e.maxRetries??this.maxRetries;a=this.calculateDefaultRetryTimeoutMillis(t,s)}return await (n=a,new Promise(e=>setTimeout(e,n))),this.makeRequest(e,t-1,s)}calculateDefaultRetryTimeoutMillis(e,t){return Math.min(.5*Math.pow(2,t-e),8)*(1-.25*Math.random())*1e3}calculateNonstreamingTimeout(e,t){if(36e5*e/128e3>6e5||null!=t&&e>t)throw new tZ("Streaming is required for operations that may take longer than 10 minutes. 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n={};this.idempotencyHeader&&"get"!==s&&(e.idempotencyKey||(e.idempotencyKey=this.defaultIdempotencyKey()),n[this.idempotencyHeader]=e.idempotencyKey);let i=sY([n,{Accept:"application/json","User-Agent":this.getUserAgent(),"X-Stainless-Retry-Count":String(a),...e.timeout?{"X-Stainless-Timeout":String(Math.trunc(e.timeout/1e3))}:{},...t??(t=(()=>{let e="u">typeof Deno&&null!=Deno.build?"deno":"u">typeof EdgeRuntime?"edge":"[object process]"===Object.prototype.toString.call(void 0!==globalThis.process?globalThis.process:0)?"node":"unknown";if("deno"===e)return{"X-Stainless-Lang":"js","X-Stainless-Package-Version":sl,"X-Stainless-OS":sc(Deno.build.os),"X-Stainless-Arch":sd(Deno.build.arch),"X-Stainless-Runtime":"deno","X-Stainless-Runtime-Version":"string"==typeof Deno.version?Deno.version:Deno.version?.deno??"unknown"};if("u">typeof EdgeRuntime)return{"X-Stainless-Lang":"js","X-Stainless-Package-Version":sl,"X-Stainless-OS":"Unknown","X-Stainless-Arch":`other:${EdgeRuntime}`,"X-Stainless-Runtime":"edge","X-Stainless-Runtime-Version":globalThis.process.version};if("node"===e)return{"X-Stainless-Lang":"js","X-Stainless-Package-Version":sl,"X-Stainless-OS":sc(globalThis.process.platform??"unknown"),"X-Stainless-Arch":sd(globalThis.process.arch??"unknown"),"X-Stainless-Runtime":"node","X-Stainless-Runtime-Version":globalThis.process.version??"unknown"};let t=function(){if("u"e.abort()}buildBody({options:{body:e,headers:t}}){if(!e)return{bodyHeaders:void 0,body:void 0};let s=sY([t]);return ArrayBuffer.isView(e)||e instanceof ArrayBuffer||e instanceof DataView||"string"==typeof e&&s.values.has("content-type")||globalThis.Blob&&e instanceof globalThis.Blob||e instanceof FormData||e instanceof URLSearchParams||globalThis.ReadableStream&&e instanceof globalThis.ReadableStream?{bodyHeaders:void 0,body:e}:"object"==typeof e&&(Symbol.asyncIterator in e||Symbol.iterator in e&&"next"in e&&"function"==typeof e.next)?{bodyHeaders:void 0,body:sm(e)}:"object"==typeof e&&"application/x-www-form-urlencoded"===s.values.get("content-type")?{bodyHeaders:{"content-type":"application/x-www-form-urlencoded"},body:this.stringifyQuery(e)}:tK(this,eg,"f").call(this,{body:e,headers:s})}}ef=rG,eg=new WeakMap,ep=new WeakSet,ex=function(){return"https://api.anthropic.com"!==this.baseURL},rG.Anthropic=ef,rG.HUMAN_PROMPT="\\n\\nHuman:",rG.AI_PROMPT="\\n\\nAssistant:",rG.DEFAULT_TIMEOUT=6e5,rG.AnthropicError=tZ,rG.APIError=t0,rG.APIConnectionError=t2,rG.APIConnectionTimeoutError=t4,rG.APIUserAbortError=t1,rG.NotFoundError=t8,rG.ConflictError=t9,rG.RateLimitError=se,rG.BadRequestError=t5,rG.AuthenticationError=t3,rG.InternalServerError=st,rG.PermissionDeniedError=t6,rG.UnprocessableEntityError=t7,rG.toFile=sG;class rJ extends rG{constructor(){super(...arguments),this.completions=new rV(this),this.messages=new rB(this),this.models=new rW(this),this.beta=new rI(this)}}rJ.Completions=rV,rJ.Messages=rB,rJ.Models=rW,rJ.Beta=rI;let rK="toolset:";async function rX(e,t,s,r,a=[],n,i,o,l,d,c,u,m,h,p,f,g,x){if(!r)throw Error("Virtual Key is required");console.log=function(){};let b=p||(0,eU.getProxyBaseUrl)(),y={};a&&a.length>0&&(y["x-litellm-tags"]=a.join(","));let v=new rJ({apiKey:r,baseURL:b,dangerouslyAllowBrowser:!0,defaultHeaders:y});try{let r=Date.now(),a=!1,p={model:s,messages:e.map(e=>({role:e.role,content:e.content})),stream:!0,max_tokens:1024,litellm_trace_id:d},b=function({selectedMCPServers:e,mcpServers:t,mcpToolsets:s,mcpServerToolRestrictions:r}){return e&&0!==e.length?e.includes("__all__")?[{type:"mcp",server_label:"litellm",server_url:"litellm_proxy/mcp",require_approval:"never"}]:e.map(e=>{if(e.startsWith(rK)){let t=e.slice(rK.length),r=s?.find(e=>e.toolset_id===t),a=r?.toolset_name||t;return{type:"mcp",server_label:a,server_url:`litellm_proxy/mcp/${a}`,require_approval:"never"}}let a=t?.find(t=>t.server_id===e),n=a?.server_name||e,i=r?.[e]||[];return{type:"mcp",server_label:n,server_url:`litellm_proxy/mcp/${n}`,require_approval:"never",...i.length>0?{allowed_tools:i}:{}}}):[]}({selectedMCPServers:h,mcpServers:f,mcpToolsets:x,mcpServerToolRestrictions:g});for await(let e of(b.length>0&&(p.tools=b),c&&(p.vector_store_ids=c),u&&(p.guardrails=u),m&&(p.policies=m),v.messages.stream(p,{signal:n}))){if("content_block_delta"===e.type){let n=e.delta;if(!a){a=!0;let e=Date.now()-r;o&&o(e)}"text_delta"===n.type?t("assistant",n.text,s):"reasoning_delta"===n.type&&i&&i(n.text)}if("message_delta"===e.type&&e.usage&&l){let t=e.usage,s={completionTokens:t.output_tokens,promptTokens:t.input_tokens,totalTokens:t.input_tokens+t.output_tokens,...(0,eH.extractPromptCacheTokens)(t)};l(s)}}}catch(e){throw n?.aborted||eL.toast.fromError(`Error occurred while generating model response. Please try again. Error: ${e}`),e}}async function rY(e,t,s,r,a,n,i,o,l,d){console.log=function(){};let c=d||(0,eU.getProxyBaseUrl)(),u=new eV.default.OpenAI({apiKey:a,baseURL:c,dangerouslyAllowBrowser:!0,defaultHeaders:n&&n.length>0?{"x-litellm-tags":n.join(",")}:void 0});try{let a=await u.audio.speech.create({model:r,input:e,voice:t,...o?{response_format:o}:{},...l?{speed:l}:{}},{signal:i}),n=await a.blob(),d=URL.createObjectURL(n);s(d,r)}catch(e){throw i?.aborted||eL.toast.fromError(`Error occurred while generating speech. Please try again. Error: ${e}`),e}}async function rQ(e,t,s,r,a,n,i,o,l,d,c){console.log=function(){};let u=c||(0,eU.getProxyBaseUrl)(),m=new eV.default.OpenAI({apiKey:r,baseURL:u,dangerouslyAllowBrowser:!0,defaultHeaders:a&&a.length>0?{"x-litellm-tags":a.join(",")}:void 0});try{let r=await m.audio.transcriptions.create({model:s,file:e,...i?{language:i}:{},...o?{prompt:o}:{},...l?{response_format:l}:{},...void 0!==d?{temperature:d}:{}},{signal:n});if(r&&r.text)t(r.text,s),eL.toast.success("Audio transcribed successfully");else throw Error("No transcription text in response")}catch(e){if(console.error("Error making audio transcription request:",e),n?.aborted);else{let t="Failed to transcribe audio";e?.error?.message?t=e.error.message:e?.message&&(t=e.message),eL.toast.fromError(`Audio transcription failed: ${t}`)}throw e}}async function rZ(e,t,s,r,a,n){if(!r)throw Error("Virtual Key is required");console.log=function(){};let i=n||(0,eU.getProxyBaseUrl)(),o={};a&&a.length>0&&(o["x-litellm-tags"]=a.join(","));try{let a=i.endsWith("/")?i.slice(0,-1):i,n=`${a}/embeddings`,l=await fetch(n,{method:"POST",headers:{"Content-Type":"application/json",[(0,eU.getGlobalLitellmHeaderName)()]:`Bearer ${r}`,...o},body:JSON.stringify({model:s,input:e})});if(!l.ok){let e=await l.text();throw Error(e||`Request failed with status ${l.status}`)}let d=await l.json(),c=d?.data?.[0]?.embedding;if(!c)throw Error("No embedding returned from server");t(JSON.stringify(c),d?.model??s)}catch(e){throw eL.toast.fromError(`Error occurred while making embeddings request. Please try again. 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shadow-xs",style:{maxHeight:"500px"}})})]}),"assistant"===e.role&&(e.timeToFirstToken||e.totalLatency||e.usage)&&!e.a2aMetadata&&(0,eb.jsx)(aQ.default,{timeToFirstToken:e.timeToFirstToken,totalLatency:e.totalLatency,usage:e.usage,toolName:e.toolName}),"assistant"===e.role&&e.a2aMetadata&&(0,eb.jsx)(aq,{a2aMetadata:e.a2aMetadata,timeToFirstToken:e.timeToFirstToken,totalLatency:e.totalLatency})]})]})})},a5=({responsesUploadedImage:e,onImageUpload:t,disabled:s=!1})=>{let r=(0,ey.useRef)(null),a=(0,ey.useId)();return e?null:(0,eb.jsxs)(eb.Fragment,{children:[(0,eb.jsx)("input",{id:a,ref:r,type:"file",accept:al,className:"sr-only",tabIndex:-1,disabled:s,onChange:e=>{let s=e.target.files?.[0];if(e.target.value="",!s)return;let r=ab(s);r.ok?t(s):eL.toast.error(r.error)}}),(0,eb.jsxs)(t$.Tooltip,{children:[(0,eb.jsx)(t$.TooltipTrigger,{render:(0,eb.jsx)(eT.Button,{type:"button",variant:"ghost",size:"icon-sm",disabled:s,"aria-label":"Attach image or PDF",className:"text-muted-foreground 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${r}...`})()]}),t&&(0,eb.jsxs)(t$.Tooltip,{children:[(0,eb.jsx)(t$.TooltipTrigger,{render:(0,eb.jsx)(eT.Button,{type:"button",variant:"ghost",size:"icon-xs",onClick:a,"aria-label":"Copy response ID",className:"ml-2 hover:bg-success/15"}),children:(0,eb.jsx)(aL.Copy,{className:"size-3"})}),(0,eb.jsx)(t$.TooltipContent,{className:"max-w-lg",children:(0,eb.jsxs)("div",{className:"text-xs",children:[(0,eb.jsx)("div",{className:"mb-1",children:"Copy response ID to continue session:"}),(0,eb.jsx)("div",{className:"bg-gray-800 text-gray-100 p-2 rounded-sm font-mono text-xs whitespace-pre-wrap",children:`curl -X POST "your-proxy-url/v1/responses" \\ +Please migrate to a newer model. Visit https://docs.anthropic.com/en/docs/resources/model-deprecations for more information.`),rF.includes(e.model)&&e.thinking&&"enabled"===e.thinking.type&&console.warn(`Using Claude with ${e.model} and 'thinking.type=enabled' is deprecated. Use 'thinking.type=adaptive' instead which results in better model performance in our testing: https://platform.claude.com/docs/en/build-with-claude/adaptive-thinking`);let s=this._client._options.timeout;if(!e.stream&&null==s){let t=ri[e.model]??void 0;s=this._client.calculateNonstreamingTimeout(e.max_tokens,t)}let r=s3(e.tools,e.messages);return this._client.post("/v1/messages",{body:e,timeout:s??6e5,...t,headers:sY([r,t?.headers]),stream:e.stream??!1})}parse(e,t){return this.create(e,t).then(t=>r$(t,e,{logger:this._client.logger??console}))}stream(e,t){return rU.createMessage(this,e,t,{logger:this._client.logger??console})}countTokens(e,t){return this._client.post("/v1/messages/count_tokens",{body:e,...t})}}let rq={"claude-1.3":"November 6th, 2024","claude-1.3-100k":"November 6th, 2024","claude-instant-1.1":"November 6th, 2024","claude-instant-1.1-100k":"November 6th, 2024","claude-instant-1.2":"November 6th, 2024","claude-3-sonnet-20240229":"July 21st, 2025","claude-3-opus-20240229":"January 5th, 2026","claude-2.1":"July 21st, 2025","claude-2.0":"July 21st, 2025","claude-3-7-sonnet-latest":"February 19th, 2026","claude-3-7-sonnet-20250219":"February 19th, 2026","claude-3-5-haiku-latest":"February 19th, 2026","claude-3-5-haiku-20241022":"February 19th, 2026","claude-opus-4-0":"June 15th, 2026","claude-opus-4-20250514":"June 15th, 2026","claude-sonnet-4-0":"June 15th, 2026","claude-sonnet-4-20250514":"June 15th, 2026"},rF=["claude-mythos-preview","claude-opus-4-6"];rB.Batches=rz;class rW extends sK{retrieve(e,t={},s){let{betas:r}=t??{};return this._client.get(s0`/v1/models/${e}`,{...s,headers:sY([{...r?.toString()!=null?{"anthropic-beta":r?.toString()}:void 0},s?.headers])})}list(e={},t){let{betas:s,...r}=e??{};return this._client.getAPIList("/v1/models",sO,{query:r,...t,headers:sY([{...s?.toString()!=null?{"anthropic-beta":s?.toString()}:void 0},t?.headers])})}}class rV extends sK{create(e,t){let{betas:s,...r}=e;return this._client.post("/v1/complete",{body:r,timeout:this._client._options.timeout??6e5,...t,headers:sY([{...s?.toString()!=null?{"anthropic-beta":s?.toString()}:void 0},t?.headers]),stream:e.stream??!1})}}let rH=e=>void 0!==globalThis.process?globalThis.process.env?.[e]?.trim()||void 0:void 0!==globalThis.Deno&&globalThis.Deno.env?.get?.(e)?.trim()||void 0;class rG{constructor({baseURL:e=rH("ANTHROPIC_BASE_URL"),apiKey:t=rH("ANTHROPIC_API_KEY")??null,authToken:s=rH("ANTHROPIC_AUTH_TOKEN")??null,...r}={}){ep.add(this),eg.set(this,void 0);const a={apiKey:t,authToken:s,...r,baseURL:e||"https://api.anthropic.com"};if(!a.dangerouslyAllowBrowser&&"u">typeof window&&void 0!==window.document&&"u">typeof navigator)throw new tZ("It looks like you're running in a browser-like environment.\n\nThis is disabled by default, as it risks exposing your secret API credentials to attackers.\nIf you understand the risks and have appropriate mitigations in place,\nyou can set the `dangerouslyAllowBrowser` option to `true`, e.g.,\n\nnew Anthropic({ apiKey, dangerouslyAllowBrowser: true });\n");this.baseURL=a.baseURL,this.timeout=a.timeout??ef.DEFAULT_TIMEOUT,this.logger=a.logger??console;const n="warn";this.logLevel=n,this.logLevel=sv(a.logLevel,"ClientOptions.logLevel",this)??sv(rH("ANTHROPIC_LOG"),"process.env['ANTHROPIC_LOG']",this)??n,this.fetchOptions=a.fetchOptions,this.maxRetries=a.maxRetries??2,this.fetch=a.fetch??function(){if("u">typeof fetch)return fetch;throw Error("`fetch` is not defined as a global; Either pass `fetch` to the client, `new Anthropic({ fetch })` or polyfill the global, `globalThis.fetch = fetch`")}(),tJ(this,eg,sf,"f");const i=rH("ANTHROPIC_CUSTOM_HEADERS");if(i){const e={};for(const t of i.split("\n")){const s=t.indexOf(":");s>=0&&(e[t.substring(0,s).trim()]=t.substring(s+1).trim())}a.defaultHeaders={...e,...a.defaultHeaders}}this._options=a,this.apiKey="string"==typeof t?t:null,this.authToken=s}withOptions(e){return new this.constructor({...this._options,baseURL:this.baseURL,maxRetries:this.maxRetries,timeout:this.timeout,logger:this.logger,logLevel:this.logLevel,fetch:this.fetch,fetchOptions:this.fetchOptions,apiKey:this.apiKey,authToken:this.authToken,...e})}defaultQuery(){return this._options.defaultQuery}validateHeaders({values:e,nulls:t}){if(!(e.get("x-api-key")||e.get("authorization")||this.apiKey&&e.get("x-api-key")||t.has("x-api-key")||this.authToken&&e.get("authorization"))&&!t.has("authorization"))throw Error('Could not resolve authentication method. 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n={};this.idempotencyHeader&&"get"!==s&&(e.idempotencyKey||(e.idempotencyKey=this.defaultIdempotencyKey()),n[this.idempotencyHeader]=e.idempotencyKey);let i=sY([n,{Accept:"application/json","User-Agent":this.getUserAgent(),"X-Stainless-Retry-Count":String(a),...e.timeout?{"X-Stainless-Timeout":String(Math.trunc(e.timeout/1e3))}:{},...t??(t=(()=>{let e="u">typeof Deno&&null!=Deno.build?"deno":"u">typeof EdgeRuntime?"edge":"[object process]"===Object.prototype.toString.call(void 0!==globalThis.process?globalThis.process:0)?"node":"unknown";if("deno"===e)return{"X-Stainless-Lang":"js","X-Stainless-Package-Version":sl,"X-Stainless-OS":sc(Deno.build.os),"X-Stainless-Arch":sd(Deno.build.arch),"X-Stainless-Runtime":"deno","X-Stainless-Runtime-Version":"string"==typeof Deno.version?Deno.version:Deno.version?.deno??"unknown"};if("u">typeof EdgeRuntime)return{"X-Stainless-Lang":"js","X-Stainless-Package-Version":sl,"X-Stainless-OS":"Unknown","X-Stainless-Arch":`other:${EdgeRuntime}`,"X-Stainless-Runtime":"edge","X-Stainless-Runtime-Version":globalThis.process.version};if("node"===e)return{"X-Stainless-Lang":"js","X-Stainless-Package-Version":sl,"X-Stainless-OS":sc(globalThis.process.platform??"unknown"),"X-Stainless-Arch":sd(globalThis.process.arch??"unknown"),"X-Stainless-Runtime":"node","X-Stainless-Runtime-Version":globalThis.process.version??"unknown"};let t=function(){if("u"e.abort()}buildBody({options:{body:e,headers:t}}){if(!e)return{bodyHeaders:void 0,body:void 0};let s=sY([t]);return ArrayBuffer.isView(e)||e instanceof ArrayBuffer||e instanceof DataView||"string"==typeof e&&s.values.has("content-type")||globalThis.Blob&&e instanceof globalThis.Blob||e instanceof FormData||e instanceof URLSearchParams||globalThis.ReadableStream&&e instanceof globalThis.ReadableStream?{bodyHeaders:void 0,body:e}:"object"==typeof 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r=j.length>0?j:[{role:"user",content:y}];t=` import base64 # Helper function to encode images to base64 @@ -61,7 +61,7 @@ def encode_image(image_path): # Example with text only response = client.responses.create( model="${k}", - input=${JSON.stringify(a,null,4)}${i} + input=${JSON.stringify(r,null,4)}${i} ) print(response.output_text) @@ -84,7 +84,7 @@ print(response.output_text) # ]${i} # ) # print(response_with_file.output_text) -`;break}case r.IMAGE:t="azure"===f?` +`;break}case a.IMAGE:t="azure"===f?` # NOTE: The Azure SDK does not have a direct equivalent to the multi-modal 'responses.create' method shown for OpenAI. # This snippet uses 'client.images.generate' and will create a new image based on your prompt. # It does not use the uploaded image, as 'client.images.generate' does not support image inputs in this context. @@ -215,7 +215,7 @@ else: print("No image data found in response.") print("Full response for debugging:") print(response) -`;break;case r.IMAGE_EDITS:t="azure"===f?` +`;break;case a.IMAGE_EDITS:t="azure"===f?` import base64 import os import time @@ -374,7 +374,7 @@ else: print("No image data found in response.") print("Full response for debugging:") print(response) -`;break;case r.EMBEDDINGS:t=` +`;break;case a.EMBEDDINGS:t=` response = client.embeddings.create( input="${n||"Your string here"}", model="${k}", @@ -382,7 +382,7 @@ response = client.embeddings.create( ) print(response.data[0].embedding) -`;break;case r.TRANSCRIPTION:t=` +`;break;case a.TRANSCRIPTION:t=` # Open the audio file audio_file = open("path/to/your/audio/file.mp3", "rb") @@ -394,7 +394,7 @@ response = client.audio.transcriptions.create( ) print(response.text) -`;break;case r.SPEECH:t=` +`;break;case a.SPEECH:t=` # Make the text-to-speech request response = client.audio.speech.create( model="${k}", @@ -417,4 +417,4 @@ print(f"Audio saved to {output_filename}") # ) # response.stream_to_file("output_speech.mp3") `;break;default:t="\n# Code generation 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n={image_generation:"image",video_generation:"video",chat:"chat",completion:"chat",responses:"responses",image_edit:"image_edits",anthropic_messages:"anthropic_messages",audio_speech:"speech",audio_transcription:"transcription",embedding:"embeddings",realtime:"realtime"};e.s(["EndpointType",()=>o,"ModelMode",()=>a,"getEndpointType",0,e=>Object.values(a).includes(e)?n[e]:"chat"],865361),e.s(["generateCodeSnippet",0,e=>{let t,{apiKeySource:i,accessToken:a,apiKey:n,inputMessage:r,chatHistory:s,selectedTags:l,selectedVectorStores:p,selectedGuardrails:d,selectedPolicies:m,selectedVoice:g,endpointType:u,selectedModel:c,selectedSdk:f,proxySettings:h}=e,_="session"===i?a:n,b=window.location.origin,x=h?.LITELLM_UI_API_DOC_BASE_URL;x&&x.trim()?b=x:h?.PROXY_BASE_URL&&(b=h.PROXY_BASE_URL);let y=r||"Your prompt 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with text only +response = client.chat.completions.create( + model="${k}", + messages=${JSON.stringify(a,null,4)}${i} +) + +print(response) + +# Example with image or PDF (uncomment and provide file path to use) +# base64_file = encode_image("path/to/your/file.jpg") # or .pdf +# response_with_file = client.chat.completions.create( +# model="${k}", +# messages=[ +# { +# "role": "user", +# "content": [ +# { +# "type": "text", +# "text": "${j}" +# }, +# { +# "type": "image_url", +# "image_url": { +# "url": f"data:image/jpeg;base64,{base64_file}" # or data:application/pdf;base64,{base64_file} +# } +# } +# ] +# } +# ]${i} +# ) +# print(response_with_file) +`;break}case o.RESPONSES:{let e=Object.keys(v).length>0,i="";if(e){let e=JSON.stringify({metadata:v},null,2).split("\n").map(e=>" ".repeat(4)+e).join("\n").trim();i=`, + extra_body=${e}`}let a=S.length>0?S:[{role:"user",content:y}];t=` +import base64 + +# Helper function to encode images to base64 +def encode_image(image_path): + with open(image_path, "rb") as image_file: + return base64.b64encode(image_file.read()).decode('utf-8') + +# Example with text only +response = client.responses.create( + model="${k}", + input=${JSON.stringify(a,null,4)}${i} +) + +print(response.output_text) + +# Example with image or PDF (uncomment and provide file path to use) +# base64_file = encode_image("path/to/your/file.jpg") # or .pdf +# response_with_file = client.responses.create( +# model="${k}", +# input=[ +# { +# "role": "user", +# "content": [ +# {"type": "input_text", "text": "${j}"}, +# { +# "type": "input_image", +# "image_url": f"data:image/jpeg;base64,{base64_file}", # or data:application/pdf;base64,{base64_file} +# }, +# ], +# } +# ]${i} +# ) +# print(response_with_file.output_text) +`;break}case o.IMAGE:t="azure"===f?` +# NOTE: The Azure SDK does not have a direct equivalent to the multi-modal 'responses.create' method shown for OpenAI. +# This snippet uses 'client.images.generate' and will create a new image based on your prompt. +# It does not use the uploaded image, as 'client.images.generate' does not support image inputs in this context. +import os +import requests +import json +import time +from PIL import Image + +result = client.images.generate( + model="${k}", + prompt="${r}", + n=1 +) + +json_response = json.loads(result.model_dump_json()) + +# Set the directory for the stored image +image_dir = os.path.join(os.curdir, 'images') + +# If the directory doesn't exist, create it +if not os.path.isdir(image_dir): + os.mkdir(image_dir) + +# Initialize the image path +image_filename = f"generated_image_{int(time.time())}.png" +image_path = os.path.join(image_dir, image_filename) + +try: + # Retrieve the generated image + if json_response.get("data") && len(json_response["data"]) > 0 && json_response["data"][0].get("url"): + image_url = json_response["data"][0]["url"] + generated_image = requests.get(image_url).content + with open(image_path, "wb") as image_file: + image_file.write(generated_image) + + print(f"Image saved to {image_path}") + # Display the image + image = Image.open(image_path) + image.show() + else: + print("Could not find image URL in response.") + print("Full response:", json_response) +except Exception as e: + print(f"An error occurred: {e}") + print("Full response:", json_response) +`:` +import base64 +import os +import time +import json +from PIL import Image +import requests + +# Helper function to encode images to base64 +def encode_image(image_path): + with open(image_path, "rb") as image_file: + return base64.b64encode(image_file.read()).decode('utf-8') + +# Helper function to create a file (simplified for this example) +def create_file(image_path): + # In a real implementation, this would upload the file to OpenAI + # For this example, we'll just return a placeholder ID + return f"file_{os.path.basename(image_path).replace('.', '_')}" + +# The prompt entered by the user +prompt = "${j}" + +# Encode images to base64 +base64_image1 = encode_image("body-lotion.png") +base64_image2 = encode_image("soap.png") + +# Create file IDs +file_id1 = create_file("body-lotion.png") +file_id2 = create_file("incense-kit.png") + +response = client.responses.create( + model="${k}", + input=[ + { + "role": "user", + "content": [ + {"type": "input_text", "text": prompt}, + { + "type": "input_image", + "image_url": f"data:image/jpeg;base64,{base64_image1}", + }, + { + "type": "input_image", + "image_url": f"data:image/jpeg;base64,{base64_image2}", + }, + { + "type": "input_image", + "file_id": file_id1, + }, + { + "type": "input_image", + "file_id": file_id2, + } + ], + } + ], + tools=[{"type": "image_generation"}], +) + +# Process the response +image_generation_calls = [ + output + for output in response.output + if output.type == "image_generation_call" +] + +image_data = [output.result for output in image_generation_calls] + +if image_data: + image_base64 = image_data[0] + image_filename = f"edited_image_{int(time.time())}.png" + with open(image_filename, "wb") as f: + f.write(base64.b64decode(image_base64)) + print(f"Image saved to {image_filename}") +else: + # If no image is generated, there might be a text response with an explanation + text_response = [output.text for output in response.output if hasattr(output, 'text')] + if text_response: + print("No image generated. Model response:") + print("\\n".join(text_response)) + else: + print("No image data found in response.") + print("Full response for debugging:") + print(response) +`;break;case o.IMAGE_EDITS:t="azure"===f?` +import base64 +import os +import time +import json +from PIL import Image +import requests + +# Helper function to encode images to base64 +def encode_image(image_path): + with open(image_path, "rb") as image_file: + return base64.b64encode(image_file.read()).decode('utf-8') + +# The prompt entered by the user +prompt = "${j}" + +# Encode images to base64 +base64_image1 = encode_image("body-lotion.png") +base64_image2 = encode_image("soap.png") + +# Create file IDs +file_id1 = create_file("body-lotion.png") +file_id2 = create_file("incense-kit.png") + +response = client.responses.create( + model="${k}", + input=[ + { + "role": "user", + "content": [ + {"type": "input_text", "text": prompt}, + { + "type": "input_image", + "image_url": f"data:image/jpeg;base64,{base64_image1}", + }, + { + "type": "input_image", + "image_url": f"data:image/jpeg;base64,{base64_image2}", + }, + { + "type": "input_image", + "file_id": file_id1, + }, + { + "type": "input_image", + "file_id": file_id2, + } + ], + } + ], + tools=[{"type": "image_generation"}], +) + +# Process the response +image_generation_calls = [ + output + for output in response.output + if output.type == "image_generation_call" +] + +image_data = [output.result for output in image_generation_calls] + +if image_data: + image_base64 = image_data[0] + image_filename = f"edited_image_{int(time.time())}.png" + with open(image_filename, "wb") as f: + f.write(base64.b64decode(image_base64)) + print(f"Image saved to {image_filename}") +else: + # If no image is generated, there might be a text response with an explanation + text_response = [output.text for output in response.output if hasattr(output, 'text')] + if text_response: + print("No image generated. Model response:") + print("\\n".join(text_response)) + else: + print("No image data found in response.") + print("Full response for debugging:") + print(response) +`:` +import base64 +import os +import time + +# Helper function to encode images to base64 +def encode_image(image_path): + with open(image_path, "rb") as image_file: + return base64.b64encode(image_file.read()).decode('utf-8') + +# Helper function to create a file (simplified for this example) +def create_file(image_path): + # In a real implementation, this would upload the file to OpenAI + # For this example, we'll just return a placeholder ID + return f"file_{os.path.basename(image_path).replace('.', '_')}" + +# The prompt entered by the user +prompt = "${j}" + +# Encode images to base64 +base64_image1 = encode_image("body-lotion.png") +base64_image2 = encode_image("soap.png") + +# Create file IDs +file_id1 = create_file("body-lotion.png") +file_id2 = create_file("incense-kit.png") + +response = client.responses.create( + model="${k}", + input=[ + { + "role": "user", + "content": [ + {"type": "input_text", "text": prompt}, + { + "type": "input_image", + "image_url": f"data:image/jpeg;base64,{base64_image1}", + }, + { + "type": "input_image", + "image_url": f"data:image/jpeg;base64,{base64_image2}", + }, + { + "type": "input_image", + "file_id": file_id1, + }, + { + "type": "input_image", + "file_id": file_id2, + } + ], + } + ], + tools=[{"type": "image_generation"}], +) + +# Process the response +image_generation_calls = [ + output + for output in response.output + if output.type == "image_generation_call" +] + +image_data = [output.result for output in image_generation_calls] + +if image_data: + image_base64 = image_data[0] + image_filename = f"edited_image_{int(time.time())}.png" + with open(image_filename, "wb") as f: + f.write(base64.b64decode(image_base64)) + print(f"Image saved to {image_filename}") +else: + # If no image is generated, there might be a text response with an explanation + text_response = [output.text for output in response.output if hasattr(output, 'text')] + if text_response: + print("No image generated. Model response:") + print("\\n".join(text_response)) + else: + print("No image data found in response.") + print("Full response for debugging:") + print(response) +`;break;case o.EMBEDDINGS:t=` +response = client.embeddings.create( + input="${r||"Your string here"}", + model="${k}", + encoding_format="base64" # or "float" +) + +print(response.data[0].embedding) +`;break;case o.TRANSCRIPTION:t=` +# Open the audio file +audio_file = open("path/to/your/audio/file.mp3", "rb") + +# Make the transcription request +response = client.audio.transcriptions.create( + model="${k}", + file=audio_file${r?`, + prompt="${r.replace(/\\/g,"\\\\").replace(/"/g,'\\"')}"`:""} +) + +print(response.text) +`;break;case o.SPEECH:t=` +# Make the text-to-speech request +response = client.audio.speech.create( + model="${k}", + input="${r||"Your text to convert to speech here"}", + voice="${g}" # Options: alloy, ash, ballad, coral, echo, fable, nova, onyx, sage, shimmer +) + +# Save the audio to a file +output_filename = "output_speech.mp3" +response.stream_to_file(output_filename) +print(f"Audio saved to {output_filename}") + +# Optional: Customize response format and speed +# response = client.audio.speech.create( +# model="${k}", +# input="${r||"Your text to convert to speech here"}", +# voice="alloy", +# response_format="mp3", # Options: mp3, opus, aac, flac, wav, pcm +# speed=1.0 # Range: 0.25 to 4.0 +# ) +# response.stream_to_file("output_speech.mp3") +`;break;default:t="\n# Code generation for this endpoint is not implemented yet."}return`${w} +${t}`}],909947)},86408,e=>{"use strict";var t=e.i(843476),i=e.i(271645),a=e.i(618566),o=e.i(934879);function n(){let e=(0,a.useSearchParams)().get("key"),[n,r]=(0,i.useState)(null);return(0,i.useEffect)(()=>{e&&r(e)},[e]),(0,t.jsx)(o.default,{accessToken:n,publicPage:!0,premiumUser:!1,userRole:null})}e.s(["default",0,function(){return(0,t.jsx)(i.Suspense,{fallback:(0,t.jsx)("div",{className:"flex items-center justify-center 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<158243242+devin-ai-integration[bot]@users.noreply.github.com> --- .../guardrails/guardrail_hooks/test_agent_365.py | 11 +++++++---- 1 file changed, 7 insertions(+), 4 deletions(-) diff --git a/tests/test_litellm/proxy/guardrails/guardrail_hooks/test_agent_365.py b/tests/test_litellm/proxy/guardrails/guardrail_hooks/test_agent_365.py index 152674aab7f..e9cdf9318a6 100644 --- a/tests/test_litellm/proxy/guardrails/guardrail_hooks/test_agent_365.py +++ b/tests/test_litellm/proxy/guardrails/guardrail_hooks/test_agent_365.py @@ -1,9 +1,8 @@ +import time from types import SimpleNamespace from typing import Any, Final import httpx -import time - import pytest from fastapi import HTTPException @@ -595,7 +594,9 @@ class TestMalformedResponses: @pytest.mark.asyncio async def test_bad_expires_in_still_allows(self): - handler: Final = FakeHandler([_response(200, {"access_token": "tok-1", "expires_in": "soon"}), _allow_response()]) + handler: Final = FakeHandler( + [_response(200, {"access_token": "tok-1", "expires_in": "soon"}), _allow_response()] + ) guardrail: Final = _make_guardrail(handler) data: Final = _mcp_data() result: Final = await _run(guardrail, data) @@ -625,7 +626,9 @@ class TestDeltaHardening: @pytest.mark.asyncio async def test_numeric_string_expires_in_honored(self): - handler: Final = FakeHandler([_response(200, {"access_token": "tok-9", "expires_in": "120"}), _allow_response()]) + handler: Final = FakeHandler( + [_response(200, {"access_token": "tok-9", "expires_in": "120"}), _allow_response()] + ) guardrail: Final = _make_guardrail(handler) await _run(guardrail, _mcp_data()) entries: Final = list(guardrail._obo_token_cache.values()) From a1ab5d42ccfdc919b7fdf4cabf31501af39c7373 Mon Sep 17 00:00:00 2001 From: yucheng Date: Thu, 10 Sep 2026 06:58:39 +0000 Subject: [PATCH 012/225] chore(agent_365): regenerate lazy OpenAPI snapshot and dashboard API types Co-Authored-By: Devin AI <158243242+devin-ai-integration[bot]@users.noreply.github.com> --- litellm/proxy/_lazy_openapi_snapshot.json | 62 ++++++++++++++++++- ui/litellm-dashboard/src/lib/http/schema.d.ts | 27 +++++++- 2 files changed, 87 insertions(+), 2 deletions(-) diff --git a/litellm/proxy/_lazy_openapi_snapshot.json b/litellm/proxy/_lazy_openapi_snapshot.json index 4d5cdcc8003..e132e633b6b 100644 --- a/litellm/proxy/_lazy_openapi_snapshot.json +++ b/litellm/proxy/_lazy_openapi_snapshot.json @@ -9904,7 +9904,7 @@ }, "unreachable_fallback": { "default": "fail_closed", - "description": "Behavior when a guardrail endpoint is unreachable due to network errors. Implemented by guardrail='generic_guardrail_api', 'akto', 'vigil_guard', 'repelloai', 'headroom', and 'compresr'. 'fail_closed' raises an error (default). 'fail_open' logs a critical error and allows the request to proceed.", + "description": "Behavior when a guardrail endpoint is unreachable due to network errors. Implemented by guardrail='generic_guardrail_api', 'agent_365', 'akto', 'vigil_guard', 'repelloai', 'headroom', and 'compresr'. 'fail_closed' raises an error (default). 'fail_open' logs a critical error and allows the request to proceed.", "enum": [ "fail_closed", "fail_open" @@ -10866,6 +10866,18 @@ "description": "Custom advisory message template used when on_flagged='inject_system_message'. Must contain a {reason} placeholder. Defaults to a generic advisory message if unset.", "title": "Advisory System Message" }, + "agent_id": { + "anyOf": [ + { + "type": "string" + }, + { + "type": "null" + } + ], + "description": "Agent identity reported to Agent 365 with every tool evaluation. When unset, the caller's key alias is used.", + "title": "Agent Id" + }, "akto_account_id": { "anyOf": [ { @@ -11368,6 +11380,30 @@ "title": "Chunk Budget Chars", "type": "integer" }, + "client_id": { + "anyOf": [ + { + "type": "string" + }, + { + "type": "null" + } + ], + "description": "Client id of the gateway's Entra app registration (a confidential client). Falls back to the AGENT365_CLIENT_ID environment variable.", + "title": "Client Id" + }, + "client_secret": { + "anyOf": [ + { + "type": "string" + }, + { + "type": "null" + } + ], + "description": "Client secret of the gateway's Entra app registration, used to perform the On-Behalf-Of exchange. Falls back to the AGENT365_CLIENT_SECRET environment variable.", + "title": "Client Secret" + }, "confidence_threshold": { "default": 0.5, "default_value": 0.5, @@ -12408,6 +12444,18 @@ "description": "The message the bot speaks aloud when a /v1/realtime guardrail fires. Falls back to violation_message_template if not set.", "title": "Realtime Violation Message" }, + "resource_app_id": { + "anyOf": [ + { + "type": "string" + }, + { + "type": "null" + } + ], + "description": "Application id of the Agent 365 resource the OBO token is minted for. Defaults to the production resource ea9ffc3e-8a23-4a7d-836d-234d7c7565c1; the Test and PreProd environments use a different id. Falls back to the AGENT365_RESOURCE_APP_ID environment variable.", + "title": "Resource App Id" + }, "rules": { "anyOf": [ { @@ -12645,6 +12693,18 @@ "description": "The ID of your Model Armor template", "title": "Template Id" }, + "tenant_id": { + "anyOf": [ + { + "type": "string" + }, + { + "type": "null" + } + ], + "description": "Entra tenant id used for the On-Behalf-Of token exchange. Falls back to the AGENT365_TENANT_ID environment variable.", + "title": "Tenant Id" + }, "timeout": { "anyOf": [ { diff --git a/ui/litellm-dashboard/src/lib/http/schema.d.ts b/ui/litellm-dashboard/src/lib/http/schema.d.ts index 6d62ce2b675..9af160c3bef 100644 --- a/ui/litellm-dashboard/src/lib/http/schema.d.ts +++ b/ui/litellm-dashboard/src/lib/http/schema.d.ts @@ -23939,7 +23939,7 @@ export interface components { timeout?: number | null; /** * Unreachable Fallback - * @description Behavior when a guardrail endpoint is unreachable due to network errors. Implemented by guardrail='generic_guardrail_api', 'akto', 'vigil_guard', 'repelloai', 'headroom', and 'compresr'. 'fail_closed' raises an error (default). 'fail_open' logs a critical error and allows the request to proceed. + * @description Behavior when a guardrail endpoint is unreachable due to network errors. Implemented by guardrail='generic_guardrail_api', 'agent_365', 'akto', 'vigil_guard', 'repelloai', 'headroom', and 'compresr'. 'fail_closed' raises an error (default). 'fail_open' logs a critical error and allows the request to proceed. * @default fail_closed * @enum {string} */ @@ -30542,6 +30542,11 @@ export interface components { * @description Custom advisory message template used when on_flagged='inject_system_message'. Must contain a {reason} placeholder. Defaults to a generic advisory message if unset. */ advisory_system_message?: string | null; + /** + * Agent Id + * @description Agent identity reported to Agent 365 with every tool evaluation. When unset, the caller's key alias is used. + */ + agent_id?: string | null; /** * Akto Account Id * @description Akto account ID for multi-tenant deployments. Env: AKTO_ACCOUNT_ID. Default: '1000000'. @@ -30743,6 +30748,16 @@ export interface components { * @default 25000 */ chunk_budget_chars: number; + /** + * Client Id + * @description Client id of the gateway's Entra app registration (a confidential client). Falls back to the AGENT365_CLIENT_ID environment variable. + */ + client_id?: string | null; + /** + * Client Secret + * @description Client secret of the gateway's Entra app registration, used to perform the On-Behalf-Of exchange. Falls back to the AGENT365_CLIENT_SECRET environment variable. + */ + client_secret?: string | null; /** * Confidence Threshold * @description Only block or mask when detection confidence >= this value; below threshold, allow or log_only. @@ -31176,6 +31191,11 @@ export interface components { * @description The message the bot speaks aloud when a /v1/realtime guardrail fires. Falls back to violation_message_template if not set. */ realtime_violation_message?: string | null; + /** + * Resource App Id + * @description Application id of the Agent 365 resource the OBO token is minted for. Defaults to the production resource ea9ffc3e-8a23-4a7d-836d-234d7c7565c1; the Test and PreProd environments use a different id. Falls back to the AGENT365_RESOURCE_APP_ID environment variable. + */ + resource_app_id?: string | null; /** * Rules * @description Ordered allow/deny rules. Patterns use regex for tool names/types and optional regex constraints on tool arguments. @@ -31277,6 +31297,11 @@ export interface components { * @description The ID of your Model Armor template */ template_id?: string | null; + /** + * Tenant Id + * @description Entra tenant id used for the On-Behalf-Of token exchange. Falls back to the AGENT365_TENANT_ID environment variable. + */ + tenant_id?: string | null; /** * Timeout * @description Per-request timeout for the guardrail provider API call (seconds). Accepts int, float, or numeric string; coerced to float on load. Each guardrail handler chooses its own default when unset. From aee5b7a0d87243cc7150dfc907a4eaa6bf7fca13 Mon Sep 17 00:00:00 2001 From: yucheng Date: Thu, 10 Sep 2026 07:26:52 +0000 Subject: [PATCH 013/225] fix(agent_365): treat a verdict without a boolean allowed field as unavailable A 200 body that is JSON but lacks a boolean allowed was recorded as a Defender Block and rejected with 400. It is malformed, so it now routes through the same unreachable_fallback handling as non-JSON and non-object bodies Co-Authored-By: Devin AI <158243242+devin-ai-integration[bot]@users.noreply.github.com> --- .../guardrail_hooks/agent_365/agent_365.py | 8 ++++- .../guardrail_hooks/test_agent_365.py | 31 +++++++++++++++++++ 2 files changed, 38 insertions(+), 1 deletion(-) diff --git a/litellm/proxy/guardrails/guardrail_hooks/agent_365/agent_365.py b/litellm/proxy/guardrails/guardrail_hooks/agent_365/agent_365.py index 0d45b5ff238..34c4d67a829 100644 --- a/litellm/proxy/guardrails/guardrail_hooks/agent_365/agent_365.py +++ b/litellm/proxy/guardrails/guardrail_hooks/agent_365/agent_365.py @@ -304,11 +304,17 @@ class Agent365Guardrail(CustomGuardrail): reason="the Agent 365 endpoint returned a non-object JSON body", ) verdict: Final[_EvaluateResponse] = parsed_verdict + allowed: Final = verdict.get("allowed") + if not isinstance(allowed, bool): + return self._handle_unavailable( + data=data, + tool_name=tool_name, + reason="the Agent 365 endpoint returned a verdict without a boolean 'allowed' field", + ) raw_defender: Final = verdict.get("defender") defender: Final = raw_defender if isinstance(raw_defender, dict) else _DefenderResult() raw_correlation_id: Final = verdict.get("correlationId") correlation_id: Final = raw_correlation_id if isinstance(raw_correlation_id, str) else None - allowed: Final = verdict.get("allowed") is True self._record_verdict( data=data, verdict="Allow" if allowed else "Block", diff --git a/tests/test_litellm/proxy/guardrails/guardrail_hooks/test_agent_365.py b/tests/test_litellm/proxy/guardrails/guardrail_hooks/test_agent_365.py index e9cdf9318a6..2f037418927 100644 --- a/tests/test_litellm/proxy/guardrails/guardrail_hooks/test_agent_365.py +++ b/tests/test_litellm/proxy/guardrails/guardrail_hooks/test_agent_365.py @@ -592,6 +592,37 @@ class TestMalformedResponses: await _run(guardrail, _mcp_data()) assert exc_info.value.status_code == 503 + @pytest.mark.asyncio + @pytest.mark.parametrize( + "verdict", + [{}, {"allowed": None}, {"allowed": "true"}, {"allowed": 1}, {"allowed": "false"}], + ids=["missing", "null", "string-true", "int-one", "string-false"], + ) + async def test_evaluate_non_boolean_allowed_fail_closed(self, verdict: dict): + handler: Final = FakeHandler([_token_response(), _response(200, verdict)]) + guardrail: Final = _make_guardrail(handler) + data: Final = _mcp_data() + with pytest.raises(HTTPException) as exc_info: + await _run(guardrail, data) + assert exc_info.value.status_code == 503 + assert "boolean 'allowed'" in exc_info.value.detail["message"] + assert _guardrail_info(data)["guardrail_response"]["verdict"] == "Unavailable" + + @pytest.mark.asyncio + @pytest.mark.parametrize( + "verdict", + [{}, {"allowed": None}, {"allowed": "true"}, {"allowed": 1}, {"allowed": "false"}], + ids=["missing", "null", "string-true", "int-one", "string-false"], + ) + async def test_evaluate_non_boolean_allowed_fail_open(self, verdict: dict): + handler: Final = FakeHandler([_token_response(), _response(200, verdict)]) + guardrail: Final = _make_guardrail(handler, unreachable_fallback="fail_open") + data: Final = _mcp_data() + result: Final = await _run(guardrail, data) + assert result is data + assert _guardrail_info(data)["guardrail_response"]["verdict"] == "Unscanned" + assert _guardrail_info(data)["guardrail_status"] == "guardrail_failed_to_respond" + @pytest.mark.asyncio async def test_bad_expires_in_still_allows(self): handler: Final = FakeHandler( From f4033ab84ab9fd22e01cceb92cdf08733ce7bd7a Mon Sep 17 00:00:00 2001 From: yucheng Date: Thu, 10 Sep 2026 08:08:33 +0000 Subject: [PATCH 014/225] fix(guardrails): keep Agent 365 missing-secret startup error readable after log redaction Co-Authored-By: Devin AI <158243242+devin-ai-integration[bot]@users.noreply.github.com> --- .../proxy/guardrails/guardrail_hooks/agent_365/__init__.py | 4 +++- .../proxy/guardrails/guardrail_hooks/test_agent_365.py | 4 +++- 2 files changed, 6 insertions(+), 2 deletions(-) diff --git a/litellm/proxy/guardrails/guardrail_hooks/agent_365/__init__.py b/litellm/proxy/guardrails/guardrail_hooks/agent_365/__init__.py index 839bf5ba888..9aacdec0602 100644 --- a/litellm/proxy/guardrails/guardrail_hooks/agent_365/__init__.py +++ b/litellm/proxy/guardrails/guardrail_hooks/agent_365/__init__.py @@ -29,7 +29,9 @@ def initialize_guardrail(litellm_params: "LitellmParams", guardrail: "Guardrail" if not client_id: raise ValueError("Microsoft Agent 365: client_id is required") if not client_secret: - raise ValueError("Microsoft Agent 365: client_secret (or api_key) is required") + raise ValueError( + "Microsoft Agent 365: client secret is required. Set client_secret, api_key, or AGENT365_CLIENT_SECRET" + ) guardrail_name: Final = guardrail.get("guardrail_name") if not guardrail_name: diff --git a/tests/test_litellm/proxy/guardrails/guardrail_hooks/test_agent_365.py b/tests/test_litellm/proxy/guardrails/guardrail_hooks/test_agent_365.py index 2f037418927..8245aba6d73 100644 --- a/tests/test_litellm/proxy/guardrails/guardrail_hooks/test_agent_365.py +++ b/tests/test_litellm/proxy/guardrails/guardrail_hooks/test_agent_365.py @@ -7,6 +7,7 @@ import pytest from fastapi import HTTPException from litellm.exceptions import Timeout as LitellmTimeout +from litellm.litellm_core_utils.secret_redaction import redact_string from litellm.proxy._types import UserAPIKeyAuth from litellm.proxy.guardrails.guardrail_hooks.agent_365 import ( Agent365Guardrail, @@ -165,8 +166,9 @@ class TestInitializeGuardrail: tenant_id="tenant-abc", client_id="client-xyz", ) - with pytest.raises(ValueError, match="client_secret"): + with pytest.raises(ValueError, match="client_secret") as exc_info: initialize_guardrail(params, {"guardrail_name": "a365"}) + assert redact_string(str(exc_info.value)) == str(exc_info.value) def test_env_var_fallbacks(self, monkeypatch): monkeypatch.delenv("AGENT365_RESOURCE_APP_ID", raising=False) From 5921e644468c0b11fcf02f11a854959752334c33 Mon Sep 17 00:00:00 2001 From: yucheng Date: Thu, 10 Sep 2026 08:14:01 +0000 Subject: [PATCH 015/225] refactor(guardrails): read key_alias from the typed UserAPIKeyAuth in Agent 365 payload Co-Authored-By: Devin AI <158243242+devin-ai-integration[bot]@users.noreply.github.com> --- litellm/proxy/guardrails/guardrail_hooks/agent_365/agent_365.py | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/litellm/proxy/guardrails/guardrail_hooks/agent_365/agent_365.py b/litellm/proxy/guardrails/guardrail_hooks/agent_365/agent_365.py index 34c4d67a829..8d0957ff2be 100644 --- a/litellm/proxy/guardrails/guardrail_hooks/agent_365/agent_365.py +++ b/litellm/proxy/guardrails/guardrail_hooks/agent_365/agent_365.py @@ -345,7 +345,7 @@ class Agent365Guardrail(CustomGuardrail): tool_name: Final = str(data.get("mcp_tool_name") or "") arguments: Final = data.get("mcp_arguments") server_name: Final = str(data.get("mcp_server_name") or "litellm") - agent_id: Final = self.agent_id or getattr(user_api_key_dict, "key_alias", None) + agent_id: Final = self.agent_id or user_api_key_dict.key_alias tool_reference: Final[_ToolReference] = {"name": tool_name} payload: Final[dict[str, object]] = { # mutable-ok: JSON body with optional fields added below "tool": tool_reference, From 239c126f2994461937d0ca6e5741be6c4c2b51e1 Mon Sep 17 00:00:00 2001 From: yucheng Date: Thu, 10 Sep 2026 08:25:48 +0000 Subject: [PATCH 016/225] refactor(guardrails): resolve the Agent 365 conversation id from the typed LiteLLM logging object Co-Authored-By: Devin AI <158243242+devin-ai-integration[bot]@users.noreply.github.com> --- .../guardrails/guardrail_hooks/agent_365/agent_365.py | 11 ++++++----- .../guardrails/guardrail_hooks/test_agent_365.py | 11 +++++++++-- 2 files changed, 15 insertions(+), 7 deletions(-) diff --git a/litellm/proxy/guardrails/guardrail_hooks/agent_365/agent_365.py b/litellm/proxy/guardrails/guardrail_hooks/agent_365/agent_365.py index 8d0957ff2be..4830bfcafb0 100644 --- a/litellm/proxy/guardrails/guardrail_hooks/agent_365/agent_365.py +++ b/litellm/proxy/guardrails/guardrail_hooks/agent_365/agent_365.py @@ -27,6 +27,7 @@ from litellm.integrations.custom_guardrail import ( CustomGuardrail, log_guardrail_information, ) +from litellm.litellm_core_utils.litellm_logging import Logging as LiteLLMLoggingObj from litellm.llms.custom_httpx.http_handler import ( AsyncHTTPHandler, get_async_httpx_client, @@ -372,16 +373,16 @@ class Agent365Guardrail(CustomGuardrail): ) if isinstance(session_id, str) and session_id: return session_id - logging_obj: Final = data.get("litellm_logging_obj") - call_details: Final = getattr(logging_obj, "model_call_details", None) - if isinstance(call_details, Mapping): - tool_call_metadata: Final = call_details.get("mcp_tool_call_metadata") + raw_logging_obj: Final = data.get("litellm_logging_obj") + logging_obj: Final = raw_logging_obj if isinstance(raw_logging_obj, LiteLLMLoggingObj) else None + if logging_obj is not None: + tool_call_metadata: Final = logging_obj.model_call_details.get("mcp_tool_call_metadata") session_from_logging: Final = ( tool_call_metadata.get("mcp_session_id") if isinstance(tool_call_metadata, Mapping) else None ) if isinstance(session_from_logging, str) and session_from_logging: return session_from_logging - call_id: Final = data.get("litellm_call_id") or getattr(logging_obj, "litellm_call_id", None) + call_id: Final = data.get("litellm_call_id") or (logging_obj.litellm_call_id if logging_obj else None) if isinstance(call_id, str) and call_id: return call_id return str(uuid.uuid4()) diff --git a/tests/test_litellm/proxy/guardrails/guardrail_hooks/test_agent_365.py b/tests/test_litellm/proxy/guardrails/guardrail_hooks/test_agent_365.py index 8245aba6d73..04460b150f5 100644 --- a/tests/test_litellm/proxy/guardrails/guardrail_hooks/test_agent_365.py +++ b/tests/test_litellm/proxy/guardrails/guardrail_hooks/test_agent_365.py @@ -7,6 +7,7 @@ import pytest from fastapi import HTTPException from litellm.exceptions import Timeout as LitellmTimeout +from litellm.litellm_core_utils.litellm_logging import Logging as LiteLLMLoggingObj from litellm.litellm_core_utils.secret_redaction import redact_string from litellm.proxy._types import UserAPIKeyAuth from litellm.proxy.guardrails.guardrail_hooks.agent_365 import ( @@ -295,10 +296,16 @@ class TestConversationId: async def test_falls_back_to_logging_obj_session_id(self): handler: Final = FakeHandler([_token_response(), _allow_response()]) guardrail: Final = _make_guardrail(handler) - logging_obj: Final = SimpleNamespace( - model_call_details={"mcp_tool_call_metadata": {"mcp_session_id": "sess-from-logging"}}, + logging_obj: Final = LiteLLMLoggingObj( + model="mcp", + messages=[], + stream=False, + call_type="call_mcp_tool", + start_time=None, litellm_call_id="call-id-1", + function_id="fn-1", ) + logging_obj.model_call_details["mcp_tool_call_metadata"] = {"mcp_session_id": "sess-from-logging"} data: Final = _mcp_data(metadata={"headers": {}}, litellm_logging_obj=logging_obj) await _run(guardrail, data) assert handler.calls[1].json["conversationId"] == "sess-from-logging" From 35a1d017dcee9bfe4e300f30c3c39f8fe938e596 Mon Sep 17 00:00:00 2001 From: yucheng Date: Fri, 11 Sep 2026 02:08:01 +0000 Subject: [PATCH 017/225] feat(guardrails): send MCP tool metadata to Agent 365 and treat unevaluated Defender verdicts as unavailable Carry the listed tool's description and inputSchema from MCPServerManager through the pre-call and during-call hook request objects into the Agent 365 evaluate payload, omitting them when the tool was never listed. An allowed verdict whose defender.status is not Evaluated (Skipped, FailedOpen, missing) now follows the unreachable_fallback policy instead of counting as a scanned allow. The conversationId prefers the proxy-owned litellm_call_id over caller-controlled mcp-session-id headers. Co-Authored-By: Devin AI <158243242+devin-ai-integration[bot]@users.noreply.github.com> --- .../mcp_server/mcp_server_manager.py | 41 ++- .../guardrail_hooks/agent_365/agent_365.py | 113 ++++++-- litellm/proxy/utils.py | 4 + litellm/types/mcp.py | 4 + .../mcp_server/test_mcp_server_manager.py | 130 +++++++++ .../guardrail_hooks/test_agent_365.py | 264 ++++++++++++++++-- .../utils/proxy_logging/test_mcp_bridging.py | 20 ++ 7 files changed, 517 insertions(+), 59 deletions(-) diff --git a/litellm/proxy/_experimental/mcp_server/mcp_server_manager.py b/litellm/proxy/_experimental/mcp_server/mcp_server_manager.py index 116bcc715d4..d8ac46a1bdf 100644 --- a/litellm/proxy/_experimental/mcp_server/mcp_server_manager.py +++ b/litellm/proxy/_experimental/mcp_server/mcp_server_manager.py @@ -1779,6 +1779,7 @@ class MCPServerManager: "gmail_send_email": "zapier_mcp_server", } """ + self._listed_tools_by_server_id: dict[str, dict[str, MCPTool]] = {} self._upstream_initialize_instructions_by_server_id: dict[str, str] = {} # Per-server monotonic timestamp of last upstream prefetch attempt (success, # empty result, or failure). Used to throttle re-probes for servers that do @@ -4239,18 +4240,18 @@ class MCPServerManager: # applied (e.g. "test_petstore-getinventory"). Do NOT pass them # through _create_prefixed_tools — that would add the prefix a second # time producing "test_petstore-test_petstore-getinventory". - if not add_prefix: - prefix: Final = get_server_prefix(server) - sep: Final = MCP_TOOL_PREFIX_SEPARATOR - tools = [ - ( - t.model_copy(update={"name": t.name[len(prefix) + len(sep) :]}) - if t.name.startswith(f"{prefix}{sep}") - else t - ) - for t in tools - ] - return tools + prefix: Final = get_server_prefix(server) + sep: Final = MCP_TOOL_PREFIX_SEPARATOR + bare_tools: Final = [ + ( + t.model_copy(update={"name": t.name[len(prefix) + len(sep) :]}) + if t.name.startswith(f"{prefix}{sep}") + else t + ) + for t in tools + ] + self._listed_tools_by_server_id[server.server_id] = {t.name: t for t in bare_tools} + return tools if add_prefix else bare_tools else: tools = await self._fetch_tools_with_timeout(client, server.name) self._remember_upstream_initialize_instructions(server, client) @@ -5131,9 +5132,14 @@ class MCPServerManager: for spelling in iter_known_tool_name_spellings(original_name, server): self.tool_name_to_mcp_server_name_mapping[spelling] = prefix + self._listed_tools_by_server_id[server.server_id] = {tool.name: tool for tool in tools} verbose_logger.info("Successfully fetched %s tools from server %s", len(prefixed_tools), server.name) return prefixed_tools + def get_listed_tool(self, server: MCPServer, name: str) -> MCPTool | None: + listed: Final = self._listed_tools_by_server_id.get(server.server_id, {}) + return listed.get(name) or listed.get(strip_known_server_prefix(name, server)) + def _create_prefixed_prompts( self, prompts: list[Prompt], server: MCPServer, add_prefix: bool = True ) -> list[Prompt]: @@ -5372,6 +5378,7 @@ class MCPServerManager: server: MCPServer, raw_headers: dict[str, str] | None = None, litellm_logging_obj: "LiteLLMLoggingObj | None" = None, + tool: MCPTool | None = None, ) -> dict[str, Any]: """ Run pre-call checks and guardrail hooks for an MCP tool call. @@ -5385,6 +5392,9 @@ class MCPServerManager: ``pre_mcp_call`` evaluation (or a block) on the spend-log row the Guardrails Monitor counts. It stays optional so callers that do no logging are unchanged. + ``tool`` is the upstream tool definition when one was listed, so guardrails + can see its description and input schema, not just the name and arguments. + Returns a dict that may contain: - "arguments": hook-modified tool arguments (only if changed) - "extra_headers": headers injected by pre_mcp_call guardrail hooks @@ -5438,6 +5448,8 @@ class MCPServerManager: "user_api_key_hash": (getattr(user_api_key_auth, "api_key_hash", None) if user_api_key_auth else None), "incoming_bearer_token": incoming_bearer_token, "headers": logging_safe_mcp_headers(raw_headers), + "tool_description": tool.description if tool is not None else None, + "tool_input_schema": tool.inputSchema if tool is not None else None, } # Create MCP request object for processing @@ -5492,6 +5504,7 @@ class MCPServerManager: proxy_logging_obj: ProxyLogging, start_time: datetime.datetime, litellm_logging_obj: "LiteLLMLoggingObj | None" = None, + tool: MCPTool | None = None, ): """Create and return a during hook task for MCP tool calls. @@ -5506,6 +5519,8 @@ class MCPServerManager: tool_name=name, arguments=arguments, server_name=server_name_from_prefix, + tool_description=tool.description if tool is not None else None, + tool_input_schema=tool.inputSchema if tool is not None else None, start_time=start_time.timestamp() if start_time else None, hidden_params=HiddenParams(), ) @@ -6101,6 +6116,7 @@ class MCPServerManager: server=mcp_server, raw_headers=raw_headers, litellm_logging_obj=litellm_logging_obj, + tool=self.get_listed_tool(mcp_server, name), ) if "arguments" in hook_result: arguments = hook_result["arguments"] @@ -6116,6 +6132,7 @@ class MCPServerManager: proxy_logging_obj=proxy_logging_obj, start_time=start_time, litellm_logging_obj=litellm_logging_obj, + tool=self.get_listed_tool(mcp_server, name), ) tasks.append(during_hook_task) diff --git a/litellm/proxy/guardrails/guardrail_hooks/agent_365/agent_365.py b/litellm/proxy/guardrails/guardrail_hooks/agent_365/agent_365.py index 4830bfcafb0..5813240f01b 100644 --- a/litellm/proxy/guardrails/guardrail_hooks/agent_365/agent_365.py +++ b/litellm/proxy/guardrails/guardrail_hooks/agent_365/agent_365.py @@ -19,8 +19,10 @@ from typing import TYPE_CHECKING, ClassVar, Final, Literal, NoReturn import httpx from fastapi import HTTPException +from pydantic import BaseModel, ConfigDict, Field, TypeAdapter, ValidationError from typing_extensions import ReadOnly, TypedDict +import litellm from litellm._logging import verbose_proxy_logger from litellm.exceptions import Timeout as LitellmTimeout from litellm.integrations.custom_guardrail import ( @@ -33,7 +35,10 @@ from litellm.llms.custom_httpx.http_handler import ( get_async_httpx_client, httpxSpecialProvider, ) +from litellm.proxy.litellm_pre_call_utils import add_guardrails_from_auth_metadata from litellm.types.guardrails import GuardrailEventHooks +from litellm.types.mcp import MCPAuth +from litellm.types.mcp_server.mcp_server_manager import MCPServer from litellm.types.proxy.guardrails.guardrail_hooks.agent_365 import ( AGENT_365_PROD_API_BASE, AGENT_365_PROD_RESOURCE_APP_ID, @@ -48,9 +53,12 @@ if TYPE_CHECKING: from litellm.types.utils import GuardrailStatus TOKEN_ENDPOINT_TEMPLATE: Final = "https://login.microsoftonline.com/{tenant_id}/oauth2/v2.0/token" +ENTRA_ISSUER_TEMPLATE: Final = "https://login.microsoftonline.com/{tenant_id}/v2.0" EVALUATE_PATH: Final = "/agents/tool-evaluation/evaluate" MCP_SESSION_ID_HEADER: Final = "mcp-session-id" +DEFENDER_STATUS_EVALUATED: Final = "Evaluated" _MCP_CALL_TYPES: Final[tuple[str, ...]] = ("mcp_call", "call_mcp_tool") +_TOOL_INPUT_SCHEMA_ADAPTER: Final = TypeAdapter(dict[str, object]) _OBO_CACHE_MAX_ENTRIES: Final = 1000 _DEFAULT_TOKEN_TTL_SECONDS: Final = 3599.0 _TOKEN_EXPIRY_SLACK_SECONDS: Final = 60.0 @@ -65,6 +73,13 @@ def _parse_expires_in(raw: object) -> float: return _DEFAULT_TOKEN_TTL_SECONDS +def _parse_tool_input_schema(raw: object) -> dict[str, object] | None: + try: + return _TOOL_INPUT_SCHEMA_ADAPTER.validate_python(raw) + except ValidationError: + return None + + class _DefenderResult(TypedDict, total=False): status: ReadOnly[str] verdict: ReadOnly[str | None] @@ -77,8 +92,12 @@ class _EvaluateResponse(TypedDict, total=False): correlationId: ReadOnly[str] -class _ToolReference(TypedDict): - name: ReadOnly[str] +class _ToolReference(BaseModel): + model_config = ConfigDict(frozen=True) + + name: str + description: str | None = None + input_schema: dict[str, object] | None = Field(default=None, serialization_alias="inputSchema") class _UnavailableDetail(TypedDict): @@ -316,11 +335,21 @@ class Agent365Guardrail(CustomGuardrail): defender: Final = raw_defender if isinstance(raw_defender, dict) else _DefenderResult() raw_correlation_id: Final = verdict.get("correlationId") correlation_id: Final = raw_correlation_id if isinstance(raw_correlation_id, str) else None + defender_status: Final = defender.get("status") + if allowed and defender_status != DEFENDER_STATUS_EVALUATED: + return self._handle_unavailable( + data=data, + tool_name=tool_name, + reason=f"Microsoft Defender did not evaluate the call (defender.status={defender_status or 'missing'})", + defender_status=defender_status, + correlation_id=correlation_id, + latency_ms=latency_ms, + ) self._record_verdict( data=data, verdict="Allow" if allowed else "Block", guardrail_status="success" if allowed else "guardrail_intervened", - defender_status=defender.get("status"), + defender_status=defender_status, correlation_id=correlation_id, latency_ms=latency_ms, ) @@ -347,9 +376,14 @@ class Agent365Guardrail(CustomGuardrail): arguments: Final = data.get("mcp_arguments") server_name: Final = str(data.get("mcp_server_name") or "litellm") agent_id: Final = self.agent_id or user_api_key_dict.key_alias - tool_reference: Final[_ToolReference] = {"name": tool_name} + description: Final = data.get("mcp_tool_description") + tool_reference: Final = _ToolReference( + name=tool_name, + description=description if isinstance(description, str) and description else None, + input_schema=_parse_tool_input_schema(data.get("mcp_tool_input_schema")), + ) payload: Final[dict[str, object]] = { # mutable-ok: JSON body with optional fields added below - "tool": tool_reference, + "tool": tool_reference.model_dump(by_alias=True, exclude_none=True), "serverName": server_name, "conversationId": self._resolve_conversation_id(data), } @@ -361,6 +395,18 @@ class Agent365Guardrail(CustomGuardrail): @staticmethod def _resolve_conversation_id(data: Mapping[str, object]) -> str: + raw_logging_obj: Final = data.get("litellm_logging_obj") + logging_obj: Final = raw_logging_obj if isinstance(raw_logging_obj, LiteLLMLoggingObj) else None + call_id: Final = data.get("litellm_call_id") or (logging_obj.litellm_call_id if logging_obj else None) + if isinstance(call_id, str) and call_id: + return call_id + if logging_obj is not None: + tool_call_metadata: Final = logging_obj.model_call_details.get("mcp_tool_call_metadata") + session_from_logging: Final = ( + tool_call_metadata.get("mcp_session_id") if isinstance(tool_call_metadata, Mapping) else None + ) + if isinstance(session_from_logging, str) and session_from_logging: + return session_from_logging metadata: Final = next( (m for m in (data.get("metadata"), data.get("litellm_metadata")) if isinstance(m, Mapping)), None, @@ -373,18 +419,6 @@ class Agent365Guardrail(CustomGuardrail): ) if isinstance(session_id, str) and session_id: return session_id - raw_logging_obj: Final = data.get("litellm_logging_obj") - logging_obj: Final = raw_logging_obj if isinstance(raw_logging_obj, LiteLLMLoggingObj) else None - if logging_obj is not None: - tool_call_metadata: Final = logging_obj.model_call_details.get("mcp_tool_call_metadata") - session_from_logging: Final = ( - tool_call_metadata.get("mcp_session_id") if isinstance(tool_call_metadata, Mapping) else None - ) - if isinstance(session_from_logging, str) and session_from_logging: - return session_from_logging - call_id: Final = data.get("litellm_call_id") or (logging_obj.litellm_call_id if logging_obj else None) - if isinstance(call_id, str) and call_id: - return call_id return str(uuid.uuid4()) async def _get_obo_token(self, assertion: str) -> str: @@ -518,6 +552,9 @@ class Agent365Guardrail(CustomGuardrail): data: dict, # mutable-ok: guardrail logging appends into the request metadata in place tool_name: str, reason: str, + defender_status: str | None = None, + correlation_id: str | None = None, + latency_ms: float | None = None, ) -> dict: # mutable-ok: returns the request data dict per hook contract if self.unreachable_fallback == "fail_open": verbose_proxy_logger.warning( @@ -530,9 +567,9 @@ class Agent365Guardrail(CustomGuardrail): data=data, verdict="Unscanned", guardrail_status="guardrail_failed_to_respond", - defender_status=None, - correlation_id=None, - latency_ms=None, + defender_status=defender_status, + correlation_id=correlation_id, + latency_ms=latency_ms, reason=reason, ) return data @@ -540,9 +577,9 @@ class Agent365Guardrail(CustomGuardrail): data=data, verdict="Unavailable", guardrail_status="guardrail_failed_to_respond", - defender_status=None, - correlation_id=None, - latency_ms=None, + defender_status=defender_status, + correlation_id=correlation_id, + latency_ms=latency_ms, reason=reason, ) unavailable_detail: Final[_UnavailableDetail] = { @@ -580,3 +617,33 @@ class Agent365Guardrail(CustomGuardrail): guardrail_provider=self.guardrail_provider, event_type=GuardrailEventHooks.pre_mcp_call, ) + + +def _applies_to_caller(guardrail: Agent365Guardrail, user_api_key_auth: "UserAPIKeyAuth") -> bool: + probe: Final[dict[str, object]] = {"metadata": {}} # mutable-ok: filled in place by the key resolver + add_guardrails_from_auth_metadata( + user_api_key_dict=user_api_key_auth, data=probe, metadata_variable_name="metadata" + ) + return guardrail.should_run_guardrail(data=probe, event_type=GuardrailEventHooks.pre_mcp_call) + + +def agent_365_authorization_servers(server: MCPServer, user_api_key_auth: "UserAPIKeyAuth | None") -> tuple[str, ...]: + """Entra issuers an MCP client signs in with before calling ``server`` through an Agent 365 guardrail. + + Empty unless the admin advertised the server's ``scopes`` (the audience the client requests), the gateway + would otherwise own sign-in for the server, and an Agent 365 guardrail applies: every registered one for the + anonymous discovery fetch, otherwise those the caller's key, team, or policies select. + """ + if not server.scopes or server.auth_type == MCPAuth.oauth2 or not server.advertises_gateway_authorization_server: + return () + registered: Final = tuple( + callback + for callback in litellm.logging_callback_manager.get_custom_loggers_for_type(Agent365Guardrail) + if isinstance(callback, Agent365Guardrail) + ) + applicable: Final = ( + registered + if user_api_key_auth is None + else tuple(g for g in registered if _applies_to_caller(g, user_api_key_auth)) + ) + return tuple(dict.fromkeys(ENTRA_ISSUER_TEMPLATE.format(tenant_id=g.tenant_id) for g in applicable)) diff --git a/litellm/proxy/utils.py b/litellm/proxy/utils.py index 00ccad33b6d..420fb29634f 100644 --- a/litellm/proxy/utils.py +++ b/litellm/proxy/utils.py @@ -1216,6 +1216,8 @@ class ProxyLogging: "user_api_key_request_route": kwargs.get("user_api_key_request_route"), "mcp_tool_name": request_obj.tool_name, # Keep original for reference "mcp_arguments": request_obj.arguments, # Keep original for reference + "mcp_tool_description": request_obj.tool_description, + "mcp_tool_input_schema": request_obj.tool_input_schema, # Surface the per-MCP-server rate-limit identity so the # ParallelRequestLimiterV3 hook can apply mcp_rpm_limit on the # synthetic call_mcp_tool payload (otherwise a key with @@ -1437,6 +1439,8 @@ class ProxyLogging: tool_name=kwargs.get("name", ""), arguments=kwargs.get("arguments", {}), server_name=kwargs.get("server_name"), + tool_description=kwargs.get("tool_description"), + tool_input_schema=kwargs.get("tool_input_schema"), user_api_key_auth=user_api_key_auth_dict, hidden_params=HiddenParams(), ) diff --git a/litellm/types/mcp.py b/litellm/types/mcp.py index a59fcb1bcb5..13a46d8b84f 100644 --- a/litellm/types/mcp.py +++ b/litellm/types/mcp.py @@ -391,6 +391,8 @@ class MCPPreCallRequestObject(BaseModel): tool_name: str arguments: dict[str, Any] server_name: str | None = None + tool_description: str | None = None + tool_input_schema: dict[str, object] | None = None user_api_key_auth: dict[str, Any] | None = None hidden_params: HiddenParams = HiddenParams() @@ -414,6 +416,8 @@ class MCPDuringCallRequestObject(BaseModel): tool_name: str arguments: dict[str, Any] server_name: str | None = None + tool_description: str | None = None + tool_input_schema: dict[str, object] | None = None start_time: float | None = None hidden_params: HiddenParams = HiddenParams() diff --git a/tests/test_litellm/proxy/_experimental/mcp_server/test_mcp_server_manager.py b/tests/test_litellm/proxy/_experimental/mcp_server/test_mcp_server_manager.py index adf985e9a21..ba7b76eae91 100644 --- a/tests/test_litellm/proxy/_experimental/mcp_server/test_mcp_server_manager.py +++ b/tests/test_litellm/proxy/_experimental/mcp_server/test_mcp_server_manager.py @@ -6403,6 +6403,136 @@ class TestMCPServerManager: # Verify the MCP client call was awaited exactly once assert mock_client.call_tool.await_count == 1 + @staticmethod + def _manager_ready_for_call_tool(listed_tools: list[MCPTool]) -> tuple[MCPServerManager, MagicMock]: + from mcp.types import CallToolResult + + manager = MCPServerManager() + server = MCPServer( + server_id="test-server", + name="test-server", + transport=MCPTransport.http, + url="http://test-server.com", + ) + manager.registry = {"test-server": server} + manager.tool_name_to_mcp_server_name_mapping["test_tool"] = "test-server" + manager.tool_name_to_mcp_server_name_mapping["test-server-test_tool"] = "test-server" + manager._create_prefixed_tools(listed_tools, server) + + mock_client = AsyncMock() + mock_client.call_tool.return_value = MagicMock(spec=CallToolResult, content=[], isError=False) + manager._create_mcp_client = AsyncMock(return_value=mock_client) + + proxy_logging_obj = MagicMock() + proxy_logging_obj._create_mcp_request_object_from_kwargs = MagicMock(return_value={}) + proxy_logging_obj._convert_mcp_to_llm_format = MagicMock(return_value={}) + proxy_logging_obj.pre_call_hook = AsyncMock(return_value={}) + proxy_logging_obj.during_call_hook = AsyncMock(return_value=None) + return manager, proxy_logging_obj + + @staticmethod + def _unrestricted_auth() -> MagicMock: + user_api_key_auth = MagicMock() + user_api_key_auth.object_permission = None + user_api_key_auth.object_permission_id = None + return user_api_key_auth + + @pytest.mark.asyncio + async def test_call_tool_hands_listed_tool_description_and_schema_to_pre_call_hooks(self): + schema = {"type": "object", "properties": {"param": {"type": "string"}}, "required": ["param"]} + listed = [MCPTool(name="test_tool", description="Runs the test tool", inputSchema=schema)] + manager, proxy_logging_obj = self._manager_ready_for_call_tool(listed) + + await manager.call_tool( + server_name="test-server", + name="test_tool", + arguments={"param": "value"}, + user_api_key_auth=self._unrestricted_auth(), + proxy_logging_obj=proxy_logging_obj, + ) + + hook_kwargs = proxy_logging_obj._create_mcp_request_object_from_kwargs.call_args.args[0] + assert (hook_kwargs["tool_description"], hook_kwargs["tool_input_schema"]) == ("Runs the test tool", schema) + + @pytest.mark.asyncio + async def test_call_tool_hands_listed_tool_metadata_to_during_call_hooks_through_real_conversion(self): + schema = {"type": "object", "properties": {"param": {"type": "string"}}} + listed = [MCPTool(name="test_tool", description="Runs the test tool", inputSchema=schema)] + manager, _ = self._manager_ready_for_call_tool(listed) + proxy_logging_obj = ProxyLogging(user_api_key_cache=DualCache()) + proxy_logging_obj.pre_call_hook = AsyncMock(return_value={}) + proxy_logging_obj.during_call_hook = AsyncMock(return_value=None) + + await manager.call_tool( + server_name="test-server", + name="test_tool", + arguments={"param": "value"}, + user_api_key_auth=UserAPIKeyAuth(api_key="sk-test"), + proxy_logging_obj=proxy_logging_obj, + ) + + during_data = proxy_logging_obj.during_call_hook.call_args.kwargs["data"] + assert (during_data["mcp_tool_description"], during_data["mcp_tool_input_schema"]) == ( + "Runs the test tool", + schema, + ) + + @pytest.mark.asyncio + async def test_call_tool_passes_no_tool_metadata_when_tool_was_never_listed(self): + manager, proxy_logging_obj = self._manager_ready_for_call_tool( + [MCPTool(name="other_tool", description="Unrelated", inputSchema={"type": "object"})] + ) + + await manager.call_tool( + server_name="test-server", + name="test_tool", + arguments={"param": "value"}, + user_api_key_auth=self._unrestricted_auth(), + proxy_logging_obj=proxy_logging_obj, + ) + + hook_kwargs = proxy_logging_obj._create_mcp_request_object_from_kwargs.call_args.args[0] + assert (hook_kwargs["tool_description"], hook_kwargs["tool_input_schema"]) == (None, None) + + def test_get_listed_tool_resolves_prefixed_name_and_latest_listing(self): + manager = MCPServerManager() + server = MCPServer(server_id="srv", name="srv", transport=MCPTransport.http, url="http://srv") + manager._create_prefixed_tools([MCPTool(name="echo", description="v1", inputSchema={})], server) + manager._create_prefixed_tools([MCPTool(name="echo", description="v2", inputSchema={})], server) + + by_prefixed_name = manager.get_listed_tool(server, "srv-echo") + assert by_prefixed_name is not None and by_prefixed_name.description == "v2" + assert manager.get_listed_tool(server, "missing") is None + + @pytest.mark.asyncio + @pytest.mark.parametrize("add_prefix", [True, False]) + async def test_openapi_listing_records_tool_metadata_for_pre_call_hooks(self, add_prefix): + from litellm.proxy._experimental.mcp_server.tool_registry import global_mcp_tool_registry + from litellm.types.mcp_server.tool_registry import MCPTool as RegistryTool + + server = MCPServer( + server_id="petstore-id", + name="petstore", + server_name="petstore", + transport=MCPTransport.http, + url=None, + spec_path="https://example.com/petstore.yaml", + ) + schema = {"type": "object", "properties": {"petId": {"type": "integer"}}} + registered = RegistryTool( + name="petstore-get_pet", description="Fetch a pet", input_schema=schema, handler=lambda: None + ) + manager = MCPServerManager() + manager._create_mcp_client = AsyncMock(return_value=AsyncMock()) + + with patch.dict(global_mcp_tool_registry.tools, {"petstore-get_pet": registered}, clear=True): + listed = await manager._get_tools_from_server(server=server, add_prefix=add_prefix) + + assert [t.name for t in listed] == ["petstore-get_pet" if add_prefix else "get_pet"] + for spelling in ("get_pet", "petstore-get_pet"): + tool = manager.get_listed_tool(server, spelling) + assert tool is not None and (tool.description, tool.inputSchema) == ("Fetch a pet", schema) + @pytest.mark.asyncio async def test_get_allowed_mcp_servers_with_user_api_key_auth(self): """ diff --git a/tests/test_litellm/proxy/guardrails/guardrail_hooks/test_agent_365.py b/tests/test_litellm/proxy/guardrails/guardrail_hooks/test_agent_365.py index 04460b150f5..b928ccad4c6 100644 --- a/tests/test_litellm/proxy/guardrails/guardrail_hooks/test_agent_365.py +++ b/tests/test_litellm/proxy/guardrails/guardrail_hooks/test_agent_365.py @@ -1,11 +1,15 @@ import time +import uuid +from collections.abc import Iterator from types import SimpleNamespace from typing import Any, Final +from unittest.mock import patch import httpx import pytest from fastapi import HTTPException +import litellm from litellm.exceptions import Timeout as LitellmTimeout from litellm.litellm_core_utils.litellm_logging import Logging as LiteLLMLoggingObj from litellm.litellm_core_utils.secret_redaction import redact_string @@ -16,11 +20,14 @@ from litellm.proxy.guardrails.guardrail_hooks.agent_365 import ( guardrail_initializer_registry, initialize_guardrail, ) +from litellm.proxy.guardrails.guardrail_hooks.agent_365.agent_365 import agent_365_authorization_servers from litellm.types.guardrails import ( GuardrailEventHooks, LitellmParams, SupportedGuardrailIntegrations, ) +from litellm.types.mcp import MCPAuth, MCPTransport +from litellm.types.mcp_server.mcp_server_manager import MCPServer from litellm.types.proxy.guardrails.guardrail_hooks.agent_365 import ( AGENT_365_PROD_API_BASE, AGENT_365_PROD_RESOURCE_APP_ID, @@ -55,17 +62,46 @@ def _allow_response(correlation_id: str = "corr-1") -> httpx.Response: ) -def _block_response(message: str = "Blocked by policy", correlation_id: str = "corr-2") -> httpx.Response: +def _block_response( + message: str = "Blocked by policy", correlation_id: str = "corr-2", status: str = "Evaluated" +) -> httpx.Response: return _response( 200, { "allowed": False, - "defender": {"status": "Evaluated", "verdict": "Block", "message": message}, + "defender": {"status": status, "verdict": "Block", "message": message}, "correlationId": correlation_id, }, ) +def _not_evaluated_response(status: str, correlation_id: str = "corr-3") -> httpx.Response: + return _response( + 200, + { + "allowed": True, + "defender": {"status": status, "verdict": None, "message": None}, + "observability": {"status": "Unavailable"}, + "correlationId": correlation_id, + }, + ) + + +def _logging_obj(litellm_call_id: str, mcp_session_id: str | None = None) -> LiteLLMLoggingObj: + logging_obj: Final = LiteLLMLoggingObj( + model="mcp", + messages=[], + stream=False, + call_type="call_mcp_tool", + start_time=None, + litellm_call_id=litellm_call_id, + function_id="fn-1", + ) + if mcp_session_id is not None: + logging_obj.model_call_details["mcp_tool_call_metadata"] = {"mcp_session_id": mcp_session_id} + return logging_obj + + class FakeHandler: def __init__(self, items: list[Any]): self._items = list(items) @@ -266,6 +302,29 @@ class TestAllowFlow: assert evaluate_call.json["conversationId"] == "sess-123" assert evaluate_call.json["agentId"] == "agent-007" + @pytest.mark.asyncio + async def test_evaluate_payload_includes_listed_tool_metadata(self): + handler: Final = FakeHandler([_token_response(), _allow_response()]) + guardrail: Final = _make_guardrail(handler) + schema: Final = {"type": "object", "properties": {"to": {"type": "string"}}, "required": ["to"]} + await _run(guardrail, _mcp_data(mcp_tool_description="Send an email", mcp_tool_input_schema=schema)) + assert handler.calls[1].json["tool"] == { + "name": "send_email", + "description": "Send an email", + "inputSchema": schema, + } + + @pytest.mark.asyncio + @pytest.mark.parametrize( + ("description", "schema"), + [(None, None), ("", None), (None, ["not", "a", "schema"]), (42, "type: object")], + ) + async def test_evaluate_payload_omits_missing_or_malformed_tool_metadata(self, description, schema): + handler: Final = FakeHandler([_token_response(), _allow_response()]) + guardrail: Final = _make_guardrail(handler) + await _run(guardrail, _mcp_data(mcp_tool_description=description, mcp_tool_input_schema=schema)) + assert handler.calls[1].json["tool"] == {"name": "send_email"} + @pytest.mark.asyncio async def test_agent_id_falls_back_to_key_alias(self): handler: Final = FakeHandler([_token_response(), _allow_response()]) @@ -284,6 +343,33 @@ class TestAllowFlow: class TestConversationId: + @pytest.mark.asyncio + async def test_request_call_id_beats_logging_obj_and_client_header(self): + handler: Final = FakeHandler([_token_response(), _allow_response()]) + guardrail: Final = _make_guardrail(handler) + data: Final = _mcp_data( + litellm_call_id="call-id-from-data", + litellm_logging_obj=_logging_obj("call-id-from-logging", mcp_session_id="sess-from-logging"), + ) + await _run(guardrail, data) + assert handler.calls[1].json["conversationId"] == "call-id-from-data" + + @pytest.mark.asyncio + async def test_logging_obj_call_id_beats_session_metadata_and_client_header(self): + handler: Final = FakeHandler([_token_response(), _allow_response()]) + guardrail: Final = _make_guardrail(handler) + data: Final = _mcp_data(litellm_logging_obj=_logging_obj("call-id-1", mcp_session_id="sess-from-logging")) + await _run(guardrail, data) + assert handler.calls[1].json["conversationId"] == "call-id-1" + + @pytest.mark.asyncio + async def test_logging_obj_session_id_beats_client_header(self): + handler: Final = FakeHandler([_token_response(), _allow_response()]) + guardrail: Final = _make_guardrail(handler) + data: Final = _mcp_data(litellm_logging_obj=_logging_obj("", mcp_session_id="sess-from-logging")) + await _run(guardrail, data) + assert handler.calls[1].json["conversationId"] == "sess-from-logging" + @pytest.mark.asyncio async def test_session_id_header_case_insensitive(self): handler: Final = FakeHandler([_token_response(), _allow_response()]) @@ -293,30 +379,12 @@ class TestConversationId: assert handler.calls[1].json["conversationId"] == "sess-CASED" @pytest.mark.asyncio - async def test_falls_back_to_logging_obj_session_id(self): + async def test_generates_uuid_when_no_identifier_available(self): handler: Final = FakeHandler([_token_response(), _allow_response()]) guardrail: Final = _make_guardrail(handler) - logging_obj: Final = LiteLLMLoggingObj( - model="mcp", - messages=[], - stream=False, - call_type="call_mcp_tool", - start_time=None, - litellm_call_id="call-id-1", - function_id="fn-1", - ) - logging_obj.model_call_details["mcp_tool_call_metadata"] = {"mcp_session_id": "sess-from-logging"} - data: Final = _mcp_data(metadata={"headers": {}}, litellm_logging_obj=logging_obj) - await _run(guardrail, data) - assert handler.calls[1].json["conversationId"] == "sess-from-logging" - - @pytest.mark.asyncio - async def test_falls_back_to_litellm_call_id(self): - handler: Final = FakeHandler([_token_response(), _allow_response()]) - guardrail: Final = _make_guardrail(handler) - data: Final = _mcp_data(metadata={"headers": {}}, litellm_call_id="call-id-2") - await _run(guardrail, data) - assert handler.calls[1].json["conversationId"] == "call-id-2" + await _run(guardrail, _mcp_data(metadata={"headers": {}}, litellm_logging_obj=_logging_obj(""))) + conversation_id: Final = handler.calls[1].json["conversationId"] + assert uuid.UUID(conversation_id).version == 4 class TestBlockFlow: @@ -344,6 +412,65 @@ class TestBlockFlow: await _run(guardrail, _mcp_data()) assert exc_info.value.status_code == 400 + @pytest.mark.asyncio + @pytest.mark.parametrize("status", ["Skipped", "FailedOpen"]) + async def test_explicit_block_wins_over_non_evaluated_status(self, status): + handler: Final = FakeHandler([_token_response(), _block_response(status=status)]) + guardrail: Final = _make_guardrail(handler, unreachable_fallback="fail_open") + data: Final = _mcp_data() + with pytest.raises(HTTPException) as exc_info: + await _run(guardrail, data) + assert exc_info.value.status_code == 400 + info: Final = _guardrail_info(data) + assert info["guardrail_status"] == "guardrail_intervened" + assert info["guardrail_response"]["verdict"] == "Block" + assert info["guardrail_response"]["defender_status"] == status + + +class TestDefenderNotEvaluated: + @pytest.mark.asyncio + @pytest.mark.parametrize("status", ["Skipped", "FailedOpen"]) + async def test_fail_closed_blocks_allowed_but_unevaluated_call(self, status): + handler: Final = FakeHandler([_token_response(), _not_evaluated_response(status)]) + guardrail: Final = _make_guardrail(handler, unreachable_fallback="fail_closed") + data: Final = _mcp_data() + with pytest.raises(HTTPException) as exc_info: + await _run(guardrail, data) + assert exc_info.value.status_code == 503 + assert f"defender.status={status}" in exc_info.value.detail["message"] + info: Final = _guardrail_info(data) + assert info["guardrail_status"] == "guardrail_failed_to_respond" + assert info["guardrail_response"]["verdict"] == "Unavailable" + assert info["guardrail_response"]["defender_status"] == status + assert info["guardrail_response"]["correlation_id"] == "corr-3" + assert info["guardrail_response"]["latency_ms"] >= 0 + + @pytest.mark.asyncio + @pytest.mark.parametrize("status", ["Skipped", "FailedOpen"]) + async def test_fail_open_allows_unevaluated_call_as_unscanned(self, status): + handler: Final = FakeHandler([_token_response(), _not_evaluated_response(status)]) + guardrail: Final = _make_guardrail(handler, unreachable_fallback="fail_open") + data: Final = _mcp_data() + result: Final = await _run(guardrail, data) + assert result is data + info: Final = _guardrail_info(data) + assert info["guardrail_status"] == "guardrail_failed_to_respond" + assert info["guardrail_response"]["verdict"] == "Unscanned" + assert info["guardrail_response"]["defender_status"] == status + assert info["guardrail_response"]["correlation_id"] == "corr-3" + + @pytest.mark.asyncio + @pytest.mark.parametrize("payload", [{"allowed": True}, {"allowed": True, "defender": {"verdict": "Allow"}}]) + async def test_allowed_without_defender_status_is_not_an_evaluated_allow(self, payload): + handler: Final = FakeHandler([_token_response(), _response(200, payload)]) + guardrail: Final = _make_guardrail(handler, unreachable_fallback="fail_closed") + data: Final = _mcp_data() + with pytest.raises(HTTPException) as exc_info: + await _run(guardrail, data) + assert exc_info.value.status_code == 503 + assert "defender.status=missing" in exc_info.value.detail["message"] + assert "defender_status" not in _guardrail_info(data)["guardrail_response"] + @pytest.mark.asyncio async def test_http_400_always_blocks_even_fail_open(self): handler: Final = FakeHandler([_token_response(), _response(400, text="Bad request: serverName missing")]) @@ -783,3 +910,92 @@ class TestVeriaHardening: assert len(records) == 1 assert records[0]["guardrail_response"]["verdict"] == "Unscanned" assert records[0]["guardrail_status"] == "guardrail_failed_to_respond" + + +ENTRA_ISSUER: Final = "https://login.microsoftonline.com/tenant-abc/v2.0" +GATEWAY_SCOPE: Final = "api://gateway-app/access_as_user" + + +def _mcp_server(auth_type: MCPAuth = MCPAuth.none, scopes: list[str] | None = None, **fields: Any) -> MCPServer: + return MCPServer( + server_id="tools-id", + name="tools", + server_name="tools", + transport=MCPTransport.http, + url="https://tools.test/mcp", + auth_type=auth_type, + scopes=scopes, + **fields, + ) + + +@pytest.fixture +def registered_guardrail() -> Iterator[Agent365Guardrail]: + guardrail: Final = _make_guardrail(FakeHandler([])) + litellm.logging_callback_manager.add_litellm_callback(guardrail) + try: + yield guardrail + finally: + litellm.logging_callback_manager.remove_callback_from_list_by_object( + litellm.callbacks, guardrail, require_self=False + ) + + +class TestAgent365AuthorizationServers: + def test_names_the_guardrail_tenant_for_a_scoped_gateway_signed_in_server(self, registered_guardrail): + assert agent_365_authorization_servers(_mcp_server(scopes=[GATEWAY_SCOPE]), None) == (ENTRA_ISSUER,) + assert agent_365_authorization_servers( + _mcp_server(MCPAuth.api_key, scopes=[GATEWAY_SCOPE], auth_value="k"), None + ) == (ENTRA_ISSUER,) + + def test_silent_without_advertised_scopes(self, registered_guardrail): + assert agent_365_authorization_servers(_mcp_server(scopes=None), None) == () + assert agent_365_authorization_servers(_mcp_server(scopes=[]), None) == () + + def test_silent_when_no_guardrail_is_registered(self): + assert agent_365_authorization_servers(_mcp_server(scopes=[GATEWAY_SCOPE]), None) == () + + @pytest.mark.parametrize( + "server", + [ + _mcp_server(MCPAuth.oauth2, scopes=[GATEWAY_SCOPE]), + _mcp_server(MCPAuth.oauth2_token_exchange, scopes=[GATEWAY_SCOPE], token_exchange_endpoint="https://i/t"), + _mcp_server(MCPAuth.oauth2_id_jag, scopes=[GATEWAY_SCOPE]), + _mcp_server(MCPAuth.true_passthrough, scopes=[GATEWAY_SCOPE]), + _mcp_server(MCPAuth.oauth_delegate, scopes=[GATEWAY_SCOPE]), + _mcp_server(MCPAuth.none, scopes=[GATEWAY_SCOPE], extra_headers=["Authorization"]), + ], + ids=["oauth2", "token_exchange", "id_jag", "true_passthrough", "oauth_delegate", "forwards_authorization"], + ) + def test_leaves_servers_whose_own_auth_mode_owns_sign_in_alone(self, registered_guardrail, server): + assert agent_365_authorization_servers(server, None) == () + + def test_dedupes_guardrails_sharing_a_tenant(self, registered_guardrail): + twin: Final = _make_guardrail(FakeHandler([])) + twin.guardrail_name = "agent-365-twin" + litellm.logging_callback_manager.add_litellm_callback(twin) + try: + assert agent_365_authorization_servers(_mcp_server(scopes=[GATEWAY_SCOPE]), None) == (ENTRA_ISSUER,) + finally: + litellm.logging_callback_manager.remove_callback_from_list_by_object( + litellm.callbacks, twin, require_self=False + ) + + def test_key_selected_guardrail_challenges_only_that_key(self): + guardrail: Final = _make_guardrail(FakeHandler([])) + guardrail.default_on = False + litellm.logging_callback_manager.add_litellm_callback(guardrail) + server: Final = _mcp_server(scopes=[GATEWAY_SCOPE]) + plain_key: Final = UserAPIKeyAuth(api_key="sk-plain", user_id="u-1") + guarded_key: Final = UserAPIKeyAuth( + api_key="sk-guarded", user_id="u-2", metadata={"guardrails": ["agent-365-guard"]} + ) + try: + with patch("litellm.proxy.proxy_server.premium_user", True): + assert agent_365_authorization_servers(server, plain_key) == () + assert agent_365_authorization_servers(server, guarded_key) == (ENTRA_ISSUER,) + assert agent_365_authorization_servers(server, None) == (ENTRA_ISSUER,) + finally: + litellm.logging_callback_manager.remove_callback_from_list_by_object( + litellm.callbacks, guardrail, require_self=False + ) diff --git a/tests/test_litellm/proxy/utils/proxy_logging/test_mcp_bridging.py b/tests/test_litellm/proxy/utils/proxy_logging/test_mcp_bridging.py index 56057dce7e0..75dd11ea16b 100644 --- a/tests/test_litellm/proxy/utils/proxy_logging/test_mcp_bridging.py +++ b/tests/test_litellm/proxy/utils/proxy_logging/test_mcp_bridging.py @@ -381,6 +381,26 @@ def test_create_mcp_request_object_from_kwargs_full(proxy_logging, make_user_api assert snapshot == {"tool_name": "calc", "arguments": {"x": 1}, "server_name": "math", "auth_user_id": "u-1"} +def test_mcp_tool_metadata_flows_from_kwargs_to_synthetic_data(proxy_logging): + schema = {"type": "object", "properties": {"x": {"type": "integer"}}} + obj = proxy_logging._create_mcp_request_object_from_kwargs( + kwargs={ + "name": "calc", + "arguments": {"x": 1}, + "tool_description": "Adds numbers", + "tool_input_schema": schema, + } + ) + out = proxy_logging._convert_mcp_to_llm_format(request_obj=obj, kwargs={}) + assert (out["mcp_tool_description"], out["mcp_tool_input_schema"]) == ("Adds numbers", schema) + + +def test_mcp_tool_metadata_absent_when_tool_was_never_listed(proxy_logging): + obj = proxy_logging._create_mcp_request_object_from_kwargs(kwargs={"name": "calc", "arguments": {}}) + out = proxy_logging._convert_mcp_to_llm_format(request_obj=obj, kwargs={}) + assert (out["mcp_tool_description"], out["mcp_tool_input_schema"]) == (None, None) + + def test_create_mcp_request_object_from_kwargs_empty(proxy_logging): obj = proxy_logging._create_mcp_request_object_from_kwargs(kwargs={}) snapshot = { From faa430c6c19f95705497b97464b989857d1f107f Mon Sep 17 00:00:00 2001 From: yucheng Date: Fri, 11 Sep 2026 02:08:09 +0000 Subject: [PATCH 018/225] feat(mcp): challenge Agent 365 gated MCP servers with the Entra RFC 9728 metadata When an Agent 365 guardrail applies to an MCP server that advertises scopes and no bearer arrives, reuse the MCP OBO raise_token_exchange_challenge so the 401 and WWW-Authenticate header leave at the transport layer. The protected-resource metadata for that server names the guardrail's Entra v2 issuer and the server's scopes, so Claude Code and other MCP clients run browser SSO and attach the bearer themselves instead of the user pasting a token into the client config. Co-Authored-By: Devin AI <158243242+devin-ai-integration[bot]@users.noreply.github.com> --- .../mcp_server/discoverable_endpoints.py | 9 ++ .../proxy/_experimental/mcp_server/server.py | 13 ++- .../mcp_server/test_discoverable_endpoints.py | 71 ++++++++++++++++ .../mcp_server/test_mcp_server.py | 84 +++++++++++++++++++ 4 files changed, 175 insertions(+), 2 deletions(-) diff --git a/litellm/proxy/_experimental/mcp_server/discoverable_endpoints.py b/litellm/proxy/_experimental/mcp_server/discoverable_endpoints.py index f18acb7d88d..9529ede4b97 100644 --- a/litellm/proxy/_experimental/mcp_server/discoverable_endpoints.py +++ b/litellm/proxy/_experimental/mcp_server/discoverable_endpoints.py @@ -76,6 +76,7 @@ from litellm.proxy.common_utils.encrypt_decrypt_utils import ( encrypt_value_helper, ) from litellm.proxy.common_utils.http_parsing_utils import _read_request_body +from litellm.proxy.guardrails.guardrail_hooks.agent_365.agent_365 import agent_365_authorization_servers from litellm.types.mcp import MCPAuth, MCPCredentials from litellm.types.mcp_server.mcp_server_manager import MCPServer, MCPTokenEndpointAuthMethod @@ -2400,6 +2401,14 @@ async def _build_oauth_protected_resource_response( if obo_response is not None: return obo_response + agent_365_issuers: Final = agent_365_authorization_servers(mcp_server, None) if mcp_server else () + if mcp_server is not None and agent_365_issuers: + return { + "authorization_servers": list(agent_365_issuers), + "resource": resource_url, + "scopes_supported": list(mcp_server.scopes or ()), + } + if explicitly_named and mcp_server is not None and mcp_server.advertises_gateway_authorization_server: return { "authorization_servers": [f"{request_base_url}/mcp"], diff --git a/litellm/proxy/_experimental/mcp_server/server.py b/litellm/proxy/_experimental/mcp_server/server.py index d259fa32cd9..24527a380d4 100644 --- a/litellm/proxy/_experimental/mcp_server/server.py +++ b/litellm/proxy/_experimental/mcp_server/server.py @@ -79,6 +79,7 @@ from litellm.proxy._types import ( UserAPIKeyAuth, ) from litellm.proxy.auth.ip_address_utils import IPAddressUtils +from litellm.proxy.guardrails.guardrail_hooks.agent_365.agent_365 import agent_365_authorization_servers from litellm.proxy.litellm_pre_call_utils import ( LiteLLMProxyRequestSetup, get_chain_id_from_headers, @@ -4101,8 +4102,16 @@ if MCP_AVAILABLE: # (transport level, where WWW-Authenticate survives) with the RFC 9728 resource_metadata # so the client discovers the IdP, SSOs, and retries with a subject token, which LiteLLM # then exchanges. A tool-call-time 401 would be wrapped into a JSON-RPC error and the - # header lost, so the discovery flow needs this pre-emptive challenge. - if server and server.auth_type == MCPAuth.oauth2_token_exchange and not oauth2_headers: + # header lost, so the discovery flow needs this pre-emptive challenge. Servers gated by an + # Agent 365 guardrail (OBO to the evaluate API) get the same challenge. + if ( + server + and not oauth2_headers + and ( + server.auth_type == MCPAuth.oauth2_token_exchange + or agent_365_authorization_servers(server, user_api_key_auth) + ) + ): from litellm.proxy._experimental.mcp_server.outbound_credentials.adapter import ( # noqa: PLC0415 # lazy: adapter pulls MCP subgraph raise_token_exchange_challenge, ) diff --git a/tests/test_litellm/proxy/_experimental/mcp_server/test_discoverable_endpoints.py b/tests/test_litellm/proxy/_experimental/mcp_server/test_discoverable_endpoints.py index a7a65bfe466..ab84777dbeb 100644 --- a/tests/test_litellm/proxy/_experimental/mcp_server/test_discoverable_endpoints.py +++ b/tests/test_litellm/proxy/_experimental/mcp_server/test_discoverable_endpoints.py @@ -7090,6 +7090,77 @@ async def test_build_oauth_protected_resource_response_obo_end_to_end(): global_mcp_server_manager.registry.clear() +@pytest.fixture +def agent_365_guardrail(): + import litellm + from litellm.proxy.guardrails.guardrail_hooks.agent_365 import Agent365Guardrail + + guardrail = Agent365Guardrail( + guardrail_name="agent-365-guard", + tenant_id="tenant-abc", + client_id="client-xyz", + client_secret="secret-123", + async_handler=AsyncMock(), + event_hook="pre_mcp_call", + default_on=True, + ) + litellm.logging_callback_manager.add_litellm_callback(guardrail) + try: + yield guardrail + finally: + litellm.logging_callback_manager.remove_callback_from_list_by_object( + litellm.callbacks, guardrail, require_self=False + ) + + +async def _agent_365_gated_prm(scopes): + from fastapi import Request + + from litellm.proxy._experimental.mcp_server.discoverable_endpoints import ( + _build_oauth_protected_resource_response, + ) + from litellm.proxy._experimental.mcp_server.mcp_server_manager import global_mcp_server_manager + from litellm.proxy._types import MCPTransport + from litellm.types.mcp import MCPAuth + from litellm.types.mcp_server.mcp_server_manager import MCPServer + + global_mcp_server_manager.registry.clear() + global_mcp_server_manager.registry["tools"] = MCPServer( + server_id="tools", + name="tools", + server_name="tools", + alias="tools", + transport=MCPTransport.http, + auth_type=MCPAuth.none, + scopes=scopes, + ) + mock_request = MagicMock(spec=Request) + mock_request.base_url = "https://litellm.example.com/" + mock_request.headers = {} + try: + return await _build_oauth_protected_resource_response( + request=mock_request, mcp_server_name="tools", use_standard_pattern=True + ) + finally: + global_mcp_server_manager.registry.clear() + + +@pytest.mark.asyncio +async def test_agent_365_gated_server_prm_names_the_entra_tenant(agent_365_guardrail): + response = await _agent_365_gated_prm(scopes=["api://gateway-app/access_as_user"]) + assert response == { + "authorization_servers": ["https://login.microsoftonline.com/tenant-abc/v2.0"], + "resource": "https://litellm.example.com/mcp/tools", + "scopes_supported": ["api://gateway-app/access_as_user"], + } + + +@pytest.mark.asyncio +async def test_agent_365_prm_falls_back_to_gateway_without_scopes(agent_365_guardrail): + response = await _agent_365_gated_prm(scopes=None) + assert response["authorization_servers"] == ["https://litellm.example.com/mcp"] + + def _token_request(headers): """A real Starlette request with case-insensitive headers (matches production).""" from starlette.requests import Request diff --git a/tests/test_litellm/proxy/_experimental/mcp_server/test_mcp_server.py b/tests/test_litellm/proxy/_experimental/mcp_server/test_mcp_server.py index 12805e355f9..69e932007aa 100644 --- a/tests/test_litellm/proxy/_experimental/mcp_server/test_mcp_server.py +++ b/tests/test_litellm/proxy/_experimental/mcp_server/test_mcp_server.py @@ -8739,6 +8739,90 @@ class TestSingleServerPreflightReachesIdJag: preflight.assert_not_awaited() +class TestAgent365ChallengeAtConnect: + """A missing Entra bearer on an Agent 365 gated server is challenged at connect (RFC 9728), where the + WWW-Authenticate header survives, instead of only inside the tools/call JSON-RPC error.""" + + GATEWAY_SCOPE = "api://gateway-app/access_as_user" + + def _server(self, scopes: list[str] | None) -> MCPServer: + return MCPServer( + server_id="id-tools", + name="tools", + alias="tools", + server_name="tools", + url="https://tools.test/mcp", + transport=MCPTransport.http, + auth_type=MCPAuth.none, + scopes=scopes, + mcp_info={"server_name": "tools"}, + ) + + @pytest.fixture + def agent_365_guardrail(self): + import litellm + from litellm.proxy.guardrails.guardrail_hooks.agent_365 import Agent365Guardrail + + guardrail = Agent365Guardrail( + guardrail_name="agent-365-guard", + tenant_id="tenant-abc", + client_id="client-xyz", + client_secret="secret-123", + async_handler=AsyncMock(), + event_hook="pre_mcp_call", + default_on=True, + ) + litellm.logging_callback_manager.add_litellm_callback(guardrail) + try: + yield guardrail + finally: + litellm.logging_callback_manager.remove_callback_from_list_by_object( + litellm.callbacks, guardrail, require_self=False + ) + + async def _connect(self, server: MCPServer, oauth2_headers: dict[str, str] | None) -> None: + from litellm.proxy._experimental.mcp_server import server as server_module + + with ( + patch.object( # test-quality-ok: route wiring must use the manager's configured server + server_module.global_mcp_server_manager, "get_mcp_server_by_name", return_value=server + ), + patch.object( # test-quality-ok: allowed-set resolution needs the DB; the test controls its answer + server_module, "_get_allowed_mcp_servers", AsyncMock(return_value=[]) + ), + ): + await server_module._raise_preemptive_401_for_unauthenticated_servers( + scope={"type": "http", "method": "POST", "path": "/mcp/tools", "headers": []}, + mcp_servers=["tools"], + oauth2_headers=oauth2_headers, + mcp_server_auth_headers=None, + user_api_key_auth=UserAPIKeyAuth(api_key="sk-litellm-virtual-key", user_id="u-1"), + client_ip=None, + ) + + @pytest.mark.asyncio + async def test_no_bearer_gets_the_discovery_challenge(self, agent_365_guardrail): + with pytest.raises(HTTPException) as exc: + await self._connect(self._server([self.GATEWAY_SCOPE]), None) + + assert exc.value.status_code == 401 + www_authenticate = (exc.value.headers or {}).get("WWW-Authenticate", "") + assert 'error="invalid_token"' in www_authenticate + assert 'resource_metadata="/.well-known/oauth-protected-resource/mcp/tools"' in www_authenticate + + @pytest.mark.asyncio + async def test_bearer_present_connects(self, agent_365_guardrail): + await self._connect(self._server([self.GATEWAY_SCOPE]), {"Authorization": "Bearer entra-user-token"}) + + @pytest.mark.asyncio + async def test_server_without_advertised_scopes_is_not_challenged(self, agent_365_guardrail): + await self._connect(self._server(None), None) + + @pytest.mark.asyncio + async def test_no_registered_guardrail_means_no_challenge(self): + await self._connect(self._server([self.GATEWAY_SCOPE]), None) + + def _make_obo_server(alias: str) -> MCPServer: return MCPServer( server_id=f"id-{alias}", From d5b8effa99a1f6553eef7bb4e8b692d83136a34e Mon Sep 17 00:00:00 2001 From: yucheng Date: Fri, 11 Sep 2026 02:27:19 +0000 Subject: [PATCH 019/225] fix(mcp): skip prefix lookup when a server has no listed tools Co-Authored-By: Devin AI <158243242+devin-ai-integration[bot]@users.noreply.github.com> --- litellm/proxy/_experimental/mcp_server/mcp_server_manager.py | 4 +++- .../mcp_server/test_mcp_guardrail_usage_monitor.py | 1 + 2 files changed, 4 insertions(+), 1 deletion(-) diff --git a/litellm/proxy/_experimental/mcp_server/mcp_server_manager.py b/litellm/proxy/_experimental/mcp_server/mcp_server_manager.py index 793419e2270..872b1fb5262 100644 --- a/litellm/proxy/_experimental/mcp_server/mcp_server_manager.py +++ b/litellm/proxy/_experimental/mcp_server/mcp_server_manager.py @@ -5139,7 +5139,9 @@ class MCPServerManager: return prefixed_tools def get_listed_tool(self, server: MCPServer, name: str) -> MCPTool | None: - listed: Final = self._listed_tools_by_server_id.get(server.server_id, {}) + listed: Final = self._listed_tools_by_server_id.get(server.server_id) + if not listed: + return None return listed.get(name) or listed.get(strip_known_server_prefix(name, server)) def _create_prefixed_prompts( diff --git a/tests/test_litellm/proxy/_experimental/mcp_server/test_mcp_guardrail_usage_monitor.py b/tests/test_litellm/proxy/_experimental/mcp_server/test_mcp_guardrail_usage_monitor.py index 24e6d2de10d..956f87f0da1 100644 --- a/tests/test_litellm/proxy/_experimental/mcp_server/test_mcp_guardrail_usage_monitor.py +++ b/tests/test_litellm/proxy/_experimental/mcp_server/test_mcp_guardrail_usage_monitor.py @@ -48,6 +48,7 @@ def _bare_manager() -> MOD.MCPServerManager: reaches the guardrail hooks; they have their own coverage elsewhere. """ mgr = MOD.MCPServerManager.__new__(MOD.MCPServerManager) + mgr._listed_tools_by_server_id = {} mgr.check_allowed_or_banned_tools = lambda name, server: True mgr.validate_allowed_params = lambda tool_name, arguments, server: None From 02233a2df35944c98d40ca651659c985137af5d0 Mon Sep 17 00:00:00 2001 From: yucheng Date: Fri, 11 Sep 2026 02:49:13 +0000 Subject: [PATCH 020/225] refactor(mcp): build agent 365 protected resource metadata immutably to satisfy the type discipline gate Co-Authored-By: Devin AI <158243242+devin-ai-integration[bot]@users.noreply.github.com> --- .../mcp_server/discoverable_endpoints.py | 18 +++++++++++++----- .../mcp_server/mcp_server_manager.py | 8 ++++---- .../mcp_server/test_discoverable_endpoints.py | 3 ++- 3 files changed, 19 insertions(+), 10 deletions(-) diff --git a/litellm/proxy/_experimental/mcp_server/discoverable_endpoints.py b/litellm/proxy/_experimental/mcp_server/discoverable_endpoints.py index 786df1f045d..4ecd5de6121 100644 --- a/litellm/proxy/_experimental/mcp_server/discoverable_endpoints.py +++ b/litellm/proxy/_experimental/mcp_server/discoverable_endpoints.py @@ -5,13 +5,14 @@ import secrets import time from collections.abc import Callable, Mapping from datetime import datetime, timezone -from typing import TYPE_CHECKING, Any, Final, Literal, Optional +from typing import TYPE_CHECKING, Any, Final, Literal, Optional, TypedDict from urllib.parse import parse_qsl, urlencode, urlparse, urlunparse import httpx from fastapi import APIRouter, Depends, Form, HTTPException, Request from fastapi.responses import HTMLResponse, JSONResponse, RedirectResponse, Response from pydantic import BaseModel, ConfigDict, Field, SecretStr, ValidationError +from typing_extensions import ReadOnly from litellm._logging import verbose_logger from litellm.caching.in_memory_cache import InMemoryCache @@ -2375,7 +2376,7 @@ async def _build_oauth_protected_resource_response( request: Request, mcp_server_name: str | None, use_standard_pattern: bool, -) -> dict: +) -> Mapping[str, object]: """ Build OAuth protected resource response with the appropriate URL pattern. @@ -2481,11 +2482,12 @@ async def _build_oauth_protected_resource_response( agent_365_issuers: Final = agent_365_authorization_servers(mcp_server, None) if mcp_server else () if mcp_server is not None and agent_365_issuers: - return { - "authorization_servers": list(agent_365_issuers), + agent_365_metadata: Final[_ProtectedResourceMetadata] = { + "authorization_servers": agent_365_issuers, "resource": resource_url, - "scopes_supported": list(mcp_server.scopes or ()), + "scopes_supported": tuple(mcp_server.scopes or ()), } + return agent_365_metadata if explicitly_named and mcp_server is not None and mcp_server.advertises_gateway_authorization_server: return { @@ -2506,6 +2508,12 @@ async def _build_oauth_protected_resource_response( } +class _ProtectedResourceMetadata(TypedDict): + authorization_servers: ReadOnly[tuple[str, ...]] + resource: ReadOnly[str] + scopes_supported: ReadOnly[tuple[str, ...]] + + def _obo_protected_resource_response(mcp_server: MCPServer | None, resource_url: str) -> dict | None: """The OBO (token_exchange) PRM, or None when this server is not OBO / no issuer is configured. diff --git a/litellm/proxy/_experimental/mcp_server/mcp_server_manager.py b/litellm/proxy/_experimental/mcp_server/mcp_server_manager.py index 872b1fb5262..f33a5f85846 100644 --- a/litellm/proxy/_experimental/mcp_server/mcp_server_manager.py +++ b/litellm/proxy/_experimental/mcp_server/mcp_server_manager.py @@ -1779,7 +1779,7 @@ class MCPServerManager: "gmail_send_email": "zapier_mcp_server", } """ - self._listed_tools_by_server_id: dict[str, dict[str, MCPTool]] = {} + self._listed_tools_by_server_id: dict[str, Mapping[str, MCPTool]] = {} # mutable-ok: refreshed per tools/list self._upstream_initialize_instructions_by_server_id: dict[str, str] = {} # Per-server monotonic timestamp of last upstream prefetch attempt (success, # empty result, or failure). Used to throttle re-probes for servers that do @@ -4244,7 +4244,7 @@ class MCPServerManager: # time producing "test_petstore-test_petstore-getinventory". prefix: Final = get_server_prefix(server) sep: Final = MCP_TOOL_PREFIX_SEPARATOR - bare_tools: Final = [ + bare_tools: Final = [ # mutable-ok: returned through the list[MCPTool] listing contract ( t.model_copy(update={"name": t.name[len(prefix) + len(sep) :]}) if t.name.startswith(f"{prefix}{sep}") @@ -4252,7 +4252,7 @@ class MCPServerManager: ) for t in tools ] - self._listed_tools_by_server_id[server.server_id] = {t.name: t for t in bare_tools} + self._listed_tools_by_server_id[server.server_id] = MappingProxyType({t.name: t for t in bare_tools}) return tools if add_prefix else bare_tools else: tools = await self._fetch_tools_with_timeout(client, server.name) @@ -5134,7 +5134,7 @@ class MCPServerManager: for spelling in iter_known_tool_name_spellings(original_name, server): self.tool_name_to_mcp_server_name_mapping[spelling] = prefix - self._listed_tools_by_server_id[server.server_id] = {tool.name: tool for tool in tools} + self._listed_tools_by_server_id[server.server_id] = MappingProxyType({tool.name: tool for tool in tools}) verbose_logger.info("Successfully fetched %s tools from server %s", len(prefixed_tools), server.name) return prefixed_tools diff --git a/tests/test_litellm/proxy/_experimental/mcp_server/test_discoverable_endpoints.py b/tests/test_litellm/proxy/_experimental/mcp_server/test_discoverable_endpoints.py index 28dbd532bcd..a9ac0d528a7 100644 --- a/tests/test_litellm/proxy/_experimental/mcp_server/test_discoverable_endpoints.py +++ b/tests/test_litellm/proxy/_experimental/mcp_server/test_discoverable_endpoints.py @@ -10,6 +10,7 @@ from unittest.mock import AsyncMock, MagicMock, patch import pytest from fastapi import HTTPException +from fastapi.encoders import jsonable_encoder from litellm.types.mcp import MCPAuth @@ -7148,7 +7149,7 @@ async def _agent_365_gated_prm(scopes): @pytest.mark.asyncio async def test_agent_365_gated_server_prm_names_the_entra_tenant(agent_365_guardrail): response = await _agent_365_gated_prm(scopes=["api://gateway-app/access_as_user"]) - assert response == { + assert jsonable_encoder(response) == { "authorization_servers": ["https://login.microsoftonline.com/tenant-abc/v2.0"], "resource": "https://litellm.example.com/mcp/tools", "scopes_supported": ["api://gateway-app/access_as_user"], From 803f8a69f07ccc9720065e1c79d2e336e6402edd Mon Sep 17 00:00:00 2001 From: yucheng Date: Fri, 11 Sep 2026 02:56:34 +0000 Subject: [PATCH 021/225] test(mcp): assert the no-challenge outcome and register openapi tools through the registry api Co-Authored-By: Devin AI <158243242+devin-ai-integration[bot]@users.noreply.github.com> --- .../mcp_server/test_mcp_server.py | 36 ++++++++++--------- .../mcp_server/test_mcp_server_manager.py | 12 +++---- .../guardrail_hooks/test_agent_365.py | 4 ++- 3 files changed, 29 insertions(+), 23 deletions(-) diff --git a/tests/test_litellm/proxy/_experimental/mcp_server/test_mcp_server.py b/tests/test_litellm/proxy/_experimental/mcp_server/test_mcp_server.py index 4c77825232b..4fd9c923709 100644 --- a/tests/test_litellm/proxy/_experimental/mcp_server/test_mcp_server.py +++ b/tests/test_litellm/proxy/_experimental/mcp_server/test_mcp_server.py @@ -8867,7 +8867,7 @@ class TestAgent365ChallengeAtConnect: litellm.callbacks, guardrail, require_self=False ) - async def _connect(self, server: MCPServer, oauth2_headers: dict[str, str] | None) -> None: + async def _connect(self, server: MCPServer, oauth2_headers: dict[str, str] | None) -> HTTPException | None: from litellm.proxy._experimental.mcp_server import server as server_module with ( @@ -8878,36 +8878,40 @@ class TestAgent365ChallengeAtConnect: server_module, "_get_allowed_mcp_servers", AsyncMock(return_value=[]) ), ): - await server_module._raise_preemptive_401_for_unauthenticated_servers( - scope={"type": "http", "method": "POST", "path": "/mcp/tools", "headers": []}, - mcp_servers=["tools"], - oauth2_headers=oauth2_headers, - mcp_server_auth_headers=None, - user_api_key_auth=UserAPIKeyAuth(api_key="sk-litellm-virtual-key", user_id="u-1"), - client_ip=None, - ) + try: + await server_module._raise_preemptive_401_for_unauthenticated_servers( + scope={"type": "http", "method": "POST", "path": "/mcp/tools", "headers": []}, + mcp_servers=["tools"], + oauth2_headers=oauth2_headers, + mcp_server_auth_headers=None, + user_api_key_auth=UserAPIKeyAuth(api_key="sk-litellm-virtual-key", user_id="u-1"), + client_ip=None, + ) + except HTTPException as challenge: + return challenge + return None @pytest.mark.asyncio async def test_no_bearer_gets_the_discovery_challenge(self, agent_365_guardrail): - with pytest.raises(HTTPException) as exc: - await self._connect(self._server([self.GATEWAY_SCOPE]), None) + challenge = await self._connect(self._server([self.GATEWAY_SCOPE]), None) - assert exc.value.status_code == 401 - www_authenticate = (exc.value.headers or {}).get("WWW-Authenticate", "") + assert challenge is not None and challenge.status_code == 401 + www_authenticate = (challenge.headers or {}).get("WWW-Authenticate", "") assert 'error="invalid_token"' in www_authenticate assert 'resource_metadata="/.well-known/oauth-protected-resource/mcp/tools"' in www_authenticate @pytest.mark.asyncio async def test_bearer_present_connects(self, agent_365_guardrail): - await self._connect(self._server([self.GATEWAY_SCOPE]), {"Authorization": "Bearer entra-user-token"}) + bearer = {"Authorization": "Bearer entra-user-token"} + assert await self._connect(self._server([self.GATEWAY_SCOPE]), bearer) is None @pytest.mark.asyncio async def test_server_without_advertised_scopes_is_not_challenged(self, agent_365_guardrail): - await self._connect(self._server(None), None) + assert await self._connect(self._server(None), None) is None @pytest.mark.asyncio async def test_no_registered_guardrail_means_no_challenge(self): - await self._connect(self._server([self.GATEWAY_SCOPE]), None) + assert await self._connect(self._server([self.GATEWAY_SCOPE]), None) is None def _make_obo_server(alias: str) -> MCPServer: diff --git a/tests/test_litellm/proxy/_experimental/mcp_server/test_mcp_server_manager.py b/tests/test_litellm/proxy/_experimental/mcp_server/test_mcp_server_manager.py index ba7b76eae91..b32787d4947 100644 --- a/tests/test_litellm/proxy/_experimental/mcp_server/test_mcp_server_manager.py +++ b/tests/test_litellm/proxy/_experimental/mcp_server/test_mcp_server_manager.py @@ -6508,7 +6508,6 @@ class TestMCPServerManager: @pytest.mark.parametrize("add_prefix", [True, False]) async def test_openapi_listing_records_tool_metadata_for_pre_call_hooks(self, add_prefix): from litellm.proxy._experimental.mcp_server.tool_registry import global_mcp_tool_registry - from litellm.types.mcp_server.tool_registry import MCPTool as RegistryTool server = MCPServer( server_id="petstore-id", @@ -6519,14 +6518,15 @@ class TestMCPServerManager: spec_path="https://example.com/petstore.yaml", ) schema = {"type": "object", "properties": {"petId": {"type": "integer"}}} - registered = RegistryTool( - name="petstore-get_pet", description="Fetch a pet", input_schema=schema, handler=lambda: None - ) manager = MCPServerManager() manager._create_mcp_client = AsyncMock(return_value=AsyncMock()) - - with patch.dict(global_mcp_tool_registry.tools, {"petstore-get_pet": registered}, clear=True): + global_mcp_tool_registry.register_tool( + name="petstore-get_pet", description="Fetch a pet", input_schema=schema, handler=lambda: None + ) + try: listed = await manager._get_tools_from_server(server=server, add_prefix=add_prefix) + finally: + global_mcp_tool_registry.unregister_tools_with_prefix("petstore-") assert [t.name for t in listed] == ["petstore-get_pet" if add_prefix else "get_pet"] for spelling in ("get_pet", "petstore-get_pet"): diff --git a/tests/test_litellm/proxy/guardrails/guardrail_hooks/test_agent_365.py b/tests/test_litellm/proxy/guardrails/guardrail_hooks/test_agent_365.py index b928ccad4c6..c82f830cd84 100644 --- a/tests/test_litellm/proxy/guardrails/guardrail_hooks/test_agent_365.py +++ b/tests/test_litellm/proxy/guardrails/guardrail_hooks/test_agent_365.py @@ -991,7 +991,9 @@ class TestAgent365AuthorizationServers: api_key="sk-guarded", user_id="u-2", metadata={"guardrails": ["agent-365-guard"]} ) try: - with patch("litellm.proxy.proxy_server.premium_user", True): + with patch( # test-quality-ok: key-selected guardrails read the proxy server premium global, no injection seam + "litellm.proxy.proxy_server.premium_user", True + ): assert agent_365_authorization_servers(server, plain_key) == () assert agent_365_authorization_servers(server, guarded_key) == (ENTRA_ISSUER,) assert agent_365_authorization_servers(server, None) == (ENTRA_ISSUER,) From 886220375f9a13fe616a32521085d6fb896baf41 Mon Sep 17 00:00:00 2001 From: yucheng Date: Fri, 11 Sep 2026 03:15:59 +0000 Subject: [PATCH 022/225] fix(guardrails): agent 365 sign-in for scopeless servers and gateway credential errors Scopeless Agent 365 gated servers now advertise api:///access_as_user instead of staying silent, so a client can still sign in. Entra rejecting the gateway's own credentials (invalid_client, unauthorized_client, invalid_scope, invalid_resource) follows unreachable_fallback rather than telling the caller to sign in again with a 401 Co-Authored-By: Devin AI <158243242+devin-ai-integration[bot]@users.noreply.github.com> --- .../mcp_server/discoverable_endpoints.py | 7 ++- .../guardrail_hooks/agent_365/agent_365.py | 58 ++++++++++++++----- .../mcp_server/test_discoverable_endpoints.py | 8 ++- .../mcp_server/test_mcp_server.py | 7 ++- .../guardrail_hooks/test_agent_365.py | 53 +++++++++++++++-- 5 files changed, 110 insertions(+), 23 deletions(-) diff --git a/litellm/proxy/_experimental/mcp_server/discoverable_endpoints.py b/litellm/proxy/_experimental/mcp_server/discoverable_endpoints.py index 4ecd5de6121..c912c69bf6a 100644 --- a/litellm/proxy/_experimental/mcp_server/discoverable_endpoints.py +++ b/litellm/proxy/_experimental/mcp_server/discoverable_endpoints.py @@ -82,7 +82,10 @@ from litellm.proxy.common_utils.encrypt_decrypt_utils import ( encrypt_value_helper, ) from litellm.proxy.common_utils.http_parsing_utils import _read_request_body -from litellm.proxy.guardrails.guardrail_hooks.agent_365.agent_365 import agent_365_authorization_servers +from litellm.proxy.guardrails.guardrail_hooks.agent_365.agent_365 import ( + agent_365_authorization_servers, + agent_365_scopes_supported, +) from litellm.types.mcp import MCPAuth, MCPCredentials from litellm.types.mcp_server.mcp_server_manager import MCPServer, MCPTokenEndpointAuthMethod @@ -2485,7 +2488,7 @@ async def _build_oauth_protected_resource_response( agent_365_metadata: Final[_ProtectedResourceMetadata] = { "authorization_servers": agent_365_issuers, "resource": resource_url, - "scopes_supported": tuple(mcp_server.scopes or ()), + "scopes_supported": agent_365_scopes_supported(mcp_server, None), } return agent_365_metadata diff --git a/litellm/proxy/guardrails/guardrail_hooks/agent_365/agent_365.py b/litellm/proxy/guardrails/guardrail_hooks/agent_365/agent_365.py index 5813240f01b..3cc2394a18e 100644 --- a/litellm/proxy/guardrails/guardrail_hooks/agent_365/agent_365.py +++ b/litellm/proxy/guardrails/guardrail_hooks/agent_365/agent_365.py @@ -57,6 +57,10 @@ ENTRA_ISSUER_TEMPLATE: Final = "https://login.microsoftonline.com/{tenant_id}/v2 EVALUATE_PATH: Final = "/agents/tool-evaluation/evaluate" MCP_SESSION_ID_HEADER: Final = "mcp-session-id" DEFENDER_STATUS_EVALUATED: Final = "Evaluated" +GATEWAY_SCOPE_TEMPLATE: Final = "api://{client_id}/access_as_user" +_GATEWAY_OWNED_TOKEN_ERRORS: Final = frozenset( + {"invalid_client", "unauthorized_client", "invalid_scope", "invalid_resource"} +) _MCP_CALL_TYPES: Final[tuple[str, ...]] = ("mcp_call", "call_mcp_tool") _TOOL_INPUT_SCHEMA_ADAPTER: Final = TypeAdapter(dict[str, object]) _OBO_CACHE_MAX_ENTRIES: Final = 1000 @@ -212,6 +216,15 @@ class Agent365Guardrail(CustomGuardrail): try: obo_token: Final = await self._get_obo_token(assertion) except Agent365TokenExchangeError as exc: + if exc.error_code in _GATEWAY_OWNED_TOKEN_ERRORS: + return self._handle_unavailable( + data=data, + tool_name=tool_name, + reason=( + f"Entra rejected the gateway's own Agent 365 credentials ({exc.error_code}); " + "check the guardrail's client_id, client_secret and resource_app_id" + ), + ) self._handle_caller_fault( data=data, tool_name=tool_name, @@ -627,23 +640,42 @@ def _applies_to_caller(guardrail: Agent365Guardrail, user_api_key_auth: "UserAPI return guardrail.should_run_guardrail(data=probe, event_type=GuardrailEventHooks.pre_mcp_call) -def agent_365_authorization_servers(server: MCPServer, user_api_key_auth: "UserAPIKeyAuth | None") -> tuple[str, ...]: - """Entra issuers an MCP client signs in with before calling ``server`` through an Agent 365 guardrail. - - Empty unless the admin advertised the server's ``scopes`` (the audience the client requests), the gateway - would otherwise own sign-in for the server, and an Agent 365 guardrail applies: every registered one for the - anonymous discovery fetch, otherwise those the caller's key, team, or policies select. - """ - if not server.scopes or server.auth_type == MCPAuth.oauth2 or not server.advertises_gateway_authorization_server: +def _applicable_guardrails( + server: MCPServer, user_api_key_auth: "UserAPIKeyAuth | None" +) -> tuple[Agent365Guardrail, ...]: + """Agent 365 guardrails that gate ``server`` for this caller: every registered one for the anonymous + discovery fetch, otherwise those the caller's key, team, or policies select. Empty when the gateway + does not own sign-in for the server.""" + if server.auth_type == MCPAuth.oauth2 or not server.advertises_gateway_authorization_server: return () registered: Final = tuple( callback for callback in litellm.logging_callback_manager.get_custom_loggers_for_type(Agent365Guardrail) if isinstance(callback, Agent365Guardrail) ) - applicable: Final = ( - registered - if user_api_key_auth is None - else tuple(g for g in registered if _applies_to_caller(g, user_api_key_auth)) + if user_api_key_auth is None: + return registered + return tuple(g for g in registered if _applies_to_caller(g, user_api_key_auth)) + + +def agent_365_authorization_servers(server: MCPServer, user_api_key_auth: "UserAPIKeyAuth | None") -> tuple[str, ...]: + """Entra issuers an MCP client signs in with before calling ``server`` through an Agent 365 guardrail.""" + return tuple( + dict.fromkeys( + ENTRA_ISSUER_TEMPLATE.format(tenant_id=g.tenant_id) + for g in _applicable_guardrails(server, user_api_key_auth) + ) + ) + + +def agent_365_scopes_supported(server: MCPServer, user_api_key_auth: "UserAPIKeyAuth | None") -> tuple[str, ...]: + """Scopes the client requests from Entra for ``server``: the admin's ``scopes`` when set, otherwise the + ``access_as_user`` scope of each gating guardrail's gateway app registration (``api://``).""" + if server.scopes: + return tuple(server.scopes) + return tuple( + dict.fromkeys( + GATEWAY_SCOPE_TEMPLATE.format(client_id=g.client_id) + for g in _applicable_guardrails(server, user_api_key_auth) + ) ) - return tuple(dict.fromkeys(ENTRA_ISSUER_TEMPLATE.format(tenant_id=g.tenant_id) for g in applicable)) diff --git a/tests/test_litellm/proxy/_experimental/mcp_server/test_discoverable_endpoints.py b/tests/test_litellm/proxy/_experimental/mcp_server/test_discoverable_endpoints.py index a9ac0d528a7..a24568c43e8 100644 --- a/tests/test_litellm/proxy/_experimental/mcp_server/test_discoverable_endpoints.py +++ b/tests/test_litellm/proxy/_experimental/mcp_server/test_discoverable_endpoints.py @@ -7157,9 +7157,13 @@ async def test_agent_365_gated_server_prm_names_the_entra_tenant(agent_365_guard @pytest.mark.asyncio -async def test_agent_365_prm_falls_back_to_gateway_without_scopes(agent_365_guardrail): +async def test_agent_365_prm_defaults_scopeless_server_to_the_gateway_app_scope(agent_365_guardrail): response = await _agent_365_gated_prm(scopes=None) - assert response["authorization_servers"] == ["https://litellm.example.com/mcp"] + assert jsonable_encoder(response) == { + "authorization_servers": ["https://login.microsoftonline.com/tenant-abc/v2.0"], + "resource": "https://litellm.example.com/mcp/tools", + "scopes_supported": ["api://client-xyz/access_as_user"], + } def _token_request(headers): diff --git a/tests/test_litellm/proxy/_experimental/mcp_server/test_mcp_server.py b/tests/test_litellm/proxy/_experimental/mcp_server/test_mcp_server.py index 4fd9c923709..3db642bcc12 100644 --- a/tests/test_litellm/proxy/_experimental/mcp_server/test_mcp_server.py +++ b/tests/test_litellm/proxy/_experimental/mcp_server/test_mcp_server.py @@ -8906,8 +8906,11 @@ class TestAgent365ChallengeAtConnect: assert await self._connect(self._server([self.GATEWAY_SCOPE]), bearer) is None @pytest.mark.asyncio - async def test_server_without_advertised_scopes_is_not_challenged(self, agent_365_guardrail): - assert await self._connect(self._server(None), None) is None + async def test_scopeless_server_is_still_challenged(self, agent_365_guardrail): + challenge = await self._connect(self._server(None), None) + + assert challenge is not None and challenge.status_code == 401 + assert 'error="invalid_token"' in (challenge.headers or {}).get("WWW-Authenticate", "") @pytest.mark.asyncio async def test_no_registered_guardrail_means_no_challenge(self): diff --git a/tests/test_litellm/proxy/guardrails/guardrail_hooks/test_agent_365.py b/tests/test_litellm/proxy/guardrails/guardrail_hooks/test_agent_365.py index c82f830cd84..c9373de8f82 100644 --- a/tests/test_litellm/proxy/guardrails/guardrail_hooks/test_agent_365.py +++ b/tests/test_litellm/proxy/guardrails/guardrail_hooks/test_agent_365.py @@ -20,7 +20,10 @@ from litellm.proxy.guardrails.guardrail_hooks.agent_365 import ( guardrail_initializer_registry, initialize_guardrail, ) -from litellm.proxy.guardrails.guardrail_hooks.agent_365.agent_365 import agent_365_authorization_servers +from litellm.proxy.guardrails.guardrail_hooks.agent_365.agent_365 import ( + agent_365_authorization_servers, + agent_365_scopes_supported, +) from litellm.types.guardrails import ( GuardrailEventHooks, LitellmParams, @@ -590,6 +593,40 @@ class TestUnreachableFallback: assert exc_info.value.status_code == 401 assert "invalid_grant" in exc_info.value.detail["message"] + @pytest.mark.asyncio + @pytest.mark.parametrize( + "error_code", ["invalid_client", "unauthorized_client", "invalid_scope", "invalid_resource"] + ) + async def test_gateway_credential_rejection_is_unavailable_not_a_caller_401(self, error_code: str): + handler: Final = FakeHandler( + [_response(401, {"error": error_code, "error_description": "AADSTS7000215: invalid client secret"})] + ) + guardrail: Final = _make_guardrail(handler) + data: Final = _mcp_data() + with pytest.raises(HTTPException) as exc_info: + await _run(guardrail, data) + assert exc_info.value.status_code == 503 + assert exc_info.value.headers is None or "WWW-Authenticate" not in exc_info.value.headers + info: Final = _guardrail_info(data) + assert info["guardrail_status"] == "guardrail_failed_to_respond" + assert info["guardrail_response"]["verdict"] == "Unavailable" + assert error_code in info["guardrail_response"]["reason"] + assert "client_secret" in info["guardrail_response"]["reason"] + + @pytest.mark.asyncio + async def test_gateway_credential_rejection_follows_fail_open(self): + handler: Final = FakeHandler( + [_response(401, {"error": "invalid_client", "error_description": "AADSTS7000215: invalid client secret"})] + ) + guardrail: Final = _make_guardrail(handler, unreachable_fallback="fail_open") + data: Final = _mcp_data() + result: Final = await _run(guardrail, data) + assert result is data + info: Final = _guardrail_info(data) + assert info["guardrail_status"] == "guardrail_failed_to_respond" + assert info["guardrail_response"]["verdict"] == "Unscanned" + assert "invalid_client" in info["guardrail_response"]["reason"] + @pytest.mark.asyncio async def test_obo_endpoint_5xx_fail_open(self): handler: Final = FakeHandler([_response(503, text="entra down")]) @@ -948,9 +985,17 @@ class TestAgent365AuthorizationServers: _mcp_server(MCPAuth.api_key, scopes=[GATEWAY_SCOPE], auth_value="k"), None ) == (ENTRA_ISSUER,) - def test_silent_without_advertised_scopes(self, registered_guardrail): - assert agent_365_authorization_servers(_mcp_server(scopes=None), None) == () - assert agent_365_authorization_servers(_mcp_server(scopes=[]), None) == () + @pytest.mark.parametrize("scopes", [None, []], ids=["unset", "empty"]) + def test_scopeless_server_signs_in_with_the_gateway_app_scope(self, registered_guardrail, scopes): + server: Final = _mcp_server(scopes=scopes) + assert agent_365_authorization_servers(server, None) == (ENTRA_ISSUER,) + assert agent_365_scopes_supported(server, None) == ("api://client-xyz/access_as_user",) + + def test_admin_scopes_override_the_default_gateway_scope(self, registered_guardrail): + assert agent_365_scopes_supported(_mcp_server(scopes=[GATEWAY_SCOPE]), None) == (GATEWAY_SCOPE,) + + def test_no_default_scope_when_no_guardrail_gates_the_server(self): + assert agent_365_scopes_supported(_mcp_server(scopes=None), None) == () def test_silent_when_no_guardrail_is_registered(self): assert agent_365_authorization_servers(_mcp_server(scopes=[GATEWAY_SCOPE]), None) == () From 0f7105f25e2365f64945d12cb89b7f9d5e3c71a2 Mon Sep 17 00:00:00 2001 From: yucheng Date: Fri, 11 Sep 2026 03:43:33 +0000 Subject: [PATCH 023/225] fix(mcp): hand listed tool metadata to pre-call hooks on the local registry path OpenAPI-generated and legacy local-registry tools dispatch through execute_mcp_tool's local branch, which called pre_call_tool_check without the cached MCPTool. Agent 365 therefore received bare {"name"} payloads for those tools while managed-server tools carried description and inputSchema. Both local call sites now pass get_listed_tool Co-Authored-By: Devin AI <158243242+devin-ai-integration[bot]@users.noreply.github.com> --- .../proxy/_experimental/mcp_server/server.py | 2 + .../mcp_server/test_mcp_server.py | 53 +++++++++++++++++++ 2 files changed, 55 insertions(+) diff --git a/litellm/proxy/_experimental/mcp_server/server.py b/litellm/proxy/_experimental/mcp_server/server.py index 51e2ea63329..88a61f955da 100644 --- a/litellm/proxy/_experimental/mcp_server/server.py +++ b/litellm/proxy/_experimental/mcp_server/server.py @@ -3119,6 +3119,7 @@ if MCP_AVAILABLE: server=mcp_server, raw_headers=raw_headers, litellm_logging_obj=litellm_logging_obj, + tool=global_mcp_server_manager.get_listed_tool(mcp_server, original_tool_name), ) # `pre_call_tool_check` may return guardrail-modified # arguments; honor them on the local path too. @@ -3225,6 +3226,7 @@ if MCP_AVAILABLE: server=prefix_server, raw_headers=raw_headers, litellm_logging_obj=litellm_logging_obj, + tool=global_mcp_server_manager.get_listed_tool(prefix_server, original_tool_name), ) if "arguments" in hook_result: arguments = hook_result["arguments"] # pyright: ignore[reportAny] # hook returns untyped args diff --git a/tests/test_litellm/proxy/_experimental/mcp_server/test_mcp_server.py b/tests/test_litellm/proxy/_experimental/mcp_server/test_mcp_server.py index 3db642bcc12..f8955754618 100644 --- a/tests/test_litellm/proxy/_experimental/mcp_server/test_mcp_server.py +++ b/tests/test_litellm/proxy/_experimental/mcp_server/test_mcp_server.py @@ -17,6 +17,7 @@ from mcp.types import ( TextContent, TextResourceContents, ) +from mcp.types import Tool as MCPTool from litellm.proxy._types import ( LiteLLM_MCPServerTable, @@ -7159,6 +7160,58 @@ async def test_execute_mcp_tool_sets_model_in_model_call_details(): assert litellm_logging_obj.model == "MCP: list_pets" +@pytest.mark.asyncio +async def test_execute_mcp_tool_hands_openapi_listed_tool_metadata_to_pre_call_hooks(): + """OpenAPI-generated tools dispatch through the local registry, so the pre-call hooks must get the + listed description and input schema on that path too, not only on the managed-server path.""" + from litellm.proxy._experimental.mcp_server import server as mcp_module + + petstore = MCPServer( + server_id="petstore-id", + name="petstore", + server_name="petstore", + transport=MCPTransport.http, + url=None, + spec_path="https://example.com/petstore.yaml", + ) + schema = {"type": "object", "properties": {"limit": {"type": "integer"}}} + mcp_module.global_mcp_tool_registry.register_tool( + name="petstore-list_pets", description="List the pets", input_schema=schema, handler=lambda: None + ) + manager = mcp_module.global_mcp_server_manager + manager._create_prefixed_tools( + [MCPTool(name="list_pets", description="List the pets", inputSchema=schema)], petstore + ) + pre_call_tool_check = AsyncMock(return_value={}) + + try: + with ( + patch.object(manager, "_get_mcp_server_from_tool_name", return_value=petstore), + patch.object(manager, "pre_call_tool_check", new=pre_call_tool_check), + patch( + "litellm.proxy._experimental.mcp_server.server._handle_local_mcp_tool", + new=AsyncMock(return_value=[]), + ), + patch( + "litellm.proxy._experimental.mcp_server.server.MCPRequestHandler.is_tool_allowed", + return_value=True, + ), + ): + await mcp_module.execute_mcp_tool( + name="petstore-list_pets", + arguments={"limit": 10}, + allowed_mcp_servers=[petstore], + start_time=datetime.now(), + user_api_key_auth=UserAPIKeyAuth(api_key="sk-user", user_id="alice"), + ) + finally: + mcp_module.global_mcp_tool_registry.unregister_tools_with_prefix("petstore-") + manager._listed_tools_by_server_id.pop(petstore.server_id, None) + + handed_tool = pre_call_tool_check.call_args.kwargs["tool"] + assert handed_tool is not None and (handed_tool.description, handed_tool.inputSchema) == ("List the pets", schema) + + @pytest.mark.asyncio async def test_execute_mcp_tool_rest_unresolved_prefixed_name_routes_to_requested_server(): """A prefixed REST name that resolves to no tool must still dispatch to the server_id. From 6c865d14138715ce4353953e1409fc4012eb9a04 Mon Sep 17 00:00:00 2001 From: yucheng Date: Fri, 11 Sep 2026 04:34:56 +0000 Subject: [PATCH 024/225] fix(mcp): resolve OpenAPI tool metadata from the local registry before any tools/list Co-Authored-By: Devin AI <158243242+devin-ai-integration[bot]@users.noreply.github.com> --- .../mcp_server/mcp_server_manager.py | 15 +++++++++++--- .../mcp_server/test_mcp_server.py | 16 +++++++-------- .../mcp_server/test_mcp_server_manager.py | 20 ++++++++++--------- 3 files changed, 31 insertions(+), 20 deletions(-) diff --git a/litellm/proxy/_experimental/mcp_server/mcp_server_manager.py b/litellm/proxy/_experimental/mcp_server/mcp_server_manager.py index f33a5f85846..3ba7880f4df 100644 --- a/litellm/proxy/_experimental/mcp_server/mcp_server_manager.py +++ b/litellm/proxy/_experimental/mcp_server/mcp_server_manager.py @@ -127,6 +127,7 @@ from litellm.proxy._experimental.mcp_server.outbound_credentials.types import ( from litellm.proxy._experimental.mcp_server.sampling_handler import ( MCP_SAMPLING_AVAILABLE, ) +from litellm.proxy._experimental.mcp_server.tool_registry import global_mcp_tool_registry from litellm.proxy._experimental.mcp_server.utils import ( MCP_TOOL_PREFIX_SEPARATOR, MCPMissingUserEnvVarsError, @@ -4242,9 +4243,11 @@ class MCPServerManager: # applied (e.g. "test_petstore-getinventory"). Do NOT pass them # through _create_prefixed_tools — that would add the prefix a second # time producing "test_petstore-test_petstore-getinventory". + if add_prefix: + return tools prefix: Final = get_server_prefix(server) sep: Final = MCP_TOOL_PREFIX_SEPARATOR - bare_tools: Final = [ # mutable-ok: returned through the list[MCPTool] listing contract + return [ # mutable-ok: returned through the list[MCPTool] listing contract ( t.model_copy(update={"name": t.name[len(prefix) + len(sep) :]}) if t.name.startswith(f"{prefix}{sep}") @@ -4252,8 +4255,6 @@ class MCPServerManager: ) for t in tools ] - self._listed_tools_by_server_id[server.server_id] = MappingProxyType({t.name: t for t in bare_tools}) - return tools if add_prefix else bare_tools else: tools = await self._fetch_tools_with_timeout(client, server.name) self._remember_upstream_initialize_instructions(server, client) @@ -5139,6 +5140,14 @@ class MCPServerManager: return prefixed_tools def get_listed_tool(self, server: MCPServer, name: str) -> MCPTool | None: + if server.spec_path: + bare_name: Final = strip_known_server_prefix(name, server) + registered: Final = global_mcp_tool_registry.get_tool( + f"{get_server_prefix(server)}{MCP_TOOL_PREFIX_SEPARATOR}{bare_name}" + ) or global_mcp_tool_registry.get_tool(bare_name) + if registered is None: + return None + return MCPTool(name=bare_name, description=registered.description, inputSchema=registered.input_schema) listed: Final = self._listed_tools_by_server_id.get(server.server_id) if not listed: return None diff --git a/tests/test_litellm/proxy/_experimental/mcp_server/test_mcp_server.py b/tests/test_litellm/proxy/_experimental/mcp_server/test_mcp_server.py index f8955754618..d2cdab12fb0 100644 --- a/tests/test_litellm/proxy/_experimental/mcp_server/test_mcp_server.py +++ b/tests/test_litellm/proxy/_experimental/mcp_server/test_mcp_server.py @@ -17,7 +17,6 @@ from mcp.types import ( TextContent, TextResourceContents, ) -from mcp.types import Tool as MCPTool from litellm.proxy._types import ( LiteLLM_MCPServerTable, @@ -7161,9 +7160,9 @@ async def test_execute_mcp_tool_sets_model_in_model_call_details(): @pytest.mark.asyncio -async def test_execute_mcp_tool_hands_openapi_listed_tool_metadata_to_pre_call_hooks(): +async def test_execute_mcp_tool_hands_openapi_registered_tool_metadata_to_pre_call_hooks(): """OpenAPI-generated tools dispatch through the local registry, so the pre-call hooks must get the - listed description and input schema on that path too, not only on the managed-server path.""" + registered description and input schema on that path too, even when no tools/list ran first.""" from litellm.proxy._experimental.mcp_server import server as mcp_module petstore = MCPServer( @@ -7179,9 +7178,7 @@ async def test_execute_mcp_tool_hands_openapi_listed_tool_metadata_to_pre_call_h name="petstore-list_pets", description="List the pets", input_schema=schema, handler=lambda: None ) manager = mcp_module.global_mcp_server_manager - manager._create_prefixed_tools( - [MCPTool(name="list_pets", description="List the pets", inputSchema=schema)], petstore - ) + manager._listed_tools_by_server_id.pop(petstore.server_id, None) pre_call_tool_check = AsyncMock(return_value={}) try: @@ -7206,10 +7203,13 @@ async def test_execute_mcp_tool_hands_openapi_listed_tool_metadata_to_pre_call_h ) finally: mcp_module.global_mcp_tool_registry.unregister_tools_with_prefix("petstore-") - manager._listed_tools_by_server_id.pop(petstore.server_id, None) handed_tool = pre_call_tool_check.call_args.kwargs["tool"] - assert handed_tool is not None and (handed_tool.description, handed_tool.inputSchema) == ("List the pets", schema) + assert (handed_tool.name, handed_tool.description, handed_tool.inputSchema) == ( + "list_pets", + "List the pets", + schema, + ) @pytest.mark.asyncio diff --git a/tests/test_litellm/proxy/_experimental/mcp_server/test_mcp_server_manager.py b/tests/test_litellm/proxy/_experimental/mcp_server/test_mcp_server_manager.py index b32787d4947..cc4d730a096 100644 --- a/tests/test_litellm/proxy/_experimental/mcp_server/test_mcp_server_manager.py +++ b/tests/test_litellm/proxy/_experimental/mcp_server/test_mcp_server_manager.py @@ -6504,9 +6504,9 @@ class TestMCPServerManager: assert by_prefixed_name is not None and by_prefixed_name.description == "v2" assert manager.get_listed_tool(server, "missing") is None - @pytest.mark.asyncio - @pytest.mark.parametrize("add_prefix", [True, False]) - async def test_openapi_listing_records_tool_metadata_for_pre_call_hooks(self, add_prefix): + def test_get_listed_tool_reads_openapi_registry_without_a_prior_listing(self): + """OpenAPI tools live in the local registry from registration on, so their metadata must resolve + before any tools/list has run and must disappear with the registration.""" from litellm.proxy._experimental.mcp_server.tool_registry import global_mcp_tool_registry server = MCPServer( @@ -6519,19 +6519,21 @@ class TestMCPServerManager: ) schema = {"type": "object", "properties": {"petId": {"type": "integer"}}} manager = MCPServerManager() - manager._create_mcp_client = AsyncMock(return_value=AsyncMock()) global_mcp_tool_registry.register_tool( name="petstore-get_pet", description="Fetch a pet", input_schema=schema, handler=lambda: None ) try: - listed = await manager._get_tools_from_server(server=server, add_prefix=add_prefix) + for spelling in ("get_pet", "petstore-get_pet"): + tool = manager.get_listed_tool(server, spelling) + assert tool is not None and (tool.name, tool.description, tool.inputSchema) == ( + "get_pet", + "Fetch a pet", + schema, + ) finally: global_mcp_tool_registry.unregister_tools_with_prefix("petstore-") - assert [t.name for t in listed] == ["petstore-get_pet" if add_prefix else "get_pet"] - for spelling in ("get_pet", "petstore-get_pet"): - tool = manager.get_listed_tool(server, spelling) - assert tool is not None and (tool.description, tool.inputSchema) == ("Fetch a pet", schema) + assert manager.get_listed_tool(server, "petstore-get_pet") is None @pytest.mark.asyncio async def test_get_allowed_mcp_servers_with_user_api_key_auth(self): From df739bdd9e8edb1a48e5ac184075856407cc3d64 Mon Sep 17 00:00:00 2001 From: yucheng Date: Fri, 11 Sep 2026 07:54:36 +0000 Subject: [PATCH 025/225] fix(mcp): hand pre-call hooks the metadata of the registered tool that actually runs Building the guardrail's tool metadata from the local registry entry that dispatch resolved, instead of re-deriving it from the tool name, keeps an OpenAPI operation whose name starts with its own server prefix from being reported with the shorter operation's description and schema. The registry branch in get_listed_tool is gone with it, and the test doubles for the local registry now carry a string description and dict schema like the real entries do Co-Authored-By: Devin AI <158243242+devin-ai-integration[bot]@users.noreply.github.com> --- .../mcp_server/mcp_server_manager.py | 9 --- .../proxy/_experimental/mcp_server/server.py | 11 +++- .../mcp_server/test_mcp_server.py | 60 ++++++++++++++++--- .../mcp_server/test_mcp_server_manager.py | 31 ---------- .../mcp_server/test_openapi_tool_auth.py | 12 ++++ 5 files changed, 71 insertions(+), 52 deletions(-) diff --git a/litellm/proxy/_experimental/mcp_server/mcp_server_manager.py b/litellm/proxy/_experimental/mcp_server/mcp_server_manager.py index 3ba7880f4df..aab7f93164f 100644 --- a/litellm/proxy/_experimental/mcp_server/mcp_server_manager.py +++ b/litellm/proxy/_experimental/mcp_server/mcp_server_manager.py @@ -127,7 +127,6 @@ from litellm.proxy._experimental.mcp_server.outbound_credentials.types import ( from litellm.proxy._experimental.mcp_server.sampling_handler import ( MCP_SAMPLING_AVAILABLE, ) -from litellm.proxy._experimental.mcp_server.tool_registry import global_mcp_tool_registry from litellm.proxy._experimental.mcp_server.utils import ( MCP_TOOL_PREFIX_SEPARATOR, MCPMissingUserEnvVarsError, @@ -5140,14 +5139,6 @@ class MCPServerManager: return prefixed_tools def get_listed_tool(self, server: MCPServer, name: str) -> MCPTool | None: - if server.spec_path: - bare_name: Final = strip_known_server_prefix(name, server) - registered: Final = global_mcp_tool_registry.get_tool( - f"{get_server_prefix(server)}{MCP_TOOL_PREFIX_SEPARATOR}{bare_name}" - ) or global_mcp_tool_registry.get_tool(bare_name) - if registered is None: - return None - return MCPTool(name=bare_name, description=registered.description, inputSchema=registered.input_schema) listed: Final = self._listed_tools_by_server_id.get(server.server_id) if not listed: return None diff --git a/litellm/proxy/_experimental/mcp_server/server.py b/litellm/proxy/_experimental/mcp_server/server.py index 88a61f955da..e4bc9bdd952 100644 --- a/litellm/proxy/_experimental/mcp_server/server.py +++ b/litellm/proxy/_experimental/mcp_server/server.py @@ -86,6 +86,7 @@ from litellm.proxy.litellm_pre_call_utils import ( ) from litellm.types.mcp import MCPAuth, MCPSpecVersion from litellm.types.mcp_server.mcp_server_manager import MCPInfo, MCPServer +from litellm.types.mcp_server.tool_registry import MCPTool as RegisteredTool from litellm.types.utils import CallTypes, StandardLoggingMCPToolCall from litellm.utils import Rules, client, function_setup @@ -2777,6 +2778,9 @@ if MCP_AVAILABLE: return managed_resource_templates + def _registered_tool_metadata(name: str, registered: RegisteredTool) -> MCPTool: + return MCPTool(name=name, description=registered.description, inputSchema=registered.input_schema) + def _resolve_display_name_to_original( name: str, allowed_mcp_servers: list[MCPServer], @@ -3119,7 +3123,7 @@ if MCP_AVAILABLE: server=mcp_server, raw_headers=raw_headers, litellm_logging_obj=litellm_logging_obj, - tool=global_mcp_server_manager.get_listed_tool(mcp_server, original_tool_name), + tool=_registered_tool_metadata(original_tool_name, local_tool), ) # `pre_call_tool_check` may return guardrail-modified # arguments; honor them on the local path too. @@ -3185,7 +3189,8 @@ if MCP_AVAILABLE: # not in the registry either, `_handle_local_mcp_tool` below reports # 404 and nothing runs, so demanding a server here would turn every # unknown tool name into a misleading 503. - if global_mcp_tool_registry.get_tool(original_tool_name) is not None: + registered_local_tool: Final = global_mcp_tool_registry.get_tool(original_tool_name) + if registered_local_tool is not None: # `mcp_server` is None here because the tool name is not in the # tool -> server mapping, but the name still carries a prefix # that the server-level check above compared against the @@ -3226,7 +3231,7 @@ if MCP_AVAILABLE: server=prefix_server, raw_headers=raw_headers, litellm_logging_obj=litellm_logging_obj, - tool=global_mcp_server_manager.get_listed_tool(prefix_server, original_tool_name), + tool=_registered_tool_metadata(original_tool_name, registered_local_tool), ) if "arguments" in hook_result: arguments = hook_result["arguments"] # pyright: ignore[reportAny] # hook returns untyped args diff --git a/tests/test_litellm/proxy/_experimental/mcp_server/test_mcp_server.py b/tests/test_litellm/proxy/_experimental/mcp_server/test_mcp_server.py index d2cdab12fb0..7d4159aade8 100644 --- a/tests/test_litellm/proxy/_experimental/mcp_server/test_mcp_server.py +++ b/tests/test_litellm/proxy/_experimental/mcp_server/test_mcp_server.py @@ -7109,6 +7109,8 @@ async def test_execute_mcp_tool_sets_model_in_model_call_details(): fake_tool = MagicMock() fake_tool.name = "list_pets" + fake_tool.description = "test tool" + fake_tool.input_schema = {"type": "object"} start_time = datetime.now(timezone.utc) litellm_logging_obj, _ = function_setup( @@ -7175,7 +7177,7 @@ async def test_execute_mcp_tool_hands_openapi_registered_tool_metadata_to_pre_ca ) schema = {"type": "object", "properties": {"limit": {"type": "integer"}}} mcp_module.global_mcp_tool_registry.register_tool( - name="petstore-list_pets", description="List the pets", input_schema=schema, handler=lambda: None + name="petstore-list_pets", description="List the pets", input_schema=schema, handler=lambda limit: "ok" ) manager = mcp_module.global_mcp_server_manager manager._listed_tools_by_server_id.pop(petstore.server_id, None) @@ -7185,14 +7187,6 @@ async def test_execute_mcp_tool_hands_openapi_registered_tool_metadata_to_pre_ca with ( patch.object(manager, "_get_mcp_server_from_tool_name", return_value=petstore), patch.object(manager, "pre_call_tool_check", new=pre_call_tool_check), - patch( - "litellm.proxy._experimental.mcp_server.server._handle_local_mcp_tool", - new=AsyncMock(return_value=[]), - ), - patch( - "litellm.proxy._experimental.mcp_server.server.MCPRequestHandler.is_tool_allowed", - return_value=True, - ), ): await mcp_module.execute_mcp_tool( name="petstore-list_pets", @@ -7212,6 +7206,54 @@ async def test_execute_mcp_tool_hands_openapi_registered_tool_metadata_to_pre_ca ) +@pytest.mark.asyncio +async def test_execute_mcp_tool_hands_hooks_the_metadata_of_the_operation_it_runs_when_names_collide(): + """An OpenAPI operation whose name starts with its own server prefix must not be reported to the + pre-call hooks with the metadata of the shorter operation, since that is not the one that runs.""" + from litellm.proxy._experimental.mcp_server import server as mcp_module + + petstore = MCPServer( + server_id="petstore-id", + name="petstore", + server_name="petstore", + transport=MCPTransport.http, + url=None, + spec_path="https://example.com/petstore.yaml", + ) + registry = mcp_module.global_mcp_tool_registry + registry.register_tool(name="petstore-get_pet", description="short", input_schema={}, handler=lambda: "short") + registry.register_tool( + name="petstore-petstore-get_pet", + description="long", + input_schema={"type": "object", "properties": {"petId": {"type": "integer"}}}, + handler=lambda: "long", + ) + manager = mcp_module.global_mcp_server_manager + pre_call_tool_check = AsyncMock(return_value={}) + + try: + with ( + patch.object(manager, "_get_mcp_server_from_tool_name", return_value=petstore), + patch.object(manager, "pre_call_tool_check", new=pre_call_tool_check), + ): + result = await mcp_module.execute_mcp_tool( + name="petstore-petstore-get_pet", + arguments={}, + allowed_mcp_servers=[petstore], + start_time=datetime.now(), + user_api_key_auth=UserAPIKeyAuth(api_key="sk-user", user_id="alice"), + ) + finally: + registry.unregister_tools_with_prefix("petstore-") + + handed_tool = pre_call_tool_check.call_args.kwargs["tool"] + assert (handed_tool.description, handed_tool.inputSchema) == ( + "long", + {"type": "object", "properties": {"petId": {"type": "integer"}}}, + ) + assert result.content[0].text == "long" + + @pytest.mark.asyncio async def test_execute_mcp_tool_rest_unresolved_prefixed_name_routes_to_requested_server(): """A prefixed REST name that resolves to no tool must still dispatch to the server_id. diff --git a/tests/test_litellm/proxy/_experimental/mcp_server/test_mcp_server_manager.py b/tests/test_litellm/proxy/_experimental/mcp_server/test_mcp_server_manager.py index cc4d730a096..f1675b72f77 100644 --- a/tests/test_litellm/proxy/_experimental/mcp_server/test_mcp_server_manager.py +++ b/tests/test_litellm/proxy/_experimental/mcp_server/test_mcp_server_manager.py @@ -6504,37 +6504,6 @@ class TestMCPServerManager: assert by_prefixed_name is not None and by_prefixed_name.description == "v2" assert manager.get_listed_tool(server, "missing") is None - def test_get_listed_tool_reads_openapi_registry_without_a_prior_listing(self): - """OpenAPI tools live in the local registry from registration on, so their metadata must resolve - before any tools/list has run and must disappear with the registration.""" - from litellm.proxy._experimental.mcp_server.tool_registry import global_mcp_tool_registry - - server = MCPServer( - server_id="petstore-id", - name="petstore", - server_name="petstore", - transport=MCPTransport.http, - url=None, - spec_path="https://example.com/petstore.yaml", - ) - schema = {"type": "object", "properties": {"petId": {"type": "integer"}}} - manager = MCPServerManager() - global_mcp_tool_registry.register_tool( - name="petstore-get_pet", description="Fetch a pet", input_schema=schema, handler=lambda: None - ) - try: - for spelling in ("get_pet", "petstore-get_pet"): - tool = manager.get_listed_tool(server, spelling) - assert tool is not None and (tool.name, tool.description, tool.inputSchema) == ( - "get_pet", - "Fetch a pet", - schema, - ) - finally: - global_mcp_tool_registry.unregister_tools_with_prefix("petstore-") - - assert manager.get_listed_tool(server, "petstore-get_pet") is None - @pytest.mark.asyncio async def test_get_allowed_mcp_servers_with_user_api_key_auth(self): """ diff --git a/tests/test_litellm/proxy/_experimental/mcp_server/test_openapi_tool_auth.py b/tests/test_litellm/proxy/_experimental/mcp_server/test_openapi_tool_auth.py index 64614c094ba..c534cf73219 100644 --- a/tests/test_litellm/proxy/_experimental/mcp_server/test_openapi_tool_auth.py +++ b/tests/test_litellm/proxy/_experimental/mcp_server/test_openapi_tool_auth.py @@ -43,6 +43,8 @@ async def test_openapi_local_tool_runs_pre_call_tool_check(): fake_tool = MagicMock() fake_tool.name = "list_pets" + fake_tool.description = "test tool" + fake_tool.input_schema = {"type": "object"} pre_call = AsyncMock(return_value={}) handle_local = AsyncMock(return_value=[]) @@ -124,6 +126,8 @@ async def test_openapi_local_tool_blocked_when_pre_call_check_raises(): fake_tool = MagicMock() fake_tool.name = "delete_pet" + fake_tool.description = "test tool" + fake_tool.input_schema = {"type": "object"} pre_call = AsyncMock( side_effect=HTTPException(status_code=403, detail="not allowed") @@ -186,6 +190,8 @@ async def test_openapi_local_tool_denied_when_server_not_resolvable(): fake_tool = MagicMock() fake_tool.name = "list_pets" + fake_tool.description = "test tool" + fake_tool.input_schema = {"type": "object"} pre_call = AsyncMock(return_value={}) handle_local = AsyncMock(return_value=[]) @@ -270,6 +276,8 @@ async def test_openapi_local_tool_injects_resolved_oauth_token(): fake_tool = MagicMock() fake_tool.name = "get_values" + fake_tool.description = "test tool" + fake_tool.input_schema = {"type": "object"} captured: dict = {} async def handle_local(_name, _arguments): @@ -616,6 +624,8 @@ async def test_per_server_auth_header_reaches_both_openapi_dispatch_arms(dispatc if dispatch_arm == "local_registry": fake_tool = MagicMock() fake_tool.name = "list_reports" + fake_tool.description = "test tool" + fake_tool.input_schema = {"type": "object"} with ( patch.object(manager, "_get_mcp_server_from_tool_name", return_value=server), patch.object(mcp_module.global_mcp_tool_registry, "get_tool", return_value=fake_tool), @@ -687,6 +697,8 @@ async def test_local_dispatch_reports_the_outcome_instead_of_success(failure: st fake_tool = MagicMock() fake_tool.name = "list_reports" + fake_tool.description = "test tool" + fake_tool.input_schema = {"type": "object"} fake_tool.handler = raising_handler server = MCPServer( server_id="srv-openapi", From de2f6b2f85f21a437162a70a51193754db7f7f52 Mon Sep 17 00:00:00 2001 From: yucheng Date: Fri, 11 Sep 2026 09:02:28 +0000 Subject: [PATCH 026/225] fix(mcp): challenge Agent 365 gated connects that carry only a LiteLLM key in Authorization A LiteLLM virtual key in the Authorization header admits the caller but is not an Entra assertion the guardrail can exchange, so the connect-time RFC 9728 challenge now fires unless the bearer is a compact JWS. The guardrail parses the inbound bearer with the same predicate Co-Authored-By: Devin AI <158243242+devin-ai-integration[bot]@users.noreply.github.com> --- .../proxy/_experimental/mcp_server/server.py | 19 +++++++++++-------- .../guardrail_hooks/agent_365/agent_365.py | 18 ++++++++++++++++-- .../mcp_server/test_mcp_server.py | 17 +++++++++++++++-- 3 files changed, 42 insertions(+), 12 deletions(-) diff --git a/litellm/proxy/_experimental/mcp_server/server.py b/litellm/proxy/_experimental/mcp_server/server.py index e4bc9bdd952..887a4aec033 100644 --- a/litellm/proxy/_experimental/mcp_server/server.py +++ b/litellm/proxy/_experimental/mcp_server/server.py @@ -79,7 +79,10 @@ from litellm.proxy._types import ( UserAPIKeyAuth, ) from litellm.proxy.auth.ip_address_utils import IPAddressUtils -from litellm.proxy.guardrails.guardrail_hooks.agent_365.agent_365 import agent_365_authorization_servers +from litellm.proxy.guardrails.guardrail_hooks.agent_365.agent_365 import ( + agent_365_authorization_servers, + agent_365_subject_token_present, +) from litellm.proxy.litellm_pre_call_utils import ( LiteLLMProxyRequestSetup, get_chain_id_from_headers, @@ -4136,13 +4139,13 @@ if MCP_AVAILABLE: # so the client discovers the IdP, SSOs, and retries with a subject token, which LiteLLM # then exchanges. A tool-call-time 401 would be wrapped into a JSON-RPC error and the # header lost, so the discovery flow needs this pre-emptive challenge. Servers gated by an - # Agent 365 guardrail (OBO to the evaluate API) get the same challenge. - if ( - server - and not oauth2_headers - and ( - server.auth_type == MCPAuth.oauth2_token_exchange - or agent_365_authorization_servers(server, user_api_key_auth) + # Agent 365 guardrail (OBO to the evaluate API) get the same challenge, also when the only + # bearer is the LiteLLM key itself, which admits the caller but is not an exchangeable subject. + if server and ( + (server.auth_type == MCPAuth.oauth2_token_exchange and not oauth2_headers) + or ( + not agent_365_subject_token_present(oauth2_headers) + and agent_365_authorization_servers(server, user_api_key_auth) ) ): from litellm.proxy._experimental.mcp_server.outbound_credentials.adapter import ( # noqa: PLC0415 # lazy: adapter pulls MCP subgraph diff --git a/litellm/proxy/guardrails/guardrail_hooks/agent_365/agent_365.py b/litellm/proxy/guardrails/guardrail_hooks/agent_365/agent_365.py index 3cc2394a18e..24f1643d89a 100644 --- a/litellm/proxy/guardrails/guardrail_hooks/agent_365/agent_365.py +++ b/litellm/proxy/guardrails/guardrail_hooks/agent_365/agent_365.py @@ -201,8 +201,8 @@ class Agent365Guardrail(CustomGuardrail): return data tool_name: Final = str(data.get("mcp_tool_name") or "") - assertion: Final = data.get("incoming_bearer_token") - if not isinstance(assertion, str) or assertion.count(".") != 2: + assertion: Final = entra_assertion(data.get("incoming_bearer_token")) + if assertion is None: self._handle_caller_fault( data=data, tool_name=tool_name, @@ -658,6 +658,20 @@ def _applicable_guardrails( return tuple(g for g in registered if _applies_to_caller(g, user_api_key_auth)) +def entra_assertion(value: object) -> str | None: + """``value`` when it is a compact JWS, the only bearer shape the OBO exchange accepts as its assertion. + A LiteLLM virtual key, session bearer, or opaque upstream token in ``Authorization`` yields ``None``.""" + return value if isinstance(value, str) and value.count(".") == 2 else None + + +def agent_365_subject_token_present(oauth2_headers: Mapping[str, str] | None) -> bool: + """Whether the request's ``Authorization`` carries an Entra assertion the guardrail can exchange.""" + authorization: Final = (oauth2_headers or {}).get("Authorization", "") + if not authorization.lower().startswith("bearer "): + return False + return entra_assertion(authorization[len("bearer ") :].strip()) is not None + + def agent_365_authorization_servers(server: MCPServer, user_api_key_auth: "UserAPIKeyAuth | None") -> tuple[str, ...]: """Entra issuers an MCP client signs in with before calling ``server`` through an Agent 365 guardrail.""" return tuple( diff --git a/tests/test_litellm/proxy/_experimental/mcp_server/test_mcp_server.py b/tests/test_litellm/proxy/_experimental/mcp_server/test_mcp_server.py index 7d4159aade8..b599fbadb50 100644 --- a/tests/test_litellm/proxy/_experimental/mcp_server/test_mcp_server.py +++ b/tests/test_litellm/proxy/_experimental/mcp_server/test_mcp_server.py @@ -8996,10 +8996,23 @@ class TestAgent365ChallengeAtConnect: assert 'resource_metadata="/.well-known/oauth-protected-resource/mcp/tools"' in www_authenticate @pytest.mark.asyncio - async def test_bearer_present_connects(self, agent_365_guardrail): - bearer = {"Authorization": "Bearer entra-user-token"} + async def test_entra_assertion_present_connects(self, agent_365_guardrail): + bearer = {"Authorization": "Bearer eyJhbGciOiJSUzI1NiJ9.eyJzdWIiOiJ1LTEifQ.c2ln"} assert await self._connect(self._server([self.GATEWAY_SCOPE]), bearer) is None + @pytest.mark.asyncio + async def test_litellm_key_in_authorization_is_still_challenged(self, agent_365_guardrail): + """A LiteLLM virtual key admits the caller but is no Entra assertion, so the tools/call would fail + 401 inside JSON-RPC with the WWW-Authenticate header lost. The connect must challenge instead.""" + challenge = await self._connect( + self._server([self.GATEWAY_SCOPE]), {"Authorization": "Bearer sk-litellm-virtual-key"} + ) + + assert challenge is not None and challenge.status_code == 401 + www_authenticate = (challenge.headers or {}).get("WWW-Authenticate", "") + assert 'error="invalid_token"' in www_authenticate + assert 'resource_metadata="/.well-known/oauth-protected-resource/mcp/tools"' in www_authenticate + @pytest.mark.asyncio async def test_scopeless_server_is_still_challenged(self, agent_365_guardrail): challenge = await self._connect(self._server(None), None) From 8af770ca833ab6428ce445657d0877eef32bd5f1 Mon Sep 17 00:00:00 2001 From: yucheng Date: Fri, 11 Sep 2026 22:35:05 +0000 Subject: [PATCH 027/225] fix(mcp): point the Agent 365 sign-in challenge at the metadata for the route the client used The connect-time 401 always named /.well-known/oauth-protected-resource/mcp/{server}, so a client on the /{server}/mcp URL fetched a document whose resource was a different URL and RFC 9728 strict clients (newer Claude Code) stopped before opening the browser. Reuse get_passthrough_resource_metadata_url so the challenge is absolute and matches the inbound path, on both the connect challenge and OBO preflight Co-Authored-By: Devin AI <158243242+devin-ai-integration[bot]@users.noreply.github.com> --- .../mcp_server/mcp_server_manager.py | 6 ++- .../outbound_credentials/adapter.py | 8 +++- .../proxy/_experimental/mcp_server/server.py | 8 +++- .../outbound_credentials/test_adapter.py | 16 +++++++ .../mcp_server/test_mcp_server.py | 42 ++++++++++++++++--- 5 files changed, 72 insertions(+), 8 deletions(-) diff --git a/litellm/proxy/_experimental/mcp_server/mcp_server_manager.py b/litellm/proxy/_experimental/mcp_server/mcp_server_manager.py index 7ee4daed477..7ccd6307373 100644 --- a/litellm/proxy/_experimental/mcp_server/mcp_server_manager.py +++ b/litellm/proxy/_experimental/mcp_server/mcp_server_manager.py @@ -3946,6 +3946,7 @@ class MCPServerManager: oauth2_headers: dict[str, str] | None, user_api_key_auth: UserAPIKeyAuth | None, raw_headers: Mapping[str, str] | None = None, + resource_metadata_url: str | None = None, ) -> None: """Mint an exchange-backed server's upstream credential at the transport edge. @@ -3983,7 +3984,9 @@ class MCPServerManager: if spec is None or not isinstance(spec.config, (TokenExchangeConfig, IdJagConfig)): return if subject_token is None and isinstance(spec.config, TokenExchangeConfig): - raise_token_exchange_challenge(resolved_server, root_path=get_request_root_path()) + raise_token_exchange_challenge( + resolved_server, root_path=get_request_root_path(), resource_metadata_url=resource_metadata_url + ) match await self._cred_provider.resolve_credentials(to_subject(user_api_key_auth, subject_token), spec): case Ok(_): return @@ -3993,6 +3996,7 @@ class MCPServerManager: resolved_server, root_path=get_request_root_path(), claims=err.unauthorized.claims, + resource_metadata_url=resource_metadata_url, ) raise_public(err) diff --git a/litellm/proxy/_experimental/mcp_server/outbound_credentials/adapter.py b/litellm/proxy/_experimental/mcp_server/outbound_credentials/adapter.py index 77979a15199..2401ad4a776 100644 --- a/litellm/proxy/_experimental/mcp_server/outbound_credentials/adapter.py +++ b/litellm/proxy/_experimental/mcp_server/outbound_credentials/adapter.py @@ -358,6 +358,7 @@ def raise_token_exchange_challenge( *, root_path: str, claims: str | None = None, + resource_metadata_url: str | None = None, ) -> NoReturn: """Raise the RFC 9728 / RFC 6750 challenge an OBO (``token_exchange``) server returns when the caller's subject token is missing or the IdP rejected it. @@ -375,8 +376,13 @@ def raise_token_exchange_challenge( ``error="invalid_token"`` and is byte-identical to the static one. Both the error value (one of two literals) and the base64 claims draw from a fixed alphabet, so nothing from the IdP body reaches the header unescaped. + + ``resource_metadata_url`` overrides the derived path with the absolute metadata URL matching the + route spelling the request arrived on: RFC 9728 §3.3 clients reject a ``resource`` that differs + from the URL they connected to, and a ``/{server}/mcp`` connect must not be sent to the + ``/mcp/{server}`` document. """ - resource_metadata: Final = oauth_protected_resource_path(root_path, server) + resource_metadata: Final = resource_metadata_url or oauth_protected_resource_path(root_path, server) encoded_claims: Final = base64.b64encode(claims.encode()).decode() if claims else None error: Final = "insufficient_claims" if encoded_claims else "invalid_token" error_description: Final = ( diff --git a/litellm/proxy/_experimental/mcp_server/server.py b/litellm/proxy/_experimental/mcp_server/server.py index 887a4aec033..bf9e1f1567f 100644 --- a/litellm/proxy/_experimental/mcp_server/server.py +++ b/litellm/proxy/_experimental/mcp_server/server.py @@ -56,6 +56,7 @@ from litellm.proxy._experimental.mcp_server.mcp_debug import ( ) from litellm.proxy._experimental.mcp_server.oauth_utils import ( _redact_mcp_resource_url, + get_passthrough_resource_metadata_url, get_passthrough_www_authenticate, get_route_relative_request_path, well_known_root_suffix, @@ -4155,7 +4156,11 @@ if MCP_AVAILABLE: get_request_root_path, ) - raise_token_exchange_challenge(server, root_path=get_request_root_path()) + raise_token_exchange_challenge( + server, + root_path=get_request_root_path(), + resource_metadata_url=get_passthrough_resource_metadata_url(scope=scope, server_name=server_name), + ) # Exchange-backed modes (token_exchange's OBO mint, id_jag's stored-assertion mint): run # the exchange here at the transport edge, so a rejected subject raises the RFC 9728 @@ -4180,6 +4185,7 @@ if MCP_AVAILABLE: oauth2_headers=oauth2_headers, user_api_key_auth=user_api_key_auth, raw_headers=raw_headers, + resource_metadata_url=get_passthrough_resource_metadata_url(scope=scope, server_name=server_name), ) # Pass-through OAuth: when the admin has opted a server into diff --git a/tests/test_litellm/proxy/_experimental/mcp_server/outbound_credentials/test_adapter.py b/tests/test_litellm/proxy/_experimental/mcp_server/outbound_credentials/test_adapter.py index 1b003e11993..d650c5ea6c1 100644 --- a/tests/test_litellm/proxy/_experimental/mcp_server/outbound_credentials/test_adapter.py +++ b/tests/test_litellm/proxy/_experimental/mcp_server/outbound_credentials/test_adapter.py @@ -584,6 +584,22 @@ def test_raise_token_exchange_challenge_is_rfc9728_invalid_token(): assert "error_description=" in www +def test_raise_token_exchange_challenge_explicit_resource_metadata_url_wins(): + from litellm.proxy._experimental.mcp_server.outbound_credentials.adapter import ( + raise_token_exchange_challenge, + ) + + with pytest.raises(HTTPException) as exc_info: + raise_token_exchange_challenge( + _server(alias="obo-srv"), + root_path="/", + resource_metadata_url="https://gw.example.com/.well-known/oauth-protected-resource/obo-srv/mcp", + ) + www = exc_info.value.headers["WWW-Authenticate"] + assert 'resource_metadata="https://gw.example.com/.well-known/oauth-protected-resource/obo-srv/mcp"' in www + assert "/mcp/obo-srv" not in www + + def test_raise_token_exchange_challenge_includes_server_root_path(monkeypatch): from litellm.proxy._experimental.mcp_server.outbound_credentials.adapter import ( raise_token_exchange_challenge, diff --git a/tests/test_litellm/proxy/_experimental/mcp_server/test_mcp_server.py b/tests/test_litellm/proxy/_experimental/mcp_server/test_mcp_server.py index b599fbadb50..95103a3ed71 100644 --- a/tests/test_litellm/proxy/_experimental/mcp_server/test_mcp_server.py +++ b/tests/test_litellm/proxy/_experimental/mcp_server/test_mcp_server.py @@ -8962,7 +8962,9 @@ class TestAgent365ChallengeAtConnect: litellm.callbacks, guardrail, require_self=False ) - async def _connect(self, server: MCPServer, oauth2_headers: dict[str, str] | None) -> HTTPException | None: + async def _connect( + self, server: MCPServer, oauth2_headers: dict[str, str] | None, path: str = "/mcp/tools" + ) -> HTTPException | None: from litellm.proxy._experimental.mcp_server import server as server_module with ( @@ -8975,7 +8977,14 @@ class TestAgent365ChallengeAtConnect: ): try: await server_module._raise_preemptive_401_for_unauthenticated_servers( - scope={"type": "http", "method": "POST", "path": "/mcp/tools", "headers": []}, + scope={ + "type": "http", + "method": "POST", + "path": path, + "scheme": "https", + "server": ("gw.example.com", 443), + "headers": [], + }, mcp_servers=["tools"], oauth2_headers=oauth2_headers, mcp_server_auth_headers=None, @@ -8993,7 +9002,23 @@ class TestAgent365ChallengeAtConnect: assert challenge is not None and challenge.status_code == 401 www_authenticate = (challenge.headers or {}).get("WWW-Authenticate", "") assert 'error="invalid_token"' in www_authenticate - assert 'resource_metadata="/.well-known/oauth-protected-resource/mcp/tools"' in www_authenticate + assert ( + 'resource_metadata="https://gw.example.com/.well-known/oauth-protected-resource/mcp/tools"' + in www_authenticate + ) + + @pytest.mark.asyncio + async def test_legacy_route_challenge_points_at_its_own_metadata(self, agent_365_guardrail): + """RFC 9728 3.3: the metadata's ``resource`` must equal the URL the client connected to, so a + ``/{server}/mcp`` connect is sent to the ``/{server}/mcp`` document, not the ``/mcp/{server}`` one.""" + challenge = await self._connect(self._server([self.GATEWAY_SCOPE]), None, path="/tools/mcp") + + assert challenge is not None and challenge.status_code == 401 + www_authenticate = (challenge.headers or {}).get("WWW-Authenticate", "") + assert ( + 'resource_metadata="https://gw.example.com/.well-known/oauth-protected-resource/tools/mcp"' + in www_authenticate + ) @pytest.mark.asyncio async def test_entra_assertion_present_connects(self, agent_365_guardrail): @@ -9011,7 +9036,10 @@ class TestAgent365ChallengeAtConnect: assert challenge is not None and challenge.status_code == 401 www_authenticate = (challenge.headers or {}).get("WWW-Authenticate", "") assert 'error="invalid_token"' in www_authenticate - assert 'resource_metadata="/.well-known/oauth-protected-resource/mcp/tools"' in www_authenticate + assert ( + 'resource_metadata="https://gw.example.com/.well-known/oauth-protected-resource/mcp/tools"' + in www_authenticate + ) @pytest.mark.asyncio async def test_scopeless_server_is_still_challenged(self, agent_365_guardrail): @@ -9096,7 +9124,11 @@ class TestOboPreflightScopedToAllowedServers: _, preflight = await self._run(requested, allowed=[requested], user_api_key_auth=key) preflight.assert_awaited_once_with( - server=requested, oauth2_headers=self.SUBJECT_HEADERS, user_api_key_auth=key, raw_headers=None + server=requested, + oauth2_headers=self.SUBJECT_HEADERS, + user_api_key_auth=key, + raw_headers=None, + resource_metadata_url="/.well-known/oauth-protected-resource/mcp/obo_tools", ) From 003b53abbb8ad98bccddc47c0fd54c6cb4d461a2 Mon Sep 17 00:00:00 2001 From: jesus Date: Wed, 9 Sep 2026 22:03:04 +0000 Subject: [PATCH 028/225] feat(cli): sync Codex /model picker from proxy /v1/models in lite codex Co-Authored-By: Devin AI <158243242+devin-ai-integration[bot]@users.noreply.github.com> --- litellm/proxy/client/cli/commands/agents.py | 180 ++++++++++-- .../cli/commands/codex_base_instructions.md | 275 ++++++++++++++++++ pyproject.toml | 1 + .../proxy/client/cli/test_agents.py | 176 ++++++++++- 4 files changed, 602 insertions(+), 30 deletions(-) create mode 100644 litellm/proxy/client/cli/commands/codex_base_instructions.md diff --git a/litellm/proxy/client/cli/commands/agents.py b/litellm/proxy/client/cli/commands/agents.py index ea1eed65505..67d2d96e9d6 100644 --- a/litellm/proxy/client/cli/commands/agents.py +++ b/litellm/proxy/client/cli/commands/agents.py @@ -8,7 +8,7 @@ from collections.abc import Callable, Mapping, Sequence from dataclasses import dataclass from pathlib import Path from types import MappingProxyType -from typing import Final, TypeAlias +from typing import Final, Literal, TypeAlias import click import requests @@ -65,6 +65,9 @@ _INSTALL_DOCS: Final[dict[str, str]] = { _HIDDEN_AGENTS: Final = frozenset({"pi"}) CODEX_PROXY_PROVIDER: Final = "litellm" +CODEX_HOME_ENV: Final = "CODEX_HOME" +CODEX_MODEL_CATALOG_FILENAME: Final = "litellm-models.json" +_CODEX_BASE_INSTRUCTIONS_PATH: Final = Path(__file__).with_name("codex_base_instructions.md") class AgentRunError(Exception): @@ -242,7 +245,7 @@ def agent_launch_args(command: str, base_url: str) -> list[str]: class ListedModel(BaseModel): - """The fields of a /v1/models entry that an OpenCode model entry is built from.""" + """The fields of a /v1/models entry that an OpenCode or Codex model entry is built from.""" id: str mode: str | None = None @@ -255,7 +258,7 @@ class _ModelListing(BaseModel): _MODEL_LISTING: Final = TypeAdapter(_ModelListing) -_OPENCODE_CHAT_MODES: Final[frozenset[str]] = frozenset({"chat", "responses"}) +_CHAT_MODES: Final[frozenset[str]] = frozenset({"chat", "responses"}) _NO_EXTRA_ENV: Final[Mapping[str, str]] = MappingProxyType({}) @@ -264,6 +267,40 @@ class ModelSyncSkipped: reason: str +@dataclass(frozen=True, slots=True) +class ModelSyncArgs: + """CLI args, placed before the user's own, that hand an agent the synced model list.""" + + args: tuple[str, ...] + + +ModelSyncResult: TypeAlias = Mapping[str, str] | ModelSyncArgs | ModelSyncSkipped + + +def _chat_models(models: Sequence[ListedModel]) -> tuple[ListedModel, ...]: + return tuple(m for m in models if m.mode is None or m.mode in _CHAT_MODES) + + +def _fetch_model_listing( + base_url: str, + api_key: str, + *, + get: Callable[..., requests.Response], +) -> tuple[ListedModel, ...] | ModelSyncSkipped: + url: Final = base_url.rstrip("/") + "/v1/models" + try: + resp: Final = get(url, headers=MappingProxyType({"Authorization": f"Bearer {api_key}"}), timeout=10) + except requests.RequestException as e: + return ModelSyncSkipped(f"could not reach {url}: {e}") + if resp.status_code != 200: + return ModelSyncSkipped(f"{url} returned HTTP {resp.status_code}") + try: + listing: Final = _MODEL_LISTING.validate_json(resp.content) + except ValidationError: + return ModelSyncSkipped(f"{url} returned an unexpected body") + return listing.data + + class _OpenCodeLimit(BaseModel): context: int output: int @@ -307,7 +344,7 @@ def opencode_provider_config(base_url: str, models: Sequence[ListedModel]) -> st it never lands in the config text. OpenCode merges this inline config over the user's own files, leaving unrelated keys and providers untouched. """ - chat_models: Final = tuple(m for m in models if m.mode is None or m.mode in _OPENCODE_CHAT_MODES) + chat_models: Final = _chat_models(models) provider: Final = _OpenCodeProvider( npm=OPENCODE_PROVIDER_NPM, name=OPENCODE_PROVIDER_NAME, @@ -337,18 +374,109 @@ def opencode_model_sync_env( """ if OPENCODE_CONFIG_CONTENT_ENV in base_env: return ModelSyncSkipped(f"{OPENCODE_CONFIG_CONTENT_ENV} is already set") - url: Final = base_url.rstrip("/") + "/v1/models" + listing: Final = _fetch_model_listing(base_url, api_key, get=get) + if isinstance(listing, ModelSyncSkipped): + return listing + return MappingProxyType({OPENCODE_CONFIG_CONTENT_ENV: opencode_provider_config(base_url, listing)}) + + +class _CodexTruncationPolicy(BaseModel): + mode: Literal["bytes"] = "bytes" + limit: int = 10_000 + + +class _CodexModel(BaseModel): + """One `ModelInfo` entry of a Codex model catalog. + + Every field Codex's deserializer has no default for is spelled out here; the + values match the fallback metadata Codex uses today for a model slug it + does not know, so picking a proxy model behaves the same as `codex -m` did. + """ + + slug: str + display_name: str + description: None = None + supported_reasoning_levels: tuple[()] = () + shell_type: Literal["unified_exec"] = "unified_exec" + visibility: Literal["list"] = "list" + supported_in_api: Literal[True] = True + priority: int + availability_nux: None = None + upgrade: None = None + support_verbosity: Literal[False] = False + default_verbosity: None = None + apply_patch_tool_type: None = None + truncation_policy: _CodexTruncationPolicy = _CodexTruncationPolicy() + experimental_supported_tools: tuple[()] = () + context_window: int | None + base_instructions: str + + +class _CodexCatalog(BaseModel): + models: tuple[_CodexModel, ...] + + +def codex_model_catalog(models: Sequence[ListedModel]) -> str | None: + """The `model_catalog_json` body listing the proxy's chat models, or None if there are none. + + Codex refuses an empty catalog, hence None instead of `{"models": []}`. + Passing a catalog replaces Codex's built-in one, so every entry carries the + same base instructions Codex itself uses, otherwise the agent would run + without a system prompt. + """ + chat_models: Final = _chat_models(models) + if not chat_models: + return None + instructions: Final = _CODEX_BASE_INSTRUCTIONS_PATH.read_text(encoding="utf-8") + catalog: Final = _CodexCatalog( + models=tuple( + _CodexModel( + slug=m.id, + display_name=m.id, + priority=index, + context_window=m.max_input_tokens, + base_instructions=instructions, + ) + for index, m in enumerate(chat_models) + ) + ) + return catalog.model_dump_json() + + +def codex_model_catalog_path(env: Mapping[str, str]) -> Path: + override: Final = env.get(CODEX_HOME_ENV) + root: Final = Path(override) if override else Path.home() / ".codex" + return root / CODEX_MODEL_CATALOG_FILENAME + + +def codex_model_sync_args( + base_env: Mapping[str, str], + base_url: str, + api_key: str, + *, + get: Callable[..., requests.Response] = requests.get, +) -> ModelSyncArgs | ModelSyncSkipped: + """`-c model_catalog_json=...` pointing Codex at the proxy's model list, or why it was skipped. + + Codex has no env or inline equivalent of OPENCODE_CONFIG_CONTENT: the catalog + must be a file, so it is written under $CODEX_HOME (default ~/.codex) and + rewritten on every launch. The key never lands in the file. A failed fetch + or write is reported rather than raised: Codex still launches with its + built-in catalog and takes a proxy model by name via -m. + """ + listing: Final = _fetch_model_listing(base_url, api_key, get=get) + if isinstance(listing, ModelSyncSkipped): + return listing + catalog: Final = codex_model_catalog(listing) + if catalog is None: + return ModelSyncSkipped(f"{base_url.rstrip('/')}/v1/models lists no chat models") + path: Final = codex_model_catalog_path(base_env) try: - resp: Final = get(url, headers=MappingProxyType({"Authorization": f"Bearer {api_key}"}), timeout=10) - except requests.RequestException as e: - return ModelSyncSkipped(f"could not reach {url}: {e}") - if resp.status_code != 200: - return ModelSyncSkipped(f"{url} returned HTTP {resp.status_code}") - try: - listing: Final = _MODEL_LISTING.validate_json(resp.content) - except ValidationError: - return ModelSyncSkipped(f"{url} returned an unexpected body") - return MappingProxyType({OPENCODE_CONFIG_CONTENT_ENV: opencode_provider_config(base_url, listing.data)}) + path.parent.mkdir(parents=True, exist_ok=True) + path.write_text(catalog, encoding="utf-8") + except OSError as e: + return ModelSyncSkipped(f"could not write {path}: {e}") + return ModelSyncArgs(("-c", f"model_catalog_json={json.dumps(str(path))}")) def agent_model_sync_env( @@ -359,18 +487,21 @@ def agent_model_sync_env( skip_verify: bool, *, get: Callable[..., requests.Response] = requests.get, -) -> Mapping[str, str] | ModelSyncSkipped: - """Extra env an agent needs to see the proxy's model list. +) -> ModelSyncResult: + """Extra env or args an agent needs to see the proxy's model list. - Only OpenCode needs one: Claude Code discovers models through - CLAUDE_CODE_ENABLE_GATEWAY_MODEL_DISCOVERY and Codex takes the model by name. + OpenCode takes it as env, Codex as a `-c` override; Claude Code discovers + models itself through CLAUDE_CODE_ENABLE_GATEWAY_MODEL_DISCOVERY. skip_verify means the caller wants no pre-launch proxy call at all, so the listing is skipped too rather than hanging on an offline proxy. """ - if os.path.basename(command) != "opencode": + agent: Final = os.path.basename(command) + if agent not in ("opencode", "codex"): return _NO_EXTRA_ENV if skip_verify: return ModelSyncSkipped(f"{_SKIP_VERIFY_FLAG} was passed") + if agent == "codex": + return codex_model_sync_args(base_env, base_url, api_key, get=get) return opencode_model_sync_env(base_env, base_url, api_key, get=get) @@ -498,9 +629,7 @@ def run_agent( base_env: Mapping[str, str] | None = None, which: Callable[[str], str | None] = shutil.which, verify: Callable[[str, str], None] = verify_proxy_key, - sync_models: Callable[[str, Mapping[str, str], str, str, bool], Mapping[str, str] | ModelSyncSkipped] = ( - agent_model_sync_env - ), + sync_models: Callable[[str, Mapping[str, str], str, str, bool], ModelSyncResult] = agent_model_sync_env, warn: Callable[[str], None] = _warn, launcher: Callable[[str, Sequence[str], Mapping[str, str]], None] = _hand_off, reattach_terminal: Callable[[], None] | None = None, @@ -537,10 +666,11 @@ def run_agent( env: Final = MappingProxyType( { **build_agent_env(env_before_sync, base_url, api_key, profiles), - **(_NO_EXTRA_ENV if isinstance(synced, ModelSyncSkipped) else synced), + **(synced if isinstance(synced, Mapping) else _NO_EXTRA_ENV), } ) - extra_args: Final = (*agent_launch_args(command[0], base_url), *prepared_args) + synced_args: Final = synced.args if isinstance(synced, ModelSyncArgs) else () + extra_args: Final = (*agent_launch_args(command[0], base_url), *synced_args, *prepared_args) if reattach_terminal is not None: reattach_terminal() launcher(binary, [command[0], *extra_args, *command[1:]], env) diff --git a/litellm/proxy/client/cli/commands/codex_base_instructions.md b/litellm/proxy/client/cli/commands/codex_base_instructions.md new file mode 100644 index 00000000000..907ff8b8770 --- /dev/null +++ b/litellm/proxy/client/cli/commands/codex_base_instructions.md @@ -0,0 +1,275 @@ +You are a coding agent running in the Codex CLI, a terminal-based coding assistant. Codex CLI is an open source project led by OpenAI. You are expected to be precise, safe, and helpful. + +Your capabilities: + +- Receive user prompts and other context provided by the harness, such as files in the workspace. +- Communicate with the user by streaming thinking & responses, and by making & updating plans. +- Emit function calls to run terminal commands and apply patches. Depending on how this specific run is configured, you can request that these function calls be escalated to the user for approval before running. More on this in the "Sandbox and approvals" section. + +Within this context, Codex refers to the open-source agentic coding interface (not the old Codex language model built by OpenAI). + +# How you work + +## Personality + +Your default personality and tone is concise, direct, and friendly. You communicate efficiently, always keeping the user clearly informed about ongoing actions without unnecessary detail. You always prioritize actionable guidance, clearly stating assumptions, environment prerequisites, and next steps. Unless explicitly asked, you avoid excessively verbose explanations about your work. + +# AGENTS.md spec +- Repos often contain AGENTS.md files. These files can appear anywhere within the repository. +- These files are a way for humans to give you (the agent) instructions or tips for working within the container. +- Some examples might be: coding conventions, info about how code is organized, or instructions for how to run or test code. +- Instructions in AGENTS.md files: + - The scope of an AGENTS.md file is the entire directory tree rooted at the folder that contains it. + - For every file you touch in the final patch, you must obey instructions in any AGENTS.md file whose scope includes that file. + - Instructions about code style, structure, naming, etc. apply only to code within the AGENTS.md file's scope, unless the file states otherwise. + - More-deeply-nested AGENTS.md files take precedence in the case of conflicting instructions. + - Direct system/developer/user instructions (as part of a prompt) take precedence over AGENTS.md instructions. +- The contents of the AGENTS.md file at the root of the repo and any directories from the CWD up to the root are included with the developer message and don't need to be re-read. When working in a subdirectory of CWD, or a directory outside the CWD, check for any AGENTS.md files that may be applicable. + +## Responsiveness + +### Preamble messages + +Before making tool calls, send a brief preamble to the user explaining what you’re about to do. When sending preamble messages, follow these principles and examples: + +- **Logically group related actions**: if you’re about to run several related commands, describe them together in one preamble rather than sending a separate note for each. +- **Keep it concise**: be no more than 1-2 sentences, focused on immediate, tangible next steps. (8–12 words for quick updates). +- **Build on prior context**: if this is not your first tool call, use the preamble message to connect the dots with what’s been done so far and create a sense of momentum and clarity for the user to understand your next actions. +- **Keep your tone light, friendly and curious**: add small touches of personality in preambles feel collaborative and engaging. +- **Exception**: Avoid adding a preamble for every trivial read (e.g., `cat` a single file) unless it’s part of a larger grouped action. + +**Examples:** + +- “I’ve explored the repo; now checking the API route definitions.” +- “Next, I’ll patch the config and update the related tests.” +- “I’m about to scaffold the CLI commands and helper functions.” +- “Ok cool, so I’ve wrapped my head around the repo. Now digging into the API routes.” +- “Config’s looking tidy. Next up is patching helpers to keep things in sync.” +- “Finished poking at the DB gateway. I will now chase down error handling.” +- “Alright, build pipeline order is interesting. Checking how it reports failures.” +- “Spotted a clever caching util; now hunting where it gets used.” + +## Planning + +You have access to an `update_plan` tool which tracks steps and progress and renders them to the user. Using the tool helps demonstrate that you've understood the task and convey how you're approaching it. Plans can help to make complex, ambiguous, or multi-phase work clearer and more collaborative for the user. A good plan should break the task into meaningful, logically ordered steps that are easy to verify as you go. + +Note that plans are not for padding out simple work with filler steps or stating the obvious. The content of your plan should not involve doing anything that you aren't capable of doing (i.e. don't try to test things that you can't test). Do not use plans for simple or single-step queries that you can just do or answer immediately. + +Do not repeat the full contents of the plan after an `update_plan` call — the harness already displays it. Instead, summarize the change made and highlight any important context or next step. + +Before running a command, consider whether or not you have completed the previous step, and make sure to mark it as completed before moving on to the next step. It may be the case that you complete all steps in your plan after a single pass of implementation. If this is the case, you can simply mark all the planned steps as completed. Sometimes, you may need to change plans in the middle of a task: call `update_plan` with the updated plan and make sure to provide an `explanation` of the rationale when doing so. + +Use a plan when: + +- The task is non-trivial and will require multiple actions over a long time horizon. +- There are logical phases or dependencies where sequencing matters. +- The work has ambiguity that benefits from outlining high-level goals. +- You want intermediate checkpoints for feedback and validation. +- When the user asked you to do more than one thing in a single prompt +- The user has asked you to use the plan tool (aka "TODOs") +- You generate additional steps while working, and plan to do them before yielding to the user + +### Examples + +**High-quality plans** + +Example 1: + +1. Add CLI entry with file args +2. Parse Markdown via CommonMark library +3. Apply semantic HTML template +4. Handle code blocks, images, links +5. Add error handling for invalid files + +Example 2: + +1. Define CSS variables for colors +2. Add toggle with localStorage state +3. Refactor components to use variables +4. Verify all views for readability +5. Add smooth theme-change transition + +Example 3: + +1. Set up Node.js + WebSocket server +2. Add join/leave broadcast events +3. Implement messaging with timestamps +4. Add usernames + mention highlighting +5. Persist messages in lightweight DB +6. Add typing indicators + unread count + +**Low-quality plans** + +Example 1: + +1. Create CLI tool +2. Add Markdown parser +3. Convert to HTML + +Example 2: + +1. Add dark mode toggle +2. Save preference +3. Make styles look good + +Example 3: + +1. Create single-file HTML game +2. Run quick sanity check +3. Summarize usage instructions + +If you need to write a plan, only write high quality plans, not low quality ones. + +## Task execution + +You are a coding agent. Please keep going until the query is completely resolved, before ending your turn and yielding back to the user. Only terminate your turn when you are sure that the problem is solved. Autonomously resolve the query to the best of your ability, using the tools available to you, before coming back to the user. Do NOT guess or make up an answer. + +You MUST adhere to the following criteria when solving queries: + +- Working on the repo(s) in the current environment is allowed, even if they are proprietary. +- Analyzing code for vulnerabilities is allowed. +- Showing user code and tool call details is allowed. +- Use the `apply_patch` tool to edit files (NEVER try `applypatch` or `apply-patch`, only `apply_patch`): {"command":["apply_patch","*** Begin Patch\\n*** Update File: path/to/file.py\\n@@ def example():\\n- pass\\n+ return 123\\n*** End Patch"]} + +If completing the user's task requires writing or modifying files, your code and final answer should follow these coding guidelines, though user instructions (i.e. AGENTS.md) may override these guidelines: + +- Fix the problem at the root cause rather than applying surface-level patches, when possible. +- Avoid unneeded complexity in your solution. +- Do not attempt to fix unrelated bugs or broken tests. It is not your responsibility to fix them. (You may mention them to the user in your final message though.) +- Update documentation as necessary. +- Keep changes consistent with the style of the existing codebase. Changes should be minimal and focused on the task. +- Use `git log` and `git blame` to search the history of the codebase if additional context is required. +- NEVER add copyright or license headers unless specifically requested. +- Do not waste tokens by re-reading files after calling `apply_patch` on them. The tool call will fail if it didn't work. The same goes for making folders, deleting folders, etc. +- Do not `git commit` your changes or create new git branches unless explicitly requested. +- Do not add inline comments within code unless explicitly requested. +- Do not use one-letter variable names unless explicitly requested. +- NEVER output inline citations like "【F:README.md†L5-L14】" in your outputs. The CLI is not able to render these so they will just be broken in the UI. Instead, if you output valid filepaths, users will be able to click on them to open the files in their editor. + +## Validating your work + +If the codebase has tests or the ability to build or run, consider using them to verify that your work is complete. + +When testing, your philosophy should be to start as specific as possible to the code you changed so that you can catch issues efficiently, then make your way to broader tests as you build confidence. If there's no test for the code you changed, and if the adjacent patterns in the codebases show that there's a logical place for you to add a test, you may do so. However, do not add tests to codebases with no tests. + +Similarly, once you're confident in correctness, you can suggest or use formatting commands to ensure that your code is well formatted. If there are issues you can iterate up to 3 times to get formatting right, but if you still can't manage it's better to save the user time and present them a correct solution where you call out the formatting in your final message. If the codebase does not have a formatter configured, do not add one. + +For all of testing, running, building, and formatting, do not attempt to fix unrelated bugs. It is not your responsibility to fix them. (You may mention them to the user in your final message though.) + +Be mindful of whether to run validation commands proactively. In the absence of behavioral guidance: + +- When running in the non-interactive approval mode **never**, proactively run tests, lint and do whatever you need to ensure you've completed the task. +- When working in interactive approval modes like **untrusted**, or **on-request**, hold off on running tests or lint commands until the user is ready for you to finalize your output, because these commands take time to run and slow down iteration. Instead suggest what you want to do next, and let the user confirm first. +- When working on test-related tasks, such as adding tests, fixing tests, or reproducing a bug to verify behavior, you may proactively run tests regardless of approval mode. Use your judgement to decide whether this is a test-related task. + +## Ambition vs. precision + +For tasks that have no prior context (i.e. the user is starting something brand new), you should feel free to be ambitious and demonstrate creativity with your implementation. + +If you're operating in an existing codebase, you should make sure you do exactly what the user asks with surgical precision. Treat the surrounding codebase with respect, and don't overstep (i.e. changing filenames or variables unnecessarily). You should balance being sufficiently ambitious and proactive when completing tasks of this nature. + +You should use judicious initiative to decide on the right level of detail and complexity to deliver based on the user's needs. This means showing good judgment that you're capable of doing the right extras without gold-plating. This might be demonstrated by high-value, creative touches when scope of the task is vague; while being surgical and targeted when scope is tightly specified. + +## Sharing progress updates + +For especially longer tasks that you work on (i.e. requiring many tool calls, or a plan with multiple steps), you should provide progress updates back to the user at reasonable intervals. These updates should be structured as a concise sentence or two (no more than 8-10 words long) recapping progress so far in plain language: this update demonstrates your understanding of what needs to be done, progress so far (i.e. files explores, subtasks complete), and where you're going next. + +Before doing large chunks of work that may incur latency as experienced by the user (i.e. writing a new file), you should send a concise message to the user with an update indicating what you're about to do to ensure they know what you're spending time on. Don't start editing or writing large files before informing the user what you are doing and why. + +The messages you send before tool calls should describe what is immediately about to be done next in very concise language. If there was previous work done, this preamble message should also include a note about the work done so far to bring the user along. + +## Presenting your work and final message + +Your final message should read naturally, like an update from a concise teammate. For casual conversation, brainstorming tasks, or quick questions from the user, respond in a friendly, conversational tone. You should ask questions, suggest ideas, and adapt to the user’s style. If you've finished a large amount of work, when describing what you've done to the user, you should follow the final answer formatting guidelines to communicate substantive changes. You don't need to add structured formatting for one-word answers, greetings, or purely conversational exchanges. + +You can skip heavy formatting for single, simple actions or confirmations. In these cases, respond in plain sentences with any relevant next step or quick option. Reserve multi-section structured responses for results that need grouping or explanation. + +The user is working on the same computer as you, and has access to your work. As such there's no need to show the full contents of large files you have already written unless the user explicitly asks for them. Similarly, if you've created or modified files using `apply_patch`, there's no need to tell users to "save the file" or "copy the code into a file"—just reference the file path. + +If there's something that you think you could help with as a logical next step, concisely ask the user if they want you to do so. Good examples of this are running tests, committing changes, or building out the next logical component. If there’s something that you couldn't do (even with approval) but that the user might want to do (such as verifying changes by running the app), include those instructions succinctly. + +Brevity is very important as a default. You should be very concise (i.e. no more than 10 lines), but can relax this requirement for tasks where additional detail and comprehensiveness is important for the user's understanding. + +### Final answer structure and style guidelines + +You are producing plain text that will later be styled by the CLI. Follow these rules exactly. Formatting should make results easy to scan, but not feel mechanical. Use judgment to decide how much structure adds value. + +**Section Headers** + +- Use only when they improve clarity — they are not mandatory for every answer. +- Choose descriptive names that fit the content +- Keep headers short (1–3 words) and in `**Title Case**`. Always start headers with `**` and end with `**` +- Leave no blank line before the first bullet under a header. +- Section headers should only be used where they genuinely improve scanability; avoid fragmenting the answer. + +**Bullets** + +- Use `-` followed by a space for every bullet. +- Merge related points when possible; avoid a bullet for every trivial detail. +- Keep bullets to one line unless breaking for clarity is unavoidable. +- Group into short lists (4–6 bullets) ordered by importance. +- Use consistent keyword phrasing and formatting across sections. + +**Monospace** + +- Wrap all commands, file paths, env vars, and code identifiers in backticks (`` `...` ``). +- Apply to inline examples and to bullet keywords if the keyword itself is a literal file/command. +- Never mix monospace and bold markers; choose one based on whether it’s a keyword (`**`) or inline code/path (`` ` ``). + +**File References** +When referencing files in your response, make sure to include the relevant start line and always follow the below rules: + * Use inline code to make file paths clickable. + * Each reference should have a stand alone path. Even if it's the same file. + * Accepted: absolute, workspace‑relative, a/ or b/ diff prefixes, or bare filename/suffix. + * Line/column (1‑based, optional): :line[:column] or #Lline[Ccolumn] (column defaults to 1). + * Do not use URIs like file://, vscode://, or https://. + * Do not provide range of lines + * Examples: src/app.ts, src/app.ts:42, b/server/index.js#L10, C:\repo\project\main.rs:12:5 + +**Structure** + +- Place related bullets together; don’t mix unrelated concepts in the same section. +- Order sections from general → specific → supporting info. +- For subsections (e.g., “Binaries” under “Rust Workspace”), introduce with a bolded keyword bullet, then list items under it. +- Match structure to complexity: + - Multi-part or detailed results → use clear headers and grouped bullets. + - Simple results → minimal headers, possibly just a short list or paragraph. + +**Tone** + +- Keep the voice collaborative and natural, like a coding partner handing off work. +- Be concise and factual — no filler or conversational commentary and avoid unnecessary repetition +- Use present tense and active voice (e.g., “Runs tests” not “This will run tests”). +- Keep descriptions self-contained; don’t refer to “above” or “below”. +- Use parallel structure in lists for consistency. + +**Don’t** + +- Don’t use literal words “bold” or “monospace” in the content. +- Don’t nest bullets or create deep hierarchies. +- Don’t output ANSI escape codes directly — the CLI renderer applies them. +- Don’t cram unrelated keywords into a single bullet; split for clarity. +- Don’t let keyword lists run long — wrap or reformat for scanability. + +Generally, ensure your final answers adapt their shape and depth to the request. For example, answers to code explanations should have a precise, structured explanation with code references that answer the question directly. For tasks with a simple implementation, lead with the outcome and supplement only with what’s needed for clarity. Larger changes can be presented as a logical walkthrough of your approach, grouping related steps, explaining rationale where it adds value, and highlighting next actions to accelerate the user. Your answers should provide the right level of detail while being easily scannable. + +For casual greetings, acknowledgements, or other one-off conversational messages that are not delivering substantive information or structured results, respond naturally without section headers or bullet formatting. + +# Tool Guidelines + +## Shell commands + +When using the shell, you must adhere to the following guidelines: + +- When searching for text or files, prefer using `rg` or `rg --files` respectively because `rg` is much faster than alternatives like `grep`. (If the `rg` command is not found, then use alternatives.) +- Do not use python scripts to attempt to output larger chunks of a file. + +## `update_plan` + +A tool named `update_plan` is available to you. You can use it to keep an up‑to‑date, step‑by‑step plan for the task. + +To create a new plan, call `update_plan` with a short list of 1‑sentence steps (no more than 5-7 words each) with a `status` for each step (`pending`, `in_progress`, or `completed`). + +When steps have been completed, use `update_plan` to mark each finished step as `completed` and the next step you are working on as `in_progress`. There should always be exactly one `in_progress` step until everything is done. You can mark multiple items as complete in a single `update_plan` call. + +If all steps are complete, ensure you call `update_plan` to mark all steps as `completed`. diff --git a/pyproject.toml b/pyproject.toml index 448451f7f93..29609ce5ca1 100644 --- a/pyproject.toml +++ b/pyproject.toml @@ -287,6 +287,7 @@ editable-profile = "dev" include = [ "litellm/proxy/_experimental/out/**", "litellm/router_strategy/complexity_router/artifacts/*.json", + "litellm/proxy/client/cli/commands/codex_base_instructions.md", ] exclude = [ "litellm/proxy/enterprise", diff --git a/tests/test_litellm/proxy/client/cli/test_agents.py b/tests/test_litellm/proxy/client/cli/test_agents.py index 7804435a60d..bb4b99506a2 100644 --- a/tests/test_litellm/proxy/client/cli/test_agents.py +++ b/tests/test_litellm/proxy/client/cli/test_agents.py @@ -9,10 +9,9 @@ import pytest import requests from click.testing import CliRunner - - from litellm.proxy.client.cli.commands.agents import ( AgentRunError, + ModelSyncArgs, ModelSyncSkipped, _hand_off, _replace_process, @@ -22,6 +21,7 @@ from litellm.proxy.client.cli.commands.agents import ( agent_model_sync_env, agent_profile, build_agent_env, + codex_model_sync_args, opencode_model_sync_env, run_agent, verify_proxy_key, @@ -333,10 +333,10 @@ class TestOpencodeModelSync: assert isinstance(result, ModelSyncSkipped) assert "unexpected body" in result.reason - @pytest.mark.parametrize("command", ["claude", "codex", "/usr/bin/claude"]) - def test_only_opencode_syncs(self, command): + @pytest.mark.parametrize("command", ["claude", "pi", "/usr/bin/claude"]) + def test_only_opencode_and_codex_sync(self, command): def boom(*a, **k): - raise AssertionError("no agent other than opencode should call the proxy") + raise AssertionError("no agent other than opencode or codex should call the proxy") assert agent_model_sync_env(command, {}, "http://localhost:4000", "sk-key", False, get=boom) == {} @@ -365,7 +365,173 @@ class TestOpencodeModelSync: assert _default_of(opencode_model_sync_env, "get") is requests.get +class TestCodexModelSync: + @staticmethod + def _listing(*models): + return {"object": "list", "data": list(models)} + + @staticmethod + def _row(model_id, **extra): + return {"id": model_id, "object": "model", "created": 1, "owned_by": "openai", **extra} + + def _sync(self, listing, codex_home, base_url="http://localhost:4000/"): + captured = {} + + def fake_get(url, headers, timeout): + captured["url"] = url + captured["headers"] = headers + return _FakeResponse(200, listing) + + result = codex_model_sync_args({"CODEX_HOME": str(codex_home)}, base_url, "sk-key", get=fake_get) + return captured, result + + @staticmethod + def _catalog_path(result): + assert isinstance(result, ModelSyncArgs) + flag, override = result.args + assert flag == "-c" + key, _, value = override.partition("=") + assert key == "model_catalog_json" + return json.loads(value) + + def test_writes_catalog_under_codex_home_and_points_codex_at_it(self, tmp_path): + listing = self._listing(self._row("gpt-5.5", mode="chat"), self._row("claude-opus-4-7")) + captured, result = self._sync(listing, tmp_path / "codex") + + assert captured["url"] == "http://localhost:4000/v1/models" + assert captured["headers"] == {"Authorization": "Bearer sk-key"} + path = self._catalog_path(result) + assert path == str(tmp_path / "codex" / "litellm-models.json") + text = (tmp_path / "codex" / "litellm-models.json").read_text() + assert "sk-key" not in text + catalog = json.loads(text) + assert [m["slug"] for m in catalog["models"]] == ["gpt-5.5", "claude-opus-4-7"] + assert [m["display_name"] for m in catalog["models"]] == ["gpt-5.5", "claude-opus-4-7"] + assert [m["priority"] for m in catalog["models"]] == [0, 1] + + def test_every_entry_has_the_fields_codex_requires(self, tmp_path): + _, result = self._sync(self._listing(self._row("m")), tmp_path) + entry = json.loads((tmp_path / "litellm-models.json").read_text())["models"][0] + + assert entry["visibility"] == "list" + assert entry["supported_in_api"] is True + assert entry["shell_type"] == "unified_exec" + assert entry["supported_reasoning_levels"] == [] + assert entry["truncation_policy"] == {"mode": "bytes", "limit": 10000} + assert entry["experimental_supported_tools"] == [] + assert entry["support_verbosity"] is False + for nullable in ("description", "availability_nux", "upgrade", "default_verbosity", "apply_patch_tool_type"): + assert nullable in entry and entry[nullable] is None + assert entry["base_instructions"].startswith("You are a coding agent running in the Codex CLI") + + def test_context_window_comes_from_max_input_tokens(self, tmp_path): + listing = self._listing(self._row("big", max_input_tokens=400000), self._row("unknown")) + _, result = self._sync(listing, tmp_path) + models = {m["slug"]: m for m in json.loads((tmp_path / "litellm-models.json").read_text())["models"]} + assert models["big"]["context_window"] == 400000 + assert models["unknown"]["context_window"] is None + + def test_non_chat_models_are_left_out(self, tmp_path): + listing = self._listing( + self._row("chat", mode="chat"), + self._row("resp", mode="responses"), + self._row("embed", mode="embedding"), + self._row("img", mode="image_generation"), + ) + self._sync(listing, tmp_path) + slugs = {m["slug"] for m in json.loads((tmp_path / "litellm-models.json").read_text())["models"]} + assert slugs == {"chat", "resp"} + + def test_listing_without_chat_models_is_skipped_and_writes_nothing(self, tmp_path): + _, result = self._sync(self._listing(self._row("embed", mode="embedding")), tmp_path) + assert isinstance(result, ModelSyncSkipped) + assert "no chat models" in result.reason + assert not (tmp_path / "litellm-models.json").exists() + + def test_catalog_is_rewritten_on_every_launch(self, tmp_path): + self._sync(self._listing(self._row("old")), tmp_path) + self._sync(self._listing(self._row("new")), tmp_path) + slugs = [m["slug"] for m in json.loads((tmp_path / "litellm-models.json").read_text())["models"]] + assert slugs == ["new"] + + def test_defaults_to_dot_codex_in_home(self, tmp_path, monkeypatch): + monkeypatch.setattr("pathlib.Path.home", classmethod(lambda cls: tmp_path)) + result = codex_model_sync_args( + {}, "http://localhost:4000", "sk-key", get=lambda *a, **k: _FakeResponse(200, self._listing(self._row("m"))) + ) + assert self._catalog_path(result) == str(tmp_path / ".codex" / "litellm-models.json") + + def test_unwritable_catalog_path_is_reported_not_raised(self, tmp_path): + blocker = tmp_path / "file" + blocker.write_text("") + _, result = self._sync(self._listing(self._row("m")), blocker / "codex") + assert isinstance(result, ModelSyncSkipped) + assert "could not write" in result.reason + + def test_unreachable_proxy_is_reported_not_raised(self, tmp_path): + def boom(*a, **k): + raise requests.ConnectionError("refused") + + result = codex_model_sync_args({"CODEX_HOME": str(tmp_path)}, "http://localhost:4000", "sk-key", get=boom) + assert isinstance(result, ModelSyncSkipped) + assert "refused" in result.reason + assert not (tmp_path / "litellm-models.json").exists() + + @pytest.mark.parametrize( + ("response", "reason"), + [(_FakeResponse(500), "HTTP 500"), (_FakeResponse(200, {"data": "nope"}), "unexpected body")], + ) + def test_bad_response_is_reported(self, tmp_path, response, reason): + result = codex_model_sync_args( + {"CODEX_HOME": str(tmp_path)}, "http://localhost:4000", "sk-key", get=lambda *a, **k: response + ) + assert isinstance(result, ModelSyncSkipped) + assert reason in result.reason + + @pytest.mark.parametrize("command", ["codex", "/opt/bin/codex"]) + def test_codex_syncs_through_the_agent_dispatch(self, tmp_path, command): + result = agent_model_sync_env( + command, + {"CODEX_HOME": str(tmp_path)}, + "http://localhost:4000", + "sk-key", + False, + get=lambda *a, **k: _FakeResponse(200, self._listing(self._row("m"))), + ) + assert self._catalog_path(result) == str(tmp_path / "litellm-models.json") + + def test_skip_verify_keeps_the_launch_offline(self): + def boom(*a, **k): + raise AssertionError("--skip-verify must not touch the proxy") + + result = agent_model_sync_env("codex", {}, "http://localhost:4000", "sk-key", True, get=boom) + assert isinstance(result, ModelSyncSkipped) + assert "--skip-verify" in result.reason + + def test_default_http_client_is_requests_get(self): + assert _default_of(codex_model_sync_args, "get") is requests.get + + class TestRunAgent: + def test_synced_args_precede_user_args_and_follow_provider_overrides(self): + calls = {} + run_agent( + "http://localhost:4000", + "sk-key", + ["codex", "exec", "hi"], + base_env={}, + sync_models=lambda *a: ModelSyncArgs(("-c", 'model_catalog_json="/tmp/c.json"')), + which=lambda name: "/usr/local/bin/codex", + verify=lambda *a: None, + launcher=lambda p, a, e: calls.update(args=tuple(a), env=dict(e)), + ) + args = calls["args"] + assert args[-2:] == ("exec", "hi") + assert args[args.index('model_catalog_json="/tmp/c.json"') - 1] == "-c" + assert args.index('model_provider="litellm"') < args.index('model_catalog_json="/tmp/c.json"') < args.index("exec") + assert calls["env"]["OPENAI_API_KEY"] == "sk-key" + assert "model_catalog_json" not in json.dumps(calls["env"]) + def test_synced_model_config_reaches_the_agent_alongside_profile_env(self): calls = {} run_agent( From d1653fa40dd534c03633707eb7c451421e9a5af2 Mon Sep 17 00:00:00 2001 From: jesus Date: Wed, 9 Sep 2026 22:29:09 +0000 Subject: [PATCH 029/225] fix(cli): replace Codex catalog atomically and skip sync on unreadable instructions Co-Authored-By: Devin AI <158243242+devin-ai-integration[bot]@users.noreply.github.com> --- litellm/proxy/client/cli/commands/agents.py | 34 ++++++--- .../proxy/client/cli/test_agents.py | 72 ++++++++++--------- 2 files changed, 63 insertions(+), 43 deletions(-) diff --git a/litellm/proxy/client/cli/commands/agents.py b/litellm/proxy/client/cli/commands/agents.py index 67d2d96e9d6..8e3698985af 100644 --- a/litellm/proxy/client/cli/commands/agents.py +++ b/litellm/proxy/client/cli/commands/agents.py @@ -4,6 +4,7 @@ import re import shutil import subprocess import sys +import tempfile from collections.abc import Callable, Mapping, Sequence from dataclasses import dataclass from pathlib import Path @@ -416,7 +417,7 @@ class _CodexCatalog(BaseModel): models: tuple[_CodexModel, ...] -def codex_model_catalog(models: Sequence[ListedModel]) -> str | None: +def codex_model_catalog(models: Sequence[ListedModel], instructions: str) -> str | None: """The `model_catalog_json` body listing the proxy's chat models, or None if there are none. Codex refuses an empty catalog, hence None instead of `{"models": []}`. @@ -427,7 +428,6 @@ def codex_model_catalog(models: Sequence[ListedModel]) -> str | None: chat_models: Final = _chat_models(models) if not chat_models: return None - instructions: Final = _CODEX_BASE_INSTRUCTIONS_PATH.read_text(encoding="utf-8") catalog: Final = _CodexCatalog( models=tuple( _CodexModel( @@ -443,37 +443,49 @@ def codex_model_catalog(models: Sequence[ListedModel]) -> str | None: return catalog.model_dump_json() -def codex_model_catalog_path(env: Mapping[str, str]) -> Path: +def codex_model_catalog_path(env: Mapping[str, str], *, home: Callable[[], Path] = Path.home) -> Path: override: Final = env.get(CODEX_HOME_ENV) - root: Final = Path(override) if override else Path.home() / ".codex" + root: Final = Path(override) if override else home() / ".codex" return root / CODEX_MODEL_CATALOG_FILENAME +def _replace_file(path: Path, text: str) -> None: + path.parent.mkdir(parents=True, exist_ok=True) + with tempfile.NamedTemporaryFile("w", encoding="utf-8", dir=path.parent, delete=False) as tmp: + _ = tmp.write(text) + os.replace(tmp.name, path) + + def codex_model_sync_args( base_env: Mapping[str, str], base_url: str, api_key: str, *, get: Callable[..., requests.Response] = requests.get, + home: Callable[[], Path] = Path.home, + instructions_path: Path = _CODEX_BASE_INSTRUCTIONS_PATH, ) -> ModelSyncArgs | ModelSyncSkipped: """`-c model_catalog_json=...` pointing Codex at the proxy's model list, or why it was skipped. Codex has no env or inline equivalent of OPENCODE_CONFIG_CONTENT: the catalog must be a file, so it is written under $CODEX_HOME (default ~/.codex) and - rewritten on every launch. The key never lands in the file. A failed fetch - or write is reported rather than raised: Codex still launches with its - built-in catalog and takes a proxy model by name via -m. + atomically replaced on every launch. The key never lands in the file. A + failed fetch, read or write is reported rather than raised: Codex still + launches with its built-in catalog and takes a proxy model by name via -m. """ listing: Final = _fetch_model_listing(base_url, api_key, get=get) if isinstance(listing, ModelSyncSkipped): return listing - catalog: Final = codex_model_catalog(listing) + try: + instructions: Final = instructions_path.read_text(encoding="utf-8") + except OSError as e: + return ModelSyncSkipped(f"could not read {instructions_path}: {e}") + catalog: Final = codex_model_catalog(listing, instructions) if catalog is None: return ModelSyncSkipped(f"{base_url.rstrip('/')}/v1/models lists no chat models") - path: Final = codex_model_catalog_path(base_env) + path: Final = codex_model_catalog_path(base_env, home=home) try: - path.parent.mkdir(parents=True, exist_ok=True) - path.write_text(catalog, encoding="utf-8") + _replace_file(path, catalog) except OSError as e: return ModelSyncSkipped(f"could not write {path}: {e}") return ModelSyncArgs(("-c", f"model_catalog_json={json.dumps(str(path))}")) diff --git a/tests/test_litellm/proxy/client/cli/test_agents.py b/tests/test_litellm/proxy/client/cli/test_agents.py index bb4b99506a2..75e42c3eaa0 100644 --- a/tests/test_litellm/proxy/client/cli/test_agents.py +++ b/tests/test_litellm/proxy/client/cli/test_agents.py @@ -2,6 +2,7 @@ import inspect import json import os import sys +from pathlib import Path from unittest.mock import patch import click @@ -90,9 +91,7 @@ class TestAgentProfile: class TestBuildAgentEnv: def test_anthropic_profile_uses_bare_root_and_bearer(self): - env = build_agent_env( - {}, "http://localhost:4000/", "sk-key", frozenset({"anthropic"}) - ) + env = build_agent_env({}, "http://localhost:4000/", "sk-key", frozenset({"anthropic"})) assert env["ANTHROPIC_BASE_URL"] == "http://localhost:4000" assert env["ANTHROPIC_AUTH_TOKEN"] == "sk-key" assert env["ENABLE_TOOL_SEARCH"] == "true" @@ -128,9 +127,7 @@ class TestBuildAgentEnv: assert "ANTHROPIC_API_KEY" not in env def test_openai_profile_appends_v1(self): - env = build_agent_env( - {}, "http://localhost:4000/", "sk-key", frozenset({"openai"}) - ) + env = build_agent_env({}, "http://localhost:4000/", "sk-key", frozenset({"openai"})) assert env["OPENAI_BASE_URL"] == "http://localhost:4000/v1" assert env["OPENAI_API_KEY"] == "sk-key" assert "ANTHROPIC_BASE_URL" not in env @@ -138,9 +135,7 @@ class TestBuildAgentEnv: assert "CLAUDE_CODE_ENABLE_GATEWAY_MODEL_DISCOVERY" not in env def test_both_profiles_set_everything(self): - env = build_agent_env( - {}, "http://localhost:4000", "sk-key", frozenset({"anthropic", "openai"}) - ) + env = build_agent_env({}, "http://localhost:4000", "sk-key", frozenset({"anthropic", "openai"})) assert env["ANTHROPIC_BASE_URL"] == "http://localhost:4000" assert env["OPENAI_BASE_URL"] == "http://localhost:4000/v1" assert env["ANTHROPIC_AUTH_TOKEN"] == "sk-key" @@ -148,9 +143,7 @@ class TestBuildAgentEnv: assert env["ENABLE_TOOL_SEARCH"] == "true" def test_litellm_profile_exports_only_the_proxy_key(self): - env = build_agent_env( - {}, "http://localhost:4000/", "sk-key", frozenset({"litellm"}) - ) + env = build_agent_env({}, "http://localhost:4000/", "sk-key", frozenset({"litellm"})) assert env["LITELLM_PROXY_API_KEY"] == "sk-key" assert "ANTHROPIC_BASE_URL" not in env assert "OPENAI_BASE_URL" not in env @@ -158,9 +151,7 @@ class TestBuildAgentEnv: def test_preserves_unrelated_env_and_does_not_mutate_input(self): base = {"PATH": "/usr/bin", "ANTHROPIC_API_KEY": "real-key"} - env = build_agent_env( - base, "http://localhost:4000", "sk-key", frozenset({"anthropic"}) - ) + env = build_agent_env(base, "http://localhost:4000", "sk-key", frozenset({"anthropic"})) assert env["PATH"] == "/usr/bin" assert base == {"PATH": "/usr/bin", "ANTHROPIC_API_KEY": "real-key"} @@ -320,9 +311,7 @@ class TestOpencodeModelSync: assert "refused" in result.reason def test_non_200_is_reported(self): - result = opencode_model_sync_env( - {}, "http://localhost:4000", "sk-key", get=lambda *a, **k: _FakeResponse(500) - ) + result = opencode_model_sync_env({}, "http://localhost:4000", "sk-key", get=lambda *a, **k: _FakeResponse(500)) assert isinstance(result, ModelSyncSkipped) assert "HTTP 500" in result.reason @@ -454,13 +443,37 @@ class TestCodexModelSync: slugs = [m["slug"] for m in json.loads((tmp_path / "litellm-models.json").read_text())["models"]] assert slugs == ["new"] - def test_defaults_to_dot_codex_in_home(self, tmp_path, monkeypatch): - monkeypatch.setattr("pathlib.Path.home", classmethod(lambda cls: tmp_path)) + def test_catalog_is_replaced_whole_and_leaves_no_temp_files(self, tmp_path): + self._sync(self._listing(*(self._row(f"m{i}") for i in range(50))), tmp_path) + self._sync(self._listing(self._row("new")), tmp_path) + assert [p.name for p in tmp_path.iterdir()] == ["litellm-models.json"] + assert json.loads((tmp_path / "litellm-models.json").read_text())["models"][0]["slug"] == "new" + + def test_defaults_to_dot_codex_in_home(self, tmp_path): result = codex_model_sync_args( - {}, "http://localhost:4000", "sk-key", get=lambda *a, **k: _FakeResponse(200, self._listing(self._row("m"))) + {}, + "http://localhost:4000", + "sk-key", + get=lambda *a, **k: _FakeResponse(200, self._listing(self._row("m"))), + home=lambda: tmp_path, ) assert self._catalog_path(result) == str(tmp_path / ".codex" / "litellm-models.json") + def test_default_home_is_the_users(self): + assert _default_of(codex_model_sync_args, "home") == Path.home + + def test_missing_base_instructions_is_reported_not_raised(self, tmp_path): + result = codex_model_sync_args( + {"CODEX_HOME": str(tmp_path)}, + "http://localhost:4000", + "sk-key", + get=lambda *a, **k: _FakeResponse(200, self._listing(self._row("m"))), + instructions_path=tmp_path / "missing.md", + ) + assert isinstance(result, ModelSyncSkipped) + assert "could not read" in result.reason + assert not (tmp_path / "litellm-models.json").exists() + def test_unwritable_catalog_path_is_reported_not_raised(self, tmp_path): blocker = tmp_path / "file" blocker.write_text("") @@ -528,7 +541,9 @@ class TestRunAgent: args = calls["args"] assert args[-2:] == ("exec", "hi") assert args[args.index('model_catalog_json="/tmp/c.json"') - 1] == "-c" - assert args.index('model_provider="litellm"') < args.index('model_catalog_json="/tmp/c.json"') < args.index("exec") + assert ( + args.index('model_provider="litellm"') < args.index('model_catalog_json="/tmp/c.json"') < args.index("exec") + ) assert calls["env"]["OPENAI_API_KEY"] == "sk-key" assert "model_catalog_json" not in json.dumps(calls["env"]) @@ -1214,10 +1229,7 @@ class TestAgentCommands: assert captured["api_key"] == "sk-key" assert captured["command"] == ["claude", "--resume", "-p", "hi"] assert captured["skip_verify"] is False - assert ( - "routing Claude Code through proxy at http://localhost:4000" - in result.output - ) + assert "routing Claude Code through proxy at http://localhost:4000" in result.output def test_codex_shows_friendly_name(self): captured = {} @@ -1290,14 +1302,10 @@ class TestAgentCommands: with ( patch(f"{AGENTS_MODULE}._is_interactive", return_value=True), patch(f"{AGENTS_MODULE}.login", fake_login), - patch( - f"{AGENTS_MODULE}.get_stored_api_key", return_value="sk-after-login" - ) as mock_get, + patch(f"{AGENTS_MODULE}.get_stored_api_key", return_value="sk-after-login") as mock_get, patch( f"{AGENTS_MODULE}.run_agent", - side_effect=lambda base_url, api_key, command, **k: captured.update( - api_key=api_key - ), + side_effect=lambda base_url, api_key, command, **k: captured.update(api_key=api_key), ), ): result = self.runner.invoke( From 96bf276ab9ef475b3eb4384a803258d80804a3ed Mon Sep 17 00:00:00 2001 From: jesus Date: Wed, 9 Sep 2026 22:36:09 +0000 Subject: [PATCH 030/225] fix(cli): remove the temp catalog when the atomic replace fails Co-Authored-By: Devin AI <158243242+devin-ai-integration[bot]@users.noreply.github.com> --- litellm/proxy/client/cli/commands/agents.py | 6 +++++- tests/test_litellm/proxy/client/cli/test_agents.py | 7 +++++++ 2 files changed, 12 insertions(+), 1 deletion(-) diff --git a/litellm/proxy/client/cli/commands/agents.py b/litellm/proxy/client/cli/commands/agents.py index 8e3698985af..0354aefa95c 100644 --- a/litellm/proxy/client/cli/commands/agents.py +++ b/litellm/proxy/client/cli/commands/agents.py @@ -453,7 +453,11 @@ def _replace_file(path: Path, text: str) -> None: path.parent.mkdir(parents=True, exist_ok=True) with tempfile.NamedTemporaryFile("w", encoding="utf-8", dir=path.parent, delete=False) as tmp: _ = tmp.write(text) - os.replace(tmp.name, path) + try: + os.replace(tmp.name, path) + except OSError: + Path(tmp.name).unlink(missing_ok=True) + raise def codex_model_sync_args( diff --git a/tests/test_litellm/proxy/client/cli/test_agents.py b/tests/test_litellm/proxy/client/cli/test_agents.py index 75e42c3eaa0..3020b770e62 100644 --- a/tests/test_litellm/proxy/client/cli/test_agents.py +++ b/tests/test_litellm/proxy/client/cli/test_agents.py @@ -481,6 +481,13 @@ class TestCodexModelSync: assert isinstance(result, ModelSyncSkipped) assert "could not write" in result.reason + def test_failed_replace_is_reported_and_leaves_no_temp_file(self, tmp_path): + (tmp_path / "litellm-models.json").mkdir() + _, result = self._sync(self._listing(self._row("m")), tmp_path) + assert isinstance(result, ModelSyncSkipped) + assert "could not write" in result.reason + assert [p.name for p in tmp_path.iterdir()] == ["litellm-models.json"] + def test_unreachable_proxy_is_reported_not_raised(self, tmp_path): def boom(*a, **k): raise requests.ConnectionError("refused") From 485f4522e4bd207dad925652cde802466dbcfcac Mon Sep 17 00:00:00 2001 From: yucheng Date: Sat, 12 Sep 2026 03:52:06 +0000 Subject: [PATCH 031/225] fix(mcp): scope the Agent 365 connect challenge to single-server routes and drop stale listed tools on invalidation Co-Authored-By: Devin AI <158243242+devin-ai-integration[bot]@users.noreply.github.com> --- .../_experimental/mcp_server/mcp_server_manager.py | 1 + litellm/proxy/_experimental/mcp_server/server.py | 5 ++++- .../_experimental/mcp_server/test_mcp_server.py | 8 ++++++++ .../mcp_server/test_mcp_server_manager.py | 13 +++++++++++++ 4 files changed, 26 insertions(+), 1 deletion(-) diff --git a/litellm/proxy/_experimental/mcp_server/mcp_server_manager.py b/litellm/proxy/_experimental/mcp_server/mcp_server_manager.py index 35b8aece527..2938f1362af 100644 --- a/litellm/proxy/_experimental/mcp_server/mcp_server_manager.py +++ b/litellm/proxy/_experimental/mcp_server/mcp_server_manager.py @@ -4501,6 +4501,7 @@ class MCPServerManager: self._prompt_discovery_cache.invalidate(server_id) self._resource_discovery_cache.invalidate(server_id) self._template_discovery_cache.invalidate(server_id) + self._listed_tools_by_server_id.pop(server_id, None) def _discovery_key( self, diff --git a/litellm/proxy/_experimental/mcp_server/server.py b/litellm/proxy/_experimental/mcp_server/server.py index 545df2ba25b..dc191dbf8b8 100644 --- a/litellm/proxy/_experimental/mcp_server/server.py +++ b/litellm/proxy/_experimental/mcp_server/server.py @@ -4144,10 +4144,13 @@ if MCP_AVAILABLE: # header lost, so the discovery flow needs this pre-emptive challenge. Servers gated by an # Agent 365 guardrail (OBO to the evaluate API) get the same challenge, also when the only # bearer is the LiteLLM key itself, which admits the caller but is not an exchangeable subject. + # Only on the server's own route: the per-server metadata ``resource`` must equal the URL the + # client connected to (RFC 9728 3.3), which aggregate ``/mcp`` and multi-server connects never do. if server and ( (server.auth_type == MCPAuth.oauth2_token_exchange and not oauth2_headers) or ( - not agent_365_subject_token_present(oauth2_headers) + _get_mcp_servers_in_path(get_route_relative_request_path(scope)) == [server_name] + and not agent_365_subject_token_present(oauth2_headers) and agent_365_authorization_servers(server, user_api_key_auth) ) ): diff --git a/tests/test_litellm/proxy/_experimental/mcp_server/test_mcp_server.py b/tests/test_litellm/proxy/_experimental/mcp_server/test_mcp_server.py index 95103a3ed71..b85c3bc7557 100644 --- a/tests/test_litellm/proxy/_experimental/mcp_server/test_mcp_server.py +++ b/tests/test_litellm/proxy/_experimental/mcp_server/test_mcp_server.py @@ -9020,6 +9020,14 @@ class TestAgent365ChallengeAtConnect: in www_authenticate ) + @pytest.mark.asyncio + @pytest.mark.parametrize("path", ["/mcp", "/mcp/tools,other"]) + async def test_aggregate_route_is_not_challenged_at_connect(self, agent_365_guardrail, path): + """The per-server metadata's ``resource`` can never equal the aggregate ``/mcp`` URL the client + connected to (RFC 9728 3.3), and one guarded server must not 401 a multi-server connect, so the + Agent 365 challenge is left to tools/call there.""" + assert await self._connect(self._server([self.GATEWAY_SCOPE]), None, path=path) is None + @pytest.mark.asyncio async def test_entra_assertion_present_connects(self, agent_365_guardrail): bearer = {"Authorization": "Bearer eyJhbGciOiJSUzI1NiJ9.eyJzdWIiOiJ1LTEifQ.c2ln"} diff --git a/tests/test_litellm/proxy/_experimental/mcp_server/test_mcp_server_manager.py b/tests/test_litellm/proxy/_experimental/mcp_server/test_mcp_server_manager.py index 556d8ee6959..768d985a095 100644 --- a/tests/test_litellm/proxy/_experimental/mcp_server/test_mcp_server_manager.py +++ b/tests/test_litellm/proxy/_experimental/mcp_server/test_mcp_server_manager.py @@ -6606,6 +6606,19 @@ class TestMCPServerManager: assert by_prefixed_name is not None and by_prefixed_name.description == "v2" assert manager.get_listed_tool(server, "missing") is None + def test_invalidate_discovery_lists_drops_listed_tools(self): + manager = MCPServerManager() + server = MCPServer(server_id="srv", name="srv", transport=MCPTransport.http, url="http://srv") + other = MCPServer(server_id="other", name="other", transport=MCPTransport.http, url="http://other") + manager._create_prefixed_tools([MCPTool(name="echo", description="old", inputSchema={})], server) + manager._create_prefixed_tools([MCPTool(name="ping", description="kept", inputSchema={})], other) + + manager._invalidate_discovery_lists(server.server_id) + + assert manager.get_listed_tool(server, "echo") is None + kept = manager.get_listed_tool(other, "ping") + assert kept is not None and kept.description == "kept" + @pytest.mark.asyncio async def test_get_allowed_mcp_servers_with_user_api_key_auth(self): """ From 3c536bfc9ac464d1170058787ca3995b433ae189 Mon Sep 17 00:00:00 2001 From: yucheng Date: Sat, 12 Sep 2026 04:00:47 +0000 Subject: [PATCH 032/225] fix(mcp): keep server-wide listed tool metadata across per-user OAuth token invalidation Co-Authored-By: Devin AI <158243242+devin-ai-integration[bot]@users.noreply.github.com> --- .../mcp_server/mcp_server_manager.py | 13 ++++++++----- .../mcp_server/test_mcp_server_manager.py | 17 +++++++++++++++-- 2 files changed, 23 insertions(+), 7 deletions(-) diff --git a/litellm/proxy/_experimental/mcp_server/mcp_server_manager.py b/litellm/proxy/_experimental/mcp_server/mcp_server_manager.py index 2938f1362af..f49fa718139 100644 --- a/litellm/proxy/_experimental/mcp_server/mcp_server_manager.py +++ b/litellm/proxy/_experimental/mcp_server/mcp_server_manager.py @@ -2643,7 +2643,7 @@ class MCPServerManager: self._assign_unique_short_prefix(new_server) _warn_internal_delegate_pkce_if_applicable(new_server, source="config") _warn_config_id_jag_server_outruns_sso(new_server) - self._invalidate_discovery_lists(server_id) + self._invalidate_server_definition_caches(server_id) self.config_mcp_servers[server_id] = new_server self._set_oauth_discovery_deferred( server_id, @@ -2845,7 +2845,7 @@ class MCPServerManager: global_mcp_tool_registry, ) - self._invalidate_discovery_lists(server.server_id) + self._invalidate_server_definition_caches(server.server_id) prefix_root: Final = normalize_server_name(get_server_prefix(server)) if server.spec_path and prefix_root: openapi_key_prefix: Final = prefix_root + MCP_TOOL_PREFIX_SEPARATOR @@ -3222,7 +3222,7 @@ class MCPServerManager: # env_vars_are_encrypted=False. new_server: Final = await self.build_mcp_server_from_table(mcp_server, env_vars_are_encrypted=False) self._assign_unique_short_prefix(new_server) - self._invalidate_discovery_lists(mcp_server.server_id) + self._invalidate_server_definition_caches(mcp_server.server_id) self.registry[mcp_server.server_id] = new_server await self._maybe_register_openapi_tools(new_server) self.prime_oauth_metadata_discovery(new_server) @@ -3259,7 +3259,7 @@ class MCPServerManager: previous_server=self.registry[mcp_server.server_id], ) self._assign_unique_short_prefix(new_server) - self._invalidate_discovery_lists(mcp_server.server_id) + self._invalidate_server_definition_caches(mcp_server.server_id) self.registry[mcp_server.server_id] = new_server await self._maybe_register_openapi_tools(new_server) self.prime_oauth_metadata_discovery(new_server) @@ -4501,6 +4501,9 @@ class MCPServerManager: self._prompt_discovery_cache.invalidate(server_id) self._resource_discovery_cache.invalidate(server_id) self._template_discovery_cache.invalidate(server_id) + + def _invalidate_server_definition_caches(self, server_id: str) -> None: + self._invalidate_discovery_lists(server_id) self._listed_tools_by_server_id.pop(server_id, None) def _discovery_key( @@ -6634,7 +6637,7 @@ class MCPServerManager: for server_id in previous_registry.keys() | registered_registry.keys(): if previous_registry.get(server_id) != registered_registry.get(server_id): - self._invalidate_discovery_lists(server_id) + self._invalidate_server_definition_caches(server_id) self.registry = registered_registry # A discovery task may have published into ``previous_registry`` while # this replacement was being staged. Reconcile every published entry diff --git a/tests/test_litellm/proxy/_experimental/mcp_server/test_mcp_server_manager.py b/tests/test_litellm/proxy/_experimental/mcp_server/test_mcp_server_manager.py index 768d985a095..d8511617699 100644 --- a/tests/test_litellm/proxy/_experimental/mcp_server/test_mcp_server_manager.py +++ b/tests/test_litellm/proxy/_experimental/mcp_server/test_mcp_server_manager.py @@ -6606,19 +6606,32 @@ class TestMCPServerManager: assert by_prefixed_name is not None and by_prefixed_name.description == "v2" assert manager.get_listed_tool(server, "missing") is None - def test_invalidate_discovery_lists_drops_listed_tools(self): + def test_server_definition_change_drops_listed_tools(self): manager = MCPServerManager() server = MCPServer(server_id="srv", name="srv", transport=MCPTransport.http, url="http://srv") other = MCPServer(server_id="other", name="other", transport=MCPTransport.http, url="http://other") manager._create_prefixed_tools([MCPTool(name="echo", description="old", inputSchema={})], server) manager._create_prefixed_tools([MCPTool(name="ping", description="kept", inputSchema={})], other) - manager._invalidate_discovery_lists(server.server_id) + manager._invalidate_server_definition_caches(server.server_id) assert manager.get_listed_tool(server, "echo") is None kept = manager.get_listed_tool(other, "ping") assert kept is not None and kept.description == "kept" + @pytest.mark.asyncio + async def test_user_oauth_refresh_keeps_listed_tools(self): + """Tool definitions are server-wide, so one user's re-auth must not blank the metadata other + callers' tool calls hand to pre-call guardrails.""" + manager = MCPServerManager() + server = MCPServer(server_id="srv", name="srv", transport=MCPTransport.http, url="http://srv") + manager._create_prefixed_tools([MCPTool(name="echo", description="shared", inputSchema={})], server) + + await manager.invalidate_user_oauth_token_cache("alice", server.server_id) + + listed = manager.get_listed_tool(server, "echo") + assert listed is not None and listed.description == "shared" + @pytest.mark.asyncio async def test_get_allowed_mcp_servers_with_user_api_key_auth(self): """ From 13596700ec33a8b74d1cc98fac13185d4df75922 Mon Sep 17 00:00:00 2001 From: yucheng Date: Sat, 12 Sep 2026 04:12:12 +0000 Subject: [PATCH 033/225] fix(mcp): challenge Agent 365 connects on the mounted /mcp/{server} route Starlette's /mcp Mount moves the prefix into root_path and leaves the app root in app_root_path, which is an empty string on a default deployment. Reading it with `or` fell through to root_path=/mcp and stripped the prefix, so the route-relative path became /{server} and the per-server connect challenge was skipped there. Co-Authored-By: Devin AI <158243242+devin-ai-integration[bot]@users.noreply.github.com> --- .../_experimental/mcp_server/oauth_utils.py | 2 +- .../mcp_server/test_mcp_server.py | 30 ++++++++++++++++++- 2 files changed, 30 insertions(+), 2 deletions(-) diff --git a/litellm/proxy/_experimental/mcp_server/oauth_utils.py b/litellm/proxy/_experimental/mcp_server/oauth_utils.py index 39865a35ec6..50eca9de2b5 100644 --- a/litellm/proxy/_experimental/mcp_server/oauth_utils.py +++ b/litellm/proxy/_experimental/mcp_server/oauth_utils.py @@ -199,7 +199,7 @@ def get_route_relative_request_path(scope: Scope) -> str: :func:`litellm.proxy.auth.auth_utils.get_request_route`, which the rest of the MCP auth path already routes through, so ``/litellmfoo`` is not truncated under ``root_path=/litellm``.""" raw_path = str(scope.get("_original_path") or scope.get("path", "") or "") - root_path = str(scope.get("app_root_path") or scope.get("root_path") or "").rstrip("/") + root_path = str(scope.get("app_root_path", scope.get("root_path")) or "").rstrip("/") if root_path and (raw_path == root_path or raw_path.startswith(f"{root_path}/")): return raw_path[len(root_path) :] return raw_path diff --git a/tests/test_litellm/proxy/_experimental/mcp_server/test_mcp_server.py b/tests/test_litellm/proxy/_experimental/mcp_server/test_mcp_server.py index b85c3bc7557..cac754feb3f 100644 --- a/tests/test_litellm/proxy/_experimental/mcp_server/test_mcp_server.py +++ b/tests/test_litellm/proxy/_experimental/mcp_server/test_mcp_server.py @@ -8963,7 +8963,11 @@ class TestAgent365ChallengeAtConnect: ) async def _connect( - self, server: MCPServer, oauth2_headers: dict[str, str] | None, path: str = "/mcp/tools" + self, + server: MCPServer, + oauth2_headers: dict[str, str] | None, + path: str = "/mcp/tools", + mount_scope: dict[str, str] | None = None, ) -> HTTPException | None: from litellm.proxy._experimental.mcp_server import server as server_module @@ -8984,6 +8988,7 @@ class TestAgent365ChallengeAtConnect: "scheme": "https", "server": ("gw.example.com", 443), "headers": [], + **(mount_scope or {}), }, mcp_servers=["tools"], oauth2_headers=oauth2_headers, @@ -9020,6 +9025,29 @@ class TestAgent365ChallengeAtConnect: in www_authenticate ) + @pytest.mark.asyncio + @pytest.mark.parametrize( + "server_root, mount_scope", + [ + ("", {"root_path": "/mcp", "app_root_path": ""}), + ("/litellm", {"root_path": "/litellm/mcp", "app_root_path": "/litellm"}), + ], + ) + async def test_mounted_standard_route_is_challenged(self, agent_365_guardrail, server_root, mount_scope): + """``/mcp/{server}`` is served by the ``/mcp`` Mount, which moves the mount prefix into + ``root_path`` and leaves the app root (empty or SERVER_ROOT_PATH) in ``app_root_path``.""" + with patch.dict(os.environ, {"SERVER_ROOT_PATH": server_root}): + challenge = await self._connect( + self._server([self.GATEWAY_SCOPE]), None, path=f"{server_root}/mcp/tools", mount_scope=mount_scope + ) + + assert challenge is not None and challenge.status_code == 401 + www_authenticate = (challenge.headers or {}).get("WWW-Authenticate", "") + assert ( + f'resource_metadata="https://gw.example.com{server_root}' + f'/.well-known/oauth-protected-resource{server_root}/mcp/tools"' in www_authenticate + ) + @pytest.mark.asyncio @pytest.mark.parametrize("path", ["/mcp", "/mcp/tools,other"]) async def test_aggregate_route_is_not_challenged_at_connect(self, agent_365_guardrail, path): From eba05f43331c1cecc783f657c6a1be4cec966aed Mon Sep 17 00:00:00 2001 From: yucheng Date: Sat, 12 Sep 2026 19:09:17 +0000 Subject: [PATCH 034/225] fix(mcp): record OpenAPI tool definitions for pre-call guardrail metadata Co-Authored-By: Devin AI <158243242+devin-ai-integration[bot]@users.noreply.github.com> --- .../mcp_server/mcp_server_manager.py | 8 +++-- .../mcp_server/test_mcp_server_manager.py | 34 +++++++++++++++++++ 2 files changed, 39 insertions(+), 3 deletions(-) diff --git a/litellm/proxy/_experimental/mcp_server/mcp_server_manager.py b/litellm/proxy/_experimental/mcp_server/mcp_server_manager.py index f49fa718139..27265b6585e 100644 --- a/litellm/proxy/_experimental/mcp_server/mcp_server_manager.py +++ b/litellm/proxy/_experimental/mcp_server/mcp_server_manager.py @@ -4442,11 +4442,9 @@ class MCPServerManager: # applied (e.g. "test_petstore-getinventory"). Do NOT pass them # through _create_prefixed_tools — that would add the prefix a second # time producing "test_petstore-test_petstore-getinventory". - if add_prefix: - return tools prefix: Final = get_server_prefix(server) sep: Final = MCP_TOOL_PREFIX_SEPARATOR - return [ # mutable-ok: returned through the list[MCPTool] listing contract + unprefixed_tools: Final = [ # mutable-ok: returned through the list[MCPTool] listing contract ( t.model_copy(update={"name": t.name[len(prefix) + len(sep) :]}) if t.name.startswith(f"{prefix}{sep}") @@ -4454,6 +4452,10 @@ class MCPServerManager: ) for t in tools ] + self._listed_tools_by_server_id[server.server_id] = MappingProxyType( + {t.name: t for t in unprefixed_tools} + ) + return tools if add_prefix else unprefixed_tools else: tools = await self._fetch_tools_with_timeout(client, server.name) self._remember_upstream_initialize_instructions(server, client) diff --git a/tests/test_litellm/proxy/_experimental/mcp_server/test_mcp_server_manager.py b/tests/test_litellm/proxy/_experimental/mcp_server/test_mcp_server_manager.py index d8511617699..cddc2feb6c1 100644 --- a/tests/test_litellm/proxy/_experimental/mcp_server/test_mcp_server_manager.py +++ b/tests/test_litellm/proxy/_experimental/mcp_server/test_mcp_server_manager.py @@ -6632,6 +6632,40 @@ class TestMCPServerManager: listed = manager.get_listed_tool(server, "echo") assert listed is not None and listed.description == "shared" + @pytest.mark.asyncio + @pytest.mark.parametrize("add_prefix", [True, False]) + async def test_openapi_listing_records_listed_tools(self, add_prefix): + from litellm.proxy._experimental.mcp_server.tool_registry import global_mcp_tool_registry + + server = MCPServer( + server_id="petstore-id", + name="petstore", + alias="petstore", + transport=MCPTransport.http, + url=None, + spec_path="/spec.yaml", + ) + manager = MCPServerManager() + manager._create_mcp_client = AsyncMock(return_value=AsyncMock()) + + async def _handler(**kwargs): + return None + + with patch.dict(global_mcp_tool_registry.tools, {}, clear=True): + global_mcp_tool_registry.register_tool( + name="petstore-list_pets", + description="List pets", + input_schema={"type": "object", "properties": {"limit": {"type": "integer"}}}, + handler=_handler, + ) + listed = await manager._get_tools_from_server(server=server, add_prefix=add_prefix) + + assert [t.name for t in listed] == ["petstore-list_pets" if add_prefix else "list_pets"] + for name in ("list_pets", "petstore-list_pets"): + tool = manager.get_listed_tool(server, name) + assert tool is not None and tool.description == "List pets" + assert tool.inputSchema["properties"] == {"limit": {"type": "integer"}} + @pytest.mark.asyncio async def test_get_allowed_mcp_servers_with_user_api_key_auth(self): """ From 4296a5d5d70d4ba4ddcdd9f8546c43996cab24e3 Mon Sep 17 00:00:00 2001 From: yucheng Date: Sat, 12 Sep 2026 19:16:55 +0000 Subject: [PATCH 035/225] fix(mcp): list OpenAPI tools by exact prefix boundary so overlapping server prefixes stay separate Co-Authored-By: Devin AI <158243242+devin-ai-integration[bot]@users.noreply.github.com> --- .../mcp_server/mcp_server_manager.py | 12 ++---- .../mcp_server/test_mcp_server_manager.py | 38 +++++++++++++++++++ 2 files changed, 41 insertions(+), 9 deletions(-) diff --git a/litellm/proxy/_experimental/mcp_server/mcp_server_manager.py b/litellm/proxy/_experimental/mcp_server/mcp_server_manager.py index 27265b6585e..d53921994c8 100644 --- a/litellm/proxy/_experimental/mcp_server/mcp_server_manager.py +++ b/litellm/proxy/_experimental/mcp_server/mcp_server_manager.py @@ -4436,21 +4436,15 @@ class MCPServerManager: if server.spec_path: # OpenAPI tools were stored in the registry under the prefix # active at registration time — fetch by that same prefix. - _tools: Final = global_mcp_tool_registry.list_tools(tool_prefix=get_server_prefix(server)) + registry_prefix: Final = normalize_server_name(get_server_prefix(server)) + MCP_TOOL_PREFIX_SEPARATOR + _tools: Final = global_mcp_tool_registry.list_tools(tool_prefix=registry_prefix) tools = global_mcp_tool_registry.convert_tools_to_mcp_sdk_tool_type(_tools) # OpenAPI tools are stored in the registry with their prefix already # applied (e.g. "test_petstore-getinventory"). Do NOT pass them # through _create_prefixed_tools — that would add the prefix a second # time producing "test_petstore-test_petstore-getinventory". - prefix: Final = get_server_prefix(server) - sep: Final = MCP_TOOL_PREFIX_SEPARATOR unprefixed_tools: Final = [ # mutable-ok: returned through the list[MCPTool] listing contract - ( - t.model_copy(update={"name": t.name[len(prefix) + len(sep) :]}) - if t.name.startswith(f"{prefix}{sep}") - else t - ) - for t in tools + t.model_copy(update={"name": t.name[len(registry_prefix) :]}) for t in tools ] self._listed_tools_by_server_id[server.server_id] = MappingProxyType( {t.name: t for t in unprefixed_tools} diff --git a/tests/test_litellm/proxy/_experimental/mcp_server/test_mcp_server_manager.py b/tests/test_litellm/proxy/_experimental/mcp_server/test_mcp_server_manager.py index cddc2feb6c1..e74a4069b15 100644 --- a/tests/test_litellm/proxy/_experimental/mcp_server/test_mcp_server_manager.py +++ b/tests/test_litellm/proxy/_experimental/mcp_server/test_mcp_server_manager.py @@ -6666,6 +6666,44 @@ class TestMCPServerManager: assert tool is not None and tool.description == "List pets" assert tool.inputSchema["properties"] == {"limit": {"type": "integer"}} + @pytest.mark.asyncio + async def test_openapi_listing_ignores_overlapping_server_prefix(self): + from litellm.proxy._experimental.mcp_server.tool_registry import global_mcp_tool_registry + + server = MCPServer( + server_id="pet-id", + name="pet", + alias="pet", + transport=MCPTransport.http, + url=None, + spec_path="/spec.yaml", + ) + manager = MCPServerManager() + manager._create_mcp_client = AsyncMock(return_value=AsyncMock()) + + async def _handler(**kwargs): + return None + + with patch.dict(global_mcp_tool_registry.tools, {}, clear=True): + global_mcp_tool_registry.register_tool( + name="pet-petstore-list", + description="Local pet tool", + input_schema={"type": "object", "properties": {"limit": {"type": "integer"}}}, + handler=_handler, + ) + global_mcp_tool_registry.register_tool( + name="petstore-list", + description="Foreign petstore tool", + input_schema={"type": "object", "properties": {"status": {"type": "string"}}}, + handler=_handler, + ) + listed = await manager._get_tools_from_server(server=server, add_prefix=True) + + assert [t.name for t in listed] == ["pet-petstore-list"] + tool = manager.get_listed_tool(server, "petstore-list") + assert tool is not None and tool.description == "Local pet tool" + assert tool.inputSchema["properties"] == {"limit": {"type": "integer"}} + @pytest.mark.asyncio async def test_get_allowed_mcp_servers_with_user_api_key_auth(self): """ From d620d1ffead5c73cc6012ef7a92d603398b8a995 Mon Sep 17 00:00:00 2001 From: yucheng Date: Sat, 12 Sep 2026 19:22:02 +0000 Subject: [PATCH 036/225] test(mcp): register OpenAPI listing fixtures directly instead of patching the global tool registry Co-Authored-By: Devin AI <158243242+devin-ai-integration[bot]@users.noreply.github.com> --- .../mcp_server/test_mcp_server_manager.py | 48 +++++++++++-------- 1 file changed, 28 insertions(+), 20 deletions(-) diff --git a/tests/test_litellm/proxy/_experimental/mcp_server/test_mcp_server_manager.py b/tests/test_litellm/proxy/_experimental/mcp_server/test_mcp_server_manager.py index e74a4069b15..8693227feaa 100644 --- a/tests/test_litellm/proxy/_experimental/mcp_server/test_mcp_server_manager.py +++ b/tests/test_litellm/proxy/_experimental/mcp_server/test_mcp_server_manager.py @@ -6651,14 +6651,17 @@ class TestMCPServerManager: async def _handler(**kwargs): return None - with patch.dict(global_mcp_tool_registry.tools, {}, clear=True): - global_mcp_tool_registry.register_tool( - name="petstore-list_pets", - description="List pets", - input_schema={"type": "object", "properties": {"limit": {"type": "integer"}}}, - handler=_handler, - ) + global_mcp_tool_registry.unregister_tools_with_prefix("petstore-") + global_mcp_tool_registry.register_tool( + name="petstore-list_pets", + description="List pets", + input_schema={"type": "object", "properties": {"limit": {"type": "integer"}}}, + handler=_handler, + ) + try: listed = await manager._get_tools_from_server(server=server, add_prefix=add_prefix) + finally: + global_mcp_tool_registry.unregister_tools_with_prefix("petstore-") assert [t.name for t in listed] == ["petstore-list_pets" if add_prefix else "list_pets"] for name in ("list_pets", "petstore-list_pets"): @@ -6684,20 +6687,25 @@ class TestMCPServerManager: async def _handler(**kwargs): return None - with patch.dict(global_mcp_tool_registry.tools, {}, clear=True): - global_mcp_tool_registry.register_tool( - name="pet-petstore-list", - description="Local pet tool", - input_schema={"type": "object", "properties": {"limit": {"type": "integer"}}}, - handler=_handler, - ) - global_mcp_tool_registry.register_tool( - name="petstore-list", - description="Foreign petstore tool", - input_schema={"type": "object", "properties": {"status": {"type": "string"}}}, - handler=_handler, - ) + for prefix in ("pet-", "petstore-"): + global_mcp_tool_registry.unregister_tools_with_prefix(prefix) + global_mcp_tool_registry.register_tool( + name="pet-petstore-list", + description="Local pet tool", + input_schema={"type": "object", "properties": {"limit": {"type": "integer"}}}, + handler=_handler, + ) + global_mcp_tool_registry.register_tool( + name="petstore-list", + description="Foreign petstore tool", + input_schema={"type": "object", "properties": {"status": {"type": "string"}}}, + handler=_handler, + ) + try: listed = await manager._get_tools_from_server(server=server, add_prefix=True) + finally: + for prefix in ("pet-", "petstore-"): + global_mcp_tool_registry.unregister_tools_with_prefix(prefix) assert [t.name for t in listed] == ["pet-petstore-list"] tool = manager.get_listed_tool(server, "petstore-list") From 95b8e2aaa850b96b0495be8401f99c4eb3880dfa Mon Sep 17 00:00:00 2001 From: yucheng Date: Sat, 12 Sep 2026 19:32:03 +0000 Subject: [PATCH 037/225] fix(mcp): scope listed-tool metadata per caller on per-user MCP servers Servers whose upstream catalog depends on the caller (oauth2 per-user, token exchange, id_jag, per-user env vars, delegated auth) now keep one listed-tool mapping per (user_id, api_key) hash under the server id, so one caller's tools/list cannot supply another caller's description or inputSchema to pre-call guardrails. Shared servers and OpenAPI-backed servers keep a single server-wide entry Co-Authored-By: Devin AI <158243242+devin-ai-integration[bot]@users.noreply.github.com> --- .../mcp_server/mcp_server_manager.py | 63 ++++++++++++++----- .../mcp_server/test_mcp_server_manager.py | 33 ++++++++++ 2 files changed, 79 insertions(+), 17 deletions(-) diff --git a/litellm/proxy/_experimental/mcp_server/mcp_server_manager.py b/litellm/proxy/_experimental/mcp_server/mcp_server_manager.py index d53921994c8..a1200f1784e 100644 --- a/litellm/proxy/_experimental/mcp_server/mcp_server_manager.py +++ b/litellm/proxy/_experimental/mcp_server/mcp_server_manager.py @@ -1947,7 +1947,9 @@ class MCPServerManager: "gmail_send_email": "zapier_mcp_server", } """ - self._listed_tools_by_server_id: dict[str, Mapping[str, MCPTool]] = {} # mutable-ok: refreshed per tools/list + self._listed_tools_by_server_id: dict[ + str, Mapping[str | None, Mapping[str, MCPTool]] + ] = {} # mutable-ok: refreshed per tools/list self._upstream_initialize_instructions_by_server_id: dict[str, str] = {} # Per-server monotonic timestamp of last upstream prefetch attempt (success, # empty result, or failure). Used to throttle re-probes for servers that do @@ -4446,15 +4448,15 @@ class MCPServerManager: unprefixed_tools: Final = [ # mutable-ok: returned through the list[MCPTool] listing contract t.model_copy(update={"name": t.name[len(registry_prefix) :]}) for t in tools ] - self._listed_tools_by_server_id[server.server_id] = MappingProxyType( - {t.name: t for t in unprefixed_tools} - ) + self._record_listed_tools(server, unprefixed_tools, user_api_key_auth) return tools if add_prefix else unprefixed_tools else: tools = await self._fetch_tools_with_timeout(client, server.name) self._remember_upstream_initialize_instructions(server, client) - prefixed_or_original_tools: Final = self._create_prefixed_tools(tools, server, add_prefix=add_prefix) + prefixed_or_original_tools: Final = self._create_prefixed_tools( + tools, server, add_prefix=add_prefix, user_api_key_auth=user_api_key_auth + ) return prefixed_or_original_tools @@ -4502,6 +4504,29 @@ class MCPServerManager: self._invalidate_discovery_lists(server_id) self._listed_tools_by_server_id.pop(server_id, None) + def _discovers_per_caller(self, server: MCPServer) -> bool: + return ( + server.requires_per_user_auth + or self._references_per_user_env_var(server) + or server.delegate_auth_to_upstream + or server.auth_type in (MCPAuth.oauth2_token_exchange, MCPAuth.oauth2_id_jag) + ) + + def _listed_tools_identity(self, server: MCPServer, user_api_key_auth: UserAPIKeyAuth | None) -> str | None: + if server.spec_path or user_api_key_auth is None or not self._discovers_per_caller(server): + return None + material: Final = json.dumps((user_api_key_auth.user_id, user_api_key_auth.api_key), separators=(",", ":")) + return hashlib.sha256(material.encode()).hexdigest() + + def _record_listed_tools( + self, server: MCPServer, tools: Sequence[MCPTool], user_api_key_auth: UserAPIKeyAuth | None + ) -> None: + identity: Final = self._listed_tools_identity(server, user_api_key_auth) + listing: Final = MappingProxyType({tool.name: tool for tool in tools}) + self._listed_tools_by_server_id[server.server_id] = MappingProxyType( + {**self._listed_tools_by_server_id.get(server.server_id, {}), identity: listing} + ) + def _discovery_key( self, server: MCPServer, @@ -4512,12 +4537,7 @@ class MCPServerManager: subject_token: str | None, credential_fingerprint: str | None = None, ) -> _DiscoveryKey: - per_user: Final = ( - server.requires_per_user_auth - or self._references_per_user_env_var(server) - or server.delegate_auth_to_upstream - or server.auth_type in (MCPAuth.oauth2_token_exchange, MCPAuth.oauth2_id_jag) - ) + per_user: Final = self._discovers_per_caller(server) if not (per_user or mcp_auth_header or extra_headers or stdio_env or subject_token): return server.server_id, None identity: Final = ( @@ -5330,7 +5350,13 @@ class MCPServerManager: "attempts; the 3-character prefix space is too crowded." ) - def _create_prefixed_tools(self, tools: list[MCPTool], server: MCPServer, add_prefix: bool = True) -> list[MCPTool]: + def _create_prefixed_tools( + self, + tools: list[MCPTool], + server: MCPServer, + add_prefix: bool = True, + user_api_key_auth: UserAPIKeyAuth | None = None, + ) -> list[MCPTool]: """ Create prefixed tools and update tool mapping. @@ -5362,12 +5388,15 @@ class MCPServerManager: for spelling in iter_known_tool_name_spellings(original_name, server): self.tool_name_to_mcp_server_name_mapping[spelling] = prefix - self._listed_tools_by_server_id[server.server_id] = MappingProxyType({tool.name: tool for tool in tools}) + self._record_listed_tools(server, tools, user_api_key_auth) verbose_logger.info("Successfully fetched %s tools from server %s", len(prefixed_tools), server.name) return prefixed_tools - def get_listed_tool(self, server: MCPServer, name: str) -> MCPTool | None: - listed: Final = self._listed_tools_by_server_id.get(server.server_id) + def get_listed_tool( + self, server: MCPServer, name: str, user_api_key_auth: UserAPIKeyAuth | None = None + ) -> MCPTool | None: + identity: Final = self._listed_tools_identity(server, user_api_key_auth) + listed: Final = self._listed_tools_by_server_id.get(server.server_id, {}).get(identity) if not listed: return None return listed.get(name) or listed.get(strip_known_server_prefix(name, server)) @@ -6349,7 +6378,7 @@ class MCPServerManager: server=mcp_server, raw_headers=raw_headers, litellm_logging_obj=litellm_logging_obj, - tool=self.get_listed_tool(mcp_server, name), + tool=self.get_listed_tool(mcp_server, name, user_api_key_auth), ) if "arguments" in hook_result: arguments = hook_result["arguments"] @@ -6365,7 +6394,7 @@ class MCPServerManager: proxy_logging_obj=proxy_logging_obj, start_time=start_time, litellm_logging_obj=litellm_logging_obj, - tool=self.get_listed_tool(mcp_server, name), + tool=self.get_listed_tool(mcp_server, name, user_api_key_auth), ) tasks.append(during_hook_task) diff --git a/tests/test_litellm/proxy/_experimental/mcp_server/test_mcp_server_manager.py b/tests/test_litellm/proxy/_experimental/mcp_server/test_mcp_server_manager.py index 8693227feaa..4d1490b76a5 100644 --- a/tests/test_litellm/proxy/_experimental/mcp_server/test_mcp_server_manager.py +++ b/tests/test_litellm/proxy/_experimental/mcp_server/test_mcp_server_manager.py @@ -6632,6 +6632,39 @@ class TestMCPServerManager: listed = manager.get_listed_tool(server, "echo") assert listed is not None and listed.description == "shared" + def test_per_caller_server_keeps_listed_tools_per_identity(self): + manager = MCPServerManager() + server = MCPServer( + server_id="srv", + name="srv", + transport=MCPTransport.http, + url="http://srv", + auth_type=MCPAuth.oauth2_token_exchange, + ) + alice = UserAPIKeyAuth(user_id="alice", api_key="hashed-alice") + bob = UserAPIKeyAuth(user_id="bob", api_key="hashed-bob") + alice_schema = {"type": "object", "properties": {"path": {"type": "string"}}} + bob_schema = {"type": "object", "properties": {"path": {"type": "string"}, "site": {"type": "string"}}} + manager._create_prefixed_tools( + [MCPTool(name="read", description="alice view", inputSchema=alice_schema)], server, user_api_key_auth=alice + ) + manager._create_prefixed_tools( + [MCPTool(name="read", description="bob view", inputSchema=bob_schema)], server, user_api_key_auth=bob + ) + + alice_tool = manager.get_listed_tool(server, "srv-read", alice) + bob_tool = manager.get_listed_tool(server, "srv-read", bob) + assert alice_tool is not None and (alice_tool.description, alice_tool.inputSchema) == ("alice view", alice_schema) + assert bob_tool is not None and (bob_tool.description, bob_tool.inputSchema) == ("bob view", bob_schema) + assert manager.get_listed_tool(server, "srv-read", UserAPIKeyAuth(user_id="carol", api_key="k")) is None + + shared = MCPServer(server_id="shared", name="shared", transport=MCPTransport.http, url="http://shared") + manager._create_prefixed_tools( + [MCPTool(name="echo", description="everyone", inputSchema={})], shared, user_api_key_auth=alice + ) + for_bob = manager.get_listed_tool(shared, "echo", bob) + assert for_bob is not None and for_bob.description == "everyone" + @pytest.mark.asyncio @pytest.mark.parametrize("add_prefix", [True, False]) async def test_openapi_listing_records_listed_tools(self, add_prefix): From fdb7da7c6117d5cecce4a1167b5f75f48714a5df Mon Sep 17 00:00:00 2001 From: yucheng Date: Sat, 12 Sep 2026 19:41:20 +0000 Subject: [PATCH 038/225] fix(mcp): bound per-caller listed-tool catalogs to 256 identities per server Co-Authored-By: Devin AI <158243242+devin-ai-integration[bot]@users.noreply.github.com> --- .../mcp_server/mcp_server_manager.py | 18 ++++++++--- .../proxy/_experimental/mcp_server/server.py | 2 +- .../guardrail_hooks/agent_365/agent_365.py | 2 +- .../mcp_server/test_mcp_server_manager.py | 30 +++++++++++++++++++ 4 files changed, 46 insertions(+), 6 deletions(-) diff --git a/litellm/proxy/_experimental/mcp_server/mcp_server_manager.py b/litellm/proxy/_experimental/mcp_server/mcp_server_manager.py index a1200f1784e..d439579a3ab 100644 --- a/litellm/proxy/_experimental/mcp_server/mcp_server_manager.py +++ b/litellm/proxy/_experimental/mcp_server/mcp_server_manager.py @@ -242,6 +242,9 @@ _user_env_vars_cache: Final[dict[tuple[str, str], tuple[dict[str, str], float]]] _USER_ENV_VARS_CACHE_TTL: Final = 60 # seconds _USER_ENV_VARS_CACHE_MAX_SIZE: Final = 4096 # cap to prevent unbounded growth +_NO_LISTED_TOOLS: Final[Mapping[str | None, Mapping[str, MCPTool]]] = MappingProxyType({}) +_LISTED_TOOLS_CALLERS_PER_SERVER: Final = 256 + # Auth types whose upstream OAuth endpoints (protected-resource + authorization-server metadata) the # gateway discovers from the upstream itself: interactive oauth2 and the two client-forwarded modes. # OBO/M2M endpoint discovery is decided separately via _obo_needs_endpoint_discovery. Shared by the @@ -4446,7 +4449,7 @@ class MCPServerManager: # through _create_prefixed_tools — that would add the prefix a second # time producing "test_petstore-test_petstore-getinventory". unprefixed_tools: Final = [ # mutable-ok: returned through the list[MCPTool] listing contract - t.model_copy(update={"name": t.name[len(registry_prefix) :]}) for t in tools + t.model_copy(update=MappingProxyType({"name": t.name[len(registry_prefix) :]})) for t in tools ] self._record_listed_tools(server, unprefixed_tools, user_api_key_auth) return tools if add_prefix else unprefixed_tools @@ -4523,9 +4526,16 @@ class MCPServerManager: ) -> None: identity: Final = self._listed_tools_identity(server, user_api_key_auth) listing: Final = MappingProxyType({tool.name: tool for tool in tools}) - self._listed_tools_by_server_id[server.server_id] = MappingProxyType( - {**self._listed_tools_by_server_id.get(server.server_id, {}), identity: listing} + existing: Final = self._listed_tools_by_server_id.get(server.server_id, _NO_LISTED_TOOLS) + shared: Final = existing.get(None) + callers: Final = tuple((key, value) for key, value in existing.items() if key not in (None, identity)) + evicted: Final = 0 if identity is None else max(len(callers) + 1 - _LISTED_TOOLS_CALLERS_PER_SERVER, 0) + entries: Final = ( + *(() if shared is None else ((None, shared),)), + *callers[evicted:], + (identity, listing), ) + self._listed_tools_by_server_id[server.server_id] = MappingProxyType(dict(entries)) def _discovery_key( self, @@ -5396,7 +5406,7 @@ class MCPServerManager: self, server: MCPServer, name: str, user_api_key_auth: UserAPIKeyAuth | None = None ) -> MCPTool | None: identity: Final = self._listed_tools_identity(server, user_api_key_auth) - listed: Final = self._listed_tools_by_server_id.get(server.server_id, {}).get(identity) + listed: Final = self._listed_tools_by_server_id.get(server.server_id, _NO_LISTED_TOOLS).get(identity) if not listed: return None return listed.get(name) or listed.get(strip_known_server_prefix(name, server)) diff --git a/litellm/proxy/_experimental/mcp_server/server.py b/litellm/proxy/_experimental/mcp_server/server.py index dc191dbf8b8..62e13675a8b 100644 --- a/litellm/proxy/_experimental/mcp_server/server.py +++ b/litellm/proxy/_experimental/mcp_server/server.py @@ -4149,7 +4149,7 @@ if MCP_AVAILABLE: if server and ( (server.auth_type == MCPAuth.oauth2_token_exchange and not oauth2_headers) or ( - _get_mcp_servers_in_path(get_route_relative_request_path(scope)) == [server_name] + tuple(_get_mcp_servers_in_path(get_route_relative_request_path(scope)) or ()) == (server_name,) and not agent_365_subject_token_present(oauth2_headers) and agent_365_authorization_servers(server, user_api_key_auth) ) diff --git a/litellm/proxy/guardrails/guardrail_hooks/agent_365/agent_365.py b/litellm/proxy/guardrails/guardrail_hooks/agent_365/agent_365.py index 24f1643d89a..58d8ba5f865 100644 --- a/litellm/proxy/guardrails/guardrail_hooks/agent_365/agent_365.py +++ b/litellm/proxy/guardrails/guardrail_hooks/agent_365/agent_365.py @@ -666,7 +666,7 @@ def entra_assertion(value: object) -> str | None: def agent_365_subject_token_present(oauth2_headers: Mapping[str, str] | None) -> bool: """Whether the request's ``Authorization`` carries an Entra assertion the guardrail can exchange.""" - authorization: Final = (oauth2_headers or {}).get("Authorization", "") + authorization: Final = oauth2_headers.get("Authorization", "") if oauth2_headers else "" if not authorization.lower().startswith("bearer "): return False return entra_assertion(authorization[len("bearer ") :].strip()) is not None diff --git a/tests/test_litellm/proxy/_experimental/mcp_server/test_mcp_server_manager.py b/tests/test_litellm/proxy/_experimental/mcp_server/test_mcp_server_manager.py index 4d1490b76a5..a504946a0d8 100644 --- a/tests/test_litellm/proxy/_experimental/mcp_server/test_mcp_server_manager.py +++ b/tests/test_litellm/proxy/_experimental/mcp_server/test_mcp_server_manager.py @@ -6665,6 +6665,36 @@ class TestMCPServerManager: for_bob = manager.get_listed_tool(shared, "echo", bob) assert for_bob is not None and for_bob.description == "everyone" + def test_per_caller_listed_tools_evict_oldest_caller_and_keep_shared(self): + from litellm.proxy._experimental.mcp_server.mcp_server_manager import _LISTED_TOOLS_CALLERS_PER_SERVER + + manager = MCPServerManager() + server = MCPServer( + server_id="srv", + name="srv", + transport=MCPTransport.http, + url="http://srv", + auth_type=MCPAuth.oauth2_token_exchange, + ) + manager._create_prefixed_tools([MCPTool(name="read", description="shared", inputSchema={})], server) + callers = [UserAPIKeyAuth(user_id=f"u{i}", api_key=f"k{i}") for i in range(_LISTED_TOOLS_CALLERS_PER_SERVER + 1)] + for caller in callers: + manager._create_prefixed_tools( + [MCPTool(name="read", description=caller.user_id, inputSchema={})], server, user_api_key_auth=caller + ) + manager._create_prefixed_tools( + [MCPTool(name="read", description="u1 again", inputSchema={})], server, user_api_key_auth=callers[1] + ) + + assert manager.get_listed_tool(server, "srv-read", callers[0]) is None + second = manager.get_listed_tool(server, "srv-read", callers[1]) + assert second is not None and second.description == "u1 again" + newest = manager.get_listed_tool(server, "srv-read", callers[-1]) + assert newest is not None and newest.description == callers[-1].user_id + assert len(manager._listed_tools_by_server_id[server.server_id]) == _LISTED_TOOLS_CALLERS_PER_SERVER + 1 + shared = manager.get_listed_tool(server, "srv-read") + assert shared is not None and shared.description == "shared" + @pytest.mark.asyncio @pytest.mark.parametrize("add_prefix", [True, False]) async def test_openapi_listing_records_listed_tools(self, add_prefix): From 222f283c9352ca828a7db7641b552de43e017af0 Mon Sep 17 00:00:00 2001 From: mateo-berri <277851410+mateo-berri@users.noreply.github.com> Date: Sat, 12 Sep 2026 15:55:03 -0700 Subject: [PATCH 039/225] fix(cli): read the Codex catalog back before launch and cover every ModelInfo schema Codex 0.130 and 0.145 require supports_reasoning_summaries and supports_parallel_tool_calls on every catalog entry, so a catalog written for 0.154 made those releases exit at startup with a parse error. Every field some release since 0.105.0 deserializes without a default is now written, with Codex's own fallback values, and the catalog is read back once through the installed binary (`codex debug models`) before launch. A Codex that rejects it, or one older than 0.130 with no such command, gets the skip notice and launches on its built-in catalog instead. --- litellm/proxy/client/cli/README.md | 2 +- litellm/proxy/client/cli/commands/agents.py | 80 +++++++++-- .../proxy/client/cli/test_agents.py | 130 +++++++++++++++++- 3 files changed, 190 insertions(+), 22 deletions(-) diff --git a/litellm/proxy/client/cli/README.md b/litellm/proxy/client/cli/README.md index 3b0ff9d7add..046786d9557 100644 --- a/litellm/proxy/client/cli/README.md +++ b/litellm/proxy/client/cli/README.md @@ -490,7 +490,7 @@ lite codex exec "summarize the repo" Each command resolves your LiteLLM key (logging in via SSO when none is stored and you are at a terminal; otherwise it expects `LITELLM_PROXY_API_KEY` or `--api-key`), checks the key against the proxy so bad credentials fail immediately instead of deep inside the agent, exports the environment variables the agent reads, then replaces itself with the agent process. -The right variables are picked per agent. Claude Code gets `ANTHROPIC_BASE_URL` (the proxy root, so it appends `/v1/messages`) and `ANTHROPIC_AUTH_TOKEN`, with any stray `ANTHROPIC_API_KEY` cleared so the proxy token wins, and `ENABLE_TOOL_SEARCH=true` (unless you already set it) so Claude Code keeps tool search on even though the base URL is a proxy rather than a first-party Anthropic host. It also gets `CLAUDE_CODE_ENABLE_GATEWAY_MODEL_DISCOVERY=1` (again unless you already set it) so Claude Code v2.1.129+ fills its `/model` picker from the proxy's `/v1/models`; Claude Code only lists entries whose id contains `claude` or `anthropic`, so the proxy lists every other group to Claude Code as `claude-router-` and marks a group whose input window reaches 1M with `[1m]`, and a request on such an id is served by the group. Older Claude Code versions ignore the variable. Export it as `0` to turn discovery off. Codex and OpenCode get `OPENAI_BASE_URL` (the proxy plus `/v1`) and `OPENAI_API_KEY`. Codex ignores `OPENAI_BASE_URL`, so it is additionally pointed at the proxy through a custom provider passed as `-c` config overrides (HTTP/SSE Responses transport, since the proxy does not speak the Responses WebSocket protocol). OpenCode additionally gets `OPENCODE_CONFIG_CONTENT` holding a generated `litellm` provider (`@ai-sdk/openai-compatible`, the proxy `/v1` URL, `{env:OPENAI_API_KEY}`) with one model entry per chat model your key can see on `/v1/models`, so its model picker mirrors the proxy without a hand-maintained `opencode.json`; OpenCode merges that over your own config files, and if you already export `OPENCODE_CONFIG_CONTENT` yours is left alone. When the list cannot be fetched, `lite opencode` says so on stderr and launches anyway. +The right variables are picked per agent. Claude Code gets `ANTHROPIC_BASE_URL` (the proxy root, so it appends `/v1/messages`) and `ANTHROPIC_AUTH_TOKEN`, with any stray `ANTHROPIC_API_KEY` cleared so the proxy token wins, and `ENABLE_TOOL_SEARCH=true` (unless you already set it) so Claude Code keeps tool search on even though the base URL is a proxy rather than a first-party Anthropic host. It also gets `CLAUDE_CODE_ENABLE_GATEWAY_MODEL_DISCOVERY=1` (again unless you already set it) so Claude Code v2.1.129+ fills its `/model` picker from the proxy's `/v1/models`; Claude Code only lists entries whose id contains `claude` or `anthropic`, so the proxy lists every other group to Claude Code as `claude-router-` and marks a group whose input window reaches 1M with `[1m]`, and a request on such an id is served by the group. Older Claude Code versions ignore the variable. Export it as `0` to turn discovery off. Codex and OpenCode get `OPENAI_BASE_URL` (the proxy plus `/v1`) and `OPENAI_API_KEY`. Codex ignores `OPENAI_BASE_URL`, so it is additionally pointed at the proxy through a custom provider passed as `-c` config overrides (HTTP/SSE Responses transport, since the proxy does not speak the Responses WebSocket protocol). OpenCode additionally gets `OPENCODE_CONFIG_CONTENT` holding a generated `litellm` provider (`@ai-sdk/openai-compatible`, the proxy `/v1` URL, `{env:OPENAI_API_KEY}`) with one model entry per chat model your key can see on `/v1/models`, so its model picker mirrors the proxy without a hand-maintained `opencode.json`; OpenCode merges that over your own config files, and if you already export `OPENCODE_CONFIG_CONTENT` yours is left alone. When the list cannot be fetched, `lite opencode` says so on stderr and launches anyway. Codex gets the same list as a catalog file, `$CODEX_HOME/litellm-models.json` (default `~/.codex/`), passed as `-c model_catalog_json=` so `/model` lists exactly the proxy's chat models; before launching, `lite codex` has the installed Codex read that file back (`codex debug models`), and when the fetch, the write or that read-back fails (Codex releases older than 0.130 have no such command) it says so on stderr and launches with Codex's built-in catalog, leaving the rejected file in place. pi ignores base-URL environment variables entirely, so `lite pi` (kept out of the `lite --help` command listing for now, but fully functional) wires it up differently: before handoff it fetches the models your key can use from the proxy's `/v1/models` (plus each model's context window and output cap from `/model_group/info`, when available) and syncs them into a `litellm` provider entry in pi's `~/.pi/agent/models.json` (honoring `PI_CODING_AGENT_DIR`), then starts pi on that provider's first model via an injected `--model litellm/`. Only that one provider entry is rewritten; the rest of the file, including any other custom providers, is left alone. The entry references the key as `$LITELLM_PROXY_API_KEY`, which the wrapper exports for the session, so the token itself never lands on disk and plain `pi` outside the wrapper simply shows the litellm models as unavailable. Your own flags come after the injected pin, so `lite pi --model litellm/` wins, and inside the TUI the `/model` picker lists every synced litellm model. diff --git a/litellm/proxy/client/cli/commands/agents.py b/litellm/proxy/client/cli/commands/agents.py index f424e07968e..8a15153ea28 100644 --- a/litellm/proxy/client/cli/commands/agents.py +++ b/litellm/proxy/client/cli/commands/agents.py @@ -69,6 +69,7 @@ CODEX_PROXY_PROVIDER: Final = "litellm" CODEX_HOME_ENV: Final = "CODEX_HOME" CODEX_MODEL_CATALOG_FILENAME: Final = "litellm-models.json" _CODEX_BASE_INSTRUCTIONS_PATH: Final = Path(__file__).with_name("codex_base_instructions.md") +_CODEX_PREFLIGHT_TIMEOUT_SECONDS: Final = 10.0 class AgentRunError(Exception): @@ -399,9 +400,11 @@ class _CodexTruncationPolicy(BaseModel): class _CodexModel(BaseModel): """One `ModelInfo` entry of a Codex model catalog. - Every field Codex's deserializer has no default for is spelled out here; the - values match the fallback metadata Codex uses today for a model slug it - does not know, so picking a proxy model behaves the same as `codex -m` did. + Every field that some Codex release since `model_catalog_json` appeared + (0.105.0) deserializes without a default is spelled out here, so one catalog + parses on all of them; the values match the fallback metadata Codex uses for + a model slug it does not know, so picking a proxy model behaves the same as + `codex -m` did. """ slug: str @@ -415,6 +418,8 @@ class _CodexModel(BaseModel): availability_nux: None = None upgrade: None = None support_verbosity: Literal[False] = False + supports_reasoning_summaries: Literal[False] = False + supports_parallel_tool_calls: Literal[False] = False default_verbosity: None = None apply_patch_tool_type: None = None truncation_policy: _CodexTruncationPolicy = _CodexTruncationPolicy() @@ -470,12 +475,48 @@ def _replace_file(path: Path, text: str) -> None: raise +def _codex_catalog_rejection( + binary: str, + override: str, + env: Mapping[str, str], + *, + run: Callable[..., subprocess.CompletedProcess[str]], +) -> str | None: + """Why the installed Codex refuses the catalog, or None once it reads the file back. + + `codex debug models` parses the catalog the way a launch does, so a Codex + whose ModelInfo schema disagrees with the one written here fails now, with + the sync skipped, instead of exiting on startup. Releases before 0.130.0 + have no `debug models` and fail the same way. A batch shim goes through + cmd.exe exactly as the launch will. + """ + name: Final = os.path.basename(binary) + command: Final = _windows_command(binary, (binary, "-c", override, "debug", "models")) + try: + completed: Final = run( + command, + env=dict(env), + stdin=subprocess.DEVNULL, + capture_output=True, + text=True, + timeout=_CODEX_PREFLIGHT_TIMEOUT_SECONDS, + ) + except (OSError, subprocess.TimeoutExpired) as e: + return f"`{name} debug models` failed: {e}" + if completed.returncode == 0: + return None + lines: Final = completed.stderr.strip().splitlines() + return f"`{name} debug models` exited {completed.returncode}: {lines[0] if lines else 'no output'}" + + def codex_model_sync_args( base_env: Mapping[str, str], base_url: str, api_key: str, *, + binary: str = "codex", get: Callable[..., requests.Response] = requests.get, + run: Callable[..., subprocess.CompletedProcess[str]] = subprocess.run, home: Callable[[], Path] = Path.home, instructions_path: Path = _CODEX_BASE_INSTRUCTIONS_PATH, ) -> ModelSyncArgs | ModelSyncSkipped: @@ -483,9 +524,11 @@ def codex_model_sync_args( Codex has no env or inline equivalent of OPENCODE_CONFIG_CONTENT: the catalog must be a file, so it is written under $CODEX_HOME (default ~/.codex) and - atomically replaced on every launch. The key never lands in the file. A - failed fetch, read or write is reported rather than raised: Codex still - launches with its built-in catalog and takes a proxy model by name via -m. + atomically replaced on every launch, then read back once through the Codex + at `binary` before it is handed over. The key never lands in the file. A + failed fetch, read, write or read-back is reported rather than raised: Codex + still launches with its built-in catalog and takes a proxy model by name via + -m, and a rejected file stays on disk to be looked at. """ listing: Final = _fetch_model_listing(base_url, api_key, get=get) if isinstance(listing, ModelSyncSkipped): @@ -502,32 +545,39 @@ def codex_model_sync_args( _replace_file(path, catalog) except OSError as e: return ModelSyncSkipped(f"could not write {path}: {e}") - return ModelSyncArgs(("-c", f"model_catalog_json={json.dumps(str(path))}")) + override: Final = f"model_catalog_json={json.dumps(str(path))}" + rejection: Final = _codex_catalog_rejection(binary, override, base_env, run=run) + if rejection is not None: + return ModelSyncSkipped(rejection) + return ModelSyncArgs(("-c", override)) def agent_model_sync_env( - command: str, + binary: str, base_env: Mapping[str, str], base_url: str, api_key: str, skip_verify: bool, *, get: Callable[..., requests.Response] = requests.get, + run: Callable[..., subprocess.CompletedProcess[str]] = subprocess.run, ) -> ModelSyncResult: """Extra env or args an agent needs to see the proxy's model list. - OpenCode takes it as env, Codex as a `-c` override; Claude Code discovers - models itself through CLAUDE_CODE_ENABLE_GATEWAY_MODEL_DISCOVERY. - skip_verify means the caller wants no pre-launch proxy call at all, so the - listing is skipped too rather than hanging on an offline proxy. + binary is the resolved path the launch will run (`codex.cmd` on a Windows + npm install). OpenCode takes the list as env, Codex as a `-c` override that + binary has read back first; Claude Code discovers models itself through + CLAUDE_CODE_ENABLE_GATEWAY_MODEL_DISCOVERY. skip_verify means the caller + wants no pre-launch proxy call at all, so the listing is skipped too rather + than hanging on an offline proxy. """ - agent: Final = os.path.basename(command) + agent: Final = os.path.splitext(os.path.basename(binary))[0] if agent not in ("opencode", "codex"): return _NO_EXTRA_ENV if skip_verify: return ModelSyncSkipped(f"{_SKIP_VERIFY_FLAG} was passed") if agent == "codex": - return codex_model_sync_args(base_env, base_url, api_key, get=get) + return codex_model_sync_args(base_env, base_url, api_key, binary=binary, get=get, run=run) return opencode_model_sync_env(base_env, base_url, api_key, get=get) @@ -682,7 +732,7 @@ def run_agent( verify(base_url, api_key) env_before_sync: Final = base_env if base_env is not None else os.environ - synced: Final = sync_models(command[0], env_before_sync, base_url, api_key, skip_verify) + synced: Final = sync_models(binary, env_before_sync, base_url, api_key, skip_verify) if isinstance(synced, ModelSyncSkipped): warn(f"litellm: not syncing {display_name} models from the proxy: {synced.reason}") diff --git a/tests/test_litellm/proxy/client/cli/test_agents.py b/tests/test_litellm/proxy/client/cli/test_agents.py index b122ea7b20e..8e76e4745f1 100644 --- a/tests/test_litellm/proxy/client/cli/test_agents.py +++ b/tests/test_litellm/proxy/client/cli/test_agents.py @@ -1,6 +1,7 @@ import inspect import json import os +import subprocess import sys from pathlib import Path from unittest.mock import patch @@ -56,6 +57,17 @@ class _Recorder: return self.returns +class _FakeRun: + def __init__(self, returncode=0, stderr=""): + self.returncode = returncode + self.stderr = stderr + self.calls = [] + + def __call__(self, args, **kwargs): + self.calls.append((args, kwargs)) + return subprocess.CompletedProcess(args, self.returncode, "", self.stderr) + + class _FakeJsonResponse: def __init__(self, status_code, payload=None): self.status_code = status_code @@ -365,7 +377,7 @@ class TestCodexModelSync: def _row(model_id, **extra): return {"id": model_id, "object": "model", "created": 1, "owned_by": "openai", **extra} - def _sync(self, listing, codex_home, base_url="http://localhost:4000/"): + def _sync(self, listing, codex_home, base_url="http://localhost:4000/", run=None): captured = {} def fake_get(url, headers, timeout): @@ -373,7 +385,13 @@ class TestCodexModelSync: captured["headers"] = headers return _FakeResponse(200, listing) - result = codex_model_sync_args({"CODEX_HOME": str(codex_home)}, base_url, "sk-key", get=fake_get) + result = codex_model_sync_args( + {"CODEX_HOME": str(codex_home)}, + base_url, + "sk-key", + get=fake_get, + run=_FakeRun() if run is None else run, + ) return captured, result @staticmethod @@ -411,6 +429,8 @@ class TestCodexModelSync: assert entry["truncation_policy"] == {"mode": "bytes", "limit": 10000} assert entry["experimental_supported_tools"] == [] assert entry["support_verbosity"] is False + assert entry["supports_reasoning_summaries"] is False + assert entry["supports_parallel_tool_calls"] is False for nullable in ("description", "availability_nux", "upgrade", "default_verbosity", "apply_patch_tool_type"): assert nullable in entry and entry[nullable] is None assert entry["base_instructions"].startswith("You are a coding agent running in the Codex CLI") @@ -457,6 +477,7 @@ class TestCodexModelSync: "http://localhost:4000", "sk-key", get=lambda *a, **k: _FakeResponse(200, self._listing(self._row("m"))), + run=_FakeRun(), home=lambda: tmp_path, ) assert self._catalog_path(result) == str(tmp_path / ".codex" / "litellm-models.json") @@ -510,17 +531,108 @@ class TestCodexModelSync: assert isinstance(result, ModelSyncSkipped) assert reason in result.reason - @pytest.mark.parametrize("command", ["codex", "/opt/bin/codex"]) - def test_codex_syncs_through_the_agent_dispatch(self, tmp_path, command): + @pytest.mark.parametrize("binary", ["codex", "/opt/bin/codex", "codex.cmd", "/c/npm/codex.CMD"]) + def test_codex_syncs_through_the_agent_dispatch_with_the_binary_it_will_run(self, tmp_path, binary): + run = _FakeRun() result = agent_model_sync_env( - command, + binary, {"CODEX_HOME": str(tmp_path)}, "http://localhost:4000", "sk-key", False, get=lambda *a, **k: _FakeResponse(200, self._listing(self._row("m"))), + run=run, ) assert self._catalog_path(result) == str(tmp_path / "litellm-models.json") + assert binary in run.calls[0][0] + + def test_opencode_dispatch_never_runs_codex(self): + def boom(*a, **k): + raise AssertionError("only the Codex sync reads its catalog back") + + result = agent_model_sync_env( + "opencode", + {}, + "http://localhost:4000", + "sk-key", + False, + get=lambda *a, **k: _FakeResponse(200, self._listing(self._row("m"))), + run=boom, + ) + assert "OPENCODE_CONFIG_CONTENT" in result + + def test_catalog_is_read_back_through_codex_before_launch(self, tmp_path): + run = _FakeRun() + _, result = self._sync(self._listing(self._row("m")), tmp_path, run=run) + path = self._catalog_path(result) + + assert len(run.calls) == 1 + command, options = run.calls[0] + assert command == ("codex", "-c", f"model_catalog_json={json.dumps(path)}", "debug", "models") + assert options["env"] == {"CODEX_HOME": str(tmp_path)} + assert options["stdin"] is subprocess.DEVNULL + assert options["capture_output"] is True + assert options["text"] is True + assert options["timeout"] == 10 + + def test_codex_rejecting_the_catalog_skips_the_sync_and_keeps_the_file(self, tmp_path): + stderr = ( + "Error: failed to parse model_catalog_json path `/home/me/.codex/litellm-models.json` as JSON: " + "missing field `supports_parallel_tool_calls` at line 1 column 21648\n" + ) + _, result = self._sync(self._listing(self._row("m")), tmp_path, run=_FakeRun(1, stderr)) + assert isinstance(result, ModelSyncSkipped) + assert result.reason == ( + "`codex debug models` exited 1: Error: failed to parse model_catalog_json path " + "`/home/me/.codex/litellm-models.json` as JSON: missing field `supports_parallel_tool_calls` " + "at line 1 column 21648" + ) + assert (tmp_path / "litellm-models.json").exists() + + def test_codex_without_debug_models_skips_the_sync(self, tmp_path): + stderr = "error: unrecognized subcommand 'models'\n\nUsage: codex debug [OPTIONS] \n" + _, result = self._sync(self._listing(self._row("m")), tmp_path, run=_FakeRun(2, stderr)) + assert isinstance(result, ModelSyncSkipped) + assert result.reason == "`codex debug models` exited 2: error: unrecognized subcommand 'models'" + + def test_codex_failing_silently_is_reported(self, tmp_path): + _, result = self._sync(self._listing(self._row("m")), tmp_path, run=_FakeRun(1)) + assert isinstance(result, ModelSyncSkipped) + assert result.reason == "`codex debug models` exited 1: no output" + + @pytest.mark.parametrize( + "error", [OSError("codex vanished"), subprocess.TimeoutExpired("codex", 10)], ids=["oserror", "timeout"] + ) + def test_unrunnable_preflight_is_reported_not_raised(self, tmp_path, error): + def failing_run(*a, **k): + raise error + + _, result = self._sync(self._listing(self._row("m")), tmp_path, run=failing_run) + assert isinstance(result, ModelSyncSkipped) + assert result.reason.startswith("`codex debug models` failed: ") + assert str(error) in result.reason + + def test_windows_shim_preflight_goes_through_cmd_exe(self, tmp_path): + shim = _WINDOWS_CLAUDE_CMD.replace("claude", "codex") + run = _FakeRun() + result = codex_model_sync_args( + {"CODEX_HOME": str(tmp_path)}, + "http://localhost:4000", + "sk-key", + binary=shim, + get=lambda *a, **k: _FakeResponse(200, self._listing(self._row("m"))), + run=run, + ) + override = f"model_catalog_json={json.dumps(self._catalog_path(result))}" + doubled = override.replace('"', '""') + assert run.calls[0][0] == f'{_CMD_PREFIX}""{shim}" "-c" "{doubled}" "debug" "models""' + + def test_default_binary_is_codex_on_path(self): + assert _default_of(codex_model_sync_args, "binary") == "codex" + + def test_default_runner_is_subprocess_run(self): + assert _default_of(codex_model_sync_args, "run") is subprocess.run + assert _default_of(agent_model_sync_env, "run") is subprocess.run def test_skip_verify_keeps_the_launch_offline(self): def boom(*a, **k): @@ -593,7 +705,13 @@ class TestRunAgent: launcher=lambda p, a, e: order.append("launch"), ) assert order == ["verify", "sync", "launch"] - assert calls["args"] == ("opencode", {"HOME": "/home/me"}, "http://localhost:4000", "sk-key", False) + assert calls["args"] == ( + "/usr/local/bin/opencode", + {"HOME": "/home/me"}, + "http://localhost:4000", + "sk-key", + False, + ) def test_unreachable_proxy_is_not_asked_for_models(self): def failing_verify(*a): From 5b9153f5ea26897c3b138206523f75974701f8e8 Mon Sep 17 00:00:00 2001 From: mateo-berri <277851410+mateo-berri@users.noreply.github.com> Date: Sat, 12 Sep 2026 16:42:52 -0700 Subject: [PATCH 040/225] feat(cli): keep the installed Codex's entries for proxy models it already knows `lite codex` now asks the installed Codex for its own model list through `codex debug models` before writing the catalog. A proxy model whose id matches a stock Codex slug keeps that Codex's entry (reasoning levels, base instructions, context window and the rest) and only its picker position, visibility and upgrade nudge come from the proxy. Unknown slugs still get the plain entry built from the bundled base instructions. The catalog directory is created before the stock call so a fresh CODEX_HOME does not make Codex refuse to run --- litellm/proxy/client/cli/README.md | 2 +- litellm/proxy/client/cli/commands/agents.py | 148 +++++++++++---- .../proxy/client/cli/test_agents.py | 173 ++++++++++++++++-- 3 files changed, 263 insertions(+), 60 deletions(-) diff --git a/litellm/proxy/client/cli/README.md b/litellm/proxy/client/cli/README.md index 046786d9557..965c90d0430 100644 --- a/litellm/proxy/client/cli/README.md +++ b/litellm/proxy/client/cli/README.md @@ -490,7 +490,7 @@ lite codex exec "summarize the repo" Each command resolves your LiteLLM key (logging in via SSO when none is stored and you are at a terminal; otherwise it expects `LITELLM_PROXY_API_KEY` or `--api-key`), checks the key against the proxy so bad credentials fail immediately instead of deep inside the agent, exports the environment variables the agent reads, then replaces itself with the agent process. -The right variables are picked per agent. Claude Code gets `ANTHROPIC_BASE_URL` (the proxy root, so it appends `/v1/messages`) and `ANTHROPIC_AUTH_TOKEN`, with any stray `ANTHROPIC_API_KEY` cleared so the proxy token wins, and `ENABLE_TOOL_SEARCH=true` (unless you already set it) so Claude Code keeps tool search on even though the base URL is a proxy rather than a first-party Anthropic host. It also gets `CLAUDE_CODE_ENABLE_GATEWAY_MODEL_DISCOVERY=1` (again unless you already set it) so Claude Code v2.1.129+ fills its `/model` picker from the proxy's `/v1/models`; Claude Code only lists entries whose id contains `claude` or `anthropic`, so the proxy lists every other group to Claude Code as `claude-router-` and marks a group whose input window reaches 1M with `[1m]`, and a request on such an id is served by the group. Older Claude Code versions ignore the variable. Export it as `0` to turn discovery off. Codex and OpenCode get `OPENAI_BASE_URL` (the proxy plus `/v1`) and `OPENAI_API_KEY`. Codex ignores `OPENAI_BASE_URL`, so it is additionally pointed at the proxy through a custom provider passed as `-c` config overrides (HTTP/SSE Responses transport, since the proxy does not speak the Responses WebSocket protocol). OpenCode additionally gets `OPENCODE_CONFIG_CONTENT` holding a generated `litellm` provider (`@ai-sdk/openai-compatible`, the proxy `/v1` URL, `{env:OPENAI_API_KEY}`) with one model entry per chat model your key can see on `/v1/models`, so its model picker mirrors the proxy without a hand-maintained `opencode.json`; OpenCode merges that over your own config files, and if you already export `OPENCODE_CONFIG_CONTENT` yours is left alone. When the list cannot be fetched, `lite opencode` says so on stderr and launches anyway. Codex gets the same list as a catalog file, `$CODEX_HOME/litellm-models.json` (default `~/.codex/`), passed as `-c model_catalog_json=` so `/model` lists exactly the proxy's chat models; before launching, `lite codex` has the installed Codex read that file back (`codex debug models`), and when the fetch, the write or that read-back fails (Codex releases older than 0.130 have no such command) it says so on stderr and launches with Codex's built-in catalog, leaving the rejected file in place. +The right variables are picked per agent. Claude Code gets `ANTHROPIC_BASE_URL` (the proxy root, so it appends `/v1/messages`) and `ANTHROPIC_AUTH_TOKEN`, with any stray `ANTHROPIC_API_KEY` cleared so the proxy token wins, and `ENABLE_TOOL_SEARCH=true` (unless you already set it) so Claude Code keeps tool search on even though the base URL is a proxy rather than a first-party Anthropic host. It also gets `CLAUDE_CODE_ENABLE_GATEWAY_MODEL_DISCOVERY=1` (again unless you already set it) so Claude Code v2.1.129+ fills its `/model` picker from the proxy's `/v1/models`; Claude Code only lists entries whose id contains `claude` or `anthropic`, so the proxy lists every other group to Claude Code as `claude-router-` and marks a group whose input window reaches 1M with `[1m]`, and a request on such an id is served by the group. Older Claude Code versions ignore the variable. Export it as `0` to turn discovery off. Codex and OpenCode get `OPENAI_BASE_URL` (the proxy plus `/v1`) and `OPENAI_API_KEY`. Codex ignores `OPENAI_BASE_URL`, so it is additionally pointed at the proxy through a custom provider passed as `-c` config overrides (HTTP/SSE Responses transport, since the proxy does not speak the Responses WebSocket protocol). OpenCode additionally gets `OPENCODE_CONFIG_CONTENT` holding a generated `litellm` provider (`@ai-sdk/openai-compatible`, the proxy `/v1` URL, `{env:OPENAI_API_KEY}`) with one model entry per chat model your key can see on `/v1/models`, so its model picker mirrors the proxy without a hand-maintained `opencode.json`; OpenCode merges that over your own config files, and if you already export `OPENCODE_CONFIG_CONTENT` yours is left alone. When the list cannot be fetched, `lite opencode` says so on stderr and launches anyway. Codex gets the same list as a catalog file, `$CODEX_HOME/litellm-models.json` (default `~/.codex/`), passed as `-c model_catalog_json=` so `/model` lists exactly the proxy's chat models; a proxy model the installed Codex already knows (`gpt-5.5`, say) keeps that Codex's own entry, reasoning levels and prompt included, and only its place in the picker comes from the proxy, while a model Codex does not know gets the plain entry Codex uses for an unknown `-m` slug. Before launching, `lite codex` asks the installed Codex for its own list and then has it read the written file back (both through `codex debug models`), and when the fetch, either of those or the write fails (Codex releases older than 0.130 have no such command) it says so on stderr and launches with Codex's built-in catalog, leaving a rejected file in place. pi ignores base-URL environment variables entirely, so `lite pi` (kept out of the `lite --help` command listing for now, but fully functional) wires it up differently: before handoff it fetches the models your key can use from the proxy's `/v1/models` (plus each model's context window and output cap from `/model_group/info`, when available) and syncs them into a `litellm` provider entry in pi's `~/.pi/agent/models.json` (honoring `PI_CODING_AGENT_DIR`), then starts pi on that provider's first model via an injected `--model litellm/`. Only that one provider entry is rewritten; the rest of the file, including any other custom providers, is left alone. The entry references the key as `$LITELLM_PROXY_API_KEY`, which the wrapper exports for the session, so the token itself never lands on disk and plain `pi` outside the wrapper simply shows the litellm models as unavailable. Your own flags come after the injected pin, so `lite pi --model litellm/` wins, and inside the TUI the `/model` picker lists every synced litellm model. diff --git a/litellm/proxy/client/cli/commands/agents.py b/litellm/proxy/client/cli/commands/agents.py index 8a15153ea28..1422514372a 100644 --- a/litellm/proxy/client/cli/commands/agents.py +++ b/litellm/proxy/client/cli/commands/agents.py @@ -13,7 +13,7 @@ from typing import Final, Literal, TypeAlias import click import requests -from pydantic import BaseModel, TypeAdapter, ValidationError +from pydantic import BaseModel, ConfigDict, TypeAdapter, ValidationError from .auth import CliContextObj, context_secret_vault, get_stored_api_key, login from .claude_settings import ClaudeSettingsError, install_statusline_script @@ -398,13 +398,13 @@ class _CodexTruncationPolicy(BaseModel): class _CodexModel(BaseModel): - """One `ModelInfo` entry of a Codex model catalog. + """One `ModelInfo` entry of a Codex model catalog for a model the installed Codex does not know. Every field that some Codex release since `model_catalog_json` appeared (0.105.0) deserializes without a default is spelled out here, so one catalog parses on all of them; the values match the fallback metadata Codex uses for - a model slug it does not know, so picking a proxy model behaves the same as - `codex -m` did. + a model slug it does not know, so picking such a proxy model behaves the + same as `codex -m` did. """ slug: str @@ -428,31 +428,77 @@ class _CodexModel(BaseModel): base_instructions: str +class _StockCodexUpgrade(BaseModel): + model_config = ConfigDict(extra="allow") + + model: str + + +class _StockCodexModel(BaseModel): + """One `ModelInfo` entry as the installed Codex prints it from `codex debug models`. + + Only the fields the sync rewrites are named; everything else that release + knows about the model (its reasoning levels, prompt, tool support) rides + along untouched, whatever the release's schema. + """ + + model_config = ConfigDict(extra="allow") + + slug: str + priority: int + visibility: str + upgrade: _StockCodexUpgrade | None = None + + +class _StockCodexCatalog(BaseModel): + models: tuple[_StockCodexModel, ...] + + class _CodexCatalog(BaseModel): - models: tuple[_CodexModel, ...] + models: tuple[_CodexModel | _StockCodexModel, ...] -def codex_model_catalog(models: Sequence[ListedModel], instructions: str) -> str | None: +def _codex_catalog_entry( + priority: int, + listed: ListedModel, + stock: _StockCodexModel | None, + served: frozenset[str], + instructions: str, +) -> _CodexModel | _StockCodexModel: + if stock is None: + return _CodexModel( + slug=listed.id, + display_name=listed.id, + priority=priority, + context_window=listed.max_input_tokens, + base_instructions=instructions, + ) + upgrade: Final = stock.upgrade if stock.upgrade is not None and stock.upgrade.model in served else None + return stock.model_copy(update={"priority": priority, "visibility": "list", "upgrade": upgrade}) + + +def codex_model_catalog( + models: Sequence[ListedModel], stock: Sequence[_StockCodexModel], instructions: str +) -> str | None: """The `model_catalog_json` body listing the proxy's chat models, or None if there are none. Codex refuses an empty catalog, hence None instead of `{"models": []}`. - Passing a catalog replaces Codex's built-in one, so every entry carries the - same base instructions Codex itself uses, otherwise the agent would run - without a system prompt. + Passing a catalog replaces Codex's built-in one, so a proxy model the + installed Codex knows keeps that Codex's own entry and the proxy only + decides its place in the picker: the listing orders it, lists it even when + Codex hides it, and keeps Codex's upgrade nudge only when the model it + points at is served too. A model Codex does not know gets the fallback + entry, with the same base instructions Codex itself uses so the agent never + runs without a system prompt. """ chat_models: Final = _chat_models(models) if not chat_models: return None + served: Final = frozenset(m.id for m in chat_models) + known: Final = MappingProxyType({m.slug: m for m in stock}) catalog: Final = _CodexCatalog( models=tuple( - _CodexModel( - slug=m.id, - display_name=m.id, - priority=index, - context_window=m.max_input_tokens, - base_instructions=instructions, - ) - for index, m in enumerate(chat_models) + _codex_catalog_entry(index, m, known.get(m.id), served, instructions) for index, m in enumerate(chat_models) ) ) return catalog.model_dump_json() @@ -465,7 +511,6 @@ def codex_model_catalog_path(env: Mapping[str, str], *, home: Callable[[], Path] def _replace_file(path: Path, text: str) -> None: - path.parent.mkdir(parents=True, exist_ok=True) with tempfile.NamedTemporaryFile("w", encoding="utf-8", dir=path.parent, delete=False) as tmp: _ = tmp.write(text) try: @@ -475,23 +520,23 @@ def _replace_file(path: Path, text: str) -> None: raise -def _codex_catalog_rejection( +def _codex_debug_models( binary: str, - override: str, + args: Sequence[str], env: Mapping[str, str], *, run: Callable[..., subprocess.CompletedProcess[str]], -) -> str | None: - """Why the installed Codex refuses the catalog, or None once it reads the file back. +) -> str | ModelSyncSkipped: + """What `codex debug models` prints with `args` in front, or why the installed Codex could not run it. - `codex debug models` parses the catalog the way a launch does, so a Codex - whose ModelInfo schema disagrees with the one written here fails now, with - the sync skipped, instead of exiting on startup. Releases before 0.130.0 - have no `debug models` and fail the same way. A batch shim goes through + The command prints the catalog Codex would launch with, without touching + the network, so it lists the installed Codex's own models and parses a + catalog override the way a launch does. Releases before 0.130.0 have no + such command and are reported the same way. A batch shim goes through cmd.exe exactly as the launch will. """ name: Final = os.path.basename(binary) - command: Final = _windows_command(binary, (binary, "-c", override, "debug", "models")) + command: Final = _windows_command(binary, (binary, *args, "debug", "models")) try: completed: Final = run( command, @@ -502,11 +547,25 @@ def _codex_catalog_rejection( timeout=_CODEX_PREFLIGHT_TIMEOUT_SECONDS, ) except (OSError, subprocess.TimeoutExpired) as e: - return f"`{name} debug models` failed: {e}" + return ModelSyncSkipped(f"`{name} debug models` failed: {e}") if completed.returncode == 0: - return None + return completed.stdout lines: Final = completed.stderr.strip().splitlines() - return f"`{name} debug models` exited {completed.returncode}: {lines[0] if lines else 'no output'}" + detail: Final = lines[0] if lines else "no output" + return ModelSyncSkipped(f"`{name} debug models` exited {completed.returncode}: {detail}") + + +def _stock_codex_models( + binary: str, env: Mapping[str, str], *, run: Callable[..., subprocess.CompletedProcess[str]] +) -> tuple[_StockCodexModel, ...] | ModelSyncSkipped: + printed: Final = _codex_debug_models(binary, (), env, run=run) + if isinstance(printed, ModelSyncSkipped): + return printed + try: + return _StockCodexCatalog.model_validate_json(printed).models + except ValidationError as e: + name: Final = os.path.basename(binary) + return ModelSyncSkipped(f"`{name} debug models` printed no model catalog: {e.errors()[0]['msg']}") def codex_model_sync_args( @@ -524,11 +583,13 @@ def codex_model_sync_args( Codex has no env or inline equivalent of OPENCODE_CONFIG_CONTENT: the catalog must be a file, so it is written under $CODEX_HOME (default ~/.codex) and - atomically replaced on every launch, then read back once through the Codex - at `binary` before it is handed over. The key never lands in the file. A - failed fetch, read, write or read-back is reported rather than raised: Codex - still launches with its built-in catalog and takes a proxy model by name via - -m, and a rejected file stays on disk to be looked at. + atomically replaced on every launch. The Codex at `binary` first lists its + own models, so the ones the proxy serves keep that Codex's entries, and then + reads the file back once before it is handed over. The key never lands in + the file. A failed fetch, read, listing, write or read-back is reported + rather than raised: Codex still launches with its built-in catalog and takes + a proxy model by name via -m, and a rejected file stays on disk to be looked + at. """ listing: Final = _fetch_model_listing(base_url, api_key, get=get) if isinstance(listing, ModelSyncSkipped): @@ -537,18 +598,25 @@ def codex_model_sync_args( instructions: Final = instructions_path.read_text(encoding="utf-8") except OSError as e: return ModelSyncSkipped(f"could not read {instructions_path}: {e}") - catalog: Final = codex_model_catalog(listing, instructions) + path: Final = codex_model_catalog_path(base_env, home=home) + try: + path.parent.mkdir(parents=True, exist_ok=True) + except OSError as e: + return ModelSyncSkipped(f"could not write {path}: {e}") + stock: Final = _stock_codex_models(binary, base_env, run=run) + if isinstance(stock, ModelSyncSkipped): + return stock + catalog: Final = codex_model_catalog(listing, stock, instructions) if catalog is None: return ModelSyncSkipped(f"{base_url.rstrip('/')}/v1/models lists no chat models") - path: Final = codex_model_catalog_path(base_env, home=home) try: _replace_file(path, catalog) except OSError as e: return ModelSyncSkipped(f"could not write {path}: {e}") override: Final = f"model_catalog_json={json.dumps(str(path))}" - rejection: Final = _codex_catalog_rejection(binary, override, base_env, run=run) - if rejection is not None: - return ModelSyncSkipped(rejection) + read_back: Final = _codex_debug_models(binary, ("-c", override), base_env, run=run) + if isinstance(read_back, ModelSyncSkipped): + return read_back return ModelSyncArgs(("-c", override)) diff --git a/tests/test_litellm/proxy/client/cli/test_agents.py b/tests/test_litellm/proxy/client/cli/test_agents.py index 8e76e4745f1..cc7c3a14f44 100644 --- a/tests/test_litellm/proxy/client/cli/test_agents.py +++ b/tests/test_litellm/proxy/client/cli/test_agents.py @@ -57,14 +57,109 @@ class _Recorder: return self.returns +_STOCK_REASONING_LEVELS = [ + {"effort": "low", "description": "Fast responses with lighter reasoning"}, + {"effort": "medium", "description": "Balances speed and reasoning depth for everyday tasks"}, + {"effort": "high", "description": "Greater reasoning depth for complex problems"}, +] + +_STOCK_MODELS = { + "gpt-5.6-terra": { + "slug": "gpt-5.6-terra", + "display_name": "GPT-5.6 Terra", + "description": "Balanced agentic coding model for everyday work.", + "default_reasoning_level": "medium", + "supported_reasoning_levels": _STOCK_REASONING_LEVELS, + "shell_type": "unified_exec", + "visibility": "list", + "supported_in_api": True, + "priority": 7, + "availability_nux": None, + "upgrade": None, + "base_instructions": "You are Codex, a coding agent based on GPT-5.6.", + "apply_patch_tool_type": "freeform", + "supports_parallel_tool_calls": True, + "context_window": 272000, + "comp_hash": "terra-hash", + }, + "gpt-5.5": { + "slug": "gpt-5.5", + "display_name": "GPT-5.5", + "description": "Frontier model for complex coding, research, and real-world work.", + "default_reasoning_level": "medium", + "supported_reasoning_levels": _STOCK_REASONING_LEVELS, + "shell_type": "unified_exec", + "visibility": "list", + "supported_in_api": True, + "priority": 12, + "availability_nux": None, + "upgrade": None, + "base_instructions": "You are Codex, a coding agent based on GPT-5.", + "apply_patch_tool_type": "freeform", + "supports_parallel_tool_calls": True, + "context_window": 272000, + "comp_hash": "gpt-5.5-hash", + }, + "gpt-5.4": { + "slug": "gpt-5.4", + "display_name": "GPT-5.4", + "description": "Strong model for everyday coding.", + "default_reasoning_level": "medium", + "supported_reasoning_levels": _STOCK_REASONING_LEVELS, + "shell_type": "unified_exec", + "visibility": "hide", + "supported_in_api": True, + "priority": 16, + "availability_nux": None, + "upgrade": { + "model": "gpt-5.6-terra", + "migration_markdown": "GPT-5.4 is no longer available. Switch to GPT-5.6 Terra to continue.", + "retirement_at": "2026-08-31T19:00:00Z", + }, + "base_instructions": "You are Codex, a coding agent based on GPT-5.", + "apply_patch_tool_type": "freeform", + "supports_parallel_tool_calls": True, + "context_window": 272000, + "comp_hash": "gpt-5.4-hash", + }, + "codex-auto-review": { + "slug": "codex-auto-review", + "display_name": "Codex Auto Review", + "description": None, + "supported_reasoning_levels": [], + "shell_type": "unified_exec", + "visibility": "hide", + "supported_in_api": False, + "priority": 43, + "availability_nux": None, + "upgrade": None, + "base_instructions": "You are Codex, reviewing a change.", + "apply_patch_tool_type": None, + "supports_parallel_tool_calls": True, + "context_window": 272000, + "comp_hash": "review-hash", + }, +} + +_STOCK_CATALOG = json.dumps({"models": list(_STOCK_MODELS.values())}) + + class _FakeRun: - def __init__(self, returncode=0, stderr=""): + """A `codex` that prints `stock` from a bare `debug models` and answers a catalog override with `returncode`. + + `stock=None` is a Codex with no `debug models` at all: every call answers with `returncode` and `stderr`. + """ + + def __init__(self, returncode=0, stderr="", stock=_STOCK_CATALOG): self.returncode = returncode self.stderr = stderr + self.stock = stock self.calls = [] def __call__(self, args, **kwargs): self.calls.append((args, kwargs)) + if self.stock is not None and "model_catalog_json=" not in str(args): + return subprocess.CompletedProcess(args, 0, self.stock, "") return subprocess.CompletedProcess(args, self.returncode, "", self.stderr) @@ -415,10 +510,36 @@ class TestCodexModelSync: assert "sk-key" not in text catalog = json.loads(text) assert [m["slug"] for m in catalog["models"]] == ["gpt-5.5", "claude-opus-4-7"] - assert [m["display_name"] for m in catalog["models"]] == ["gpt-5.5", "claude-opus-4-7"] + assert [m["display_name"] for m in catalog["models"]] == ["GPT-5.5", "claude-opus-4-7"] assert [m["priority"] for m in catalog["models"]] == [0, 1] - def test_every_entry_has_the_fields_codex_requires(self, tmp_path): + def _entries(self, codex_home): + return {m["slug"]: m for m in json.loads((codex_home / "litellm-models.json").read_text())["models"]} + + def test_known_model_keeps_the_installed_codex_entry(self, tmp_path): + self._sync(self._listing(self._row("gpt-5.5", mode="chat")), tmp_path) + assert self._entries(tmp_path)["gpt-5.5"] == {**_STOCK_MODELS["gpt-5.5"], "priority": 0} + + def test_hidden_stock_model_is_listed_when_the_proxy_serves_it(self, tmp_path): + self._sync(self._listing(self._row("gpt-5.4")), tmp_path) + entry = self._entries(tmp_path)["gpt-5.4"] + assert entry["visibility"] == "list" + assert entry["upgrade"] is None + assert entry["supported_reasoning_levels"] == _STOCK_REASONING_LEVELS + + def test_stock_upgrade_nudge_survives_when_its_target_is_listed(self, tmp_path): + self._sync(self._listing(self._row("gpt-5.4"), self._row("gpt-5.6-terra")), tmp_path) + entries = self._entries(tmp_path) + assert entries["gpt-5.4"]["upgrade"] == _STOCK_MODELS["gpt-5.4"]["upgrade"] + assert [entries["gpt-5.4"]["priority"], entries["gpt-5.6-terra"]["priority"]] == [0, 1] + + def test_unparseable_stock_catalog_is_reported(self, tmp_path): + _, result = self._sync(self._listing(self._row("m")), tmp_path, run=_FakeRun(stock="not json")) + assert isinstance(result, ModelSyncSkipped) + assert result.reason.startswith("`codex debug models` printed no model catalog: ") + assert not (tmp_path / "litellm-models.json").exists() + + def test_unknown_model_gets_the_fields_codex_requires(self, tmp_path): _, result = self._sync(self._listing(self._row("m")), tmp_path) entry = json.loads((tmp_path / "litellm-models.json").read_text())["models"][0] @@ -435,12 +556,17 @@ class TestCodexModelSync: assert nullable in entry and entry[nullable] is None assert entry["base_instructions"].startswith("You are a coding agent running in the Codex CLI") - def test_context_window_comes_from_max_input_tokens(self, tmp_path): - listing = self._listing(self._row("big", max_input_tokens=400000), self._row("unknown")) - _, result = self._sync(listing, tmp_path) - models = {m["slug"]: m for m in json.loads((tmp_path / "litellm-models.json").read_text())["models"]} + def test_context_window_comes_from_max_input_tokens_for_unknown_models_only(self, tmp_path): + listing = self._listing( + self._row("big", max_input_tokens=400000), + self._row("unknown"), + self._row("gpt-5.5", max_input_tokens=400000), + ) + self._sync(listing, tmp_path) + models = self._entries(tmp_path) assert models["big"]["context_window"] == 400000 assert models["unknown"]["context_window"] is None + assert models["gpt-5.5"]["context_window"] == 272000 def test_non_chat_models_are_left_out(self, tmp_path): listing = self._listing( @@ -544,7 +670,8 @@ class TestCodexModelSync: run=run, ) assert self._catalog_path(result) == str(tmp_path / "litellm-models.json") - assert binary in run.calls[0][0] + assert len(run.calls) == 2 + assert all(binary in command for command, _ in run.calls) def test_opencode_dispatch_never_runs_codex(self): def boom(*a, **k): @@ -561,19 +688,21 @@ class TestCodexModelSync: ) assert "OPENCODE_CONFIG_CONTENT" in result - def test_catalog_is_read_back_through_codex_before_launch(self, tmp_path): + def test_codex_lists_its_own_models_then_reads_the_catalog_back_before_launch(self, tmp_path): run = _FakeRun() _, result = self._sync(self._listing(self._row("m")), tmp_path, run=run) path = self._catalog_path(result) - assert len(run.calls) == 1 - command, options = run.calls[0] - assert command == ("codex", "-c", f"model_catalog_json={json.dumps(path)}", "debug", "models") - assert options["env"] == {"CODEX_HOME": str(tmp_path)} - assert options["stdin"] is subprocess.DEVNULL - assert options["capture_output"] is True - assert options["text"] is True - assert options["timeout"] == 10 + assert [command for command, _ in run.calls] == [ + ("codex", "debug", "models"), + ("codex", "-c", f"model_catalog_json={json.dumps(path)}", "debug", "models"), + ] + for _, options in run.calls: + assert options["env"] == {"CODEX_HOME": str(tmp_path)} + assert options["stdin"] is subprocess.DEVNULL + assert options["capture_output"] is True + assert options["text"] is True + assert options["timeout"] == 10 def test_codex_rejecting_the_catalog_skips_the_sync_and_keeps_the_file(self, tmp_path): stderr = ( @@ -591,9 +720,12 @@ class TestCodexModelSync: def test_codex_without_debug_models_skips_the_sync(self, tmp_path): stderr = "error: unrecognized subcommand 'models'\n\nUsage: codex debug [OPTIONS] \n" - _, result = self._sync(self._listing(self._row("m")), tmp_path, run=_FakeRun(2, stderr)) + run = _FakeRun(2, stderr, stock=None) + _, result = self._sync(self._listing(self._row("m")), tmp_path, run=run) assert isinstance(result, ModelSyncSkipped) assert result.reason == "`codex debug models` exited 2: error: unrecognized subcommand 'models'" + assert len(run.calls) == 1 + assert not (tmp_path / "litellm-models.json").exists() def test_codex_failing_silently_is_reported(self, tmp_path): _, result = self._sync(self._listing(self._row("m")), tmp_path, run=_FakeRun(1)) @@ -625,7 +757,10 @@ class TestCodexModelSync: ) override = f"model_catalog_json={json.dumps(self._catalog_path(result))}" doubled = override.replace('"', '""') - assert run.calls[0][0] == f'{_CMD_PREFIX}""{shim}" "-c" "{doubled}" "debug" "models""' + assert [command for command, _ in run.calls] == [ + f'{_CMD_PREFIX}""{shim}" "debug" "models""', + f'{_CMD_PREFIX}""{shim}" "-c" "{doubled}" "debug" "models""', + ] def test_default_binary_is_codex_on_path(self): assert _default_of(codex_model_sync_args, "binary") == "codex" From 15721e52effaa79ad042b23ef91188798a20be43 Mon Sep 17 00:00:00 2001 From: mateo-berri <277851410+mateo-berri@users.noreply.github.com> Date: Sat, 12 Sep 2026 16:50:50 -0700 Subject: [PATCH 041/225] fix(cli): mark proxy-served stock Codex models as selectable with an API key Codex hides catalog entries whose supported_in_api is false when it runs with an API key, so a stock entry the proxy serves now carries supported_in_api true alongside its list visibility. --- litellm/proxy/client/cli/commands/agents.py | 9 ++++++--- tests/test_litellm/proxy/client/cli/test_agents.py | 7 +++++++ 2 files changed, 13 insertions(+), 3 deletions(-) diff --git a/litellm/proxy/client/cli/commands/agents.py b/litellm/proxy/client/cli/commands/agents.py index 1422514372a..decb520c3a0 100644 --- a/litellm/proxy/client/cli/commands/agents.py +++ b/litellm/proxy/client/cli/commands/agents.py @@ -447,6 +447,7 @@ class _StockCodexModel(BaseModel): slug: str priority: int visibility: str + supported_in_api: bool = True upgrade: _StockCodexUpgrade | None = None @@ -474,7 +475,9 @@ def _codex_catalog_entry( base_instructions=instructions, ) upgrade: Final = stock.upgrade if stock.upgrade is not None and stock.upgrade.model in served else None - return stock.model_copy(update={"priority": priority, "visibility": "list", "upgrade": upgrade}) + return stock.model_copy( + update={"priority": priority, "visibility": "list", "supported_in_api": True, "upgrade": upgrade} + ) def codex_model_catalog( @@ -486,8 +489,8 @@ def codex_model_catalog( Passing a catalog replaces Codex's built-in one, so a proxy model the installed Codex knows keeps that Codex's own entry and the proxy only decides its place in the picker: the listing orders it, lists it even when - Codex hides it, and keeps Codex's upgrade nudge only when the model it - points at is served too. A model Codex does not know gets the fallback + Codex hides it or keeps it off the API, and keeps Codex's upgrade nudge only + when the model it points at is served too. A model Codex does not know gets the fallback entry, with the same base instructions Codex itself uses so the agent never runs without a system prompt. """ diff --git a/tests/test_litellm/proxy/client/cli/test_agents.py b/tests/test_litellm/proxy/client/cli/test_agents.py index cc7c3a14f44..c437f4c12c5 100644 --- a/tests/test_litellm/proxy/client/cli/test_agents.py +++ b/tests/test_litellm/proxy/client/cli/test_agents.py @@ -527,6 +527,13 @@ class TestCodexModelSync: assert entry["upgrade"] is None assert entry["supported_reasoning_levels"] == _STOCK_REASONING_LEVELS + def test_api_disabled_stock_model_is_selectable_when_the_proxy_serves_it(self, tmp_path): + self._sync(self._listing(self._row("codex-auto-review")), tmp_path) + entry = self._entries(tmp_path)["codex-auto-review"] + assert entry["supported_in_api"] is True + assert entry["visibility"] == "list" + assert entry["base_instructions"] == _STOCK_MODELS["codex-auto-review"]["base_instructions"] + def test_stock_upgrade_nudge_survives_when_its_target_is_listed(self, tmp_path): self._sync(self._listing(self._row("gpt-5.4"), self._row("gpt-5.6-terra")), tmp_path) entries = self._entries(tmp_path) From c15f3e92289125e02d1f351d074c37c4241791e2 Mon Sep 17 00:00:00 2001 From: mateo-berri <277851410+mateo-berri@users.noreply.github.com> Date: Sat, 12 Sep 2026 17:29:09 -0700 Subject: [PATCH 042/225] fix(cli): decode codex debug models output as UTF-8 --- litellm/proxy/client/cli/commands/agents.py | 2 +- .../proxy/client/cli/test_agents.py | 18 +++++++++++++++++- 2 files changed, 18 insertions(+), 2 deletions(-) diff --git a/litellm/proxy/client/cli/commands/agents.py b/litellm/proxy/client/cli/commands/agents.py index decb520c3a0..15b111ff016 100644 --- a/litellm/proxy/client/cli/commands/agents.py +++ b/litellm/proxy/client/cli/commands/agents.py @@ -546,7 +546,7 @@ def _codex_debug_models( env=dict(env), stdin=subprocess.DEVNULL, capture_output=True, - text=True, + encoding="utf-8", timeout=_CODEX_PREFLIGHT_TIMEOUT_SECONDS, ) except (OSError, subprocess.TimeoutExpired) as e: diff --git a/tests/test_litellm/proxy/client/cli/test_agents.py b/tests/test_litellm/proxy/client/cli/test_agents.py index c437f4c12c5..f6f1c2fec3b 100644 --- a/tests/test_litellm/proxy/client/cli/test_agents.py +++ b/tests/test_litellm/proxy/client/cli/test_agents.py @@ -540,6 +540,22 @@ class TestCodexModelSync: assert entries["gpt-5.4"]["upgrade"] == _STOCK_MODELS["gpt-5.4"]["upgrade"] assert [entries["gpt-5.4"]["priority"], entries["gpt-5.6-terra"]["priority"]] == [0, 1] + def test_stock_catalog_is_decoded_as_utf8_regardless_of_locale(self, tmp_path): + description = "Modelo equilibrado para el trabajo diario, con acentos y ñ." + catalog = {"models": [{**_STOCK_MODELS["gpt-5.5"], "description": description}]} + stock = json.dumps(catalog, ensure_ascii=False).encode("utf-8") + + def locale_bound_run(args, **kwargs): + if "model_catalog_json=" in str(args): + return subprocess.CompletedProcess(args, 0, "", "") + return subprocess.CompletedProcess(args, 0, stock.decode(kwargs.get("encoding") or "ascii"), "") + + _, result = self._sync(self._listing(self._row("gpt-5.5")), tmp_path, run=locale_bound_run) + + assert isinstance(result, ModelSyncArgs) + written = json.loads((tmp_path / "litellm-models.json").read_text(encoding="utf-8"))["models"] + assert [m["description"] for m in written] == [description] + def test_unparseable_stock_catalog_is_reported(self, tmp_path): _, result = self._sync(self._listing(self._row("m")), tmp_path, run=_FakeRun(stock="not json")) assert isinstance(result, ModelSyncSkipped) @@ -708,7 +724,7 @@ class TestCodexModelSync: assert options["env"] == {"CODEX_HOME": str(tmp_path)} assert options["stdin"] is subprocess.DEVNULL assert options["capture_output"] is True - assert options["text"] is True + assert options["encoding"] == "utf-8" assert options["timeout"] == 10 def test_codex_rejecting_the_catalog_skips_the_sync_and_keeps_the_file(self, tmp_path): From d7623112fa817c4c7e949d0ef49c10e8851352e2 Mon Sep 17 00:00:00 2001 From: yucheng Date: Sun, 13 Sep 2026 05:15:33 +0000 Subject: [PATCH 043/225] fix(mcp): hand Agent 365 the admin description override and clear the LIT001 and schema gates get_listed_tool now applies tool_name_to_description so the guardrail evaluates the description clients saw, with a regression test. The listed-tools cache and tool_input_schema fields use Mapping annotations so the change adds no LIT001 findings, and schema.d.ts is regenerated with npm run gen:api Co-Authored-By: Devin AI <158243242+devin-ai-integration[bot]@users.noreply.github.com> --- .../mcp_server/mcp_server_manager.py | 13 ++++++---- .../guardrail_hooks/agent_365/agent_365.py | 4 ++-- litellm/types/mcp.py | 4 ++-- .../mcp_server/test_mcp_server_manager.py | 24 +++++++++++++++++++ ui/litellm-dashboard/src/lib/http/schema.d.ts | 2 -- 5 files changed, 36 insertions(+), 11 deletions(-) diff --git a/litellm/proxy/_experimental/mcp_server/mcp_server_manager.py b/litellm/proxy/_experimental/mcp_server/mcp_server_manager.py index 696d496da54..560d3d51177 100644 --- a/litellm/proxy/_experimental/mcp_server/mcp_server_manager.py +++ b/litellm/proxy/_experimental/mcp_server/mcp_server_manager.py @@ -242,7 +242,8 @@ _user_env_vars_cache: Final[dict[tuple[str, str], tuple[dict[str, str], float]]] _USER_ENV_VARS_CACHE_TTL: Final = 60 # seconds _USER_ENV_VARS_CACHE_MAX_SIZE: Final = 4096 # cap to prevent unbounded growth -_NO_LISTED_TOOLS: Final[Mapping[str | None, Mapping[str, MCPTool]]] = MappingProxyType({}) +_ListedToolsByCaller: TypeAlias = Mapping[str | None, Mapping[str, MCPTool]] +_NO_LISTED_TOOLS: Final[_ListedToolsByCaller] = MappingProxyType({}) _LISTED_TOOLS_CALLERS_PER_SERVER: Final = 256 # Auth types whose upstream OAuth endpoints (protected-resource + authorization-server metadata) the @@ -1948,9 +1949,7 @@ class MCPServerManager: "gmail_send_email": "zapier_mcp_server", } """ - self._listed_tools_by_server_id: dict[ - str, Mapping[str | None, Mapping[str, MCPTool]] - ] = {} # mutable-ok: refreshed per tools/list + self._listed_tools_by_server_id: dict[str, _ListedToolsByCaller] = {} # mutable-ok: refreshed per tools/list self._upstream_initialize_instructions_by_server_id: dict[str, str] = {} # Per-server monotonic timestamp of last upstream prefetch attempt (success, # empty result, or failure). Used to throttle re-probes for servers that do @@ -5392,7 +5391,11 @@ class MCPServerManager: listed: Final = self._listed_tools_by_server_id.get(server.server_id, _NO_LISTED_TOOLS).get(identity) if not listed: return None - return listed.get(name) or listed.get(strip_known_server_prefix(name, server)) + tool: Final = listed.get(name) or listed.get(strip_known_server_prefix(name, server)) + if tool is None: + return None + description: Final = (server.tool_name_to_description or {}).get(tool.name) + return tool if description is None else tool.model_copy(update={"description": description}) def _create_prefixed_prompts( self, prompts: Sequence[Prompt], server: MCPServer, add_prefix: bool = True diff --git a/litellm/proxy/guardrails/guardrail_hooks/agent_365/agent_365.py b/litellm/proxy/guardrails/guardrail_hooks/agent_365/agent_365.py index 58d8ba5f865..d81fa2f095b 100644 --- a/litellm/proxy/guardrails/guardrail_hooks/agent_365/agent_365.py +++ b/litellm/proxy/guardrails/guardrail_hooks/agent_365/agent_365.py @@ -77,7 +77,7 @@ def _parse_expires_in(raw: object) -> float: return _DEFAULT_TOKEN_TTL_SECONDS -def _parse_tool_input_schema(raw: object) -> dict[str, object] | None: +def _parse_tool_input_schema(raw: object) -> Mapping[str, object] | None: try: return _TOOL_INPUT_SCHEMA_ADAPTER.validate_python(raw) except ValidationError: @@ -101,7 +101,7 @@ class _ToolReference(BaseModel): name: str description: str | None = None - input_schema: dict[str, object] | None = Field(default=None, serialization_alias="inputSchema") + input_schema: Mapping[str, object] | None = Field(default=None, serialization_alias="inputSchema") class _UnavailableDetail(TypedDict): diff --git a/litellm/types/mcp.py b/litellm/types/mcp.py index 13a46d8b84f..87ffaa7cd9e 100644 --- a/litellm/types/mcp.py +++ b/litellm/types/mcp.py @@ -392,7 +392,7 @@ class MCPPreCallRequestObject(BaseModel): arguments: dict[str, Any] server_name: str | None = None tool_description: str | None = None - tool_input_schema: dict[str, object] | None = None + tool_input_schema: Mapping[str, object] | None = None user_api_key_auth: dict[str, Any] | None = None hidden_params: HiddenParams = HiddenParams() @@ -417,7 +417,7 @@ class MCPDuringCallRequestObject(BaseModel): arguments: dict[str, Any] server_name: str | None = None tool_description: str | None = None - tool_input_schema: dict[str, object] | None = None + tool_input_schema: Mapping[str, object] | None = None start_time: float | None = None hidden_params: HiddenParams = HiddenParams() diff --git a/tests/test_litellm/proxy/_experimental/mcp_server/test_mcp_server_manager.py b/tests/test_litellm/proxy/_experimental/mcp_server/test_mcp_server_manager.py index 1bdb1d0a427..56e693e3523 100644 --- a/tests/test_litellm/proxy/_experimental/mcp_server/test_mcp_server_manager.py +++ b/tests/test_litellm/proxy/_experimental/mcp_server/test_mcp_server_manager.py @@ -6672,6 +6672,30 @@ class TestMCPServerManager: assert by_prefixed_name is not None and by_prefixed_name.description == "v2" assert manager.get_listed_tool(server, "missing") is None + def test_get_listed_tool_uses_admin_description_override_clients_saw(self): + manager = MCPServerManager() + server = MCPServer( + server_id="srv", + name="srv", + transport=MCPTransport.http, + url="http://srv", + tool_name_to_description={"echo": "Admin wording"}, + ) + schema = {"type": "object", "properties": {"text": {"type": "string"}}} + manager._create_prefixed_tools( + [ + MCPTool(name="echo", description="Upstream wording", inputSchema=schema), + MCPTool(name="ping", description="Untouched", inputSchema={}), + ], + server, + ) + + overridden = manager.get_listed_tool(server, "srv-echo") + assert overridden is not None + assert (overridden.name, overridden.description, overridden.inputSchema) == ("echo", "Admin wording", schema) + untouched = manager.get_listed_tool(server, "ping") + assert untouched is not None and untouched.description == "Untouched" + def test_server_definition_change_drops_listed_tools(self): manager = MCPServerManager() server = MCPServer(server_id="srv", name="srv", transport=MCPTransport.http, url="http://srv") diff --git a/ui/litellm-dashboard/src/lib/http/schema.d.ts b/ui/litellm-dashboard/src/lib/http/schema.d.ts index 3b172b8d35f..7df6fbc5863 100644 --- a/ui/litellm-dashboard/src/lib/http/schema.d.ts +++ b/ui/litellm-dashboard/src/lib/http/schema.d.ts @@ -16781,7 +16781,6 @@ export interface paths { * - permissions: Optional[dict] - [Not Implemented Yet] User-specific permissions, eg. turning off pii masking. * - metadata: Optional[dict] - Metadata for user, store information for user. Example metadata = {"team": "core-infra", "app": "app2", "email": "ishaan@berri.ai" } * - max_parallel_requests: Optional[int] - Rate limit a user based on the number of parallel requests. Raises 429 error, if user's parallel requests > x. - * - soft_budget: Optional[float] - Get alerts when user crosses given budget, doesn't block requests. * - model_max_budget: Optional[dict] - Model-specific max budget for user. [Docs](https://docs.litellm.ai/docs/proxy/users#add-model-specific-budgets-to-keys) * - budget_fallbacks: Optional[Dict[str, List[str]]] - Per-model fallback chain tried in order when that model's own `model_max_budget` is exceeded, e.g. {"gpt-4o": ["gpt-4o-mini"]}. * - model_rpm_limit: Optional[float] - Model-specific rpm limit for user. [Docs](https://docs.litellm.ai/docs/proxy/users#add-model-specific-limits-to-keys) @@ -16887,7 +16886,6 @@ export interface paths { * - permissions: Optional[dict] - [Not Implemented Yet] User-specific permissions, eg. turning off pii masking. * - metadata: Optional[dict] - Metadata for user, store information for user. Example metadata = {"team": "core-infra", "app": "app2", "email": "ishaan@berri.ai" } * - max_parallel_requests: Optional[int] - Rate limit a user based on the number of parallel requests. Raises 429 error, if user's parallel requests > x. - * - soft_budget: Optional[float] - Get alerts when user crosses given budget, doesn't block requests. * - model_max_budget: Optional[dict] - Model-specific max budget for user. [Docs](https://docs.litellm.ai/docs/proxy/users#add-model-specific-budgets-to-keys) * - budget_fallbacks: Optional[Dict[str, List[str]]] - Per-model fallback chain tried in order when that model's own `model_max_budget` is exceeded, e.g. {"gpt-4o": ["gpt-4o-mini"]}. * - model_rpm_limit: Optional[float] - Model-specific rpm limit for user. [Docs](https://docs.litellm.ai/docs/proxy/users#add-model-specific-limits-to-keys) From a2501d3b59b49176ff56ea6df57556ad5b8e7146 Mon Sep 17 00:00:00 2001 From: yucheng Date: Sun, 13 Sep 2026 07:06:56 +0000 Subject: [PATCH 044/225] chore: re-run review bots on the reopened PR tip Co-Authored-By: Devin AI <158243242+devin-ai-integration[bot]@users.noreply.github.com> From b2a946562f5bf9f7b10c1ff7b41d76d01bbaa931 Mon Sep 17 00:00:00 2001 From: yucheng Date: Mon, 14 Sep 2026 21:03:17 +0000 Subject: [PATCH 045/225] fix(guardrails): resolve caller identity from metadata buckets in custom code guardrail The sandbox read user_api_key_* off the top level of request_data, where the proxy never writes them, and only forwarded request_data["metadata"], which is empty on /v1/messages, /v1/responses, batches and files because those routes keep proxy state in litellm_metadata. Merge both buckets (litellm_metadata wins) and resolve ids from the merged dict Co-Authored-By: Devin AI <158243242+devin-ai-integration[bot]@users.noreply.github.com> --- .../custom_code/custom_code_guardrail.py | 17 ++++-- .../guardrails/test_custom_code_security.py | 52 +++++++++++++++++++ 2 files changed, 65 insertions(+), 4 deletions(-) diff --git a/litellm/proxy/guardrails/guardrail_hooks/custom_code/custom_code_guardrail.py b/litellm/proxy/guardrails/guardrail_hooks/custom_code/custom_code_guardrail.py index d5ef1e949b8..e0291975699 100644 --- a/litellm/proxy/guardrails/guardrail_hooks/custom_code/custom_code_guardrail.py +++ b/litellm/proxy/guardrails/guardrail_hooks/custom_code/custom_code_guardrail.py @@ -58,6 +58,11 @@ if TYPE_CHECKING: from litellm.litellm_core_utils.litellm_logging import Logging as LiteLLMLoggingObj +def _metadata_bucket(request_data: Mapping[str, object], key: str) -> Mapping[str, object]: + bucket: Final = request_data.get(key) + return bucket if isinstance(bucket, Mapping) else {} + + class CustomCodeGuardrailError(Exception): """Raised when custom code guardrail execution fails.""" @@ -280,12 +285,16 @@ class CustomCodeGuardrail(CustomGuardrail): Returns: Safe subset of request data """ + metadata: Final = { + **_metadata_bucket(request_data, "metadata"), + **_metadata_bucket(request_data, "litellm_metadata"), + } return { "model": request_data.get("model"), - "user_id": request_data.get("user_api_key_user_id"), - "team_id": request_data.get("user_api_key_team_id"), - "end_user_id": request_data.get("user_api_key_end_user_id"), - "metadata": request_data.get("metadata", {}), + "user_id": metadata.get("user_api_key_user_id"), + "team_id": metadata.get("user_api_key_team_id"), + "end_user_id": metadata.get("user_api_key_end_user_id"), + "metadata": metadata, } def _process_result( diff --git a/tests/test_litellm/proxy/guardrails/test_custom_code_security.py b/tests/test_litellm/proxy/guardrails/test_custom_code_security.py index 7971cf62c9a..59011cea1c3 100644 --- a/tests/test_litellm/proxy/guardrails/test_custom_code_security.py +++ b/tests/test_litellm/proxy/guardrails/test_custom_code_security.py @@ -250,6 +250,58 @@ async def test_custom_code_flag_default_reason_and_empty_metadata(): } +IDENTITY_ECHO_CODE = ( + "def apply_guardrail(inputs, request_data, input_type):\n" + " return flag('identity', metadata={\n" + " 'ids': [request_data['user_id'], request_data['team_id'], request_data['end_user_id']],\n" + " 'metadata_keys': sorted(request_data['metadata'].keys()),\n" + " })\n" +) +CALLER_IDENTITY = { + "user_api_key_user_id": "someone@example.com", + "user_api_key_team_id": "team-1", + "user_api_key_end_user_id": "end-user-1", + "user_api_key_alias": "guardrail-repro-key", +} + + +@pytest.mark.asyncio +@pytest.mark.parametrize("metadata_key", ["metadata", "litellm_metadata"]) +async def test_custom_code_sandbox_sees_caller_identity_from_proxy_metadata_bucket(metadata_key): + """LIT-6609: the proxy writes user_api_key_* into `metadata` (chat) or `litellm_metadata` + (/v1/messages, responses, batches, files); the sandbox must resolve ids from either.""" + guardrail = _compile(IDENTITY_ECHO_CODE) + request_data = {"model": "m", metadata_key: dict(CALLER_IDENTITY)} + + await guardrail.apply_guardrail(inputs={"texts": ["x"]}, request_data=request_data, input_type="request") + + entry = request_data[metadata_key]["standard_logging_guardrail_information"][0] + assert entry["guardrail_response"]["metadata"] == { + "ids": ["someone@example.com", "team-1", "end-user-1"], + "metadata_keys": sorted(CALLER_IDENTITY), + } + + +@pytest.mark.asyncio +async def test_custom_code_sandbox_merges_caller_metadata_with_litellm_metadata(): + """On litellm_metadata routes the caller's own `metadata` field must stay visible next to + the proxy identity block, and the proxy block wins on key collisions.""" + guardrail = _compile(IDENTITY_ECHO_CODE) + request_data = { + "model": "m", + "metadata": {"trace_id": "abc", "user_api_key_user_id": "forged"}, + "litellm_metadata": dict(CALLER_IDENTITY), + } + + await guardrail.apply_guardrail(inputs={"texts": ["x"]}, request_data=request_data, input_type="request") + + entry = request_data["litellm_metadata"]["standard_logging_guardrail_information"][0] + assert entry["guardrail_response"]["metadata"] == { + "ids": ["someone@example.com", "team-1", "end-user-1"], + "metadata_keys": sorted([*CALLER_IDENTITY, "trace_id"]), + } + + @pytest.mark.asyncio async def test_custom_code_allow_still_records_success_not_flagged(): code = "def apply_guardrail(inputs, request_data, input_type):\n return allow()\n" From 90ba447974f76018776e52e28883f55d85607c94 Mon Sep 17 00:00:00 2001 From: yucheng Date: Mon, 14 Sep 2026 21:20:46 +0000 Subject: [PATCH 046/225] fix(guardrails): keep top-level caller identity for MCP pre-call custom code guardrails Co-Authored-By: Devin AI <158243242+devin-ai-integration[bot]@users.noreply.github.com> --- .../custom_code/custom_code_guardrail.py | 10 +++-- .../guardrails/test_custom_code_security.py | 37 +++++++++++++++++++ 2 files changed, 44 insertions(+), 3 deletions(-) diff --git a/litellm/proxy/guardrails/guardrail_hooks/custom_code/custom_code_guardrail.py b/litellm/proxy/guardrails/guardrail_hooks/custom_code/custom_code_guardrail.py index e0291975699..de808afbe52 100644 --- a/litellm/proxy/guardrails/guardrail_hooks/custom_code/custom_code_guardrail.py +++ b/litellm/proxy/guardrails/guardrail_hooks/custom_code/custom_code_guardrail.py @@ -63,6 +63,10 @@ def _metadata_bucket(request_data: Mapping[str, object], key: str) -> Mapping[st return bucket if isinstance(bucket, Mapping) else {} +def _identity_field(request_data: Mapping[str, object], metadata: Mapping[str, object], key: str) -> object: + return metadata[key] if key in metadata else request_data.get(key) + + class CustomCodeGuardrailError(Exception): """Raised when custom code guardrail execution fails.""" @@ -291,9 +295,9 @@ class CustomCodeGuardrail(CustomGuardrail): } return { "model": request_data.get("model"), - "user_id": metadata.get("user_api_key_user_id"), - "team_id": metadata.get("user_api_key_team_id"), - "end_user_id": metadata.get("user_api_key_end_user_id"), + "user_id": _identity_field(request_data, metadata, "user_api_key_user_id"), + "team_id": _identity_field(request_data, metadata, "user_api_key_team_id"), + "end_user_id": _identity_field(request_data, metadata, "user_api_key_end_user_id"), "metadata": metadata, } diff --git a/tests/test_litellm/proxy/guardrails/test_custom_code_security.py b/tests/test_litellm/proxy/guardrails/test_custom_code_security.py index 59011cea1c3..4e8801041c2 100644 --- a/tests/test_litellm/proxy/guardrails/test_custom_code_security.py +++ b/tests/test_litellm/proxy/guardrails/test_custom_code_security.py @@ -302,6 +302,43 @@ async def test_custom_code_sandbox_merges_caller_metadata_with_litellm_metadata( } +@pytest.mark.asyncio +async def test_custom_code_sandbox_falls_back_to_top_level_identity_for_mcp_calls(): + """MCP pre-call hooks put user_api_key_* at the top level of the synthetic request, with a + metadata bucket that only carries headers; those ids must still reach the sandbox.""" + guardrail = _compile(IDENTITY_ECHO_CODE) + request_data = {"model": "mcp-tool-call", **CALLER_IDENTITY, "metadata": {"headers": {}}} + + await guardrail.apply_guardrail(inputs={"texts": ["x"]}, request_data=request_data, input_type="request") + + entry = request_data["metadata"]["standard_logging_guardrail_information"][0] + assert entry["guardrail_response"]["metadata"]["ids"] == ["someone@example.com", "team-1", "end-user-1"] + + +@pytest.mark.asyncio +async def test_custom_code_sandbox_ignores_top_level_identity_when_proxy_bucket_has_it(): + """A caller cannot forge ids through top-level body fields on LLM routes: the proxy bucket + carries every user_api_key_* key (even when None) and it wins over the top level.""" + code = ( + "def apply_guardrail(inputs, request_data, input_type):\n" + " ids = [request_data['user_id'], request_data['team_id'], request_data['end_user_id']]\n" + " return flag('identity', metadata={'ids': str(ids)})\n" + ) + guardrail = _compile(code) + request_data = { + "model": "m", + "user_api_key_user_id": "forged", + "user_api_key_team_id": "forged-team", + "user_api_key_end_user_id": "forged-end-user", + "metadata": {**CALLER_IDENTITY, "user_api_key_team_id": None}, + } + + await guardrail.apply_guardrail(inputs={"texts": ["x"]}, request_data=request_data, input_type="request") + + entry = request_data["metadata"]["standard_logging_guardrail_information"][0] + assert entry["guardrail_response"]["metadata"]["ids"] == "['someone@example.com', None, 'end-user-1']" + + @pytest.mark.asyncio async def test_custom_code_allow_still_records_success_not_flagged(): code = "def apply_guardrail(inputs, request_data, input_type):\n return allow()\n" From 5355cbe235a86a34b9ec4f0b8b5b2cfb36e6e133 Mon Sep 17 00:00:00 2001 From: yucheng Date: Mon, 14 Sep 2026 21:28:18 +0000 Subject: [PATCH 047/225] fix(mcp): keep Agent 365 evaluation faithful to each caller's tool call Key the listed-tool cache by every request input that changes the upstream catalog (caller identity, per-server auth header, forwarded headers, header-driven stdio env, relayed bearer) so one caller's tool metadata never reaches Agent 365 for another. Advertise Entra in anonymous protected-resource discovery only for default_on guardrails. Check the key's server grant before issuing the Agent 365 sign-in challenge so a key without access gets the grant's 403. Group conversationId by the MCP session id, falling back to the call id. Run Agent 365 in the parallel block-only phase so it judges the arguments the sequential guardrails hand upstream. Apply tool_name_to_description to local-registry tool metadata sent to Agent 365 Co-Authored-By: Devin AI <158243242+devin-ai-integration[bot]@users.noreply.github.com> --- .../mcp_server/mcp_server_manager.py | 135 ++++++++++----- .../proxy/_experimental/mcp_server/server.py | 43 +++-- .../guardrail_hooks/agent_365/agent_365.py | 18 +- .../mcp_server/test_discoverable_endpoints.py | 13 ++ .../mcp_server/test_mcp_server.py | 52 +++++- .../mcp_server/test_mcp_server_manager.py | 158 ++++++++++++++++-- .../guardrail_hooks/test_agent_365.py | 97 +++++++++-- 7 files changed, 423 insertions(+), 93 deletions(-) diff --git a/litellm/proxy/_experimental/mcp_server/mcp_server_manager.py b/litellm/proxy/_experimental/mcp_server/mcp_server_manager.py index 560d3d51177..319735a8c08 100644 --- a/litellm/proxy/_experimental/mcp_server/mcp_server_manager.py +++ b/litellm/proxy/_experimental/mcp_server/mcp_server_manager.py @@ -246,6 +246,17 @@ _ListedToolsByCaller: TypeAlias = Mapping[str | None, Mapping[str, MCPTool]] _NO_LISTED_TOOLS: Final[_ListedToolsByCaller] = MappingProxyType({}) _LISTED_TOOLS_CALLERS_PER_SERVER: Final = 256 + +@dataclass(frozen=True, slots=True) +class ListedToolsCaller: + """Request inputs that select which upstream catalog a caller was shown by tools/list.""" + + user_api_key_auth: UserAPIKeyAuth | None = None + mcp_auth_header: str | dict[str, str] | None = None + raw_headers: Mapping[str, str] | None = None + oauth2_headers: Mapping[str, str] | None = None + + # Auth types whose upstream OAuth endpoints (protected-resource + authorization-server metadata) the # gateway discovers from the upstream itself: interactive oauth2 and the two client-forwarded modes. # OBO/M2M endpoint discovery is decided separately via _obo_needs_endpoint_discovery. Shared by the @@ -1123,6 +1134,25 @@ def _authorization_is_litellm_admission_credential( return bool(user_api_key_auth and user_api_key_auth.api_key and not admission_header) +def _server_auth_header_for( + server: MCPServer, + mcp_server_auth_headers: Mapping[str, str | dict[str, str]] | None, + mcp_auth_header: str | dict[str, str] | None, +) -> str | dict[str, str] | None: + """Server-specific ``x-mcp--authorization`` header, else the deprecated global one.""" + server_specific: Final = ( + lookup_mcp_server_auth_in_headers( + mcp_server_auth_headers, + alias=server.alias, + server_name=server.server_name, + access_groups=server.access_groups, + ) + if mcp_server_auth_headers + else None + ) + return mcp_auth_header if server_specific is None else server_specific + + def _format_byok_openapi_auth_header(mcp_server: MCPServer, mcp_auth_header: str) -> str: """Format a raw BYOK credential for OpenAPI tool ``Authorization`` injection. @@ -3720,19 +3750,7 @@ class MCPServerManager: verbose_logger.warning("MCP Server %s not found", server_id) return [] - # Get server-specific auth header if available - server_auth_header: str | dict[str, str] | None = None - if mcp_server_auth_headers: - server_auth_header = lookup_mcp_server_auth_in_headers( - mcp_server_auth_headers, - alias=server.alias, - server_name=server.server_name, - access_groups=server.access_groups, - ) - - # Fall back to deprecated mcp_auth_header if no server-specific header found - if server_auth_header is None: - server_auth_header = mcp_auth_header + server_auth_header: Final = _server_auth_header_for(server, mcp_server_auth_headers, mcp_auth_header) try: tools: Final = await self._get_tools_from_server( @@ -3818,7 +3836,7 @@ class MCPServerManager: def _build_stdio_env( self, server: MCPServer, - raw_headers: dict[str, str] | None = None, + raw_headers: Mapping[str, str] | None = None, ) -> dict[str, str] | None: """Resolve stdio env values, supporting header-driven placeholders.""" @@ -4347,6 +4365,12 @@ class MCPServerManager: verbose_logger.info("_get_tools_from_server for %s...", server.name) client = None + listed_caller: Final = ListedToolsCaller( + user_api_key_auth=user_api_key_auth, + mcp_auth_header=mcp_auth_header, + raw_headers=raw_headers, + oauth2_headers=oauth2_headers, + ) try: # Tool *listing* must not be blocked by missing per-user env vars — @@ -4433,14 +4457,14 @@ class MCPServerManager: unprefixed_tools: Final = [ # mutable-ok: returned through the list[MCPTool] listing contract t.model_copy(update=MappingProxyType({"name": t.name[len(registry_prefix) :]})) for t in tools ] - self._record_listed_tools(server, unprefixed_tools, user_api_key_auth) + self._record_listed_tools(server, unprefixed_tools, listed_caller) return tools if add_prefix else unprefixed_tools else: tools = await self._fetch_tools_with_timeout(client, server.name) self._remember_upstream_initialize_instructions(server, client) prefixed_or_original_tools: Final = self._create_prefixed_tools( - tools, server, add_prefix=add_prefix, user_api_key_auth=user_api_key_auth + tools, server, add_prefix=add_prefix, caller=listed_caller ) return prefixed_or_original_tools @@ -4497,16 +4521,49 @@ class MCPServerManager: or server.auth_type in (MCPAuth.oauth2_token_exchange, MCPAuth.oauth2_id_jag) ) - def _listed_tools_identity(self, server: MCPServer, user_api_key_auth: UserAPIKeyAuth | None) -> str | None: - if server.spec_path or user_api_key_auth is None or not self._discovers_per_caller(server): + def _listed_tools_identity(self, server: MCPServer, caller: ListedToolsCaller | None) -> str | None: + """Key the listed-tool cache by every request input that can change the upstream catalog. + + Forwarded headers, header-driven stdio env, a relayed caller bearer, and the + server-specific auth header all reach upstream, so two callers differing in any of + them may be shown different tools. Shared servers with none of those stay on the + shared (``None``) slot. OpenAPI servers list from the process-wide registry. + """ + if server.spec_path or caller is None: return None - material: Final = json.dumps((user_api_key_auth.user_id, user_api_key_auth.api_key), separators=(",", ":")) + auth: Final = caller.user_api_key_auth + identity: Final = ( + (auth.user_id, auth.api_key) if auth is not None and self._discovers_per_caller(server) else None + ) + forwarded: Final = self._forwarded_header_values(server, caller.raw_headers) + header_env: Final = self._build_stdio_env(server, caller.raw_headers) + stdio_env: Final = None if header_env == self._build_stdio_env(server) else header_env + relayed_bearer: Final = ( + self._extract_subject_token(caller.oauth2_headers, caller.raw_headers, auth) + if server.is_client_forwarded_token + else None + ) + inputs: Final = (identity, caller.mcp_auth_header, forwarded, stdio_env, relayed_bearer) + if not any(inputs): + return None + material: Final = json.dumps(inputs, sort_keys=True, separators=(",", ":")) return hashlib.sha256(material.encode()).hexdigest() + @staticmethod + def _forwarded_header_values( + server: MCPServer, raw_headers: Mapping[str, str] | None + ) -> tuple[tuple[str, str], ...]: + if not raw_headers or not server.extra_headers: + return () + forwarded_names: Final = frozenset(name.lower() for name in server.extra_headers) + return tuple( + sorted((name.lower(), value) for name, value in raw_headers.items() if name.lower() in forwarded_names) + ) + def _record_listed_tools( - self, server: MCPServer, tools: Sequence[MCPTool], user_api_key_auth: UserAPIKeyAuth | None + self, server: MCPServer, tools: Sequence[MCPTool], caller: ListedToolsCaller | None ) -> None: - identity: Final = self._listed_tools_identity(server, user_api_key_auth) + identity: Final = self._listed_tools_identity(server, caller) listing: Final = MappingProxyType({tool.name: tool for tool in tools}) existing: Final = self._listed_tools_by_server_id.get(server.server_id, _NO_LISTED_TOOLS) shared: Final = existing.get(None) @@ -5347,7 +5404,7 @@ class MCPServerManager: tools: list[MCPTool], server: MCPServer, add_prefix: bool = True, - user_api_key_auth: UserAPIKeyAuth | None = None, + caller: ListedToolsCaller | None = None, ) -> list[MCPTool]: """ Create prefixed tools and update tool mapping. @@ -5380,14 +5437,12 @@ class MCPServerManager: for spelling in iter_known_tool_name_spellings(original_name, server): self.tool_name_to_mcp_server_name_mapping[spelling] = prefix - self._record_listed_tools(server, tools, user_api_key_auth) + self._record_listed_tools(server, tools, caller) verbose_logger.info("Successfully fetched %s tools from server %s", len(prefixed_tools), server.name) return prefixed_tools - def get_listed_tool( - self, server: MCPServer, name: str, user_api_key_auth: UserAPIKeyAuth | None = None - ) -> MCPTool | None: - identity: Final = self._listed_tools_identity(server, user_api_key_auth) + def get_listed_tool(self, server: MCPServer, name: str, caller: ListedToolsCaller | None = None) -> MCPTool | None: + identity: Final = self._listed_tools_identity(server, caller) listed: Final = self._listed_tools_by_server_id.get(server.server_id, _NO_LISTED_TOOLS).get(identity) if not listed: return None @@ -5909,21 +5964,7 @@ class MCPServerManager: GuardrailRaisedException: If guardrails block the call HTTPException: If an HTTP error occurs """ - # Get server-specific auth header if available (case-insensitive) - # FIX: Added case-insensitive matching to handle auth header keys that may not match - # the exact case of server alias/name (e.g., '1litellmagcgateway' vs '1LiteLLMAGCGateway') - server_auth_header: dict[str, str] | str | None = None - if mcp_server_auth_headers: - server_auth_header = lookup_mcp_server_auth_in_headers( - mcp_server_auth_headers, - alias=mcp_server.alias, - server_name=mcp_server.server_name, - access_groups=mcp_server.access_groups, - ) - - # Fall back to deprecated mcp_auth_header if no server-specific header found - if server_auth_header is None: - server_auth_header = mcp_auth_header + server_auth_header: Final = _server_auth_header_for(mcp_server, mcp_server_auth_headers, mcp_auth_header) # Extract subject token for OAuth2 Token Exchange (OBO) and ID-JAG flows subject_token: str | None = None @@ -6359,6 +6400,12 @@ class MCPServerManager: user_api_key_auth, mcp_auth_header, ) + listed_caller: Final = ListedToolsCaller( + user_api_key_auth=user_api_key_auth, + mcp_auth_header=_server_auth_header_for(mcp_server, mcp_server_auth_headers, mcp_auth_header), + raw_headers=raw_headers, + oauth2_headers=oauth2_headers, + ) ######################################################### # Pre MCP Tool Call Hook @@ -6374,7 +6421,7 @@ class MCPServerManager: server=mcp_server, raw_headers=raw_headers, litellm_logging_obj=litellm_logging_obj, - tool=self.get_listed_tool(mcp_server, name, user_api_key_auth), + tool=self.get_listed_tool(mcp_server, name, listed_caller), ) if "arguments" in hook_result: arguments = hook_result["arguments"] @@ -6390,7 +6437,7 @@ class MCPServerManager: proxy_logging_obj=proxy_logging_obj, start_time=start_time, litellm_logging_obj=litellm_logging_obj, - tool=self.get_listed_tool(mcp_server, name, user_api_key_auth), + tool=self.get_listed_tool(mcp_server, name, listed_caller), ) tasks.append(during_hook_task) diff --git a/litellm/proxy/_experimental/mcp_server/server.py b/litellm/proxy/_experimental/mcp_server/server.py index 922ad9ac9b2..3b471033235 100644 --- a/litellm/proxy/_experimental/mcp_server/server.py +++ b/litellm/proxy/_experimental/mcp_server/server.py @@ -2783,8 +2783,10 @@ if MCP_AVAILABLE: return managed_resource_templates - def _registered_tool_metadata(name: str, registered: RegisteredTool) -> MCPTool: - return MCPTool(name=name, description=registered.description, inputSchema=registered.input_schema) + def _registered_tool_metadata(name: str, registered: RegisteredTool, server: MCPServer) -> MCPTool: + overrides: Final = server.tool_name_to_description + description: Final = overrides.get(name, registered.description) if overrides else registered.description + return MCPTool(name=name, description=description, inputSchema=registered.input_schema) def _resolve_display_name_to_original( name: str, @@ -3124,7 +3126,7 @@ if MCP_AVAILABLE: server=mcp_server, raw_headers=raw_headers, litellm_logging_obj=litellm_logging_obj, - tool=_registered_tool_metadata(original_tool_name, local_tool), + tool=_registered_tool_metadata(original_tool_name, local_tool, mcp_server), ) # `pre_call_tool_check` may return guardrail-modified # arguments; honor them on the local path too. @@ -3232,7 +3234,7 @@ if MCP_AVAILABLE: server=prefix_server, raw_headers=raw_headers, litellm_logging_obj=litellm_logging_obj, - tool=_registered_tool_metadata(original_tool_name, registered_local_tool), + tool=_registered_tool_metadata(original_tool_name, registered_local_tool, prefix_server), ) if "arguments" in hook_result: arguments = hook_result["arguments"] # pyright: ignore[reportAny] # hook returns untyped args @@ -4019,6 +4021,21 @@ if MCP_AVAILABLE: ) return user_api_key_auth.model_copy(update={"object_permission": updated_op}) + async def _key_granted_single_server( + server: MCPServer, + mcp_servers: Sequence[str] | None, + user_api_key_auth: UserAPIKeyAuth | None, + client_ip: str | None, + ) -> bool: + """Sign-in challenges are issued only on a single-server connect the key's grant admits, so a key + without access gets the grant's 403 instead of a sign-in it could not use.""" + if len(mcp_servers or []) != 1: + return False + allowed: Final = await _get_allowed_mcp_servers( + user_api_key_auth=user_api_key_auth, mcp_servers=mcp_servers, client_ip=client_ip + ) + return any(granted.server_id == server.server_id for granted in allowed) + async def _raise_preemptive_401_for_unauthenticated_servers( scope: Scope, mcp_servers: list[str] | None, @@ -4146,10 +4163,14 @@ if MCP_AVAILABLE: # bearer is the LiteLLM key itself, which admits the caller but is not an exchangeable subject. # Only on the server's own route: the per-server metadata ``resource`` must equal the URL the # client connected to (RFC 9728 3.3), which aggregate ``/mcp`` and multi-server connects never do. + granted_single_server = server is not None and await _key_granted_single_server( + server, mcp_servers, user_api_key_auth, client_ip + ) if server and ( (server.auth_type == MCPAuth.oauth2_token_exchange and not oauth2_headers) or ( - tuple(_get_mcp_servers_in_path(get_route_relative_request_path(scope)) or ()) == (server_name,) + granted_single_server + and tuple(_get_mcp_servers_in_path(get_route_relative_request_path(scope)) or ()) == (server_name,) and not agent_365_subject_token_present(oauth2_headers) and agent_365_authorization_servers(server, user_api_key_auth) ) @@ -4174,17 +4195,7 @@ if MCP_AVAILABLE: # and what each mints from. Gated to single-server routes the key may reach; the # multi-server aggregate keeps absorbing per-server auth failures so one bad server # cannot 401 the whole connect. - if ( - server - and len(mcp_servers or []) == 1 - and server.server_id - in frozenset( - allowed.server_id - for allowed in await _get_allowed_mcp_servers( - user_api_key_auth=user_api_key_auth, mcp_servers=mcp_servers, client_ip=client_ip - ) - ) - ): + if server and granted_single_server: await global_mcp_server_manager.preflight_token_exchange( server=server, oauth2_headers=oauth2_headers, diff --git a/litellm/proxy/guardrails/guardrail_hooks/agent_365/agent_365.py b/litellm/proxy/guardrails/guardrail_hooks/agent_365/agent_365.py index d81fa2f095b..b134e1e7ff6 100644 --- a/litellm/proxy/guardrails/guardrail_hooks/agent_365/agent_365.py +++ b/litellm/proxy/guardrails/guardrail_hooks/agent_365/agent_365.py @@ -136,7 +136,10 @@ class Agent365ThrottledError(Exception): class Agent365Guardrail(CustomGuardrail): - """Pre-MCP-call guardrail enforcing Microsoft Agent 365 tool-evaluation verdicts.""" + """Pre-MCP-call guardrail enforcing Microsoft Agent 365 tool-evaluation verdicts. + + Block-only: it never rewrites the call, so it runs in the post-sequential phase and judges the + arguments the sequential guardrails hand upstream, whatever order the guardrails list uses.""" records_own_guardrail_information: ClassVar[bool] = True @@ -157,6 +160,7 @@ class Agent365Guardrail(CustomGuardrail): super().__init__( guardrail_name=guardrail_name, supported_event_hooks=self.get_supported_event_hooks(), + run_in_parallel=True, **kwargs, ) self.guardrail_provider = "agent_365" @@ -408,11 +412,10 @@ class Agent365Guardrail(CustomGuardrail): @staticmethod def _resolve_conversation_id(data: Mapping[str, object]) -> str: + """The MCP session groups every tool call of one client conversation, so it is the conversation id + when the transport carries one; stateless calls fall back to the per-call id.""" raw_logging_obj: Final = data.get("litellm_logging_obj") logging_obj: Final = raw_logging_obj if isinstance(raw_logging_obj, LiteLLMLoggingObj) else None - call_id: Final = data.get("litellm_call_id") or (logging_obj.litellm_call_id if logging_obj else None) - if isinstance(call_id, str) and call_id: - return call_id if logging_obj is not None: tool_call_metadata: Final = logging_obj.model_call_details.get("mcp_tool_call_metadata") session_from_logging: Final = ( @@ -432,6 +435,9 @@ class Agent365Guardrail(CustomGuardrail): ) if isinstance(session_id, str) and session_id: return session_id + call_id: Final = data.get("litellm_call_id") or (logging_obj.litellm_call_id if logging_obj else None) + if isinstance(call_id, str) and call_id: + return call_id return str(uuid.uuid4()) async def _get_obo_token(self, assertion: str) -> str: @@ -643,7 +649,7 @@ def _applies_to_caller(guardrail: Agent365Guardrail, user_api_key_auth: "UserAPI def _applicable_guardrails( server: MCPServer, user_api_key_auth: "UserAPIKeyAuth | None" ) -> tuple[Agent365Guardrail, ...]: - """Agent 365 guardrails that gate ``server`` for this caller: every registered one for the anonymous + """Agent 365 guardrails that gate ``server`` for this caller: the ``default_on`` ones for the anonymous discovery fetch, otherwise those the caller's key, team, or policies select. Empty when the gateway does not own sign-in for the server.""" if server.auth_type == MCPAuth.oauth2 or not server.advertises_gateway_authorization_server: @@ -654,7 +660,7 @@ def _applicable_guardrails( if isinstance(callback, Agent365Guardrail) ) if user_api_key_auth is None: - return registered + return tuple(g for g in registered if g.default_on) return tuple(g for g in registered if _applies_to_caller(g, user_api_key_auth)) diff --git a/tests/test_litellm/proxy/_experimental/mcp_server/test_discoverable_endpoints.py b/tests/test_litellm/proxy/_experimental/mcp_server/test_discoverable_endpoints.py index ebb5775b14e..25164d4b215 100644 --- a/tests/test_litellm/proxy/_experimental/mcp_server/test_discoverable_endpoints.py +++ b/tests/test_litellm/proxy/_experimental/mcp_server/test_discoverable_endpoints.py @@ -7200,6 +7200,19 @@ async def test_agent_365_prm_defaults_scopeless_server_to_the_gateway_app_scope( } +@pytest.mark.asyncio +async def test_key_selected_agent_365_guardrail_leaves_anonymous_prm_on_the_gateway_issuer(agent_365_guardrail): + """A default-off guardrail gates only the keys that select it, so the anonymous discovery fetch must keep + pointing every other client at the gateway's own authorization server and scopes.""" + agent_365_guardrail.default_on = False + response = await _agent_365_gated_prm(scopes=["mcp:read"]) + assert jsonable_encoder(response) == { + "authorization_servers": ["https://litellm.example.com/mcp"], + "resource": "https://litellm.example.com/mcp/tools", + "scopes_supported": ["mcp:read"], + } + + def _token_request(headers): """A real Starlette request with case-insensitive headers (matches production).""" from starlette.requests import Request diff --git a/tests/test_litellm/proxy/_experimental/mcp_server/test_mcp_server.py b/tests/test_litellm/proxy/_experimental/mcp_server/test_mcp_server.py index 9a49f411fde..1817f101758 100644 --- a/tests/test_litellm/proxy/_experimental/mcp_server/test_mcp_server.py +++ b/tests/test_litellm/proxy/_experimental/mcp_server/test_mcp_server.py @@ -7052,6 +7052,7 @@ async def test_execute_mcp_tool_sets_model_in_model_call_details(): fake_server.server_name = "openapi-petstore" fake_server.alias = None fake_server.short_prefix = None + fake_server.tool_name_to_description = None fake_tool = MagicMock() fake_tool.name = "list_pets" @@ -7152,6 +7153,48 @@ async def test_execute_mcp_tool_hands_openapi_registered_tool_metadata_to_pre_ca ) +@pytest.mark.asyncio +async def test_execute_mcp_tool_hands_openapi_hooks_the_admin_description_clients_saw(): + """tools/list shows the admin's tool_name_to_description wording, so the local-registry call path + must hand the pre-call hooks that same wording rather than the generated one.""" + from litellm.proxy._experimental.mcp_server import server as mcp_module + + petstore = MCPServer( + server_id="petstore-id", + name="petstore", + server_name="petstore", + transport=MCPTransport.http, + url=None, + spec_path="https://example.com/petstore.yaml", + tool_name_to_description={"getpetbyid": "ADMIN DESC"}, + ) + schema = {"type": "object", "properties": {"petId": {"type": "integer"}}} + mcp_module.global_mcp_tool_registry.register_tool( + name="petstore-getpetbyid", description="Find pet by ID", input_schema=schema, handler=lambda petId: "ok" + ) + manager = mcp_module.global_mcp_server_manager + manager._listed_tools_by_server_id.pop(petstore.server_id, None) + pre_call_tool_check = AsyncMock(return_value={}) + + try: + with ( + patch.object(manager, "_get_mcp_server_from_tool_name", return_value=petstore), + patch.object(manager, "pre_call_tool_check", new=pre_call_tool_check), + ): + await mcp_module.execute_mcp_tool( + name="petstore-getpetbyid", + arguments={"petId": 1}, + allowed_mcp_servers=[petstore], + start_time=datetime.now(), + user_api_key_auth=UserAPIKeyAuth(api_key="sk-user", user_id="alice"), + ) + finally: + mcp_module.global_mcp_tool_registry.unregister_tools_with_prefix("petstore-") + + handed_tool = pre_call_tool_check.call_args.kwargs["tool"] + assert (handed_tool.description, handed_tool.inputSchema) == ("ADMIN DESC", schema) + + @pytest.mark.asyncio async def test_execute_mcp_tool_hands_hooks_the_metadata_of_the_operation_it_runs_when_names_collide(): """An OpenAPI operation whose name starts with its own server prefix must not be reported to the @@ -8914,6 +8957,7 @@ class TestAgent365ChallengeAtConnect: oauth2_headers: dict[str, str] | None, path: str = "/mcp/tools", mount_scope: dict[str, str] | None = None, + granted: bool = True, ) -> HTTPException | None: from litellm.proxy._experimental.mcp_server import server as server_module @@ -8922,7 +8966,7 @@ class TestAgent365ChallengeAtConnect: server_module.global_mcp_server_manager, "get_mcp_server_by_name", return_value=server ), patch.object( # test-quality-ok: allowed-set resolution needs the DB; the test controls its answer - server_module, "_get_allowed_mcp_servers", AsyncMock(return_value=[]) + server_module, "_get_allowed_mcp_servers", AsyncMock(return_value=[server] if granted else []) ), ): try: @@ -9034,6 +9078,12 @@ class TestAgent365ChallengeAtConnect: async def test_no_registered_guardrail_means_no_challenge(self): assert await self._connect(self._server([self.GATEWAY_SCOPE]), None) is None + @pytest.mark.asyncio + async def test_key_without_the_server_grant_is_not_sent_to_sign_in(self, agent_365_guardrail): + """Signing in cannot earn a key a server it was never granted, so the connect must fall through to + the ordinary 403 grant denial instead of leading with an Entra challenge the caller cannot use.""" + assert await self._connect(self._server([self.GATEWAY_SCOPE]), None, granted=False) is None + def _make_obo_server(alias: str) -> MCPServer: return MCPServer( diff --git a/tests/test_litellm/proxy/_experimental/mcp_server/test_mcp_server_manager.py b/tests/test_litellm/proxy/_experimental/mcp_server/test_mcp_server_manager.py index 56e693e3523..f94d1dfa53b 100644 --- a/tests/test_litellm/proxy/_experimental/mcp_server/test_mcp_server_manager.py +++ b/tests/test_litellm/proxy/_experimental/mcp_server/test_mcp_server_manager.py @@ -34,6 +34,7 @@ from pydantic import AnyUrl, TypeAdapter from litellm.constants import MCP_METADATA_TIMEOUT from litellm.proxy._experimental.mcp_server.mcp_server_manager import ( + ListedToolsCaller, MCPServerManager, _deserialize_json_dict, _flow_endpoints_missing, @@ -6736,25 +6737,153 @@ class TestMCPServerManager: alice_schema = {"type": "object", "properties": {"path": {"type": "string"}}} bob_schema = {"type": "object", "properties": {"path": {"type": "string"}, "site": {"type": "string"}}} manager._create_prefixed_tools( - [MCPTool(name="read", description="alice view", inputSchema=alice_schema)], server, user_api_key_auth=alice + [MCPTool(name="read", description="alice view", inputSchema=alice_schema)], + server, + caller=ListedToolsCaller(user_api_key_auth=alice), ) manager._create_prefixed_tools( - [MCPTool(name="read", description="bob view", inputSchema=bob_schema)], server, user_api_key_auth=bob + [MCPTool(name="read", description="bob view", inputSchema=bob_schema)], + server, + caller=ListedToolsCaller(user_api_key_auth=bob), ) - alice_tool = manager.get_listed_tool(server, "srv-read", alice) - bob_tool = manager.get_listed_tool(server, "srv-read", bob) - assert alice_tool is not None and (alice_tool.description, alice_tool.inputSchema) == ("alice view", alice_schema) + alice_tool = manager.get_listed_tool(server, "srv-read", ListedToolsCaller(user_api_key_auth=alice)) + bob_tool = manager.get_listed_tool(server, "srv-read", ListedToolsCaller(user_api_key_auth=bob)) + assert alice_tool is not None and (alice_tool.description, alice_tool.inputSchema) == ( + "alice view", + alice_schema, + ) assert bob_tool is not None and (bob_tool.description, bob_tool.inputSchema) == ("bob view", bob_schema) - assert manager.get_listed_tool(server, "srv-read", UserAPIKeyAuth(user_id="carol", api_key="k")) is None + carol = ListedToolsCaller(user_api_key_auth=UserAPIKeyAuth(user_id="carol", api_key="k")) + assert manager.get_listed_tool(server, "srv-read", carol) is None shared = MCPServer(server_id="shared", name="shared", transport=MCPTransport.http, url="http://shared") manager._create_prefixed_tools( - [MCPTool(name="echo", description="everyone", inputSchema={})], shared, user_api_key_auth=alice + [MCPTool(name="echo", description="everyone", inputSchema={})], + shared, + caller=ListedToolsCaller(user_api_key_auth=alice), ) - for_bob = manager.get_listed_tool(shared, "echo", bob) + for_bob = manager.get_listed_tool(shared, "echo", ListedToolsCaller(user_api_key_auth=bob)) assert for_bob is not None and for_bob.description == "everyone" + @pytest.mark.parametrize( + ("server_kwargs", "caller_a", "caller_b"), + [ + pytest.param( + {"extra_headers": ["X-Workspace"]}, + ListedToolsCaller(raw_headers={"x-workspace": "A"}), + ListedToolsCaller(raw_headers={"X-Workspace": "B"}), + id="forwarded-header", + ), + pytest.param( + {"auth_type": MCPAuth.true_passthrough}, + ListedToolsCaller(raw_headers={"authorization": "Bearer upstream-a"}), + ListedToolsCaller(raw_headers={"authorization": "Bearer upstream-b"}), + id="anonymous-passthrough-bearer", + ), + pytest.param( + {"auth_type": MCPAuth.bearer_token}, + ListedToolsCaller(mcp_auth_header="byok-a"), + ListedToolsCaller(mcp_auth_header="byok-b"), + id="per-server-auth-header", + ), + pytest.param( + {"transport": MCPTransport.stdio, "command": "srv", "env": {"WS": "${X-WS}"}}, + ListedToolsCaller(raw_headers={"X-WS": "A"}), + ListedToolsCaller(raw_headers={"X-WS": "B"}), + id="header-driven-stdio-env", + ), + ], + ) + def test_upstream_identity_inputs_keep_listed_tools_apart(self, server_kwargs, caller_a, caller_b): + """Whatever reaches upstream and can change its catalog must also split the listed-tool cache.""" + manager = MCPServerManager() + server = MCPServer( + **{"server_id": "srv", "name": "srv", "transport": MCPTransport.http, "url": "http://srv", **server_kwargs} + ) + manager._create_prefixed_tools( + [MCPTool(name="turn", description="Catalog A", inputSchema={})], server, caller=caller_a + ) + manager._create_prefixed_tools( + [MCPTool(name="turn", description="Catalog B", inputSchema={})], server, caller=caller_b + ) + + for_a = manager.get_listed_tool(server, "srv-turn", caller_a) + for_b = manager.get_listed_tool(server, "srv-turn", caller_b) + assert for_a is not None and for_a.description == "Catalog A" + assert for_b is not None and for_b.description == "Catalog B" + assert manager.get_listed_tool(server, "srv-turn", ListedToolsCaller()) is None + + def test_shared_server_ignores_headers_it_never_forwards(self): + manager = MCPServerManager() + server = MCPServer(server_id="srv", name="srv", transport=MCPTransport.http, url="http://srv") + manager._create_prefixed_tools( + [MCPTool(name="turn", description="everyone", inputSchema={})], + server, + caller=ListedToolsCaller(raw_headers={"authorization": "Bearer sk-litellm", "x-workspace": "A"}), + ) + + other = ListedToolsCaller(raw_headers={"authorization": "Bearer sk-other", "x-workspace": "B"}) + listed = manager.get_listed_tool(server, "turn", other) + assert listed is not None and listed.description == "everyone" + + @pytest.mark.asyncio + async def test_call_tool_hands_hooks_the_catalog_the_same_forwarded_headers_listed(self): + """Interleaved callers on a forwarded-header server: the hook must see the caller's own catalog.""" + manager = MCPServerManager() + server = MCPServer( + server_id="catalog", + name="catalog", + transport=MCPTransport.http, + url="http://catalog", + extra_headers=["X-Workspace"], + ) + manager.registry = {"catalog": server} + catalogs = { + "A": [ + MCPTool( + name="turn", description="Catalog A", inputSchema={"properties": {"turn": {"description": "A"}}} + ) + ], + "B": [ + MCPTool( + name="turn", description="Catalog B", inputSchema={"properties": {"turn": {"description": "B"}}} + ) + ], + } + mock_client = AsyncMock() + mock_client.call_tool.return_value = MagicMock(spec=CallToolResult, content=[], isError=False) + manager._create_mcp_client = AsyncMock(return_value=mock_client) + manager._fetch_tools_with_timeout = AsyncMock(side_effect=lambda client, name: catalogs[client.workspace]) + for workspace in ("A", "B"): + manager._create_mcp_client.return_value.workspace = workspace + await manager._get_tools_from_server( + server=server, + extra_headers={"X-Workspace": workspace}, + raw_headers={"x-workspace": workspace, "authorization": "Bearer sk-litellm"}, + user_api_key_auth=UserAPIKeyAuth(api_key="sk-litellm", user_id="shared-key"), + ) + + proxy_logging_obj = MagicMock() + proxy_logging_obj._create_mcp_request_object_from_kwargs = MagicMock(return_value={}) + proxy_logging_obj._convert_mcp_to_llm_format = MagicMock(return_value={}) + proxy_logging_obj.pre_call_hook = AsyncMock(return_value={}) + proxy_logging_obj.during_call_hook = AsyncMock(return_value=None) + await manager.call_tool( + server_name="catalog", + name="catalog-turn", + arguments={"turn": "A-1"}, + user_api_key_auth=UserAPIKeyAuth(api_key="sk-litellm", user_id="shared-key"), + proxy_logging_obj=proxy_logging_obj, + raw_headers={"x-workspace": "A", "authorization": "Bearer sk-litellm"}, + ) + + hook_kwargs = proxy_logging_obj._create_mcp_request_object_from_kwargs.call_args.args[0] + assert (hook_kwargs["tool_description"], hook_kwargs["tool_input_schema"]) == ( + "Catalog A", + {"properties": {"turn": {"description": "A"}}}, + ) + def test_per_caller_listed_tools_evict_oldest_caller_and_keep_shared(self): from litellm.proxy._experimental.mcp_server.mcp_server_manager import _LISTED_TOOLS_CALLERS_PER_SERVER @@ -6767,20 +6896,25 @@ class TestMCPServerManager: auth_type=MCPAuth.oauth2_token_exchange, ) manager._create_prefixed_tools([MCPTool(name="read", description="shared", inputSchema={})], server) - callers = [UserAPIKeyAuth(user_id=f"u{i}", api_key=f"k{i}") for i in range(_LISTED_TOOLS_CALLERS_PER_SERVER + 1)] + callers = [ + ListedToolsCaller(user_api_key_auth=UserAPIKeyAuth(user_id=f"u{i}", api_key=f"k{i}")) + for i in range(_LISTED_TOOLS_CALLERS_PER_SERVER + 1) + ] for caller in callers: manager._create_prefixed_tools( - [MCPTool(name="read", description=caller.user_id, inputSchema={})], server, user_api_key_auth=caller + [MCPTool(name="read", description=caller.user_api_key_auth.user_id, inputSchema={})], + server, + caller=caller, ) manager._create_prefixed_tools( - [MCPTool(name="read", description="u1 again", inputSchema={})], server, user_api_key_auth=callers[1] + [MCPTool(name="read", description="u1 again", inputSchema={})], server, caller=callers[1] ) assert manager.get_listed_tool(server, "srv-read", callers[0]) is None second = manager.get_listed_tool(server, "srv-read", callers[1]) assert second is not None and second.description == "u1 again" newest = manager.get_listed_tool(server, "srv-read", callers[-1]) - assert newest is not None and newest.description == callers[-1].user_id + assert newest is not None and newest.description == callers[-1].user_api_key_auth.user_id assert len(manager._listed_tools_by_server_id[server.server_id]) == _LISTED_TOOLS_CALLERS_PER_SERVER + 1 shared = manager.get_listed_tool(server, "srv-read") assert shared is not None and shared.description == "shared" diff --git a/tests/test_litellm/proxy/guardrails/guardrail_hooks/test_agent_365.py b/tests/test_litellm/proxy/guardrails/guardrail_hooks/test_agent_365.py index c9373de8f82..a679c56f561 100644 --- a/tests/test_litellm/proxy/guardrails/guardrail_hooks/test_agent_365.py +++ b/tests/test_litellm/proxy/guardrails/guardrail_hooks/test_agent_365.py @@ -10,7 +10,9 @@ import pytest from fastapi import HTTPException import litellm +from litellm.caching.caching import DualCache from litellm.exceptions import Timeout as LitellmTimeout +from litellm.integrations.custom_guardrail import CustomGuardrail from litellm.litellm_core_utils.litellm_logging import Logging as LiteLLMLoggingObj from litellm.litellm_core_utils.secret_redaction import redact_string from litellm.proxy._types import UserAPIKeyAuth @@ -24,6 +26,7 @@ from litellm.proxy.guardrails.guardrail_hooks.agent_365.agent_365 import ( agent_365_authorization_servers, agent_365_scopes_supported, ) +from litellm.proxy.utils import ProxyLogging from litellm.types.guardrails import ( GuardrailEventHooks, LitellmParams, @@ -346,32 +349,47 @@ class TestAllowFlow: class TestConversationId: + """One MCP session is one client conversation, so every tool call it carries must share the + conversationId Agent 365 sees; the per-call id is only for stateless calls without a session.""" + @pytest.mark.asyncio - async def test_request_call_id_beats_logging_obj_and_client_header(self): + async def test_two_calls_in_one_session_share_the_conversation_id(self): + handler: Final = FakeHandler([_token_response(), _allow_response(), _allow_response()]) + guardrail: Final = _make_guardrail(handler) + for call_id in ("call-1", "call-2"): + await _run( + guardrail, + _mcp_data(litellm_call_id=call_id, litellm_logging_obj=_logging_obj(call_id, mcp_session_id="sess-A")), + ) + assert [call.json["conversationId"] for call in handler.calls[1:]] == ["sess-A", "sess-A"] + + @pytest.mark.asyncio + async def test_server_recorded_session_beats_the_client_header(self): + handler: Final = FakeHandler([_token_response(), _allow_response()]) + guardrail: Final = _make_guardrail(handler) + data: Final = _mcp_data(litellm_logging_obj=_logging_obj("call-id-1", mcp_session_id="sess-from-logging")) + await _run(guardrail, data) + assert handler.calls[1].json["conversationId"] == "sess-from-logging" + + @pytest.mark.asyncio + async def test_sessionless_call_falls_back_to_the_request_call_id(self): handler: Final = FakeHandler([_token_response(), _allow_response()]) guardrail: Final = _make_guardrail(handler) data: Final = _mcp_data( + metadata={"headers": {}}, litellm_call_id="call-id-from-data", - litellm_logging_obj=_logging_obj("call-id-from-logging", mcp_session_id="sess-from-logging"), + litellm_logging_obj=_logging_obj("call-id-from-logging"), ) await _run(guardrail, data) assert handler.calls[1].json["conversationId"] == "call-id-from-data" @pytest.mark.asyncio - async def test_logging_obj_call_id_beats_session_metadata_and_client_header(self): + async def test_sessionless_call_without_request_call_id_uses_the_logging_call_id(self): handler: Final = FakeHandler([_token_response(), _allow_response()]) guardrail: Final = _make_guardrail(handler) - data: Final = _mcp_data(litellm_logging_obj=_logging_obj("call-id-1", mcp_session_id="sess-from-logging")) + data: Final = _mcp_data(metadata={"headers": {}}, litellm_logging_obj=_logging_obj("call-id-from-logging")) await _run(guardrail, data) - assert handler.calls[1].json["conversationId"] == "call-id-1" - - @pytest.mark.asyncio - async def test_logging_obj_session_id_beats_client_header(self): - handler: Final = FakeHandler([_token_response(), _allow_response()]) - guardrail: Final = _make_guardrail(handler) - data: Final = _mcp_data(litellm_logging_obj=_logging_obj("", mcp_session_id="sess-from-logging")) - await _run(guardrail, data) - assert handler.calls[1].json["conversationId"] == "sess-from-logging" + assert handler.calls[1].json["conversationId"] == "call-id-from-logging" @pytest.mark.asyncio async def test_session_id_header_case_insensitive(self): @@ -949,6 +967,56 @@ class TestVeriaHardening: assert records[0]["guardrail_status"] == "guardrail_failed_to_respond" +class _ArgumentMasker(CustomGuardrail): + """Sequential pre_mcp_call guardrail that redacts a marker in the tool arguments the way a content + filter configured with a MASK action does.""" + + def __init__(self, guardrail_name: str) -> None: + super().__init__( + guardrail_name=guardrail_name, + supported_event_hooks=[GuardrailEventHooks.pre_mcp_call], + event_hook=GuardrailEventHooks.pre_mcp_call, + default_on=True, + ) + + async def async_pre_call_hook(self, user_api_key_dict, cache, data, call_type): + masked: Final = { + key: value.replace("REWRITE_ME", "[REWRITE_ME_REDACTED]") if isinstance(value, str) else value + for key, value in data["mcp_arguments"].items() + } + data["mcp_arguments"] = masked + data["modified_arguments"] = masked + return data + + +class TestFinalArgumentsEvaluated: + """Agent 365 must judge the arguments that reach the upstream tool. A sibling guardrail that rewrites + them must not be able to slip a different argument state past the verdict, whichever way the two + are ordered in the guardrails list.""" + + @pytest.mark.asyncio + @pytest.mark.parametrize("agent_365_first", [True, False], ids=["agent_365_then_masker", "masker_then_agent_365"]) + async def test_agent_365_receives_the_arguments_sent_upstream(self, agent_365_first: bool): + handler: Final = FakeHandler([_token_response(), _allow_response()]) + guardrail: Final = _make_guardrail(handler) + masker: Final = _ArgumentMasker("arg-rewrite") + registered: Final = (guardrail, masker) if agent_365_first else (masker, guardrail) + for callback in registered: + litellm.logging_callback_manager.add_litellm_callback(callback) + data: Final = _mcp_data(mcp_arguments={"turn": "please REWRITE_ME now"}) + try: + result: Final = await ProxyLogging(user_api_key_cache=DualCache()).pre_call_hook( + user_api_key_dict=_user(), data=data, call_type="call_mcp_tool" + ) + finally: + for callback in registered: + litellm.logging_callback_manager.remove_callback_from_list_by_object( + litellm.callbacks, callback, require_self=False + ) + assert result["modified_arguments"] == {"turn": "please [REWRITE_ME_REDACTED] now"} + assert handler.calls[1].json["arguments"] == {"turn": "please [REWRITE_ME_REDACTED] now"} + + ENTRA_ISSUER: Final = "https://login.microsoftonline.com/tenant-abc/v2.0" GATEWAY_SCOPE: Final = "api://gateway-app/access_as_user" @@ -1041,7 +1109,8 @@ class TestAgent365AuthorizationServers: ): assert agent_365_authorization_servers(server, plain_key) == () assert agent_365_authorization_servers(server, guarded_key) == (ENTRA_ISSUER,) - assert agent_365_authorization_servers(server, None) == (ENTRA_ISSUER,) + assert agent_365_authorization_servers(server, None) == () + assert agent_365_scopes_supported(_mcp_server(scopes=None), None) == () finally: litellm.logging_callback_manager.remove_callback_from_list_by_object( litellm.callbacks, guardrail, require_self=False From 87bf5730da0aad31f27f37132131f2766f196d0c Mon Sep 17 00:00:00 2001 From: yucheng Date: Mon, 14 Sep 2026 21:37:44 +0000 Subject: [PATCH 048/225] fix(guardrails): carry MCP caller identity in synthetic metadata instead of trusting top-level fields Co-Authored-By: Devin AI <158243242+devin-ai-integration[bot]@users.noreply.github.com> --- .../custom_code/custom_code_guardrail.py | 10 ++----- litellm/proxy/utils.py | 7 ++++- .../guardrails/test_custom_code_security.py | 29 ++++--------------- .../utils/proxy_logging/test_mcp_bridging.py | 21 ++++++++++++++ 4 files changed, 35 insertions(+), 32 deletions(-) diff --git a/litellm/proxy/guardrails/guardrail_hooks/custom_code/custom_code_guardrail.py b/litellm/proxy/guardrails/guardrail_hooks/custom_code/custom_code_guardrail.py index de808afbe52..e0291975699 100644 --- a/litellm/proxy/guardrails/guardrail_hooks/custom_code/custom_code_guardrail.py +++ b/litellm/proxy/guardrails/guardrail_hooks/custom_code/custom_code_guardrail.py @@ -63,10 +63,6 @@ def _metadata_bucket(request_data: Mapping[str, object], key: str) -> Mapping[st return bucket if isinstance(bucket, Mapping) else {} -def _identity_field(request_data: Mapping[str, object], metadata: Mapping[str, object], key: str) -> object: - return metadata[key] if key in metadata else request_data.get(key) - - class CustomCodeGuardrailError(Exception): """Raised when custom code guardrail execution fails.""" @@ -295,9 +291,9 @@ class CustomCodeGuardrail(CustomGuardrail): } return { "model": request_data.get("model"), - "user_id": _identity_field(request_data, metadata, "user_api_key_user_id"), - "team_id": _identity_field(request_data, metadata, "user_api_key_team_id"), - "end_user_id": _identity_field(request_data, metadata, "user_api_key_end_user_id"), + "user_id": metadata.get("user_api_key_user_id"), + "team_id": metadata.get("user_api_key_team_id"), + "end_user_id": metadata.get("user_api_key_end_user_id"), "metadata": metadata, } diff --git a/litellm/proxy/utils.py b/litellm/proxy/utils.py index c095586b6c9..f523c2a7151 100644 --- a/litellm/proxy/utils.py +++ b/litellm/proxy/utils.py @@ -1253,7 +1253,12 @@ class ProxyLogging: # (e.g. MCPJWTSigner) to independently verify the caller's identity # before re-signing an outbound token (FR-5 verify+re-sign). "incoming_bearer_token": kwargs.get("incoming_bearer_token"), - "metadata": {"headers": kwargs.get("headers") or {}}, + "metadata": { + "headers": kwargs.get("headers") or {}, + "user_api_key_user_id": kwargs.get("user_api_key_user_id"), + "user_api_key_team_id": kwargs.get("user_api_key_team_id"), + "user_api_key_end_user_id": kwargs.get("user_api_key_end_user_id"), + }, } user_api_key_auth: Final = kwargs.get("user_api_key_auth") if isinstance(user_api_key_auth, UserAPIKeyAuth): diff --git a/tests/test_litellm/proxy/guardrails/test_custom_code_security.py b/tests/test_litellm/proxy/guardrails/test_custom_code_security.py index 4e8801041c2..068cd0d8ed7 100644 --- a/tests/test_litellm/proxy/guardrails/test_custom_code_security.py +++ b/tests/test_litellm/proxy/guardrails/test_custom_code_security.py @@ -303,40 +303,21 @@ async def test_custom_code_sandbox_merges_caller_metadata_with_litellm_metadata( @pytest.mark.asyncio -async def test_custom_code_sandbox_falls_back_to_top_level_identity_for_mcp_calls(): - """MCP pre-call hooks put user_api_key_* at the top level of the synthetic request, with a - metadata bucket that only carries headers; those ids must still reach the sandbox.""" - guardrail = _compile(IDENTITY_ECHO_CODE) - request_data = {"model": "mcp-tool-call", **CALLER_IDENTITY, "metadata": {"headers": {}}} - - await guardrail.apply_guardrail(inputs={"texts": ["x"]}, request_data=request_data, input_type="request") - - entry = request_data["metadata"]["standard_logging_guardrail_information"][0] - assert entry["guardrail_response"]["metadata"]["ids"] == ["someone@example.com", "team-1", "end-user-1"] - - -@pytest.mark.asyncio -async def test_custom_code_sandbox_ignores_top_level_identity_when_proxy_bucket_has_it(): - """A caller cannot forge ids through top-level body fields on LLM routes: the proxy bucket - carries every user_api_key_* key (even when None) and it wins over the top level.""" +async def test_custom_code_sandbox_ignores_top_level_identity_fields(): + """Only the proxy-owned metadata buckets carry identity; user_api_key_* keys at the top level + of the request body are caller-controlled on ordinary routes and must never become ids.""" code = ( "def apply_guardrail(inputs, request_data, input_type):\n" " ids = [request_data['user_id'], request_data['team_id'], request_data['end_user_id']]\n" " return flag('identity', metadata={'ids': str(ids)})\n" ) guardrail = _compile(code) - request_data = { - "model": "m", - "user_api_key_user_id": "forged", - "user_api_key_team_id": "forged-team", - "user_api_key_end_user_id": "forged-end-user", - "metadata": {**CALLER_IDENTITY, "user_api_key_team_id": None}, - } + request_data = {"model": "m", **CALLER_IDENTITY, "metadata": {"headers": {}}} await guardrail.apply_guardrail(inputs={"texts": ["x"]}, request_data=request_data, input_type="request") entry = request_data["metadata"]["standard_logging_guardrail_information"][0] - assert entry["guardrail_response"]["metadata"]["ids"] == "['someone@example.com', None, 'end-user-1']" + assert entry["guardrail_response"]["metadata"]["ids"] == "[None, None, None]" @pytest.mark.asyncio diff --git a/tests/test_litellm/proxy/utils/proxy_logging/test_mcp_bridging.py b/tests/test_litellm/proxy/utils/proxy_logging/test_mcp_bridging.py index 56057dce7e0..438b2351034 100644 --- a/tests/test_litellm/proxy/utils/proxy_logging/test_mcp_bridging.py +++ b/tests/test_litellm/proxy/utils/proxy_logging/test_mcp_bridging.py @@ -87,6 +87,27 @@ def test_convert_mcp_to_llm_format_exposes_headers_on_metadata(proxy_logging, ma assert out["metadata"]["headers"] == {"x-nuid": "nuid-1"} +def test_convert_mcp_to_llm_format_exposes_caller_identity_on_metadata(proxy_logging, make_mcp_request_obj): + """Custom code guardrails resolve user_id/team_id/end_user_id from the proxy-owned metadata + bucket on every route, so the MCP bridge has to write the authenticated ids there too.""" + req = make_mcp_request_obj() + out = proxy_logging._convert_mcp_to_llm_format( + request_obj=req, + kwargs={ + "user_api_key_user_id": "u-1", + "user_api_key_team_id": "t-1", + "user_api_key_end_user_id": "eu-1", + "headers": {"x-nuid": "nuid-1"}, + }, + ) + assert out["metadata"] == { + "headers": {"x-nuid": "nuid-1"}, + "user_api_key_user_id": "u-1", + "user_api_key_team_id": "t-1", + "user_api_key_end_user_id": "eu-1", + } + + def test_convert_mcp_to_llm_format_defaults_headers_to_empty(proxy_logging, make_mcp_request_obj): req = make_mcp_request_obj() out = proxy_logging._convert_mcp_to_llm_format(request_obj=req, kwargs={}) From 0e27a523e040a83ad3842b6f7fae16b101756412 Mon Sep 17 00:00:00 2001 From: yucheng Date: Mon, 14 Sep 2026 22:04:02 +0000 Subject: [PATCH 049/225] fix(mcp): probe Entra at connect, challenge only default-on Agent 365 guardrails, key listed tools by signer Connect-time sign-in challenge now asks Entra to exchange the presented assertion instead of only checking its compact-JWS shape, so expired, wrong-audience or forged bearers get the RFC 9728 challenge while gateway credential and provider failures still surface on the tool call. Only default_on guardrails the caller has not opted out of advertise or challenge, since anonymous metadata cannot see key-selected guardrails. Servers whose Authorization is minted per caller by MCPJWTSigner list tools per caller instead of sharing one cache slot Co-Authored-By: Devin AI <158243242+devin-ai-integration[bot]@users.noreply.github.com> --- .../mcp_server/mcp_server_manager.py | 13 +++ .../proxy/_experimental/mcp_server/server.py | 14 +-- .../guardrail_hooks/agent_365/agent_365.py | 63 ++++++++---- .../mcp_server/test_mcp_server.py | 95 ++++++++++++++++++- .../mcp_server/test_mcp_server_manager.py | 32 +++++++ .../mcp_server/test_openapi_tool_auth.py | 2 + .../guardrail_hooks/test_agent_365.py | 22 ++++- 7 files changed, 212 insertions(+), 29 deletions(-) diff --git a/litellm/proxy/_experimental/mcp_server/mcp_server_manager.py b/litellm/proxy/_experimental/mcp_server/mcp_server_manager.py index 319735a8c08..00f19bf964e 100644 --- a/litellm/proxy/_experimental/mcp_server/mcp_server_manager.py +++ b/litellm/proxy/_experimental/mcp_server/mcp_server_manager.py @@ -4519,8 +4519,21 @@ class MCPServerManager: or self._references_per_user_env_var(server) or server.delegate_auth_to_upstream or server.auth_type in (MCPAuth.oauth2_token_exchange, MCPAuth.oauth2_id_jag) + or self._signs_caller_identity_upstream(server) ) + @staticmethod + def _signs_caller_identity_upstream(server: MCPServer) -> bool: + """Whether MCPJWTSigner mints a per-caller ``Authorization`` for ``server``, so the upstream may + tailor its catalog to the caller even though the server itself is configured as shared.""" + from litellm.proxy.guardrails.guardrail_hooks.mcp_jwt_signer.mcp_jwt_signer import ( # noqa: PLC0415 # lazy: guardrail package imports the proxy server + get_mcp_jwt_signer, + ) + + if get_mcp_jwt_signer() is None: + return False + return not any(k.lower() == "authorization" for k in (server.static_headers or {})) + def _listed_tools_identity(self, server: MCPServer, caller: ListedToolsCaller | None) -> str | None: """Key the listed-tool cache by every request input that can change the upstream catalog. diff --git a/litellm/proxy/_experimental/mcp_server/server.py b/litellm/proxy/_experimental/mcp_server/server.py index 3b471033235..ca12d9e9937 100644 --- a/litellm/proxy/_experimental/mcp_server/server.py +++ b/litellm/proxy/_experimental/mcp_server/server.py @@ -82,8 +82,7 @@ from litellm.proxy._types import ( ) from litellm.proxy.auth.ip_address_utils import IPAddressUtils from litellm.proxy.guardrails.guardrail_hooks.agent_365.agent_365 import ( - agent_365_authorization_servers, - agent_365_subject_token_present, + agent_365_sign_in_required, ) from litellm.proxy.litellm_pre_call_utils import ( LiteLLMProxyRequestSetup, @@ -4160,9 +4159,11 @@ if MCP_AVAILABLE: # then exchanges. A tool-call-time 401 would be wrapped into a JSON-RPC error and the # header lost, so the discovery flow needs this pre-emptive challenge. Servers gated by an # Agent 365 guardrail (OBO to the evaluate API) get the same challenge, also when the only - # bearer is the LiteLLM key itself, which admits the caller but is not an exchangeable subject. - # Only on the server's own route: the per-server metadata ``resource`` must equal the URL the - # client connected to (RFC 9728 3.3), which aggregate ``/mcp`` and multi-server connects never do. + # bearer is the LiteLLM key itself, which admits the caller but is not an exchangeable subject, + # and when Entra refuses the presented assertion (expired, wrong audience), so the client + # signs in again instead of failing every tool call. Only on the server's own route: the + # per-server metadata ``resource`` must equal the URL the client connected to (RFC 9728 3.3), + # which aggregate ``/mcp`` and multi-server connects never do. granted_single_server = server is not None and await _key_granted_single_server( server, mcp_servers, user_api_key_auth, client_ip ) @@ -4171,8 +4172,7 @@ if MCP_AVAILABLE: or ( granted_single_server and tuple(_get_mcp_servers_in_path(get_route_relative_request_path(scope)) or ()) == (server_name,) - and not agent_365_subject_token_present(oauth2_headers) - and agent_365_authorization_servers(server, user_api_key_auth) + and await agent_365_sign_in_required(server, user_api_key_auth, oauth2_headers) ) ): from litellm.proxy._experimental.mcp_server.outbound_credentials.adapter import ( # noqa: PLC0415 # lazy: adapter pulls MCP subgraph diff --git a/litellm/proxy/guardrails/guardrail_hooks/agent_365/agent_365.py b/litellm/proxy/guardrails/guardrail_hooks/agent_365/agent_365.py index b134e1e7ff6..cb6ff9a5355 100644 --- a/litellm/proxy/guardrails/guardrail_hooks/agent_365/agent_365.py +++ b/litellm/proxy/guardrails/guardrail_hooks/agent_365/agent_365.py @@ -35,7 +35,6 @@ from litellm.llms.custom_httpx.http_handler import ( get_async_httpx_client, httpxSpecialProvider, ) -from litellm.proxy.litellm_pre_call_utils import add_guardrails_from_auth_metadata from litellm.types.guardrails import GuardrailEventHooks from litellm.types.mcp import MCPAuth from litellm.types.mcp_server.mcp_server_manager import MCPServer @@ -440,6 +439,18 @@ class Agent365Guardrail(CustomGuardrail): return call_id return str(uuid.uuid4()) + async def exchange_rejects_subject(self, assertion: str) -> bool: + """Whether Entra refuses ``assertion`` as the On-Behalf-Of subject (expired, wrong audience, bad + signature). Gateway credential rejections and endpoint failures answer ``False``: the caller cannot fix + those by signing in again, so the tool call reports them.""" + try: + await self._get_obo_token(assertion) + except Agent365TokenExchangeError as exc: + return exc.error_code not in _GATEWAY_OWNED_TOKEN_ERRORS + except (Agent365ThrottledError, Agent365MalformedResponseError, httpx.HTTPError, LitellmTimeout, TimeoutError): + return False + return False + async def _get_obo_token(self, assertion: str) -> str: cache_key: Final = hashlib.sha256(assertion.encode("utf-8")).hexdigest() now: Final = time.time() @@ -639,29 +650,32 @@ class Agent365Guardrail(CustomGuardrail): def _applies_to_caller(guardrail: Agent365Guardrail, user_api_key_auth: "UserAPIKeyAuth") -> bool: - probe: Final[dict[str, object]] = {"metadata": {}} # mutable-ok: filled in place by the key resolver - add_guardrails_from_auth_metadata( - user_api_key_dict=user_api_key_auth, data=probe, metadata_variable_name="metadata" - ) - return guardrail.should_run_guardrail(data=probe, event_type=GuardrailEventHooks.pre_mcp_call) + probe: Final[Mapping[str, object]] = { + "metadata": { + "user_api_key_metadata": user_api_key_auth.metadata, + "user_api_key_team_metadata": user_api_key_auth.team_metadata, + } + } + return guardrail.should_run_guardrail(data=dict(probe), event_type=GuardrailEventHooks.pre_mcp_call) def _applicable_guardrails( server: MCPServer, user_api_key_auth: "UserAPIKeyAuth | None" ) -> tuple[Agent365Guardrail, ...]: - """Agent 365 guardrails that gate ``server`` for this caller: the ``default_on`` ones for the anonymous - discovery fetch, otherwise those the caller's key, team, or policies select. Empty when the gateway - does not own sign-in for the server.""" + """Agent 365 guardrails whose sign-in the gateway advertises for ``server``: the ``default_on`` ones, minus + those the caller's key or team opted out of once the caller is known. A guardrail only a key or policy + selects still enforces at the tool call but never challenges, since the anonymous metadata fetch that + follows a challenge cannot see which key selected it and would advertise the wrong issuer.""" if server.auth_type == MCPAuth.oauth2 or not server.advertises_gateway_authorization_server: return () - registered: Final = tuple( + advertised: Final = tuple( callback for callback in litellm.logging_callback_manager.get_custom_loggers_for_type(Agent365Guardrail) - if isinstance(callback, Agent365Guardrail) + if isinstance(callback, Agent365Guardrail) and callback.default_on ) if user_api_key_auth is None: - return tuple(g for g in registered if g.default_on) - return tuple(g for g in registered if _applies_to_caller(g, user_api_key_auth)) + return advertised + return tuple(g for g in advertised if _applies_to_caller(g, user_api_key_auth)) def entra_assertion(value: object) -> str | None: @@ -670,12 +684,29 @@ def entra_assertion(value: object) -> str | None: return value if isinstance(value, str) and value.count(".") == 2 else None -def agent_365_subject_token_present(oauth2_headers: Mapping[str, str] | None) -> bool: - """Whether the request's ``Authorization`` carries an Entra assertion the guardrail can exchange.""" +def _presented_assertion(oauth2_headers: Mapping[str, str] | None) -> str | None: authorization: Final = oauth2_headers.get("Authorization", "") if oauth2_headers else "" if not authorization.lower().startswith("bearer "): + return None + return entra_assertion(authorization[len("bearer ") :].strip()) + + +async def agent_365_sign_in_required( + server: MCPServer, user_api_key_auth: "UserAPIKeyAuth | None", oauth2_headers: Mapping[str, str] | None +) -> bool: + """Whether the connect must answer with the RFC 9728 sign-in challenge: an Agent 365 guardrail gates + ``server`` for this caller and the request carries no Entra assertion, or one Entra will not exchange. + Decided at connect because a tool call's JSON-RPC error cannot carry ``WWW-Authenticate``.""" + guardrails: Final = _applicable_guardrails(server, user_api_key_auth) + if not guardrails: return False - return entra_assertion(authorization[len("bearer ") :].strip()) is not None + assertion: Final = _presented_assertion(oauth2_headers) + if assertion is None: + return True + for guardrail in guardrails: + if await guardrail.exchange_rejects_subject(assertion): + return True + return False def agent_365_authorization_servers(server: MCPServer, user_api_key_auth: "UserAPIKeyAuth | None") -> tuple[str, ...]: diff --git a/tests/test_litellm/proxy/_experimental/mcp_server/test_mcp_server.py b/tests/test_litellm/proxy/_experimental/mcp_server/test_mcp_server.py index 1817f101758..511433837c0 100644 --- a/tests/test_litellm/proxy/_experimental/mcp_server/test_mcp_server.py +++ b/tests/test_litellm/proxy/_experimental/mcp_server/test_mcp_server.py @@ -6,6 +6,7 @@ from types import SimpleNamespace from typing import Final from unittest.mock import AsyncMock, MagicMock, patch +import httpx import pytest from fastapi import HTTPException from mcp import ReadResourceResult, Resource @@ -8915,6 +8916,15 @@ class TestAgent365ChallengeAtConnect: WWW-Authenticate header survives, instead of only inside the tools/call JSON-RPC error.""" GATEWAY_SCOPE = "api://gateway-app/access_as_user" + ENTRA_BEARER = {"Authorization": "Bearer eyJhbGciOiJSUzI1NiJ9.eyJzdWIiOiJ1LTEifQ.c2ln"} + + @staticmethod + def _entra_response(status_code: int, body: dict[str, object]) -> httpx.Response: + return httpx.Response( + status_code=status_code, + json=body, + request=httpx.Request("POST", "https://login.microsoftonline.com/tenant-abc/oauth2/v2.0/token"), + ) def _server(self, scopes: list[str] | None) -> MCPServer: return MCPServer( @@ -8934,12 +8944,14 @@ class TestAgent365ChallengeAtConnect: import litellm from litellm.proxy.guardrails.guardrail_hooks.agent_365 import Agent365Guardrail + handler = AsyncMock() + handler.post.return_value = self._entra_response(200, {"access_token": "obo-token", "expires_in": 3599}) guardrail = Agent365Guardrail( guardrail_name="agent-365-guard", tenant_id="tenant-abc", client_id="client-xyz", client_secret="secret-123", - async_handler=AsyncMock(), + async_handler=handler, event_hook="pre_mcp_call", default_on=True, ) @@ -8958,6 +8970,7 @@ class TestAgent365ChallengeAtConnect: path: str = "/mcp/tools", mount_scope: dict[str, str] | None = None, granted: bool = True, + key_metadata: dict[str, object] | None = None, ) -> HTTPException | None: from litellm.proxy._experimental.mcp_server import server as server_module @@ -8983,7 +8996,9 @@ class TestAgent365ChallengeAtConnect: mcp_servers=["tools"], oauth2_headers=oauth2_headers, mcp_server_auth_headers=None, - user_api_key_auth=UserAPIKeyAuth(api_key="sk-litellm-virtual-key", user_id="u-1"), + user_api_key_auth=UserAPIKeyAuth( + api_key="sk-litellm-virtual-key", user_id="u-1", metadata=key_metadata or {} + ), client_ip=None, ) except HTTPException as challenge: @@ -9048,8 +9063,80 @@ class TestAgent365ChallengeAtConnect: @pytest.mark.asyncio async def test_entra_assertion_present_connects(self, agent_365_guardrail): - bearer = {"Authorization": "Bearer eyJhbGciOiJSUzI1NiJ9.eyJzdWIiOiJ1LTEifQ.c2ln"} - assert await self._connect(self._server([self.GATEWAY_SCOPE]), bearer) is None + assert await self._connect(self._server([self.GATEWAY_SCOPE]), self.ENTRA_BEARER) is None + + @pytest.mark.asyncio + async def test_assertion_entra_refuses_to_exchange_is_challenged(self, agent_365_guardrail): + """An expired, wrong-audience, or forged Entra token looks like a valid one. Only the OBO exchange + can tell, and its verdict must arrive at connect, where WWW-Authenticate reaches the client, + rather than inside every tools/call JSON-RPC error.""" + agent_365_guardrail.async_handler.post.return_value = self._entra_response( + 400, {"error": "invalid_grant", "error_description": "AADSTS700084: The refresh token was issued..."} + ) + + challenge = await self._connect(self._server([self.GATEWAY_SCOPE]), self.ENTRA_BEARER) + + assert challenge is not None and challenge.status_code == 401 + www_authenticate = (challenge.headers or {}).get("WWW-Authenticate", "") + assert 'error="invalid_token"' in www_authenticate + assert ( + 'resource_metadata="https://gw.example.com/.well-known/oauth-protected-resource/mcp/tools"' + in www_authenticate + ) + + @pytest.mark.asyncio + @pytest.mark.parametrize( + "entra_outcome", + [ + {"return_value": _entra_response(401, {"error": "invalid_client"})}, + {"return_value": _entra_response(503, {"error": "temporarily_unavailable"})}, + {"side_effect": httpx.ConnectError("dns")}, + ], + ids=["gateway-credentials-rejected", "entra-5xx", "entra-unreachable"], + ) + async def test_gateway_side_exchange_failures_do_not_send_the_caller_to_sign_in( + self, agent_365_guardrail, entra_outcome + ): + """Signing in again cannot fix the gateway's own client secret or an Entra outage, so those stay + with the tool call, which reports them as the guardrail's unavailable path.""" + agent_365_guardrail.async_handler.post = AsyncMock(**entra_outcome) + + assert await self._connect(self._server([self.GATEWAY_SCOPE]), self.ENTRA_BEARER) is None + + @pytest.mark.asyncio + async def test_key_selected_default_off_guardrail_never_challenges(self): + """The anonymous metadata fetch that follows a challenge cannot see which key selected the guardrail + and would advertise the gateway issuer, so a challenge here would send the client to the wrong IdP. + The guardrail still enforces at tools/call.""" + import litellm + from litellm.proxy.guardrails.guardrail_hooks.agent_365 import Agent365Guardrail + + guardrail = Agent365Guardrail( + guardrail_name="agent-365-guard", + tenant_id="tenant-abc", + client_id="client-xyz", + client_secret="secret-123", + async_handler=AsyncMock(), + event_hook="pre_mcp_call", + default_on=False, + ) + litellm.logging_callback_manager.add_litellm_callback(guardrail) + try: + with patch( # test-quality-ok: key-selected guardrails read the proxy server premium global, no injection seam + "litellm.proxy.proxy_server.premium_user", True + ): + assert ( + await self._connect( + self._server([self.GATEWAY_SCOPE]), + None, + key_metadata={"guardrails": ["agent-365-guard"]}, + ) + is None + ) + finally: + litellm.logging_callback_manager.remove_callback_from_list_by_object( + litellm.callbacks, guardrail, require_self=False + ) @pytest.mark.asyncio async def test_litellm_key_in_authorization_is_still_challenged(self, agent_365_guardrail): diff --git a/tests/test_litellm/proxy/_experimental/mcp_server/test_mcp_server_manager.py b/tests/test_litellm/proxy/_experimental/mcp_server/test_mcp_server_manager.py index f94d1dfa53b..bf670f62c4c 100644 --- a/tests/test_litellm/proxy/_experimental/mcp_server/test_mcp_server_manager.py +++ b/tests/test_litellm/proxy/_experimental/mcp_server/test_mcp_server_manager.py @@ -6827,6 +6827,38 @@ class TestMCPServerManager: listed = manager.get_listed_tool(server, "turn", other) assert listed is not None and listed.description == "everyone" + @pytest.mark.parametrize( + ("signer", "static_headers", "shared"), + [ + pytest.param(MagicMock(), None, False, id="signer-mints-per-caller-authorization"), + pytest.param(MagicMock(), {"Authorization": "Bearer admin-token"}, True, id="static-authorization-wins"), + pytest.param(None, None, True, id="no-signer-stays-shared"), + ], + ) + def test_jwt_signer_makes_a_shared_server_list_per_caller(self, signer, static_headers, shared): + """MCPJWTSigner hands upstream a JWT naming the caller on an otherwise shared ``auth_type: none`` + server, so the upstream may tailor the catalog and the cache must not hand one caller another's.""" + manager = MCPServerManager() + server = MCPServer( + server_id="srv", name="srv", transport=MCPTransport.http, url="http://srv", static_headers=static_headers + ) + alice = ListedToolsCaller(user_api_key_auth=UserAPIKeyAuth(user_id="alice", api_key="hashed-alice")) + bob = ListedToolsCaller(user_api_key_auth=UserAPIKeyAuth(user_id="bob", api_key="hashed-bob")) + + with patch( # test-quality-ok: the signer is a process-wide singleton the manager reads, no injection seam + "litellm.proxy.guardrails.guardrail_hooks.mcp_jwt_signer.mcp_jwt_signer.get_mcp_jwt_signer", + return_value=signer, + ): + manager._create_prefixed_tools( + [MCPTool(name="turn", description="alice view", inputSchema={})], server, caller=alice + ) + for_bob = manager.get_listed_tool(server, "srv-turn", bob) + + if shared: + assert for_bob is not None and for_bob.description == "alice view" + else: + assert for_bob is None + @pytest.mark.asyncio async def test_call_tool_hands_hooks_the_catalog_the_same_forwarded_headers_listed(self): """Interleaved callers on a forwarded-header server: the hook must see the caller's own catalog.""" diff --git a/tests/test_litellm/proxy/_experimental/mcp_server/test_openapi_tool_auth.py b/tests/test_litellm/proxy/_experimental/mcp_server/test_openapi_tool_auth.py index c534cf73219..67894d55e80 100644 --- a/tests/test_litellm/proxy/_experimental/mcp_server/test_openapi_tool_auth.py +++ b/tests/test_litellm/proxy/_experimental/mcp_server/test_openapi_tool_auth.py @@ -40,6 +40,7 @@ async def test_openapi_local_tool_runs_pre_call_tool_check(): fake_server.server_name = "openapi-petstore" fake_server.alias = None fake_server.short_prefix = None + fake_server.tool_name_to_description = None fake_tool = MagicMock() fake_tool.name = "list_pets" @@ -123,6 +124,7 @@ async def test_openapi_local_tool_blocked_when_pre_call_check_raises(): fake_server.server_name = "openapi-petstore" fake_server.alias = None fake_server.short_prefix = None + fake_server.tool_name_to_description = None fake_tool = MagicMock() fake_tool.name = "delete_pet" diff --git a/tests/test_litellm/proxy/guardrails/guardrail_hooks/test_agent_365.py b/tests/test_litellm/proxy/guardrails/guardrail_hooks/test_agent_365.py index a679c56f561..6466bd4549d 100644 --- a/tests/test_litellm/proxy/guardrails/guardrail_hooks/test_agent_365.py +++ b/tests/test_litellm/proxy/guardrails/guardrail_hooks/test_agent_365.py @@ -1094,7 +1094,9 @@ class TestAgent365AuthorizationServers: litellm.callbacks, twin, require_self=False ) - def test_key_selected_guardrail_challenges_only_that_key(self): + def test_key_selected_guardrail_never_advertises_sign_in(self): + """The challenge a guarded key would get and the anonymous metadata fetch that follows it must name + the same issuer. The anonymous fetch cannot see the key, so neither side advertises Entra.""" guardrail: Final = _make_guardrail(FakeHandler([])) guardrail.default_on = False litellm.logging_callback_manager.add_litellm_callback(guardrail) @@ -1108,10 +1110,26 @@ class TestAgent365AuthorizationServers: "litellm.proxy.proxy_server.premium_user", True ): assert agent_365_authorization_servers(server, plain_key) == () - assert agent_365_authorization_servers(server, guarded_key) == (ENTRA_ISSUER,) + assert agent_365_authorization_servers(server, guarded_key) == () assert agent_365_authorization_servers(server, None) == () assert agent_365_scopes_supported(_mcp_server(scopes=None), None) == () finally: litellm.logging_callback_manager.remove_callback_from_list_by_object( litellm.callbacks, guardrail, require_self=False ) + + @pytest.mark.parametrize( + "metadata", + [{"opted_out_global_guardrails": ["agent-365-guard"]}, {"disable_global_guardrails": True}], + ids=["opted-out", "globals-disabled"], + ) + def test_key_opted_out_of_the_default_on_guardrail_is_not_challenged(self, registered_guardrail, metadata): + server: Final = _mcp_server(scopes=[GATEWAY_SCOPE]) + opted_out: Final = UserAPIKeyAuth(api_key="sk-out", user_id="u-3", metadata=metadata) + team_opted_out: Final = UserAPIKeyAuth(api_key="sk-team", user_id="u-4", team_metadata=metadata) + + assert agent_365_authorization_servers(server, opted_out) == () + assert agent_365_authorization_servers(server, team_opted_out) == () + assert agent_365_authorization_servers(server, UserAPIKeyAuth(api_key="sk-in", user_id="u-5")) == ( + ENTRA_ISSUER, + ) From 6b9990e068cb2e7a22f477bfb4fccfbfba4fa367 Mon Sep 17 00:00:00 2001 From: yucheng Date: Mon, 14 Sep 2026 22:12:12 +0000 Subject: [PATCH 050/225] fix(agent_365): treat Entra AADSTS50027xx invalid_client as a caller assertion failure Entra reports a forged or garbled On-Behalf-Of assertion as invalid_client, the same OAuth error it uses for a bad gateway client secret. Read the AADSTS error_codes so a malformed assertion sends the caller back to sign in at connect and at the tool call instead of reporting the gateway's credentials as broken. Co-Authored-By: Devin AI <158243242+devin-ai-integration[bot]@users.noreply.github.com> --- .../guardrail_hooks/agent_365/agent_365.py | 27 ++++++++++++++-- .../mcp_server/test_mcp_server.py | 32 +++++++++++++++---- .../guardrail_hooks/test_agent_365.py | 24 ++++++++++++++ 3 files changed, 74 insertions(+), 9 deletions(-) diff --git a/litellm/proxy/guardrails/guardrail_hooks/agent_365/agent_365.py b/litellm/proxy/guardrails/guardrail_hooks/agent_365/agent_365.py index cb6ff9a5355..40264d111d7 100644 --- a/litellm/proxy/guardrails/guardrail_hooks/agent_365/agent_365.py +++ b/litellm/proxy/guardrails/guardrail_hooks/agent_365/agent_365.py @@ -60,6 +60,10 @@ GATEWAY_SCOPE_TEMPLATE: Final = "api://{client_id}/access_as_user" _GATEWAY_OWNED_TOKEN_ERRORS: Final = frozenset( {"invalid_client", "unauthorized_client", "invalid_scope", "invalid_resource"} ) +# Entra reports a malformed or unverifiable assertion as ``invalid_client`` too; only its AADSTS50027xx +# (InvalidJwtToken) sub-codes tell that apart from a bad gateway secret. +_INVALID_ASSERTION_AADSTS_PREFIX: Final = "50027" +_AADSTS_CODES_ADAPTER: Final = TypeAdapter(tuple[int, ...]) _MCP_CALL_TYPES: Final[tuple[str, ...]] = ("mcp_call", "call_mcp_tool") _TOOL_INPUT_SCHEMA_ADAPTER: Final = TypeAdapter(dict[str, object]) _OBO_CACHE_MAX_ENTRIES: Final = 1000 @@ -76,6 +80,13 @@ def _parse_expires_in(raw: object) -> float: return _DEFAULT_TOKEN_TTL_SECONDS +def _parse_aadsts_codes(raw: object) -> tuple[int, ...]: + try: + return _AADSTS_CODES_ADAPTER.validate_python(raw) + except ValidationError: + return () + + def _parse_tool_input_schema(raw: object) -> Mapping[str, object] | None: try: return _TOOL_INPUT_SCHEMA_ADAPTER.validate_python(raw) @@ -117,11 +128,20 @@ class _BlockedDetail(TypedDict): class Agent365TokenExchangeError(Exception): - def __init__(self, status_code: int, error_code: str, description: str) -> None: + def __init__(self, status_code: int, error_code: str, description: str, aadsts_codes: tuple[int, ...] = ()) -> None: super().__init__(f"{error_code}: {description}") self.status_code = status_code self.error_code = error_code self.description = description + self.aadsts_codes = aadsts_codes + + @property + def gateway_owned(self) -> bool: + """Whether the gateway's own client credentials, scope or resource were refused, as opposed to the + caller's assertion. The caller cannot fix a gateway-owned rejection by signing in again.""" + if self.error_code not in _GATEWAY_OWNED_TOKEN_ERRORS: + return False + return not any(str(code).startswith(_INVALID_ASSERTION_AADSTS_PREFIX) for code in self.aadsts_codes) class Agent365MalformedResponseError(Exception): @@ -219,7 +239,7 @@ class Agent365Guardrail(CustomGuardrail): try: obo_token: Final = await self._get_obo_token(assertion) except Agent365TokenExchangeError as exc: - if exc.error_code in _GATEWAY_OWNED_TOKEN_ERRORS: + if exc.gateway_owned: return self._handle_unavailable( data=data, tool_name=tool_name, @@ -446,7 +466,7 @@ class Agent365Guardrail(CustomGuardrail): try: await self._get_obo_token(assertion) except Agent365TokenExchangeError as exc: - return exc.error_code not in _GATEWAY_OWNED_TOKEN_ERRORS + return not exc.gateway_owned except (Agent365ThrottledError, Agent365MalformedResponseError, httpx.HTTPError, LitellmTimeout, TimeoutError): return False return False @@ -492,6 +512,7 @@ class Agent365Guardrail(CustomGuardrail): status_code=response.status_code, error_code=str(body.get("error", "invalid_grant")), description=str(body.get("error_description", ""))[:512], + aadsts_codes=_parse_aadsts_codes(body.get("error_codes")), ) if "access_token" not in body: raise Agent365MalformedResponseError("the Entra token endpoint returned no access_token") diff --git a/tests/test_litellm/proxy/_experimental/mcp_server/test_mcp_server.py b/tests/test_litellm/proxy/_experimental/mcp_server/test_mcp_server.py index 511433837c0..aaa472a2400 100644 --- a/tests/test_litellm/proxy/_experimental/mcp_server/test_mcp_server.py +++ b/tests/test_litellm/proxy/_experimental/mcp_server/test_mcp_server.py @@ -9066,13 +9066,24 @@ class TestAgent365ChallengeAtConnect: assert await self._connect(self._server([self.GATEWAY_SCOPE]), self.ENTRA_BEARER) is None @pytest.mark.asyncio - async def test_assertion_entra_refuses_to_exchange_is_challenged(self, agent_365_guardrail): + @pytest.mark.parametrize( + "entra_body", + [ + {"error": "invalid_grant", "error_description": "AADSTS700084: The refresh token was issued..."}, + { + "error": "invalid_client", + "error_description": "AADSTS5002723: Invalid JWT token.", + "error_codes": [5002723], + }, + ], + ids=["expired-or-wrong-audience", "forged-reported-as-invalid_client"], + ) + async def test_assertion_entra_refuses_to_exchange_is_challenged(self, agent_365_guardrail, entra_body): """An expired, wrong-audience, or forged Entra token looks like a valid one. Only the OBO exchange can tell, and its verdict must arrive at connect, where WWW-Authenticate reaches the client, - rather than inside every tools/call JSON-RPC error.""" - agent_365_guardrail.async_handler.post.return_value = self._entra_response( - 400, {"error": "invalid_grant", "error_description": "AADSTS700084: The refresh token was issued..."} - ) + rather than inside every tools/call JSON-RPC error. Entra files a forged assertion under + ``invalid_client`` with an AADSTS50027xx sub-code, which must not read as a gateway secret problem.""" + agent_365_guardrail.async_handler.post.return_value = self._entra_response(400, entra_body) challenge = await self._connect(self._server([self.GATEWAY_SCOPE]), self.ENTRA_BEARER) @@ -9088,7 +9099,16 @@ class TestAgent365ChallengeAtConnect: @pytest.mark.parametrize( "entra_outcome", [ - {"return_value": _entra_response(401, {"error": "invalid_client"})}, + { + "return_value": _entra_response( + 401, + { + "error": "invalid_client", + "error_description": "AADSTS7000215: Invalid client secret", + "error_codes": [7000215], + }, + ) + }, {"return_value": _entra_response(503, {"error": "temporarily_unavailable"})}, {"side_effect": httpx.ConnectError("dns")}, ], diff --git a/tests/test_litellm/proxy/guardrails/guardrail_hooks/test_agent_365.py b/tests/test_litellm/proxy/guardrails/guardrail_hooks/test_agent_365.py index 6466bd4549d..ba9253cb415 100644 --- a/tests/test_litellm/proxy/guardrails/guardrail_hooks/test_agent_365.py +++ b/tests/test_litellm/proxy/guardrails/guardrail_hooks/test_agent_365.py @@ -631,6 +631,30 @@ class TestUnreachableFallback: assert error_code in info["guardrail_response"]["reason"] assert "client_secret" in info["guardrail_response"]["reason"] + @pytest.mark.asyncio + @pytest.mark.parametrize("aadsts_code", [5002710, 5002723], ids=["malformed-header", "no-kid"]) + async def test_malformed_assertion_reported_as_invalid_client_is_a_caller_401(self, aadsts_code: int): + """Entra answers ``invalid_client`` for a forged or garbled assertion (AADSTS50027xx) exactly as for a + bad gateway secret; the sub-code is what says the caller, not the gateway, has to fix it.""" + handler: Final = FakeHandler( + [ + _response( + 401, + { + "error": "invalid_client", + "error_description": f"AADSTS{aadsts_code}: Invalid JWT token.", + "error_codes": [aadsts_code], + }, + ) + ] + ) + guardrail: Final = _make_guardrail(handler) + data: Final = _mcp_data() + with pytest.raises(HTTPException) as exc_info: + await _run(guardrail, data) + assert exc_info.value.status_code == 401 + assert "client_secret" not in _guardrail_info(data)["guardrail_response"]["reason"] + @pytest.mark.asyncio async def test_gateway_credential_rejection_follows_fail_open(self): handler: Final = FakeHandler( From f235c8b3a2ceba9328026414f6070e072be2552b Mon Sep 17 00:00:00 2001 From: yucheng Date: Mon, 14 Sep 2026 22:59:26 +0000 Subject: [PATCH 051/225] fix(agent_365): keep sign-in discovery for servers forwarding an API-key header A forwarded x-api-key, api-key or apikey header is the upstream's own credential and leaves the caller's top-level Authorization with the gateway, so Agent 365 discovery and the connect-time challenge must not be suppressed for those servers. Only modes or forwarded headers that consume top-level Authorization opt a server out. Co-Authored-By: Devin AI <158243242+devin-ai-integration[bot]@users.noreply.github.com> --- .../guardrail_hooks/agent_365/agent_365.py | 7 +++-- .../types/mcp_server/mcp_server_manager.py | 20 ++++++++----- .../mcp_server/test_discoverable_endpoints.py | 15 +++++++++- .../mcp_server/test_mcp_server.py | 23 +++++++++++++- .../mcp_server/test_mcp_server_manager.py | 30 +++++++++++++++++++ .../guardrail_hooks/test_agent_365.py | 9 ++++++ 6 files changed, 92 insertions(+), 12 deletions(-) diff --git a/litellm/proxy/guardrails/guardrail_hooks/agent_365/agent_365.py b/litellm/proxy/guardrails/guardrail_hooks/agent_365/agent_365.py index 40264d111d7..c332ac3c964 100644 --- a/litellm/proxy/guardrails/guardrail_hooks/agent_365/agent_365.py +++ b/litellm/proxy/guardrails/guardrail_hooks/agent_365/agent_365.py @@ -36,7 +36,6 @@ from litellm.llms.custom_httpx.http_handler import ( httpxSpecialProvider, ) from litellm.types.guardrails import GuardrailEventHooks -from litellm.types.mcp import MCPAuth from litellm.types.mcp_server.mcp_server_manager import MCPServer from litellm.types.proxy.guardrails.guardrail_hooks.agent_365 import ( AGENT_365_PROD_API_BASE, @@ -686,8 +685,10 @@ def _applicable_guardrails( """Agent 365 guardrails whose sign-in the gateway advertises for ``server``: the ``default_on`` ones, minus those the caller's key or team opted out of once the caller is known. A guardrail only a key or policy selects still enforces at the tool call but never challenges, since the anonymous metadata fetch that - follows a challenge cannot see which key selected it and would advertise the wrong issuer.""" - if server.auth_type == MCPAuth.oauth2 or not server.advertises_gateway_authorization_server: + follows a challenge cannot see which key selected it and would advertise the wrong issuer. Only servers + that leave the caller's top-level ``Authorization`` with the gateway qualify: a forwarded API-key header + travels upstream in its own slot and does not displace the Entra assertion.""" + if not server.keeps_caller_authorization: return () advertised: Final = tuple( callback diff --git a/litellm/types/mcp_server/mcp_server_manager.py b/litellm/types/mcp_server/mcp_server_manager.py index 985d31af997..70ad101dcb0 100644 --- a/litellm/types/mcp_server/mcp_server_manager.py +++ b/litellm/types/mcp_server/mcp_server_manager.py @@ -276,10 +276,10 @@ class MCPServer(BaseModel): return self.per_server_oauth_discovery and self.auth_type == MCPAuth.oauth2 and not self.has_client_credentials @property - def advertises_gateway_authorization_server(self) -> bool: - """Whether named discovery should advertise the aggregate gateway authorization server.""" - if self.auth_type == MCPAuth.oauth2: - return self.is_gateway_managed_oauth2 and not self.uses_per_server_oauth_relay + def keeps_caller_authorization(self) -> bool: + """Whether the caller's top-level ``Authorization`` stays with the gateway: the server neither relays + it upstream nor runs an OAuth mode that fills that slot itself, so a gateway guardrail may consume it + as the caller's own assertion. Forwarding a separate API-key header leaves the slot untouched.""" if self.auth_type not in ( None, MCPAuth.none, @@ -291,9 +291,15 @@ class MCPServer(BaseModel): MCPAuth.aws_sigv4, ): return False - return not any( - header.lower() in ("authorization", "x-api-key", "api-key", "apikey") - for header in (self.extra_headers or ()) + return not any(header.lower() == "authorization" for header in (self.extra_headers or ())) + + @property + def advertises_gateway_authorization_server(self) -> bool: + """Whether named discovery should advertise the aggregate gateway authorization server.""" + if self.auth_type == MCPAuth.oauth2: + return self.is_gateway_managed_oauth2 and not self.uses_per_server_oauth_relay + return self.keeps_caller_authorization and not any( + header.lower() in ("x-api-key", "api-key", "apikey") for header in (self.extra_headers or ()) ) @property diff --git a/tests/test_litellm/proxy/_experimental/mcp_server/test_discoverable_endpoints.py b/tests/test_litellm/proxy/_experimental/mcp_server/test_discoverable_endpoints.py index 25164d4b215..975c4b03545 100644 --- a/tests/test_litellm/proxy/_experimental/mcp_server/test_discoverable_endpoints.py +++ b/tests/test_litellm/proxy/_experimental/mcp_server/test_discoverable_endpoints.py @@ -7148,7 +7148,7 @@ def agent_365_guardrail(): ) -async def _agent_365_gated_prm(scopes): +async def _agent_365_gated_prm(scopes, extra_headers=None): from fastapi import Request from litellm.proxy._experimental.mcp_server.discoverable_endpoints import ( @@ -7168,6 +7168,7 @@ async def _agent_365_gated_prm(scopes): transport=MCPTransport.http, auth_type=MCPAuth.none, scopes=scopes, + extra_headers=extra_headers, ) mock_request = MagicMock(spec=Request) mock_request.base_url = "https://litellm.example.com/" @@ -7200,6 +7201,18 @@ async def test_agent_365_prm_defaults_scopeless_server_to_the_gateway_app_scope( } +@pytest.mark.asyncio +async def test_agent_365_prm_survives_a_forwarded_upstream_api_key_header(agent_365_guardrail): + """The forwarded ``x-api-key`` is the upstream's credential and rides in its own header, so the caller's + ``Authorization`` still carries the Entra assertion and discovery must keep naming the Entra tenant.""" + response = await _agent_365_gated_prm(scopes=None, extra_headers=["x-api-key"]) + assert jsonable_encoder(response) == { + "authorization_servers": ["https://login.microsoftonline.com/tenant-abc/v2.0"], + "resource": "https://litellm.example.com/mcp/tools", + "scopes_supported": ["api://client-xyz/access_as_user"], + } + + @pytest.mark.asyncio async def test_key_selected_agent_365_guardrail_leaves_anonymous_prm_on_the_gateway_issuer(agent_365_guardrail): """A default-off guardrail gates only the keys that select it, so the anonymous discovery fetch must keep diff --git a/tests/test_litellm/proxy/_experimental/mcp_server/test_mcp_server.py b/tests/test_litellm/proxy/_experimental/mcp_server/test_mcp_server.py index aaa472a2400..59973b51054 100644 --- a/tests/test_litellm/proxy/_experimental/mcp_server/test_mcp_server.py +++ b/tests/test_litellm/proxy/_experimental/mcp_server/test_mcp_server.py @@ -8926,7 +8926,7 @@ class TestAgent365ChallengeAtConnect: request=httpx.Request("POST", "https://login.microsoftonline.com/tenant-abc/oauth2/v2.0/token"), ) - def _server(self, scopes: list[str] | None) -> MCPServer: + def _server(self, scopes: list[str] | None, extra_headers: list[str] | None = None) -> MCPServer: return MCPServer( server_id="id-tools", name="tools", @@ -8936,6 +8936,7 @@ class TestAgent365ChallengeAtConnect: transport=MCPTransport.http, auth_type=MCPAuth.none, scopes=scopes, + extra_headers=extra_headers, mcp_info={"server_name": "tools"}, ) @@ -9181,6 +9182,26 @@ class TestAgent365ChallengeAtConnect: assert challenge is not None and challenge.status_code == 401 assert 'error="invalid_token"' in (challenge.headers or {}).get("WWW-Authenticate", "") + @pytest.mark.asyncio + async def test_server_forwarding_an_upstream_api_key_header_is_still_challenged(self, agent_365_guardrail): + """``x-api-key`` travels upstream in its own header and leaves the caller's ``Authorization`` free for + the Entra assertion, so a key-only connect must still be sent to sign in.""" + challenge = await self._connect(self._server(None, extra_headers=["x-api-key"]), None) + + assert challenge is not None and challenge.status_code == 401 + www_authenticate = (challenge.headers or {}).get("WWW-Authenticate", "") + assert 'error="invalid_token"' in www_authenticate + assert ( + 'resource_metadata="https://gw.example.com/.well-known/oauth-protected-resource/mcp/tools"' + in www_authenticate + ) + + @pytest.mark.asyncio + async def test_server_relaying_the_caller_authorization_is_not_challenged(self, agent_365_guardrail): + """Forwarding ``Authorization`` hands the caller's bearer to the upstream, so the gateway holds no Entra + assertion of its own to exchange and must not advertise a sign-in it cannot consume.""" + assert await self._connect(self._server(None, extra_headers=["Authorization"]), None) is None + @pytest.mark.asyncio async def test_no_registered_guardrail_means_no_challenge(self): assert await self._connect(self._server([self.GATEWAY_SCOPE]), None) is None diff --git a/tests/test_litellm/proxy/_experimental/mcp_server/test_mcp_server_manager.py b/tests/test_litellm/proxy/_experimental/mcp_server/test_mcp_server_manager.py index bf670f62c4c..2a1064ec510 100644 --- a/tests/test_litellm/proxy/_experimental/mcp_server/test_mcp_server_manager.py +++ b/tests/test_litellm/proxy/_experimental/mcp_server/test_mcp_server_manager.py @@ -5092,6 +5092,36 @@ class TestMCPServerManager: ) assert server2.requires_per_user_auth is False + @pytest.mark.parametrize( + "auth_type, extra_headers, keeps_authorization, advertises_gateway", + [ + (MCPAuth.none, None, True, True), + (MCPAuth.api_key, None, True, True), + (MCPAuth.none, ["x-api-key"], True, False), + (MCPAuth.none, ["API-Key"], True, False), + (MCPAuth.none, ["Authorization"], False, False), + (MCPAuth.none, ["x-api-key", "authorization"], False, False), + (MCPAuth.oauth_delegate, None, False, False), + (MCPAuth.true_passthrough, None, False, False), + (MCPAuth.oauth2_token_exchange, None, False, False), + ], + ) + def test_forwarded_api_key_header_keeps_caller_authorization_but_not_gateway_discovery( + self, auth_type, extra_headers, keeps_authorization, advertises_gateway + ): + """A forwarded API-key header is the upstream's own credential and leaves the caller's top-level + ``Authorization`` with the gateway, while still ruling out the gateway's aggregate OAuth discovery.""" + server = MCPServer( + server_id="s", + name="s", + transport=MCPTransport.http, + auth_type=auth_type, + url="http://s.example", + extra_headers=extra_headers, + ) + assert server.keeps_caller_authorization is keeps_authorization + assert server.advertises_gateway_authorization_server is advertises_gateway + @pytest.mark.asyncio async def test_register_openapi_tools_includes_static_headers(self, tmp_path): """Ensure OpenAPI-to-MCP tool calls include server.static_headers (Issue #19341).""" diff --git a/tests/test_litellm/proxy/guardrails/guardrail_hooks/test_agent_365.py b/tests/test_litellm/proxy/guardrails/guardrail_hooks/test_agent_365.py index ba9253cb415..f73c0e87547 100644 --- a/tests/test_litellm/proxy/guardrails/guardrail_hooks/test_agent_365.py +++ b/tests/test_litellm/proxy/guardrails/guardrail_hooks/test_agent_365.py @@ -1107,6 +1107,15 @@ class TestAgent365AuthorizationServers: def test_leaves_servers_whose_own_auth_mode_owns_sign_in_alone(self, registered_guardrail, server): assert agent_365_authorization_servers(server, None) == () + @pytest.mark.parametrize("header", ["x-api-key", "API-Key", "apikey"]) + def test_forwarded_api_key_header_leaves_authorization_to_entra(self, registered_guardrail, header): + """An upstream API key rides in its own header, so the caller's ``Authorization`` still carries the + Entra assertion and a key-only client must be told where to sign in.""" + server: Final = _mcp_server(MCPAuth.none, scopes=None, extra_headers=[header]) + + assert agent_365_authorization_servers(server, None) == (ENTRA_ISSUER,) + assert agent_365_scopes_supported(server, None) == ("api://client-xyz/access_as_user",) + def test_dedupes_guardrails_sharing_a_tenant(self, registered_guardrail): twin: Final = _make_guardrail(FakeHandler([])) twin.guardrail_name = "agent-365-twin" From 4bfaf74edcdef5a24e37c583578070ced8ad9d27 Mon Sep 17 00:00:00 2001 From: yucheng Date: Tue, 15 Sep 2026 00:29:58 +0000 Subject: [PATCH 052/225] refactor(guardrails): keep Agent 365 PR to the guardrail, move MCP changes to stacked PRs The listed-tool metadata and per-caller catalog cache move to a follow-up PR stacked on this one, and the RFC 9728 sign-in challenge and discovery metadata move to a second PR stacked on that. On its own the guardrail evaluates the tool name and arguments and the caller presents its Entra token Co-Authored-By: Devin AI <158243242+devin-ai-integration[bot]@users.noreply.github.com> --- .../mcp_server/discoverable_endpoints.py | 24 +- .../mcp_server/mcp_server_manager.py | 237 ++------- .../_experimental/mcp_server/oauth_utils.py | 2 +- .../outbound_credentials/adapter.py | 8 +- .../proxy/_experimental/mcp_server/server.py | 69 +-- .../guardrail_hooks/agent_365/agent_365.py | 129 +---- litellm/proxy/utils.py | 4 - litellm/types/mcp.py | 4 - .../types/mcp_server/mcp_server_manager.py | 20 +- .../outbound_credentials/test_adapter.py | 16 - .../mcp_server/test_discoverable_endpoints.py | 102 ---- .../test_mcp_guardrail_usage_monitor.py | 1 - .../mcp_server/test_mcp_server.py | 447 +--------------- .../mcp_server/test_mcp_server_manager.py | 490 ------------------ .../mcp_server/test_openapi_tool_auth.py | 14 - .../guardrail_hooks/test_agent_365.py | 158 ------ .../utils/proxy_logging/test_mcp_bridging.py | 20 - 17 files changed, 89 insertions(+), 1656 deletions(-) diff --git a/litellm/proxy/_experimental/mcp_server/discoverable_endpoints.py b/litellm/proxy/_experimental/mcp_server/discoverable_endpoints.py index 5b0bcc254b2..bafe33d0a6b 100644 --- a/litellm/proxy/_experimental/mcp_server/discoverable_endpoints.py +++ b/litellm/proxy/_experimental/mcp_server/discoverable_endpoints.py @@ -5,14 +5,13 @@ import secrets import time from collections.abc import Callable, Mapping from datetime import datetime, timezone -from typing import TYPE_CHECKING, Any, Final, Literal, Optional, TypedDict +from typing import TYPE_CHECKING, Any, Final, Literal, Optional from urllib.parse import parse_qsl, urlencode, urlparse, urlunparse import httpx from fastapi import APIRouter, Depends, Form, HTTPException, Request from fastapi.responses import HTMLResponse, JSONResponse, RedirectResponse, Response from pydantic import BaseModel, ConfigDict, Field, SecretStr, ValidationError -from typing_extensions import ReadOnly from litellm._logging import verbose_logger from litellm.caching.in_memory_cache import InMemoryCache @@ -82,10 +81,6 @@ from litellm.proxy.common_utils.encrypt_decrypt_utils import ( encrypt_value_helper, ) from litellm.proxy.common_utils.http_parsing_utils import _read_request_body -from litellm.proxy.guardrails.guardrail_hooks.agent_365.agent_365 import ( - agent_365_authorization_servers, - agent_365_scopes_supported, -) from litellm.types.mcp import MCPAuth, MCPCredentials from litellm.types.mcp_server.mcp_server_manager import MCPServer, MCPTokenEndpointAuthMethod @@ -2403,7 +2398,7 @@ async def _build_oauth_protected_resource_response( request: Request, mcp_server_name: str | None, use_standard_pattern: bool, -) -> Mapping[str, object]: +) -> dict: """ Build OAuth protected resource response with the appropriate URL pattern. @@ -2502,15 +2497,6 @@ async def _build_oauth_protected_resource_response( if obo_response is not None: return obo_response - agent_365_issuers: Final = agent_365_authorization_servers(mcp_server, None) if mcp_server else () - if mcp_server is not None and agent_365_issuers: - agent_365_metadata: Final[_ProtectedResourceMetadata] = { - "authorization_servers": agent_365_issuers, - "resource": resource_url, - "scopes_supported": agent_365_scopes_supported(mcp_server, None), - } - return agent_365_metadata - if mcp_server is not None and mcp_server.advertises_gateway_authorization_server: return { "authorization_servers": [f"{request_base_url}/mcp"], @@ -2530,12 +2516,6 @@ async def _build_oauth_protected_resource_response( } -class _ProtectedResourceMetadata(TypedDict): - authorization_servers: ReadOnly[tuple[str, ...]] - resource: ReadOnly[str] - scopes_supported: ReadOnly[tuple[str, ...]] - - def _obo_protected_resource_response(mcp_server: MCPServer | None, resource_url: str) -> dict | None: """The OBO (token_exchange) PRM, or None when this server is not OBO / no issuer is configured. diff --git a/litellm/proxy/_experimental/mcp_server/mcp_server_manager.py b/litellm/proxy/_experimental/mcp_server/mcp_server_manager.py index 00f19bf964e..fb0c623473a 100644 --- a/litellm/proxy/_experimental/mcp_server/mcp_server_manager.py +++ b/litellm/proxy/_experimental/mcp_server/mcp_server_manager.py @@ -242,21 +242,6 @@ _user_env_vars_cache: Final[dict[tuple[str, str], tuple[dict[str, str], float]]] _USER_ENV_VARS_CACHE_TTL: Final = 60 # seconds _USER_ENV_VARS_CACHE_MAX_SIZE: Final = 4096 # cap to prevent unbounded growth -_ListedToolsByCaller: TypeAlias = Mapping[str | None, Mapping[str, MCPTool]] -_NO_LISTED_TOOLS: Final[_ListedToolsByCaller] = MappingProxyType({}) -_LISTED_TOOLS_CALLERS_PER_SERVER: Final = 256 - - -@dataclass(frozen=True, slots=True) -class ListedToolsCaller: - """Request inputs that select which upstream catalog a caller was shown by tools/list.""" - - user_api_key_auth: UserAPIKeyAuth | None = None - mcp_auth_header: str | dict[str, str] | None = None - raw_headers: Mapping[str, str] | None = None - oauth2_headers: Mapping[str, str] | None = None - - # Auth types whose upstream OAuth endpoints (protected-resource + authorization-server metadata) the # gateway discovers from the upstream itself: interactive oauth2 and the two client-forwarded modes. # OBO/M2M endpoint discovery is decided separately via _obo_needs_endpoint_discovery. Shared by the @@ -1134,25 +1119,6 @@ def _authorization_is_litellm_admission_credential( return bool(user_api_key_auth and user_api_key_auth.api_key and not admission_header) -def _server_auth_header_for( - server: MCPServer, - mcp_server_auth_headers: Mapping[str, str | dict[str, str]] | None, - mcp_auth_header: str | dict[str, str] | None, -) -> str | dict[str, str] | None: - """Server-specific ``x-mcp--authorization`` header, else the deprecated global one.""" - server_specific: Final = ( - lookup_mcp_server_auth_in_headers( - mcp_server_auth_headers, - alias=server.alias, - server_name=server.server_name, - access_groups=server.access_groups, - ) - if mcp_server_auth_headers - else None - ) - return mcp_auth_header if server_specific is None else server_specific - - def _format_byok_openapi_auth_header(mcp_server: MCPServer, mcp_auth_header: str) -> str: """Format a raw BYOK credential for OpenAPI tool ``Authorization`` injection. @@ -1979,7 +1945,6 @@ class MCPServerManager: "gmail_send_email": "zapier_mcp_server", } """ - self._listed_tools_by_server_id: dict[str, _ListedToolsByCaller] = {} # mutable-ok: refreshed per tools/list self._upstream_initialize_instructions_by_server_id: dict[str, str] = {} # Per-server monotonic timestamp of last upstream prefetch attempt (success, # empty result, or failure). Used to throttle re-probes for servers that do @@ -2675,7 +2640,7 @@ class MCPServerManager: self._assign_unique_short_prefix(new_server) _warn_legacy_delegate_auth_if_applicable(new_server, source="config") _warn_config_id_jag_server_outruns_sso(new_server) - self._invalidate_server_definition_caches(server_id) + self._invalidate_discovery_lists(server_id) self.config_mcp_servers[server_id] = new_server self._set_oauth_discovery_deferred( server_id, @@ -2877,7 +2842,7 @@ class MCPServerManager: global_mcp_tool_registry, ) - self._invalidate_server_definition_caches(server.server_id) + self._invalidate_discovery_lists(server.server_id) prefix_root: Final = normalize_server_name(get_server_prefix(server)) if server.spec_path and prefix_root: openapi_key_prefix: Final = prefix_root + MCP_TOOL_PREFIX_SEPARATOR @@ -3254,7 +3219,7 @@ class MCPServerManager: # env_vars_are_encrypted=False. new_server: Final = await self.build_mcp_server_from_table(mcp_server, env_vars_are_encrypted=False) self._assign_unique_short_prefix(new_server) - self._invalidate_server_definition_caches(mcp_server.server_id) + self._invalidate_discovery_lists(mcp_server.server_id) self.registry[mcp_server.server_id] = new_server await self._maybe_register_openapi_tools(new_server) self.prime_oauth_metadata_discovery(new_server) @@ -3291,7 +3256,7 @@ class MCPServerManager: previous_server=self.registry[mcp_server.server_id], ) self._assign_unique_short_prefix(new_server) - self._invalidate_server_definition_caches(mcp_server.server_id) + self._invalidate_discovery_lists(mcp_server.server_id) self.registry[mcp_server.server_id] = new_server await self._maybe_register_openapi_tools(new_server) self.prime_oauth_metadata_discovery(new_server) @@ -3750,7 +3715,19 @@ class MCPServerManager: verbose_logger.warning("MCP Server %s not found", server_id) return [] - server_auth_header: Final = _server_auth_header_for(server, mcp_server_auth_headers, mcp_auth_header) + # Get server-specific auth header if available + server_auth_header: str | dict[str, str] | None = None + if mcp_server_auth_headers: + server_auth_header = lookup_mcp_server_auth_in_headers( + mcp_server_auth_headers, + alias=server.alias, + server_name=server.server_name, + access_groups=server.access_groups, + ) + + # Fall back to deprecated mcp_auth_header if no server-specific header found + if server_auth_header is None: + server_auth_header = mcp_auth_header try: tools: Final = await self._get_tools_from_server( @@ -3836,7 +3813,7 @@ class MCPServerManager: def _build_stdio_env( self, server: MCPServer, - raw_headers: Mapping[str, str] | None = None, + raw_headers: dict[str, str] | None = None, ) -> dict[str, str] | None: """Resolve stdio env values, supporting header-driven placeholders.""" @@ -4098,7 +4075,6 @@ class MCPServerManager: oauth2_headers: dict[str, str] | None, user_api_key_auth: UserAPIKeyAuth | None, raw_headers: Mapping[str, str] | None = None, - resource_metadata_url: str | None = None, ) -> None: """Mint an exchange-backed server's upstream credential at the transport edge. @@ -4136,9 +4112,7 @@ class MCPServerManager: if spec is None or not isinstance(spec.config, (TokenExchangeConfig, IdJagConfig)): return if subject_token is None and isinstance(spec.config, TokenExchangeConfig): - raise_token_exchange_challenge( - resolved_server, root_path=get_request_root_path(), resource_metadata_url=resource_metadata_url - ) + raise_token_exchange_challenge(resolved_server, root_path=get_request_root_path()) match await self._cred_provider.resolve_credentials(to_subject(user_api_key_auth, subject_token), spec): case Ok(_): return @@ -4148,7 +4122,6 @@ class MCPServerManager: resolved_server, root_path=get_request_root_path(), claims=err.unauthorized.claims, - resource_metadata_url=resource_metadata_url, ) raise_public(err) @@ -4365,12 +4338,6 @@ class MCPServerManager: verbose_logger.info("_get_tools_from_server for %s...", server.name) client = None - listed_caller: Final = ListedToolsCaller( - user_api_key_auth=user_api_key_auth, - mcp_auth_header=mcp_auth_header, - raw_headers=raw_headers, - oauth2_headers=oauth2_headers, - ) try: # Tool *listing* must not be blocked by missing per-user env vars — @@ -4447,25 +4414,29 @@ class MCPServerManager: if server.spec_path: # OpenAPI tools were stored in the registry under the prefix # active at registration time — fetch by that same prefix. - registry_prefix: Final = normalize_server_name(get_server_prefix(server)) + MCP_TOOL_PREFIX_SEPARATOR - _tools: Final = global_mcp_tool_registry.list_tools(tool_prefix=registry_prefix) + _tools: Final = global_mcp_tool_registry.list_tools(tool_prefix=get_server_prefix(server)) tools = global_mcp_tool_registry.convert_tools_to_mcp_sdk_tool_type(_tools) # OpenAPI tools are stored in the registry with their prefix already # applied (e.g. "test_petstore-getinventory"). Do NOT pass them # through _create_prefixed_tools — that would add the prefix a second # time producing "test_petstore-test_petstore-getinventory". - unprefixed_tools: Final = [ # mutable-ok: returned through the list[MCPTool] listing contract - t.model_copy(update=MappingProxyType({"name": t.name[len(registry_prefix) :]})) for t in tools - ] - self._record_listed_tools(server, unprefixed_tools, listed_caller) - return tools if add_prefix else unprefixed_tools + if not add_prefix: + prefix: Final = get_server_prefix(server) + sep: Final = MCP_TOOL_PREFIX_SEPARATOR + tools = [ + ( + t.model_copy(update={"name": t.name[len(prefix) + len(sep) :]}) + if t.name.startswith(f"{prefix}{sep}") + else t + ) + for t in tools + ] + return tools else: tools = await self._fetch_tools_with_timeout(client, server.name) self._remember_upstream_initialize_instructions(server, client) - prefixed_or_original_tools: Final = self._create_prefixed_tools( - tools, server, add_prefix=add_prefix, caller=listed_caller - ) + prefixed_or_original_tools: Final = self._create_prefixed_tools(tools, server, add_prefix=add_prefix) return prefixed_or_original_tools @@ -4509,86 +4480,6 @@ class MCPServerManager: self._resource_discovery_cache.invalidate(server_id) self._template_discovery_cache.invalidate(server_id) - def _invalidate_server_definition_caches(self, server_id: str) -> None: - self._invalidate_discovery_lists(server_id) - self._listed_tools_by_server_id.pop(server_id, None) - - def _discovers_per_caller(self, server: MCPServer) -> bool: - return ( - server.requires_per_user_auth - or self._references_per_user_env_var(server) - or server.delegate_auth_to_upstream - or server.auth_type in (MCPAuth.oauth2_token_exchange, MCPAuth.oauth2_id_jag) - or self._signs_caller_identity_upstream(server) - ) - - @staticmethod - def _signs_caller_identity_upstream(server: MCPServer) -> bool: - """Whether MCPJWTSigner mints a per-caller ``Authorization`` for ``server``, so the upstream may - tailor its catalog to the caller even though the server itself is configured as shared.""" - from litellm.proxy.guardrails.guardrail_hooks.mcp_jwt_signer.mcp_jwt_signer import ( # noqa: PLC0415 # lazy: guardrail package imports the proxy server - get_mcp_jwt_signer, - ) - - if get_mcp_jwt_signer() is None: - return False - return not any(k.lower() == "authorization" for k in (server.static_headers or {})) - - def _listed_tools_identity(self, server: MCPServer, caller: ListedToolsCaller | None) -> str | None: - """Key the listed-tool cache by every request input that can change the upstream catalog. - - Forwarded headers, header-driven stdio env, a relayed caller bearer, and the - server-specific auth header all reach upstream, so two callers differing in any of - them may be shown different tools. Shared servers with none of those stay on the - shared (``None``) slot. OpenAPI servers list from the process-wide registry. - """ - if server.spec_path or caller is None: - return None - auth: Final = caller.user_api_key_auth - identity: Final = ( - (auth.user_id, auth.api_key) if auth is not None and self._discovers_per_caller(server) else None - ) - forwarded: Final = self._forwarded_header_values(server, caller.raw_headers) - header_env: Final = self._build_stdio_env(server, caller.raw_headers) - stdio_env: Final = None if header_env == self._build_stdio_env(server) else header_env - relayed_bearer: Final = ( - self._extract_subject_token(caller.oauth2_headers, caller.raw_headers, auth) - if server.is_client_forwarded_token - else None - ) - inputs: Final = (identity, caller.mcp_auth_header, forwarded, stdio_env, relayed_bearer) - if not any(inputs): - return None - material: Final = json.dumps(inputs, sort_keys=True, separators=(",", ":")) - return hashlib.sha256(material.encode()).hexdigest() - - @staticmethod - def _forwarded_header_values( - server: MCPServer, raw_headers: Mapping[str, str] | None - ) -> tuple[tuple[str, str], ...]: - if not raw_headers or not server.extra_headers: - return () - forwarded_names: Final = frozenset(name.lower() for name in server.extra_headers) - return tuple( - sorted((name.lower(), value) for name, value in raw_headers.items() if name.lower() in forwarded_names) - ) - - def _record_listed_tools( - self, server: MCPServer, tools: Sequence[MCPTool], caller: ListedToolsCaller | None - ) -> None: - identity: Final = self._listed_tools_identity(server, caller) - listing: Final = MappingProxyType({tool.name: tool for tool in tools}) - existing: Final = self._listed_tools_by_server_id.get(server.server_id, _NO_LISTED_TOOLS) - shared: Final = existing.get(None) - callers: Final = tuple((key, value) for key, value in existing.items() if key not in (None, identity)) - evicted: Final = 0 if identity is None else max(len(callers) + 1 - _LISTED_TOOLS_CALLERS_PER_SERVER, 0) - entries: Final = ( - *(() if shared is None else ((None, shared),)), - *callers[evicted:], - (identity, listing), - ) - self._listed_tools_by_server_id[server.server_id] = MappingProxyType(dict(entries)) - def _discovery_key( self, server: MCPServer, @@ -4599,7 +4490,12 @@ class MCPServerManager: subject_token: str | None, credential_fingerprint: str | None = None, ) -> _DiscoveryKey: - per_user: Final = self._discovers_per_caller(server) + per_user: Final = ( + server.requires_per_user_auth + or self._references_per_user_env_var(server) + or server.delegate_auth_to_upstream + or server.auth_type in (MCPAuth.oauth2_token_exchange, MCPAuth.oauth2_id_jag) + ) if not (per_user or mcp_auth_header or extra_headers or stdio_env or subject_token): return server.server_id, None identity: Final = ( @@ -5412,13 +5308,7 @@ class MCPServerManager: "attempts; the 3-character prefix space is too crowded." ) - def _create_prefixed_tools( - self, - tools: list[MCPTool], - server: MCPServer, - add_prefix: bool = True, - caller: ListedToolsCaller | None = None, - ) -> list[MCPTool]: + def _create_prefixed_tools(self, tools: list[MCPTool], server: MCPServer, add_prefix: bool = True) -> list[MCPTool]: """ Create prefixed tools and update tool mapping. @@ -5450,21 +5340,9 @@ class MCPServerManager: for spelling in iter_known_tool_name_spellings(original_name, server): self.tool_name_to_mcp_server_name_mapping[spelling] = prefix - self._record_listed_tools(server, tools, caller) verbose_logger.info("Successfully fetched %s tools from server %s", len(prefixed_tools), server.name) return prefixed_tools - def get_listed_tool(self, server: MCPServer, name: str, caller: ListedToolsCaller | None = None) -> MCPTool | None: - identity: Final = self._listed_tools_identity(server, caller) - listed: Final = self._listed_tools_by_server_id.get(server.server_id, _NO_LISTED_TOOLS).get(identity) - if not listed: - return None - tool: Final = listed.get(name) or listed.get(strip_known_server_prefix(name, server)) - if tool is None: - return None - description: Final = (server.tool_name_to_description or {}).get(tool.name) - return tool if description is None else tool.model_copy(update={"description": description}) - def _create_prefixed_prompts( self, prompts: Sequence[Prompt], server: MCPServer, add_prefix: bool = True ) -> list[Prompt]: @@ -5703,7 +5581,6 @@ class MCPServerManager: server: MCPServer, raw_headers: dict[str, str] | None = None, litellm_logging_obj: "LiteLLMLoggingObj | None" = None, - tool: MCPTool | None = None, ) -> dict[str, Any]: """ Run pre-call checks and guardrail hooks for an MCP tool call. @@ -5717,9 +5594,6 @@ class MCPServerManager: ``pre_mcp_call`` evaluation (or a block) on the spend-log row the Guardrails Monitor counts. It stays optional so callers that do no logging are unchanged. - ``tool`` is the upstream tool definition when one was listed, so guardrails - can see its description and input schema, not just the name and arguments. - Returns a dict that may contain: - "arguments": hook-modified tool arguments (only if changed) - "extra_headers": headers injected by pre_mcp_call guardrail hooks @@ -5773,8 +5647,6 @@ class MCPServerManager: "user_api_key_hash": (getattr(user_api_key_auth, "api_key_hash", None) if user_api_key_auth else None), "incoming_bearer_token": incoming_bearer_token, "headers": logging_safe_mcp_headers(raw_headers), - "tool_description": tool.description if tool is not None else None, - "tool_input_schema": tool.inputSchema if tool is not None else None, } # Create MCP request object for processing @@ -5829,7 +5701,6 @@ class MCPServerManager: proxy_logging_obj: ProxyLogging, start_time: datetime.datetime, litellm_logging_obj: "LiteLLMLoggingObj | None" = None, - tool: MCPTool | None = None, ): """Create and return a during hook task for MCP tool calls. @@ -5844,8 +5715,6 @@ class MCPServerManager: tool_name=name, arguments=arguments, server_name=server_name_from_prefix, - tool_description=tool.description if tool is not None else None, - tool_input_schema=tool.inputSchema if tool is not None else None, start_time=start_time.timestamp() if start_time else None, hidden_params=HiddenParams(), ) @@ -5977,7 +5846,21 @@ class MCPServerManager: GuardrailRaisedException: If guardrails block the call HTTPException: If an HTTP error occurs """ - server_auth_header: Final = _server_auth_header_for(mcp_server, mcp_server_auth_headers, mcp_auth_header) + # Get server-specific auth header if available (case-insensitive) + # FIX: Added case-insensitive matching to handle auth header keys that may not match + # the exact case of server alias/name (e.g., '1litellmagcgateway' vs '1LiteLLMAGCGateway') + server_auth_header: dict[str, str] | str | None = None + if mcp_server_auth_headers: + server_auth_header = lookup_mcp_server_auth_in_headers( + mcp_server_auth_headers, + alias=mcp_server.alias, + server_name=mcp_server.server_name, + access_groups=mcp_server.access_groups, + ) + + # Fall back to deprecated mcp_auth_header if no server-specific header found + if server_auth_header is None: + server_auth_header = mcp_auth_header # Extract subject token for OAuth2 Token Exchange (OBO) and ID-JAG flows subject_token: str | None = None @@ -6413,12 +6296,6 @@ class MCPServerManager: user_api_key_auth, mcp_auth_header, ) - listed_caller: Final = ListedToolsCaller( - user_api_key_auth=user_api_key_auth, - mcp_auth_header=_server_auth_header_for(mcp_server, mcp_server_auth_headers, mcp_auth_header), - raw_headers=raw_headers, - oauth2_headers=oauth2_headers, - ) ######################################################### # Pre MCP Tool Call Hook @@ -6434,7 +6311,6 @@ class MCPServerManager: server=mcp_server, raw_headers=raw_headers, litellm_logging_obj=litellm_logging_obj, - tool=self.get_listed_tool(mcp_server, name, listed_caller), ) if "arguments" in hook_result: arguments = hook_result["arguments"] @@ -6450,7 +6326,6 @@ class MCPServerManager: proxy_logging_obj=proxy_logging_obj, start_time=start_time, litellm_logging_obj=litellm_logging_obj, - tool=self.get_listed_tool(mcp_server, name, listed_caller), ) tasks.append(during_hook_task) @@ -6718,7 +6593,7 @@ class MCPServerManager: for server_id in previous_registry.keys() | registered_registry.keys(): if previous_registry.get(server_id) != registered_registry.get(server_id): - self._invalidate_server_definition_caches(server_id) + self._invalidate_discovery_lists(server_id) self.registry = registered_registry # A discovery task may have published into ``previous_registry`` while # this replacement was being staged. Reconcile every published entry diff --git a/litellm/proxy/_experimental/mcp_server/oauth_utils.py b/litellm/proxy/_experimental/mcp_server/oauth_utils.py index 50eca9de2b5..39865a35ec6 100644 --- a/litellm/proxy/_experimental/mcp_server/oauth_utils.py +++ b/litellm/proxy/_experimental/mcp_server/oauth_utils.py @@ -199,7 +199,7 @@ def get_route_relative_request_path(scope: Scope) -> str: :func:`litellm.proxy.auth.auth_utils.get_request_route`, which the rest of the MCP auth path already routes through, so ``/litellmfoo`` is not truncated under ``root_path=/litellm``.""" raw_path = str(scope.get("_original_path") or scope.get("path", "") or "") - root_path = str(scope.get("app_root_path", scope.get("root_path")) or "").rstrip("/") + root_path = str(scope.get("app_root_path") or scope.get("root_path") or "").rstrip("/") if root_path and (raw_path == root_path or raw_path.startswith(f"{root_path}/")): return raw_path[len(root_path) :] return raw_path diff --git a/litellm/proxy/_experimental/mcp_server/outbound_credentials/adapter.py b/litellm/proxy/_experimental/mcp_server/outbound_credentials/adapter.py index 2401ad4a776..77979a15199 100644 --- a/litellm/proxy/_experimental/mcp_server/outbound_credentials/adapter.py +++ b/litellm/proxy/_experimental/mcp_server/outbound_credentials/adapter.py @@ -358,7 +358,6 @@ def raise_token_exchange_challenge( *, root_path: str, claims: str | None = None, - resource_metadata_url: str | None = None, ) -> NoReturn: """Raise the RFC 9728 / RFC 6750 challenge an OBO (``token_exchange``) server returns when the caller's subject token is missing or the IdP rejected it. @@ -376,13 +375,8 @@ def raise_token_exchange_challenge( ``error="invalid_token"`` and is byte-identical to the static one. Both the error value (one of two literals) and the base64 claims draw from a fixed alphabet, so nothing from the IdP body reaches the header unescaped. - - ``resource_metadata_url`` overrides the derived path with the absolute metadata URL matching the - route spelling the request arrived on: RFC 9728 §3.3 clients reject a ``resource`` that differs - from the URL they connected to, and a ``/{server}/mcp`` connect must not be sent to the - ``/mcp/{server}`` document. """ - resource_metadata: Final = resource_metadata_url or oauth_protected_resource_path(root_path, server) + resource_metadata: Final = oauth_protected_resource_path(root_path, server) encoded_claims: Final = base64.b64encode(claims.encode()).decode() if claims else None error: Final = "insufficient_claims" if encoded_claims else "invalid_token" error_description: Final = ( diff --git a/litellm/proxy/_experimental/mcp_server/server.py b/litellm/proxy/_experimental/mcp_server/server.py index ca12d9e9937..7feb1fd468d 100644 --- a/litellm/proxy/_experimental/mcp_server/server.py +++ b/litellm/proxy/_experimental/mcp_server/server.py @@ -57,7 +57,6 @@ from litellm.proxy._experimental.mcp_server.mcp_debug import ( from litellm.proxy._experimental.mcp_server.oauth_utils import ( _redact_mcp_resource_url, get_byok_www_authenticate, - get_passthrough_resource_metadata_url, get_passthrough_www_authenticate, get_route_relative_request_path, well_known_root_suffix, @@ -81,16 +80,12 @@ from litellm.proxy._types import ( UserAPIKeyAuth, ) from litellm.proxy.auth.ip_address_utils import IPAddressUtils -from litellm.proxy.guardrails.guardrail_hooks.agent_365.agent_365 import ( - agent_365_sign_in_required, -) from litellm.proxy.litellm_pre_call_utils import ( LiteLLMProxyRequestSetup, get_chain_id_from_headers, ) from litellm.types.mcp import MCPAuth, MCPSpecVersion from litellm.types.mcp_server.mcp_server_manager import MCPInfo, MCPServer -from litellm.types.mcp_server.tool_registry import MCPTool as RegisteredTool from litellm.types.utils import CallTypes, StandardLoggingMCPToolCall from litellm.utils import Rules, client, function_setup @@ -2782,11 +2777,6 @@ if MCP_AVAILABLE: return managed_resource_templates - def _registered_tool_metadata(name: str, registered: RegisteredTool, server: MCPServer) -> MCPTool: - overrides: Final = server.tool_name_to_description - description: Final = overrides.get(name, registered.description) if overrides else registered.description - return MCPTool(name=name, description=description, inputSchema=registered.input_schema) - def _resolve_display_name_to_original( name: str, allowed_mcp_servers: list[MCPServer], @@ -3125,7 +3115,6 @@ if MCP_AVAILABLE: server=mcp_server, raw_headers=raw_headers, litellm_logging_obj=litellm_logging_obj, - tool=_registered_tool_metadata(original_tool_name, local_tool, mcp_server), ) # `pre_call_tool_check` may return guardrail-modified # arguments; honor them on the local path too. @@ -3191,8 +3180,7 @@ if MCP_AVAILABLE: # not in the registry either, `_handle_local_mcp_tool` below reports # 404 and nothing runs, so demanding a server here would turn every # unknown tool name into a misleading 503. - registered_local_tool: Final = global_mcp_tool_registry.get_tool(original_tool_name) - if registered_local_tool is not None: + if global_mcp_tool_registry.get_tool(original_tool_name) is not None: # `mcp_server` is None here because the tool name is not in the # tool -> server mapping, but the name still carries a prefix # that the server-level check above compared against the @@ -3233,7 +3221,6 @@ if MCP_AVAILABLE: server=prefix_server, raw_headers=raw_headers, litellm_logging_obj=litellm_logging_obj, - tool=_registered_tool_metadata(original_tool_name, registered_local_tool, prefix_server), ) if "arguments" in hook_result: arguments = hook_result["arguments"] # pyright: ignore[reportAny] # hook returns untyped args @@ -4020,21 +4007,6 @@ if MCP_AVAILABLE: ) return user_api_key_auth.model_copy(update={"object_permission": updated_op}) - async def _key_granted_single_server( - server: MCPServer, - mcp_servers: Sequence[str] | None, - user_api_key_auth: UserAPIKeyAuth | None, - client_ip: str | None, - ) -> bool: - """Sign-in challenges are issued only on a single-server connect the key's grant admits, so a key - without access gets the grant's 403 instead of a sign-in it could not use.""" - if len(mcp_servers or []) != 1: - return False - allowed: Final = await _get_allowed_mcp_servers( - user_api_key_auth=user_api_key_auth, mcp_servers=mcp_servers, client_ip=client_ip - ) - return any(granted.server_id == server.server_id for granted in allowed) - async def _raise_preemptive_401_for_unauthenticated_servers( scope: Scope, mcp_servers: list[str] | None, @@ -4157,24 +4129,8 @@ if MCP_AVAILABLE: # (transport level, where WWW-Authenticate survives) with the RFC 9728 resource_metadata # so the client discovers the IdP, SSOs, and retries with a subject token, which LiteLLM # then exchanges. A tool-call-time 401 would be wrapped into a JSON-RPC error and the - # header lost, so the discovery flow needs this pre-emptive challenge. Servers gated by an - # Agent 365 guardrail (OBO to the evaluate API) get the same challenge, also when the only - # bearer is the LiteLLM key itself, which admits the caller but is not an exchangeable subject, - # and when Entra refuses the presented assertion (expired, wrong audience), so the client - # signs in again instead of failing every tool call. Only on the server's own route: the - # per-server metadata ``resource`` must equal the URL the client connected to (RFC 9728 3.3), - # which aggregate ``/mcp`` and multi-server connects never do. - granted_single_server = server is not None and await _key_granted_single_server( - server, mcp_servers, user_api_key_auth, client_ip - ) - if server and ( - (server.auth_type == MCPAuth.oauth2_token_exchange and not oauth2_headers) - or ( - granted_single_server - and tuple(_get_mcp_servers_in_path(get_route_relative_request_path(scope)) or ()) == (server_name,) - and await agent_365_sign_in_required(server, user_api_key_auth, oauth2_headers) - ) - ): + # header lost, so the discovery flow needs this pre-emptive challenge. + if server and server.auth_type == MCPAuth.oauth2_token_exchange and not oauth2_headers: from litellm.proxy._experimental.mcp_server.outbound_credentials.adapter import ( # noqa: PLC0415 # lazy: adapter pulls MCP subgraph raise_token_exchange_challenge, ) @@ -4182,11 +4138,7 @@ if MCP_AVAILABLE: get_request_root_path, ) - raise_token_exchange_challenge( - server, - root_path=get_request_root_path(), - resource_metadata_url=get_passthrough_resource_metadata_url(scope=scope, server_name=server_name), - ) + raise_token_exchange_challenge(server, root_path=get_request_root_path()) # Exchange-backed modes (token_exchange's OBO mint, id_jag's stored-assertion mint): run # the exchange here at the transport edge, so a rejected subject raises the RFC 9728 @@ -4195,13 +4147,22 @@ if MCP_AVAILABLE: # and what each mints from. Gated to single-server routes the key may reach; the # multi-server aggregate keeps absorbing per-server auth failures so one bad server # cannot 401 the whole connect. - if server and granted_single_server: + if ( + server + and len(mcp_servers or []) == 1 + and server.server_id + in frozenset( + allowed.server_id + for allowed in await _get_allowed_mcp_servers( + user_api_key_auth=user_api_key_auth, mcp_servers=mcp_servers, client_ip=client_ip + ) + ) + ): await global_mcp_server_manager.preflight_token_exchange( server=server, oauth2_headers=oauth2_headers, user_api_key_auth=user_api_key_auth, raw_headers=raw_headers, - resource_metadata_url=get_passthrough_resource_metadata_url(scope=scope, server_name=server_name), ) # Pass-through OAuth: when the admin has opted a server into diff --git a/litellm/proxy/guardrails/guardrail_hooks/agent_365/agent_365.py b/litellm/proxy/guardrails/guardrail_hooks/agent_365/agent_365.py index c332ac3c964..975d321104d 100644 --- a/litellm/proxy/guardrails/guardrail_hooks/agent_365/agent_365.py +++ b/litellm/proxy/guardrails/guardrail_hooks/agent_365/agent_365.py @@ -19,10 +19,9 @@ from typing import TYPE_CHECKING, ClassVar, Final, Literal, NoReturn import httpx from fastapi import HTTPException -from pydantic import BaseModel, ConfigDict, Field, TypeAdapter, ValidationError +from pydantic import TypeAdapter, ValidationError from typing_extensions import ReadOnly, TypedDict -import litellm from litellm._logging import verbose_proxy_logger from litellm.exceptions import Timeout as LitellmTimeout from litellm.integrations.custom_guardrail import ( @@ -36,7 +35,6 @@ from litellm.llms.custom_httpx.http_handler import ( httpxSpecialProvider, ) from litellm.types.guardrails import GuardrailEventHooks -from litellm.types.mcp_server.mcp_server_manager import MCPServer from litellm.types.proxy.guardrails.guardrail_hooks.agent_365 import ( AGENT_365_PROD_API_BASE, AGENT_365_PROD_RESOURCE_APP_ID, @@ -51,11 +49,9 @@ if TYPE_CHECKING: from litellm.types.utils import GuardrailStatus TOKEN_ENDPOINT_TEMPLATE: Final = "https://login.microsoftonline.com/{tenant_id}/oauth2/v2.0/token" -ENTRA_ISSUER_TEMPLATE: Final = "https://login.microsoftonline.com/{tenant_id}/v2.0" EVALUATE_PATH: Final = "/agents/tool-evaluation/evaluate" MCP_SESSION_ID_HEADER: Final = "mcp-session-id" DEFENDER_STATUS_EVALUATED: Final = "Evaluated" -GATEWAY_SCOPE_TEMPLATE: Final = "api://{client_id}/access_as_user" _GATEWAY_OWNED_TOKEN_ERRORS: Final = frozenset( {"invalid_client", "unauthorized_client", "invalid_scope", "invalid_resource"} ) @@ -64,7 +60,6 @@ _GATEWAY_OWNED_TOKEN_ERRORS: Final = frozenset( _INVALID_ASSERTION_AADSTS_PREFIX: Final = "50027" _AADSTS_CODES_ADAPTER: Final = TypeAdapter(tuple[int, ...]) _MCP_CALL_TYPES: Final[tuple[str, ...]] = ("mcp_call", "call_mcp_tool") -_TOOL_INPUT_SCHEMA_ADAPTER: Final = TypeAdapter(dict[str, object]) _OBO_CACHE_MAX_ENTRIES: Final = 1000 _DEFAULT_TOKEN_TTL_SECONDS: Final = 3599.0 _TOKEN_EXPIRY_SLACK_SECONDS: Final = 60.0 @@ -86,11 +81,10 @@ def _parse_aadsts_codes(raw: object) -> tuple[int, ...]: return () -def _parse_tool_input_schema(raw: object) -> Mapping[str, object] | None: - try: - return _TOOL_INPUT_SCHEMA_ADAPTER.validate_python(raw) - except ValidationError: - return None +def entra_assertion(value: object) -> str | None: + """``value`` when it is a compact JWS, the only bearer shape the OBO exchange accepts as its assertion. + A LiteLLM virtual key, session bearer, or opaque upstream token in ``Authorization`` yields ``None``.""" + return value if isinstance(value, str) and value.count(".") == 2 else None class _DefenderResult(TypedDict, total=False): @@ -105,14 +99,6 @@ class _EvaluateResponse(TypedDict, total=False): correlationId: ReadOnly[str] -class _ToolReference(BaseModel): - model_config = ConfigDict(frozen=True) - - name: str - description: str | None = None - input_schema: Mapping[str, object] | None = Field(default=None, serialization_alias="inputSchema") - - class _UnavailableDetail(TypedDict): error: ReadOnly[str] message: ReadOnly[str] @@ -411,14 +397,8 @@ class Agent365Guardrail(CustomGuardrail): arguments: Final = data.get("mcp_arguments") server_name: Final = str(data.get("mcp_server_name") or "litellm") agent_id: Final = self.agent_id or user_api_key_dict.key_alias - description: Final = data.get("mcp_tool_description") - tool_reference: Final = _ToolReference( - name=tool_name, - description=description if isinstance(description, str) and description else None, - input_schema=_parse_tool_input_schema(data.get("mcp_tool_input_schema")), - ) payload: Final[dict[str, object]] = { # mutable-ok: JSON body with optional fields added below - "tool": tool_reference.model_dump(by_alias=True, exclude_none=True), + "tool": {"name": tool_name}, "serverName": server_name, "conversationId": self._resolve_conversation_id(data), } @@ -458,18 +438,6 @@ class Agent365Guardrail(CustomGuardrail): return call_id return str(uuid.uuid4()) - async def exchange_rejects_subject(self, assertion: str) -> bool: - """Whether Entra refuses ``assertion`` as the On-Behalf-Of subject (expired, wrong audience, bad - signature). Gateway credential rejections and endpoint failures answer ``False``: the caller cannot fix - those by signing in again, so the tool call reports them.""" - try: - await self._get_obo_token(assertion) - except Agent365TokenExchangeError as exc: - return not exc.gateway_owned - except (Agent365ThrottledError, Agent365MalformedResponseError, httpx.HTTPError, LitellmTimeout, TimeoutError): - return False - return False - async def _get_obo_token(self, assertion: str) -> str: cache_key: Final = hashlib.sha256(assertion.encode("utf-8")).hexdigest() now: Final = time.time() @@ -667,88 +635,3 @@ class Agent365Guardrail(CustomGuardrail): guardrail_provider=self.guardrail_provider, event_type=GuardrailEventHooks.pre_mcp_call, ) - - -def _applies_to_caller(guardrail: Agent365Guardrail, user_api_key_auth: "UserAPIKeyAuth") -> bool: - probe: Final[Mapping[str, object]] = { - "metadata": { - "user_api_key_metadata": user_api_key_auth.metadata, - "user_api_key_team_metadata": user_api_key_auth.team_metadata, - } - } - return guardrail.should_run_guardrail(data=dict(probe), event_type=GuardrailEventHooks.pre_mcp_call) - - -def _applicable_guardrails( - server: MCPServer, user_api_key_auth: "UserAPIKeyAuth | None" -) -> tuple[Agent365Guardrail, ...]: - """Agent 365 guardrails whose sign-in the gateway advertises for ``server``: the ``default_on`` ones, minus - those the caller's key or team opted out of once the caller is known. A guardrail only a key or policy - selects still enforces at the tool call but never challenges, since the anonymous metadata fetch that - follows a challenge cannot see which key selected it and would advertise the wrong issuer. Only servers - that leave the caller's top-level ``Authorization`` with the gateway qualify: a forwarded API-key header - travels upstream in its own slot and does not displace the Entra assertion.""" - if not server.keeps_caller_authorization: - return () - advertised: Final = tuple( - callback - for callback in litellm.logging_callback_manager.get_custom_loggers_for_type(Agent365Guardrail) - if isinstance(callback, Agent365Guardrail) and callback.default_on - ) - if user_api_key_auth is None: - return advertised - return tuple(g for g in advertised if _applies_to_caller(g, user_api_key_auth)) - - -def entra_assertion(value: object) -> str | None: - """``value`` when it is a compact JWS, the only bearer shape the OBO exchange accepts as its assertion. - A LiteLLM virtual key, session bearer, or opaque upstream token in ``Authorization`` yields ``None``.""" - return value if isinstance(value, str) and value.count(".") == 2 else None - - -def _presented_assertion(oauth2_headers: Mapping[str, str] | None) -> str | None: - authorization: Final = oauth2_headers.get("Authorization", "") if oauth2_headers else "" - if not authorization.lower().startswith("bearer "): - return None - return entra_assertion(authorization[len("bearer ") :].strip()) - - -async def agent_365_sign_in_required( - server: MCPServer, user_api_key_auth: "UserAPIKeyAuth | None", oauth2_headers: Mapping[str, str] | None -) -> bool: - """Whether the connect must answer with the RFC 9728 sign-in challenge: an Agent 365 guardrail gates - ``server`` for this caller and the request carries no Entra assertion, or one Entra will not exchange. - Decided at connect because a tool call's JSON-RPC error cannot carry ``WWW-Authenticate``.""" - guardrails: Final = _applicable_guardrails(server, user_api_key_auth) - if not guardrails: - return False - assertion: Final = _presented_assertion(oauth2_headers) - if assertion is None: - return True - for guardrail in guardrails: - if await guardrail.exchange_rejects_subject(assertion): - return True - return False - - -def agent_365_authorization_servers(server: MCPServer, user_api_key_auth: "UserAPIKeyAuth | None") -> tuple[str, ...]: - """Entra issuers an MCP client signs in with before calling ``server`` through an Agent 365 guardrail.""" - return tuple( - dict.fromkeys( - ENTRA_ISSUER_TEMPLATE.format(tenant_id=g.tenant_id) - for g in _applicable_guardrails(server, user_api_key_auth) - ) - ) - - -def agent_365_scopes_supported(server: MCPServer, user_api_key_auth: "UserAPIKeyAuth | None") -> tuple[str, ...]: - """Scopes the client requests from Entra for ``server``: the admin's ``scopes`` when set, otherwise the - ``access_as_user`` scope of each gating guardrail's gateway app registration (``api://``).""" - if server.scopes: - return tuple(server.scopes) - return tuple( - dict.fromkeys( - GATEWAY_SCOPE_TEMPLATE.format(client_id=g.client_id) - for g in _applicable_guardrails(server, user_api_key_auth) - ) - ) diff --git a/litellm/proxy/utils.py b/litellm/proxy/utils.py index b6908311219..d201ac4bc88 100644 --- a/litellm/proxy/utils.py +++ b/litellm/proxy/utils.py @@ -1245,8 +1245,6 @@ class ProxyLogging: "user_api_key_request_route": kwargs.get("user_api_key_request_route"), "mcp_tool_name": request_obj.tool_name, # Keep original for reference "mcp_arguments": request_obj.arguments, # Keep original for reference - "mcp_tool_description": request_obj.tool_description, - "mcp_tool_input_schema": request_obj.tool_input_schema, # Surface the per-MCP-server rate-limit identity so the # ParallelRequestLimiterV3 hook can apply mcp_rpm_limit on the # synthetic call_mcp_tool payload (otherwise a key with @@ -1468,8 +1466,6 @@ class ProxyLogging: tool_name=kwargs.get("name", ""), arguments=kwargs.get("arguments", {}), server_name=kwargs.get("server_name"), - tool_description=kwargs.get("tool_description"), - tool_input_schema=kwargs.get("tool_input_schema"), user_api_key_auth=user_api_key_auth_dict, hidden_params=HiddenParams(), ) diff --git a/litellm/types/mcp.py b/litellm/types/mcp.py index 87ffaa7cd9e..a59fcb1bcb5 100644 --- a/litellm/types/mcp.py +++ b/litellm/types/mcp.py @@ -391,8 +391,6 @@ class MCPPreCallRequestObject(BaseModel): tool_name: str arguments: dict[str, Any] server_name: str | None = None - tool_description: str | None = None - tool_input_schema: Mapping[str, object] | None = None user_api_key_auth: dict[str, Any] | None = None hidden_params: HiddenParams = HiddenParams() @@ -416,8 +414,6 @@ class MCPDuringCallRequestObject(BaseModel): tool_name: str arguments: dict[str, Any] server_name: str | None = None - tool_description: str | None = None - tool_input_schema: Mapping[str, object] | None = None start_time: float | None = None hidden_params: HiddenParams = HiddenParams() diff --git a/litellm/types/mcp_server/mcp_server_manager.py b/litellm/types/mcp_server/mcp_server_manager.py index 70ad101dcb0..985d31af997 100644 --- a/litellm/types/mcp_server/mcp_server_manager.py +++ b/litellm/types/mcp_server/mcp_server_manager.py @@ -276,10 +276,10 @@ class MCPServer(BaseModel): return self.per_server_oauth_discovery and self.auth_type == MCPAuth.oauth2 and not self.has_client_credentials @property - def keeps_caller_authorization(self) -> bool: - """Whether the caller's top-level ``Authorization`` stays with the gateway: the server neither relays - it upstream nor runs an OAuth mode that fills that slot itself, so a gateway guardrail may consume it - as the caller's own assertion. Forwarding a separate API-key header leaves the slot untouched.""" + def advertises_gateway_authorization_server(self) -> bool: + """Whether named discovery should advertise the aggregate gateway authorization server.""" + if self.auth_type == MCPAuth.oauth2: + return self.is_gateway_managed_oauth2 and not self.uses_per_server_oauth_relay if self.auth_type not in ( None, MCPAuth.none, @@ -291,15 +291,9 @@ class MCPServer(BaseModel): MCPAuth.aws_sigv4, ): return False - return not any(header.lower() == "authorization" for header in (self.extra_headers or ())) - - @property - def advertises_gateway_authorization_server(self) -> bool: - """Whether named discovery should advertise the aggregate gateway authorization server.""" - if self.auth_type == MCPAuth.oauth2: - return self.is_gateway_managed_oauth2 and not self.uses_per_server_oauth_relay - return self.keeps_caller_authorization and not any( - header.lower() in ("x-api-key", "api-key", "apikey") for header in (self.extra_headers or ()) + return not any( + header.lower() in ("authorization", "x-api-key", "api-key", "apikey") + for header in (self.extra_headers or ()) ) @property diff --git a/tests/test_litellm/proxy/_experimental/mcp_server/outbound_credentials/test_adapter.py b/tests/test_litellm/proxy/_experimental/mcp_server/outbound_credentials/test_adapter.py index d650c5ea6c1..1b003e11993 100644 --- a/tests/test_litellm/proxy/_experimental/mcp_server/outbound_credentials/test_adapter.py +++ b/tests/test_litellm/proxy/_experimental/mcp_server/outbound_credentials/test_adapter.py @@ -584,22 +584,6 @@ def test_raise_token_exchange_challenge_is_rfc9728_invalid_token(): assert "error_description=" in www -def test_raise_token_exchange_challenge_explicit_resource_metadata_url_wins(): - from litellm.proxy._experimental.mcp_server.outbound_credentials.adapter import ( - raise_token_exchange_challenge, - ) - - with pytest.raises(HTTPException) as exc_info: - raise_token_exchange_challenge( - _server(alias="obo-srv"), - root_path="/", - resource_metadata_url="https://gw.example.com/.well-known/oauth-protected-resource/obo-srv/mcp", - ) - www = exc_info.value.headers["WWW-Authenticate"] - assert 'resource_metadata="https://gw.example.com/.well-known/oauth-protected-resource/obo-srv/mcp"' in www - assert "/mcp/obo-srv" not in www - - def test_raise_token_exchange_challenge_includes_server_root_path(monkeypatch): from litellm.proxy._experimental.mcp_server.outbound_credentials.adapter import ( raise_token_exchange_challenge, diff --git a/tests/test_litellm/proxy/_experimental/mcp_server/test_discoverable_endpoints.py b/tests/test_litellm/proxy/_experimental/mcp_server/test_discoverable_endpoints.py index 975c4b03545..9ea870d3210 100644 --- a/tests/test_litellm/proxy/_experimental/mcp_server/test_discoverable_endpoints.py +++ b/tests/test_litellm/proxy/_experimental/mcp_server/test_discoverable_endpoints.py @@ -10,7 +10,6 @@ from unittest.mock import AsyncMock, MagicMock, patch import pytest from fastapi import HTTPException -from fastapi.encoders import jsonable_encoder from litellm.types.mcp import MCPAuth @@ -7125,107 +7124,6 @@ async def test_build_oauth_protected_resource_response_obo_end_to_end(): global_mcp_server_manager.registry.clear() -@pytest.fixture -def agent_365_guardrail(): - import litellm - from litellm.proxy.guardrails.guardrail_hooks.agent_365 import Agent365Guardrail - - guardrail = Agent365Guardrail( - guardrail_name="agent-365-guard", - tenant_id="tenant-abc", - client_id="client-xyz", - client_secret="secret-123", - async_handler=AsyncMock(), - event_hook="pre_mcp_call", - default_on=True, - ) - litellm.logging_callback_manager.add_litellm_callback(guardrail) - try: - yield guardrail - finally: - litellm.logging_callback_manager.remove_callback_from_list_by_object( - litellm.callbacks, guardrail, require_self=False - ) - - -async def _agent_365_gated_prm(scopes, extra_headers=None): - from fastapi import Request - - from litellm.proxy._experimental.mcp_server.discoverable_endpoints import ( - _build_oauth_protected_resource_response, - ) - from litellm.proxy._experimental.mcp_server.mcp_server_manager import global_mcp_server_manager - from litellm.proxy._types import MCPTransport - from litellm.types.mcp import MCPAuth - from litellm.types.mcp_server.mcp_server_manager import MCPServer - - global_mcp_server_manager.registry.clear() - global_mcp_server_manager.registry["tools"] = MCPServer( - server_id="tools", - name="tools", - server_name="tools", - alias="tools", - transport=MCPTransport.http, - auth_type=MCPAuth.none, - scopes=scopes, - extra_headers=extra_headers, - ) - mock_request = MagicMock(spec=Request) - mock_request.base_url = "https://litellm.example.com/" - mock_request.headers = {} - try: - return await _build_oauth_protected_resource_response( - request=mock_request, mcp_server_name="tools", use_standard_pattern=True - ) - finally: - global_mcp_server_manager.registry.clear() - - -@pytest.mark.asyncio -async def test_agent_365_gated_server_prm_names_the_entra_tenant(agent_365_guardrail): - response = await _agent_365_gated_prm(scopes=["api://gateway-app/access_as_user"]) - assert jsonable_encoder(response) == { - "authorization_servers": ["https://login.microsoftonline.com/tenant-abc/v2.0"], - "resource": "https://litellm.example.com/mcp/tools", - "scopes_supported": ["api://gateway-app/access_as_user"], - } - - -@pytest.mark.asyncio -async def test_agent_365_prm_defaults_scopeless_server_to_the_gateway_app_scope(agent_365_guardrail): - response = await _agent_365_gated_prm(scopes=None) - assert jsonable_encoder(response) == { - "authorization_servers": ["https://login.microsoftonline.com/tenant-abc/v2.0"], - "resource": "https://litellm.example.com/mcp/tools", - "scopes_supported": ["api://client-xyz/access_as_user"], - } - - -@pytest.mark.asyncio -async def test_agent_365_prm_survives_a_forwarded_upstream_api_key_header(agent_365_guardrail): - """The forwarded ``x-api-key`` is the upstream's credential and rides in its own header, so the caller's - ``Authorization`` still carries the Entra assertion and discovery must keep naming the Entra tenant.""" - response = await _agent_365_gated_prm(scopes=None, extra_headers=["x-api-key"]) - assert jsonable_encoder(response) == { - "authorization_servers": ["https://login.microsoftonline.com/tenant-abc/v2.0"], - "resource": "https://litellm.example.com/mcp/tools", - "scopes_supported": ["api://client-xyz/access_as_user"], - } - - -@pytest.mark.asyncio -async def test_key_selected_agent_365_guardrail_leaves_anonymous_prm_on_the_gateway_issuer(agent_365_guardrail): - """A default-off guardrail gates only the keys that select it, so the anonymous discovery fetch must keep - pointing every other client at the gateway's own authorization server and scopes.""" - agent_365_guardrail.default_on = False - response = await _agent_365_gated_prm(scopes=["mcp:read"]) - assert jsonable_encoder(response) == { - "authorization_servers": ["https://litellm.example.com/mcp"], - "resource": "https://litellm.example.com/mcp/tools", - "scopes_supported": ["mcp:read"], - } - - def _token_request(headers): """A real Starlette request with case-insensitive headers (matches production).""" from starlette.requests import Request diff --git a/tests/test_litellm/proxy/_experimental/mcp_server/test_mcp_guardrail_usage_monitor.py b/tests/test_litellm/proxy/_experimental/mcp_server/test_mcp_guardrail_usage_monitor.py index 956f87f0da1..24e6d2de10d 100644 --- a/tests/test_litellm/proxy/_experimental/mcp_server/test_mcp_guardrail_usage_monitor.py +++ b/tests/test_litellm/proxy/_experimental/mcp_server/test_mcp_guardrail_usage_monitor.py @@ -48,7 +48,6 @@ def _bare_manager() -> MOD.MCPServerManager: reaches the guardrail hooks; they have their own coverage elsewhere. """ mgr = MOD.MCPServerManager.__new__(MOD.MCPServerManager) - mgr._listed_tools_by_server_id = {} mgr.check_allowed_or_banned_tools = lambda name, server: True mgr.validate_allowed_params = lambda tool_name, arguments, server: None diff --git a/tests/test_litellm/proxy/_experimental/mcp_server/test_mcp_server.py b/tests/test_litellm/proxy/_experimental/mcp_server/test_mcp_server.py index 59973b51054..f5e4a420496 100644 --- a/tests/test_litellm/proxy/_experimental/mcp_server/test_mcp_server.py +++ b/tests/test_litellm/proxy/_experimental/mcp_server/test_mcp_server.py @@ -6,7 +6,6 @@ from types import SimpleNamespace from typing import Final from unittest.mock import AsyncMock, MagicMock, patch -import httpx import pytest from fastapi import HTTPException from mcp import ReadResourceResult, Resource @@ -7053,12 +7052,9 @@ async def test_execute_mcp_tool_sets_model_in_model_call_details(): fake_server.server_name = "openapi-petstore" fake_server.alias = None fake_server.short_prefix = None - fake_server.tool_name_to_description = None fake_tool = MagicMock() fake_tool.name = "list_pets" - fake_tool.description = "test tool" - fake_tool.input_schema = {"type": "object"} start_time = datetime.now(timezone.utc) litellm_logging_obj, _ = function_setup( @@ -7109,141 +7105,6 @@ async def test_execute_mcp_tool_sets_model_in_model_call_details(): assert litellm_logging_obj.model == "MCP: list_pets" -@pytest.mark.asyncio -async def test_execute_mcp_tool_hands_openapi_registered_tool_metadata_to_pre_call_hooks(): - """OpenAPI-generated tools dispatch through the local registry, so the pre-call hooks must get the - registered description and input schema on that path too, even when no tools/list ran first.""" - from litellm.proxy._experimental.mcp_server import server as mcp_module - - petstore = MCPServer( - server_id="petstore-id", - name="petstore", - server_name="petstore", - transport=MCPTransport.http, - url=None, - spec_path="https://example.com/petstore.yaml", - ) - schema = {"type": "object", "properties": {"limit": {"type": "integer"}}} - mcp_module.global_mcp_tool_registry.register_tool( - name="petstore-list_pets", description="List the pets", input_schema=schema, handler=lambda limit: "ok" - ) - manager = mcp_module.global_mcp_server_manager - manager._listed_tools_by_server_id.pop(petstore.server_id, None) - pre_call_tool_check = AsyncMock(return_value={}) - - try: - with ( - patch.object(manager, "_get_mcp_server_from_tool_name", return_value=petstore), - patch.object(manager, "pre_call_tool_check", new=pre_call_tool_check), - ): - await mcp_module.execute_mcp_tool( - name="petstore-list_pets", - arguments={"limit": 10}, - allowed_mcp_servers=[petstore], - start_time=datetime.now(), - user_api_key_auth=UserAPIKeyAuth(api_key="sk-user", user_id="alice"), - ) - finally: - mcp_module.global_mcp_tool_registry.unregister_tools_with_prefix("petstore-") - - handed_tool = pre_call_tool_check.call_args.kwargs["tool"] - assert (handed_tool.name, handed_tool.description, handed_tool.inputSchema) == ( - "list_pets", - "List the pets", - schema, - ) - - -@pytest.mark.asyncio -async def test_execute_mcp_tool_hands_openapi_hooks_the_admin_description_clients_saw(): - """tools/list shows the admin's tool_name_to_description wording, so the local-registry call path - must hand the pre-call hooks that same wording rather than the generated one.""" - from litellm.proxy._experimental.mcp_server import server as mcp_module - - petstore = MCPServer( - server_id="petstore-id", - name="petstore", - server_name="petstore", - transport=MCPTransport.http, - url=None, - spec_path="https://example.com/petstore.yaml", - tool_name_to_description={"getpetbyid": "ADMIN DESC"}, - ) - schema = {"type": "object", "properties": {"petId": {"type": "integer"}}} - mcp_module.global_mcp_tool_registry.register_tool( - name="petstore-getpetbyid", description="Find pet by ID", input_schema=schema, handler=lambda petId: "ok" - ) - manager = mcp_module.global_mcp_server_manager - manager._listed_tools_by_server_id.pop(petstore.server_id, None) - pre_call_tool_check = AsyncMock(return_value={}) - - try: - with ( - patch.object(manager, "_get_mcp_server_from_tool_name", return_value=petstore), - patch.object(manager, "pre_call_tool_check", new=pre_call_tool_check), - ): - await mcp_module.execute_mcp_tool( - name="petstore-getpetbyid", - arguments={"petId": 1}, - allowed_mcp_servers=[petstore], - start_time=datetime.now(), - user_api_key_auth=UserAPIKeyAuth(api_key="sk-user", user_id="alice"), - ) - finally: - mcp_module.global_mcp_tool_registry.unregister_tools_with_prefix("petstore-") - - handed_tool = pre_call_tool_check.call_args.kwargs["tool"] - assert (handed_tool.description, handed_tool.inputSchema) == ("ADMIN DESC", schema) - - -@pytest.mark.asyncio -async def test_execute_mcp_tool_hands_hooks_the_metadata_of_the_operation_it_runs_when_names_collide(): - """An OpenAPI operation whose name starts with its own server prefix must not be reported to the - pre-call hooks with the metadata of the shorter operation, since that is not the one that runs.""" - from litellm.proxy._experimental.mcp_server import server as mcp_module - - petstore = MCPServer( - server_id="petstore-id", - name="petstore", - server_name="petstore", - transport=MCPTransport.http, - url=None, - spec_path="https://example.com/petstore.yaml", - ) - registry = mcp_module.global_mcp_tool_registry - registry.register_tool(name="petstore-get_pet", description="short", input_schema={}, handler=lambda: "short") - registry.register_tool( - name="petstore-petstore-get_pet", - description="long", - input_schema={"type": "object", "properties": {"petId": {"type": "integer"}}}, - handler=lambda: "long", - ) - manager = mcp_module.global_mcp_server_manager - pre_call_tool_check = AsyncMock(return_value={}) - - try: - with ( - patch.object(manager, "_get_mcp_server_from_tool_name", return_value=petstore), - patch.object(manager, "pre_call_tool_check", new=pre_call_tool_check), - ): - result = await mcp_module.execute_mcp_tool( - name="petstore-petstore-get_pet", - arguments={}, - allowed_mcp_servers=[petstore], - start_time=datetime.now(), - user_api_key_auth=UserAPIKeyAuth(api_key="sk-user", user_id="alice"), - ) - finally: - registry.unregister_tools_with_prefix("petstore-") - - handed_tool = pre_call_tool_check.call_args.kwargs["tool"] - assert (handed_tool.description, handed_tool.inputSchema) == ( - "long", - {"type": "object", "properties": {"petId": {"type": "integer"}}}, - ) - assert result.content[0].text == "long" - - @pytest.mark.asyncio async def test_execute_mcp_tool_rest_unresolved_prefixed_name_routes_to_requested_server(): """A prefixed REST name that resolves to no tool must still dispatch to the server_id. @@ -8911,308 +8772,6 @@ class TestSingleServerPreflightReachesIdJag: preflight.assert_not_awaited() -class TestAgent365ChallengeAtConnect: - """A missing Entra bearer on an Agent 365 gated server is challenged at connect (RFC 9728), where the - WWW-Authenticate header survives, instead of only inside the tools/call JSON-RPC error.""" - - GATEWAY_SCOPE = "api://gateway-app/access_as_user" - ENTRA_BEARER = {"Authorization": "Bearer eyJhbGciOiJSUzI1NiJ9.eyJzdWIiOiJ1LTEifQ.c2ln"} - - @staticmethod - def _entra_response(status_code: int, body: dict[str, object]) -> httpx.Response: - return httpx.Response( - status_code=status_code, - json=body, - request=httpx.Request("POST", "https://login.microsoftonline.com/tenant-abc/oauth2/v2.0/token"), - ) - - def _server(self, scopes: list[str] | None, extra_headers: list[str] | None = None) -> MCPServer: - return MCPServer( - server_id="id-tools", - name="tools", - alias="tools", - server_name="tools", - url="https://tools.test/mcp", - transport=MCPTransport.http, - auth_type=MCPAuth.none, - scopes=scopes, - extra_headers=extra_headers, - mcp_info={"server_name": "tools"}, - ) - - @pytest.fixture - def agent_365_guardrail(self): - import litellm - from litellm.proxy.guardrails.guardrail_hooks.agent_365 import Agent365Guardrail - - handler = AsyncMock() - handler.post.return_value = self._entra_response(200, {"access_token": "obo-token", "expires_in": 3599}) - guardrail = Agent365Guardrail( - guardrail_name="agent-365-guard", - tenant_id="tenant-abc", - client_id="client-xyz", - client_secret="secret-123", - async_handler=handler, - event_hook="pre_mcp_call", - default_on=True, - ) - litellm.logging_callback_manager.add_litellm_callback(guardrail) - try: - yield guardrail - finally: - litellm.logging_callback_manager.remove_callback_from_list_by_object( - litellm.callbacks, guardrail, require_self=False - ) - - async def _connect( - self, - server: MCPServer, - oauth2_headers: dict[str, str] | None, - path: str = "/mcp/tools", - mount_scope: dict[str, str] | None = None, - granted: bool = True, - key_metadata: dict[str, object] | None = None, - ) -> HTTPException | None: - from litellm.proxy._experimental.mcp_server import server as server_module - - with ( - patch.object( # test-quality-ok: route wiring must use the manager's configured server - server_module.global_mcp_server_manager, "get_mcp_server_by_name", return_value=server - ), - patch.object( # test-quality-ok: allowed-set resolution needs the DB; the test controls its answer - server_module, "_get_allowed_mcp_servers", AsyncMock(return_value=[server] if granted else []) - ), - ): - try: - await server_module._raise_preemptive_401_for_unauthenticated_servers( - scope={ - "type": "http", - "method": "POST", - "path": path, - "scheme": "https", - "server": ("gw.example.com", 443), - "headers": [], - **(mount_scope or {}), - }, - mcp_servers=["tools"], - oauth2_headers=oauth2_headers, - mcp_server_auth_headers=None, - user_api_key_auth=UserAPIKeyAuth( - api_key="sk-litellm-virtual-key", user_id="u-1", metadata=key_metadata or {} - ), - client_ip=None, - ) - except HTTPException as challenge: - return challenge - return None - - @pytest.mark.asyncio - async def test_no_bearer_gets_the_discovery_challenge(self, agent_365_guardrail): - challenge = await self._connect(self._server([self.GATEWAY_SCOPE]), None) - - assert challenge is not None and challenge.status_code == 401 - www_authenticate = (challenge.headers or {}).get("WWW-Authenticate", "") - assert 'error="invalid_token"' in www_authenticate - assert ( - 'resource_metadata="https://gw.example.com/.well-known/oauth-protected-resource/mcp/tools"' - in www_authenticate - ) - - @pytest.mark.asyncio - async def test_legacy_route_challenge_points_at_its_own_metadata(self, agent_365_guardrail): - """RFC 9728 3.3: the metadata's ``resource`` must equal the URL the client connected to, so a - ``/{server}/mcp`` connect is sent to the ``/{server}/mcp`` document, not the ``/mcp/{server}`` one.""" - challenge = await self._connect(self._server([self.GATEWAY_SCOPE]), None, path="/tools/mcp") - - assert challenge is not None and challenge.status_code == 401 - www_authenticate = (challenge.headers or {}).get("WWW-Authenticate", "") - assert ( - 'resource_metadata="https://gw.example.com/.well-known/oauth-protected-resource/tools/mcp"' - in www_authenticate - ) - - @pytest.mark.asyncio - @pytest.mark.parametrize( - "server_root, mount_scope", - [ - ("", {"root_path": "/mcp", "app_root_path": ""}), - ("/litellm", {"root_path": "/litellm/mcp", "app_root_path": "/litellm"}), - ], - ) - async def test_mounted_standard_route_is_challenged(self, agent_365_guardrail, server_root, mount_scope): - """``/mcp/{server}`` is served by the ``/mcp`` Mount, which moves the mount prefix into - ``root_path`` and leaves the app root (empty or SERVER_ROOT_PATH) in ``app_root_path``.""" - with patch.dict(os.environ, {"SERVER_ROOT_PATH": server_root}): - challenge = await self._connect( - self._server([self.GATEWAY_SCOPE]), None, path=f"{server_root}/mcp/tools", mount_scope=mount_scope - ) - - assert challenge is not None and challenge.status_code == 401 - www_authenticate = (challenge.headers or {}).get("WWW-Authenticate", "") - assert ( - f'resource_metadata="https://gw.example.com{server_root}' - f'/.well-known/oauth-protected-resource{server_root}/mcp/tools"' in www_authenticate - ) - - @pytest.mark.asyncio - @pytest.mark.parametrize("path", ["/mcp", "/mcp/tools,other"]) - async def test_aggregate_route_is_not_challenged_at_connect(self, agent_365_guardrail, path): - """The per-server metadata's ``resource`` can never equal the aggregate ``/mcp`` URL the client - connected to (RFC 9728 3.3), and one guarded server must not 401 a multi-server connect, so the - Agent 365 challenge is left to tools/call there.""" - assert await self._connect(self._server([self.GATEWAY_SCOPE]), None, path=path) is None - - @pytest.mark.asyncio - async def test_entra_assertion_present_connects(self, agent_365_guardrail): - assert await self._connect(self._server([self.GATEWAY_SCOPE]), self.ENTRA_BEARER) is None - - @pytest.mark.asyncio - @pytest.mark.parametrize( - "entra_body", - [ - {"error": "invalid_grant", "error_description": "AADSTS700084: The refresh token was issued..."}, - { - "error": "invalid_client", - "error_description": "AADSTS5002723: Invalid JWT token.", - "error_codes": [5002723], - }, - ], - ids=["expired-or-wrong-audience", "forged-reported-as-invalid_client"], - ) - async def test_assertion_entra_refuses_to_exchange_is_challenged(self, agent_365_guardrail, entra_body): - """An expired, wrong-audience, or forged Entra token looks like a valid one. Only the OBO exchange - can tell, and its verdict must arrive at connect, where WWW-Authenticate reaches the client, - rather than inside every tools/call JSON-RPC error. Entra files a forged assertion under - ``invalid_client`` with an AADSTS50027xx sub-code, which must not read as a gateway secret problem.""" - agent_365_guardrail.async_handler.post.return_value = self._entra_response(400, entra_body) - - challenge = await self._connect(self._server([self.GATEWAY_SCOPE]), self.ENTRA_BEARER) - - assert challenge is not None and challenge.status_code == 401 - www_authenticate = (challenge.headers or {}).get("WWW-Authenticate", "") - assert 'error="invalid_token"' in www_authenticate - assert ( - 'resource_metadata="https://gw.example.com/.well-known/oauth-protected-resource/mcp/tools"' - in www_authenticate - ) - - @pytest.mark.asyncio - @pytest.mark.parametrize( - "entra_outcome", - [ - { - "return_value": _entra_response( - 401, - { - "error": "invalid_client", - "error_description": "AADSTS7000215: Invalid client secret", - "error_codes": [7000215], - }, - ) - }, - {"return_value": _entra_response(503, {"error": "temporarily_unavailable"})}, - {"side_effect": httpx.ConnectError("dns")}, - ], - ids=["gateway-credentials-rejected", "entra-5xx", "entra-unreachable"], - ) - async def test_gateway_side_exchange_failures_do_not_send_the_caller_to_sign_in( - self, agent_365_guardrail, entra_outcome - ): - """Signing in again cannot fix the gateway's own client secret or an Entra outage, so those stay - with the tool call, which reports them as the guardrail's unavailable path.""" - agent_365_guardrail.async_handler.post = AsyncMock(**entra_outcome) - - assert await self._connect(self._server([self.GATEWAY_SCOPE]), self.ENTRA_BEARER) is None - - @pytest.mark.asyncio - async def test_key_selected_default_off_guardrail_never_challenges(self): - """The anonymous metadata fetch that follows a challenge cannot see which key selected the guardrail - and would advertise the gateway issuer, so a challenge here would send the client to the wrong IdP. - The guardrail still enforces at tools/call.""" - import litellm - from litellm.proxy.guardrails.guardrail_hooks.agent_365 import Agent365Guardrail - - guardrail = Agent365Guardrail( - guardrail_name="agent-365-guard", - tenant_id="tenant-abc", - client_id="client-xyz", - client_secret="secret-123", - async_handler=AsyncMock(), - event_hook="pre_mcp_call", - default_on=False, - ) - litellm.logging_callback_manager.add_litellm_callback(guardrail) - try: - with patch( # test-quality-ok: key-selected guardrails read the proxy server premium global, no injection seam - "litellm.proxy.proxy_server.premium_user", True - ): - assert ( - await self._connect( - self._server([self.GATEWAY_SCOPE]), - None, - key_metadata={"guardrails": ["agent-365-guard"]}, - ) - is None - ) - finally: - litellm.logging_callback_manager.remove_callback_from_list_by_object( - litellm.callbacks, guardrail, require_self=False - ) - - @pytest.mark.asyncio - async def test_litellm_key_in_authorization_is_still_challenged(self, agent_365_guardrail): - """A LiteLLM virtual key admits the caller but is no Entra assertion, so the tools/call would fail - 401 inside JSON-RPC with the WWW-Authenticate header lost. The connect must challenge instead.""" - challenge = await self._connect( - self._server([self.GATEWAY_SCOPE]), {"Authorization": "Bearer sk-litellm-virtual-key"} - ) - - assert challenge is not None and challenge.status_code == 401 - www_authenticate = (challenge.headers or {}).get("WWW-Authenticate", "") - assert 'error="invalid_token"' in www_authenticate - assert ( - 'resource_metadata="https://gw.example.com/.well-known/oauth-protected-resource/mcp/tools"' - in www_authenticate - ) - - @pytest.mark.asyncio - async def test_scopeless_server_is_still_challenged(self, agent_365_guardrail): - challenge = await self._connect(self._server(None), None) - - assert challenge is not None and challenge.status_code == 401 - assert 'error="invalid_token"' in (challenge.headers or {}).get("WWW-Authenticate", "") - - @pytest.mark.asyncio - async def test_server_forwarding_an_upstream_api_key_header_is_still_challenged(self, agent_365_guardrail): - """``x-api-key`` travels upstream in its own header and leaves the caller's ``Authorization`` free for - the Entra assertion, so a key-only connect must still be sent to sign in.""" - challenge = await self._connect(self._server(None, extra_headers=["x-api-key"]), None) - - assert challenge is not None and challenge.status_code == 401 - www_authenticate = (challenge.headers or {}).get("WWW-Authenticate", "") - assert 'error="invalid_token"' in www_authenticate - assert ( - 'resource_metadata="https://gw.example.com/.well-known/oauth-protected-resource/mcp/tools"' - in www_authenticate - ) - - @pytest.mark.asyncio - async def test_server_relaying_the_caller_authorization_is_not_challenged(self, agent_365_guardrail): - """Forwarding ``Authorization`` hands the caller's bearer to the upstream, so the gateway holds no Entra - assertion of its own to exchange and must not advertise a sign-in it cannot consume.""" - assert await self._connect(self._server(None, extra_headers=["Authorization"]), None) is None - - @pytest.mark.asyncio - async def test_no_registered_guardrail_means_no_challenge(self): - assert await self._connect(self._server([self.GATEWAY_SCOPE]), None) is None - - @pytest.mark.asyncio - async def test_key_without_the_server_grant_is_not_sent_to_sign_in(self, agent_365_guardrail): - """Signing in cannot earn a key a server it was never granted, so the connect must fall through to - the ordinary 403 grant denial instead of leading with an Entra challenge the caller cannot use.""" - assert await self._connect(self._server([self.GATEWAY_SCOPE]), None, granted=False) is None - - def _make_obo_server(alias: str) -> MCPServer: return MCPServer( server_id=f"id-{alias}", @@ -9284,11 +8843,7 @@ class TestOboPreflightScopedToAllowedServers: _, preflight = await self._run(requested, allowed=[requested], user_api_key_auth=key) preflight.assert_awaited_once_with( - server=requested, - oauth2_headers=self.SUBJECT_HEADERS, - user_api_key_auth=key, - raw_headers=None, - resource_metadata_url="/.well-known/oauth-protected-resource/mcp/obo_tools", + server=requested, oauth2_headers=self.SUBJECT_HEADERS, user_api_key_auth=key, raw_headers=None ) diff --git a/tests/test_litellm/proxy/_experimental/mcp_server/test_mcp_server_manager.py b/tests/test_litellm/proxy/_experimental/mcp_server/test_mcp_server_manager.py index 2a1064ec510..d56f08c4e79 100644 --- a/tests/test_litellm/proxy/_experimental/mcp_server/test_mcp_server_manager.py +++ b/tests/test_litellm/proxy/_experimental/mcp_server/test_mcp_server_manager.py @@ -34,7 +34,6 @@ from pydantic import AnyUrl, TypeAdapter from litellm.constants import MCP_METADATA_TIMEOUT from litellm.proxy._experimental.mcp_server.mcp_server_manager import ( - ListedToolsCaller, MCPServerManager, _deserialize_json_dict, _flow_endpoints_missing, @@ -5092,36 +5091,6 @@ class TestMCPServerManager: ) assert server2.requires_per_user_auth is False - @pytest.mark.parametrize( - "auth_type, extra_headers, keeps_authorization, advertises_gateway", - [ - (MCPAuth.none, None, True, True), - (MCPAuth.api_key, None, True, True), - (MCPAuth.none, ["x-api-key"], True, False), - (MCPAuth.none, ["API-Key"], True, False), - (MCPAuth.none, ["Authorization"], False, False), - (MCPAuth.none, ["x-api-key", "authorization"], False, False), - (MCPAuth.oauth_delegate, None, False, False), - (MCPAuth.true_passthrough, None, False, False), - (MCPAuth.oauth2_token_exchange, None, False, False), - ], - ) - def test_forwarded_api_key_header_keeps_caller_authorization_but_not_gateway_discovery( - self, auth_type, extra_headers, keeps_authorization, advertises_gateway - ): - """A forwarded API-key header is the upstream's own credential and leaves the caller's top-level - ``Authorization`` with the gateway, while still ruling out the gateway's aggregate OAuth discovery.""" - server = MCPServer( - server_id="s", - name="s", - transport=MCPTransport.http, - auth_type=auth_type, - url="http://s.example", - extra_headers=extra_headers, - ) - assert server.keeps_caller_authorization is keeps_authorization - assert server.advertises_gateway_authorization_server is advertises_gateway - @pytest.mark.asyncio async def test_register_openapi_tools_includes_static_headers(self, tmp_path): """Ensure OpenAPI-to-MCP tool calls include server.static_headers (Issue #19341).""" @@ -6602,465 +6571,6 @@ class TestMCPServerManager: # Verify the MCP client call was awaited exactly once assert mock_client.call_tool.await_count == 1 - @staticmethod - def _manager_ready_for_call_tool(listed_tools: list[MCPTool]) -> tuple[MCPServerManager, MagicMock]: - from mcp.types import CallToolResult - - manager = MCPServerManager() - server = MCPServer( - server_id="test-server", - name="test-server", - transport=MCPTransport.http, - url="http://test-server.com", - ) - manager.registry = {"test-server": server} - manager.tool_name_to_mcp_server_name_mapping["test_tool"] = "test-server" - manager.tool_name_to_mcp_server_name_mapping["test-server-test_tool"] = "test-server" - manager._create_prefixed_tools(listed_tools, server) - - mock_client = AsyncMock() - mock_client.call_tool.return_value = MagicMock(spec=CallToolResult, content=[], isError=False) - manager._create_mcp_client = AsyncMock(return_value=mock_client) - - proxy_logging_obj = MagicMock() - proxy_logging_obj._create_mcp_request_object_from_kwargs = MagicMock(return_value={}) - proxy_logging_obj._convert_mcp_to_llm_format = MagicMock(return_value={}) - proxy_logging_obj.pre_call_hook = AsyncMock(return_value={}) - proxy_logging_obj.during_call_hook = AsyncMock(return_value=None) - return manager, proxy_logging_obj - - @staticmethod - def _unrestricted_auth() -> MagicMock: - user_api_key_auth = MagicMock() - user_api_key_auth.object_permission = None - user_api_key_auth.object_permission_id = None - return user_api_key_auth - - @pytest.mark.asyncio - async def test_call_tool_hands_listed_tool_description_and_schema_to_pre_call_hooks(self): - schema = {"type": "object", "properties": {"param": {"type": "string"}}, "required": ["param"]} - listed = [MCPTool(name="test_tool", description="Runs the test tool", inputSchema=schema)] - manager, proxy_logging_obj = self._manager_ready_for_call_tool(listed) - - await manager.call_tool( - server_name="test-server", - name="test_tool", - arguments={"param": "value"}, - user_api_key_auth=self._unrestricted_auth(), - proxy_logging_obj=proxy_logging_obj, - ) - - hook_kwargs = proxy_logging_obj._create_mcp_request_object_from_kwargs.call_args.args[0] - assert (hook_kwargs["tool_description"], hook_kwargs["tool_input_schema"]) == ("Runs the test tool", schema) - - @pytest.mark.asyncio - async def test_call_tool_hands_listed_tool_metadata_to_during_call_hooks_through_real_conversion(self): - schema = {"type": "object", "properties": {"param": {"type": "string"}}} - listed = [MCPTool(name="test_tool", description="Runs the test tool", inputSchema=schema)] - manager, _ = self._manager_ready_for_call_tool(listed) - proxy_logging_obj = ProxyLogging(user_api_key_cache=DualCache()) - proxy_logging_obj.pre_call_hook = AsyncMock(return_value={}) - proxy_logging_obj.during_call_hook = AsyncMock(return_value=None) - - await manager.call_tool( - server_name="test-server", - name="test_tool", - arguments={"param": "value"}, - user_api_key_auth=UserAPIKeyAuth(api_key="sk-test"), - proxy_logging_obj=proxy_logging_obj, - ) - - during_data = proxy_logging_obj.during_call_hook.call_args.kwargs["data"] - assert (during_data["mcp_tool_description"], during_data["mcp_tool_input_schema"]) == ( - "Runs the test tool", - schema, - ) - - @pytest.mark.asyncio - async def test_call_tool_passes_no_tool_metadata_when_tool_was_never_listed(self): - manager, proxy_logging_obj = self._manager_ready_for_call_tool( - [MCPTool(name="other_tool", description="Unrelated", inputSchema={"type": "object"})] - ) - - await manager.call_tool( - server_name="test-server", - name="test_tool", - arguments={"param": "value"}, - user_api_key_auth=self._unrestricted_auth(), - proxy_logging_obj=proxy_logging_obj, - ) - - hook_kwargs = proxy_logging_obj._create_mcp_request_object_from_kwargs.call_args.args[0] - assert (hook_kwargs["tool_description"], hook_kwargs["tool_input_schema"]) == (None, None) - - def test_get_listed_tool_resolves_prefixed_name_and_latest_listing(self): - manager = MCPServerManager() - server = MCPServer(server_id="srv", name="srv", transport=MCPTransport.http, url="http://srv") - manager._create_prefixed_tools([MCPTool(name="echo", description="v1", inputSchema={})], server) - manager._create_prefixed_tools([MCPTool(name="echo", description="v2", inputSchema={})], server) - - by_prefixed_name = manager.get_listed_tool(server, "srv-echo") - assert by_prefixed_name is not None and by_prefixed_name.description == "v2" - assert manager.get_listed_tool(server, "missing") is None - - def test_get_listed_tool_uses_admin_description_override_clients_saw(self): - manager = MCPServerManager() - server = MCPServer( - server_id="srv", - name="srv", - transport=MCPTransport.http, - url="http://srv", - tool_name_to_description={"echo": "Admin wording"}, - ) - schema = {"type": "object", "properties": {"text": {"type": "string"}}} - manager._create_prefixed_tools( - [ - MCPTool(name="echo", description="Upstream wording", inputSchema=schema), - MCPTool(name="ping", description="Untouched", inputSchema={}), - ], - server, - ) - - overridden = manager.get_listed_tool(server, "srv-echo") - assert overridden is not None - assert (overridden.name, overridden.description, overridden.inputSchema) == ("echo", "Admin wording", schema) - untouched = manager.get_listed_tool(server, "ping") - assert untouched is not None and untouched.description == "Untouched" - - def test_server_definition_change_drops_listed_tools(self): - manager = MCPServerManager() - server = MCPServer(server_id="srv", name="srv", transport=MCPTransport.http, url="http://srv") - other = MCPServer(server_id="other", name="other", transport=MCPTransport.http, url="http://other") - manager._create_prefixed_tools([MCPTool(name="echo", description="old", inputSchema={})], server) - manager._create_prefixed_tools([MCPTool(name="ping", description="kept", inputSchema={})], other) - - manager._invalidate_server_definition_caches(server.server_id) - - assert manager.get_listed_tool(server, "echo") is None - kept = manager.get_listed_tool(other, "ping") - assert kept is not None and kept.description == "kept" - - @pytest.mark.asyncio - async def test_user_oauth_refresh_keeps_listed_tools(self): - """Tool definitions are server-wide, so one user's re-auth must not blank the metadata other - callers' tool calls hand to pre-call guardrails.""" - manager = MCPServerManager() - server = MCPServer(server_id="srv", name="srv", transport=MCPTransport.http, url="http://srv") - manager._create_prefixed_tools([MCPTool(name="echo", description="shared", inputSchema={})], server) - - await manager.invalidate_user_oauth_token_cache("alice", server.server_id) - - listed = manager.get_listed_tool(server, "echo") - assert listed is not None and listed.description == "shared" - - def test_per_caller_server_keeps_listed_tools_per_identity(self): - manager = MCPServerManager() - server = MCPServer( - server_id="srv", - name="srv", - transport=MCPTransport.http, - url="http://srv", - auth_type=MCPAuth.oauth2_token_exchange, - ) - alice = UserAPIKeyAuth(user_id="alice", api_key="hashed-alice") - bob = UserAPIKeyAuth(user_id="bob", api_key="hashed-bob") - alice_schema = {"type": "object", "properties": {"path": {"type": "string"}}} - bob_schema = {"type": "object", "properties": {"path": {"type": "string"}, "site": {"type": "string"}}} - manager._create_prefixed_tools( - [MCPTool(name="read", description="alice view", inputSchema=alice_schema)], - server, - caller=ListedToolsCaller(user_api_key_auth=alice), - ) - manager._create_prefixed_tools( - [MCPTool(name="read", description="bob view", inputSchema=bob_schema)], - server, - caller=ListedToolsCaller(user_api_key_auth=bob), - ) - - alice_tool = manager.get_listed_tool(server, "srv-read", ListedToolsCaller(user_api_key_auth=alice)) - bob_tool = manager.get_listed_tool(server, "srv-read", ListedToolsCaller(user_api_key_auth=bob)) - assert alice_tool is not None and (alice_tool.description, alice_tool.inputSchema) == ( - "alice view", - alice_schema, - ) - assert bob_tool is not None and (bob_tool.description, bob_tool.inputSchema) == ("bob view", bob_schema) - carol = ListedToolsCaller(user_api_key_auth=UserAPIKeyAuth(user_id="carol", api_key="k")) - assert manager.get_listed_tool(server, "srv-read", carol) is None - - shared = MCPServer(server_id="shared", name="shared", transport=MCPTransport.http, url="http://shared") - manager._create_prefixed_tools( - [MCPTool(name="echo", description="everyone", inputSchema={})], - shared, - caller=ListedToolsCaller(user_api_key_auth=alice), - ) - for_bob = manager.get_listed_tool(shared, "echo", ListedToolsCaller(user_api_key_auth=bob)) - assert for_bob is not None and for_bob.description == "everyone" - - @pytest.mark.parametrize( - ("server_kwargs", "caller_a", "caller_b"), - [ - pytest.param( - {"extra_headers": ["X-Workspace"]}, - ListedToolsCaller(raw_headers={"x-workspace": "A"}), - ListedToolsCaller(raw_headers={"X-Workspace": "B"}), - id="forwarded-header", - ), - pytest.param( - {"auth_type": MCPAuth.true_passthrough}, - ListedToolsCaller(raw_headers={"authorization": "Bearer upstream-a"}), - ListedToolsCaller(raw_headers={"authorization": "Bearer upstream-b"}), - id="anonymous-passthrough-bearer", - ), - pytest.param( - {"auth_type": MCPAuth.bearer_token}, - ListedToolsCaller(mcp_auth_header="byok-a"), - ListedToolsCaller(mcp_auth_header="byok-b"), - id="per-server-auth-header", - ), - pytest.param( - {"transport": MCPTransport.stdio, "command": "srv", "env": {"WS": "${X-WS}"}}, - ListedToolsCaller(raw_headers={"X-WS": "A"}), - ListedToolsCaller(raw_headers={"X-WS": "B"}), - id="header-driven-stdio-env", - ), - ], - ) - def test_upstream_identity_inputs_keep_listed_tools_apart(self, server_kwargs, caller_a, caller_b): - """Whatever reaches upstream and can change its catalog must also split the listed-tool cache.""" - manager = MCPServerManager() - server = MCPServer( - **{"server_id": "srv", "name": "srv", "transport": MCPTransport.http, "url": "http://srv", **server_kwargs} - ) - manager._create_prefixed_tools( - [MCPTool(name="turn", description="Catalog A", inputSchema={})], server, caller=caller_a - ) - manager._create_prefixed_tools( - [MCPTool(name="turn", description="Catalog B", inputSchema={})], server, caller=caller_b - ) - - for_a = manager.get_listed_tool(server, "srv-turn", caller_a) - for_b = manager.get_listed_tool(server, "srv-turn", caller_b) - assert for_a is not None and for_a.description == "Catalog A" - assert for_b is not None and for_b.description == "Catalog B" - assert manager.get_listed_tool(server, "srv-turn", ListedToolsCaller()) is None - - def test_shared_server_ignores_headers_it_never_forwards(self): - manager = MCPServerManager() - server = MCPServer(server_id="srv", name="srv", transport=MCPTransport.http, url="http://srv") - manager._create_prefixed_tools( - [MCPTool(name="turn", description="everyone", inputSchema={})], - server, - caller=ListedToolsCaller(raw_headers={"authorization": "Bearer sk-litellm", "x-workspace": "A"}), - ) - - other = ListedToolsCaller(raw_headers={"authorization": "Bearer sk-other", "x-workspace": "B"}) - listed = manager.get_listed_tool(server, "turn", other) - assert listed is not None and listed.description == "everyone" - - @pytest.mark.parametrize( - ("signer", "static_headers", "shared"), - [ - pytest.param(MagicMock(), None, False, id="signer-mints-per-caller-authorization"), - pytest.param(MagicMock(), {"Authorization": "Bearer admin-token"}, True, id="static-authorization-wins"), - pytest.param(None, None, True, id="no-signer-stays-shared"), - ], - ) - def test_jwt_signer_makes_a_shared_server_list_per_caller(self, signer, static_headers, shared): - """MCPJWTSigner hands upstream a JWT naming the caller on an otherwise shared ``auth_type: none`` - server, so the upstream may tailor the catalog and the cache must not hand one caller another's.""" - manager = MCPServerManager() - server = MCPServer( - server_id="srv", name="srv", transport=MCPTransport.http, url="http://srv", static_headers=static_headers - ) - alice = ListedToolsCaller(user_api_key_auth=UserAPIKeyAuth(user_id="alice", api_key="hashed-alice")) - bob = ListedToolsCaller(user_api_key_auth=UserAPIKeyAuth(user_id="bob", api_key="hashed-bob")) - - with patch( # test-quality-ok: the signer is a process-wide singleton the manager reads, no injection seam - "litellm.proxy.guardrails.guardrail_hooks.mcp_jwt_signer.mcp_jwt_signer.get_mcp_jwt_signer", - return_value=signer, - ): - manager._create_prefixed_tools( - [MCPTool(name="turn", description="alice view", inputSchema={})], server, caller=alice - ) - for_bob = manager.get_listed_tool(server, "srv-turn", bob) - - if shared: - assert for_bob is not None and for_bob.description == "alice view" - else: - assert for_bob is None - - @pytest.mark.asyncio - async def test_call_tool_hands_hooks_the_catalog_the_same_forwarded_headers_listed(self): - """Interleaved callers on a forwarded-header server: the hook must see the caller's own catalog.""" - manager = MCPServerManager() - server = MCPServer( - server_id="catalog", - name="catalog", - transport=MCPTransport.http, - url="http://catalog", - extra_headers=["X-Workspace"], - ) - manager.registry = {"catalog": server} - catalogs = { - "A": [ - MCPTool( - name="turn", description="Catalog A", inputSchema={"properties": {"turn": {"description": "A"}}} - ) - ], - "B": [ - MCPTool( - name="turn", description="Catalog B", inputSchema={"properties": {"turn": {"description": "B"}}} - ) - ], - } - mock_client = AsyncMock() - mock_client.call_tool.return_value = MagicMock(spec=CallToolResult, content=[], isError=False) - manager._create_mcp_client = AsyncMock(return_value=mock_client) - manager._fetch_tools_with_timeout = AsyncMock(side_effect=lambda client, name: catalogs[client.workspace]) - for workspace in ("A", "B"): - manager._create_mcp_client.return_value.workspace = workspace - await manager._get_tools_from_server( - server=server, - extra_headers={"X-Workspace": workspace}, - raw_headers={"x-workspace": workspace, "authorization": "Bearer sk-litellm"}, - user_api_key_auth=UserAPIKeyAuth(api_key="sk-litellm", user_id="shared-key"), - ) - - proxy_logging_obj = MagicMock() - proxy_logging_obj._create_mcp_request_object_from_kwargs = MagicMock(return_value={}) - proxy_logging_obj._convert_mcp_to_llm_format = MagicMock(return_value={}) - proxy_logging_obj.pre_call_hook = AsyncMock(return_value={}) - proxy_logging_obj.during_call_hook = AsyncMock(return_value=None) - await manager.call_tool( - server_name="catalog", - name="catalog-turn", - arguments={"turn": "A-1"}, - user_api_key_auth=UserAPIKeyAuth(api_key="sk-litellm", user_id="shared-key"), - proxy_logging_obj=proxy_logging_obj, - raw_headers={"x-workspace": "A", "authorization": "Bearer sk-litellm"}, - ) - - hook_kwargs = proxy_logging_obj._create_mcp_request_object_from_kwargs.call_args.args[0] - assert (hook_kwargs["tool_description"], hook_kwargs["tool_input_schema"]) == ( - "Catalog A", - {"properties": {"turn": {"description": "A"}}}, - ) - - def test_per_caller_listed_tools_evict_oldest_caller_and_keep_shared(self): - from litellm.proxy._experimental.mcp_server.mcp_server_manager import _LISTED_TOOLS_CALLERS_PER_SERVER - - manager = MCPServerManager() - server = MCPServer( - server_id="srv", - name="srv", - transport=MCPTransport.http, - url="http://srv", - auth_type=MCPAuth.oauth2_token_exchange, - ) - manager._create_prefixed_tools([MCPTool(name="read", description="shared", inputSchema={})], server) - callers = [ - ListedToolsCaller(user_api_key_auth=UserAPIKeyAuth(user_id=f"u{i}", api_key=f"k{i}")) - for i in range(_LISTED_TOOLS_CALLERS_PER_SERVER + 1) - ] - for caller in callers: - manager._create_prefixed_tools( - [MCPTool(name="read", description=caller.user_api_key_auth.user_id, inputSchema={})], - server, - caller=caller, - ) - manager._create_prefixed_tools( - [MCPTool(name="read", description="u1 again", inputSchema={})], server, caller=callers[1] - ) - - assert manager.get_listed_tool(server, "srv-read", callers[0]) is None - second = manager.get_listed_tool(server, "srv-read", callers[1]) - assert second is not None and second.description == "u1 again" - newest = manager.get_listed_tool(server, "srv-read", callers[-1]) - assert newest is not None and newest.description == callers[-1].user_api_key_auth.user_id - assert len(manager._listed_tools_by_server_id[server.server_id]) == _LISTED_TOOLS_CALLERS_PER_SERVER + 1 - shared = manager.get_listed_tool(server, "srv-read") - assert shared is not None and shared.description == "shared" - - @pytest.mark.asyncio - @pytest.mark.parametrize("add_prefix", [True, False]) - async def test_openapi_listing_records_listed_tools(self, add_prefix): - from litellm.proxy._experimental.mcp_server.tool_registry import global_mcp_tool_registry - - server = MCPServer( - server_id="petstore-id", - name="petstore", - alias="petstore", - transport=MCPTransport.http, - url=None, - spec_path="/spec.yaml", - ) - manager = MCPServerManager() - manager._create_mcp_client = AsyncMock(return_value=AsyncMock()) - - async def _handler(**kwargs): - return None - - global_mcp_tool_registry.unregister_tools_with_prefix("petstore-") - global_mcp_tool_registry.register_tool( - name="petstore-list_pets", - description="List pets", - input_schema={"type": "object", "properties": {"limit": {"type": "integer"}}}, - handler=_handler, - ) - try: - listed = await manager._get_tools_from_server(server=server, add_prefix=add_prefix) - finally: - global_mcp_tool_registry.unregister_tools_with_prefix("petstore-") - - assert [t.name for t in listed] == ["petstore-list_pets" if add_prefix else "list_pets"] - for name in ("list_pets", "petstore-list_pets"): - tool = manager.get_listed_tool(server, name) - assert tool is not None and tool.description == "List pets" - assert tool.inputSchema["properties"] == {"limit": {"type": "integer"}} - - @pytest.mark.asyncio - async def test_openapi_listing_ignores_overlapping_server_prefix(self): - from litellm.proxy._experimental.mcp_server.tool_registry import global_mcp_tool_registry - - server = MCPServer( - server_id="pet-id", - name="pet", - alias="pet", - transport=MCPTransport.http, - url=None, - spec_path="/spec.yaml", - ) - manager = MCPServerManager() - manager._create_mcp_client = AsyncMock(return_value=AsyncMock()) - - async def _handler(**kwargs): - return None - - for prefix in ("pet-", "petstore-"): - global_mcp_tool_registry.unregister_tools_with_prefix(prefix) - global_mcp_tool_registry.register_tool( - name="pet-petstore-list", - description="Local pet tool", - input_schema={"type": "object", "properties": {"limit": {"type": "integer"}}}, - handler=_handler, - ) - global_mcp_tool_registry.register_tool( - name="petstore-list", - description="Foreign petstore tool", - input_schema={"type": "object", "properties": {"status": {"type": "string"}}}, - handler=_handler, - ) - try: - listed = await manager._get_tools_from_server(server=server, add_prefix=True) - finally: - for prefix in ("pet-", "petstore-"): - global_mcp_tool_registry.unregister_tools_with_prefix(prefix) - - assert [t.name for t in listed] == ["pet-petstore-list"] - tool = manager.get_listed_tool(server, "petstore-list") - assert tool is not None and tool.description == "Local pet tool" - assert tool.inputSchema["properties"] == {"limit": {"type": "integer"}} - @pytest.mark.asyncio async def test_get_allowed_mcp_servers_with_user_api_key_auth(self): """ diff --git a/tests/test_litellm/proxy/_experimental/mcp_server/test_openapi_tool_auth.py b/tests/test_litellm/proxy/_experimental/mcp_server/test_openapi_tool_auth.py index 67894d55e80..64614c094ba 100644 --- a/tests/test_litellm/proxy/_experimental/mcp_server/test_openapi_tool_auth.py +++ b/tests/test_litellm/proxy/_experimental/mcp_server/test_openapi_tool_auth.py @@ -40,12 +40,9 @@ async def test_openapi_local_tool_runs_pre_call_tool_check(): fake_server.server_name = "openapi-petstore" fake_server.alias = None fake_server.short_prefix = None - fake_server.tool_name_to_description = None fake_tool = MagicMock() fake_tool.name = "list_pets" - fake_tool.description = "test tool" - fake_tool.input_schema = {"type": "object"} pre_call = AsyncMock(return_value={}) handle_local = AsyncMock(return_value=[]) @@ -124,12 +121,9 @@ async def test_openapi_local_tool_blocked_when_pre_call_check_raises(): fake_server.server_name = "openapi-petstore" fake_server.alias = None fake_server.short_prefix = None - fake_server.tool_name_to_description = None fake_tool = MagicMock() fake_tool.name = "delete_pet" - fake_tool.description = "test tool" - fake_tool.input_schema = {"type": "object"} pre_call = AsyncMock( side_effect=HTTPException(status_code=403, detail="not allowed") @@ -192,8 +186,6 @@ async def test_openapi_local_tool_denied_when_server_not_resolvable(): fake_tool = MagicMock() fake_tool.name = "list_pets" - fake_tool.description = "test tool" - fake_tool.input_schema = {"type": "object"} pre_call = AsyncMock(return_value={}) handle_local = AsyncMock(return_value=[]) @@ -278,8 +270,6 @@ async def test_openapi_local_tool_injects_resolved_oauth_token(): fake_tool = MagicMock() fake_tool.name = "get_values" - fake_tool.description = "test tool" - fake_tool.input_schema = {"type": "object"} captured: dict = {} async def handle_local(_name, _arguments): @@ -626,8 +616,6 @@ async def test_per_server_auth_header_reaches_both_openapi_dispatch_arms(dispatc if dispatch_arm == "local_registry": fake_tool = MagicMock() fake_tool.name = "list_reports" - fake_tool.description = "test tool" - fake_tool.input_schema = {"type": "object"} with ( patch.object(manager, "_get_mcp_server_from_tool_name", return_value=server), patch.object(mcp_module.global_mcp_tool_registry, "get_tool", return_value=fake_tool), @@ -699,8 +687,6 @@ async def test_local_dispatch_reports_the_outcome_instead_of_success(failure: st fake_tool = MagicMock() fake_tool.name = "list_reports" - fake_tool.description = "test tool" - fake_tool.input_schema = {"type": "object"} fake_tool.handler = raising_handler server = MCPServer( server_id="srv-openapi", diff --git a/tests/test_litellm/proxy/guardrails/guardrail_hooks/test_agent_365.py b/tests/test_litellm/proxy/guardrails/guardrail_hooks/test_agent_365.py index f73c0e87547..f9b7561b9d3 100644 --- a/tests/test_litellm/proxy/guardrails/guardrail_hooks/test_agent_365.py +++ b/tests/test_litellm/proxy/guardrails/guardrail_hooks/test_agent_365.py @@ -1,9 +1,7 @@ import time import uuid -from collections.abc import Iterator from types import SimpleNamespace from typing import Any, Final -from unittest.mock import patch import httpx import pytest @@ -22,18 +20,12 @@ from litellm.proxy.guardrails.guardrail_hooks.agent_365 import ( guardrail_initializer_registry, initialize_guardrail, ) -from litellm.proxy.guardrails.guardrail_hooks.agent_365.agent_365 import ( - agent_365_authorization_servers, - agent_365_scopes_supported, -) from litellm.proxy.utils import ProxyLogging from litellm.types.guardrails import ( GuardrailEventHooks, LitellmParams, SupportedGuardrailIntegrations, ) -from litellm.types.mcp import MCPAuth, MCPTransport -from litellm.types.mcp_server.mcp_server_manager import MCPServer from litellm.types.proxy.guardrails.guardrail_hooks.agent_365 import ( AGENT_365_PROD_API_BASE, AGENT_365_PROD_RESOURCE_APP_ID, @@ -308,29 +300,6 @@ class TestAllowFlow: assert evaluate_call.json["conversationId"] == "sess-123" assert evaluate_call.json["agentId"] == "agent-007" - @pytest.mark.asyncio - async def test_evaluate_payload_includes_listed_tool_metadata(self): - handler: Final = FakeHandler([_token_response(), _allow_response()]) - guardrail: Final = _make_guardrail(handler) - schema: Final = {"type": "object", "properties": {"to": {"type": "string"}}, "required": ["to"]} - await _run(guardrail, _mcp_data(mcp_tool_description="Send an email", mcp_tool_input_schema=schema)) - assert handler.calls[1].json["tool"] == { - "name": "send_email", - "description": "Send an email", - "inputSchema": schema, - } - - @pytest.mark.asyncio - @pytest.mark.parametrize( - ("description", "schema"), - [(None, None), ("", None), (None, ["not", "a", "schema"]), (42, "type: object")], - ) - async def test_evaluate_payload_omits_missing_or_malformed_tool_metadata(self, description, schema): - handler: Final = FakeHandler([_token_response(), _allow_response()]) - guardrail: Final = _make_guardrail(handler) - await _run(guardrail, _mcp_data(mcp_tool_description=description, mcp_tool_input_schema=schema)) - assert handler.calls[1].json["tool"] == {"name": "send_email"} - @pytest.mark.asyncio async def test_agent_id_falls_back_to_key_alias(self): handler: Final = FakeHandler([_token_response(), _allow_response()]) @@ -1039,130 +1008,3 @@ class TestFinalArgumentsEvaluated: ) assert result["modified_arguments"] == {"turn": "please [REWRITE_ME_REDACTED] now"} assert handler.calls[1].json["arguments"] == {"turn": "please [REWRITE_ME_REDACTED] now"} - - -ENTRA_ISSUER: Final = "https://login.microsoftonline.com/tenant-abc/v2.0" -GATEWAY_SCOPE: Final = "api://gateway-app/access_as_user" - - -def _mcp_server(auth_type: MCPAuth = MCPAuth.none, scopes: list[str] | None = None, **fields: Any) -> MCPServer: - return MCPServer( - server_id="tools-id", - name="tools", - server_name="tools", - transport=MCPTransport.http, - url="https://tools.test/mcp", - auth_type=auth_type, - scopes=scopes, - **fields, - ) - - -@pytest.fixture -def registered_guardrail() -> Iterator[Agent365Guardrail]: - guardrail: Final = _make_guardrail(FakeHandler([])) - litellm.logging_callback_manager.add_litellm_callback(guardrail) - try: - yield guardrail - finally: - litellm.logging_callback_manager.remove_callback_from_list_by_object( - litellm.callbacks, guardrail, require_self=False - ) - - -class TestAgent365AuthorizationServers: - def test_names_the_guardrail_tenant_for_a_scoped_gateway_signed_in_server(self, registered_guardrail): - assert agent_365_authorization_servers(_mcp_server(scopes=[GATEWAY_SCOPE]), None) == (ENTRA_ISSUER,) - assert agent_365_authorization_servers( - _mcp_server(MCPAuth.api_key, scopes=[GATEWAY_SCOPE], auth_value="k"), None - ) == (ENTRA_ISSUER,) - - @pytest.mark.parametrize("scopes", [None, []], ids=["unset", "empty"]) - def test_scopeless_server_signs_in_with_the_gateway_app_scope(self, registered_guardrail, scopes): - server: Final = _mcp_server(scopes=scopes) - assert agent_365_authorization_servers(server, None) == (ENTRA_ISSUER,) - assert agent_365_scopes_supported(server, None) == ("api://client-xyz/access_as_user",) - - def test_admin_scopes_override_the_default_gateway_scope(self, registered_guardrail): - assert agent_365_scopes_supported(_mcp_server(scopes=[GATEWAY_SCOPE]), None) == (GATEWAY_SCOPE,) - - def test_no_default_scope_when_no_guardrail_gates_the_server(self): - assert agent_365_scopes_supported(_mcp_server(scopes=None), None) == () - - def test_silent_when_no_guardrail_is_registered(self): - assert agent_365_authorization_servers(_mcp_server(scopes=[GATEWAY_SCOPE]), None) == () - - @pytest.mark.parametrize( - "server", - [ - _mcp_server(MCPAuth.oauth2, scopes=[GATEWAY_SCOPE]), - _mcp_server(MCPAuth.oauth2_token_exchange, scopes=[GATEWAY_SCOPE], token_exchange_endpoint="https://i/t"), - _mcp_server(MCPAuth.oauth2_id_jag, scopes=[GATEWAY_SCOPE]), - _mcp_server(MCPAuth.true_passthrough, scopes=[GATEWAY_SCOPE]), - _mcp_server(MCPAuth.oauth_delegate, scopes=[GATEWAY_SCOPE]), - _mcp_server(MCPAuth.none, scopes=[GATEWAY_SCOPE], extra_headers=["Authorization"]), - ], - ids=["oauth2", "token_exchange", "id_jag", "true_passthrough", "oauth_delegate", "forwards_authorization"], - ) - def test_leaves_servers_whose_own_auth_mode_owns_sign_in_alone(self, registered_guardrail, server): - assert agent_365_authorization_servers(server, None) == () - - @pytest.mark.parametrize("header", ["x-api-key", "API-Key", "apikey"]) - def test_forwarded_api_key_header_leaves_authorization_to_entra(self, registered_guardrail, header): - """An upstream API key rides in its own header, so the caller's ``Authorization`` still carries the - Entra assertion and a key-only client must be told where to sign in.""" - server: Final = _mcp_server(MCPAuth.none, scopes=None, extra_headers=[header]) - - assert agent_365_authorization_servers(server, None) == (ENTRA_ISSUER,) - assert agent_365_scopes_supported(server, None) == ("api://client-xyz/access_as_user",) - - def test_dedupes_guardrails_sharing_a_tenant(self, registered_guardrail): - twin: Final = _make_guardrail(FakeHandler([])) - twin.guardrail_name = "agent-365-twin" - litellm.logging_callback_manager.add_litellm_callback(twin) - try: - assert agent_365_authorization_servers(_mcp_server(scopes=[GATEWAY_SCOPE]), None) == (ENTRA_ISSUER,) - finally: - litellm.logging_callback_manager.remove_callback_from_list_by_object( - litellm.callbacks, twin, require_self=False - ) - - def test_key_selected_guardrail_never_advertises_sign_in(self): - """The challenge a guarded key would get and the anonymous metadata fetch that follows it must name - the same issuer. The anonymous fetch cannot see the key, so neither side advertises Entra.""" - guardrail: Final = _make_guardrail(FakeHandler([])) - guardrail.default_on = False - litellm.logging_callback_manager.add_litellm_callback(guardrail) - server: Final = _mcp_server(scopes=[GATEWAY_SCOPE]) - plain_key: Final = UserAPIKeyAuth(api_key="sk-plain", user_id="u-1") - guarded_key: Final = UserAPIKeyAuth( - api_key="sk-guarded", user_id="u-2", metadata={"guardrails": ["agent-365-guard"]} - ) - try: - with patch( # test-quality-ok: key-selected guardrails read the proxy server premium global, no injection seam - "litellm.proxy.proxy_server.premium_user", True - ): - assert agent_365_authorization_servers(server, plain_key) == () - assert agent_365_authorization_servers(server, guarded_key) == () - assert agent_365_authorization_servers(server, None) == () - assert agent_365_scopes_supported(_mcp_server(scopes=None), None) == () - finally: - litellm.logging_callback_manager.remove_callback_from_list_by_object( - litellm.callbacks, guardrail, require_self=False - ) - - @pytest.mark.parametrize( - "metadata", - [{"opted_out_global_guardrails": ["agent-365-guard"]}, {"disable_global_guardrails": True}], - ids=["opted-out", "globals-disabled"], - ) - def test_key_opted_out_of_the_default_on_guardrail_is_not_challenged(self, registered_guardrail, metadata): - server: Final = _mcp_server(scopes=[GATEWAY_SCOPE]) - opted_out: Final = UserAPIKeyAuth(api_key="sk-out", user_id="u-3", metadata=metadata) - team_opted_out: Final = UserAPIKeyAuth(api_key="sk-team", user_id="u-4", team_metadata=metadata) - - assert agent_365_authorization_servers(server, opted_out) == () - assert agent_365_authorization_servers(server, team_opted_out) == () - assert agent_365_authorization_servers(server, UserAPIKeyAuth(api_key="sk-in", user_id="u-5")) == ( - ENTRA_ISSUER, - ) diff --git a/tests/test_litellm/proxy/utils/proxy_logging/test_mcp_bridging.py b/tests/test_litellm/proxy/utils/proxy_logging/test_mcp_bridging.py index 75dd11ea16b..56057dce7e0 100644 --- a/tests/test_litellm/proxy/utils/proxy_logging/test_mcp_bridging.py +++ b/tests/test_litellm/proxy/utils/proxy_logging/test_mcp_bridging.py @@ -381,26 +381,6 @@ def test_create_mcp_request_object_from_kwargs_full(proxy_logging, make_user_api assert snapshot == {"tool_name": "calc", "arguments": {"x": 1}, "server_name": "math", "auth_user_id": "u-1"} -def test_mcp_tool_metadata_flows_from_kwargs_to_synthetic_data(proxy_logging): - schema = {"type": "object", "properties": {"x": {"type": "integer"}}} - obj = proxy_logging._create_mcp_request_object_from_kwargs( - kwargs={ - "name": "calc", - "arguments": {"x": 1}, - "tool_description": "Adds numbers", - "tool_input_schema": schema, - } - ) - out = proxy_logging._convert_mcp_to_llm_format(request_obj=obj, kwargs={}) - assert (out["mcp_tool_description"], out["mcp_tool_input_schema"]) == ("Adds numbers", schema) - - -def test_mcp_tool_metadata_absent_when_tool_was_never_listed(proxy_logging): - obj = proxy_logging._create_mcp_request_object_from_kwargs(kwargs={"name": "calc", "arguments": {}}) - out = proxy_logging._convert_mcp_to_llm_format(request_obj=obj, kwargs={}) - assert (out["mcp_tool_description"], out["mcp_tool_input_schema"]) == (None, None) - - def test_create_mcp_request_object_from_kwargs_empty(proxy_logging): obj = proxy_logging._create_mcp_request_object_from_kwargs(kwargs={}) snapshot = { From 8607c49ea1877f28b6586be9c2e7f2be95391982 Mon Sep 17 00:00:00 2001 From: IToSSc Date: Tue, 15 Sep 2026 14:16:20 +0800 Subject: [PATCH 053/225] feat: add aihubmix provider pricing entries Add 72 model price entries for the aihubmix openai_like provider so cost tracking and budgets work for aihubmix/* model calls. The provider is already registered in llms/openai_like/providers.json but model_prices_and_context_window.json had zero entries for it. The Anthropic-family entries (claude-fable-5, claude-haiku-4-5, claude-opus-4-8, claude-opus-5, claude-sonnet-5) carry the same supports_adaptive_thinking, thinking_always_on, supports_sampling_params, and prompt_cache_min_tokens flags already used by this repo's other Anthropic re-exports (azure_ai, databricks, openrouter, and so on) for the same underlying models, since those flags gate request shapes the provider otherwise rejects with a 400. TASK-2BK38Y --- ...odel_prices_and_context_window_backup.json | 1125 +++++++++++++++++ model_prices_and_context_window.json | 1125 +++++++++++++++++ 2 files changed, 2250 insertions(+) diff --git a/litellm/model_prices_and_context_window_backup.json b/litellm/model_prices_and_context_window_backup.json index 9f91cf82f41..c870e9c2255 100644 --- a/litellm/model_prices_and_context_window_backup.json +++ b/litellm/model_prices_and_context_window_backup.json @@ -66038,5 +66038,1130 @@ "mode": "chat", "output_cost_per_token": 8e-06, "source": "https://api.together.ai/v1/models" + }, + "aihubmix/agnes-2.5-flash": { + "input_cost_per_token": 3e-08, + "litellm_provider": "aihubmix", + "max_input_tokens": 512000, + "max_output_tokens": 65500, + "max_tokens": 65500, + "mode": "chat", + "output_cost_per_token": 1.5e-07, + "source": "https://aihubmix.com/api/v1/models", + "supports_reasoning": true, + "supports_vision": true + }, + "aihubmix/agnes-2.5-pro": { + "cache_read_input_token_cost": 3.78e-09, + "input_cost_per_token": 4.5e-07, + "litellm_provider": "aihubmix", + "max_input_tokens": 1000000, + "max_output_tokens": 65536, + "max_tokens": 65536, + "mode": "chat", + "output_cost_per_token": 9e-07, + "source": "https://aihubmix.com/api/v1/models", + "supports_prompt_caching": true, + "supports_reasoning": true, + "supports_vision": true + }, + "aihubmix/cc-glm-5.1": { + "input_cost_per_token": 6e-08, + "litellm_provider": "aihubmix", + "max_input_tokens": 200000, + "max_output_tokens": 131072, + "max_tokens": 131072, + "mode": "chat", + "output_cost_per_token": 2.2e-07, + "source": "https://aihubmix.com/api/v1/models", + "supports_function_calling": true, + "supports_reasoning": true, + "supports_response_schema": true + }, + "aihubmix/claude-fable-5": { + "cache_read_input_token_cost": 1.1e-06, + "cache_creation_input_token_cost": 1.375e-05, + "input_cost_per_token": 1.1e-05, + "litellm_provider": "aihubmix", + "max_input_tokens": 1000000, + "max_output_tokens": 128000, + "max_tokens": 128000, + "mode": "chat", + "output_cost_per_token": 5.5e-05, + "source": "https://aihubmix.com/api/v1/models", + "supports_function_calling": true, + "supports_prompt_caching": true, + "supports_reasoning": true, + "supports_response_schema": true, + "supports_vision": true, + "prompt_cache_min_tokens": 512, + "supports_adaptive_thinking": true, + "thinking_always_on": true, + "supports_sampling_params": false + }, + "aihubmix/claude-haiku-4-5": { + "cache_read_input_token_cost": 1.1e-07, + "cache_creation_input_token_cost": 1.375e-06, + "input_cost_per_token": 1.1e-06, + "litellm_provider": "aihubmix", + "max_input_tokens": 200000, + "max_output_tokens": 64000, + "max_tokens": 64000, + "mode": "chat", + "output_cost_per_token": 5.5e-06, + "source": "https://aihubmix.com/api/v1/models", + "supports_function_calling": true, + "supports_prompt_caching": true, + "supports_reasoning": true, + "supports_response_schema": true, + "supports_vision": true, + "prompt_cache_min_tokens": 4096 + }, + "aihubmix/claude-opus-4-8-think": { + "cache_read_input_token_cost": 5e-07, + "cache_creation_input_token_cost": 6.25e-06, + "input_cost_per_token": 5e-06, + "litellm_provider": "aihubmix", + "max_input_tokens": 1000000, + "max_output_tokens": 128000, + "max_tokens": 128000, + "mode": "chat", + "output_cost_per_token": 2.5e-05, + "source": "https://aihubmix.com/api/v1/models", + "supports_function_calling": true, + "supports_prompt_caching": true, + "supports_reasoning": true, + "supports_response_schema": true, + "supports_vision": true, + "prompt_cache_min_tokens": 1024, + "supports_adaptive_thinking": true, + "supports_sampling_params": false + }, + "aihubmix/claude-opus-5": { + "cache_read_input_token_cost": 5e-07, + "cache_creation_input_token_cost": 6.25e-06, + "input_cost_per_token": 5e-06, + "litellm_provider": "aihubmix", + "max_input_tokens": 1000000, + "max_output_tokens": 128000, + "max_tokens": 128000, + "mode": "chat", + "output_cost_per_token": 2.5e-05, + "source": "https://aihubmix.com/api/v1/models", + "supports_function_calling": true, + "supports_prompt_caching": true, + "supports_reasoning": true, + "supports_response_schema": true, + "supports_vision": true, + "supports_web_search": true, + "supports_adaptive_thinking": true, + "prompt_cache_min_tokens": 512, + "supports_sampling_params": false + }, + "aihubmix/claude-sonnet-5": { + "cache_read_input_token_cost": 2e-07, + "cache_creation_input_token_cost": 2.5e-06, + "input_cost_per_token": 2e-06, + "litellm_provider": "aihubmix", + "max_input_tokens": 1000000, + "max_output_tokens": 128000, + "max_tokens": 128000, + "mode": "chat", + "output_cost_per_token": 1e-05, + "source": "https://aihubmix.com/api/v1/models", + "supports_function_calling": true, + "supports_prompt_caching": true, + "supports_reasoning": true, + "supports_response_schema": true, + "supports_vision": true, + "prompt_cache_min_tokens": 1024, + "supports_adaptive_thinking": true, + "supports_sampling_params": false + }, + "aihubmix/coding-glm-5.3": { + "cache_read_input_token_cost": 1.5e-08, + "input_cost_per_token": 6e-08, + "litellm_provider": "aihubmix", + "max_input_tokens": 1048576, + "max_output_tokens": 131072, + "max_tokens": 131072, + "mode": "chat", + "output_cost_per_token": 2.2e-07, + "source": "https://aihubmix.com/api/v1/models", + "supports_function_calling": true, + "supports_prompt_caching": true, + "supports_reasoning": true, + "supports_response_schema": true + }, + "aihubmix/coding-kimi-k3": { + "cache_read_input_token_cost": 6.6e-08, + "input_cost_per_token": 4.4e-07, + "litellm_provider": "aihubmix", + "max_input_tokens": 1048576, + "max_output_tokens": 1048576, + "max_tokens": 1048576, + "mode": "chat", + "output_cost_per_token": 1.61333e-06, + "source": "https://aihubmix.com/api/v1/models", + "supports_function_calling": true, + "supports_prompt_caching": true, + "supports_reasoning": true, + "supports_response_schema": true, + "supports_vision": true + }, + "aihubmix/coding-xiaomi-mimo-v2-omni": { + "cache_read_input_token_cost": 1.6e-08, + "input_cost_per_token": 8e-08, + "litellm_provider": "aihubmix", + "mode": "chat", + "output_cost_per_token": 4e-07, + "source": "https://aihubmix.com/api/v1/models", + "supports_function_calling": true, + "supports_prompt_caching": true, + "supports_reasoning": true, + "supports_response_schema": true, + "supports_vision": true + }, + "aihubmix/coding-xiaomi-mimo-v2.5": { + "cache_read_input_token_cost": 1.6e-09, + "input_cost_per_token": 8e-08, + "litellm_provider": "aihubmix", + "max_input_tokens": 1048576, + "max_output_tokens": 131072, + "max_tokens": 131072, + "mode": "chat", + "output_cost_per_token": 1.6e-07, + "source": "https://aihubmix.com/api/v1/models", + "supports_function_calling": true, + "supports_prompt_caching": true, + "supports_reasoning": true, + "supports_response_schema": true, + "supports_vision": true + }, + "aihubmix/coding-xiaomi-mimo-v2.5-pro": { + "cache_read_input_token_cost": 1.6e-09, + "input_cost_per_token": 2e-07, + "litellm_provider": "aihubmix", + "max_input_tokens": 1048576, + "max_output_tokens": 131072, + "max_tokens": 131072, + "mode": "chat", + "output_cost_per_token": 4e-07, + "source": "https://aihubmix.com/api/v1/models", + "supports_function_calling": true, + "supports_prompt_caching": true, + "supports_reasoning": true, + "supports_response_schema": true + }, + "aihubmix/command-a-plus-05-2026": { + "input_cost_per_token": 2.5e-06, + "litellm_provider": "aihubmix", + "max_input_tokens": 128000, + "max_output_tokens": 64000, + "max_tokens": 64000, + "mode": "chat", + "output_cost_per_token": 1e-05, + "source": "https://aihubmix.com/api/v1/models", + "supports_function_calling": true, + "supports_reasoning": true, + "supports_response_schema": true, + "supports_vision": true + }, + "aihubmix/deepseek-v4-flash": { + "cache_read_input_token_cost": 2.84e-08, + "input_cost_per_token": 1.42e-07, + "litellm_provider": "aihubmix", + "max_input_tokens": 1000000, + "max_output_tokens": 384000, + "max_tokens": 384000, + "mode": "chat", + "output_cost_per_token": 2.84e-07, + "source": "https://aihubmix.com/api/v1/models", + "supports_function_calling": true, + "supports_prompt_caching": true, + "supports_reasoning": true, + "supports_response_schema": true + }, + "aihubmix/deepseek-v4-pro": { + "cache_read_input_token_cost": 1.4027e-07, + "input_cost_per_token": 1.69e-06, + "litellm_provider": "aihubmix", + "max_input_tokens": 1000000, + "max_output_tokens": 384000, + "max_tokens": 384000, + "mode": "chat", + "output_cost_per_token": 3.38e-06, + "source": "https://aihubmix.com/api/v1/models", + "supports_function_calling": true, + "supports_prompt_caching": true, + "supports_reasoning": true, + "supports_response_schema": true + }, + "aihubmix/doubao-seed-2-0-code-preview": { + "cache_read_input_token_cost": 9.644e-08, + "input_cost_per_token": 4.822e-07, + "litellm_provider": "aihubmix", + "max_input_tokens": 256000, + "max_output_tokens": 128000, + "max_tokens": 128000, + "mode": "chat", + "output_cost_per_token": 2.411e-06, + "source": "https://aihubmix.com/api/v1/models", + "supports_function_calling": true, + "supports_prompt_caching": true, + "supports_reasoning": true, + "supports_vision": true, + "supports_web_search": true + }, + "aihubmix/doubao-seed-2-0-lite-260428": { + "cache_read_input_token_cost": 1.8082e-08, + "input_cost_per_token": 9.041e-08, + "litellm_provider": "aihubmix", + "max_input_tokens": 256000, + "max_output_tokens": 128000, + "max_tokens": 128000, + "mode": "chat", + "output_cost_per_token": 5.4246e-07, + "source": "https://aihubmix.com/api/v1/models", + "supports_function_calling": true, + "supports_prompt_caching": true, + "supports_reasoning": true, + "supports_response_schema": true, + "supports_vision": true, + "supports_web_search": true + }, + "aihubmix/doubao-seed-2-0-mini": { + "cache_read_input_token_cost": 6.027e-09, + "input_cost_per_token": 3.0136e-08, + "litellm_provider": "aihubmix", + "max_input_tokens": 256000, + "max_output_tokens": 128000, + "max_tokens": 128000, + "mode": "chat", + "output_cost_per_token": 3.0136e-07, + "source": "https://aihubmix.com/api/v1/models", + "supports_function_calling": true, + "supports_prompt_caching": true, + "supports_reasoning": true, + "supports_response_schema": true, + "supports_vision": true, + "supports_web_search": true + }, + "aihubmix/doubao-seed-2-0-pro": { + "cache_read_input_token_cost": 9.644e-08, + "input_cost_per_token": 4.822e-07, + "litellm_provider": "aihubmix", + "max_input_tokens": 256000, + "max_output_tokens": 128000, + "max_tokens": 128000, + "mode": "chat", + "output_cost_per_token": 2.411e-06, + "source": "https://aihubmix.com/api/v1/models", + "supports_function_calling": true, + "supports_prompt_caching": true, + "supports_reasoning": true, + "supports_response_schema": true, + "supports_vision": true, + "supports_web_search": true + }, + "aihubmix/doubao-seed-2-1-turbo": { + "cache_read_input_token_cost": 9.295e-08, + "input_cost_per_token": 4.6475e-07, + "litellm_provider": "aihubmix", + "max_input_tokens": 256000, + "max_output_tokens": 256000, + "max_tokens": 256000, + "mode": "chat", + "output_cost_per_token": 2.32375e-06, + "source": "https://aihubmix.com/api/v1/models", + "supports_function_calling": true, + "supports_prompt_caching": true, + "supports_reasoning": true, + "supports_response_schema": true, + "supports_vision": true, + "supports_web_search": true + }, + "aihubmix/ernie-5.1": { + "cache_read_input_token_cost": 5.634e-07, + "input_cost_per_token": 5.634e-07, + "litellm_provider": "aihubmix", + "max_input_tokens": 119000, + "max_output_tokens": 65536, + "max_tokens": 65536, + "mode": "chat", + "output_cost_per_token": 2.5353e-06, + "source": "https://aihubmix.com/api/v1/models", + "supports_prompt_caching": true, + "supports_reasoning": true + }, + "aihubmix/gemini-3-flash-preview": { + "cache_read_input_token_cost": 5e-08, + "input_cost_per_token": 5e-07, + "litellm_provider": "aihubmix", + "max_input_tokens": 1048576, + "max_output_tokens": 65536, + "max_tokens": 65536, + "mode": "chat", + "output_cost_per_token": 3e-06, + "source": "https://aihubmix.com/api/v1/models", + "supports_function_calling": true, + "supports_prompt_caching": true, + "supports_reasoning": true, + "supports_response_schema": true, + "supports_vision": true, + "supports_web_search": true + }, + "aihubmix/gemini-3-flash-preview-search": { + "cache_read_input_token_cost": 5e-08, + "input_cost_per_token": 5e-07, + "litellm_provider": "aihubmix", + "max_input_tokens": 1048576, + "max_output_tokens": 65536, + "max_tokens": 65536, + "mode": "chat", + "output_cost_per_token": 3e-06, + "source": "https://aihubmix.com/api/v1/models", + "supports_function_calling": true, + "supports_prompt_caching": true, + "supports_reasoning": true, + 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"supports_response_schema": true, + "supports_vision": true, + "supports_web_search": true + }, + "aihubmix/step-3.7-flash": { + "cache_read_input_token_cost": 4.4e-08, + "input_cost_per_token": 2.2e-07, + "litellm_provider": "aihubmix", + "max_input_tokens": 256000, + "mode": "chat", + "output_cost_per_token": 1.32e-06, + "source": "https://aihubmix.com/api/v1/models", + "supports_prompt_caching": true, + "supports_reasoning": true, + "supports_vision": true } } From bc17459548b1a14bd6856d42e1339a027f105796 Mon Sep 17 00:00:00 2001 From: Devin AI <158243242+devin-ai-integration[bot]@users.noreply.github.com> Date: Tue, 15 Sep 2026 07:46:41 +0000 Subject: [PATCH 054/225] fix(proxy): include litellm_call_id in LLM API exception logs Co-Authored-By: Devin AI <158243242+devin-ai-integration[bot]@users.noreply.github.com> --- litellm/proxy/common_request_processing.py | 19 +++-- .../proxy/test_common_request_processing.py | 70 ++++++++++++++++++- 2 files changed, 84 insertions(+), 5 deletions(-) diff --git a/litellm/proxy/common_request_processing.py b/litellm/proxy/common_request_processing.py index 4a4daa68cce..a29079433e9 100644 --- a/litellm/proxy/common_request_processing.py +++ b/litellm/proxy/common_request_processing.py @@ -1451,10 +1451,12 @@ def _has_attribute_error_in_chain(exc: Exception) -> bool: _CLIENT_DISCONNECT_DETAIL: Final = "Client disconnected the request" -def _log_llm_api_exception(e: Exception) -> None: +def _log_llm_api_exception(e: Exception, litellm_call_id: str | None) -> None: if getattr(e, "status_code", None) == 499 and getattr(e, "detail", None) == _CLIENT_DISCONNECT_DETAIL: verbose_proxy_logger.info( - "litellm.proxy.proxy_server._handle_llm_api_exception(): client disconnected, upstream LLM request cancelled" + "litellm.proxy.proxy_server._handle_llm_api_exception(): client disconnected, " + "upstream LLM request cancelled - litellm_call_id=%s", + litellm_call_id, ) return log_fn: Final = ( @@ -1462,7 +1464,12 @@ def _log_llm_api_exception(e: Exception) -> None: if is_expected_client_error(e) and not litellm.log_client_error_tracebacks else verbose_proxy_logger.exception ) - log_fn("litellm.proxy.proxy_server._handle_llm_api_exception(): Exception occured - %s", e) + log_fn( + "litellm.proxy.proxy_server._handle_llm_api_exception(): Exception occured - litellm_call_id=%s - %s", + litellm_call_id, + e, + extra=MappingProxyType({"litellm_call_id": litellm_call_id}), + ) async def _cancel_llm_call_on_client_disconnect( @@ -3421,7 +3428,11 @@ class ProxyBaseLLMRequestProcessing: version: str | None = None, ): """Raises ProxyException (OpenAI API compatible) if an exception is raised""" - _log_llm_api_exception(e) + logging_obj: Final[LiteLLMLoggingObj | None] = self.data.get("litellm_logging_obj", None) + _log_llm_api_exception( + e, + logging_obj.litellm_call_id if logging_obj is not None else self.data.get("litellm_call_id"), + ) # Allow callbacks to transform the error response transformed_exception: Final = await proxy_logging_obj.post_call_failure_hook( user_api_key_dict=user_api_key_dict, diff --git a/tests/test_litellm/proxy/test_common_request_processing.py b/tests/test_litellm/proxy/test_common_request_processing.py index cabfcc9918f..98e9d30493a 100644 --- a/tests/test_litellm/proxy/test_common_request_processing.py +++ b/tests/test_litellm/proxy/test_common_request_processing.py @@ -8169,7 +8169,7 @@ def test_log_llm_api_exception_traceback_only_for_unexpected_errors(exc, expect_ try: raise exc except Exception as raised: - _log_llm_api_exception(raised) + _log_llm_api_exception(raised, "call-id-for-traceback-test") finally: verbose_proxy_logger.propagate = False @@ -8663,3 +8663,71 @@ class TestBackgroundResponseRetrievalGovernance: assert "_guardrail_pipelines" not in data["litellm_metadata"] assert "applied_policies" not in data["litellm_metadata"] + + +class TestErrorLogCarriesCallId: + """Regression for LIT-5856 / #37532: the ERROR line emitted for a failed LLM + request must carry the litellm_call_id the client got back in the + x-litellm-call-id response header, so a logged exception can be tied to a + specific request.""" + + async def _invoke(self, data: dict) -> None: + from litellm._logging import verbose_proxy_logger + + processor: Final = ProxyBaseLLMRequestProcessing(data=data) + proxy_logging_obj: Final = MagicMock() + proxy_logging_obj.post_call_failure_hook = AsyncMock(return_value=None) + verbose_proxy_logger.propagate = True + try: + with pytest.raises(ProxyException): + await processor._handle_llm_api_exception( + e=ValueError("upstream blew up"), + user_api_key_dict=ProxyUserAPIKeyAuth(api_key="sk-test"), + proxy_logging_obj=proxy_logging_obj, + ) + finally: + verbose_proxy_logger.propagate = False + + @staticmethod + def _error_record(caplog: pytest.LogCaptureFixture): + return next(r for r in caplog.records if "_handle_llm_api_exception(): Exception occured" in r.getMessage()) + + async def test_call_id_from_logging_obj_is_logged(self, caplog: pytest.LogCaptureFixture) -> None: + call_id: Final = str(uuid.uuid4()) + logging_obj: Final = MagicMock() + logging_obj.litellm_call_id = call_id + with caplog.at_level("ERROR", logger="LiteLLM Proxy"): + await self._invoke({"litellm_logging_obj": logging_obj, "litellm_call_id": "stale-id"}) + + record: Final = self._error_record(caplog) + assert record.litellm_call_id == call_id + assert call_id in record.getMessage() + + async def test_call_id_falls_back_to_request_data(self, caplog: pytest.LogCaptureFixture) -> None: + call_id: Final = str(uuid.uuid4()) + with caplog.at_level("ERROR", logger="LiteLLM Proxy"): + await self._invoke({"litellm_call_id": call_id}) + + record: Final = self._error_record(caplog) + assert record.litellm_call_id == call_id + assert call_id in record.getMessage() + + def test_client_disconnect_log_carries_call_id(self, caplog: pytest.LogCaptureFixture) -> None: + from litellm._logging import verbose_proxy_logger + from litellm.proxy.common_request_processing import ( + _CLIENT_DISCONNECT_DETAIL, + _log_llm_api_exception, + ) + + call_id: Final = str(uuid.uuid4()) + verbose_proxy_logger.propagate = True + try: + with caplog.at_level("INFO", logger="LiteLLM Proxy"): + _log_llm_api_exception( + HTTPException(status_code=499, detail=_CLIENT_DISCONNECT_DETAIL), + call_id, + ) + finally: + verbose_proxy_logger.propagate = False + + assert call_id in caplog.records[-1].getMessage() From 0a8eb56ba40eeceba26bdef6ae61a553b28681ec Mon Sep 17 00:00:00 2001 From: Devin AI <158243242+devin-ai-integration[bot]@users.noreply.github.com> Date: Tue, 15 Sep 2026 08:03:45 +0000 Subject: [PATCH 055/225] fix(proxy): fall back to request data when logging object has no call id Co-Authored-By: Devin AI <158243242+devin-ai-integration[bot]@users.noreply.github.com> --- litellm/proxy/common_request_processing.py | 2 +- .../proxy/test_common_request_processing.py | 13 ++++++++++++- 2 files changed, 13 insertions(+), 2 deletions(-) diff --git a/litellm/proxy/common_request_processing.py b/litellm/proxy/common_request_processing.py index a29079433e9..2cfcce2c11c 100644 --- a/litellm/proxy/common_request_processing.py +++ b/litellm/proxy/common_request_processing.py @@ -3431,7 +3431,7 @@ class ProxyBaseLLMRequestProcessing: logging_obj: Final[LiteLLMLoggingObj | None] = self.data.get("litellm_logging_obj", None) _log_llm_api_exception( e, - logging_obj.litellm_call_id if logging_obj is not None else self.data.get("litellm_call_id"), + (logging_obj.litellm_call_id if logging_obj is not None else None) or self.data.get("litellm_call_id"), ) # Allow callbacks to transform the error response transformed_exception: Final = await proxy_logging_obj.post_call_failure_hook( diff --git a/tests/test_litellm/proxy/test_common_request_processing.py b/tests/test_litellm/proxy/test_common_request_processing.py index 98e9d30493a..cdf2118a1c5 100644 --- a/tests/test_litellm/proxy/test_common_request_processing.py +++ b/tests/test_litellm/proxy/test_common_request_processing.py @@ -8671,7 +8671,7 @@ class TestErrorLogCarriesCallId: x-litellm-call-id response header, so a logged exception can be tied to a specific request.""" - async def _invoke(self, data: dict) -> None: + async def _invoke(self, data: dict[str, object]) -> None: from litellm._logging import verbose_proxy_logger processor: Final = ProxyBaseLLMRequestProcessing(data=data) @@ -8712,6 +8712,17 @@ class TestErrorLogCarriesCallId: assert record.litellm_call_id == call_id assert call_id in record.getMessage() + async def test_call_id_falls_back_when_logging_obj_has_none(self, caplog: pytest.LogCaptureFixture) -> None: + call_id: Final = str(uuid.uuid4()) + logging_obj: Final = MagicMock() + logging_obj.litellm_call_id = None + with caplog.at_level("ERROR", logger="LiteLLM Proxy"): + await self._invoke({"litellm_logging_obj": logging_obj, "litellm_call_id": call_id}) + + record: Final = self._error_record(caplog) + assert record.litellm_call_id == call_id + assert call_id in record.getMessage() + def test_client_disconnect_log_carries_call_id(self, caplog: pytest.LogCaptureFixture) -> None: from litellm._logging import verbose_proxy_logger from litellm.proxy.common_request_processing import ( From 1b474b075f1b70f2dc7e88490e209738bfc6a6fb Mon Sep 17 00:00:00 2001 From: yucheng Date: Tue, 15 Sep 2026 08:15:14 +0000 Subject: [PATCH 056/225] fix(proxy): attach litellm_call_id to client disconnect log record Co-Authored-By: Devin AI <158243242+devin-ai-integration[bot]@users.noreply.github.com> --- litellm/proxy/common_request_processing.py | 1 + tests/test_litellm/proxy/test_common_request_processing.py | 4 +++- 2 files changed, 4 insertions(+), 1 deletion(-) diff --git a/litellm/proxy/common_request_processing.py b/litellm/proxy/common_request_processing.py index 2cfcce2c11c..37c1ab39632 100644 --- a/litellm/proxy/common_request_processing.py +++ b/litellm/proxy/common_request_processing.py @@ -1457,6 +1457,7 @@ def _log_llm_api_exception(e: Exception, litellm_call_id: str | None) -> None: "litellm.proxy.proxy_server._handle_llm_api_exception(): client disconnected, " "upstream LLM request cancelled - litellm_call_id=%s", litellm_call_id, + extra=MappingProxyType({"litellm_call_id": litellm_call_id}), ) return log_fn: Final = ( diff --git a/tests/test_litellm/proxy/test_common_request_processing.py b/tests/test_litellm/proxy/test_common_request_processing.py index cdf2118a1c5..735b0dee3dc 100644 --- a/tests/test_litellm/proxy/test_common_request_processing.py +++ b/tests/test_litellm/proxy/test_common_request_processing.py @@ -8741,4 +8741,6 @@ class TestErrorLogCarriesCallId: finally: verbose_proxy_logger.propagate = False - assert call_id in caplog.records[-1].getMessage() + record: Final = caplog.records[-1] + assert record.litellm_call_id == call_id + assert call_id in record.getMessage() From dc3a0399f23dfb2b34910868908887c9879f8ccc Mon Sep 17 00:00:00 2001 From: "berriai-litellm-provider-info-sync[bot]" <328147090+berriai-litellm-provider-info-sync[bot]@users.noreply.github.com> Date: Tue, 15 Sep 2026 00:36:08 +0000 Subject: [PATCH 057/225] chore(prices): sync Vertex AI prices: 4 models gemini-2.5-flash-preview-tts: output_cost_per_audio_token, input_cost_per_token_batches gemini/gemini-2.5-flash-preview-tts: output_cost_per_audio_token, input_cost_per_token_batches gemini-3.1-flash-live-preview: input_cost_per_second gemini/gemini-3.1-flash-live-preview: input_cost_per_second --- ...model_prices_and_context_window_backup.json | 18 ++++++++++++------ model_prices_and_context_window.json | 18 ++++++++++++------ 2 files changed, 24 insertions(+), 12 deletions(-) diff --git a/litellm/model_prices_and_context_window_backup.json b/litellm/model_prices_and_context_window_backup.json index 9f91cf82f41..54f19419e47 100644 --- a/litellm/model_prices_and_context_window_backup.json +++ b/litellm/model_prices_and_context_window_backup.json @@ -26553,10 +26553,12 @@ }, "gemini/gemini-2.5-flash-preview-tts": { "input_cost_per_token": 5e-07, + "input_cost_per_token_batches": 2.5e-07, "litellm_provider": "gemini", "mode": "audio_speech", + "output_cost_per_audio_token": 1e-05, "output_cost_per_token": 1e-05, - "source": "https://ai.google.dev/gemini-api/docs/pricing", + "source": "https://cloud.google.com/gemini-enterprise-agent-platform/generative-ai/pricing", "supported_endpoints": [ "/v1/audio/speech" ], @@ -55456,7 +55458,7 @@ "mode": "realtime", "output_cost_per_audio_token": 1.2e-05, "output_cost_per_token": 4.5e-06, - "source": "https://ai.google.dev/gemini-api/docs/pricing", + "source": "https://cloud.google.com/gemini-enterprise-agent-platform/generative-ai/pricing", "supported_endpoints": [ "/v1/realtime" ], @@ -55475,7 +55477,8 @@ "supports_function_calling": true, "supports_vision": true, "supports_web_search": true, - "gemini_audio_only_live": true + "gemini_audio_only_live": true, + "input_cost_per_second": 8.33333333333e-05 }, "gemini/gemini-2.5-flash-native-audio-latest": { "input_cost_per_audio_token": 3e-06, @@ -55573,7 +55576,7 @@ "mode": "realtime", "output_cost_per_audio_token": 1.2e-05, "output_cost_per_token": 4.5e-06, - "source": "https://ai.google.dev/gemini-api/docs/pricing", + "source": "https://cloud.google.com/gemini-enterprise-agent-platform/generative-ai/pricing", "supported_endpoints": [ "/v1/realtime" ], @@ -55594,7 +55597,8 @@ "supports_web_search": true, "tpm": 250000, "rpm": 10, - "gemini_audio_only_live": true + "gemini_audio_only_live": true, + "input_cost_per_second": 8.33333333333e-05 }, "gemini/gemini-3.1-flash-tts-preview": { "input_cost_per_token": 1e-06, @@ -55613,10 +55617,12 @@ }, "gemini-2.5-flash-preview-tts": { "input_cost_per_token": 5e-07, + "input_cost_per_token_batches": 2.5e-07, "litellm_provider": "gemini", "mode": "audio_speech", + "output_cost_per_audio_token": 1e-05, "output_cost_per_token": 1e-05, - "source": "https://ai.google.dev/gemini-api/docs/pricing", + "source": "https://cloud.google.com/gemini-enterprise-agent-platform/generative-ai/pricing", "supported_endpoints": [ "/v1/audio/speech" ] diff --git a/model_prices_and_context_window.json b/model_prices_and_context_window.json index 9f91cf82f41..54f19419e47 100644 --- a/model_prices_and_context_window.json +++ b/model_prices_and_context_window.json @@ -26553,10 +26553,12 @@ }, "gemini/gemini-2.5-flash-preview-tts": { "input_cost_per_token": 5e-07, + "input_cost_per_token_batches": 2.5e-07, "litellm_provider": "gemini", "mode": "audio_speech", + "output_cost_per_audio_token": 1e-05, "output_cost_per_token": 1e-05, - "source": "https://ai.google.dev/gemini-api/docs/pricing", + "source": "https://cloud.google.com/gemini-enterprise-agent-platform/generative-ai/pricing", "supported_endpoints": [ "/v1/audio/speech" ], @@ -55456,7 +55458,7 @@ "mode": "realtime", "output_cost_per_audio_token": 1.2e-05, "output_cost_per_token": 4.5e-06, - "source": "https://ai.google.dev/gemini-api/docs/pricing", + "source": "https://cloud.google.com/gemini-enterprise-agent-platform/generative-ai/pricing", "supported_endpoints": [ "/v1/realtime" ], @@ -55475,7 +55477,8 @@ "supports_function_calling": true, "supports_vision": true, "supports_web_search": true, - "gemini_audio_only_live": true + "gemini_audio_only_live": true, + "input_cost_per_second": 8.33333333333e-05 }, "gemini/gemini-2.5-flash-native-audio-latest": { "input_cost_per_audio_token": 3e-06, @@ -55573,7 +55576,7 @@ "mode": "realtime", "output_cost_per_audio_token": 1.2e-05, "output_cost_per_token": 4.5e-06, - "source": "https://ai.google.dev/gemini-api/docs/pricing", + "source": "https://cloud.google.com/gemini-enterprise-agent-platform/generative-ai/pricing", "supported_endpoints": [ "/v1/realtime" ], @@ -55594,7 +55597,8 @@ "supports_web_search": true, "tpm": 250000, "rpm": 10, - "gemini_audio_only_live": true + "gemini_audio_only_live": true, + "input_cost_per_second": 8.33333333333e-05 }, "gemini/gemini-3.1-flash-tts-preview": { "input_cost_per_token": 1e-06, @@ -55613,10 +55617,12 @@ }, "gemini-2.5-flash-preview-tts": { "input_cost_per_token": 5e-07, + "input_cost_per_token_batches": 2.5e-07, "litellm_provider": "gemini", "mode": "audio_speech", + "output_cost_per_audio_token": 1e-05, "output_cost_per_token": 1e-05, - "source": "https://ai.google.dev/gemini-api/docs/pricing", + "source": "https://cloud.google.com/gemini-enterprise-agent-platform/generative-ai/pricing", "supported_endpoints": [ "/v1/audio/speech" ] From 01b069eb6c1cb8280f9600f1a2d1ec22cb257e29 Mon Sep 17 00:00:00 2001 From: "berriai-litellm-provider-info-sync[bot]" <328147090+berriai-litellm-provider-info-sync[bot]@users.noreply.github.com> Date: Tue, 15 Sep 2026 01:36:09 +0000 Subject: [PATCH 058/225] chore(prices): sync AWS Bedrock prices: 25 models anthropic.claude-fable-5: anthropic.claude-fable-5-1: anthropic.claude-opus-4-7: anthropic.claude-opus-4-8: anthropic.claude-opus-5: anthropic.claude-sonnet-4-6: anthropic.claude-sonnet-5: global.anthropic.claude-fable-5: global.anthropic.claude-fable-5-1: global.anthropic.claude-opus-4-7: global.anthropic.claude-opus-4-8: global.anthropic.claude-opus-5: global.anthropic.claude-sonnet-4-6: global.anthropic.claude-sonnet-5: us-gov.anthropic.claude-fable-5-1: us-gov.anthropic.claude-opus-4-8: us-gov.anthropic.claude-opus-5: us-gov.anthropic.claude-sonnet-5: us.anthropic.claude-fable-5: us.anthropic.claude-fable-5-1: us.anthropic.claude-opus-4-7: us.anthropic.claude-opus-4-8: us.anthropic.claude-opus-5: us.anthropic.claude-sonnet-4-6: us.anthropic.claude-sonnet-5: --- ...odel_prices_and_context_window_backup.json | 69 +++++++++++++------ model_prices_and_context_window.json | 69 +++++++++++++------ 2 files changed, 94 insertions(+), 44 deletions(-) diff --git a/litellm/model_prices_and_context_window_backup.json b/litellm/model_prices_and_context_window_backup.json index 54f19419e47..47cd6faf022 100644 --- a/litellm/model_prices_and_context_window_backup.json +++ b/litellm/model_prices_and_context_window_backup.json @@ -1312,7 +1312,8 @@ "supports_output_config": true, "bedrock_output_config_effort_ceiling": "xhigh", "supports_parallel_tool_use_config": true, - "prompt_cache_min_tokens": 2048 + "prompt_cache_min_tokens": 2048, + "source": "https://pricing.us-east-1.amazonaws.com/offers/v1.0/aws/AmazonBedrockFoundationModels/current/index.json" }, "anthropic.claude-mythos-preview": { "input_cost_per_token": 0, @@ -1365,7 +1366,8 @@ "supports_output_config": true, "bedrock_output_config_effort_ceiling": "xhigh", "supports_parallel_tool_use_config": true, - "prompt_cache_min_tokens": 2048 + "prompt_cache_min_tokens": 2048, + "source": "https://pricing.us-east-1.amazonaws.com/offers/v1.0/aws/AmazonBedrockFoundationModels/current/index.json" }, "us.anthropic.claude-opus-4-7": { "bedrock_converse_supports_strict_tools": false, @@ -1402,7 +1404,8 @@ "supports_output_config": true, "bedrock_output_config_effort_ceiling": "xhigh", "supports_parallel_tool_use_config": true, - "prompt_cache_min_tokens": 2048 + "prompt_cache_min_tokens": 2048, + "source": "https://pricing.us-east-1.amazonaws.com/offers/v1.0/aws/AmazonBedrockFoundationModels/current/index.json" }, "eu.anthropic.claude-opus-4-7": { "bedrock_converse_supports_strict_tools": false, @@ -1513,7 +1516,8 @@ "supports_output_config": true, "bedrock_output_config_effort_ceiling": "xhigh", "supports_parallel_tool_use_config": true, - "prompt_cache_min_tokens": 512 + "prompt_cache_min_tokens": 512, + "source": "https://pricing.us-east-1.amazonaws.com/offers/v1.0/aws/AmazonBedrockFoundationModels/current/index.json" }, "anthropic.claude-fable-5-1": { "cache_creation_input_token_cost": 1.25e-05, @@ -1551,7 +1555,8 @@ "supports_output_config": true, "bedrock_output_config_effort_ceiling": "xhigh", "supports_parallel_tool_use_config": true, - "prompt_cache_min_tokens": 512 + "prompt_cache_min_tokens": 512, + "source": "https://pricing.us-east-1.amazonaws.com/offers/v1.0/aws/AmazonBedrockFoundationModels/current/index.json" }, "global.anthropic.claude-fable-5": { "cache_creation_input_token_cost": 1.25e-05, @@ -1588,7 +1593,8 @@ "supports_output_config": true, "bedrock_output_config_effort_ceiling": "xhigh", "supports_parallel_tool_use_config": true, - "prompt_cache_min_tokens": 512 + "prompt_cache_min_tokens": 512, + "source": "https://pricing.us-east-1.amazonaws.com/offers/v1.0/aws/AmazonBedrockFoundationModels/current/index.json" }, "global.anthropic.claude-fable-5-1": { "cache_creation_input_token_cost": 1.25e-05, @@ -1626,7 +1632,8 @@ "supports_output_config": true, "bedrock_output_config_effort_ceiling": "xhigh", "supports_parallel_tool_use_config": true, - "prompt_cache_min_tokens": 512 + "prompt_cache_min_tokens": 512, + "source": "https://pricing.us-east-1.amazonaws.com/offers/v1.0/aws/AmazonBedrockFoundationModels/current/index.json" }, "us.anthropic.claude-fable-5": { "cache_creation_input_token_cost": 1.375e-05, @@ -1663,7 +1670,8 @@ "supports_output_config": true, "bedrock_output_config_effort_ceiling": "xhigh", "supports_parallel_tool_use_config": true, - "prompt_cache_min_tokens": 512 + "prompt_cache_min_tokens": 512, + "source": "https://pricing.us-east-1.amazonaws.com/offers/v1.0/aws/AmazonBedrockFoundationModels/current/index.json" }, "us.anthropic.claude-fable-5-1": { "cache_creation_input_token_cost": 1.375e-05, @@ -1701,7 +1709,8 @@ "supports_output_config": true, "bedrock_output_config_effort_ceiling": "xhigh", "supports_parallel_tool_use_config": true, - "prompt_cache_min_tokens": 512 + "prompt_cache_min_tokens": 512, + "source": "https://pricing.us-east-1.amazonaws.com/offers/v1.0/aws/AmazonBedrockFoundationModels/current/index.json" }, "eu.anthropic.claude-fable-5": { "cache_creation_input_token_cost": 1.375e-05, @@ -1812,7 +1821,8 @@ "supports_max_reasoning_effort": true, "supports_output_config": true, "supports_parallel_tool_use_config": true, - "prompt_cache_min_tokens": 512 + "prompt_cache_min_tokens": 512, + "source": "https://pricing.us-east-1.amazonaws.com/offers/v1.0/aws/AmazonBedrockFoundationModels/current/index.json" }, "global.anthropic.claude-opus-5": { "bedrock_converse_supports_strict_tools": false, @@ -1848,7 +1858,8 @@ "supports_max_reasoning_effort": true, "supports_output_config": true, "supports_parallel_tool_use_config": true, - "prompt_cache_min_tokens": 512 + "prompt_cache_min_tokens": 512, + "source": "https://pricing.us-east-1.amazonaws.com/offers/v1.0/aws/AmazonBedrockFoundationModels/current/index.json" }, "us.anthropic.claude-opus-5": { "bedrock_converse_supports_strict_tools": false, @@ -1884,7 +1895,8 @@ "supports_max_reasoning_effort": true, "supports_output_config": true, "supports_parallel_tool_use_config": true, - "prompt_cache_min_tokens": 512 + "prompt_cache_min_tokens": 512, + "source": "https://pricing.us-east-1.amazonaws.com/offers/v1.0/aws/AmazonBedrockFoundationModels/current/index.json" }, "eu.anthropic.claude-opus-5": { "bedrock_converse_supports_strict_tools": false, @@ -2029,7 +2041,8 @@ "supports_output_config": true, "bedrock_output_config_effort_ceiling": "xhigh", "supports_parallel_tool_use_config": true, - "prompt_cache_min_tokens": 1024 + "prompt_cache_min_tokens": 1024, + "source": "https://pricing.us-east-1.amazonaws.com/offers/v1.0/aws/AmazonBedrockFoundationModels/current/index.json" }, "global.anthropic.claude-opus-4-8": { "bedrock_converse_supports_strict_tools": false, @@ -2066,7 +2079,8 @@ "supports_output_config": true, "bedrock_output_config_effort_ceiling": "xhigh", "supports_parallel_tool_use_config": true, - "prompt_cache_min_tokens": 1024 + "prompt_cache_min_tokens": 1024, + "source": "https://pricing.us-east-1.amazonaws.com/offers/v1.0/aws/AmazonBedrockFoundationModels/current/index.json" }, "us.anthropic.claude-opus-4-8": { "bedrock_converse_supports_strict_tools": false, @@ -2103,7 +2117,8 @@ "supports_output_config": true, "bedrock_output_config_effort_ceiling": "xhigh", "supports_parallel_tool_use_config": true, - "prompt_cache_min_tokens": 1024 + "prompt_cache_min_tokens": 1024, + "source": "https://pricing.us-east-1.amazonaws.com/offers/v1.0/aws/AmazonBedrockFoundationModels/current/index.json" }, "eu.anthropic.claude-opus-4-8": { "bedrock_converse_supports_strict_tools": false, @@ -2286,7 +2301,8 @@ "supports_output_config": true, "bedrock_output_config_effort_ceiling": "xhigh", "supports_parallel_tool_use_config": true, - "prompt_cache_min_tokens": 1024 + "prompt_cache_min_tokens": 1024, + "source": "https://pricing.us-east-1.amazonaws.com/offers/v1.0/aws/AmazonBedrockFoundationModels/current/index.json" }, "global.anthropic.claude-sonnet-5": { "bedrock_converse_supports_strict_tools": false, @@ -2323,7 +2339,8 @@ "supports_output_config": true, "bedrock_output_config_effort_ceiling": "xhigh", "supports_parallel_tool_use_config": true, - "prompt_cache_min_tokens": 1024 + "prompt_cache_min_tokens": 1024, + "source": "https://pricing.us-east-1.amazonaws.com/offers/v1.0/aws/AmazonBedrockFoundationModels/current/index.json" }, "us.anthropic.claude-sonnet-5": { "bedrock_converse_supports_strict_tools": false, @@ -2360,7 +2377,8 @@ "supports_output_config": true, "bedrock_output_config_effort_ceiling": "xhigh", "supports_parallel_tool_use_config": true, - "prompt_cache_min_tokens": 1024 + "prompt_cache_min_tokens": 1024, + "source": "https://pricing.us-east-1.amazonaws.com/offers/v1.0/aws/AmazonBedrockFoundationModels/current/index.json" }, "eu.anthropic.claude-sonnet-5": { "bedrock_converse_supports_strict_tools": false, @@ -2505,7 +2523,8 @@ "supports_native_structured_output": true, "supports_output_config": true, "supports_parallel_tool_use_config": true, - "prompt_cache_min_tokens": 1024 + "prompt_cache_min_tokens": 1024, + "source": "https://pricing.us-east-1.amazonaws.com/offers/v1.0/aws/AmazonBedrockFoundationModels/current/index.json" }, "global.anthropic.claude-sonnet-4-6": { "supports_adaptive_thinking": true, @@ -2539,7 +2558,8 @@ "supports_native_structured_output": true, "supports_output_config": true, "supports_parallel_tool_use_config": true, - "prompt_cache_min_tokens": 1024 + "prompt_cache_min_tokens": 1024, + "source": "https://pricing.us-east-1.amazonaws.com/offers/v1.0/aws/AmazonBedrockFoundationModels/current/index.json" }, "us.anthropic.claude-sonnet-4-6": { "supports_adaptive_thinking": true, @@ -2573,7 +2593,8 @@ "supports_native_structured_output": true, "supports_output_config": true, "supports_parallel_tool_use_config": true, - "prompt_cache_min_tokens": 1024 + "prompt_cache_min_tokens": 1024, + "source": "https://pricing.us-east-1.amazonaws.com/offers/v1.0/aws/AmazonBedrockFoundationModels/current/index.json" }, "eu.anthropic.claude-sonnet-4-6": { "supports_adaptive_thinking": true, @@ -44977,6 +44998,7 @@ "mode": "chat", "output_cost_per_token": 1.2e-05, "prompt_cache_min_tokens": 1024, + "source": "https://pricing.us-east-1.amazonaws.com/offers/v1.0/aws/AmazonBedrockFoundationModels/current/index.json", "supports_adaptive_thinking": true, "supports_assistant_prefill": false, "supports_computer_use": true, @@ -45009,6 +45031,7 @@ "mode": "chat", "output_cost_per_token": 3e-05, "prompt_cache_min_tokens": 1024, + "source": "https://pricing.us-east-1.amazonaws.com/offers/v1.0/aws/AmazonBedrockFoundationModels/current/index.json", "supports_adaptive_thinking": true, "supports_assistant_prefill": false, "supports_computer_use": true, @@ -45040,6 +45063,7 @@ "mode": "chat", "output_cost_per_token": 3e-05, "prompt_cache_min_tokens": 512, + "source": "https://pricing.us-east-1.amazonaws.com/offers/v1.0/aws/AmazonBedrockFoundationModels/current/index.json", "supports_adaptive_thinking": true, "supports_assistant_prefill": false, "supports_computer_use": true, @@ -45089,7 +45113,8 @@ "supports_output_config": true, "bedrock_output_config_effort_ceiling": "xhigh", "supports_parallel_tool_use_config": true, - "prompt_cache_min_tokens": 512 + "prompt_cache_min_tokens": 512, + "source": "https://pricing.us-east-1.amazonaws.com/offers/v1.0/aws/AmazonBedrockFoundationModels/current/index.json" }, "us-gov.nvidia.nemotron-nano-3-30b": { "input_cost_per_token": 7.2e-08, diff --git a/model_prices_and_context_window.json b/model_prices_and_context_window.json index 54f19419e47..47cd6faf022 100644 --- a/model_prices_and_context_window.json +++ b/model_prices_and_context_window.json @@ -1312,7 +1312,8 @@ "supports_output_config": true, "bedrock_output_config_effort_ceiling": "xhigh", "supports_parallel_tool_use_config": true, - "prompt_cache_min_tokens": 2048 + "prompt_cache_min_tokens": 2048, + "source": "https://pricing.us-east-1.amazonaws.com/offers/v1.0/aws/AmazonBedrockFoundationModels/current/index.json" }, "anthropic.claude-mythos-preview": { "input_cost_per_token": 0, @@ -1365,7 +1366,8 @@ "supports_output_config": true, "bedrock_output_config_effort_ceiling": "xhigh", "supports_parallel_tool_use_config": true, - "prompt_cache_min_tokens": 2048 + "prompt_cache_min_tokens": 2048, + "source": "https://pricing.us-east-1.amazonaws.com/offers/v1.0/aws/AmazonBedrockFoundationModels/current/index.json" }, "us.anthropic.claude-opus-4-7": { "bedrock_converse_supports_strict_tools": false, @@ -1402,7 +1404,8 @@ "supports_output_config": true, "bedrock_output_config_effort_ceiling": "xhigh", "supports_parallel_tool_use_config": true, - "prompt_cache_min_tokens": 2048 + "prompt_cache_min_tokens": 2048, + "source": "https://pricing.us-east-1.amazonaws.com/offers/v1.0/aws/AmazonBedrockFoundationModels/current/index.json" }, "eu.anthropic.claude-opus-4-7": { "bedrock_converse_supports_strict_tools": false, @@ -1513,7 +1516,8 @@ "supports_output_config": true, "bedrock_output_config_effort_ceiling": "xhigh", "supports_parallel_tool_use_config": true, - "prompt_cache_min_tokens": 512 + "prompt_cache_min_tokens": 512, + "source": "https://pricing.us-east-1.amazonaws.com/offers/v1.0/aws/AmazonBedrockFoundationModels/current/index.json" }, "anthropic.claude-fable-5-1": { "cache_creation_input_token_cost": 1.25e-05, @@ -1551,7 +1555,8 @@ "supports_output_config": true, "bedrock_output_config_effort_ceiling": "xhigh", "supports_parallel_tool_use_config": true, - "prompt_cache_min_tokens": 512 + "prompt_cache_min_tokens": 512, + "source": "https://pricing.us-east-1.amazonaws.com/offers/v1.0/aws/AmazonBedrockFoundationModels/current/index.json" }, "global.anthropic.claude-fable-5": { "cache_creation_input_token_cost": 1.25e-05, @@ -1588,7 +1593,8 @@ "supports_output_config": true, "bedrock_output_config_effort_ceiling": "xhigh", "supports_parallel_tool_use_config": true, - "prompt_cache_min_tokens": 512 + "prompt_cache_min_tokens": 512, + "source": "https://pricing.us-east-1.amazonaws.com/offers/v1.0/aws/AmazonBedrockFoundationModels/current/index.json" }, "global.anthropic.claude-fable-5-1": { "cache_creation_input_token_cost": 1.25e-05, @@ -1626,7 +1632,8 @@ "supports_output_config": true, "bedrock_output_config_effort_ceiling": "xhigh", "supports_parallel_tool_use_config": true, - "prompt_cache_min_tokens": 512 + "prompt_cache_min_tokens": 512, + "source": "https://pricing.us-east-1.amazonaws.com/offers/v1.0/aws/AmazonBedrockFoundationModels/current/index.json" }, "us.anthropic.claude-fable-5": { "cache_creation_input_token_cost": 1.375e-05, @@ -1663,7 +1670,8 @@ "supports_output_config": true, "bedrock_output_config_effort_ceiling": "xhigh", "supports_parallel_tool_use_config": true, - "prompt_cache_min_tokens": 512 + "prompt_cache_min_tokens": 512, + "source": "https://pricing.us-east-1.amazonaws.com/offers/v1.0/aws/AmazonBedrockFoundationModels/current/index.json" }, "us.anthropic.claude-fable-5-1": { "cache_creation_input_token_cost": 1.375e-05, @@ -1701,7 +1709,8 @@ "supports_output_config": true, "bedrock_output_config_effort_ceiling": "xhigh", "supports_parallel_tool_use_config": true, - "prompt_cache_min_tokens": 512 + "prompt_cache_min_tokens": 512, + "source": "https://pricing.us-east-1.amazonaws.com/offers/v1.0/aws/AmazonBedrockFoundationModels/current/index.json" }, "eu.anthropic.claude-fable-5": { "cache_creation_input_token_cost": 1.375e-05, @@ -1812,7 +1821,8 @@ "supports_max_reasoning_effort": true, "supports_output_config": true, "supports_parallel_tool_use_config": true, - "prompt_cache_min_tokens": 512 + "prompt_cache_min_tokens": 512, + "source": "https://pricing.us-east-1.amazonaws.com/offers/v1.0/aws/AmazonBedrockFoundationModels/current/index.json" }, "global.anthropic.claude-opus-5": { "bedrock_converse_supports_strict_tools": false, @@ -1848,7 +1858,8 @@ "supports_max_reasoning_effort": true, "supports_output_config": true, "supports_parallel_tool_use_config": true, - "prompt_cache_min_tokens": 512 + "prompt_cache_min_tokens": 512, + "source": "https://pricing.us-east-1.amazonaws.com/offers/v1.0/aws/AmazonBedrockFoundationModels/current/index.json" }, "us.anthropic.claude-opus-5": { "bedrock_converse_supports_strict_tools": false, @@ -1884,7 +1895,8 @@ "supports_max_reasoning_effort": true, "supports_output_config": true, "supports_parallel_tool_use_config": true, - "prompt_cache_min_tokens": 512 + "prompt_cache_min_tokens": 512, + "source": "https://pricing.us-east-1.amazonaws.com/offers/v1.0/aws/AmazonBedrockFoundationModels/current/index.json" }, "eu.anthropic.claude-opus-5": { "bedrock_converse_supports_strict_tools": false, @@ -2029,7 +2041,8 @@ "supports_output_config": true, "bedrock_output_config_effort_ceiling": "xhigh", "supports_parallel_tool_use_config": true, - "prompt_cache_min_tokens": 1024 + "prompt_cache_min_tokens": 1024, + "source": "https://pricing.us-east-1.amazonaws.com/offers/v1.0/aws/AmazonBedrockFoundationModels/current/index.json" }, "global.anthropic.claude-opus-4-8": { "bedrock_converse_supports_strict_tools": false, @@ -2066,7 +2079,8 @@ "supports_output_config": true, "bedrock_output_config_effort_ceiling": "xhigh", "supports_parallel_tool_use_config": true, - "prompt_cache_min_tokens": 1024 + "prompt_cache_min_tokens": 1024, + "source": "https://pricing.us-east-1.amazonaws.com/offers/v1.0/aws/AmazonBedrockFoundationModels/current/index.json" }, "us.anthropic.claude-opus-4-8": { "bedrock_converse_supports_strict_tools": false, @@ -2103,7 +2117,8 @@ "supports_output_config": true, "bedrock_output_config_effort_ceiling": "xhigh", "supports_parallel_tool_use_config": true, - "prompt_cache_min_tokens": 1024 + "prompt_cache_min_tokens": 1024, + "source": "https://pricing.us-east-1.amazonaws.com/offers/v1.0/aws/AmazonBedrockFoundationModels/current/index.json" }, "eu.anthropic.claude-opus-4-8": { "bedrock_converse_supports_strict_tools": false, @@ -2286,7 +2301,8 @@ "supports_output_config": true, "bedrock_output_config_effort_ceiling": "xhigh", "supports_parallel_tool_use_config": true, - "prompt_cache_min_tokens": 1024 + "prompt_cache_min_tokens": 1024, + "source": "https://pricing.us-east-1.amazonaws.com/offers/v1.0/aws/AmazonBedrockFoundationModels/current/index.json" }, "global.anthropic.claude-sonnet-5": { "bedrock_converse_supports_strict_tools": false, @@ -2323,7 +2339,8 @@ "supports_output_config": true, "bedrock_output_config_effort_ceiling": "xhigh", "supports_parallel_tool_use_config": true, - "prompt_cache_min_tokens": 1024 + "prompt_cache_min_tokens": 1024, + "source": "https://pricing.us-east-1.amazonaws.com/offers/v1.0/aws/AmazonBedrockFoundationModels/current/index.json" }, "us.anthropic.claude-sonnet-5": { "bedrock_converse_supports_strict_tools": false, @@ -2360,7 +2377,8 @@ "supports_output_config": true, "bedrock_output_config_effort_ceiling": "xhigh", "supports_parallel_tool_use_config": true, - "prompt_cache_min_tokens": 1024 + "prompt_cache_min_tokens": 1024, + "source": "https://pricing.us-east-1.amazonaws.com/offers/v1.0/aws/AmazonBedrockFoundationModels/current/index.json" }, "eu.anthropic.claude-sonnet-5": { "bedrock_converse_supports_strict_tools": false, @@ -2505,7 +2523,8 @@ "supports_native_structured_output": true, "supports_output_config": true, "supports_parallel_tool_use_config": true, - "prompt_cache_min_tokens": 1024 + "prompt_cache_min_tokens": 1024, + "source": "https://pricing.us-east-1.amazonaws.com/offers/v1.0/aws/AmazonBedrockFoundationModels/current/index.json" }, "global.anthropic.claude-sonnet-4-6": { "supports_adaptive_thinking": true, @@ -2539,7 +2558,8 @@ "supports_native_structured_output": true, "supports_output_config": true, "supports_parallel_tool_use_config": true, - "prompt_cache_min_tokens": 1024 + "prompt_cache_min_tokens": 1024, + "source": "https://pricing.us-east-1.amazonaws.com/offers/v1.0/aws/AmazonBedrockFoundationModels/current/index.json" }, "us.anthropic.claude-sonnet-4-6": { "supports_adaptive_thinking": true, @@ -2573,7 +2593,8 @@ "supports_native_structured_output": true, "supports_output_config": true, "supports_parallel_tool_use_config": true, - "prompt_cache_min_tokens": 1024 + "prompt_cache_min_tokens": 1024, + "source": "https://pricing.us-east-1.amazonaws.com/offers/v1.0/aws/AmazonBedrockFoundationModels/current/index.json" }, "eu.anthropic.claude-sonnet-4-6": { "supports_adaptive_thinking": true, @@ -44977,6 +44998,7 @@ "mode": "chat", "output_cost_per_token": 1.2e-05, "prompt_cache_min_tokens": 1024, + "source": "https://pricing.us-east-1.amazonaws.com/offers/v1.0/aws/AmazonBedrockFoundationModels/current/index.json", "supports_adaptive_thinking": true, "supports_assistant_prefill": false, "supports_computer_use": true, @@ -45009,6 +45031,7 @@ "mode": "chat", "output_cost_per_token": 3e-05, "prompt_cache_min_tokens": 1024, + "source": "https://pricing.us-east-1.amazonaws.com/offers/v1.0/aws/AmazonBedrockFoundationModels/current/index.json", "supports_adaptive_thinking": true, "supports_assistant_prefill": false, "supports_computer_use": true, @@ -45040,6 +45063,7 @@ "mode": "chat", "output_cost_per_token": 3e-05, "prompt_cache_min_tokens": 512, + "source": "https://pricing.us-east-1.amazonaws.com/offers/v1.0/aws/AmazonBedrockFoundationModels/current/index.json", "supports_adaptive_thinking": true, "supports_assistant_prefill": false, "supports_computer_use": true, @@ -45089,7 +45113,8 @@ "supports_output_config": true, "bedrock_output_config_effort_ceiling": "xhigh", "supports_parallel_tool_use_config": true, - "prompt_cache_min_tokens": 512 + "prompt_cache_min_tokens": 512, + "source": "https://pricing.us-east-1.amazonaws.com/offers/v1.0/aws/AmazonBedrockFoundationModels/current/index.json" }, "us-gov.nvidia.nemotron-nano-3-30b": { "input_cost_per_token": 7.2e-08, From 8a9a08caf0b4be092df6bc5da5759a0b699edb8e Mon Sep 17 00:00:00 2001 From: "berriai-litellm-provider-info-sync[bot]" <328147090+berriai-litellm-provider-info-sync[bot]@users.noreply.github.com> Date: Tue, 15 Sep 2026 16:31:04 +0000 Subject: [PATCH 059/225] chore(prices): sync prices for 2 providers: 14 models chatgpt-image-latest: input_cost_per_image_token_batches gemini-2.0-flash: input_cost_per_audio_token_batches gemini-2.0-flash-lite: input_cost_per_audio_token_batches gemini-2.5-flash: input_cost_per_audio_token_batches gemini-2.5-flash-lite: input_cost_per_audio_token_batches gemini-3-flash-preview: input_cost_per_audio_token_batches vertex_ai/gemini-3-flash-preview: input_cost_per_audio_token_batches gemini-3.1-flash-lite: input_cost_per_audio_token_batches vertex_ai/gemini-3.1-flash-lite: input_cost_per_audio_token_batches gpt-image-1: input_cost_per_image_token_batches gpt-image-1-mini: input_cost_per_image_token_batches gpt-image-1.5: input_cost_per_image_token_batches gpt-image-1.5-2025-12-16: input_cost_per_image_token_batches gpt-image-2: input_cost_per_image_token_batches --- ...model_prices_and_context_window_backup.json | 18 ++++++++++++++++-- model_prices_and_context_window.json | 18 ++++++++++++++++-- 2 files changed, 32 insertions(+), 4 deletions(-) diff --git a/litellm/model_prices_and_context_window_backup.json b/litellm/model_prices_and_context_window_backup.json index 47cd6faf022..e63e2e07216 100644 --- a/litellm/model_prices_and_context_window_backup.json +++ b/litellm/model_prices_and_context_window_backup.json @@ -23683,6 +23683,7 @@ "cache_read_input_token_cost": 2.5e-08, "deprecation_date": "2026-06-01", "input_cost_per_audio_token": 1e-06, + "input_cost_per_audio_token_batches": 5e-07, "input_cost_per_character": 3.75e-08, "input_cost_per_token": 1.5e-07, "input_cost_per_token_batches": 7.5e-08, @@ -23762,6 +23763,7 @@ "cache_read_input_token_cost": 1.875e-08, "deprecation_date": "2026-06-01", "input_cost_per_audio_token": 7.5e-08, + "input_cost_per_audio_token_batches": 3.75e-08, "input_cost_per_character": 1.875e-08, "input_cost_per_token": 7.5e-08, "input_cost_per_token_batches": 3.75e-08, @@ -23879,6 +23881,7 @@ "search_context_size_high": 0.035 }, "google_maps_grounding_cost_per_query": 0.025, + "input_cost_per_audio_token_batches": 5e-07, "input_cost_per_token_batches": 1.5e-07, "input_cost_per_token_flex": 1.5e-07, "input_cost_per_token_priority": 5.4e-07, @@ -24261,7 +24264,8 @@ "search_context_size_high": 0.014 }, "web_search_billing_unit": "per_query", - "google_maps_grounding_cost_per_query": 0.014 + "google_maps_grounding_cost_per_query": 0.014, + "input_cost_per_audio_token_batches": 2.5e-07 }, "gemini-3.5-flash-lite": { "deprecation_date": "2027-07-21", @@ -24403,6 +24407,7 @@ "search_context_size_high": 0.035 }, "google_maps_grounding_cost_per_query": 0.025, + "input_cost_per_audio_token_batches": 5e-08, "input_cost_per_token_batches": 5e-08, "input_cost_per_token_flex": 5e-08, "input_cost_per_token_priority": 1.8e-07, @@ -25020,6 +25025,7 @@ }, "web_search_billing_unit": "per_query", "google_maps_grounding_cost_per_query": 0.014, + "input_cost_per_audio_token_batches": 5e-07, "input_cost_per_token_batches": 2.5e-07, "input_cost_per_token_flex": 2.5e-07, "output_cost_per_token_batches": 1.5e-06, @@ -27387,6 +27393,7 @@ }, "web_search_billing_unit": "per_query", "google_maps_grounding_cost_per_query": 0.014, + "input_cost_per_audio_token_batches": 5e-07, "input_cost_per_token_batches": 2.5e-07, "input_cost_per_token_flex": 2.5e-07, "output_cost_per_token_batches": 1.5e-06, @@ -30244,6 +30251,7 @@ "mode": "image_generation", "output_cost_per_token": 1e-05, "input_cost_per_image_token": 8e-06, + "input_cost_per_image_token_batches": 4e-06, "input_cost_per_token_batches": 2.5e-06, "output_cost_per_image_token": 3.2e-05, "output_cost_per_token_batches": 5e-06, @@ -30262,6 +30270,7 @@ "mode": "image_generation", "output_cost_per_token": 1e-05, "input_cost_per_image_token": 8e-06, + "input_cost_per_image_token_batches": 4e-06, "input_cost_per_token_batches": 2.5e-06, "output_cost_per_image_token": 3.2e-05, "output_cost_per_token_batches": 5e-06, @@ -30278,6 +30287,7 @@ "litellm_provider": "openai", "mode": "image_generation", "input_cost_per_image_token": 8e-06, + "input_cost_per_image_token_batches": 4e-06, "input_cost_per_token_batches": 2.5e-06, "output_cost_per_image_token": 3e-05, "source": "https://developers.openai.com/api/docs/pricing", @@ -33054,6 +33064,7 @@ "cache_read_input_token_cost": 1.25e-06, "deprecation_date": "2026-10-23", "input_cost_per_image_token": 1e-05, + "input_cost_per_image_token_batches": 5e-06, "input_cost_per_token": 5e-06, "input_cost_per_token_batches": 2.5e-06, "litellm_provider": "openai", @@ -33069,6 +33080,7 @@ "cache_read_input_token_cost": 2e-07, "deprecation_date": "2026-12-01", "input_cost_per_image_token": 2.5e-06, + "input_cost_per_image_token_batches": 1.25e-06, "input_cost_per_token": 2e-06, "input_cost_per_token_batches": 1e-06, "litellm_provider": "openai", @@ -48246,7 +48258,8 @@ "search_context_size_high": 0.014 }, "web_search_billing_unit": "per_query", - "google_maps_grounding_cost_per_query": 0.014 + "google_maps_grounding_cost_per_query": 0.014, + "input_cost_per_audio_token_batches": 2.5e-07 }, "vertex_ai/gemini-3.5-flash-lite": { "deprecation_date": "2027-07-21", @@ -55302,6 +55315,7 @@ "cache_read_input_token_cost": 1.25e-06, "deprecation_date": "2026-12-01", "input_cost_per_image_token": 8e-06, + "input_cost_per_image_token_batches": 4e-06, "input_cost_per_token": 5e-06, "input_cost_per_token_batches": 2.5e-06, "litellm_provider": "openai", diff --git a/model_prices_and_context_window.json b/model_prices_and_context_window.json index 47cd6faf022..e63e2e07216 100644 --- a/model_prices_and_context_window.json +++ b/model_prices_and_context_window.json @@ -23683,6 +23683,7 @@ "cache_read_input_token_cost": 2.5e-08, "deprecation_date": "2026-06-01", "input_cost_per_audio_token": 1e-06, + "input_cost_per_audio_token_batches": 5e-07, "input_cost_per_character": 3.75e-08, "input_cost_per_token": 1.5e-07, "input_cost_per_token_batches": 7.5e-08, @@ -23762,6 +23763,7 @@ "cache_read_input_token_cost": 1.875e-08, "deprecation_date": "2026-06-01", "input_cost_per_audio_token": 7.5e-08, + "input_cost_per_audio_token_batches": 3.75e-08, "input_cost_per_character": 1.875e-08, "input_cost_per_token": 7.5e-08, "input_cost_per_token_batches": 3.75e-08, @@ -23879,6 +23881,7 @@ "search_context_size_high": 0.035 }, "google_maps_grounding_cost_per_query": 0.025, + "input_cost_per_audio_token_batches": 5e-07, "input_cost_per_token_batches": 1.5e-07, "input_cost_per_token_flex": 1.5e-07, "input_cost_per_token_priority": 5.4e-07, @@ -24261,7 +24264,8 @@ "search_context_size_high": 0.014 }, "web_search_billing_unit": "per_query", - "google_maps_grounding_cost_per_query": 0.014 + "google_maps_grounding_cost_per_query": 0.014, + "input_cost_per_audio_token_batches": 2.5e-07 }, "gemini-3.5-flash-lite": { "deprecation_date": "2027-07-21", @@ -24403,6 +24407,7 @@ "search_context_size_high": 0.035 }, "google_maps_grounding_cost_per_query": 0.025, + "input_cost_per_audio_token_batches": 5e-08, "input_cost_per_token_batches": 5e-08, "input_cost_per_token_flex": 5e-08, "input_cost_per_token_priority": 1.8e-07, @@ -25020,6 +25025,7 @@ }, "web_search_billing_unit": "per_query", "google_maps_grounding_cost_per_query": 0.014, + "input_cost_per_audio_token_batches": 5e-07, "input_cost_per_token_batches": 2.5e-07, "input_cost_per_token_flex": 2.5e-07, "output_cost_per_token_batches": 1.5e-06, @@ -27387,6 +27393,7 @@ }, "web_search_billing_unit": "per_query", "google_maps_grounding_cost_per_query": 0.014, + "input_cost_per_audio_token_batches": 5e-07, "input_cost_per_token_batches": 2.5e-07, "input_cost_per_token_flex": 2.5e-07, "output_cost_per_token_batches": 1.5e-06, @@ -30244,6 +30251,7 @@ "mode": "image_generation", "output_cost_per_token": 1e-05, "input_cost_per_image_token": 8e-06, + "input_cost_per_image_token_batches": 4e-06, "input_cost_per_token_batches": 2.5e-06, "output_cost_per_image_token": 3.2e-05, "output_cost_per_token_batches": 5e-06, @@ -30262,6 +30270,7 @@ "mode": "image_generation", "output_cost_per_token": 1e-05, "input_cost_per_image_token": 8e-06, + "input_cost_per_image_token_batches": 4e-06, "input_cost_per_token_batches": 2.5e-06, "output_cost_per_image_token": 3.2e-05, "output_cost_per_token_batches": 5e-06, @@ -30278,6 +30287,7 @@ "litellm_provider": "openai", "mode": "image_generation", "input_cost_per_image_token": 8e-06, + "input_cost_per_image_token_batches": 4e-06, "input_cost_per_token_batches": 2.5e-06, "output_cost_per_image_token": 3e-05, "source": "https://developers.openai.com/api/docs/pricing", @@ -33054,6 +33064,7 @@ "cache_read_input_token_cost": 1.25e-06, "deprecation_date": "2026-10-23", "input_cost_per_image_token": 1e-05, + "input_cost_per_image_token_batches": 5e-06, "input_cost_per_token": 5e-06, "input_cost_per_token_batches": 2.5e-06, "litellm_provider": "openai", @@ -33069,6 +33080,7 @@ "cache_read_input_token_cost": 2e-07, "deprecation_date": "2026-12-01", "input_cost_per_image_token": 2.5e-06, + "input_cost_per_image_token_batches": 1.25e-06, "input_cost_per_token": 2e-06, "input_cost_per_token_batches": 1e-06, "litellm_provider": "openai", @@ -48246,7 +48258,8 @@ "search_context_size_high": 0.014 }, "web_search_billing_unit": "per_query", - "google_maps_grounding_cost_per_query": 0.014 + "google_maps_grounding_cost_per_query": 0.014, + "input_cost_per_audio_token_batches": 2.5e-07 }, "vertex_ai/gemini-3.5-flash-lite": { "deprecation_date": "2027-07-21", @@ -55302,6 +55315,7 @@ "cache_read_input_token_cost": 1.25e-06, "deprecation_date": "2026-12-01", "input_cost_per_image_token": 8e-06, + "input_cost_per_image_token_batches": 4e-06, "input_cost_per_token": 5e-06, "input_cost_per_token_batches": 2.5e-06, "litellm_provider": "openai", From 68f97321dd1831dce82e9fbff926309057a24f9c Mon Sep 17 00:00:00 2001 From: kerry Date: Tue, 15 Sep 2026 16:45:03 +0000 Subject: [PATCH 060/225] chore(prices): drop source from us-gov rows to match usgov pricing test Co-Authored-By: Devin AI <158243242+devin-ai-integration[bot]@users.noreply.github.com> --- litellm/model_prices_and_context_window_backup.json | 6 +----- model_prices_and_context_window.json | 6 +----- 2 files changed, 2 insertions(+), 10 deletions(-) diff --git a/litellm/model_prices_and_context_window_backup.json b/litellm/model_prices_and_context_window_backup.json index e63e2e07216..7a85c332d1a 100644 --- a/litellm/model_prices_and_context_window_backup.json +++ b/litellm/model_prices_and_context_window_backup.json @@ -45010,7 +45010,6 @@ "mode": "chat", "output_cost_per_token": 1.2e-05, "prompt_cache_min_tokens": 1024, - "source": "https://pricing.us-east-1.amazonaws.com/offers/v1.0/aws/AmazonBedrockFoundationModels/current/index.json", "supports_adaptive_thinking": true, "supports_assistant_prefill": false, "supports_computer_use": true, @@ -45043,7 +45042,6 @@ "mode": "chat", "output_cost_per_token": 3e-05, "prompt_cache_min_tokens": 1024, - "source": "https://pricing.us-east-1.amazonaws.com/offers/v1.0/aws/AmazonBedrockFoundationModels/current/index.json", "supports_adaptive_thinking": true, "supports_assistant_prefill": false, "supports_computer_use": true, @@ -45075,7 +45073,6 @@ "mode": "chat", "output_cost_per_token": 3e-05, "prompt_cache_min_tokens": 512, - "source": "https://pricing.us-east-1.amazonaws.com/offers/v1.0/aws/AmazonBedrockFoundationModels/current/index.json", "supports_adaptive_thinking": true, "supports_assistant_prefill": false, "supports_computer_use": true, @@ -45125,8 +45122,7 @@ "supports_output_config": true, "bedrock_output_config_effort_ceiling": "xhigh", "supports_parallel_tool_use_config": true, - "prompt_cache_min_tokens": 512, - "source": "https://pricing.us-east-1.amazonaws.com/offers/v1.0/aws/AmazonBedrockFoundationModels/current/index.json" + "prompt_cache_min_tokens": 512 }, "us-gov.nvidia.nemotron-nano-3-30b": { "input_cost_per_token": 7.2e-08, diff --git a/model_prices_and_context_window.json b/model_prices_and_context_window.json index e63e2e07216..7a85c332d1a 100644 --- a/model_prices_and_context_window.json +++ b/model_prices_and_context_window.json @@ -45010,7 +45010,6 @@ "mode": "chat", "output_cost_per_token": 1.2e-05, "prompt_cache_min_tokens": 1024, - "source": "https://pricing.us-east-1.amazonaws.com/offers/v1.0/aws/AmazonBedrockFoundationModels/current/index.json", "supports_adaptive_thinking": true, "supports_assistant_prefill": false, "supports_computer_use": true, @@ -45043,7 +45042,6 @@ "mode": "chat", "output_cost_per_token": 3e-05, "prompt_cache_min_tokens": 1024, - "source": "https://pricing.us-east-1.amazonaws.com/offers/v1.0/aws/AmazonBedrockFoundationModels/current/index.json", "supports_adaptive_thinking": true, "supports_assistant_prefill": false, "supports_computer_use": true, @@ -45075,7 +45073,6 @@ "mode": "chat", "output_cost_per_token": 3e-05, "prompt_cache_min_tokens": 512, - "source": "https://pricing.us-east-1.amazonaws.com/offers/v1.0/aws/AmazonBedrockFoundationModels/current/index.json", "supports_adaptive_thinking": true, "supports_assistant_prefill": false, "supports_computer_use": true, @@ -45125,8 +45122,7 @@ "supports_output_config": true, "bedrock_output_config_effort_ceiling": "xhigh", "supports_parallel_tool_use_config": true, - "prompt_cache_min_tokens": 512, - "source": "https://pricing.us-east-1.amazonaws.com/offers/v1.0/aws/AmazonBedrockFoundationModels/current/index.json" + "prompt_cache_min_tokens": 512 }, "us-gov.nvidia.nemotron-nano-3-30b": { "input_cost_per_token": 7.2e-08, From 1b69cfcf3cbf259c97d8a27bd966f6154889acfe Mon Sep 17 00:00:00 2001 From: "berriai-litellm-provider-info-sync[bot]" <328147090+berriai-litellm-provider-info-sync[bot]@users.noreply.github.com> Date: Tue, 15 Sep 2026 16:51:09 +0000 Subject: [PATCH 061/225] chore(prices): sync AWS Bedrock prices: 4 models us-gov.anthropic.claude-fable-5-1: us-gov.anthropic.claude-opus-4-8: us-gov.anthropic.claude-opus-5: us-gov.anthropic.claude-sonnet-5: --- litellm/model_prices_and_context_window_backup.json | 6 +++++- model_prices_and_context_window.json | 6 +++++- 2 files changed, 10 insertions(+), 2 deletions(-) diff --git a/litellm/model_prices_and_context_window_backup.json b/litellm/model_prices_and_context_window_backup.json index 7a85c332d1a..e63e2e07216 100644 --- a/litellm/model_prices_and_context_window_backup.json +++ b/litellm/model_prices_and_context_window_backup.json @@ -45010,6 +45010,7 @@ "mode": "chat", "output_cost_per_token": 1.2e-05, "prompt_cache_min_tokens": 1024, + "source": "https://pricing.us-east-1.amazonaws.com/offers/v1.0/aws/AmazonBedrockFoundationModels/current/index.json", "supports_adaptive_thinking": true, "supports_assistant_prefill": false, "supports_computer_use": true, @@ -45042,6 +45043,7 @@ "mode": "chat", "output_cost_per_token": 3e-05, "prompt_cache_min_tokens": 1024, + "source": "https://pricing.us-east-1.amazonaws.com/offers/v1.0/aws/AmazonBedrockFoundationModels/current/index.json", "supports_adaptive_thinking": true, "supports_assistant_prefill": false, "supports_computer_use": true, @@ -45073,6 +45075,7 @@ "mode": "chat", "output_cost_per_token": 3e-05, "prompt_cache_min_tokens": 512, + "source": "https://pricing.us-east-1.amazonaws.com/offers/v1.0/aws/AmazonBedrockFoundationModels/current/index.json", "supports_adaptive_thinking": true, "supports_assistant_prefill": false, "supports_computer_use": true, @@ -45122,7 +45125,8 @@ "supports_output_config": true, "bedrock_output_config_effort_ceiling": "xhigh", "supports_parallel_tool_use_config": true, - "prompt_cache_min_tokens": 512 + "prompt_cache_min_tokens": 512, + "source": "https://pricing.us-east-1.amazonaws.com/offers/v1.0/aws/AmazonBedrockFoundationModels/current/index.json" }, "us-gov.nvidia.nemotron-nano-3-30b": { "input_cost_per_token": 7.2e-08, diff --git a/model_prices_and_context_window.json b/model_prices_and_context_window.json index 7a85c332d1a..e63e2e07216 100644 --- a/model_prices_and_context_window.json +++ b/model_prices_and_context_window.json @@ -45010,6 +45010,7 @@ "mode": "chat", "output_cost_per_token": 1.2e-05, "prompt_cache_min_tokens": 1024, + "source": "https://pricing.us-east-1.amazonaws.com/offers/v1.0/aws/AmazonBedrockFoundationModels/current/index.json", "supports_adaptive_thinking": true, "supports_assistant_prefill": false, "supports_computer_use": true, @@ -45042,6 +45043,7 @@ "mode": "chat", "output_cost_per_token": 3e-05, "prompt_cache_min_tokens": 1024, + "source": "https://pricing.us-east-1.amazonaws.com/offers/v1.0/aws/AmazonBedrockFoundationModels/current/index.json", "supports_adaptive_thinking": true, "supports_assistant_prefill": false, "supports_computer_use": true, @@ -45073,6 +45075,7 @@ "mode": "chat", "output_cost_per_token": 3e-05, "prompt_cache_min_tokens": 512, + "source": "https://pricing.us-east-1.amazonaws.com/offers/v1.0/aws/AmazonBedrockFoundationModels/current/index.json", "supports_adaptive_thinking": true, "supports_assistant_prefill": false, "supports_computer_use": true, @@ -45122,7 +45125,8 @@ "supports_output_config": true, "bedrock_output_config_effort_ceiling": "xhigh", "supports_parallel_tool_use_config": true, - "prompt_cache_min_tokens": 512 + "prompt_cache_min_tokens": 512, + "source": "https://pricing.us-east-1.amazonaws.com/offers/v1.0/aws/AmazonBedrockFoundationModels/current/index.json" }, "us-gov.nvidia.nemotron-nano-3-30b": { "input_cost_per_token": 7.2e-08, From a8dd1394f941cf930107f3f617676ae7c10f2705 Mon Sep 17 00:00:00 2001 From: kerry Date: Tue, 15 Sep 2026 17:04:09 +0000 Subject: [PATCH 062/225] chore(prices): drop source from us-gov rows again Co-Authored-By: Devin AI <158243242+devin-ai-integration[bot]@users.noreply.github.com> --- litellm/model_prices_and_context_window_backup.json | 6 +----- model_prices_and_context_window.json | 6 +----- 2 files changed, 2 insertions(+), 10 deletions(-) diff --git a/litellm/model_prices_and_context_window_backup.json b/litellm/model_prices_and_context_window_backup.json index e63e2e07216..7a85c332d1a 100644 --- a/litellm/model_prices_and_context_window_backup.json +++ b/litellm/model_prices_and_context_window_backup.json @@ -45010,7 +45010,6 @@ "mode": "chat", "output_cost_per_token": 1.2e-05, "prompt_cache_min_tokens": 1024, - "source": "https://pricing.us-east-1.amazonaws.com/offers/v1.0/aws/AmazonBedrockFoundationModels/current/index.json", "supports_adaptive_thinking": true, "supports_assistant_prefill": false, "supports_computer_use": true, @@ -45043,7 +45042,6 @@ "mode": "chat", "output_cost_per_token": 3e-05, "prompt_cache_min_tokens": 1024, - "source": "https://pricing.us-east-1.amazonaws.com/offers/v1.0/aws/AmazonBedrockFoundationModels/current/index.json", "supports_adaptive_thinking": true, "supports_assistant_prefill": false, "supports_computer_use": true, @@ -45075,7 +45073,6 @@ "mode": "chat", "output_cost_per_token": 3e-05, "prompt_cache_min_tokens": 512, - "source": "https://pricing.us-east-1.amazonaws.com/offers/v1.0/aws/AmazonBedrockFoundationModels/current/index.json", "supports_adaptive_thinking": true, "supports_assistant_prefill": false, "supports_computer_use": true, @@ -45125,8 +45122,7 @@ "supports_output_config": true, "bedrock_output_config_effort_ceiling": "xhigh", "supports_parallel_tool_use_config": true, - "prompt_cache_min_tokens": 512, - "source": "https://pricing.us-east-1.amazonaws.com/offers/v1.0/aws/AmazonBedrockFoundationModels/current/index.json" + "prompt_cache_min_tokens": 512 }, "us-gov.nvidia.nemotron-nano-3-30b": { "input_cost_per_token": 7.2e-08, diff --git a/model_prices_and_context_window.json b/model_prices_and_context_window.json index e63e2e07216..7a85c332d1a 100644 --- a/model_prices_and_context_window.json +++ b/model_prices_and_context_window.json @@ -45010,7 +45010,6 @@ "mode": "chat", "output_cost_per_token": 1.2e-05, "prompt_cache_min_tokens": 1024, - "source": "https://pricing.us-east-1.amazonaws.com/offers/v1.0/aws/AmazonBedrockFoundationModels/current/index.json", "supports_adaptive_thinking": true, "supports_assistant_prefill": false, "supports_computer_use": true, @@ -45043,7 +45042,6 @@ "mode": "chat", "output_cost_per_token": 3e-05, "prompt_cache_min_tokens": 1024, - "source": "https://pricing.us-east-1.amazonaws.com/offers/v1.0/aws/AmazonBedrockFoundationModels/current/index.json", "supports_adaptive_thinking": true, "supports_assistant_prefill": false, "supports_computer_use": true, @@ -45075,7 +45073,6 @@ "mode": "chat", "output_cost_per_token": 3e-05, "prompt_cache_min_tokens": 512, - "source": "https://pricing.us-east-1.amazonaws.com/offers/v1.0/aws/AmazonBedrockFoundationModels/current/index.json", "supports_adaptive_thinking": true, "supports_assistant_prefill": false, "supports_computer_use": true, @@ -45125,8 +45122,7 @@ "supports_output_config": true, "bedrock_output_config_effort_ceiling": "xhigh", "supports_parallel_tool_use_config": true, - "prompt_cache_min_tokens": 512, - "source": "https://pricing.us-east-1.amazonaws.com/offers/v1.0/aws/AmazonBedrockFoundationModels/current/index.json" + "prompt_cache_min_tokens": 512 }, "us-gov.nvidia.nemotron-nano-3-30b": { "input_cost_per_token": 7.2e-08, From 363b7835a3cc17310c5910a0e74784fd839322ab Mon Sep 17 00:00:00 2001 From: "berriai-litellm-provider-info-sync[bot]" <328147090+berriai-litellm-provider-info-sync[bot]@users.noreply.github.com> Date: Tue, 15 Sep 2026 17:06:11 +0000 Subject: [PATCH 063/225] chore(prices): sync AWS Bedrock prices: 4 models us-gov.anthropic.claude-fable-5-1: us-gov.anthropic.claude-opus-4-8: us-gov.anthropic.claude-opus-5: us-gov.anthropic.claude-sonnet-5: --- litellm/model_prices_and_context_window_backup.json | 6 +++++- model_prices_and_context_window.json | 6 +++++- 2 files changed, 10 insertions(+), 2 deletions(-) diff --git a/litellm/model_prices_and_context_window_backup.json b/litellm/model_prices_and_context_window_backup.json index 7a85c332d1a..e63e2e07216 100644 --- a/litellm/model_prices_and_context_window_backup.json +++ b/litellm/model_prices_and_context_window_backup.json @@ -45010,6 +45010,7 @@ "mode": "chat", "output_cost_per_token": 1.2e-05, "prompt_cache_min_tokens": 1024, + "source": "https://pricing.us-east-1.amazonaws.com/offers/v1.0/aws/AmazonBedrockFoundationModels/current/index.json", "supports_adaptive_thinking": true, "supports_assistant_prefill": false, "supports_computer_use": true, @@ -45042,6 +45043,7 @@ "mode": "chat", "output_cost_per_token": 3e-05, "prompt_cache_min_tokens": 1024, + "source": "https://pricing.us-east-1.amazonaws.com/offers/v1.0/aws/AmazonBedrockFoundationModels/current/index.json", "supports_adaptive_thinking": true, "supports_assistant_prefill": false, "supports_computer_use": true, @@ -45073,6 +45075,7 @@ "mode": "chat", "output_cost_per_token": 3e-05, "prompt_cache_min_tokens": 512, + "source": "https://pricing.us-east-1.amazonaws.com/offers/v1.0/aws/AmazonBedrockFoundationModels/current/index.json", "supports_adaptive_thinking": true, "supports_assistant_prefill": false, "supports_computer_use": true, @@ -45122,7 +45125,8 @@ "supports_output_config": true, "bedrock_output_config_effort_ceiling": "xhigh", "supports_parallel_tool_use_config": true, - "prompt_cache_min_tokens": 512 + "prompt_cache_min_tokens": 512, + "source": "https://pricing.us-east-1.amazonaws.com/offers/v1.0/aws/AmazonBedrockFoundationModels/current/index.json" }, "us-gov.nvidia.nemotron-nano-3-30b": { "input_cost_per_token": 7.2e-08, diff --git a/model_prices_and_context_window.json b/model_prices_and_context_window.json index 7a85c332d1a..e63e2e07216 100644 --- a/model_prices_and_context_window.json +++ b/model_prices_and_context_window.json @@ -45010,6 +45010,7 @@ "mode": "chat", "output_cost_per_token": 1.2e-05, "prompt_cache_min_tokens": 1024, + "source": "https://pricing.us-east-1.amazonaws.com/offers/v1.0/aws/AmazonBedrockFoundationModels/current/index.json", "supports_adaptive_thinking": true, "supports_assistant_prefill": false, "supports_computer_use": true, @@ -45042,6 +45043,7 @@ "mode": "chat", "output_cost_per_token": 3e-05, "prompt_cache_min_tokens": 1024, + "source": "https://pricing.us-east-1.amazonaws.com/offers/v1.0/aws/AmazonBedrockFoundationModels/current/index.json", "supports_adaptive_thinking": true, "supports_assistant_prefill": false, "supports_computer_use": true, @@ -45073,6 +45075,7 @@ "mode": "chat", "output_cost_per_token": 3e-05, "prompt_cache_min_tokens": 512, + "source": "https://pricing.us-east-1.amazonaws.com/offers/v1.0/aws/AmazonBedrockFoundationModels/current/index.json", "supports_adaptive_thinking": true, "supports_assistant_prefill": false, "supports_computer_use": true, @@ -45122,7 +45125,8 @@ "supports_output_config": true, "bedrock_output_config_effort_ceiling": "xhigh", "supports_parallel_tool_use_config": true, - "prompt_cache_min_tokens": 512 + "prompt_cache_min_tokens": 512, + "source": "https://pricing.us-east-1.amazonaws.com/offers/v1.0/aws/AmazonBedrockFoundationModels/current/index.json" }, "us-gov.nvidia.nemotron-nano-3-30b": { "input_cost_per_token": 7.2e-08, From a426df108af97e0ea353c34a3b1098353bbc8a72 Mon Sep 17 00:00:00 2001 From: Devin AI <158243242+devin-ai-integration[bot]@users.noreply.github.com> Date: Tue, 15 Sep 2026 17:54:03 +0000 Subject: [PATCH 064/225] feat(http): opt-in outbound HTTP/2 for httpx clients Co-Authored-By: Devin AI <158243242+devin-ai-integration[bot]@users.noreply.github.com> --- litellm/__init__.py | 1 + litellm/llms/custom_httpx/http_handler.py | 22 +++- litellm/llms/openai/common_utils.py | 3 + pyproject.toml | 2 +- tests/test_litellm/llms/conftest.py | 124 ++++++++++++++++++ .../llms/custom_httpx/test_http_handler.py | 68 ++++++++++ .../llms/openai/test_openai_common_utils.py | 17 +++ uv.lock | 9 +- 8 files changed, 239 insertions(+), 7 deletions(-) create mode 100644 tests/test_litellm/llms/conftest.py diff --git a/litellm/__init__.py b/litellm/__init__.py index ccfbf80369f..5e4a76503cd 100644 --- a/litellm/__init__.py +++ b/litellm/__init__.py @@ -524,6 +524,7 @@ aiohttp_trust_env: bool = False # set to true to use HTTP_ Proxy settings disable_aiohttp_transport: bool = False # Set this to true to use httpx instead disable_aiohttp_trust_env: bool = False # When False, aiohttp will respect HTTP(S)_PROXY env vars force_ipv4: bool = False # when True, litellm will force ipv4 for all LLM requests. Some users have seen httpx ConnectionError when using ipv6. +http2: bool = False # when True, LiteLLM-built httpx clients negotiate HTTP/2 over TLS (falls back to HTTP/1.1); bypasses aiohttp transport network_mock: bool = False # When True, use mock transport — no real network calls ####### STOP SEQUENCE LIMIT ####### diff --git a/litellm/llms/custom_httpx/http_handler.py b/litellm/llms/custom_httpx/http_handler.py index f4883b57fbc..cc37d1d9eca 100644 --- a/litellm/llms/custom_httpx/http_handler.py +++ b/litellm/llms/custom_httpx/http_handler.py @@ -74,6 +74,12 @@ _IPV4_LOCAL_ADDRESS: Final = "0.0.0.0" _HttpxTransportT = TypeVar("_HttpxTransportT", HTTPTransport, AsyncHTTPTransport) +def http2_enabled() -> bool: + from litellm.secret_managers.main import str_to_bool + + return litellm.http2 is True or str_to_bool(os.getenv("LITELLM_HTTP2", "False")) is True + + def _environment_proxy_mounts( build_proxy_transport: Callable[[str], _HttpxTransportT], ) -> Mapping[str, _HttpxTransportT | None]: @@ -638,6 +644,7 @@ class AsyncHTTPHandler: headers=default_headers, cookies=blocked_cookie_jar(), follow_redirects=True, + http2=http2_enabled(), ) async def close(self): @@ -1157,6 +1164,10 @@ class AsyncHTTPHandler: from litellm.secret_managers.main import str_to_bool + if http2_enabled(): + verbose_logger.debug("LITELLM_HTTP2 enabled, using httpx transport (aiohttp has no HTTP/2 support)") + return False + ######################################################### # Check if user disabled aiohttp transport ######################################################## @@ -1287,7 +1298,7 @@ class AsyncHTTPHandler: - [Default] If force_ipv4 is False, it will return None """ if litellm.force_ipv4: - return AsyncHTTPTransport(local_address=_IPV4_LOCAL_ADDRESS) + return AsyncHTTPTransport(local_address=_IPV4_LOCAL_ADDRESS, http2=http2_enabled()) else: return None @@ -1300,7 +1311,7 @@ class AsyncHTTPHandler: if not isinstance(transport, AsyncHTTPTransport): return None return _environment_proxy_mounts( - lambda proxy_url: AsyncHTTPTransport(proxy=proxy_url, verify=verify, cert=cert) + lambda proxy_url: AsyncHTTPTransport(proxy=proxy_url, verify=verify, cert=cert, http2=http2_enabled()) ) @@ -1342,6 +1353,7 @@ class HTTPHandler: headers=default_headers, cookies=blocked_cookie_jar(), follow_redirects=True, + http2=http2_enabled(), ) @property @@ -1616,7 +1628,7 @@ class HTTPHandler: Some users have seen httpx ConnectionError when using ipv6 - forcing ipv4 resolves the issue for them """ if litellm.force_ipv4: - return HTTPTransport(local_address=_IPV4_LOCAL_ADDRESS) + return HTTPTransport(local_address=_IPV4_LOCAL_ADDRESS, http2=http2_enabled()) else: return getattr(litellm, "sync_transport", None) @@ -1627,7 +1639,9 @@ class HTTPHandler: ) -> Mapping[str, HTTPTransport | None] | None: if not litellm.force_ipv4: return None - return _environment_proxy_mounts(lambda proxy_url: HTTPTransport(proxy=proxy_url, verify=verify, cert=cert)) + return _environment_proxy_mounts( + lambda proxy_url: HTTPTransport(proxy=proxy_url, verify=verify, cert=cert, http2=http2_enabled()) + ) def get_async_httpx_client( diff --git a/litellm/llms/openai/common_utils.py b/litellm/llms/openai/common_utils.py index 2db6d78a218..cb6a5e4e96a 100644 --- a/litellm/llms/openai/common_utils.py +++ b/litellm/llms/openai/common_utils.py @@ -32,6 +32,7 @@ from litellm.llms.custom_httpx.http_handler import ( _DEFAULT_TTL_FOR_HTTPX_CLIENTS, AsyncHTTPHandler, get_ssl_configuration, + http2_enabled, ) @@ -325,6 +326,7 @@ class BaseOpenAILLM: transport=transport, mounts=AsyncHTTPHandler._create_httpx_proxy_mounts(transport, verify=ssl_config, cert=None), follow_redirects=True, + http2=http2_enabled(), ) @staticmethod @@ -343,6 +345,7 @@ class BaseOpenAILLM: return httpx.Client( verify=ssl_config, follow_redirects=True, + http2=http2_enabled(), ) diff --git a/pyproject.toml b/pyproject.toml index 62ce4b4fd61..de6a6e9c9ba 100644 --- a/pyproject.toml +++ b/pyproject.toml @@ -15,7 +15,7 @@ dependencies = [ # When changing a floor, verify it installs + imports on every supported # Python with: `uv pip install --resolution=lowest-direct .` "fastuuid>=0.14.0,<1.0", - "httpx>=0.28.0,<1.0", + "httpx[http2]>=0.28.0,<1.0", "openai>=2.20.0,<3.0.0", "python-dotenv>=1.0.0,<2.0", "tiktoken>=0.8.0,<1.0", diff --git a/tests/test_litellm/llms/conftest.py b/tests/test_litellm/llms/conftest.py new file mode 100644 index 00000000000..2905b606e51 --- /dev/null +++ b/tests/test_litellm/llms/conftest.py @@ -0,0 +1,124 @@ +import asyncio +import datetime +import ipaddress +import socket +import threading +import time + +import pytest + + +def _write_self_signed_cert(cert_dir): + from cryptography import x509 + from cryptography.hazmat.primitives import hashes, serialization + from cryptography.hazmat.primitives.asymmetric import rsa + + key = rsa.generate_private_key(public_exponent=65537, key_size=2048) + now = datetime.datetime.now(datetime.timezone.utc) + cert = ( + x509.CertificateBuilder() + .subject_name(x509.Name([x509.NameAttribute(x509.oid.NameOID.COMMON_NAME, "localhost")])) + .issuer_name(x509.Name([x509.NameAttribute(x509.oid.NameOID.COMMON_NAME, "localhost")])) + .public_key(key.public_key()) + .serial_number(x509.random_serial_number()) + .not_valid_before(now - datetime.timedelta(days=1)) + .not_valid_after(now + datetime.timedelta(days=7)) + .add_extension( + x509.SubjectAlternativeName( + [x509.DNSName("localhost"), x509.IPAddress(ipaddress.ip_address("127.0.0.1"))] + ), + critical=False, + ) + .sign(key, hashes.SHA256()) + ) + cert_file = cert_dir / "cert.pem" + key_file = cert_dir / "key.pem" + cert_file.write_bytes(cert.public_bytes(serialization.Encoding.PEM)) + key_file.write_bytes( + key.private_bytes( + serialization.Encoding.PEM, + serialization.PrivateFormat.TraditionalOpenSSL, + serialization.NoEncryption(), + ) + ) + return cert_file, key_file + + +async def _asgi_app(scope, receive, send): + if scope["type"] != "http": + return + while True: + message = await receive() + if message["type"] == "http.request" and not message.get("more_body"): + break + if message["type"] == "http.disconnect": + return + if scope["path"] == "/stream": + await send( + { + "type": "http.response.start", + "status": 200, + "headers": [(b"content-type", b"text/event-stream")], + } + ) + for index in range(3): + await send( + { + "type": "http.response.body", + "body": f"data: chunk-{index}\n\n".encode(), + "more_body": True, + } + ) + await send({"type": "http.response.body", "body": b"", "more_body": False}) + return + await send( + { + "type": "http.response.start", + "status": 200, + "headers": [(b"content-type", b"application/json")], + } + ) + await send({"type": "http.response.body", "body": b'{"ok": true}'}) + + +@pytest.fixture(scope="module") +def http2_tls_server(tmp_path_factory): + """Hypercorn TLS server on an ephemeral port that negotiates h2 or http/1.1 via ALPN.""" + from hypercorn.asyncio import serve + from hypercorn.config import Config + + cert_dir = tmp_path_factory.mktemp("h2certs") + cert_file, key_file = _write_self_signed_cert(cert_dir) + + with socket.socket() as sock: + sock.bind(("127.0.0.1", 0)) + port = sock.getsockname()[1] + + shutdown = threading.Event() + + def _serve() -> None: + loop = asyncio.new_event_loop() + config = Config() + config.bind = [f"127.0.0.1:{port}"] + config.certfile = str(cert_file) + config.keyfile = str(key_file) + config.alpn_protocols = ["h2", "http/1.1"] + loop.run_until_complete(serve(_asgi_app, config, shutdown_trigger=lambda: asyncio.to_thread(shutdown.wait))) + loop.close() + + thread = threading.Thread(target=_serve, daemon=True) + thread.start() + + for _ in range(100): + try: + with socket.create_connection(("127.0.0.1", port), timeout=0.2): + break + except OSError: + time.sleep(0.05) + else: + pytest.fail("hypercorn test server did not start") + + yield f"https://127.0.0.1:{port}" + + shutdown.set() + thread.join(timeout=10) diff --git a/tests/test_litellm/llms/custom_httpx/test_http_handler.py b/tests/test_litellm/llms/custom_httpx/test_http_handler.py index f8868cfaf83..e170a7f7a78 100644 --- a/tests/test_litellm/llms/custom_httpx/test_http_handler.py +++ b/tests/test_litellm/llms/custom_httpx/test_http_handler.py @@ -1675,3 +1675,71 @@ async def test_bounded_get_closes_stream_on_cancellation(respx_mock, monkeypatch finally: await handler.close() assert closed.is_set() + + +@pytest.mark.asyncio +async def test_http2_flag_bypasses_aiohttp_transport(monkeypatch: pytest.MonkeyPatch): + monkeypatch.setattr(litellm, "disable_aiohttp_transport", False) + monkeypatch.setattr(litellm, "force_ipv4", False) + monkeypatch.delenv("LITELLM_HTTP2", raising=False) + monkeypatch.delenv("DISABLE_AIOHTTP_TRANSPORT", raising=False) + + monkeypatch.setattr(litellm, "http2", True) + assert AsyncHTTPHandler._should_use_aiohttp_transport() is False + assert AsyncHTTPHandler._create_async_transport() is None + + monkeypatch.setattr(litellm, "http2", False) + monkeypatch.setenv("LITELLM_HTTP2", "True") + assert AsyncHTTPHandler._should_use_aiohttp_transport() is False + assert AsyncHTTPHandler._create_async_transport() is None + + +@pytest.mark.asyncio +async def test_http2_disabled_by_default(monkeypatch: pytest.MonkeyPatch): + monkeypatch.setattr(litellm, "http2", False) + monkeypatch.delenv("LITELLM_HTTP2", raising=False) + monkeypatch.delenv("DISABLE_AIOHTTP_TRANSPORT", raising=False) + monkeypatch.setattr(litellm, "disable_aiohttp_transport", False) + + assert AsyncHTTPHandler._should_use_aiohttp_transport() is True + + +@pytest.mark.asyncio +@pytest.mark.parametrize("use_http2", [True, False]) +@pytest.mark.parametrize("handler_kind", ["async", "sync"]) +async def test_http_version_negotiated_over_tls(monkeypatch, http2_tls_server, handler_kind, use_http2): + monkeypatch.setattr(litellm, "http2", use_http2) + monkeypatch.delenv("LITELLM_HTTP2", raising=False) + monkeypatch.setattr(litellm, "disable_aiohttp_transport", True) + monkeypatch.setattr(litellm, "force_ipv4", False) + + expected_version = "HTTP/2" if use_http2 else "HTTP/1.1" + + if handler_kind == "async": + handler = AsyncHTTPHandler(ssl_verify=False) + try: + response = await handler.post(f"{http2_tls_server}/echo", json={"ping": "pong"}) + assert response.status_code == 200 + assert response.http_version == expected_version + + stream_response = await handler.post(f"{http2_tls_server}/stream", stream=True) + assert stream_response.http_version == expected_version + chunks = [chunk async for chunk in stream_response.aiter_bytes()] + assert chunks + await stream_response.aclose() + finally: + await handler.close() + else: + handler = HTTPHandler(ssl_verify=False) + try: + response = handler.post(f"{http2_tls_server}/echo", json={"ping": "pong"}) + assert response.status_code == 200 + assert response.http_version == expected_version + + stream_response = handler.post(f"{http2_tls_server}/stream", stream=True) + assert stream_response.http_version == expected_version + chunks = list(stream_response.iter_bytes()) + assert chunks + stream_response.close() + finally: + handler.close() diff --git a/tests/test_litellm/llms/openai/test_openai_common_utils.py b/tests/test_litellm/llms/openai/test_openai_common_utils.py index d3c21c5bd5a..3dc37c21e7a 100644 --- a/tests/test_litellm/llms/openai/test_openai_common_utils.py +++ b/tests/test_litellm/llms/openai/test_openai_common_utils.py @@ -411,3 +411,20 @@ async def test_async_genuine_bad_request_still_raises(provider, stream): ) def test_is_openai_backed_api_base_decides_by_hostname_only(api_base, expected): assert is_openai_backed_api_base(api_base) is expected + + +def test_litellm_built_http_clients_negotiate_http2_only_when_enabled(monkeypatch): + monkeypatch.delenv("LITELLM_HTTP2", raising=False) + monkeypatch.setattr(litellm, "disable_aiohttp_transport", True) + + monkeypatch.setattr(litellm, "http2", False) + async_client = BaseOpenAILLM._get_async_http_client() + sync_client = BaseOpenAILLM._get_sync_http_client() + assert async_client is not None and async_client._transport._pool._http2 is False + assert sync_client is not None and sync_client._transport._pool._http2 is False + + monkeypatch.setattr(litellm, "http2", True) + async_client = BaseOpenAILLM._get_async_http_client() + sync_client = BaseOpenAILLM._get_sync_http_client() + assert async_client is not None and async_client._transport._pool._http2 is True + assert sync_client is not None and sync_client._transport._pool._http2 is True diff --git a/uv.lock b/uv.lock index eb4cdef76f1..4917dd2f5cb 100644 --- a/uv.lock +++ b/uv.lock @@ -3295,6 +3295,11 @@ wheels = [ { url = "https://files.pythonhosted.org/packages/2a/39/e50c7c3a983047577ee07d2a9e53faf5a69493943ec3f6a384bdc792deb2/httpx-0.28.1-py3-none-any.whl", hash = "sha256:d909fcccc110f8c7faf814ca82a9a4d816bc5a6dbfea25d6591d6985b8ba59ad", size = 73517, upload-time = "2024-12-06T15:37:21.509Z" }, ] +[package.optional-dependencies] +http2 = [ + { name = "h2" }, +] + [[package]] name = "httpx-sse" version = "0.4.3" @@ -4365,7 +4370,7 @@ dependencies = [ { name = "boto3" }, { name = "click" }, { name = "fastuuid" }, - { name = "httpx" }, + { name = "httpx", extra = ["http2"] }, { name = "importlib-metadata" }, { name = "jinja2" }, { name = "jsonschema" }, @@ -4617,7 +4622,7 @@ requires-dist = [ { name = "grpcio", marker = "extra == 'proxy-runtime'", specifier = "==1.78.0" }, { name = "gunicorn", marker = "extra == 'proxy'", specifier = ">=23.0.0,<24.0" }, { name = "hiredis", marker = "extra == 'proxy'", specifier = ">=3.0.0,<4.0" }, - { name = "httpx", specifier = ">=0.28.0,<1.0" }, + { name = "httpx", extras = ["http2"], specifier = ">=0.28.0,<1.0" }, { name = "importlib-metadata", specifier = ">=8.0.0,<9.0" }, { name = "inquirerpy", marker = "extra == 'cli'", specifier = ">=0.3.4,<1.0" }, { name = "inquirerpy", marker = "extra == 'proxy'", specifier = ">=0.3.4,<1.0" }, From fd2fb4c44e5d62f2fab08e748b46d8dd12a4792f Mon Sep 17 00:00:00 2001 From: Devin AI <158243242+devin-ai-integration[bot]@users.noreply.github.com> Date: Tue, 15 Sep 2026 18:25:26 +0000 Subject: [PATCH 065/225] fix(http): address review on outbound HTTP/2 Co-Authored-By: Devin AI <158243242+devin-ai-integration[bot]@users.noreply.github.com> --- litellm/__init__.py | 2 +- litellm/main.py | 6 +- .../test_outbound_http2_e2e.py | 208 ++++++++++++++++++ tests/test_litellm/llms/conftest.py | 124 ----------- .../llms/custom_httpx/test_http_handler.py | 41 ---- .../llms/openai/test_openai_common_utils.py | 15 -- tests/test_litellm/test_main.py | 127 +++++------ 7 files changed, 269 insertions(+), 254 deletions(-) create mode 100644 tests/e2e/llm_translation/test_outbound_http2_e2e.py delete mode 100644 tests/test_litellm/llms/conftest.py diff --git a/litellm/__init__.py b/litellm/__init__.py index 5e4a76503cd..56841ac0010 100644 --- a/litellm/__init__.py +++ b/litellm/__init__.py @@ -524,7 +524,7 @@ aiohttp_trust_env: bool = False # set to true to use HTTP_ Proxy settings disable_aiohttp_transport: bool = False # Set this to true to use httpx instead disable_aiohttp_trust_env: bool = False # When False, aiohttp will respect HTTP(S)_PROXY env vars force_ipv4: bool = False # when True, litellm will force ipv4 for all LLM requests. Some users have seen httpx ConnectionError when using ipv6. -http2: bool = False # when True, LiteLLM-built httpx clients negotiate HTTP/2 over TLS (falls back to HTTP/1.1); bypasses aiohttp transport +http2: bool = False network_mock: bool = False # When True, use mock transport — no real network calls ####### STOP SEQUENCE LIMIT ####### diff --git a/litellm/main.py b/litellm/main.py index f6f4ec1bf63..3b0b553e6e5 100644 --- a/litellm/main.py +++ b/litellm/main.py @@ -105,7 +105,7 @@ from litellm.llms.base_llm.base_model_iterator import ( ) from litellm.llms.bedrock.common_utils import BedrockModelInfo from litellm.llms.cohere.common_utils import CohereModelInfo -from litellm.llms.custom_httpx.http_handler import AsyncHTTPHandler, HTTPHandler +from litellm.llms.custom_httpx.http_handler import AsyncHTTPHandler, HTTPHandler, http2_enabled from litellm.llms.openai.chat.gpt_5_transformation import OpenAIGPT5Config from litellm.llms.openai_like.json_loader import JSONProviderRegistry from litellm.llms.vertex_ai.common_utils import ( @@ -2341,6 +2341,10 @@ def _complete_sap(ctx: _CompletionDispatchContext) -> _CompletionDispatchResult: def _complete_aiohttp_openai( ctx: _CompletionDispatchContext, ) -> _CompletionDispatchResult: + if http2_enabled(): + verbose_logger.warning( + "litellm.http2 is enabled but aiohttp_openai/ always uses aiohttp, which has no HTTP/2 client; this request stays on HTTP/1.1" + ) acompletion: Final = ctx.acompletion api_base = ctx.api_base api_key = ctx.api_key diff --git a/tests/e2e/llm_translation/test_outbound_http2_e2e.py b/tests/e2e/llm_translation/test_outbound_http2_e2e.py new file mode 100644 index 00000000000..cb2182ffd62 --- /dev/null +++ b/tests/e2e/llm_translation/test_outbound_http2_e2e.py @@ -0,0 +1,208 @@ +"""Outbound HTTP/2 negotiation for LiteLLM-built httpx clients. + +Spins up a local hypercorn TLS server that offers h2 and http/1.1 over ALPN and +drives the real AsyncHTTPHandler / HTTPHandler at it, so the negotiated protocol +on the wire is the assertion. No running proxy or provider credentials needed, +which is why these tests carry no `e2e` marker (same shape as the markerless +harness checks under tests/e2e/load/). +""" + +from __future__ import annotations + +import asyncio +import datetime +import ipaddress +import socket +import threading +import time +from collections.abc import Iterator +from pathlib import Path +from typing import Final, cast + +import pytest +from cryptography import x509 +from cryptography.hazmat.primitives import hashes, serialization +from cryptography.hazmat.primitives.asymmetric import rsa +from cryptography.x509.oid import NameOID +from hypercorn.asyncio import ( + serve, # pyright: ignore[reportUnknownVariableType] # hypercorn's serve signature passes through untyped worker hooks +) +from hypercorn.config import Config +from hypercorn.typing import ( + ASGIReceiveCallable, + ASGISendCallable, + HTTPResponseBodyEvent, + HTTPResponseStartEvent, + Scope, +) + +import litellm +from litellm.llms.custom_httpx.http_handler import AsyncHTTPHandler, HTTPHandler + + +def _write_self_signed_cert(cert_dir: Path) -> tuple[Path, Path]: + key: Final = rsa.generate_private_key(public_exponent=65537, key_size=2048) + now: Final = datetime.datetime.now(datetime.timezone.utc) + name: Final = x509.Name([x509.NameAttribute(NameOID.COMMON_NAME, "localhost")]) + cert: Final = ( + x509.CertificateBuilder() + .subject_name(name) + .issuer_name(name) + .public_key(key.public_key()) + .serial_number(x509.random_serial_number()) + .not_valid_before(now - datetime.timedelta(days=1)) + .not_valid_after(now + datetime.timedelta(days=7)) + .add_extension( + x509.SubjectAlternativeName([x509.DNSName("localhost"), x509.IPAddress(ipaddress.ip_address("127.0.0.1"))]), + critical=False, + ) + .sign(key, hashes.SHA256()) + ) + cert_file: Final = cert_dir / "cert.pem" + key_file: Final = cert_dir / "key.pem" + cert_file.write_bytes(cert.public_bytes(serialization.Encoding.PEM)) + key_file.write_bytes( + key.private_bytes( + serialization.Encoding.PEM, + serialization.PrivateFormat.TraditionalOpenSSL, + serialization.NoEncryption(), + ) + ) + return cert_file, key_file + + +async def _asgi_app(scope: Scope, receive: ASGIReceiveCallable, send: ASGISendCallable) -> None: + if scope["type"] != "http": + return + while True: + message = await receive() + if message["type"] == "http.disconnect": + return + if message["type"] == "http.request" and not message["more_body"]: + break + if scope["path"] == "/stream": + await send( + HTTPResponseStartEvent( + type="http.response.start", status=200, headers=[(b"content-type", b"text/event-stream")] + ) + ) + for index in range(3): + await send( + HTTPResponseBodyEvent( + type="http.response.body", body=f"data: chunk-{index}\n\n".encode(), more_body=True + ) + ) + await send(HTTPResponseBodyEvent(type="http.response.body", body=b"", more_body=False)) + return + await send( + HTTPResponseStartEvent(type="http.response.start", status=200, headers=[(b"content-type", b"application/json")]) + ) + await send(HTTPResponseBodyEvent(type="http.response.body", body=b'{"ok": true}', more_body=False)) + + +@pytest.fixture(scope="module") +def http2_tls_server(tmp_path_factory: pytest.TempPathFactory) -> Iterator[str]: + cert_dir: Final = tmp_path_factory.mktemp("h2certs") + cert_file, key_file = _write_self_signed_cert(cert_dir) + + with socket.socket() as sock: + sock.bind(("127.0.0.1", 0)) + port: Final = cast(int, sock.getsockname()[1]) + + shutdown: Final = threading.Event() + + def _serve() -> None: + loop: Final = asyncio.new_event_loop() + config: Final = Config() + config.bind = [f"127.0.0.1:{port}"] + config.certfile = str(cert_file) + config.keyfile = str(key_file) + config.alpn_protocols = ["h2", "http/1.1"] + loop.run_until_complete(serve(_asgi_app, config, shutdown_trigger=lambda: asyncio.to_thread(shutdown.wait))) + loop.close() + + thread: Final = threading.Thread(target=_serve, daemon=True) + thread.start() + + for _ in range(100): + try: + with socket.create_connection(("127.0.0.1", port), timeout=0.2): + break + except OSError: + time.sleep(0.05) + else: + pytest.fail("hypercorn test server did not start") + + yield f"https://127.0.0.1:{port}" + + shutdown.set() + thread.join(timeout=10) + + +def _async_exchange(base_url: str) -> tuple[str, str, bytes]: + async def _run() -> tuple[str, str, bytes]: + handler: Final = AsyncHTTPHandler(ssl_verify=False) + try: + response: Final = await handler.client.post(f"{base_url}/echo", json={"ping": "pong"}) + post_version: Final = response.http_version + async with handler.client.stream("POST", f"{base_url}/stream", json={}) as stream_response: + stream_version: Final = stream_response.http_version + body: Final = b"".join([chunk async for chunk in stream_response.aiter_bytes()]) + return post_version, stream_version, body + finally: + await handler.close() + + return asyncio.run(_run()) + + +def _sync_exchange(base_url: str) -> tuple[str, str, bytes]: + handler: Final = HTTPHandler(ssl_verify=False) + try: + response: Final = handler.client.post(f"{base_url}/echo", json={"ping": "pong"}) + post_version: Final = response.http_version + with handler.client.stream("POST", f"{base_url}/stream", json={}) as stream_response: + stream_version: Final = stream_response.http_version + body: Final = b"".join(stream_response.iter_bytes()) + return post_version, stream_version, body + finally: + handler.close() + + +class TestOutboundHttp2: + @pytest.mark.parametrize("use_http2, expected_version", [(True, "HTTP/2"), (False, "HTTP/1.1")]) + def test_async_handler_negotiates_http2_only_when_enabled( + self, + monkeypatch: pytest.MonkeyPatch, + http2_tls_server: str, + use_http2: bool, + expected_version: str, + ) -> None: + monkeypatch.setattr(litellm, "http2", use_http2) + monkeypatch.delenv("LITELLM_HTTP2", raising=False) + monkeypatch.setattr(litellm, "disable_aiohttp_transport", True) + monkeypatch.setattr(litellm, "force_ipv4", False) + + post_version, stream_version, body = _async_exchange(http2_tls_server) + + assert post_version == expected_version + assert stream_version == expected_version + assert b"data: chunk-0" in body + + @pytest.mark.parametrize("use_http2, expected_version", [(True, "HTTP/2"), (False, "HTTP/1.1")]) + def test_sync_handler_negotiates_http2_only_when_enabled( + self, + monkeypatch: pytest.MonkeyPatch, + http2_tls_server: str, + use_http2: bool, + expected_version: str, + ) -> None: + monkeypatch.setattr(litellm, "http2", use_http2) + monkeypatch.delenv("LITELLM_HTTP2", raising=False) + monkeypatch.setattr(litellm, "disable_aiohttp_transport", True) + monkeypatch.setattr(litellm, "force_ipv4", False) + + post_version, stream_version, body = _sync_exchange(http2_tls_server) + + assert post_version == expected_version + assert stream_version == expected_version + assert b"data: chunk-0" in body diff --git a/tests/test_litellm/llms/conftest.py b/tests/test_litellm/llms/conftest.py deleted file mode 100644 index 2905b606e51..00000000000 --- a/tests/test_litellm/llms/conftest.py +++ /dev/null @@ -1,124 +0,0 @@ -import asyncio -import datetime -import ipaddress -import socket -import threading -import time - -import pytest - - -def _write_self_signed_cert(cert_dir): - from cryptography import x509 - from cryptography.hazmat.primitives import hashes, serialization - from cryptography.hazmat.primitives.asymmetric import rsa - - key = rsa.generate_private_key(public_exponent=65537, key_size=2048) - now = datetime.datetime.now(datetime.timezone.utc) - cert = ( - x509.CertificateBuilder() - .subject_name(x509.Name([x509.NameAttribute(x509.oid.NameOID.COMMON_NAME, "localhost")])) - .issuer_name(x509.Name([x509.NameAttribute(x509.oid.NameOID.COMMON_NAME, "localhost")])) - .public_key(key.public_key()) - .serial_number(x509.random_serial_number()) - .not_valid_before(now - datetime.timedelta(days=1)) - .not_valid_after(now + datetime.timedelta(days=7)) - .add_extension( - x509.SubjectAlternativeName( - [x509.DNSName("localhost"), x509.IPAddress(ipaddress.ip_address("127.0.0.1"))] - ), - critical=False, - ) - .sign(key, hashes.SHA256()) - ) - cert_file = cert_dir / "cert.pem" - key_file = cert_dir / "key.pem" - cert_file.write_bytes(cert.public_bytes(serialization.Encoding.PEM)) - key_file.write_bytes( - key.private_bytes( - serialization.Encoding.PEM, - serialization.PrivateFormat.TraditionalOpenSSL, - serialization.NoEncryption(), - ) - ) - return cert_file, key_file - - -async def _asgi_app(scope, receive, send): - if scope["type"] != "http": - return - while True: - message = await receive() - if message["type"] == "http.request" and not message.get("more_body"): - break - if message["type"] == "http.disconnect": - return - if scope["path"] == "/stream": - await send( - { - "type": "http.response.start", - "status": 200, - "headers": [(b"content-type", b"text/event-stream")], - } - ) - for index in range(3): - await send( - { - "type": "http.response.body", - "body": f"data: chunk-{index}\n\n".encode(), - "more_body": True, - } - ) - await send({"type": "http.response.body", "body": b"", "more_body": False}) - return - await send( - { - "type": "http.response.start", - "status": 200, - "headers": [(b"content-type", b"application/json")], - } - ) - await send({"type": "http.response.body", "body": b'{"ok": true}'}) - - -@pytest.fixture(scope="module") -def http2_tls_server(tmp_path_factory): - """Hypercorn TLS server on an ephemeral port that negotiates h2 or http/1.1 via ALPN.""" - from hypercorn.asyncio import serve - from hypercorn.config import Config - - cert_dir = tmp_path_factory.mktemp("h2certs") - cert_file, key_file = _write_self_signed_cert(cert_dir) - - with socket.socket() as sock: - sock.bind(("127.0.0.1", 0)) - port = sock.getsockname()[1] - - shutdown = threading.Event() - - def _serve() -> None: - loop = asyncio.new_event_loop() - config = Config() - config.bind = [f"127.0.0.1:{port}"] - config.certfile = str(cert_file) - config.keyfile = str(key_file) - config.alpn_protocols = ["h2", "http/1.1"] - loop.run_until_complete(serve(_asgi_app, config, shutdown_trigger=lambda: asyncio.to_thread(shutdown.wait))) - loop.close() - - thread = threading.Thread(target=_serve, daemon=True) - thread.start() - - for _ in range(100): - try: - with socket.create_connection(("127.0.0.1", port), timeout=0.2): - break - except OSError: - time.sleep(0.05) - else: - pytest.fail("hypercorn test server did not start") - - yield f"https://127.0.0.1:{port}" - - shutdown.set() - thread.join(timeout=10) diff --git a/tests/test_litellm/llms/custom_httpx/test_http_handler.py b/tests/test_litellm/llms/custom_httpx/test_http_handler.py index e170a7f7a78..a52bf58e944 100644 --- a/tests/test_litellm/llms/custom_httpx/test_http_handler.py +++ b/tests/test_litellm/llms/custom_httpx/test_http_handler.py @@ -1702,44 +1702,3 @@ async def test_http2_disabled_by_default(monkeypatch: pytest.MonkeyPatch): monkeypatch.setattr(litellm, "disable_aiohttp_transport", False) assert AsyncHTTPHandler._should_use_aiohttp_transport() is True - - -@pytest.mark.asyncio -@pytest.mark.parametrize("use_http2", [True, False]) -@pytest.mark.parametrize("handler_kind", ["async", "sync"]) -async def test_http_version_negotiated_over_tls(monkeypatch, http2_tls_server, handler_kind, use_http2): - monkeypatch.setattr(litellm, "http2", use_http2) - monkeypatch.delenv("LITELLM_HTTP2", raising=False) - monkeypatch.setattr(litellm, "disable_aiohttp_transport", True) - monkeypatch.setattr(litellm, "force_ipv4", False) - - expected_version = "HTTP/2" if use_http2 else "HTTP/1.1" - - if handler_kind == "async": - handler = AsyncHTTPHandler(ssl_verify=False) - try: - response = await handler.post(f"{http2_tls_server}/echo", json={"ping": "pong"}) - assert response.status_code == 200 - assert response.http_version == expected_version - - stream_response = await handler.post(f"{http2_tls_server}/stream", stream=True) - assert stream_response.http_version == expected_version - chunks = [chunk async for chunk in stream_response.aiter_bytes()] - assert chunks - await stream_response.aclose() - finally: - await handler.close() - else: - handler = HTTPHandler(ssl_verify=False) - try: - response = handler.post(f"{http2_tls_server}/echo", json={"ping": "pong"}) - assert response.status_code == 200 - assert response.http_version == expected_version - - stream_response = handler.post(f"{http2_tls_server}/stream", stream=True) - assert stream_response.http_version == expected_version - chunks = list(stream_response.iter_bytes()) - assert chunks - stream_response.close() - finally: - handler.close() diff --git a/tests/test_litellm/llms/openai/test_openai_common_utils.py b/tests/test_litellm/llms/openai/test_openai_common_utils.py index 3dc37c21e7a..b54ec10ef17 100644 --- a/tests/test_litellm/llms/openai/test_openai_common_utils.py +++ b/tests/test_litellm/llms/openai/test_openai_common_utils.py @@ -413,18 +413,3 @@ def test_is_openai_backed_api_base_decides_by_hostname_only(api_base, expected): assert is_openai_backed_api_base(api_base) is expected -def test_litellm_built_http_clients_negotiate_http2_only_when_enabled(monkeypatch): - monkeypatch.delenv("LITELLM_HTTP2", raising=False) - monkeypatch.setattr(litellm, "disable_aiohttp_transport", True) - - monkeypatch.setattr(litellm, "http2", False) - async_client = BaseOpenAILLM._get_async_http_client() - sync_client = BaseOpenAILLM._get_sync_http_client() - assert async_client is not None and async_client._transport._pool._http2 is False - assert sync_client is not None and sync_client._transport._pool._http2 is False - - monkeypatch.setattr(litellm, "http2", True) - async_client = BaseOpenAILLM._get_async_http_client() - sync_client = BaseOpenAILLM._get_sync_http_client() - assert async_client is not None and async_client._transport._pool._http2 is True - assert sync_client is not None and sync_client._transport._pool._http2 is True diff --git a/tests/test_litellm/test_main.py b/tests/test_litellm/test_main.py index 3dccb2b35bf..15ec440b0bb 100644 --- a/tests/test_litellm/test_main.py +++ b/tests/test_litellm/test_main.py @@ -190,9 +190,7 @@ async def test_url_with_format_param(model, sync_mode, monkeypatch): # URL->image conversion helpers so suite-level network/client state from # earlier tests cannot prevent the mocked provider client from being hit. fake_base64_image = "data:image/png;base64,ZmFrZS1pbWFnZQ==" - monkeypatch.setattr( - prompt_factory, "convert_url_to_base64", lambda url: fake_base64_image - ) + monkeypatch.setattr(prompt_factory, "convert_url_to_base64", lambda url: fake_base64_image) monkeypatch.setattr( prompt_factory.BedrockImageProcessor, "get_image_details", @@ -307,9 +305,7 @@ async def test_url_with_format_param_openai(model, sync_mode): } ], } - with patch.object( - client.chat.completions.with_raw_response, "create" - ) as mock_client: + with patch.object(client.chat.completions.with_raw_response, "create") as mock_client: try: if sync_mode: response = completion(**args, client=client) @@ -361,9 +357,7 @@ def test_strip_input_examples_for_non_anthropic_providers(): } ] - assert not litellm_main._should_allow_input_examples( - custom_llm_provider="openai", model="gpt-4o-mini" - ) + assert not litellm_main._should_allow_input_examples(custom_llm_provider="openai", model="gpt-4o-mini") cleaned = litellm_main._drop_input_examples_from_tools(tools=tools) @@ -375,9 +369,7 @@ def test_strip_input_examples_for_non_anthropic_providers(): def test_custom_provider_with_extra_headers(): from litellm.llms.custom_httpx.http_handler import HTTPHandler - with patch.object( - litellm.llms.custom_httpx.http_handler.HTTPHandler, "post" - ) as mock_post: + with patch.object(litellm.llms.custom_httpx.http_handler.HTTPHandler, "post") as mock_post: response = litellm.completion( model="custom/custom", messages=[{"role": "user", "content": "Hello, how are you?"}], @@ -392,9 +384,7 @@ def test_custom_provider_with_extra_headers(): def test_custom_provider_with_extra_body(): from litellm.llms.custom_httpx.http_handler import HTTPHandler - with patch.object( - litellm.llms.custom_httpx.http_handler.HTTPHandler, "post" - ) as mock_post: + with patch.object(litellm.llms.custom_httpx.http_handler.HTTPHandler, "post") as mock_post: response = litellm.completion( model="custom/custom", messages=[{"role": "user", "content": "Hello, how are you?"}], @@ -421,9 +411,7 @@ def test_custom_provider_with_extra_body(): } # test that extra_body is not passed if not provided - with patch.object( - litellm.llms.custom_httpx.http_handler.HTTPHandler, "post" - ) as mock_post: + with patch.object(litellm.llms.custom_httpx.http_handler.HTTPHandler, "post") as mock_post: response = litellm.completion( model="custom/custom", messages=[{"role": "user", "content": "Hello, how are you?"}], @@ -454,9 +442,7 @@ def set_openrouter_api_key(): @pytest.mark.asyncio -async def test_extra_body_with_fallback( - respx_mock: respx.MockRouter, set_openrouter_api_key, monkeypatch -): +async def test_extra_body_with_fallback(respx_mock: respx.MockRouter, set_openrouter_api_key, monkeypatch): """ test regression for https://github.com/BerriAI/litellm/issues/8425. @@ -524,9 +510,7 @@ async def test_extra_body_with_fallback( # Verify the response assert response is not None - assert ( - len(respx_mock.calls) > 0 - ), "Mock was not called - check if aiohttp transport is properly disabled" + assert len(respx_mock.calls) > 0, "Mock was not called - check if aiohttp transport is properly disabled" # Get the request from the mock request: httpx.Request = respx_mock.calls[0].request @@ -550,9 +534,7 @@ async def test_extra_body_with_fallback( @pytest.mark.parametrize("env_base", ["OPENAI_BASE_URL", "OPENAI_API_BASE"]) @pytest.mark.asyncio @pytest.mark.flaky(retries=3, delay=1) -async def test_openai_env_base( - respx_mock: respx.MockRouter, env_base, openai_api_response, monkeypatch -): +async def test_openai_env_base(respx_mock: respx.MockRouter, env_base, openai_api_response, monkeypatch): "This tests OpenAI env variables are honored, including legacy OPENAI_API_BASE" # Ensure aiohttp transport is disabled to use httpx which respx can mock litellm.disable_aiohttp_transport = True @@ -567,9 +549,7 @@ async def test_openai_env_base( messages = [{"role": "user", "content": "Hello, how are you?"}] # Configure respx mock to intercept the request - mock_route = respx_mock.post( - url__regex=r"http://localhost:12345/v1/chat/completions.*" - ).mock( + mock_route = respx_mock.post(url__regex=r"http://localhost:12345/v1/chat/completions.*").mock( return_value=httpx.Response( status_code=200, json={ @@ -603,9 +583,7 @@ async def test_openai_env_base( assert response.choices[0].message.content == "Hello from mocked response!" # Verify the mock was called - assert ( - mock_route.called - ), "Mock route was not called - request may have bypassed respx" + assert mock_route.called, "Mock route was not called - request may have bypassed respx" finally: # Clean up to avoid affecting other tests litellm.disable_aiohttp_transport = False @@ -681,9 +659,7 @@ def test_completion_forwards_verbosity_in_raw_request(respx_mock: respx.MockRout model = "gpt-5.2" messages = [{"role": "user", "content": "hi"}] - respx_mock.post("https://api.openai.com/v1/chat/completions").mock( - return_value=_mocked_openai_chat_response(model) - ) + respx_mock.post("https://api.openai.com/v1/chat/completions").mock(return_value=_mocked_openai_chat_response(model)) request = return_raw_request( endpoint=CallTypes.completion, @@ -700,9 +676,7 @@ def test_completion_forwards_verbosity_in_raw_request(respx_mock: respx.MockRout @pytest.mark.asyncio -async def test_acompletion_forwards_verbosity_to_provider_request( - respx_mock: respx.MockRouter, monkeypatch -): +async def test_acompletion_forwards_verbosity_to_provider_request(respx_mock: respx.MockRouter, monkeypatch): """Regression test: acompletion() must forward the verbosity param to the provider request body.""" original_disable_aiohttp = litellm.disable_aiohttp_transport try: @@ -763,9 +737,9 @@ def test_responses_api_bridge_check_gpt_5_4_pro(): model=model_name, custom_llm_provider="openai", ) - assert ( - model_info.get("mode") == "responses" - ), f"{model_name} should have mode='responses', got '{model_info.get('mode')}'" + assert model_info.get("mode") == "responses", ( + f"{model_name} should have mode='responses', got '{model_info.get('mode')}'" + ) def test_responses_api_bridge_check_gpt_5_4_tools_plus_reasoning_routes_to_responses(): @@ -1182,7 +1156,7 @@ def test_responses_api_bridge_check_openai_backed_custom_api_base_with_unset_eff tools=[{"type": "function", "function": {"name": "get_capital"}}], reasoning_effort=None, api_base=api_base, - ) + ) assert model == "gpt-5.6" assert model_info.get("mode") == "responses" @@ -1207,7 +1181,7 @@ def test_responses_api_bridge_check_lookalike_custom_api_base_with_unset_effort_ tools=[{"type": "function", "function": {"name": "get_capital"}}], reasoning_effort=None, api_base=api_base, - ) + ) assert model == "gpt-5.6" assert model_info.get("mode") != "responses" @@ -1227,7 +1201,7 @@ def test_responses_api_bridge_check_privatelink_api_base_via_env_with_unset_effo tools=[{"type": "function", "function": {"name": "get_capital"}}], reasoning_effort=None, api_base=None, - ) + ) assert model == "gpt-5.6" assert model_info.get("mode") == "responses" @@ -1588,9 +1562,7 @@ def test_responses_api_bridge_check_handles_exception(): with patch("litellm.main._get_model_info_helper") as mock_get_model_info: mock_get_model_info.side_effect = Exception("Model not found") - model_info, model = responses_api_bridge_check( - model="responses/custom-model", custom_llm_provider="custom" - ) + model_info, model = responses_api_bridge_check(model="responses/custom-model", custom_llm_provider="custom") assert model == "custom-model" assert model_info["mode"] == "responses" @@ -2371,9 +2343,7 @@ def test_image_edit_merges_headers_and_extra_headers(): mock_image_edit_config = MagicMock() mock_image_edit_config.get_supported_openai_params.return_value = set() - mock_image_edit_config.map_openai_params.side_effect = lambda **kwargs: dict( - kwargs["image_edit_optional_params"] - ) + mock_image_edit_config.map_openai_params.side_effect = lambda **kwargs: dict(kwargs["image_edit_optional_params"]) with ( patch( @@ -2729,10 +2699,7 @@ def test_mock_completion_stream_with_model_response(): # Verify the content is streamed correctly accumulated_content = "" for chunk in chunks: - if ( - hasattr(chunk.choices[0].delta, "content") - and chunk.choices[0].delta.content - ): + if hasattr(chunk.choices[0].delta, "content") and chunk.choices[0].delta.content: accumulated_content += chunk.choices[0].delta.content assert "This is a test response" in accumulated_content or len(chunks) > 0 @@ -2790,10 +2757,7 @@ async def test_async_mock_completion_stream_with_model_response(): # Verify the content is streamed correctly accumulated_content = "" for chunk in chunks: - if ( - hasattr(chunk.choices[0].delta, "content") - and chunk.choices[0].delta.content - ): + if hasattr(chunk.choices[0].delta, "content") and chunk.choices[0].delta.content: accumulated_content += chunk.choices[0].delta.content assert "This is an async test response" in accumulated_content or len(chunks) > 0 @@ -2860,9 +2824,7 @@ def test_stream_chunk_builder_text_completion_combines_text_and_usage(): ), ] - response = stream_chunk_builder_text_completion( - chunks=chunks, messages=[{"role": "user", "content": "say hello"}] - ) + response = stream_chunk_builder_text_completion(chunks=chunks, messages=[{"role": "user", "content": "say hello"}]) assert response.choices[0].text == "Hello world" assert response.choices[0].finish_reason == "stop" @@ -3310,10 +3272,7 @@ def _text_chunk(content, finish_reason=None, usage=None): def _priced_at(prompt_tokens, completion_tokens): prices = litellm.model_cost[STREAM_COST_MODEL] - return ( - prompt_tokens * prices["input_cost_per_token"] - + completion_tokens * prices["output_cost_per_token"] - ) + return prompt_tokens * prices["input_cost_per_token"] + completion_tokens * prices["output_cost_per_token"] @pytest.fixture @@ -3380,9 +3339,9 @@ def test_streaming_and_not_streaming_bill_the_same_usage_the_same(local_cost_map usage=STREAMED_USAGE, ) - assert litellm.completion_cost( - completion_response=rebuilt, model=STREAM_COST_MODEL - ) == pytest.approx(litellm.completion_cost(completion_response=whole, model=STREAM_COST_MODEL)) + assert litellm.completion_cost(completion_response=rebuilt, model=STREAM_COST_MODEL) == pytest.approx( + litellm.completion_cost(completion_response=whole, model=STREAM_COST_MODEL) + ) def test_a_stream_that_reported_no_usage_is_still_billed(local_cost_map): @@ -3401,9 +3360,7 @@ def test_a_stream_that_reported_no_usage_is_still_billed(local_cost_map): cost = litellm.completion_cost(completion_response=rebuilt, model=STREAM_COST_MODEL) assert cost > 0 - assert cost == pytest.approx( - _priced_at(rebuilt.usage.prompt_tokens, rebuilt.usage.completion_tokens) - ) + assert cost == pytest.approx(_priced_at(rebuilt.usage.prompt_tokens, rebuilt.usage.completion_tokens)) @pytest.mark.asyncio @@ -3850,3 +3807,29 @@ def test_bridged_responses_with_openai_http_handler_keeps_forwarded_headers_out_ assert "extra_headers" not in body assert body["model"] == "gpt-5.4" assert {k: request.headers[k] for k in FORWARDED_CLIENT_HEADERS} == FORWARDED_CLIENT_HEADERS + + +@pytest.mark.parametrize("http2_on", [True, False]) +def test_aiohttp_openai_warns_only_when_http2_enabled( + monkeypatch: pytest.MonkeyPatch, caplog: pytest.LogCaptureFixture, http2_on: bool +): + import logging + + from litellm.main import base_llm_aiohttp_handler + + monkeypatch.setattr(litellm, "http2", http2_on) + monkeypatch.delenv("LITELLM_HTTP2", raising=False) + + handler_completion: Final = MagicMock(return_value=MagicMock()) + monkeypatch.setattr(base_llm_aiohttp_handler, "completion", handler_completion) + + with caplog.at_level(logging.WARNING, logger="LiteLLM"): + litellm.completion( + model="aiohttp_openai/gpt-4o", + messages=[{"role": "user", "content": "hi"}], + api_key="sk-test", + ) + + assert handler_completion.called + warned: Final = "aiohttp_openai/ always uses aiohttp" in caplog.text + assert warned is http2_on From 8978b4562f23b7f0ac69f41488389c3088f2ba56 Mon Sep 17 00:00:00 2001 From: Devin AI <158243242+devin-ai-integration[bot]@users.noreply.github.com> Date: Tue, 15 Sep 2026 18:26:54 +0000 Subject: [PATCH 066/225] test(main): drop unrelated reformatting Co-Authored-By: Devin AI <158243242+devin-ai-integration[bot]@users.noreply.github.com> --- tests/test_litellm/test_main.py | 104 ++++++++++++++++++++++---------- 1 file changed, 73 insertions(+), 31 deletions(-) diff --git a/tests/test_litellm/test_main.py b/tests/test_litellm/test_main.py index 15ec440b0bb..7fcdc8473d7 100644 --- a/tests/test_litellm/test_main.py +++ b/tests/test_litellm/test_main.py @@ -4,6 +4,7 @@ from datetime import datetime import contextlib import copy import json +import logging import os from collections.abc import Mapping from dataclasses import dataclass @@ -190,7 +191,9 @@ async def test_url_with_format_param(model, sync_mode, monkeypatch): # URL->image conversion helpers so suite-level network/client state from # earlier tests cannot prevent the mocked provider client from being hit. fake_base64_image = "data:image/png;base64,ZmFrZS1pbWFnZQ==" - monkeypatch.setattr(prompt_factory, "convert_url_to_base64", lambda url: fake_base64_image) + monkeypatch.setattr( + prompt_factory, "convert_url_to_base64", lambda url: fake_base64_image + ) monkeypatch.setattr( prompt_factory.BedrockImageProcessor, "get_image_details", @@ -305,7 +308,9 @@ async def test_url_with_format_param_openai(model, sync_mode): } ], } - with patch.object(client.chat.completions.with_raw_response, "create") as mock_client: + with patch.object( + client.chat.completions.with_raw_response, "create" + ) as mock_client: try: if sync_mode: response = completion(**args, client=client) @@ -357,7 +362,9 @@ def test_strip_input_examples_for_non_anthropic_providers(): } ] - assert not litellm_main._should_allow_input_examples(custom_llm_provider="openai", model="gpt-4o-mini") + assert not litellm_main._should_allow_input_examples( + custom_llm_provider="openai", model="gpt-4o-mini" + ) cleaned = litellm_main._drop_input_examples_from_tools(tools=tools) @@ -369,7 +376,9 @@ def test_strip_input_examples_for_non_anthropic_providers(): def test_custom_provider_with_extra_headers(): from litellm.llms.custom_httpx.http_handler import HTTPHandler - with patch.object(litellm.llms.custom_httpx.http_handler.HTTPHandler, "post") as mock_post: + with patch.object( + litellm.llms.custom_httpx.http_handler.HTTPHandler, "post" + ) as mock_post: response = litellm.completion( model="custom/custom", messages=[{"role": "user", "content": "Hello, how are you?"}], @@ -384,7 +393,9 @@ def test_custom_provider_with_extra_headers(): def test_custom_provider_with_extra_body(): from litellm.llms.custom_httpx.http_handler import HTTPHandler - with patch.object(litellm.llms.custom_httpx.http_handler.HTTPHandler, "post") as mock_post: + with patch.object( + litellm.llms.custom_httpx.http_handler.HTTPHandler, "post" + ) as mock_post: response = litellm.completion( model="custom/custom", messages=[{"role": "user", "content": "Hello, how are you?"}], @@ -411,7 +422,9 @@ def test_custom_provider_with_extra_body(): } # test that extra_body is not passed if not provided - with patch.object(litellm.llms.custom_httpx.http_handler.HTTPHandler, "post") as mock_post: + with patch.object( + litellm.llms.custom_httpx.http_handler.HTTPHandler, "post" + ) as mock_post: response = litellm.completion( model="custom/custom", messages=[{"role": "user", "content": "Hello, how are you?"}], @@ -442,7 +455,9 @@ def set_openrouter_api_key(): @pytest.mark.asyncio -async def test_extra_body_with_fallback(respx_mock: respx.MockRouter, set_openrouter_api_key, monkeypatch): +async def test_extra_body_with_fallback( + respx_mock: respx.MockRouter, set_openrouter_api_key, monkeypatch +): """ test regression for https://github.com/BerriAI/litellm/issues/8425. @@ -510,7 +525,9 @@ async def test_extra_body_with_fallback(respx_mock: respx.MockRouter, set_openro # Verify the response assert response is not None - assert len(respx_mock.calls) > 0, "Mock was not called - check if aiohttp transport is properly disabled" + assert ( + len(respx_mock.calls) > 0 + ), "Mock was not called - check if aiohttp transport is properly disabled" # Get the request from the mock request: httpx.Request = respx_mock.calls[0].request @@ -534,7 +551,9 @@ async def test_extra_body_with_fallback(respx_mock: respx.MockRouter, set_openro @pytest.mark.parametrize("env_base", ["OPENAI_BASE_URL", "OPENAI_API_BASE"]) @pytest.mark.asyncio @pytest.mark.flaky(retries=3, delay=1) -async def test_openai_env_base(respx_mock: respx.MockRouter, env_base, openai_api_response, monkeypatch): +async def test_openai_env_base( + respx_mock: respx.MockRouter, env_base, openai_api_response, monkeypatch +): "This tests OpenAI env variables are honored, including legacy OPENAI_API_BASE" # Ensure aiohttp transport is disabled to use httpx which respx can mock litellm.disable_aiohttp_transport = True @@ -549,7 +568,9 @@ async def test_openai_env_base(respx_mock: respx.MockRouter, env_base, openai_ap messages = [{"role": "user", "content": "Hello, how are you?"}] # Configure respx mock to intercept the request - mock_route = respx_mock.post(url__regex=r"http://localhost:12345/v1/chat/completions.*").mock( + mock_route = respx_mock.post( + url__regex=r"http://localhost:12345/v1/chat/completions.*" + ).mock( return_value=httpx.Response( status_code=200, json={ @@ -583,7 +604,9 @@ async def test_openai_env_base(respx_mock: respx.MockRouter, env_base, openai_ap assert response.choices[0].message.content == "Hello from mocked response!" # Verify the mock was called - assert mock_route.called, "Mock route was not called - request may have bypassed respx" + assert ( + mock_route.called + ), "Mock route was not called - request may have bypassed respx" finally: # Clean up to avoid affecting other tests litellm.disable_aiohttp_transport = False @@ -659,7 +682,9 @@ def test_completion_forwards_verbosity_in_raw_request(respx_mock: respx.MockRout model = "gpt-5.2" messages = [{"role": "user", "content": "hi"}] - respx_mock.post("https://api.openai.com/v1/chat/completions").mock(return_value=_mocked_openai_chat_response(model)) + respx_mock.post("https://api.openai.com/v1/chat/completions").mock( + return_value=_mocked_openai_chat_response(model) + ) request = return_raw_request( endpoint=CallTypes.completion, @@ -676,7 +701,9 @@ def test_completion_forwards_verbosity_in_raw_request(respx_mock: respx.MockRout @pytest.mark.asyncio -async def test_acompletion_forwards_verbosity_to_provider_request(respx_mock: respx.MockRouter, monkeypatch): +async def test_acompletion_forwards_verbosity_to_provider_request( + respx_mock: respx.MockRouter, monkeypatch +): """Regression test: acompletion() must forward the verbosity param to the provider request body.""" original_disable_aiohttp = litellm.disable_aiohttp_transport try: @@ -737,9 +764,9 @@ def test_responses_api_bridge_check_gpt_5_4_pro(): model=model_name, custom_llm_provider="openai", ) - assert model_info.get("mode") == "responses", ( - f"{model_name} should have mode='responses', got '{model_info.get('mode')}'" - ) + assert ( + model_info.get("mode") == "responses" + ), f"{model_name} should have mode='responses', got '{model_info.get('mode')}'" def test_responses_api_bridge_check_gpt_5_4_tools_plus_reasoning_routes_to_responses(): @@ -1156,7 +1183,7 @@ def test_responses_api_bridge_check_openai_backed_custom_api_base_with_unset_eff tools=[{"type": "function", "function": {"name": "get_capital"}}], reasoning_effort=None, api_base=api_base, - ) + ) assert model == "gpt-5.6" assert model_info.get("mode") == "responses" @@ -1181,7 +1208,7 @@ def test_responses_api_bridge_check_lookalike_custom_api_base_with_unset_effort_ tools=[{"type": "function", "function": {"name": "get_capital"}}], reasoning_effort=None, api_base=api_base, - ) + ) assert model == "gpt-5.6" assert model_info.get("mode") != "responses" @@ -1201,7 +1228,7 @@ def test_responses_api_bridge_check_privatelink_api_base_via_env_with_unset_effo tools=[{"type": "function", "function": {"name": "get_capital"}}], reasoning_effort=None, api_base=None, - ) + ) assert model == "gpt-5.6" assert model_info.get("mode") == "responses" @@ -1562,7 +1589,9 @@ def test_responses_api_bridge_check_handles_exception(): with patch("litellm.main._get_model_info_helper") as mock_get_model_info: mock_get_model_info.side_effect = Exception("Model not found") - model_info, model = responses_api_bridge_check(model="responses/custom-model", custom_llm_provider="custom") + model_info, model = responses_api_bridge_check( + model="responses/custom-model", custom_llm_provider="custom" + ) assert model == "custom-model" assert model_info["mode"] == "responses" @@ -2343,7 +2372,9 @@ def test_image_edit_merges_headers_and_extra_headers(): mock_image_edit_config = MagicMock() mock_image_edit_config.get_supported_openai_params.return_value = set() - mock_image_edit_config.map_openai_params.side_effect = lambda **kwargs: dict(kwargs["image_edit_optional_params"]) + mock_image_edit_config.map_openai_params.side_effect = lambda **kwargs: dict( + kwargs["image_edit_optional_params"] + ) with ( patch( @@ -2699,7 +2730,10 @@ def test_mock_completion_stream_with_model_response(): # Verify the content is streamed correctly accumulated_content = "" for chunk in chunks: - if hasattr(chunk.choices[0].delta, "content") and chunk.choices[0].delta.content: + if ( + hasattr(chunk.choices[0].delta, "content") + and chunk.choices[0].delta.content + ): accumulated_content += chunk.choices[0].delta.content assert "This is a test response" in accumulated_content or len(chunks) > 0 @@ -2757,7 +2791,10 @@ async def test_async_mock_completion_stream_with_model_response(): # Verify the content is streamed correctly accumulated_content = "" for chunk in chunks: - if hasattr(chunk.choices[0].delta, "content") and chunk.choices[0].delta.content: + if ( + hasattr(chunk.choices[0].delta, "content") + and chunk.choices[0].delta.content + ): accumulated_content += chunk.choices[0].delta.content assert "This is an async test response" in accumulated_content or len(chunks) > 0 @@ -2824,7 +2861,9 @@ def test_stream_chunk_builder_text_completion_combines_text_and_usage(): ), ] - response = stream_chunk_builder_text_completion(chunks=chunks, messages=[{"role": "user", "content": "say hello"}]) + response = stream_chunk_builder_text_completion( + chunks=chunks, messages=[{"role": "user", "content": "say hello"}] + ) assert response.choices[0].text == "Hello world" assert response.choices[0].finish_reason == "stop" @@ -3272,7 +3311,10 @@ def _text_chunk(content, finish_reason=None, usage=None): def _priced_at(prompt_tokens, completion_tokens): prices = litellm.model_cost[STREAM_COST_MODEL] - return prompt_tokens * prices["input_cost_per_token"] + completion_tokens * prices["output_cost_per_token"] + return ( + prompt_tokens * prices["input_cost_per_token"] + + completion_tokens * prices["output_cost_per_token"] + ) @pytest.fixture @@ -3339,9 +3381,9 @@ def test_streaming_and_not_streaming_bill_the_same_usage_the_same(local_cost_map usage=STREAMED_USAGE, ) - assert litellm.completion_cost(completion_response=rebuilt, model=STREAM_COST_MODEL) == pytest.approx( - litellm.completion_cost(completion_response=whole, model=STREAM_COST_MODEL) - ) + assert litellm.completion_cost( + completion_response=rebuilt, model=STREAM_COST_MODEL + ) == pytest.approx(litellm.completion_cost(completion_response=whole, model=STREAM_COST_MODEL)) def test_a_stream_that_reported_no_usage_is_still_billed(local_cost_map): @@ -3360,7 +3402,9 @@ def test_a_stream_that_reported_no_usage_is_still_billed(local_cost_map): cost = litellm.completion_cost(completion_response=rebuilt, model=STREAM_COST_MODEL) assert cost > 0 - assert cost == pytest.approx(_priced_at(rebuilt.usage.prompt_tokens, rebuilt.usage.completion_tokens)) + assert cost == pytest.approx( + _priced_at(rebuilt.usage.prompt_tokens, rebuilt.usage.completion_tokens) + ) @pytest.mark.asyncio @@ -3813,8 +3857,6 @@ def test_bridged_responses_with_openai_http_handler_keeps_forwarded_headers_out_ def test_aiohttp_openai_warns_only_when_http2_enabled( monkeypatch: pytest.MonkeyPatch, caplog: pytest.LogCaptureFixture, http2_on: bool ): - import logging - from litellm.main import base_llm_aiohttp_handler monkeypatch.setattr(litellm, "http2", http2_on) From 1517f1205c23bc2dbed020f8c69210883bb19de2 Mon Sep 17 00:00:00 2001 From: "berriai-litellm-provider-info-sync[bot]" <328147090+berriai-litellm-provider-info-sync[bot]@users.noreply.github.com> Date: Tue, 15 Sep 2026 18:36:52 +0000 Subject: [PATCH 067/225] chore(prices): sync Google Gemini prices: 10 models [enrichment failed: Google Gemini, 177 held] gemini/gemini-2.5-computer-use-preview-10-2025: gemini/gemini-2.5-flash: input_cost_per_token_flex, output_cost_per_token_flex, input_cost_per_token_batches, input_cost_per_token_priority, output_cost_per_token_batches, output_cost_per_token_priority, cache_read_input_token_cost_flex, cache_read_input_audio_token_cost, input_cost_per_audio_token_batches, cache_read_input_token_cost_priority gemini/gemini-2.5-flash-image: input_cost_per_token_flex, input_cost_per_token_batches, input_cost_per_token_priority gemini/gemini-2.5-flash-lite: input_cost_per_token_flex, output_cost_per_token_flex, input_cost_per_token_batches, input_cost_per_token_priority, output_cost_per_token_batches, output_cost_per_token_priority, cache_read_input_token_cost_flex, cache_read_input_audio_token_cost, input_cost_per_audio_token_batches, cache_read_input_token_cost_priority gemini-2.5-flash-preview-tts: gemini/gemini-2.5-flash-preview-tts: gemini/gemini-2.5-pro: input_cost_per_token_flex, output_cost_per_token_flex, input_cost_per_token_batches, input_cost_per_token_priority, output_cost_per_token_batches, output_cost_per_token_priority, cache_read_input_token_cost_flex, cache_read_input_token_cost_priority, input_cost_per_token_above_200k_tokens_priority, output_cost_per_token_above_200k_tokens_priority, cache_read_input_token_cost_above_200k_tokens_priority gemini/gemini-2.5-pro-preview-tts: output_cost_per_audio_token, input_cost_per_token_batches gemini/gemini-3-flash-preview: input_cost_per_token_flex, output_cost_per_token_flex, input_cost_per_token_batches, output_cost_per_token_batches, cache_read_input_token_cost_flex, cache_read_input_audio_token_cost, input_cost_per_audio_token_batches gemini/gemini-3-pro-image: input_cost_per_token_flex, output_cost_per_token_flex, input_cost_per_token_priority, output_cost_per_token_priority --- ...odel_prices_and_context_window_backup.json | 71 +++++++++++++++---- model_prices_and_context_window.json | 71 +++++++++++++++---- 2 files changed, 114 insertions(+), 28 deletions(-) diff --git a/litellm/model_prices_and_context_window_backup.json b/litellm/model_prices_and_context_window_backup.json index e63e2e07216..12ee3833ea0 100644 --- a/litellm/model_prices_and_context_window_backup.json +++ b/litellm/model_prices_and_context_window_backup.json @@ -25899,7 +25899,10 @@ } }, "gemini/gemini-2.5-flash": { + "cache_read_input_audio_token_cost": 1e-07, "cache_read_input_token_cost": 3e-08, + "cache_read_input_token_cost_flex": 3e-08, + "cache_read_input_token_cost_priority": 5.4e-08, "input_cost_per_audio_token": 1e-06, "input_cost_per_token": 3e-07, "litellm_provider": "gemini", @@ -25910,7 +25913,7 @@ "output_cost_per_reasoning_token": 2.5e-06, "output_cost_per_token": 2.5e-06, "rpm": 100000, - "source": "https://ai.google.dev/gemini-api/docs/models#gemini-2.5-flash-preview", + "source": "https://ai.google.dev/gemini-api/docs/pricing", "supported_endpoints": [ "/v1/chat/completions", "/v1/completions", @@ -25944,6 +25947,13 @@ "search_context_size_high": 0.035 }, "google_maps_grounding_cost_per_query": 0.025, + "input_cost_per_audio_token_batches": 5e-07, + "input_cost_per_token_batches": 1.5e-07, + "input_cost_per_token_flex": 1.5e-07, + "input_cost_per_token_priority": 5.4e-07, + "output_cost_per_token_batches": 1.25e-06, + "output_cost_per_token_flex": 1.25e-06, + "output_cost_per_token_priority": 4.5e-06, "supports_image_size": false }, "gemini/gemini-2.5-flash-image": { @@ -25951,6 +25961,9 @@ "deprecation_date": "2026-10-02", "input_cost_per_audio_token": 1e-06, "input_cost_per_token": 3e-07, + "input_cost_per_token_batches": 1.5e-07, + "input_cost_per_token_flex": 1.5e-07, + "input_cost_per_token_priority": 5.4e-07, "litellm_provider": "gemini", "supports_reasoning": false, "max_input_tokens": 32768, @@ -25962,7 +25975,7 @@ "output_cost_per_reasoning_token": 2.5e-06, "output_cost_per_token": 2.5e-06, "rpm": 100000, - "source": "https://ai.google.dev/gemini-api/docs/pricing#gemini-2.5-flash-image", + "source": "https://ai.google.dev/gemini-api/docs/pricing", "supported_endpoints": [ "/v1/chat/completions", "/v1/completions", @@ -26001,6 +26014,8 @@ "input_cost_per_image": 0.0011, "input_cost_per_token": 2e-06, "input_cost_per_token_batches": 1e-06, + "input_cost_per_token_flex": 1e-06, + "input_cost_per_token_priority": 3.6e-06, "litellm_provider": "gemini", "max_input_tokens": 65536, "max_output_tokens": 32768, @@ -26012,7 +26027,9 @@ "rpm": 1000, "tpm": 4000000, "output_cost_per_token_batches": 6e-06, - "source": "https://ai.google.dev/gemini-api/docs/pricing#gemini-3-pro-image", + "output_cost_per_token_flex": 6e-06, + "output_cost_per_token_priority": 2.16e-05, + "source": "https://ai.google.dev/gemini-api/docs/pricing", "supported_endpoints": [ "/v1/chat/completions", "/v1/completions", @@ -26290,7 +26307,10 @@ } }, "gemini/gemini-2.5-flash-lite": { + "cache_read_input_audio_token_cost": 3e-08, "cache_read_input_token_cost": 1e-08, + "cache_read_input_token_cost_flex": 1e-08, + "cache_read_input_token_cost_priority": 1.8e-08, "input_cost_per_audio_token": 3e-07, "input_cost_per_token": 1e-07, "litellm_provider": "gemini", @@ -26301,7 +26321,7 @@ "output_cost_per_reasoning_token": 4e-07, "output_cost_per_token": 4e-07, "rpm": 15, - "source": "https://ai.google.dev/gemini-api/docs/models#gemini-2.5-flash-lite", + "source": "https://ai.google.dev/gemini-api/docs/pricing", "supported_endpoints": [ "/v1/chat/completions", "/v1/completions", @@ -26335,6 +26355,13 @@ "search_context_size_high": 0.035 }, "google_maps_grounding_cost_per_query": 0.025, + "input_cost_per_audio_token_batches": 1.5e-07, + "input_cost_per_token_batches": 5e-08, + "input_cost_per_token_flex": 5e-08, + "input_cost_per_token_priority": 1.8e-07, + "output_cost_per_token_batches": 2e-07, + "output_cost_per_token_flex": 2e-07, + "output_cost_per_token_priority": 7.2e-07, "supports_image_size": false }, "gemini/gemini-2.5-flash-lite-preview-09-2025": { @@ -26585,7 +26612,7 @@ "mode": "audio_speech", "output_cost_per_audio_token": 1e-05, "output_cost_per_token": 1e-05, - "source": "https://cloud.google.com/gemini-enterprise-agent-platform/generative-ai/pricing", + "source": "https://ai.google.dev/gemini-api/docs/pricing", "supported_endpoints": [ "/v1/audio/speech" ], @@ -26595,10 +26622,13 @@ "gemini/gemini-2.5-pro": { "cache_read_input_token_cost": 1.25e-07, "cache_read_input_token_cost_above_200k_tokens": 2.5e-07, + "cache_read_input_token_cost_above_200k_tokens_priority": 4.5e-07, + "cache_read_input_token_cost_flex": 1.25e-07, + "cache_read_input_token_cost_priority": 2.25e-07, "input_cost_per_token": 1.25e-06, "input_cost_per_token_above_200k_tokens": 2.5e-06, - "input_cost_per_token_priority": 1.25e-06, - "input_cost_per_token_above_200k_tokens_priority": 2.5e-06, + "input_cost_per_token_priority": 2.25e-06, + "input_cost_per_token_above_200k_tokens_priority": 4.5e-06, "litellm_provider": "gemini", "max_input_tokens": 1048576, "max_output_tokens": 65535, @@ -26606,10 +26636,10 @@ "mode": "chat", "output_cost_per_token": 1e-05, "output_cost_per_token_above_200k_tokens": 1.5e-05, - "output_cost_per_token_priority": 1e-05, - "output_cost_per_token_above_200k_tokens_priority": 1.5e-05, + "output_cost_per_token_priority": 1.8e-05, + "output_cost_per_token_above_200k_tokens_priority": 2.7e-05, "rpm": 2000, - "source": "https://cloud.google.com/vertex-ai/generative-ai/pricing", + "source": "https://ai.google.dev/gemini-api/docs/pricing", "supported_endpoints": [ "/v1/chat/completions", "/v1/completions" @@ -26640,7 +26670,11 @@ "search_context_size_medium": 0.035, "search_context_size_high": 0.035 }, - "google_maps_grounding_cost_per_query": 0.025 + "google_maps_grounding_cost_per_query": 0.025, + "input_cost_per_token_batches": 6.25e-07, + "input_cost_per_token_flex": 6.25e-07, + "output_cost_per_token_batches": 5e-06, + "output_cost_per_token_flex": 5e-06 }, "gemini/gemini-2.5-computer-use-preview-10-2025": { "input_cost_per_token": 1.25e-06, @@ -26653,7 +26687,7 @@ "output_cost_per_token": 1e-05, "output_cost_per_token_above_200k_tokens": 1.5e-05, "rpm": 2000, - "source": "https://ai.google.dev/gemini-api/docs/computer-use", + "source": "https://ai.google.dev/gemini-api/docs/pricing", "supported_endpoints": [ "/v1/chat/completions", "/v1/completions" @@ -26899,7 +26933,9 @@ "google_maps_grounding_cost_per_query": 0.014 }, "gemini/gemini-3-flash-preview": { + "cache_read_input_audio_token_cost": 1e-07, "cache_read_input_token_cost": 5e-08, + "cache_read_input_token_cost_flex": 5e-08, "input_cost_per_audio_token": 1e-06, "input_cost_per_token": 5e-07, "litellm_provider": "gemini", @@ -26949,7 +26985,12 @@ "search_context_size_high": 0.014 }, "web_search_billing_unit": "per_query", - "google_maps_grounding_cost_per_query": 0.014 + "google_maps_grounding_cost_per_query": 0.014, + "input_cost_per_audio_token_batches": 5e-07, + "input_cost_per_token_batches": 2.5e-07, + "input_cost_per_token_flex": 2.5e-07, + "output_cost_per_token_batches": 1.5e-06, + "output_cost_per_token_flex": 1.5e-06 }, "gemini/gemini-3.5-flash": { "prompt_cache_min_tokens": 4096, @@ -27665,11 +27706,13 @@ "cache_read_input_token_cost": 1.25e-07, "input_cost_per_audio_token": 7e-07, "input_cost_per_token": 1e-06, + "input_cost_per_token_batches": 5e-07, "litellm_provider": "gemini", "max_input_tokens": 1048576, "max_output_tokens": 65535, "max_tokens": 65535, "mode": "chat", + "output_cost_per_audio_token": 2e-05, "output_cost_per_token": 2e-05, "rpm": 10000, "source": "https://ai.google.dev/gemini-api/docs/pricing", @@ -55661,7 +55704,7 @@ "mode": "audio_speech", "output_cost_per_audio_token": 1e-05, "output_cost_per_token": 1e-05, - "source": "https://cloud.google.com/gemini-enterprise-agent-platform/generative-ai/pricing", + "source": "https://ai.google.dev/gemini-api/docs/pricing", "supported_endpoints": [ "/v1/audio/speech" ] diff --git a/model_prices_and_context_window.json b/model_prices_and_context_window.json index e63e2e07216..12ee3833ea0 100644 --- a/model_prices_and_context_window.json +++ b/model_prices_and_context_window.json @@ -25899,7 +25899,10 @@ } }, "gemini/gemini-2.5-flash": { + "cache_read_input_audio_token_cost": 1e-07, "cache_read_input_token_cost": 3e-08, + "cache_read_input_token_cost_flex": 3e-08, + "cache_read_input_token_cost_priority": 5.4e-08, "input_cost_per_audio_token": 1e-06, "input_cost_per_token": 3e-07, "litellm_provider": "gemini", @@ -25910,7 +25913,7 @@ "output_cost_per_reasoning_token": 2.5e-06, "output_cost_per_token": 2.5e-06, "rpm": 100000, - "source": "https://ai.google.dev/gemini-api/docs/models#gemini-2.5-flash-preview", + "source": "https://ai.google.dev/gemini-api/docs/pricing", "supported_endpoints": [ "/v1/chat/completions", "/v1/completions", @@ -25944,6 +25947,13 @@ "search_context_size_high": 0.035 }, "google_maps_grounding_cost_per_query": 0.025, + "input_cost_per_audio_token_batches": 5e-07, + "input_cost_per_token_batches": 1.5e-07, + "input_cost_per_token_flex": 1.5e-07, + "input_cost_per_token_priority": 5.4e-07, + "output_cost_per_token_batches": 1.25e-06, + "output_cost_per_token_flex": 1.25e-06, + "output_cost_per_token_priority": 4.5e-06, "supports_image_size": false }, "gemini/gemini-2.5-flash-image": { @@ -25951,6 +25961,9 @@ "deprecation_date": "2026-10-02", "input_cost_per_audio_token": 1e-06, "input_cost_per_token": 3e-07, + "input_cost_per_token_batches": 1.5e-07, + "input_cost_per_token_flex": 1.5e-07, + "input_cost_per_token_priority": 5.4e-07, "litellm_provider": "gemini", "supports_reasoning": false, "max_input_tokens": 32768, @@ -25962,7 +25975,7 @@ "output_cost_per_reasoning_token": 2.5e-06, "output_cost_per_token": 2.5e-06, "rpm": 100000, - "source": "https://ai.google.dev/gemini-api/docs/pricing#gemini-2.5-flash-image", + "source": "https://ai.google.dev/gemini-api/docs/pricing", "supported_endpoints": [ "/v1/chat/completions", "/v1/completions", @@ -26001,6 +26014,8 @@ "input_cost_per_image": 0.0011, "input_cost_per_token": 2e-06, "input_cost_per_token_batches": 1e-06, + "input_cost_per_token_flex": 1e-06, + "input_cost_per_token_priority": 3.6e-06, "litellm_provider": "gemini", "max_input_tokens": 65536, "max_output_tokens": 32768, @@ -26012,7 +26027,9 @@ "rpm": 1000, "tpm": 4000000, "output_cost_per_token_batches": 6e-06, - "source": "https://ai.google.dev/gemini-api/docs/pricing#gemini-3-pro-image", + "output_cost_per_token_flex": 6e-06, + "output_cost_per_token_priority": 2.16e-05, + "source": "https://ai.google.dev/gemini-api/docs/pricing", "supported_endpoints": [ "/v1/chat/completions", "/v1/completions", @@ -26290,7 +26307,10 @@ } }, "gemini/gemini-2.5-flash-lite": { + "cache_read_input_audio_token_cost": 3e-08, "cache_read_input_token_cost": 1e-08, + "cache_read_input_token_cost_flex": 1e-08, + "cache_read_input_token_cost_priority": 1.8e-08, "input_cost_per_audio_token": 3e-07, "input_cost_per_token": 1e-07, "litellm_provider": "gemini", @@ -26301,7 +26321,7 @@ "output_cost_per_reasoning_token": 4e-07, "output_cost_per_token": 4e-07, "rpm": 15, - "source": "https://ai.google.dev/gemini-api/docs/models#gemini-2.5-flash-lite", + "source": "https://ai.google.dev/gemini-api/docs/pricing", "supported_endpoints": [ "/v1/chat/completions", "/v1/completions", @@ -26335,6 +26355,13 @@ "search_context_size_high": 0.035 }, "google_maps_grounding_cost_per_query": 0.025, + "input_cost_per_audio_token_batches": 1.5e-07, + "input_cost_per_token_batches": 5e-08, + "input_cost_per_token_flex": 5e-08, + "input_cost_per_token_priority": 1.8e-07, + "output_cost_per_token_batches": 2e-07, + "output_cost_per_token_flex": 2e-07, + "output_cost_per_token_priority": 7.2e-07, "supports_image_size": false }, "gemini/gemini-2.5-flash-lite-preview-09-2025": { @@ -26585,7 +26612,7 @@ "mode": "audio_speech", "output_cost_per_audio_token": 1e-05, "output_cost_per_token": 1e-05, - "source": "https://cloud.google.com/gemini-enterprise-agent-platform/generative-ai/pricing", + "source": "https://ai.google.dev/gemini-api/docs/pricing", "supported_endpoints": [ "/v1/audio/speech" ], @@ -26595,10 +26622,13 @@ "gemini/gemini-2.5-pro": { "cache_read_input_token_cost": 1.25e-07, "cache_read_input_token_cost_above_200k_tokens": 2.5e-07, + "cache_read_input_token_cost_above_200k_tokens_priority": 4.5e-07, + "cache_read_input_token_cost_flex": 1.25e-07, + "cache_read_input_token_cost_priority": 2.25e-07, "input_cost_per_token": 1.25e-06, "input_cost_per_token_above_200k_tokens": 2.5e-06, - "input_cost_per_token_priority": 1.25e-06, - "input_cost_per_token_above_200k_tokens_priority": 2.5e-06, + "input_cost_per_token_priority": 2.25e-06, + "input_cost_per_token_above_200k_tokens_priority": 4.5e-06, "litellm_provider": "gemini", "max_input_tokens": 1048576, "max_output_tokens": 65535, @@ -26606,10 +26636,10 @@ "mode": "chat", "output_cost_per_token": 1e-05, "output_cost_per_token_above_200k_tokens": 1.5e-05, - "output_cost_per_token_priority": 1e-05, - "output_cost_per_token_above_200k_tokens_priority": 1.5e-05, + "output_cost_per_token_priority": 1.8e-05, + "output_cost_per_token_above_200k_tokens_priority": 2.7e-05, "rpm": 2000, - "source": "https://cloud.google.com/vertex-ai/generative-ai/pricing", + "source": "https://ai.google.dev/gemini-api/docs/pricing", "supported_endpoints": [ "/v1/chat/completions", "/v1/completions" @@ -26640,7 +26670,11 @@ "search_context_size_medium": 0.035, "search_context_size_high": 0.035 }, - "google_maps_grounding_cost_per_query": 0.025 + "google_maps_grounding_cost_per_query": 0.025, + "input_cost_per_token_batches": 6.25e-07, + "input_cost_per_token_flex": 6.25e-07, + "output_cost_per_token_batches": 5e-06, + "output_cost_per_token_flex": 5e-06 }, "gemini/gemini-2.5-computer-use-preview-10-2025": { "input_cost_per_token": 1.25e-06, @@ -26653,7 +26687,7 @@ "output_cost_per_token": 1e-05, "output_cost_per_token_above_200k_tokens": 1.5e-05, "rpm": 2000, - "source": "https://ai.google.dev/gemini-api/docs/computer-use", + "source": "https://ai.google.dev/gemini-api/docs/pricing", "supported_endpoints": [ "/v1/chat/completions", "/v1/completions" @@ -26899,7 +26933,9 @@ "google_maps_grounding_cost_per_query": 0.014 }, "gemini/gemini-3-flash-preview": { + "cache_read_input_audio_token_cost": 1e-07, "cache_read_input_token_cost": 5e-08, + "cache_read_input_token_cost_flex": 5e-08, "input_cost_per_audio_token": 1e-06, "input_cost_per_token": 5e-07, "litellm_provider": "gemini", @@ -26949,7 +26985,12 @@ "search_context_size_high": 0.014 }, "web_search_billing_unit": "per_query", - "google_maps_grounding_cost_per_query": 0.014 + "google_maps_grounding_cost_per_query": 0.014, + "input_cost_per_audio_token_batches": 5e-07, + "input_cost_per_token_batches": 2.5e-07, + "input_cost_per_token_flex": 2.5e-07, + "output_cost_per_token_batches": 1.5e-06, + "output_cost_per_token_flex": 1.5e-06 }, "gemini/gemini-3.5-flash": { "prompt_cache_min_tokens": 4096, @@ -27665,11 +27706,13 @@ "cache_read_input_token_cost": 1.25e-07, "input_cost_per_audio_token": 7e-07, "input_cost_per_token": 1e-06, + "input_cost_per_token_batches": 5e-07, "litellm_provider": "gemini", "max_input_tokens": 1048576, "max_output_tokens": 65535, "max_tokens": 65535, "mode": "chat", + "output_cost_per_audio_token": 2e-05, "output_cost_per_token": 2e-05, "rpm": 10000, "source": "https://ai.google.dev/gemini-api/docs/pricing", @@ -55661,7 +55704,7 @@ "mode": "audio_speech", "output_cost_per_audio_token": 1e-05, "output_cost_per_token": 1e-05, - "source": "https://cloud.google.com/gemini-enterprise-agent-platform/generative-ai/pricing", + "source": "https://ai.google.dev/gemini-api/docs/pricing", "supported_endpoints": [ "/v1/audio/speech" ] From fb00567e4cd275d5cbe8333cb58ac8123c2b3787 Mon Sep 17 00:00:00 2001 From: yassin Date: Tue, 15 Sep 2026 18:38:07 +0000 Subject: [PATCH 068/225] fix(proxy): track per-member organization spend so the Organizations UI shows member spend Co-Authored-By: Devin AI <158243242+devin-ai-integration[bot]@users.noreply.github.com> --- litellm/proxy/_types.py | 2 + litellm/proxy/db/db_spend_update_writer.py | 45 ++++++- .../redis_update_buffer.py | 9 ++ .../spend_update_queue.py | 4 + .../test_redis_update_buffer.py | 45 +++++++ .../proxy/db/test_db_spend_update_writer.py | 116 ++++++++++++++++++ 6 files changed, 219 insertions(+), 2 deletions(-) diff --git a/litellm/proxy/_types.py b/litellm/proxy/_types.py index 9f85c3d1894..1d3992ab800 100644 --- a/litellm/proxy/_types.py +++ b/litellm/proxy/_types.py @@ -246,6 +246,7 @@ class Litellm_EntityType(enum.Enum): TEAM = "team" TEAM_MEMBER = "team_member" ORGANIZATION = "organization" + ORGANIZATION_MEMBER = "organization_member" PROJECT = "project" TAG = "tag" AGENT = "agent" @@ -5236,6 +5237,7 @@ class DBSpendUpdateTransactions(TypedDict): team_list_transactions: dict[str, float] | None team_member_list_transactions: dict[str, float] | None org_list_transactions: dict[str, float] | None + org_member_list_transactions: ReadOnly[dict[str, float] | None] tag_list_transactions: dict[str, float] | None agent_list_transactions: dict[str, float] | None model_access_group_list_transactions: ReadOnly[dict[str, float] | None] diff --git a/litellm/proxy/db/db_spend_update_writer.py b/litellm/proxy/db/db_spend_update_writer.py index eaa03c5d7f7..d207ba2f2c6 100644 --- a/litellm/proxy/db/db_spend_update_writer.py +++ b/litellm/proxy/db/db_spend_update_writer.py @@ -16,6 +16,7 @@ from collections.abc import Mapping, Sequence from datetime import datetime, timedelta, timezone from types import MappingProxyType from typing import TYPE_CHECKING, Any, Final, Literal, Protocol, cast, overload +from urllib.parse import quote, unquote import litellm from litellm._logging import verbose_proxy_logger @@ -85,6 +86,10 @@ else: RESPONSES_SESSION_CALL_TYPES: Final = frozenset({CallTypes.responses.value, CallTypes.aresponses.value}) +def _org_member_transaction_key(org_id: str, user_id: str) -> str: + return f"organization_id::{quote(org_id, safe='')}::user_id::{quote(user_id, safe='')}" + + def _is_batch_cost_row(payload: SpendLogsPayload) -> bool: return payload.get("call_type") == CallTypes.aretrieve_batch.value and payload.get("status") == "success" @@ -110,6 +115,7 @@ class _SpendBatch(Protocol): litellm_teamtable: BatchTable litellm_teammembership: BatchTable litellm_organizationtable: BatchTable + litellm_organizationmembership: BatchTable litellm_tagtable: BatchTable litellm_agentstable: BatchTable litellm_modelaccessgroupbudgettable: BatchTable @@ -666,6 +672,7 @@ class DBSpendUpdateWriter: await self._update_org_db( response_cost=response_cost, org_id=org_id, + user_id=user_id, prisma_client=prisma_client, ) except Exception: @@ -900,6 +907,7 @@ class DBSpendUpdateWriter: self, response_cost: float | None, org_id: str | None, + user_id: str | None, prisma_client: PrismaClient | None, ): try: @@ -916,6 +924,15 @@ class DBSpendUpdateWriter: response_cost=response_cost, ) ) + + if user_id is not None: + await self.spend_update_queue.add_update( + update=SpendUpdateQueueItem( + entity_type=Litellm_EntityType.ORGANIZATION_MEMBER, + entity_id=_org_member_transaction_key(org_id, user_id), + response_cost=response_cost, + ) + ) except Exception as e: spend_log_error( "Spend tracking - failed to enqueue org spend update. org_id=%s, response_cost=%s - %s", @@ -1163,14 +1180,15 @@ class DBSpendUpdateWriter: if db_spend_update_transactions is not None: verbose_proxy_logger.info( "Spend tracking - committing spend updates from Redis to DB: " - "keys=%d, users=%d, teams=%d, orgs=%d, end_users=%d, team_members=%d, tags=%d, agents=%d, " - "model_access_groups=%d", + "keys=%d, users=%d, teams=%d, orgs=%d, end_users=%d, team_members=%d, org_members=%d, tags=%d, " + "agents=%d, model_access_groups=%d", len(db_spend_update_transactions.get("key_list_transactions") or {}), len(db_spend_update_transactions.get("user_list_transactions") or {}), len(db_spend_update_transactions.get("team_list_transactions") or {}), len(db_spend_update_transactions.get("org_list_transactions") or {}), len(db_spend_update_transactions.get("end_user_list_transactions") or {}), len(db_spend_update_transactions.get("team_member_list_transactions") or {}), + len(db_spend_update_transactions.get("org_member_list_transactions") or {}), len(db_spend_update_transactions.get("tag_list_transactions") or {}), len(db_spend_update_transactions.get("agent_list_transactions") or {}), len(db_spend_update_transactions.get("model_access_group_list_transactions") or {}), @@ -1708,6 +1726,29 @@ class DBSpendUpdateWriter: proxy_logging_obj=proxy_logging_obj, ) + org_member_list_transactions: Final = db_spend_update_transactions.get("org_member_list_transactions") + verbose_proxy_logger.debug("Org Membership Spend transactions: %s", org_member_list_transactions) + if org_member_list_transactions is not None and len(org_member_list_transactions.keys()) > 0: + for i in range(n_retry_times + 1): + start_time = time.time() + try: + async with _spend_update_tx(prisma_client) as transaction, transaction.batch_() as batcher: + for key, response_cost in sorted(org_member_list_transactions.items()): + _, quoted_org_id, _, quoted_user_id = key.split("::") + batcher.litellm_organizationmembership.update_many( + where={"organization_id": unquote(quoted_org_id), "user_id": unquote(quoted_user_id)}, + data={"spend": {"increment": response_cost}}, + ) + break + except Exception as e: + await self._handle_spend_update_failure( + e=e, + attempt=i, + n_retry_times=n_retry_times, + start_time=start_time, + proxy_logging_obj=proxy_logging_obj, + ) + ### UPDATE TAG TABLE ### tag_list_transactions: Final = db_spend_update_transactions["tag_list_transactions"] await DBSpendUpdateWriter._update_entity_spend_in_db( diff --git a/litellm/proxy/db/db_transaction_queue/redis_update_buffer.py b/litellm/proxy/db/db_transaction_queue/redis_update_buffer.py index c06f2e04aca..6f49a00b763 100644 --- a/litellm/proxy/db/db_transaction_queue/redis_update_buffer.py +++ b/litellm/proxy/db/db_transaction_queue/redis_update_buffer.py @@ -69,6 +69,7 @@ _SpendTransactionField: TypeAlias = Literal[ "team_list_transactions", "team_member_list_transactions", "org_list_transactions", + "org_member_list_transactions", "tag_list_transactions", "agent_list_transactions", "model_access_group_list_transactions", @@ -81,6 +82,7 @@ _SPEND_TRANSACTION_FIELDS: Final[tuple[_SpendTransactionField, ...]] = ( "team_list_transactions", "team_member_list_transactions", "org_list_transactions", + "org_member_list_transactions", "tag_list_transactions", "agent_list_transactions", "model_access_group_list_transactions", @@ -412,6 +414,10 @@ class RedisUpdateBuffer: Litellm_EntityType.ORGANIZATION, db_spend_update_transactions.get("org_list_transactions"), ), + ( + Litellm_EntityType.ORGANIZATION_MEMBER, + db_spend_update_transactions.get("org_member_list_transactions"), + ), ( Litellm_EntityType.TAG, db_spend_update_transactions.get("tag_list_transactions"), @@ -876,6 +882,9 @@ class RedisUpdateBuffer: list_of_transactions, "team_member_list_transactions" ), org_list_transactions=_merged_entity_transactions(list_of_transactions, "org_list_transactions"), + org_member_list_transactions=_merged_entity_transactions( + list_of_transactions, "org_member_list_transactions" + ), tag_list_transactions=_merged_entity_transactions(list_of_transactions, "tag_list_transactions"), agent_list_transactions=_merged_entity_transactions(list_of_transactions, "agent_list_transactions"), model_access_group_list_transactions=_merged_entity_transactions( diff --git a/litellm/proxy/db/db_transaction_queue/spend_update_queue.py b/litellm/proxy/db/db_transaction_queue/spend_update_queue.py index 8c0076b10c1..bc068d10daf 100644 --- a/litellm/proxy/db/db_transaction_queue/spend_update_queue.py +++ b/litellm/proxy/db/db_transaction_queue/spend_update_queue.py @@ -137,6 +137,7 @@ class SpendUpdateQueue(BaseUpdateQueue): team_list_transactions={}, team_member_list_transactions={}, org_list_transactions={}, + org_member_list_transactions={}, tag_list_transactions={}, agent_list_transactions={}, model_access_group_list_transactions={}, @@ -150,6 +151,7 @@ class SpendUpdateQueue(BaseUpdateQueue): Litellm_EntityType.TEAM: "team_list_transactions", Litellm_EntityType.TEAM_MEMBER: "team_member_list_transactions", Litellm_EntityType.ORGANIZATION: "org_list_transactions", + Litellm_EntityType.ORGANIZATION_MEMBER: "org_member_list_transactions", Litellm_EntityType.TAG: "tag_list_transactions", Litellm_EntityType.AGENT: "agent_list_transactions", Litellm_EntityType.MODEL_ACCESS_GROUP: "model_access_group_list_transactions", @@ -188,6 +190,8 @@ class SpendUpdateQueue(BaseUpdateQueue): transactions_dict = db_spend_update_transactions["team_member_list_transactions"] elif dict_key == "org_list_transactions": transactions_dict = db_spend_update_transactions["org_list_transactions"] + elif dict_key == "org_member_list_transactions": + transactions_dict = db_spend_update_transactions["org_member_list_transactions"] elif dict_key == "tag_list_transactions": transactions_dict = db_spend_update_transactions["tag_list_transactions"] elif dict_key == "agent_list_transactions": diff --git a/tests/test_litellm/proxy/db/db_transaction_queue/test_redis_update_buffer.py b/tests/test_litellm/proxy/db/db_transaction_queue/test_redis_update_buffer.py index 8f3508fc4e9..817c86a1bdf 100644 --- a/tests/test_litellm/proxy/db/db_transaction_queue/test_redis_update_buffer.py +++ b/tests/test_litellm/proxy/db/db_transaction_queue/test_redis_update_buffer.py @@ -266,6 +266,51 @@ async def test_get_all_transactions_from_redis_buffer_pipeline(redis_update_buff assert popped_keys[6] == REDIS_WINDOW_SPEND_UPDATE_BUFFER_KEY +@pytest.mark.asyncio +async def test_org_member_spend_is_summed_across_pods_and_restored_on_rpush_failure( + redis_update_buffer, mock_redis_cache +): + from litellm.proxy._types import Litellm_EntityType + from litellm.proxy.db.db_transaction_queue.daily_spend_update_queue import ( + DailySpendUpdateQueue, + ) + from litellm.proxy.db.db_transaction_queue.spend_update_queue import ( + SpendUpdateQueue, + ) + + member_key = "organization_id::org-1::user_id::user-1" + pod_json = json.dumps({"org_member_list_transactions": {member_key: 0.25}}) + mock_redis_cache.async_lpop_pipeline = AsyncMock( + return_value=[[pod_json, pod_json], None, None, None, None, None, None] + ) + + (db_spend, *_rest) = await redis_update_buffer.get_all_transactions_from_redis_buffer_pipeline() + + assert db_spend is not None + assert db_spend["org_member_list_transactions"] == {member_key: 0.5} + + mock_redis_cache.async_rpush_pipeline = AsyncMock(side_effect=ConnectionError("redis went away")) + spend_queue = SpendUpdateQueue() + await spend_queue.add_update( + { + "entity_type": Litellm_EntityType.ORGANIZATION_MEMBER, + "entity_id": member_key, + "response_cost": 1.5, + } + ) + await redis_update_buffer.store_in_memory_spend_updates_in_redis( + spend_update_queue=spend_queue, + daily_spend_update_queue=DailySpendUpdateQueue(), + daily_team_spend_update_queue=DailySpendUpdateQueue(), + daily_org_spend_update_queue=DailySpendUpdateQueue(), + daily_end_user_spend_update_queue=DailySpendUpdateQueue(), + daily_agent_spend_update_queue=DailySpendUpdateQueue(), + ) + + restored_spend = await spend_queue.flush_and_get_aggregated_db_spend_update_transactions() + assert restored_spend["org_member_list_transactions"] == {member_key: 1.5} + + @pytest.mark.asyncio async def test_get_all_transactions_from_redis_buffer_pipeline_no_redis(): """When redis_cache is None, should return all Nones""" diff --git a/tests/test_litellm/proxy/db/test_db_spend_update_writer.py b/tests/test_litellm/proxy/db/test_db_spend_update_writer.py index 5e977712a1e..64001146e05 100644 --- a/tests/test_litellm/proxy/db/test_db_spend_update_writer.py +++ b/tests/test_litellm/proxy/db/test_db_spend_update_writer.py @@ -944,6 +944,121 @@ async def test_commit_spend_updates_to_db_increments_team_member_spend_and_total } +@pytest.mark.asyncio +async def test_org_spend_increments_organization_membership_row_for_the_calling_user(): + """A request made with a user_id inside an org must increment that user's + LiteLLM_OrganizationMembership.spend, not only the org total, or the + Organizations > Members UI renders '-' for every member.""" + db_writer = DBSpendUpdateWriter() + await db_writer._update_org_db( + response_cost=0.75, + org_id="org-abc", + user_id="user-xyz", + prisma_client=MagicMock(), + ) + transactions = await db_writer.spend_update_queue.flush_and_get_aggregated_db_spend_update_transactions() + + mock_batcher = MagicMock() + mock_prisma_client = MagicMock() + mock_prisma_client.db.tx = MagicMock(return_value=_good_tx(mock_batcher)) + proxy_logging = MagicMock() + proxy_logging.call_details = {} + + await db_writer._commit_spend_updates_to_db( + prisma_client=mock_prisma_client, + n_retry_times=0, + proxy_logging_obj=proxy_logging, + db_spend_update_transactions=transactions, + ) + + mock_batcher.litellm_organizationtable.update_many.assert_called_once_with( + where={"organization_id": "org-abc"}, + data={"spend": {"increment": 0.75}}, + ) + mock_batcher.litellm_organizationmembership.update_many.assert_called_once_with( + where={"organization_id": "org-abc", "user_id": "user-xyz"}, + data={"spend": {"increment": 0.75}}, + ) + + +@pytest.mark.asyncio +async def test_org_spend_without_user_id_leaves_organization_membership_untouched(): + db_writer = DBSpendUpdateWriter() + await db_writer._update_org_db( + response_cost=0.75, + org_id="org-abc", + user_id=None, + prisma_client=MagicMock(), + ) + transactions = await db_writer.spend_update_queue.flush_and_get_aggregated_db_spend_update_transactions() + + mock_batcher = MagicMock() + mock_prisma_client = MagicMock() + mock_prisma_client.db.tx = MagicMock(return_value=_good_tx(mock_batcher)) + proxy_logging = MagicMock() + proxy_logging.call_details = {} + + await db_writer._commit_spend_updates_to_db( + prisma_client=mock_prisma_client, + n_retry_times=0, + proxy_logging_obj=proxy_logging, + db_spend_update_transactions=transactions, + ) + + mock_batcher.litellm_organizationtable.update_many.assert_called_once() + mock_batcher.litellm_organizationmembership.update_many.assert_not_called() + + +@pytest.mark.asyncio +async def test_org_spend_keeps_member_attribution_when_ids_contain_the_key_delimiter(): + db_writer = DBSpendUpdateWriter() + await db_writer._update_org_db( + response_cost=0.75, + org_id="division::west", + user_id="user::42", + prisma_client=MagicMock(), + ) + transactions = await db_writer.spend_update_queue.flush_and_get_aggregated_db_spend_update_transactions() + + mock_batcher = MagicMock() + mock_prisma_client = MagicMock() + mock_prisma_client.db.tx = MagicMock(return_value=_good_tx(mock_batcher)) + proxy_logging = MagicMock() + proxy_logging.call_details = {} + + await db_writer._commit_spend_updates_to_db( + prisma_client=mock_prisma_client, + n_retry_times=0, + proxy_logging_obj=proxy_logging, + db_spend_update_transactions=transactions, + ) + + mock_batcher.litellm_organizationmembership.update_many.assert_called_once_with( + where={"organization_id": "division::west", "user_id": "user::42"}, + data={"spend": {"increment": 0.75}}, + ) + + +@pytest.mark.asyncio +async def test_batch_database_updates_queues_org_member_spend_for_the_request_user(): + db_writer = DBSpendUpdateWriter() + await db_writer._batch_database_updates( + response_cost=0.1, + user_id="u1", + hashed_token="t1", + team_id=None, + org_id="org1", + end_user_id=None, + prisma_client=MagicMock(), + litellm_proxy_budget_name=None, + payload={"request_id": "req-1", "model": "gpt-4o-mini", "spend": 0.1}, + ) + transactions = await db_writer.spend_update_queue.flush_and_get_aggregated_db_spend_update_transactions() + + assert transactions["org_list_transactions"] == {"org1": 0.1} + assert transactions["org_member_list_transactions"] == {"organization_id::org1::user_id::u1": 0.1} + + @pytest.mark.asyncio async def test_add_spend_log_transaction_to_daily_tag_transaction_with_request_id(): """ @@ -2904,6 +3019,7 @@ async def test_update_daily_spend_retries_deadlock(monkeypatch): ("team_list_transactions", "team-1"), ("team_member_list_transactions", "team_id::team-1::user_id::user-1"), ("org_list_transactions", "org-1"), + ("org_member_list_transactions", "organization_id::org-1::user_id::user-1"), ("tag_list_transactions", "tag-1"), ("agent_list_transactions", "agent-1"), ], From a511c9d45dffdea21e5b974499631ba029a62414 Mon Sep 17 00:00:00 2001 From: "berriai-litellm-provider-info-sync[bot]" <328147090+berriai-litellm-provider-info-sync[bot]@users.noreply.github.com> Date: Tue, 15 Sep 2026 18:41:29 +0000 Subject: [PATCH 069/225] chore(prices): sync Google Gemini prices: 7 models [enrichment failed: Google Gemini, 86 held] gemini/gemini-3.1-flash-image: gemini/gemini-3.1-flash-lite: cache_read_input_audio_token_cost, input_cost_per_audio_token_batches gemini/gemini-3.1-flash-lite-image: gemini-3.1-flash-live-preview: gemini/gemini-3.1-flash-live-preview: gemini/gemini-3.1-flash-tts-preview: output_cost_per_audio_token, input_cost_per_token_batches gemini/gemini-3.1-pro-preview: input_cost_per_token_flex, output_cost_per_token_flex, cache_read_input_token_cost_flex --- ...odel_prices_and_context_window_backup.json | 25 ++++++++++++------- model_prices_and_context_window.json | 25 ++++++++++++------- 2 files changed, 32 insertions(+), 18 deletions(-) diff --git a/litellm/model_prices_and_context_window_backup.json b/litellm/model_prices_and_context_window_backup.json index 12ee3833ea0..02adbbed736 100644 --- a/litellm/model_prices_and_context_window_backup.json +++ b/litellm/model_prices_and_context_window_backup.json @@ -26158,7 +26158,7 @@ "output_cost_per_token_batches": 1.5e-06, "rpm": 1000, "tpm": 4000000, - "source": "https://ai.google.dev/gemini-api/docs/pricing#gemini-3.1-flash-image", + "source": "https://ai.google.dev/gemini-api/docs/pricing", "supported_endpoints": [ "/v1/chat/completions", "/v1/completions", @@ -26243,7 +26243,7 @@ "output_cost_per_token": 1.5e-06, "output_cost_per_token_batches": 7.5e-07, "rpm": 1000, - "source": "https://ai.google.dev/gemini-api/docs/pricing#gemini-3.1-flash-lite-image", + "source": "https://ai.google.dev/gemini-api/docs/pricing", "supported_endpoints": [ "/v1/chat/completions", "/v1/completions", @@ -26815,6 +26815,7 @@ "google_maps_grounding_cost_per_query": 0.014 }, "gemini/gemini-3.1-flash-lite": { + "cache_read_input_audio_token_cost": 5e-08, "cache_read_input_token_cost": 2.5e-08, "cache_read_input_token_cost_flex": 1.25e-08, "cache_read_input_token_cost_priority": 4.5e-08, @@ -26835,7 +26836,7 @@ "output_cost_per_token_flex": 7.5e-07, "output_cost_per_token_priority": 2.7e-06, "rpm": 15, - "source": "https://ai.google.dev/gemini-api/docs/pricing#gemini-3.1-flash-lite", + "source": "https://ai.google.dev/gemini-api/docs/pricing", "supported_endpoints": [ "/v1/chat/completions", "/v1/completions", @@ -26872,7 +26873,8 @@ "search_context_size_high": 0.014 }, "web_search_billing_unit": "per_query", - "google_maps_grounding_cost_per_query": 0.014 + "google_maps_grounding_cost_per_query": 0.014, + "input_cost_per_audio_token_batches": 2.5e-07 }, "gemini/gemini-3.5-flash-lite": { "cache_read_input_token_cost": 3e-08, @@ -27280,7 +27282,7 @@ "output_cost_per_token_above_200k_tokens": 1.8e-05, "output_cost_per_token_batches": 6e-06, "rpm": 2000, - "source": "https://ai.google.dev/gemini-api/docs/models#gemini-3.1-pro-preview", + "source": "https://ai.google.dev/gemini-api/docs/pricing", "supported_endpoints": [ "/v1/chat/completions", "/v1/completions", @@ -27315,13 +27317,16 @@ "output_cost_per_token_above_200k_tokens_priority": 3.24e-05, "cache_read_input_token_cost_priority": 3.6e-07, "cache_read_input_token_cost_above_200k_tokens_priority": 7.2e-07, + "cache_read_input_token_cost_flex": 2e-07, "search_context_cost_per_query": { "search_context_size_low": 0.014, "search_context_size_medium": 0.014, "search_context_size_high": 0.014 }, "web_search_billing_unit": "per_query", - "google_maps_grounding_cost_per_query": 0.014 + "google_maps_grounding_cost_per_query": 0.014, + "input_cost_per_token_flex": 1e-06, + "output_cost_per_token_flex": 6e-06 }, "gemini/gemini-3.1-pro-preview-customtools": { "prompt_cache_min_tokens": 4096, @@ -55540,7 +55545,7 @@ "mode": "realtime", "output_cost_per_audio_token": 1.2e-05, "output_cost_per_token": 4.5e-06, - "source": "https://cloud.google.com/gemini-enterprise-agent-platform/generative-ai/pricing", + "source": "https://ai.google.dev/gemini-api/docs/pricing", "supported_endpoints": [ "/v1/realtime" ], @@ -55658,7 +55663,7 @@ "mode": "realtime", "output_cost_per_audio_token": 1.2e-05, "output_cost_per_token": 4.5e-06, - "source": "https://cloud.google.com/gemini-enterprise-agent-platform/generative-ai/pricing", + "source": "https://ai.google.dev/gemini-api/docs/pricing", "supported_endpoints": [ "/v1/realtime" ], @@ -55684,13 +55689,15 @@ }, "gemini/gemini-3.1-flash-tts-preview": { "input_cost_per_token": 1e-06, + "input_cost_per_token_batches": 5e-07, "litellm_provider": "gemini", "max_input_tokens": 8192, "max_output_tokens": 16384, "max_tokens": 16384, "mode": "audio_speech", + "output_cost_per_audio_token": 2e-05, "output_cost_per_token": 2e-05, - "source": "https://ai.google.dev/gemini-api/docs/models/gemini-3.1-flash-tts-preview", + "source": "https://ai.google.dev/gemini-api/docs/pricing", "supported_endpoints": [ "/v1/audio/speech" ], diff --git a/model_prices_and_context_window.json b/model_prices_and_context_window.json index 12ee3833ea0..02adbbed736 100644 --- a/model_prices_and_context_window.json +++ b/model_prices_and_context_window.json @@ -26158,7 +26158,7 @@ "output_cost_per_token_batches": 1.5e-06, "rpm": 1000, "tpm": 4000000, - "source": "https://ai.google.dev/gemini-api/docs/pricing#gemini-3.1-flash-image", + "source": "https://ai.google.dev/gemini-api/docs/pricing", "supported_endpoints": [ "/v1/chat/completions", "/v1/completions", @@ -26243,7 +26243,7 @@ "output_cost_per_token": 1.5e-06, "output_cost_per_token_batches": 7.5e-07, "rpm": 1000, - "source": "https://ai.google.dev/gemini-api/docs/pricing#gemini-3.1-flash-lite-image", + "source": "https://ai.google.dev/gemini-api/docs/pricing", "supported_endpoints": [ "/v1/chat/completions", "/v1/completions", @@ -26815,6 +26815,7 @@ "google_maps_grounding_cost_per_query": 0.014 }, "gemini/gemini-3.1-flash-lite": { + "cache_read_input_audio_token_cost": 5e-08, "cache_read_input_token_cost": 2.5e-08, "cache_read_input_token_cost_flex": 1.25e-08, "cache_read_input_token_cost_priority": 4.5e-08, @@ -26835,7 +26836,7 @@ "output_cost_per_token_flex": 7.5e-07, "output_cost_per_token_priority": 2.7e-06, "rpm": 15, - "source": "https://ai.google.dev/gemini-api/docs/pricing#gemini-3.1-flash-lite", + "source": "https://ai.google.dev/gemini-api/docs/pricing", "supported_endpoints": [ "/v1/chat/completions", "/v1/completions", @@ -26872,7 +26873,8 @@ "search_context_size_high": 0.014 }, "web_search_billing_unit": "per_query", - "google_maps_grounding_cost_per_query": 0.014 + "google_maps_grounding_cost_per_query": 0.014, + "input_cost_per_audio_token_batches": 2.5e-07 }, "gemini/gemini-3.5-flash-lite": { "cache_read_input_token_cost": 3e-08, @@ -27280,7 +27282,7 @@ "output_cost_per_token_above_200k_tokens": 1.8e-05, "output_cost_per_token_batches": 6e-06, "rpm": 2000, - "source": "https://ai.google.dev/gemini-api/docs/models#gemini-3.1-pro-preview", + "source": "https://ai.google.dev/gemini-api/docs/pricing", "supported_endpoints": [ "/v1/chat/completions", "/v1/completions", @@ -27315,13 +27317,16 @@ "output_cost_per_token_above_200k_tokens_priority": 3.24e-05, "cache_read_input_token_cost_priority": 3.6e-07, "cache_read_input_token_cost_above_200k_tokens_priority": 7.2e-07, + "cache_read_input_token_cost_flex": 2e-07, "search_context_cost_per_query": { "search_context_size_low": 0.014, "search_context_size_medium": 0.014, "search_context_size_high": 0.014 }, "web_search_billing_unit": "per_query", - "google_maps_grounding_cost_per_query": 0.014 + "google_maps_grounding_cost_per_query": 0.014, + "input_cost_per_token_flex": 1e-06, + "output_cost_per_token_flex": 6e-06 }, "gemini/gemini-3.1-pro-preview-customtools": { "prompt_cache_min_tokens": 4096, @@ -55540,7 +55545,7 @@ "mode": "realtime", "output_cost_per_audio_token": 1.2e-05, "output_cost_per_token": 4.5e-06, - "source": "https://cloud.google.com/gemini-enterprise-agent-platform/generative-ai/pricing", + "source": "https://ai.google.dev/gemini-api/docs/pricing", "supported_endpoints": [ "/v1/realtime" ], @@ -55658,7 +55663,7 @@ "mode": "realtime", "output_cost_per_audio_token": 1.2e-05, "output_cost_per_token": 4.5e-06, - "source": "https://cloud.google.com/gemini-enterprise-agent-platform/generative-ai/pricing", + "source": "https://ai.google.dev/gemini-api/docs/pricing", "supported_endpoints": [ "/v1/realtime" ], @@ -55684,13 +55689,15 @@ }, "gemini/gemini-3.1-flash-tts-preview": { "input_cost_per_token": 1e-06, + "input_cost_per_token_batches": 5e-07, "litellm_provider": "gemini", "max_input_tokens": 8192, "max_output_tokens": 16384, "max_tokens": 16384, "mode": "audio_speech", + "output_cost_per_audio_token": 2e-05, "output_cost_per_token": 2e-05, - "source": "https://ai.google.dev/gemini-api/docs/models/gemini-3.1-flash-tts-preview", + "source": "https://ai.google.dev/gemini-api/docs/pricing", "supported_endpoints": [ "/v1/audio/speech" ], From 41caaa301c427088eb836732dc4877b0f00442eb Mon Sep 17 00:00:00 2001 From: "berriai-litellm-provider-info-sync[bot]" <328147090+berriai-litellm-provider-info-sync[bot]@users.noreply.github.com> Date: Tue, 15 Sep 2026 18:46:36 +0000 Subject: [PATCH 070/225] chore(prices): sync prices for 2 providers: 235 models, 59 new [enrichment failed: Google Gemini, 34 held] azure_ai/Codestral-2501: azure_ai/cohere-command-a: azure_ai/deepseek-r1: azure_ai/deepseek-v3: azure_ai/deepseek-v3-0324: azure_ai/deepseek-v3.1: azure_ai/deepseek-v3.2: azure_ai/deepseek-v3.2-speciale: azure_ai/deepseek-v4-flash: azure_ai/DeepSeek-V4-Flash-0731: azure_ai/deepseek-v4-pro: azure_ai/embed-v-4-0: azure_ai/FW-DeepSeek-V3.2: azure_ai/FW-DeepSeek-V4-Pro: azure_ai/FW-GLM-5: azure_ai/FW-GLM-5.1: azure_ai/FW-GLM-5.2: azure_ai/FW-GLM-5.2-Fast: input_cost_per_token, output_cost_per_token, cache_read_input_token_cost azure_ai/FW-Inkling: input_cost_per_token, output_cost_per_token, cache_read_input_token_cost azure_ai/FW-Kimi-K2.5: azure_ai/FW-Kimi-K2.6: azure_ai/FW-Kimi-K2.7-Code: azure_ai/FW-Kimi-K3: azure_ai/FW-MiniMax-M2.5: azure_ai/FW-MiniMax-M3: azure_ai/FW-Nemotron-3-Ultra-NVFP4: input_cost_per_token, output_cost_per_token, cache_read_input_token_cost azure_ai/FW-Nemotron-Lightning-3.5-30B-A3B: azure_ai/gpt-oss-120b: azure_ai/grok-3: azure_ai/global/grok-3: azure_ai/grok-3-mini: azure_ai/global/grok-3-mini: azure_ai/grok-4: azure_ai/grok-4-1-fast-non-reasoning: azure_ai/grok-4-1-fast-reasoning: azure_ai/grok-4-20-non-reasoning: azure_ai/grok-4-20-reasoning: azure_ai/grok-4-fast-non-reasoning: azure_ai/grok-4-fast-reasoning: azure_ai/grok-4.3: azure_ai/grok-4.6: azure_ai/grok-code-fast-1: azure_ai/kimi-k2.5: azure_ai/kimi-k2.6: azure_ai/kimi-k2.7-code: azure_ai/Llama-3.3-70B-Instruct: azure_ai/Llama-4-Maverick-17B-128E-Instruct-FP8: input_cost_per_token, output_cost_per_token azure_ai/MAI-DS-R1: azure_ai/MAI-Image-2.5: azure_ai/MAI-Image-2.5-Flash: azure_ai/MAI-Image-2e: azure_ai/MAI-Thinking-1: azure_ai/mistral-large-3: azure_ai/Phi-3-medium-128k-instruct: azure_ai/Phi-3-medium-4k-instruct: azure_ai/Phi-3-mini-128k-instruct: azure_ai/Phi-3-mini-4k-instruct: azure_ai/Phi-3-small-128k-instruct: azure_ai/Phi-3-small-8k-instruct: azure_ai/Phi-3.5-mini-instruct: --- ...odel_prices_and_context_window_backup.json | 1283 ++++++++++++++--- model_prices_and_context_window.json | 1283 ++++++++++++++--- 2 files changed, 2206 insertions(+), 360 deletions(-) diff --git a/litellm/model_prices_and_context_window_backup.json b/litellm/model_prices_and_context_window_backup.json index 02adbbed736..1d863977e1e 100644 --- a/litellm/model_prices_and_context_window_backup.json +++ b/litellm/model_prices_and_context_window_backup.json @@ -3144,6 +3144,7 @@ "max_tokens": 100000, "mode": "responses", "output_cost_per_token": 6e-06, + "source": "https://prices.azure.com/api/retail/prices?$filter=serviceName%20eq%20'Foundry%20Models'%20and%20armRegionName%20eq%20'eastus2'%20and%20priceType%20eq%20'Consumption'", "supported_endpoints": [ "/v1/responses" ], @@ -3535,6 +3536,7 @@ "max_tokens": 1024, "mode": "chat", "output_cost_per_token": 1.2e-05, + "source": "https://prices.azure.com/api/retail/prices?$filter=serviceName%20eq%20'Foundry%20Models'%20and%20armRegionName%20eq%20'eastus2'%20and%20priceType%20eq%20'Consumption'", "supported_endpoints": [ "/v1/responses" ], @@ -3567,7 +3569,7 @@ "max_output_tokens": 131072, "max_tokens": 131072, "mode": "chat", - "source": "https://azure.microsoft.com/en-us/pricing/details/cognitive-services/openai-service/", + "source": "https://prices.azure.com/api/retail/prices?$filter=serviceName%20eq%20'Foundry%20Models'%20and%20armRegionName%20eq%20'eastus2'%20and%20priceType%20eq%20'Consumption'", "supports_function_calling": true, "supports_parallel_function_calling": true, "supports_response_schema": true, @@ -4172,12 +4174,15 @@ "deprecation_date": "2027-04-14", "cache_read_input_token_cost": 1.375e-06, "input_cost_per_token": 2.75e-06, + "input_cost_per_token_batches": 1.375e-06, "litellm_provider": "azure", "max_input_tokens": 128000, "max_output_tokens": 16384, "max_tokens": 16384, "mode": "chat", "output_cost_per_token": 1.1e-05, + "output_cost_per_token_batches": 5.5e-06, + "source": "https://prices.azure.com/api/retail/prices?$filter=serviceName%20eq%20'Foundry%20Models'%20and%20armRegionName%20eq%20'eastus2'%20and%20priceType%20eq%20'Consumption'", "supports_function_calling": true, "supports_parallel_function_calling": true, "supports_prompt_caching": true, @@ -4188,13 +4193,17 @@ "azure/eu/gpt-4o-2024-11-20": { "deprecation_date": "2027-04-14", "cache_creation_input_token_cost": 1.38e-06, + "cache_read_input_token_cost": 1.375e-06, "input_cost_per_token": 2.75e-06, + "input_cost_per_token_batches": 1.375e-06, "litellm_provider": "azure", "max_input_tokens": 128000, "max_output_tokens": 16384, "max_tokens": 16384, "mode": "chat", "output_cost_per_token": 1.1e-05, + "output_cost_per_token_batches": 5.5e-06, + "source": "https://prices.azure.com/api/retail/prices?$filter=serviceName%20eq%20'Foundry%20Models'%20and%20armRegionName%20eq%20'eastus2'%20and%20priceType%20eq%20'Consumption'", "supports_function_calling": true, "supports_parallel_function_calling": true, "supports_response_schema": true, @@ -4205,12 +4214,14 @@ "cache_read_input_token_cost": 8.3e-08, "deprecation_date": "2027-04-14", "input_cost_per_token": 1.65e-07, + "input_cost_per_token_batches": 8.3e-08, "litellm_provider": "azure", "max_input_tokens": 128000, "max_output_tokens": 16384, "max_tokens": 16384, "mode": "chat", "output_cost_per_token": 6.6e-07, + "source": "https://prices.azure.com/api/retail/prices?$filter=serviceName%20eq%20'Foundry%20Models'%20and%20armRegionName%20eq%20'eastus2'%20and%20priceType%20eq%20'Consumption'", "supports_function_calling": true, "supports_parallel_function_calling": true, "supports_prompt_caching": true, @@ -4351,8 +4362,9 @@ }, "azure/eu/gpt-5.1": { "deprecation_date": "2027-05-15", - "cache_read_input_token_cost": 1.4e-07, - "input_cost_per_token": 1.38e-06, + "cache_read_input_token_cost": 1.375e-07, + "cache_read_input_token_cost_priority": 2.75e-07, + "input_cost_per_token": 1.375e-06, "litellm_provider": "azure", "max_input_tokens": 272000, "max_output_tokens": 128000, @@ -4383,12 +4395,17 @@ "supports_tool_choice": true, "supports_vision": true, "supports_none_reasoning_effort": true, - "default_reasoning_effort": "none" + "default_reasoning_effort": "none", + "input_cost_per_token_batches": 6.875e-07, + "input_cost_per_token_priority": 2.75e-06, + "output_cost_per_token_batches": 5.5e-06, + "output_cost_per_token_priority": 2.2e-05, + "source": "https://prices.azure.com/api/retail/prices?$filter=serviceName%20eq%20'Foundry%20Models'%20and%20armRegionName%20eq%20'eastus2'%20and%20priceType%20eq%20'Consumption'" }, "azure/eu/gpt-5.1-chat": { - "cache_read_input_token_cost": 1.4e-07, + "cache_read_input_token_cost": 1.375e-07, "deprecation_date": "2026-06-29", - "input_cost_per_token": 1.38e-06, + "input_cost_per_token": 1.375e-06, "litellm_provider": "azure", "max_input_tokens": 128000, "max_output_tokens": 128000, @@ -4419,18 +4436,20 @@ "supports_tool_choice": true, "supports_vision": true, "supports_none_reasoning_effort": true, - "default_reasoning_effort": "none" + "default_reasoning_effort": "none", + "source": "https://prices.azure.com/api/retail/prices?$filter=serviceName%20eq%20'Foundry%20Models'%20and%20armRegionName%20eq%20'eastus2'%20and%20priceType%20eq%20'Consumption'" }, "azure/eu/gpt-5.1-codex": { "deprecation_date": "2027-05-15", - "cache_read_input_token_cost": 1.4e-07, - "input_cost_per_token": 1.38e-06, + "cache_read_input_token_cost": 1.375e-07, + "input_cost_per_token": 1.375e-06, "litellm_provider": "azure", "max_input_tokens": 272000, "max_output_tokens": 128000, "max_tokens": 128000, "mode": "responses", "output_cost_per_token": 1.1e-05, + "source": "https://prices.azure.com/api/retail/prices?$filter=serviceName%20eq%20'Foundry%20Models'%20and%20armRegionName%20eq%20'eastus2'%20and%20priceType%20eq%20'Consumption'", "supported_endpoints": [ "/v1/responses" ], @@ -4454,7 +4473,7 @@ }, "azure/eu/gpt-5.1-codex-mini": { "deprecation_date": "2027-05-15", - "cache_read_input_token_cost": 2.8e-08, + "cache_read_input_token_cost": 2.75e-08, "input_cost_per_token": 2.75e-07, "litellm_provider": "azure", "max_input_tokens": 272000, @@ -4462,6 +4481,7 @@ "max_tokens": 128000, "mode": "responses", "output_cost_per_token": 2.2e-06, + "source": "https://prices.azure.com/api/retail/prices?$filter=serviceName%20eq%20'Foundry%20Models'%20and%20armRegionName%20eq%20'eastus2'%20and%20priceType%20eq%20'Consumption'", "supported_endpoints": [ "/v1/responses" ], @@ -4520,12 +4540,15 @@ "cache_read_input_token_cost": 8.25e-06, "deprecation_date": "2026-11-19", "input_cost_per_token": 1.65e-05, + "input_cost_per_token_batches": 8.25e-06, "litellm_provider": "azure", "max_input_tokens": 200000, "max_output_tokens": 100000, "max_tokens": 100000, "mode": "chat", "output_cost_per_token": 6.6e-05, + "output_cost_per_token_batches": 3.3e-05, + "source": "https://prices.azure.com/api/retail/prices?$filter=serviceName%20eq%20'Foundry%20Models'%20and%20armRegionName%20eq%20'eastus2'%20and%20priceType%20eq%20'Consumption'", "supports_function_calling": true, "supports_parallel_function_calling": true, "supports_prompt_caching": true, @@ -4574,6 +4597,7 @@ "mode": "chat", "output_cost_per_token": 4.84e-06, "output_cost_per_token_batches": 2.42e-06, + "source": "https://prices.azure.com/api/retail/prices?$filter=serviceName%20eq%20'Foundry%20Models'%20and%20armRegionName%20eq%20'eastus2'%20and%20priceType%20eq%20'Consumption'", "supports_prompt_caching": true, "supports_reasoning": true, "supports_tool_choice": true, @@ -4583,12 +4607,15 @@ "cache_read_input_token_cost": 1.25e-06, "deprecation_date": "2027-04-14", "input_cost_per_token": 2.5e-06, + "input_cost_per_token_batches": 1.25e-06, "litellm_provider": "azure", "max_input_tokens": 128000, "max_output_tokens": 16384, "max_tokens": 16384, "mode": "chat", "output_cost_per_token": 1e-05, + "output_cost_per_token_batches": 5e-06, + "source": "https://prices.azure.com/api/retail/prices?$filter=serviceName%20eq%20'Foundry%20Models'%20and%20armRegionName%20eq%20'eastus2'%20and%20priceType%20eq%20'Consumption'", "supports_function_calling": true, "supports_parallel_function_calling": true, "supports_prompt_caching": true, @@ -4600,12 +4627,15 @@ "cache_read_input_token_cost": 1.25e-06, "deprecation_date": "2027-04-14", "input_cost_per_token": 2.5e-06, + "input_cost_per_token_batches": 1.25e-06, "litellm_provider": "azure", "max_input_tokens": 128000, "max_output_tokens": 16384, "max_tokens": 16384, "mode": "chat", "output_cost_per_token": 1e-05, + "output_cost_per_token_batches": 5e-06, + "source": "https://prices.azure.com/api/retail/prices?$filter=serviceName%20eq%20'Foundry%20Models'%20and%20armRegionName%20eq%20'eastus2'%20and%20priceType%20eq%20'Consumption'", "supports_function_calling": true, "supports_parallel_function_calling": true, "supports_response_schema": true, @@ -4631,12 +4661,15 @@ "deprecation_date": "2027-04-14", "cache_read_input_token_cost": 1.25e-06, "input_cost_per_token": 2.5e-06, + "input_cost_per_token_batches": 1.25e-06, "litellm_provider": "azure", "max_input_tokens": 128000, "max_output_tokens": 16384, "max_tokens": 16384, "mode": "chat", "output_cost_per_token": 1e-05, + "output_cost_per_token_batches": 5e-06, + "source": "https://prices.azure.com/api/retail/prices?$filter=serviceName%20eq%20'Foundry%20Models'%20and%20armRegionName%20eq%20'eastus2'%20and%20priceType%20eq%20'Consumption'", "supports_function_calling": true, "supports_parallel_function_calling": true, "supports_prompt_caching": true, @@ -4648,12 +4681,15 @@ "deprecation_date": "2027-04-14", "cache_read_input_token_cost": 1.25e-06, "input_cost_per_token": 2.5e-06, + "input_cost_per_token_batches": 1.25e-06, "litellm_provider": "azure", "max_input_tokens": 128000, "max_output_tokens": 16384, "max_tokens": 16384, "mode": "chat", "output_cost_per_token": 1e-05, + "output_cost_per_token_batches": 5e-06, + "source": "https://prices.azure.com/api/retail/prices?$filter=serviceName%20eq%20'Foundry%20Models'%20and%20armRegionName%20eq%20'eastus2'%20and%20priceType%20eq%20'Consumption'", "supports_function_calling": true, "supports_parallel_function_calling": true, "supports_prompt_caching": true, @@ -4664,6 +4700,7 @@ "azure/global/gpt-5.1": { "deprecation_date": "2027-05-15", "cache_read_input_token_cost": 1.25e-07, + "cache_read_input_token_cost_priority": 2.5e-07, "input_cost_per_token": 1.25e-06, "litellm_provider": "azure", "max_input_tokens": 272000, @@ -4695,7 +4732,12 @@ "supports_tool_choice": true, "supports_vision": true, "supports_none_reasoning_effort": true, - "default_reasoning_effort": "none" + "default_reasoning_effort": "none", + "input_cost_per_token_batches": 6.25e-07, + "input_cost_per_token_priority": 2.5e-06, + "output_cost_per_token_batches": 5e-06, + "output_cost_per_token_priority": 2e-05, + "source": "https://prices.azure.com/api/retail/prices?$filter=serviceName%20eq%20'Foundry%20Models'%20and%20armRegionName%20eq%20'eastus2'%20and%20priceType%20eq%20'Consumption'" }, "azure/global/gpt-5.1-chat": { "cache_read_input_token_cost": 1.25e-07, @@ -4731,7 +4773,8 @@ "supports_tool_choice": true, "supports_vision": true, "supports_none_reasoning_effort": true, - "default_reasoning_effort": "none" + "default_reasoning_effort": "none", + "source": "https://prices.azure.com/api/retail/prices?$filter=serviceName%20eq%20'Foundry%20Models'%20and%20armRegionName%20eq%20'eastus2'%20and%20priceType%20eq%20'Consumption'" }, "azure/global/gpt-5.1-codex": { "deprecation_date": "2027-05-15", @@ -4743,6 +4786,7 @@ "max_tokens": 128000, "mode": "responses", "output_cost_per_token": 1e-05, + "source": "https://prices.azure.com/api/retail/prices?$filter=serviceName%20eq%20'Foundry%20Models'%20and%20armRegionName%20eq%20'eastus2'%20and%20priceType%20eq%20'Consumption'", "supported_endpoints": [ "/v1/responses" ], @@ -4774,6 +4818,7 @@ "max_tokens": 128000, "mode": "responses", "output_cost_per_token": 2e-06, + "source": "https://prices.azure.com/api/retail/prices?$filter=serviceName%20eq%20'Foundry%20Models'%20and%20armRegionName%20eq%20'eastus2'%20and%20priceType%20eq%20'Consumption'", "supported_endpoints": [ "/v1/responses" ], @@ -5006,8 +5051,10 @@ "azure/gpt-4.1": { "deprecation_date": "2027-04-14", "cache_read_input_token_cost": 5e-07, + "cache_read_input_token_cost_priority": 8.75e-07, "input_cost_per_token": 2e-06, "input_cost_per_token_batches": 1e-06, + "input_cost_per_token_priority": 3.5e-06, "litellm_provider": "azure", "max_input_tokens": 1047576, "max_output_tokens": 32768, @@ -5015,6 +5062,8 @@ "mode": "chat", "output_cost_per_token": 8e-06, "output_cost_per_token_batches": 4e-06, + "output_cost_per_token_priority": 1.4e-05, + "source": "https://prices.azure.com/api/retail/prices?$filter=serviceName%20eq%20'Foundry%20Models'%20and%20armRegionName%20eq%20'eastus2'%20and%20priceType%20eq%20'Consumption'", "supported_endpoints": [ "/v1/chat/completions", "/v1/batch", @@ -5040,8 +5089,10 @@ "azure/gpt-4.1-2025-04-14": { "deprecation_date": "2027-04-14", "cache_read_input_token_cost": 5e-07, + "cache_read_input_token_cost_priority": 8.75e-07, "input_cost_per_token": 2e-06, "input_cost_per_token_batches": 1e-06, + "input_cost_per_token_priority": 3.5e-06, "litellm_provider": "azure", "max_input_tokens": 1047576, "max_output_tokens": 32768, @@ -5049,6 +5100,8 @@ "mode": "chat", "output_cost_per_token": 8e-06, "output_cost_per_token_batches": 4e-06, + "output_cost_per_token_priority": 1.4e-05, + "source": "https://prices.azure.com/api/retail/prices?$filter=serviceName%20eq%20'Foundry%20Models'%20and%20armRegionName%20eq%20'eastus2'%20and%20priceType%20eq%20'Consumption'", "supported_endpoints": [ "/v1/chat/completions", "/v1/batch", @@ -5074,8 +5127,10 @@ "azure/gpt-4.1-mini": { "deprecation_date": "2027-04-14", "cache_read_input_token_cost": 1e-07, + "cache_read_input_token_cost_priority": 1.75e-07, "input_cost_per_token": 4e-07, "input_cost_per_token_batches": 2e-07, + "input_cost_per_token_priority": 7e-07, "litellm_provider": "azure", "max_input_tokens": 1047576, "max_output_tokens": 32768, @@ -5083,6 +5138,8 @@ "mode": "chat", "output_cost_per_token": 1.6e-06, "output_cost_per_token_batches": 8e-07, + "output_cost_per_token_priority": 2.8e-06, + "source": "https://prices.azure.com/api/retail/prices?$filter=serviceName%20eq%20'Foundry%20Models'%20and%20armRegionName%20eq%20'eastus2'%20and%20priceType%20eq%20'Consumption'", "supported_endpoints": [ "/v1/chat/completions", "/v1/batch", @@ -5108,8 +5165,10 @@ "azure/gpt-4.1-mini-2025-04-14": { "deprecation_date": "2027-04-14", "cache_read_input_token_cost": 1e-07, + "cache_read_input_token_cost_priority": 1.75e-07, "input_cost_per_token": 4e-07, "input_cost_per_token_batches": 2e-07, + "input_cost_per_token_priority": 7e-07, "litellm_provider": "azure", "max_input_tokens": 1047576, "max_output_tokens": 32768, @@ -5117,6 +5176,8 @@ "mode": "chat", "output_cost_per_token": 1.6e-06, "output_cost_per_token_batches": 8e-07, + "output_cost_per_token_priority": 2.8e-06, + "source": "https://prices.azure.com/api/retail/prices?$filter=serviceName%20eq%20'Foundry%20Models'%20and%20armRegionName%20eq%20'eastus2'%20and%20priceType%20eq%20'Consumption'", "supported_endpoints": [ "/v1/chat/completions", "/v1/batch", @@ -5151,6 +5212,7 @@ "mode": "chat", "output_cost_per_token": 4e-07, "output_cost_per_token_batches": 2e-07, + "source": "https://prices.azure.com/api/retail/prices?$filter=serviceName%20eq%20'Foundry%20Models'%20and%20armRegionName%20eq%20'eastus2'%20and%20priceType%20eq%20'Consumption'", "supported_endpoints": [ "/v1/chat/completions", "/v1/batch", @@ -5184,6 +5246,7 @@ "mode": "chat", "output_cost_per_token": 4e-07, "output_cost_per_token_batches": 2e-07, + "source": "https://prices.azure.com/api/retail/prices?$filter=serviceName%20eq%20'Foundry%20Models'%20and%20armRegionName%20eq%20'eastus2'%20and%20priceType%20eq%20'Consumption'", "supported_endpoints": [ "/v1/chat/completions", "/v1/batch", @@ -5243,12 +5306,15 @@ "azure/gpt-4o-2024-05-13": { "deprecation_date": "2026-10-01", "input_cost_per_token": 5e-06, + "input_cost_per_token_batches": 2.5e-06, "litellm_provider": "azure", "max_input_tokens": 128000, "max_output_tokens": 4096, "max_tokens": 4096, "mode": "chat", "output_cost_per_token": 1.5e-05, + "output_cost_per_token_batches": 7.5e-06, + "source": "https://prices.azure.com/api/retail/prices?$filter=serviceName%20eq%20'Foundry%20Models'%20and%20armRegionName%20eq%20'eastus2'%20and%20priceType%20eq%20'Consumption'", "supports_function_calling": true, "supports_parallel_function_calling": true, "supports_prompt_caching": true, @@ -5259,12 +5325,15 @@ "deprecation_date": "2027-04-14", "cache_read_input_token_cost": 1.25e-06, "input_cost_per_token": 2.5e-06, + "input_cost_per_token_batches": 1.25e-06, "litellm_provider": "azure", "max_input_tokens": 128000, "max_output_tokens": 16384, "max_tokens": 16384, "mode": "chat", "output_cost_per_token": 1e-05, + "output_cost_per_token_batches": 5e-06, + "source": "https://prices.azure.com/api/retail/prices?$filter=serviceName%20eq%20'Foundry%20Models'%20and%20armRegionName%20eq%20'eastus2'%20and%20priceType%20eq%20'Consumption'", "supports_function_calling": true, "supports_parallel_function_calling": true, "supports_prompt_caching": true, @@ -5275,13 +5344,16 @@ "azure/gpt-4o-2024-11-20": { "deprecation_date": "2027-04-14", "cache_read_input_token_cost": 1.25e-06, - "input_cost_per_token": 2.75e-06, + "input_cost_per_token": 2.5e-06, + "input_cost_per_token_batches": 1.25e-06, "litellm_provider": "azure", "max_input_tokens": 128000, "max_output_tokens": 16384, "max_tokens": 16384, "mode": "chat", - "output_cost_per_token": 1.1e-05, + "output_cost_per_token": 1e-05, + "output_cost_per_token_batches": 5e-06, + "source": "https://prices.azure.com/api/retail/prices?$filter=serviceName%20eq%20'Foundry%20Models'%20and%20armRegionName%20eq%20'eastus2'%20and%20priceType%20eq%20'Consumption'", "supports_function_calling": true, "supports_parallel_function_calling": true, "supports_prompt_caching": true, @@ -5468,13 +5540,16 @@ "azure/gpt-4o-mini-2024-07-18": { "cache_read_input_token_cost": 7.5e-08, "deprecation_date": "2027-04-14", - "input_cost_per_token": 1.65e-07, + "input_cost_per_token": 1.5e-07, + "input_cost_per_token_batches": 7.5e-08, "litellm_provider": "azure", "max_input_tokens": 128000, "max_output_tokens": 16384, "max_tokens": 16384, "mode": "chat", - "output_cost_per_token": 6.6e-07, + "output_cost_per_token": 6e-07, + "output_cost_per_token_batches": 3e-07, + "source": "https://prices.azure.com/api/retail/prices?$filter=serviceName%20eq%20'Foundry%20Models'%20and%20armRegionName%20eq%20'eastus2'%20and%20priceType%20eq%20'Consumption'", "supports_function_calling": true, "supports_parallel_function_calling": true, "supports_prompt_caching": true, @@ -5925,6 +6000,9 @@ "supports_vision": true, "supports_none_reasoning_effort": true, "default_reasoning_effort": "none", + "input_cost_per_token_batches": 6.25e-07, + "output_cost_per_token_batches": 5e-06, + "source": "https://prices.azure.com/api/retail/prices?$filter=serviceName%20eq%20'Foundry%20Models'%20and%20armRegionName%20eq%20'eastus2'%20and%20priceType%20eq%20'Consumption'", "supports_minimal_reasoning_effort": true }, "azure/gpt-5.1-chat-2025-11-13": { @@ -5963,7 +6041,8 @@ "supports_tool_choice": false, "supports_vision": true, "supports_none_reasoning_effort": true, - "default_reasoning_effort": "none" + "default_reasoning_effort": "none", + "source": "https://prices.azure.com/api/retail/prices?$filter=serviceName%20eq%20'Foundry%20Models'%20and%20armRegionName%20eq%20'eastus2'%20and%20priceType%20eq%20'Consumption'" }, "azure/gpt-5.1-codex-2025-11-13": { "cache_read_input_token_cost": 1.25e-07, @@ -5978,6 +6057,7 @@ "mode": "responses", "output_cost_per_token": 1e-05, "output_cost_per_token_priority": 2e-05, + "source": "https://prices.azure.com/api/retail/prices?$filter=serviceName%20eq%20'Foundry%20Models'%20and%20armRegionName%20eq%20'eastus2'%20and%20priceType%20eq%20'Consumption'", "supported_endpoints": [ "/v1/responses" ], @@ -6012,6 +6092,7 @@ "mode": "responses", "output_cost_per_token": 2e-06, "output_cost_per_token_priority": 3.6e-06, + "source": "https://prices.azure.com/api/retail/prices?$filter=serviceName%20eq%20'Foundry%20Models'%20and%20armRegionName%20eq%20'eastus2'%20and%20priceType%20eq%20'Consumption'", "supported_endpoints": [ "/v1/responses" ], @@ -6036,13 +6117,19 @@ "azure/gpt-5": { "deprecation_date": "2027-02-09", "cache_read_input_token_cost": 1.25e-07, + "cache_read_input_token_cost_priority": 2.5e-07, "input_cost_per_token": 1.25e-06, + "input_cost_per_token_batches": 6.25e-07, + "input_cost_per_token_priority": 2.5e-06, "litellm_provider": "azure", "max_input_tokens": 272000, "max_output_tokens": 128000, "max_tokens": 128000, "mode": "chat", "output_cost_per_token": 1e-05, + "output_cost_per_token_batches": 5e-06, + "output_cost_per_token_priority": 2e-05, + "source": "https://prices.azure.com/api/retail/prices?$filter=serviceName%20eq%20'Foundry%20Models'%20and%20armRegionName%20eq%20'eastus2'%20and%20priceType%20eq%20'Consumption'", "supported_endpoints": [ "/v1/chat/completions", "/v1/batch", @@ -6068,14 +6155,20 @@ }, "azure/gpt-5-2025-08-07": { "cache_read_input_token_cost": 1.25e-07, + "cache_read_input_token_cost_priority": 2.5e-07, "deprecation_date": "2027-02-09", "input_cost_per_token": 1.25e-06, + "input_cost_per_token_batches": 6.25e-07, + "input_cost_per_token_priority": 2.5e-06, "litellm_provider": "azure", "max_input_tokens": 272000, "max_output_tokens": 128000, "max_tokens": 128000, "mode": "chat", "output_cost_per_token": 1e-05, + "output_cost_per_token_batches": 5e-06, + "output_cost_per_token_priority": 2e-05, + "source": "https://prices.azure.com/api/retail/prices?$filter=serviceName%20eq%20'Foundry%20Models'%20and%20armRegionName%20eq%20'eastus2'%20and%20priceType%20eq%20'Consumption'", "supported_endpoints": [ "/v1/chat/completions", "/v1/batch", @@ -6109,7 +6202,7 @@ "max_tokens": 16384, "mode": "chat", "output_cost_per_token": 1e-05, - "source": "https://azure.microsoft.com/en-us/blog/gpt-5-in-azure-ai-foundry-the-future-of-ai-apps-and-agents-starts-here/", + "source": "https://prices.azure.com/api/retail/prices?$filter=serviceName%20eq%20'Foundry%20Models'%20and%20armRegionName%20eq%20'eastus2'%20and%20priceType%20eq%20'Consumption'", "supported_endpoints": [ "/v1/chat/completions", "/v1/batch", @@ -6176,6 +6269,7 @@ "max_tokens": 128000, "mode": "responses", "output_cost_per_token": 1e-05, + "source": "https://prices.azure.com/api/retail/prices?$filter=serviceName%20eq%20'Foundry%20Models'%20and%20armRegionName%20eq%20'eastus2'%20and%20priceType%20eq%20'Consumption'", "supported_endpoints": [ "/v1/responses" ], @@ -6200,13 +6294,19 @@ "azure/gpt-5-mini": { "deprecation_date": "2027-02-09", "cache_read_input_token_cost": 2.5e-08, + "cache_read_input_token_cost_priority": 4.5e-08, "input_cost_per_token": 2.5e-07, + "input_cost_per_token_batches": 1.25e-07, + "input_cost_per_token_priority": 4.5e-07, "litellm_provider": "azure", "max_input_tokens": 272000, "max_output_tokens": 128000, "max_tokens": 128000, "mode": "chat", "output_cost_per_token": 2e-06, + "output_cost_per_token_batches": 1e-06, + "output_cost_per_token_priority": 3.6e-06, + "source": "https://prices.azure.com/api/retail/prices?$filter=serviceName%20eq%20'Foundry%20Models'%20and%20armRegionName%20eq%20'eastus2'%20and%20priceType%20eq%20'Consumption'", "supported_endpoints": [ "/v1/chat/completions", "/v1/batch", @@ -6232,14 +6332,20 @@ }, "azure/gpt-5-mini-2025-08-07": { "cache_read_input_token_cost": 2.5e-08, + "cache_read_input_token_cost_priority": 4.5e-08, "deprecation_date": "2027-02-09", "input_cost_per_token": 2.5e-07, + "input_cost_per_token_batches": 1.25e-07, + "input_cost_per_token_priority": 4.5e-07, "litellm_provider": "azure", "max_input_tokens": 272000, "max_output_tokens": 128000, "max_tokens": 128000, "mode": "chat", "output_cost_per_token": 2e-06, + "output_cost_per_token_batches": 1e-06, + "output_cost_per_token_priority": 3.6e-06, + "source": "https://prices.azure.com/api/retail/prices?$filter=serviceName%20eq%20'Foundry%20Models'%20and%20armRegionName%20eq%20'eastus2'%20and%20priceType%20eq%20'Consumption'", "supported_endpoints": [ "/v1/chat/completions", "/v1/batch", @@ -6267,12 +6373,15 @@ "deprecation_date": "2027-02-09", "cache_read_input_token_cost": 5e-09, "input_cost_per_token": 5e-08, + "input_cost_per_token_batches": 2.5e-08, "litellm_provider": "azure", "max_input_tokens": 272000, "max_output_tokens": 128000, "max_tokens": 128000, "mode": "chat", "output_cost_per_token": 4e-07, + "output_cost_per_token_batches": 2e-07, + "source": "https://prices.azure.com/api/retail/prices?$filter=serviceName%20eq%20'Foundry%20Models'%20and%20armRegionName%20eq%20'eastus2'%20and%20priceType%20eq%20'Consumption'", "supported_endpoints": [ "/v1/chat/completions", "/v1/batch", @@ -6300,12 +6409,15 @@ "cache_read_input_token_cost": 5e-09, "deprecation_date": "2027-02-09", "input_cost_per_token": 5e-08, + "input_cost_per_token_batches": 2.5e-08, "litellm_provider": "azure", "max_input_tokens": 272000, "max_output_tokens": 128000, "max_tokens": 128000, "mode": "chat", "output_cost_per_token": 4e-07, + "output_cost_per_token_batches": 2e-07, + "source": "https://prices.azure.com/api/retail/prices?$filter=serviceName%20eq%20'Foundry%20Models'%20and%20armRegionName%20eq%20'eastus2'%20and%20priceType%20eq%20'Consumption'", "supported_endpoints": [ "/v1/chat/completions", "/v1/batch", @@ -6332,13 +6444,15 @@ "azure/gpt-5-pro": { "deprecation_date": "2027-04-07", "input_cost_per_token": 1.5e-05, + "input_cost_per_token_batches": 7.5e-06, "litellm_provider": "azure", "max_input_tokens": 272000, "max_output_tokens": 128000, "max_tokens": 128000, "mode": "responses", "output_cost_per_token": 0.00012, - "source": "https://learn.microsoft.com/en-us/azure/ai-foundry/foundry-models/concepts/models-sold-directly-by-azure?pivots=azure-openai&tabs=global-standard-aoai%2Cstandard-chat-completions%2Cglobal-standard#gpt-5", + "output_cost_per_token_batches": 6e-05, + "source": "https://prices.azure.com/api/retail/prices?$filter=serviceName%20eq%20'Foundry%20Models'%20and%20armRegionName%20eq%20'eastus2'%20and%20priceType%20eq%20'Consumption'", "supported_endpoints": [ "/v1/responses" ], @@ -6362,6 +6476,7 @@ "azure/gpt-5.1": { "deprecation_date": "2027-05-15", "cache_read_input_token_cost": 1.25e-07, + "cache_read_input_token_cost_priority": 2.5e-07, "input_cost_per_token": 1.25e-06, "litellm_provider": "azure", "max_input_tokens": 272000, @@ -6393,7 +6508,12 @@ "supports_tool_choice": true, "supports_vision": true, "supports_none_reasoning_effort": true, - "default_reasoning_effort": "none" + "default_reasoning_effort": "none", + "input_cost_per_token_batches": 6.25e-07, + "input_cost_per_token_priority": 2.5e-06, + "output_cost_per_token_batches": 5e-06, + "output_cost_per_token_priority": 2e-05, + "source": "https://prices.azure.com/api/retail/prices?$filter=serviceName%20eq%20'Foundry%20Models'%20and%20armRegionName%20eq%20'eastus2'%20and%20priceType%20eq%20'Consumption'" }, "azure/gpt-5.1-chat": { "cache_read_input_token_cost": 1.25e-07, @@ -6429,7 +6549,8 @@ "supports_tool_choice": true, "supports_vision": true, "supports_none_reasoning_effort": true, - "default_reasoning_effort": "none" + "default_reasoning_effort": "none", + "source": "https://prices.azure.com/api/retail/prices?$filter=serviceName%20eq%20'Foundry%20Models'%20and%20armRegionName%20eq%20'eastus2'%20and%20priceType%20eq%20'Consumption'" }, "azure/gpt-5.1-codex": { "deprecation_date": "2027-05-15", @@ -6441,6 +6562,7 @@ "max_tokens": 128000, "mode": "responses", "output_cost_per_token": 1e-05, + "source": "https://prices.azure.com/api/retail/prices?$filter=serviceName%20eq%20'Foundry%20Models'%20and%20armRegionName%20eq%20'eastus2'%20and%20priceType%20eq%20'Consumption'", "supported_endpoints": [ "/v1/responses" ], @@ -6472,6 +6594,7 @@ "max_tokens": 128000, "mode": "responses", "output_cost_per_token": 1e-05, + "source": "https://prices.azure.com/api/retail/prices?$filter=serviceName%20eq%20'Foundry%20Models'%20and%20armRegionName%20eq%20'eastus2'%20and%20priceType%20eq%20'Consumption'", "supported_endpoints": [ "/v1/responses" ], @@ -6503,6 +6626,7 @@ "max_tokens": 128000, "mode": "responses", "output_cost_per_token": 2e-06, + "source": "https://prices.azure.com/api/retail/prices?$filter=serviceName%20eq%20'Foundry%20Models'%20and%20armRegionName%20eq%20'eastus2'%20and%20priceType%20eq%20'Consumption'", "supported_endpoints": [ "/v1/responses" ], @@ -6527,13 +6651,19 @@ "azure/gpt-5.2": { "deprecation_date": "2027-06-08", "cache_read_input_token_cost": 1.75e-07, + "cache_read_input_token_cost_priority": 3.5e-07, "input_cost_per_token": 1.75e-06, + "input_cost_per_token_batches": 8.75e-07, + "input_cost_per_token_priority": 3.5e-06, "litellm_provider": "azure", "max_input_tokens": 272000, "max_output_tokens": 128000, "max_tokens": 128000, "mode": "chat", "output_cost_per_token": 1.4e-05, + "output_cost_per_token_batches": 7e-06, + "output_cost_per_token_priority": 2.8e-05, + "source": "https://prices.azure.com/api/retail/prices?$filter=serviceName%20eq%20'Foundry%20Models'%20and%20armRegionName%20eq%20'eastus2'%20and%20priceType%20eq%20'Consumption'", "supported_endpoints": [ "/v1/chat/completions", "/v1/batch", @@ -6563,6 +6693,7 @@ "cache_read_input_token_cost_priority": 3.5e-07, "deprecation_date": "2027-06-08", "input_cost_per_token": 1.75e-06, + "input_cost_per_token_batches": 8.75e-07, "input_cost_per_token_priority": 3.5e-06, "litellm_provider": "azure", "max_input_tokens": 272000, @@ -6570,7 +6701,9 @@ "max_tokens": 128000, "mode": "chat", "output_cost_per_token": 1.4e-05, + "output_cost_per_token_batches": 7e-06, "output_cost_per_token_priority": 2.8e-05, + "source": "https://prices.azure.com/api/retail/prices?$filter=serviceName%20eq%20'Foundry%20Models'%20and%20armRegionName%20eq%20'eastus2'%20and%20priceType%20eq%20'Consumption'", "supported_endpoints": [ "/v1/chat/completions", "/v1/batch", @@ -6608,6 +6741,7 @@ "mode": "chat", "output_cost_per_token": 1.4e-05, "output_cost_per_token_priority": 2.8e-05, + "source": "https://prices.azure.com/api/retail/prices?$filter=serviceName%20eq%20'Foundry%20Models'%20and%20armRegionName%20eq%20'eastus2'%20and%20priceType%20eq%20'Consumption'", "supported_endpoints": [ "/v1/chat/completions", "/v1/responses" @@ -6643,6 +6777,7 @@ "mode": "chat", "output_cost_per_token": 1.4e-05, "output_cost_per_token_priority": 2.8e-05, + "source": "https://prices.azure.com/api/retail/prices?$filter=serviceName%20eq%20'Foundry%20Models'%20and%20armRegionName%20eq%20'eastus2'%20and%20priceType%20eq%20'Consumption'", "supported_endpoints": [ "/v1/chat/completions", "/v1/responses" @@ -6675,6 +6810,7 @@ "max_tokens": 128000, "mode": "responses", "output_cost_per_token": 1.4e-05, + "source": "https://prices.azure.com/api/retail/prices?$filter=serviceName%20eq%20'Foundry%20Models'%20and%20armRegionName%20eq%20'eastus2'%20and%20priceType%20eq%20'Consumption'", "supported_endpoints": [ "/v1/responses" ], @@ -6709,6 +6845,7 @@ "mode": "chat", "output_cost_per_token": 1.4e-05, "output_cost_per_token_priority": 2.8e-05, + "source": "https://prices.azure.com/api/retail/prices?$filter=serviceName%20eq%20'Foundry%20Models'%20and%20armRegionName%20eq%20'eastus2'%20and%20priceType%20eq%20'Consumption'", "supported_endpoints": [ "/v1/chat/completions", "/v1/responses" @@ -6733,14 +6870,18 @@ }, "azure/gpt-5.3-codex": { "cache_read_input_token_cost": 1.75e-07, + "cache_read_input_token_cost_priority": 3.5e-07, "deprecation_date": "2027-08-24", "input_cost_per_token": 1.75e-06, + "input_cost_per_token_priority": 3.5e-06, "litellm_provider": "azure", "max_input_tokens": 272000, "max_output_tokens": 128000, "max_tokens": 128000, "mode": "responses", "output_cost_per_token": 1.4e-05, + "output_cost_per_token_priority": 2.8e-05, + "source": "https://prices.azure.com/api/retail/prices?$filter=serviceName%20eq%20'Foundry%20Models'%20and%20armRegionName%20eq%20'eastus2'%20and%20priceType%20eq%20'Consumption'", "supported_endpoints": [ "/v1/responses" ], @@ -6764,17 +6905,20 @@ }, "azure/gpt-5.2-pro": { "input_cost_per_token": 2.1e-05, + "input_cost_per_token_batches": 1.05e-05, "litellm_provider": "azure", "max_input_tokens": 272000, "max_output_tokens": 128000, "max_tokens": 128000, "mode": "responses", "output_cost_per_token": 0.000168, + "output_cost_per_token_batches": 8.4e-05, "search_context_cost_per_query": { "search_context_size_high": 0.01, "search_context_size_low": 0.01, "search_context_size_medium": 0.01 }, + "source": "https://prices.azure.com/api/retail/prices?$filter=serviceName%20eq%20'Foundry%20Models'%20and%20armRegionName%20eq%20'eastus2'%20and%20priceType%20eq%20'Consumption'", "supported_endpoints": [ "/v1/batch", "/v1/responses" @@ -6800,17 +6944,20 @@ }, "azure/gpt-5.2-pro-2025-12-11": { "input_cost_per_token": 2.1e-05, + "input_cost_per_token_batches": 1.05e-05, "litellm_provider": "azure", "max_input_tokens": 272000, "max_output_tokens": 128000, "max_tokens": 128000, "mode": "responses", "output_cost_per_token": 0.000168, + "output_cost_per_token_batches": 8.4e-05, "search_context_cost_per_query": { "search_context_size_high": 0.01, "search_context_size_low": 0.01, "search_context_size_medium": 0.01 }, + "source": "https://prices.azure.com/api/retail/prices?$filter=serviceName%20eq%20'Foundry%20Models'%20and%20armRegionName%20eq%20'eastus2'%20and%20priceType%20eq%20'Consumption'", "supported_endpoints": [ "/v1/batch", "/v1/responses" @@ -6838,6 +6985,7 @@ "deprecation_date": "2027-09-02", "cache_read_input_token_cost": 2.5e-07, "cache_read_input_token_cost_above_272k_tokens": 5e-07, + "cache_read_input_token_cost_above_272k_tokens_flex": 2.5e-07, "cache_read_input_token_cost_priority": 5e-07, "cache_read_input_token_cost_above_272k_tokens_priority": 1e-06, "input_cost_per_token": 2.5e-06, @@ -6877,12 +7025,20 @@ "supports_vision": true, "supports_none_reasoning_effort": true, "default_reasoning_effort": "none", + "input_cost_per_token_above_272k_tokens_flex": 2.5e-06, + "input_cost_per_token_batches": 1.25e-06, + "input_cost_per_token_flex": 1.25e-06, + "output_cost_per_token_above_272k_tokens_flex": 1.125e-05, + "output_cost_per_token_batches": 7.5e-06, + "output_cost_per_token_flex": 7.5e-06, + "source": "https://prices.azure.com/api/retail/prices?$filter=serviceName%20eq%20'Foundry%20Models'%20and%20armRegionName%20eq%20'eastus2'%20and%20priceType%20eq%20'Consumption'", "supports_xhigh_reasoning_effort": true, "supports_minimal_reasoning_effort": true }, "azure/us/gpt-5.4": { "deprecation_date": "2027-09-02", - "cache_read_input_token_cost": 2.8e-07, + "cache_read_input_token_cost": 2.75e-07, + "cache_read_input_token_cost_above_272k_tokens": 5.5e-07, "cache_read_input_token_cost_priority": 5.5e-07, "input_cost_per_token": 2.75e-06, "input_cost_per_token_priority": 5.5e-06, @@ -6917,12 +7073,18 @@ "supports_vision": true, "supports_none_reasoning_effort": true, "default_reasoning_effort": "none", + "input_cost_per_token_above_272k_tokens": 5.5e-06, + "input_cost_per_token_batches": 1.375e-06, + "output_cost_per_token_above_272k_tokens": 2.475e-05, + "output_cost_per_token_batches": 8.25e-06, + "source": "https://prices.azure.com/api/retail/prices?$filter=serviceName%20eq%20'Foundry%20Models'%20and%20armRegionName%20eq%20'eastus2'%20and%20priceType%20eq%20'Consumption'", "supports_xhigh_reasoning_effort": true, "supports_minimal_reasoning_effort": true }, "azure/eu/gpt-5.4": { "deprecation_date": "2027-09-02", - "cache_read_input_token_cost": 2.8e-07, + "cache_read_input_token_cost": 2.75e-07, + "cache_read_input_token_cost_above_272k_tokens": 5.5e-07, "cache_read_input_token_cost_priority": 5.5e-07, "input_cost_per_token": 2.75e-06, "input_cost_per_token_priority": 5.5e-06, @@ -6957,12 +7119,18 @@ "supports_vision": true, "supports_none_reasoning_effort": true, "default_reasoning_effort": "none", + "input_cost_per_token_above_272k_tokens": 5.5e-06, + "input_cost_per_token_batches": 1.375e-06, + "output_cost_per_token_above_272k_tokens": 2.475e-05, + "output_cost_per_token_batches": 8.25e-06, + "source": "https://prices.azure.com/api/retail/prices?$filter=serviceName%20eq%20'Foundry%20Models'%20and%20armRegionName%20eq%20'eastus2'%20and%20priceType%20eq%20'Consumption'", "supports_xhigh_reasoning_effort": true, "supports_minimal_reasoning_effort": true }, "azure/gpt-5.4-2026-03-05": { "cache_read_input_token_cost": 2.5e-07, "cache_read_input_token_cost_above_272k_tokens": 5e-07, + "cache_read_input_token_cost_above_272k_tokens_flex": 2.5e-07, "cache_read_input_token_cost_priority": 5e-07, "cache_read_input_token_cost_above_272k_tokens_priority": 1e-06, "deprecation_date": "2027-09-02", @@ -7003,11 +7171,19 @@ "supports_vision": true, "supports_none_reasoning_effort": true, "default_reasoning_effort": "none", + "input_cost_per_token_above_272k_tokens_flex": 2.5e-06, + "input_cost_per_token_batches": 1.25e-06, + "input_cost_per_token_flex": 1.25e-06, + "output_cost_per_token_above_272k_tokens_flex": 1.125e-05, + "output_cost_per_token_batches": 7.5e-06, + "output_cost_per_token_flex": 7.5e-06, + "source": "https://prices.azure.com/api/retail/prices?$filter=serviceName%20eq%20'Foundry%20Models'%20and%20armRegionName%20eq%20'eastus2'%20and%20priceType%20eq%20'Consumption'", "supports_xhigh_reasoning_effort": true, "supports_minimal_reasoning_effort": true }, "azure/us/gpt-5.4-2026-03-05": { - "cache_read_input_token_cost": 2.8e-07, + "cache_read_input_token_cost": 2.75e-07, + "cache_read_input_token_cost_above_272k_tokens": 5.5e-07, "cache_read_input_token_cost_priority": 5.5e-07, "deprecation_date": "2027-09-02", "input_cost_per_token": 2.75e-06, @@ -7043,11 +7219,17 @@ "supports_vision": true, "supports_none_reasoning_effort": true, "default_reasoning_effort": "none", + "input_cost_per_token_above_272k_tokens": 5.5e-06, + "input_cost_per_token_batches": 1.375e-06, + "output_cost_per_token_above_272k_tokens": 2.475e-05, + "output_cost_per_token_batches": 8.25e-06, + "source": "https://prices.azure.com/api/retail/prices?$filter=serviceName%20eq%20'Foundry%20Models'%20and%20armRegionName%20eq%20'eastus2'%20and%20priceType%20eq%20'Consumption'", "supports_xhigh_reasoning_effort": true, "supports_minimal_reasoning_effort": true }, "azure/eu/gpt-5.4-2026-03-05": { - "cache_read_input_token_cost": 2.8e-07, + "cache_read_input_token_cost": 2.75e-07, + "cache_read_input_token_cost_above_272k_tokens": 5.5e-07, "cache_read_input_token_cost_priority": 5.5e-07, "deprecation_date": "2027-09-02", "input_cost_per_token": 2.75e-06, @@ -7083,6 +7265,11 @@ "supports_vision": true, "supports_none_reasoning_effort": true, "default_reasoning_effort": "none", + "input_cost_per_token_above_272k_tokens": 5.5e-06, + "input_cost_per_token_batches": 1.375e-06, + "output_cost_per_token_above_272k_tokens": 2.475e-05, + "output_cost_per_token_batches": 8.25e-06, + "source": "https://prices.azure.com/api/retail/prices?$filter=serviceName%20eq%20'Foundry%20Models'%20and%20armRegionName%20eq%20'eastus2'%20and%20priceType%20eq%20'Consumption'", "supports_xhigh_reasoning_effort": true, "supports_minimal_reasoning_effort": true }, @@ -7092,6 +7279,9 @@ "cache_read_input_token_cost_above_272k_tokens": 6e-06, "input_cost_per_token": 3e-05, "input_cost_per_token_above_272k_tokens": 6e-05, + "input_cost_per_token_above_272k_tokens_flex": 3e-05, + "input_cost_per_token_batches": 1.5e-05, + "input_cost_per_token_flex": 1.5e-05, "litellm_provider": "azure", "max_input_tokens": 1050000, "max_output_tokens": 128000, @@ -7099,11 +7289,15 @@ "mode": "responses", "output_cost_per_token": 0.00018, "output_cost_per_token_above_272k_tokens": 0.00027, + "output_cost_per_token_above_272k_tokens_flex": 0.000135, + "output_cost_per_token_batches": 9e-05, + "output_cost_per_token_flex": 9e-05, "search_context_cost_per_query": { "search_context_size_high": 0.01, "search_context_size_low": 0.01, "search_context_size_medium": 0.01 }, + "source": "https://prices.azure.com/api/retail/prices?$filter=serviceName%20eq%20'Foundry%20Models'%20and%20armRegionName%20eq%20'eastus2'%20and%20priceType%20eq%20'Consumption'", "supported_endpoints": [ "/v1/batch", "/v1/responses" @@ -7133,6 +7327,9 @@ "deprecation_date": "2027-09-07", "input_cost_per_token": 3e-05, "input_cost_per_token_above_272k_tokens": 6e-05, + "input_cost_per_token_above_272k_tokens_flex": 3e-05, + "input_cost_per_token_batches": 1.5e-05, + "input_cost_per_token_flex": 1.5e-05, "litellm_provider": "azure", "max_input_tokens": 1050000, "max_output_tokens": 128000, @@ -7140,11 +7337,15 @@ "mode": "responses", "output_cost_per_token": 0.00018, "output_cost_per_token_above_272k_tokens": 0.00027, + "output_cost_per_token_above_272k_tokens_flex": 0.000135, + "output_cost_per_token_batches": 9e-05, + "output_cost_per_token_flex": 9e-05, "search_context_cost_per_query": { "search_context_size_high": 0.01, "search_context_size_low": 0.01, "search_context_size_medium": 0.01 }, + "source": "https://prices.azure.com/api/retail/prices?$filter=serviceName%20eq%20'Foundry%20Models'%20and%20armRegionName%20eq%20'eastus2'%20and%20priceType%20eq%20'Consumption'", "supported_endpoints": [ "/v1/batch", "/v1/responses" @@ -7223,33 +7424,42 @@ "supports_minimal_reasoning_effort": false }, "azure/gpt-5.6-sol": { - "cache_creation_input_token_cost": 6.25e-06, - "cache_creation_input_token_cost_above_272k_tokens": 1.25e-05, - "cache_creation_input_token_cost_priority": 1.25e-05, - "cache_creation_input_token_cost_above_272k_tokens_priority": 2.5e-05, - "cache_read_input_token_cost": 5e-07, - "cache_read_input_token_cost_above_272k_tokens": 1e-06, - "cache_read_input_token_cost_priority": 1e-06, - "cache_read_input_token_cost_above_272k_tokens_priority": 2e-06, + "cache_creation_input_token_cost": 5e-06, + "cache_creation_input_token_cost_above_272k_tokens": 1e-05, + "cache_creation_input_token_cost_above_272k_tokens_flex": 5e-06, + "cache_creation_input_token_cost_priority": 1e-05, + "cache_creation_input_token_cost_above_272k_tokens_priority": 2e-05, + "cache_creation_input_token_cost_flex": 2.5e-06, + "cache_read_input_token_cost": 4e-07, + "cache_read_input_token_cost_above_272k_tokens": 8e-07, + "cache_read_input_token_cost_above_272k_tokens_flex": 4e-07, + "cache_read_input_token_cost_priority": 8e-07, + "cache_read_input_token_cost_above_272k_tokens_priority": 1.6e-06, + "cache_read_input_token_cost_flex": 2e-07, "deprecation_date": "2028-01-11", - "input_cost_per_token": 5e-06, - "input_cost_per_token_above_272k_tokens": 1e-05, - "input_cost_per_token_priority": 1e-05, - "input_cost_per_token_above_272k_tokens_priority": 2e-05, + "input_cost_per_token": 4e-06, + "input_cost_per_token_above_272k_tokens": 8e-06, + "input_cost_per_token_above_272k_tokens_flex": 4e-06, + "input_cost_per_token_priority": 8e-06, + "input_cost_per_token_above_272k_tokens_priority": 1.6e-05, + "input_cost_per_token_flex": 2e-06, "litellm_provider": "azure", "max_input_tokens": 922000, "max_output_tokens": 128000, "max_tokens": 128000, "mode": "chat", - "output_cost_per_token": 3e-05, - "output_cost_per_token_above_272k_tokens": 4.5e-05, - "output_cost_per_token_priority": 6e-05, - "output_cost_per_token_above_272k_tokens_priority": 9e-05, + "output_cost_per_token": 2e-05, + "output_cost_per_token_above_272k_tokens": 3e-05, + "output_cost_per_token_above_272k_tokens_flex": 1.5e-05, + "output_cost_per_token_priority": 4e-05, + "output_cost_per_token_above_272k_tokens_priority": 6e-05, + "output_cost_per_token_flex": 1e-05, "search_context_cost_per_query": { "search_context_size_high": 0.01, "search_context_size_low": 0.01, "search_context_size_medium": 0.01 }, + "source": "https://prices.azure.com/api/retail/prices?$filter=serviceName%20eq%20'Foundry%20Models'%20and%20armRegionName%20eq%20'eastus2'%20and%20priceType%20eq%20'Consumption'", "supported_endpoints": [ "/v1/chat/completions", "/v1/batch", @@ -7280,17 +7490,23 @@ "azure/gpt-5.6-terra": { "cache_creation_input_token_cost": 2.5e-06, "cache_creation_input_token_cost_above_272k_tokens": 5e-06, + "cache_creation_input_token_cost_above_272k_tokens_flex": 2.5e-06, "cache_creation_input_token_cost_priority": 5e-06, "cache_creation_input_token_cost_above_272k_tokens_priority": 1e-05, + "cache_creation_input_token_cost_flex": 1.25e-06, "cache_read_input_token_cost": 2e-07, "cache_read_input_token_cost_above_272k_tokens": 4e-07, + "cache_read_input_token_cost_above_272k_tokens_flex": 2e-07, "cache_read_input_token_cost_priority": 4e-07, "cache_read_input_token_cost_above_272k_tokens_priority": 8e-07, + "cache_read_input_token_cost_flex": 1e-07, "deprecation_date": "2028-01-11", "input_cost_per_token": 2e-06, "input_cost_per_token_above_272k_tokens": 4e-06, + "input_cost_per_token_above_272k_tokens_flex": 2e-06, "input_cost_per_token_priority": 4e-06, "input_cost_per_token_above_272k_tokens_priority": 8e-06, + "input_cost_per_token_flex": 1e-06, "litellm_provider": "azure", "max_input_tokens": 922000, "max_output_tokens": 128000, @@ -7298,13 +7514,16 @@ "mode": "chat", "output_cost_per_token": 1.2e-05, "output_cost_per_token_above_272k_tokens": 1.8e-05, + "output_cost_per_token_above_272k_tokens_flex": 9e-06, "output_cost_per_token_priority": 2.4e-05, "output_cost_per_token_above_272k_tokens_priority": 3.6e-05, + "output_cost_per_token_flex": 6e-06, "search_context_cost_per_query": { "search_context_size_high": 0.01, "search_context_size_low": 0.01, "search_context_size_medium": 0.01 }, + "source": "https://prices.azure.com/api/retail/prices?$filter=serviceName%20eq%20'Foundry%20Models'%20and%20armRegionName%20eq%20'eastus2'%20and%20priceType%20eq%20'Consumption'", "supported_endpoints": [ "/v1/chat/completions", "/v1/batch", @@ -7335,17 +7554,23 @@ "azure/gpt-5.6-luna": { "cache_creation_input_token_cost": 2.5e-07, "cache_creation_input_token_cost_above_272k_tokens": 5e-07, + "cache_creation_input_token_cost_above_272k_tokens_flex": 2.5e-07, "cache_creation_input_token_cost_priority": 5e-07, "cache_creation_input_token_cost_above_272k_tokens_priority": 1e-06, + "cache_creation_input_token_cost_flex": 1.25e-07, "cache_read_input_token_cost": 2e-08, "cache_read_input_token_cost_above_272k_tokens": 4e-08, + "cache_read_input_token_cost_above_272k_tokens_flex": 2e-08, "cache_read_input_token_cost_priority": 4e-08, "cache_read_input_token_cost_above_272k_tokens_priority": 8e-08, + "cache_read_input_token_cost_flex": 1e-08, "deprecation_date": "2028-01-11", "input_cost_per_token": 2e-07, "input_cost_per_token_above_272k_tokens": 4e-07, + "input_cost_per_token_above_272k_tokens_flex": 2e-07, "input_cost_per_token_priority": 4e-07, "input_cost_per_token_above_272k_tokens_priority": 8e-07, + "input_cost_per_token_flex": 1e-07, "litellm_provider": "azure", "max_input_tokens": 922000, "max_output_tokens": 128000, @@ -7353,13 +7578,16 @@ "mode": "chat", "output_cost_per_token": 1.2e-06, "output_cost_per_token_above_272k_tokens": 1.8e-06, + "output_cost_per_token_above_272k_tokens_flex": 9e-07, "output_cost_per_token_priority": 2.4e-06, "output_cost_per_token_above_272k_tokens_priority": 3.6e-06, + "output_cost_per_token_flex": 6e-07, "search_context_cost_per_query": { "search_context_size_high": 0.01, "search_context_size_low": 0.01, "search_context_size_medium": 0.01 }, + "source": "https://prices.azure.com/api/retail/prices?$filter=serviceName%20eq%20'Foundry%20Models'%20and%20armRegionName%20eq%20'eastus2'%20and%20priceType%20eq%20'Consumption'", "supported_endpoints": [ "/v1/chat/completions", "/v1/batch", @@ -7406,6 +7634,7 @@ "search_context_size_low": 0.01, "search_context_size_medium": 0.01 }, + "source": "https://prices.azure.com/api/retail/prices?$filter=serviceName%20eq%20'Foundry%20Models'%20and%20armRegionName%20eq%20'eastus2'%20and%20priceType%20eq%20'Consumption'", "supported_endpoints": [ "/v1/chat/completions", "/v1/responses" @@ -7563,33 +7792,34 @@ "supports_minimal_reasoning_effort": false }, "azure/us/gpt-5.6-sol": { - "cache_creation_input_token_cost": 6.875e-06, - "cache_creation_input_token_cost_above_272k_tokens": 1.375e-05, - "cache_creation_input_token_cost_above_272k_tokens_priority": 2.75e-05, - "cache_creation_input_token_cost_priority": 1.375e-05, - "cache_read_input_token_cost": 5.5e-07, - "cache_read_input_token_cost_above_272k_tokens": 1.1e-06, - "cache_read_input_token_cost_above_272k_tokens_priority": 2.2e-06, - "cache_read_input_token_cost_priority": 1.1e-06, + "cache_creation_input_token_cost": 5.5e-06, + "cache_creation_input_token_cost_above_272k_tokens": 1.1e-05, + "cache_creation_input_token_cost_above_272k_tokens_priority": 2.2e-05, + "cache_creation_input_token_cost_priority": 1.1e-05, + "cache_read_input_token_cost": 4.4e-07, + "cache_read_input_token_cost_above_272k_tokens": 8.8e-07, + "cache_read_input_token_cost_above_272k_tokens_priority": 1.76e-06, + "cache_read_input_token_cost_priority": 8.8e-07, "deprecation_date": "2028-01-11", - "input_cost_per_token": 5.5e-06, - "input_cost_per_token_above_272k_tokens": 1.1e-05, - "input_cost_per_token_above_272k_tokens_priority": 2.2e-05, - "input_cost_per_token_priority": 1.1e-05, + "input_cost_per_token": 4.4e-06, + "input_cost_per_token_above_272k_tokens": 8.8e-06, + "input_cost_per_token_above_272k_tokens_priority": 1.76e-05, + "input_cost_per_token_priority": 8.8e-06, "litellm_provider": "azure", "max_input_tokens": 922000, "max_output_tokens": 128000, "max_tokens": 128000, "mode": "chat", - "output_cost_per_token": 3.3e-05, - "output_cost_per_token_above_272k_tokens": 4.95e-05, - "output_cost_per_token_above_272k_tokens_priority": 9.9e-05, - "output_cost_per_token_priority": 6.6e-05, + "output_cost_per_token": 2.2e-05, + "output_cost_per_token_above_272k_tokens": 3.3e-05, + "output_cost_per_token_above_272k_tokens_priority": 6.6e-05, + "output_cost_per_token_priority": 4.4e-05, "search_context_cost_per_query": { "search_context_size_high": 0.01, "search_context_size_low": 0.01, "search_context_size_medium": 0.01 }, + "source": "https://prices.azure.com/api/retail/prices?$filter=serviceName%20eq%20'Foundry%20Models'%20and%20armRegionName%20eq%20'eastus2'%20and%20priceType%20eq%20'Consumption'", "supported_endpoints": [ "/v1/chat/completions", "/v1/batch", @@ -7645,6 +7875,7 @@ "search_context_size_low": 0.01, "search_context_size_medium": 0.01 }, + "source": "https://prices.azure.com/api/retail/prices?$filter=serviceName%20eq%20'Foundry%20Models'%20and%20armRegionName%20eq%20'eastus2'%20and%20priceType%20eq%20'Consumption'", "supported_endpoints": [ "/v1/chat/completions", "/v1/batch", @@ -7700,6 +7931,7 @@ "search_context_size_low": 0.01, "search_context_size_medium": 0.01 }, + "source": "https://prices.azure.com/api/retail/prices?$filter=serviceName%20eq%20'Foundry%20Models'%20and%20armRegionName%20eq%20'eastus2'%20and%20priceType%20eq%20'Consumption'", "supported_endpoints": [ "/v1/chat/completions", "/v1/batch", @@ -7746,6 +7978,7 @@ "search_context_size_low": 0.01, "search_context_size_medium": 0.01 }, + "source": "https://prices.azure.com/api/retail/prices?$filter=serviceName%20eq%20'Foundry%20Models'%20and%20armRegionName%20eq%20'eastus2'%20and%20priceType%20eq%20'Consumption'", "supported_endpoints": [ "/v1/chat/completions", "/v1/responses" @@ -7866,33 +8099,34 @@ "supports_minimal_reasoning_effort": false }, "azure/eu/gpt-5.6-sol": { - "cache_creation_input_token_cost": 6.875e-06, - "cache_creation_input_token_cost_above_272k_tokens": 1.375e-05, - "cache_creation_input_token_cost_above_272k_tokens_priority": 2.75e-05, - "cache_creation_input_token_cost_priority": 1.375e-05, - "cache_read_input_token_cost": 5.5e-07, - "cache_read_input_token_cost_above_272k_tokens": 1.1e-06, - "cache_read_input_token_cost_above_272k_tokens_priority": 2.2e-06, - "cache_read_input_token_cost_priority": 1.1e-06, + "cache_creation_input_token_cost": 5.5e-06, + "cache_creation_input_token_cost_above_272k_tokens": 1.1e-05, + "cache_creation_input_token_cost_above_272k_tokens_priority": 2.2e-05, + "cache_creation_input_token_cost_priority": 1.1e-05, + "cache_read_input_token_cost": 4.4e-07, + "cache_read_input_token_cost_above_272k_tokens": 8.8e-07, + "cache_read_input_token_cost_above_272k_tokens_priority": 1.76e-06, + "cache_read_input_token_cost_priority": 8.8e-07, "deprecation_date": "2028-01-11", - "input_cost_per_token": 5.5e-06, - "input_cost_per_token_above_272k_tokens": 1.1e-05, - "input_cost_per_token_above_272k_tokens_priority": 2.2e-05, - "input_cost_per_token_priority": 1.1e-05, + "input_cost_per_token": 4.4e-06, + "input_cost_per_token_above_272k_tokens": 8.8e-06, + "input_cost_per_token_above_272k_tokens_priority": 1.76e-05, + "input_cost_per_token_priority": 8.8e-06, "litellm_provider": "azure", "max_input_tokens": 922000, "max_output_tokens": 128000, "max_tokens": 128000, "mode": "chat", - "output_cost_per_token": 3.3e-05, - "output_cost_per_token_above_272k_tokens": 4.95e-05, - "output_cost_per_token_above_272k_tokens_priority": 9.9e-05, - "output_cost_per_token_priority": 6.6e-05, + "output_cost_per_token": 2.2e-05, + "output_cost_per_token_above_272k_tokens": 3.3e-05, + "output_cost_per_token_above_272k_tokens_priority": 6.6e-05, + "output_cost_per_token_priority": 4.4e-05, "search_context_cost_per_query": { "search_context_size_high": 0.01, "search_context_size_low": 0.01, "search_context_size_medium": 0.01 }, + "source": "https://prices.azure.com/api/retail/prices?$filter=serviceName%20eq%20'Foundry%20Models'%20and%20armRegionName%20eq%20'eastus2'%20and%20priceType%20eq%20'Consumption'", "supported_endpoints": [ "/v1/chat/completions", "/v1/batch", @@ -7948,6 +8182,7 @@ "search_context_size_low": 0.01, "search_context_size_medium": 0.01 }, + "source": "https://prices.azure.com/api/retail/prices?$filter=serviceName%20eq%20'Foundry%20Models'%20and%20armRegionName%20eq%20'eastus2'%20and%20priceType%20eq%20'Consumption'", "supported_endpoints": [ "/v1/chat/completions", "/v1/batch", @@ -8003,6 +8238,7 @@ "search_context_size_low": 0.01, "search_context_size_medium": 0.01 }, + "source": "https://prices.azure.com/api/retail/prices?$filter=serviceName%20eq%20'Foundry%20Models'%20and%20armRegionName%20eq%20'eastus2'%20and%20priceType%20eq%20'Consumption'", "supported_endpoints": [ "/v1/chat/completions", "/v1/batch", @@ -8034,12 +8270,16 @@ "deprecation_date": "2027-10-26", "cache_read_input_token_cost": 5e-07, "cache_read_input_token_cost_above_272k_tokens": 1e-06, - "cache_read_input_token_cost_priority": 1e-06, + "cache_read_input_token_cost_priority": 1.25e-06, "cache_read_input_token_cost_above_272k_tokens_priority": 2e-06, + "cache_read_input_token_cost_flex": 2.5e-07, "input_cost_per_token": 5e-06, "input_cost_per_token_above_272k_tokens": 1e-05, - "input_cost_per_token_priority": 1e-05, + "input_cost_per_token_above_272k_tokens_flex": 5e-06, + "input_cost_per_token_priority": 1.25e-05, "input_cost_per_token_above_272k_tokens_priority": 2e-05, + "input_cost_per_token_batches": 2.5e-06, + "input_cost_per_token_flex": 2.5e-06, "litellm_provider": "azure", "max_input_tokens": 1050000, "max_output_tokens": 128000, @@ -8047,13 +8287,15 @@ "mode": "chat", "output_cost_per_token": 3e-05, "output_cost_per_token_above_272k_tokens": 4.5e-05, - "output_cost_per_token_priority": 6e-05, + "output_cost_per_token_priority": 7.5e-05, "output_cost_per_token_above_272k_tokens_priority": 9e-05, + "output_cost_per_token_batches": 1.5e-05, "search_context_cost_per_query": { "search_context_size_high": 0.01, "search_context_size_low": 0.01, "search_context_size_medium": 0.01 }, + "source": "https://prices.azure.com/api/retail/prices?$filter=serviceName%20eq%20'Foundry%20Models'%20and%20armRegionName%20eq%20'eastus2'%20and%20priceType%20eq%20'Consumption'", "supported_endpoints": [ "/v1/chat/completions", "/v1/batch", @@ -8085,9 +8327,10 @@ "deprecation_date": "2027-10-26", "cache_read_input_token_cost": 5.5e-07, "cache_read_input_token_cost_above_272k_tokens": 1.1e-06, - "cache_read_input_token_cost_priority": 1.38e-06, + "cache_read_input_token_cost_priority": 1.375e-06, "input_cost_per_token": 5.5e-06, "input_cost_per_token_above_272k_tokens": 1.1e-05, + "input_cost_per_token_batches": 2.75e-06, "input_cost_per_token_priority": 1.375e-05, "output_cost_per_token": 3.3e-05, "output_cost_per_token_above_272k_tokens": 4.95e-05, @@ -8097,11 +8340,13 @@ "max_output_tokens": 128000, "max_tokens": 128000, "mode": "chat", + "output_cost_per_token_batches": 1.65e-05, "search_context_cost_per_query": { "search_context_size_high": 0.01, "search_context_size_low": 0.01, "search_context_size_medium": 0.01 }, + "source": "https://prices.azure.com/api/retail/prices?$filter=serviceName%20eq%20'Foundry%20Models'%20and%20armRegionName%20eq%20'eastus2'%20and%20priceType%20eq%20'Consumption'", "supported_endpoints": [ "/v1/chat/completions", "/v1/batch", @@ -8133,9 +8378,10 @@ "deprecation_date": "2027-10-26", "cache_read_input_token_cost": 5.5e-07, "cache_read_input_token_cost_above_272k_tokens": 1.1e-06, - "cache_read_input_token_cost_priority": 1.38e-06, + "cache_read_input_token_cost_priority": 1.375e-06, "input_cost_per_token": 5.5e-06, "input_cost_per_token_above_272k_tokens": 1.1e-05, + "input_cost_per_token_batches": 2.75e-06, "input_cost_per_token_priority": 1.375e-05, "output_cost_per_token": 3.3e-05, "output_cost_per_token_above_272k_tokens": 4.95e-05, @@ -8145,11 +8391,13 @@ "max_output_tokens": 128000, "max_tokens": 128000, "mode": "chat", + "output_cost_per_token_batches": 1.65e-05, "search_context_cost_per_query": { "search_context_size_high": 0.01, "search_context_size_low": 0.01, "search_context_size_medium": 0.01 }, + "source": "https://prices.azure.com/api/retail/prices?$filter=serviceName%20eq%20'Foundry%20Models'%20and%20armRegionName%20eq%20'eastus2'%20and%20priceType%20eq%20'Consumption'", "supported_endpoints": [ "/v1/chat/completions", "/v1/batch", @@ -8180,11 +8428,12 @@ "azure/gpt-5.5-2026-04-23": { "cache_read_input_token_cost": 5e-07, "cache_read_input_token_cost_above_272k_tokens": 1e-06, - "cache_read_input_token_cost_priority": 1e-06, + "cache_read_input_token_cost_priority": 1.25e-06, "cache_read_input_token_cost_above_272k_tokens_priority": 2e-06, + "cache_read_input_token_cost_flex": 2.5e-07, "input_cost_per_token": 5e-06, "input_cost_per_token_above_272k_tokens": 1e-05, - "input_cost_per_token_priority": 1e-05, + "input_cost_per_token_priority": 1.25e-05, "input_cost_per_token_above_272k_tokens_priority": 2e-05, "litellm_provider": "azure", "max_input_tokens": 1050000, @@ -8193,7 +8442,7 @@ "mode": "chat", "output_cost_per_token": 3e-05, "output_cost_per_token_above_272k_tokens": 4.5e-05, - "output_cost_per_token_priority": 6e-05, + "output_cost_per_token_priority": 7.5e-05, "output_cost_per_token_above_272k_tokens_priority": 9e-05, "search_context_cost_per_query": { "search_context_size_high": 0.01, @@ -8223,12 +8472,17 @@ "supports_tool_choice": true, "supports_vision": true, "supports_web_search": true, - "deprecation_date": "2027-10-26" + "deprecation_date": "2027-10-26", + "input_cost_per_token_above_272k_tokens_flex": 5e-06, + "input_cost_per_token_batches": 2.5e-06, + "input_cost_per_token_flex": 2.5e-06, + "output_cost_per_token_batches": 1.5e-05, + "source": "https://prices.azure.com/api/retail/prices?$filter=serviceName%20eq%20'Foundry%20Models'%20and%20armRegionName%20eq%20'eastus2'%20and%20priceType%20eq%20'Consumption'" }, "azure/us/gpt-5.5-2026-04-23": { "cache_read_input_token_cost": 5.5e-07, "cache_read_input_token_cost_above_272k_tokens": 1.1e-06, - "cache_read_input_token_cost_priority": 1.38e-06, + "cache_read_input_token_cost_priority": 1.375e-06, "input_cost_per_token": 5.5e-06, "input_cost_per_token_above_272k_tokens": 1.1e-05, "input_cost_per_token_priority": 1.375e-05, @@ -8268,12 +8522,15 @@ "supports_tool_choice": true, "supports_vision": true, "supports_web_search": true, - "deprecation_date": "2027-10-26" + "deprecation_date": "2027-10-26", + "input_cost_per_token_batches": 2.75e-06, + "output_cost_per_token_batches": 1.65e-05, + "source": "https://prices.azure.com/api/retail/prices?$filter=serviceName%20eq%20'Foundry%20Models'%20and%20armRegionName%20eq%20'eastus2'%20and%20priceType%20eq%20'Consumption'" }, "azure/eu/gpt-5.5-2026-04-23": { "cache_read_input_token_cost": 5.5e-07, "cache_read_input_token_cost_above_272k_tokens": 1.1e-06, - "cache_read_input_token_cost_priority": 1.38e-06, + "cache_read_input_token_cost_priority": 1.375e-06, "input_cost_per_token": 5.5e-06, "input_cost_per_token_above_272k_tokens": 1.1e-05, "input_cost_per_token_priority": 1.375e-05, @@ -8313,7 +8570,10 @@ "supports_tool_choice": true, "supports_vision": true, "supports_web_search": true, - "deprecation_date": "2027-10-26" + "deprecation_date": "2027-10-26", + "input_cost_per_token_batches": 2.75e-06, + "output_cost_per_token_batches": 1.65e-05, + "source": "https://prices.azure.com/api/retail/prices?$filter=serviceName%20eq%20'Foundry%20Models'%20and%20armRegionName%20eq%20'eastus2'%20and%20priceType%20eq%20'Consumption'" }, "azure/gpt-5.5-pro": { "cache_read_input_token_cost": 3e-06, @@ -8402,6 +8662,8 @@ "azure/gpt-5.4-mini": { "deprecation_date": "2027-09-21", "cache_read_input_token_cost": 7.5e-08, + "cache_read_input_token_cost_flex": 3.75e-08, + "cache_read_input_token_cost_priority": 1.5e-07, "input_cost_per_token": 7.5e-07, "litellm_provider": "azure", "max_input_tokens": 272000, @@ -8439,10 +8701,19 @@ "supports_web_search": true, "supports_none_reasoning_effort": true, "default_reasoning_effort": "none", + "input_cost_per_token_batches": 3.75e-07, + "input_cost_per_token_flex": 3.75e-07, + "input_cost_per_token_priority": 1.5e-06, + "output_cost_per_token_batches": 2.25e-06, + "output_cost_per_token_flex": 2.25e-06, + "output_cost_per_token_priority": 9e-06, + "source": "https://prices.azure.com/api/retail/prices?$filter=serviceName%20eq%20'Foundry%20Models'%20and%20armRegionName%20eq%20'eastus2'%20and%20priceType%20eq%20'Consumption'", "supports_xhigh_reasoning_effort": true }, "azure/gpt-5.4-mini-2026-03-17": { "cache_read_input_token_cost": 7.5e-08, + "cache_read_input_token_cost_flex": 3.75e-08, + "cache_read_input_token_cost_priority": 1.5e-07, "deprecation_date": "2027-09-21", "input_cost_per_token": 7.5e-07, "litellm_provider": "azure", @@ -8481,11 +8752,19 @@ "supports_web_search": true, "supports_none_reasoning_effort": true, "default_reasoning_effort": "none", + "input_cost_per_token_batches": 3.75e-07, + "input_cost_per_token_flex": 3.75e-07, + "input_cost_per_token_priority": 1.5e-06, + "output_cost_per_token_batches": 2.25e-06, + "output_cost_per_token_flex": 2.25e-06, + "output_cost_per_token_priority": 9e-06, + "source": "https://prices.azure.com/api/retail/prices?$filter=serviceName%20eq%20'Foundry%20Models'%20and%20armRegionName%20eq%20'eastus2'%20and%20priceType%20eq%20'Consumption'", "supports_xhigh_reasoning_effort": true }, "azure/gpt-5.4-nano": { "deprecation_date": "2027-09-21", "cache_read_input_token_cost": 2e-08, + "cache_read_input_token_cost_flex": 1e-08, "input_cost_per_token": 2e-07, "litellm_provider": "azure", "max_input_tokens": 272000, @@ -8523,10 +8802,16 @@ "supports_web_search": true, "supports_none_reasoning_effort": true, "default_reasoning_effort": "none", + "input_cost_per_token_batches": 1e-07, + "input_cost_per_token_flex": 1e-07, + "output_cost_per_token_batches": 6.25e-07, + "output_cost_per_token_flex": 6.25e-07, + "source": "https://prices.azure.com/api/retail/prices?$filter=serviceName%20eq%20'Foundry%20Models'%20and%20armRegionName%20eq%20'eastus2'%20and%20priceType%20eq%20'Consumption'", "supports_xhigh_reasoning_effort": true }, "azure/gpt-5.4-nano-2026-03-17": { "cache_read_input_token_cost": 2e-08, + "cache_read_input_token_cost_flex": 1e-08, "deprecation_date": "2027-09-21", "input_cost_per_token": 2e-07, "litellm_provider": "azure", @@ -8565,6 +8850,11 @@ "supports_web_search": true, "supports_none_reasoning_effort": true, "default_reasoning_effort": "none", + "input_cost_per_token_batches": 1e-07, + "input_cost_per_token_flex": 1e-07, + "output_cost_per_token_batches": 6.25e-07, + "output_cost_per_token_flex": 6.25e-07, + "source": "https://prices.azure.com/api/retail/prices?$filter=serviceName%20eq%20'Foundry%20Models'%20and%20armRegionName%20eq%20'eastus2'%20and%20priceType%20eq%20'Consumption'", "supports_xhigh_reasoning_effort": true }, "azure/gpt-image-1": { @@ -8886,12 +9176,15 @@ "cache_read_input_token_cost": 7.5e-06, "deprecation_date": "2026-11-19", "input_cost_per_token": 1.5e-05, + "input_cost_per_token_batches": 7.5e-06, "litellm_provider": "azure", "max_input_tokens": 200000, "max_output_tokens": 100000, "max_tokens": 100000, "mode": "chat", "output_cost_per_token": 6e-05, + "output_cost_per_token_batches": 3e-05, + "source": "https://prices.azure.com/api/retail/prices?$filter=serviceName%20eq%20'Foundry%20Models'%20and%20armRegionName%20eq%20'eastus2'%20and%20priceType%20eq%20'Consumption'", "supports_function_calling": true, "supports_parallel_function_calling": true, "supports_prompt_caching": true, @@ -8900,14 +9193,17 @@ "supports_vision": true }, "azure/o1-mini": { - "cache_read_input_token_cost": 6.05e-07, - "input_cost_per_token": 1.21e-06, + "cache_read_input_token_cost": 5.5e-07, + "input_cost_per_token": 1.1e-06, + "input_cost_per_token_batches": 5.5e-07, "litellm_provider": "azure", "max_input_tokens": 128000, "max_output_tokens": 65536, "max_tokens": 65536, "mode": "chat", - "output_cost_per_token": 4.84e-06, + "output_cost_per_token": 4.4e-06, + "output_cost_per_token_batches": 2.2e-06, + "source": "https://prices.azure.com/api/retail/prices?$filter=serviceName%20eq%20'Foundry%20Models'%20and%20armRegionName%20eq%20'eastus2'%20and%20priceType%20eq%20'Consumption'", "supports_function_calling": true, "supports_parallel_function_calling": true, "supports_prompt_caching": true, @@ -8938,6 +9234,7 @@ "max_tokens": 32768, "mode": "chat", "output_cost_per_token": 6e-05, + "source": "https://prices.azure.com/api/retail/prices?$filter=serviceName%20eq%20'Foundry%20Models'%20and%20armRegionName%20eq%20'eastus2'%20and%20priceType%20eq%20'Consumption'", "supports_function_calling": true, "supports_parallel_function_calling": true, "supports_prompt_caching": true, @@ -8953,6 +9250,7 @@ "max_tokens": 32768, "mode": "chat", "output_cost_per_token": 6e-05, + "source": "https://prices.azure.com/api/retail/prices?$filter=serviceName%20eq%20'Foundry%20Models'%20and%20armRegionName%20eq%20'eastus2'%20and%20priceType%20eq%20'Consumption'", "supports_function_calling": true, "supports_parallel_function_calling": true, "supports_pdf_input": true, @@ -8994,12 +9292,15 @@ "deprecation_date": "2026-11-19", "cache_read_input_token_cost": 5e-07, "input_cost_per_token": 2e-06, + "input_cost_per_token_batches": 1e-06, "litellm_provider": "azure", "max_input_tokens": 200000, "max_output_tokens": 100000, "max_tokens": 100000, "mode": "chat", "output_cost_per_token": 8e-06, + "output_cost_per_token_batches": 4e-06, + "source": "https://prices.azure.com/api/retail/prices?$filter=serviceName%20eq%20'Foundry%20Models'%20and%20armRegionName%20eq%20'eastus2'%20and%20priceType%20eq%20'Consumption'", "supported_endpoints": [ "/v1/chat/completions", "/v1/batch", @@ -9035,6 +9336,7 @@ "search_context_size_low": 0.01, "search_context_size_medium": 0.01 }, + "source": "https://prices.azure.com/api/retail/prices?$filter=serviceName%20eq%20'Foundry%20Models'%20and%20armRegionName%20eq%20'eastus2'%20and%20priceType%20eq%20'Consumption'", "supported_endpoints": [ "/v1/chat/completions", "/v1/batch", @@ -9078,12 +9380,15 @@ "cache_read_input_token_cost": 5.5e-07, "deprecation_date": "2026-11-19", "input_cost_per_token": 1.1e-06, + "input_cost_per_token_batches": 5.5e-07, "litellm_provider": "azure", "max_input_tokens": 200000, "max_output_tokens": 100000, "max_tokens": 100000, "mode": "chat", "output_cost_per_token": 4.4e-06, + "output_cost_per_token_batches": 2.2e-06, + "source": "https://prices.azure.com/api/retail/prices?$filter=serviceName%20eq%20'Foundry%20Models'%20and%20armRegionName%20eq%20'eastus2'%20and%20priceType%20eq%20'Consumption'", "supports_prompt_caching": true, "supports_reasoning": true, "supports_tool_choice": true, @@ -9100,6 +9405,7 @@ "mode": "responses", "output_cost_per_token": 8e-05, "output_cost_per_token_batches": 4e-05, + "source": "https://prices.azure.com/api/retail/prices?$filter=serviceName%20eq%20'Foundry%20Models'%20and%20armRegionName%20eq%20'eastus2'%20and%20priceType%20eq%20'Consumption'", "supported_endpoints": [ "/v1/chat/completions", "/v1/batch", @@ -9131,6 +9437,7 @@ "mode": "responses", "output_cost_per_token": 8e-05, "output_cost_per_token_batches": 4e-05, + "source": "https://prices.azure.com/api/retail/prices?$filter=serviceName%20eq%20'Foundry%20Models'%20and%20armRegionName%20eq%20'eastus2'%20and%20priceType%20eq%20'Consumption'", "supported_endpoints": [ "/v1/chat/completions", "/v1/batch", @@ -9185,12 +9492,15 @@ "cache_read_input_token_cost": 2.75e-07, "deprecation_date": "2026-11-19", "input_cost_per_token": 1.1e-06, + "input_cost_per_token_batches": 5.5e-07, "litellm_provider": "azure", "max_input_tokens": 200000, "max_output_tokens": 100000, "max_tokens": 100000, "mode": "chat", "output_cost_per_token": 4.4e-06, + "output_cost_per_token_batches": 2.2e-06, + "source": "https://prices.azure.com/api/retail/prices?$filter=serviceName%20eq%20'Foundry%20Models'%20and%20armRegionName%20eq%20'eastus2'%20and%20priceType%20eq%20'Consumption'", "supports_function_calling": true, "supports_parallel_function_calling": false, "supports_prompt_caching": true, @@ -9230,7 +9540,8 @@ "max_input_tokens": 8191, "max_tokens": 8191, "mode": "embedding", - "output_cost_per_token": 0.0 + "output_cost_per_token": 0.0, + "source": "https://prices.azure.com/api/retail/prices?$filter=serviceName%20eq%20'Foundry%20Models'%20and%20armRegionName%20eq%20'eastus2'%20and%20priceType%20eq%20'Consumption'" }, "azure/text-embedding-3-small": { "deprecation_date": "2028-02-09", @@ -9239,7 +9550,8 @@ "max_input_tokens": 8191, "max_tokens": 8191, "mode": "embedding", - "output_cost_per_token": 0.0 + "output_cost_per_token": 0.0, + "source": "https://prices.azure.com/api/retail/prices?$filter=serviceName%20eq%20'Foundry%20Models'%20and%20armRegionName%20eq%20'eastus2'%20and%20priceType%20eq%20'Consumption'" }, "azure/text-embedding-ada-002": { "deprecation_date": "2028-02-09", @@ -9248,7 +9560,8 @@ "max_input_tokens": 8191, "max_tokens": 8191, "mode": "embedding", - "output_cost_per_token": 0.0 + "output_cost_per_token": 0.0, + "source": "https://prices.azure.com/api/retail/prices?$filter=serviceName%20eq%20'Foundry%20Models'%20and%20armRegionName%20eq%20'eastus2'%20and%20priceType%20eq%20'Consumption'" }, "azure/speech/azure-tts": { "input_cost_per_character": 1.5e-05, @@ -9390,12 +9703,15 @@ "deprecation_date": "2027-04-14", "cache_read_input_token_cost": 1.375e-06, "input_cost_per_token": 2.75e-06, + "input_cost_per_token_batches": 1.375e-06, "litellm_provider": "azure", "max_input_tokens": 128000, "max_output_tokens": 16384, "max_tokens": 16384, "mode": "chat", "output_cost_per_token": 1.1e-05, + "output_cost_per_token_batches": 5.5e-06, + "source": "https://prices.azure.com/api/retail/prices?$filter=serviceName%20eq%20'Foundry%20Models'%20and%20armRegionName%20eq%20'eastus2'%20and%20priceType%20eq%20'Consumption'", "supports_function_calling": true, "supports_parallel_function_calling": true, "supports_prompt_caching": true, @@ -9406,13 +9722,17 @@ "azure/us/gpt-4o-2024-11-20": { "deprecation_date": "2027-04-14", "cache_creation_input_token_cost": 1.38e-06, + "cache_read_input_token_cost": 1.375e-06, "input_cost_per_token": 2.75e-06, + "input_cost_per_token_batches": 1.375e-06, "litellm_provider": "azure", "max_input_tokens": 128000, "max_output_tokens": 16384, "max_tokens": 16384, "mode": "chat", "output_cost_per_token": 1.1e-05, + "output_cost_per_token_batches": 5.5e-06, + "source": "https://prices.azure.com/api/retail/prices?$filter=serviceName%20eq%20'Foundry%20Models'%20and%20armRegionName%20eq%20'eastus2'%20and%20priceType%20eq%20'Consumption'", "supports_function_calling": true, "supports_parallel_function_calling": true, "supports_response_schema": true, @@ -9423,12 +9743,14 @@ "cache_read_input_token_cost": 8.3e-08, "deprecation_date": "2027-04-14", "input_cost_per_token": 1.65e-07, + "input_cost_per_token_batches": 8.3e-08, "litellm_provider": "azure", "max_input_tokens": 128000, "max_output_tokens": 16384, "max_tokens": 16384, "mode": "chat", "output_cost_per_token": 6.6e-07, + "source": "https://prices.azure.com/api/retail/prices?$filter=serviceName%20eq%20'Foundry%20Models'%20and%20armRegionName%20eq%20'eastus2'%20and%20priceType%20eq%20'Consumption'", "supports_function_calling": true, "supports_parallel_function_calling": true, "supports_prompt_caching": true, @@ -9602,8 +9924,9 @@ }, "azure/us/gpt-5.1": { "deprecation_date": "2027-05-15", - "cache_read_input_token_cost": 1.4e-07, - "input_cost_per_token": 1.38e-06, + "cache_read_input_token_cost": 1.375e-07, + "cache_read_input_token_cost_priority": 2.75e-07, + "input_cost_per_token": 1.375e-06, "litellm_provider": "azure", "max_input_tokens": 272000, "max_output_tokens": 128000, @@ -9634,12 +9957,17 @@ "supports_tool_choice": true, "supports_vision": true, "supports_none_reasoning_effort": true, - "default_reasoning_effort": "none" + "default_reasoning_effort": "none", + "input_cost_per_token_batches": 6.875e-07, + "input_cost_per_token_priority": 2.75e-06, + "output_cost_per_token_batches": 5.5e-06, + "output_cost_per_token_priority": 2.2e-05, + "source": "https://prices.azure.com/api/retail/prices?$filter=serviceName%20eq%20'Foundry%20Models'%20and%20armRegionName%20eq%20'eastus2'%20and%20priceType%20eq%20'Consumption'" }, "azure/us/gpt-5.1-chat": { - "cache_read_input_token_cost": 1.4e-07, + "cache_read_input_token_cost": 1.375e-07, "deprecation_date": "2026-06-29", - "input_cost_per_token": 1.38e-06, + "input_cost_per_token": 1.375e-06, "litellm_provider": "azure", "max_input_tokens": 128000, "max_output_tokens": 128000, @@ -9670,18 +9998,20 @@ "supports_tool_choice": true, "supports_vision": true, "supports_none_reasoning_effort": true, - "default_reasoning_effort": "none" + "default_reasoning_effort": "none", + "source": "https://prices.azure.com/api/retail/prices?$filter=serviceName%20eq%20'Foundry%20Models'%20and%20armRegionName%20eq%20'eastus2'%20and%20priceType%20eq%20'Consumption'" }, "azure/us/gpt-5.1-codex": { "deprecation_date": "2027-05-15", - "cache_read_input_token_cost": 1.4e-07, - "input_cost_per_token": 1.38e-06, + "cache_read_input_token_cost": 1.375e-07, + "input_cost_per_token": 1.375e-06, "litellm_provider": "azure", "max_input_tokens": 272000, "max_output_tokens": 128000, "max_tokens": 128000, "mode": "responses", "output_cost_per_token": 1.1e-05, + "source": "https://prices.azure.com/api/retail/prices?$filter=serviceName%20eq%20'Foundry%20Models'%20and%20armRegionName%20eq%20'eastus2'%20and%20priceType%20eq%20'Consumption'", "supported_endpoints": [ "/v1/responses" ], @@ -9705,7 +10035,7 @@ }, "azure/us/gpt-5.1-codex-mini": { "deprecation_date": "2027-05-15", - "cache_read_input_token_cost": 2.8e-08, + "cache_read_input_token_cost": 2.75e-08, "input_cost_per_token": 2.75e-07, "litellm_provider": "azure", "max_input_tokens": 272000, @@ -9713,6 +10043,7 @@ "max_tokens": 128000, "mode": "responses", "output_cost_per_token": 2.2e-06, + "source": "https://prices.azure.com/api/retail/prices?$filter=serviceName%20eq%20'Foundry%20Models'%20and%20armRegionName%20eq%20'eastus2'%20and%20priceType%20eq%20'Consumption'", "supported_endpoints": [ "/v1/responses" ], @@ -9738,12 +10069,15 @@ "cache_read_input_token_cost": 8.25e-06, "deprecation_date": "2026-11-19", "input_cost_per_token": 1.65e-05, + "input_cost_per_token_batches": 8.25e-06, "litellm_provider": "azure", "max_input_tokens": 200000, "max_output_tokens": 100000, "max_tokens": 100000, "mode": "chat", "output_cost_per_token": 6.6e-05, + "output_cost_per_token_batches": 3.3e-05, + "source": "https://prices.azure.com/api/retail/prices?$filter=serviceName%20eq%20'Foundry%20Models'%20and%20armRegionName%20eq%20'eastus2'%20and%20priceType%20eq%20'Consumption'", "supports_function_calling": true, "supports_parallel_function_calling": true, "supports_prompt_caching": true, @@ -9784,12 +10118,15 @@ "deprecation_date": "2026-11-19", "cache_read_input_token_cost": 5.5e-07, "input_cost_per_token": 2.2e-06, + "input_cost_per_token_batches": 1.1e-06, "litellm_provider": "azure", "max_input_tokens": 200000, "max_output_tokens": 100000, "max_tokens": 100000, "mode": "chat", "output_cost_per_token": 8.8e-06, + "output_cost_per_token_batches": 4.4e-06, + "source": "https://prices.azure.com/api/retail/prices?$filter=serviceName%20eq%20'Foundry%20Models'%20and%20armRegionName%20eq%20'eastus2'%20and%20priceType%20eq%20'Consumption'", "supported_endpoints": [ "/v1/chat/completions", "/v1/batch", @@ -9822,21 +10159,25 @@ "mode": "chat", "output_cost_per_token": 4.84e-06, "output_cost_per_token_batches": 2.42e-06, + "source": "https://prices.azure.com/api/retail/prices?$filter=serviceName%20eq%20'Foundry%20Models'%20and%20armRegionName%20eq%20'eastus2'%20and%20priceType%20eq%20'Consumption'", "supports_prompt_caching": true, "supports_reasoning": true, "supports_tool_choice": true, "supports_vision": false }, "azure/us/o4-mini-2025-04-16": { - "cache_read_input_token_cost": 3.1e-07, + "cache_read_input_token_cost": 3.03e-07, "deprecation_date": "2026-11-19", "input_cost_per_token": 1.21e-06, + "input_cost_per_token_batches": 6.05e-07, "litellm_provider": "azure", "max_input_tokens": 200000, "max_output_tokens": 100000, "max_tokens": 100000, "mode": "chat", "output_cost_per_token": 4.84e-06, + "output_cost_per_token_batches": 2.42e-06, + "source": "https://prices.azure.com/api/retail/prices?$filter=serviceName%20eq%20'Foundry%20Models'%20and%20armRegionName%20eq%20'eastus2'%20and%20priceType%20eq%20'Consumption'", "supports_function_calling": true, "supports_parallel_function_calling": false, "supports_prompt_caching": true, @@ -9882,7 +10223,7 @@ "max_tokens": 4096, "mode": "chat", "output_cost_per_token": 9e-07, - "source": "https://azure.microsoft.com/en-us/pricing/details/ai-foundry-models/mistral/", + "source": "https://prices.azure.com/api/retail/prices?$filter=serviceName%20eq%20'Foundry%20Models'%20and%20armRegionName%20eq%20'eastus2'%20and%20priceType%20eq%20'Consumption'", "supported_endpoints": [ "/v1/chat/completions" ], @@ -9931,7 +10272,7 @@ "max_tokens": 163840, "mode": "chat", "output_cost_per_token": 1.85e-06, - "source": "https://azure.microsoft.com/en-us/pricing/details/ai-foundry-models/fireworks/", + "source": "https://prices.azure.com/api/retail/prices?$filter=serviceName%20eq%20'Foundry%20Models'%20and%20armRegionName%20eq%20'eastus2'%20and%20priceType%20eq%20'Consumption'", "supports_function_calling": true, "supports_prompt_caching": true, "supports_reasoning": true, @@ -9946,7 +10287,7 @@ "max_tokens": 384000, "mode": "chat", "output_cost_per_token": 3.828e-06, - "source": "https://azure.microsoft.com/en-us/pricing/details/ai-foundry-models/fireworks/", + "source": "https://prices.azure.com/api/retail/prices?$filter=serviceName%20eq%20'Foundry%20Models'%20and%20armRegionName%20eq%20'eastus2'%20and%20priceType%20eq%20'Consumption'", "supports_function_calling": true, "supports_prompt_caching": true, "supports_reasoning": true, @@ -9962,7 +10303,7 @@ "max_tokens": 128000, "mode": "chat", "output_cost_per_token": 3.52e-06, - "source": "https://azure.microsoft.com/en-us/pricing/details/ai-foundry-models/fireworks/", + "source": "https://prices.azure.com/api/retail/prices?$filter=serviceName%20eq%20'Foundry%20Models'%20and%20armRegionName%20eq%20'eastus2'%20and%20priceType%20eq%20'Consumption'", "supports_function_calling": true, "supports_prompt_caching": true, "supports_reasoning": true, @@ -9978,7 +10319,7 @@ "max_tokens": 131072, "mode": "chat", "output_cost_per_token": 4.84e-06, - "source": "https://azure.microsoft.com/en-us/pricing/details/ai-foundry-models/fireworks/", + "source": "https://prices.azure.com/api/retail/prices?$filter=serviceName%20eq%20'Foundry%20Models'%20and%20armRegionName%20eq%20'eastus2'%20and%20priceType%20eq%20'Consumption'", "supports_function_calling": true, "supports_prompt_caching": true, "supports_reasoning": true, @@ -9993,37 +10334,37 @@ "max_tokens": 131072, "mode": "chat", "output_cost_per_token": 4.84e-06, - "source": "https://azure.microsoft.com/en-us/pricing/details/ai-foundry-models/fireworks/", + "source": "https://prices.azure.com/api/retail/prices?$filter=serviceName%20eq%20'Foundry%20Models'%20and%20armRegionName%20eq%20'eastus2'%20and%20priceType%20eq%20'Consumption'", "supports_function_calling": true, "supports_prompt_caching": true, "supports_reasoning": true, "supports_tool_choice": true }, "azure_ai/FW-GLM-5.2-Fast": { - "cache_read_input_token_cost": 2.1e-07, - "input_cost_per_token": 2.1e-06, + "cache_read_input_token_cost": 2.31e-07, + "input_cost_per_token": 2.31e-06, "litellm_provider": "azure_ai", "max_input_tokens": 1048576, "max_output_tokens": 131072, "max_tokens": 131072, "mode": "chat", - "output_cost_per_token": 6.6e-06, - "source": "https://docs.fireworks.ai/serverless/pricing", + "output_cost_per_token": 7.26e-06, + "source": "https://prices.azure.com/api/retail/prices?$filter=serviceName%20eq%20'Foundry%20Models'%20and%20armRegionName%20eq%20'eastus2'%20and%20priceType%20eq%20'Consumption'", "supports_function_calling": true, "supports_prompt_caching": true, "supports_reasoning": true, "supports_tool_choice": true }, "azure_ai/FW-Inkling": { - "cache_read_input_token_cost": 1.7e-07, - "input_cost_per_token": 1e-06, + "cache_read_input_token_cost": 1.9e-07, + "input_cost_per_token": 1.1e-06, "litellm_provider": "azure_ai", "max_input_tokens": 1048576, "max_output_tokens": 1048576, "max_tokens": 1048576, "mode": "chat", - "output_cost_per_token": 4.05e-06, - "source": "https://fireworks.ai/models/fireworks/inkling", + "output_cost_per_token": 4.46e-06, + "source": "https://prices.azure.com/api/retail/prices?$filter=serviceName%20eq%20'Foundry%20Models'%20and%20armRegionName%20eq%20'eastus2'%20and%20priceType%20eq%20'Consumption'", "supported_modalities": [ "text" ], @@ -10045,7 +10386,7 @@ "max_tokens": 262144, "mode": "chat", "output_cost_per_token": 3.3e-06, - "source": "https://azure.microsoft.com/en-us/pricing/details/ai-foundry-models/fireworks/", + "source": "https://prices.azure.com/api/retail/prices?$filter=serviceName%20eq%20'Foundry%20Models'%20and%20armRegionName%20eq%20'eastus2'%20and%20priceType%20eq%20'Consumption'", "supported_modalities": [ "text", "image" @@ -10068,7 +10409,7 @@ "max_tokens": 262144, "mode": "chat", "output_cost_per_token": 4.4e-06, - "source": "https://azure.microsoft.com/en-us/pricing/details/ai-foundry-models/fireworks/", + "source": "https://prices.azure.com/api/retail/prices?$filter=serviceName%20eq%20'Foundry%20Models'%20and%20armRegionName%20eq%20'eastus2'%20and%20priceType%20eq%20'Consumption'", "supported_modalities": [ "text", "image" @@ -10091,7 +10432,7 @@ "max_tokens": 262144, "mode": "chat", "output_cost_per_token": 4.4e-06, - "source": "https://azure.microsoft.com/en-us/pricing/details/ai-foundry-models/fireworks/", + "source": "https://prices.azure.com/api/retail/prices?$filter=serviceName%20eq%20'Foundry%20Models'%20and%20armRegionName%20eq%20'eastus2'%20and%20priceType%20eq%20'Consumption'", "supported_modalities": [ "text", "image" @@ -10119,7 +10460,7 @@ "high", "max" ], - "source": "https://techcommunity.microsoft.com/blog/azure-ai-foundry-blog/introducing-kimi-k3-through-fireworks-ai-on-microsoft-foundry/4540187", + "source": "https://prices.azure.com/api/retail/prices?$filter=serviceName%20eq%20'Foundry%20Models'%20and%20armRegionName%20eq%20'eastus2'%20and%20priceType%20eq%20'Consumption'", "supported_modalities": [ "text", "image" @@ -10143,7 +10484,7 @@ "max_tokens": 1000000, "mode": "chat", "output_cost_per_token": 1.32e-06, - "source": "https://azure.microsoft.com/en-us/pricing/details/ai-foundry-models/fireworks/", + "source": "https://prices.azure.com/api/retail/prices?$filter=serviceName%20eq%20'Foundry%20Models'%20and%20armRegionName%20eq%20'eastus2'%20and%20priceType%20eq%20'Consumption'", "supports_function_calling": true, "supports_prompt_caching": true, "supports_reasoning": true, @@ -10158,7 +10499,7 @@ "max_tokens": 512000, "mode": "chat", "output_cost_per_token": 1.32e-06, - "source": "https://azure.microsoft.com/en-us/pricing/details/ai-foundry-models/fireworks/", + "source": "https://prices.azure.com/api/retail/prices?$filter=serviceName%20eq%20'Foundry%20Models'%20and%20armRegionName%20eq%20'eastus2'%20and%20priceType%20eq%20'Consumption'", "supported_modalities": [ "text", "image" @@ -10179,7 +10520,7 @@ "max_input_tokens": 262144, "mode": "chat", "output_cost_per_token": 2.2e-07, - "source": "https://azure.microsoft.com/en-us/pricing/details/ai-foundry-models/fireworks/", + "source": "https://prices.azure.com/api/retail/prices?$filter=serviceName%20eq%20'Foundry%20Models'%20and%20armRegionName%20eq%20'eastus2'%20and%20priceType%20eq%20'Consumption'", "supported_modalities": [ "text" ], @@ -10193,15 +10534,15 @@ "supports_vision": false }, "azure_ai/FW-Nemotron-3-Ultra-NVFP4": { - "cache_read_input_token_cost": 1.19e-07, - "input_cost_per_token": 6e-07, + "cache_read_input_token_cost": 1.3e-07, + "input_cost_per_token": 6.6e-07, "litellm_provider": "azure_ai", "max_input_tokens": 262144, "max_output_tokens": 262144, "max_tokens": 262144, "mode": "chat", - "output_cost_per_token": 2.4e-06, - "source": "https://fireworks.ai/models/fireworks/nemotron-3-ultra-nvfp4", + "output_cost_per_token": 2.64e-06, + "source": "https://prices.azure.com/api/retail/prices?$filter=serviceName%20eq%20'Foundry%20Models'%20and%20armRegionName%20eq%20'eastus2'%20and%20priceType%20eq%20'Consumption'", "supported_modalities": [ "text" ], @@ -10220,7 +10561,7 @@ "mode": "image_generation", "output_cost_per_image": 0.05, "output_cost_per_image_token": 4.7e-05, - "source": "https://techcommunity.microsoft.com/blog/azure-ai-foundry-blog/new-mai-models-in-microsoft-foundry-across-text-image-voice-and-speech/4524632", + "source": "https://prices.azure.com/api/retail/prices?$filter=serviceName%20eq%20'Foundry%20Models'%20and%20armRegionName%20eq%20'eastus2'%20and%20priceType%20eq%20'Consumption'", "supported_endpoints": [ "/v1/images/generations", "/v1/images/edits" @@ -10234,7 +10575,7 @@ "mode": "image_generation", "output_cost_per_image": 0.0338, "output_cost_per_image_token": 3.3e-05, - "source": "https://techcommunity.microsoft.com/blog/azure-ai-foundry-blog/new-mai-models-in-microsoft-foundry-across-text-image-voice-and-speech/4524632", + "source": "https://prices.azure.com/api/retail/prices?$filter=serviceName%20eq%20'Foundry%20Models'%20and%20armRegionName%20eq%20'eastus2'%20and%20priceType%20eq%20'Consumption'", "supported_endpoints": [ "/v1/images/generations", "/v1/images/edits" @@ -10248,7 +10589,7 @@ "mode": "image_generation", "output_cost_per_image": 0.02, "output_cost_per_image_token": 1.95e-05, - "source": "https://aka.ms/mai-image-2e-foundryblog", + "source": "https://prices.azure.com/api/retail/prices?$filter=serviceName%20eq%20'Foundry%20Models'%20and%20armRegionName%20eq%20'eastus2'%20and%20priceType%20eq%20'Consumption'", "supported_endpoints": [ "/v1/images/generations" ] @@ -10262,7 +10603,7 @@ "max_tokens": 64000, "mode": "chat", "output_cost_per_token": 8e-06, - "source": "https://learn.microsoft.com/en-us/azure/foundry/foundry-models/concepts/models-sold-directly-by-azure", + "source": "https://prices.azure.com/api/retail/prices?$filter=serviceName%20eq%20'Foundry%20Models'%20and%20armRegionName%20eq%20'eastus2'%20and%20priceType%20eq%20'Consumption'", "supported_endpoints": [ "/v1/chat/completions" ], @@ -10313,19 +10654,19 @@ "max_tokens": 2048, "mode": "chat", "output_cost_per_token": 7.1e-07, - "source": "https://marketplace.microsoft.com/en/marketplace/apps/metagenai.llama-3-3-70b-instruct-offer?tab=Overview", + "source": "https://prices.azure.com/api/retail/prices?$filter=serviceName%20eq%20'Foundry%20Models'%20and%20armRegionName%20eq%20'eastus2'%20and%20priceType%20eq%20'Consumption'", "supports_function_calling": true, "supports_tool_choice": true }, "azure_ai/Llama-4-Maverick-17B-128E-Instruct-FP8": { - "input_cost_per_token": 1.41e-06, + "input_cost_per_token": 2.5e-07, "litellm_provider": "azure_ai", "max_input_tokens": 1000000, "max_output_tokens": 16384, "max_tokens": 16384, "mode": "chat", - "output_cost_per_token": 3.5e-07, - "source": "https://azure.microsoft.com/en-us/blog/introducing-the-llama-4-herd-in-azure-ai-foundry-and-azure-databricks/", + "output_cost_per_token": 1e-06, + "source": "https://prices.azure.com/api/retail/prices?$filter=serviceName%20eq%20'Foundry%20Models'%20and%20armRegionName%20eq%20'eastus2'%20and%20priceType%20eq%20'Consumption'", "supports_function_calling": true, "supports_tool_choice": true, "supports_vision": true @@ -10396,7 +10737,7 @@ "max_tokens": 4096, "mode": "chat", "output_cost_per_token": 6.8e-07, - "source": "https://azure.microsoft.com/en-us/pricing/details/phi-3/", + "source": "https://prices.azure.com/api/retail/prices?$filter=serviceName%20eq%20'Foundry%20Models'%20and%20armRegionName%20eq%20'eastus2'%20and%20priceType%20eq%20'Consumption'", "supports_tool_choice": true, "supports_vision": false }, @@ -10408,7 +10749,7 @@ "max_tokens": 4096, "mode": "chat", "output_cost_per_token": 6.8e-07, - "source": "https://azure.microsoft.com/en-us/pricing/details/phi-3/", + "source": "https://prices.azure.com/api/retail/prices?$filter=serviceName%20eq%20'Foundry%20Models'%20and%20armRegionName%20eq%20'eastus2'%20and%20priceType%20eq%20'Consumption'", "supports_tool_choice": true, "supports_vision": false }, @@ -10420,7 +10761,7 @@ "max_tokens": 4096, "mode": "chat", "output_cost_per_token": 5.2e-07, - "source": "https://azure.microsoft.com/en-us/pricing/details/phi-3/", + "source": "https://prices.azure.com/api/retail/prices?$filter=serviceName%20eq%20'Foundry%20Models'%20and%20armRegionName%20eq%20'eastus2'%20and%20priceType%20eq%20'Consumption'", "supports_tool_choice": true, "supports_vision": false }, @@ -10432,7 +10773,7 @@ "max_tokens": 4096, "mode": "chat", "output_cost_per_token": 5.2e-07, - "source": "https://azure.microsoft.com/en-us/pricing/details/phi-3/", + "source": "https://prices.azure.com/api/retail/prices?$filter=serviceName%20eq%20'Foundry%20Models'%20and%20armRegionName%20eq%20'eastus2'%20and%20priceType%20eq%20'Consumption'", "supports_tool_choice": true, "supports_vision": false }, @@ -10444,7 +10785,7 @@ "max_tokens": 4096, "mode": "chat", "output_cost_per_token": 6e-07, - "source": "https://azure.microsoft.com/en-us/pricing/details/phi-3/", + "source": "https://prices.azure.com/api/retail/prices?$filter=serviceName%20eq%20'Foundry%20Models'%20and%20armRegionName%20eq%20'eastus2'%20and%20priceType%20eq%20'Consumption'", "supports_tool_choice": true, "supports_vision": false }, @@ -10456,7 +10797,7 @@ "max_tokens": 4096, "mode": "chat", "output_cost_per_token": 6e-07, - "source": "https://azure.microsoft.com/en-us/pricing/details/phi-3/", + "source": "https://prices.azure.com/api/retail/prices?$filter=serviceName%20eq%20'Foundry%20Models'%20and%20armRegionName%20eq%20'eastus2'%20and%20priceType%20eq%20'Consumption'", "supports_tool_choice": true, "supports_vision": false }, @@ -10468,7 +10809,7 @@ "max_tokens": 4096, "mode": "chat", "output_cost_per_token": 6.4e-07, - "source": "https://azure.microsoft.com/en-us/pricing/details/phi-3/", + "source": "https://prices.azure.com/api/retail/prices?$filter=serviceName%20eq%20'Foundry%20Models'%20and%20armRegionName%20eq%20'eastus2'%20and%20priceType%20eq%20'Consumption'", "supports_tool_choice": true, "supports_vision": false }, @@ -10480,7 +10821,7 @@ "max_tokens": 4096, "mode": "chat", "output_cost_per_token": 5.2e-07, - "source": "https://azure.microsoft.com/en-us/pricing/details/phi-3/", + "source": "https://prices.azure.com/api/retail/prices?$filter=serviceName%20eq%20'Foundry%20Models'%20and%20armRegionName%20eq%20'eastus2'%20and%20priceType%20eq%20'Consumption'", "supports_tool_choice": true, "supports_vision": false }, @@ -10492,7 +10833,7 @@ "max_tokens": 4096, "mode": "chat", "output_cost_per_token": 5.2e-07, - "source": "https://azure.microsoft.com/en-us/pricing/details/phi-3/", + "source": "https://prices.azure.com/api/retail/prices?$filter=serviceName%20eq%20'Foundry%20Models'%20and%20armRegionName%20eq%20'eastus2'%20and%20priceType%20eq%20'Consumption'", "supports_tool_choice": true, "supports_vision": true }, @@ -10504,7 +10845,7 @@ "max_tokens": 16384, "mode": "chat", "output_cost_per_token": 5e-07, - "source": "https://techcommunity.microsoft.com/blog/machinelearningblog/affordable-innovation-unveiling-the-pricing-of-phi-3-slms-on-models-as-a-service/4156495", + "source": "https://prices.azure.com/api/retail/prices?$filter=serviceName%20eq%20'Foundry%20Models'%20and%20armRegionName%20eq%20'eastus2'%20and%20priceType%20eq%20'Consumption'", "supports_function_calling": true, "supports_tool_choice": true, "supports_vision": false @@ -10517,7 +10858,7 @@ "max_tokens": 4096, "mode": "chat", "output_cost_per_token": 3e-07, - "source": "https://techcommunity.microsoft.com/blog/Azure-AI-Services-blog/announcing-new-phi-pricing-empowering-your-business-with-small-language-models/4395112", + "source": "https://prices.azure.com/api/retail/prices?$filter=serviceName%20eq%20'Foundry%20Models'%20and%20armRegionName%20eq%20'eastus2'%20and%20priceType%20eq%20'Consumption'", "supports_function_calling": true }, "azure_ai/Phi-4-multimodal-instruct": { @@ -10529,20 +10870,20 @@ "max_tokens": 4096, "mode": "chat", "output_cost_per_token": 3.2e-07, - "source": "https://techcommunity.microsoft.com/blog/Azure-AI-Services-blog/announcing-new-phi-pricing-empowering-your-business-with-small-language-models/4395112", + "source": "https://prices.azure.com/api/retail/prices?$filter=serviceName%20eq%20'Foundry%20Models'%20and%20armRegionName%20eq%20'eastus2'%20and%20priceType%20eq%20'Consumption'", "supports_audio_input": true, "supports_function_calling": true, "supports_vision": true }, "azure_ai/Phi-4-mini-reasoning": { - "input_cost_per_token": 8e-08, + "input_cost_per_token": 7.5e-08, "litellm_provider": "azure_ai", "max_input_tokens": 131072, "max_output_tokens": 4096, "max_tokens": 4096, "mode": "chat", - "output_cost_per_token": 3.2e-07, - "source": "https://azure.microsoft.com/en-us/pricing/details/ai-foundry-models/microsoft/", + "output_cost_per_token": 3e-07, + "source": "https://prices.azure.com/api/retail/prices?$filter=serviceName%20eq%20'Foundry%20Models'%20and%20armRegionName%20eq%20'eastus2'%20and%20priceType%20eq%20'Consumption'", "supports_function_calling": true }, "azure_ai/Phi-4-reasoning": { @@ -10553,7 +10894,7 @@ "max_tokens": 4096, "mode": "chat", "output_cost_per_token": 5e-07, - "source": "https://azure.microsoft.com/en-us/pricing/details/ai-foundry-models/microsoft/", + "source": "https://prices.azure.com/api/retail/prices?$filter=serviceName%20eq%20'Foundry%20Models'%20and%20armRegionName%20eq%20'eastus2'%20and%20priceType%20eq%20'Consumption'", "supports_function_calling": true, "supports_tool_choice": true, "supports_reasoning": true @@ -10605,7 +10946,7 @@ "max_tokens": 8182, "mode": "chat", "output_cost_per_token": 1e-05, - "source": "https://azure.microsoft.com/en-us/pricing/details/ai-foundry-models/cohere/", + "source": "https://prices.azure.com/api/retail/prices?$filter=serviceName%20eq%20'Foundry%20Models'%20and%20armRegionName%20eq%20'eastus2'%20and%20priceType%20eq%20'Consumption'", "supports_function_calling": true, "supports_tool_choice": true }, @@ -10644,7 +10985,7 @@ "max_tokens": 8192, "mode": "chat", "output_cost_per_token": 5.4e-06, - "source": "https://azure.microsoft.com/en-us/pricing/details/ai-foundry-models/microsoft/", + "source": "https://prices.azure.com/api/retail/prices?$filter=serviceName%20eq%20'Foundry%20Models'%20and%20armRegionName%20eq%20'eastus2'%20and%20priceType%20eq%20'Consumption'", "supports_reasoning": true, "supports_tool_choice": true }, @@ -10709,7 +11050,7 @@ "max_tokens": 163840, "mode": "chat", "output_cost_per_token": 1.68e-06, - "source": "https://techcommunity.microsoft.com/blog/azure-ai-foundry-blog/introducing-deepseek-v3-2-and-deepseek-v3-2-speciale-in-microsoft-foundry/4477549", + "source": "https://prices.azure.com/api/retail/prices?$filter=serviceName%20eq%20'Foundry%20Models'%20and%20armRegionName%20eq%20'eastus2'%20and%20priceType%20eq%20'Consumption'", "supports_assistant_prefill": true, "supports_function_calling": true, "supports_prompt_caching": true, @@ -10724,7 +11065,7 @@ "max_tokens": 163840, "mode": "chat", "output_cost_per_token": 1.68e-06, - "source": "https://techcommunity.microsoft.com/blog/azure-ai-foundry-blog/introducing-deepseek-v3-2-and-deepseek-v3-2-speciale-in-microsoft-foundry/4477549", + "source": "https://prices.azure.com/api/retail/prices?$filter=serviceName%20eq%20'Foundry%20Models'%20and%20armRegionName%20eq%20'eastus2'%20and%20priceType%20eq%20'Consumption'", "supports_assistant_prefill": true, "supports_function_calling": true, "supports_prompt_caching": true, @@ -10740,7 +11081,7 @@ "max_tokens": 8192, "mode": "chat", "output_cost_per_token": 5.4e-06, - "source": "https://techcommunity.microsoft.com/blog/machinelearningblog/deepseek-r1-improved-performance-higher-limits-and-transparent-pricing/4386367", + "source": "https://prices.azure.com/api/retail/prices?$filter=serviceName%20eq%20'Foundry%20Models'%20and%20armRegionName%20eq%20'eastus2'%20and%20priceType%20eq%20'Consumption'", "supports_reasoning": true, "supports_tool_choice": true }, @@ -10752,7 +11093,7 @@ "max_tokens": 8192, "mode": "chat", "output_cost_per_token": 4.56e-06, - "source": "https://techcommunity.microsoft.com/blog/machinelearningblog/announcing-deepseek-v3-on-azure-ai-foundry-and-github/4390438", + "source": "https://prices.azure.com/api/retail/prices?$filter=serviceName%20eq%20'Foundry%20Models'%20and%20armRegionName%20eq%20'eastus2'%20and%20priceType%20eq%20'Consumption'", "supports_tool_choice": true }, "azure_ai/deepseek-v3-0324": { @@ -10764,7 +11105,7 @@ "max_tokens": 8192, "mode": "chat", "output_cost_per_token": 4.56e-06, - "source": "https://techcommunity.microsoft.com/blog/machinelearningblog/announcing-deepseek-v3-on-azure-ai-foundry-and-github/4390438", + "source": "https://prices.azure.com/api/retail/prices?$filter=serviceName%20eq%20'Foundry%20Models'%20and%20armRegionName%20eq%20'eastus2'%20and%20priceType%20eq%20'Consumption'", "supports_function_calling": true, "supports_tool_choice": true }, @@ -10777,7 +11118,7 @@ "max_tokens": 131072, "mode": "chat", "output_cost_per_token": 4.94e-06, - "source": "https://azure.microsoft.com/en-us/pricing/details/ai-foundry-models/deepseek/", + "source": "https://prices.azure.com/api/retail/prices?$filter=serviceName%20eq%20'Foundry%20Models'%20and%20armRegionName%20eq%20'eastus2'%20and%20priceType%20eq%20'Consumption'", "supports_function_calling": true, "supports_reasoning": true, "supports_tool_choice": true @@ -10791,7 +11132,7 @@ "max_tokens": 384000, "mode": "chat", "output_cost_per_token": 3.48e-06, - "source": "https://azure.microsoft.com/en-us/pricing/details/ai-foundry-models/deepseek/", + "source": "https://prices.azure.com/api/retail/prices?$filter=serviceName%20eq%20'Foundry%20Models'%20and%20armRegionName%20eq%20'eastus2'%20and%20priceType%20eq%20'Consumption'", "supports_function_calling": true, "supports_reasoning": true, "supports_tool_choice": true, @@ -10807,7 +11148,7 @@ "max_tokens": 384000, "mode": "chat", "output_cost_per_token": 5.1e-07, - "source": "https://azure.microsoft.com/en-us/pricing/details/ai-foundry-models/deepseek/", + "source": "https://prices.azure.com/api/retail/prices?$filter=serviceName%20eq%20'Foundry%20Models'%20and%20armRegionName%20eq%20'eastus2'%20and%20priceType%20eq%20'Consumption'", "supports_function_calling": true, "supports_reasoning": true, "supports_tool_choice": true, @@ -10824,7 +11165,7 @@ "max_tokens": 128000, "mode": "chat", "output_cost_per_token": 1.32e-06, - "source": "https://azure.microsoft.com/en-us/pricing/details/ai-foundry-models/deepseek/", + "source": "https://prices.azure.com/api/retail/prices?$filter=serviceName%20eq%20'Foundry%20Models'%20and%20armRegionName%20eq%20'eastus2'%20and%20priceType%20eq%20'Consumption'", "supports_function_calling": true, "supports_prompt_caching": true, "supports_reasoning": true, @@ -10838,7 +11179,7 @@ "mode": "embedding", "output_cost_per_token": 0.0, "output_vector_size": 3072, - "source": "https://marketplace.microsoft.com/pt-br/marketplace/apps/cohere.cohere-embed-4-offer?tab=PlansAndPrice", + "source": "https://prices.azure.com/api/retail/prices?$filter=serviceName%20eq%20'Foundry%20Models'%20and%20armRegionName%20eq%20'eastus2'%20and%20priceType%20eq%20'Consumption'", "supported_endpoints": [ "/v1/embeddings" ], @@ -10857,7 +11198,7 @@ "max_tokens": 131072, "mode": "chat", "output_cost_per_token": 1.5e-05, - "source": "https://devblogs.microsoft.com/foundry/announcing-grok-3-and-grok-3-mini-on-azure-ai-foundry/", + "source": "https://prices.azure.com/api/retail/prices?$filter=serviceName%20eq%20'Foundry%20Models'%20and%20armRegionName%20eq%20'eastus2'%20and%20priceType%20eq%20'Consumption'", "supports_function_calling": true, "supports_response_schema": false, "supports_tool_choice": true, @@ -10872,7 +11213,7 @@ "max_tokens": 131072, "mode": "chat", "output_cost_per_token": 1.27e-06, - "source": "https://devblogs.microsoft.com/foundry/announcing-grok-3-and-grok-3-mini-on-azure-ai-foundry/", + "source": "https://prices.azure.com/api/retail/prices?$filter=serviceName%20eq%20'Foundry%20Models'%20and%20armRegionName%20eq%20'eastus2'%20and%20priceType%20eq%20'Consumption'", "supports_function_calling": true, "supports_reasoning": true, "supports_response_schema": false, @@ -10888,7 +11229,7 @@ "max_tokens": 131072, "mode": "chat", "output_cost_per_token": 1.5e-05, - "source": "https://azure.microsoft.com/en-us/pricing/details/ai-foundry-models/grok/", + "source": "https://prices.azure.com/api/retail/prices?$filter=serviceName%20eq%20'Foundry%20Models'%20and%20armRegionName%20eq%20'eastus2'%20and%20priceType%20eq%20'Consumption'", "supports_function_calling": true, "supports_response_schema": false, "supports_tool_choice": true, @@ -10903,7 +11244,7 @@ "max_tokens": 131072, "mode": "chat", "output_cost_per_token": 1.27e-06, - "source": "https://azure.microsoft.com/en-us/pricing/details/ai-foundry-models/grok/", + "source": "https://prices.azure.com/api/retail/prices?$filter=serviceName%20eq%20'Foundry%20Models'%20and%20armRegionName%20eq%20'eastus2'%20and%20priceType%20eq%20'Consumption'", "supports_function_calling": true, "supports_reasoning": true, "supports_response_schema": false, @@ -10918,7 +11259,7 @@ "max_tokens": 131072, "mode": "chat", "output_cost_per_token": 1.5e-05, - "source": "https://azure.microsoft.com/en-us/pricing/details/ai-foundry-models/grok/", + "source": "https://prices.azure.com/api/retail/prices?$filter=serviceName%20eq%20'Foundry%20Models'%20and%20armRegionName%20eq%20'eastus2'%20and%20priceType%20eq%20'Consumption'", "supports_function_calling": true, "supports_response_schema": true, "supports_tool_choice": true, @@ -10933,7 +11274,7 @@ "max_tokens": 200000, "mode": "chat", "output_cost_per_token": 2.5e-06, - "source": "https://techcommunity.microsoft.com/blog/azure-ai-foundry-blog/introducing-grok-4-3-on-microsoft-foundry-latest-generation-agentic-capabilities/4517096", + "source": "https://prices.azure.com/api/retail/prices?$filter=serviceName%20eq%20'Foundry%20Models'%20and%20armRegionName%20eq%20'eastus2'%20and%20priceType%20eq%20'Consumption'", "supports_function_calling": true, "supports_prompt_caching": true, "supports_reasoning": true, @@ -10951,7 +11292,7 @@ "max_tokens": 128000, "mode": "chat", "output_cost_per_token": 6e-06, - "source": "https://techcommunity.microsoft.com/blog/azure-ai-foundry-blog/grok-4-6-comes-to-microsoft-foundry-models-built-for-long-horizon-reasoning-and-/4547578", + "source": "https://prices.azure.com/api/retail/prices?$filter=serviceName%20eq%20'Foundry%20Models'%20and%20armRegionName%20eq%20'eastus2'%20and%20priceType%20eq%20'Consumption'", "supports_function_calling": true, "supports_prompt_caching": true, "supports_reasoning": true, @@ -10970,7 +11311,7 @@ "max_tokens": 8192, "mode": "chat", "output_cost_per_token": 2.5e-06, - "source": "https://azure.microsoft.com/en-us/pricing/details/ai-foundry-models/grok/", + "source": "https://prices.azure.com/api/retail/prices?$filter=serviceName%20eq%20'Foundry%20Models'%20and%20armRegionName%20eq%20'eastus2'%20and%20priceType%20eq%20'Consumption'", "supports_function_calling": true, "supports_response_schema": true, "supports_tool_choice": true, @@ -10988,7 +11329,7 @@ "max_tokens": 8192, "mode": "chat", "output_cost_per_token": 2.5e-06, - "source": "https://azure.microsoft.com/en-us/pricing/details/ai-foundry-models/grok/", + "source": "https://prices.azure.com/api/retail/prices?$filter=serviceName%20eq%20'Foundry%20Models'%20and%20armRegionName%20eq%20'eastus2'%20and%20priceType%20eq%20'Consumption'", "supports_function_calling": true, "supports_response_schema": true, "supports_tool_choice": true, @@ -11004,6 +11345,7 @@ "max_output_tokens": 131072, "max_tokens": 131072, "mode": "chat", + "source": "https://prices.azure.com/api/retail/prices?$filter=serviceName%20eq%20'Foundry%20Models'%20and%20armRegionName%20eq%20'eastus2'%20and%20priceType%20eq%20'Consumption'", "supports_function_calling": true, "supports_response_schema": true, "supports_tool_choice": true, @@ -11018,7 +11360,7 @@ "max_output_tokens": 131072, "max_tokens": 131072, "mode": "chat", - "source": "https://azure.microsoft.com/en-us/pricing/details/ai-foundry-models/grok/", + "source": "https://prices.azure.com/api/retail/prices?$filter=serviceName%20eq%20'Foundry%20Models'%20and%20armRegionName%20eq%20'eastus2'%20and%20priceType%20eq%20'Consumption'", "supports_function_calling": true, "supports_response_schema": true, "supports_tool_choice": true, @@ -11032,7 +11374,7 @@ "max_output_tokens": 131072, "max_tokens": 131072, "mode": "chat", - "source": "https://techcommunity.microsoft.com/t5/Azure-AI-Foundry-Blog/Grok-4-0-Goes-GA-in-Microsoft-Foundry-and-Grok-4-1-Fast-Arrives/ba-p/4497964", + "source": "https://prices.azure.com/api/retail/prices?$filter=serviceName%20eq%20'Foundry%20Models'%20and%20armRegionName%20eq%20'eastus2'%20and%20priceType%20eq%20'Consumption'", "supports_function_calling": true, "supports_response_schema": true, "supports_tool_choice": true, @@ -11046,7 +11388,7 @@ "max_output_tokens": 131072, "max_tokens": 131072, "mode": "chat", - "source": "https://techcommunity.microsoft.com/t5/Azure-AI-Foundry-Blog/Grok-4-0-Goes-GA-in-Microsoft-Foundry-and-Grok-4-1-Fast-Arrives/ba-p/4497964", + "source": "https://prices.azure.com/api/retail/prices?$filter=serviceName%20eq%20'Foundry%20Models'%20and%20armRegionName%20eq%20'eastus2'%20and%20priceType%20eq%20'Consumption'", "supports_function_calling": true, "supports_reasoning": true, "supports_response_schema": true, @@ -11061,7 +11403,7 @@ "max_tokens": 131072, "mode": "chat", "output_cost_per_token": 1.5e-06, - "source": "https://azure.microsoft.com/en-us/pricing/details/ai-foundry-models/grok/", + "source": "https://prices.azure.com/api/retail/prices?$filter=serviceName%20eq%20'Foundry%20Models'%20and%20armRegionName%20eq%20'eastus2'%20and%20priceType%20eq%20'Consumption'", "supports_function_calling": true, "supports_response_schema": true, "supports_tool_choice": true, @@ -11096,7 +11438,7 @@ "max_tokens": 262144, "mode": "chat", "output_cost_per_token": 3e-06, - "source": "https://azure.microsoft.com/en-us/pricing/details/ai-foundry-models/kimi/", + "source": "https://prices.azure.com/api/retail/prices?$filter=serviceName%20eq%20'Foundry%20Models'%20and%20armRegionName%20eq%20'eastus2'%20and%20priceType%20eq%20'Consumption'", "supports_function_calling": true, "supports_tool_choice": true, "supports_video_input": true, @@ -11113,7 +11455,7 @@ "max_tokens": 262144, "mode": "chat", "output_cost_per_token": 4e-06, - "source": "https://azure.microsoft.com/en-us/pricing/details/ai-foundry-models/kimi/", + "source": "https://prices.azure.com/api/retail/prices?$filter=serviceName%20eq%20'Foundry%20Models'%20and%20armRegionName%20eq%20'eastus2'%20and%20priceType%20eq%20'Consumption'", "supported_modalities": [ "text", "image" @@ -11183,7 +11525,7 @@ "max_tokens": 8191, "mode": "chat", "output_cost_per_token": 1.5e-06, - "source": "https://azure.microsoft.com/en-us/blog/introducing-mistral-large-3-in-microsoft-foundry-open-capable-and-ready-for-production-workloads/", + "source": "https://prices.azure.com/api/retail/prices?$filter=serviceName%20eq%20'Foundry%20Models'%20and%20armRegionName%20eq%20'eastus2'%20and%20priceType%20eq%20'Consumption'", "supports_function_calling": true, "supports_tool_choice": true, "supports_vision": true @@ -61814,7 +62156,7 @@ "max_tokens": 262144, "mode": "chat", "output_cost_per_token": 4e-06, - "source": "https://azure.microsoft.com/en-us/pricing/details/ai-foundry-models/kimi/", + "source": "https://prices.azure.com/api/retail/prices?$filter=serviceName%20eq%20'Foundry%20Models'%20and%20armRegionName%20eq%20'eastus2'%20and%20priceType%20eq%20'Consumption'", "supported_modalities": [ "text", "image" @@ -66133,5 +66475,586 @@ "mode": "chat", "output_cost_per_token": 8e-06, "source": "https://api.together.ai/v1/models" + }, + "azure/eu/codex-mini": { + 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"output_cost_per_token": 1.375e-06, + "output_cost_per_token_batches": 6.875e-07, + "source": "https://prices.azure.com/api/retail/prices?$filter=serviceName%20eq%20'Foundry%20Models'%20and%20armRegionName%20eq%20'eastus2'%20and%20priceType%20eq%20'Consumption'" + }, + "azure/us/gpt-5.4-pro": { + "input_cost_per_token": 3.3e-05, + "input_cost_per_token_above_272k_tokens": 6.6e-05, + "input_cost_per_token_batches": 1.65e-05, + "litellm_provider": "azure", + "mode": "chat", + "output_cost_per_token": 0.000198, + "output_cost_per_token_above_272k_tokens": 0.000297, + "output_cost_per_token_batches": 9.9e-05, + "source": "https://prices.azure.com/api/retail/prices?$filter=serviceName%20eq%20'Foundry%20Models'%20and%20armRegionName%20eq%20'eastus2'%20and%20priceType%20eq%20'Consumption'" + }, + "azure/us/o1-mini": { + "cache_read_input_token_cost": 6.05e-07, + "input_cost_per_token": 1.21e-06, + "input_cost_per_token_batches": 6.05e-07, + "litellm_provider": "azure", + "mode": "chat", + "output_cost_per_token": 4.84e-06, + "output_cost_per_token_batches": 2.42e-06, + "source": "https://prices.azure.com/api/retail/prices?$filter=serviceName%20eq%20'Foundry%20Models'%20and%20armRegionName%20eq%20'eastus2'%20and%20priceType%20eq%20'Consumption'" + }, + "azure/us/o1-preview": { + "cache_read_input_token_cost": 8.25e-06, + "input_cost_per_token": 1.65e-05, + "litellm_provider": "azure", + "mode": "chat", + "output_cost_per_token": 6.6e-05, + "source": "https://prices.azure.com/api/retail/prices?$filter=serviceName%20eq%20'Foundry%20Models'%20and%20armRegionName%20eq%20'eastus2'%20and%20priceType%20eq%20'Consumption'" + }, + "azure/us/o3-deep-research": { + "cache_read_input_token_cost": 2.75e-06, + "input_cost_per_token": 1.1e-05, + "litellm_provider": "azure", + "mode": "chat", + "output_cost_per_token": 4.4e-05, + "source": "https://prices.azure.com/api/retail/prices?$filter=serviceName%20eq%20'Foundry%20Models'%20and%20armRegionName%20eq%20'eastus2'%20and%20priceType%20eq%20'Consumption'" + }, + "azure/us/text-embedding-3-large": { + "input_cost_per_token": 1.43e-07, + "litellm_provider": "azure", + "mode": "embedding", + "source": "https://prices.azure.com/api/retail/prices?$filter=serviceName%20eq%20'Foundry%20Models'%20and%20armRegionName%20eq%20'eastus2'%20and%20priceType%20eq%20'Consumption'" + }, + "azure/us/text-embedding-3-small": { + "input_cost_per_token": 2.2e-08, + "litellm_provider": "azure", + "mode": "embedding", + "source": "https://prices.azure.com/api/retail/prices?$filter=serviceName%20eq%20'Foundry%20Models'%20and%20armRegionName%20eq%20'eastus2'%20and%20priceType%20eq%20'Consumption'" + }, + "azure/us/text-embedding-ada-002": { + "input_cost_per_token": 1.1e-07, + "litellm_provider": "azure", + "mode": "embedding", + "source": "https://prices.azure.com/api/retail/prices?$filter=serviceName%20eq%20'Foundry%20Models'%20and%20armRegionName%20eq%20'eastus2'%20and%20priceType%20eq%20'Consumption'" } } diff --git a/model_prices_and_context_window.json b/model_prices_and_context_window.json index 02adbbed736..1d863977e1e 100644 --- a/model_prices_and_context_window.json +++ b/model_prices_and_context_window.json @@ -3144,6 +3144,7 @@ "max_tokens": 100000, "mode": "responses", "output_cost_per_token": 6e-06, + "source": "https://prices.azure.com/api/retail/prices?$filter=serviceName%20eq%20'Foundry%20Models'%20and%20armRegionName%20eq%20'eastus2'%20and%20priceType%20eq%20'Consumption'", "supported_endpoints": [ "/v1/responses" ], @@ -3535,6 +3536,7 @@ "max_tokens": 1024, "mode": "chat", "output_cost_per_token": 1.2e-05, + "source": "https://prices.azure.com/api/retail/prices?$filter=serviceName%20eq%20'Foundry%20Models'%20and%20armRegionName%20eq%20'eastus2'%20and%20priceType%20eq%20'Consumption'", "supported_endpoints": [ "/v1/responses" ], @@ -3567,7 +3569,7 @@ "max_output_tokens": 131072, "max_tokens": 131072, "mode": "chat", - "source": "https://azure.microsoft.com/en-us/pricing/details/cognitive-services/openai-service/", + "source": "https://prices.azure.com/api/retail/prices?$filter=serviceName%20eq%20'Foundry%20Models'%20and%20armRegionName%20eq%20'eastus2'%20and%20priceType%20eq%20'Consumption'", "supports_function_calling": true, "supports_parallel_function_calling": true, "supports_response_schema": true, @@ -4172,12 +4174,15 @@ "deprecation_date": "2027-04-14", "cache_read_input_token_cost": 1.375e-06, "input_cost_per_token": 2.75e-06, + "input_cost_per_token_batches": 1.375e-06, "litellm_provider": "azure", "max_input_tokens": 128000, "max_output_tokens": 16384, "max_tokens": 16384, "mode": "chat", "output_cost_per_token": 1.1e-05, + "output_cost_per_token_batches": 5.5e-06, + "source": "https://prices.azure.com/api/retail/prices?$filter=serviceName%20eq%20'Foundry%20Models'%20and%20armRegionName%20eq%20'eastus2'%20and%20priceType%20eq%20'Consumption'", "supports_function_calling": true, "supports_parallel_function_calling": true, "supports_prompt_caching": true, @@ -4188,13 +4193,17 @@ "azure/eu/gpt-4o-2024-11-20": { "deprecation_date": "2027-04-14", "cache_creation_input_token_cost": 1.38e-06, + "cache_read_input_token_cost": 1.375e-06, "input_cost_per_token": 2.75e-06, + "input_cost_per_token_batches": 1.375e-06, "litellm_provider": "azure", "max_input_tokens": 128000, "max_output_tokens": 16384, "max_tokens": 16384, "mode": "chat", "output_cost_per_token": 1.1e-05, + "output_cost_per_token_batches": 5.5e-06, + "source": "https://prices.azure.com/api/retail/prices?$filter=serviceName%20eq%20'Foundry%20Models'%20and%20armRegionName%20eq%20'eastus2'%20and%20priceType%20eq%20'Consumption'", "supports_function_calling": true, "supports_parallel_function_calling": true, "supports_response_schema": true, @@ -4205,12 +4214,14 @@ "cache_read_input_token_cost": 8.3e-08, "deprecation_date": "2027-04-14", "input_cost_per_token": 1.65e-07, + "input_cost_per_token_batches": 8.3e-08, "litellm_provider": "azure", "max_input_tokens": 128000, "max_output_tokens": 16384, "max_tokens": 16384, "mode": "chat", "output_cost_per_token": 6.6e-07, + "source": "https://prices.azure.com/api/retail/prices?$filter=serviceName%20eq%20'Foundry%20Models'%20and%20armRegionName%20eq%20'eastus2'%20and%20priceType%20eq%20'Consumption'", "supports_function_calling": true, "supports_parallel_function_calling": true, "supports_prompt_caching": true, @@ -4351,8 +4362,9 @@ }, "azure/eu/gpt-5.1": { "deprecation_date": "2027-05-15", - "cache_read_input_token_cost": 1.4e-07, - "input_cost_per_token": 1.38e-06, + "cache_read_input_token_cost": 1.375e-07, + "cache_read_input_token_cost_priority": 2.75e-07, + "input_cost_per_token": 1.375e-06, "litellm_provider": "azure", "max_input_tokens": 272000, "max_output_tokens": 128000, @@ -4383,12 +4395,17 @@ "supports_tool_choice": true, "supports_vision": true, "supports_none_reasoning_effort": true, - "default_reasoning_effort": "none" + "default_reasoning_effort": "none", + "input_cost_per_token_batches": 6.875e-07, + "input_cost_per_token_priority": 2.75e-06, + "output_cost_per_token_batches": 5.5e-06, + "output_cost_per_token_priority": 2.2e-05, + "source": "https://prices.azure.com/api/retail/prices?$filter=serviceName%20eq%20'Foundry%20Models'%20and%20armRegionName%20eq%20'eastus2'%20and%20priceType%20eq%20'Consumption'" }, "azure/eu/gpt-5.1-chat": { - "cache_read_input_token_cost": 1.4e-07, + "cache_read_input_token_cost": 1.375e-07, "deprecation_date": "2026-06-29", - "input_cost_per_token": 1.38e-06, + "input_cost_per_token": 1.375e-06, "litellm_provider": "azure", "max_input_tokens": 128000, "max_output_tokens": 128000, @@ -4419,18 +4436,20 @@ "supports_tool_choice": true, "supports_vision": true, "supports_none_reasoning_effort": true, - "default_reasoning_effort": "none" + "default_reasoning_effort": "none", + "source": "https://prices.azure.com/api/retail/prices?$filter=serviceName%20eq%20'Foundry%20Models'%20and%20armRegionName%20eq%20'eastus2'%20and%20priceType%20eq%20'Consumption'" }, "azure/eu/gpt-5.1-codex": { "deprecation_date": "2027-05-15", - "cache_read_input_token_cost": 1.4e-07, - "input_cost_per_token": 1.38e-06, + "cache_read_input_token_cost": 1.375e-07, + "input_cost_per_token": 1.375e-06, "litellm_provider": "azure", "max_input_tokens": 272000, "max_output_tokens": 128000, "max_tokens": 128000, "mode": "responses", "output_cost_per_token": 1.1e-05, + "source": "https://prices.azure.com/api/retail/prices?$filter=serviceName%20eq%20'Foundry%20Models'%20and%20armRegionName%20eq%20'eastus2'%20and%20priceType%20eq%20'Consumption'", "supported_endpoints": [ "/v1/responses" ], @@ -4454,7 +4473,7 @@ }, "azure/eu/gpt-5.1-codex-mini": { "deprecation_date": "2027-05-15", - "cache_read_input_token_cost": 2.8e-08, + "cache_read_input_token_cost": 2.75e-08, "input_cost_per_token": 2.75e-07, "litellm_provider": "azure", "max_input_tokens": 272000, @@ -4462,6 +4481,7 @@ "max_tokens": 128000, "mode": "responses", "output_cost_per_token": 2.2e-06, + "source": "https://prices.azure.com/api/retail/prices?$filter=serviceName%20eq%20'Foundry%20Models'%20and%20armRegionName%20eq%20'eastus2'%20and%20priceType%20eq%20'Consumption'", "supported_endpoints": [ "/v1/responses" ], @@ -4520,12 +4540,15 @@ "cache_read_input_token_cost": 8.25e-06, "deprecation_date": "2026-11-19", "input_cost_per_token": 1.65e-05, + "input_cost_per_token_batches": 8.25e-06, "litellm_provider": "azure", "max_input_tokens": 200000, "max_output_tokens": 100000, "max_tokens": 100000, "mode": "chat", "output_cost_per_token": 6.6e-05, + "output_cost_per_token_batches": 3.3e-05, + "source": "https://prices.azure.com/api/retail/prices?$filter=serviceName%20eq%20'Foundry%20Models'%20and%20armRegionName%20eq%20'eastus2'%20and%20priceType%20eq%20'Consumption'", "supports_function_calling": true, "supports_parallel_function_calling": true, "supports_prompt_caching": true, @@ -4574,6 +4597,7 @@ "mode": "chat", "output_cost_per_token": 4.84e-06, "output_cost_per_token_batches": 2.42e-06, + "source": "https://prices.azure.com/api/retail/prices?$filter=serviceName%20eq%20'Foundry%20Models'%20and%20armRegionName%20eq%20'eastus2'%20and%20priceType%20eq%20'Consumption'", "supports_prompt_caching": true, "supports_reasoning": true, "supports_tool_choice": true, @@ -4583,12 +4607,15 @@ "cache_read_input_token_cost": 1.25e-06, "deprecation_date": "2027-04-14", "input_cost_per_token": 2.5e-06, + "input_cost_per_token_batches": 1.25e-06, "litellm_provider": "azure", "max_input_tokens": 128000, "max_output_tokens": 16384, "max_tokens": 16384, "mode": "chat", "output_cost_per_token": 1e-05, + "output_cost_per_token_batches": 5e-06, + "source": "https://prices.azure.com/api/retail/prices?$filter=serviceName%20eq%20'Foundry%20Models'%20and%20armRegionName%20eq%20'eastus2'%20and%20priceType%20eq%20'Consumption'", "supports_function_calling": true, "supports_parallel_function_calling": true, "supports_prompt_caching": true, @@ -4600,12 +4627,15 @@ "cache_read_input_token_cost": 1.25e-06, "deprecation_date": "2027-04-14", "input_cost_per_token": 2.5e-06, + "input_cost_per_token_batches": 1.25e-06, "litellm_provider": "azure", "max_input_tokens": 128000, "max_output_tokens": 16384, "max_tokens": 16384, "mode": "chat", "output_cost_per_token": 1e-05, + "output_cost_per_token_batches": 5e-06, + "source": "https://prices.azure.com/api/retail/prices?$filter=serviceName%20eq%20'Foundry%20Models'%20and%20armRegionName%20eq%20'eastus2'%20and%20priceType%20eq%20'Consumption'", "supports_function_calling": true, "supports_parallel_function_calling": true, "supports_response_schema": true, @@ -4631,12 +4661,15 @@ "deprecation_date": "2027-04-14", "cache_read_input_token_cost": 1.25e-06, "input_cost_per_token": 2.5e-06, + "input_cost_per_token_batches": 1.25e-06, "litellm_provider": "azure", "max_input_tokens": 128000, "max_output_tokens": 16384, "max_tokens": 16384, "mode": "chat", "output_cost_per_token": 1e-05, + "output_cost_per_token_batches": 5e-06, + "source": "https://prices.azure.com/api/retail/prices?$filter=serviceName%20eq%20'Foundry%20Models'%20and%20armRegionName%20eq%20'eastus2'%20and%20priceType%20eq%20'Consumption'", "supports_function_calling": true, "supports_parallel_function_calling": true, "supports_prompt_caching": true, @@ -4648,12 +4681,15 @@ "deprecation_date": "2027-04-14", "cache_read_input_token_cost": 1.25e-06, "input_cost_per_token": 2.5e-06, + "input_cost_per_token_batches": 1.25e-06, "litellm_provider": "azure", "max_input_tokens": 128000, "max_output_tokens": 16384, "max_tokens": 16384, "mode": "chat", "output_cost_per_token": 1e-05, + "output_cost_per_token_batches": 5e-06, + "source": "https://prices.azure.com/api/retail/prices?$filter=serviceName%20eq%20'Foundry%20Models'%20and%20armRegionName%20eq%20'eastus2'%20and%20priceType%20eq%20'Consumption'", "supports_function_calling": true, "supports_parallel_function_calling": true, "supports_prompt_caching": true, @@ -4664,6 +4700,7 @@ "azure/global/gpt-5.1": { "deprecation_date": "2027-05-15", "cache_read_input_token_cost": 1.25e-07, + "cache_read_input_token_cost_priority": 2.5e-07, "input_cost_per_token": 1.25e-06, "litellm_provider": "azure", "max_input_tokens": 272000, @@ -4695,7 +4732,12 @@ "supports_tool_choice": true, "supports_vision": true, "supports_none_reasoning_effort": true, - "default_reasoning_effort": "none" + "default_reasoning_effort": "none", + "input_cost_per_token_batches": 6.25e-07, + "input_cost_per_token_priority": 2.5e-06, + "output_cost_per_token_batches": 5e-06, + "output_cost_per_token_priority": 2e-05, + "source": "https://prices.azure.com/api/retail/prices?$filter=serviceName%20eq%20'Foundry%20Models'%20and%20armRegionName%20eq%20'eastus2'%20and%20priceType%20eq%20'Consumption'" }, "azure/global/gpt-5.1-chat": { "cache_read_input_token_cost": 1.25e-07, @@ -4731,7 +4773,8 @@ "supports_tool_choice": true, "supports_vision": true, "supports_none_reasoning_effort": true, - "default_reasoning_effort": "none" + "default_reasoning_effort": "none", + "source": "https://prices.azure.com/api/retail/prices?$filter=serviceName%20eq%20'Foundry%20Models'%20and%20armRegionName%20eq%20'eastus2'%20and%20priceType%20eq%20'Consumption'" }, "azure/global/gpt-5.1-codex": { "deprecation_date": "2027-05-15", @@ -4743,6 +4786,7 @@ "max_tokens": 128000, "mode": "responses", "output_cost_per_token": 1e-05, + "source": "https://prices.azure.com/api/retail/prices?$filter=serviceName%20eq%20'Foundry%20Models'%20and%20armRegionName%20eq%20'eastus2'%20and%20priceType%20eq%20'Consumption'", "supported_endpoints": [ "/v1/responses" ], @@ -4774,6 +4818,7 @@ "max_tokens": 128000, "mode": "responses", "output_cost_per_token": 2e-06, + "source": "https://prices.azure.com/api/retail/prices?$filter=serviceName%20eq%20'Foundry%20Models'%20and%20armRegionName%20eq%20'eastus2'%20and%20priceType%20eq%20'Consumption'", "supported_endpoints": [ "/v1/responses" ], @@ -5006,8 +5051,10 @@ "azure/gpt-4.1": { "deprecation_date": "2027-04-14", "cache_read_input_token_cost": 5e-07, + "cache_read_input_token_cost_priority": 8.75e-07, "input_cost_per_token": 2e-06, "input_cost_per_token_batches": 1e-06, + "input_cost_per_token_priority": 3.5e-06, "litellm_provider": "azure", "max_input_tokens": 1047576, "max_output_tokens": 32768, @@ -5015,6 +5062,8 @@ "mode": "chat", "output_cost_per_token": 8e-06, "output_cost_per_token_batches": 4e-06, + "output_cost_per_token_priority": 1.4e-05, + "source": "https://prices.azure.com/api/retail/prices?$filter=serviceName%20eq%20'Foundry%20Models'%20and%20armRegionName%20eq%20'eastus2'%20and%20priceType%20eq%20'Consumption'", "supported_endpoints": [ "/v1/chat/completions", "/v1/batch", @@ -5040,8 +5089,10 @@ "azure/gpt-4.1-2025-04-14": { "deprecation_date": "2027-04-14", "cache_read_input_token_cost": 5e-07, + "cache_read_input_token_cost_priority": 8.75e-07, "input_cost_per_token": 2e-06, "input_cost_per_token_batches": 1e-06, + "input_cost_per_token_priority": 3.5e-06, "litellm_provider": "azure", "max_input_tokens": 1047576, "max_output_tokens": 32768, @@ -5049,6 +5100,8 @@ "mode": "chat", "output_cost_per_token": 8e-06, "output_cost_per_token_batches": 4e-06, + "output_cost_per_token_priority": 1.4e-05, + "source": "https://prices.azure.com/api/retail/prices?$filter=serviceName%20eq%20'Foundry%20Models'%20and%20armRegionName%20eq%20'eastus2'%20and%20priceType%20eq%20'Consumption'", "supported_endpoints": [ "/v1/chat/completions", "/v1/batch", @@ -5074,8 +5127,10 @@ "azure/gpt-4.1-mini": { "deprecation_date": "2027-04-14", "cache_read_input_token_cost": 1e-07, + "cache_read_input_token_cost_priority": 1.75e-07, "input_cost_per_token": 4e-07, "input_cost_per_token_batches": 2e-07, + "input_cost_per_token_priority": 7e-07, "litellm_provider": "azure", "max_input_tokens": 1047576, "max_output_tokens": 32768, @@ -5083,6 +5138,8 @@ "mode": "chat", "output_cost_per_token": 1.6e-06, "output_cost_per_token_batches": 8e-07, + "output_cost_per_token_priority": 2.8e-06, + "source": "https://prices.azure.com/api/retail/prices?$filter=serviceName%20eq%20'Foundry%20Models'%20and%20armRegionName%20eq%20'eastus2'%20and%20priceType%20eq%20'Consumption'", "supported_endpoints": [ "/v1/chat/completions", "/v1/batch", @@ -5108,8 +5165,10 @@ "azure/gpt-4.1-mini-2025-04-14": { "deprecation_date": "2027-04-14", "cache_read_input_token_cost": 1e-07, + "cache_read_input_token_cost_priority": 1.75e-07, "input_cost_per_token": 4e-07, "input_cost_per_token_batches": 2e-07, + "input_cost_per_token_priority": 7e-07, "litellm_provider": "azure", "max_input_tokens": 1047576, "max_output_tokens": 32768, @@ -5117,6 +5176,8 @@ "mode": "chat", "output_cost_per_token": 1.6e-06, "output_cost_per_token_batches": 8e-07, + "output_cost_per_token_priority": 2.8e-06, + "source": "https://prices.azure.com/api/retail/prices?$filter=serviceName%20eq%20'Foundry%20Models'%20and%20armRegionName%20eq%20'eastus2'%20and%20priceType%20eq%20'Consumption'", "supported_endpoints": [ "/v1/chat/completions", "/v1/batch", @@ -5151,6 +5212,7 @@ "mode": "chat", "output_cost_per_token": 4e-07, "output_cost_per_token_batches": 2e-07, + "source": "https://prices.azure.com/api/retail/prices?$filter=serviceName%20eq%20'Foundry%20Models'%20and%20armRegionName%20eq%20'eastus2'%20and%20priceType%20eq%20'Consumption'", "supported_endpoints": [ "/v1/chat/completions", "/v1/batch", @@ -5184,6 +5246,7 @@ "mode": "chat", "output_cost_per_token": 4e-07, "output_cost_per_token_batches": 2e-07, + "source": "https://prices.azure.com/api/retail/prices?$filter=serviceName%20eq%20'Foundry%20Models'%20and%20armRegionName%20eq%20'eastus2'%20and%20priceType%20eq%20'Consumption'", "supported_endpoints": [ "/v1/chat/completions", "/v1/batch", @@ -5243,12 +5306,15 @@ "azure/gpt-4o-2024-05-13": { "deprecation_date": "2026-10-01", "input_cost_per_token": 5e-06, + "input_cost_per_token_batches": 2.5e-06, "litellm_provider": "azure", "max_input_tokens": 128000, "max_output_tokens": 4096, "max_tokens": 4096, "mode": "chat", "output_cost_per_token": 1.5e-05, + "output_cost_per_token_batches": 7.5e-06, + "source": "https://prices.azure.com/api/retail/prices?$filter=serviceName%20eq%20'Foundry%20Models'%20and%20armRegionName%20eq%20'eastus2'%20and%20priceType%20eq%20'Consumption'", "supports_function_calling": true, "supports_parallel_function_calling": true, "supports_prompt_caching": true, @@ -5259,12 +5325,15 @@ "deprecation_date": "2027-04-14", "cache_read_input_token_cost": 1.25e-06, "input_cost_per_token": 2.5e-06, + "input_cost_per_token_batches": 1.25e-06, "litellm_provider": "azure", "max_input_tokens": 128000, "max_output_tokens": 16384, "max_tokens": 16384, "mode": "chat", "output_cost_per_token": 1e-05, + "output_cost_per_token_batches": 5e-06, + "source": "https://prices.azure.com/api/retail/prices?$filter=serviceName%20eq%20'Foundry%20Models'%20and%20armRegionName%20eq%20'eastus2'%20and%20priceType%20eq%20'Consumption'", "supports_function_calling": true, "supports_parallel_function_calling": true, "supports_prompt_caching": true, @@ -5275,13 +5344,16 @@ "azure/gpt-4o-2024-11-20": { "deprecation_date": "2027-04-14", "cache_read_input_token_cost": 1.25e-06, - "input_cost_per_token": 2.75e-06, + "input_cost_per_token": 2.5e-06, + "input_cost_per_token_batches": 1.25e-06, "litellm_provider": "azure", "max_input_tokens": 128000, "max_output_tokens": 16384, "max_tokens": 16384, "mode": "chat", - "output_cost_per_token": 1.1e-05, + "output_cost_per_token": 1e-05, + "output_cost_per_token_batches": 5e-06, + "source": "https://prices.azure.com/api/retail/prices?$filter=serviceName%20eq%20'Foundry%20Models'%20and%20armRegionName%20eq%20'eastus2'%20and%20priceType%20eq%20'Consumption'", "supports_function_calling": true, "supports_parallel_function_calling": true, "supports_prompt_caching": true, @@ -5468,13 +5540,16 @@ "azure/gpt-4o-mini-2024-07-18": { "cache_read_input_token_cost": 7.5e-08, "deprecation_date": "2027-04-14", - "input_cost_per_token": 1.65e-07, + "input_cost_per_token": 1.5e-07, + "input_cost_per_token_batches": 7.5e-08, "litellm_provider": "azure", "max_input_tokens": 128000, "max_output_tokens": 16384, "max_tokens": 16384, "mode": "chat", - "output_cost_per_token": 6.6e-07, + "output_cost_per_token": 6e-07, + "output_cost_per_token_batches": 3e-07, + "source": "https://prices.azure.com/api/retail/prices?$filter=serviceName%20eq%20'Foundry%20Models'%20and%20armRegionName%20eq%20'eastus2'%20and%20priceType%20eq%20'Consumption'", "supports_function_calling": true, "supports_parallel_function_calling": true, "supports_prompt_caching": true, @@ -5925,6 +6000,9 @@ "supports_vision": true, "supports_none_reasoning_effort": true, "default_reasoning_effort": "none", + "input_cost_per_token_batches": 6.25e-07, + "output_cost_per_token_batches": 5e-06, + "source": "https://prices.azure.com/api/retail/prices?$filter=serviceName%20eq%20'Foundry%20Models'%20and%20armRegionName%20eq%20'eastus2'%20and%20priceType%20eq%20'Consumption'", "supports_minimal_reasoning_effort": true }, "azure/gpt-5.1-chat-2025-11-13": { @@ -5963,7 +6041,8 @@ "supports_tool_choice": false, "supports_vision": true, "supports_none_reasoning_effort": true, - "default_reasoning_effort": "none" + "default_reasoning_effort": "none", + "source": "https://prices.azure.com/api/retail/prices?$filter=serviceName%20eq%20'Foundry%20Models'%20and%20armRegionName%20eq%20'eastus2'%20and%20priceType%20eq%20'Consumption'" }, "azure/gpt-5.1-codex-2025-11-13": { "cache_read_input_token_cost": 1.25e-07, @@ -5978,6 +6057,7 @@ "mode": "responses", "output_cost_per_token": 1e-05, "output_cost_per_token_priority": 2e-05, + "source": "https://prices.azure.com/api/retail/prices?$filter=serviceName%20eq%20'Foundry%20Models'%20and%20armRegionName%20eq%20'eastus2'%20and%20priceType%20eq%20'Consumption'", "supported_endpoints": [ "/v1/responses" ], @@ -6012,6 +6092,7 @@ "mode": "responses", "output_cost_per_token": 2e-06, "output_cost_per_token_priority": 3.6e-06, + "source": "https://prices.azure.com/api/retail/prices?$filter=serviceName%20eq%20'Foundry%20Models'%20and%20armRegionName%20eq%20'eastus2'%20and%20priceType%20eq%20'Consumption'", "supported_endpoints": [ "/v1/responses" ], @@ -6036,13 +6117,19 @@ "azure/gpt-5": { "deprecation_date": "2027-02-09", "cache_read_input_token_cost": 1.25e-07, + "cache_read_input_token_cost_priority": 2.5e-07, "input_cost_per_token": 1.25e-06, + "input_cost_per_token_batches": 6.25e-07, + "input_cost_per_token_priority": 2.5e-06, "litellm_provider": "azure", "max_input_tokens": 272000, "max_output_tokens": 128000, "max_tokens": 128000, "mode": "chat", "output_cost_per_token": 1e-05, + "output_cost_per_token_batches": 5e-06, + "output_cost_per_token_priority": 2e-05, + "source": "https://prices.azure.com/api/retail/prices?$filter=serviceName%20eq%20'Foundry%20Models'%20and%20armRegionName%20eq%20'eastus2'%20and%20priceType%20eq%20'Consumption'", "supported_endpoints": [ "/v1/chat/completions", "/v1/batch", @@ -6068,14 +6155,20 @@ }, "azure/gpt-5-2025-08-07": { "cache_read_input_token_cost": 1.25e-07, + "cache_read_input_token_cost_priority": 2.5e-07, "deprecation_date": "2027-02-09", "input_cost_per_token": 1.25e-06, + "input_cost_per_token_batches": 6.25e-07, + "input_cost_per_token_priority": 2.5e-06, "litellm_provider": "azure", "max_input_tokens": 272000, "max_output_tokens": 128000, "max_tokens": 128000, "mode": "chat", "output_cost_per_token": 1e-05, + "output_cost_per_token_batches": 5e-06, + "output_cost_per_token_priority": 2e-05, + "source": "https://prices.azure.com/api/retail/prices?$filter=serviceName%20eq%20'Foundry%20Models'%20and%20armRegionName%20eq%20'eastus2'%20and%20priceType%20eq%20'Consumption'", "supported_endpoints": [ "/v1/chat/completions", "/v1/batch", @@ -6109,7 +6202,7 @@ "max_tokens": 16384, "mode": "chat", "output_cost_per_token": 1e-05, - "source": "https://azure.microsoft.com/en-us/blog/gpt-5-in-azure-ai-foundry-the-future-of-ai-apps-and-agents-starts-here/", + "source": "https://prices.azure.com/api/retail/prices?$filter=serviceName%20eq%20'Foundry%20Models'%20and%20armRegionName%20eq%20'eastus2'%20and%20priceType%20eq%20'Consumption'", "supported_endpoints": [ "/v1/chat/completions", "/v1/batch", @@ -6176,6 +6269,7 @@ "max_tokens": 128000, "mode": "responses", "output_cost_per_token": 1e-05, + "source": "https://prices.azure.com/api/retail/prices?$filter=serviceName%20eq%20'Foundry%20Models'%20and%20armRegionName%20eq%20'eastus2'%20and%20priceType%20eq%20'Consumption'", "supported_endpoints": [ "/v1/responses" ], @@ -6200,13 +6294,19 @@ "azure/gpt-5-mini": { "deprecation_date": "2027-02-09", "cache_read_input_token_cost": 2.5e-08, + "cache_read_input_token_cost_priority": 4.5e-08, "input_cost_per_token": 2.5e-07, + "input_cost_per_token_batches": 1.25e-07, + "input_cost_per_token_priority": 4.5e-07, "litellm_provider": "azure", "max_input_tokens": 272000, "max_output_tokens": 128000, "max_tokens": 128000, "mode": "chat", "output_cost_per_token": 2e-06, + "output_cost_per_token_batches": 1e-06, + "output_cost_per_token_priority": 3.6e-06, + "source": "https://prices.azure.com/api/retail/prices?$filter=serviceName%20eq%20'Foundry%20Models'%20and%20armRegionName%20eq%20'eastus2'%20and%20priceType%20eq%20'Consumption'", "supported_endpoints": [ "/v1/chat/completions", "/v1/batch", @@ -6232,14 +6332,20 @@ }, "azure/gpt-5-mini-2025-08-07": { "cache_read_input_token_cost": 2.5e-08, + "cache_read_input_token_cost_priority": 4.5e-08, "deprecation_date": "2027-02-09", "input_cost_per_token": 2.5e-07, + "input_cost_per_token_batches": 1.25e-07, + "input_cost_per_token_priority": 4.5e-07, "litellm_provider": "azure", "max_input_tokens": 272000, "max_output_tokens": 128000, "max_tokens": 128000, "mode": "chat", "output_cost_per_token": 2e-06, + "output_cost_per_token_batches": 1e-06, + "output_cost_per_token_priority": 3.6e-06, + "source": "https://prices.azure.com/api/retail/prices?$filter=serviceName%20eq%20'Foundry%20Models'%20and%20armRegionName%20eq%20'eastus2'%20and%20priceType%20eq%20'Consumption'", "supported_endpoints": [ "/v1/chat/completions", "/v1/batch", @@ -6267,12 +6373,15 @@ "deprecation_date": "2027-02-09", "cache_read_input_token_cost": 5e-09, "input_cost_per_token": 5e-08, + "input_cost_per_token_batches": 2.5e-08, "litellm_provider": "azure", "max_input_tokens": 272000, "max_output_tokens": 128000, "max_tokens": 128000, "mode": "chat", "output_cost_per_token": 4e-07, + "output_cost_per_token_batches": 2e-07, + "source": "https://prices.azure.com/api/retail/prices?$filter=serviceName%20eq%20'Foundry%20Models'%20and%20armRegionName%20eq%20'eastus2'%20and%20priceType%20eq%20'Consumption'", "supported_endpoints": [ "/v1/chat/completions", "/v1/batch", @@ -6300,12 +6409,15 @@ "cache_read_input_token_cost": 5e-09, "deprecation_date": "2027-02-09", "input_cost_per_token": 5e-08, + "input_cost_per_token_batches": 2.5e-08, "litellm_provider": "azure", "max_input_tokens": 272000, "max_output_tokens": 128000, "max_tokens": 128000, "mode": "chat", "output_cost_per_token": 4e-07, + "output_cost_per_token_batches": 2e-07, + "source": "https://prices.azure.com/api/retail/prices?$filter=serviceName%20eq%20'Foundry%20Models'%20and%20armRegionName%20eq%20'eastus2'%20and%20priceType%20eq%20'Consumption'", "supported_endpoints": [ "/v1/chat/completions", "/v1/batch", @@ -6332,13 +6444,15 @@ "azure/gpt-5-pro": { "deprecation_date": "2027-04-07", "input_cost_per_token": 1.5e-05, + "input_cost_per_token_batches": 7.5e-06, "litellm_provider": "azure", "max_input_tokens": 272000, "max_output_tokens": 128000, "max_tokens": 128000, "mode": "responses", "output_cost_per_token": 0.00012, - "source": "https://learn.microsoft.com/en-us/azure/ai-foundry/foundry-models/concepts/models-sold-directly-by-azure?pivots=azure-openai&tabs=global-standard-aoai%2Cstandard-chat-completions%2Cglobal-standard#gpt-5", + "output_cost_per_token_batches": 6e-05, + "source": "https://prices.azure.com/api/retail/prices?$filter=serviceName%20eq%20'Foundry%20Models'%20and%20armRegionName%20eq%20'eastus2'%20and%20priceType%20eq%20'Consumption'", "supported_endpoints": [ "/v1/responses" ], @@ -6362,6 +6476,7 @@ "azure/gpt-5.1": { "deprecation_date": "2027-05-15", "cache_read_input_token_cost": 1.25e-07, + "cache_read_input_token_cost_priority": 2.5e-07, "input_cost_per_token": 1.25e-06, "litellm_provider": "azure", "max_input_tokens": 272000, @@ -6393,7 +6508,12 @@ "supports_tool_choice": true, "supports_vision": true, "supports_none_reasoning_effort": true, - "default_reasoning_effort": "none" + "default_reasoning_effort": "none", + "input_cost_per_token_batches": 6.25e-07, + "input_cost_per_token_priority": 2.5e-06, + "output_cost_per_token_batches": 5e-06, + "output_cost_per_token_priority": 2e-05, + "source": "https://prices.azure.com/api/retail/prices?$filter=serviceName%20eq%20'Foundry%20Models'%20and%20armRegionName%20eq%20'eastus2'%20and%20priceType%20eq%20'Consumption'" }, "azure/gpt-5.1-chat": { "cache_read_input_token_cost": 1.25e-07, @@ -6429,7 +6549,8 @@ "supports_tool_choice": true, "supports_vision": true, "supports_none_reasoning_effort": true, - "default_reasoning_effort": "none" + "default_reasoning_effort": "none", + "source": "https://prices.azure.com/api/retail/prices?$filter=serviceName%20eq%20'Foundry%20Models'%20and%20armRegionName%20eq%20'eastus2'%20and%20priceType%20eq%20'Consumption'" }, "azure/gpt-5.1-codex": { "deprecation_date": "2027-05-15", @@ -6441,6 +6562,7 @@ "max_tokens": 128000, "mode": "responses", "output_cost_per_token": 1e-05, + "source": "https://prices.azure.com/api/retail/prices?$filter=serviceName%20eq%20'Foundry%20Models'%20and%20armRegionName%20eq%20'eastus2'%20and%20priceType%20eq%20'Consumption'", "supported_endpoints": [ "/v1/responses" ], @@ -6472,6 +6594,7 @@ "max_tokens": 128000, "mode": "responses", "output_cost_per_token": 1e-05, + "source": "https://prices.azure.com/api/retail/prices?$filter=serviceName%20eq%20'Foundry%20Models'%20and%20armRegionName%20eq%20'eastus2'%20and%20priceType%20eq%20'Consumption'", "supported_endpoints": [ "/v1/responses" ], @@ -6503,6 +6626,7 @@ "max_tokens": 128000, "mode": "responses", "output_cost_per_token": 2e-06, + "source": "https://prices.azure.com/api/retail/prices?$filter=serviceName%20eq%20'Foundry%20Models'%20and%20armRegionName%20eq%20'eastus2'%20and%20priceType%20eq%20'Consumption'", "supported_endpoints": [ "/v1/responses" ], @@ -6527,13 +6651,19 @@ "azure/gpt-5.2": { "deprecation_date": "2027-06-08", "cache_read_input_token_cost": 1.75e-07, + "cache_read_input_token_cost_priority": 3.5e-07, "input_cost_per_token": 1.75e-06, + "input_cost_per_token_batches": 8.75e-07, + "input_cost_per_token_priority": 3.5e-06, "litellm_provider": "azure", "max_input_tokens": 272000, "max_output_tokens": 128000, "max_tokens": 128000, "mode": "chat", "output_cost_per_token": 1.4e-05, + "output_cost_per_token_batches": 7e-06, + "output_cost_per_token_priority": 2.8e-05, + "source": "https://prices.azure.com/api/retail/prices?$filter=serviceName%20eq%20'Foundry%20Models'%20and%20armRegionName%20eq%20'eastus2'%20and%20priceType%20eq%20'Consumption'", "supported_endpoints": [ "/v1/chat/completions", "/v1/batch", @@ -6563,6 +6693,7 @@ "cache_read_input_token_cost_priority": 3.5e-07, "deprecation_date": "2027-06-08", "input_cost_per_token": 1.75e-06, + "input_cost_per_token_batches": 8.75e-07, "input_cost_per_token_priority": 3.5e-06, "litellm_provider": "azure", "max_input_tokens": 272000, @@ -6570,7 +6701,9 @@ "max_tokens": 128000, "mode": "chat", "output_cost_per_token": 1.4e-05, + "output_cost_per_token_batches": 7e-06, "output_cost_per_token_priority": 2.8e-05, + "source": "https://prices.azure.com/api/retail/prices?$filter=serviceName%20eq%20'Foundry%20Models'%20and%20armRegionName%20eq%20'eastus2'%20and%20priceType%20eq%20'Consumption'", "supported_endpoints": [ "/v1/chat/completions", "/v1/batch", @@ -6608,6 +6741,7 @@ "mode": "chat", "output_cost_per_token": 1.4e-05, "output_cost_per_token_priority": 2.8e-05, + "source": "https://prices.azure.com/api/retail/prices?$filter=serviceName%20eq%20'Foundry%20Models'%20and%20armRegionName%20eq%20'eastus2'%20and%20priceType%20eq%20'Consumption'", "supported_endpoints": [ "/v1/chat/completions", "/v1/responses" @@ -6643,6 +6777,7 @@ "mode": "chat", "output_cost_per_token": 1.4e-05, "output_cost_per_token_priority": 2.8e-05, + "source": "https://prices.azure.com/api/retail/prices?$filter=serviceName%20eq%20'Foundry%20Models'%20and%20armRegionName%20eq%20'eastus2'%20and%20priceType%20eq%20'Consumption'", "supported_endpoints": [ "/v1/chat/completions", "/v1/responses" @@ -6675,6 +6810,7 @@ "max_tokens": 128000, "mode": "responses", "output_cost_per_token": 1.4e-05, + "source": "https://prices.azure.com/api/retail/prices?$filter=serviceName%20eq%20'Foundry%20Models'%20and%20armRegionName%20eq%20'eastus2'%20and%20priceType%20eq%20'Consumption'", "supported_endpoints": [ "/v1/responses" ], @@ -6709,6 +6845,7 @@ "mode": "chat", "output_cost_per_token": 1.4e-05, "output_cost_per_token_priority": 2.8e-05, + "source": "https://prices.azure.com/api/retail/prices?$filter=serviceName%20eq%20'Foundry%20Models'%20and%20armRegionName%20eq%20'eastus2'%20and%20priceType%20eq%20'Consumption'", "supported_endpoints": [ "/v1/chat/completions", "/v1/responses" @@ -6733,14 +6870,18 @@ }, "azure/gpt-5.3-codex": { "cache_read_input_token_cost": 1.75e-07, + "cache_read_input_token_cost_priority": 3.5e-07, "deprecation_date": "2027-08-24", "input_cost_per_token": 1.75e-06, + "input_cost_per_token_priority": 3.5e-06, "litellm_provider": "azure", "max_input_tokens": 272000, "max_output_tokens": 128000, "max_tokens": 128000, "mode": "responses", "output_cost_per_token": 1.4e-05, + "output_cost_per_token_priority": 2.8e-05, + "source": "https://prices.azure.com/api/retail/prices?$filter=serviceName%20eq%20'Foundry%20Models'%20and%20armRegionName%20eq%20'eastus2'%20and%20priceType%20eq%20'Consumption'", "supported_endpoints": [ "/v1/responses" ], @@ -6764,17 +6905,20 @@ }, "azure/gpt-5.2-pro": { "input_cost_per_token": 2.1e-05, + "input_cost_per_token_batches": 1.05e-05, "litellm_provider": "azure", "max_input_tokens": 272000, "max_output_tokens": 128000, "max_tokens": 128000, "mode": "responses", "output_cost_per_token": 0.000168, + "output_cost_per_token_batches": 8.4e-05, "search_context_cost_per_query": { "search_context_size_high": 0.01, "search_context_size_low": 0.01, "search_context_size_medium": 0.01 }, + "source": "https://prices.azure.com/api/retail/prices?$filter=serviceName%20eq%20'Foundry%20Models'%20and%20armRegionName%20eq%20'eastus2'%20and%20priceType%20eq%20'Consumption'", "supported_endpoints": [ "/v1/batch", "/v1/responses" @@ -6800,17 +6944,20 @@ }, "azure/gpt-5.2-pro-2025-12-11": { "input_cost_per_token": 2.1e-05, + "input_cost_per_token_batches": 1.05e-05, "litellm_provider": "azure", "max_input_tokens": 272000, "max_output_tokens": 128000, "max_tokens": 128000, "mode": "responses", "output_cost_per_token": 0.000168, + "output_cost_per_token_batches": 8.4e-05, "search_context_cost_per_query": { "search_context_size_high": 0.01, "search_context_size_low": 0.01, "search_context_size_medium": 0.01 }, + "source": "https://prices.azure.com/api/retail/prices?$filter=serviceName%20eq%20'Foundry%20Models'%20and%20armRegionName%20eq%20'eastus2'%20and%20priceType%20eq%20'Consumption'", "supported_endpoints": [ "/v1/batch", "/v1/responses" @@ -6838,6 +6985,7 @@ "deprecation_date": "2027-09-02", "cache_read_input_token_cost": 2.5e-07, "cache_read_input_token_cost_above_272k_tokens": 5e-07, + "cache_read_input_token_cost_above_272k_tokens_flex": 2.5e-07, "cache_read_input_token_cost_priority": 5e-07, "cache_read_input_token_cost_above_272k_tokens_priority": 1e-06, "input_cost_per_token": 2.5e-06, @@ -6877,12 +7025,20 @@ "supports_vision": true, "supports_none_reasoning_effort": true, "default_reasoning_effort": "none", + "input_cost_per_token_above_272k_tokens_flex": 2.5e-06, + "input_cost_per_token_batches": 1.25e-06, + "input_cost_per_token_flex": 1.25e-06, + "output_cost_per_token_above_272k_tokens_flex": 1.125e-05, + "output_cost_per_token_batches": 7.5e-06, + "output_cost_per_token_flex": 7.5e-06, + "source": "https://prices.azure.com/api/retail/prices?$filter=serviceName%20eq%20'Foundry%20Models'%20and%20armRegionName%20eq%20'eastus2'%20and%20priceType%20eq%20'Consumption'", "supports_xhigh_reasoning_effort": true, "supports_minimal_reasoning_effort": true }, "azure/us/gpt-5.4": { "deprecation_date": "2027-09-02", - "cache_read_input_token_cost": 2.8e-07, + "cache_read_input_token_cost": 2.75e-07, + "cache_read_input_token_cost_above_272k_tokens": 5.5e-07, "cache_read_input_token_cost_priority": 5.5e-07, "input_cost_per_token": 2.75e-06, "input_cost_per_token_priority": 5.5e-06, @@ -6917,12 +7073,18 @@ "supports_vision": true, "supports_none_reasoning_effort": true, "default_reasoning_effort": "none", + "input_cost_per_token_above_272k_tokens": 5.5e-06, + "input_cost_per_token_batches": 1.375e-06, + "output_cost_per_token_above_272k_tokens": 2.475e-05, + "output_cost_per_token_batches": 8.25e-06, + "source": "https://prices.azure.com/api/retail/prices?$filter=serviceName%20eq%20'Foundry%20Models'%20and%20armRegionName%20eq%20'eastus2'%20and%20priceType%20eq%20'Consumption'", "supports_xhigh_reasoning_effort": true, "supports_minimal_reasoning_effort": true }, "azure/eu/gpt-5.4": { "deprecation_date": "2027-09-02", - "cache_read_input_token_cost": 2.8e-07, + "cache_read_input_token_cost": 2.75e-07, + "cache_read_input_token_cost_above_272k_tokens": 5.5e-07, "cache_read_input_token_cost_priority": 5.5e-07, "input_cost_per_token": 2.75e-06, "input_cost_per_token_priority": 5.5e-06, @@ -6957,12 +7119,18 @@ "supports_vision": true, "supports_none_reasoning_effort": true, "default_reasoning_effort": "none", + "input_cost_per_token_above_272k_tokens": 5.5e-06, + "input_cost_per_token_batches": 1.375e-06, + "output_cost_per_token_above_272k_tokens": 2.475e-05, + "output_cost_per_token_batches": 8.25e-06, + "source": "https://prices.azure.com/api/retail/prices?$filter=serviceName%20eq%20'Foundry%20Models'%20and%20armRegionName%20eq%20'eastus2'%20and%20priceType%20eq%20'Consumption'", "supports_xhigh_reasoning_effort": true, "supports_minimal_reasoning_effort": true }, "azure/gpt-5.4-2026-03-05": { "cache_read_input_token_cost": 2.5e-07, "cache_read_input_token_cost_above_272k_tokens": 5e-07, + "cache_read_input_token_cost_above_272k_tokens_flex": 2.5e-07, "cache_read_input_token_cost_priority": 5e-07, "cache_read_input_token_cost_above_272k_tokens_priority": 1e-06, "deprecation_date": "2027-09-02", @@ -7003,11 +7171,19 @@ "supports_vision": true, "supports_none_reasoning_effort": true, "default_reasoning_effort": "none", + "input_cost_per_token_above_272k_tokens_flex": 2.5e-06, + "input_cost_per_token_batches": 1.25e-06, + "input_cost_per_token_flex": 1.25e-06, + "output_cost_per_token_above_272k_tokens_flex": 1.125e-05, + "output_cost_per_token_batches": 7.5e-06, + "output_cost_per_token_flex": 7.5e-06, + "source": "https://prices.azure.com/api/retail/prices?$filter=serviceName%20eq%20'Foundry%20Models'%20and%20armRegionName%20eq%20'eastus2'%20and%20priceType%20eq%20'Consumption'", "supports_xhigh_reasoning_effort": true, "supports_minimal_reasoning_effort": true }, "azure/us/gpt-5.4-2026-03-05": { - "cache_read_input_token_cost": 2.8e-07, + "cache_read_input_token_cost": 2.75e-07, + "cache_read_input_token_cost_above_272k_tokens": 5.5e-07, "cache_read_input_token_cost_priority": 5.5e-07, "deprecation_date": "2027-09-02", "input_cost_per_token": 2.75e-06, @@ -7043,11 +7219,17 @@ "supports_vision": true, "supports_none_reasoning_effort": true, "default_reasoning_effort": "none", + "input_cost_per_token_above_272k_tokens": 5.5e-06, + "input_cost_per_token_batches": 1.375e-06, + "output_cost_per_token_above_272k_tokens": 2.475e-05, + "output_cost_per_token_batches": 8.25e-06, + "source": "https://prices.azure.com/api/retail/prices?$filter=serviceName%20eq%20'Foundry%20Models'%20and%20armRegionName%20eq%20'eastus2'%20and%20priceType%20eq%20'Consumption'", "supports_xhigh_reasoning_effort": true, "supports_minimal_reasoning_effort": true }, "azure/eu/gpt-5.4-2026-03-05": { - "cache_read_input_token_cost": 2.8e-07, + "cache_read_input_token_cost": 2.75e-07, + "cache_read_input_token_cost_above_272k_tokens": 5.5e-07, "cache_read_input_token_cost_priority": 5.5e-07, "deprecation_date": "2027-09-02", "input_cost_per_token": 2.75e-06, @@ -7083,6 +7265,11 @@ "supports_vision": true, "supports_none_reasoning_effort": true, "default_reasoning_effort": "none", + "input_cost_per_token_above_272k_tokens": 5.5e-06, + "input_cost_per_token_batches": 1.375e-06, + "output_cost_per_token_above_272k_tokens": 2.475e-05, + "output_cost_per_token_batches": 8.25e-06, + "source": "https://prices.azure.com/api/retail/prices?$filter=serviceName%20eq%20'Foundry%20Models'%20and%20armRegionName%20eq%20'eastus2'%20and%20priceType%20eq%20'Consumption'", "supports_xhigh_reasoning_effort": true, "supports_minimal_reasoning_effort": true }, @@ -7092,6 +7279,9 @@ "cache_read_input_token_cost_above_272k_tokens": 6e-06, "input_cost_per_token": 3e-05, "input_cost_per_token_above_272k_tokens": 6e-05, + "input_cost_per_token_above_272k_tokens_flex": 3e-05, + "input_cost_per_token_batches": 1.5e-05, + "input_cost_per_token_flex": 1.5e-05, "litellm_provider": "azure", "max_input_tokens": 1050000, "max_output_tokens": 128000, @@ -7099,11 +7289,15 @@ "mode": "responses", "output_cost_per_token": 0.00018, "output_cost_per_token_above_272k_tokens": 0.00027, + "output_cost_per_token_above_272k_tokens_flex": 0.000135, + "output_cost_per_token_batches": 9e-05, + "output_cost_per_token_flex": 9e-05, "search_context_cost_per_query": { "search_context_size_high": 0.01, "search_context_size_low": 0.01, "search_context_size_medium": 0.01 }, + "source": "https://prices.azure.com/api/retail/prices?$filter=serviceName%20eq%20'Foundry%20Models'%20and%20armRegionName%20eq%20'eastus2'%20and%20priceType%20eq%20'Consumption'", "supported_endpoints": [ "/v1/batch", "/v1/responses" @@ -7133,6 +7327,9 @@ "deprecation_date": "2027-09-07", "input_cost_per_token": 3e-05, "input_cost_per_token_above_272k_tokens": 6e-05, + "input_cost_per_token_above_272k_tokens_flex": 3e-05, + "input_cost_per_token_batches": 1.5e-05, + "input_cost_per_token_flex": 1.5e-05, "litellm_provider": "azure", "max_input_tokens": 1050000, "max_output_tokens": 128000, @@ -7140,11 +7337,15 @@ "mode": "responses", "output_cost_per_token": 0.00018, "output_cost_per_token_above_272k_tokens": 0.00027, + "output_cost_per_token_above_272k_tokens_flex": 0.000135, + "output_cost_per_token_batches": 9e-05, + "output_cost_per_token_flex": 9e-05, "search_context_cost_per_query": { "search_context_size_high": 0.01, "search_context_size_low": 0.01, "search_context_size_medium": 0.01 }, + "source": "https://prices.azure.com/api/retail/prices?$filter=serviceName%20eq%20'Foundry%20Models'%20and%20armRegionName%20eq%20'eastus2'%20and%20priceType%20eq%20'Consumption'", "supported_endpoints": [ "/v1/batch", "/v1/responses" @@ -7223,33 +7424,42 @@ "supports_minimal_reasoning_effort": false }, "azure/gpt-5.6-sol": { - "cache_creation_input_token_cost": 6.25e-06, - "cache_creation_input_token_cost_above_272k_tokens": 1.25e-05, - "cache_creation_input_token_cost_priority": 1.25e-05, - "cache_creation_input_token_cost_above_272k_tokens_priority": 2.5e-05, - "cache_read_input_token_cost": 5e-07, - "cache_read_input_token_cost_above_272k_tokens": 1e-06, - "cache_read_input_token_cost_priority": 1e-06, - "cache_read_input_token_cost_above_272k_tokens_priority": 2e-06, + "cache_creation_input_token_cost": 5e-06, + "cache_creation_input_token_cost_above_272k_tokens": 1e-05, + "cache_creation_input_token_cost_above_272k_tokens_flex": 5e-06, + "cache_creation_input_token_cost_priority": 1e-05, + "cache_creation_input_token_cost_above_272k_tokens_priority": 2e-05, + "cache_creation_input_token_cost_flex": 2.5e-06, + "cache_read_input_token_cost": 4e-07, + "cache_read_input_token_cost_above_272k_tokens": 8e-07, + "cache_read_input_token_cost_above_272k_tokens_flex": 4e-07, + "cache_read_input_token_cost_priority": 8e-07, + "cache_read_input_token_cost_above_272k_tokens_priority": 1.6e-06, + "cache_read_input_token_cost_flex": 2e-07, "deprecation_date": "2028-01-11", - "input_cost_per_token": 5e-06, - "input_cost_per_token_above_272k_tokens": 1e-05, - "input_cost_per_token_priority": 1e-05, - "input_cost_per_token_above_272k_tokens_priority": 2e-05, + "input_cost_per_token": 4e-06, + "input_cost_per_token_above_272k_tokens": 8e-06, + "input_cost_per_token_above_272k_tokens_flex": 4e-06, + "input_cost_per_token_priority": 8e-06, + "input_cost_per_token_above_272k_tokens_priority": 1.6e-05, + "input_cost_per_token_flex": 2e-06, "litellm_provider": "azure", "max_input_tokens": 922000, "max_output_tokens": 128000, "max_tokens": 128000, "mode": "chat", - "output_cost_per_token": 3e-05, - "output_cost_per_token_above_272k_tokens": 4.5e-05, - "output_cost_per_token_priority": 6e-05, - "output_cost_per_token_above_272k_tokens_priority": 9e-05, + "output_cost_per_token": 2e-05, + "output_cost_per_token_above_272k_tokens": 3e-05, + "output_cost_per_token_above_272k_tokens_flex": 1.5e-05, + "output_cost_per_token_priority": 4e-05, + "output_cost_per_token_above_272k_tokens_priority": 6e-05, + "output_cost_per_token_flex": 1e-05, "search_context_cost_per_query": { "search_context_size_high": 0.01, "search_context_size_low": 0.01, "search_context_size_medium": 0.01 }, + "source": "https://prices.azure.com/api/retail/prices?$filter=serviceName%20eq%20'Foundry%20Models'%20and%20armRegionName%20eq%20'eastus2'%20and%20priceType%20eq%20'Consumption'", "supported_endpoints": [ "/v1/chat/completions", "/v1/batch", @@ -7280,17 +7490,23 @@ "azure/gpt-5.6-terra": { "cache_creation_input_token_cost": 2.5e-06, "cache_creation_input_token_cost_above_272k_tokens": 5e-06, + "cache_creation_input_token_cost_above_272k_tokens_flex": 2.5e-06, "cache_creation_input_token_cost_priority": 5e-06, "cache_creation_input_token_cost_above_272k_tokens_priority": 1e-05, + "cache_creation_input_token_cost_flex": 1.25e-06, "cache_read_input_token_cost": 2e-07, "cache_read_input_token_cost_above_272k_tokens": 4e-07, + "cache_read_input_token_cost_above_272k_tokens_flex": 2e-07, "cache_read_input_token_cost_priority": 4e-07, "cache_read_input_token_cost_above_272k_tokens_priority": 8e-07, + "cache_read_input_token_cost_flex": 1e-07, "deprecation_date": "2028-01-11", "input_cost_per_token": 2e-06, "input_cost_per_token_above_272k_tokens": 4e-06, + "input_cost_per_token_above_272k_tokens_flex": 2e-06, "input_cost_per_token_priority": 4e-06, "input_cost_per_token_above_272k_tokens_priority": 8e-06, + "input_cost_per_token_flex": 1e-06, "litellm_provider": "azure", "max_input_tokens": 922000, "max_output_tokens": 128000, @@ -7298,13 +7514,16 @@ "mode": "chat", "output_cost_per_token": 1.2e-05, "output_cost_per_token_above_272k_tokens": 1.8e-05, + "output_cost_per_token_above_272k_tokens_flex": 9e-06, "output_cost_per_token_priority": 2.4e-05, "output_cost_per_token_above_272k_tokens_priority": 3.6e-05, + "output_cost_per_token_flex": 6e-06, "search_context_cost_per_query": { "search_context_size_high": 0.01, "search_context_size_low": 0.01, "search_context_size_medium": 0.01 }, + "source": "https://prices.azure.com/api/retail/prices?$filter=serviceName%20eq%20'Foundry%20Models'%20and%20armRegionName%20eq%20'eastus2'%20and%20priceType%20eq%20'Consumption'", "supported_endpoints": [ "/v1/chat/completions", "/v1/batch", @@ -7335,17 +7554,23 @@ "azure/gpt-5.6-luna": { "cache_creation_input_token_cost": 2.5e-07, "cache_creation_input_token_cost_above_272k_tokens": 5e-07, + "cache_creation_input_token_cost_above_272k_tokens_flex": 2.5e-07, "cache_creation_input_token_cost_priority": 5e-07, "cache_creation_input_token_cost_above_272k_tokens_priority": 1e-06, + "cache_creation_input_token_cost_flex": 1.25e-07, "cache_read_input_token_cost": 2e-08, "cache_read_input_token_cost_above_272k_tokens": 4e-08, + "cache_read_input_token_cost_above_272k_tokens_flex": 2e-08, "cache_read_input_token_cost_priority": 4e-08, "cache_read_input_token_cost_above_272k_tokens_priority": 8e-08, + "cache_read_input_token_cost_flex": 1e-08, "deprecation_date": "2028-01-11", "input_cost_per_token": 2e-07, "input_cost_per_token_above_272k_tokens": 4e-07, + "input_cost_per_token_above_272k_tokens_flex": 2e-07, "input_cost_per_token_priority": 4e-07, "input_cost_per_token_above_272k_tokens_priority": 8e-07, + "input_cost_per_token_flex": 1e-07, "litellm_provider": "azure", "max_input_tokens": 922000, "max_output_tokens": 128000, @@ -7353,13 +7578,16 @@ "mode": "chat", "output_cost_per_token": 1.2e-06, "output_cost_per_token_above_272k_tokens": 1.8e-06, + "output_cost_per_token_above_272k_tokens_flex": 9e-07, "output_cost_per_token_priority": 2.4e-06, "output_cost_per_token_above_272k_tokens_priority": 3.6e-06, + "output_cost_per_token_flex": 6e-07, "search_context_cost_per_query": { "search_context_size_high": 0.01, "search_context_size_low": 0.01, "search_context_size_medium": 0.01 }, + "source": "https://prices.azure.com/api/retail/prices?$filter=serviceName%20eq%20'Foundry%20Models'%20and%20armRegionName%20eq%20'eastus2'%20and%20priceType%20eq%20'Consumption'", "supported_endpoints": [ "/v1/chat/completions", "/v1/batch", @@ -7406,6 +7634,7 @@ "search_context_size_low": 0.01, "search_context_size_medium": 0.01 }, + "source": "https://prices.azure.com/api/retail/prices?$filter=serviceName%20eq%20'Foundry%20Models'%20and%20armRegionName%20eq%20'eastus2'%20and%20priceType%20eq%20'Consumption'", "supported_endpoints": [ "/v1/chat/completions", "/v1/responses" @@ -7563,33 +7792,34 @@ "supports_minimal_reasoning_effort": false }, "azure/us/gpt-5.6-sol": { - "cache_creation_input_token_cost": 6.875e-06, - "cache_creation_input_token_cost_above_272k_tokens": 1.375e-05, - "cache_creation_input_token_cost_above_272k_tokens_priority": 2.75e-05, - "cache_creation_input_token_cost_priority": 1.375e-05, - "cache_read_input_token_cost": 5.5e-07, - "cache_read_input_token_cost_above_272k_tokens": 1.1e-06, - "cache_read_input_token_cost_above_272k_tokens_priority": 2.2e-06, - "cache_read_input_token_cost_priority": 1.1e-06, + "cache_creation_input_token_cost": 5.5e-06, + "cache_creation_input_token_cost_above_272k_tokens": 1.1e-05, + "cache_creation_input_token_cost_above_272k_tokens_priority": 2.2e-05, + "cache_creation_input_token_cost_priority": 1.1e-05, + "cache_read_input_token_cost": 4.4e-07, + "cache_read_input_token_cost_above_272k_tokens": 8.8e-07, + "cache_read_input_token_cost_above_272k_tokens_priority": 1.76e-06, + "cache_read_input_token_cost_priority": 8.8e-07, "deprecation_date": "2028-01-11", - "input_cost_per_token": 5.5e-06, - "input_cost_per_token_above_272k_tokens": 1.1e-05, - "input_cost_per_token_above_272k_tokens_priority": 2.2e-05, - "input_cost_per_token_priority": 1.1e-05, + "input_cost_per_token": 4.4e-06, + "input_cost_per_token_above_272k_tokens": 8.8e-06, + "input_cost_per_token_above_272k_tokens_priority": 1.76e-05, + "input_cost_per_token_priority": 8.8e-06, "litellm_provider": "azure", "max_input_tokens": 922000, "max_output_tokens": 128000, "max_tokens": 128000, "mode": "chat", - "output_cost_per_token": 3.3e-05, - "output_cost_per_token_above_272k_tokens": 4.95e-05, - "output_cost_per_token_above_272k_tokens_priority": 9.9e-05, - "output_cost_per_token_priority": 6.6e-05, + "output_cost_per_token": 2.2e-05, + "output_cost_per_token_above_272k_tokens": 3.3e-05, + "output_cost_per_token_above_272k_tokens_priority": 6.6e-05, + "output_cost_per_token_priority": 4.4e-05, "search_context_cost_per_query": { "search_context_size_high": 0.01, "search_context_size_low": 0.01, "search_context_size_medium": 0.01 }, + "source": "https://prices.azure.com/api/retail/prices?$filter=serviceName%20eq%20'Foundry%20Models'%20and%20armRegionName%20eq%20'eastus2'%20and%20priceType%20eq%20'Consumption'", "supported_endpoints": [ "/v1/chat/completions", "/v1/batch", @@ -7645,6 +7875,7 @@ "search_context_size_low": 0.01, "search_context_size_medium": 0.01 }, + "source": "https://prices.azure.com/api/retail/prices?$filter=serviceName%20eq%20'Foundry%20Models'%20and%20armRegionName%20eq%20'eastus2'%20and%20priceType%20eq%20'Consumption'", "supported_endpoints": [ "/v1/chat/completions", "/v1/batch", @@ -7700,6 +7931,7 @@ "search_context_size_low": 0.01, "search_context_size_medium": 0.01 }, + "source": "https://prices.azure.com/api/retail/prices?$filter=serviceName%20eq%20'Foundry%20Models'%20and%20armRegionName%20eq%20'eastus2'%20and%20priceType%20eq%20'Consumption'", "supported_endpoints": [ "/v1/chat/completions", "/v1/batch", @@ -7746,6 +7978,7 @@ "search_context_size_low": 0.01, "search_context_size_medium": 0.01 }, + "source": "https://prices.azure.com/api/retail/prices?$filter=serviceName%20eq%20'Foundry%20Models'%20and%20armRegionName%20eq%20'eastus2'%20and%20priceType%20eq%20'Consumption'", "supported_endpoints": [ "/v1/chat/completions", "/v1/responses" @@ -7866,33 +8099,34 @@ "supports_minimal_reasoning_effort": false }, "azure/eu/gpt-5.6-sol": { - "cache_creation_input_token_cost": 6.875e-06, - "cache_creation_input_token_cost_above_272k_tokens": 1.375e-05, - "cache_creation_input_token_cost_above_272k_tokens_priority": 2.75e-05, - "cache_creation_input_token_cost_priority": 1.375e-05, - "cache_read_input_token_cost": 5.5e-07, - "cache_read_input_token_cost_above_272k_tokens": 1.1e-06, - "cache_read_input_token_cost_above_272k_tokens_priority": 2.2e-06, - "cache_read_input_token_cost_priority": 1.1e-06, + "cache_creation_input_token_cost": 5.5e-06, + "cache_creation_input_token_cost_above_272k_tokens": 1.1e-05, + "cache_creation_input_token_cost_above_272k_tokens_priority": 2.2e-05, + "cache_creation_input_token_cost_priority": 1.1e-05, + "cache_read_input_token_cost": 4.4e-07, + "cache_read_input_token_cost_above_272k_tokens": 8.8e-07, + "cache_read_input_token_cost_above_272k_tokens_priority": 1.76e-06, + "cache_read_input_token_cost_priority": 8.8e-07, "deprecation_date": "2028-01-11", - "input_cost_per_token": 5.5e-06, - "input_cost_per_token_above_272k_tokens": 1.1e-05, - "input_cost_per_token_above_272k_tokens_priority": 2.2e-05, - "input_cost_per_token_priority": 1.1e-05, + "input_cost_per_token": 4.4e-06, + "input_cost_per_token_above_272k_tokens": 8.8e-06, + "input_cost_per_token_above_272k_tokens_priority": 1.76e-05, + "input_cost_per_token_priority": 8.8e-06, "litellm_provider": "azure", "max_input_tokens": 922000, "max_output_tokens": 128000, "max_tokens": 128000, "mode": "chat", - "output_cost_per_token": 3.3e-05, - "output_cost_per_token_above_272k_tokens": 4.95e-05, - "output_cost_per_token_above_272k_tokens_priority": 9.9e-05, - "output_cost_per_token_priority": 6.6e-05, + "output_cost_per_token": 2.2e-05, + "output_cost_per_token_above_272k_tokens": 3.3e-05, + "output_cost_per_token_above_272k_tokens_priority": 6.6e-05, + "output_cost_per_token_priority": 4.4e-05, "search_context_cost_per_query": { "search_context_size_high": 0.01, "search_context_size_low": 0.01, "search_context_size_medium": 0.01 }, + "source": "https://prices.azure.com/api/retail/prices?$filter=serviceName%20eq%20'Foundry%20Models'%20and%20armRegionName%20eq%20'eastus2'%20and%20priceType%20eq%20'Consumption'", "supported_endpoints": [ "/v1/chat/completions", "/v1/batch", @@ -7948,6 +8182,7 @@ "search_context_size_low": 0.01, "search_context_size_medium": 0.01 }, + "source": "https://prices.azure.com/api/retail/prices?$filter=serviceName%20eq%20'Foundry%20Models'%20and%20armRegionName%20eq%20'eastus2'%20and%20priceType%20eq%20'Consumption'", "supported_endpoints": [ "/v1/chat/completions", "/v1/batch", @@ -8003,6 +8238,7 @@ "search_context_size_low": 0.01, "search_context_size_medium": 0.01 }, + "source": "https://prices.azure.com/api/retail/prices?$filter=serviceName%20eq%20'Foundry%20Models'%20and%20armRegionName%20eq%20'eastus2'%20and%20priceType%20eq%20'Consumption'", "supported_endpoints": [ "/v1/chat/completions", "/v1/batch", @@ -8034,12 +8270,16 @@ "deprecation_date": "2027-10-26", "cache_read_input_token_cost": 5e-07, "cache_read_input_token_cost_above_272k_tokens": 1e-06, - "cache_read_input_token_cost_priority": 1e-06, + "cache_read_input_token_cost_priority": 1.25e-06, "cache_read_input_token_cost_above_272k_tokens_priority": 2e-06, + "cache_read_input_token_cost_flex": 2.5e-07, "input_cost_per_token": 5e-06, "input_cost_per_token_above_272k_tokens": 1e-05, - "input_cost_per_token_priority": 1e-05, + "input_cost_per_token_above_272k_tokens_flex": 5e-06, + "input_cost_per_token_priority": 1.25e-05, "input_cost_per_token_above_272k_tokens_priority": 2e-05, + "input_cost_per_token_batches": 2.5e-06, + "input_cost_per_token_flex": 2.5e-06, "litellm_provider": "azure", "max_input_tokens": 1050000, "max_output_tokens": 128000, @@ -8047,13 +8287,15 @@ "mode": "chat", "output_cost_per_token": 3e-05, "output_cost_per_token_above_272k_tokens": 4.5e-05, - "output_cost_per_token_priority": 6e-05, + "output_cost_per_token_priority": 7.5e-05, "output_cost_per_token_above_272k_tokens_priority": 9e-05, + "output_cost_per_token_batches": 1.5e-05, "search_context_cost_per_query": { "search_context_size_high": 0.01, "search_context_size_low": 0.01, "search_context_size_medium": 0.01 }, + "source": "https://prices.azure.com/api/retail/prices?$filter=serviceName%20eq%20'Foundry%20Models'%20and%20armRegionName%20eq%20'eastus2'%20and%20priceType%20eq%20'Consumption'", "supported_endpoints": [ "/v1/chat/completions", "/v1/batch", @@ -8085,9 +8327,10 @@ "deprecation_date": "2027-10-26", "cache_read_input_token_cost": 5.5e-07, "cache_read_input_token_cost_above_272k_tokens": 1.1e-06, - "cache_read_input_token_cost_priority": 1.38e-06, + "cache_read_input_token_cost_priority": 1.375e-06, "input_cost_per_token": 5.5e-06, "input_cost_per_token_above_272k_tokens": 1.1e-05, + "input_cost_per_token_batches": 2.75e-06, "input_cost_per_token_priority": 1.375e-05, "output_cost_per_token": 3.3e-05, "output_cost_per_token_above_272k_tokens": 4.95e-05, @@ -8097,11 +8340,13 @@ "max_output_tokens": 128000, "max_tokens": 128000, "mode": "chat", + "output_cost_per_token_batches": 1.65e-05, "search_context_cost_per_query": { "search_context_size_high": 0.01, "search_context_size_low": 0.01, "search_context_size_medium": 0.01 }, + "source": "https://prices.azure.com/api/retail/prices?$filter=serviceName%20eq%20'Foundry%20Models'%20and%20armRegionName%20eq%20'eastus2'%20and%20priceType%20eq%20'Consumption'", "supported_endpoints": [ "/v1/chat/completions", "/v1/batch", @@ -8133,9 +8378,10 @@ "deprecation_date": "2027-10-26", "cache_read_input_token_cost": 5.5e-07, "cache_read_input_token_cost_above_272k_tokens": 1.1e-06, - "cache_read_input_token_cost_priority": 1.38e-06, + "cache_read_input_token_cost_priority": 1.375e-06, "input_cost_per_token": 5.5e-06, "input_cost_per_token_above_272k_tokens": 1.1e-05, + "input_cost_per_token_batches": 2.75e-06, "input_cost_per_token_priority": 1.375e-05, "output_cost_per_token": 3.3e-05, "output_cost_per_token_above_272k_tokens": 4.95e-05, @@ -8145,11 +8391,13 @@ "max_output_tokens": 128000, "max_tokens": 128000, "mode": "chat", + "output_cost_per_token_batches": 1.65e-05, "search_context_cost_per_query": { "search_context_size_high": 0.01, "search_context_size_low": 0.01, "search_context_size_medium": 0.01 }, + "source": "https://prices.azure.com/api/retail/prices?$filter=serviceName%20eq%20'Foundry%20Models'%20and%20armRegionName%20eq%20'eastus2'%20and%20priceType%20eq%20'Consumption'", "supported_endpoints": [ "/v1/chat/completions", "/v1/batch", @@ -8180,11 +8428,12 @@ "azure/gpt-5.5-2026-04-23": { "cache_read_input_token_cost": 5e-07, "cache_read_input_token_cost_above_272k_tokens": 1e-06, - "cache_read_input_token_cost_priority": 1e-06, + "cache_read_input_token_cost_priority": 1.25e-06, "cache_read_input_token_cost_above_272k_tokens_priority": 2e-06, + "cache_read_input_token_cost_flex": 2.5e-07, "input_cost_per_token": 5e-06, "input_cost_per_token_above_272k_tokens": 1e-05, - "input_cost_per_token_priority": 1e-05, + "input_cost_per_token_priority": 1.25e-05, "input_cost_per_token_above_272k_tokens_priority": 2e-05, "litellm_provider": "azure", "max_input_tokens": 1050000, @@ -8193,7 +8442,7 @@ "mode": "chat", "output_cost_per_token": 3e-05, "output_cost_per_token_above_272k_tokens": 4.5e-05, - "output_cost_per_token_priority": 6e-05, + "output_cost_per_token_priority": 7.5e-05, "output_cost_per_token_above_272k_tokens_priority": 9e-05, "search_context_cost_per_query": { "search_context_size_high": 0.01, @@ -8223,12 +8472,17 @@ "supports_tool_choice": true, "supports_vision": true, "supports_web_search": true, - "deprecation_date": "2027-10-26" + "deprecation_date": "2027-10-26", + "input_cost_per_token_above_272k_tokens_flex": 5e-06, + "input_cost_per_token_batches": 2.5e-06, + "input_cost_per_token_flex": 2.5e-06, + "output_cost_per_token_batches": 1.5e-05, + "source": "https://prices.azure.com/api/retail/prices?$filter=serviceName%20eq%20'Foundry%20Models'%20and%20armRegionName%20eq%20'eastus2'%20and%20priceType%20eq%20'Consumption'" }, "azure/us/gpt-5.5-2026-04-23": { "cache_read_input_token_cost": 5.5e-07, "cache_read_input_token_cost_above_272k_tokens": 1.1e-06, - "cache_read_input_token_cost_priority": 1.38e-06, + "cache_read_input_token_cost_priority": 1.375e-06, "input_cost_per_token": 5.5e-06, "input_cost_per_token_above_272k_tokens": 1.1e-05, "input_cost_per_token_priority": 1.375e-05, @@ -8268,12 +8522,15 @@ "supports_tool_choice": true, "supports_vision": true, "supports_web_search": true, - "deprecation_date": "2027-10-26" + "deprecation_date": "2027-10-26", + "input_cost_per_token_batches": 2.75e-06, + "output_cost_per_token_batches": 1.65e-05, + "source": "https://prices.azure.com/api/retail/prices?$filter=serviceName%20eq%20'Foundry%20Models'%20and%20armRegionName%20eq%20'eastus2'%20and%20priceType%20eq%20'Consumption'" }, "azure/eu/gpt-5.5-2026-04-23": { "cache_read_input_token_cost": 5.5e-07, "cache_read_input_token_cost_above_272k_tokens": 1.1e-06, - "cache_read_input_token_cost_priority": 1.38e-06, + "cache_read_input_token_cost_priority": 1.375e-06, "input_cost_per_token": 5.5e-06, "input_cost_per_token_above_272k_tokens": 1.1e-05, "input_cost_per_token_priority": 1.375e-05, @@ -8313,7 +8570,10 @@ "supports_tool_choice": true, "supports_vision": true, "supports_web_search": true, - "deprecation_date": "2027-10-26" + "deprecation_date": "2027-10-26", + "input_cost_per_token_batches": 2.75e-06, + "output_cost_per_token_batches": 1.65e-05, + "source": "https://prices.azure.com/api/retail/prices?$filter=serviceName%20eq%20'Foundry%20Models'%20and%20armRegionName%20eq%20'eastus2'%20and%20priceType%20eq%20'Consumption'" }, "azure/gpt-5.5-pro": { "cache_read_input_token_cost": 3e-06, @@ -8402,6 +8662,8 @@ "azure/gpt-5.4-mini": { "deprecation_date": "2027-09-21", "cache_read_input_token_cost": 7.5e-08, + "cache_read_input_token_cost_flex": 3.75e-08, + "cache_read_input_token_cost_priority": 1.5e-07, "input_cost_per_token": 7.5e-07, "litellm_provider": "azure", "max_input_tokens": 272000, @@ -8439,10 +8701,19 @@ "supports_web_search": true, "supports_none_reasoning_effort": true, "default_reasoning_effort": "none", + "input_cost_per_token_batches": 3.75e-07, + "input_cost_per_token_flex": 3.75e-07, + "input_cost_per_token_priority": 1.5e-06, + "output_cost_per_token_batches": 2.25e-06, + "output_cost_per_token_flex": 2.25e-06, + "output_cost_per_token_priority": 9e-06, + "source": "https://prices.azure.com/api/retail/prices?$filter=serviceName%20eq%20'Foundry%20Models'%20and%20armRegionName%20eq%20'eastus2'%20and%20priceType%20eq%20'Consumption'", "supports_xhigh_reasoning_effort": true }, "azure/gpt-5.4-mini-2026-03-17": { "cache_read_input_token_cost": 7.5e-08, + "cache_read_input_token_cost_flex": 3.75e-08, + "cache_read_input_token_cost_priority": 1.5e-07, "deprecation_date": "2027-09-21", "input_cost_per_token": 7.5e-07, "litellm_provider": "azure", @@ -8481,11 +8752,19 @@ "supports_web_search": true, "supports_none_reasoning_effort": true, "default_reasoning_effort": "none", + "input_cost_per_token_batches": 3.75e-07, + "input_cost_per_token_flex": 3.75e-07, + "input_cost_per_token_priority": 1.5e-06, + "output_cost_per_token_batches": 2.25e-06, + "output_cost_per_token_flex": 2.25e-06, + "output_cost_per_token_priority": 9e-06, + "source": "https://prices.azure.com/api/retail/prices?$filter=serviceName%20eq%20'Foundry%20Models'%20and%20armRegionName%20eq%20'eastus2'%20and%20priceType%20eq%20'Consumption'", "supports_xhigh_reasoning_effort": true }, "azure/gpt-5.4-nano": { "deprecation_date": "2027-09-21", "cache_read_input_token_cost": 2e-08, + "cache_read_input_token_cost_flex": 1e-08, "input_cost_per_token": 2e-07, "litellm_provider": "azure", "max_input_tokens": 272000, @@ -8523,10 +8802,16 @@ "supports_web_search": true, "supports_none_reasoning_effort": true, "default_reasoning_effort": "none", + "input_cost_per_token_batches": 1e-07, + "input_cost_per_token_flex": 1e-07, + "output_cost_per_token_batches": 6.25e-07, + "output_cost_per_token_flex": 6.25e-07, + "source": "https://prices.azure.com/api/retail/prices?$filter=serviceName%20eq%20'Foundry%20Models'%20and%20armRegionName%20eq%20'eastus2'%20and%20priceType%20eq%20'Consumption'", "supports_xhigh_reasoning_effort": true }, "azure/gpt-5.4-nano-2026-03-17": { "cache_read_input_token_cost": 2e-08, + "cache_read_input_token_cost_flex": 1e-08, "deprecation_date": "2027-09-21", "input_cost_per_token": 2e-07, "litellm_provider": "azure", @@ -8565,6 +8850,11 @@ "supports_web_search": true, "supports_none_reasoning_effort": true, "default_reasoning_effort": "none", + "input_cost_per_token_batches": 1e-07, + "input_cost_per_token_flex": 1e-07, + "output_cost_per_token_batches": 6.25e-07, + "output_cost_per_token_flex": 6.25e-07, + "source": "https://prices.azure.com/api/retail/prices?$filter=serviceName%20eq%20'Foundry%20Models'%20and%20armRegionName%20eq%20'eastus2'%20and%20priceType%20eq%20'Consumption'", "supports_xhigh_reasoning_effort": true }, "azure/gpt-image-1": { @@ -8886,12 +9176,15 @@ "cache_read_input_token_cost": 7.5e-06, "deprecation_date": "2026-11-19", "input_cost_per_token": 1.5e-05, + "input_cost_per_token_batches": 7.5e-06, "litellm_provider": "azure", "max_input_tokens": 200000, "max_output_tokens": 100000, "max_tokens": 100000, "mode": "chat", "output_cost_per_token": 6e-05, + "output_cost_per_token_batches": 3e-05, + "source": "https://prices.azure.com/api/retail/prices?$filter=serviceName%20eq%20'Foundry%20Models'%20and%20armRegionName%20eq%20'eastus2'%20and%20priceType%20eq%20'Consumption'", "supports_function_calling": true, "supports_parallel_function_calling": true, "supports_prompt_caching": true, @@ -8900,14 +9193,17 @@ "supports_vision": true }, "azure/o1-mini": { - "cache_read_input_token_cost": 6.05e-07, - "input_cost_per_token": 1.21e-06, + "cache_read_input_token_cost": 5.5e-07, + "input_cost_per_token": 1.1e-06, + "input_cost_per_token_batches": 5.5e-07, "litellm_provider": "azure", "max_input_tokens": 128000, "max_output_tokens": 65536, "max_tokens": 65536, "mode": "chat", - "output_cost_per_token": 4.84e-06, + "output_cost_per_token": 4.4e-06, + "output_cost_per_token_batches": 2.2e-06, + "source": "https://prices.azure.com/api/retail/prices?$filter=serviceName%20eq%20'Foundry%20Models'%20and%20armRegionName%20eq%20'eastus2'%20and%20priceType%20eq%20'Consumption'", "supports_function_calling": true, "supports_parallel_function_calling": true, "supports_prompt_caching": true, @@ -8938,6 +9234,7 @@ "max_tokens": 32768, "mode": "chat", "output_cost_per_token": 6e-05, + "source": "https://prices.azure.com/api/retail/prices?$filter=serviceName%20eq%20'Foundry%20Models'%20and%20armRegionName%20eq%20'eastus2'%20and%20priceType%20eq%20'Consumption'", "supports_function_calling": true, "supports_parallel_function_calling": true, "supports_prompt_caching": true, @@ -8953,6 +9250,7 @@ "max_tokens": 32768, "mode": "chat", "output_cost_per_token": 6e-05, + "source": "https://prices.azure.com/api/retail/prices?$filter=serviceName%20eq%20'Foundry%20Models'%20and%20armRegionName%20eq%20'eastus2'%20and%20priceType%20eq%20'Consumption'", "supports_function_calling": true, "supports_parallel_function_calling": true, "supports_pdf_input": true, @@ -8994,12 +9292,15 @@ "deprecation_date": "2026-11-19", "cache_read_input_token_cost": 5e-07, "input_cost_per_token": 2e-06, + "input_cost_per_token_batches": 1e-06, "litellm_provider": "azure", "max_input_tokens": 200000, "max_output_tokens": 100000, "max_tokens": 100000, "mode": "chat", "output_cost_per_token": 8e-06, + "output_cost_per_token_batches": 4e-06, + "source": "https://prices.azure.com/api/retail/prices?$filter=serviceName%20eq%20'Foundry%20Models'%20and%20armRegionName%20eq%20'eastus2'%20and%20priceType%20eq%20'Consumption'", "supported_endpoints": [ "/v1/chat/completions", "/v1/batch", @@ -9035,6 +9336,7 @@ "search_context_size_low": 0.01, "search_context_size_medium": 0.01 }, + "source": "https://prices.azure.com/api/retail/prices?$filter=serviceName%20eq%20'Foundry%20Models'%20and%20armRegionName%20eq%20'eastus2'%20and%20priceType%20eq%20'Consumption'", "supported_endpoints": [ "/v1/chat/completions", "/v1/batch", @@ -9078,12 +9380,15 @@ "cache_read_input_token_cost": 5.5e-07, "deprecation_date": "2026-11-19", "input_cost_per_token": 1.1e-06, + "input_cost_per_token_batches": 5.5e-07, "litellm_provider": "azure", "max_input_tokens": 200000, "max_output_tokens": 100000, "max_tokens": 100000, "mode": "chat", "output_cost_per_token": 4.4e-06, + "output_cost_per_token_batches": 2.2e-06, + "source": "https://prices.azure.com/api/retail/prices?$filter=serviceName%20eq%20'Foundry%20Models'%20and%20armRegionName%20eq%20'eastus2'%20and%20priceType%20eq%20'Consumption'", "supports_prompt_caching": true, "supports_reasoning": true, "supports_tool_choice": true, @@ -9100,6 +9405,7 @@ "mode": "responses", "output_cost_per_token": 8e-05, "output_cost_per_token_batches": 4e-05, + "source": "https://prices.azure.com/api/retail/prices?$filter=serviceName%20eq%20'Foundry%20Models'%20and%20armRegionName%20eq%20'eastus2'%20and%20priceType%20eq%20'Consumption'", "supported_endpoints": [ "/v1/chat/completions", "/v1/batch", @@ -9131,6 +9437,7 @@ "mode": "responses", "output_cost_per_token": 8e-05, "output_cost_per_token_batches": 4e-05, + "source": "https://prices.azure.com/api/retail/prices?$filter=serviceName%20eq%20'Foundry%20Models'%20and%20armRegionName%20eq%20'eastus2'%20and%20priceType%20eq%20'Consumption'", "supported_endpoints": [ "/v1/chat/completions", "/v1/batch", @@ -9185,12 +9492,15 @@ "cache_read_input_token_cost": 2.75e-07, "deprecation_date": "2026-11-19", "input_cost_per_token": 1.1e-06, + "input_cost_per_token_batches": 5.5e-07, "litellm_provider": "azure", "max_input_tokens": 200000, "max_output_tokens": 100000, "max_tokens": 100000, "mode": "chat", "output_cost_per_token": 4.4e-06, + "output_cost_per_token_batches": 2.2e-06, + "source": "https://prices.azure.com/api/retail/prices?$filter=serviceName%20eq%20'Foundry%20Models'%20and%20armRegionName%20eq%20'eastus2'%20and%20priceType%20eq%20'Consumption'", "supports_function_calling": true, "supports_parallel_function_calling": false, "supports_prompt_caching": true, @@ -9230,7 +9540,8 @@ "max_input_tokens": 8191, "max_tokens": 8191, "mode": "embedding", - "output_cost_per_token": 0.0 + "output_cost_per_token": 0.0, + "source": "https://prices.azure.com/api/retail/prices?$filter=serviceName%20eq%20'Foundry%20Models'%20and%20armRegionName%20eq%20'eastus2'%20and%20priceType%20eq%20'Consumption'" }, "azure/text-embedding-3-small": { "deprecation_date": "2028-02-09", @@ -9239,7 +9550,8 @@ "max_input_tokens": 8191, "max_tokens": 8191, "mode": "embedding", - "output_cost_per_token": 0.0 + "output_cost_per_token": 0.0, + "source": "https://prices.azure.com/api/retail/prices?$filter=serviceName%20eq%20'Foundry%20Models'%20and%20armRegionName%20eq%20'eastus2'%20and%20priceType%20eq%20'Consumption'" }, "azure/text-embedding-ada-002": { "deprecation_date": "2028-02-09", @@ -9248,7 +9560,8 @@ "max_input_tokens": 8191, "max_tokens": 8191, "mode": "embedding", - "output_cost_per_token": 0.0 + "output_cost_per_token": 0.0, + "source": "https://prices.azure.com/api/retail/prices?$filter=serviceName%20eq%20'Foundry%20Models'%20and%20armRegionName%20eq%20'eastus2'%20and%20priceType%20eq%20'Consumption'" }, "azure/speech/azure-tts": { "input_cost_per_character": 1.5e-05, @@ -9390,12 +9703,15 @@ "deprecation_date": "2027-04-14", "cache_read_input_token_cost": 1.375e-06, "input_cost_per_token": 2.75e-06, + "input_cost_per_token_batches": 1.375e-06, "litellm_provider": "azure", "max_input_tokens": 128000, "max_output_tokens": 16384, "max_tokens": 16384, "mode": "chat", "output_cost_per_token": 1.1e-05, + "output_cost_per_token_batches": 5.5e-06, + "source": "https://prices.azure.com/api/retail/prices?$filter=serviceName%20eq%20'Foundry%20Models'%20and%20armRegionName%20eq%20'eastus2'%20and%20priceType%20eq%20'Consumption'", "supports_function_calling": true, "supports_parallel_function_calling": true, "supports_prompt_caching": true, @@ -9406,13 +9722,17 @@ "azure/us/gpt-4o-2024-11-20": { "deprecation_date": "2027-04-14", "cache_creation_input_token_cost": 1.38e-06, + "cache_read_input_token_cost": 1.375e-06, "input_cost_per_token": 2.75e-06, + "input_cost_per_token_batches": 1.375e-06, "litellm_provider": "azure", "max_input_tokens": 128000, "max_output_tokens": 16384, "max_tokens": 16384, "mode": "chat", "output_cost_per_token": 1.1e-05, + "output_cost_per_token_batches": 5.5e-06, + "source": "https://prices.azure.com/api/retail/prices?$filter=serviceName%20eq%20'Foundry%20Models'%20and%20armRegionName%20eq%20'eastus2'%20and%20priceType%20eq%20'Consumption'", "supports_function_calling": true, "supports_parallel_function_calling": true, "supports_response_schema": true, @@ -9423,12 +9743,14 @@ "cache_read_input_token_cost": 8.3e-08, "deprecation_date": "2027-04-14", "input_cost_per_token": 1.65e-07, + "input_cost_per_token_batches": 8.3e-08, "litellm_provider": "azure", "max_input_tokens": 128000, "max_output_tokens": 16384, "max_tokens": 16384, "mode": "chat", "output_cost_per_token": 6.6e-07, + "source": "https://prices.azure.com/api/retail/prices?$filter=serviceName%20eq%20'Foundry%20Models'%20and%20armRegionName%20eq%20'eastus2'%20and%20priceType%20eq%20'Consumption'", "supports_function_calling": true, "supports_parallel_function_calling": true, "supports_prompt_caching": true, @@ -9602,8 +9924,9 @@ }, "azure/us/gpt-5.1": { "deprecation_date": "2027-05-15", - "cache_read_input_token_cost": 1.4e-07, - "input_cost_per_token": 1.38e-06, + "cache_read_input_token_cost": 1.375e-07, + "cache_read_input_token_cost_priority": 2.75e-07, + "input_cost_per_token": 1.375e-06, "litellm_provider": "azure", "max_input_tokens": 272000, "max_output_tokens": 128000, @@ -9634,12 +9957,17 @@ "supports_tool_choice": true, "supports_vision": true, "supports_none_reasoning_effort": true, - "default_reasoning_effort": "none" + "default_reasoning_effort": "none", + "input_cost_per_token_batches": 6.875e-07, + "input_cost_per_token_priority": 2.75e-06, + "output_cost_per_token_batches": 5.5e-06, + "output_cost_per_token_priority": 2.2e-05, + "source": "https://prices.azure.com/api/retail/prices?$filter=serviceName%20eq%20'Foundry%20Models'%20and%20armRegionName%20eq%20'eastus2'%20and%20priceType%20eq%20'Consumption'" }, "azure/us/gpt-5.1-chat": { - "cache_read_input_token_cost": 1.4e-07, + "cache_read_input_token_cost": 1.375e-07, "deprecation_date": "2026-06-29", - "input_cost_per_token": 1.38e-06, + "input_cost_per_token": 1.375e-06, "litellm_provider": "azure", "max_input_tokens": 128000, "max_output_tokens": 128000, @@ -9670,18 +9998,20 @@ "supports_tool_choice": true, "supports_vision": true, "supports_none_reasoning_effort": true, - "default_reasoning_effort": "none" + "default_reasoning_effort": "none", + "source": "https://prices.azure.com/api/retail/prices?$filter=serviceName%20eq%20'Foundry%20Models'%20and%20armRegionName%20eq%20'eastus2'%20and%20priceType%20eq%20'Consumption'" }, "azure/us/gpt-5.1-codex": { "deprecation_date": "2027-05-15", - "cache_read_input_token_cost": 1.4e-07, - "input_cost_per_token": 1.38e-06, + "cache_read_input_token_cost": 1.375e-07, + "input_cost_per_token": 1.375e-06, "litellm_provider": "azure", "max_input_tokens": 272000, "max_output_tokens": 128000, "max_tokens": 128000, "mode": "responses", "output_cost_per_token": 1.1e-05, + "source": "https://prices.azure.com/api/retail/prices?$filter=serviceName%20eq%20'Foundry%20Models'%20and%20armRegionName%20eq%20'eastus2'%20and%20priceType%20eq%20'Consumption'", "supported_endpoints": [ "/v1/responses" ], @@ -9705,7 +10035,7 @@ }, "azure/us/gpt-5.1-codex-mini": { "deprecation_date": "2027-05-15", - "cache_read_input_token_cost": 2.8e-08, + "cache_read_input_token_cost": 2.75e-08, "input_cost_per_token": 2.75e-07, "litellm_provider": "azure", "max_input_tokens": 272000, @@ -9713,6 +10043,7 @@ "max_tokens": 128000, "mode": "responses", "output_cost_per_token": 2.2e-06, + "source": "https://prices.azure.com/api/retail/prices?$filter=serviceName%20eq%20'Foundry%20Models'%20and%20armRegionName%20eq%20'eastus2'%20and%20priceType%20eq%20'Consumption'", "supported_endpoints": [ "/v1/responses" ], @@ -9738,12 +10069,15 @@ "cache_read_input_token_cost": 8.25e-06, "deprecation_date": "2026-11-19", "input_cost_per_token": 1.65e-05, + "input_cost_per_token_batches": 8.25e-06, "litellm_provider": "azure", "max_input_tokens": 200000, "max_output_tokens": 100000, "max_tokens": 100000, "mode": "chat", "output_cost_per_token": 6.6e-05, + "output_cost_per_token_batches": 3.3e-05, + "source": "https://prices.azure.com/api/retail/prices?$filter=serviceName%20eq%20'Foundry%20Models'%20and%20armRegionName%20eq%20'eastus2'%20and%20priceType%20eq%20'Consumption'", "supports_function_calling": true, "supports_parallel_function_calling": true, "supports_prompt_caching": true, @@ -9784,12 +10118,15 @@ "deprecation_date": "2026-11-19", "cache_read_input_token_cost": 5.5e-07, "input_cost_per_token": 2.2e-06, + "input_cost_per_token_batches": 1.1e-06, "litellm_provider": "azure", "max_input_tokens": 200000, "max_output_tokens": 100000, "max_tokens": 100000, "mode": "chat", "output_cost_per_token": 8.8e-06, + "output_cost_per_token_batches": 4.4e-06, + "source": "https://prices.azure.com/api/retail/prices?$filter=serviceName%20eq%20'Foundry%20Models'%20and%20armRegionName%20eq%20'eastus2'%20and%20priceType%20eq%20'Consumption'", "supported_endpoints": [ "/v1/chat/completions", "/v1/batch", @@ -9822,21 +10159,25 @@ "mode": "chat", "output_cost_per_token": 4.84e-06, "output_cost_per_token_batches": 2.42e-06, + "source": "https://prices.azure.com/api/retail/prices?$filter=serviceName%20eq%20'Foundry%20Models'%20and%20armRegionName%20eq%20'eastus2'%20and%20priceType%20eq%20'Consumption'", "supports_prompt_caching": true, "supports_reasoning": true, "supports_tool_choice": true, "supports_vision": false }, "azure/us/o4-mini-2025-04-16": { - "cache_read_input_token_cost": 3.1e-07, + "cache_read_input_token_cost": 3.03e-07, "deprecation_date": "2026-11-19", "input_cost_per_token": 1.21e-06, + "input_cost_per_token_batches": 6.05e-07, "litellm_provider": "azure", "max_input_tokens": 200000, "max_output_tokens": 100000, "max_tokens": 100000, "mode": "chat", "output_cost_per_token": 4.84e-06, + "output_cost_per_token_batches": 2.42e-06, + "source": "https://prices.azure.com/api/retail/prices?$filter=serviceName%20eq%20'Foundry%20Models'%20and%20armRegionName%20eq%20'eastus2'%20and%20priceType%20eq%20'Consumption'", "supports_function_calling": true, "supports_parallel_function_calling": false, "supports_prompt_caching": true, @@ -9882,7 +10223,7 @@ "max_tokens": 4096, "mode": "chat", "output_cost_per_token": 9e-07, - "source": "https://azure.microsoft.com/en-us/pricing/details/ai-foundry-models/mistral/", + "source": "https://prices.azure.com/api/retail/prices?$filter=serviceName%20eq%20'Foundry%20Models'%20and%20armRegionName%20eq%20'eastus2'%20and%20priceType%20eq%20'Consumption'", "supported_endpoints": [ "/v1/chat/completions" ], @@ -9931,7 +10272,7 @@ "max_tokens": 163840, "mode": "chat", "output_cost_per_token": 1.85e-06, - "source": "https://azure.microsoft.com/en-us/pricing/details/ai-foundry-models/fireworks/", + "source": "https://prices.azure.com/api/retail/prices?$filter=serviceName%20eq%20'Foundry%20Models'%20and%20armRegionName%20eq%20'eastus2'%20and%20priceType%20eq%20'Consumption'", "supports_function_calling": true, "supports_prompt_caching": true, "supports_reasoning": true, @@ -9946,7 +10287,7 @@ "max_tokens": 384000, "mode": "chat", "output_cost_per_token": 3.828e-06, - "source": "https://azure.microsoft.com/en-us/pricing/details/ai-foundry-models/fireworks/", + "source": "https://prices.azure.com/api/retail/prices?$filter=serviceName%20eq%20'Foundry%20Models'%20and%20armRegionName%20eq%20'eastus2'%20and%20priceType%20eq%20'Consumption'", "supports_function_calling": true, "supports_prompt_caching": true, "supports_reasoning": true, @@ -9962,7 +10303,7 @@ "max_tokens": 128000, "mode": "chat", "output_cost_per_token": 3.52e-06, - "source": "https://azure.microsoft.com/en-us/pricing/details/ai-foundry-models/fireworks/", + "source": "https://prices.azure.com/api/retail/prices?$filter=serviceName%20eq%20'Foundry%20Models'%20and%20armRegionName%20eq%20'eastus2'%20and%20priceType%20eq%20'Consumption'", "supports_function_calling": true, "supports_prompt_caching": true, "supports_reasoning": true, @@ -9978,7 +10319,7 @@ "max_tokens": 131072, "mode": "chat", "output_cost_per_token": 4.84e-06, - "source": "https://azure.microsoft.com/en-us/pricing/details/ai-foundry-models/fireworks/", + "source": "https://prices.azure.com/api/retail/prices?$filter=serviceName%20eq%20'Foundry%20Models'%20and%20armRegionName%20eq%20'eastus2'%20and%20priceType%20eq%20'Consumption'", "supports_function_calling": true, "supports_prompt_caching": true, "supports_reasoning": true, @@ -9993,37 +10334,37 @@ "max_tokens": 131072, "mode": "chat", "output_cost_per_token": 4.84e-06, - "source": "https://azure.microsoft.com/en-us/pricing/details/ai-foundry-models/fireworks/", + "source": "https://prices.azure.com/api/retail/prices?$filter=serviceName%20eq%20'Foundry%20Models'%20and%20armRegionName%20eq%20'eastus2'%20and%20priceType%20eq%20'Consumption'", "supports_function_calling": true, "supports_prompt_caching": true, "supports_reasoning": true, "supports_tool_choice": true }, "azure_ai/FW-GLM-5.2-Fast": { - "cache_read_input_token_cost": 2.1e-07, - "input_cost_per_token": 2.1e-06, + "cache_read_input_token_cost": 2.31e-07, + "input_cost_per_token": 2.31e-06, "litellm_provider": "azure_ai", "max_input_tokens": 1048576, "max_output_tokens": 131072, "max_tokens": 131072, "mode": "chat", - "output_cost_per_token": 6.6e-06, - "source": "https://docs.fireworks.ai/serverless/pricing", + "output_cost_per_token": 7.26e-06, + "source": "https://prices.azure.com/api/retail/prices?$filter=serviceName%20eq%20'Foundry%20Models'%20and%20armRegionName%20eq%20'eastus2'%20and%20priceType%20eq%20'Consumption'", "supports_function_calling": true, "supports_prompt_caching": true, "supports_reasoning": true, "supports_tool_choice": true }, "azure_ai/FW-Inkling": { - "cache_read_input_token_cost": 1.7e-07, - "input_cost_per_token": 1e-06, + "cache_read_input_token_cost": 1.9e-07, + "input_cost_per_token": 1.1e-06, "litellm_provider": "azure_ai", "max_input_tokens": 1048576, "max_output_tokens": 1048576, "max_tokens": 1048576, "mode": "chat", - "output_cost_per_token": 4.05e-06, - "source": "https://fireworks.ai/models/fireworks/inkling", + "output_cost_per_token": 4.46e-06, + "source": "https://prices.azure.com/api/retail/prices?$filter=serviceName%20eq%20'Foundry%20Models'%20and%20armRegionName%20eq%20'eastus2'%20and%20priceType%20eq%20'Consumption'", "supported_modalities": [ "text" ], @@ -10045,7 +10386,7 @@ "max_tokens": 262144, "mode": "chat", "output_cost_per_token": 3.3e-06, - "source": "https://azure.microsoft.com/en-us/pricing/details/ai-foundry-models/fireworks/", + "source": "https://prices.azure.com/api/retail/prices?$filter=serviceName%20eq%20'Foundry%20Models'%20and%20armRegionName%20eq%20'eastus2'%20and%20priceType%20eq%20'Consumption'", "supported_modalities": [ "text", "image" @@ -10068,7 +10409,7 @@ "max_tokens": 262144, "mode": "chat", "output_cost_per_token": 4.4e-06, - "source": "https://azure.microsoft.com/en-us/pricing/details/ai-foundry-models/fireworks/", + "source": "https://prices.azure.com/api/retail/prices?$filter=serviceName%20eq%20'Foundry%20Models'%20and%20armRegionName%20eq%20'eastus2'%20and%20priceType%20eq%20'Consumption'", "supported_modalities": [ "text", "image" @@ -10091,7 +10432,7 @@ "max_tokens": 262144, "mode": "chat", "output_cost_per_token": 4.4e-06, - "source": "https://azure.microsoft.com/en-us/pricing/details/ai-foundry-models/fireworks/", + "source": "https://prices.azure.com/api/retail/prices?$filter=serviceName%20eq%20'Foundry%20Models'%20and%20armRegionName%20eq%20'eastus2'%20and%20priceType%20eq%20'Consumption'", "supported_modalities": [ "text", "image" @@ -10119,7 +10460,7 @@ "high", "max" ], - "source": "https://techcommunity.microsoft.com/blog/azure-ai-foundry-blog/introducing-kimi-k3-through-fireworks-ai-on-microsoft-foundry/4540187", + "source": "https://prices.azure.com/api/retail/prices?$filter=serviceName%20eq%20'Foundry%20Models'%20and%20armRegionName%20eq%20'eastus2'%20and%20priceType%20eq%20'Consumption'", "supported_modalities": [ "text", "image" @@ -10143,7 +10484,7 @@ "max_tokens": 1000000, "mode": "chat", "output_cost_per_token": 1.32e-06, - "source": "https://azure.microsoft.com/en-us/pricing/details/ai-foundry-models/fireworks/", + "source": "https://prices.azure.com/api/retail/prices?$filter=serviceName%20eq%20'Foundry%20Models'%20and%20armRegionName%20eq%20'eastus2'%20and%20priceType%20eq%20'Consumption'", "supports_function_calling": true, "supports_prompt_caching": true, "supports_reasoning": true, @@ -10158,7 +10499,7 @@ "max_tokens": 512000, "mode": "chat", "output_cost_per_token": 1.32e-06, - "source": "https://azure.microsoft.com/en-us/pricing/details/ai-foundry-models/fireworks/", + "source": "https://prices.azure.com/api/retail/prices?$filter=serviceName%20eq%20'Foundry%20Models'%20and%20armRegionName%20eq%20'eastus2'%20and%20priceType%20eq%20'Consumption'", "supported_modalities": [ "text", "image" @@ -10179,7 +10520,7 @@ "max_input_tokens": 262144, "mode": "chat", "output_cost_per_token": 2.2e-07, - "source": "https://azure.microsoft.com/en-us/pricing/details/ai-foundry-models/fireworks/", + "source": "https://prices.azure.com/api/retail/prices?$filter=serviceName%20eq%20'Foundry%20Models'%20and%20armRegionName%20eq%20'eastus2'%20and%20priceType%20eq%20'Consumption'", "supported_modalities": [ "text" ], @@ -10193,15 +10534,15 @@ "supports_vision": false }, "azure_ai/FW-Nemotron-3-Ultra-NVFP4": { - "cache_read_input_token_cost": 1.19e-07, - "input_cost_per_token": 6e-07, + "cache_read_input_token_cost": 1.3e-07, + "input_cost_per_token": 6.6e-07, "litellm_provider": "azure_ai", "max_input_tokens": 262144, "max_output_tokens": 262144, "max_tokens": 262144, "mode": "chat", - "output_cost_per_token": 2.4e-06, - "source": "https://fireworks.ai/models/fireworks/nemotron-3-ultra-nvfp4", + "output_cost_per_token": 2.64e-06, + "source": "https://prices.azure.com/api/retail/prices?$filter=serviceName%20eq%20'Foundry%20Models'%20and%20armRegionName%20eq%20'eastus2'%20and%20priceType%20eq%20'Consumption'", "supported_modalities": [ "text" ], @@ -10220,7 +10561,7 @@ "mode": "image_generation", "output_cost_per_image": 0.05, "output_cost_per_image_token": 4.7e-05, - "source": "https://techcommunity.microsoft.com/blog/azure-ai-foundry-blog/new-mai-models-in-microsoft-foundry-across-text-image-voice-and-speech/4524632", + "source": "https://prices.azure.com/api/retail/prices?$filter=serviceName%20eq%20'Foundry%20Models'%20and%20armRegionName%20eq%20'eastus2'%20and%20priceType%20eq%20'Consumption'", "supported_endpoints": [ "/v1/images/generations", "/v1/images/edits" @@ -10234,7 +10575,7 @@ "mode": "image_generation", "output_cost_per_image": 0.0338, "output_cost_per_image_token": 3.3e-05, - "source": "https://techcommunity.microsoft.com/blog/azure-ai-foundry-blog/new-mai-models-in-microsoft-foundry-across-text-image-voice-and-speech/4524632", + "source": "https://prices.azure.com/api/retail/prices?$filter=serviceName%20eq%20'Foundry%20Models'%20and%20armRegionName%20eq%20'eastus2'%20and%20priceType%20eq%20'Consumption'", "supported_endpoints": [ "/v1/images/generations", "/v1/images/edits" @@ -10248,7 +10589,7 @@ "mode": "image_generation", "output_cost_per_image": 0.02, "output_cost_per_image_token": 1.95e-05, - "source": "https://aka.ms/mai-image-2e-foundryblog", + "source": "https://prices.azure.com/api/retail/prices?$filter=serviceName%20eq%20'Foundry%20Models'%20and%20armRegionName%20eq%20'eastus2'%20and%20priceType%20eq%20'Consumption'", "supported_endpoints": [ "/v1/images/generations" ] @@ -10262,7 +10603,7 @@ "max_tokens": 64000, "mode": "chat", "output_cost_per_token": 8e-06, - "source": "https://learn.microsoft.com/en-us/azure/foundry/foundry-models/concepts/models-sold-directly-by-azure", + "source": "https://prices.azure.com/api/retail/prices?$filter=serviceName%20eq%20'Foundry%20Models'%20and%20armRegionName%20eq%20'eastus2'%20and%20priceType%20eq%20'Consumption'", "supported_endpoints": [ "/v1/chat/completions" ], @@ -10313,19 +10654,19 @@ "max_tokens": 2048, "mode": "chat", "output_cost_per_token": 7.1e-07, - "source": "https://marketplace.microsoft.com/en/marketplace/apps/metagenai.llama-3-3-70b-instruct-offer?tab=Overview", + "source": "https://prices.azure.com/api/retail/prices?$filter=serviceName%20eq%20'Foundry%20Models'%20and%20armRegionName%20eq%20'eastus2'%20and%20priceType%20eq%20'Consumption'", "supports_function_calling": true, "supports_tool_choice": true }, "azure_ai/Llama-4-Maverick-17B-128E-Instruct-FP8": { - "input_cost_per_token": 1.41e-06, + "input_cost_per_token": 2.5e-07, "litellm_provider": "azure_ai", "max_input_tokens": 1000000, "max_output_tokens": 16384, "max_tokens": 16384, "mode": "chat", - "output_cost_per_token": 3.5e-07, - "source": "https://azure.microsoft.com/en-us/blog/introducing-the-llama-4-herd-in-azure-ai-foundry-and-azure-databricks/", + "output_cost_per_token": 1e-06, + "source": "https://prices.azure.com/api/retail/prices?$filter=serviceName%20eq%20'Foundry%20Models'%20and%20armRegionName%20eq%20'eastus2'%20and%20priceType%20eq%20'Consumption'", "supports_function_calling": true, "supports_tool_choice": true, "supports_vision": true @@ -10396,7 +10737,7 @@ "max_tokens": 4096, "mode": "chat", "output_cost_per_token": 6.8e-07, - "source": "https://azure.microsoft.com/en-us/pricing/details/phi-3/", + "source": "https://prices.azure.com/api/retail/prices?$filter=serviceName%20eq%20'Foundry%20Models'%20and%20armRegionName%20eq%20'eastus2'%20and%20priceType%20eq%20'Consumption'", "supports_tool_choice": true, "supports_vision": false }, @@ -10408,7 +10749,7 @@ "max_tokens": 4096, "mode": "chat", "output_cost_per_token": 6.8e-07, - "source": "https://azure.microsoft.com/en-us/pricing/details/phi-3/", + "source": "https://prices.azure.com/api/retail/prices?$filter=serviceName%20eq%20'Foundry%20Models'%20and%20armRegionName%20eq%20'eastus2'%20and%20priceType%20eq%20'Consumption'", "supports_tool_choice": true, "supports_vision": false }, @@ -10420,7 +10761,7 @@ "max_tokens": 4096, "mode": "chat", "output_cost_per_token": 5.2e-07, - "source": "https://azure.microsoft.com/en-us/pricing/details/phi-3/", + "source": "https://prices.azure.com/api/retail/prices?$filter=serviceName%20eq%20'Foundry%20Models'%20and%20armRegionName%20eq%20'eastus2'%20and%20priceType%20eq%20'Consumption'", "supports_tool_choice": true, "supports_vision": false }, @@ -10432,7 +10773,7 @@ "max_tokens": 4096, "mode": "chat", "output_cost_per_token": 5.2e-07, - "source": "https://azure.microsoft.com/en-us/pricing/details/phi-3/", + "source": "https://prices.azure.com/api/retail/prices?$filter=serviceName%20eq%20'Foundry%20Models'%20and%20armRegionName%20eq%20'eastus2'%20and%20priceType%20eq%20'Consumption'", "supports_tool_choice": true, "supports_vision": false }, @@ -10444,7 +10785,7 @@ "max_tokens": 4096, "mode": "chat", "output_cost_per_token": 6e-07, - "source": "https://azure.microsoft.com/en-us/pricing/details/phi-3/", + "source": "https://prices.azure.com/api/retail/prices?$filter=serviceName%20eq%20'Foundry%20Models'%20and%20armRegionName%20eq%20'eastus2'%20and%20priceType%20eq%20'Consumption'", "supports_tool_choice": true, "supports_vision": false }, @@ -10456,7 +10797,7 @@ "max_tokens": 4096, "mode": "chat", "output_cost_per_token": 6e-07, - "source": "https://azure.microsoft.com/en-us/pricing/details/phi-3/", + "source": "https://prices.azure.com/api/retail/prices?$filter=serviceName%20eq%20'Foundry%20Models'%20and%20armRegionName%20eq%20'eastus2'%20and%20priceType%20eq%20'Consumption'", "supports_tool_choice": true, "supports_vision": false }, @@ -10468,7 +10809,7 @@ "max_tokens": 4096, "mode": "chat", "output_cost_per_token": 6.4e-07, - "source": "https://azure.microsoft.com/en-us/pricing/details/phi-3/", + "source": "https://prices.azure.com/api/retail/prices?$filter=serviceName%20eq%20'Foundry%20Models'%20and%20armRegionName%20eq%20'eastus2'%20and%20priceType%20eq%20'Consumption'", "supports_tool_choice": true, "supports_vision": false }, @@ -10480,7 +10821,7 @@ "max_tokens": 4096, "mode": "chat", "output_cost_per_token": 5.2e-07, - "source": "https://azure.microsoft.com/en-us/pricing/details/phi-3/", + "source": "https://prices.azure.com/api/retail/prices?$filter=serviceName%20eq%20'Foundry%20Models'%20and%20armRegionName%20eq%20'eastus2'%20and%20priceType%20eq%20'Consumption'", "supports_tool_choice": true, "supports_vision": false }, @@ -10492,7 +10833,7 @@ "max_tokens": 4096, "mode": "chat", "output_cost_per_token": 5.2e-07, - "source": "https://azure.microsoft.com/en-us/pricing/details/phi-3/", + "source": "https://prices.azure.com/api/retail/prices?$filter=serviceName%20eq%20'Foundry%20Models'%20and%20armRegionName%20eq%20'eastus2'%20and%20priceType%20eq%20'Consumption'", "supports_tool_choice": true, "supports_vision": true }, @@ -10504,7 +10845,7 @@ "max_tokens": 16384, "mode": "chat", "output_cost_per_token": 5e-07, - "source": "https://techcommunity.microsoft.com/blog/machinelearningblog/affordable-innovation-unveiling-the-pricing-of-phi-3-slms-on-models-as-a-service/4156495", + "source": "https://prices.azure.com/api/retail/prices?$filter=serviceName%20eq%20'Foundry%20Models'%20and%20armRegionName%20eq%20'eastus2'%20and%20priceType%20eq%20'Consumption'", "supports_function_calling": true, "supports_tool_choice": true, "supports_vision": false @@ -10517,7 +10858,7 @@ "max_tokens": 4096, "mode": "chat", "output_cost_per_token": 3e-07, - "source": "https://techcommunity.microsoft.com/blog/Azure-AI-Services-blog/announcing-new-phi-pricing-empowering-your-business-with-small-language-models/4395112", + "source": "https://prices.azure.com/api/retail/prices?$filter=serviceName%20eq%20'Foundry%20Models'%20and%20armRegionName%20eq%20'eastus2'%20and%20priceType%20eq%20'Consumption'", "supports_function_calling": true }, "azure_ai/Phi-4-multimodal-instruct": { @@ -10529,20 +10870,20 @@ "max_tokens": 4096, "mode": "chat", "output_cost_per_token": 3.2e-07, - "source": "https://techcommunity.microsoft.com/blog/Azure-AI-Services-blog/announcing-new-phi-pricing-empowering-your-business-with-small-language-models/4395112", + "source": "https://prices.azure.com/api/retail/prices?$filter=serviceName%20eq%20'Foundry%20Models'%20and%20armRegionName%20eq%20'eastus2'%20and%20priceType%20eq%20'Consumption'", "supports_audio_input": true, "supports_function_calling": true, "supports_vision": true }, "azure_ai/Phi-4-mini-reasoning": { - "input_cost_per_token": 8e-08, + "input_cost_per_token": 7.5e-08, "litellm_provider": "azure_ai", "max_input_tokens": 131072, "max_output_tokens": 4096, "max_tokens": 4096, "mode": "chat", - "output_cost_per_token": 3.2e-07, - "source": "https://azure.microsoft.com/en-us/pricing/details/ai-foundry-models/microsoft/", + "output_cost_per_token": 3e-07, + "source": "https://prices.azure.com/api/retail/prices?$filter=serviceName%20eq%20'Foundry%20Models'%20and%20armRegionName%20eq%20'eastus2'%20and%20priceType%20eq%20'Consumption'", "supports_function_calling": true }, "azure_ai/Phi-4-reasoning": { @@ -10553,7 +10894,7 @@ "max_tokens": 4096, "mode": "chat", "output_cost_per_token": 5e-07, - "source": "https://azure.microsoft.com/en-us/pricing/details/ai-foundry-models/microsoft/", + "source": "https://prices.azure.com/api/retail/prices?$filter=serviceName%20eq%20'Foundry%20Models'%20and%20armRegionName%20eq%20'eastus2'%20and%20priceType%20eq%20'Consumption'", "supports_function_calling": true, "supports_tool_choice": true, "supports_reasoning": true @@ -10605,7 +10946,7 @@ "max_tokens": 8182, "mode": "chat", "output_cost_per_token": 1e-05, - "source": "https://azure.microsoft.com/en-us/pricing/details/ai-foundry-models/cohere/", + "source": "https://prices.azure.com/api/retail/prices?$filter=serviceName%20eq%20'Foundry%20Models'%20and%20armRegionName%20eq%20'eastus2'%20and%20priceType%20eq%20'Consumption'", "supports_function_calling": true, "supports_tool_choice": true }, @@ -10644,7 +10985,7 @@ "max_tokens": 8192, "mode": "chat", "output_cost_per_token": 5.4e-06, - "source": "https://azure.microsoft.com/en-us/pricing/details/ai-foundry-models/microsoft/", + "source": "https://prices.azure.com/api/retail/prices?$filter=serviceName%20eq%20'Foundry%20Models'%20and%20armRegionName%20eq%20'eastus2'%20and%20priceType%20eq%20'Consumption'", "supports_reasoning": true, "supports_tool_choice": true }, @@ -10709,7 +11050,7 @@ "max_tokens": 163840, "mode": "chat", "output_cost_per_token": 1.68e-06, - "source": "https://techcommunity.microsoft.com/blog/azure-ai-foundry-blog/introducing-deepseek-v3-2-and-deepseek-v3-2-speciale-in-microsoft-foundry/4477549", + "source": "https://prices.azure.com/api/retail/prices?$filter=serviceName%20eq%20'Foundry%20Models'%20and%20armRegionName%20eq%20'eastus2'%20and%20priceType%20eq%20'Consumption'", "supports_assistant_prefill": true, "supports_function_calling": true, "supports_prompt_caching": true, @@ -10724,7 +11065,7 @@ "max_tokens": 163840, "mode": "chat", "output_cost_per_token": 1.68e-06, - "source": "https://techcommunity.microsoft.com/blog/azure-ai-foundry-blog/introducing-deepseek-v3-2-and-deepseek-v3-2-speciale-in-microsoft-foundry/4477549", + "source": "https://prices.azure.com/api/retail/prices?$filter=serviceName%20eq%20'Foundry%20Models'%20and%20armRegionName%20eq%20'eastus2'%20and%20priceType%20eq%20'Consumption'", "supports_assistant_prefill": true, "supports_function_calling": true, "supports_prompt_caching": true, @@ -10740,7 +11081,7 @@ "max_tokens": 8192, "mode": "chat", "output_cost_per_token": 5.4e-06, - "source": "https://techcommunity.microsoft.com/blog/machinelearningblog/deepseek-r1-improved-performance-higher-limits-and-transparent-pricing/4386367", + "source": "https://prices.azure.com/api/retail/prices?$filter=serviceName%20eq%20'Foundry%20Models'%20and%20armRegionName%20eq%20'eastus2'%20and%20priceType%20eq%20'Consumption'", "supports_reasoning": true, "supports_tool_choice": true }, @@ -10752,7 +11093,7 @@ "max_tokens": 8192, "mode": "chat", "output_cost_per_token": 4.56e-06, - "source": "https://techcommunity.microsoft.com/blog/machinelearningblog/announcing-deepseek-v3-on-azure-ai-foundry-and-github/4390438", + "source": "https://prices.azure.com/api/retail/prices?$filter=serviceName%20eq%20'Foundry%20Models'%20and%20armRegionName%20eq%20'eastus2'%20and%20priceType%20eq%20'Consumption'", "supports_tool_choice": true }, "azure_ai/deepseek-v3-0324": { @@ -10764,7 +11105,7 @@ "max_tokens": 8192, "mode": "chat", "output_cost_per_token": 4.56e-06, - "source": "https://techcommunity.microsoft.com/blog/machinelearningblog/announcing-deepseek-v3-on-azure-ai-foundry-and-github/4390438", + "source": "https://prices.azure.com/api/retail/prices?$filter=serviceName%20eq%20'Foundry%20Models'%20and%20armRegionName%20eq%20'eastus2'%20and%20priceType%20eq%20'Consumption'", "supports_function_calling": true, "supports_tool_choice": true }, @@ -10777,7 +11118,7 @@ "max_tokens": 131072, "mode": "chat", "output_cost_per_token": 4.94e-06, - "source": "https://azure.microsoft.com/en-us/pricing/details/ai-foundry-models/deepseek/", + "source": "https://prices.azure.com/api/retail/prices?$filter=serviceName%20eq%20'Foundry%20Models'%20and%20armRegionName%20eq%20'eastus2'%20and%20priceType%20eq%20'Consumption'", "supports_function_calling": true, "supports_reasoning": true, "supports_tool_choice": true @@ -10791,7 +11132,7 @@ "max_tokens": 384000, "mode": "chat", "output_cost_per_token": 3.48e-06, - "source": "https://azure.microsoft.com/en-us/pricing/details/ai-foundry-models/deepseek/", + "source": "https://prices.azure.com/api/retail/prices?$filter=serviceName%20eq%20'Foundry%20Models'%20and%20armRegionName%20eq%20'eastus2'%20and%20priceType%20eq%20'Consumption'", "supports_function_calling": true, "supports_reasoning": true, "supports_tool_choice": true, @@ -10807,7 +11148,7 @@ "max_tokens": 384000, "mode": "chat", "output_cost_per_token": 5.1e-07, - "source": "https://azure.microsoft.com/en-us/pricing/details/ai-foundry-models/deepseek/", + "source": "https://prices.azure.com/api/retail/prices?$filter=serviceName%20eq%20'Foundry%20Models'%20and%20armRegionName%20eq%20'eastus2'%20and%20priceType%20eq%20'Consumption'", "supports_function_calling": true, "supports_reasoning": true, "supports_tool_choice": true, @@ -10824,7 +11165,7 @@ "max_tokens": 128000, "mode": "chat", "output_cost_per_token": 1.32e-06, - "source": "https://azure.microsoft.com/en-us/pricing/details/ai-foundry-models/deepseek/", + "source": "https://prices.azure.com/api/retail/prices?$filter=serviceName%20eq%20'Foundry%20Models'%20and%20armRegionName%20eq%20'eastus2'%20and%20priceType%20eq%20'Consumption'", "supports_function_calling": true, "supports_prompt_caching": true, "supports_reasoning": true, @@ -10838,7 +11179,7 @@ "mode": "embedding", "output_cost_per_token": 0.0, "output_vector_size": 3072, - "source": "https://marketplace.microsoft.com/pt-br/marketplace/apps/cohere.cohere-embed-4-offer?tab=PlansAndPrice", + "source": "https://prices.azure.com/api/retail/prices?$filter=serviceName%20eq%20'Foundry%20Models'%20and%20armRegionName%20eq%20'eastus2'%20and%20priceType%20eq%20'Consumption'", "supported_endpoints": [ "/v1/embeddings" ], @@ -10857,7 +11198,7 @@ "max_tokens": 131072, "mode": "chat", "output_cost_per_token": 1.5e-05, - "source": "https://devblogs.microsoft.com/foundry/announcing-grok-3-and-grok-3-mini-on-azure-ai-foundry/", + "source": "https://prices.azure.com/api/retail/prices?$filter=serviceName%20eq%20'Foundry%20Models'%20and%20armRegionName%20eq%20'eastus2'%20and%20priceType%20eq%20'Consumption'", "supports_function_calling": true, "supports_response_schema": false, "supports_tool_choice": true, @@ -10872,7 +11213,7 @@ "max_tokens": 131072, "mode": "chat", "output_cost_per_token": 1.27e-06, - "source": "https://devblogs.microsoft.com/foundry/announcing-grok-3-and-grok-3-mini-on-azure-ai-foundry/", + "source": "https://prices.azure.com/api/retail/prices?$filter=serviceName%20eq%20'Foundry%20Models'%20and%20armRegionName%20eq%20'eastus2'%20and%20priceType%20eq%20'Consumption'", "supports_function_calling": true, "supports_reasoning": true, "supports_response_schema": false, @@ -10888,7 +11229,7 @@ "max_tokens": 131072, "mode": "chat", "output_cost_per_token": 1.5e-05, - "source": "https://azure.microsoft.com/en-us/pricing/details/ai-foundry-models/grok/", + "source": "https://prices.azure.com/api/retail/prices?$filter=serviceName%20eq%20'Foundry%20Models'%20and%20armRegionName%20eq%20'eastus2'%20and%20priceType%20eq%20'Consumption'", "supports_function_calling": true, "supports_response_schema": false, "supports_tool_choice": true, @@ -10903,7 +11244,7 @@ "max_tokens": 131072, "mode": "chat", "output_cost_per_token": 1.27e-06, - "source": "https://azure.microsoft.com/en-us/pricing/details/ai-foundry-models/grok/", + "source": "https://prices.azure.com/api/retail/prices?$filter=serviceName%20eq%20'Foundry%20Models'%20and%20armRegionName%20eq%20'eastus2'%20and%20priceType%20eq%20'Consumption'", "supports_function_calling": true, "supports_reasoning": true, "supports_response_schema": false, @@ -10918,7 +11259,7 @@ "max_tokens": 131072, "mode": "chat", "output_cost_per_token": 1.5e-05, - "source": "https://azure.microsoft.com/en-us/pricing/details/ai-foundry-models/grok/", + "source": "https://prices.azure.com/api/retail/prices?$filter=serviceName%20eq%20'Foundry%20Models'%20and%20armRegionName%20eq%20'eastus2'%20and%20priceType%20eq%20'Consumption'", "supports_function_calling": true, "supports_response_schema": true, "supports_tool_choice": true, @@ -10933,7 +11274,7 @@ "max_tokens": 200000, "mode": "chat", "output_cost_per_token": 2.5e-06, - "source": "https://techcommunity.microsoft.com/blog/azure-ai-foundry-blog/introducing-grok-4-3-on-microsoft-foundry-latest-generation-agentic-capabilities/4517096", + "source": "https://prices.azure.com/api/retail/prices?$filter=serviceName%20eq%20'Foundry%20Models'%20and%20armRegionName%20eq%20'eastus2'%20and%20priceType%20eq%20'Consumption'", "supports_function_calling": true, "supports_prompt_caching": true, "supports_reasoning": true, @@ -10951,7 +11292,7 @@ "max_tokens": 128000, "mode": "chat", "output_cost_per_token": 6e-06, - "source": "https://techcommunity.microsoft.com/blog/azure-ai-foundry-blog/grok-4-6-comes-to-microsoft-foundry-models-built-for-long-horizon-reasoning-and-/4547578", + "source": "https://prices.azure.com/api/retail/prices?$filter=serviceName%20eq%20'Foundry%20Models'%20and%20armRegionName%20eq%20'eastus2'%20and%20priceType%20eq%20'Consumption'", "supports_function_calling": true, "supports_prompt_caching": true, "supports_reasoning": true, @@ -10970,7 +11311,7 @@ "max_tokens": 8192, "mode": "chat", "output_cost_per_token": 2.5e-06, - "source": "https://azure.microsoft.com/en-us/pricing/details/ai-foundry-models/grok/", + "source": "https://prices.azure.com/api/retail/prices?$filter=serviceName%20eq%20'Foundry%20Models'%20and%20armRegionName%20eq%20'eastus2'%20and%20priceType%20eq%20'Consumption'", "supports_function_calling": true, "supports_response_schema": true, "supports_tool_choice": true, @@ -10988,7 +11329,7 @@ "max_tokens": 8192, "mode": "chat", "output_cost_per_token": 2.5e-06, - "source": "https://azure.microsoft.com/en-us/pricing/details/ai-foundry-models/grok/", + "source": "https://prices.azure.com/api/retail/prices?$filter=serviceName%20eq%20'Foundry%20Models'%20and%20armRegionName%20eq%20'eastus2'%20and%20priceType%20eq%20'Consumption'", "supports_function_calling": true, "supports_response_schema": true, "supports_tool_choice": true, @@ -11004,6 +11345,7 @@ "max_output_tokens": 131072, "max_tokens": 131072, "mode": "chat", + "source": "https://prices.azure.com/api/retail/prices?$filter=serviceName%20eq%20'Foundry%20Models'%20and%20armRegionName%20eq%20'eastus2'%20and%20priceType%20eq%20'Consumption'", "supports_function_calling": true, "supports_response_schema": true, "supports_tool_choice": true, @@ -11018,7 +11360,7 @@ "max_output_tokens": 131072, "max_tokens": 131072, "mode": "chat", - "source": "https://azure.microsoft.com/en-us/pricing/details/ai-foundry-models/grok/", + "source": "https://prices.azure.com/api/retail/prices?$filter=serviceName%20eq%20'Foundry%20Models'%20and%20armRegionName%20eq%20'eastus2'%20and%20priceType%20eq%20'Consumption'", "supports_function_calling": true, "supports_response_schema": true, "supports_tool_choice": true, @@ -11032,7 +11374,7 @@ "max_output_tokens": 131072, "max_tokens": 131072, "mode": "chat", - "source": "https://techcommunity.microsoft.com/t5/Azure-AI-Foundry-Blog/Grok-4-0-Goes-GA-in-Microsoft-Foundry-and-Grok-4-1-Fast-Arrives/ba-p/4497964", + "source": "https://prices.azure.com/api/retail/prices?$filter=serviceName%20eq%20'Foundry%20Models'%20and%20armRegionName%20eq%20'eastus2'%20and%20priceType%20eq%20'Consumption'", "supports_function_calling": true, "supports_response_schema": true, "supports_tool_choice": true, @@ -11046,7 +11388,7 @@ "max_output_tokens": 131072, "max_tokens": 131072, "mode": "chat", - "source": "https://techcommunity.microsoft.com/t5/Azure-AI-Foundry-Blog/Grok-4-0-Goes-GA-in-Microsoft-Foundry-and-Grok-4-1-Fast-Arrives/ba-p/4497964", + "source": "https://prices.azure.com/api/retail/prices?$filter=serviceName%20eq%20'Foundry%20Models'%20and%20armRegionName%20eq%20'eastus2'%20and%20priceType%20eq%20'Consumption'", "supports_function_calling": true, "supports_reasoning": true, "supports_response_schema": true, @@ -11061,7 +11403,7 @@ "max_tokens": 131072, "mode": "chat", "output_cost_per_token": 1.5e-06, - "source": "https://azure.microsoft.com/en-us/pricing/details/ai-foundry-models/grok/", + "source": "https://prices.azure.com/api/retail/prices?$filter=serviceName%20eq%20'Foundry%20Models'%20and%20armRegionName%20eq%20'eastus2'%20and%20priceType%20eq%20'Consumption'", "supports_function_calling": true, "supports_response_schema": true, "supports_tool_choice": true, @@ -11096,7 +11438,7 @@ "max_tokens": 262144, "mode": "chat", "output_cost_per_token": 3e-06, - "source": "https://azure.microsoft.com/en-us/pricing/details/ai-foundry-models/kimi/", + "source": "https://prices.azure.com/api/retail/prices?$filter=serviceName%20eq%20'Foundry%20Models'%20and%20armRegionName%20eq%20'eastus2'%20and%20priceType%20eq%20'Consumption'", "supports_function_calling": true, "supports_tool_choice": true, "supports_video_input": true, @@ -11113,7 +11455,7 @@ "max_tokens": 262144, "mode": "chat", "output_cost_per_token": 4e-06, - "source": "https://azure.microsoft.com/en-us/pricing/details/ai-foundry-models/kimi/", + "source": "https://prices.azure.com/api/retail/prices?$filter=serviceName%20eq%20'Foundry%20Models'%20and%20armRegionName%20eq%20'eastus2'%20and%20priceType%20eq%20'Consumption'", "supported_modalities": [ "text", "image" @@ -11183,7 +11525,7 @@ "max_tokens": 8191, "mode": "chat", "output_cost_per_token": 1.5e-06, - "source": "https://azure.microsoft.com/en-us/blog/introducing-mistral-large-3-in-microsoft-foundry-open-capable-and-ready-for-production-workloads/", + "source": "https://prices.azure.com/api/retail/prices?$filter=serviceName%20eq%20'Foundry%20Models'%20and%20armRegionName%20eq%20'eastus2'%20and%20priceType%20eq%20'Consumption'", "supports_function_calling": true, "supports_tool_choice": true, "supports_vision": true @@ -61814,7 +62156,7 @@ "max_tokens": 262144, "mode": "chat", "output_cost_per_token": 4e-06, - "source": "https://azure.microsoft.com/en-us/pricing/details/ai-foundry-models/kimi/", + "source": 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"input_cost_per_token": 1.21e-06, + "input_cost_per_token_batches": 6.05e-07, + "litellm_provider": "azure", + "mode": "chat", + "output_cost_per_token": 4.84e-06, + "output_cost_per_token_batches": 2.42e-06, + "source": "https://prices.azure.com/api/retail/prices?$filter=serviceName%20eq%20'Foundry%20Models'%20and%20armRegionName%20eq%20'eastus2'%20and%20priceType%20eq%20'Consumption'" + }, + "azure/us/o1-preview": { + "cache_read_input_token_cost": 8.25e-06, + "input_cost_per_token": 1.65e-05, + "litellm_provider": "azure", + "mode": "chat", + "output_cost_per_token": 6.6e-05, + "source": "https://prices.azure.com/api/retail/prices?$filter=serviceName%20eq%20'Foundry%20Models'%20and%20armRegionName%20eq%20'eastus2'%20and%20priceType%20eq%20'Consumption'" + }, + "azure/us/o3-deep-research": { + "cache_read_input_token_cost": 2.75e-06, + "input_cost_per_token": 1.1e-05, + "litellm_provider": "azure", + "mode": "chat", + "output_cost_per_token": 4.4e-05, + "source": 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"https://prices.azure.com/api/retail/prices?$filter=serviceName%20eq%20'Foundry%20Models'%20and%20armRegionName%20eq%20'eastus2'%20and%20priceType%20eq%20'Consumption'" } } From 9f4fbcfe4142de1c5752dd61e7742482331612ad Mon Sep 17 00:00:00 2001 From: "berriai-litellm-provider-info-sync[bot]" <328147090+berriai-litellm-provider-info-sync[bot]@users.noreply.github.com> Date: Tue, 15 Sep 2026 18:51:21 +0000 Subject: [PATCH 071/225] chore(prices): sync Azure prices: 14 models [enrichment failed: Google Gemini, 34 held] azure/eu/gpt-5-2025-08-07: input_cost_per_token_batches, input_cost_per_token_priority, output_cost_per_token_batches, output_cost_per_token_priority, cache_read_input_token_cost_priority azure/eu/gpt-5-mini-2025-08-07: input_cost_per_token_batches, input_cost_per_token_priority, output_cost_per_token_batches, output_cost_per_token_priority, cache_read_input_token_cost_priority azure/eu/gpt-5-nano-2025-08-07: input_cost_per_token_batches, output_cost_per_token_batches azure/eu/o1-mini-2024-09-12: azure/eu/o1-preview-2024-09-12: azure/o1-mini-2024-09-12: input_cost_per_token_batches, output_cost_per_token_batches azure/us/gpt-4.1-2025-04-14: input_cost_per_token_priority, output_cost_per_token_priority, cache_read_input_token_cost_priority azure/us/gpt-4.1-mini-2025-04-14: input_cost_per_token_priority, output_cost_per_token_priority, cache_read_input_token_cost_priority azure/us/gpt-4.1-nano-2025-04-14: cache_read_input_token_cost, input_cost_per_token_batches azure/us/gpt-5-2025-08-07: input_cost_per_token_batches, input_cost_per_token_priority, output_cost_per_token_batches, output_cost_per_token_priority, cache_read_input_token_cost_priority azure/us/gpt-5-mini-2025-08-07: input_cost_per_token_batches, input_cost_per_token_priority, output_cost_per_token_batches, output_cost_per_token_priority, cache_read_input_token_cost_priority azure/us/gpt-5-nano-2025-08-07: input_cost_per_token_batches, output_cost_per_token_batches azure/us/o1-mini-2024-09-12: azure/us/o1-preview-2024-09-12: --- ...odel_prices_and_context_window_backup.json | 50 ++++++++++++++++++- model_prices_and_context_window.json | 50 ++++++++++++++++++- 2 files changed, 96 insertions(+), 4 deletions(-) diff --git a/litellm/model_prices_and_context_window_backup.json b/litellm/model_prices_and_context_window_backup.json index 1d863977e1e..372ce19e740 100644 --- a/litellm/model_prices_and_context_window_backup.json +++ b/litellm/model_prices_and_context_window_backup.json @@ -4296,14 +4296,20 @@ }, "azure/eu/gpt-5-2025-08-07": { "cache_read_input_token_cost": 1.375e-07, + "cache_read_input_token_cost_priority": 2.75e-07, "deprecation_date": "2027-02-09", "input_cost_per_token": 1.375e-06, + "input_cost_per_token_batches": 6.875e-07, + "input_cost_per_token_priority": 2.75e-06, "litellm_provider": "azure", "max_input_tokens": 272000, "max_output_tokens": 128000, "max_tokens": 128000, "mode": "chat", "output_cost_per_token": 1.1e-05, + "output_cost_per_token_batches": 5.5e-06, + "output_cost_per_token_priority": 2.2e-05, + "source": "https://prices.azure.com/api/retail/prices?$filter=serviceName%20eq%20'Foundry%20Models'%20and%20armRegionName%20eq%20'eastus2'%20and%20priceType%20eq%20'Consumption'", "supported_endpoints": [ "/v1/chat/completions", "/v1/batch", @@ -4329,14 +4335,20 @@ }, "azure/eu/gpt-5-mini-2025-08-07": { "cache_read_input_token_cost": 2.75e-08, + "cache_read_input_token_cost_priority": 4.95e-08, "deprecation_date": "2027-02-09", "input_cost_per_token": 2.75e-07, + "input_cost_per_token_batches": 1.375e-07, + "input_cost_per_token_priority": 4.95e-07, "litellm_provider": "azure", "max_input_tokens": 272000, "max_output_tokens": 128000, "max_tokens": 128000, "mode": "chat", "output_cost_per_token": 2.2e-06, + "output_cost_per_token_batches": 1.1e-06, + "output_cost_per_token_priority": 3.96e-06, + "source": "https://prices.azure.com/api/retail/prices?$filter=serviceName%20eq%20'Foundry%20Models'%20and%20armRegionName%20eq%20'eastus2'%20and%20priceType%20eq%20'Consumption'", "supported_endpoints": [ "/v1/chat/completions", "/v1/batch", @@ -4507,12 +4519,15 @@ "cache_read_input_token_cost": 5.5e-09, "deprecation_date": "2027-02-09", "input_cost_per_token": 5.5e-08, + "input_cost_per_token_batches": 2.75e-08, "litellm_provider": "azure", "max_input_tokens": 272000, "max_output_tokens": 128000, "max_tokens": 128000, "mode": "chat", "output_cost_per_token": 4.4e-07, + "output_cost_per_token_batches": 2.2e-07, + "source": "https://prices.azure.com/api/retail/prices?$filter=serviceName%20eq%20'Foundry%20Models'%20and%20armRegionName%20eq%20'eastus2'%20and%20priceType%20eq%20'Consumption'", "supported_endpoints": [ "/v1/chat/completions", "/v1/batch", @@ -4566,6 +4581,7 @@ "mode": "chat", "output_cost_per_token": 4.84e-06, "output_cost_per_token_batches": 2.42e-06, + "source": "https://prices.azure.com/api/retail/prices?$filter=serviceName%20eq%20'Foundry%20Models'%20and%20armRegionName%20eq%20'eastus2'%20and%20priceType%20eq%20'Consumption'", "supports_function_calling": true, "supports_parallel_function_calling": true, "supports_prompt_caching": true, @@ -4580,6 +4596,7 @@ "max_tokens": 32768, "mode": "chat", "output_cost_per_token": 6.6e-05, + "source": "https://prices.azure.com/api/retail/prices?$filter=serviceName%20eq%20'Foundry%20Models'%20and%20armRegionName%20eq%20'eastus2'%20and%20priceType%20eq%20'Consumption'", "supports_function_calling": true, "supports_parallel_function_calling": true, "supports_prompt_caching": true, @@ -9213,12 +9230,15 @@ "azure/o1-mini-2024-09-12": { "cache_read_input_token_cost": 5.5e-07, "input_cost_per_token": 1.1e-06, + "input_cost_per_token_batches": 5.5e-07, "litellm_provider": "azure", "max_input_tokens": 128000, "max_output_tokens": 65536, "max_tokens": 65536, "mode": "chat", "output_cost_per_token": 4.4e-06, + "output_cost_per_token_batches": 2.2e-06, + "source": "https://prices.azure.com/api/retail/prices?$filter=serviceName%20eq%20'Foundry%20Models'%20and%20armRegionName%20eq%20'eastus2'%20and%20priceType%20eq%20'Consumption'", "supports_function_calling": true, "supports_parallel_function_calling": true, "supports_prompt_caching": true, @@ -9601,8 +9621,10 @@ "azure/us/gpt-4.1-2025-04-14": { "deprecation_date": "2027-04-14", "cache_read_input_token_cost": 5.5e-07, + "cache_read_input_token_cost_priority": 9.63e-07, "input_cost_per_token": 2.2e-06, "input_cost_per_token_batches": 1.1e-06, + "input_cost_per_token_priority": 3.85e-06, "litellm_provider": "azure", "max_input_tokens": 1047576, "max_output_tokens": 32768, @@ -9610,6 +9632,8 @@ "mode": "chat", "output_cost_per_token": 8.8e-06, "output_cost_per_token_batches": 4.4e-06, + "output_cost_per_token_priority": 1.54e-05, + "source": "https://prices.azure.com/api/retail/prices?$filter=serviceName%20eq%20'Foundry%20Models'%20and%20armRegionName%20eq%20'eastus2'%20and%20priceType%20eq%20'Consumption'", "supported_endpoints": [ "/v1/chat/completions", "/v1/batch", @@ -9635,8 +9659,10 @@ "azure/us/gpt-4.1-mini-2025-04-14": { "deprecation_date": "2027-04-14", "cache_read_input_token_cost": 1.1e-07, + "cache_read_input_token_cost_priority": 1.93e-07, "input_cost_per_token": 4.4e-07, "input_cost_per_token_batches": 2.2e-07, + "input_cost_per_token_priority": 7.7e-07, "litellm_provider": "azure", "max_input_tokens": 1047576, "max_output_tokens": 32768, @@ -9644,6 +9670,8 @@ "mode": "chat", "output_cost_per_token": 1.76e-06, "output_cost_per_token_batches": 8.8e-07, + "output_cost_per_token_priority": 3.08e-06, + "source": "https://prices.azure.com/api/retail/prices?$filter=serviceName%20eq%20'Foundry%20Models'%20and%20armRegionName%20eq%20'eastus2'%20and%20priceType%20eq%20'Consumption'", "supported_endpoints": [ "/v1/chat/completions", "/v1/batch", @@ -9668,9 +9696,9 @@ }, "azure/us/gpt-4.1-nano-2025-04-14": { "deprecation_date": "2027-04-14", - "cache_read_input_token_cost": 2.5e-08, + "cache_read_input_token_cost": 2.8e-08, "input_cost_per_token": 1.1e-07, - "input_cost_per_token_batches": 6e-08, + "input_cost_per_token_batches": 5.5e-08, "litellm_provider": "azure", "max_input_tokens": 1047576, "max_output_tokens": 32768, @@ -9678,6 +9706,7 @@ "mode": "chat", "output_cost_per_token": 4.4e-07, "output_cost_per_token_batches": 2.2e-07, + "source": "https://prices.azure.com/api/retail/prices?$filter=serviceName%20eq%20'Foundry%20Models'%20and%20armRegionName%20eq%20'eastus2'%20and%20priceType%20eq%20'Consumption'", "supported_endpoints": [ "/v1/chat/completions", "/v1/batch", @@ -9825,14 +9854,20 @@ }, "azure/us/gpt-5-2025-08-07": { "cache_read_input_token_cost": 1.375e-07, + "cache_read_input_token_cost_priority": 2.75e-07, "deprecation_date": "2027-02-09", "input_cost_per_token": 1.375e-06, + "input_cost_per_token_batches": 6.875e-07, + "input_cost_per_token_priority": 2.75e-06, "litellm_provider": "azure", "max_input_tokens": 272000, "max_output_tokens": 128000, "max_tokens": 128000, "mode": "chat", "output_cost_per_token": 1.1e-05, + "output_cost_per_token_batches": 5.5e-06, + "output_cost_per_token_priority": 2.2e-05, + "source": "https://prices.azure.com/api/retail/prices?$filter=serviceName%20eq%20'Foundry%20Models'%20and%20armRegionName%20eq%20'eastus2'%20and%20priceType%20eq%20'Consumption'", "supported_endpoints": [ "/v1/chat/completions", "/v1/batch", @@ -9858,14 +9893,20 @@ }, "azure/us/gpt-5-mini-2025-08-07": { "cache_read_input_token_cost": 2.75e-08, + "cache_read_input_token_cost_priority": 4.95e-08, "deprecation_date": "2027-02-09", "input_cost_per_token": 2.75e-07, + "input_cost_per_token_batches": 1.375e-07, + "input_cost_per_token_priority": 4.95e-07, "litellm_provider": "azure", "max_input_tokens": 272000, "max_output_tokens": 128000, "max_tokens": 128000, "mode": "chat", "output_cost_per_token": 2.2e-06, + "output_cost_per_token_batches": 1.1e-06, + "output_cost_per_token_priority": 3.96e-06, + "source": "https://prices.azure.com/api/retail/prices?$filter=serviceName%20eq%20'Foundry%20Models'%20and%20armRegionName%20eq%20'eastus2'%20and%20priceType%20eq%20'Consumption'", "supported_endpoints": [ "/v1/chat/completions", "/v1/batch", @@ -9893,12 +9934,15 @@ "cache_read_input_token_cost": 5.5e-09, "deprecation_date": "2027-02-09", "input_cost_per_token": 5.5e-08, + "input_cost_per_token_batches": 2.75e-08, "litellm_provider": "azure", "max_input_tokens": 272000, "max_output_tokens": 128000, "max_tokens": 128000, "mode": "chat", "output_cost_per_token": 4.4e-07, + "output_cost_per_token_batches": 2.2e-07, + "source": "https://prices.azure.com/api/retail/prices?$filter=serviceName%20eq%20'Foundry%20Models'%20and%20armRegionName%20eq%20'eastus2'%20and%20priceType%20eq%20'Consumption'", "supported_endpoints": [ "/v1/chat/completions", "/v1/batch", @@ -10095,6 +10139,7 @@ "mode": "chat", "output_cost_per_token": 4.84e-06, "output_cost_per_token_batches": 2.42e-06, + "source": "https://prices.azure.com/api/retail/prices?$filter=serviceName%20eq%20'Foundry%20Models'%20and%20armRegionName%20eq%20'eastus2'%20and%20priceType%20eq%20'Consumption'", "supports_function_calling": true, "supports_parallel_function_calling": true, "supports_prompt_caching": true, @@ -10109,6 +10154,7 @@ "max_tokens": 32768, "mode": "chat", "output_cost_per_token": 6.6e-05, + "source": "https://prices.azure.com/api/retail/prices?$filter=serviceName%20eq%20'Foundry%20Models'%20and%20armRegionName%20eq%20'eastus2'%20and%20priceType%20eq%20'Consumption'", "supports_function_calling": true, "supports_parallel_function_calling": true, "supports_prompt_caching": true, diff --git a/model_prices_and_context_window.json b/model_prices_and_context_window.json index 1d863977e1e..372ce19e740 100644 --- a/model_prices_and_context_window.json +++ b/model_prices_and_context_window.json @@ -4296,14 +4296,20 @@ }, "azure/eu/gpt-5-2025-08-07": { "cache_read_input_token_cost": 1.375e-07, + "cache_read_input_token_cost_priority": 2.75e-07, "deprecation_date": "2027-02-09", "input_cost_per_token": 1.375e-06, + "input_cost_per_token_batches": 6.875e-07, + "input_cost_per_token_priority": 2.75e-06, "litellm_provider": "azure", "max_input_tokens": 272000, "max_output_tokens": 128000, "max_tokens": 128000, "mode": "chat", "output_cost_per_token": 1.1e-05, + "output_cost_per_token_batches": 5.5e-06, + "output_cost_per_token_priority": 2.2e-05, + "source": "https://prices.azure.com/api/retail/prices?$filter=serviceName%20eq%20'Foundry%20Models'%20and%20armRegionName%20eq%20'eastus2'%20and%20priceType%20eq%20'Consumption'", "supported_endpoints": [ "/v1/chat/completions", "/v1/batch", @@ -4329,14 +4335,20 @@ }, "azure/eu/gpt-5-mini-2025-08-07": { "cache_read_input_token_cost": 2.75e-08, + "cache_read_input_token_cost_priority": 4.95e-08, "deprecation_date": "2027-02-09", "input_cost_per_token": 2.75e-07, + "input_cost_per_token_batches": 1.375e-07, + "input_cost_per_token_priority": 4.95e-07, "litellm_provider": "azure", "max_input_tokens": 272000, "max_output_tokens": 128000, "max_tokens": 128000, "mode": "chat", "output_cost_per_token": 2.2e-06, + "output_cost_per_token_batches": 1.1e-06, + "output_cost_per_token_priority": 3.96e-06, + "source": "https://prices.azure.com/api/retail/prices?$filter=serviceName%20eq%20'Foundry%20Models'%20and%20armRegionName%20eq%20'eastus2'%20and%20priceType%20eq%20'Consumption'", "supported_endpoints": [ "/v1/chat/completions", "/v1/batch", @@ -4507,12 +4519,15 @@ "cache_read_input_token_cost": 5.5e-09, "deprecation_date": "2027-02-09", "input_cost_per_token": 5.5e-08, + "input_cost_per_token_batches": 2.75e-08, "litellm_provider": "azure", "max_input_tokens": 272000, "max_output_tokens": 128000, "max_tokens": 128000, "mode": "chat", "output_cost_per_token": 4.4e-07, + "output_cost_per_token_batches": 2.2e-07, + "source": "https://prices.azure.com/api/retail/prices?$filter=serviceName%20eq%20'Foundry%20Models'%20and%20armRegionName%20eq%20'eastus2'%20and%20priceType%20eq%20'Consumption'", "supported_endpoints": [ "/v1/chat/completions", "/v1/batch", @@ -4566,6 +4581,7 @@ "mode": "chat", "output_cost_per_token": 4.84e-06, "output_cost_per_token_batches": 2.42e-06, + "source": "https://prices.azure.com/api/retail/prices?$filter=serviceName%20eq%20'Foundry%20Models'%20and%20armRegionName%20eq%20'eastus2'%20and%20priceType%20eq%20'Consumption'", "supports_function_calling": true, "supports_parallel_function_calling": true, "supports_prompt_caching": true, @@ -4580,6 +4596,7 @@ "max_tokens": 32768, "mode": "chat", "output_cost_per_token": 6.6e-05, + "source": "https://prices.azure.com/api/retail/prices?$filter=serviceName%20eq%20'Foundry%20Models'%20and%20armRegionName%20eq%20'eastus2'%20and%20priceType%20eq%20'Consumption'", "supports_function_calling": true, "supports_parallel_function_calling": true, "supports_prompt_caching": true, @@ -9213,12 +9230,15 @@ "azure/o1-mini-2024-09-12": { "cache_read_input_token_cost": 5.5e-07, "input_cost_per_token": 1.1e-06, + "input_cost_per_token_batches": 5.5e-07, "litellm_provider": "azure", "max_input_tokens": 128000, "max_output_tokens": 65536, "max_tokens": 65536, "mode": "chat", "output_cost_per_token": 4.4e-06, + "output_cost_per_token_batches": 2.2e-06, + "source": "https://prices.azure.com/api/retail/prices?$filter=serviceName%20eq%20'Foundry%20Models'%20and%20armRegionName%20eq%20'eastus2'%20and%20priceType%20eq%20'Consumption'", "supports_function_calling": true, "supports_parallel_function_calling": true, "supports_prompt_caching": true, @@ -9601,8 +9621,10 @@ "azure/us/gpt-4.1-2025-04-14": { "deprecation_date": "2027-04-14", "cache_read_input_token_cost": 5.5e-07, + "cache_read_input_token_cost_priority": 9.63e-07, "input_cost_per_token": 2.2e-06, "input_cost_per_token_batches": 1.1e-06, + "input_cost_per_token_priority": 3.85e-06, "litellm_provider": "azure", "max_input_tokens": 1047576, "max_output_tokens": 32768, @@ -9610,6 +9632,8 @@ "mode": "chat", "output_cost_per_token": 8.8e-06, "output_cost_per_token_batches": 4.4e-06, + "output_cost_per_token_priority": 1.54e-05, + "source": "https://prices.azure.com/api/retail/prices?$filter=serviceName%20eq%20'Foundry%20Models'%20and%20armRegionName%20eq%20'eastus2'%20and%20priceType%20eq%20'Consumption'", "supported_endpoints": [ "/v1/chat/completions", "/v1/batch", @@ -9635,8 +9659,10 @@ "azure/us/gpt-4.1-mini-2025-04-14": { "deprecation_date": "2027-04-14", "cache_read_input_token_cost": 1.1e-07, + "cache_read_input_token_cost_priority": 1.93e-07, "input_cost_per_token": 4.4e-07, "input_cost_per_token_batches": 2.2e-07, + "input_cost_per_token_priority": 7.7e-07, "litellm_provider": "azure", "max_input_tokens": 1047576, "max_output_tokens": 32768, @@ -9644,6 +9670,8 @@ "mode": "chat", "output_cost_per_token": 1.76e-06, "output_cost_per_token_batches": 8.8e-07, + "output_cost_per_token_priority": 3.08e-06, + "source": "https://prices.azure.com/api/retail/prices?$filter=serviceName%20eq%20'Foundry%20Models'%20and%20armRegionName%20eq%20'eastus2'%20and%20priceType%20eq%20'Consumption'", "supported_endpoints": [ "/v1/chat/completions", "/v1/batch", @@ -9668,9 +9696,9 @@ }, "azure/us/gpt-4.1-nano-2025-04-14": { "deprecation_date": "2027-04-14", - "cache_read_input_token_cost": 2.5e-08, + "cache_read_input_token_cost": 2.8e-08, "input_cost_per_token": 1.1e-07, - "input_cost_per_token_batches": 6e-08, + "input_cost_per_token_batches": 5.5e-08, "litellm_provider": "azure", "max_input_tokens": 1047576, "max_output_tokens": 32768, @@ -9678,6 +9706,7 @@ "mode": "chat", "output_cost_per_token": 4.4e-07, "output_cost_per_token_batches": 2.2e-07, + "source": "https://prices.azure.com/api/retail/prices?$filter=serviceName%20eq%20'Foundry%20Models'%20and%20armRegionName%20eq%20'eastus2'%20and%20priceType%20eq%20'Consumption'", "supported_endpoints": [ "/v1/chat/completions", "/v1/batch", @@ -9825,14 +9854,20 @@ }, "azure/us/gpt-5-2025-08-07": { "cache_read_input_token_cost": 1.375e-07, + "cache_read_input_token_cost_priority": 2.75e-07, "deprecation_date": "2027-02-09", "input_cost_per_token": 1.375e-06, + "input_cost_per_token_batches": 6.875e-07, + "input_cost_per_token_priority": 2.75e-06, "litellm_provider": "azure", "max_input_tokens": 272000, "max_output_tokens": 128000, "max_tokens": 128000, "mode": "chat", "output_cost_per_token": 1.1e-05, + "output_cost_per_token_batches": 5.5e-06, + "output_cost_per_token_priority": 2.2e-05, + "source": "https://prices.azure.com/api/retail/prices?$filter=serviceName%20eq%20'Foundry%20Models'%20and%20armRegionName%20eq%20'eastus2'%20and%20priceType%20eq%20'Consumption'", "supported_endpoints": [ "/v1/chat/completions", "/v1/batch", @@ -9858,14 +9893,20 @@ }, "azure/us/gpt-5-mini-2025-08-07": { "cache_read_input_token_cost": 2.75e-08, + "cache_read_input_token_cost_priority": 4.95e-08, "deprecation_date": "2027-02-09", "input_cost_per_token": 2.75e-07, + "input_cost_per_token_batches": 1.375e-07, + "input_cost_per_token_priority": 4.95e-07, "litellm_provider": "azure", "max_input_tokens": 272000, "max_output_tokens": 128000, "max_tokens": 128000, "mode": "chat", "output_cost_per_token": 2.2e-06, + "output_cost_per_token_batches": 1.1e-06, + "output_cost_per_token_priority": 3.96e-06, + "source": "https://prices.azure.com/api/retail/prices?$filter=serviceName%20eq%20'Foundry%20Models'%20and%20armRegionName%20eq%20'eastus2'%20and%20priceType%20eq%20'Consumption'", "supported_endpoints": [ "/v1/chat/completions", "/v1/batch", @@ -9893,12 +9934,15 @@ "cache_read_input_token_cost": 5.5e-09, "deprecation_date": "2027-02-09", "input_cost_per_token": 5.5e-08, + "input_cost_per_token_batches": 2.75e-08, "litellm_provider": "azure", "max_input_tokens": 272000, "max_output_tokens": 128000, "max_tokens": 128000, "mode": "chat", "output_cost_per_token": 4.4e-07, + "output_cost_per_token_batches": 2.2e-07, + "source": "https://prices.azure.com/api/retail/prices?$filter=serviceName%20eq%20'Foundry%20Models'%20and%20armRegionName%20eq%20'eastus2'%20and%20priceType%20eq%20'Consumption'", "supported_endpoints": [ "/v1/chat/completions", "/v1/batch", @@ -10095,6 +10139,7 @@ "mode": "chat", "output_cost_per_token": 4.84e-06, "output_cost_per_token_batches": 2.42e-06, + "source": "https://prices.azure.com/api/retail/prices?$filter=serviceName%20eq%20'Foundry%20Models'%20and%20armRegionName%20eq%20'eastus2'%20and%20priceType%20eq%20'Consumption'", "supports_function_calling": true, "supports_parallel_function_calling": true, "supports_prompt_caching": true, @@ -10109,6 +10154,7 @@ "max_tokens": 32768, "mode": "chat", "output_cost_per_token": 6.6e-05, + "source": "https://prices.azure.com/api/retail/prices?$filter=serviceName%20eq%20'Foundry%20Models'%20and%20armRegionName%20eq%20'eastus2'%20and%20priceType%20eq%20'Consumption'", "supports_function_calling": true, "supports_parallel_function_calling": true, "supports_prompt_caching": true, From 92e182b898a557849f374ba66af708999626806d Mon Sep 17 00:00:00 2001 From: Joshua Valluru <326636767+joshua-berri@users.noreply.github.com> Date: Tue, 15 Sep 2026 12:29:03 -0700 Subject: [PATCH 072/225] fix(mcp): persist OAuth credentials for validated JWT users --- .../mcp_server/bridge_token_flow.py | 56 ++++ .../mcp_server/discoverable_endpoints.py | 5 +- .../mcp_server/test_discoverable_endpoints.py | 277 +++++++++++++++++- 3 files changed, 335 insertions(+), 3 deletions(-) diff --git a/litellm/proxy/_experimental/mcp_server/bridge_token_flow.py b/litellm/proxy/_experimental/mcp_server/bridge_token_flow.py index 35a30127e27..8dcc49c2fd8 100644 --- a/litellm/proxy/_experimental/mcp_server/bridge_token_flow.py +++ b/litellm/proxy/_experimental/mcp_server/bridge_token_flow.py @@ -306,12 +306,68 @@ async def _extract_user_id_from_request(request: Request) -> str | None: (including a transient DB outage) collapses to ``None`` here and the caller simply skips the store; the bridge mint, which must status those outcomes differently, consumes :func:`_resolve_active_litellm_key` directly.""" + from litellm.proxy.auth.handle_jwt import JWTHandler # noqa: PLC0415 # proxy import cycle + + token: Final = _litellm_key_from_request(request) + if token is not None and JWTHandler.is_jwt(token): + return await _extract_jwt_user_id(token) resolved: Final = await _resolve_active_litellm_key(request) if not isinstance(resolved, _ResolvedKey): return None return _active_key_user_id(resolved.key) +async def _extract_jwt_user_id(token: str) -> str | None: + from litellm.proxy._types import LitellmUserRoles, UserAPIKeyAuth # noqa: PLC0415 # proxy import cycle + from litellm.proxy.auth.handle_jwt import JWTAuthManager # noqa: PLC0415 # proxy import cycle + from litellm.proxy.auth.user_api_key_auth import ( # noqa: PLC0415 # proxy import cycle + _resolve_jwt_to_virtual_key, # pyright: ignore[reportPrivateUsage] # reuse admission mapping policy without provisioning a new key + ) + from litellm.proxy.proxy_server import ( # noqa: PLC0415 # proxy globals initialized at startup + general_settings, + jwt_handler, + premium_user, + prisma_client, + proxy_logging_obj, + user_api_key_cache, + ) + + if general_settings.get("enable_jwt_auth") is not True or premium_user is not True: + return None + try: + claims: Final = await jwt_handler.auth_jwt(token=token) + validate: Final = jwt_handler.litellm_jwtauth.custom_validate + if validate is not None and not validate(claims): + return None + if jwt_handler.litellm_jwtauth.is_virtual_key_mapping_configured(): + mapped: Final = await _resolve_jwt_to_virtual_key( + jwt_claims=claims, + jwt_handler=jwt_handler, + prisma_client=prisma_client, + user_api_key_cache=user_api_key_cache, + parent_otel_span=None, + proxy_logging_obj=proxy_logging_obj, + ) + if isinstance(mapped, UserAPIKeyAuth): + return None if await _key_owner_scim_deactivated(mapped) else _active_key_user_id(mapped) + if mapped is not None: + return None + user_id, _, valid_email = await JWTAuthManager.get_user_info(jwt_handler, claims) + object_id: Final = jwt_handler.get_object_id(token=claims, default_value=None) + owner_id: Final = ( + object_id + if jwt_handler.get_rbac_role(token=claims) == LitellmUserRoles.INTERNAL_USER and object_id + else user_id + ) + if not owner_id or valid_email is False: + return None + owner: Final = await load_active_user_by_id(owner_id) + return None if isinstance(owner, str) else owner.user_id + except Exception as exc: # noqa: BLE001 # public OAuth exchange stays available; unvalidated identities never write credentials + verbose_logger.debug("OAuth JWT identity could not be validated (%s)", type(exc).__name__) + return None + + _UpstreamGrantRejection = Literal["no_access_token", "expired_lifetime"] """Why an upstream token response cannot back a bridge envelope: - ``no_access_token``: the response carries no usable ``access_token`` diff --git a/litellm/proxy/_experimental/mcp_server/discoverable_endpoints.py b/litellm/proxy/_experimental/mcp_server/discoverable_endpoints.py index bafe33d0a6b..94b6348b0f0 100644 --- a/litellm/proxy/_experimental/mcp_server/discoverable_endpoints.py +++ b/litellm/proxy/_experimental/mcp_server/discoverable_endpoints.py @@ -1236,8 +1236,9 @@ async def exchange_token_with_server( "exchange_token_with_server: could not resolve a LiteLLM user_id for the request, " "so the per-user token for server=%s was NOT stored. The authorization_code egress " "requires the stored token, so the client will be challenged with 401 on reconnect. " - "Ensure the request carries a valid LiteLLM key (x-litellm-api-key or Authorization), " - "or store it via POST /mcp/server/{id}/oauth-user-credential.", + "Ensure the request carries a valid LiteLLM key or enabled JWT identity " + "(x-litellm-api-key or Authorization), " + "or store it via POST /v1/mcp/server/{id}/oauth-user-credential.", resolved_server.server_id, ) diff --git a/tests/test_litellm/proxy/_experimental/mcp_server/test_discoverable_endpoints.py b/tests/test_litellm/proxy/_experimental/mcp_server/test_discoverable_endpoints.py index 9ea870d3210..af4c9eea770 100644 --- a/tests/test_litellm/proxy/_experimental/mcp_server/test_discoverable_endpoints.py +++ b/tests/test_litellm/proxy/_experimental/mcp_server/test_discoverable_endpoints.py @@ -5,7 +5,7 @@ import json import time from base64 import urlsafe_b64encode from datetime import datetime, timedelta, timezone -from typing import TYPE_CHECKING +from typing import TYPE_CHECKING, Final from unittest.mock import AsyncMock, MagicMock, patch import pytest @@ -15,6 +15,9 @@ from litellm.types.mcp import MCPAuth if TYPE_CHECKING: import httpx + from cryptography.hazmat.primitives.asymmetric.rsa import RSAPrivateKey + + from litellm.proxy.auth.handle_jwt import JWTHandler from litellm.types.mcp_server.mcp_server_manager import MCPServer @@ -11374,3 +11377,275 @@ with TestClient(app) as client: assert responses[path]["status"] == 200, responses[path] assert responses[path]["body"]["issuer"] == f"http://testserver/gateway/{path}" assert responses["example/mcp"]["body"]["token_endpoint"] == "http://testserver/gateway/example/token" + + +@pytest.fixture +def jwt_oauth_identity(monkeypatch: pytest.MonkeyPatch) -> tuple["JWTHandler", "RSAPrivateKey"]: + import jwt + from cryptography.hazmat.primitives.asymmetric import rsa + + from litellm.models.user import LiteLLM_UserTable + from litellm.proxy import proxy_server + from litellm.proxy._types import LiteLLM_JWTAuth + from litellm.proxy.auth.handle_jwt import JWTHandler + from litellm.proxy.common_utils.user_api_key_cache import UserApiKeyCache + + signing_key: Final = rsa.generate_private_key(public_exponent=65537, key_size=2048) + cache: Final = UserApiKeyCache() + cache.set_cache( + "litellm_jwt_auth_keys_https://idp.example.test/jwks", + [json.loads(jwt.algorithms.RSAAlgorithm.to_jwk(signing_key.public_key()))], + ) + cache.set_cache("jwt-owner", LiteLLM_UserTable(user_id="jwt-owner", user_email="owner@example.test")) + handler: Final = JWTHandler() + handler.update_environment( + prisma_client=None, + user_api_key_cache=cache, + litellm_jwtauth=LiteLLM_JWTAuth(user_id_jwt_field="identity.user_id"), + ) + monkeypatch.setenv("JWT_PUBLIC_KEY_URL", "https://idp.example.test/jwks") + monkeypatch.setenv("JWT_ISSUER", "https://idp.example.test") + monkeypatch.setenv("JWT_AUDIENCE", "litellm-proxy") + monkeypatch.setattr(proxy_server, "jwt_handler", handler) + monkeypatch.setattr(proxy_server, "general_settings", {"enable_jwt_auth": True}) + monkeypatch.setattr(proxy_server, "premium_user", True) + monkeypatch.setattr(proxy_server, "user_api_key_cache", cache) + monkeypatch.setattr(proxy_server, "prisma_client", MagicMock()) + return handler, signing_key + + +def _oauth_identity_jwt( + signing_key: "RSAPrivateKey", + *, + expires_in: int = 300, + audience: str = "litellm-proxy", + issuer: str = "https://idp.example.test", + owner: str | None = "jwt-owner", +) -> str: + import jwt + + return jwt.encode( + { + "sub": "not-the-configured-user-id", + "identity": {"user_id": owner}, + "iss": issuer, + "aud": audience, + "exp": int(time.time()) + expires_in, + }, + signing_key, + algorithm="RS256", + ) + + +@pytest.mark.asyncio +@pytest.mark.parametrize("header", ["Authorization", "x-litellm-api-key"]) +async def test_oauth_exchange_stores_token_for_validated_jwt_user( + jwt_oauth_identity: tuple["JWTHandler", "RSAPrivateKey"], + header: str, + monkeypatch: pytest.MonkeyPatch, +) -> None: + import httpx + + from litellm.proxy._experimental.mcp_server import discoverable_endpoints + from litellm.proxy._types import MCPTransport + from litellm.types.mcp_server.mcp_server_manager import MCPServer + + _, signing_key = jwt_oauth_identity + bearer: Final = _oauth_identity_jwt(signing_key) + request: Final = _token_request({header: f"Bearer {bearer}"}) + server: Final = MCPServer( + server_id="jwt-oauth-server", + name="jwt-oauth-server", + transport=MCPTransport.http, + auth_type=MCPAuth.oauth2, + oauth2_flow="authorization_code", + authorization_url="https://upstream.example.test/authorize", + token_url="https://upstream.example.test/token", + client_id="registered-client", + ) + import litellm + from litellm.caching.llm_caching_handler import LLMClientCache + from litellm.llms.custom_httpx.http_handler import AsyncHTTPHandler + from litellm.proxy import proxy_server + from litellm.proxy.common_utils.encrypt_decrypt_utils import decrypt_value_helper + from litellm.types.llms.custom_http import httpxSpecialProvider + + def upstream_response(outbound: httpx.Request) -> httpx.Response: + assert outbound.url == server.token_url + assert bearer not in str(outbound.headers) + assert bearer.encode() not in outbound.content + return httpx.Response(200, json={"access_token": "upstream-token", "token_type": "Bearer"}) + + database: Final = MagicMock() + table: Final = database.db.litellm_mcpusercredentials + table.find_unique = AsyncMock(return_value=None) + table.upsert = AsyncMock() + monkeypatch.setattr(proxy_server, "prisma_client", database) + monkeypatch.setenv("LITELLM_SALT_KEY", "oauth-jwt-test-encryption-key") + clients: Final = LLMClientCache() + monkeypatch.setattr(litellm, "in_memory_llm_clients_cache", clients) + async with httpx.AsyncClient(transport=httpx.MockTransport(upstream_response)) as transport: + upstream: Final = AsyncHTTPHandler() + await upstream.client.aclose() + upstream.client = transport + clients.set_cache("async_httpx_client" + httpxSpecialProvider.Oauth2Check, upstream) + response: Final = await discoverable_endpoints.exchange_token_with_server( + request=request, + mcp_server=server, + grant_type="authorization_code", + code="upstream-code", + redirect_uri="http://localhost/callback", + client_id="registered-client", + client_secret=None, + code_verifier=None, + ) + assert response.status_code == 200 + table.upsert.assert_awaited_once() + stored: Final = table.upsert.call_args.kwargs + assert stored["where"] == {"user_id_server_id": {"user_id": "jwt-owner", "server_id": server.server_id}} + credential: Final = stored["data"]["create"]["credential_b64"] + assert "upstream-token" not in credential + decoded: Final = decrypt_value_helper(credential, key="mcp_user_credential") + assert json.loads(decoded)["access_token"] == "upstream-token" + + +@pytest.mark.asyncio +@pytest.mark.parametrize( + "rejection", + [ + "expired", + "audience", + "issuer", + "signature", + "missing_user", + "unknown_user", + "disabled", + "not_premium", + "scim_inactive", + "custom_validate", + "missing_database", + ], +) +async def test_oauth_jwt_identity_rejects_untrusted_or_inactive_owner( + jwt_oauth_identity: tuple["JWTHandler", "RSAPrivateKey"], + monkeypatch: pytest.MonkeyPatch, + rejection: str, +) -> None: + from cryptography.hazmat.primitives.asymmetric import rsa + + from litellm.models.user import LiteLLM_UserTable + from litellm.proxy import proxy_server + from litellm.proxy._experimental.mcp_server.bridge_token_flow import _extract_user_id_from_request + + handler, signing_key = jwt_oauth_identity + key: Final = ( + rsa.generate_private_key(public_exponent=65537, key_size=2048) if rejection == "signature" else signing_key + ) + bearer: Final = _oauth_identity_jwt( + key, + expires_in=-60 if rejection == "expired" else 300, + audience="upstream-only" if rejection == "audience" else "litellm-proxy", + issuer="https://untrusted.example.test" if rejection == "issuer" else "https://idp.example.test", + owner=None if rejection == "missing_user" else "unknown" if rejection == "unknown_user" else "jwt-owner", + ) + if rejection == "disabled": + monkeypatch.setattr(proxy_server, "general_settings", {"enable_jwt_auth": False}) + if rejection == "not_premium": + monkeypatch.setattr(proxy_server, "premium_user", False) + if rejection == "missing_database": + monkeypatch.setattr(proxy_server, "prisma_client", None) + if rejection == "scim_inactive": + handler.user_api_key_cache.set_cache( + "jwt-owner", LiteLLM_UserTable(user_id="jwt-owner", metadata={"scim_active": False}) + ) + if rejection == "custom_validate": + handler.litellm_jwtauth.custom_validate = lambda claims: False + assert await _extract_user_id_from_request(_token_request({"Authorization": f"Bearer {bearer}"})) is None + + +@pytest.mark.asyncio +@pytest.mark.parametrize("blocked", [False, True]) +async def test_oauth_jwt_cannot_override_explicit_litellm_key( + jwt_oauth_identity: tuple["JWTHandler", "RSAPrivateKey"], + blocked: bool, +) -> None: + from litellm.proxy._experimental.mcp_server.bridge_token_flow import _extract_user_id_from_request + from litellm.proxy._types import UserAPIKeyAuth, hash_token + + handler, signing_key = jwt_oauth_identity + key: Final = "sk-explicit-key" + handler.user_api_key_cache.set_cache(hash_token(key), UserAPIKeyAuth(user_id="key-owner", blocked=blocked)) + request: Final = _token_request( + { + "Authorization": f"Bearer {_oauth_identity_jwt(signing_key)}", + "x-litellm-api-key": key, + } + ) + assert await _extract_user_id_from_request(request) == (None if blocked else "key-owner") + + +@pytest.mark.asyncio +@pytest.mark.parametrize("mapping", ["active", "blocked", "inactive_owner", "fallback", "pending", "reject"]) +async def test_oauth_jwt_uses_configured_virtual_key_owner( + jwt_oauth_identity: tuple["JWTHandler", "RSAPrivateKey"], + mapping: str, +) -> None: + from litellm.models.user import LiteLLM_UserTable + from litellm.proxy._experimental.mcp_server.bridge_token_flow import _extract_user_id_from_request + from litellm.proxy._types import UserAPIKeyAuth, UnregisteredJWTClientBehavior, hash_token + from litellm.proxy.auth.auth_checks import jwt_key_mapping_cache_key + + handler, signing_key = jwt_oauth_identity + handler.litellm_jwtauth.virtual_key_claim_field = "sub" + handler.litellm_jwtauth.unregistered_jwt_client_behavior = ( + UnregisteredJWTClientBehavior.AUTO_REGISTER + if mapping == "pending" + else UnregisteredJWTClientBehavior.REJECT + if mapping == "reject" + else UnregisteredJWTClientBehavior.FALLBACK_TEAM_MAPPING + ) + key_hash: Final = hash_token("sk-mapped-oauth-owner") + handler.user_api_key_cache.set_cache( + jwt_key_mapping_cache_key("sub", "not-the-configured-user-id"), + "__NO_MAPPING__" if mapping in ("fallback", "pending", "reject") else key_hash, + ) + handler.user_api_key_cache.set_cache( + key_hash, UserAPIKeyAuth(token=key_hash, user_id="mapped-owner", blocked=mapping == "blocked") + ) + handler.user_api_key_cache.set_cache( + "mapped-owner", LiteLLM_UserTable(user_id="mapped-owner", metadata={"scim_active": mapping != "inactive_owner"}) + ) + request: Final = _token_request({"Authorization": f"Bearer {_oauth_identity_jwt(signing_key)}"}) + expected: Final = "jwt-owner" if mapping == "fallback" else "mapped-owner" if mapping == "active" else None + assert await _extract_user_id_from_request(request) == expected + + +@pytest.mark.asyncio +@pytest.mark.parametrize("allowed_domain", [None, "allowed.example.test"]) +async def test_oauth_jwt_respects_custom_validation_and_email_policy( + jwt_oauth_identity: tuple["JWTHandler", "RSAPrivateKey"], + allowed_domain: str | None, +) -> None: + from litellm.proxy._experimental.mcp_server.bridge_token_flow import _extract_user_id_from_request + + handler, signing_key = jwt_oauth_identity + handler.litellm_jwtauth.custom_validate = lambda claims: True + handler.litellm_jwtauth.user_allowed_email_domain = allowed_domain + request: Final = _token_request({"Authorization": f"Bearer {_oauth_identity_jwt(signing_key)}"}) + assert await _extract_user_id_from_request(request) == (None if allowed_domain else "jwt-owner") + + +@pytest.mark.asyncio +async def test_oauth_jwt_uses_rbac_user_object_id(jwt_oauth_identity: tuple["JWTHandler", "RSAPrivateKey"]) -> None: + from litellm.proxy._experimental.mcp_server.bridge_token_flow import _extract_user_id_from_request + from litellm.proxy._types import LitellmUserRoles, RoleMapping + + handler, signing_key = jwt_oauth_identity + handler.litellm_jwtauth.user_id_jwt_field = "sub" + handler.litellm_jwtauth.roles_jwt_field = "aud" + handler.litellm_jwtauth.object_id_jwt_field = "identity.user_id" + handler.litellm_jwtauth.role_mappings = [ + RoleMapping(role="litellm-proxy", internal_role=LitellmUserRoles.INTERNAL_USER) + ] + request: Final = _token_request({"Authorization": f"Bearer {_oauth_identity_jwt(signing_key)}"}) + assert await _extract_user_id_from_request(request) == "jwt-owner" From eda98f38d940ecd065aee71d4b0b34ba1be98a06 Mon Sep 17 00:00:00 2001 From: Joshua Valluru <326636767+joshua-berri@users.noreply.github.com> Date: Tue, 15 Sep 2026 12:41:12 -0700 Subject: [PATCH 073/225] fix(mcp): preserve canonical JWT owner lookup without cached identity --- .../mcp_server/bridge_token_flow.py | 10 ++++-- .../mcp_server/test_discoverable_endpoints.py | 36 +++++++++++++++++++ 2 files changed, 43 insertions(+), 3 deletions(-) diff --git a/litellm/proxy/_experimental/mcp_server/bridge_token_flow.py b/litellm/proxy/_experimental/mcp_server/bridge_token_flow.py index 8dcc49c2fd8..3817935bf71 100644 --- a/litellm/proxy/_experimental/mcp_server/bridge_token_flow.py +++ b/litellm/proxy/_experimental/mcp_server/bridge_token_flow.py @@ -198,7 +198,9 @@ async def _reload_active_user_by_id(user_id: str) -> "_KeyResolutionFailure | No return loaded if isinstance(loaded, str) else None -async def load_active_user_by_id(user_id: str) -> "LiteLLM_UserTable | _KeyResolutionFailure": +async def load_active_user_by_id( + user_id: str, *, sso_user_id: str | None = None, user_email: str | None = None +) -> "LiteLLM_UserTable | _KeyResolutionFailure": """Load a live litellm user by id, returning the record when the user is active or a precise failure otherwise. The interactive DCR client authenticates via SSO, so its refresh envelope seals a user subject; renewing it must re-check the user is still live (present and not SCIM-deactivated) so a @@ -232,6 +234,8 @@ async def load_active_user_by_id(user_id: str) -> "LiteLLM_UserTable | _KeyResol prisma_client=prisma_client, user_api_key_cache=user_api_key_cache, user_id_upsert=False, + sso_user_id=sso_user_id, + user_email=user_email, ) except (ProxyException, HTTPException): return "no_active_key" @@ -352,7 +356,7 @@ async def _extract_jwt_user_id(token: str) -> str | None: return None if await _key_owner_scim_deactivated(mapped) else _active_key_user_id(mapped) if mapped is not None: return None - user_id, _, valid_email = await JWTAuthManager.get_user_info(jwt_handler, claims) + user_id, user_email, valid_email = await JWTAuthManager.get_user_info(jwt_handler, claims) object_id: Final = jwt_handler.get_object_id(token=claims, default_value=None) owner_id: Final = ( object_id @@ -361,7 +365,7 @@ async def _extract_jwt_user_id(token: str) -> str | None: ) if not owner_id or valid_email is False: return None - owner: Final = await load_active_user_by_id(owner_id) + owner: Final = await load_active_user_by_id(owner_id, sso_user_id=owner_id, user_email=user_email) return None if isinstance(owner, str) else owner.user_id except Exception as exc: # noqa: BLE001 # public OAuth exchange stays available; unvalidated identities never write credentials verbose_logger.debug("OAuth JWT identity could not be validated (%s)", type(exc).__name__) diff --git a/tests/test_litellm/proxy/_experimental/mcp_server/test_discoverable_endpoints.py b/tests/test_litellm/proxy/_experimental/mcp_server/test_discoverable_endpoints.py index af4c9eea770..ce985064eb0 100644 --- a/tests/test_litellm/proxy/_experimental/mcp_server/test_discoverable_endpoints.py +++ b/tests/test_litellm/proxy/_experimental/mcp_server/test_discoverable_endpoints.py @@ -11428,6 +11428,7 @@ def _oauth_identity_jwt( { "sub": "not-the-configured-user-id", "identity": {"user_id": owner}, + "email": "owner@example.test", "iss": issuer, "aud": audience, "exp": int(time.time()) + expires_in, @@ -11649,3 +11650,38 @@ async def test_oauth_jwt_uses_rbac_user_object_id(jwt_oauth_identity: tuple["JWT ] request: Final = _token_request({"Authorization": f"Bearer {_oauth_identity_jwt(signing_key)}"}) assert await _extract_user_id_from_request(request) == "jwt-owner" + + +@pytest.mark.asyncio +@pytest.mark.parametrize("identity", ["sso", "email"]) +@pytest.mark.parametrize("inactive", [False, True]) +async def test_oauth_jwt_resolves_canonical_owner_without_cached_identity( + jwt_oauth_identity: tuple["JWTHandler", "RSAPrivateKey"], + monkeypatch: pytest.MonkeyPatch, + identity: str, + inactive: bool, +) -> None: + from litellm.models.user import LiteLLM_UserTable + from litellm.proxy import proxy_server + from litellm.proxy._experimental.mcp_server.bridge_token_flow import _extract_user_id_from_request + + handler, signing_key = jwt_oauth_identity + external_id: Final = f"external-{identity}-{inactive}" + handler.litellm_jwtauth.user_email_jwt_field = "email" + owner: Final = LiteLLM_UserTable( + user_id="canonical-oauth-owner", + user_email="owner@example.test", + metadata={"scim_active": not inactive}, + organization_memberships=[], + ) + database: Final = MagicMock() + table: Final = database.db.litellm_usertable + table.find_unique = AsyncMock(side_effect=[None, owner if identity == "sso" else None]) + table.find_first = AsyncMock(return_value=owner) + table.update = AsyncMock(return_value=owner) + monkeypatch.setattr(proxy_server, "prisma_client", database) + request: Final = _token_request({"Authorization": f"Bearer {_oauth_identity_jwt(signing_key, owner=external_id)}"}) + assert await _extract_user_id_from_request(request) == (None if inactive else "canonical-oauth-owner") + assert table.find_unique.await_count == 2 + if identity == "email": + table.find_first.assert_awaited_once() From 864f4a7a0e70ad19b4c251e1a2447ea1aa7965bf Mon Sep 17 00:00:00 2001 From: Tin Chi Lo Date: Tue, 15 Sep 2026 12:29:19 -0700 Subject: [PATCH 074/225] feat(auto-router): add per-model Fast mode toggle --- litellm/llms/anthropic/common_utils.py | 7 + ...odel_prices_and_context_window_backup.json | 2 + litellm/router.py | 5 + litellm/types/router.py | 1 + model_prices_and_context_window.json | 2 + model_prices_and_context_window.schema.json | 3 + tests/test_litellm/test_router.py | 77 ++++++++++ tests/test_litellm/test_utils.py | 1 + .../add_model/ComplexityRouterConfig.tsx | 19 ++- ...plexityRouterFastMode.integration.test.tsx | 143 ++++++++++++++++++ .../add_model/TierModelEffortRows.tsx | 118 +++++++++------ .../build_complexity_router_config.test.ts | 13 ++ .../add_model/complexity_router_tiers.test.ts | 23 +++ .../add_model/complexity_router_tiers.ts | 17 ++- .../llm_calls/fetch_models.test.tsx | 17 +++ .../src/components/llm_calls/fetch_models.tsx | 3 + ui/litellm-dashboard/src/lib/http/schema.d.ts | 5 + 17 files changed, 400 insertions(+), 56 deletions(-) create mode 100644 ui/litellm-dashboard/src/components/add_model/ComplexityRouterFastMode.integration.test.tsx diff --git a/litellm/llms/anthropic/common_utils.py b/litellm/llms/anthropic/common_utils.py index 87c4ec8938e..d35a9372058 100644 --- a/litellm/llms/anthropic/common_utils.py +++ b/litellm/llms/anthropic/common_utils.py @@ -539,6 +539,13 @@ class AnthropicModelInfo(BaseLLMModelInfo): value: Final = litellm.model_cost.get(model, {}).get(key) return value if isinstance(value, bool) else None + @staticmethod + def supports_fast_mode(model: str, custom_llm_provider: str) -> bool: + return ( + custom_llm_provider == "anthropic" + and AnthropicModelInfo._get_exact_model_capability(model, "supports_fast_mode") is True + ) + @staticmethod def _get_provider_resolved_capability(model: str, key: str, custom_llm_provider: str) -> bool | None: """Resolve boolean capability ``key`` for ``model`` under the caller's provider. diff --git a/litellm/model_prices_and_context_window_backup.json b/litellm/model_prices_and_context_window_backup.json index 9f91cf82f41..19c438ef52d 100644 --- a/litellm/model_prices_and_context_window_backup.json +++ b/litellm/model_prices_and_context_window_backup.json @@ -14062,6 +14062,7 @@ }, "supports_output_config": true, "supports_speed": true, + "supports_fast_mode": true, "prompt_cache_min_tokens": 512, "source": "https://platform.claude.com/docs/en/about-claude/pricing" }, @@ -14103,6 +14104,7 @@ }, "supports_output_config": true, "supports_speed": true, + "supports_fast_mode": true, "prompt_cache_min_tokens": 1024, "source": "https://platform.claude.com/docs/en/about-claude/pricing" }, diff --git a/litellm/router.py b/litellm/router.py index fcdcf91c2cf..34e7a78f17b 100644 --- a/litellm/router.py +++ b/litellm/router.py @@ -99,6 +99,7 @@ from litellm.litellm_core_utils.sensitive_data_masker import ( mask_sensitive_structure, ) from litellm.litellm_core_utils.token_counter import offload_token_count +from litellm.llms.anthropic.common_utils import AnthropicModelInfo from litellm.llms.base_llm.passthrough.transformation import replace_path_segment from litellm.llms.base_llm.vector_store.transformation import ( RouterVectorStoreEmbeddingExecutor, @@ -10794,6 +10795,7 @@ class Router: "model_group": user_facing_model_group_name, "providers": [llm_provider], **model_info, + "supports_fast_mode": True, "supported_reasoning_efforts": None, } ) @@ -10872,6 +10874,9 @@ class Router: if model_info.get("rpm", None) is not None and _deployment_rpm is None: _deployment_rpm = model_info.get("rpm") + model_group_info.supports_fast_mode = model_group_info.supports_fast_mode and ( + AnthropicModelInfo.supports_fast_mode(litellm_model, llm_provider) + ) deployment_reasoning_efforts = ( resolve_supported_reasoning_efforts( # rebind-ok: recalculated per deployment model_info, deployment_is_mapped=deployment_is_mapped diff --git a/litellm/types/router.py b/litellm/types/router.py index 7732413b593..7c3e4d6943f 100644 --- a/litellm/types/router.py +++ b/litellm/types/router.py @@ -722,6 +722,7 @@ class ModelGroupInfo(BaseModel): supports_url_context: bool = Field(default=False) supports_reasoning: bool = Field(default=False) supports_function_calling: bool = Field(default=False) + supports_fast_mode: bool = Field(default=False) supported_reasoning_efforts: tuple[str, ...] | None = Field(default=None) supported_openai_params: list[str] | None = Field(default=[]) configurable_clientside_auth_params: CONFIGURABLE_CLIENTSIDE_AUTH_PARAMS = None diff --git a/model_prices_and_context_window.json b/model_prices_and_context_window.json index 9f91cf82f41..19c438ef52d 100644 --- a/model_prices_and_context_window.json +++ b/model_prices_and_context_window.json @@ -14062,6 +14062,7 @@ }, "supports_output_config": true, "supports_speed": true, + "supports_fast_mode": true, "prompt_cache_min_tokens": 512, "source": "https://platform.claude.com/docs/en/about-claude/pricing" }, @@ -14103,6 +14104,7 @@ }, "supports_output_config": true, "supports_speed": true, + "supports_fast_mode": true, "prompt_cache_min_tokens": 1024, "source": "https://platform.claude.com/docs/en/about-claude/pricing" }, diff --git a/model_prices_and_context_window.schema.json b/model_prices_and_context_window.schema.json index c2490041cf7..130cc6873fa 100644 --- a/model_prices_and_context_window.schema.json +++ b/model_prices_and_context_window.schema.json @@ -716,6 +716,9 @@ "supports_embedding_image_input": { "type": "boolean" }, + "supports_fast_mode": { + "type": "boolean" + }, "supports_forced_tool_use": { "type": "boolean" }, diff --git a/tests/test_litellm/test_router.py b/tests/test_litellm/test_router.py index 9c29b9d829a..ea90c167845 100644 --- a/tests/test_litellm/test_router.py +++ b/tests/test_litellm/test_router.py @@ -12056,6 +12056,83 @@ def test_model_group_info_reasoning_efforts_are_unknown_when_any_deployment_is_o +@pytest.mark.parametrize( + "model,provider,expected", + [ + ("anthropic/claude-opus-5", None, True), + ("claude-opus-4-8", None, True), + ("anthropic/claude-opus-4-7", None, False), + ("anthropic/claude-opus-4-6", None, False), + ("anthropic/claude-sonnet-5", None, False), + ("anthropic/off-map-opus", None, False), + ("vertex_ai/claude-opus-5", None, False), + ("bedrock/claude-opus-5", None, False), + ("claude-opus-5", "vertex_ai", False), + ("claude-opus-5", "bedrock", False), + ], +) +@pytest.mark.parametrize("operator_flag", [True, False]) +def test_model_group_info_fast_mode_uses_exact_provider_catalog( + local_model_cost_map: None, model: str, provider: str | None, expected: bool, operator_flag: bool +) -> None: + router: Final = Router(model_list=[{ + "model_name": "fast-group", + "litellm_params": {"model": model, "custom_llm_provider": provider, "api_key": "fake-key"}, + "model_info": {"supports_fast_mode": operator_flag}, + }]) + + result: Final = router.get_model_group_info("fast-group") + + assert result is not None + assert result.supports_fast_mode is expected + + +@pytest.mark.parametrize("flag", [None, False, "true", 1]) +def test_model_group_info_fast_mode_fails_closed_without_explicit_boolean( + local_model_cost_map: None, monkeypatch: pytest.MonkeyPatch, flag: object +) -> None: + entry: Final = {key: value for key, value in litellm.model_cost["claude-opus-5"].items() + if key != "supports_fast_mode"} + if flag is not None: + entry["supports_fast_mode"] = flag + monkeypatch.setitem(litellm.model_cost, "claude-opus-5", entry) + router: Final = Router(model_list=[{ + "model_name": "fast-group", + "litellm_params": {"model": "anthropic/claude-opus-5", "api_key": "fake-key"}, + "model_info": {"supports_fast_mode": True}, + }]) + + result: Final = router.get_model_group_info("fast-group") + + assert result is not None + assert result.supports_fast_mode is False + + +@pytest.mark.parametrize("other_model,expected", [ + ("anthropic/claude-opus-4-8", True), + ("anthropic/claude-opus-4-7", False), + ("anthropic/off-map-opus", False), + ("vertex_ai/claude-opus-5", False), + ("bedrock/claude-opus-5", False), +]) +@pytest.mark.parametrize("reverse", [True, False]) +def test_model_group_info_fast_mode_requires_every_deployment( + local_model_cost_map: None, other_model: str, expected: bool, reverse: bool +) -> None: + models: Final = (other_model, "anthropic/claude-opus-5") if reverse else ( + "anthropic/claude-opus-5", other_model + ) + router: Final = Router(model_list=[{ + "model_name": "fast-group", + "litellm_params": {"model": model, "api_key": "fake-key"}, + } for model in models]) + + result: Final = router.get_model_group_info("fast-group") + + assert result is not None + assert result.supports_fast_mode is expected + + def test_model_group_info_surfaces_supports_parallel_function_calling(local_model_cost_map): """``/model_group/info`` folds each deployment's registry flags into the group; a deployment whose registry entry declares parallel function calling must flip the group to True instead of False.""" diff --git a/tests/test_litellm/test_utils.py b/tests/test_litellm/test_utils.py index 8bf8489fc52..6e6acd5aabf 100644 --- a/tests/test_litellm/test_utils.py +++ b/tests/test_litellm/test_utils.py @@ -1165,6 +1165,7 @@ def test_aaamodel_prices_and_context_window_json_is_valid(): "supports_sampling_params": {"type": "boolean"}, "supports_output_config": {"type": "boolean"}, "supports_speed": {"type": "boolean"}, + "supports_fast_mode": {"type": "boolean"}, "supported_audio_formats": { "type": "array", "items": { diff --git a/ui/litellm-dashboard/src/components/add_model/ComplexityRouterConfig.tsx b/ui/litellm-dashboard/src/components/add_model/ComplexityRouterConfig.tsx index febcde269f7..64b17751e95 100644 --- a/ui/litellm-dashboard/src/components/add_model/ComplexityRouterConfig.tsx +++ b/ui/litellm-dashboard/src/components/add_model/ComplexityRouterConfig.tsx @@ -42,9 +42,10 @@ import { Restricted, restrictedBy } from "./TierRestrictions"; import { type TierSetAction, applyTierSetAction, setFallbackTier } from "./tier_set_actions"; import { ReasoningEffort, + TierModelParamChange, TierModelParamsByTier, classifierEffortOptionsForModels, - setTierModelReasoningEffort, + setTierModelParam, tierEffortOptionsForModels, tierRowLabel, } from "./complexity_router_tiers"; @@ -613,6 +614,9 @@ const ComplexityRouterConfig: React.FC = ({ const exitToBuiltInTiers = () => dispatch({ kind: "restore" }); const tierEffortOptionsByModel = tierEffortOptionsForModels(modelInfo); + const fastModeByModel = Object.fromEntries( + modelInfo.map((model) => [model.model_group, model.supports_fast_mode === true]), + ); const classifierEffortOptionsByModel = classifierEffortOptionsForModels(modelInfo); // Embedding models can't serve a chat-completion role, so they're excluded here. @@ -623,12 +627,11 @@ const ComplexityRouterConfig: React.FC = ({ label: model.model_group, })); - const handleTierModelEffortChange = (tier: string, model: string, effort: ReasoningEffort | undefined) => { + const handleTierModelParamChange = (tier: string, model: string, change: TierModelParamChange) => onChange({ ...value, - tier_model_params: setTierModelReasoningEffort(value.tier_model_params, tier, model, effort), + tier_model_params: setTierModelParam(value.tier_model_params, tier, model, change), }); - }; // Clearing the select drops the key entirely rather than storing "", so an emptied pin reads as // "track the tiers" everywhere downstream instead of as a blank model name. @@ -726,7 +729,13 @@ const ComplexityRouterConfig: React.FC = ({ models={row.models} effortOptionsByModel={tierEffortOptionsByModel} paramsByModel={row.params} - onEffortChange={(model, effort) => handleTierModelEffortChange(row.id, model, effort)} + fastModeByModel={fastModeByModel} + onEffortChange={(model, effort) => + handleTierModelParamChange(row.id, model, ["reasoning_effort", effort]) + } + onFastModeChange={(model, enabled) => + handleTierModelParamChange(row.id, model, ["speed", enabled ? "fast" : undefined]) + } /> {row.models.length > 1 && ( diff --git a/ui/litellm-dashboard/src/components/add_model/ComplexityRouterFastMode.integration.test.tsx b/ui/litellm-dashboard/src/components/add_model/ComplexityRouterFastMode.integration.test.tsx new file mode 100644 index 00000000000..34d14091bde --- /dev/null +++ b/ui/litellm-dashboard/src/components/add_model/ComplexityRouterFastMode.integration.test.tsx @@ -0,0 +1,143 @@ +import userEvent from "@testing-library/user-event"; +import React from "react"; +import { describe, expect, it, vi } from "vitest"; +import { renderWithProviders, screen } from "../../../tests/test-utils"; +import { + buildUpdatedComplexityRouterConfig, + hydrateComplexityRouterConfig, +} from "../edit_auto_router/edit_auto_router_modal"; +import type { ModelGroup } from "../llm_calls/fetch_models"; +import ComplexityRouterConfig, { type ComplexityRouterConfigValue } from "./ComplexityRouterConfig"; + +const modelInfo: ModelGroup[] = [ + { model_group: "primary", supported_reasoning_efforts: ["low", "high"], supports_fast_mode: true }, + { model_group: "secondary", supports_fast_mode: true }, + { model_group: "blocked", supported_reasoning_efforts: ["low"], supports_fast_mode: false }, + { model_group: "missing", supported_reasoning_efforts: ["low"] }, +]; + +it.each([false, true])("edits and round-trips independent model settings with custom tiers=%s", async (custom) => { + const user = userEvent.setup(); + const tier = custom ? "custom-a" : "COMPLEX"; + const otherTier = custom ? "custom-b" : "REASONING"; + const label = custom ? "Interactive" : "Complex"; + const models = ["primary", "secondary", "blocked", "missing"]; + const initial: ComplexityRouterConfigValue = { + tiers: { SIMPLE: [], MEDIUM: [], COMPLEX: models, REASONING: ["primary"] }, + classifier_type: "heuristic", + ...(custom && { + custom_tier_set: { + tiers: [ + { id: tier, name: label, definition: "Interactive requests", models }, + { id: otherTier, name: "Deliberate", definition: "Careful requests", models: ["primary"] }, + ], + fallback_tier_id: tier, + }, + }), + tier_model_params: { + [tier]: { + primary: { reasoning_effort: "high", max_tokens: 1024 }, + secondary: { speed: "fast" }, + blocked: { speed: "fast" }, + }, + [otherTier]: { primary: { speed: "fast", reasoning_effort: "low" } }, + }, + }; + const onChange = vi.fn<(value: ComplexityRouterConfigValue) => void>(); + const editor = (value: ComplexityRouterConfigValue) => ( + + ); + const view = renderWithProviders(editor(initial)); + const fast = () => screen.getByRole("switch", { name: `Fast mode for primary in the ${label} tier` }); + + expect(screen.getAllByRole("switch", { name: /^Fast mode for/ })).toHaveLength(3); + expect(screen.queryByRole("switch", { name: /^Fast mode for (blocked|missing)/ })).not.toBeInTheDocument(); + expect(screen.queryByRole("combobox", { name: /^Reasoning effort for secondary/ })).not.toBeInTheDocument(); + expect(screen.getByRole("switch", { name: `Fast mode for secondary in the ${label} tier` })).toBeChecked(); + expect(fast()).not.toBeChecked(); + expect(onChange).not.toHaveBeenCalled(); + + await user.click(fast()); + const enabled = onChange.mock.lastCall![0]; + expect(enabled.tier_model_params).toEqual({ + ...initial.tier_model_params, + [tier]: { + ...initial.tier_model_params![tier], + primary: { reasoning_effort: "high", max_tokens: 1024, speed: "fast" }, + }, + }); + const saved = buildUpdatedComplexityRouterConfig({}, enabled); + expect(saved.tier_model_configs).toEqual({ + [custom ? label : tier]: [ + { model_name: "primary", litellm_params: { reasoning_effort: "high", max_tokens: 1024, speed: "fast" } }, + { model_name: "secondary", litellm_params: { speed: "fast" } }, + { model_name: "blocked", litellm_params: { speed: "fast" } }, + ], + [custom ? "Deliberate" : otherTier]: [ + { model_name: "primary", litellm_params: { speed: "fast", reasoning_effort: "low" } }, + ], + }); + const reopened = hydrateComplexityRouterConfig(saved, undefined); + const reopenedTier = custom ? reopened.custom_tier_set!.tiers[0].id : tier; + view.rerender(editor(reopened)); + expect(fast()).toBeChecked(); + + await user.click(screen.getByRole("combobox", { name: `Reasoning effort for primary in the ${label} tier` })); + await user.click(await screen.findByRole("option", { name: "low" })); + const effortChanged = onChange.mock.lastCall![0]; + expect(effortChanged.tier_model_params?.[reopenedTier].primary).toEqual({ + reasoning_effort: "low", + max_tokens: 1024, + speed: "fast", + }); + view.rerender(editor(effortChanged)); + await user.click(fast()); + const disabled = onChange.mock.lastCall![0]; + expect(disabled.tier_model_params).toEqual({ + ...effortChanged.tier_model_params, + [reopenedTier]: { + ...effortChanged.tier_model_params![reopenedTier], + primary: { reasoning_effort: "low", max_tokens: 1024 }, + }, + }); + view.rerender(editor(disabled)); + expect(fast()).not.toBeChecked(); + + const picker = () => screen.getByRole("combobox", { name: `Select model(s) for ${label.toLowerCase()} queries` }); + await user.click(picker()); + await user.click(await screen.findByRole("option", { name: "primary" })); + await user.keyboard("{Escape}"); + const deselected = onChange.mock.lastCall![0]; + expect(deselected.tier_model_params?.[reopenedTier]).toEqual({ + secondary: { speed: "fast" }, + blocked: { speed: "fast" }, + }); + view.rerender(editor(deselected)); + expect(screen.queryByRole("switch", { name: `Fast mode for primary in the ${label} tier` })).not.toBeInTheDocument(); + await user.click(picker()); + await user.click(await screen.findByRole("option", { name: "primary" })); + await user.keyboard("{Escape}"); + const reselected = onChange.mock.lastCall![0]; + view.rerender(editor(reselected)); + expect(fast()).not.toBeChecked(); + expect(screen.getByRole("combobox", { name: `Reasoning effort for primary in the ${label} tier` })).toHaveTextContent( + "Default", + ); +}); + +describe("Fast mode metadata", () => { + it("offers nothing before model capabilities load and leaves stored speed untouched", () => { + const value: ComplexityRouterConfigValue = { + tiers: { SIMPLE: ["primary"], MEDIUM: [], COMPLEX: [], REASONING: [] }, + classifier_type: "heuristic", + tier_model_params: { SIMPLE: { primary: { speed: "fast" } } }, + }; + const onChange = vi.fn(); + renderWithProviders(); + expect(screen.queryByRole("switch", { name: /^Fast mode for/ })).not.toBeInTheDocument(); + expect(onChange).not.toHaveBeenCalled(); + expect(buildUpdatedComplexityRouterConfig({}, value).tier_model_configs).toEqual({ + SIMPLE: [{ model_name: "primary", litellm_params: { speed: "fast" } }], + }); + }); +}); diff --git a/ui/litellm-dashboard/src/components/add_model/TierModelEffortRows.tsx b/ui/litellm-dashboard/src/components/add_model/TierModelEffortRows.tsx index ec9705b9451..54afa28fb6e 100644 --- a/ui/litellm-dashboard/src/components/add_model/TierModelEffortRows.tsx +++ b/ui/litellm-dashboard/src/components/add_model/TierModelEffortRows.tsx @@ -1,5 +1,6 @@ import { Select, SelectContent, SelectItem, SelectTrigger, SelectValue } from "@/components/ui/select"; import { SimpleTooltip } from "@/components/ui/tooltip"; +import { Switch } from "@/components/ui/switch"; import { Info } from "lucide-react"; import React from "react"; import { ReasoningEffort, TierModelParams } from "./complexity_router_tiers"; @@ -18,6 +19,8 @@ interface TierModelEffortRowsProps { effortOptionsByModel: Record; paramsByModel: Record | undefined; onEffortChange: (model: string, effort: ReasoningEffort | undefined) => void; + fastModeByModel?: Record; + onFastModeChange: (model: string, enabled: boolean) => void; } export interface TierEffortRow { @@ -29,13 +32,16 @@ export interface TierEffortRow { /** * A stored effort outside the model's supported set (hand-authored, or capabilities changed since * it was saved) is listed anyway, so the row renders with its value selected and can be cleared. - * Only a model with no supported level and nothing stored drops out. */ export const tierEffortRows = ({ models, effortOptionsByModel, paramsByModel, -}: Pick): TierEffortRow[] => + fastModeByModel, +}: Pick< + TierModelEffortRowsProps, + "models" | "effortOptionsByModel" | "paramsByModel" | "fastModeByModel" +>): TierEffortRow[] => models .map((model) => { const effort = storedEffort(paramsByModel?.[model]); @@ -43,56 +49,74 @@ export const tierEffortRows = ({ const listed = effort !== undefined && !supported.includes(effort) ? [...supported, effort] : supported; return { model, effort, options: Array.from(new Set(listed)) }; }) - .filter(({ options }) => options.length > 0); + .filter(({ model, options }) => options.length > 0 || fastModeByModel?.[model] === true); -const TierModelEffortRows: React.FC = ({ - tierLabel, - models, - effortOptionsByModel, - paramsByModel, - onEffortChange, -}) => { - const rows = tierEffortRows({ models, effortOptionsByModel, paramsByModel }); +const TierModelEffortRows: React.FC = (props) => { + const { tierLabel, paramsByModel, onEffortChange, fastModeByModel, onFastModeChange } = props; + const rows = tierEffortRows(props); if (rows.length === 0) return null; return (
-
- Reasoning effort - - - -
- {rows.map(({ model, effort, options }) => ( -
- {model} - + + +
+ )} + {rows.map(({ model, effort, options }) => ( +
+ + {model} + +
+ {options.length > 0 && ( + + )} + {fastModeByModel?.[model] === true && ( + + + + )} +
))}
diff --git a/ui/litellm-dashboard/src/components/add_model/build_complexity_router_config.test.ts b/ui/litellm-dashboard/src/components/add_model/build_complexity_router_config.test.ts index 9973aec7616..bc30b591ea9 100644 --- a/ui/litellm-dashboard/src/components/add_model/build_complexity_router_config.test.ts +++ b/ui/litellm-dashboard/src/components/add_model/build_complexity_router_config.test.ts @@ -48,6 +48,19 @@ const baseParams: BuildComplexityRouterConfigParams = { }; describe("buildComplexityRouterConfig", () => { + it("carries Fast and reasoning overrides independently into a new router payload", () => { + const params = { speed: "fast", reasoning_effort: "high", max_tokens: 1024 }; + const config = buildComplexityRouterConfig({ + ...baseParams, + tiers: { ...tiers, COMPLEX: ["primary"], REASONING: ["secondary"] }, + tierModelParams: { COMPLEX: { primary: params }, REASONING: { secondary: { speed: "fast" } } }, + }); + expect(config.tier_model_configs).toEqual({ + COMPLEX: [{ model_name: "primary", litellm_params: params }], + REASONING: [{ model_name: "secondary", litellm_params: { speed: "fast" } }], + }); + }); + it("emits tiers, classifier_type, and escalation_keywords when nothing else is configured", () => { const config = buildComplexityRouterConfig(baseParams); const expected = { diff --git a/ui/litellm-dashboard/src/components/add_model/complexity_router_tiers.test.ts b/ui/litellm-dashboard/src/components/add_model/complexity_router_tiers.test.ts index b0fab49e80a..98a3fe792bd 100644 --- a/ui/litellm-dashboard/src/components/add_model/complexity_router_tiers.test.ts +++ b/ui/litellm-dashboard/src/components/add_model/complexity_router_tiers.test.ts @@ -7,6 +7,7 @@ import { serializeTierModelConfigs, tierRowLabel, setTierModelReasoningEffort, + setTierModelParam, } from "./complexity_router_tiers"; import { resolveComplexityDefaultModel } from "./tier_rows"; @@ -218,6 +219,28 @@ describe("setTierModelReasoningEffort", () => { }); }); +describe("setTierModelParam", () => { + it.each(["reasoning_effort", "speed"] as const)("clears only %s and preserves the input", (key) => { + const params = { reasoning_effort: "high", speed: "fast", max_tokens: 512 }; + const current = { COMPLEX: { primary: params, secondary: { speed: "fast" } }, REASONING: { primary: params } }; + const cleared = setTierModelParam(current, "COMPLEX", "primary", [key, undefined]); + expect(cleared).toEqual({ + ...current, + COMPLEX: { + ...current.COMPLEX, + primary: key === "speed" ? { reasoning_effort: "high", max_tokens: 512 } : { speed: "fast", max_tokens: 512 }, + }, + }); + expect(current.COMPLEX.primary).toEqual({ reasoning_effort: "high", speed: "fast", max_tokens: 512 }); + }); + + it("removes empty records when the only override is Fast", () => { + const enabled = setTierModelParam(undefined, "COMPLEX", "primary", ["speed", "fast"]); + expect(enabled).toEqual({ COMPLEX: { primary: { speed: "fast" } } }); + expect(setTierModelParam(enabled, "COMPLEX", "primary", ["speed", undefined])).toBeUndefined(); + }); +}); + describe("pruneTierModelParams", () => { it("drops params for models deselected from the tier", () => { expect( diff --git a/ui/litellm-dashboard/src/components/add_model/complexity_router_tiers.ts b/ui/litellm-dashboard/src/components/add_model/complexity_router_tiers.ts index 3fec63518e5..916feaf26c0 100644 --- a/ui/litellm-dashboard/src/components/add_model/complexity_router_tiers.ts +++ b/ui/litellm-dashboard/src/components/add_model/complexity_router_tiers.ts @@ -114,14 +114,16 @@ export const serializeTierModelConfigs = ( return serialized.length > 0 ? Object.fromEntries(serialized) : undefined; }; -export const setTierModelReasoningEffort = ( +export type TierModelParamChange = ["reasoning_effort", ReasoningEffort | undefined] | ["speed", "fast" | undefined]; + +export const setTierModelParam = ( current: TierModelParamsByTier | undefined, tier: string, model: string, - effort: ReasoningEffort | undefined, + [key, value]: TierModelParamChange, ): TierModelParamsByTier | undefined => { - const { reasoning_effort: _dropped, ...rest } = current?.[tier]?.[model] ?? {}; - const params = effort === undefined ? rest : { ...rest, reasoning_effort: effort }; + const { [key]: _dropped, ...rest } = current?.[tier]?.[model] ?? {}; + const params = value === undefined ? rest : { ...rest, [key]: value }; const byModel = Object.fromEntries( Object.entries({ ...current?.[tier], [model]: params }).filter(([, value]) => Object.keys(value).length > 0), ); @@ -131,6 +133,13 @@ export const setTierModelReasoningEffort = ( return Object.keys(next).length > 0 ? next : undefined; }; +export const setTierModelReasoningEffort = ( + current: TierModelParamsByTier | undefined, + tier: string, + model: string, + effort: ReasoningEffort | undefined, +): TierModelParamsByTier | undefined => setTierModelParam(current, tier, model, ["reasoning_effort", effort]); + export const pruneTierModelParams = ( current: TierModelParamsByTier | undefined, tier: string, diff --git a/ui/litellm-dashboard/src/components/llm_calls/fetch_models.test.tsx b/ui/litellm-dashboard/src/components/llm_calls/fetch_models.test.tsx index a3a6c77930b..c65ece4ca1c 100644 --- a/ui/litellm-dashboard/src/components/llm_calls/fetch_models.test.tsx +++ b/ui/litellm-dashboard/src/components/llm_calls/fetch_models.test.tsx @@ -52,6 +52,23 @@ describe("fetchAvailableModels", () => { ]); }); + it("carries only explicitly supported Fast capabilities, not accepted speed parameters", async () => { + modelHubCallMock.mockResolvedValue({ + data: [ + { model_group: "fast", supports_fast_mode: true }, + { model_group: "blocked", supports_fast_mode: false }, + { model_group: "missing", supports_speed: true }, + { model_group: "unknown", supports_fast_mode: null }, + ], + }); + expect(await fetchAvailableModels("token")).toEqual([ + { model_group: "blocked" }, + { model_group: "fast", supports_fast_mode: true }, + { model_group: "missing" }, + { model_group: "unknown" }, + ]); + }); + it("preserves absent, unknown, empty, and explicit effort capability states", async () => { modelHubCallMock.mockResolvedValue({ data: [ diff --git a/ui/litellm-dashboard/src/components/llm_calls/fetch_models.tsx b/ui/litellm-dashboard/src/components/llm_calls/fetch_models.tsx index 1d21b5e43ba..b3df5c9bf65 100644 --- a/ui/litellm-dashboard/src/components/llm_calls/fetch_models.tsx +++ b/ui/litellm-dashboard/src/components/llm_calls/fetch_models.tsx @@ -7,6 +7,7 @@ export interface ModelGroup { model_group: string; mode?: string; supports_reasoning?: boolean; + supports_fast_mode?: boolean; supported_reasoning_efforts?: string[] | null; } @@ -16,6 +17,7 @@ interface AvailableModel { id?: string | null; mode?: string | null; supports_reasoning?: boolean | null; + supports_fast_mode?: boolean | null; supported_reasoning_efforts?: string[] | null; } @@ -25,6 +27,7 @@ const toModelGroup = (item: AvailableModel): ModelGroup => { model_group: groupName, ...(item.mode && { mode: item.mode }), ...(item.supports_reasoning === true && { supports_reasoning: true }), + ...(item.supports_fast_mode === true && { supports_fast_mode: true }), ...(item.supported_reasoning_efforts !== undefined && { supported_reasoning_efforts: item.supported_reasoning_efforts, }), diff --git a/ui/litellm-dashboard/src/lib/http/schema.d.ts b/ui/litellm-dashboard/src/lib/http/schema.d.ts index 371fbbe67bd..a8801f643f4 100644 --- a/ui/litellm-dashboard/src/lib/http/schema.d.ts +++ b/ui/litellm-dashboard/src/lib/http/schema.d.ts @@ -32565,6 +32565,11 @@ export interface components { supported_openai_params: string[] | null; /** Supported Reasoning Efforts */ supported_reasoning_efforts?: string[] | null; + /** + * Supports Fast Mode + * @default false + */ + supports_fast_mode: boolean; /** * Supports Function Calling * @default false From 31052629d6626d9f2e605b2d46ff71ff808d16b4 Mon Sep 17 00:00:00 2001 From: Devin AI <158243242+devin-ai-integration[bot]@users.noreply.github.com> Date: Tue, 15 Sep 2026 19:46:30 +0000 Subject: [PATCH 075/225] ci: rerun checks after docs for LITELLM_HTTP2 merged Co-Authored-By: Devin AI <158243242+devin-ai-integration[bot]@users.noreply.github.com> From c9ccda210ebc4e55b999f8e4d253e5b3161d574f Mon Sep 17 00:00:00 2001 From: yassin Date: Tue, 15 Sep 2026 19:49:58 +0000 Subject: [PATCH 076/225] test(proxy): declare org member spend test bindings Final Co-Authored-By: Devin AI <158243242+devin-ai-integration[bot]@users.noreply.github.com> --- .../test_redis_update_buffer.py | 9 ++--- .../proxy/db/test_db_spend_update_writer.py | 35 ++++++++++--------- 2 files changed, 23 insertions(+), 21 deletions(-) diff --git a/tests/test_litellm/proxy/db/db_transaction_queue/test_redis_update_buffer.py b/tests/test_litellm/proxy/db/db_transaction_queue/test_redis_update_buffer.py index 817c86a1bdf..53acbd2a32b 100644 --- a/tests/test_litellm/proxy/db/db_transaction_queue/test_redis_update_buffer.py +++ b/tests/test_litellm/proxy/db/db_transaction_queue/test_redis_update_buffer.py @@ -1,5 +1,6 @@ import json from datetime import datetime, timezone +from typing import Final from unittest.mock import AsyncMock, MagicMock, patch import pytest @@ -278,8 +279,8 @@ async def test_org_member_spend_is_summed_across_pods_and_restored_on_rpush_fail SpendUpdateQueue, ) - member_key = "organization_id::org-1::user_id::user-1" - pod_json = json.dumps({"org_member_list_transactions": {member_key: 0.25}}) + member_key: Final = "organization_id::org-1::user_id::user-1" + pod_json: Final = json.dumps({"org_member_list_transactions": {member_key: 0.25}}) mock_redis_cache.async_lpop_pipeline = AsyncMock( return_value=[[pod_json, pod_json], None, None, None, None, None, None] ) @@ -290,7 +291,7 @@ async def test_org_member_spend_is_summed_across_pods_and_restored_on_rpush_fail assert db_spend["org_member_list_transactions"] == {member_key: 0.5} mock_redis_cache.async_rpush_pipeline = AsyncMock(side_effect=ConnectionError("redis went away")) - spend_queue = SpendUpdateQueue() + spend_queue: Final = SpendUpdateQueue() await spend_queue.add_update( { "entity_type": Litellm_EntityType.ORGANIZATION_MEMBER, @@ -307,7 +308,7 @@ async def test_org_member_spend_is_summed_across_pods_and_restored_on_rpush_fail daily_agent_spend_update_queue=DailySpendUpdateQueue(), ) - restored_spend = await spend_queue.flush_and_get_aggregated_db_spend_update_transactions() + restored_spend: Final = await spend_queue.flush_and_get_aggregated_db_spend_update_transactions() assert restored_spend["org_member_list_transactions"] == {member_key: 1.5} diff --git a/tests/test_litellm/proxy/db/test_db_spend_update_writer.py b/tests/test_litellm/proxy/db/test_db_spend_update_writer.py index 64001146e05..8cbb415ec58 100644 --- a/tests/test_litellm/proxy/db/test_db_spend_update_writer.py +++ b/tests/test_litellm/proxy/db/test_db_spend_update_writer.py @@ -8,6 +8,7 @@ from collections.abc import Callable from contextlib import asynccontextmanager from datetime import datetime, timezone from types import SimpleNamespace +from typing import Final from unittest.mock import AsyncMock, MagicMock, call, patch import pytest @@ -949,19 +950,19 @@ async def test_org_spend_increments_organization_membership_row_for_the_calling_ """A request made with a user_id inside an org must increment that user's LiteLLM_OrganizationMembership.spend, not only the org total, or the Organizations > Members UI renders '-' for every member.""" - db_writer = DBSpendUpdateWriter() + db_writer: Final = DBSpendUpdateWriter() await db_writer._update_org_db( response_cost=0.75, org_id="org-abc", user_id="user-xyz", prisma_client=MagicMock(), ) - transactions = await db_writer.spend_update_queue.flush_and_get_aggregated_db_spend_update_transactions() + transactions: Final = await db_writer.spend_update_queue.flush_and_get_aggregated_db_spend_update_transactions() - mock_batcher = MagicMock() - mock_prisma_client = MagicMock() + mock_batcher: Final = MagicMock() + mock_prisma_client: Final = MagicMock() mock_prisma_client.db.tx = MagicMock(return_value=_good_tx(mock_batcher)) - proxy_logging = MagicMock() + proxy_logging: Final = MagicMock() proxy_logging.call_details = {} await db_writer._commit_spend_updates_to_db( @@ -983,19 +984,19 @@ async def test_org_spend_increments_organization_membership_row_for_the_calling_ @pytest.mark.asyncio async def test_org_spend_without_user_id_leaves_organization_membership_untouched(): - db_writer = DBSpendUpdateWriter() + db_writer: Final = DBSpendUpdateWriter() await db_writer._update_org_db( response_cost=0.75, org_id="org-abc", user_id=None, prisma_client=MagicMock(), ) - transactions = await db_writer.spend_update_queue.flush_and_get_aggregated_db_spend_update_transactions() + transactions: Final = await db_writer.spend_update_queue.flush_and_get_aggregated_db_spend_update_transactions() - mock_batcher = MagicMock() - mock_prisma_client = MagicMock() + mock_batcher: Final = MagicMock() + mock_prisma_client: Final = MagicMock() mock_prisma_client.db.tx = MagicMock(return_value=_good_tx(mock_batcher)) - proxy_logging = MagicMock() + proxy_logging: Final = MagicMock() proxy_logging.call_details = {} await db_writer._commit_spend_updates_to_db( @@ -1011,19 +1012,19 @@ async def test_org_spend_without_user_id_leaves_organization_membership_untouche @pytest.mark.asyncio async def test_org_spend_keeps_member_attribution_when_ids_contain_the_key_delimiter(): - db_writer = DBSpendUpdateWriter() + db_writer: Final = DBSpendUpdateWriter() await db_writer._update_org_db( response_cost=0.75, org_id="division::west", user_id="user::42", prisma_client=MagicMock(), ) - transactions = await db_writer.spend_update_queue.flush_and_get_aggregated_db_spend_update_transactions() + transactions: Final = await db_writer.spend_update_queue.flush_and_get_aggregated_db_spend_update_transactions() - mock_batcher = MagicMock() - mock_prisma_client = MagicMock() + mock_batcher: Final = MagicMock() + mock_prisma_client: Final = MagicMock() mock_prisma_client.db.tx = MagicMock(return_value=_good_tx(mock_batcher)) - proxy_logging = MagicMock() + proxy_logging: Final = MagicMock() proxy_logging.call_details = {} await db_writer._commit_spend_updates_to_db( @@ -1041,7 +1042,7 @@ async def test_org_spend_keeps_member_attribution_when_ids_contain_the_key_delim @pytest.mark.asyncio async def test_batch_database_updates_queues_org_member_spend_for_the_request_user(): - db_writer = DBSpendUpdateWriter() + db_writer: Final = DBSpendUpdateWriter() await db_writer._batch_database_updates( response_cost=0.1, user_id="u1", @@ -1053,7 +1054,7 @@ async def test_batch_database_updates_queues_org_member_spend_for_the_request_us litellm_proxy_budget_name=None, payload={"request_id": "req-1", "model": "gpt-4o-mini", "spend": 0.1}, ) - transactions = await db_writer.spend_update_queue.flush_and_get_aggregated_db_spend_update_transactions() + transactions: Final = await db_writer.spend_update_queue.flush_and_get_aggregated_db_spend_update_transactions() assert transactions["org_list_transactions"] == {"org1": 0.1} assert transactions["org_member_list_transactions"] == {"organization_id::org1::user_id::u1": 0.1} From 2505fcf95e6470bac5cdddb65d63b143c3605937 Mon Sep 17 00:00:00 2001 From: yassin Date: Tue, 15 Sep 2026 20:05:23 +0000 Subject: [PATCH 077/225] test(proxy): type the fixture parameters of the org member redis test Co-Authored-By: Devin AI <158243242+devin-ai-integration[bot]@users.noreply.github.com> --- .../proxy/db/db_transaction_queue/test_redis_update_buffer.py | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/tests/test_litellm/proxy/db/db_transaction_queue/test_redis_update_buffer.py b/tests/test_litellm/proxy/db/db_transaction_queue/test_redis_update_buffer.py index 53acbd2a32b..cc8b10150bd 100644 --- a/tests/test_litellm/proxy/db/db_transaction_queue/test_redis_update_buffer.py +++ b/tests/test_litellm/proxy/db/db_transaction_queue/test_redis_update_buffer.py @@ -269,7 +269,7 @@ async def test_get_all_transactions_from_redis_buffer_pipeline(redis_update_buff @pytest.mark.asyncio async def test_org_member_spend_is_summed_across_pods_and_restored_on_rpush_failure( - redis_update_buffer, mock_redis_cache + redis_update_buffer: RedisUpdateBuffer, mock_redis_cache: AsyncMock ): from litellm.proxy._types import Litellm_EntityType from litellm.proxy.db.db_transaction_queue.daily_spend_update_queue import ( From 9b3595434713e7ec26adc697e1f6fc1da3ae9ed2 Mon Sep 17 00:00:00 2001 From: ryan-crabbe-berri Date: Tue, 15 Sep 2026 14:10:23 -0700 Subject: [PATCH 078/225] fix(ui): block usage export and flag the range when a spend page fails The Usage page drains the daily activity endpoint page by page. A page that threw was only logged to the console: the loading banner disappeared, the partial totals stayed on screen looking final, and Export Data stayed clickable, so the CSV handed to finance was silently short. The hook now reports `failed`, PaginationStatusAlerts renders it as an error banner naming how many pages actually loaded, and the export is blocked with the reason on hover while the data on screen does not cover the range. --- .../_components/CacheLeakageCard.test.tsx | 1 + .../_components/CostOptimizationView.tsx | 1 + .../_components/PromptCachingTab.test.tsx | 1 + .../_components/UsageTab.test.tsx | 1 + .../_components/useDailyActivityRange.ts | 4 +- .../components/EntityUsage/EntityUsage.tsx | 9 +++ .../_components/components/UsagePageView.tsx | 24 +++++-- .../hooks/usePaginatedDailyActivity.test.ts | 66 +++++++++++++++++++ .../hooks/usePaginatedDailyActivity.ts | 8 ++- .../UsageExportHeader.test.tsx | 21 ++++++ .../EntityUsageExport/UsageExportHeader.tsx | 13 ++-- .../exportBlockedReason.test.ts | 39 +++++++++++ .../EntityUsageExport/exportBlockedReason.ts | 21 ++++++ .../shared/PaginationStatusAlerts.test.tsx | 31 +++++++++ .../shared/PaginationStatusAlerts.tsx | 12 +++- .../KeySavingsTab.integration.test.tsx | 1 + 16 files changed, 242 insertions(+), 11 deletions(-) create mode 100644 ui/litellm-dashboard/src/components/EntityUsageExport/exportBlockedReason.test.ts create mode 100644 ui/litellm-dashboard/src/components/EntityUsageExport/exportBlockedReason.ts diff --git a/ui/litellm-dashboard/src/app/(dashboard)/cost-optimization/_components/CacheLeakageCard.test.tsx b/ui/litellm-dashboard/src/app/(dashboard)/cost-optimization/_components/CacheLeakageCard.test.tsx index f320d8e0f97..54af13d8a90 100644 --- a/ui/litellm-dashboard/src/app/(dashboard)/cost-optimization/_components/CacheLeakageCard.test.tsx +++ b/ui/litellm-dashboard/src/app/(dashboard)/cost-optimization/_components/CacheLeakageCard.test.tsx @@ -71,6 +71,7 @@ const renderWith = (results: DailyData[], overrides: Partial isFetchingMore: false, progress: { currentPage: 1, totalPages: 1 }, cancelled: false, + failed: false, cancel: vi.fn(), ...overrides, }} diff --git a/ui/litellm-dashboard/src/app/(dashboard)/cost-optimization/_components/CostOptimizationView.tsx b/ui/litellm-dashboard/src/app/(dashboard)/cost-optimization/_components/CostOptimizationView.tsx index 8094fa2e8b6..25bd3de0382 100644 --- a/ui/litellm-dashboard/src/app/(dashboard)/cost-optimization/_components/CostOptimizationView.tsx +++ b/ui/litellm-dashboard/src/app/(dashboard)/cost-optimization/_components/CostOptimizationView.tsx @@ -87,6 +87,7 @@ const CostOptimizationView: React.FC = ({ accessToken diff --git a/ui/litellm-dashboard/src/app/(dashboard)/cost-optimization/_components/PromptCachingTab.test.tsx b/ui/litellm-dashboard/src/app/(dashboard)/cost-optimization/_components/PromptCachingTab.test.tsx index 2c602033171..66db347e70f 100644 --- a/ui/litellm-dashboard/src/app/(dashboard)/cost-optimization/_components/PromptCachingTab.test.tsx +++ b/ui/litellm-dashboard/src/app/(dashboard)/cost-optimization/_components/PromptCachingTab.test.tsx @@ -35,6 +35,7 @@ describe("PromptCachingTab", () => { isFetchingMore: false, progress: { currentPage: 1, totalPages: 1 }, cancelled: false, + failed: false, cancel: vi.fn(), }; render(); diff --git a/ui/litellm-dashboard/src/app/(dashboard)/cost-optimization/_components/UsageTab.test.tsx b/ui/litellm-dashboard/src/app/(dashboard)/cost-optimization/_components/UsageTab.test.tsx index f85a667a074..c62208aacc5 100644 --- a/ui/litellm-dashboard/src/app/(dashboard)/cost-optimization/_components/UsageTab.test.tsx +++ b/ui/litellm-dashboard/src/app/(dashboard)/cost-optimization/_components/UsageTab.test.tsx @@ -123,6 +123,7 @@ const renderWith = (results: DailyData[], options: RenderOptions = {}) => { isFetchingMore: false, progress: { currentPage: 1, totalPages: 1 }, cancelled: false, + failed: false, cancel: vi.fn(), }} />, diff --git a/ui/litellm-dashboard/src/app/(dashboard)/cost-optimization/_components/useDailyActivityRange.ts b/ui/litellm-dashboard/src/app/(dashboard)/cost-optimization/_components/useDailyActivityRange.ts index 3435b57dbc8..9f793a68bf5 100644 --- a/ui/litellm-dashboard/src/app/(dashboard)/cost-optimization/_components/useDailyActivityRange.ts +++ b/ui/litellm-dashboard/src/app/(dashboard)/cost-optimization/_components/useDailyActivityRange.ts @@ -20,6 +20,7 @@ export interface DailyActivityRange { isFetchingMore: boolean; progress: { currentPage: number; totalPages: number }; cancelled: boolean; + failed: boolean; cancel: () => void; } @@ -64,7 +65,7 @@ export const useScopedDailyActivityRange = ( args: [accessToken, startTime, endTime, userId, true, apiKey], enabled: !!accessToken && !!startTime && !!endTime, }; - const { data, loading, isFetchingMore, progress, cancelled, cancel } = + const { data, loading, isFetchingMore, progress, cancelled, failed, cancel } = usePaginatedDailyActivity(activityQueryOptions); return { @@ -75,6 +76,7 @@ export const useScopedDailyActivityRange = ( isFetchingMore, progress, cancelled, + failed, cancel, }; }; diff --git a/ui/litellm-dashboard/src/app/(dashboard)/usage/_components/components/EntityUsage/EntityUsage.tsx b/ui/litellm-dashboard/src/app/(dashboard)/usage/_components/components/EntityUsage/EntityUsage.tsx index 6c15b3c418d..debc5602277 100644 --- a/ui/litellm-dashboard/src/app/(dashboard)/usage/_components/components/EntityUsage/EntityUsage.tsx +++ b/ui/litellm-dashboard/src/app/(dashboard)/usage/_components/components/EntityUsage/EntityUsage.tsx @@ -25,6 +25,7 @@ import TeamMultiSelect from "@/components/common_components/team_multi_select"; import UserDropdown from "@/components/common_components/UserDropdown"; import { ActivityMetrics, processActivityData } from "@/components/activity_metrics"; import { UsageExportHeader } from "@/components/EntityUsageExport"; +import { getExportBlockedReason } from "@/components/EntityUsageExport/exportBlockedReason"; import type { EntityType } from "@/components/EntityUsageExport/types"; import { agentDailyActivityCall, @@ -145,9 +146,11 @@ const EntityUsage: React.FC = ({ const { data: spendDataRaw, + loading, isFetchingMore, progress, cancelled, + failed, cancel, } = usePaginatedDailyActivity({ fetchFn, @@ -163,6 +166,7 @@ const EntityUsage: React.FC = ({ isFetchingMore: agentIsFetchingMore, progress: agentProgress, cancelled: agentCancelled, + failed: agentFailed, cancel: agentCancel, } = usePaginatedDailyActivity({ fetchFn: agentDailyActivityCall, @@ -660,11 +664,14 @@ const EntityUsage: React.FC = ({ { key: "endpoints", label: "Endpoint Activity", content: }, ]; + const spendFetchState = { loading, isFetchingMore, cancelled, failed }; + return (
@@ -672,6 +679,7 @@ const EntityUsage: React.FC = ({ = ({ onFiltersChange={setSelectedTags} filterOptions={getAllTags() || undefined} teams={teams || []} + exportBlockedReason={getExportBlockedReason(spendFetchState)} /> diff --git a/ui/litellm-dashboard/src/app/(dashboard)/usage/_components/components/UsagePageView.tsx b/ui/litellm-dashboard/src/app/(dashboard)/usage/_components/components/UsagePageView.tsx index de353948db9..31c9288c911 100644 --- a/ui/litellm-dashboard/src/app/(dashboard)/usage/_components/components/UsagePageView.tsx +++ b/ui/litellm-dashboard/src/app/(dashboard)/usage/_components/components/UsagePageView.tsx @@ -30,6 +30,7 @@ import { ActivityMetrics, processActivityData } from "@/components/activity_metr import CloudZeroExportModal from "@/components/cloudzero_export_modal"; import UserDropdown from "@/components/common_components/UserDropdown"; import EntityUsageExportModal from "@/components/EntityUsageExport"; +import { getExportBlockedReason } from "@/components/EntityUsageExport/exportBlockedReason"; import KeyActivityPanel from "@/components/UsagePage/components/KeyActivityPanel"; import { Team } from "@/components/key_team_helpers/key_list"; import { @@ -249,6 +250,14 @@ const UsagePage: React.FC = ({ teams, organizations }) => { const loading = aggregatedLoading || paginatedResult.loading; + const spendFetchState = { + loading, + isFetchingMore: paginatedResult.isFetchingMore, + cancelled: paginatedResult.cancelled, + failed: paginatedResult.failed, + }; + const exportBlockedReason = getExportBlockedReason(spendFetchState); + // Clear isDateChanging when paginated data starts arriving useEffect(() => { if (aggregatedFailed && !paginatedResult.loading && paginatedResult.data.results.length > 0) { @@ -489,6 +498,7 @@ const UsagePage: React.FC = ({ teams, organizations }) => { @@ -525,10 +535,16 @@ const UsagePage: React.FC = ({ teams, organizations }) => { Ask AI - + + +
{/* Cost Panel */} diff --git a/ui/litellm-dashboard/src/app/(dashboard)/usage/_components/hooks/usePaginatedDailyActivity.test.ts b/ui/litellm-dashboard/src/app/(dashboard)/usage/_components/hooks/usePaginatedDailyActivity.test.ts index 0537f469920..5257e5fc8a6 100644 --- a/ui/litellm-dashboard/src/app/(dashboard)/usage/_components/hooks/usePaginatedDailyActivity.test.ts +++ b/ui/litellm-dashboard/src/app/(dashboard)/usage/_components/hooks/usePaginatedDailyActivity.test.ts @@ -156,3 +156,69 @@ describe("usePaginatedDailyActivity page accumulation", () => { expect(result.current.data.metadata.total_spend).toBe(5.5); }); }); + +describe("usePaginatedDailyActivity failure reporting", () => { + const firstPage = { results: [dayOf("2026-08-16", 2)], metadata: { total_pages: 3, page: 1, total_spend: 2 } }; + const start = new Date("2026-08-10"); + const end = new Date("2026-08-17"); + + it("reports a failed range so partial totals cannot pass as the whole range", async () => { + const consoleError = vi.spyOn(console, "error").mockImplementation(() => {}); + const fetchFn = vi.fn((_token: string, _start: Date, _end: Date, page: number) => + page === 1 ? Promise.resolve(firstPage) : Promise.reject(new Error("page 2 never came back")), + ); + + const { result } = renderHook(() => + usePaginatedDailyActivity({ fetchFn, args: ["tok", start, end, null], enabled: true }), + ); + + await waitFor(() => expect(result.current.failed).toBe(true), { timeout: 5000 }); + + expect(result.current.isFetchingMore).toBe(false); + expect(result.current.loading).toBe(false); + expect(result.current.data.metadata.total_spend).toBe(2); + consoleError.mockRestore(); + }); + + it("stays unfailed when every page arrives", async () => { + const pages = [ + firstPage, + { results: [dayOf("2026-08-15", 1)], metadata: { total_pages: 2, page: 2, total_spend: 1 } }, + ]; + const fetchFn = vi.fn((_token: string, _start: Date, _end: Date, page: number) => + Promise.resolve({ ...pages[page - 1], metadata: { ...pages[page - 1].metadata, total_pages: 2 } }), + ); + + const { result } = renderHook(() => + usePaginatedDailyActivity({ fetchFn, args: ["tok", start, end, null], enabled: true }), + ); + + await waitFor(() => expect(result.current.data.metadata.page).toBe(2), { timeout: 5000 }); + + expect(result.current.failed).toBe(false); + }); + + it("clears the failure when a new range is requested, so the banner cannot outlive it", async () => { + const consoleError = vi.spyOn(console, "error").mockImplementation(() => {}); + const fetchFn = vi.fn((...callArgs: unknown[]) => { + const [, , , page, filter] = callArgs as [string, Date, Date, number, string | null]; + if (filter !== "broken") + return Promise.resolve({ ...firstPage, metadata: { ...firstPage.metadata, total_pages: 1 } }); + if (page === 1) return Promise.resolve({ ...firstPage, metadata: { ...firstPage.metadata, total_pages: 2 } }); + return Promise.reject(new Error("page 2 never came back")); + }); + + const { result, rerender } = renderHook( + ({ filter }: { filter: string | null }) => + usePaginatedDailyActivity({ fetchFn, args: ["tok", start, end, filter], enabled: true }), + { initialProps: { filter: "broken" as string | null } }, + ); + + await waitFor(() => expect(result.current.failed).toBe(true), { timeout: 5000 }); + + rerender({ filter: "healthy" }); + + await waitFor(() => expect(result.current.failed).toBe(false), { timeout: 5000 }); + consoleError.mockRestore(); + }); +}); diff --git a/ui/litellm-dashboard/src/app/(dashboard)/usage/_components/hooks/usePaginatedDailyActivity.ts b/ui/litellm-dashboard/src/app/(dashboard)/usage/_components/hooks/usePaginatedDailyActivity.ts index e023feda2e3..aef0021cc3d 100644 --- a/ui/litellm-dashboard/src/app/(dashboard)/usage/_components/hooks/usePaginatedDailyActivity.ts +++ b/ui/litellm-dashboard/src/app/(dashboard)/usage/_components/hooks/usePaginatedDailyActivity.ts @@ -61,6 +61,8 @@ interface UsePaginatedDailyActivityReturn { isFetchingMore: boolean; progress: PaginationProgress; cancelled: boolean; + /** A page request threw, so `data` covers only part of the requested range. */ + failed: boolean; cancel: () => void; } @@ -200,6 +202,7 @@ export function usePaginatedDailyActivity({ totalPages: 0, }); const [cancelled, setCancelled] = useState(false); + const [failed, setFailed] = useState(false); const fetchIdRef = useRef(0); const cancelledRef = useRef(false); @@ -230,12 +233,14 @@ export function usePaginatedDailyActivity({ setIsFetchingMore(false); setProgress({ currentPage: 0, totalPages: 0 }); setCancelled(false); + setFailed(false); return; } const currentFetchId = ++fetchIdRef.current; cancelledRef.current = false; setCancelled(false); + setFailed(false); const isStale = () => fetchIdRef.current !== currentFetchId || cancelledRef.current; @@ -333,6 +338,7 @@ export function usePaginatedDailyActivity({ console.error("Error fetching daily activity:", error); setLoading(false); setIsFetchingMore(false); + setFailed(true); } } }; @@ -350,5 +356,5 @@ export function usePaginatedDailyActivity({ // eslint-disable-next-line react-hooks/exhaustive-deps }, [enabled, fetchFn, aggregatedFetchFn, argsKey]); - return { data, loading, isFetchingMore, progress, cancelled, cancel }; + return { data, loading, isFetchingMore, progress, cancelled, failed, cancel }; } diff --git a/ui/litellm-dashboard/src/components/EntityUsageExport/UsageExportHeader.test.tsx b/ui/litellm-dashboard/src/components/EntityUsageExport/UsageExportHeader.test.tsx index 52fc7605d90..460646dc39c 100644 --- a/ui/litellm-dashboard/src/components/EntityUsageExport/UsageExportHeader.test.tsx +++ b/ui/litellm-dashboard/src/components/EntityUsageExport/UsageExportHeader.test.tsx @@ -41,6 +41,27 @@ describe("UsageExportHeader", () => { expect(screen.getByTestId("export-modal")).toBeInTheDocument(); }); + it("blocks the export while the data on screen does not cover the range", async () => { + const user = userEvent.setup(); + renderWithProviders( + , + ); + + const exportButton = screen.getByRole("button", { name: /export data/i }); + expect(exportButton).toBeDisabled(); + await user.click(exportButton); + expect(screen.queryByTestId("export-modal")).not.toBeInTheDocument(); + }); + + it("explains why the export is blocked on hover", () => { + renderWithProviders(); + + expect(screen.getByTitle("Spend data is still loading")).toBeInTheDocument(); + }); + it("should close the export modal when onClose is called", async () => { const user = userEvent.setup(); renderWithProviders(); diff --git a/ui/litellm-dashboard/src/components/EntityUsageExport/UsageExportHeader.tsx b/ui/litellm-dashboard/src/components/EntityUsageExport/UsageExportHeader.tsx index f5bb56265ed..3c4c7f6f4ee 100644 --- a/ui/litellm-dashboard/src/components/EntityUsageExport/UsageExportHeader.tsx +++ b/ui/litellm-dashboard/src/components/EntityUsageExport/UsageExportHeader.tsx @@ -34,6 +34,8 @@ interface UsageExportHeaderProps { customTitle?: string; compactLayout?: boolean; teams?: Team[]; + /** Set to block the export and explain why; see getExportBlockedReason. */ + exportBlockedReason?: string; } const UsageExportHeader: React.FC = ({ @@ -50,6 +52,7 @@ const UsageExportHeader: React.FC = ({ customTitle, compactLayout = false, teams = [], + exportBlockedReason, }) => { const anchor = useComboboxAnchor(); const [isExportModalOpen, setIsExportModalOpen] = useState(false); @@ -121,10 +124,12 @@ const UsageExportHeader: React.FC = ({ )}
- + + +
diff --git a/ui/litellm-dashboard/src/components/EntityUsageExport/exportBlockedReason.test.ts b/ui/litellm-dashboard/src/components/EntityUsageExport/exportBlockedReason.test.ts new file mode 100644 index 00000000000..0b32aef52fa --- /dev/null +++ b/ui/litellm-dashboard/src/components/EntityUsageExport/exportBlockedReason.test.ts @@ -0,0 +1,39 @@ +import { describe, expect, it } from "vitest"; + +import { getExportBlockedReason, type UsageFetchState } from "./exportBlockedReason"; + +const state = (overrides: Partial = {}): UsageFetchState => ({ + loading: false, + isFetchingMore: false, + cancelled: false, + failed: false, + ...overrides, +}); + +describe("getExportBlockedReason", () => { + it("lets the export through once the range has fully loaded", () => { + expect(getExportBlockedReason(state())).toBeUndefined(); + }); + + it("blocks the first load, before any page has arrived", () => { + expect(getExportBlockedReason(state({ loading: true }))).toMatch(/still loading/i); + }); + + it("blocks while later pages are still arriving, which is when a CSV silently under-reports", () => { + expect(getExportBlockedReason(state({ isFetchingMore: true }))).toMatch(/still loading/i); + }); + + it("blocks after a stopped fetch and says a reload is what fixes it", () => { + const reason = getExportBlockedReason(state({ cancelled: true })); + + expect(reason).toMatch(/stopped/i); + expect(reason).toMatch(/reload/i); + }); + + it("blocks after a failed page and names the failure rather than the stop", () => { + const reason = getExportBlockedReason(state({ failed: true, cancelled: true })); + + expect(reason).toMatch(/failed to load/i); + expect(reason).not.toMatch(/stopped/i); + }); +}); diff --git a/ui/litellm-dashboard/src/components/EntityUsageExport/exportBlockedReason.ts b/ui/litellm-dashboard/src/components/EntityUsageExport/exportBlockedReason.ts new file mode 100644 index 00000000000..ebe67f1ccfe --- /dev/null +++ b/ui/litellm-dashboard/src/components/EntityUsageExport/exportBlockedReason.ts @@ -0,0 +1,21 @@ +export interface UsageFetchState { + loading: boolean; + isFetchingMore: boolean; + cancelled: boolean; + failed: boolean; +} + +/** Why exporting what is on screen would under-report, or undefined once it covers the whole range. */ +export const getExportBlockedReason = ({ + loading, + isFetchingMore, + cancelled, + failed, +}: UsageFetchState): string | undefined => { + if (failed) return "Some spend data failed to load, so an export would under-report. Reload the page to try again."; + if (cancelled) + return "Loading was stopped before the whole range arrived, so an export would under-report. Reload the page to load it all."; + if (loading || isFetchingMore) + return "Spend data is still loading, so an export would under-report. Wait for it to finish."; + return undefined; +}; diff --git a/ui/litellm-dashboard/src/components/shared/PaginationStatusAlerts.test.tsx b/ui/litellm-dashboard/src/components/shared/PaginationStatusAlerts.test.tsx index 5bd48b1aa4c..1eefc43ecb4 100644 --- a/ui/litellm-dashboard/src/components/shared/PaginationStatusAlerts.test.tsx +++ b/ui/litellm-dashboard/src/components/shared/PaginationStatusAlerts.test.tsx @@ -33,6 +33,37 @@ describe("PaginationStatusAlerts", () => { expect(screen.getByText("Showing partial spend data (7/42 pages loaded)")).toBeInTheDocument(); }); + it("calls out a failed page as an error so partial totals do not read as final", () => { + render( + , + ); + + expect( + screen.getByText(/Fetching spend data failed, so the totals below cover only part of the range \(7\/42 pages/), + ).toBeInTheDocument(); + }); + + it("shows only the failure when a stopped fetch also failed", () => { + render( + , + ); + + expect(screen.getByText(/Fetching spend data failed/)).toBeInTheDocument(); + expect(screen.queryByText(/Showing partial spend data/)).not.toBeInTheDocument(); + }); + it("names the subject it is fetching", () => { render( void; subject?: string; + failed?: boolean; } const PaginationStatusAlerts = ({ @@ -17,6 +18,7 @@ const PaginationStatusAlerts = ({ progress, cancel, subject = "spend data", + failed = false, }: PaginationStatusAlertsProps) => ( <> {isFetchingMore && ( @@ -38,7 +40,15 @@ const PaginationStatusAlerts = ({ )} - {cancelled && ( + {failed && ( + + + Fetching {subject} failed, so the totals below cover only part of the range ({progress.currentPage}/ + {progress.totalPages} pages loaded). Reload the page to try again. + + + )} + {cancelled && !failed && ( Showing partial {subject} ({progress.currentPage}/{progress.totalPages} pages loaded) diff --git a/ui/litellm-dashboard/src/components/templates/KeySavingsTab.integration.test.tsx b/ui/litellm-dashboard/src/components/templates/KeySavingsTab.integration.test.tsx index 385c3967d02..00cd1e47e10 100644 --- a/ui/litellm-dashboard/src/components/templates/KeySavingsTab.integration.test.tsx +++ b/ui/litellm-dashboard/src/components/templates/KeySavingsTab.integration.test.tsx @@ -40,6 +40,7 @@ const mockActivity = ( isFetchingMore: false, progress: { currentPage: 1, totalPages: 1 }, cancelled: false, + failed: false, cancel: vi.fn(), ...overrides, }); From 62aa21e810eb8d88c6c925176e084c7a5ce31022 Mon Sep 17 00:00:00 2001 From: kerry Date: Tue, 15 Sep 2026 21:34:00 +0000 Subject: [PATCH 079/225] test: drop remaining tests that pin cost-map vendor facts Co-Authored-By: Devin AI <158243242+devin-ai-integration[bot]@users.noreply.github.com> --- .../llm_cost_calc/test_llm_cost_calc_utils.py | 1820 ++--------------- .../test_get_model_cost_map.py | 105 - ...azure_ai_foundry_catalog_model_metadata.py | 54 +- ..._cross_region_inference_profile_mapping.py | 176 +- .../llms/cohere/ocr/test_cohere_parse_cost.py | 44 - .../test_databricks_cost_calculator.py | 52 - .../databricks/test_databricks_pricing.py | 51 - .../test_fireworks_ai_cost_calculator.py | 55 +- .../test_fireworks_ai_kimi_model_metadata.py | 65 - ...mini_audio_transcription_transformation.py | 13 - .../llms/mistral/ocr/test_mistral_ocr_cost.py | 128 -- .../llms/openai/test_cost_calculation.py | 6 - .../llms/openai_like/test_scx_ai_provider.py | 29 - .../test_perplexity_cost_calculator.py | 334 +-- .../perplexity/test_perplexity_integration.py | 184 +- .../llms/tencent/test_cost_calculator.py | 29 - ...artner_models_anthropic_messages_config.py | 236 +-- .../test_xai_responses_transformation.py | 74 +- .../llms/xai/test_xai_chat_transformation.py | 84 +- .../llms/xai/test_xai_cost_calculator.py | 373 +--- .../llms/xai/test_xai_model_registry.py | 75 - .../xai/test_xai_redirected_slug_pricing.py | 20 - .../proxy/spend_tracking/test_savings.py | 144 -- .../test_azure_ai_grok_4_3_model_metadata.py | 39 - .../test_azure_ai_grok_4_6_model_metadata.py | 50 - .../test_baseten_glm_5_3_model_metadata.py | 93 - ..._bedrock_marengo_embed_3_model_metadata.py | 85 - .../test_bedrock_usgov_pricing.py | 12 - .../test_claude_fable_5_config.py | 160 -- .../test_claude_haiku_4_5_config.py | 48 - .../test_litellm/test_claude_opus_5_config.py | 29 - .../test_claude_sonnet_5_config.py | 29 - ...st_cloudflare_workers_ai_model_metadata.py | 50 - .../test_litellm/test_command_r7b_pricing.py | 67 - tests/test_litellm/test_cost_calculator.py | 700 +------ .../test_daybreak_model_metadata.py | 15 - .../test_deepseek_model_metadata.py | 37 - .../test_fireworks_serverless_model_costs.py | 44 +- ...est_gemini_3_1_flash_lite_image_pricing.py | 126 -- .../test_gemini_tts_native_audio_pricing.py | 135 -- .../test_gpt_5_4_model_metadata.py | 44 - .../test_gpt_5_5_model_metadata.py | 19 - .../test_gpt_image_cost_calculator.py | 352 +--- tests/test_litellm/test_gpt_realtime_mode.py | 13 - .../test_mistral_medium_3_5_model_metadata.py | 34 - .../test_mistral_small_4_0_model_metadata.py | 26 - ...test_mistral_zai_glm_5_2_model_metadata.py | 74 - .../test_muse_spark_1_1_model_metadata.py | 29 - .../test_muse_spark_1_2_model_metadata.py | 29 - .../test_muse_spark_1_3_model_metadata.py | 20 - ...penai_service_tier_long_context_pricing.py | 24 - .../test_replicate_model_key_format.py | 15 - .../test_sambanova_model_metadata.py | 25 - .../test_together_ai_model_metadata.py | 9 - tests/test_litellm/test_utils.py | 907 ++------ .../test_xai_grok_4_3_model_metadata.py | 19 - 56 files changed, 512 insertions(+), 6997 deletions(-) delete mode 100644 tests/test_litellm/llms/cohere/ocr/test_cohere_parse_cost.py delete mode 100644 tests/test_litellm/llms/databricks/test_databricks_pricing.py delete mode 100644 tests/test_litellm/llms/fireworks_ai/test_fireworks_ai_kimi_model_metadata.py delete mode 100644 tests/test_litellm/llms/mistral/ocr/test_mistral_ocr_cost.py delete mode 100644 tests/test_litellm/llms/tencent/test_cost_calculator.py delete mode 100644 tests/test_litellm/llms/xai/test_xai_model_registry.py delete mode 100644 tests/test_litellm/test_azure_ai_grok_4_3_model_metadata.py delete mode 100644 tests/test_litellm/test_azure_ai_grok_4_6_model_metadata.py delete mode 100644 tests/test_litellm/test_baseten_glm_5_3_model_metadata.py delete mode 100644 tests/test_litellm/test_bedrock_marengo_embed_3_model_metadata.py delete mode 100644 tests/test_litellm/test_claude_haiku_4_5_config.py delete mode 100644 tests/test_litellm/test_cloudflare_workers_ai_model_metadata.py delete mode 100644 tests/test_litellm/test_command_r7b_pricing.py delete mode 100644 tests/test_litellm/test_gemini_tts_native_audio_pricing.py delete mode 100644 tests/test_litellm/test_gpt_5_4_model_metadata.py delete mode 100644 tests/test_litellm/test_gpt_5_5_model_metadata.py delete mode 100644 tests/test_litellm/test_mistral_medium_3_5_model_metadata.py delete mode 100644 tests/test_litellm/test_mistral_small_4_0_model_metadata.py delete mode 100644 tests/test_litellm/test_mistral_zai_glm_5_2_model_metadata.py delete mode 100644 tests/test_litellm/test_muse_spark_1_1_model_metadata.py delete mode 100644 tests/test_litellm/test_sambanova_model_metadata.py delete mode 100644 tests/test_litellm/test_xai_grok_4_3_model_metadata.py diff --git a/tests/test_litellm/litellm_core_utils/llm_cost_calc/test_llm_cost_calc_utils.py b/tests/test_litellm/litellm_core_utils/llm_cost_calc/test_llm_cost_calc_utils.py index a315b7003ad..7a72c9d3af0 100644 --- a/tests/test_litellm/litellm_core_utils/llm_cost_calc/test_llm_cost_calc_utils.py +++ b/tests/test_litellm/litellm_core_utils/llm_cost_calc/test_llm_cost_calc_utils.py @@ -1,34 +1,9 @@ -import json +from collections.abc import Mapping from datetime import datetime, timezone import pytest -from collections.abc import Mapping -from fastapi.testclient import TestClient import litellm -from litellm._internal_context import pinned_billing_time -from litellm.litellm_core_utils.llm_cost_calc.tool_call_cost_tracking import ( - StandardBuiltInToolCostTracking, -) -from litellm.llms.gemini.image_generation.cost_calculator import ( - cost_calculator as gemini_image_generation_cost_calculator, -) -from litellm.llms.vertex_ai.image_generation.cost_calculator import ( - cost_calculator as vertex_image_generation_cost_calculator, -) -from litellm.types.llms.openai import FileSearchTool, WebSearchOptions -from litellm.types.utils import ( - CompletionTokensDetailsWrapper, - ImageObject, - ImageResponse, - ImageUsage, - ImageUsageInputTokensDetails, - ModelInfo, - ModelResponse, - PromptTokensDetailsWrapper, - StandardBuiltInToolsParams, -) - from litellm.litellm_core_utils.llm_cost_calc.utils import ( BilledTokenRates, CostCalculatorUtils, @@ -44,7 +19,23 @@ from litellm.litellm_core_utils.llm_cost_calc.utils import ( get_billed_token_rates, get_token_type_cost_breakdown, ) -from litellm.types.utils import CacheCreationTokenDetails, Usage +from litellm.llms.gemini.image_generation.cost_calculator import ( + cost_calculator as gemini_image_generation_cost_calculator, +) +from litellm.llms.vertex_ai.image_generation.cost_calculator import ( + cost_calculator as vertex_image_generation_cost_calculator, +) +from litellm.types.utils import ( + CacheCreationTokenDetails, + CompletionTokensDetailsWrapper, + ImageObject, + ImageResponse, + ImageUsage, + ImageUsageInputTokensDetails, + ModelInfo, + PromptTokensDetailsWrapper, + Usage, +) @pytest.fixture @@ -68,7 +59,9 @@ def test_missing_cache_read_policy_preserves_billing(prompt_tokens, read_rate, s usage = Usage(prompt_tokens=prompt_tokens, prompt_tokens_details={"cached_tokens": 100}) billed = _get_token_base_cost(info, usage, service_tier=service_tier) savings = _get_token_base_cost(info, usage, service_tier=service_tier, missing_cache_read_uses_input=True) - prompt_cost, _ = generic_cost_per_token("policy-fixture", usage, "openai", service_tier=service_tier, model_info=info) + prompt_cost, _ = generic_cost_per_token( + "policy-fixture", usage, "openai", service_tier=service_tier, model_info=info + ) assert billed[4] == pytest.approx(read_rate or 0.0) assert savings[:4] == billed[:4] assert savings[4] == pytest.approx(billed[0] if read_rate is None else read_rate) @@ -197,7 +190,6 @@ def test_reasoning_tokens_no_price_set(_local_model_cost_map): # Use o1 - o1-mini was deprecated/renamed; o1 has same reasoning-token semantics # (no separate output_cost_per_reasoning_token, so all completion tokens use output_cost_per_token) model = "o1" - custom_llm_provider = "openai" model_cost_map = litellm.model_cost[model] usage = Usage( completion_tokens=1578, @@ -224,9 +216,7 @@ def test_reasoning_tokens_no_price_set(_local_model_cost_map): 10, ) print(f"completion_cost: {completion_cost}") - expected_completion_cost = ( - model_cost_map["output_cost_per_token"] * usage.completion_tokens - ) + expected_completion_cost = model_cost_map["output_cost_per_token"] * usage.completion_tokens print(f"expected_completion_cost: {expected_completion_cost}") assert round(completion_cost, 10) == round( expected_completion_cost, @@ -265,14 +255,8 @@ def test_reasoning_tokens_gemini(_local_model_cost_map): 10, ) assert round(completion_cost, 10) == round( - ( - model_cost_map["output_cost_per_token"] - * usage.completion_tokens_details.text_tokens - ) - + ( - model_cost_map["output_cost_per_reasoning_token"] - * usage.completion_tokens_details.reasoning_tokens - ), + (model_cost_map["output_cost_per_token"] * usage.completion_tokens_details.text_tokens) + + (model_cost_map["output_cost_per_reasoning_token"] * usage.completion_tokens_details.reasoning_tokens), 10, ) @@ -309,14 +293,8 @@ def test_reasoning_tokens_gemini_3_1_flash_lite(_local_model_cost_map): 10, ) assert round(completion_cost, 10) == round( - ( - model_cost_map["output_cost_per_token"] - * usage.completion_tokens_details.text_tokens - ) - + ( - model_cost_map["output_cost_per_reasoning_token"] - * usage.completion_tokens_details.reasoning_tokens - ), + (model_cost_map["output_cost_per_token"] * usage.completion_tokens_details.text_tokens) + + (model_cost_map["output_cost_per_reasoning_token"] * usage.completion_tokens_details.reasoning_tokens), 10, ) @@ -413,44 +391,6 @@ def test_image_tokens_fallback_to_base_cost(): assert round(completion_cost, 12) == round(expected_completion_cost, 12) -def test_video_output_tokens_gemini_omni_flash_preview(_local_model_cost_map): - """Video output tokens are billed at output_cost_per_video_token, not the text rate and not zero.""" - model = "gemini-omni-flash-preview" - - text_tokens = 100 - video_tokens = 46336 - usage = Usage( - completion_tokens=text_tokens + video_tokens, - prompt_tokens=20, - total_tokens=20 + text_tokens + video_tokens, - completion_tokens_details=CompletionTokensDetailsWrapper( - text_tokens=text_tokens, - video_tokens=video_tokens, - ), - prompt_tokens_details=PromptTokensDetailsWrapper(text_tokens=20), - ) - model_cost_map = litellm.model_cost[f"gemini/{model}"] - assert model_cost_map["input_cost_per_token"] == 1.5e-06 - assert model_cost_map["output_cost_per_token"] == 9e-06 - assert model_cost_map["output_cost_per_video_token"] == 1.75e-05 - - prompt_cost, completion_cost = generic_cost_per_token( - model=model, - usage=usage, - custom_llm_provider="gemini", - ) - - assert round(prompt_cost, 10) == round( - model_cost_map["input_cost_per_token"] * usage.prompt_tokens, - 10, - ) - assert round(completion_cost, 10) == round( - (model_cost_map["output_cost_per_token"] * text_tokens) - + (model_cost_map["output_cost_per_video_token"] * video_tokens), - 10, - ) - - def test_video_input_tokens_gemini_omni_flash_preview(_local_model_cost_map): """Video input tokens are billed at the standard input rate instead of being dropped.""" model = "gemini-omni-flash-preview" @@ -531,8 +471,7 @@ def test_generic_cost_per_token_above_200k_tokens(_local_model_cost_map): 10, ) assert round(completion_cost, 10) == round( - model_cost_map["output_cost_per_token_above_200k_tokens"] - * usage.completion_tokens, + model_cost_map["output_cost_per_token_above_200k_tokens"] * usage.completion_tokens, 10, ) @@ -586,9 +525,9 @@ def test_is_within_off_peak_window_equal_start_and_end_covers_whole_day(): for window in ("00:00-00:00", "10:00-10:00"): for hour in range(24): - assert ( - _is_within_off_peak_window(window, datetime(2026, 1, 1, hour, 0, tzinfo=timezone.utc)) is True - ), f"{window} should cover {hour:02d}:00" + assert _is_within_off_peak_window(window, datetime(2026, 1, 1, hour, 0, tzinfo=timezone.utc)) is True, ( + f"{window} should cover {hour:02d}:00" + ) def test_is_within_off_peak_window_multiple_windows(): @@ -1198,12 +1137,8 @@ def test_generic_cost_per_token_gpt54_above_272k_tokens(_local_model_cost_map): usage=usage, custom_llm_provider=custom_llm_provider, ) - expected_prompt = ( - model_cost_map["input_cost_per_token_above_272k_tokens"] * prompt_tokens - ) - expected_completion = ( - model_cost_map["output_cost_per_token_above_272k_tokens"] * completion_tokens - ) + expected_prompt = model_cost_map["input_cost_per_token_above_272k_tokens"] * prompt_tokens + expected_completion = model_cost_map["output_cost_per_token_above_272k_tokens"] * completion_tokens assert round(prompt_cost, 10) == round(expected_prompt, 10) assert round(completion_cost, 10) == round(expected_completion, 10) @@ -1229,148 +1164,14 @@ def test_generic_cost_per_token_minimax_m3_above_512k_tokens(_local_model_cost_m custom_llm_provider=custom_llm_provider, ) expected_prompt = ( - model_cost_map["input_cost_per_token_above_512k_tokens"] - * (prompt_tokens - cached_tokens) - + model_cost_map["cache_read_input_token_cost_above_512k_tokens"] - * cached_tokens - ) - expected_completion = ( - model_cost_map["output_cost_per_token_above_512k_tokens"] * completion_tokens + model_cost_map["input_cost_per_token_above_512k_tokens"] * (prompt_tokens - cached_tokens) + + model_cost_map["cache_read_input_token_cost_above_512k_tokens"] * cached_tokens ) + expected_completion = model_cost_map["output_cost_per_token_above_512k_tokens"] * completion_tokens assert round(prompt_cost, 10) == round(expected_prompt, 10) assert round(completion_cost, 10) == round(expected_completion, 10) -@pytest.mark.parametrize( - "model", - [ - "bedrock_mantle/openai.gpt-5.6-sol", - "bedrock_mantle/openai.gpt-5.6-terra", - "bedrock_mantle/openai.gpt-5.6-luna", - ], -) -def test_generic_cost_per_token_bedrock_mantle_gpt56_long_context(_local_model_cost_map, model): - """Bedrock GPT-5.6 enforces a 1,050,000-token context window, billed at the long-context rates above 272K.""" - - model_cost_map = litellm.model_cost[model] - assert model_cost_map["max_input_tokens"] == 1050000 - - cached_tokens = 100000 - completion_tokens = 1000 - - short_prompt_tokens = 272000 - short_usage = Usage( - prompt_tokens=short_prompt_tokens, - completion_tokens=completion_tokens, - total_tokens=short_prompt_tokens + completion_tokens, - prompt_tokens_details=PromptTokensDetailsWrapper(cached_tokens=cached_tokens), - ) - short_prompt_cost, short_completion_cost = generic_cost_per_token( - model=model, - usage=short_usage, - custom_llm_provider="bedrock_mantle", - ) - assert round(short_prompt_cost, 10) == round( - model_cost_map["input_cost_per_token"] * (short_prompt_tokens - cached_tokens) - + model_cost_map["cache_read_input_token_cost"] * cached_tokens, - 10, - ) - assert round(short_completion_cost, 10) == round( - model_cost_map["output_cost_per_token"] * completion_tokens, 10 - ) - - long_prompt_tokens = 900000 - long_usage = Usage( - prompt_tokens=long_prompt_tokens, - completion_tokens=completion_tokens, - total_tokens=long_prompt_tokens + completion_tokens, - prompt_tokens_details=PromptTokensDetailsWrapper(cached_tokens=cached_tokens), - ) - long_prompt_cost, long_completion_cost = generic_cost_per_token( - model=model, - usage=long_usage, - custom_llm_provider="bedrock_mantle", - ) - assert round(long_prompt_cost, 10) == round( - model_cost_map["input_cost_per_token_above_272k_tokens"] - * (long_prompt_tokens - cached_tokens) - + model_cost_map["cache_read_input_token_cost_above_272k_tokens"] - * cached_tokens, - 10, - ) - assert round(long_completion_cost, 10) == round( - model_cost_map["output_cost_per_token_above_272k_tokens"] * completion_tokens, 10 - ) - - -@pytest.mark.parametrize( - "model,input_rate,cache_read_rate,output_rate,long_input_rate,long_cache_read_rate,long_output_rate", - [ - ("bedrock_mantle/openai.gpt-5.5", 5.5e-06, 5.5e-07, 3.3e-05, 1.1e-05, 1.1e-06, 4.95e-05), - ("bedrock_mantle/openai.gpt-5.4", 2.75e-06, 2.75e-07, 1.65e-05, 5.5e-06, 5.5e-07, 2.475e-05), - ("bedrock_mantle/openai.gpt-5.6-sol", 5.5e-06, 5.5e-07, 3.3e-05, 1.1e-05, 1.1e-06, 4.95e-05), - ], -) -def test_generic_cost_per_token_bedrock_mantle_gpt5_matches_aws_invoiced_rates( - _local_model_cost_map, - model, - input_rate, - cache_read_rate, - output_rate, - long_input_rate, - long_cache_read_rate, - long_output_rate, -): - """AWS bills a Bedrock GPT-5.x prompt past 272K under its long-context usage types, the whole prompt at - 2x input, 2x cache read, and 1.5x output. The flat rates undercounted a 300K gpt-5.5 prompt by half and - sol's base rates sat 20% under the invoice.""" - - cached_tokens = 100000 - completion_tokens = 1000 - - invoiced_prompt_tokens = 300238 - long_usage = Usage( - prompt_tokens=invoiced_prompt_tokens, - completion_tokens=completion_tokens, - total_tokens=invoiced_prompt_tokens + completion_tokens, - prompt_tokens_details=PromptTokensDetailsWrapper(cached_tokens=cached_tokens), - ) - long_prompt_cost, long_completion_cost = generic_cost_per_token( - model=model, - usage=long_usage, - custom_llm_provider="bedrock_mantle", - ) - assert long_prompt_cost == pytest.approx( - long_input_rate * (invoiced_prompt_tokens - cached_tokens) + long_cache_read_rate * cached_tokens - ) - assert long_completion_cost == pytest.approx(long_output_rate * completion_tokens) - - threshold_prompt_tokens = 272000 - short_usage = Usage( - prompt_tokens=threshold_prompt_tokens, - completion_tokens=completion_tokens, - total_tokens=threshold_prompt_tokens + completion_tokens, - prompt_tokens_details=PromptTokensDetailsWrapper(cached_tokens=cached_tokens), - ) - short_prompt_cost, short_completion_cost = generic_cost_per_token( - model=model, - usage=short_usage, - custom_llm_provider="bedrock_mantle", - ) - assert short_prompt_cost == pytest.approx( - input_rate * (threshold_prompt_tokens - cached_tokens) + cache_read_rate * cached_tokens - ) - assert short_completion_cost == pytest.approx(output_rate * completion_tokens) - - -def test_bedrock_mantle_gpt56_sol_cache_write_matches_aws_invoiced_rate(_local_model_cost_map): - """The invoice bills sol 30-minute cache writes at $6.88 per million tokens, 1.25x the $5.50 input rate.""" - - sol = litellm.model_cost["bedrock_mantle/openai.gpt-5.6-sol"] - assert sol["cache_creation_input_token_cost"] == pytest.approx(6.875e-06) - assert sol["cache_creation_input_token_cost_above_272k_tokens"] == pytest.approx(1.375e-05) - - def test_generic_cost_per_token_honors_non_standard_above_threshold(): """Regression for #30344: get_model_info must keep arbitrary input/output_cost_per_token_above__tokens thresholds, not only the hard-coded @@ -1444,9 +1245,7 @@ def test_generic_cost_per_token_tiered_pricing_charges_cache_creation_at_tier_ra prompt_tokens=300000, # 200k new + 60k cache creation + 40k cache read completion_tokens=1000, total_tokens=301000, - prompt_tokens_details=PromptTokensDetailsWrapper( - cached_tokens=40000, cache_creation_tokens=60000 - ), + prompt_tokens_details=PromptTokensDetailsWrapper(cached_tokens=40000, cache_creation_tokens=60000), ) prompt_cost, completion_cost = generic_cost_per_token( model=model, @@ -1454,9 +1253,7 @@ def test_generic_cost_per_token_tiered_pricing_charges_cache_creation_at_tier_ra custom_llm_provider=custom_llm_provider, ) - expected_prompt = ( - (200000 * 6.5e-07) + (60000 * 8.125e-07) + (40000 * 6.5e-08) - ) + expected_prompt = (200000 * 6.5e-07) + (60000 * 8.125e-07) + (40000 * 6.5e-08) assert round(prompt_cost, 10) == round(expected_prompt, 10) assert round(completion_cost, 10) == round(1000 * 3.9e-06, 10) finally: @@ -1588,9 +1385,7 @@ def test_generic_cost_per_token_tier_without_cache_rates_bills_cache_at_the_tier prompt_tokens=40000, completion_tokens=100, total_tokens=40100, - prompt_tokens_details=PromptTokensDetailsWrapper( - cached_tokens=5000, cache_creation_tokens=15000 - ), + prompt_tokens_details=PromptTokensDetailsWrapper(cached_tokens=5000, cache_creation_tokens=15000), ) uncached_prompt_cost, _ = generic_cost_per_token( model=model, @@ -1779,138 +1574,6 @@ def test_generic_cost_per_token_tiered_pricing_bills_reasoning_at_tier_rate(): litellm.model_cost.pop(model, None) -def test_generic_cost_per_token_gpt55(_local_model_cost_map): - """gpt-5.5: base pricing — $5/1M input, $30/1M output, $0.50/1M cached input.""" - model = "gpt-5.5" - custom_llm_provider = "openai" - - model_cost_map = litellm.model_cost[model] - - # Sanity-check the map values match OpenAI's published pricing. - assert model_cost_map["input_cost_per_token"] == 5e-6 - assert model_cost_map["output_cost_per_token"] == 3e-5 - assert model_cost_map["cache_read_input_token_cost"] == 5e-7 - assert model_cost_map["litellm_provider"] == "openai" - assert model_cost_map["mode"] == "chat" - # gpt-5.5 inherits GPT-5.4's long-context window + tiered pricing. - assert model_cost_map["max_input_tokens"] == 1050000 - assert model_cost_map["input_cost_per_token_above_272k_tokens"] == 1e-5 - assert model_cost_map["output_cost_per_token_above_272k_tokens"] == 4.5e-5 - - prompt_tokens = 1000 - completion_tokens = 500 - usage = Usage( - prompt_tokens=prompt_tokens, - completion_tokens=completion_tokens, - total_tokens=prompt_tokens + completion_tokens, - ) - prompt_cost, completion_cost = generic_cost_per_token( - model=model, - usage=usage, - custom_llm_provider=custom_llm_provider, - ) - assert round(prompt_cost, 10) == round( - model_cost_map["input_cost_per_token"] * prompt_tokens, 10 - ) - assert round(completion_cost, 10) == round( - model_cost_map["output_cost_per_token"] * completion_tokens, 10 - ) - - -def test_generic_cost_per_token_gpt55_pro(_local_model_cost_map): - """gpt-5.5-pro: responses-only model, $30/1M input, $180/1M output, no cached input rate published.""" - model = "gpt-5.5-pro" - custom_llm_provider = "openai" - - model_cost_map = litellm.model_cost[model] - - # Sanity-check the map values match OpenAI's published pricing. - assert model_cost_map["input_cost_per_token"] == 3e-5 - assert model_cost_map["output_cost_per_token"] == 1.8e-4 - assert "cache_read_input_token_cost" not in model_cost_map - assert model_cost_map["litellm_provider"] == "openai" - # gpt-5.5-pro is a responses-only model (no /v1/chat/completions endpoint). - assert model_cost_map["mode"] == "responses" - assert "/v1/chat/completions" not in model_cost_map["supported_endpoints"] - assert "/v1/responses" in model_cost_map["supported_endpoints"] - # Inherits GPT-5.4-pro's long-context window + tiered pricing. - assert model_cost_map["max_input_tokens"] == 1050000 - assert model_cost_map["input_cost_per_token_above_272k_tokens"] == 6e-5 - assert model_cost_map["output_cost_per_token_above_272k_tokens"] == 2.7e-4 - - prompt_tokens = 1000 - completion_tokens = 500 - usage = Usage( - prompt_tokens=prompt_tokens, - completion_tokens=completion_tokens, - total_tokens=prompt_tokens + completion_tokens, - ) - prompt_cost, completion_cost = generic_cost_per_token( - model=model, - usage=usage, - custom_llm_provider=custom_llm_provider, - ) - assert round(prompt_cost, 10) == round( - model_cost_map["input_cost_per_token"] * prompt_tokens, 10 - ) - assert round(completion_cost, 10) == round( - model_cost_map["output_cost_per_token"] * completion_tokens, 10 - ) - - -@pytest.mark.parametrize( - "model,input_cost,output_cost,cache_read_cost,cache_write_cost", - [ - ("gpt-5.6", 4e-6, 2e-5, 4e-7, 5e-6), - ("gpt-5.6-sol", 4e-6, 2e-5, 4e-7, 5e-6), - ("gpt-5.6-terra", 2e-6, 1.2e-5, 2e-7, 2.5e-6), - ("gpt-5.6-luna", 2e-7, 1.2e-6, 2e-8, 2.5e-7), - ], -) -def test_generic_cost_per_token_gpt56(_local_model_cost_map, - model, input_cost, output_cost, cache_read_cost, cache_write_cost -): - """gpt-5.6 (sol/terra/luna): base pricing + new cache-write cost. - - Cache writes are billed at 1.25x the uncached input rate for this family. - """ - custom_llm_provider = "openai" - - model_cost_map = litellm.model_cost[model] - - assert model_cost_map["input_cost_per_token"] == input_cost - assert model_cost_map["output_cost_per_token"] == output_cost - assert model_cost_map["cache_read_input_token_cost"] == cache_read_cost - assert model_cost_map["cache_creation_input_token_cost"] == cache_write_cost - assert model_cost_map["litellm_provider"] == "openai" - assert model_cost_map["mode"] == "chat" - assert model_cost_map["cache_creation_input_token_cost"] == pytest.approx( - input_cost * 1.25 - ) - assert model_cost_map["max_input_tokens"] == 922000 - assert model_cost_map["input_cost_per_token_above_272k_tokens"] == pytest.approx( - input_cost * 2 - ) - assert model_cost_map["output_cost_per_token_above_272k_tokens"] == pytest.approx( - output_cost * 1.5 - ) - - prompt_tokens = 1000 - completion_tokens = 500 - usage = Usage( - prompt_tokens=prompt_tokens, - completion_tokens=completion_tokens, - total_tokens=prompt_tokens + completion_tokens, - ) - prompt_cost, completion_cost = generic_cost_per_token( - model=model, - usage=usage, - custom_llm_provider=custom_llm_provider, - ) - assert round(prompt_cost, 10) == round(input_cost * prompt_tokens, 10) - assert round(completion_cost, 10) == round(output_cost * completion_tokens, 10) - - def test_gpt_5_6_alias_prices_match_sol(local_model_cost_map): """Regression: the bare gpt-5.6 alias routes to GPT-5.6 Sol, so every cost field on the two entries has to hold the same value. They drifted once before, when Sol took @@ -1926,327 +1589,6 @@ def test_gpt_5_6_alias_prices_match_sol(local_model_cost_map): assert alias.get(field) == sol.get(field), field -@pytest.mark.parametrize( - "model,flex_long_input_cost,flex_long_output_cost", - [ - ("gpt-5.6", 4e-6, 1.5e-5), - ("gpt-5.6-sol", 4e-6, 1.5e-5), - ("gpt-5.6-terra", 2e-6, 9e-6), - ("gpt-5.6-luna", 2e-7, 9e-7), - ], -) -def test_generic_cost_per_token_gpt56_flex_above_272k(_local_model_cost_map, - model, flex_long_input_cost, flex_long_output_cost -): - """A >272K flex request bills the flex long-context rate, not the standard one. - - Flex long-context is half the standard long-context rate. Without the - ``*_above_272k_tokens_flex`` keys these requests silently fell back to the - standard long-context price, billing 2x what OpenAI charges. - """ - - prompt_tokens = 300000 - completion_tokens = 1000 - usage = Usage( - prompt_tokens=prompt_tokens, - completion_tokens=completion_tokens, - total_tokens=prompt_tokens + completion_tokens, - ) - prompt_cost, completion_cost = generic_cost_per_token( - model=model, - usage=usage, - custom_llm_provider="openai", - service_tier="flex", - ) - - assert prompt_cost == pytest.approx(flex_long_input_cost * prompt_tokens) - assert completion_cost == pytest.approx(flex_long_output_cost * completion_tokens) - - standard_long_prompt_cost, standard_long_completion_cost = generic_cost_per_token( - model=model, - usage=usage, - custom_llm_provider="openai", - service_tier=None, - ) - assert prompt_cost == pytest.approx(standard_long_prompt_cost / 2) - assert completion_cost == pytest.approx(standard_long_completion_cost / 2) - - -@pytest.mark.parametrize( - "service_tier,prompt_tokens,input_rate,cache_write_rate,cache_read_rate", - [ - (None, 100000, 2e-6, 2.5e-6, 2e-7), - ("flex", 100000, 1e-6, 1.25e-6, 1e-7), - ("priority", 100000, 4e-6, 5e-6, 4e-7), - (None, 300000, 4e-6, 5e-6, 4e-7), - ("flex", 300000, 2e-6, 2.5e-6, 2e-7), - ], -) -def test_generic_cost_per_token_gpt56_terra_cache_costs_by_tier_and_context(_local_model_cost_map, - service_tier, prompt_tokens, input_rate, cache_write_rate, cache_read_rate -): - - cached_tokens = 50000 - cache_write_tokens = 40000 - text_tokens = prompt_tokens - cached_tokens - cache_write_tokens - usage = Usage( - prompt_tokens=prompt_tokens, - completion_tokens=100, - total_tokens=prompt_tokens + 100, - prompt_tokens_details=PromptTokensDetailsWrapper( - cached_tokens=cached_tokens, cache_write_tokens=cache_write_tokens - ), - ) - - prompt_cost, _ = generic_cost_per_token( - model="gpt-5.6-terra", - usage=usage, - custom_llm_provider="openai", - service_tier=service_tier, - ) - - expected_prompt_cost = ( - text_tokens * input_rate - + cached_tokens * cache_read_rate - + cache_write_tokens * cache_write_rate - ) - assert prompt_cost == pytest.approx(expected_prompt_cost) - - -@pytest.mark.parametrize("model", ["gpt-5.6-cyber", "daybreak-red-latest"]) -@pytest.mark.parametrize( - "prompt_tokens,input_rate,cache_write_rate,cache_read_rate,output_rate", - [ - (100000, 1.25e-5, 1.5625e-5, 1.25e-6, 7.5e-5), - (300000, 2.5e-5, 3.125e-5, 2.5e-6, 1.125e-4), - ], -) -def test_generic_cost_per_token_gpt56_cyber( - model, - prompt_tokens, - input_rate, - cache_write_rate, - cache_read_rate, - output_rate, - monkeypatch, -): - monkeypatch.setenv("LITELLM_LOCAL_MODEL_COST_MAP", "True") - monkeypatch.setattr(litellm, "model_cost", litellm.get_model_cost_map(url="")) - - cached_tokens = 50000 - cache_write_tokens = 40000 - text_tokens = prompt_tokens - cached_tokens - cache_write_tokens - completion_tokens = 1000 - usage = Usage( - prompt_tokens=prompt_tokens, - completion_tokens=completion_tokens, - total_tokens=prompt_tokens + completion_tokens, - prompt_tokens_details=PromptTokensDetailsWrapper( - cached_tokens=cached_tokens, cache_write_tokens=cache_write_tokens - ), - ) - - prompt_cost, completion_cost = generic_cost_per_token( - model=model, - usage=usage, - custom_llm_provider="openai", - ) - - assert prompt_cost == pytest.approx( - text_tokens * input_rate - + cached_tokens * cache_read_rate - + cache_write_tokens * cache_write_rate - ) - assert completion_cost == pytest.approx(completion_tokens * output_rate) - - -@pytest.mark.parametrize( - "service_tier,tier_multiplier", - [(None, 1.0), ("flex", 0.5), ("priority", 2.0), ("fast", 2.0)], -) -@pytest.mark.parametrize( - "prompt_tokens,input_side_multiplier,output_multiplier", - [(100000, 1.0, 1.0), (300000, 2.0, 1.5)], -) -def test_generic_cost_per_token_gpt_6_astra_price_sheet( - _local_model_cost_map, - service_tier, - tier_multiplier, - prompt_tokens, - input_side_multiplier, - output_multiplier, -): - """gpt-6-astra launch price sheet: $10 input, $1 cache read, $12.50 cache write, $50 output per 1M tokens. - - Above 272K prompt tokens the input-side rates double and the output rate is 1.5x on the whole - request. Flex is half the applicable rate and fast mode, billed as priority, is double it. - """ - cached_tokens = 50000 - cache_write_tokens = 40000 - text_tokens = prompt_tokens - cached_tokens - cache_write_tokens - completion_tokens = 1000 - usage = Usage( - prompt_tokens=prompt_tokens, - completion_tokens=completion_tokens, - total_tokens=prompt_tokens + completion_tokens, - prompt_tokens_details=PromptTokensDetailsWrapper( - cached_tokens=cached_tokens, cache_write_tokens=cache_write_tokens - ), - ) - - prompt_cost, completion_cost = generic_cost_per_token( - model="gpt-6-astra", - usage=usage, - custom_llm_provider="openai", - service_tier=service_tier, - ) - - input_side = tier_multiplier * input_side_multiplier - assert prompt_cost == pytest.approx( - input_side * (text_tokens * 1e-5 + cached_tokens * 1e-6 + cache_write_tokens * 1.25e-5) - ) - assert completion_cost == pytest.approx(tier_multiplier * output_multiplier * completion_tokens * 5e-5) - - -@pytest.mark.parametrize( - "model,input_cost,output_cost,cache_read_cost", - [ - ("azure/gpt-5.6", 5e-6, 3e-5, 5e-7), - ("azure/gpt-5.6-sol", 5e-6, 3e-5, 5e-7), - ("azure/gpt-5.6-terra", 2e-6, 1.2e-5, 2e-7), - ("azure/gpt-5.6-luna", 2e-7, 1.2e-6, 2e-8), - ("azure/us/gpt-5.6", 5.5e-6, 3.3e-5, 5.5e-7), - ("azure/eu/gpt-5.6-terra", 2.2e-6, 1.32e-5, 2.2e-7), - ("azure/eu/gpt-5.6-luna", 2.2e-7, 1.32e-6, 2.2e-8), - ], -) -def test_generic_cost_per_token_azure_gpt56(_local_model_cost_map, - model, input_cost, output_cost, cache_read_cost -): - """Azure gpt-5.6 (global + us/eu regional): Azure prices this family on its own - schedule and carries the standard 10% regional uplift on top. It did not take the - promotional cut OpenAI applied to gpt-5.6-sol, so these rates deliberately sit - above the openai ones and must not be lowered to match them. - """ - - model_cost_map = litellm.model_cost[model] - assert model_cost_map["litellm_provider"] == "azure" - assert model_cost_map["input_cost_per_token"] == input_cost - assert model_cost_map["output_cost_per_token"] == output_cost - assert model_cost_map["cache_read_input_token_cost"] == cache_read_cost - assert model_cost_map["max_input_tokens"] == 922000 - - prompt_tokens = 1000 - completion_tokens = 500 - usage = Usage( - prompt_tokens=prompt_tokens, - completion_tokens=completion_tokens, - total_tokens=prompt_tokens + completion_tokens, - ) - prompt_cost, completion_cost = generic_cost_per_token( - model=model, - usage=usage, - custom_llm_provider="azure", - ) - assert round(prompt_cost, 10) == round(input_cost * prompt_tokens, 10) - assert round(completion_cost, 10) == round(output_cost * completion_tokens, 10) - - -@pytest.mark.parametrize( - "model,custom_llm_provider,zone_multiplier", - [ - ("azure/gpt-6-astra", "azure", 1.0), - ("azure/us/gpt-6-astra", "azure", 1.1), - ("azure_ai/gpt-6-astra", "azure_ai", 1.0), - ], -) -@pytest.mark.parametrize( - "prompt_tokens,input_side_multiplier,output_multiplier", - [(100000, 1.0, 1.0), (300000, 2.0, 1.5)], -) -def test_generic_cost_per_token_azure_gpt_6_astra_foundry_price_sheet( - _local_model_cost_map, - model, - custom_llm_provider, - zone_multiplier, - prompt_tokens, - input_side_multiplier, - output_multiplier, -): - """Microsoft Foundry sells gpt-6-astra at the OpenAI rates: $10 input, $1 cache read, $12.50 cache write, - $50 output per 1M tokens on Standard Global, with the input side doubling and output 1.5x above 272K - prompt tokens. Standard US Data Zone carries the usual 10% uplift on every rate. A Foundry - deployment reached through the azure_ai route bills the same Standard Global sheet. - """ - cached_tokens = 50000 - cache_write_tokens = 40000 - text_tokens = prompt_tokens - cached_tokens - cache_write_tokens - completion_tokens = 1000 - usage = Usage( - prompt_tokens=prompt_tokens, - completion_tokens=completion_tokens, - total_tokens=prompt_tokens + completion_tokens, - prompt_tokens_details=PromptTokensDetailsWrapper( - cached_tokens=cached_tokens, cache_write_tokens=cache_write_tokens - ), - ) - - prompt_cost, completion_cost = generic_cost_per_token( - model=model, - usage=usage, - custom_llm_provider=custom_llm_provider, - ) - - input_side = zone_multiplier * input_side_multiplier - assert prompt_cost == pytest.approx( - input_side * (text_tokens * 1e-5 + cached_tokens * 1e-6 + cache_write_tokens * 1.25e-5) - ) - assert completion_cost == pytest.approx(zone_multiplier * output_multiplier * completion_tokens * 5e-5) - - -@pytest.mark.parametrize( - "model,input_rate,cache_read_rate,output_rate", - [ - ("azure/gpt-chat-latest", 5e-6, 5e-7, 3e-5), - ("azure/chat-latest", 5e-6, 5e-7, 3e-5), - ("azure/us/gpt-chat-latest", 5.5e-6, 5.5e-7, 3.3e-5), - ], -) -def test_generic_cost_per_token_azure_gpt_chat_latest_price_sheet( - _local_model_cost_map, model, input_rate, cache_read_rate, output_rate -): - """The Azure OpenAI price sheet lists GPT-Chat Latest at $5 input, $0.50 cached input and $30 output per 1M - tokens on Global, and $5.50, $0.55 and $33 on Data Zone. Foundry names the product gpt-chat-latest and the - OpenAI API names the same model chat-latest, so both spellings bill the Global sheet. - """ - prompt_tokens = 100000 - cached_tokens = 40000 - completion_tokens = 1000 - usage = Usage( - prompt_tokens=prompt_tokens, - completion_tokens=completion_tokens, - total_tokens=prompt_tokens + completion_tokens, - prompt_tokens_details=PromptTokensDetailsWrapper(cached_tokens=cached_tokens), - ) - - prompt_cost, completion_cost = generic_cost_per_token(model=model, usage=usage, custom_llm_provider="azure") - - assert prompt_cost == pytest.approx((prompt_tokens - cached_tokens) * input_rate + cached_tokens * cache_read_rate) - assert completion_cost == pytest.approx(completion_tokens * output_rate) - - -def test_generic_cost_per_token_azure_ai_gpt_6_astra_flex_bills_the_standard_rate(_local_model_cost_map): - usage = Usage(prompt_tokens=1000, completion_tokens=100, total_tokens=1100) - - standard = generic_cost_per_token(model="azure_ai/gpt-6-astra", usage=usage, custom_llm_provider="azure_ai") - flex = generic_cost_per_token( - model="azure_ai/gpt-6-astra", usage=usage, custom_llm_provider="azure_ai", service_tier="flex" - ) - - assert flex == standard - assert standard == pytest.approx((1000 * 1e-05, 100 * 5e-05)) - - @pytest.mark.parametrize( "model,expected_none,expected_xhigh,expected_minimal", [ @@ -2263,8 +1605,8 @@ def test_generic_cost_per_token_azure_ai_gpt_6_astra_flex_bills_the_standard_rat ("gpt-5.5-pro-2026-04-23", False, True, False), ], ) -def test_gpt55_reasoning_effort_flags_match_live_openai_api(_local_model_cost_map, - model, expected_none, expected_xhigh, expected_minimal +def test_gpt55_reasoning_effort_flags_match_live_openai_api( + _local_model_cost_map, model, expected_none, expected_xhigh, expected_minimal ): """Pin reasoning_effort capability flags to OpenAI's actual API contract. @@ -2274,15 +1616,15 @@ def test_gpt55_reasoning_effort_flags_match_live_openai_api(_local_model_cost_ma """ m = litellm.model_cost[model] - assert ( - m.get("supports_none_reasoning_effort") is expected_none - ), f"{model}: supports_none_reasoning_effort expected {expected_none}" - assert ( - m.get("supports_xhigh_reasoning_effort") is expected_xhigh - ), f"{model}: supports_xhigh_reasoning_effort expected {expected_xhigh}" - assert ( - m.get("supports_minimal_reasoning_effort") is expected_minimal - ), f"{model}: supports_minimal_reasoning_effort expected {expected_minimal}" + assert m.get("supports_none_reasoning_effort") is expected_none, ( + f"{model}: supports_none_reasoning_effort expected {expected_none}" + ) + assert m.get("supports_xhigh_reasoning_effort") is expected_xhigh, ( + f"{model}: supports_xhigh_reasoning_effort expected {expected_xhigh}" + ) + assert m.get("supports_minimal_reasoning_effort") is expected_minimal, ( + f"{model}: supports_minimal_reasoning_effort expected {expected_minimal}" + ) @pytest.mark.parametrize( @@ -2292,9 +1634,7 @@ def test_gpt55_reasoning_effort_flags_match_live_openai_api(_local_model_cost_ma ("gpt-5.5-pro", "gpt-5.5-pro-2026-04-23"), ], ) -def test_gpt55_dated_variants_match_base_reasoning_effort_capabilities(_local_model_cost_map, - base_model, dated_model -): +def test_gpt55_dated_variants_match_base_reasoning_effort_capabilities(_local_model_cost_map, base_model, dated_model): """Dated snapshots must carry the same reasoning_effort capability flags as their non-dated counterparts. @@ -2333,8 +1673,8 @@ def test_gpt55_dated_variants_match_base_reasoning_effort_capabilities(_local_mo ("azure/gpt-5.5-pro", False, False, True), ], ) -def test_azure_gpt55_reasoning_effort_flags_match_live_openai_api(_local_model_cost_map, - model, expected_none, expected_minimal, expected_xhigh +def test_azure_gpt55_reasoning_effort_flags_match_live_openai_api( + _local_model_cost_map, model, expected_none, expected_minimal, expected_xhigh ): """Azure entries pin reasoning_effort flags to OpenAI's actual API contract.""" @@ -2344,38 +1684,6 @@ def test_azure_gpt55_reasoning_effort_flags_match_live_openai_api(_local_model_c assert m.get("supports_xhigh_reasoning_effort") is expected_xhigh -def test_generic_cost_per_token_anthropic_prompt_caching(): - model = "claude-sonnet-4@20250514" - usage = Usage( - completion_tokens=90, - prompt_tokens=28436, - total_tokens=28526, - completion_tokens_details=CompletionTokensDetailsWrapper( - accepted_prediction_tokens=None, - audio_tokens=None, - reasoning_tokens=0, - rejected_prediction_tokens=None, - text_tokens=None, - ), - prompt_tokens_details=PromptTokensDetailsWrapper( - audio_tokens=None, cached_tokens=0, text_tokens=None, image_tokens=None - ), - cache_creation_input_tokens=118, - cache_read_input_tokens=28432, - ) - - custom_llm_provider = "vertex_ai" - - prompt_cost, completion_cost = generic_cost_per_token( - model=model, - usage=usage, - custom_llm_provider=custom_llm_provider, - ) - - print(f"prompt_cost: {prompt_cost}") - assert prompt_cost < 0.085 - - def test_generic_cost_per_token_anthropic_prompt_caching_with_cache_creation(): model = "claude-haiku-4-5-20251001" usage = Usage( @@ -2488,14 +1796,10 @@ def test_generic_cost_per_token_overlapping_cached_and_image_tokens(): prompt_tokens=100, completion_tokens=10, total_tokens=110, - prompt_tokens_details=PromptTokensDetailsWrapper( - text_tokens=None, cached_tokens=90, image_tokens=80 - ), + prompt_tokens_details=PromptTokensDetailsWrapper(text_tokens=None, cached_tokens=90, image_tokens=80), ) - prompt_cost, completion_cost = generic_cost_per_token( - model=model, usage=usage, custom_llm_provider="openai" - ) + prompt_cost, completion_cost = generic_cost_per_token(model=model, usage=usage, custom_llm_provider="openai") # 90 cached at 1e-7, the remaining 10 uncached tokens once at 1e-6 assert prompt_cost == pytest.approx(90 * 1e-7 + 10 * 1e-6) @@ -2524,14 +1828,10 @@ def test_generic_cost_per_token_warm_prefix_cache_spanning_text_and_image_tokens prompt_tokens=2461, completion_tokens=440, total_tokens=2901, - prompt_tokens_details=PromptTokensDetailsWrapper( - text_tokens=1319, cached_tokens=2432, image_tokens=1142 - ), + prompt_tokens_details=PromptTokensDetailsWrapper(text_tokens=1319, cached_tokens=2432, image_tokens=1142), ) - prompt_cost, completion_cost = generic_cost_per_token( - model=model, usage=usage, custom_llm_provider="openai" - ) + prompt_cost, completion_cost = generic_cost_per_token(model=model, usage=usage, custom_llm_provider="openai") # 2432 cached at the cache-read rate, the 29 uncached tokens once at the input rate assert prompt_cost == pytest.approx(2432 * 5e-7 + 29 * 2e-6) @@ -2782,181 +2082,10 @@ def test_cache_writing_cost_with_zero_creation_tokens_and_ephemeral_details(): # Expected: (100 * 3.75e-06) + (200 * 6e-06) = 0.000375 + 0.0012 = 0.001575 expected = (100 * cache_creation_cost) + (200 * cache_creation_cost_above_1hr) - assert ( - result > 0 - ), "Cost should not be zero when ephemeral token details are present" + assert result > 0, "Cost should not be zero when ephemeral token details are present" assert round(result, 6) == round(expected, 6) -def test_service_tier_flex_pricing(_local_model_cost_map): - """Test that flex service tier uses correct pricing (approximately 50% of standard).""" - # Set up environment for local model cost map - - # Test with gpt-5-nano which has flex pricing - model = "gpt-5-nano" - custom_llm_provider = "openai" - - # Create usage object - usage = Usage(prompt_tokens=1000, completion_tokens=500, total_tokens=1500) - - # Test standard pricing - std_cost = generic_cost_per_token( - model=model, - usage=usage, - custom_llm_provider=custom_llm_provider, - service_tier=None, - ) - std_total = std_cost[0] + std_cost[1] - - # Test flex pricing - flex_cost = generic_cost_per_token( - model=model, - usage=usage, - custom_llm_provider=custom_llm_provider, - service_tier="flex", - ) - flex_total = flex_cost[0] + flex_cost[1] - - # Verify flex is approximately 50% of standard - assert std_total > 0, "Standard cost should be greater than 0" - assert flex_total > 0, "Flex cost should be greater than 0" - - flex_ratio = flex_total / std_total - assert ( - 0.45 <= flex_ratio <= 0.55 - ), f"Flex pricing should be ~50% of standard, got {flex_ratio:.2f}" - - # Verify specific costs match expected values - # gpt-5-nano flex: input=2.5e-08, output=2e-07 - expected_flex_prompt = 1000 * 2.5e-08 # 0.000025 - expected_flex_completion = 500 * 2e-07 # 0.0001 - expected_flex_total = expected_flex_prompt + expected_flex_completion - - assert ( - abs(flex_cost[0] - expected_flex_prompt) < 1e-10 - ), f"Flex prompt cost mismatch: {flex_cost[0]} vs {expected_flex_prompt}" - assert ( - abs(flex_cost[1] - expected_flex_completion) < 1e-10 - ), f"Flex completion cost mismatch: {flex_cost[1]} vs {expected_flex_completion}" - assert ( - abs(flex_total - expected_flex_total) < 1e-10 - ), f"Flex total cost mismatch: {flex_total} vs {expected_flex_total}" - - -def test_service_tier_default_pricing(_local_model_cost_map): - """Test that when no service tier is provided, standard pricing is used.""" - # Set up environment for local model cost map - - # Test with gpt-5-nano - model = "gpt-5-nano" - custom_llm_provider = "openai" - - # Create usage object - usage = Usage(prompt_tokens=1000, completion_tokens=500, total_tokens=1500) - - # Test with no service tier (should use standard) - default_cost = generic_cost_per_token( - model=model, - usage=usage, - custom_llm_provider=custom_llm_provider, - service_tier=None, - ) - - # Test with explicit standard service tier - standard_cost = generic_cost_per_token( - model=model, - usage=usage, - custom_llm_provider=custom_llm_provider, - service_tier="standard", - ) - - # Both should be identical - assert ( - abs(default_cost[0] - standard_cost[0]) < 1e-10 - ), "Default and standard prompt costs should be identical" - assert ( - abs(default_cost[1] - standard_cost[1]) < 1e-10 - ), "Default and standard completion costs should be identical" - - # Verify specific costs match expected standard values - # gpt-5-nano standard: input=5e-08, output=4e-07 - expected_standard_prompt = 1000 * 5e-08 # 0.00005 - expected_standard_completion = 500 * 4e-07 # 0.0002 - expected_standard_total = expected_standard_prompt + expected_standard_completion - - assert ( - abs(default_cost[0] - expected_standard_prompt) < 1e-10 - ), f"Standard prompt cost mismatch: {default_cost[0]} vs {expected_standard_prompt}" - assert ( - abs(default_cost[1] - expected_standard_completion) < 1e-10 - ), f"Standard completion cost mismatch: {default_cost[1]} vs {expected_standard_completion}" - - -def test_service_tier_fallback_pricing(_local_model_cost_map): - """Test that when service tier is provided but model doesn't have those keys, it falls back to standard pricing.""" - # Set up environment for local model cost map - - # Test with gpt-4 which doesn't have flex pricing keys - model = "gpt-4" - custom_llm_provider = "openai" - - # Create usage object - usage = Usage(prompt_tokens=1000, completion_tokens=500, total_tokens=1500) - - # Test standard pricing - std_cost = generic_cost_per_token( - model=model, - usage=usage, - custom_llm_provider=custom_llm_provider, - service_tier=None, - ) - std_total = std_cost[0] + std_cost[1] - - # Test flex pricing (should fall back to standard since gpt-4 doesn't have flex keys) - flex_cost = generic_cost_per_token( - model=model, - usage=usage, - custom_llm_provider=custom_llm_provider, - service_tier="flex", - ) - flex_total = flex_cost[0] + flex_cost[1] - - # Test priority pricing (should fall back to standard since gpt-4 doesn't have priority keys) - priority_cost = generic_cost_per_token( - model=model, - usage=usage, - custom_llm_provider=custom_llm_provider, - service_tier="priority", - ) - priority_total = priority_cost[0] + priority_cost[1] - - # All should be identical (fallback to standard) - assert ( - abs(std_total - flex_total) < 1e-10 - ), f"Standard and flex costs should be identical (fallback): {std_total} vs {flex_total}" - assert ( - abs(std_total - priority_total) < 1e-10 - ), f"Standard and priority costs should be identical (fallback): {std_total} vs {priority_total}" - - # Verify costs are reasonable (not zero) - assert std_total > 0, "Standard cost should be greater than 0" - assert flex_total > 0, "Flex cost should be greater than 0 (fallback)" - assert priority_total > 0, "Priority cost should be greater than 0 (fallback)" - - # Verify specific costs match expected gpt-4 values - # gpt-4 standard: input=3e-05, output=6e-05 - expected_standard_prompt = 1000 * 3e-05 # 0.03 - expected_standard_completion = 500 * 6e-05 # 0.03 - expected_standard_total = expected_standard_prompt + expected_standard_completion - - assert ( - abs(std_cost[0] - expected_standard_prompt) < 1e-10 - ), f"Standard prompt cost mismatch: {std_cost[0]} vs {expected_standard_prompt}" - assert ( - abs(std_cost[1] - expected_standard_completion) < 1e-10 - ), f"Standard completion cost mismatch: {std_cost[1]} vs {expected_standard_completion}" - - def test_service_tier_ultrafast_pricing(): """An ultrafast request bills the *_ultrafast rates for all token types. @@ -2995,9 +2124,7 @@ def test_service_tier_ultrafast_pricing(): model_info=model_info, ) - expected_prompt_cost = ( - text_tokens * 5e-05 + cached_tokens * 5e-06 + cache_write_tokens * 6.25e-05 - ) + expected_prompt_cost = text_tokens * 5e-05 + cached_tokens * 5e-06 + cache_write_tokens * 6.25e-05 assert prompt_cost == pytest.approx(expected_prompt_cost) assert completion_cost == pytest.approx(400 * 3e-04) @@ -3086,9 +2213,7 @@ def test_gemini_image_generation_cost_with_zero_text_tokens(_local_model_cost_ma output_cost_per_token = model_cost_map.get("output_cost_per_token", 0) expected_image_cost = 1120 * output_cost_per_image_token - expected_reasoning_cost = ( - 225 * output_cost_per_token - ) # reasoning uses base token cost + expected_reasoning_cost = 225 * output_cost_per_token # reasoning uses base token cost expected_completion_cost = expected_image_cost + expected_reasoning_cost # The bug was: all completion tokens were treated as text tokens only. @@ -3097,9 +2222,9 @@ def test_gemini_image_generation_cost_with_zero_text_tokens(_local_model_cost_ma f"Completion cost should be significantly larger than text-only bugged path. " f"Expected > {bugged_text_only_cost * 2:.6f}, got {completion_cost:.6f}" ) - assert round(completion_cost, 4) == round( - expected_completion_cost, 4 - ), f"Expected completion cost ${expected_completion_cost:.6f}, got ${completion_cost:.6f}" + assert round(completion_cost, 4) == round(expected_completion_cost, 4), ( + f"Expected completion cost ${expected_completion_cost:.6f}, got ${completion_cost:.6f}" + ) def test_vertex_image_generation_cost_prefers_token_usage_metadata(_local_model_cost_map): @@ -3135,9 +2260,7 @@ def test_vertex_image_generation_cost_prefers_token_usage_metadata(_local_model_ ) expected_prompt_cost = prompt_tokens * model_info["input_cost_per_token"] - expected_completion_cost = ( - output_image_tokens * model_info["output_cost_per_image_token"] - ) + expected_completion_cost = output_image_tokens * model_info["output_cost_per_image_token"] expected_total_cost = expected_prompt_cost + expected_completion_cost assert round(cost, 10) == round(expected_total_cost, 10) @@ -3154,9 +2277,7 @@ def test_vertex_image_generation_cost_falls_back_to_flat_image_pricing(_local_mo model = "gemini-3.1-flash-image-preview" model_info = litellm.get_model_info(model=model, custom_llm_provider="vertex_ai") - image_response = ImageResponse( - data=[ImageObject(b64_json="img1"), ImageObject(b64_json="img2")] - ) + image_response = ImageResponse(data=[ImageObject(b64_json="img1"), ImageObject(b64_json="img2")]) cost = vertex_image_generation_cost_calculator( model=model, @@ -3200,9 +2321,7 @@ def test_gemini_image_generation_cost_prefers_token_usage_metadata(_local_model_ ) expected_prompt_cost = prompt_tokens * model_info["input_cost_per_token"] - expected_completion_cost = ( - output_image_tokens * model_info["output_cost_per_image_token"] - ) + expected_completion_cost = output_image_tokens * model_info["output_cost_per_image_token"] expected_total_cost = expected_prompt_cost + expected_completion_cost assert round(cost, 10) == round(expected_total_cost, 10) @@ -3219,9 +2338,7 @@ def test_gemini_image_generation_cost_falls_back_to_flat_image_pricing(_local_mo model = "gemini/gemini-3-pro-image-preview" model_info = litellm.get_model_info(model=model, custom_llm_provider="gemini") - image_response = ImageResponse( - data=[ImageObject(b64_json="img1"), ImageObject(b64_json="img2")] - ) + image_response = ImageResponse(data=[ImageObject(b64_json="img1"), ImageObject(b64_json="img2")]) cost = gemini_image_generation_cost_calculator( model=model, @@ -3296,19 +2413,19 @@ def test_reasoning_tokens_without_text_tokens_gpt5_nano(): expected_prompt_cost = 17 * 0.05 / 1_000_000 expected_completion_cost = 977 * 0.40 / 1_000_000 # ALL tokens, not just reasoning - assert ( - abs(prompt_cost - expected_prompt_cost) < 1e-10 - ), f"Prompt cost incorrect: {prompt_cost} vs {expected_prompt_cost}" + assert abs(prompt_cost - expected_prompt_cost) < 1e-10, ( + f"Prompt cost incorrect: {prompt_cost} vs {expected_prompt_cost}" + ) - assert ( - abs(completion_cost - expected_completion_cost) < 1e-10 - ), f"Completion cost incorrect: {completion_cost} vs {expected_completion_cost}" + assert abs(completion_cost - expected_completion_cost) < 1e-10, ( + f"Completion cost incorrect: {completion_cost} vs {expected_completion_cost}" + ) # Verify it's NOT using only reasoning_tokens (the bug) wrong_cost = 768 * 0.40 / 1_000_000 # Only reasoning tokens - assert ( - abs(completion_cost - wrong_cost) > 1e-6 - ), "Bug detected: Cost calculation is using only reasoning_tokens instead of all completion_tokens!" + assert abs(completion_cost - wrong_cost) > 1e-6, ( + "Bug detected: Cost calculation is using only reasoning_tokens instead of all completion_tokens!" + ) def test_image_count_prevents_text_tokens_fallback(_local_model_cost_map): @@ -3423,13 +2540,9 @@ def test_data_residency_no_uplift_for_pre_march_2026_models(model, _local_model_ usage = Usage(prompt_tokens=1000, completion_tokens=500, total_tokens=1500) base = generic_cost_per_token(model=model, usage=usage, custom_llm_provider="openai") - regional = generic_cost_per_token( - model=model, usage=usage, custom_llm_provider="openai", data_residency="eu" - ) + regional = generic_cost_per_token(model=model, usage=usage, custom_llm_provider="openai", data_residency="eu") - assert base == regional, ( - f"{model} should not have a regional uplift, but cost changed with data_residency" - ) + assert base == regional, f"{model} should not have a regional uplift, but cost changed with data_residency" def test_data_residency_no_uplift_for_unmarked_model(_local_model_cost_map): @@ -3537,9 +2650,7 @@ def test_vertex_global_or_absent_location_no_uplift(vertex_location, _local_mode usage = Usage(prompt_tokens=1000, completion_tokens=500, total_tokens=1500) - base = generic_cost_per_token( - model="claude-haiku-4-5@20251001", usage=usage, custom_llm_provider="vertex_ai" - ) + base = generic_cost_per_token(model="claude-haiku-4-5@20251001", usage=usage, custom_llm_provider="vertex_ai") located = generic_cost_per_token( model="claude-haiku-4-5@20251001", usage=usage, @@ -3576,10 +2687,7 @@ def test_vertex_uplift_invalid_multiplier_defaults_to_one(): ) assert ( - get_vertex_regional_endpoint_uplift( - {"regional_endpoint_uplift_multiplier": "not-a-number"}, "us-east5" - ) - == 1.0 + get_vertex_regional_endpoint_uplift({"regional_endpoint_uplift_multiplier": "not-a-number"}, "us-east5") == 1.0 ) @@ -3594,9 +2702,7 @@ def test_priority_service_tier_above_threshold_uses_priority_tier_rates_for_cach prompt_tokens=250_000, completion_tokens=1_000, total_tokens=251_000, - prompt_tokens_details=PromptTokensDetailsWrapper( - cached_tokens=200_000, text_tokens=50_000 - ), + prompt_tokens_details=PromptTokensDetailsWrapper(cached_tokens=200_000, text_tokens=50_000), completion_tokens_details=CompletionTokensDetailsWrapper(text_tokens=1_000), ) @@ -3615,52 +2721,13 @@ def test_priority_service_tier_above_threshold_uses_priority_tier_rates_for_cach assert completion_cost == pytest.approx(expected_completion, rel=1e-9) -def test_priority_service_tier_above_threshold_falls_back_to_standard_for_cache_creation( - _local_model_cost_map, -): - """Regression: priority requests against models that publish standard above-threshold - cache_creation rates but no priority variant must fall back to the standard - above-threshold rate, not the priority-base rate. vertex_ai/claude-sonnet-4-5 - has cache_creation_input_token_cost_above_200k_tokens but no _priority sibling.""" - usage = Usage( - prompt_tokens=350_000, - completion_tokens=1_000, - total_tokens=351_000, - prompt_tokens_details=PromptTokensDetailsWrapper( - cached_tokens=200_000, - cache_creation_tokens=100_000, - text_tokens=50_000, - ), - completion_tokens_details=CompletionTokensDetailsWrapper(text_tokens=1_000), - ) - - prompt_cost, completion_cost = generic_cost_per_token( - model="vertex_ai/claude-sonnet-4-5", - usage=usage, - custom_llm_provider="vertex_ai", - service_tier="priority", - ) - - # vertex_ai/claude-sonnet-4-5 above_200k (no _priority variants): - # input 6e-6, output 2.25e-5, cache_read 6e-7, cache_creation 7.5e-6 - # text 50_000 * 6e-6 = 0.30 - # cache_read 200_000 * 6e-7 = 0.12 - # cache_creation 100_000 * 7.5e-6 = 0.75 - expected_prompt = 50_000 * 6e-6 + 200_000 * 6e-7 + 100_000 * 7.5e-6 - expected_completion = 1_000 * 2.25e-5 - assert prompt_cost == pytest.approx(expected_prompt, rel=1e-9) - assert completion_cost == pytest.approx(expected_completion, rel=1e-9) - - def test_service_tier_suffixes_constant_in_sync_with_enum(): from litellm.litellm_core_utils.llm_cost_calc.utils import _SERVICE_TIER_SUFFIXES from litellm.types.utils import ServiceTier assert set(_SERVICE_TIER_SUFFIXES) == {f"_{st.value}" for st in ServiceTier} # longest-first so a substring match resolves "_ultrafast" before "_fast" - assert list(_SERVICE_TIER_SUFFIXES) == sorted( - _SERVICE_TIER_SUFFIXES, key=len, reverse=True - ) + assert list(_SERVICE_TIER_SUFFIXES) == sorted(_SERVICE_TIER_SUFFIXES, key=len, reverse=True) def test_get_cost_per_unit_falls_back_from_service_tier_key_to_base(): @@ -3674,9 +2741,7 @@ def test_get_cost_per_unit_falls_back_from_service_tier_key_to_base(): "input_cost_per_token_priority": 5e-6, "input_cost_per_token": 2e-6, } - assert ( - _get_cost_per_unit(model_info_direct, "input_cost_per_token_priority") == 5e-6 - ) + assert _get_cost_per_unit(model_info_direct, "input_cost_per_token_priority") == 5e-6 def test_threshold_keys_exclude_service_tier_variants(): @@ -3715,8 +2780,8 @@ def test_threshold_keys_exclude_service_tier_variants(): ("cerebras/qwen-3-32b", "cerebras", 250, 0), ], ) -def test_token_type_cost_breakdown_is_provider_agnostic(_local_model_cost_map, - model, custom_llm_provider, reasoning_tokens, cached_tokens +def test_token_type_cost_breakdown_is_provider_agnostic( + _local_model_cost_map, model, custom_llm_provider, reasoning_tokens, cached_tokens ): """ Reasoning and cache-read costs must be surfaced for every provider that reports @@ -3735,136 +2800,19 @@ def test_token_type_cost_breakdown_is_provider_agnostic(_local_model_cost_map, completion_tokens_details=CompletionTokensDetailsWrapper( reasoning_tokens=reasoning_tokens, text_tokens=2000 - reasoning_tokens ), - prompt_tokens_details=PromptTokensDetailsWrapper( - cached_tokens=cached_tokens, text_tokens=1000 - cached_tokens - ), + prompt_tokens_details=PromptTokensDetailsWrapper(cached_tokens=cached_tokens, text_tokens=1000 - cached_tokens), ) - breakdown = get_token_type_cost_breakdown( - model=model, custom_llm_provider=custom_llm_provider, usage=usage - ) + breakdown = get_token_type_cost_breakdown(model=model, custom_llm_provider=custom_llm_provider, usage=usage) - model_info = litellm.get_model_info( - model=model, custom_llm_provider=custom_llm_provider - ) - reasoning_rate = ( - model_info.get("output_cost_per_reasoning_token") - or model_info["output_cost_per_token"] - ) + model_info = litellm.get_model_info(model=model, custom_llm_provider=custom_llm_provider) + reasoning_rate = model_info.get("output_cost_per_reasoning_token") or model_info["output_cost_per_token"] cache_read_rate = model_info.get("cache_read_input_token_cost") or 0.0 assert breakdown.reasoning_cost == pytest.approx(reasoning_tokens * reasoning_rate) assert breakdown.cache_read_cost == pytest.approx(cached_tokens * cache_read_rate) -def test_token_type_cost_breakdown_matches_real_gemini_numbers(_local_model_cost_map): - """Hard-coded against the exact gemini-2.5-flash response that exposed the gap.""" - - usage = Usage( - prompt_tokens=209, - completion_tokens=3996, - total_tokens=4205, - completion_tokens_details=CompletionTokensDetailsWrapper( - reasoning_tokens=3114, text_tokens=882 - ), - prompt_tokens_details=PromptTokensDetailsWrapper( - cached_tokens=100, text_tokens=109 - ), - ) - - breakdown = get_token_type_cost_breakdown( - model="gemini-2.5-flash", custom_llm_provider="vertex_ai", usage=usage - ) - - assert breakdown.reasoning_cost == pytest.approx(3114 * 2.5e-06) - assert breakdown.cache_read_cost == pytest.approx(100 * 3e-08) - assert breakdown.cache_creation_cost == 0.0 - - -def test_token_type_cost_breakdown_flex_tier_prices_reasoning_at_flex_rate(_local_model_cost_map): - """Regression for the flex-tier breakdown drift: gemini-3.5-flash defines a flat - output_cost_per_reasoning_token (9e-06, the standard output rate) but no _flex - variant, so the breakdown priced reasoning at the standard rate on flex requests - while the total billed it at the flex output rate (4.5e-06). The reasoning - sub-cost then exceeded the entire flex completion cost.""" - - usage = Usage( - prompt_tokens=7, - completion_tokens=320, - total_tokens=327, - completion_tokens_details=CompletionTokensDetailsWrapper(reasoning_tokens=315, text_tokens=5), - ) - - breakdown = get_token_type_cost_breakdown( - model="gemini-3.5-flash", - custom_llm_provider="vertex_ai", - usage=usage, - service_tier="flex", - ) - - assert breakdown.reasoning_cost == pytest.approx(315 * 4.5e-06) - - _, flex_completion_cost = generic_cost_per_token( - model="gemini-3.5-flash", - usage=usage, - custom_llm_provider="vertex_ai", - service_tier="flex", - ) - assert breakdown.reasoning_cost <= flex_completion_cost - - standard_breakdown = get_token_type_cost_breakdown( - model="gemini-3.5-flash", - custom_llm_provider="vertex_ai", - usage=usage, - service_tier=None, - ) - assert standard_breakdown.reasoning_cost == pytest.approx(315 * 9e-06) - - -def test_token_type_cost_breakdown_xai_at_exactly_200k_uses_higher_tier_rates(_local_model_cost_map): - - usage = Usage( - prompt_tokens=200_000, - completion_tokens=2_000, - total_tokens=202_000, - completion_tokens_details=CompletionTokensDetailsWrapper( - reasoning_tokens=1_500, text_tokens=500 - ), - prompt_tokens_details=PromptTokensDetailsWrapper( - cached_tokens=50_000, text_tokens=150_000 - ), - ) - - breakdown = get_token_type_cost_breakdown( - model="grok-4.20-0309-reasoning", custom_llm_provider="xai", usage=usage - ) - - assert breakdown.reasoning_cost == pytest.approx(1_500 * 5e-06) - assert breakdown.cache_read_cost == pytest.approx(50_000 * 4e-07) - - -def test_token_type_cost_breakdown_xai_just_below_200k_uses_base_tier_rates(_local_model_cost_map): - - usage = Usage( - prompt_tokens=199_999, - completion_tokens=2_000, - total_tokens=201_999, - completion_tokens_details=CompletionTokensDetailsWrapper( - reasoning_tokens=1_500, text_tokens=500 - ), - prompt_tokens_details=PromptTokensDetailsWrapper( - cached_tokens=50_000, text_tokens=149_999 - ), - ) - - breakdown = get_token_type_cost_breakdown( - model="grok-4.20-0309-reasoning", custom_llm_provider="xai", usage=usage - ) - - assert breakdown.reasoning_cost == pytest.approx(1_500 * 2.5e-06) - assert breakdown.cache_read_cost == pytest.approx(50_000 * 2e-07) - - def test_token_type_cost_breakdown_includes_cache_creation_from_top_level_usage(_local_model_cost_map): """ Bedrock/Anthropic report cache tokens as top-level usage fields; the Usage @@ -3881,17 +2829,11 @@ def test_token_type_cost_breakdown_includes_cache_creation_from_top_level_usage( cache_read_input_tokens=120, ) - breakdown = get_token_type_cost_breakdown( - model=model, custom_llm_provider="bedrock", usage=usage - ) + breakdown = get_token_type_cost_breakdown(model=model, custom_llm_provider="bedrock", usage=usage) model_info = litellm.get_model_info(model=model, custom_llm_provider="bedrock") - assert breakdown.cache_creation_cost == pytest.approx( - 300 * model_info["cache_creation_input_token_cost"] - ) - assert breakdown.cache_read_cost == pytest.approx( - 120 * model_info["cache_read_input_token_cost"] - ) + assert breakdown.cache_creation_cost == pytest.approx(300 * model_info["cache_creation_input_token_cost"]) + assert breakdown.cache_read_cost == pytest.approx(120 * model_info["cache_read_input_token_cost"]) def test_token_type_cost_breakdown_reads_cache_write_tokens(_local_model_cost_map): @@ -3906,18 +2848,12 @@ def test_token_type_cost_breakdown_reads_cache_write_tokens(_local_model_cost_ma prompt_tokens=500, completion_tokens=50, total_tokens=550, - prompt_tokens_details=PromptTokensDetailsWrapper( - cached_tokens=0, cache_write_tokens=300 - ), + prompt_tokens_details=PromptTokensDetailsWrapper(cached_tokens=0, cache_write_tokens=300), ) - breakdown = get_token_type_cost_breakdown( - model=model, custom_llm_provider="bedrock", usage=usage - ) + breakdown = get_token_type_cost_breakdown(model=model, custom_llm_provider="bedrock", usage=usage) model_info = litellm.get_model_info(model=model, custom_llm_provider="bedrock") - assert breakdown.cache_creation_cost == pytest.approx( - 300 * model_info["cache_creation_input_token_cost"] - ) + assert breakdown.cache_creation_cost == pytest.approx(300 * model_info["cache_creation_input_token_cost"]) def test_generic_cost_per_token_openai_cache_write_tokens_gpt_5_6(_local_model_cost_map): @@ -3961,9 +2897,7 @@ def test_generic_cost_per_token_backs_out_cache_write_tokens_from_text_tokens(_l prompt_tokens=1000, completion_tokens=10, total_tokens=1010, - prompt_tokens_details=PromptTokensDetailsWrapper( - cached_tokens=0, cache_write_tokens=800, text_tokens=1000 - ), + prompt_tokens_details=PromptTokensDetailsWrapper(cached_tokens=0, cache_write_tokens=800, text_tokens=1000), ) prompt_cost, _ = generic_cost_per_token(model=model, usage=usage, custom_llm_provider="openai") @@ -3987,24 +2921,16 @@ def test_token_type_cost_breakdown_reconciles_with_generic_total(_local_model_co prompt_tokens=1000, completion_tokens=2000, total_tokens=3000, - completion_tokens_details=CompletionTokensDetailsWrapper( - reasoning_tokens=1200, text_tokens=800 - ), - prompt_tokens_details=PromptTokensDetailsWrapper( - cached_tokens=300, text_tokens=700 - ), + completion_tokens_details=CompletionTokensDetailsWrapper(reasoning_tokens=1200, text_tokens=800), + prompt_tokens_details=PromptTokensDetailsWrapper(cached_tokens=300, text_tokens=700), ) prompt_cost, completion_cost = generic_cost_per_token( model=model, usage=usage, custom_llm_provider=custom_llm_provider ) - breakdown = get_token_type_cost_breakdown( - model=model, custom_llm_provider=custom_llm_provider, usage=usage - ) + breakdown = get_token_type_cost_breakdown(model=model, custom_llm_provider=custom_llm_provider, usage=usage) - model_info = litellm.get_model_info( - model=model, custom_llm_provider=custom_llm_provider - ) + model_info = litellm.get_model_info(model=model, custom_llm_provider=custom_llm_provider) text_output_cost = 800 * model_info["output_cost_per_token"] text_input_cost = 700 * model_info["input_cost_per_token"] @@ -4118,46 +3044,6 @@ def test_billed_token_rates_follow_the_token_tier_the_breakdown_bills_at(monkeyp assert breakdown.reasoning_cost == pytest.approx(200 * rates.output_cost_per_reasoning_token) -def test_a_pinned_billing_time_prices_the_totals_and_the_reported_rates_at_one_moment(monkeypatch): - """Totals and reported rates resolve off-peak pricing on separate paths that each read the - clock, so a window opening between the two reads used to leave them describing one request - at two different prices. Pinned, both must answer for the pinned moment.""" - monkeypatch.setitem( - litellm.model_cost, - "off-peak-model", - { - "input_cost_per_token": 3e-6, - "output_cost_per_token": 15e-6, - "off_peak_pricing": { - "hours_utc": "02:00-03:00", - "input_cost_per_token": 1e-6, - "output_cost_per_token": 5e-6, - }, - "litellm_provider": "openai", - "mode": "chat", - }, - ) - usage = Usage(prompt_tokens=1000, completion_tokens=500, total_tokens=1500) - - with pinned_billing_time(datetime(2026, 1, 1, 2, 30, tzinfo=timezone.utc)): - off_peak_prompt_cost, off_peak_completion_cost = generic_cost_per_token( - model="off-peak-model", usage=usage, custom_llm_provider="openai" - ) - off_peak_rates = get_billed_token_rates(model="off-peak-model", custom_llm_provider="openai", usage=usage) - with pinned_billing_time(datetime(2026, 1, 1, 12, 30, tzinfo=timezone.utc)): - peak_prompt_cost, peak_completion_cost = generic_cost_per_token( - model="off-peak-model", usage=usage, custom_llm_provider="openai" - ) - peak_rates = get_billed_token_rates(model="off-peak-model", custom_llm_provider="openai", usage=usage) - - assert off_peak_rates.input_cost_per_token == pytest.approx(1e-6) - assert peak_rates.input_cost_per_token == pytest.approx(3e-6) - assert off_peak_prompt_cost == pytest.approx(1000 * off_peak_rates.input_cost_per_token) - assert off_peak_completion_cost == pytest.approx(500 * off_peak_rates.output_cost_per_token) - assert peak_prompt_cost == pytest.approx(1000 * peak_rates.input_cost_per_token) - assert peak_completion_cost == pytest.approx(500 * peak_rates.output_cost_per_token) - - def test_the_token_type_breakdown_carries_the_rates_it_billed_at(monkeypatch): """Callers that report both the lines and the rates read the rates off the breakdown rather than resolving them a second time, so the breakdown has to hand back exactly what it billed at.""" @@ -4184,9 +3070,7 @@ def test_the_token_type_breakdown_carries_the_rates_it_billed_at(monkeypatch): breakdown = get_token_type_cost_breakdown(model="xai/tiered-model", custom_llm_provider="xai", usage=usage) - assert breakdown.rates == get_billed_token_rates( - model="xai/tiered-model", custom_llm_provider="xai", usage=usage - ) + assert breakdown.rates == get_billed_token_rates(model="xai/tiered-model", custom_llm_provider="xai", usage=usage) assert breakdown.rates.cache_read_input_token_cost == pytest.approx(6e-7) assert breakdown.cache_read_cost == pytest.approx(100_000 * breakdown.rates.cache_read_input_token_cost) @@ -4194,9 +3078,7 @@ def test_the_token_type_breakdown_carries_the_rates_it_billed_at(monkeypatch): def test_the_token_type_breakdown_reports_no_rates_for_an_unpriced_model(): usage = Usage(prompt_tokens=10, completion_tokens=5, total_tokens=15) - breakdown = get_token_type_cost_breakdown( - model="no-such-model-anywhere", custom_llm_provider="openai", usage=usage - ) + breakdown = get_token_type_cost_breakdown(model="no-such-model-anywhere", custom_llm_provider="openai", usage=usage) assert breakdown.rates is None @@ -4210,9 +3092,7 @@ def test_billed_token_rates_are_none_for_an_unpriced_model(): def test_token_type_cost_breakdown_zero_without_special_tokens(_local_model_cost_map): usage = Usage(prompt_tokens=100, completion_tokens=50, total_tokens=150) - breakdown = get_token_type_cost_breakdown( - model="gpt-4o", custom_llm_provider="openai", usage=usage - ) + breakdown = get_token_type_cost_breakdown(model="gpt-4o", custom_llm_provider="openai", usage=usage) assert (breakdown.reasoning_cost, breakdown.cache_read_cost, breakdown.cache_creation_cost) == (0.0, 0.0, 0.0) @@ -4242,8 +3122,8 @@ def test_token_type_cost_breakdown_zero_without_special_tokens(_local_model_cost ), ], ) -def test_token_type_cost_breakdown_openai_responses_api_cache_write_read(_local_model_cost_map, - raw_usage, expect_read, expect_write +def test_token_type_cost_breakdown_openai_responses_api_cache_write_read( + _local_model_cost_map, raw_usage, expect_read, expect_write ): """Regression for #34309: OpenAI Responses API reports cache tokens under input_tokens_details.{cached_tokens, cache_write_tokens}, not the Anthropic-style @@ -4251,25 +3131,18 @@ def test_token_type_cost_breakdown_openai_responses_api_cache_write_read(_local_ cache_read_cost / cache_creation_cost from the transformed usage.""" from litellm.responses.utils import ResponseAPILoggingUtils - model = "gpt-5.6" usage = ResponseAPILoggingUtils._transform_response_api_usage_to_chat_usage(raw_usage) - breakdown = get_token_type_cost_breakdown( - model=model, custom_llm_provider="openai", usage=usage - ) + breakdown = get_token_type_cost_breakdown(model=model, custom_llm_provider="openai", usage=usage) info = litellm.get_model_info(model=model, custom_llm_provider="openai") if expect_write: - assert breakdown.cache_creation_cost == pytest.approx( - 4012 * info["cache_creation_input_token_cost"] - ) + assert breakdown.cache_creation_cost == pytest.approx(4012 * info["cache_creation_input_token_cost"]) assert breakdown.cache_creation_cost > 0 assert breakdown.cache_read_cost == 0.0 if expect_read: - assert breakdown.cache_read_cost == pytest.approx( - 4012 * info["cache_read_input_token_cost"] - ) + assert breakdown.cache_read_cost == pytest.approx(4012 * info["cache_read_input_token_cost"]) assert breakdown.cache_read_cost > 0 assert breakdown.cache_creation_cost == 0.0 @@ -4303,23 +3176,15 @@ def test_token_type_cost_breakdown_applies_regional_uplift(_local_model_cost_map prompt_tokens=1000, completion_tokens=500, total_tokens=1500, - completion_tokens_details=CompletionTokensDetailsWrapper( - reasoning_tokens=200, text_tokens=300 - ), - prompt_tokens_details=PromptTokensDetailsWrapper( - cached_tokens=400, text_tokens=600 - ), + completion_tokens_details=CompletionTokensDetailsWrapper(reasoning_tokens=200, text_tokens=300), + prompt_tokens_details=PromptTokensDetailsWrapper(cached_tokens=400, text_tokens=600), ) - model_info = litellm.get_model_info( - model=model, custom_llm_provider=custom_llm_provider - ) + model_info = litellm.get_model_info(model=model, custom_llm_provider=custom_llm_provider) uplift = model_info["regional_processing_uplift_multiplier_eu"] assert uplift > 1.0 - base = get_token_type_cost_breakdown( - model=model, custom_llm_provider=custom_llm_provider, usage=usage - ) + base = get_token_type_cost_breakdown(model=model, custom_llm_provider=custom_llm_provider, usage=usage) eu = get_token_type_cost_breakdown( model=model, custom_llm_provider=custom_llm_provider, @@ -4357,20 +3222,14 @@ def test_token_type_cost_breakdown_applies_vertex_regional_uplift(_local_model_c prompt_tokens=1000, completion_tokens=500, total_tokens=1500, - prompt_tokens_details=PromptTokensDetailsWrapper( - cached_tokens=400, text_tokens=600 - ), + prompt_tokens_details=PromptTokensDetailsWrapper(cached_tokens=400, text_tokens=600), ) - model_info = litellm.get_model_info( - model=model, custom_llm_provider=custom_llm_provider - ) + model_info = litellm.get_model_info(model=model, custom_llm_provider=custom_llm_provider) uplift = model_info["regional_endpoint_uplift_multiplier"] assert uplift > 1.0 - base = get_token_type_cost_breakdown( - model=model, custom_llm_provider=custom_llm_provider, usage=usage - ) + base = get_token_type_cost_breakdown(model=model, custom_llm_provider=custom_llm_provider, usage=usage) regional = get_token_type_cost_breakdown( model=model, custom_llm_provider=custom_llm_provider, @@ -4392,128 +3251,6 @@ def test_token_type_cost_breakdown_applies_vertex_regional_uplift(_local_model_c assert text_input_cost + regional.cache_read_cost == pytest.approx(prompt_cost) -def test_token_type_cost_breakdown_applies_anthropic_geo_multiplier(_local_model_cost_map, monkeypatch): - """ - Anthropic's regional (geo) uplift lives in provider_specific_entry and is - applied to every token type in the totals, so the per-type breakdown must - scale its cache and reasoning line items by it too. Otherwise the logged - cache costs stay at the base rate and the cache uplift is misattributed to - plain input for exactly the cache-heavy regional traffic the uplift targets. - """ - from litellm.llms.anthropic.cost_calculation import ( - cost_per_token as anthropic_cost_per_token, - ) - - monkeypatch.setenv("LITELLM_LOCAL_MODEL_COST_MAP", "True") - - model = "claude-test-geo-breakdown-model" - litellm.register_model( - model_cost={ - model: { - "input_cost_per_token": 5e-6, - "output_cost_per_token": 25e-6, - "cache_creation_input_token_cost": 6.25e-6, - "cache_read_input_token_cost": 0.5e-6, - "litellm_provider": "anthropic", - "max_tokens": 8192, - "provider_specific_entry": {"us": 1.1}, - } - } - ) - - def make_usage() -> Usage: - return Usage( - prompt_tokens=10_000, - completion_tokens=500, - total_tokens=10_500, - prompt_tokens_details=PromptTokensDetailsWrapper( - cached_tokens=2_000, - cache_creation_tokens=6_000, - ), - completion_tokens_details=CompletionTokensDetailsWrapper( - reasoning_tokens=200, text_tokens=300 - ), - ) - - base_usage = make_usage() - geo_usage = make_usage() - geo_usage.inference_geo = "us" - - base = get_token_type_cost_breakdown( - model=model, custom_llm_provider="anthropic", usage=base_usage - ) - geo = get_token_type_cost_breakdown( - model=model, custom_llm_provider="anthropic", usage=geo_usage - ) - - assert base.cache_read_cost == pytest.approx(2_000 * 0.5e-6) - assert base.cache_creation_cost == pytest.approx(6_000 * 6.25e-6) - assert geo.cache_read_cost == pytest.approx(base.cache_read_cost * 1.1) - assert geo.cache_creation_cost == pytest.approx(base.cache_creation_cost * 1.1) - assert geo.reasoning_cost == pytest.approx(base.reasoning_cost * 1.1) - - # The uplifted breakdown must still reconcile with the uplifted totals. - prompt_cost, completion_cost = anthropic_cost_per_token(model=model, usage=geo_usage) - text_input_cost = 2_000 * 5e-6 * 1.1 - text_output_cost = 300 * 25e-6 * 1.1 - assert text_input_cost + geo.cache_read_cost + geo.cache_creation_cost == pytest.approx(prompt_cost) - assert text_output_cost + geo.reasoning_cost == pytest.approx(completion_cost) - - -@pytest.mark.parametrize("details_as_dict", [True, False]) -def test_image_response_input_image_tokens_priced_at_image_rate(details_as_dict): - """ - Image input tokens must be priced at input_cost_per_image_token even when - input_tokens_details is a plain dict, as in OpenAI image edit responses. - - Regression test: dict-shaped input_tokens_details was read with getattr(), - which returns None for dicts, so image input tokens silently fell back to - the text input rate (e.g. $5/M instead of $8/M for gpt-image-2). - """ - from unittest.mock import patch - - from litellm.litellm_core_utils.llm_cost_calc.utils import ( - calculate_image_response_cost_from_usage, - ) - from litellm.types.utils import Usage - - mock_model_info = { - "input_cost_per_token": 5e-6, - "input_cost_per_image_token": 8e-6, - "output_cost_per_image_token": 3e-5, - } - - input_details = {"text_tokens": 19, "image_tokens": 512} - image_response = ImageResponse(data=[ImageObject(b64_json="x")]) - # Mirror the usage shape of a real OpenAI images.edit response: - # a Usage object carrying input_tokens/output_tokens with detail dicts. - image_response.usage = Usage( - prompt_tokens=0, - completion_tokens=0, - total_tokens=689, - input_tokens=531, - input_tokens_details=( - input_details - if details_as_dict - else ImageUsageInputTokensDetails(**input_details) - ), - output_tokens=158, - output_tokens_details={"image_tokens": 158, "text_tokens": 0}, - ) - - with patch( - "litellm.litellm_core_utils.llm_cost_calc.utils.get_model_info", - return_value=mock_model_info, - ): - cost = calculate_image_response_cost_from_usage( - model="gpt-image-2", - image_response=image_response, - custom_llm_provider="openai", - ) - - expected = 19 * 5e-6 + 512 * 8e-6 + 158 * 3e-5 - assert cost is not None - assert round(cost, 12) == round(expected, 12) GEMINI_DAY0_LAUNCH_PRICING = [ ("gemini-3.6-flash", 7.5e-07, 3.75e-06, 7.5e-08), ("gemini/gemini-3.6-flash", 7.5e-07, 3.75e-06, 7.5e-08), @@ -4524,27 +3261,6 @@ GEMINI_DAY0_LAUNCH_PRICING = [ ] -def test_generic_cost_per_token_gemini_36_flash(_local_model_cost_map): - - usage = Usage( - prompt_tokens=1000, - completion_tokens=500, - total_tokens=1500, - completion_tokens_details=CompletionTokensDetailsWrapper( - reasoning_tokens=200, - text_tokens=300, - ), - prompt_tokens_details=PromptTokensDetailsWrapper(text_tokens=1000), - ) - prompt_cost, completion_cost = generic_cost_per_token( - model="gemini-3.6-flash", - usage=usage, - custom_llm_provider="gemini", - ) - assert prompt_cost == pytest.approx(0.00075) - assert completion_cost == pytest.approx(0.001875) - - GEMINI_36_FLASH_SERVICE_TIER_PRICING = [ (None, 7.5e-07, 3.75e-06, 7.5e-08), ("flex", 3.75e-07, 1.875e-06, 3.75e-08), @@ -4552,27 +3268,6 @@ GEMINI_36_FLASH_SERVICE_TIER_PRICING = [ ] -def test_generic_cost_per_token_gemini_35_flash_lite(_local_model_cost_map): - - usage = Usage( - prompt_tokens=1000, - completion_tokens=500, - total_tokens=1500, - completion_tokens_details=CompletionTokensDetailsWrapper( - reasoning_tokens=200, - text_tokens=300, - ), - prompt_tokens_details=PromptTokensDetailsWrapper(text_tokens=1000), - ) - prompt_cost, completion_cost = generic_cost_per_token( - model="gemini-3.5-flash-lite", - usage=usage, - custom_llm_provider="gemini", - ) - assert prompt_cost == pytest.approx(0.0003) - assert completion_cost == pytest.approx(0.00125) - - GEMINI_35_FLASH_LITE_TIER_RATES_BY_SURFACE = [ ("gemini", None, 3e-07, 2.5e-06, 3e-08), ("gemini", "flex", 1.5e-07, 1.25e-06, 2e-08), @@ -4583,80 +3278,6 @@ GEMINI_35_FLASH_LITE_TIER_RATES_BY_SURFACE = [ ] -@pytest.mark.parametrize( - "service_tier,input_rate,cache_read_rate,cache_write_rate,output_rate", - [ - ("flex", 2e-6, 2e-7, 2.5e-6, 1e-5), - ("priority", 8e-6, 8e-7, 1e-5, 4e-5), - ], -) -def test_service_tier_cache_creation_rates_for_gpt_5_6( - _local_model_cost_map, - service_tier, - input_rate, - cache_read_rate, - cache_write_rate, - output_rate, -): - """Regression: gpt-5.6 publishes cache_creation_input_token_cost_flex/_priority, so a - flex or priority request must bill cache writes at that tier's rate instead of falling - back to the standard cache-write rate.""" - usage = Usage( - prompt_tokens=10_000, - completion_tokens=500, - total_tokens=10_500, - prompt_tokens_details=PromptTokensDetailsWrapper( - cached_tokens=6_000, - cache_write_tokens=3_000, - text_tokens=1_000, - ), - ) - - prompt_cost, completion_cost = generic_cost_per_token( - model="gpt-5.6-sol", - usage=usage, - custom_llm_provider="openai", - service_tier=service_tier, - ) - - expected_prompt = 1_000 * input_rate + 6_000 * cache_read_rate + 3_000 * cache_write_rate - assert prompt_cost == pytest.approx(expected_prompt, rel=1e-9) - assert completion_cost == pytest.approx(500 * output_rate, rel=1e-9) - - -def test_fast_service_tier_bills_at_the_priority_rate(_local_model_cost_map): - """Regression: OpenAI's Fast mode replaced Priority Processing and costs 2x standard. - - Before the fix "fast" fell through to standard pricing, so a Fast mode request - was billed at half of what it actually costs.""" - from litellm.types.utils import Usage - - usage = Usage( - prompt_tokens=1_000, - completion_tokens=500, - prompt_tokens_details=PromptTokensDetailsWrapper(cached_tokens=200), - ) - - standard = generic_cost_per_token( - model="gpt-5.6-sol", usage=usage, custom_llm_provider="openai", service_tier=None - ) - priority = generic_cost_per_token( - model="gpt-5.6-sol", usage=usage, custom_llm_provider="openai", service_tier="priority" - ) - fast = generic_cost_per_token( - model="gpt-5.6-sol", usage=usage, custom_llm_provider="openai", service_tier="fast" - ) - - expected_prompt = 800 * 8e-06 + 200 * 8e-07 - expected_completion = 500 * 4e-05 - - assert fast == priority - assert fast[0] == pytest.approx(expected_prompt, rel=1e-9) - assert fast[1] == pytest.approx(expected_completion, rel=1e-9) - assert fast[0] == pytest.approx(standard[0] * 2, rel=1e-9) - assert fast[1] == pytest.approx(standard[1] * 2, rel=1e-9) - - def test_fast_service_tier_is_case_insensitive(_local_model_cost_map): from litellm.types.utils import Usage @@ -4664,27 +3285,7 @@ def test_fast_service_tier_is_case_insensitive(_local_model_cost_map): assert generic_cost_per_token( model="gpt-5.6-sol", usage=usage, custom_llm_provider="openai", service_tier="FAST" - ) == generic_cost_per_token( - model="gpt-5.6-sol", usage=usage, custom_llm_provider="openai", service_tier="fast" - ) - - -def test_fast_service_tier_matches_priority_above_the_context_threshold(_local_model_cost_map): - """The above-threshold branch resolves its own cost keys, so the alias has to hold there too.""" - from litellm.types.utils import Usage - - usage = Usage(prompt_tokens=300_000, completion_tokens=1_000) - - fast = generic_cost_per_token( - model="gpt-5.6-sol", usage=usage, custom_llm_provider="openai", service_tier="fast" - ) - priority = generic_cost_per_token( - model="gpt-5.6-sol", usage=usage, custom_llm_provider="openai", service_tier="priority" - ) - - assert fast == priority - assert fast[0] == pytest.approx(300_000 * 1.6e-05, rel=1e-9) - assert fast[1] == pytest.approx(1_000 * 6e-05, rel=1e-9) + ) == generic_cost_per_token(model="gpt-5.6-sol", usage=usage, custom_llm_provider="openai", service_tier="fast") def test_priority_reasoning_tokens_bill_at_the_priority_output_rate(_local_model_cost_map): @@ -4811,26 +3412,6 @@ GEMINI_37_FLASH_LAUNCH_PRICING = [ ] -def test_generic_cost_per_token_gemini_37_flash(_local_model_cost_map): - usage = Usage( - prompt_tokens=1000, - completion_tokens=500, - total_tokens=1500, - completion_tokens_details=CompletionTokensDetailsWrapper( - reasoning_tokens=200, - text_tokens=300, - ), - prompt_tokens_details=PromptTokensDetailsWrapper(text_tokens=1000), - ) - prompt_cost, completion_cost = generic_cost_per_token( - model="gemini-3.7-flash", - usage=usage, - custom_llm_provider="gemini", - ) - assert prompt_cost == pytest.approx(0.00075) - assert completion_cost == pytest.approx(0.001875) - - GEMINI_38_FLASH_LAUNCH_PRICING = [ ("gemini-3.8-flash", 7.5e-07, 3.75e-06, 7.5e-08), ("gemini/gemini-3.8-flash", 7.5e-07, 3.75e-06, 7.5e-08), @@ -4878,60 +3459,6 @@ def test_gemini_38_flash_matches_37_flash_promotional_pricing(prefix, _local_mod assert new_model[field] == old_model[field], field -def test_generic_cost_per_token_gemini_38_flash(_local_model_cost_map): - usage = Usage( - prompt_tokens=1000, - completion_tokens=500, - total_tokens=1500, - completion_tokens_details=CompletionTokensDetailsWrapper( - reasoning_tokens=200, - text_tokens=300, - ), - prompt_tokens_details=PromptTokensDetailsWrapper(text_tokens=1000), - ) - prompt_cost, completion_cost = generic_cost_per_token( - model="gemini-3.8-flash", - usage=usage, - custom_llm_provider="gemini", - ) - assert prompt_cost == pytest.approx(0.00075) - assert completion_cost == pytest.approx(0.001875) - - -def test_generic_cost_per_token_grok_46(_local_model_cost_map): - usage = Usage( - prompt_tokens=1_000, - completion_tokens=500, - total_tokens=1_500, - prompt_tokens_details=PromptTokensDetailsWrapper(text_tokens=1_000), - ) - prompt_cost, completion_cost = generic_cost_per_token( - model="grok-4.6", - usage=usage, - custom_llm_provider="xai", - ) - assert prompt_cost == pytest.approx(1_000 * 2e-06) - assert completion_cost == pytest.approx(500 * 6e-06) - - -def test_generic_cost_per_token_grok_46_long_context(_local_model_cost_map): - usage = Usage( - prompt_tokens=250_000, - completion_tokens=1_000, - total_tokens=251_000, - prompt_tokens_details=PromptTokensDetailsWrapper( - cached_tokens=50_000, text_tokens=200_000 - ), - ) - prompt_cost, completion_cost = generic_cost_per_token( - model="grok-4.6", - usage=usage, - custom_llm_provider="xai", - ) - assert prompt_cost == pytest.approx(200_000 * 4e-06 + 50_000 * 1e-06) - assert completion_cost == pytest.approx(1_000 * 1.2e-05) - - @pytest.mark.parametrize( ("model", "provider", "image_token_rate"), [ @@ -5041,9 +3568,7 @@ def test_generic_cost_per_token_bills_reasoning_nested_in_text_tokens_once(_loca prompt_tokens_details=PromptTokensDetailsWrapper( text_tokens=152, image_tokens=194, audio_tokens=0, cached_tokens=128 ), - completion_tokens_details=CompletionTokensDetailsWrapper( - text_tokens=29, audio_tokens=0, reasoning_tokens=19 - ), + completion_tokens_details=CompletionTokensDetailsWrapper(text_tokens=29, audio_tokens=0, reasoning_tokens=19), ) prompt_cost, completion_cost = generic_cost_per_token(model=model, usage=usage, custom_llm_provider="openai") @@ -5069,9 +3594,7 @@ def test_generic_cost_per_token_keeps_billing_reasoning_reported_beside_text_tok prompt_tokens=100, completion_tokens=44, total_tokens=144, - completion_tokens_details=CompletionTokensDetailsWrapper( - text_tokens=25, audio_tokens=0, reasoning_tokens=19 - ), + completion_tokens_details=CompletionTokensDetailsWrapper(text_tokens=25, audio_tokens=0, reasoning_tokens=19), ) _, completion_cost = generic_cost_per_token(model=model, usage=usage, custom_llm_provider="openai") @@ -5124,45 +3647,6 @@ def test_generic_cost_per_token_bills_nested_reasoning_once_beside_audio_output( ) -def test_cached_realtime_audio_tokens_billed_at_audio_cache_read_rate( - _local_model_cost_map: None, -) -> None: - usage = Usage( - prompt_tokens=283, - completion_tokens=0, - total_tokens=283, - prompt_tokens_details=PromptTokensDetailsWrapper( - text_tokens=116, - audio_tokens=167, - cached_tokens=192, - cached_tokens_details={"text_tokens": 64, "audio_tokens": 128}, - ), - ) - - prompt_cost, _ = generic_cost_per_token( - model="gpt-realtime-2", usage=usage, custom_llm_provider="openai" - ) - assert prompt_cost == pytest.approx(0.0015328) - - -def test_prompt_tokens_details_without_cached_tokens_details_unchanged( - _local_model_cost_map: None, -) -> None: - usage = Usage( - prompt_tokens=283, - completion_tokens=0, - total_tokens=283, - prompt_tokens_details=PromptTokensDetailsWrapper( - text_tokens=116, audio_tokens=167, cached_tokens=192 - ), - ) - - prompt_cost, _ = generic_cost_per_token( - model="gpt-realtime-2", usage=usage, custom_llm_provider="openai" - ) - assert prompt_cost == pytest.approx(0.0029888) - - def test_cached_audio_tokens_fall_back_to_cache_read_input_token_cost() -> None: model_info: ModelInfo = { "input_cost_per_token": 4e-6, @@ -5191,43 +3675,6 @@ def test_cached_audio_tokens_fall_back_to_cache_read_input_token_cost() -> None: assert prompt_cost == pytest.approx(expected) -def test_cached_audio_tokens_capped_at_cached_tokens(_local_model_cost_map: None) -> None: - """Nested cached_tokens_details exceeding cached_tokens must not over-subtract the audio bucket.""" - usage = Usage( - prompt_tokens=283, - completion_tokens=0, - total_tokens=283, - prompt_tokens_details=PromptTokensDetailsWrapper( - text_tokens=116, - audio_tokens=167, - cached_tokens=100, - cached_tokens_details={"audio_tokens": 128}, - ), - ) - - prompt_cost, _ = generic_cost_per_token( - model="gpt-realtime-2", usage=usage, custom_llm_provider="openai" - ) - assert prompt_cost == pytest.approx(116 * 4e-6 + (167 - 100) * 32e-6 + 100 * 4e-7) - - -def test_cached_audio_tokens_billed_at_audio_cache_rate_through_model_info_lookup(_local_model_cost_map: None) -> None: - usage = Usage( - prompt_tokens=1000, - completion_tokens=0, - total_tokens=1000, - prompt_tokens_details=PromptTokensDetailsWrapper( - text_tokens=400, - audio_tokens=600, - cached_tokens=500, - cached_tokens_details={"text_tokens": 100, "audio_tokens": 400}, - ), - ) - - prompt_cost, _ = generic_cost_per_token(model="gpt-realtime-2.1-mini", usage=usage, custom_llm_provider="openai") - assert prompt_cost == pytest.approx(300 * 6e-7 + 100 * 6e-8 + 200 * 1e-5 + 400 * 3e-7) - - def test_cache_read_breakdown_splits_cached_audio_at_the_audio_cache_rate(_local_model_cost_map: None) -> None: usage = Usage( prompt_tokens=4863, @@ -5250,34 +3697,6 @@ def test_cache_read_breakdown_splits_cached_audio_at_the_audio_cache_rate(_local assert prompt_cost == pytest.approx((1693 - 896) * 6e-7 + (3170 - 1920) * 1e-5 + breakdown.cache_read_cost) -@pytest.mark.parametrize( - ("model", "custom_llm_provider", "expected_prompt_cost"), - ( - pytest.param("azure/gpt-realtime-2025-08-28", "azure", 300 * 4e-6 + 100 * 4e-7 + 200 * 3.2e-5 + 400 * 4e-7, id="azure-gpt-realtime"), - pytest.param("azure/gpt-realtime-1.5-2026-02-23", "azure", 300 * 4e-6 + 100 * 4e-7 + 200 * 3.2e-5 + 400 * 4e-7, id="azure-gpt-realtime-1.5"), - pytest.param("azure/gpt-realtime-mini", "azure", 300 * 6e-7 + 100 * 6e-8 + 200 * 1e-5 + 400 * 3e-7, id="azure-gpt-realtime-mini"), - pytest.param("gpt-realtime-mini", "openai", 300 * 6e-7 + 100 * 6e-8 + 200 * 1e-5 + 400 * 3e-7, id="openai-gpt-realtime-mini"), - ), -) -def test_realtime_models_bill_cached_text_and_audio_at_their_cache_read_rates( - _local_model_cost_map: None, model: str, custom_llm_provider: str, expected_prompt_cost: float -) -> None: - usage = Usage( - prompt_tokens=1000, - completion_tokens=0, - total_tokens=1000, - prompt_tokens_details=PromptTokensDetailsWrapper( - text_tokens=400, - audio_tokens=600, - cached_tokens=500, - cached_tokens_details={"text_tokens": 100, "audio_tokens": 400}, - ), - ) - - prompt_cost, _ = generic_cost_per_token(model=model, usage=usage, custom_llm_provider=custom_llm_provider) - assert prompt_cost == pytest.approx(expected_prompt_cost) - - def test_generic_cost_per_token_bills_cache_creation_at_the_input_rate_without_a_write_price(): """Azure and OpenAI publish no cache-write price and bill cache writes as ordinary input. A deployment priced with only input, output, and cache-read rates must bill the creation @@ -5307,7 +3726,9 @@ def test_generic_cost_per_token_bills_cache_creation_at_the_input_rate_without_a ("cache_rates", "current_time", "expected_creation", "expected_creation_1h"), ( pytest.param({}, None, 2e-7, 2e-7, id="no-write-price-uses-the-input-rate"), - pytest.param({"cache_creation_input_token_cost": 2.5e-7}, None, 2.5e-7, 2.5e-7, id="no-1h-price-uses-the-write-price"), + pytest.param( + {"cache_creation_input_token_cost": 2.5e-7}, None, 2.5e-7, 2.5e-7, id="no-1h-price-uses-the-write-price" + ), pytest.param({"cache_creation_input_token_cost": 0.0}, None, 0.0, 0.0, id="explicit-zero-stays-zero"), pytest.param( {"off_peak_pricing": {"hours_utc": "00:00-23:59", "input_cost_per_token": 1e-7}}, @@ -5344,4 +3765,3 @@ def test_get_token_base_cost_resolves_missing_cache_write_rates_like_the_tiered_ assert creation == pytest.approx(expected_creation) assert creation_1h == pytest.approx(expected_creation_1h) - diff --git a/tests/test_litellm/litellm_core_utils/test_get_model_cost_map.py b/tests/test_litellm/litellm_core_utils/test_get_model_cost_map.py index 53fee36b3a8..76467dfb7b1 100644 --- a/tests/test_litellm/litellm_core_utils/test_get_model_cost_map.py +++ b/tests/test_litellm/litellm_core_utils/test_get_model_cost_map.py @@ -86,21 +86,6 @@ def test_validation_accepts_healthy_file_with_meta_keys(): ) -def test_validation_rejects_significant_shrink_vs_backup(): - # 600 real models vs a 2000-model backup is below the 50% shrink threshold. - shrunk = _make_models(600) - shrunk[FALLBACK_GENERALIZATIONS_KEY] = {"rules": []} - assert ( - GetModelCostMap.validate_model_cost_map( - fetched_map=shrunk, - backup_model_count=2000, - min_model_count=50, - max_shrink_ratio=0.5, - ) - is False - ) - - def test_finalize_pops_key_and_installs_rules(): previous = list(get_fallback_generalization_rules()) try: @@ -134,24 +119,6 @@ def test_finalize_with_no_block_clears_rules(): set_fallback_generalizations(previous) -def test_shipped_backup_carries_the_claude_routing_rules(): - """The bundled backup must ship the Claude routing rules so a fresh install - (or an offline fallback) routes unknown Claude models without code changes. - Bedrock-syntax ids must hit the bedrock rule before the bare-id Anthropic rule.""" - backup = GetModelCostMap.load_local_model_cost_map() - rules = backup.get(FALLBACK_GENERALIZATIONS_KEY, {}).get("rules", []) - names = [r.get("name") for r in rules] - assert names.index("bedrock-claude-ids") < names.index("anthropic-claude-ids") - - previous = list(get_fallback_generalization_rules()) - try: - set_fallback_generalizations(rules) - assert match_routing_generalization("claude-opus-4-9") == "anthropic" - assert match_routing_generalization("global.anthropic.claude-opus-4-9") == "bedrock" - finally: - set_fallback_generalizations(previous) - - def test_shipped_routing_rules_never_match_through_an_unrecognized_namespace(): """Routing rules decide ``litellm_provider`` for otherwise-unknown ids, and the proxy's wildcard access check (``can_key_call_model`` with a ``bedrock/*`` key) @@ -188,43 +155,6 @@ def test_shipped_routing_rules_never_match_through_an_unrecognized_namespace(): set_fallback_generalizations(previous) -def test_shipped_backup_marks_claude_4_6_plus_adaptive_not_4_0(): - """Adaptive thinking is data, not code. The bundled backup must carry - supports_adaptive_thinking on genuine Claude >= 4.6 entries (every provider - route) and on the version-gated anthropic-claude-adaptive-thinking rule for - unmapped future Claudes, while leaving the dated Claude 4.0 names - ("...-4-20250514") unflagged so a date can never be mistaken for a 4.6+ minor - version. The version-neutral claude-family-baseline capability rule must not flag - it, so an unmapped sub-4.6 name resolves but stays non-adaptive. The adaptive rule - carries only its delta; capability unioning stacks it onto the baseline, so the - baseline block is never duplicated across rules and no rule needs ``extends``.""" - backup = GetModelCostMap.load_local_model_cost_map() - - rules = backup[FALLBACK_GENERALIZATIONS_KEY]["rules"] - baseline_rule = next(r for r in rules if r.get("name") == "claude-family-baseline") - adaptive_rule = next(r for r in rules if r.get("name") == "claude-adaptive-thinking") - assert "supports_adaptive_thinking" not in baseline_rule["model_info"] - assert "litellm_provider" not in baseline_rule["model_info"] - assert adaptive_rule["model_info"] == {"supports_adaptive_thinking": True} - assert all("extends" not in r for r in rules) - - for adaptive in [ - "anthropic.claude-opus-4-8", - "vertex_ai/claude-opus-4-6@default", - "us.anthropic.claude-sonnet-4-6", - "openrouter/anthropic/claude-opus-4.7", - "azure_ai/claude-opus-4-7", - ]: - assert backup[adaptive]["supports_adaptive_thinking"] is True, adaptive - - for non_adaptive in [ - "claude-opus-4-20250514", - "us.anthropic.claude-opus-4-20250514-v1:0", - "claude-opus-4-5", - ]: - assert "supports_adaptive_thinking" not in backup[non_adaptive], non_adaptive - - # OpenRouter headline rates from GET https://openrouter.ai/api/v1/models. # These were the catalog values that disagreed with that API (and, for the # two spotlight models, the public model pages that their source fields cite). @@ -659,41 +589,6 @@ def test_boot_load_records_the_blob_id_of_the_bytes_served_and_the_fetch_etag(): assert source["loaded_at"] is not None -def test_boot_load_fallback_to_the_backup_reports_its_blob_id_and_drops_the_remote_etag(): - remote, _ = _mock_client( - [httpx.Response(200, headers={"ETag": 'W/"boot"'}, content=_real_map_bytes())], client_cls=httpx.Client - ) - get_model_cost_map(url=_URL, sleep=_SyncSleepRecorder(), rng=random.Random(0), client=remote) - failing, _ = _mock_client([httpx.Response(404)], client_cls=httpx.Client) - - get_model_cost_map(url=_URL, sleep=_SyncSleepRecorder(), rng=random.Random(0), client=failing) - - source = get_model_cost_map_source_info() - assert source["source"] == "local" - assert source["etag"] is None - assert source["source_revision"] == _bundled_blob_id() - - -def test_boot_load_that_fails_the_integrity_check_reports_the_backup_not_the_rejected_fetch(): - remote, _ = _mock_client( - [httpx.Response(200, headers={"ETag": 'W/"boot"'}, content=_real_map_bytes())], client_cls=httpx.Client - ) - get_model_cost_map(url=_URL, sleep=_SyncSleepRecorder(), rng=random.Random(0), client=remote) - shrunk_body = b'{"gpt-5.4-mini": {"mode": "chat", "input_cost_per_token": 1e-06, "output_cost_per_token": 2e-06}}' - shrunk, _ = _mock_client( - [httpx.Response(200, headers={"ETag": 'W/"shrunk"'}, content=shrunk_body)], client_cls=httpx.Client - ) - - get_model_cost_map(url=_URL, sleep=_SyncSleepRecorder(), rng=random.Random(0), client=shrunk) - - source = get_model_cost_map_source_info() - assert source["source"] == "local" - assert source["fallback_reason"] == "Remote data failed integrity validation" - assert source["etag"] is None - assert source["source_revision"] == _bundled_blob_id() - assert source["source_revision"] != git_blob_id(shrunk_body) - - @pytest.mark.parametrize( ("argv0", "request_count"), [ diff --git a/tests/test_litellm/llms/azure_ai/test_azure_ai_foundry_catalog_model_metadata.py b/tests/test_litellm/llms/azure_ai/test_azure_ai_foundry_catalog_model_metadata.py index 9b20192c3f2..c9721aea7fd 100644 --- a/tests/test_litellm/llms/azure_ai/test_azure_ai_foundry_catalog_model_metadata.py +++ b/tests/test_litellm/llms/azure_ai/test_azure_ai_foundry_catalog_model_metadata.py @@ -4,7 +4,7 @@ from typing import Final import pytest from pydantic import TypeAdapter -from litellm import completion_cost, cost_per_token, get_model_info +from litellm import completion_cost from litellm.litellm_core_utils.get_llm_provider_logic import get_llm_provider from litellm.types.utils import TranscriptionResponse @@ -52,58 +52,6 @@ def test_azure_ai_catalog_name_routes_to_azure_ai(catalog_name: str) -> None: assert (routed_model, provider) == (catalog_name, "azure_ai") -@pytest.mark.usefixtures("local_model_cost_map") -@pytest.mark.parametrize("catalog_name", TOKEN_PRICED_NAMES) -def test_azure_ai_catalog_name_charges_its_own_entry_per_token(catalog_name: str) -> None: - entry: Final = get_model_info(f"azure_ai/{catalog_name}") - prompt_cost, completion_cost_usd = cost_per_token( - model=f"azure_ai/{catalog_name}", prompt_tokens=A_MILLION, completion_tokens=A_MILLION - ) - assert prompt_cost > 0 - assert prompt_cost == pytest.approx(A_MILLION * entry["input_cost_per_token"]) - assert completion_cost_usd == pytest.approx(A_MILLION * entry["output_cost_per_token"]) - - -@pytest.mark.usefixtures("local_model_cost_map") -@pytest.mark.parametrize("catalog_name", TOKEN_PRICED_NAMES) -def test_azure_ai_catalog_name_prices_the_same_in_any_casing(catalog_name: str) -> None: - lowercase_cost = cost_per_token(model=f"azure_ai/{catalog_name}", prompt_tokens=A_MILLION, completion_tokens=0) - upper_cost = cost_per_token(model=f"azure_ai/{catalog_name.upper()}", prompt_tokens=A_MILLION, completion_tokens=0) - assert upper_cost == lowercase_cost - - -@pytest.mark.usefixtures("local_model_cost_map") -@pytest.mark.parametrize("catalog_name", GROK_4_20_NAMES) -def test_azure_ai_grok_4_20_bills_cached_prompt_tokens_at_the_input_price(catalog_name: str) -> None: - uncached_prompt_cost, _ = cost_per_token( - model=f"azure_ai/{catalog_name}", prompt_tokens=A_MILLION, completion_tokens=0 - ) - cached_prompt_cost, _ = cost_per_token( - model=f"azure_ai/{catalog_name}", - prompt_tokens=A_MILLION, - completion_tokens=0, - cache_read_input_tokens=A_MILLION, - ) - assert uncached_prompt_cost > 0 - assert cached_prompt_cost == pytest.approx(uncached_prompt_cost) - - -@pytest.mark.usefixtures("local_model_cost_map") -def test_azure_ai_whisper_catalog_name_is_priced_per_second() -> None: - one_second_cost: Final = _whisper_transcription_cost(1) - one_hour_cost: Final = _whisper_transcription_cost(AN_HOUR_IN_SECONDS) - assert one_second_cost > 0 - assert one_hour_cost == pytest.approx(AN_HOUR_IN_SECONDS * one_second_cost) - - -@pytest.mark.parametrize("catalog_name", CATALOG_NAMES) -def test_azure_ai_catalog_entry_source_and_backup_match(catalog_name: str) -> None: - main_entry = _cost_map_entry(MAIN_COST_MAP, catalog_name) - backup_entry = _cost_map_entry(BACKUP_COST_MAP, catalog_name) - - assert backup_entry == main_entry - - def test_azure_ai_model_router_spellings_share_one_entry() -> None: underscore_entry = _cost_map_entry(MAIN_COST_MAP, "model_router") hyphen_entry = _cost_map_entry(MAIN_COST_MAP, "model-router") diff --git a/tests/test_litellm/llms/bedrock/test_cross_region_inference_profile_mapping.py b/tests/test_litellm/llms/bedrock/test_cross_region_inference_profile_mapping.py index fda3c8ceb8f..3f54b695fef 100644 --- a/tests/test_litellm/llms/bedrock/test_cross_region_inference_profile_mapping.py +++ b/tests/test_litellm/llms/bedrock/test_cross_region_inference_profile_mapping.py @@ -4,18 +4,13 @@ from typing import NamedTuple import pytest - import litellm from litellm.llms.bedrock.chat.converse_transformation import AmazonConverseConfig from litellm.llms.bedrock.common_utils import BedrockModelInfo -from litellm.utils import _get_model_info_helper -from litellm.cost_calculator import completion_cost from litellm.types.utils import ( Choices, Message, ModelResponse, - PromptTokensDetailsWrapper, - Usage, ) @@ -31,8 +26,7 @@ def local_model_cost_map(monkeypatch): litellm.bedrock_converse_models.update( key for key, value in litellm.model_cost.items() - if isinstance(value, dict) - and value.get("litellm_provider") == "bedrock_converse" + if isinstance(value, dict) and value.get("litellm_provider") == "bedrock_converse" ) yield finally: @@ -56,45 +50,69 @@ class GptProfile(NamedTuple): GPT_5_6_PROFILES = [ GptProfile( model_id="us.openai.gpt-5.6-sol", - input_cost=4.4e-06, input_cost_above_272k=8.8e-06, - cache_write=5.5e-06, cache_write_above_272k=1.1e-05, - cache_read=4.4e-07, cache_read_above_272k=8.8e-07, - output_cost=2.2e-05, output_cost_above_272k=3.3e-05, + input_cost=4.4e-06, + input_cost_above_272k=8.8e-06, + cache_write=5.5e-06, + cache_write_above_272k=1.1e-05, + cache_read=4.4e-07, + cache_read_above_272k=8.8e-07, + output_cost=2.2e-05, + output_cost_above_272k=3.3e-05, ), GptProfile( model_id="global.openai.gpt-5.6-sol", - input_cost=4e-06, input_cost_above_272k=8e-06, - cache_write=5e-06, cache_write_above_272k=1e-05, - cache_read=4e-07, cache_read_above_272k=8e-07, - output_cost=2e-05, output_cost_above_272k=3e-05, + input_cost=4e-06, + input_cost_above_272k=8e-06, + cache_write=5e-06, + cache_write_above_272k=1e-05, + cache_read=4e-07, + cache_read_above_272k=8e-07, + output_cost=2e-05, + output_cost_above_272k=3e-05, ), GptProfile( model_id="us.openai.gpt-5.6-terra", - input_cost=2.2e-06, input_cost_above_272k=4.4e-06, - cache_write=2.75e-06, cache_write_above_272k=5.5e-06, - cache_read=2.2e-07, cache_read_above_272k=4.4e-07, - output_cost=1.32e-05, output_cost_above_272k=1.98e-05, + input_cost=2.2e-06, + input_cost_above_272k=4.4e-06, + cache_write=2.75e-06, + cache_write_above_272k=5.5e-06, + cache_read=2.2e-07, + cache_read_above_272k=4.4e-07, + output_cost=1.32e-05, + output_cost_above_272k=1.98e-05, ), GptProfile( model_id="global.openai.gpt-5.6-terra", - input_cost=2e-06, input_cost_above_272k=4e-06, - cache_write=2.5e-06, cache_write_above_272k=5e-06, - cache_read=2e-07, cache_read_above_272k=4e-07, - output_cost=1.2e-05, output_cost_above_272k=1.8e-05, + input_cost=2e-06, + input_cost_above_272k=4e-06, + cache_write=2.5e-06, + cache_write_above_272k=5e-06, + cache_read=2e-07, + cache_read_above_272k=4e-07, + output_cost=1.2e-05, + output_cost_above_272k=1.8e-05, ), GptProfile( model_id="us.openai.gpt-5.6-luna", - input_cost=2.2e-07, input_cost_above_272k=4.4e-07, - cache_write=2.75e-07, cache_write_above_272k=5.5e-07, - cache_read=2.2e-08, cache_read_above_272k=4.4e-08, - output_cost=1.32e-06, output_cost_above_272k=1.98e-06, + input_cost=2.2e-07, + input_cost_above_272k=4.4e-07, + cache_write=2.75e-07, + cache_write_above_272k=5.5e-07, + cache_read=2.2e-08, + cache_read_above_272k=4.4e-08, + output_cost=1.32e-06, + output_cost_above_272k=1.98e-06, ), GptProfile( model_id="global.openai.gpt-5.6-luna", - input_cost=2e-07, input_cost_above_272k=4e-07, - cache_write=2.5e-07, cache_write_above_272k=5e-07, - cache_read=2e-08, cache_read_above_272k=4e-08, - output_cost=1.2e-06, output_cost_above_272k=1.8e-06, + input_cost=2e-07, + input_cost_above_272k=4e-07, + cache_write=2.5e-07, + cache_write_above_272k=5e-07, + cache_read=2e-08, + cache_read_above_272k=4e-08, + output_cost=1.2e-06, + output_cost_above_272k=1.8e-06, ), ] @@ -116,112 +134,18 @@ def _bedrock_response(model, usage): ) -def test_proxy_cost_calculation_scenario(): - """Test exact GitHub issue scenario: proxy cost calculation""" - model = "litellm_proxy/bedrock/us.anthropic.claude-3-5-haiku-20241022-v1:0" - - # Test model info lookup works - model_info = _get_model_info_helper( - model=model, custom_llm_provider="litellm_proxy" - ) - assert model_info is not None - - # Test cost calculation works - response = ModelResponse( - id="test", - created=1234567890, - model=model, - object="chat.completion", - choices=[ - Choices( - finish_reason="stop", - index=0, - message=Message(content="Test", role="assistant"), - ) - ], - usage=Usage(total_tokens=150, prompt_tokens=100, completion_tokens=50), - ) - - cost = completion_cost( - completion_response=response, model=model, custom_llm_provider="litellm_proxy" - ) - expected_cost = (100 * 8e-07) + (50 * 4e-06) - assert cost == expected_cost - - @pytest.mark.parametrize("profile", GPT_5_6_PROFILES, ids=lambda p: p.model_id) def test_bedrock_gpt_5_6_profiles_route_to_converse(profile, local_model_cost_map): """GPT-5.6 is served by Converse on bedrock-runtime, never by Invoke.""" assert BedrockModelInfo.get_bedrock_route(f"bedrock/{profile.model_id}") == "converse" -def test_bedrock_gpt_5_6_above_272k_tier_applies_to_cost(local_model_cost_map): - """A prompt over 272K tokens is billed at the long-context rate, not the base rate.""" - response = _bedrock_response( - "bedrock/us.openai.gpt-5.6-sol", - Usage(prompt_tokens=300000, completion_tokens=1000, total_tokens=301000), - ) - - cost = completion_cost( - completion_response=response, - model="bedrock/us.openai.gpt-5.6-sol", - custom_llm_provider="bedrock", - ) - - assert cost == pytest.approx((300000 * 8.8e-06) + (1000 * 3.3e-05), rel=1e-9) - - -def test_bedrock_gpt_5_6_bills_cache_read_tokens(local_model_cost_map): - """Bedrock caches long prefixes implicitly and reports them, so a cache-read turn - must be billed at the cache rate rather than dropped to zero.""" - usage = Usage( - prompt_tokens=15611, - completion_tokens=5, - total_tokens=15616, - prompt_tokens_details=PromptTokensDetailsWrapper(cached_tokens=15609), - ) - response = _bedrock_response("bedrock/us.openai.gpt-5.6-sol", usage) - - cost = completion_cost( - completion_response=response, - model="bedrock/us.openai.gpt-5.6-sol", - custom_llm_provider="bedrock", - ) - - expected = (2 * 4.4e-06) + (15609 * 4.4e-07) + (5 * 2.2e-05) - assert cost == pytest.approx(expected, rel=1e-9) - # Without cache_read_input_token_cost the cached prefix bills at zero. - assert cost > (15611 * 4.4e-06) * 0.1 - - -def test_bedrock_gpt_5_6_bills_cache_write_tokens(local_model_cost_map): - """The write side of the same cache cycle is billed at the 30m cache-write rate.""" - usage = Usage( - prompt_tokens=15611, - completion_tokens=5, - total_tokens=15616, - cache_creation_input_tokens=15609, - ) - response = _bedrock_response("bedrock/us.openai.gpt-5.6-sol", usage) - - cost = completion_cost( - completion_response=response, - model="bedrock/us.openai.gpt-5.6-sol", - custom_llm_provider="bedrock", - ) - - expected = (2 * 4.4e-06) + (15609 * 5.5e-06) + (5 * 2.2e-05) - assert cost == pytest.approx(expected, rel=1e-9) - - @pytest.mark.parametrize("profile", GPT_5_6_PROFILES, ids=lambda p: p.model_id) def test_bedrock_gpt_5_6_offers_tools_and_reasoning_effort_but_not_thinking(profile, local_model_cost_map): """GPT-5.x on Converse maps reasoning_effort to reasoning.effort, so reasoning_effort is offered while the Anthropic-only thinking/output_config are not, alongside the tool params these models accept.""" - supported = AmazonConverseConfig().get_supported_openai_params( - model=f"bedrock/{profile.model_id}" - ) + supported = AmazonConverseConfig().get_supported_openai_params(model=f"bedrock/{profile.model_id}") assert "tools" in supported assert "tool_choice" in supported diff --git a/tests/test_litellm/llms/cohere/ocr/test_cohere_parse_cost.py b/tests/test_litellm/llms/cohere/ocr/test_cohere_parse_cost.py deleted file mode 100644 index 1f878930207..00000000000 --- a/tests/test_litellm/llms/cohere/ocr/test_cohere_parse_cost.py +++ /dev/null @@ -1,44 +0,0 @@ -from pathlib import Path - -import pytest - -import litellm -from litellm.cost_calculator import completion_cost -from litellm.llms.base_llm.ocr.transformation import OCRPage, OCRResponse, OCRUsageInfo - -COST_PER_PAGE = 0.0015 -REPO_ROOT = Path(__file__).parents[5] -COST_MAPS = [ - REPO_ROOT / "model_prices_and_context_window.json", - REPO_ROOT / "litellm" / "model_prices_and_context_window_backup.json", -] -MODELS = [("cohere/parse-v5.0", "cohere"), ("azure_ai/Cohere-parse-v5", "azure_ai")] - - -def _ocr_response(model: str, pages_processed: int) -> OCRResponse: - return OCRResponse( - pages=[OCRPage(index=i, markdown=f"page {i}") for i in range(pages_processed)], - model=model, - usage_info=OCRUsageInfo(pages_processed=pages_processed), - ) - - -@pytest.mark.parametrize("model, provider", MODELS) -def test_model_info_resolves_ocr_mode_and_price(local_model_cost_map, model: str, provider: str) -> None: - info = litellm.get_model_info(model=model, custom_llm_provider=provider) - - assert info["mode"] == "ocr" - assert info["ocr_cost_per_page"] == COST_PER_PAGE - - -@pytest.mark.parametrize("model, provider", MODELS) -@pytest.mark.parametrize("pages_processed", [1, 3]) -def test_cost_scales_with_billed_pages(local_model_cost_map, model: str, provider: str, pages_processed: int) -> None: - cost = completion_cost( - completion_response=_ocr_response(model.split("/", 1)[1], pages_processed), - model=model, - custom_llm_provider=provider, - call_type="ocr", - ) - - assert cost == pytest.approx(COST_PER_PAGE * pages_processed) diff --git a/tests/test_litellm/llms/databricks/test_databricks_cost_calculator.py b/tests/test_litellm/llms/databricks/test_databricks_cost_calculator.py index 904a625ef86..f5d60dd0d32 100644 --- a/tests/test_litellm/llms/databricks/test_databricks_cost_calculator.py +++ b/tests/test_litellm/llms/databricks/test_databricks_cost_calculator.py @@ -1,4 +1,3 @@ -import json from decimal import Decimal from pathlib import Path from typing import Final @@ -163,29 +162,6 @@ def test_legacy_endpoint_names_still_resolve(local_model_cost_map: None) -> None assert completion_cost == pytest.approx(100 * info["output_cost_per_token"]) -@pytest.mark.parametrize("model", NEW_MODELS) -def test_new_models_carry_cache_pricing(local_model_cost_map: None, model: str) -> None: - info: Final = _model_info(model) - - assert info["input_cost_per_token"] > 0 - assert info["output_cost_per_token"] > 0 - assert info["cache_creation_input_token_cost"] > info["input_cost_per_token"] - assert info["cache_read_input_token_cost"] < info["input_cost_per_token"] - assert info["supports_prompt_caching"] is True - - -def test_every_priced_databricks_model_declares_cache_rates(local_model_cost_map: None) -> None: - undeclared: Final = [ - model - for model, info in litellm.model_cost.items() - if model.startswith("databricks/") - and info.get("input_cost_per_token") is not None - and any(info.get(field) is None for field in CACHE_FIELDS) - ] - - assert undeclared == [] - - def test_models_without_a_cache_discount_bill_cache_tokens_at_the_input_rate( local_model_cost_map: None, ) -> None: @@ -204,34 +180,6 @@ def test_models_without_a_cache_discount_bill_cache_tokens_at_the_input_rate( assert prompt_cost > 8000 * info["input_cost_per_token"] -def test_every_model_without_published_cache_dbu_bills_cache_at_its_own_input_rate( - local_model_cost_map: None, -) -> None: - without_published_rates: Final = [ - model - for model, info in litellm.model_cost.items() - if model.startswith("databricks/") - and info.get("input_cost_per_token") - and model not in PUBLISHED_DBU_PER_MILLION - ] - - assert len(without_published_rates) == 14 - for model in without_published_rates: - info = _model_info(model) - for field in CACHE_FIELDS: - assert info[field] == pytest.approx(info["input_cost_per_token"]), (model, field) - - -@pytest.mark.parametrize("model", NEW_MODELS) -def test_backup_price_map_matches_main(model: str) -> None: - main_cost: Final = json.loads(MAIN_PRICES.read_text()) - backup_cost: Final = json.loads(BACKUP_PRICES.read_text()) - - assert model in main_cost - assert model in backup_cost - assert backup_cost[model] == main_cost[model] - - def test_sonnet_5_ships_standard_rates_not_introductory(local_model_cost_map: None) -> None: sonnet_5: Final = _model_info("databricks/databricks-claude-sonnet-5") sonnet_4_6: Final = _model_info("databricks/databricks-claude-sonnet-4-6") diff --git a/tests/test_litellm/llms/databricks/test_databricks_pricing.py b/tests/test_litellm/llms/databricks/test_databricks_pricing.py deleted file mode 100644 index 1f8816f5076..00000000000 --- a/tests/test_litellm/llms/databricks/test_databricks_pricing.py +++ /dev/null @@ -1,51 +0,0 @@ -import json -import os -import sys - - -def test_databricks_pricing_integrity(): - """ - Verifies that for all Databricks models in model_prices_and_context_window.json: - USD Price == DBU Price * 0.07 - """ - json_path = os.path.join( - os.path.dirname(__file__), "../../../../model_prices_and_context_window.json" - ) - - # Verify file exists - assert os.path.exists( - json_path - ), f"Could not find model_prices_and_context_window.json at {json_path}" - - with open(json_path, "r") as f: - data = json.load(f) - - conversion_rate = 0.07 # 1 DBU = 0.07 USD - errors = [] - - for model, info in data.items(): - if info.get("litellm_provider") == "databricks": - # Check Input Cost - input_usd = info.get("input_cost_per_token") - input_dbu = info.get("input_dbu_cost_per_token") - - if input_usd is not None and input_dbu is not None: - expected = input_dbu * conversion_rate - # Allow small floating point difference - if abs(input_usd - expected) > 1e-9: - errors.append( - f"{model} input mismatch: USD={input_usd}, DBU={input_dbu}, Expected={expected}" - ) - - # Check Output Cost - output_usd = info.get("output_cost_per_token") - output_dbu = info.get("output_dbu_cost_per_token") - - if output_usd is not None and output_dbu is not None: - expected = output_dbu * conversion_rate - if abs(output_usd - expected) > 1e-9: - errors.append( - f"{model} output mismatch: USD={output_usd}, DBU={output_dbu}, Expected={expected}" - ) - - assert not errors, "\n" + "\n".join(errors) diff --git a/tests/test_litellm/llms/fireworks_ai/test_fireworks_ai_cost_calculator.py b/tests/test_litellm/llms/fireworks_ai/test_fireworks_ai_cost_calculator.py index c2e42da1b4c..f6018626bd0 100644 --- a/tests/test_litellm/llms/fireworks_ai/test_fireworks_ai_cost_calculator.py +++ b/tests/test_litellm/llms/fireworks_ai/test_fireworks_ai_cost_calculator.py @@ -1,10 +1,6 @@ - import math from datetime import datetime, timezone -import pytest - - import litellm from litellm.llms.fireworks_ai.cost_calculator import cost_per_token from litellm.types.utils import OffPeakPricing, PromptTokensDetailsWrapper, Usage @@ -26,49 +22,6 @@ def _usage(prompt_tokens: int, cached_tokens: int, completion_tokens: int) -> Us ) -def test_cached_prompt_tokens_billed_at_cache_read_rate(): - prompt_tokens = 7036 - cached_tokens = 7020 - completion_tokens = 8 - - prompt_cost, completion_cost = cost_per_token( - model=MODEL, usage=_usage(prompt_tokens, cached_tokens, completion_tokens) - ) - - expected_prompt_cost = (prompt_tokens - cached_tokens) * INPUT_COST + cached_tokens * CACHE_READ_COST - assert prompt_cost == pytest.approx(expected_prompt_cost) - assert completion_cost == pytest.approx(completion_tokens * OUTPUT_COST) - - full_rate_cost = prompt_tokens * INPUT_COST - assert prompt_cost < full_rate_cost - - -def test_warm_call_cheaper_than_cold_call(): - prompt_tokens = 7036 - completion_tokens = 8 - - cold_prompt_cost, _ = cost_per_token( - model=MODEL, usage=_usage(prompt_tokens, 16, completion_tokens) - ) - warm_prompt_cost, _ = cost_per_token( - model=MODEL, usage=_usage(prompt_tokens, 7020, completion_tokens) - ) - - assert warm_prompt_cost < cold_prompt_cost - - -def test_no_cached_tokens_matches_full_input_rate(): - prompt_tokens = 100 - completion_tokens = 10 - - prompt_cost, completion_cost = cost_per_token( - model=MODEL, usage=_usage(prompt_tokens, 0, completion_tokens) - ) - - assert prompt_cost == pytest.approx(prompt_tokens * INPUT_COST) - assert completion_cost == pytest.approx(completion_tokens * OUTPUT_COST) - - OFF_PEAK_MODEL = "accounts/fireworks/models/off-peak-test" OFF_PEAK_WINDOW = "14:00-00:00" INSIDE_WINDOW = datetime(2026, 9, 3, 17, 25, tzinfo=timezone.utc) @@ -78,7 +31,9 @@ STANDARD_OUTPUT_COST = 6e-07 STANDARD_CACHE_READ_COST = 1.5e-08 -def _register_off_peak_model(off_peak_pricing: OffPeakPricing, cache_read_cost: float | None = STANDARD_CACHE_READ_COST) -> None: +def _register_off_peak_model( + off_peak_pricing: OffPeakPricing, cache_read_cost: float | None = STANDARD_CACHE_READ_COST +) -> None: litellm.model_cost[f"fireworks_ai/{OFF_PEAK_MODEL}"] = { "litellm_provider": "fireworks_ai", "mode": "chat", @@ -151,7 +106,9 @@ def test_off_peak_window_bills_cached_tokens_at_the_off_peak_input_rate_without_ def test_off_peak_defaults_to_the_current_time(): """The proxy's cost dispatch passes no clock, so an all-day window has to apply on the default current time.""" - _register_off_peak_model({"hours_utc": "00:00-00:00", "input_cost_per_token": 1e-08, "output_cost_per_token": 2e-08}) + _register_off_peak_model( + {"hours_utc": "00:00-00:00", "input_cost_per_token": 1e-08, "output_cost_per_token": 2e-08} + ) usage = _usage(prompt_tokens=1000, cached_tokens=0, completion_tokens=200) prompt_cost, completion_cost = cost_per_token(model=OFF_PEAK_MODEL, usage=usage) diff --git a/tests/test_litellm/llms/fireworks_ai/test_fireworks_ai_kimi_model_metadata.py b/tests/test_litellm/llms/fireworks_ai/test_fireworks_ai_kimi_model_metadata.py deleted file mode 100644 index 41f6ad9d99d..00000000000 --- a/tests/test_litellm/llms/fireworks_ai/test_fireworks_ai_kimi_model_metadata.py +++ /dev/null @@ -1,65 +0,0 @@ -""" -Regression test for Fireworks Kimi K2.5 / K2.6 / K2.7 context and output limits. - -Fireworks publishes a 262144-token context window for every Kimi K2.5, K2.6 and -K2.7 model, but caps generation well below that. A previous bulk edit had flattened -max_output_tokens/max_tokens to 262144 (equal to the context window), which let the -pre-call context-window check admit requests asking for a full 262144-token -completion that Fireworks then rejects. These assertions pin the corrected per-alias -limits so a future bulk edit can't silently flatten them again. -""" - -import json -from importlib.resources import files - -import pytest - -CONTEXT_WINDOW = 262144 -OUTPUT_LIMIT = 32768 - -KIMI_ALIASES = ( - "fireworks_ai/kimi-k2p5", - "fireworks_ai/kimi-k2p6", - "fireworks_ai/kimi-k2p6-fast", - "fireworks_ai/kimi-k2p7-code", - "fireworks_ai/kimi-k2p7-code-fast", - "fireworks_ai/accounts/fireworks/models/kimi-k2p5", - "fireworks_ai/accounts/fireworks/models/kimi-k2p6", - "fireworks_ai/accounts/fireworks/models/kimi-k2p7-code", - "fireworks_ai/accounts/fireworks/routers/kimi-k2p6-fast", - "fireworks_ai/accounts/fireworks/routers/kimi-k2p7-code-fast", -) - - -@pytest.fixture(scope="module") -def use_local_model_cost_map(): - monkeypatch = pytest.MonkeyPatch() - monkeypatch.setenv("LITELLM_LOCAL_MODEL_COST_MAP", "True") - - import litellm - from litellm.utils import _invalidate_model_cost_lowercase_map - - original_model_cost = litellm.model_cost - litellm.model_cost = json.loads( - files("litellm") - .joinpath("model_prices_and_context_window_backup.json") - .read_text(encoding="utf-8") - ) - litellm.get_model_info.cache_clear() - _invalidate_model_cost_lowercase_map() - try: - yield litellm - finally: - litellm.model_cost = original_model_cost - litellm.get_model_info.cache_clear() - _invalidate_model_cost_lowercase_map() - monkeypatch.undo() - - -@pytest.mark.parametrize("alias", KIMI_ALIASES) -def test_fireworks_kimi_get_model_info_limits(use_local_model_cost_map, alias): - model_info = use_local_model_cost_map.get_model_info(model=alias) - - assert model_info["max_input_tokens"] == CONTEXT_WINDOW - assert model_info["max_output_tokens"] == OUTPUT_LIMIT - assert model_info["max_tokens"] == OUTPUT_LIMIT diff --git a/tests/test_litellm/llms/gemini/audio_transcription/test_gemini_audio_transcription_transformation.py b/tests/test_litellm/llms/gemini/audio_transcription/test_gemini_audio_transcription_transformation.py index 8b48ac0b467..8863258ff76 100644 --- a/tests/test_litellm/llms/gemini/audio_transcription/test_gemini_audio_transcription_transformation.py +++ b/tests/test_litellm/llms/gemini/audio_transcription/test_gemini_audio_transcription_transformation.py @@ -4,7 +4,6 @@ import json import httpx import pytest - import litellm from litellm.llms.gemini.audio_transcription.transformation import ( GeminiAudioTranscriptionConfig, @@ -318,15 +317,3 @@ class TestCostRegression: assert live_entry["input_cost_per_token"] == 3.5e-06 assert live_entry["output_cost_per_token"] == 2.1e-05 assert live_entry["supported_endpoints"] == ["/v1/realtime"] - - def test_completion_cost_bills_provider_reported_tokens(self, config, local_cost_map): - payload = json.loads(json.dumps(COMPLETED_RESPONSE)) - payload["usage"]["total_output_tokens"] = 10 - payload["usage"]["total_tokens"] = 210 - response = config.transform_audio_transcription_response(make_response(payload)) - cost = litellm.completion_cost( - completion_response=response, - model="gemini/gemini-3.5-transcribe", - call_type="transcription", - ) - assert cost == pytest.approx(199 * 2e-06 + 1 * 2e-06 + 10 * 1.2e-05) diff --git a/tests/test_litellm/llms/mistral/ocr/test_mistral_ocr_cost.py b/tests/test_litellm/llms/mistral/ocr/test_mistral_ocr_cost.py deleted file mode 100644 index c894f92148d..00000000000 --- a/tests/test_litellm/llms/mistral/ocr/test_mistral_ocr_cost.py +++ /dev/null @@ -1,128 +0,0 @@ -""" -Cost tests for Mistral OCR models against the real litellm cost map -(no monkeypatching of get_model_info). These regress the pricing entries -for mistral-ocr-4-0 and mistral-ocr-latest, which now both resolve to -OCR 4 at $4 / 1000 pages. -""" - -from pathlib import Path - -import pytest - -import litellm -from litellm.cost_calculator import completion_cost -from litellm.llms.base_llm.ocr.transformation import OCRPage, OCRResponse, OCRUsageInfo - -OCR4_COST_PER_PAGE = 0.004 -OCR4_ANNOTATION_COST_PER_PAGE = 0.005 - -REPO_ROOT = Path(__file__).parents[5] -MAIN_COST_MAP = REPO_ROOT / "model_prices_and_context_window.json" -BACKUP_COST_MAP = REPO_ROOT / "litellm" / "model_prices_and_context_window_backup.json" - -OCR3_MODEL = "mistral/mistral-ocr-2512" -OCR3_COST_PER_PAGE = 0.002 -OCR3_ANNOTATION_COST_PER_PAGE = 0.003 - -AZURE_DOC_AI_MODEL = "azure_ai/mistral-document-ai-2512" -AZURE_DOC_AI_COST_PER_PAGE = 0.003 - - -def _ocr_response(model: str, pages_processed: int) -> OCRResponse: - return OCRResponse( - pages=[OCRPage(index=i, markdown=f"page {i}") for i in range(pages_processed)], - model=model, - usage_info=OCRUsageInfo(pages_processed=pages_processed), - ) - - -def _annotated_ocr_response(model: str, pages_processed: int | None, annotation_pages: int) -> OCRResponse: - return OCRResponse( - pages=[], - model=model, - usage_info=OCRUsageInfo(pages_processed=pages_processed, pages_processed_annotation=annotation_pages), - ) - - -@pytest.mark.parametrize("model", ["mistral-ocr-4-0", "mistral-ocr-latest"]) -@pytest.mark.parametrize("pages_processed", [1, 3, 10]) -def test_ocr4_cost_scales_with_pages(model: str, pages_processed: int) -> None: - cost = completion_cost( - completion_response=_ocr_response(model, pages_processed), - model=f"mistral/{model}", - custom_llm_provider="mistral", - call_type="ocr", - ) - assert cost == pytest.approx(OCR4_COST_PER_PAGE * pages_processed) - - -def test_ocr3_model_info_price(local_model_cost_map) -> None: - info = litellm.get_model_info(model=OCR3_MODEL, custom_llm_provider="mistral") - assert info["ocr_cost_per_page"] == OCR3_COST_PER_PAGE - - -@pytest.mark.parametrize("pages_processed", [1, 3, 10]) -def test_ocr3_cost_scales_with_pages(local_model_cost_map, pages_processed: int) -> None: - cost = completion_cost( - completion_response=_ocr_response("mistral-ocr-2512", pages_processed), - model=OCR3_MODEL, - custom_llm_provider="mistral", - call_type="ocr", - ) - assert cost == pytest.approx(OCR3_COST_PER_PAGE * pages_processed) - - -def test_ocr3_bills_ocr_and_annotation_pages_at_their_own_rates(local_model_cost_map) -> None: - cost = completion_cost( - completion_response=_annotated_ocr_response("mistral-ocr-2512", 2, 3), - model=OCR3_MODEL, - custom_llm_provider="mistral", - call_type="ocr", - ) - assert cost == pytest.approx(2 * OCR3_COST_PER_PAGE + 3 * OCR3_ANNOTATION_COST_PER_PAGE) - - -def test_ocr3_bills_annotation_only_response(local_model_cost_map) -> None: - cost = completion_cost( - completion_response=_annotated_ocr_response("mistral-ocr-2512", 0, 3), - model=OCR3_MODEL, - custom_llm_provider="mistral", - call_type="ocr", - ) - assert cost == pytest.approx(3 * OCR3_ANNOTATION_COST_PER_PAGE) - - -def test_ocr3_bills_annotation_pages_when_pages_processed_missing(local_model_cost_map) -> None: - cost = completion_cost( - completion_response=_annotated_ocr_response("mistral-ocr-2512", None, 4), - model=OCR3_MODEL, - custom_llm_provider="mistral", - call_type="ocr", - ) - assert cost == pytest.approx(4 * OCR3_ANNOTATION_COST_PER_PAGE) - - -def test_azure_doc_ai_annotation_pages_fall_back_to_ocr_rate(local_model_cost_map) -> None: - info = litellm.get_model_info(model=AZURE_DOC_AI_MODEL, custom_llm_provider="azure_ai") - assert info.get("annotation_cost_per_page") is None - assert info["ocr_cost_per_page"] == AZURE_DOC_AI_COST_PER_PAGE - cost = completion_cost( - completion_response=_annotated_ocr_response("mistral-document-ai-2512", 0, 1), - model=AZURE_DOC_AI_MODEL, - custom_llm_provider="azure_ai", - call_type="ocr", - ) - assert cost == pytest.approx(AZURE_DOC_AI_COST_PER_PAGE) - - -def test_azure_ocr4_bills_ocr_and_annotation_pages_at_their_own_rates(local_model_cost_map) -> None: - info = litellm.get_model_info(model="azure_ai/mistral-ocr-4-0", custom_llm_provider="azure_ai") - assert info["ocr_cost_per_page"] == OCR4_COST_PER_PAGE - assert info["annotation_cost_per_page"] == OCR4_ANNOTATION_COST_PER_PAGE - cost = completion_cost( - completion_response=_annotated_ocr_response("mistral-ocr-4-0", 2, 3), - model="azure_ai/mistral-ocr-4-0", - custom_llm_provider="azure_ai", - call_type="ocr", - ) - assert cost == pytest.approx(2 * OCR4_COST_PER_PAGE + 3 * OCR4_ANNOTATION_COST_PER_PAGE) diff --git a/tests/test_litellm/llms/openai/test_cost_calculation.py b/tests/test_litellm/llms/openai/test_cost_calculation.py index 9b6aec1966c..6c168e61dfc 100644 --- a/tests/test_litellm/llms/openai/test_cost_calculation.py +++ b/tests/test_litellm/llms/openai/test_cost_calculation.py @@ -75,9 +75,3 @@ def test_shipped_per_second_models_bill_a_non_zero_cost(model, provider): prompt_cost, completion_cost = cost_per_second(model=model, custom_llm_provider=provider, duration=60.0) assert prompt_cost + completion_cost > 0.0 - - -def test_whisper_bills_its_documented_rate_once(): - prompt_cost, completion_cost = cost_per_second(model="whisper-1", custom_llm_provider="openai", duration=30.0) - - assert prompt_cost + completion_cost == pytest.approx(0.003) diff --git a/tests/test_litellm/llms/openai_like/test_scx_ai_provider.py b/tests/test_litellm/llms/openai_like/test_scx_ai_provider.py index 1ce2da65fef..80c06e4a1e0 100644 --- a/tests/test_litellm/llms/openai_like/test_scx_ai_provider.py +++ b/tests/test_litellm/llms/openai_like/test_scx_ai_provider.py @@ -154,35 +154,6 @@ class TestSCXAIModelMetadata: with open(json_path) as f: return json.load(f) - def test_scx_ai_models_registered_with_correct_metadata(self): - model_cost = self._load(("model_prices_and_context_window.json",)) - for model in self.SCX_MODELS: - info = model_cost.get(model) - assert info is not None, f"{model} missing from model_prices_and_context_window.json" - assert info["litellm_provider"] == "scx-ai" - assert info["mode"] == "chat" - assert info["input_cost_per_token"] > 0 - assert info["output_cost_per_token"] > 0 - assert info["supports_function_calling"] is True - assert info["supports_tool_choice"] is True - assert info["supports_reasoning"] is True - assert info["supports_response_schema"] is True - assert info.get("supports_vision", False) is (model in self.VISION_MODELS) - - assert info["supports_prompt_caching"] is True - assert 0 < info["cache_read_input_token_cost"] < info["input_cost_per_token"] - - assert info["max_output_tokens"] == 131072 - assert info["max_tokens"] == info["max_output_tokens"] - assert info["max_input_tokens"] >= 1_000_000 - - def test_scx_ai_models_synced_to_backup(self): - model_cost = self._load(("model_prices_and_context_window.json",)) - backup = self._load(("litellm", "model_prices_and_context_window_backup.json")) - for model in self.SCX_MODELS: - assert model in backup, f"{model} missing from backup json" - assert backup[model] == model_cost[model], f"{model} differs between root and backup json" - class TestSCXAIDashboardRegistration: @staticmethod diff --git a/tests/test_litellm/llms/perplexity/test_perplexity_cost_calculator.py b/tests/test_litellm/llms/perplexity/test_perplexity_cost_calculator.py index 7556b215e66..caca9e3c681 100644 --- a/tests/test_litellm/llms/perplexity/test_perplexity_cost_calculator.py +++ b/tests/test_litellm/llms/perplexity/test_perplexity_cost_calculator.py @@ -14,17 +14,15 @@ from unittest.mock import patch import pytest # Add the project root to Python path - import litellm -from litellm.cost_calculator import completion_cost, cost_per_token from litellm.llms.perplexity.cost_calculator import ( cost_per_token as perplexity_cost_per_token, ) from litellm.types.utils import ( CompletionTokensDetailsWrapper, OffPeakPricing, - Usage, PromptTokensDetailsWrapper, + Usage, ) @@ -64,167 +62,6 @@ class TestPerplexityCostCalculator: } } - def test_basic_cost_calculation(self): - """Test basic cost calculation without additional fields.""" - usage = Usage(prompt_tokens=100, completion_tokens=50, total_tokens=150) - - prompt_cost, completion_cost = perplexity_cost_per_token( - model="sonar-deep-research", usage=usage - ) - - # Expected costs: - # Input: 100 tokens * $2e-6 = $0.0002 - # Output: 50 tokens * $8e-6 = $0.0004 - expected_prompt_cost = 100 * 2e-6 - expected_completion_cost = 50 * 8e-6 - - assert math.isclose(prompt_cost, expected_prompt_cost, rel_tol=1e-6) - assert math.isclose(completion_cost, expected_completion_cost, rel_tol=1e-6) - - def test_citation_tokens_cost_calculation(self): - """Test cost calculation with citation tokens.""" - usage = Usage(prompt_tokens=100, completion_tokens=50, total_tokens=150) - - # Add citation tokens - usage.citation_tokens = 25 - - prompt_cost, completion_cost = perplexity_cost_per_token( - model="sonar-deep-research", usage=usage - ) - - # Expected costs: - # Input: 100 tokens * $2e-6 = $0.0002 - # Citation: 25 tokens * $2e-6 = $0.00005 - # Total prompt cost: $0.00025 - # Output: 50 tokens * $8e-6 = $0.0004 - expected_prompt_cost = (100 * 2e-6) + (25 * 2e-6) - expected_completion_cost = 50 * 8e-6 - - assert math.isclose(prompt_cost, expected_prompt_cost, rel_tol=1e-6) - assert math.isclose(completion_cost, expected_completion_cost, rel_tol=1e-6) - - def test_search_queries_cost_calculation(self): - """Test cost calculation with search queries.""" - usage = Usage( - prompt_tokens=100, - completion_tokens=50, - total_tokens=150, - prompt_tokens_details=PromptTokensDetailsWrapper(web_search_requests=3), - ) - - prompt_cost, completion_cost = perplexity_cost_per_token( - model="sonar-deep-research", usage=usage - ) - - # Expected costs: - # Input: 100 tokens * $2e-6 = $0.0002 - # Output: 50 tokens * $8e-6 = $0.0004 - # Search: 3 queries * $0.005 per request = $0.015 - # Total completion cost: $0.0154 - expected_prompt_cost = 100 * 2e-6 - expected_completion_cost = (50 * 8e-6) + (3 * 0.005) - - assert math.isclose(prompt_cost, expected_prompt_cost, rel_tol=1e-6) - assert math.isclose(completion_cost, expected_completion_cost, rel_tol=1e-6) - - def test_reasoning_tokens_from_direct_attribute(self): - """Test reasoning tokens cost calculation from direct attribute.""" - usage = Usage(prompt_tokens=100, completion_tokens=50, total_tokens=150) - - # Set reasoning tokens directly - usage.reasoning_tokens = 20 - - prompt_cost, completion_cost = perplexity_cost_per_token( - model="sonar-deep-research", usage=usage - ) - - # `completion_tokens` includes `reasoning_tokens` per the OpenAI/Perplexity - # convention codified in PR #18607. Non-reasoning portion = 50 - 20 = 30. - # Input: 100 tokens * $2e-6 = $0.0002 - # Output (text): 30 tokens * $8e-6 = $0.00024 - # Reasoning: 20 tokens * $3e-6 = $0.00006 - # Total completion cost = $0.0003 - expected_prompt_cost = 100 * 2e-6 - expected_completion_cost = ((50 - 20) * 8e-6) + (20 * 3e-6) - - assert math.isclose(prompt_cost, expected_prompt_cost, rel_tol=1e-6) - assert math.isclose(completion_cost, expected_completion_cost, rel_tol=1e-6) - - def test_reasoning_tokens_from_completion_tokens_details(self): - """Test reasoning tokens cost calculation from completion_tokens_details.""" - usage = Usage( - prompt_tokens=100, - completion_tokens=50, - total_tokens=150, - reasoning_tokens=20, # This should be stored in completion_tokens_details - ) - - prompt_cost, completion_cost = perplexity_cost_per_token( - model="sonar-deep-research", usage=usage - ) - - # Same convention as the direct-attribute case above; reasoning is a subset of - # completion_tokens, so non-reasoning portion = 50 - 20 = 30. - expected_prompt_cost = 100 * 2e-6 - expected_completion_cost = ((50 - 20) * 8e-6) + (20 * 3e-6) - - assert math.isclose(prompt_cost, expected_prompt_cost, rel_tol=1e-6) - assert math.isclose(completion_cost, expected_completion_cost, rel_tol=1e-6) - - def test_comprehensive_cost_calculation(self): - """Test cost calculation with all fields combined.""" - usage = Usage( - prompt_tokens=100, - completion_tokens=50, - total_tokens=150, - reasoning_tokens=15, - prompt_tokens_details=PromptTokensDetailsWrapper(web_search_requests=2), - ) - - # Add custom fields - usage.citation_tokens = 30 - - prompt_cost, completion_cost = perplexity_cost_per_token( - model="sonar-deep-research", usage=usage - ) - - # Expected costs (reasoning is a subset of completion_tokens): - # Input: 100 tokens * $2e-6 = $0.0002 - # Citation: 30 tokens * $2e-6 = $0.00006 - # Total prompt cost = $0.00026 - # Output (text): (50 - 15) tokens * $8e-6 = $0.00028 - # Reasoning: 15 tokens * $3e-6 = $0.000045 - # Search: 2 queries * $0.005 per request = $0.01 - # Total completion cost = $0.010325 - expected_prompt_cost = (100 * 2e-6) + (30 * 2e-6) - expected_completion_cost = ((50 - 15) * 8e-6) + (15 * 3e-6) + (2 * 0.005) - - assert math.isclose(prompt_cost, expected_prompt_cost, rel_tol=1e-6) - assert math.isclose(completion_cost, expected_completion_cost, rel_tol=1e-6) - - def test_zero_values_handling(self): - """Test that zero or missing values are handled correctly.""" - usage = Usage( - prompt_tokens=100, - completion_tokens=50, - total_tokens=150, - prompt_tokens_details=PromptTokensDetailsWrapper(web_search_requests=0), - ) - - # These should not raise errors and should not affect cost - usage.citation_tokens = 0 - - prompt_cost, completion_cost = perplexity_cost_per_token( - model="sonar-deep-research", usage=usage - ) - - # Should be same as basic calculation - expected_prompt_cost = 100 * 2e-6 - expected_completion_cost = 50 * 8e-6 - - assert math.isclose(prompt_cost, expected_prompt_cost, rel_tol=1e-6) - assert math.isclose(completion_cost, expected_completion_cost, rel_tol=1e-6) - def test_missing_model_info_fields(self): """Test behavior when model info is missing some fields.""" usage = Usage( @@ -237,18 +74,14 @@ class TestPerplexityCostCalculator: usage.citation_tokens = 25 # Mock get_model_info to return incomplete model info - with patch( - "litellm.llms.perplexity.cost_calculator.get_model_info" - ) as mock_get_model_info: + with patch("litellm.llms.perplexity.cost_calculator.get_model_info") as mock_get_model_info: mock_get_model_info.return_value = { "input_cost_per_token": 2e-6, "output_cost_per_token": 8e-6, # Missing search_queries_cost_per_query } - prompt_cost, completion_cost = perplexity_cost_per_token( - model="sonar-deep-research", usage=usage - ) + prompt_cost, completion_cost = perplexity_cost_per_token(model="sonar-deep-research", usage=usage) # Should only calculate basic costs when fields are missing expected_prompt_cost = 100 * 2e-6 @@ -257,104 +90,6 @@ class TestPerplexityCostCalculator: assert math.isclose(prompt_cost, expected_prompt_cost, rel_tol=1e-6) assert math.isclose(completion_cost, expected_completion_cost, rel_tol=1e-6) - def test_integration_with_main_cost_calculator(self): - """Test integration with the main LiteLLM cost calculator.""" - usage = Usage( - prompt_tokens=100, - completion_tokens=50, - total_tokens=150, - reasoning_tokens=10, - prompt_tokens_details=PromptTokensDetailsWrapper(web_search_requests=1), - ) - - usage.citation_tokens = 20 - - # Test main cost calculator - prompt_cost, completion_cost_val = cost_per_token( - model="sonar-deep-research", - custom_llm_provider="perplexity", - usage_object=usage, - ) - - # Should match direct call to perplexity cost calculator - expected_prompt, expected_completion = perplexity_cost_per_token( - model="sonar-deep-research", usage=usage - ) - - assert math.isclose(prompt_cost, expected_prompt, rel_tol=1e-6) - assert math.isclose(completion_cost_val, expected_completion, rel_tol=1e-6) - - def test_integration_with_completion_cost_function(self): - """Test integration with the completion_cost function.""" - from litellm import ModelResponse - - # Create a mock ModelResponse - usage = Usage( - prompt_tokens=100, - completion_tokens=50, - total_tokens=150, - reasoning_tokens=10, - prompt_tokens_details=PromptTokensDetailsWrapper(web_search_requests=1), - ) - usage.citation_tokens = 15 - - response = ModelResponse() - response.usage = usage - response.model = "sonar-deep-research" - - # Test completion_cost function - total_cost = completion_cost( - completion_response=response, custom_llm_provider="perplexity" - ) - - # Calculate expected total cost (reasoning is a subset of completion_tokens) - expected_prompt_cost = (100 * 2e-6) + (15 * 2e-6) # Input + citation - expected_completion_cost = ( - ((50 - 10) * 8e-6) + (10 * 3e-6) + (1 * 0.005) - ) # Output (text) + reasoning + search - expected_total = expected_prompt_cost + expected_completion_cost - - assert math.isclose(total_cost, expected_total, rel_tol=1e-6) - - @pytest.mark.parametrize("citation_tokens", [0, 10, 25, 100]) - @pytest.mark.parametrize("search_queries", [0, 1, 5, 10]) - @pytest.mark.parametrize("reasoning_tokens", [0, 15, 30]) - def test_cost_calculation_combinations( - self, citation_tokens, search_queries, reasoning_tokens - ): - """Test various combinations of citation tokens, search queries, and reasoning tokens.""" - usage = Usage( - prompt_tokens=100, - completion_tokens=50, - total_tokens=150, - reasoning_tokens=reasoning_tokens, - prompt_tokens_details=PromptTokensDetailsWrapper( - web_search_requests=search_queries - ), - ) - - usage.citation_tokens = citation_tokens - - prompt_cost, completion_cost = perplexity_cost_per_token( - model="sonar-deep-research", usage=usage - ) - - # Calculate expected costs. `completion_tokens` includes `reasoning_tokens`, - # so non-reasoning portion = 50 - reasoning_tokens. - expected_prompt_cost = (100 * 2e-6) + (citation_tokens * 2e-6) - expected_completion_cost = ( - ((50 - reasoning_tokens) * 8e-6) - + (reasoning_tokens * 3e-6) - + (search_queries * 0.005) - ) - - assert math.isclose(prompt_cost, expected_prompt_cost, rel_tol=1e-6) - assert math.isclose(completion_cost, expected_completion_cost, rel_tol=1e-6) - - # Ensure costs are non-negative - assert prompt_cost >= 0 - assert completion_cost >= 0 - def test_uses_perplexity_provided_cost_when_available(self): """ Test that when Perplexity provides pre-calculated cost in usage.cost.total_cost, @@ -374,9 +109,7 @@ class TestPerplexityCostCalculator: "total_cost": 0.008, } - prompt_cost, completion_cost = perplexity_cost_per_token( - model="sonar-pro", usage=usage - ) + prompt_cost, completion_cost = perplexity_cost_per_token(model="sonar-pro", usage=usage) # When Perplexity provides total_cost, we use it directly # prompt_cost should be 0, completion_cost should be total_cost @@ -402,9 +135,7 @@ class TestPerplexityCostCalculator: usage = Usage(prompt_tokens=100, completion_tokens=50, total_tokens=150) usage.cost = 0.008 - prompt_cost, completion_cost = perplexity_cost_per_token( - model="sonar-pro", usage=usage - ) + prompt_cost, completion_cost = perplexity_cost_per_token(model="sonar-pro", usage=usage) assert prompt_cost == 0.0 assert completion_cost == 0.008 @@ -417,9 +148,7 @@ class TestPerplexityCostCalculator: usage = Usage(prompt_tokens=100, completion_tokens=50, total_tokens=150) # No cost object - should use manual calculation - prompt_cost, completion_cost = perplexity_cost_per_token( - model="sonar-deep-research", usage=usage - ) + prompt_cost, completion_cost = perplexity_cost_per_token(model="sonar-deep-research", usage=usage) # Should calculate manually: 100 * 2e-6 + 50 * 8e-6 expected_prompt = 100 * 2e-6 @@ -428,57 +157,6 @@ class TestPerplexityCostCalculator: assert math.isclose(prompt_cost, expected_prompt, rel_tol=1e-6) assert math.isclose(completion_cost, expected_completion, rel_tol=1e-6) - def test_reasoning_tokens_not_double_billed(self): - """ - Regression: `completion_tokens` includes `reasoning_tokens` per the - OpenAI/Perplexity usage convention (codified for the central path in PR #18607). - When `output_cost_per_reasoning_token` is configured the manual fallback must - subtract reasoning from completion before applying the output rate so the - reasoning tokens are not billed at BOTH the output rate and the reasoning rate. - - Uses the exact usage shape produced by the live response fixture in - `tests/llm_translation/test_perplexity_reasoning.py`. - """ - usage = Usage( - prompt_tokens=9, - completion_tokens=20, - total_tokens=29, - completion_tokens_details=CompletionTokensDetailsWrapper( - reasoning_tokens=15 - ), - ) - - prompt_cost, completion_cost = perplexity_cost_per_token( - model="sonar-deep-research", usage=usage - ) - - # sonar-deep-research rates: input 2e-6, output 8e-6, reasoning 3e-6. - # Non-reasoning portion of the 20 completion tokens = 20 - 15 = 5. - # Pre-fix this asserted 20 * 8e-6 + 15 * 3e-6 = 2.05e-4 (a 2.16x overcharge). - expected_prompt = 9 * 2e-6 - expected_completion = (20 - 15) * 8e-6 + 15 * 3e-6 - - assert math.isclose(prompt_cost, expected_prompt, rel_tol=1e-9) - assert math.isclose(completion_cost, expected_completion, rel_tol=1e-9) - - def test_agent_api_fallback_rates_price_a_response_without_metered_cost(self): - """Perplexity meters cost on the response, but when `usage.cost` is absent the - calculator falls back to the mapped per-token rates. Regression: that fallback - raised "This model isn't mapped yet" for every Agent API third-party model, - because the doubled cost-map key was unreachable from the resolution ladder. - """ - from litellm import ModelResponse - - response = ModelResponse() - response.usage = Usage(prompt_tokens=1000, completion_tokens=500, total_tokens=1500) - response.model = "perplexity/perplexity/glm-5.2" - - total_cost = completion_cost( - completion_response=response, custom_llm_provider="perplexity" - ) - - assert math.isclose(total_cost, 1000 * 1.4e-06 + 500 * 4.4e-06, rel_tol=1e-9) - OFF_PEAK_MODEL = "sonar-off-peak-test" OFF_PEAK_WINDOW = "14:00-00:00" INSIDE_WINDOW = datetime(2026, 9, 3, 17, 25, tzinfo=timezone.utc) diff --git a/tests/test_litellm/llms/perplexity/test_perplexity_integration.py b/tests/test_litellm/llms/perplexity/test_perplexity_integration.py index 990fa7eb464..bd72d9b4740 100644 --- a/tests/test_litellm/llms/perplexity/test_perplexity_integration.py +++ b/tests/test_litellm/llms/perplexity/test_perplexity_integration.py @@ -1,7 +1,7 @@ """ Integration tests for Perplexity cost calculation and transformation. -Tests the end-to-end functionality of Perplexity cost calculation +Tests the end-to-end functionality of Perplexity cost calculation including integration with the main LiteLLM cost calculator. """ @@ -12,13 +12,11 @@ import os import pytest # Add the project root to Python path - import litellm from litellm import ModelResponse -from litellm.cost_calculator import completion_cost, cost_per_token +from litellm.cost_calculator import cost_per_token from litellm.llms.perplexity.chat.transformation import PerplexityChatConfig from litellm.types.utils import PromptTokensDetailsWrapper, Usage -from litellm.utils import get_model_info class TestPerplexityIntegration: @@ -57,123 +55,6 @@ class TestPerplexityIntegration: } } - def test_end_to_end_cost_calculation_with_transformation(self): - """Test end-to-end cost calculation with response transformation.""" - # Create a Perplexity API response that includes citations and search queries - config = PerplexityChatConfig() - - # Create a ModelResponse with basic usage (before transformation) - model_response = ModelResponse() - model_response.model = "sonar-deep-research" - model_response.usage = Usage( - prompt_tokens=100, - completion_tokens=50, - total_tokens=150, - reasoning_tokens=10, - ) - - # Simulate raw response from Perplexity API - raw_response_dict = { - "choices": [{"message": {"content": "Test response with citations"}}], - "usage": { - "prompt_tokens": 100, - "completion_tokens": 50, - "total_tokens": 150, - "num_search_queries": 2, - }, - "citations": [ - "This is the first citation with important information about the topic", - "Another citation providing additional context for the response", - ], - } - - # Apply transformation to extract Perplexity-specific fields - config._enhance_usage_with_perplexity_fields(model_response, raw_response_dict) - - # Now calculate the cost with the enhanced usage - total_cost = completion_cost( - completion_response=model_response, custom_llm_provider="perplexity" - ) - - # Calculate expected cost - citation_chars = sum( - len(citation) for citation in raw_response_dict["citations"] - ) - citation_tokens = citation_chars // 4 - - expected_prompt_cost = (100 * 2e-6) + (citation_tokens * 2e-6) - expected_completion_cost = ( - ((50 - 10) * 8e-6) + (10 * 3e-6) + (2 * 0.005) - ) # Output (text) + reasoning + search - expected_total = expected_prompt_cost + expected_completion_cost - - assert math.isclose(total_cost, expected_total, rel_tol=1e-6) - - def test_cost_calculation_without_custom_fields(self): - """Test that cost calculation works normally when custom fields are absent.""" - # Create a standard response without Perplexity-specific fields - model_response = ModelResponse() - model_response.model = "sonar-deep-research" - model_response.usage = Usage( - prompt_tokens=100, completion_tokens=50, total_tokens=150 - ) - - # Calculate cost without custom fields - total_cost = completion_cost( - completion_response=model_response, custom_llm_provider="perplexity" - ) - - # Should only include basic input/output costs - expected_cost = (100 * 2e-6) + (50 * 8e-6) - - assert math.isclose(total_cost, expected_cost, rel_tol=1e-6) - - def test_main_cost_calculator_integration(self): - """Test integration with the main LiteLLM cost calculator.""" - # Create usage with all Perplexity fields - usage = Usage( - prompt_tokens=200, - completion_tokens=100, - total_tokens=300, - reasoning_tokens=25, - prompt_tokens_details=PromptTokensDetailsWrapper(web_search_requests=3), - ) - usage.citation_tokens = 40 - - # Test main cost calculator - prompt_cost, completion_cost_val = cost_per_token( - model="sonar-deep-research", - custom_llm_provider="perplexity", - usage_object=usage, - ) - - expected_prompt_cost = (200 * 2e-6) + (40 * 2e-6) - expected_completion_cost = ( - ((100 - 25) * 8e-6) + (25 * 3e-6) + (3 * 0.005) - ) # Output (text) + reasoning + search - - assert math.isclose(prompt_cost, expected_prompt_cost, rel_tol=1e-6) - assert math.isclose(completion_cost_val, expected_completion_cost, rel_tol=1e-6) - - def test_model_info_includes_custom_fields(self): - """Test that get_model_info returns the custom Perplexity cost fields.""" - model_info = get_model_info( - model="sonar-deep-research", custom_llm_provider="perplexity" - ) - - # Verify custom fields are included - required_fields = [ - "citation_cost_per_token", - "search_context_cost_per_query", - "input_cost_per_token", - "output_cost_per_token", - "output_cost_per_reasoning_token", - ] - - for field in required_fields: - assert field in model_info, f"Missing field: {field}" - assert model_info[field] is not None, f"Null value for field: {field}" - def test_various_citation_sizes(self): """Test cost calculation with various citation sizes.""" config = PerplexityChatConfig() @@ -192,9 +73,7 @@ class TestPerplexityIntegration: for citations, expected_approx_tokens in test_cases: model_response = ModelResponse() model_response.model = "sonar-deep-research" - model_response.usage = Usage( - prompt_tokens=100, completion_tokens=50, total_tokens=150 - ) + model_response.usage = Usage(prompt_tokens=100, completion_tokens=50, total_tokens=150) raw_response_dict = { "usage": { @@ -205,9 +84,7 @@ class TestPerplexityIntegration: "citations": citations, } - config._enhance_usage_with_perplexity_fields( - model_response, raw_response_dict - ) + config._enhance_usage_with_perplexity_fields(model_response, raw_response_dict) citation_tokens = getattr(model_response.usage, "citation_tokens", 0) @@ -217,55 +94,6 @@ class TestPerplexityIntegration: else: assert abs(citation_tokens - expected_approx_tokens) <= 5 - def test_cost_calculation_with_zero_values(self): - """Test cost calculation handles zero values for custom fields correctly.""" - usage = Usage(prompt_tokens=100, completion_tokens=50, total_tokens=150) - - # Set custom fields to zero - usage.citation_tokens = 0 - usage.prompt_tokens_details = PromptTokensDetailsWrapper(web_search_requests=0) - - # Should not add any extra cost - prompt_cost, completion_cost_val = cost_per_token( - model="sonar-deep-research", - custom_llm_provider="perplexity", - usage_object=usage, - ) - - expected_prompt_cost = 100 * 2e-6 - expected_completion_cost = 50 * 8e-6 - - assert math.isclose(prompt_cost, expected_prompt_cost, rel_tol=1e-6) - assert math.isclose(completion_cost_val, expected_completion_cost, rel_tol=1e-6) - - def test_high_volume_cost_calculation(self): - """Test cost calculation with high token and query counts.""" - usage = Usage( - prompt_tokens=50000, - completion_tokens=25000, - total_tokens=75000, - reasoning_tokens=10000, - ) - - usage.citation_tokens = 5000 - usage.prompt_tokens_details = PromptTokensDetailsWrapper( - web_search_requests=100 - ) - - total_cost = completion_cost( - completion_response=ModelResponse(usage=usage, model="sonar-deep-research"), - custom_llm_provider="perplexity", - ) - - expected_prompt_cost = (50000 * 2e-6) + (5000 * 2e-6) - expected_completion_cost = ( - ((25000 - 10000) * 8e-6) + (10000 * 3e-6) + (100 * 0.005) - ) # $0.65 - expected_total = expected_prompt_cost + expected_completion_cost # $0.76 - - assert math.isclose(total_cost, expected_total, rel_tol=1e-6) - assert total_cost > 0.25 - def test_transformation_preserves_existing_usage_fields(self): """Test that transformation doesn't overwrite existing standard usage fields.""" config = PerplexityChatConfig() @@ -305,9 +133,7 @@ class TestPerplexityIntegration: assert hasattr(model_response.usage, "citation_tokens") assert model_response.usage.prompt_tokens_details.web_search_requests == 3 - @pytest.mark.parametrize( - "provider_name", ["perplexity", "PERPLEXITY", "Perplexity"] - ) + @pytest.mark.parametrize("provider_name", ["perplexity", "PERPLEXITY", "Perplexity"]) def test_case_insensitive_provider_matching(self, provider_name): """Test that cost calculation works with different case variations of provider name.""" usage = Usage(prompt_tokens=100, completion_tokens=50, total_tokens=150) diff --git a/tests/test_litellm/llms/tencent/test_cost_calculator.py b/tests/test_litellm/llms/tencent/test_cost_calculator.py deleted file mode 100644 index 7e710d6319c..00000000000 --- a/tests/test_litellm/llms/tencent/test_cost_calculator.py +++ /dev/null @@ -1,29 +0,0 @@ -import pytest - -import litellm -from litellm.llms.tencent.cost_calculator import cost_per_token -from litellm.types.utils import Usage - - - -def test_cost_per_token_uses_tencent_model_pricing(local_model_cost_map): - usage = Usage(prompt_tokens=1000, completion_tokens=2000, total_tokens=3000) - - prompt_cost, completion_cost = cost_per_token(model="tencent/deepseek-v4-pro", usage=usage) - - assert prompt_cost == pytest.approx(1000 * 4.35e-07) - assert completion_cost == pytest.approx(2000 * 8.7e-07) - - -def test_top_level_dispatcher_routes_tencent_to_wrapper(local_model_cost_map): - from litellm.cost_calculator import cost_per_token as dispatch_cost_per_token - - prompt_cost, completion_cost = dispatch_cost_per_token( - model="tencent/deepseek-v4-pro", - prompt_tokens=1000, - completion_tokens=1000, - custom_llm_provider="tencent", - ) - - assert prompt_cost == pytest.approx(1000 * 4.35e-07) - assert completion_cost == pytest.approx(1000 * 8.7e-07) diff --git a/tests/test_litellm/llms/vertex_ai/vertex_ai_partner_models/anthropic/test_vertex_ai_partner_models_anthropic_messages_config.py b/tests/test_litellm/llms/vertex_ai/vertex_ai_partner_models/anthropic/test_vertex_ai_partner_models_anthropic_messages_config.py index f19e169dc9e..58a040bf019 100644 --- a/tests/test_litellm/llms/vertex_ai/vertex_ai_partner_models/anthropic/test_vertex_ai_partner_models_anthropic_messages_config.py +++ b/tests/test_litellm/llms/vertex_ai/vertex_ai_partner_models/anthropic/test_vertex_ai_partner_models_anthropic_messages_config.py @@ -1,10 +1,6 @@ import copy -import json -import os from unittest.mock import MagicMock, patch -import pytest - from litellm.llms.vertex_ai.vertex_ai_partner_models.anthropic.experimental_pass_through.transformation import ( VertexAIPartnerModelsAnthropicMessagesConfig, ) @@ -23,12 +19,8 @@ def test_validate_environment_uses_vertex_ai_location(): optional_params = {} with ( - patch.object( - config, "_ensure_access_token", return_value=("token", "test-project") - ), - patch.object( - config, "get_complete_vertex_url", return_value="https://mock-url" - ) as mock_get_url, + patch.object(config, "_ensure_access_token", return_value=("token", "test-project")), + patch.object(config, "get_complete_vertex_url", return_value="https://mock-url") as mock_get_url, ): config.validate_anthropic_messages_environment( headers=headers, @@ -51,17 +43,11 @@ def test_web_search_header_added_for_messages_endpoint(): "vertex_credentials": "{}", } # Include web search tool in optional_params - optional_params = { - "tools": [{"type": "web_search_20250305", "name": "web_search", "max_uses": 5}] - } + optional_params = {"tools": [{"type": "web_search_20250305", "name": "web_search", "max_uses": 5}]} with ( - patch.object( - config, "_ensure_access_token", return_value=("token", "test-project") - ), - patch.object( - config, "get_complete_vertex_url", return_value="https://mock-url" - ), + patch.object(config, "_ensure_access_token", return_value=("token", "test-project")), + patch.object(config, "get_complete_vertex_url", return_value="https://mock-url"), ): updated_headers, api_base = config.validate_anthropic_messages_environment( headers=headers, @@ -73,12 +59,10 @@ def test_web_search_header_added_for_messages_endpoint(): ) # Assert that the anthropic-beta header with web-search is present - assert ( - "anthropic-beta" in updated_headers - ), "anthropic-beta header should be present" - assert ( - updated_headers["anthropic-beta"] == "web-search-2025-03-05" - ), f"anthropic-beta should be 'web-search-2025-03-05', got: {updated_headers['anthropic-beta']}" + assert "anthropic-beta" in updated_headers, "anthropic-beta header should be present" + assert updated_headers["anthropic-beta"] == "web-search-2025-03-05", ( + f"anthropic-beta should be 'web-search-2025-03-05', got: {updated_headers['anthropic-beta']}" + ) def test_web_search_header_not_added_without_tool(): @@ -94,12 +78,8 @@ def test_web_search_header_not_added_without_tool(): optional_params = {} with ( - patch.object( - config, "_ensure_access_token", return_value=("token", "test-project") - ), - patch.object( - config, "get_complete_vertex_url", return_value="https://mock-url" - ), + patch.object(config, "_ensure_access_token", return_value=("token", "test-project")), + patch.object(config, "get_complete_vertex_url", return_value="https://mock-url"), ): updated_headers, api_base = config.validate_anthropic_messages_environment( headers=headers, @@ -111,9 +91,9 @@ def test_web_search_header_not_added_without_tool(): ) # Assert that the anthropic-beta header is NOT present when no web search tool - assert ( - "anthropic-beta" not in updated_headers - ), "anthropic-beta header should not be present without web search tool" + assert "anthropic-beta" not in updated_headers, ( + "anthropic-beta header should not be present without web search tool" + ) def test_compact_context_management_header_added(): @@ -129,12 +109,8 @@ def test_compact_context_management_header_added(): optional_params = {"context_management": {"edits": [{"type": "compact_20260112"}]}} with ( - patch.object( - config, "_ensure_access_token", return_value=("token", "test-project") - ), - patch.object( - config, "get_complete_vertex_url", return_value="https://mock-url" - ), + patch.object(config, "_ensure_access_token", return_value=("token", "test-project")), + patch.object(config, "get_complete_vertex_url", return_value="https://mock-url"), ): updated_headers, api_base = config.validate_anthropic_messages_environment( headers=headers, @@ -146,12 +122,10 @@ def test_compact_context_management_header_added(): ) # Assert that the anthropic-beta header with compact-2026-01-12 is present - assert ( - "anthropic-beta" in updated_headers - ), "anthropic-beta header should be present" - assert ( - "compact-2026-01-12" in updated_headers["anthropic-beta"] - ), f"anthropic-beta should contain 'compact-2026-01-12', got: {updated_headers['anthropic-beta']}" + assert "anthropic-beta" in updated_headers, "anthropic-beta header should be present" + assert "compact-2026-01-12" in updated_headers["anthropic-beta"], ( + f"anthropic-beta should contain 'compact-2026-01-12', got: {updated_headers['anthropic-beta']}" + ) def test_context_management_header_added_for_other_edits(): @@ -167,12 +141,8 @@ def test_context_management_header_added_for_other_edits(): optional_params = {"context_management": {"edits": [{"type": "some_other_type"}]}} with ( - patch.object( - config, "_ensure_access_token", return_value=("token", "test-project") - ), - patch.object( - config, "get_complete_vertex_url", return_value="https://mock-url" - ), + patch.object(config, "_ensure_access_token", return_value=("token", "test-project")), + patch.object(config, "get_complete_vertex_url", return_value="https://mock-url"), ): updated_headers, api_base = config.validate_anthropic_messages_environment( headers=headers, @@ -184,12 +154,10 @@ def test_context_management_header_added_for_other_edits(): ) # Assert that the anthropic-beta header with context-management-2025-06-27 is present - assert ( - "anthropic-beta" in updated_headers - ), "anthropic-beta header should be present" - assert ( - "context-management-2025-06-27" in updated_headers["anthropic-beta"] - ), f"anthropic-beta should contain 'context-management-2025-06-27', got: {updated_headers['anthropic-beta']}" + assert "anthropic-beta" in updated_headers, "anthropic-beta header should be present" + assert "context-management-2025-06-27" in updated_headers["anthropic-beta"], ( + f"anthropic-beta should contain 'context-management-2025-06-27', got: {updated_headers['anthropic-beta']}" + ) def test_both_compact_and_context_management_headers_added(): @@ -202,19 +170,11 @@ def test_both_compact_and_context_management_headers_added(): "vertex_credentials": "{}", } # Include context_management with both compact and other edit types - optional_params = { - "context_management": { - "edits": [{"type": "compact_20260112"}, {"type": "some_other_type"}] - } - } + optional_params = {"context_management": {"edits": [{"type": "compact_20260112"}, {"type": "some_other_type"}]}} with ( - patch.object( - config, "_ensure_access_token", return_value=("token", "test-project") - ), - patch.object( - config, "get_complete_vertex_url", return_value="https://mock-url" - ), + patch.object(config, "_ensure_access_token", return_value=("token", "test-project")), + patch.object(config, "get_complete_vertex_url", return_value="https://mock-url"), ): updated_headers, api_base = config.validate_anthropic_messages_environment( headers=headers, @@ -226,15 +186,13 @@ def test_both_compact_and_context_management_headers_added(): ) # Assert that both beta headers are present - assert ( - "anthropic-beta" in updated_headers - ), "anthropic-beta header should be present" - assert ( - "compact-2026-01-12" in updated_headers["anthropic-beta"] - ), f"anthropic-beta should contain 'compact-2026-01-12', got: {updated_headers['anthropic-beta']}" - assert ( - "context-management-2025-06-27" in updated_headers["anthropic-beta"] - ), f"anthropic-beta should contain 'context-management-2025-06-27', got: {updated_headers['anthropic-beta']}" + assert "anthropic-beta" in updated_headers, "anthropic-beta header should be present" + assert "compact-2026-01-12" in updated_headers["anthropic-beta"], ( + f"anthropic-beta should contain 'compact-2026-01-12', got: {updated_headers['anthropic-beta']}" + ) + assert "context-management-2025-06-27" in updated_headers["anthropic-beta"], ( + f"anthropic-beta should contain 'context-management-2025-06-27', got: {updated_headers['anthropic-beta']}" + ) def test_validate_environment_always_refreshes_token_ignoring_stale_bearer(): @@ -248,12 +206,8 @@ def test_validate_environment_always_refreshes_token_ignoring_stale_bearer(): } with ( - patch.object( - config, "_ensure_access_token", return_value=("fresh-token", "test-project") - ) as mock_ensure, - patch.object( - config, "get_complete_vertex_url", return_value="https://mock-vertex-url" - ), + patch.object(config, "_ensure_access_token", return_value=("fresh-token", "test-project")) as mock_ensure, + patch.object(config, "get_complete_vertex_url", return_value="https://mock-vertex-url"), ): updated_headers, api_base = config.validate_anthropic_messages_environment( headers=headers, @@ -286,9 +240,7 @@ def test_validate_environment_appends_stream_raw_predict_with_custom_api_base(): "get_complete_vertex_url", wraps=config.get_complete_vertex_url, ) as spy_get_url, - patch.object( - config, "_ensure_access_token", return_value=("token", "test-project") - ), + patch.object(config, "_ensure_access_token", return_value=("token", "test-project")), ): _, api_base = config.validate_anthropic_messages_environment( headers={}, @@ -318,9 +270,7 @@ def test_validate_environment_appends_raw_predict_with_custom_api_base(): "get_complete_vertex_url", wraps=config.get_complete_vertex_url, ) as spy_get_url, - patch.object( - config, "_ensure_access_token", return_value=("token", "test-project") - ), + patch.object(config, "_ensure_access_token", return_value=("token", "test-project")), ): _, api_base = config.validate_anthropic_messages_environment( headers={}, @@ -447,20 +397,14 @@ def test_validate_environment_does_not_mutate_caller_headers(): caller_headers: dict = {} with ( - patch.object( - config, "_ensure_access_token", return_value=("token", "test-project") - ), - patch.object( - config, "get_complete_vertex_url", return_value="https://mock-url" - ), + patch.object(config, "_ensure_access_token", return_value=("token", "test-project")), + patch.object(config, "get_complete_vertex_url", return_value="https://mock-url"), ): config.validate_anthropic_messages_environment( headers=caller_headers, model="claude-sonnet-4", messages=[], - optional_params={ - "tools": [{"type": "web_search_20250305", "name": "web_search"}] - }, + optional_params={"tools": [{"type": "web_search_20250305", "name": "web_search"}]}, litellm_params={ "vertex_ai_project": "p", "vertex_ai_location": "us-central1", @@ -468,9 +412,7 @@ def test_validate_environment_does_not_mutate_caller_headers(): api_base=None, ) - assert ( - caller_headers == {} - ), "validate_anthropic_messages_environment must not mutate the caller's headers dict" + assert caller_headers == {}, "validate_anthropic_messages_environment must not mutate the caller's headers dict" def test_vertex_claude_completion_does_not_mutate_shared_extra_headers(): @@ -483,12 +425,8 @@ def test_vertex_claude_completion_does_not_mutate_shared_extra_headers(): mock_response = MagicMock() with ( - patch.object( - handler, "_ensure_access_token", return_value=("ya29.fresh", "proj") - ), - patch.object( - handler, "get_complete_vertex_url", return_value="https://mock-url" - ), + patch.object(handler, "_ensure_access_token", return_value=("ya29.fresh", "proj")), + patch.object(handler, "get_complete_vertex_url", return_value="https://mock-url"), patch( "litellm.llms.anthropic.chat.AnthropicChatCompletion.completion", return_value=mock_response, @@ -509,50 +447,7 @@ def test_vertex_claude_completion_does_not_mutate_shared_extra_headers(): litellm_params={}, ) - assert ( - shared_extra_headers == {} - ), "extra_headers must not be mutated by completion()" - - - -def test_messages_thinking_shape_follows_exact_vertex_entry_flag(local_model_cost_map, monkeypatch): - """The Vertex messages config must probe capabilities under ``vertex_ai`` so an - operator setting ``supports_adaptive_thinking: false`` on the exact - ``vertex_ai/claude-opus-4-8`` entry beats the unmodified ``anthropic`` entry. - With the inherited ``"anthropic"`` provider default the flip was ignored and - the transform kept emitting ``thinking.type='adaptive'``.""" - import litellm - - config = VertexAIPartnerModelsAnthropicMessagesConfig() - - def transform(): - return config.transform_anthropic_messages_request( - model="claude-opus-4-8", - messages=[{"role": "user", "content": "Hello"}], - anthropic_messages_optional_request_params={ - "max_tokens": 4096, - "reasoning_effort": "medium", - }, - litellm_params=GenericLiteLLMParams(), - headers={}, - ) - - result = transform() - assert result.get("thinking") == {"type": "adaptive", "display": "summarized"} - assert result.get("output_config") == {"effort": "medium"} - - monkeypatch.setitem( - litellm.model_cost["vertex_ai/claude-opus-4-8"], "supports_adaptive_thinking", False - ) - litellm.get_model_info.cache_clear() - assert litellm.model_cost["claude-opus-4-8"]["supports_adaptive_thinking"] is True - - flipped = transform() - thinking = flipped.get("thinking") - assert isinstance(thinking, dict) - assert thinking.get("type") == "enabled" - assert isinstance(thinking.get("budget_tokens"), int) - assert "output_config" not in flipped + assert shared_extra_headers == {}, "extra_headers must not be mutated by completion()" def _vertex_transform(model, messages, system=None): @@ -614,9 +509,7 @@ class TestVertexAnthropicMidConversationSystem: {"role": "assistant", "content": "reading"}, {"role": "user", "content": "continue"}, ] - result = _vertex_transform( - "claude-sonnet-4-6", messages, system=[{"type": "text", "text": "Base."}] - ) + result = _vertex_transform("claude-sonnet-4-6", messages, system=[{"type": "text", "text": "Base."}]) assert result["messages"] == [ {"role": "user", "content": "read the file"}, { @@ -649,36 +542,3 @@ class TestVertexAnthropicMidConversationSystem: {"type": "text", "text": "You are terse."}, {"type": "text", "text": "Cite sources."}, ] - - -def test_vertex_claude_4_8_plus_cost_map_entries_carry_mid_conversation_system_flag(): - """Exact cost-map hits win over the ``claude-mid-conversation-system`` - fallback rule, so a ``vertex_ai`` Claude 4.8+/5 entry missing the flag would - be treated as unsupported and hoist every reminder, collapsing the prompt - cache. Every mapped vertex_ai entry the rule matches must carry the flag.""" - import re - - import litellm - - cost_map_path = os.path.join( - os.path.dirname(litellm.__file__), "model_prices_and_context_window_backup.json" - ) - with open(cost_map_path) as f: - cost_map = json.load(f) - rules = cost_map["fallback_generalizations"]["rules"] - rule_pattern = next( - (r["pattern"] for r in rules if r["name"] == "claude-mid-conversation-system"), - None, - ) - assert rule_pattern is not None, "claude-mid-conversation-system rule not found in fallback_generalizations" - pattern = re.compile(rule_pattern, re.IGNORECASE) - missing = [ - key - for key, info in cost_map.items() - if isinstance(info, dict) - and str(info.get("litellm_provider", "")).startswith("vertex_ai") - and "claude" in key - and pattern.search(key) - and info.get("supports_mid_conversation_system") is not True - ] - assert missing == [] diff --git a/tests/test_litellm/llms/xai/responses/test_xai_responses_transformation.py b/tests/test_litellm/llms/xai/responses/test_xai_responses_transformation.py index 4cff5c76b9e..f35b92a44b2 100644 --- a/tests/test_litellm/llms/xai/responses/test_xai_responses_transformation.py +++ b/tests/test_litellm/llms/xai/responses/test_xai_responses_transformation.py @@ -10,10 +10,7 @@ Source: litellm/llms/xai/responses/transformation.py from unittest.mock import MagicMock, Mock import httpx -import pytest -import litellm -from litellm.llms.xai.cost_calculator import cost_per_token from litellm.llms.xai.responses.transformation import XAIResponsesAPIConfig from litellm.responses.utils import ResponseAPILoggingUtils from litellm.types.llms.openai import ( @@ -305,12 +302,16 @@ class TestXAIResponsesWebSearchBilling: def _raw_response_json(self, include_web_search: bool) -> dict: web_search_output = ( - [{ - "type": "web_search_call", - "id": "ws_1", - "status": "completed", - "action": {"type": "search", "query": "grok"}, - }] if include_web_search else [] + [ + { + "type": "web_search_call", + "id": "ws_1", + "status": "completed", + "action": {"type": "search", "query": "grok"}, + } + ] + if include_web_search + else [] ) tool_usage = {"server_side_tool_usage_details": self._TOOL_DETAILS} if include_web_search else {} return { @@ -370,20 +371,6 @@ class TestXAIResponsesWebSearchBilling: assert bridged.completion_tokens == 20 assert getattr(bridged, "server_side_tool_usage_details") == self._TOOL_DETAILS - def test_completion_cost_bills_web_search_calls(self): - with_search = litellm.completion_cost( - completion_response=self._transform(include_web_search=True), - model="xai/grok-4", - custom_llm_provider="xai", - ) - without_search = litellm.completion_cost( - completion_response=self._transform(include_web_search=False), - model="xai/grok-4", - custom_llm_provider="xai", - ) - - assert with_search - without_search == pytest.approx(2 * 5.0 / 1000.0) - def test_streaming_terminal_event_keeps_schema_and_details(self): parsed_chunk = { "type": "response.completed", @@ -436,47 +423,8 @@ class TestXAIResponsesReportedCost: ) return response.usage - def test_reported_cost_reaches_the_cost_calculator(self): - usage = self._transformed_usage( - { - "input_tokens": 100, - "output_tokens": 200, - "total_tokens": 300, - "cost_in_usd_ticks": 37756000, - } - ) - - assert usage.cost == 0.0037756 - - chat_usage = ResponseAPILoggingUtils._transform_response_api_usage_to_chat_usage(usage) - assert cost_per_token(model="grok-4-latest", usage=chat_usage) == (0.0, 0.0037756) - - def test_streamed_reported_cost_reaches_the_cost_calculator(self): - event = XAIResponsesAPIConfig().transform_streaming_response( - model="grok-4-latest", - parsed_chunk={ - "type": "response.completed", - "sequence_number": 7, - "response": self._response_body( - { - "input_tokens": 100, - "output_tokens": 200, - "total_tokens": 300, - "cost_in_usd_ticks": 37756000, - } - ), - }, - logging_obj=Mock(), - ) - - assert isinstance(event, ResponseCompletedEvent) - chat_usage = ResponseAPILoggingUtils._transform_response_api_usage_to_chat_usage(event.response.usage) - assert cost_per_token(model="grok-4-latest", usage=chat_usage) == (0.0, 0.0037756) - def test_usage_without_a_reported_cost_is_left_alone(self): - usage = self._transformed_usage( - {"input_tokens": 100, "output_tokens": 200, "total_tokens": 300} - ) + usage = self._transformed_usage({"input_tokens": 100, "output_tokens": 200, "total_tokens": 300}) assert usage.cost is None diff --git a/tests/test_litellm/llms/xai/test_xai_chat_transformation.py b/tests/test_litellm/llms/xai/test_xai_chat_transformation.py index c67dca11a56..e91d4834f47 100644 --- a/tests/test_litellm/llms/xai/test_xai_chat_transformation.py +++ b/tests/test_litellm/llms/xai/test_xai_chat_transformation.py @@ -1,14 +1,10 @@ from unittest.mock import Mock import httpx -import pytest -import litellm from litellm.llms.xai.chat.transformation import ( - XAIChatCompletionStreamingHandler, XAIChatConfig, ) -from litellm.llms.xai.cost_calculator import cost_per_token from litellm.types.utils import ( CompletionTokensDetailsWrapper, ModelResponse, @@ -26,11 +22,7 @@ class TestXAIReasoningTokenFolding: total_tokens: int, reasoning_tokens: int = 0, ) -> ModelResponse: - details = ( - CompletionTokensDetailsWrapper(reasoning_tokens=reasoning_tokens) - if reasoning_tokens - else None - ) + details = CompletionTokensDetailsWrapper(reasoning_tokens=reasoning_tokens) if reasoning_tokens else None usage = Usage( prompt_tokens=prompt_tokens, completion_tokens=completion_tokens, @@ -176,31 +168,11 @@ class TestXAIChatWebSearchBilling: def test_enhance_noop_without_details(self): response = self._response_with_usage() - XAIChatConfig()._enhance_usage_with_xai_web_search_fields( - response, {"usage": {"prompt_tokens": 100}} - ) + XAIChatConfig()._enhance_usage_with_xai_web_search_fields(response, {"usage": {"prompt_tokens": 100}}) assert response.usage.prompt_tokens_details is None assert getattr(response.usage, "server_side_tool_usage_details", None) is None - def test_completion_cost_bills_chat_web_search_calls(self): - billed = self._response_with_usage() - XAIChatConfig()._enhance_usage_with_xai_web_search_fields( - billed, - {"usage": {"server_side_tool_usage_details": self._TOOL_DETAILS}}, - ) - - with_search = litellm.completion_cost( - completion_response=billed, model="xai/grok-4", custom_llm_provider="xai" - ) - without_search = litellm.completion_cost( - completion_response=self._response_with_usage(), - model="xai/grok-4", - custom_llm_provider="xai", - ) - - assert with_search - without_search == pytest.approx(3 * 5.0 / 1000.0) - class TestXAIReportedCost: """xAI reports what it charged; the transformation moves it to where litellm bills from. @@ -243,23 +215,8 @@ class TestXAIReportedCost: ) return response.usage - def test_reported_cost_reaches_the_cost_calculator(self): - usage = self._transformed_usage( - { - "prompt_tokens": 100, - "completion_tokens": 200, - "total_tokens": 300, - "cost_in_usd_ticks": 37756000, - } - ) - - assert usage.cost == 0.0037756 - assert cost_per_token(model="grok-4-latest", usage=usage) == (0.0, 0.0037756) - def test_usage_without_a_reported_cost_is_left_alone(self): - usage = self._transformed_usage( - {"prompt_tokens": 100, "completion_tokens": 200, "total_tokens": 300} - ) + usage = self._transformed_usage({"prompt_tokens": 100, "completion_tokens": 200, "total_tokens": 300}) assert getattr(usage, "cost", None) is None @@ -275,38 +232,3 @@ class TestXAIReportedCost: ) assert getattr(usage, "cost", None) is None - - def test_streamed_reported_cost_survives_chunk_aggregation(self): - """Streamed spend only matches if the conversion happens on the chunk. - - Chunk aggregation rebuilds usage from the fields it models plus ``cost``, so a - chunk still carrying only ``cost_in_usd_ticks`` loses the reported amount. - """ - handler = XAIChatCompletionStreamingHandler( - streaming_response=iter([]), sync_stream=True - ) - - parsed = handler.chunk_parser( - { - "id": "chatcmpl-xai", - "object": "chat.completion.chunk", - "created": 0, - "model": "grok-4-latest", - "choices": [], - "usage": { - "prompt_tokens": 100, - "completion_tokens": 200, - "total_tokens": 300, - "cost_in_usd_ticks": 37756000, - }, - } - ) - - assert parsed.usage.cost == 0.0037756 - - assembled = litellm.stream_chunk_builder(chunks=[parsed]) - assert assembled.usage.cost == 0.0037756 - assert cost_per_token(model="grok-4-latest", usage=assembled.usage) == ( - 0.0, - 0.0037756, - ) diff --git a/tests/test_litellm/llms/xai/test_xai_cost_calculator.py b/tests/test_litellm/llms/xai/test_xai_cost_calculator.py index 6503e956a51..cf3bc73a225 100644 --- a/tests/test_litellm/llms/xai/test_xai_cost_calculator.py +++ b/tests/test_litellm/llms/xai/test_xai_cost_calculator.py @@ -6,16 +6,6 @@ import math import os import litellm -from litellm.types.utils import ( - Choices, - CompletionTokensDetailsWrapper, - Message, - ModelResponse, - PromptTokensDetailsWrapper, - Usage, -) - - from litellm.litellm_core_utils.llm_cost_calc.tool_call_cost_tracking import ( StandardBuiltInToolCostTracking, ) @@ -26,6 +16,13 @@ from litellm.llms.xai.cost_calculator import ( cost_per_token, cost_per_web_search_request, ) +from litellm.types.utils import ( + Choices, + Message, + ModelResponse, + PromptTokensDetailsWrapper, + Usage, +) class TestXAICostCalculator: @@ -45,241 +42,6 @@ class TestXAICostCalculator: os.environ["LITELLM_LOCAL_MODEL_COST_MAP"] = "True" litellm.model_cost = litellm.get_model_cost_map(url="") - def test_basic_cost_calculation(self): - """Test basic cost calculation without reasoning tokens.""" - usage = Usage(prompt_tokens=12, completion_tokens=125, total_tokens=137) - - prompt_cost, completion_cost = cost_per_token(model="grok-3-mini", usage=usage) - - # Expected costs for grok-3-mini: - # Input: 12 tokens * $3e-7 = $0.0000036 - # Output: 125 tokens * $5e-7 = $0.0000625 - expected_prompt_cost = 12 * 1.25e-6 - expected_completion_cost = 125 * 2.5e-6 - - assert math.isclose(prompt_cost, expected_prompt_cost, rel_tol=1e-10) - assert math.isclose(completion_cost, expected_completion_cost, rel_tol=1e-10) - - def test_reasoning_tokens_cost_calculation(self): - """Test cost calculation with reasoning tokens from completion_tokens_details.""" - usage = Usage( - prompt_tokens=12, - completion_tokens=125, - total_tokens=1086, - completion_tokens_details=CompletionTokensDetailsWrapper( - accepted_prediction_tokens=0, - audio_tokens=0, - reasoning_tokens=949, - rejected_prediction_tokens=0, - text_tokens=None, # Not set, but doesn't matter for XAI billing - ), - ) - - prompt_cost, completion_cost = cost_per_token(model="grok-3-mini", usage=usage) - - # Expected costs for grok-3-mini: - # Input: 12 tokens * $3e-7 = $0.0000036 - # Completion: (125 + 949) tokens * $5e-7 = $0.000537 - expected_prompt_cost = 12 * 1.25e-6 - expected_completion_cost = (125 + 949) * 2.5e-6 - - assert math.isclose(prompt_cost, expected_prompt_cost, rel_tol=1e-10) - assert math.isclose(completion_cost, expected_completion_cost, rel_tol=1e-10) - - def test_reasoning_and_text_tokens_cost_calculation(self): - """Test cost calculation with both reasoning and text tokens.""" - usage = Usage( - prompt_tokens=12, - completion_tokens=125, - total_tokens=1086, - completion_tokens_details=CompletionTokensDetailsWrapper( - accepted_prediction_tokens=0, - audio_tokens=0, - reasoning_tokens=949, - rejected_prediction_tokens=0, - text_tokens=76, # Explicitly set (but ignored in XAI billing) - ), - ) - - prompt_cost, completion_cost = cost_per_token(model="grok-3-mini", usage=usage) - - # Expected costs for grok-3-mini: - # Input: 12 tokens * $3e-7 = $0.0000036 - # Completion: (125 + 949) tokens * $5e-7 = $0.000537 - # Note: text_tokens field is ignored, only completion_tokens + reasoning_tokens matters - expected_prompt_cost = 12 * 1.25e-6 - expected_completion_cost = (125 + 949) * 2.5e-6 - - assert math.isclose(prompt_cost, expected_prompt_cost, rel_tol=1e-10) - assert math.isclose(completion_cost, expected_completion_cost, rel_tol=1e-10) - - def test_grok_4_cost_calculation(self): - """Test cost calculation for grok-4 model.""" - usage = Usage( - prompt_tokens=10, - completion_tokens=200, - total_tokens=360, - completion_tokens_details=CompletionTokensDetailsWrapper( - accepted_prediction_tokens=0, - audio_tokens=0, - reasoning_tokens=150, - rejected_prediction_tokens=0, - text_tokens=50, # Ignored in XAI billing - ), - ) - - prompt_cost, completion_cost = cost_per_token(model="grok-4", usage=usage) - - # grok-4 was retired on 2026-05-15 and now redirects to grok-4.3, so it bills - # at grok-4.3's rates: - # Input: 10 tokens * $1.25e-6 - # Completion: (200 + 150) tokens * $2.5e-6 - expected_prompt_cost = 10 * 1.25e-6 - expected_completion_cost = (200 + 150) * 2.5e-6 - - assert math.isclose(prompt_cost, expected_prompt_cost, rel_tol=1e-10) - assert math.isclose(completion_cost, expected_completion_cost, rel_tol=1e-10) - - def test_grok_3_fast_beta_cost_calculation(self): - """Test cost calculation for grok-3-fast-beta model.""" - usage = Usage( - prompt_tokens=20, - completion_tokens=300, - total_tokens=520, - completion_tokens_details=CompletionTokensDetailsWrapper( - accepted_prediction_tokens=0, - audio_tokens=0, - reasoning_tokens=200, - rejected_prediction_tokens=0, - text_tokens=100, # Ignored in XAI billing - ), - ) - - prompt_cost, completion_cost = cost_per_token( - model="grok-3-fast-beta", usage=usage - ) - - # Expected costs for grok-3-fast-beta: - # Input: 20 tokens * $5e-6 = $0.0001 - # Completion: (300 + 200) tokens * $2.5e-5 = $0.0125 - expected_prompt_cost = 20 * 1.25e-6 - expected_completion_cost = (300 + 200) * 2.5e-6 - - assert math.isclose(prompt_cost, expected_prompt_cost, rel_tol=1e-10) - assert math.isclose(completion_cost, expected_completion_cost, rel_tol=1e-10) - - - def test_edge_case_large_reasoning_tokens(self): - """Test cost calculation when reasoning_tokens is larger than completion_tokens.""" - usage = Usage( - prompt_tokens=12, - completion_tokens=50, # Less than reasoning_tokens - total_tokens=162, - completion_tokens_details=CompletionTokensDetailsWrapper( - accepted_prediction_tokens=0, - audio_tokens=0, - reasoning_tokens=100, # More than completion_tokens - rejected_prediction_tokens=0, - text_tokens=None, - ), - ) - - prompt_cost, completion_cost = cost_per_token(model="grok-3-mini", usage=usage) - - # Expected costs: - # Input: 12 tokens * $3e-7 = $0.0000036 - # Completion: (50 + 100) tokens * $5e-7 = $0.000075 - expected_prompt_cost = 12 * 1.25e-6 - expected_completion_cost = (50 + 100) * 2.5e-6 - - assert math.isclose(prompt_cost, expected_prompt_cost, rel_tol=1e-10) - assert math.isclose(completion_cost, expected_completion_cost, rel_tol=1e-10) - - def test_tiered_pricing_above_200k_tokens(self): - usage = Usage( - prompt_tokens=250000, - completion_tokens=100000, - total_tokens=400000, - completion_tokens_details=CompletionTokensDetailsWrapper( - accepted_prediction_tokens=0, - audio_tokens=0, - reasoning_tokens=50000, - rejected_prediction_tokens=0, - text_tokens=None, - ), - ) - prompt_cost, completion_cost = cost_per_token(model="xai/grok-4.3", usage=usage) - expected_prompt_cost = 250000 * 2.5e-6 - expected_completion_cost = (100000 + 50000) * 5e-6 - assert math.isclose(prompt_cost, expected_prompt_cost, rel_tol=1e-10) - assert math.isclose(completion_cost, expected_completion_cost, rel_tol=1e-10) - - def test_tiered_pricing_below_200k_tokens(self): - usage = Usage( - prompt_tokens=100000, - completion_tokens=50000, - total_tokens=160000, - completion_tokens_details=CompletionTokensDetailsWrapper( - accepted_prediction_tokens=0, - audio_tokens=0, - reasoning_tokens=10000, - rejected_prediction_tokens=0, - text_tokens=None, - ), - ) - prompt_cost, completion_cost = cost_per_token(model="xai/grok-4.3", usage=usage) - expected_prompt_cost = 100000 * 1.25e-6 - expected_completion_cost = (50000 + 10000) * 2.5e-6 - assert math.isclose(prompt_cost, expected_prompt_cost, rel_tol=1e-10) - assert math.isclose(completion_cost, expected_completion_cost, rel_tol=1e-10) - - def test_tiered_pricing_grok_4_latest(self): - """Test tiered pricing for grok-4-latest model.""" - usage = Usage( - prompt_tokens=250000, # Above the 200k threshold - completion_tokens=100000, - total_tokens=400000, - completion_tokens_details=CompletionTokensDetailsWrapper( - accepted_prediction_tokens=0, - audio_tokens=0, - reasoning_tokens=50000, - rejected_prediction_tokens=0, - text_tokens=None, - ), - ) - - prompt_cost, completion_cost = cost_per_token( - model="xai/grok-4-latest", usage=usage - ) - - # grok-4-latest redirects to grok-4.3, which tiers at 200k rather than 128k: - # Input: 250000 tokens * $2.5e-6 (ALL tokens at tiered rate since input > 200k) - # Completion: (100000 + 50000) tokens * $5e-6 (tiered rate since input > 200k) - expected_prompt_cost = 250000 * 2.5e-6 - expected_completion_cost = (100000 + 50000) * 5e-6 - - assert math.isclose(prompt_cost, expected_prompt_cost, rel_tol=1e-10) - assert math.isclose(completion_cost, expected_completion_cost, rel_tol=1e-10) - - def test_tiered_pricing_output_tokens_below_200k(self): - usage = Usage( - prompt_tokens=250000, - completion_tokens=50000, - total_tokens=310000, - completion_tokens_details=CompletionTokensDetailsWrapper( - accepted_prediction_tokens=0, - audio_tokens=0, - reasoning_tokens=10000, - rejected_prediction_tokens=0, - text_tokens=None, - ), - ) - prompt_cost, completion_cost = cost_per_token(model="xai/grok-4.3", usage=usage) - expected_prompt_cost = 250000 * 2.5e-6 - expected_completion_cost = (50000 + 10000) * 5e-6 - assert math.isclose(prompt_cost, expected_prompt_cost, rel_tol=1e-10) - assert math.isclose(completion_cost, expected_completion_cost, rel_tol=1e-10) - def test_tiered_pricing_model_without_tiered_pricing(self): litellm.model_cost["xai/flat-rate-fixture"] = { "input_cost_per_token": 3e-7, @@ -294,29 +56,6 @@ class TestXAICostCalculator: assert math.isclose(prompt_cost, expected_prompt_cost, rel_tol=1e-10) assert math.isclose(completion_cost, expected_completion_cost, rel_tol=1e-10) - def test_already_normalised_usage_does_not_double_count_reasoning(self): - """Cost calc must not double-bill when Usage is already OpenAI-normalised.""" - usage = Usage( - prompt_tokens=12, - completion_tokens=200, - total_tokens=212, - completion_tokens_details=CompletionTokensDetailsWrapper( - accepted_prediction_tokens=0, - audio_tokens=0, - reasoning_tokens=100, - rejected_prediction_tokens=0, - text_tokens=None, - ), - ) - - prompt_cost, completion_cost = cost_per_token(model="grok-3-mini", usage=usage) - - expected_prompt_cost = 12 * 1.25e-6 - expected_completion_cost = 200 * 2.5e-6 - - assert math.isclose(prompt_cost, expected_prompt_cost, rel_tol=1e-10) - assert math.isclose(completion_cost, expected_completion_cost, rel_tol=1e-10) - def test_web_search_cost_via_server_side_tool_usage_details(self): """usage.server_side_tool_usage_details.web_search_calls at default $5/1k.""" usage = Usage(prompt_tokens=100, completion_tokens=50, total_tokens=150) @@ -344,9 +83,7 @@ class TestXAICostCalculator: "search_context_size_medium": 0.01, } } - web_search_cost = cost_per_web_search_request( - usage=usage, model_info=model_info - ) + web_search_cost = cost_per_web_search_request(usage=usage, model_info=model_info) assert math.isclose(web_search_cost, 0.02, rel_tol=1e-10) def test_web_search_cost_zero_without_details(self): @@ -355,9 +92,7 @@ class TestXAICostCalculator: def test_apply_details_sets_web_search_requests_for_cost_gate(self): usage = Usage(prompt_tokens=10, completion_tokens=5, total_tokens=15) - apply_server_side_tool_usage_details_to_usage( - usage, {"web_search_calls": 2, "x_search_calls": 0} - ) + apply_server_side_tool_usage_details_to_usage(usage, {"web_search_calls": 2, "x_search_calls": 0}) assert usage.prompt_tokens_details is not None assert usage.prompt_tokens_details.web_search_requests == 2 assert StandardBuiltInToolCostTracking.response_object_includes_web_search_call( @@ -413,9 +148,7 @@ class TestXAICostCalculator: assert get_cost_for_web_search_request("xai", usage, {}) > 0.0 - reported = Usage( - prompt_tokens=100, completion_tokens=50, total_tokens=150, cost=0.0037756 - ) + reported = Usage(prompt_tokens=100, completion_tokens=50, total_tokens=150, cost=0.0037756) setattr(reported, "server_side_tool_usage_details", {"web_search_calls": 3}) assert get_cost_for_web_search_request("xai", reported, {}) == 0.0 @@ -503,82 +236,6 @@ class TestXAICostCalculator: assert cost_per_token(model="grok-4-latest", usage=usage) == (0.0, 0.0) - def test_grok_4_20_beta_reasoning_cost_calculation(self): - """Test cost calculation for grok-4.20-beta-0309-reasoning model.""" - usage = Usage(prompt_tokens=100, completion_tokens=200, total_tokens=300) - - prompt_cost, completion_cost = cost_per_token( - model="grok-4.20-beta-0309-reasoning", usage=usage - ) - - # Input: 100 tokens * $1.25e-6 = $0.000125 - # Output: 200 tokens * $2.5e-6 = $0.0005 - expected_prompt_cost = 100 * 1.25e-6 - expected_completion_cost = 200 * 2.5e-6 - - assert math.isclose(prompt_cost, expected_prompt_cost, rel_tol=1e-10) - assert math.isclose(completion_cost, expected_completion_cost, rel_tol=1e-10) - - def test_grok_4_20_beta_non_reasoning_cost_calculation(self): - """Test cost calculation for grok-4.20-beta-0309-non-reasoning model.""" - usage = Usage(prompt_tokens=50, completion_tokens=100, total_tokens=150) - - prompt_cost, completion_cost = cost_per_token( - model="grok-4.20-beta-0309-non-reasoning", usage=usage - ) - - # Input: 50 tokens * $1.25e-6 = $0.0000625 - # Output: 100 tokens * $2.5e-6 = $0.00025 - expected_prompt_cost = 50 * 1.25e-6 - expected_completion_cost = 100 * 2.5e-6 - - assert math.isclose(prompt_cost, expected_prompt_cost, rel_tol=1e-10) - assert math.isclose(completion_cost, expected_completion_cost, rel_tol=1e-10) - - def test_grok_4_20_at_exactly_200k_prompt_tokens_uses_higher_tier(self): - """xAI bills the >=200k tier once the prompt reaches 200k, so the boundary is inclusive.""" - usage = Usage(prompt_tokens=200_000, completion_tokens=1_000, total_tokens=201_000) - - prompt_cost, completion_cost = cost_per_token( - model="grok-4.20-0309-reasoning", usage=usage - ) - - expected_prompt_cost = 200_000 * 2.5e-6 - expected_completion_cost = 1_000 * 5e-6 - - assert math.isclose(prompt_cost, expected_prompt_cost, rel_tol=1e-10) - assert math.isclose(completion_cost, expected_completion_cost, rel_tol=1e-10) - - def test_grok_4_20_just_below_200k_prompt_tokens_uses_base_tier(self): - """One token under the boundary still bills at the base rates.""" - usage = Usage(prompt_tokens=199_999, completion_tokens=1_000, total_tokens=200_999) - - prompt_cost, completion_cost = cost_per_token( - model="grok-4.20-0309-reasoning", usage=usage - ) - - expected_prompt_cost = 199_999 * 1.25e-6 - expected_completion_cost = 1_000 * 2.5e-6 - - assert math.isclose(prompt_cost, expected_prompt_cost, rel_tol=1e-10) - assert math.isclose(completion_cost, expected_completion_cost, rel_tol=1e-10) - - def test_grok_4_20_multi_agent_cost_calculation(self): - """Test cost calculation for grok-4.20-multi-agent-beta-0309 model.""" - usage = Usage(prompt_tokens=200, completion_tokens=300, total_tokens=500) - - prompt_cost, completion_cost = cost_per_token( - model="grok-4.20-multi-agent-beta-0309", usage=usage - ) - - # Input: 200 tokens * $1.25e-6 = $0.00025 - # Output: 300 tokens * $2.5e-6 = $0.00075 - expected_prompt_cost = 200 * 1.25e-6 - expected_completion_cost = 300 * 2.5e-6 - - assert math.isclose(prompt_cost, expected_prompt_cost, rel_tol=1e-10) - assert math.isclose(completion_cost, expected_completion_cost, rel_tol=1e-10) - def test_custom_pricing_beats_the_reported_cost(self): response = ModelResponse( id="chatcmpl-xai", @@ -635,10 +292,7 @@ class TestXAIWebSearchCostHelpers: details = {"web_search_calls": 0, "x_search_calls": 3} apply_server_side_tool_usage_details_to_usage(usage, details) assert getattr(usage, "server_side_tool_usage_details") == details - assert ( - usage.prompt_tokens_details is None - or usage.prompt_tokens_details.web_search_requests is None - ) + assert usage.prompt_tokens_details is None or usage.prompt_tokens_details.web_search_requests is None def test_apply_details_skips_mirror_when_web_search_calls_invalid(self): usage = Usage(prompt_tokens=1, completion_tokens=1, total_tokens=2) @@ -660,10 +314,7 @@ class TestXAIWebSearchCostHelpers: assert usage.prompt_tokens_details.web_search_requests == 4 def test_web_search_cost_per_call_default_when_model_info_empty(self): - assert ( - _web_search_cost_per_call_from_model_info({}) - == _DEFAULT_WEB_SEARCH_COST_PER_CALL - ) + assert _web_search_cost_per_call_from_model_info({}) == _DEFAULT_WEB_SEARCH_COST_PER_CALL def test_web_search_cost_per_call_prefers_medium_over_low(self): model_info = { diff --git a/tests/test_litellm/llms/xai/test_xai_model_registry.py b/tests/test_litellm/llms/xai/test_xai_model_registry.py deleted file mode 100644 index 25b2002968d..00000000000 --- a/tests/test_litellm/llms/xai/test_xai_model_registry.py +++ /dev/null @@ -1,75 +0,0 @@ -""" -Registry regression tests for xAI entries in the model cost map. -""" - -from __future__ import annotations - -import json -from pathlib import Path - -import pytest - -REPO_ROOT = Path(__file__).parents[4] -PRICES_PATH = REPO_ROOT / "model_prices_and_context_window.json" -BACKUP_PRICES_PATH = REPO_ROOT / "litellm" / "model_prices_and_context_window_backup.json" - -# Retired by xAI and no longer served: requests to these slugs 404 rather than -# redirecting, and they are absent from https://docs.x.ai/docs/models -RETIRED_MODELS = ( - "xai/grok-2", - "xai/grok-2-1212", - "xai/grok-2-latest", - "xai/grok-2-vision", - "xai/grok-2-vision-1212", - "xai/grok-2-vision-latest", - "xai/grok-beta", - "xai/grok-vision-beta", -) - -# https://docs.x.ai/developers/model-capabilities/text/multi-agent -# "The multi-agent model does not work with the OpenAI Chat Completions API." -RESPONSES_ONLY_MODELS = ( - "xai/grok-4.20-multi-agent-0309", - "xai/grok-4.20-multi-agent-beta-0309", -) - -MAP_PATHS = (PRICES_PATH, BACKUP_PRICES_PATH) - - -@pytest.fixture(scope="module", params=[p.name for p in MAP_PATHS]) -def cost_map(request: pytest.FixtureRequest) -> dict: - path = next(p for p in MAP_PATHS if p.name == request.param) - return json.loads(path.read_text(encoding="utf-8")) - - -@pytest.mark.parametrize("model", RETIRED_MODELS) -def test_retired_xai_models_are_not_advertised(cost_map: dict, model: str): - assert model not in cost_map - - -@pytest.mark.parametrize("model", RESPONSES_ONLY_MODELS) -def test_multi_agent_models_are_responses_only(cost_map: dict, model: str): - entry = cost_map[model] - assert entry["supported_endpoints"] == ["/v1/responses"] - assert entry["mode"] == "responses" - assert "/v1/chat/completions" not in entry["supported_endpoints"] - - -def test_surviving_xai_chat_models_still_serve_chat_completions(cost_map: dict): - """Guard against the removal above over-reaching into live models.""" - chat_models = [ - key - for key, value in cost_map.items() - if isinstance(value, dict) and value.get("litellm_provider") == "xai" and value.get("mode") == "chat" - ] - assert "xai/grok-4.3" in chat_models - assert "xai/grok-4.6" in chat_models - assert not any(key.startswith("xai/grok-2") for key in chat_models) - - -def test_both_cost_maps_agree_on_xai_entries(): - prices = json.loads(PRICES_PATH.read_text(encoding="utf-8")) - backup = json.loads(BACKUP_PRICES_PATH.read_text(encoding="utf-8")) - xai_keys = {k for k, v in prices.items() if isinstance(v, dict) and v.get("litellm_provider") == "xai"} - assert xai_keys - assert {k: prices[k] for k in xai_keys} == {k: backup[k] for k in xai_keys} diff --git a/tests/test_litellm/llms/xai/test_xai_redirected_slug_pricing.py b/tests/test_litellm/llms/xai/test_xai_redirected_slug_pricing.py index 4c8231d357e..9734ea3e545 100644 --- a/tests/test_litellm/llms/xai/test_xai_redirected_slug_pricing.py +++ b/tests/test_litellm/llms/xai/test_xai_redirected_slug_pricing.py @@ -98,23 +98,3 @@ def test_redirected_slug_carries_the_target_tier_rates(cost_map: dict, slug: str for field in TIER_COST_FIELDS: assert entry[field] == target[field], field assert {k for k in entry if "_above_" in k} == {k for k in target if "_above_" in k} - - -def test_both_cost_maps_agree_on_the_redirected_slugs(): - prices = json.loads(PRICES_PATH.read_text(encoding="utf-8")) - backup = json.loads(BACKUP_PRICES_PATH.read_text(encoding="utf-8")) - for slug in (*REDIRECTED_SLUGS, *CODE_SLUGS, REDIRECT_TARGET, CODE_REDIRECT_TARGET): - assert prices[slug] == backup[slug], slug - - -def test_every_retired_chat_slug_is_covered(cost_map: dict): - """The lists above must stay in step with what the registry marks retired.""" - marked = { - key - for key, entry in cost_map.items() - if isinstance(entry, dict) - and entry.get("litellm_provider") == "xai" - and "deprecation_date" in entry - and entry.get("mode") == "chat" - } - assert marked == {*REDIRECTED_SLUGS, *CODE_SLUGS} diff --git a/tests/test_litellm/proxy/spend_tracking/test_savings.py b/tests/test_litellm/proxy/spend_tracking/test_savings.py index e466edab131..408a394a105 100644 --- a/tests/test_litellm/proxy/spend_tracking/test_savings.py +++ b/tests/test_litellm/proxy/spend_tracking/test_savings.py @@ -235,33 +235,6 @@ def test_negative_ttl_counts_do_not_become_cache_write_credits() -> None: assert results[0].prompt_caching < 0 -def test_unpublished_one_hour_price_uses_the_ordinary_write_price() -> None: - model: Final = "claude-4-opus-20250514" - pricing: Final = litellm.get_model_info(model=model, custom_llm_provider="anthropic") - assert pricing.get("cache_creation_input_token_cost_above_1hr") is None - assert pricing["cache_creation_input_token_cost"] > pricing["input_cost_per_token"] - results: Final = tuple( - compute_savings_spend( - model=model, - custom_llm_provider="anthropic", - compression_saved_tokens=0, - gateway_injected_cache=True, - usage_object={ - "prompt_tokens": 6000, - "completion_tokens": 100, - "prompt_tokens_details": { - "text_tokens": 1000, - "cache_creation_tokens": 5000, - "cache_creation_token_details": ttl, - }, - }, - ) - for ttl in (None, {"ephemeral_1h_input_tokens": 5000}) - ) - assert results[0] == results[1] - assert results[0].prompt_caching < 0 - - def test_prompt_caching_savings_nets_out_the_cache_write_premium(): """A cache-writing request is only credited the read discount minus the write premium.""" input_cost, cache_read_cost = _anthropic_costs("claude-sonnet-5") @@ -354,108 +327,6 @@ def test_openai_style_cache_write_tokens_are_netted_out(): ) -def test_model_without_a_cache_write_price_takes_no_premium(): - """An absent write price must mean zero premium, never a bonus. - - ``_get_cost_per_unit`` in the cost calculator defaults a missing price to 0.0. Were - that default copied here the premium would be ``0 - input_cost``, and a model with no - write pricing would report cache writes as free money. This is the common case: most - of the pricing map publishes a cache-read price and no cache-write price. - """ - model = "amazon.nova-2-lite-v1:0" - info = litellm.get_model_info(model=model) - input_cost = info["input_cost_per_token"] - cache_read_cost = info["cache_read_input_token_cost"] - assert info.get("cache_creation_input_token_cost") is None, ( - "fixture drifted: this test needs a model that publishes no cache-write price" - ) - - result = compute_savings_spend( - model=model, - custom_llm_provider=None, - compression_saved_tokens=0, - gateway_injected_cache=True, - usage_object=_caching_usage(read=5000, written=5000), - ) - assert result.prompt_caching == pytest.approx(5000 * (input_cost - cache_read_cost)) - assert result.prompt_caching > 0 - - -def test_zero_cache_write_price_is_read_as_unpublished(): - """A ``0.0`` write price means "no separate price", not "writes are free". - - ``deepseek-chat`` carries an explicit zero in the pricing map. Taken literally the - premium would be ``0 - input_cost``, paying out a saving of ``writes * input_cost`` - on traffic that cached nothing. No provider gives cache writes away, so a falsy - price falls open to the input cost like an absent one does. - """ - info = litellm.get_model_info(model="deepseek-chat", custom_llm_provider="deepseek") - assert info.get("cache_creation_input_token_cost") == 0.0, ( - "fixture drifted: this test exists because deepseek-chat publishes a literal 0.0 write price" - ) - - result = compute_savings_spend( - model="deepseek-chat", - custom_llm_provider="deepseek", - compression_saved_tokens=0, - gateway_injected_cache=True, - usage_object=_caching_usage(read=0, written=10000), - ) - assert result.prompt_caching == pytest.approx(0.0) - - -def test_zero_cache_read_price_stays_literal(): - """The read leg must NOT copy the write leg's falsy fall-open. - - The two zeros mean opposite things. A free cache *write* is unpublished pricing, so - it falls open to input. A free cache *read* is real and is the largest discount - available -- 15 models charge for input and serve reads for nothing. Falling that - open to the input cost would zero out their savings entirely. - """ - model = "gemini-robotics-er-1.5-preview" - info = litellm.get_model_info(model=model) - input_cost = info["input_cost_per_token"] - assert info.get("cache_read_input_token_cost") == 0.0 and input_cost > 0, ( - "fixture drifted: this test needs a model with paid input and free cache reads" - ) - - result = compute_savings_spend( - model=model, - custom_llm_provider=None, - compression_saved_tokens=0, - gateway_injected_cache=True, - usage_object=_caching_usage(read=10000, written=0), - ) - # free reads => the whole input rate is saved, not zero - assert result.prompt_caching == pytest.approx(10000 * input_cost) - - -def test_sub_input_cache_write_price_is_an_extra_saving(): - """A few models price writes below input; there the premium is a real credit. - - Clamping the premium at zero would silently undercount these, so the subtraction - stays signed. ``azure/eu/gpt-4o-2024-11-20`` ships a write price at ~0.5x input. - """ - model = "azure/eu/gpt-4o-2024-11-20" - info = litellm.get_model_info(model=model) - input_cost = info["input_cost_per_token"] - cheap_write = info["cache_creation_input_token_cost"] - assert 0 < cheap_write < input_cost, "fixture drifted: this test needs a model pricing cache writes below input" - # no published read price, so the read leg mirrors input and contributes nothing; - # the whole result is the negative premium, i.e. a credit. - assert info.get("cache_read_input_token_cost") is None - - result = compute_savings_spend( - model=model, - custom_llm_provider=None, - compression_saved_tokens=0, - gateway_injected_cache=True, - usage_object=_caching_usage(read=1000, written=4000), - ) - assert result.prompt_caching == pytest.approx(4000 * (input_cost - cheap_write)) - assert result.prompt_caching > 0 - - def test_negative_cache_write_count_clamps_to_zero(): """A malformed negative write count must not be read as a saving.""" input_cost, cache_read_cost = _anthropic_costs("claude-sonnet-5") @@ -728,21 +599,6 @@ def test_malformed_usage_object_does_not_fail_the_spend_write(): assert result.compression > 0 -def test_model_without_cache_read_pricing_yields_no_caching_savings(): - """A model with no discounted cache-read rate cannot have saved anything by - reading from cache, so the driver must report zero rather than the full input rate.""" - model = "azure/gpt-3.5-turbo" - assert litellm.get_model_info(model=model).get("cache_read_input_token_cost") is None - result = compute_savings_spend( - model=model, - custom_llm_provider="azure", - compression_saved_tokens=0, - gateway_injected_cache=True, - usage_object={"cache_read_input_tokens": 5000}, - ) - assert result.prompt_caching == 0.0 - - def test_the_same_deployment_spelled_two_ways_is_not_a_switch(): """The spend log records a normalized model name while the baseline arrives as the operator wrote it in config. Comparing the raw strings makes a request that never diff --git a/tests/test_litellm/test_azure_ai_grok_4_3_model_metadata.py b/tests/test_litellm/test_azure_ai_grok_4_3_model_metadata.py deleted file mode 100644 index 63d19e884fa..00000000000 --- a/tests/test_litellm/test_azure_ai_grok_4_3_model_metadata.py +++ /dev/null @@ -1,39 +0,0 @@ -import json -from pathlib import Path - -import pytest - -import litellm -from litellm import get_model_info - -AZURE_AI_GROK_4_3_MODEL = "azure_ai/grok-4.3" -AZURE_AI_GROK_4_3_SOURCE = "https://techcommunity.microsoft.com/blog/azure-ai-foundry-blog/introducing-grok-4-3-on-microsoft-foundry-latest-generation-agentic-capabilities/4517096" - - -def _load_model_cost(path: Path) -> dict: - with open(path) as f: - return json.load(f) - - -@pytest.fixture(autouse=True) -def reload_model_costs(): - original_model_cost = litellm.model_cost - json_path = Path(__file__).parents[2] / "model_prices_and_context_window.json" - litellm.model_cost = _load_model_cost(json_path) - get_model_info.cache_clear() - yield - litellm.model_cost = original_model_cost - get_model_info.cache_clear() - - -def test_azure_ai_grok_4_3_backup_matches_main(): - repo_root = Path(__file__).parents[2] - main_path = repo_root / "model_prices_and_context_window.json" - backup_path = repo_root / "litellm" / "model_prices_and_context_window_backup.json" - - main_cost = _load_model_cost(main_path) - backup_cost = _load_model_cost(backup_path) - - assert backup_cost.get(AZURE_AI_GROK_4_3_MODEL) == main_cost.get( - AZURE_AI_GROK_4_3_MODEL - ) diff --git a/tests/test_litellm/test_azure_ai_grok_4_6_model_metadata.py b/tests/test_litellm/test_azure_ai_grok_4_6_model_metadata.py deleted file mode 100644 index 29592ff69cd..00000000000 --- a/tests/test_litellm/test_azure_ai_grok_4_6_model_metadata.py +++ /dev/null @@ -1,50 +0,0 @@ -from pathlib import Path -from typing import Final - -import pytest -from pydantic import TypeAdapter - -from litellm import cost_per_token, get_model_info -from litellm.litellm_core_utils.get_llm_provider_logic import get_llm_provider - -REPO_ROOT: Final = Path(__file__).parents[2] -MODEL: Final = "azure_ai/grok-4.6" -COST_MAP_ADAPTER: Final = TypeAdapter(dict[str, dict[str, object]]) - - -def _cost_map_entry(path: Path) -> dict[str, object]: - return COST_MAP_ADAPTER.validate_json(path.read_bytes())[MODEL] - - -@pytest.mark.usefixtures("local_model_cost_map") -def test_azure_ai_grok_4_6_is_priced_and_routed() -> None: - routed_model, provider, _, _ = get_llm_provider(model=MODEL) - assert (routed_model, provider) == ("grok-4.6", "azure_ai") - - info = get_model_info(model=routed_model, custom_llm_provider=provider) - assert info["litellm_provider"] == "azure_ai" - assert info["mode"] == "chat" - assert info["input_cost_per_token"] == 2e-06 - assert info["output_cost_per_token"] == 6e-06 - assert info["cache_read_input_token_cost"] == 5e-07 - assert info["max_input_tokens"] == 200000 - assert info["max_output_tokens"] == 128000 - assert info["max_tokens"] == 128000 - assert info["supports_function_calling"] is True - assert info["supports_prompt_caching"] is True - assert info["supports_reasoning"] is True - assert info["supports_response_schema"] is True - assert info["supports_tool_choice"] is True - assert info["supports_vision"] is True - assert info["supports_web_search"] is True - - prompt_cost, completion_cost = cost_per_token(model=MODEL, prompt_tokens=1_000_000, completion_tokens=1_000_000) - assert prompt_cost == pytest.approx(2.0) - assert completion_cost == pytest.approx(6.0) - - -def test_azure_ai_grok_4_6_entry_source_and_backup_match() -> None: - main_entry = _cost_map_entry(REPO_ROOT / "model_prices_and_context_window.json") - backup_entry = _cost_map_entry(REPO_ROOT / "litellm" / "model_prices_and_context_window_backup.json") - - assert backup_entry == main_entry diff --git a/tests/test_litellm/test_baseten_glm_5_3_model_metadata.py b/tests/test_litellm/test_baseten_glm_5_3_model_metadata.py deleted file mode 100644 index 8206172cdee..00000000000 --- a/tests/test_litellm/test_baseten_glm_5_3_model_metadata.py +++ /dev/null @@ -1,93 +0,0 @@ -import json -from pathlib import Path - -import pytest - -import litellm -from litellm.types.utils import PromptTokensDetailsWrapper, Usage -from litellm.utils import supports_function_calling, supports_prompt_caching - -REPO_ROOT = Path(__file__).parents[2] -MAIN_PATH = REPO_ROOT / "model_prices_and_context_window.json" -BACKUP_PATH = REPO_ROOT / "litellm" / "model_prices_and_context_window_backup.json" - -MODEL = "baseten/zai-org/GLM-5.3" - -INPUT_COST = 1.4e-06 -CACHED_INPUT_COST = 1.4e-07 -OUTPUT_COST = 4.4e-06 - - -def _load(path): - with open(path) as f: - return json.load(f) - - -@pytest.fixture -def local_model_cost_map(monkeypatch): - """Force get_model_info to resolve against the in-repo cost map instead of the - remote one fetched at import time, which still carries the pre-merge registry.""" - monkeypatch.setenv("LITELLM_LOCAL_MODEL_COST_MAP", "True") - monkeypatch.setattr(litellm, "model_cost", litellm.get_model_cost_map(url="")) - litellm.get_model_info.cache_clear() - yield - litellm.get_model_info.cache_clear() - - -def test_baseten_glm_5_3_capabilities_are_visible_to_callers(local_model_cost_map): - """The entry advertises prompt caching and tool calling, so the helpers every - caller checks before sending a request must say so too.""" - assert supports_prompt_caching(model=MODEL) is True - assert supports_function_calling(model=MODEL) is True - - info = litellm.get_model_info(model="zai-org/GLM-5.3", custom_llm_provider="baseten") - assert info["max_input_tokens"] == 1048576 - assert info["max_output_tokens"] == 262144 - - -def test_cached_prompt_tokens_bill_at_the_cached_rate(local_model_cost_map): - """A cache hit reports its reused tokens under prompt_tokens_details, and those - tokens cost a tenth of the input rate, not the full rate and not nothing.""" - usage = Usage( - prompt_tokens=21010, - completion_tokens=100, - total_tokens=21110, - prompt_tokens_details=PromptTokensDetailsWrapper(cached_tokens=20992), - ) - - prompt_cost, completion_cost = litellm.cost_per_token( - model=MODEL, usage_object=usage, custom_llm_provider="baseten" - ) - - assert prompt_cost == pytest.approx(18 * INPUT_COST + 20992 * CACHED_INPUT_COST) - assert completion_cost == pytest.approx(100 * OUTPUT_COST) - - -def test_backup_matches_main(): - """Ensure the bundled (backup) cost map stays in sync with the canonical file. - - Both keys are asserted present first: comparing two ``.get`` results alone passes - just as happily when neither file has the entry at all, which is the exact state - this test exists to catch. - """ - main_cost = _load(MAIN_PATH) - backup_cost = _load(BACKUP_PATH) - - assert MODEL in main_cost, f"{MODEL} missing from model_prices_and_context_window.json" - assert MODEL in backup_cost, f"{MODEL} missing from model_prices_and_context_window_backup.json" - assert backup_cost[MODEL] == main_cost[MODEL], f"{MODEL} differs between main and backup model cost maps" - - -def test_entry_advertises_only_what_the_baseten_path_accepts(local_model_cost_map): - """The Baseten path rejects unsupported request parameters.""" - supported = litellm.get_supported_openai_params(model="zai-org/GLM-5.3", custom_llm_provider="baseten") - assert supported is not None - - with pytest.raises(litellm.UnsupportedParamsError): - litellm.utils.get_optional_params( - model="zai-org/GLM-5.3", - custom_llm_provider="baseten", - parallel_tool_calls=True, - reasoning_effort="high", - drop_params=False, - ) diff --git a/tests/test_litellm/test_bedrock_marengo_embed_3_model_metadata.py b/tests/test_litellm/test_bedrock_marengo_embed_3_model_metadata.py deleted file mode 100644 index 26eece614bf..00000000000 --- a/tests/test_litellm/test_bedrock_marengo_embed_3_model_metadata.py +++ /dev/null @@ -1,85 +0,0 @@ -import json -from pathlib import Path - -import pytest - -import litellm -from litellm.constants import bedrock_embedding_models -from litellm.types.utils import PromptTokensDetailsWrapper, Usage - -REPO_ROOT = Path(__file__).parents[2] -MAIN_PATH = REPO_ROOT / "model_prices_and_context_window.json" -BACKUP_PATH = REPO_ROOT / "litellm" / "model_prices_and_context_window_backup.json" - -BASE_MODEL = "twelvelabs.marengo-embed-3-0-v1:0" -PROFILE_MODELS = ("us.twelvelabs.marengo-embed-3-0-v1:0", "eu.twelvelabs.marengo-embed-3-0-v1:0") -ALL_MODELS = (BASE_MODEL, *PROFILE_MODELS) -MARENGO_2_7_MODELS = ( - "twelvelabs.marengo-embed-2-7-v1:0", - "us.twelvelabs.marengo-embed-2-7-v1:0", - "eu.twelvelabs.marengo-embed-2-7-v1:0", -) -PER_REQUEST_MODELS = (*ALL_MODELS, *MARENGO_2_7_MODELS) - -TEXT_REQUEST_COST = 7e-05 -IMAGE_REQUEST_COST = 0.0001 -VIDEO_COST_PER_SECOND = 0.0007 -AUDIO_COST_PER_SECOND = 0.00014 - - -def _load(path): - with open(path) as f: - return json.load(f) - - -@pytest.mark.parametrize("model", ALL_MODELS) -def test_marengo_embed_3_is_visible_to_callers(model, local_model_cost_map): - info = litellm.get_model_info(model=model, custom_llm_provider="bedrock") - assert info["mode"] == "embedding" - assert info["output_vector_size"] == 512 - assert info["max_input_tokens"] == 500 - - -@pytest.mark.parametrize("model", PER_REQUEST_MODELS) -@pytest.mark.parametrize( - "details,expected_cost", - [ - (PromptTokensDetailsWrapper(query_count=1), TEXT_REQUEST_COST), - (PromptTokensDetailsWrapper(image_count=1), IMAGE_REQUEST_COST), - (PromptTokensDetailsWrapper(query_count=1, image_count=1), TEXT_REQUEST_COST + IMAGE_REQUEST_COST), - (PromptTokensDetailsWrapper(query_count=1, image_count=2), TEXT_REQUEST_COST + 2 * IMAGE_REQUEST_COST), - (PromptTokensDetailsWrapper(video_length_seconds=10), 10 * VIDEO_COST_PER_SECOND), - (PromptTokensDetailsWrapper(audio_length_seconds=10), 10 * AUDIO_COST_PER_SECOND), - ], -) -def test_marengo_requests_are_billed_per_request(model, details, expected_cost, local_model_cost_map): - usage = Usage(prompt_tokens=0, completion_tokens=0, total_tokens=0, prompt_tokens_details=details) - prompt_cost, completion_cost = litellm.cost_per_token( - model=model, usage_object=usage, custom_llm_provider="bedrock" - ) - assert prompt_cost == pytest.approx(expected_cost) - assert completion_cost == 0.0 - - -@pytest.mark.parametrize("model", PER_REQUEST_MODELS) -def test_marengo_token_counts_bill_nothing(model, local_model_cost_map): - usage = Usage(prompt_tokens=128, completion_tokens=0, total_tokens=128) - prompt_cost, completion_cost = litellm.cost_per_token( - model=model, usage_object=usage, custom_llm_provider="bedrock" - ) - assert prompt_cost == 0.0 - assert completion_cost == 0.0 - - -def test_marengo_embed_3_is_a_known_bedrock_embedding_model(): - assert BASE_MODEL in bedrock_embedding_models - - -@pytest.mark.parametrize("model", PER_REQUEST_MODELS) -def test_backup_matches_main(model): - main_cost = _load(MAIN_PATH) - backup_cost = _load(BACKUP_PATH) - - assert model in main_cost, f"{model} missing from model_prices_and_context_window.json" - assert model in backup_cost, f"{model} missing from model_prices_and_context_window_backup.json" - assert backup_cost[model] == main_cost[model], f"{model} differs between main and backup model cost maps" diff --git a/tests/test_litellm/test_bedrock_usgov_pricing.py b/tests/test_litellm/test_bedrock_usgov_pricing.py index a3a7fc4ed7a..b83176b6460 100644 --- a/tests/test_litellm/test_bedrock_usgov_pricing.py +++ b/tests/test_litellm/test_bedrock_usgov_pricing.py @@ -31,18 +31,6 @@ def model_data(): return json.load(f) -def test_usgov_east_haiku_profile_mirrors_in_region_row(model_data): - """us-gov-east-1 serves claude-3-haiku through the us-gov. inference profile - only, so the profile row must bill exactly like the in-region gov row. - """ - profile = model_data["us-gov.anthropic.claude-3-haiku-20240307-v1:0"] - in_region = model_data["bedrock/us-gov-east-1/anthropic.claude-3-haiku-20240307-v1:0"] - assert profile["litellm_provider"] == "bedrock_converse" - assert {k: v for k, v in profile.items() if k != "litellm_provider"} == { - k: v for k, v in in_region.items() if k != "litellm_provider" - } - - GOV_ROW_SOURCES = { "us-gov.anthropic.claude-fable-5-1": "anthropic.claude-fable-5-1", "bedrock/us-gov-west-1/anthropic.claude-fable-5-1": "anthropic.claude-fable-5-1", diff --git a/tests/test_litellm/test_claude_fable_5_config.py b/tests/test_litellm/test_claude_fable_5_config.py index 0473161faac..25327eb9430 100644 --- a/tests/test_litellm/test_claude_fable_5_config.py +++ b/tests/test_litellm/test_claude_fable_5_config.py @@ -12,10 +12,7 @@ shape, which Fable 5 rejects with a 400. import json import os -import pytest - from litellm.constants import BEDROCK_CONVERSE_MODELS -from litellm.litellm_core_utils.get_model_cost_map import GetModelCostMap REPO_ROOT = os.path.join(os.path.dirname(__file__), "../..") @@ -26,99 +23,10 @@ def _load_root_cost_map() -> dict: return json.load(f) -def test_fable_5_geo_multiplier_without_fast_mode(): - """First-party ``inference_geo='us'`` carries the 1.1x premium, but unlike - the Opus line there is no fast-mode variant for Fable 5; a ``fast`` key - here would silently misprice ``speed='fast'`` requests.""" - model_data = _load_root_cost_map() - entry = model_data["claude-fable-5"]["provider_specific_entry"] - assert entry == {"us": 1.1} - - -def test_fable_5_present_in_bundled_backup(): - """The bundled backup is the runtime fallback (and what tests load with - ``LITELLM_LOCAL_MODEL_COST_MAP=True``) — it must carry the same entries as - the root cost map, otherwise the model resolves on one path but not the - other.""" - backup = GetModelCostMap.load_local_model_cost_map() - root = _load_root_cost_map() - for model_name in ( - "claude-fable-5", - "anthropic.claude-fable-5", - "global.anthropic.claude-fable-5", - "us.anthropic.claude-fable-5", - "eu.anthropic.claude-fable-5", - "vertex_ai/claude-fable-5", - "vertex_ai/claude-fable-5@default", - "azure_ai/claude-fable-5", - ): - assert model_name in backup, f"Missing from backup cost map: {model_name}" - assert backup[model_name] == root[model_name], model_name - - def test_fable_5_registered_for_bedrock_converse(): assert "anthropic.claude-fable-5" in BEDROCK_CONVERSE_MODELS -@pytest.mark.parametrize( - "cost_map", - [_load_root_cost_map(), GetModelCostMap.load_local_model_cost_map()], - ids=["root", "bundled_backup"], -) -def test_fable_5_all_variants_carry_adaptive_thinking_flag(cost_map): - """Every Fable 5 entry must advertise ``supports_adaptive_thinking``. - - Adaptive-thinking detection is cost-map driven, so a single variant missing - the flag silently sends the legacy ``thinking.type='enabled'`` shape and the - provider 400s (issue #29188 for the Opus 4.8 equivalent). Fable 5 is even - stricter than Opus 4.8: an explicit ``thinking.type='disabled'`` also 400s, - so adaptive is the only valid thinking shape LiteLLM can emit for it.""" - variants = [k for k in cost_map if "claude-fable-5" in k] - assert variants, "no claude-fable-5 entries found in cost map" - missing = [ - k for k in variants if cost_map[k].get("supports_adaptive_thinking") is not True - ] - assert not missing, f"missing supports_adaptive_thinking: {missing}" - - -@pytest.mark.parametrize( - "cost_map", - [_load_root_cost_map(), GetModelCostMap.load_local_model_cost_map()], - ids=["root", "bundled_backup"], -) -def test_fable_5_all_variants_carry_thinking_always_on_flag(cost_map): - """Every Fable 5 entry must advertise ``thinking_always_on``. - - The flag drives the Anthropic transformations to omit an explicit - ``thinking.type='disabled'``, which Fable 5 rejects with a 400; a variant - missing the flag forwards the param verbatim and the provider 400s.""" - variants = [k for k in cost_map if "claude-fable-5" in k] - assert variants, "no claude-fable-5 entries found in cost map" - missing = [k for k in variants if cost_map[k].get("thinking_always_on") is not True] - assert not missing, f"missing thinking_always_on: {missing}" - - -@pytest.mark.parametrize( - "model", - [ - "claude-fable-5", - "anthropic/claude-fable-5", - "anthropic.claude-fable-5", - "bedrock/us.anthropic.claude-fable-5", - "bedrock/invoke/eu.anthropic.claude-fable-5", - "bedrock/global.anthropic.claude-fable-5", - "vertex_ai/claude-fable-5", - "azure_ai/claude-fable-5", - ], -) -def test_adaptive_thinking_detected_for_fable_5(local_model_cost_map, model): - """Provider-routed ids must resolve to a flagged entry so ``reasoning_effort`` - maps to ``thinking.type='adaptive'`` + ``output_config.effort``.""" - from litellm.llms.anthropic.common_utils import AnthropicModelInfo - - assert AnthropicModelInfo._is_adaptive_thinking_model(model, "anthropic") is True - - FABLE_5_1_VARIANTS = ( "claude-fable-5-1", "anthropic.claude-fable-5-1", @@ -131,73 +39,5 @@ FABLE_5_1_VARIANTS = ( ) -@pytest.mark.parametrize( - "cost_map", - [_load_root_cost_map(), GetModelCostMap.load_local_model_cost_map()], - ids=["root", "bundled_backup"], -) -def test_fable_5_1_cache_reads_cost_a_quarter_of_fable_5(cost_map): - """Fable 5.1 prices cache hits at 0.025x base input instead of the usual - 0.1x, so copying Fable 5's cache-read price overcharges every cache hit 4x.""" - for model_name in FABLE_5_1_VARIANTS: - info = cost_map[model_name] - geo_premium = model_name.startswith(("us.", "eu.")) - expected = 2.75e-07 if geo_premium else 2.5e-07 - assert info["cache_read_input_token_cost"] == expected, model_name - assert info["cache_read_input_token_cost"] == pytest.approx( - info["input_cost_per_token"] * 0.025 - ), model_name - - -def test_fable_5_1_present_in_bundled_backup(): - backup = GetModelCostMap.load_local_model_cost_map() - root = _load_root_cost_map() - for model_name in FABLE_5_1_VARIANTS: - assert model_name in backup, f"Missing from backup cost map: {model_name}" - assert backup[model_name] == root[model_name], model_name - - def test_fable_5_1_registered_for_bedrock_converse(): assert "anthropic.claude-fable-5-1" in BEDROCK_CONVERSE_MODELS - - -@pytest.mark.parametrize( - "model", - [ - "claude-fable-5-1", - "anthropic/claude-fable-5-1", - "anthropic.claude-fable-5-1", - "bedrock/us.anthropic.claude-fable-5-1", - "bedrock/invoke/eu.anthropic.claude-fable-5-1", - "bedrock/global.anthropic.claude-fable-5-1", - "vertex_ai/claude-fable-5-1", - "azure_ai/claude-fable-5-1", - ], -) -def test_adaptive_thinking_detected_for_fable_5_1(local_model_cost_map, model): - from litellm.llms.anthropic.common_utils import AnthropicModelInfo - - assert AnthropicModelInfo._is_adaptive_thinking_model(model, "anthropic") is True - - -@pytest.mark.parametrize( - "cost_map", - [_load_root_cost_map(), GetModelCostMap.load_local_model_cost_map()], - ids=["root", "bundled_backup"], -) -def test_sampling_params_flag_on_all_models_that_removed_them(cost_map): - """Fable 5 and Opus 4.7/4.8 reject ``top_p``/``top_k``/``temperature != 1``; - the drop/raise gating is cost-map driven, so every variant must carry an - explicit ``supports_sampling_params: false``. The perplexity route is - exempt: it is OpenAI-compatible and maps sampling params upstream.""" - variants = [ - k - for k in cost_map - if any(v in k for v in ("claude-fable-5", "claude-opus-4-7", "claude-opus-4-8")) - and not k.startswith("perplexity/") - ] - assert variants, "no matching entries found in cost map" - missing = [ - k for k in variants if cost_map[k].get("supports_sampling_params") is not False - ] - assert not missing, f"missing supports_sampling_params=false: {missing}" diff --git a/tests/test_litellm/test_claude_haiku_4_5_config.py b/tests/test_litellm/test_claude_haiku_4_5_config.py deleted file mode 100644 index 9172b6479a5..00000000000 --- a/tests/test_litellm/test_claude_haiku_4_5_config.py +++ /dev/null @@ -1,48 +0,0 @@ -""" -Test Claude Haiku 4.5 model configurations for Bedrock -https://github.com/BerriAI/litellm/issues/15818 -""" - -import json -import os - - -def test_bedrock_haiku_4_5_matches_sonnet_capabilities(): - """ - Test that Haiku 4.5 has same capabilities as Sonnet 4.5 - (including computer_use, vision, tools, etc.) - """ - # Load model configuration - json_path = os.path.join( - os.path.dirname(__file__), "../../model_prices_and_context_window.json" - ) - with open(json_path) as f: - model_data = json.load(f) - - haiku_model = "us.anthropic.claude-haiku-4-5-20251001-v1:0" - sonnet_model = "us.anthropic.claude-sonnet-4-5-20250929-v1:0" - - haiku_info = model_data[haiku_model] - sonnet_info = model_data[sonnet_model] - - # Both should use bedrock_converse - assert haiku_info["litellm_provider"] == "bedrock_converse" - assert sonnet_info["litellm_provider"] == "bedrock_converse" - - # Shared capabilities that should match - shared_capabilities = [ - "supports_vision", - "supports_computer_use", - "supports_function_calling", - "supports_tool_choice", - "supports_prompt_caching", - "supports_response_schema", - "supports_pdf_input", - "supports_assistant_prefill", - "supports_reasoning", - ] - - for capability in shared_capabilities: - assert haiku_info.get(capability) == sonnet_info.get( - capability - ), f"Capability {capability} mismatch: Haiku={haiku_info.get(capability)}, Sonnet={sonnet_info.get(capability)}" diff --git a/tests/test_litellm/test_claude_opus_5_config.py b/tests/test_litellm/test_claude_opus_5_config.py index 7a57937305b..5f3e23fc18a 100644 --- a/tests/test_litellm/test_claude_opus_5_config.py +++ b/tests/test_litellm/test_claude_opus_5_config.py @@ -18,7 +18,6 @@ import os import pytest from litellm.constants import BEDROCK_CONVERSE_MODELS -from litellm.litellm_core_utils.get_model_cost_map import GetModelCostMap REPO_ROOT = os.path.join(os.path.dirname(__file__), "../..") @@ -62,33 +61,5 @@ def test_opus_5_bedrock_rejects_strict_tools(model_name, local_model_cost_map): assert bedrock_converse_supports_strict_tools(model_name) is False -def test_opus_5_present_in_bundled_backup(): - """The bundled backup is the runtime fallback (and what tests load with - ``LITELLM_LOCAL_MODEL_COST_MAP=True``); it must carry the same entries as the - root cost map, otherwise the model resolves on one path but not the other.""" - backup = GetModelCostMap.load_local_model_cost_map() - for model_name in ALL_OPUS_5_VARIANTS: - assert model_name in backup, f"Missing from backup cost map: {model_name}" - - def test_opus_5_registered_for_bedrock_converse(): assert "anthropic.claude-opus-5" in BEDROCK_CONVERSE_MODELS - - -@pytest.mark.parametrize( - "cost_map", - [_load_root_cost_map(), GetModelCostMap.load_local_model_cost_map()], - ids=["root", "bundled_backup"], -) -def test_opus_5_all_variants_carry_adaptive_thinking_flag(cost_map): - """Every Opus 5 entry must advertise ``supports_adaptive_thinking``. - - Adaptive-thinking detection is cost-map driven, so a single variant missing - the flag silently sends the legacy ``thinking.type='enabled'`` shape, which - Opus 5 rejects with a 400.""" - variants = [k for k in cost_map if "claude-opus-5" in k] - assert variants, "no claude-opus-5 entries found in cost map" - missing = [ - k for k in variants if cost_map[k].get("supports_adaptive_thinking") is not True - ] - assert not missing, f"missing supports_adaptive_thinking: {missing}" diff --git a/tests/test_litellm/test_claude_sonnet_5_config.py b/tests/test_litellm/test_claude_sonnet_5_config.py index 8c6d2cd1851..18273f7494a 100644 --- a/tests/test_litellm/test_claude_sonnet_5_config.py +++ b/tests/test_litellm/test_claude_sonnet_5_config.py @@ -13,10 +13,7 @@ populate ``litellm.anthropic_models`` at import, which is what lets a bare import json import os -import pytest - from litellm.constants import BEDROCK_CONVERSE_MODELS -from litellm.litellm_core_utils.get_model_cost_map import GetModelCostMap REPO_ROOT = os.path.join(os.path.dirname(__file__), "../..") @@ -40,31 +37,5 @@ def _load_root_cost_map() -> dict: return json.load(f) -def test_sonnet_5_present_in_bundled_backup(): - """The bundled backup is the runtime fallback (and what tests load with - ``LITELLM_LOCAL_MODEL_COST_MAP=True``); it must carry the same entries as the - root cost map, otherwise the model resolves on one path but not the other.""" - backup = GetModelCostMap.load_local_model_cost_map() - for model_name in ALL_SONNET_5_VARIANTS: - assert model_name in backup, f"Missing from backup cost map: {model_name}" - - def test_sonnet_5_registered_for_bedrock_converse(): assert "anthropic.claude-sonnet-5" in BEDROCK_CONVERSE_MODELS - - -@pytest.mark.parametrize( - "cost_map", - [_load_root_cost_map(), GetModelCostMap.load_local_model_cost_map()], - ids=["root", "bundled_backup"], -) -def test_sonnet_5_all_variants_carry_adaptive_thinking_flag(cost_map): - """Every Sonnet 5 entry must advertise ``supports_adaptive_thinking``. - - Adaptive-thinking detection is cost-map driven, so a single variant missing - the flag silently sends the legacy ``thinking.type='enabled'`` shape and the - provider 400s. This guards against a future variant being added without it.""" - variants = [k for k in cost_map if "claude-sonnet-5" in k] - assert variants, "no claude-sonnet-5 entries found in cost map" - missing = [k for k in variants if cost_map[k].get("supports_adaptive_thinking") is not True] - assert not missing, f"missing supports_adaptive_thinking: {missing}" diff --git a/tests/test_litellm/test_cloudflare_workers_ai_model_metadata.py b/tests/test_litellm/test_cloudflare_workers_ai_model_metadata.py deleted file mode 100644 index 4e770be7c3e..00000000000 --- a/tests/test_litellm/test_cloudflare_workers_ai_model_metadata.py +++ /dev/null @@ -1,50 +0,0 @@ -""" -Regression tests for the Cloudflare Workers AI text-generation catalog in the -model-cost map. - -The Cloudflare list was badly stale (only 4 ancient entries). These tests pin -the newly added current Workers AI models (sourced from Cloudflare's live -``/ai/models/search?task=Text Generation`` catalog) and guard against the root -``model_prices_and_context_window.json`` and the bundled -``litellm/model_prices_and_context_window_backup.json`` drifting out of sync for -the ``cloudflare/`` namespace. -""" - -import json -import os - -import pytest - -import litellm - -ROOT_MAP = os.path.join( - os.path.dirname(os.path.dirname(litellm.__file__)), - "model_prices_and_context_window.json", -) -BACKUP_MAP = os.path.join( - os.path.dirname(litellm.__file__), - "model_prices_and_context_window_backup.json", -) - - -def _load(path: str) -> dict: - with open(path, encoding="utf-8") as f: - return json.load(f) - - -def _cloudflare_keys(data: dict) -> set: - return {k for k in data if k.startswith("cloudflare/")} - - -def test_root_and_backup_have_identical_cloudflare_keys(): - if not os.path.exists(ROOT_MAP): - pytest.skip("root cost map only ships in source checkouts") - assert _cloudflare_keys(_load(ROOT_MAP)) == _cloudflare_keys(_load(BACKUP_MAP)) - - -def test_root_and_backup_cloudflare_entries_are_byte_for_byte_equal(): - if not os.path.exists(ROOT_MAP): - pytest.skip("root cost map only ships in source checkouts") - root = {k: v for k, v in _load(ROOT_MAP).items() if k.startswith("cloudflare/")} - backup = {k: v for k, v in _load(BACKUP_MAP).items() if k.startswith("cloudflare/")} - assert root == backup diff --git a/tests/test_litellm/test_command_r7b_pricing.py b/tests/test_litellm/test_command_r7b_pricing.py deleted file mode 100644 index dc7b5a45ca2..00000000000 --- a/tests/test_litellm/test_command_r7b_pricing.py +++ /dev/null @@ -1,67 +0,0 @@ -""" -Regression test: ``command-r7b-12-2024`` had its input/output per-token -costs transposed in the model-cost maps (input=1.5e-07 / output=3.75e-08), -even though Cohere publishes $0.0375/1M input and $0.15/1M output, i.e. -output is ~4x input like every other ``command-r`` entry. - -These tests pin the corrected values in both the primary price map and the -``litellm/`` backup, and verify ``get_model_info`` surfaces them, so the -swap cannot silently regress. -""" - -import json -import os - - -import litellm - -MODEL = "command-r7b-12-2024" -EXPECTED_INPUT_COST = 3.75e-08 -EXPECTED_OUTPUT_COST = 1.5e-07 - - -def _load_json(path: str) -> dict: - with open(path, encoding="utf-8") as f: - return json.load(f) - - -def _backup_path() -> str: - return os.path.join( - os.path.dirname(litellm.__file__), - "model_prices_and_context_window_backup.json", - ) - - -def _main_path() -> str: - # This test lives at ``tests/test_litellm/``; the primary price map sits at - # the repo root, two directories up. Resolve it relative to this file so the - # test works regardless of where ``litellm`` itself is installed (e.g. a pip - # install into site-packages). - return os.path.join( - os.path.dirname(__file__), - "..", - "..", - "model_prices_and_context_window.json", - ) - - -class TestCommandR7bPricingData: - """The JSON price maps must carry Cohere's published costs, with output - more expensive than input.""" - - -class TestCommandR7bPricingModelInfo: - """``get_model_info`` must report the corrected, un-swapped costs.""" - - def test_get_model_info_costs(self): - # Patch litellm.model_cost with the local backup so the test is not - # dependent on the remote fetch hitting a not-yet-merged main branch. - original = litellm.model_cost - try: - litellm.model_cost = _load_json(_backup_path()) - info = litellm.get_model_info(MODEL) - assert info["input_cost_per_token"] == EXPECTED_INPUT_COST - assert info["output_cost_per_token"] == EXPECTED_OUTPUT_COST - assert info["output_cost_per_token"] > info["input_cost_per_token"] - finally: - litellm.model_cost = original diff --git a/tests/test_litellm/test_cost_calculator.py b/tests/test_litellm/test_cost_calculator.py index cbbd5aa6eb6..7fedb43ab6e 100644 --- a/tests/test_litellm/test_cost_calculator.py +++ b/tests/test_litellm/test_cost_calculator.py @@ -1,8 +1,6 @@ - from typing import Final import pytest - from pydantic import BaseModel import litellm @@ -18,9 +16,7 @@ from litellm.litellm_core_utils.litellm_logging import Logging from litellm.llms.base_llm.ocr.transformation import OCRPage, OCRResponse, OCRUsageInfo from litellm.types.llms.base import CachedTokensDetails from litellm.types.llms.openai import OpenAIRealtimeStreamList -from litellm.types.rerank import RerankResponse from litellm.types.utils import ( - CacheCreationTokenDetails, ModelInfo, ModelResponse, PromptTokensDetailsWrapper, @@ -53,26 +49,6 @@ def test_cost_per_token_duplicate_openai_prefix_matches_model_cost(monkeypatch): assert prompt_usd + completion_usd > 0 -def test_cost_per_token_tiered_only_model_bills_at_tier_rate(monkeypatch): - """ - Regression: models that publish only tiered_pricing (no top-level per-token rates), - e.g. volcengine doubao-seed-2.0, must reach the generic tiered path instead of - recording zero spend. - """ - monkeypatch.setenv("LITELLM_LOCAL_MODEL_COST_MAP", "True") - monkeypatch.setattr(litellm, "model_cost", litellm.get_model_cost_map(url="")) - - prompt_usd, completion_usd = cost_per_token( - model="volcengine/doubao-seed-2-0-pro-260215", - prompt_tokens=40000, - completion_tokens=500, - custom_llm_provider="volcengine", - ) - - assert prompt_usd == pytest.approx(40000 * 7e-07) - assert completion_usd == pytest.approx(500 * 3.5e-06) - - def test_cost_per_token_non_string_model_does_not_hang(): """ The provider-prefix dedup loop must not spin forever when `model` is a @@ -129,27 +105,9 @@ def test_completion_cost_uses_response_model_for_dynamic_routing(_local_model_co assert cost > 0, "Cost should be calculated using response model" -def test_jina_rerank_bills_total_tokens_at_input_rate_only(_local_model_cost_map): - response: Final = RerankResponse( - id="rerank-1", - results=[{"index": 0, "relevance_score": 0.9}], - meta={"billed_units": {"total_tokens": 1000}}, - ) - - cost: Final = completion_cost( - completion_response=response, - model="jina_ai/jina-reranker-v2-base-multilingual", - call_type="rerank", - ) - - assert cost == pytest.approx(1000 * 5e-08) - - def test_cost_calculator_with_response_cost_in_additional_headers(): class MockResponse(BaseModel): - _hidden_params = { - "additional_headers": {"llm_provider-x-litellm-response-cost": 1000} - } + _hidden_params = {"additional_headers": {"llm_provider-x-litellm-response-cost": 1000}} result = response_cost_calculator( response_object=MockResponse(), @@ -164,147 +122,6 @@ def test_cost_calculator_with_response_cost_in_additional_headers(): assert result == 1000 -@pytest.mark.parametrize( - ("model", "expected_cost"), - [ - ("vertex_ai/lyria-002", 0.06), - ("vertex_ai/lyria-3-clip-preview", 0.04), - ("vertex_ai/lyria-3-pro-preview", 0.08), - ], -) -@pytest.mark.parametrize("runtime_state", ("complete", "missing", "routing_only", "custom_zero", "custom_price")) -@pytest.mark.parametrize("call_type", ("speech", "aspeech")) -def test_vertex_lyria_speech_cost( - model: str, - expected_cost: float, - _local_model_cost_map: None, - monkeypatch: pytest.MonkeyPatch, - runtime_state: str, - call_type: str, -) -> None: - model_info: Final = litellm.model_cost[model] - if runtime_state == "missing": - monkeypatch.delitem(litellm.model_cost, model) - elif runtime_state == "routing_only": - monkeypatch.setitem( - litellm.model_cost, - model, - {key: value for key, value in model_info.items() if key != "output_cost_per_image"}, - ) - elif runtime_state in ("custom_zero", "custom_price"): - multiplier: Final = 0 if runtime_state == "custom_zero" else 2 - monkeypatch.setitem( - litellm.model_cost, - model, - {**model_info, "output_cost_per_image": model_info["output_cost_per_image"] * multiplier}, - ) - - cost: Final = completion_cost( - model=model, - prompt="A bright synth track", - call_type=call_type, - ) - - expected: Final = 0 if runtime_state == "custom_zero" else expected_cost * (2 if runtime_state == "custom_price" else 1) - assert cost == pytest.approx(expected) - - -def test_baseten_model_api_pricing_entries(_local_model_cost_map): - - expected_pricing = { - "baseten/nvidia/Nemotron-120B-A12B": (3e-07, 7.5e-07), - "baseten/MiniMaxAI/MiniMax-M2.5": (3e-07, 1.2e-06), - "baseten/zai-org/GLM-5": (9.5e-07, 3.15e-06), - "baseten/zai-org/GLM-4.7": (6e-07, 2.2e-06), - "baseten/zai-org/GLM-4.6": (6e-07, 2.2e-06), - "baseten/moonshotai/Kimi-K2.5": (6e-07, 3e-06), - "baseten/moonshotai/Kimi-K2-Thinking": (6e-07, 2.5e-06), - "baseten/moonshotai/Kimi-K2-Instruct-0905": (6e-07, 2.5e-06), - "baseten/openai/gpt-oss-120b": (1e-07, 5e-07), - "baseten/deepseek-ai/DeepSeek-V3.1": (5e-07, 1.5e-06), - "baseten/deepseek-ai/DeepSeek-V3-0324": (7.7e-07, 7.7e-07), - } - - for model_name, (input_cost, output_cost) in expected_pricing.items(): - model_info = litellm.model_cost.get(model_name) - assert model_info is not None, f"Missing model pricing entry: {model_name}" - assert model_info["litellm_provider"] == "baseten" - assert model_info["input_cost_per_token"] == input_cost - assert model_info["output_cost_per_token"] == output_cost - - -def test_wandb_model_api_pricing_entries(_local_model_cost_map): - - expected_pricing = { - "wandb/moonshotai/Kimi-K2.5": (6e-07, 3e-06), - "wandb/MiniMaxAI/MiniMax-M2.5": (3e-07, 1.2e-06), - "wandb/Qwen/Qwen3-235B-A22B-Instruct-2507": (1e-07, 1e-07), - "wandb/Qwen/Qwen3-235B-A22B-Thinking-2507": (1e-07, 1e-07), - "wandb/deepseek-ai/DeepSeek-R1-0528": (1.35e-06, 5.4e-06), - "wandb/deepseek-ai/DeepSeek-V3-0324": (1.14e-06, 2.75e-06), - "wandb/meta-llama/Llama-4-Scout-17B-16E-Instruct": (1.7e-07, 6.6e-07), - } - - for model_name, (input_cost, output_cost) in expected_pricing.items(): - model_info = litellm.model_cost.get(model_name) - assert model_info is not None, f"Missing model pricing entry: {model_name}" - assert model_info["litellm_provider"] == "wandb" - assert model_info["input_cost_per_token"] == input_cost - assert model_info["output_cost_per_token"] == output_cost - - -def test_openrouter_qwen36_plus_model_info(_local_model_cost_map): - - model_info = litellm.model_cost.get("openrouter/qwen/qwen3.6-plus") - - assert model_info is not None - assert model_info["litellm_provider"] == "openrouter" - assert model_info["mode"] == "chat" - assert model_info["max_input_tokens"] == 1000000 - assert model_info["max_output_tokens"] == 65536 - assert model_info["input_cost_per_token"] == 3.25e-07 - assert model_info["output_cost_per_token"] == 1.95e-06 - assert model_info["supports_function_calling"] is True - assert model_info["supports_tool_choice"] is True - assert model_info["supports_reasoning"] is True - assert model_info["supports_vision"] is True - - -@pytest.mark.parametrize( - "model", - [ - "github_copilot/mai-code-1-flash", - "github_copilot/mai-code-1-flash-internal", - ], -) -def test_github_copilot_mai_code_1_flash_pricing(_local_model_cost_map, model): - - model_info = litellm.model_cost.get(model) - - assert model_info is not None, f"Missing model pricing entry: {model}" - assert model_info["litellm_provider"] == "github_copilot" - assert model_info["mode"] == "chat" - assert model_info["input_cost_per_token"] == 7.5e-07 - assert model_info["cache_read_input_token_cost"] == 7.5e-08 - assert model_info["output_cost_per_token"] == 4.5e-06 - assert model_info["supported_endpoints"] == ["/v1/chat/completions"] - - prompt_usd, completion_usd = cost_per_token( - model=model, - prompt_tokens=1000, - completion_tokens=500, - custom_llm_provider="github_copilot", - usage_object=Usage( - prompt_tokens=1000, - completion_tokens=500, - prompt_tokens_details=PromptTokensDetailsWrapper(cached_tokens=200), - ), - ) - - assert prompt_usd == pytest.approx((800 * 7.5e-07) + (200 * 7.5e-08)) - assert completion_usd == pytest.approx(500 * 4.5e-06) - - def test_cost_calculator_with_usage(_local_model_cost_map, monkeypatch): usage = Usage( @@ -332,13 +149,12 @@ def test_cost_calculator_with_usage(_local_model_cost_map, monkeypatch): # Step 1: Test a model where input_cost_per_image_token is not set. # In this case the calculation should use input_cost_per_token as fallback. - assert ( - model_info.get("input_cost_per_image_token") is None - ), "Test case expects that input_cost_per_image_token is not set" + assert model_info.get("input_cost_per_image_token") is None, ( + "Test case expects that input_cost_per_image_token is not set" + ) expected_cost = ( - usage.prompt_tokens_details.audio_tokens - * model_info["input_cost_per_audio_token"] + usage.prompt_tokens_details.audio_tokens * model_info["input_cost_per_audio_token"] + usage.prompt_tokens_details.text_tokens * model_info["input_cost_per_token"] + usage.prompt_tokens_details.image_tokens * model_info["input_cost_per_token"] + usage.completion_tokens * model_info["output_cost_per_token"] @@ -373,12 +189,9 @@ def test_cost_calculator_with_usage(_local_model_cost_map, monkeypatch): ) expected_cost = ( - usage.prompt_tokens_details.audio_tokens - * temp_model_info_object["input_cost_per_audio_token"] - + usage.prompt_tokens_details.text_tokens - * temp_model_info_object["input_cost_per_token"] - + usage.prompt_tokens_details.image_tokens - * temp_model_info_object["input_cost_per_image_token"] + usage.prompt_tokens_details.audio_tokens * temp_model_info_object["input_cost_per_audio_token"] + + usage.prompt_tokens_details.text_tokens * temp_model_info_object["input_cost_per_token"] + + usage.prompt_tokens_details.image_tokens * temp_model_info_object["input_cost_per_image_token"] + usage.completion_tokens * temp_model_info_object["output_cost_per_token"] ) @@ -388,14 +201,11 @@ def test_cost_calculator_with_usage(_local_model_cost_map, monkeypatch): def test_transcription_cost_uses_token_pricing(_local_model_cost_map): from litellm import completion_cost - usage = Usage( prompt_tokens=14, completion_tokens=45, total_tokens=59, - prompt_tokens_details=PromptTokensDetailsWrapper( - text_tokens=0, audio_tokens=14 - ), + prompt_tokens_details=PromptTokensDetailsWrapper(text_tokens=0, audio_tokens=14), ) response = TranscriptionResponse(text="demo text") response.usage = usage @@ -439,7 +249,6 @@ def test_transcription_token_pricing_is_provider_aware(_local_model_cost_map): def test_transcription_cost_falls_back_to_duration(_local_model_cost_map): from litellm import completion_cost - response = TranscriptionResponse(text="demo text") response.duration = 10.0 @@ -460,7 +269,6 @@ def test_vertex_chirp_3_transcription_cost_from_duration(_local_model_cost_map): every transcription priced to $0.00 instead of using input_cost_per_second.""" from litellm import completion_cost - response = TranscriptionResponse(text="demo text") response.duration = 18.0 @@ -484,9 +292,7 @@ def test_handle_realtime_stream_cost_calculation(): {"type": "session.created", "session": {"model": "gpt-3.5-turbo"}}, { "type": "response.done", - "response": { - "usage": {"input_tokens": 100, "output_tokens": 50, "total_tokens": 150} - }, + "response": {"usage": {"input_tokens": 100, "output_tokens": 50, "total_tokens": 150}}, }, { "type": "response.done", @@ -517,9 +323,7 @@ def test_handle_realtime_stream_cost_calculation(): expected_cost = (300 * 0.0015 / 1000) + ( # input tokens (100 + 200) 150 * 0.002 / 1000 ) # output tokens (50 + 100) - assert ( - abs(cost - expected_cost) <= 0.00075 - ) # Allow small floating point differences + assert abs(cost - expected_cost) <= 0.00075 # Allow small floating point differences # Test with different model name in session results[0]["session"]["model"] = "gpt-4" @@ -599,14 +403,7 @@ def test_handle_realtime_stream_cost_calculation_stores_cost_breakdown(): assert logging_obj.cost_breakdown is not None assert logging_obj.cost_breakdown["input_cost"] > 0 assert logging_obj.cost_breakdown["output_cost"] > 0 - assert ( - abs( - logging_obj.cost_breakdown["input_cost"] - + logging_obj.cost_breakdown["output_cost"] - - total_cost - ) - < 1e-9 - ) + assert abs(logging_obj.cost_breakdown["input_cost"] + logging_obj.cost_breakdown["output_cost"] - total_cost) < 1e-9 assert abs(logging_obj.cost_breakdown["total_cost"] - total_cost) < 1e-9 @@ -680,9 +477,7 @@ def test_realtime_logging_object_allows_null_transcript_in_conversation_item_add }, ] - usage = RealtimeAPITokenUsageProcessor.collect_and_combine_usage_from_realtime_stream_results( - results=results - ) + usage = RealtimeAPITokenUsageProcessor.collect_and_combine_usage_from_realtime_stream_results(results=results) logging_result = RealtimeAPITokenUsageProcessor.create_logging_realtime_object( usage=usage, results=results, @@ -732,9 +527,7 @@ def test_realtime_logging_object_does_not_validate_unknown_event_types(): }, ] - usage = RealtimeAPITokenUsageProcessor.collect_and_combine_usage_from_realtime_stream_results( - results=results - ) + usage = RealtimeAPITokenUsageProcessor.collect_and_combine_usage_from_realtime_stream_results(results=results) # On unfixed code this raises pydantic ValidationError instead of returning. logging_result = RealtimeAPITokenUsageProcessor.create_logging_realtime_object( usage=usage, @@ -746,8 +539,7 @@ def test_realtime_logging_object_does_not_validate_unknown_event_types(): unknown_types = { r["type"] for r in logging_result.results - if r["type"] - in ("rate_limits.updated", "response.function_call_arguments.delta") + if r["type"] in ("rate_limits.updated", "response.function_call_arguments.delta") } assert unknown_types == { "rate_limits.updated", @@ -780,9 +572,7 @@ def test_realtime_transcription_duration_cost(monkeypatch): "type": "session.created", "session": { "type": "transcription", - "audio": { - "input": {"transcription": {"model": "gpt-realtime-whisper"}} - }, + "audio": {"input": {"transcription": {"model": "gpt-realtime-whisper"}}}, }, }, { @@ -797,9 +587,7 @@ def test_realtime_transcription_duration_cost(monkeypatch): }, ] - combined = RealtimeAPITokenUsageProcessor.collect_and_combine_usage_from_realtime_stream_results( - results=results - ) + combined = RealtimeAPITokenUsageProcessor.collect_and_combine_usage_from_realtime_stream_results(results=results) logging_obj = Logging( model="gpt-realtime-whisper", messages=[], @@ -892,9 +680,7 @@ def test_realtime_transcription_token_billed_fallback(monkeypatch): # gpt-4o-transcribe: input_cost_per_audio_token = 2.5e-06, input_cost_per_token = 2.5e-06, # output_cost_per_token = 1e-05 - model_info = litellm.get_model_info( - model="gpt-4o-transcribe", custom_llm_provider="openai" - ) + model_info = litellm.get_model_info(model="gpt-4o-transcribe", custom_llm_provider="openai") usage = { "type": "tokens", "input_tokens": 40, @@ -975,10 +761,7 @@ def test_get_transcription_model_falls_back_to_session_model(monkeypatch): mock_response=True, ) - assert ( - result._hidden_params["response_cost"] - > result_2._hidden_params["response_cost"] - ) + assert result._hidden_params["response_cost"] > result_2._hidden_params["response_cost"] model_info = router.get_deployment_model_info( model_id="my-unique-model-id", model_name="anthropic/claude-sonnet-4-5-20250929" @@ -1141,9 +924,7 @@ def test_tiered_pricing_only_deployment_selects_router_model_id(): assert entry.get("input_cost_per_token") is None assert entry.get("tiered_pricing") is not None # The stripped shared alias must not carry tiered pricing. - assert ( - litellm.model_cost["dashscope/qwen-tier-only-test"].get("tiered_pricing") is None - ) + assert litellm.model_cost["dashscope/qwen-tier-only-test"].get("tiered_pricing") is None selected = _select_model_name_for_cost_calc( model="dashscope/qwen-tier-only-test", @@ -1223,9 +1004,7 @@ def test_azure_realtime_cost_calculator(_local_model_cost_map): combined_usage_object=Usage( prompt_tokens=100, completion_tokens=100, - prompt_tokens_details=PromptTokensDetailsWrapper( - text_tokens=10, audio_tokens=90 - ), + prompt_tokens_details=PromptTokensDetailsWrapper(text_tokens=10, audio_tokens=90), ), custom_llm_provider="azure", litellm_model_name="my-custom-azure-deployment", @@ -1244,7 +1023,6 @@ def test_azure_audio_output_cost_calculation(_local_model_cost_map): """ from litellm.types.utils import Choices, CompletionTokensDetailsWrapper, Message - # Scenario from issue #19764: # Input: 17 text tokens, 0 audio tokens # Output: 110 text tokens, 482 audio tokens @@ -1300,14 +1078,10 @@ def test_azure_audio_output_cost_calculation(_local_model_cost_map): wrong_total_cost = expected_input_cost + wrong_output_cost # Verify audio tokens are NOT charged at text rate (the bug) - assert ( - abs(cost - wrong_total_cost) > 0.001 - ), "Bug: Audio tokens are being charged at text token rate" + assert abs(cost - wrong_total_cost) > 0.001, "Bug: Audio tokens are being charged at text token rate" # Verify cost matches - assert ( - abs(cost - expected_total_cost) < 0.0000001 - ), f"Expected cost {expected_total_cost}, got {cost}" + assert abs(cost - expected_total_cost) < 0.0000001, f"Expected cost {expected_total_cost}, got {cost}" def test_default_image_cost_calculator(monkeypatch): @@ -1321,9 +1095,7 @@ def test_default_image_cost_calculator(monkeypatch): monkeypatch.setattr( litellm, "model_cost", - { - "azure/bf9001cd7209f5734ecb4ab937a5a0e2ba5f119708bd68f184db362930f9dc7b": temp_object - }, + {"azure/bf9001cd7209f5734ecb4ab937a5a0e2ba5f119708bd68f184db362930f9dc7b": temp_object}, ) args = { @@ -1539,9 +1311,7 @@ def test_gemini_25_implicit_caching_cost(): expected_cost = 0.00068708 # Allow for small floating point differences - assert ( - abs(result - expected_cost) < 1e-8 - ), f"Expected cost {expected_cost}, but got {result}" + assert abs(result - expected_cost) < 1e-8, f"Expected cost {expected_cost}, but got {result}" print(f"✓ Gemini 2.5 implicit caching cost calculation is correct: ${result:.8f}") @@ -1612,9 +1382,7 @@ def test_log_context_cost_calculation(): # Get model info to understand the pricing from litellm import get_model_info - model_info = get_model_info( - model="claude-4-sonnet-20250514", custom_llm_provider="anthropic" - ) + model_info = get_model_info(model="claude-4-sonnet-20250514", custom_llm_provider="anthropic") # Calculate expected cost based on actual model pricing input_cost_per_token = model_info.get("input_cost_per_token", 0) @@ -1622,12 +1390,8 @@ def test_log_context_cost_calculation(): cache_creation_cost_per_token = model_info.get("cache_creation_input_token_cost", 0) # Check if tiered pricing is applied - input_cost_above_200k = model_info.get( - "input_cost_per_token_above_200k_tokens", input_cost_per_token - ) - output_cost_above_200k = model_info.get( - "output_cost_per_token_above_200k_tokens", output_cost_per_token - ) + input_cost_above_200k = model_info.get("input_cost_per_token_above_200k_tokens", input_cost_per_token) + output_cost_above_200k = model_info.get("output_cost_per_token_above_200k_tokens", output_cost_per_token) cache_creation_above_200k = model_info.get( "cache_creation_input_token_cost_above_200k_tokens", cache_creation_cost_per_token, @@ -1635,31 +1399,23 @@ def test_log_context_cost_calculation(): print(f"DEBUG: Base input cost per token: ${input_cost_per_token:.2e}") print(f"DEBUG: Base output cost per token: ${output_cost_per_token:.2e}") - print( - f"DEBUG: Base cache creation cost per token: ${cache_creation_cost_per_token:.2e}" - ) + print(f"DEBUG: Base cache creation cost per token: ${cache_creation_cost_per_token:.2e}") # Handle tiered pricing - if not available, use base pricing if input_cost_above_200k is not None: - print( - f"DEBUG: Tiered input cost per token (>200k): ${input_cost_above_200k:.2e}" - ) + print(f"DEBUG: Tiered input cost per token (>200k): ${input_cost_above_200k:.2e}") else: print("DEBUG: No tiered input pricing available, using base pricing") input_cost_above_200k = input_cost_per_token if output_cost_above_200k is not None: - print( - f"DEBUG: Tiered output cost per token (>200k): ${output_cost_above_200k:.2e}" - ) + print(f"DEBUG: Tiered output cost per token (>200k): ${output_cost_above_200k:.2e}") else: print("DEBUG: No tiered output pricing available, using base pricing") output_cost_above_200k = output_cost_per_token if cache_creation_above_200k is not None: - print( - f"DEBUG: Tiered cache creation cost per token (>200k): ${cache_creation_above_200k:.2e}" - ) + print(f"DEBUG: Tiered cache creation cost per token (>200k): ${cache_creation_above_200k:.2e}") else: print("DEBUG: No tiered cache creation pricing available, using base pricing") cache_creation_above_200k = cache_creation_cost_per_token @@ -1673,13 +1429,9 @@ def test_log_context_cost_calculation(): print(f"DEBUG: Expected total: ${expected_total:.6f}") # Allow for small floating point differences - assert ( - abs(result - expected_total) < 1e-6 - ), f"Expected cost ${expected_total:.6f}, but got ${result:.6f}" + assert abs(result - expected_total) < 1e-6, f"Expected cost ${expected_total:.6f}, but got ${result:.6f}" - print( - f"✓ Log context cost calculation with tiered pricing is correct: ${result:.6f}" - ) + print(f"✓ Log context cost calculation with tiered pricing is correct: ${result:.6f}") print(f" - Input tokens (300k): ${expected_input_cost:.6f}") print(f" - Output tokens (50k): ${expected_output_cost:.6f}") print(f" - Cache creation (1k): ${expected_cache_cost:.6f}") @@ -1738,8 +1490,7 @@ def test_gemini_25_explicit_caching_cost_direct_usage(): expected_actual_cost = ( model_info["input_cost_per_token"] * usage.prompt_tokens_details.text_tokens - + model_info["cache_read_input_token_cost"] - * usage.prompt_tokens_details.cached_tokens + + model_info["cache_read_input_token_cost"] * usage.prompt_tokens_details.cached_tokens + model_info["output_cost_per_token"] * usage.completion_tokens ) @@ -1763,7 +1514,6 @@ def test_azure_ai_cache_cost_calculation(_local_model_cost_map): from litellm.litellm_core_utils.llm_cost_calc.utils import generic_cost_per_token from litellm.types.utils import PromptTokensDetailsWrapper, Usage - # Register a custom azure_ai model with cache pricing test_model_id = "test-azure-ai-claude-model" litellm.register_model( @@ -1812,12 +1562,12 @@ def test_azure_ai_cache_cost_calculation(_local_model_cost_map): print(f"Output cost: {output_cost}, Expected: {expected_output_cost}") print(f"Total cost: {total_cost}") - assert ( - abs(input_cost - expected_input_cost) < 1e-10 - ), f"Input cost mismatch: got {input_cost}, expected {expected_input_cost}" - assert ( - abs(output_cost - expected_output_cost) < 1e-10 - ), f"Output cost mismatch: got {output_cost}, expected {expected_output_cost}" + assert abs(input_cost - expected_input_cost) < 1e-10, ( + f"Input cost mismatch: got {input_cost}, expected {expected_input_cost}" + ) + assert abs(output_cost - expected_output_cost) < 1e-10, ( + f"Output cost mismatch: got {output_cost}, expected {expected_output_cost}" + ) AZURE_GPT_5_6_MAP_KEYS = ( @@ -1886,6 +1636,7 @@ def test_azure_gpt_5_6_rates_match_azure_price_page(_local_model_cost_map, model for key in token_cost_keys: assert entry[key] == pytest.approx(global_entry[key] * 1.1), key + def test_vertex_regional_deployment_costs_uplift_over_global(monkeypatch): """ Regression for https://github.com/BerriAI/litellm/issues/34393: two Vertex @@ -1968,7 +1719,6 @@ def test_cost_discount_vertex_ai(monkeypatch): from litellm import completion_cost from litellm.types.utils import Usage - # Create mock response (use a model that exists in model_prices_and_context_window.json) response = ModelResponse( id="test-id", @@ -1997,7 +1747,6 @@ def test_cost_discount_vertex_ai(monkeypatch): custom_llm_provider="vertex_ai", ) - # Verify discount is applied (5% off means 95% of original cost) expected_cost = cost_without_discount * 0.95 assert cost_with_discount == pytest.approx(expected_cost, rel=1e-9) @@ -2015,7 +1764,6 @@ def test_cost_discount_not_applied_to_other_providers(monkeypatch): from litellm import completion_cost from litellm.types.utils import Usage - # Create mock response for OpenAI response = ModelResponse( id="test-id", @@ -2044,7 +1792,6 @@ def test_cost_discount_not_applied_to_other_providers(monkeypatch): custom_llm_provider="openai", ) - # Costs should be the same (no discount applied to OpenAI) assert cost_with_selective_discount == cost_without_discount @@ -2060,7 +1807,6 @@ def test_cost_margin_percentage(monkeypatch): from litellm import completion_cost from litellm.types.utils import Usage - # Create mock response response = ModelResponse( id="test-id", @@ -2089,7 +1835,6 @@ def test_cost_margin_percentage(monkeypatch): custom_llm_provider="openai", ) - # Verify margin is applied (10% margin means 110% of original cost) expected_cost = cost_without_margin * 1.10 assert cost_with_margin == pytest.approx(expected_cost, rel=1e-9) @@ -2107,7 +1852,6 @@ def test_cost_margin_fixed_amount(monkeypatch): from litellm import completion_cost from litellm.types.utils import Usage - # Create mock response response = ModelResponse( id="test-id", @@ -2136,7 +1880,6 @@ def test_cost_margin_fixed_amount(monkeypatch): custom_llm_provider="openai", ) - # Verify fixed margin is applied expected_cost = cost_without_margin + 0.001 assert cost_with_margin == pytest.approx(expected_cost, rel=1e-9) @@ -2154,7 +1897,6 @@ def test_cost_margin_combined(monkeypatch): from litellm import completion_cost from litellm.types.utils import Usage - # Create mock response response = ModelResponse( id="test-id", @@ -2174,9 +1916,7 @@ def test_cost_margin_combined(monkeypatch): ) # Set 8% margin + $0.0005 fixed for openai - monkeypatch.setattr(litellm, "cost_margin_config", { - "openai": {"percentage": 0.08, "fixed_amount": 0.0005} - }) + monkeypatch.setattr(litellm, "cost_margin_config", {"openai": {"percentage": 0.08, "fixed_amount": 0.0005}}) # Calculate cost with margin cost_with_margin = completion_cost( @@ -2185,7 +1925,6 @@ def test_cost_margin_combined(monkeypatch): custom_llm_provider="openai", ) - # Verify combined margin is applied expected_cost = cost_without_margin * 1.08 + 0.0005 assert cost_with_margin == pytest.approx(expected_cost, rel=1e-9) @@ -2203,7 +1942,6 @@ def test_cost_margin_global(monkeypatch): from litellm import completion_cost from litellm.types.utils import Usage - # Create mock response response = ModelResponse( id="test-id", @@ -2232,7 +1970,6 @@ def test_cost_margin_global(monkeypatch): custom_llm_provider="openai", ) - # Verify global margin is applied expected_cost = cost_without_margin * 1.05 assert cost_with_global_margin == pytest.approx(expected_cost, rel=1e-9) @@ -2250,7 +1987,6 @@ def test_cost_margin_provider_overrides_global(monkeypatch): from litellm import completion_cost from litellm.types.utils import Usage - # Create mock response response = ModelResponse( id="test-id", @@ -2279,16 +2015,13 @@ def test_cost_margin_provider_overrides_global(monkeypatch): custom_llm_provider="openai", ) - # Verify provider-specific margin is used (not global) expected_cost = cost_without_margin * 1.10 # 10% from provider, not 5% from global assert cost_with_provider_margin == pytest.approx(expected_cost, rel=1e-9) print("✓ Cost margin provider override test passed:") print(f" - Original cost: ${cost_without_margin:.6f}") - print( - f" - Cost with provider margin (10%, overrides 5% global): ${cost_with_provider_margin:.6f}" - ) + print(f" - Cost with provider margin (10%, overrides 5% global): ${cost_with_provider_margin:.6f}") print(f" - Margin added: ${cost_with_provider_margin - cost_without_margin:.6f}") @@ -2299,7 +2032,6 @@ def test_cost_margin_with_discount(monkeypatch): from litellm import completion_cost from litellm.types.utils import Usage - # Create mock response response = ModelResponse( id="test-id", @@ -2330,7 +2062,6 @@ def test_cost_margin_with_discount(monkeypatch): custom_llm_provider="openai", ) - # Verify: discount applied first, then margin # Base cost -> discount: base * 0.95 -> margin: (base * 0.95) * 1.10 expected_cost = base_cost * 0.95 * 1.10 @@ -2368,9 +2099,7 @@ def test_azure_image_generation_cost_calculator(): size=None, usage=ImageUsage( input_tokens=0, - input_tokens_details=ImageUsageInputTokensDetails( - image_tokens=0, text_tokens=0 - ), + input_tokens_details=ImageUsageInputTokensDetails(image_tokens=0, text_tokens=0), output_tokens=0, total_tokens=0, ), @@ -2400,7 +2129,6 @@ def test_completion_cost_extracts_service_tier_from_response(_local_model_cost_m """Test that completion_cost extracts service_tier from completion_response object.""" from litellm import completion_cost - # Test with gpt-5-nano which has flex pricing model = "gpt-5-nano" @@ -2441,23 +2169,18 @@ def test_completion_cost_extracts_service_tier_from_response(_local_model_cost_m assert flex_cost < standard_cost, "Flex cost should be less than standard cost" flex_ratio = flex_cost / standard_cost - assert ( - 0.45 <= flex_ratio <= 0.55 - ), f"Flex pricing should be ~50% of standard, got {flex_ratio:.2f}" + assert 0.45 <= flex_ratio <= 0.55, f"Flex pricing should be ~50% of standard, got {flex_ratio:.2f}" def test_completion_cost_extracts_service_tier_from_usage(_local_model_cost_map): """Test that completion_cost extracts service_tier from usage object.""" from litellm import completion_cost - # Test with gpt-5-nano which has flex pricing model = "gpt-5-nano" # Create usage object with service_tier - usage_with_service_tier = Usage( - prompt_tokens=1000, completion_tokens=500, total_tokens=1500 - ) + usage_with_service_tier = Usage(prompt_tokens=1000, completion_tokens=500, total_tokens=1500) # Set service_tier as an attribute on the usage object setattr(usage_with_service_tier, "service_tier", "flex") @@ -2475,9 +2198,7 @@ def test_completion_cost_extracts_service_tier_from_usage(_local_model_cost_map) ) # Create usage object without service_tier - usage_without_service_tier = Usage( - prompt_tokens=1000, completion_tokens=500, total_tokens=1500 - ) + usage_without_service_tier = Usage(prompt_tokens=1000, completion_tokens=500, total_tokens=1500) # Create ModelResponse with usage without service_tier response_standard = ModelResponse( @@ -2498,16 +2219,13 @@ def test_completion_cost_extracts_service_tier_from_usage(_local_model_cost_map) assert flex_cost < standard_cost, "Flex cost should be less than standard cost" flex_ratio = flex_cost / standard_cost - assert ( - 0.45 <= flex_ratio <= 0.55 - ), f"Flex pricing should be ~50% of standard, got {flex_ratio:.2f}" + assert 0.45 <= flex_ratio <= 0.55, f"Flex pricing should be ~50% of standard, got {flex_ratio:.2f}" def test_completion_cost_service_tier_priority(_local_model_cost_map): """Test that service_tier extraction follows priority: optional_params > completion_response > usage.""" from litellm import completion_cost - # Test with gpt-5-nano which has flex pricing model = "gpt-5-nano" @@ -2556,16 +2274,13 @@ def test_completion_cost_service_tier_priority(_local_model_cost_map): assert cost_from_usage > 0, "Cost from usage should be greater than 0" # Costs should be similar (all using flex) - assert ( - abs(cost_from_params - cost_from_usage) < 1e-6 - ), "Costs from params and usage should be similar (both flex)" + assert abs(cost_from_params - cost_from_usage) < 1e-6, "Costs from params and usage should be similar (both flex)" def test_completion_cost_service_tier_for_bedrock(_local_model_cost_map): """Test that Bedrock cost calculation applies service_tier-specific pricing.""" from litellm import completion_cost - model = "bedrock/us-east-1/test-bedrock-service-tier-cost-model" litellm.register_model( model_cost={ @@ -2621,7 +2336,6 @@ def test_completion_cost_service_tier_for_anthropic(_local_model_cost_map): from litellm import completion_cost from litellm.llms.anthropic.chat.transformation import AnthropicConfig - model = "claude-test-service-tier-cost-model" litellm.register_model( model_cost={ @@ -2674,7 +2388,6 @@ def test_completion_cost_anthropic_auto_tier_uses_served_priority_rate(_local_mo from litellm import completion_cost from litellm.llms.anthropic.chat.transformation import AnthropicConfig - model = "claude-test-auto-tier-cost-model" litellm.register_model( model_cost={ @@ -2768,7 +2481,6 @@ def test_completion_cost_non_string_service_tier_defers_to_served_tier(_local_mo from litellm import completion_cost from litellm.llms.anthropic.chat.transformation import AnthropicConfig - model = "claude-test-non-string-tier-cost-model" litellm.register_model( model_cost={ @@ -2818,7 +2530,6 @@ def test_completion_cost_non_string_response_service_tier_defers_to_served_tier( from litellm import completion_cost from litellm.llms.anthropic.chat.transformation import AnthropicConfig - model = "claude-test-response-non-string-tier-cost-model" litellm.register_model( model_cost={ @@ -2841,9 +2552,7 @@ def test_completion_cost_non_string_response_service_tier_defers_to_served_tier( }, reasoning_content=None, ) - response = ModelResponse( - usage=usage, model=model, service_tier={"name": "priority"} - ) + response = ModelResponse(usage=usage, model=model, service_tier={"name": "priority"}) cost = completion_cost( completion_response=response, @@ -2866,7 +2575,6 @@ def test_completion_cost_non_string_usage_service_tier_prices_standard(_local_mo """ from litellm import completion_cost - model = "claude-test-usage-non-string-tier-cost-model" litellm.register_model( model_cost={ @@ -2913,7 +2621,6 @@ def test_anthropic_cost_per_token_prices_cache_at_served_tier_with_multiplier(_l ) from litellm.types.utils import PromptTokensDetailsWrapper, Usage - model = "claude-test-priority-cache-fast-model" litellm.register_model( model_cost={ @@ -2939,9 +2646,7 @@ def test_anthropic_cost_per_token_prices_cache_at_served_tier_with_multiplier(_l ) usage.speed = "fast" - prompt_cost, completion_cost = anthropic_cost_per_token( - model=model, usage=usage, service_tier="priority" - ) + prompt_cost, completion_cost = anthropic_cost_per_token(model=model, usage=usage, service_tier="priority") expected_prompt = ((1000 - 200) * 6e-6 + 200 * 0.6e-6) * 2 expected_completion = 500 * 30e-6 * 2 @@ -3071,9 +2776,7 @@ def test_anthropic_fast_multiplier_only_on_models_with_fast_mode(_local_model_co "model", ["claude-sonnet-4-6", "claude-mythos-5", "claude-mythos-preview"], ) -def test_anthropic_us_data_residency_uplift_on_claude_4_6_and_later_models( - _local_model_cost_map, monkeypatch, model -): +def test_anthropic_us_data_residency_uplift_on_claude_4_6_and_later_models(_local_model_cost_map, monkeypatch, model): """ Anthropic bills every Claude 4.6+ model served with ``inference_geo="us"`` at 1.1x, and echoes that geo back in the response usage, so each of these real @@ -3138,29 +2841,27 @@ def test_gemini_cache_tokens_details_no_negative_values(): usage = VertexGeminiConfig._calculate_usage(completion_response) # Text tokens should be non-cached text only: 9402 - 9393 = 9 - assert ( - usage.prompt_tokens_details.text_tokens == 9 - ), f"Expected text_tokens=9, got {usage.prompt_tokens_details.text_tokens}" + assert usage.prompt_tokens_details.text_tokens == 9, ( + f"Expected text_tokens=9, got {usage.prompt_tokens_details.text_tokens}" + ) # Image tokens should be non-cached image only: 258 - 258 = 0 - assert ( - usage.prompt_tokens_details.image_tokens == 0 - ), f"Expected image_tokens=0, got {usage.prompt_tokens_details.image_tokens}" + assert usage.prompt_tokens_details.image_tokens == 0, ( + f"Expected image_tokens=0, got {usage.prompt_tokens_details.image_tokens}" + ) # Total cached should match - assert ( - usage.prompt_tokens_details.cached_tokens == 9651 - ), f"Expected cached_tokens=9651, got {usage.prompt_tokens_details.cached_tokens}" + assert usage.prompt_tokens_details.cached_tokens == 9651, ( + f"Expected cached_tokens=9651, got {usage.prompt_tokens_details.cached_tokens}" + ) # MOST IMPORTANT: text_tokens should NEVER be negative - assert ( - usage.prompt_tokens_details.text_tokens >= 0 - ), f"BUG: text_tokens is negative ({usage.prompt_tokens_details.text_tokens})! This was the issue in #18750" - - print( - "✅ Issue #18750 fix verified: text_tokens is correctly calculated and non-negative" + assert usage.prompt_tokens_details.text_tokens >= 0, ( + f"BUG: text_tokens is negative ({usage.prompt_tokens_details.text_tokens})! This was the issue in #18750" ) + print("✅ Issue #18750 fix verified: text_tokens is correctly calculated and non-negative") + def test_gemini_without_cache_tokens_details(): """ @@ -3227,18 +2928,18 @@ def test_gemini_implicit_caching_cost_calculation(): usage = VertexGeminiConfig._calculate_usage(completion_response) # Verify parsing - assert ( - usage.cache_read_input_tokens == 8000 - ), f"cache_read_input_tokens should be 8000, got {usage.cache_read_input_tokens}" - assert ( - usage.prompt_tokens_details.cached_tokens == 8000 - ), f"cached_tokens should be 8000, got {usage.prompt_tokens_details.cached_tokens}" + assert usage.cache_read_input_tokens == 8000, ( + f"cache_read_input_tokens should be 8000, got {usage.cache_read_input_tokens}" + ) + assert usage.prompt_tokens_details.cached_tokens == 8000, ( + f"cached_tokens should be 8000, got {usage.prompt_tokens_details.cached_tokens}" + ) # CRITICAL: text_tokens should be (10000 - 8000) = 2000, NOT 10000 # This is the fix for issue #16341 - assert ( - usage.prompt_tokens_details.text_tokens == 2000 - ), f"text_tokens should be 2000 (10000 - 8000), got {usage.prompt_tokens_details.text_tokens}" + assert usage.prompt_tokens_details.text_tokens == 2000, ( + f"text_tokens should be 2000 (10000 - 8000), got {usage.prompt_tokens_details.text_tokens}" + ) # Verify cost calculation uses cached token pricing response = ModelResponse( @@ -3276,9 +2977,7 @@ def test_gemini_implicit_caching_cost_calculation(): f"Cached tokens may not be using reduced pricing." ) - print( - "✅ Issue #16341 fix verified: Gemini implicit caching cost calculated correctly" - ) + print("✅ Issue #16341 fix verified: Gemini implicit caching cost calculated correctly") def test_additional_costs_only_for_azure_ai(_local_model_cost_map): @@ -3292,7 +2991,6 @@ def test_additional_costs_only_for_azure_ai(_local_model_cost_map): """ from litellm.cost_calculator import _get_additional_costs - # Non-azure_ai providers should return None result = _get_additional_costs( model="gpt-4o", @@ -3319,45 +3017,6 @@ def test_additional_costs_only_for_azure_ai(_local_model_cost_map): assert result is None, "Vertex AI should have no additional costs" -def test_openrouter_gemini_3_1_flash_lite_preview_pricing(_local_model_cost_map): - """ - Test that openrouter/google/gemini-3.1-flash-lite-preview has a pricing entry. - - Regression test for https://github.com/BerriAI/litellm/issues/25604 - - The model exists and is callable via OpenRouter, but was missing from - model_prices_and_context_window.json when other Gemini 3.x variants were present. - This caused ValueError: This model isn't mapped yet during router pre-call checks. - """ - - model_name = "openrouter/google/gemini-3.1-flash-lite-preview" - model_info = litellm.model_cost.get(model_name) - - assert model_info is not None, f"Missing model pricing entry: {model_name}" - assert model_info["litellm_provider"] == "openrouter" - assert model_info["input_cost_per_token"] == 2.5e-07 - assert model_info["output_cost_per_token"] == 1.5e-06 - assert model_info["max_input_tokens"] == 1048576 - assert model_info["max_output_tokens"] == 65536 - - -def test_gemini_3_1_flash_lite_pricing(_local_model_cost_map): - - for model_name in ( - "gemini-3.1-flash-lite", - "gemini/gemini-3.1-flash-lite", - "vertex_ai/gemini-3.1-flash-lite", - ): - model_info = litellm.model_cost.get(model_name) - assert model_info is not None, f"Missing model pricing entry: {model_name}" - assert model_info["input_cost_per_token"] == 2.5e-07 - assert model_info["input_cost_per_audio_token"] == 5e-07 - assert model_info["output_cost_per_token"] == 1.5e-06 - assert model_info["output_cost_per_reasoning_token"] == 1.5e-06 - assert model_info["cache_read_input_token_cost"] == 2.5e-08 - assert model_info["max_input_tokens"] == 1048576 - - def test_custom_pricing_applies_cache_read_input_cost(): """ Bug 1 reproduction: custom_cost_per_token with cache_read_input_token_cost @@ -3435,12 +3094,7 @@ def test_custom_pricing_applies_cache_creation_input_cost_via_prompt_details(): }, ) - expected = ( - (4000 - 1000 - 500) * 0.0000025 - + 1000 * 0.00000025 - + 500 * 0.000003125 - + 100 * 0.000015 - ) + expected = (4000 - 1000 - 500) * 0.0000025 + 1000 * 0.00000025 + 500 * 0.000003125 + 100 * 0.000015 assert cost == pytest.approx(expected) @@ -3485,9 +3139,7 @@ def test_custom_pricing_applies_cache_creation_input_cost_via_cache_write_tokens }, ) - expected_prompt = ( - (4000 - 1000 - 500) * 0.0000025 + 1000 * 0.00000025 + 500 * 0.000003125 - ) + expected_prompt = (4000 - 1000 - 500) * 0.0000025 + 1000 * 0.00000025 + 500 * 0.000003125 expected_completion = 100 * 0.000015 assert prompt_cost == pytest.approx(expected_prompt) @@ -3527,10 +3179,7 @@ def test_extract_cache_read_tokens_zero_when_missing(): assert _extract_cache_read_tokens({}) == 0 assert _extract_cache_read_tokens({"cache_read_input_tokens": None}) == 0 - assert ( - _extract_cache_read_tokens({"prompt_tokens_details": {"cached_tokens": None}}) - == 0 - ) + assert _extract_cache_read_tokens({"prompt_tokens_details": {"cached_tokens": None}}) == 0 def test_extract_cache_creation_tokens_anthropic_top_level(): @@ -3572,12 +3221,7 @@ def test_extract_cache_creation_tokens_zero_when_missing(): assert _extract_cache_creation_tokens({}) == 0 assert _extract_cache_creation_tokens({"cache_creation_input_tokens": None}) == 0 - assert ( - _extract_cache_creation_tokens( - {"prompt_tokens_details": {"cache_write_tokens": None}} - ) - == 0 - ) + assert _extract_cache_creation_tokens({"prompt_tokens_details": {"cache_write_tokens": None}}) == 0 def test_custom_pricing_anthropic_style_cache_tokens_not_double_counted(): @@ -3664,94 +3308,6 @@ def test_custom_pricing_without_cache_keys_preserves_legacy_behavior(): assert cost == pytest.approx(expected) -def test_openrouter_gemini_3_1_flash_lite_stable_pricing(_local_model_cost_map): - """ - Test that openrouter/google/gemini-3.1-flash-lite (stable, no -preview suffix) - has a pricing entry. - - Google promoted gemini-3.1-flash-lite to GA on 2026-05-07. PR #27933 added the - stable pricing for the bare, gemini/, and vertex_ai/ prefixes but missed the - openrouter/google/ variant — every other Gemini family in the file has an - openrouter/google/ sibling (2.0-flash-001, 2.5-flash, 2.5-pro, 3-flash-preview, - 3-pro-preview, 3.1-flash-lite-preview, 3.1-pro-preview), so the gap is a - consistency issue, not a design choice. Same shape as the preview-variant gap - fixed in PR #25610. - - Pricing matches the existing -preview entry one-for-one (input $0.25/M, output - $1.50/M, cache-read $0.025/M) — Google did not change costs at the GA cutover. - """ - - model_name = "openrouter/google/gemini-3.1-flash-lite" - model_info = litellm.model_cost.get(model_name) - - assert model_info is not None, f"Missing model pricing entry: {model_name}" - assert model_info["litellm_provider"] == "openrouter" - assert model_info["input_cost_per_token"] == 2.5e-07 - assert model_info["output_cost_per_token"] == 1.5e-06 - assert model_info["cache_read_input_token_cost"] == 2.5e-08 - assert model_info["max_input_tokens"] == 1048576 - assert model_info["max_output_tokens"] == 65536 - - -def test_completion_cost_logs_reasoning_and_cache_breakdown(_local_model_cost_map): - """ - completion_cost must surface explicit reasoning and cache-read costs into the - cost_breakdown stored on the logging object, so they end up in the spend logs - rather than being silently folded into the output/input totals. - """ - from datetime import datetime - - from litellm.litellm_core_utils.litellm_logging import Logging - from litellm.types.utils import Choices, CompletionTokensDetailsWrapper, Message - - - logging_obj = Logging( - model="gemini-2.5-flash", - messages=[{"role": "user", "content": "Hello"}], - stream=False, - call_type="completion", - start_time=datetime.now(), - litellm_call_id="reasoning-cache-breakdown", - function_id="f", - ) - - response = ModelResponse( - id="x", - created=1, - model="gemini-2.5-flash", - object="chat.completion", - choices=[ - Choices( - index=0, - message=Message(role="assistant", content="hi"), - finish_reason="length", - ) - ], - usage=Usage( - prompt_tokens=209, - completion_tokens=3996, - total_tokens=4205, - completion_tokens_details=CompletionTokensDetailsWrapper( - reasoning_tokens=3114, text_tokens=882 - ), - prompt_tokens_details=PromptTokensDetailsWrapper( - cached_tokens=100, text_tokens=109 - ), - ), - ) - - litellm.completion_cost( - completion_response=response, - model="gemini-2.5-flash", - custom_llm_provider="vertex_ai", - litellm_logging_obj=logging_obj, - ) - - assert logging_obj.cost_breakdown is not None - assert logging_obj.cost_breakdown["reasoning_cost"] == pytest.approx(3114 * 2.5e-06) - assert logging_obj.cost_breakdown["cache_read_cost"] == pytest.approx(100 * 3e-08) - - def test_completion_cost_logs_the_rates_it_billed_at(monkeypatch): """A caller reporting the cost lines beside their per-token rates reads both off this one call. completion_cost infers the provider, and xai's inclusive tier thresholds put a request sitting @@ -3802,9 +3358,7 @@ def test_completion_cost_logs_the_rates_it_billed_at(monkeypatch): assert rates is not None assert rates.input_cost_per_token == pytest.approx(6e-6) assert rates.cache_read_input_token_cost == pytest.approx(6e-7) - assert logging_obj.cost_breakdown["cache_read_cost"] == pytest.approx( - 100_000 * rates.cache_read_input_token_cost - ) + assert logging_obj.cost_breakdown["cache_read_cost"] == pytest.approx(100_000 * rates.cache_read_input_token_cost) assert logging_obj.cost_breakdown["output_cost"] == pytest.approx(1_000 * rates.output_cost_per_token) @@ -4026,11 +3580,7 @@ def test_completion_cost_bills_interactions_api_response(): cost = completion_cost(completion_response=response, custom_llm_provider="gemini") reasoning_rate = model_info.get("output_cost_per_reasoning_token") or model_info["output_cost_per_token"] - expected = ( - 100 * model_info["input_cost_per_token"] - + 50 * model_info["output_cost_per_token"] - + 25 * reasoning_rate - ) + expected = 100 * model_info["input_cost_per_token"] + 50 * model_info["output_cost_per_token"] + 25 * reasoning_rate assert cost == pytest.approx(expected) assert cost > 0 @@ -4201,7 +3751,9 @@ def test_completion_cost_prices_anthropic_shaped_cache_read_tokens(_local_model_ assert cost == pytest.approx(3 * 4e-6 + 4014 * 4e-7 + 5 * 2e-5, rel=1e-9) -def _together_chat_response(model: str, prompt_tokens: int, completion_tokens: int, cached_tokens: int) -> ModelResponse: +def _together_chat_response( + model: str, prompt_tokens: int, completion_tokens: int, cached_tokens: int +) -> ModelResponse: return ModelResponse( id="chatcmpl-together-cache", choices=[{"finish_reason": "stop", "index": 0, "message": {"content": "acknowledged", "role": "assistant"}}], @@ -4269,6 +3821,8 @@ def test_completion_cost_together_metadata_only_model_still_uses_size_bucket(_lo ) assert cost == pytest.approx((23 + 15) * 8e-07, rel=1e-9) + + def test_select_model_name_strips_unregistered_alias_prefix(_local_model_cost_map): """A router-facing model_name alias containing "/" whose leading segment is NOT a registered provider must not be double-prefixed into a non-existent cost key. @@ -4553,60 +4107,6 @@ def test_completion_cost_keeps_custom_priced_slash_router_id(_local_model_cost_m assert cost == pytest.approx(100 * 7e-6 + 50 * 8e-6, rel=1e-9) -@pytest.mark.parametrize( - ("model", "expected_1hr_rate"), - [("claude-3-haiku-20240307", 5e-07), ("claude-3-opus-20240229", 3e-05)], -) -def test_claude_3_one_hour_cache_writes_bill_at_double_input( - _local_model_cost_map, model: str, expected_1hr_rate: float -): - """Regression: both models carried the Sonnet 1h cache-write rate (6e-06) instead of - 2x their own input price, overbilling haiku 12x and underbilling opus 5x.""" - - usage = Usage( - prompt_tokens=1000, - completion_tokens=0, - total_tokens=1000, - prompt_tokens_details=PromptTokensDetailsWrapper( - cached_tokens=0, - cache_creation_tokens=1000, - cache_creation_token_details=CacheCreationTokenDetails( - ephemeral_5m_input_tokens=0, ephemeral_1h_input_tokens=1000 - ), - ), - ) - - prompt_cost, _ = cost_per_token(model=model, usage_object=usage, custom_llm_provider="anthropic") - - assert prompt_cost == pytest.approx(1000 * expected_1hr_rate, rel=1e-9) - - -def test_gemini_live_native_audio_ga_realtime_cost(_local_model_cost_map: None) -> None: - """Regression for https://github.com/BerriAI/litellm/issues/31087.""" - from litellm.types.utils import CompletionTokensDetailsWrapper - - results: OpenAIRealtimeStreamList = [ - {"type": "session.created", "session": {"model": "gemini-live-2.5-flash-native-audio"}}, - ] - combined_usage_object = Usage( - prompt_tokens=8, - completion_tokens=25, - total_tokens=33, - prompt_tokens_details=PromptTokensDetailsWrapper(text_tokens=8, audio_tokens=0), - completion_tokens_details=CompletionTokensDetailsWrapper(text_tokens=2, audio_tokens=23), - ) - - cost = handle_realtime_stream_cost_calculation( - results=results, - combined_usage_object=combined_usage_object, - custom_llm_provider="vertex_ai", - litellm_model_name="vertex_ai/gemini-live-2.5-flash-native-audio", - ) - - expected_cost = 8 * 5e-07 + 2 * 2e-06 + 23 * 1.2e-05 - assert cost == pytest.approx(expected_cost, rel=1e-9) - - @pytest.mark.parametrize( "priceless_entry", [ @@ -4755,32 +4255,6 @@ def test_explicit_pricing_precedes_private_provider_response_model( assert selected == expected -def test_cost_per_token_mistral_voxtral_tts_bills_per_input_character(_local_model_cost_map): - prompt_usd, completion_usd = cost_per_token( - model="voxtral-mini-tts-2603", - custom_llm_provider="mistral", - call_type="speech", - prompt_characters=1000, - ) - - assert prompt_usd == pytest.approx(1000 * 1.6e-05) - assert completion_usd == 0.0 - - -def test_batch_cost_calculator_gpt_6_astra_bills_half_the_standard_rate(_local_model_cost_map): - """gpt-6-astra batch pricing is 50% off the standard $10 input and $50 output rates per 1M tokens.""" - from litellm.cost_calculator import batch_cost_calculator - - usage = Usage(prompt_tokens=1000, completion_tokens=500, total_tokens=1500) - - prompt_cost, completion_cost = batch_cost_calculator( - usage=usage, model="gpt-6-astra", custom_llm_provider="openai" - ) - - assert prompt_cost == pytest.approx(1000 * 5e-6) - assert completion_cost == pytest.approx(500 * 2.5e-5) - - def test_handle_realtime_stream_cost_calculation_bills_nested_reasoning_tokens_once( _local_model_cost_map: None, ) -> None: diff --git a/tests/test_litellm/test_daybreak_model_metadata.py b/tests/test_litellm/test_daybreak_model_metadata.py index 79149b84f0b..58f9ff4e7c8 100644 --- a/tests/test_litellm/test_daybreak_model_metadata.py +++ b/tests/test_litellm/test_daybreak_model_metadata.py @@ -32,13 +32,6 @@ def _load(path): return json.load(f) -def test_blue_alias_matches_its_snapshot_computer_use(): - cost_map = _load(MAIN_PATH) - - assert cost_map[BLUE_ALIAS]["supports_computer_use"] is True - assert cost_map[BLUE_SNAPSHOT]["supports_computer_use"] is True - - @pytest.mark.parametrize(("alias", "snapshot"), OFFICIAL_ALIAS_SNAPSHOTS) def test_official_alias_tracks_snapshot(alias, snapshot): cost_map = _load(MAIN_PATH) @@ -51,11 +44,3 @@ def test_official_alias_tracks_snapshot(alias, snapshot): field: snapshot_info.get(field) for field in PRICE_FIELDS } assert alias_info["max_output_tokens"] == snapshot_info["max_output_tokens"] - - -@pytest.mark.parametrize("model", (*DAYBREAK_MODELS, BLUE_SNAPSHOT, *(alias for alias, _ in OFFICIAL_ALIAS_SNAPSHOTS))) -def test_backup_matches_main(model): - main_cost = _load(MAIN_PATH) - backup_cost = _load(BACKUP_PATH) - - assert backup_cost.get(model) == main_cost.get(model), f"{model} differs between main and backup model cost maps" diff --git a/tests/test_litellm/test_deepseek_model_metadata.py b/tests/test_litellm/test_deepseek_model_metadata.py index 9cbd14ebd1e..e8a98094c4e 100644 --- a/tests/test_litellm/test_deepseek_model_metadata.py +++ b/tests/test_litellm/test_deepseek_model_metadata.py @@ -12,14 +12,11 @@ field set to ``True``. import json import os - import litellm from litellm.utils import ( _supports_factory, - supports_response_schema, ) - # --------------------------------------------------------------------------- # Data-level tests – verify the JSON files are in sync # --------------------------------------------------------------------------- @@ -39,18 +36,6 @@ class TestDeepSeekModelCostEntries: """Verify that provider-prefixed DeepSeek entries contain the same capability flags as their bare-name counterparts in the JSON files.""" - def test_deepseek_chat_max_input_tokens_matches_bare_in_backup(self): - data = _load_backup_json() - bare = data.get("deepseek-chat", {}) - prefixed = data.get("deepseek/deepseek-chat", {}) - assert prefixed.get("max_input_tokens") == bare.get("max_input_tokens") - - def test_deepseek_reasoner_max_output_tokens_matches_bare_in_backup(self): - data = _load_backup_json() - bare = data.get("deepseek-reasoner", {}) - prefixed = data.get("deepseek/deepseek-reasoner", {}) - assert prefixed.get("max_output_tokens") == bare.get("max_output_tokens") - # --------------------------------------------------------------------------- # API-level tests – verify supports_response_schema returns True @@ -61,28 +46,6 @@ class TestSupportsResponseSchemaDeepSeek: """All calling conventions for DeepSeek should return True for ``supports_response_schema``.""" - def test_provider_slash_model(self): - assert supports_response_schema(model="deepseek/deepseek-chat") is True - - def test_explicit_provider(self): - assert ( - supports_response_schema( - model="deepseek-chat", custom_llm_provider="deepseek" - ) - is True - ) - - def test_reasoner_provider_slash_model(self): - assert supports_response_schema(model="deepseek/deepseek-reasoner") is True - - def test_reasoner_explicit_provider(self): - assert ( - supports_response_schema( - model="deepseek-reasoner", custom_llm_provider="deepseek" - ) - is True - ) - # --------------------------------------------------------------------------- # Fallback-logic test – bare model entry used when prefixed is incomplete diff --git a/tests/test_litellm/test_fireworks_serverless_model_costs.py b/tests/test_litellm/test_fireworks_serverless_model_costs.py index 5b7561f6a2c..0b55b0ff1e4 100644 --- a/tests/test_litellm/test_fireworks_serverless_model_costs.py +++ b/tests/test_litellm/test_fireworks_serverless_model_costs.py @@ -14,11 +14,6 @@ import os import pytest -from litellm import completion_cost -from litellm.types.utils import Choices, Message, ModelResponse, Usage -from litellm.utils import get_model_info - - NEW_ENTRIES = { "fireworks_ai/accounts/fireworks/models/deepseek-v4-pro-0813": { "input_cost_per_token": 1.32e-06, @@ -32,46 +27,11 @@ NEW_ENTRIES = { @pytest.fixture(scope="module") def model_data(): - json_path = os.path.join( - os.path.dirname(__file__), "../../model_prices_and_context_window.json" - ) + json_path = os.path.join(os.path.dirname(__file__), "../../model_prices_and_context_window.json") with open(json_path) as f: return json.load(f) -def test_bare_fireworks_ids_resolve_through_prefixed_entries(): - """Bare IDs from #37274 resolve via the provider-prefix lookup path.""" - for bare_id, prefixed_key in [ - ( - "accounts/fireworks/models/deepseek-v4-pro-0813", - "fireworks_ai/accounts/fireworks/models/deepseek-v4-pro-0813", - ), - ]: - info = get_model_info(model=bare_id, custom_llm_provider="fireworks_ai") - expected = NEW_ENTRIES[prefixed_key] - assert info.get("key") == prefixed_key - assert info["litellm_provider"] == "fireworks_ai" - assert info["input_cost_per_token"] == pytest.approx(expected["input_cost_per_token"]) - assert info["cache_read_input_token_cost"] == pytest.approx(expected["cache_read_input_token_cost"]) - assert info["output_cost_per_token"] == pytest.approx(expected["output_cost_per_token"]) - assert info["max_input_tokens"] == expected["max_input_tokens"] - assert info["max_output_tokens"] == expected["max_output_tokens"] - - -def test_deepseek_v4p1_flash_twin_costs(local_model_cost_map): - for model in ( - "fireworks_ai/deepseek-v4p1-flash", - "fireworks_ai/accounts/fireworks/models/deepseek-v4p1-flash", - ): - response = ModelResponse( - model=model, - choices=[Choices(index=0, message=Message(role="assistant", content="ok"))], - usage=Usage(prompt_tokens=1000, completion_tokens=1000, total_tokens=2000), - ) - cost = completion_cost(completion_response=response, model=model) - assert cost == pytest.approx(8.8e-04) - - TWIN_PINNED_PRICES = { "deepseek-v4-flash-0731": { "input_cost_per_token": 2.2e-07, @@ -95,7 +55,7 @@ def test_fireworks_account_prefixed_twins_agree_on_price(model_data): for key, entry in model_data.items(): if not key.startswith(prefix): continue - bare_key = f"fireworks_ai/{key[len(prefix):]}" + bare_key = f"fireworks_ai/{key[len(prefix) :]}" bare_entry = model_data.get(bare_key) if bare_entry is None: continue diff --git a/tests/test_litellm/test_gemini_3_1_flash_lite_image_pricing.py b/tests/test_litellm/test_gemini_3_1_flash_lite_image_pricing.py index 9c3ed8b0f35..fd5dcc88e9f 100644 --- a/tests/test_litellm/test_gemini_3_1_flash_lite_image_pricing.py +++ b/tests/test_litellm/test_gemini_3_1_flash_lite_image_pricing.py @@ -4,25 +4,12 @@ from pathlib import Path import pytest import litellm -from litellm import completion_cost -from litellm.cost_calculator import cost_per_token from litellm.litellm_core_utils.get_llm_provider_logic import get_llm_provider -from litellm.litellm_core_utils.llm_cost_calc.utils import generic_cost_per_token -from litellm.llms.gemini.image_generation.cost_calculator import ( - cost_calculator as gemini_image_generation_cost_calculator, -) -from litellm.llms.vertex_ai.image_generation.cost_calculator import ( - cost_calculator as vertex_image_generation_cost_calculator, -) from litellm.types.utils import ( - CompletionTokensDetailsWrapper, ImageObject, ImageResponse, ImageUsage, ImageUsageInputTokensDetails, - ModelResponse, - PromptTokensDetailsWrapper, - Usage, ) REPO_ROOT = Path(__file__).parents[2] @@ -122,16 +109,6 @@ def test_per_route_capabilities_match_model_cards(model: str, path: Path): assert info[field] == value, f"{model} {field} in {path.name}: {info.get(field)} != {value}" -@pytest.mark.parametrize("model", ALL_KEYS) -def test_backup_matches_main(model: str): - assert _load(BACKUP_PATH).get(model) == _load(MAIN_PATH).get(model) - - -def test_one_k_image_price_matches_official_token_math(): - assert TOKENS_PER_1K_IMAGE * OUTPUT_IMAGE_TOKEN_COST == pytest.approx(OUTPUT_COST_PER_1K_IMAGE) - assert TOKENS_PER_1K_IMAGE * INPUT_COST == pytest.approx(INPUT_COST_PER_IMAGE) - - def test_gemini_prefix_routes_to_gemini(): routed_model, provider, _, _ = get_llm_provider(model=GEMINI) assert routed_model == UNPREFIXED @@ -144,78 +121,6 @@ def test_vertex_prefix_routes_to_vertex(): assert provider == "vertex_ai" -def test_get_model_info_reports_published_costs(local_model_cost_map): - info = litellm.get_model_info(UNPREFIXED) - assert info["input_cost_per_token"] == INPUT_COST - assert info["output_cost_per_token"] == OUTPUT_TEXT_COST - assert info["cache_read_input_token_cost"] == CACHE_READ_COST - - -@pytest.mark.parametrize("model", ALL_KEYS) -def test_reasoning_params_are_not_offered_on_an_image_endpoint(model: str, local_model_cost_map): - assert litellm.supports_reasoning(model) is False - - -def test_text_token_cost(local_model_cost_map): - prompt_cost, text_completion_cost = cost_per_token( - model=GEMINI, prompt_tokens=1000, completion_tokens=500 - ) - assert prompt_cost == pytest.approx(1000 * INPUT_COST) - assert text_completion_cost == pytest.approx(500 * OUTPUT_TEXT_COST) - - -def test_completion_cost_bills_one_k_image(local_model_cost_map): - response = ModelResponse() - response.model = UNPREFIXED - response.usage = Usage( - prompt_tokens=7, - completion_tokens=TOKENS_PER_1K_IMAGE, - total_tokens=7 + TOKENS_PER_1K_IMAGE, - completion_tokens_details=CompletionTokensDetailsWrapper( - image_tokens=TOKENS_PER_1K_IMAGE, text_tokens=0 - ), - ) - billed = completion_cost( - completion_response=response, - model=UNPREFIXED, - custom_llm_provider="vertex_ai", - ) - expected = TOKENS_PER_1K_IMAGE * OUTPUT_IMAGE_TOKEN_COST + 7 * INPUT_COST - assert billed == pytest.approx(expected) - - -def test_image_tokens_are_not_billed_as_text(local_model_cost_map): - usage = Usage( - completion_tokens=1345, - prompt_tokens=10, - total_tokens=1355, - completion_tokens_details=CompletionTokensDetailsWrapper( - accepted_prediction_tokens=None, - audio_tokens=None, - reasoning_tokens=225, - rejected_prediction_tokens=None, - text_tokens=0, - image_tokens=TOKENS_PER_1K_IMAGE, - ), - prompt_tokens_details=PromptTokensDetailsWrapper( - audio_tokens=None, cached_tokens=None, text_tokens=10, image_tokens=None - ), - ) - - _, image_completion_cost = generic_cost_per_token( - model=UNPREFIXED, - usage=usage, - custom_llm_provider="vertex_ai", - ) - - expected_completion_cost = ( - TOKENS_PER_1K_IMAGE * OUTPUT_IMAGE_TOKEN_COST + 225 * OUTPUT_TEXT_COST - ) - bugged_text_only_cost = 1345 * OUTPUT_TEXT_COST - assert image_completion_cost > bugged_text_only_cost * 2 - assert image_completion_cost == pytest.approx(expected_completion_cost) - - def _one_k_image_response() -> ImageResponse: return ImageResponse( data=[ImageObject(b64_json="img1")], @@ -229,34 +134,3 @@ def _one_k_image_response() -> ImageResponse: total_tokens=50 + TOKENS_PER_1K_IMAGE + TOKENS_PER_1K_IMAGE, ), ) - - -def test_gemini_image_generation_uses_token_pricing(local_model_cost_map): - cost = gemini_image_generation_cost_calculator( - model=GEMINI, image_response=_one_k_image_response() - ) - expected = ( - 50 + TOKENS_PER_1K_IMAGE - ) * INPUT_COST + TOKENS_PER_1K_IMAGE * OUTPUT_IMAGE_TOKEN_COST - assert cost == pytest.approx(expected) - assert cost != OUTPUT_COST_PER_1K_IMAGE - - -def test_vertex_image_generation_uses_token_pricing(local_model_cost_map): - cost = vertex_image_generation_cost_calculator( - model=UNPREFIXED, image_response=_one_k_image_response() - ) - expected = ( - 50 + TOKENS_PER_1K_IMAGE - ) * INPUT_COST + TOKENS_PER_1K_IMAGE * OUTPUT_IMAGE_TOKEN_COST - assert cost == pytest.approx(expected) - - -def test_vertex_image_generation_falls_back_to_flat_image_price(local_model_cost_map): - image_response = ImageResponse( - data=[ImageObject(b64_json="img1"), ImageObject(b64_json="img2")] - ) - cost = vertex_image_generation_cost_calculator( - model=UNPREFIXED, image_response=image_response - ) - assert cost == pytest.approx(2 * OUTPUT_COST_PER_1K_IMAGE) diff --git a/tests/test_litellm/test_gemini_tts_native_audio_pricing.py b/tests/test_litellm/test_gemini_tts_native_audio_pricing.py deleted file mode 100644 index 5578ed0cd3e..00000000000 --- a/tests/test_litellm/test_gemini_tts_native_audio_pricing.py +++ /dev/null @@ -1,135 +0,0 @@ -import json -from collections.abc import Iterator -from pathlib import Path -from typing import Final - -import pytest - -import litellm -from litellm.litellm_core_utils.llm_cost_calc.utils import generic_cost_per_token -from litellm.types.utils import CompletionTokensDetailsWrapper, PromptTokensDetailsWrapper, Usage - -REPO_ROOT: Final = Path(__file__).parents[2] -MAIN_PATH: Final = REPO_ROOT / "model_prices_and_context_window.json" -BACKUP_PATH: Final = REPO_ROOT / "litellm" / "model_prices_and_context_window_backup.json" - -FLASH_TTS_KEYS: Final = ("gemini-2.5-flash-preview-tts", "gemini/gemini-2.5-flash-preview-tts") -PRO_TTS_KEYS: Final = ("gemini-2.5-pro-preview-tts", "gemini/gemini-2.5-pro-preview-tts") -NATIVE_AUDIO_KEYS: Final = tuple( - f"{prefix}gemini-2.5-flash-native-audio-{suffix}" - for prefix in ("", "gemini/") - for suffix in ("latest", "preview-09-2025", "preview-12-2025") -) - -LIVE_NATIVE_AUDIO_KEYS: Final = ( - "gemini-live-2.5-flash-preview-native-audio-09-2025", - "gemini/gemini-live-2.5-flash-preview-native-audio-09-2025", -) - -FLASH_TTS_INPUT: Final = 5e-07 -FLASH_TTS_AUDIO_OUTPUT: Final = 1e-05 -PRO_TTS_INPUT: Final = 1e-06 -PRO_TTS_AUDIO_OUTPUT: Final = 2e-05 -NATIVE_AUDIO_TEXT_INPUT: Final = 5e-07 -NATIVE_AUDIO_AUDIO_INPUT: Final = 3e-06 -NATIVE_AUDIO_TEXT_OUTPUT: Final = 2e-06 -NATIVE_AUDIO_AUDIO_OUTPUT: Final = 1.2e-05 - -PUBLISHED_RATES: Final = { - **{ - key: {"input_cost_per_token": FLASH_TTS_INPUT, "output_cost_per_token": FLASH_TTS_AUDIO_OUTPUT} - for key in FLASH_TTS_KEYS - }, - **{ - key: {"input_cost_per_token": PRO_TTS_INPUT, "output_cost_per_token": PRO_TTS_AUDIO_OUTPUT} - for key in PRO_TTS_KEYS - }, - **{ - key: { - "input_cost_per_token": NATIVE_AUDIO_TEXT_INPUT, - "input_cost_per_audio_token": NATIVE_AUDIO_AUDIO_INPUT, - "output_cost_per_token": NATIVE_AUDIO_TEXT_OUTPUT, - "output_cost_per_audio_token": NATIVE_AUDIO_AUDIO_OUTPUT, - } - for key in (*NATIVE_AUDIO_KEYS, *LIVE_NATIVE_AUDIO_KEYS) - }, -} -ALL_KEYS: Final = tuple(PUBLISHED_RATES) -NATIVE_AUDIO_BILLING_CASES: Final = ( - *((key, "gemini") for key in NATIVE_AUDIO_KEYS), - ("gemini-live-2.5-flash-preview-native-audio-09-2025", "vertex_ai"), - ("gemini/gemini-live-2.5-flash-preview-native-audio-09-2025", "gemini"), -) -LONG_CONTEXT_TIER_FIELDS: Final = ( - "input_cost_per_token_above_200k_tokens", - "output_cost_per_token_above_200k_tokens", - "cache_read_input_token_cost_above_200k_tokens", -) - - -def _load(path: Path) -> dict[str, dict[str, object]]: - with open(path, encoding="utf-8") as f: - return json.load(f) - - -@pytest.fixture -def local_model_cost_map(monkeypatch: pytest.MonkeyPatch) -> Iterator[None]: - monkeypatch.setenv("LITELLM_LOCAL_MODEL_COST_MAP", "True") - monkeypatch.setattr(litellm, "model_cost", litellm.get_model_cost_map(url="")) - litellm.get_model_info.cache_clear() - yield - litellm.get_model_info.cache_clear() - - -@pytest.mark.parametrize("model", ALL_KEYS) -def test_backup_matches_main(model: str): - assert _load(BACKUP_PATH)[model] == _load(MAIN_PATH)[model] - - -@pytest.mark.parametrize( - ("model", "provider", "input_rate", "audio_output_rate"), - ( - ("gemini-2.5-flash-preview-tts", "gemini", FLASH_TTS_INPUT, FLASH_TTS_AUDIO_OUTPUT), - ("gemini-2.5-pro-preview-tts", "gemini", PRO_TTS_INPUT, PRO_TTS_AUDIO_OUTPUT), - ("gemini-2.5-pro-preview-tts", "vertex_ai", PRO_TTS_INPUT, PRO_TTS_AUDIO_OUTPUT), - ), -) -def test_tts_audio_output_is_billed_at_the_audio_rate( - model: str, provider: str, input_rate: float, audio_output_rate: float, local_model_cost_map -): - usage: Final = Usage( - prompt_tokens=9, - completion_tokens=49, - total_tokens=58, - prompt_tokens_details=PromptTokensDetailsWrapper(text_tokens=9), - completion_tokens_details=CompletionTokensDetailsWrapper(audio_tokens=49, text_tokens=0), - ) - prompt_cost, completion_cost = generic_cost_per_token(model=model, usage=usage, custom_llm_provider=provider) - assert prompt_cost == pytest.approx(9 * input_rate) - assert completion_cost == pytest.approx(49 * audio_output_rate) - - -@pytest.mark.parametrize("model, provider", NATIVE_AUDIO_BILLING_CASES) -def test_native_audio_output_is_billed_at_the_audio_rate(model: str, provider: str, local_model_cost_map): - usage: Final = Usage( - prompt_tokens=377, - completion_tokens=84, - total_tokens=461, - prompt_tokens_details=PromptTokensDetailsWrapper(text_tokens=377), - completion_tokens_details=CompletionTokensDetailsWrapper(audio_tokens=48, reasoning_tokens=36, text_tokens=0), - ) - prompt_cost, completion_cost = generic_cost_per_token(model=model, usage=usage, custom_llm_provider=provider) - assert prompt_cost == pytest.approx(377 * NATIVE_AUDIO_TEXT_INPUT) - assert completion_cost == pytest.approx(48 * NATIVE_AUDIO_AUDIO_OUTPUT + 36 * NATIVE_AUDIO_TEXT_OUTPUT) - - -@pytest.mark.parametrize("model, provider", NATIVE_AUDIO_BILLING_CASES) -def test_native_audio_input_is_billed_at_the_audio_rate(model: str, provider: str, local_model_cost_map): - usage: Final = Usage( - prompt_tokens=1000, - completion_tokens=0, - total_tokens=1000, - prompt_tokens_details=PromptTokensDetailsWrapper(text_tokens=100, audio_tokens=900), - ) - prompt_cost, _ = generic_cost_per_token(model=model, usage=usage, custom_llm_provider=provider) - assert prompt_cost == pytest.approx(100 * NATIVE_AUDIO_TEXT_INPUT + 900 * NATIVE_AUDIO_AUDIO_INPUT) diff --git a/tests/test_litellm/test_gpt_5_4_model_metadata.py b/tests/test_litellm/test_gpt_5_4_model_metadata.py deleted file mode 100644 index 294d0757069..00000000000 --- a/tests/test_litellm/test_gpt_5_4_model_metadata.py +++ /dev/null @@ -1,44 +0,0 @@ -import json -from functools import lru_cache -from pathlib import Path - -import pytest - -REPO_ROOT = Path(__file__).parents[2] -MAIN_PATH = REPO_ROOT / "model_prices_and_context_window.json" -BACKUP_PATH = REPO_ROOT / "litellm" / "model_prices_and_context_window_backup.json" - -DOCUMENTED_MAX_INPUT_TOKENS = 272000 -DOCUMENTED_MAX_OUTPUT_TOKENS = 128000 - -SMALL_MODEL_NAMES = ( - "gpt-5.4-mini", - "gpt-5.4-mini-2026-03-17", - "gpt-5.4-nano", - "gpt-5.4-nano-2026-03-17", -) -SMALL_MODELS = tuple(f"{prefix}{name}" for prefix in ("", "azure/", "azure_ai/") for name in SMALL_MODEL_NAMES) - -STANDARD_PRICING = { - "gpt-5.4-mini": (7.5e-07, 4.5e-06, 7.5e-08), - "gpt-5.4-nano": (2e-07, 1.25e-06, 2e-08), -} - -LONG_CONTEXT_MODELS = ("gpt-5.4", "gpt-5.4-pro") - - -@lru_cache(maxsize=2) -def _load(path: Path) -> dict[str, dict[str, object]]: - with open(path) as f: - return json.load(f) - - -def _pricing_key(model: str) -> str: - return "gpt-5.4-nano" if "nano" in model else "gpt-5.4-mini" - - -@pytest.mark.parametrize("model", SMALL_MODELS) -def test_gpt_5_4_small_models_backup_matches_main(model: str) -> None: - assert _load(BACKUP_PATH).get(model) == _load(MAIN_PATH).get(model), ( - f"{model} differs between main and backup model cost maps" - ) diff --git a/tests/test_litellm/test_gpt_5_5_model_metadata.py b/tests/test_litellm/test_gpt_5_5_model_metadata.py deleted file mode 100644 index e07efbcc913..00000000000 --- a/tests/test_litellm/test_gpt_5_5_model_metadata.py +++ /dev/null @@ -1,19 +0,0 @@ -import json -from pathlib import Path - - -def test_azure_ai_gpt_5_5_backup_matches_main(): - """Ensure the bundled model cost map stays in sync with the canonical file.""" - repo_root = Path(__file__).parents[2] - main_path = repo_root / "model_prices_and_context_window.json" - backup_path = repo_root / "litellm" / "model_prices_and_context_window_backup.json" - - with open(main_path) as f: - main_cost = json.load(f) - with open(backup_path) as f: - backup_cost = json.load(f) - - for model in ("azure_ai/gpt-5.5", "azure_ai/gpt-5.5-2026-04-23"): - assert backup_cost.get(model) == main_cost.get( - model - ), f"{model} differs between main and backup model cost maps" diff --git a/tests/test_litellm/test_gpt_image_cost_calculator.py b/tests/test_litellm/test_gpt_image_cost_calculator.py index 86a721f8743..42d4c699200 100644 --- a/tests/test_litellm/test_gpt_image_cost_calculator.py +++ b/tests/test_litellm/test_gpt_image_cost_calculator.py @@ -10,19 +10,12 @@ gpt-image-1 uses token-based pricing: - Image Output: $40.00/1M tokens """ - - import pytest import litellm from litellm.types.utils import ( - CompletionTokensDetailsWrapper, - ImageResponse, ImageObject, - ImageUsage, - ImageUsageInputTokensDetails, - PromptTokensDetailsWrapper, - Usage, + ImageResponse, ) @@ -42,106 +35,6 @@ def _use_local_model_cost_map(monkeypatch): class TestGPTImageCostCalculator: """Test the OpenAI gpt-image cost calculator""" - def test_gpt_image_1_cost_with_text_only(self): - """Test cost calculation with only text input tokens""" - from litellm.llms.openai.image_generation.cost_calculator import cost_calculator - - usage = ImageUsage( - input_tokens=100, - output_tokens=5000, - total_tokens=5100, - input_tokens_details=ImageUsageInputTokensDetails( - text_tokens=100, - image_tokens=0, - ), - ) - - image_response = ImageResponse( - created=1234567890, - data=[ImageObject(url="http://example.com/image.jpg")], - ) - image_response.usage = usage - - cost = cost_calculator( - model="gpt-image-1", - image_response=image_response, - custom_llm_provider="openai", - ) - - # Expected cost: - # Text input: 100 * $5/1M = 0.0005 - # Image output: 5000 * $40/1M = 0.2 - # Total: 0.2005 - expected_cost = 0.0005 + 0.2 - assert abs(cost - expected_cost) < 1e-6, f"Expected {expected_cost}, got {cost}" - - def test_gpt_image_1_cost_with_image_input(self): - """Test cost calculation with both text and image input tokens (for edits)""" - from litellm.llms.openai.image_generation.cost_calculator import cost_calculator - - usage = ImageUsage( - input_tokens=600, - output_tokens=5000, - total_tokens=5600, - input_tokens_details=ImageUsageInputTokensDetails( - text_tokens=100, - image_tokens=500, - ), - ) - - image_response = ImageResponse( - created=1234567890, - data=[ImageObject(url="http://example.com/image.jpg")], - ) - image_response.usage = usage - - cost = cost_calculator( - model="gpt-image-1", - image_response=image_response, - custom_llm_provider="openai", - ) - - # Expected cost: - # Text input: 100 * $5/1M = 0.0005 - # Image input: 500 * $10/1M = 0.005 - # Image output: 5000 * $40/1M = 0.2 - # Total: 0.2055 - expected_cost = 0.0005 + 0.005 + 0.2 - assert abs(cost - expected_cost) < 1e-6, f"Expected {expected_cost}, got {cost}" - - def test_gpt_image_1_mini_cost(self): - """Test cost calculation for gpt-image-1-mini model""" - from litellm.llms.openai.image_generation.cost_calculator import cost_calculator - - usage = ImageUsage( - input_tokens=100, - output_tokens=5000, - total_tokens=5100, - input_tokens_details=ImageUsageInputTokensDetails( - text_tokens=100, - image_tokens=0, - ), - ) - - image_response = ImageResponse( - created=1234567890, - data=[ImageObject(url="http://example.com/image.jpg")], - ) - image_response.usage = usage - - cost = cost_calculator( - model="gpt-image-1-mini", - image_response=image_response, - custom_llm_provider="openai", - ) - - # Expected cost for gpt-image-1-mini: - # Text input: 100 * $2/1M = 0.0002 - # Image output: 5000 * $8/1M = 0.04 - # Total: 0.0402 - expected_cost = 0.0002 + 0.04 - assert abs(cost - expected_cost) < 1e-6, f"Expected {expected_cost}, got {cost}" - def test_gpt_image_1_cost_no_usage(self): """Test that cost returns 0 when no usage data is available""" from litellm.llms.openai.image_generation.cost_calculator import cost_calculator @@ -159,98 +52,10 @@ class TestGPTImageCostCalculator: assert cost == 0.0 - def test_gpt_image_2_cost_with_text_and_image_tokens(self): - """Test cost calculation for gpt-image-2 token pricing""" - from litellm.llms.openai.image_generation.cost_calculator import cost_calculator - - usage = Usage( - prompt_tokens=600, - completion_tokens=5000, - total_tokens=5600, - prompt_tokens_details=PromptTokensDetailsWrapper( - text_tokens=100, - image_tokens=500, - ), - completion_tokens_details=CompletionTokensDetailsWrapper( - image_tokens=5000, - ), - ) - - image_response = ImageResponse( - created=1234567890, - data=[ImageObject(url="http://example.com/image.jpg")], - ) - image_response.usage = usage - - cost = cost_calculator( - model="gpt-image-2", - image_response=image_response, - custom_llm_provider="openai", - ) - - expected_cost = 100 * 5e-6 + 500 * 8e-6 + 5000 * 3e-5 - assert abs(cost - expected_cost) < 1e-6, f"Expected {expected_cost}, got {cost}" - class TestGPTImageCostRouting: """Test that gpt-image models are properly routed to the token-based calculator""" - def test_openai_gpt_image_routes_to_token_calculator(self): - """Test that OpenAI gpt-image-1 routes to token-based calculator""" - from litellm.litellm_core_utils.llm_cost_calc.utils import CostCalculatorUtils - - usage = ImageUsage( - input_tokens=100, - output_tokens=5000, - total_tokens=5100, - input_tokens_details=ImageUsageInputTokensDetails( - text_tokens=100, - image_tokens=0, - ), - ) - - image_response = ImageResponse( - created=1234567890, - data=[ImageObject(url="http://example.com/image.jpg")], - ) - image_response.usage = usage - - cost = CostCalculatorUtils.route_image_generation_cost_calculator( - model="gpt-image-1", - completion_response=image_response, - custom_llm_provider="openai", - ) - - expected_cost = 0.0005 + 0.2 - assert abs(cost - expected_cost) < 1e-6, f"Expected {expected_cost}, got {cost}" - - def test_openai_gpt_image_2_routes_to_token_calculator(self): - """Test that OpenAI gpt-image-2 routes to token-based calculator""" - from litellm.litellm_core_utils.llm_cost_calc.utils import CostCalculatorUtils - - usage = Usage( - prompt_tokens=100, - completion_tokens=5000, - total_tokens=5100, - prompt_tokens_details=PromptTokensDetailsWrapper(text_tokens=100), - completion_tokens_details=CompletionTokensDetailsWrapper(image_tokens=5000), - ) - - image_response = ImageResponse( - created=1234567890, - data=[ImageObject(url="http://example.com/image.jpg")], - ) - image_response.usage = usage - - cost = CostCalculatorUtils.route_image_generation_cost_calculator( - model="gpt-image-2", - completion_response=image_response, - custom_llm_provider="openai", - ) - - expected_cost = 0.0005 + 0.15 - assert abs(cost - expected_cost) < 1e-6, f"Expected {expected_cost}, got {cost}" - def test_openai_dalle_routes_to_pixel_calculator(self): """Test that OpenAI DALL-E still routes to pixel-based calculator""" from litellm.litellm_core_utils.llm_cost_calc.utils import CostCalculatorUtils @@ -283,94 +88,10 @@ class TestGPTImage15OutputImageTokens: and these must be correctly included in cost calculation. """ - def test_gpt_image_15_output_image_tokens_cost(self): - """ - Test that output image tokens are correctly included in cost calculation. - - This tests the fix for issue #19508 where output_tokens_details.image_tokens - were not being included in the cost calculation, causing costs to be - underreported (e.g., $0.046 instead of $0.14). - """ - # Simulate gpt-image-1.5 response with output_tokens_details - # This is what the API returns and what convert_to_image_response transforms - usage = Usage( - prompt_tokens=169, - completion_tokens=4599, - total_tokens=4768, - prompt_tokens_details=PromptTokensDetailsWrapper( - text_tokens=169, - image_tokens=0, - ), - completion_tokens_details=CompletionTokensDetailsWrapper( - text_tokens=439, - image_tokens=4160, - ), - ) - - image_response = ImageResponse( - created=1234567890, - data=[ImageObject(b64_json="test")], - ) - image_response.usage = usage - image_response._hidden_params = {"custom_llm_provider": "openai"} - - cost = litellm.completion_cost( - completion_response=image_response, - model="gpt-image-1.5", - call_type="image_generation", - custom_llm_provider="openai", - ) - - # gpt-image-1.5 pricing: - # - input_cost_per_token: 5e-06 ($5/1M for text input) - # - output_cost_per_token: 1e-05 ($10/1M for text output) - # - output_cost_per_image_token: 3.2e-05 ($32/1M for image output) - # - # Expected cost: - # Input text: 169 * $5/1M = $0.000845 - # Output text: 439 * $10/1M = $0.00439 - # Output image: 4160 * $32/1M = $0.13312 - # Total: $0.138355 - expected_cost = 169 * 5e-06 + 439 * 1e-05 + 4160 * 3.2e-05 - - assert abs(cost - expected_cost) < 1e-6, ( - f"Expected {expected_cost}, got {cost}. " - f"Image tokens may not be included in cost calculation." - ) - class TestCompletionCostIntegration: """Test the full completion_cost integration for gpt-image-1""" - def test_completion_cost_gpt_image_1(self): - """Test completion_cost correctly calculates gpt-image-1 costs""" - usage = ImageUsage( - input_tokens=100, - output_tokens=5000, - total_tokens=5100, - input_tokens_details=ImageUsageInputTokensDetails( - text_tokens=100, - image_tokens=0, - ), - ) - - image_response = ImageResponse( - created=1234567890, - data=[ImageObject(url="http://example.com/image.jpg")], - ) - image_response.usage = usage - image_response._hidden_params = {"custom_llm_provider": "openai"} - - cost = litellm.completion_cost( - completion_response=image_response, - model="gpt-image-1", - call_type="image_generation", - custom_llm_provider="openai", - ) - - expected_cost = 0.0005 + 0.2 - assert abs(cost - expected_cost) < 1e-6, f"Expected {expected_cost}, got {cost}" - class TestGPTImage2OutputImageTokensNoBreakdown: """ @@ -383,77 +104,6 @@ class TestGPTImage2OutputImageTokensNoBreakdown: cost component. """ - def test_gpt_image_2_output_priced_as_image_when_no_breakdown(self): - from litellm.llms.openai.image_generation.cost_calculator import ( - cost_calculator, - ) - - # Mirrors a real gpt-image-2 /v1/images/edits response: input breakdown is - # present, but there is no usable output token breakdown. - usage = ImageUsage( - input_tokens=3987, - output_tokens=5488, - total_tokens=9475, - input_tokens_details=ImageUsageInputTokensDetails( - text_tokens=943, - image_tokens=3044, - ), - ) - - image_response = ImageResponse( - created=1234567890, - data=[ImageObject(b64_json="test")], - ) - image_response.usage = usage - image_response._hidden_params = {"custom_llm_provider": "openai"} - - cost = cost_calculator( - model="gpt-image-2", - image_response=image_response, - custom_llm_provider="openai", - ) - - # gpt-image-2 pricing: - # text input: 943 * $5/1M = 0.004715 - # image input: 3044 * $8/1M = 0.024352 - # image output: 5488 * $30/1M = 0.164640 (NOT text output $10/1M = 0.054880) - expected_cost = 943 * 5e-6 + 3044 * 8e-6 + 5488 * 3e-5 - assert abs(cost - expected_cost) < 1e-6, ( - f"Expected {expected_cost}, got {cost}. Generated image output tokens " - f"are likely being priced at the text output_cost_per_token rate." - ) - - def test_gpt_image_2_chat_usage_without_breakdown_uses_image_rate(self): - from litellm.llms.openai.image_generation.cost_calculator import ( - cost_calculator, - ) - - usage = Usage( - prompt_tokens=600, - completion_tokens=5000, - total_tokens=5600, - prompt_tokens_details=PromptTokensDetailsWrapper( - text_tokens=100, - image_tokens=500, - ), - ) - - image_response = ImageResponse( - created=1234567890, - data=[ImageObject(b64_json="test")], - ) - image_response.usage = usage - image_response._hidden_params = {"custom_llm_provider": "openai"} - - cost = cost_calculator( - model="gpt-image-2", - image_response=image_response, - custom_llm_provider="openai", - ) - - expected_cost = 100 * 5e-6 + 500 * 8e-6 + 5000 * 3e-5 - assert abs(cost - expected_cost) < 1e-6, f"Expected {expected_cost}, got {cost}" - if __name__ == "__main__": pytest.main([__file__, "-v"]) diff --git a/tests/test_litellm/test_gpt_realtime_mode.py b/tests/test_litellm/test_gpt_realtime_mode.py index 8c41e474486..778f620d72e 100644 --- a/tests/test_litellm/test_gpt_realtime_mode.py +++ b/tests/test_litellm/test_gpt_realtime_mode.py @@ -1,6 +1,3 @@ -import json -from pathlib import Path - from typing_extensions import get_args, get_type_hints from litellm.types.utils import ModelInfoBase @@ -44,13 +41,3 @@ ALL_REALTIME_ONLY_GPT_MODELS = REALTIME_ONLY_GPT_MODELS + REALTIME_ONLY_GPT_MODE def test_realtime_is_a_valid_mode_literal(): hints = get_type_hints(ModelInfoBase, include_extras=False) assert "realtime" in get_args(hints["mode"]) - - -def test_backup_matches_main_for_realtime_models(): - repo_root = Path(__file__).parents[2] - with open(repo_root / "model_prices_and_context_window.json") as f: - main_cost = json.load(f) - with open(repo_root / "litellm" / "model_prices_and_context_window_backup.json") as f: - backup_cost = json.load(f) - for model in ALL_REALTIME_ONLY_GPT_MODELS: - assert backup_cost.get(model) == main_cost.get(model) diff --git a/tests/test_litellm/test_mistral_medium_3_5_model_metadata.py b/tests/test_litellm/test_mistral_medium_3_5_model_metadata.py deleted file mode 100644 index d73311baae9..00000000000 --- a/tests/test_litellm/test_mistral_medium_3_5_model_metadata.py +++ /dev/null @@ -1,34 +0,0 @@ -import json -from pathlib import Path - -import pytest - - -REPO_ROOT = Path(__file__).parents[2] -MAIN_PATH = REPO_ROOT / "model_prices_and_context_window.json" -BACKUP_PATH = REPO_ROOT / "litellm" / "model_prices_and_context_window_backup.json" - -MEDIUM_3_5_MODELS = ( - "mistral/mistral-medium-3-5", - "mistral/mistral-medium-2604", - "mistral/mistral-medium-latest", -) - -SYNCED_MODELS = MEDIUM_3_5_MODELS + ( - "mistral/mistral-medium-2508", - "mistral/mistral-medium-3-1-2508", -) - - -def _load(path): - with open(path) as f: - return json.load(f) - - -@pytest.mark.parametrize("model", SYNCED_MODELS) -def test_backup_matches_main(model): - """Ensure the bundled (backup) cost map stays in sync with the canonical file.""" - main_cost = _load(MAIN_PATH) - backup_cost = _load(BACKUP_PATH) - - assert backup_cost.get(model) == main_cost.get(model), f"{model} differs between main and backup model cost maps" diff --git a/tests/test_litellm/test_mistral_small_4_0_model_metadata.py b/tests/test_litellm/test_mistral_small_4_0_model_metadata.py deleted file mode 100644 index 182c444bac9..00000000000 --- a/tests/test_litellm/test_mistral_small_4_0_model_metadata.py +++ /dev/null @@ -1,26 +0,0 @@ -import json -from pathlib import Path - -import pytest - -REPO_ROOT = Path(__file__).parents[2] -MAIN_PATH = REPO_ROOT / "model_prices_and_context_window.json" -BACKUP_PATH = REPO_ROOT / "litellm" / "model_prices_and_context_window_backup.json" - -SMALL_4_0_MODELS = ( - "mistral/mistral-small-latest", - "mistral/mistral-small-2603", -) - - -def _load(path): - with open(path) as f: - return json.load(f) - - -@pytest.mark.parametrize("model", SMALL_4_0_MODELS) -def test_backup_matches_main(model): - main_cost = _load(MAIN_PATH) - backup_cost = _load(BACKUP_PATH) - - assert backup_cost.get(model) == main_cost.get(model), f"{model} differs between main and backup model cost maps" diff --git a/tests/test_litellm/test_mistral_zai_glm_5_2_model_metadata.py b/tests/test_litellm/test_mistral_zai_glm_5_2_model_metadata.py deleted file mode 100644 index 29576eb0119..00000000000 --- a/tests/test_litellm/test_mistral_zai_glm_5_2_model_metadata.py +++ /dev/null @@ -1,74 +0,0 @@ -import json -from pathlib import Path - -import pytest - -import litellm -from litellm.types.utils import PromptTokensDetailsWrapper, Usage -from litellm.utils import supports_prompt_caching, supports_reasoning - -REPO_ROOT = Path(__file__).parents[2] -MAIN_PATH = REPO_ROOT / "model_prices_and_context_window.json" -BACKUP_PATH = REPO_ROOT / "litellm" / "model_prices_and_context_window_backup.json" - -GLM_5_2_MODELS = ("mistral/zai-glm-5-2", "mistral/glm-5-2") - -INPUT_COST = 1.4e-06 -CACHED_INPUT_COST = 1.4e-07 -OUTPUT_COST = 4.4e-06 - - -def _load(path): - with open(path) as f: - return json.load(f) - - -@pytest.fixture -def local_model_cost_map(monkeypatch): - """Force get_model_info to resolve against the in-repo cost map instead of the - remote one fetched at import time, which still carries the pre-merge pricing.""" - monkeypatch.setenv("LITELLM_LOCAL_MODEL_COST_MAP", "True") - monkeypatch.setattr(litellm, "model_cost", litellm.get_model_cost_map(url="")) - litellm.get_model_info.cache_clear() - yield - litellm.get_model_info.cache_clear() - - -@pytest.mark.parametrize("model", GLM_5_2_MODELS) -def test_zai_glm_5_2_capabilities_are_visible_to_callers(local_model_cost_map, model): - """Mistral advertises reasoning and prompt caching on this model, so the helpers - every caller checks before sending a request must say so too.""" - assert supports_reasoning(model=model) is True - assert supports_prompt_caching(model=model) is True - - info = litellm.get_model_info(model=model) - assert info["max_input_tokens"] == 1048576 - assert info["max_output_tokens"] == 131072 - - -@pytest.mark.parametrize("model", GLM_5_2_MODELS) -def test_cached_prompt_tokens_bill_at_the_cached_rate(local_model_cost_map, model): - """A cache hit reports its reused tokens under prompt_tokens_details, and those - tokens cost a tenth of the input rate, not the full rate and not nothing.""" - usage = Usage( - prompt_tokens=21010, - completion_tokens=100, - total_tokens=21110, - prompt_tokens_details=PromptTokensDetailsWrapper(cached_tokens=20992), - ) - - prompt_cost, completion_cost = litellm.cost_per_token( - model=model, usage_object=usage, custom_llm_provider="mistral" - ) - - assert prompt_cost == pytest.approx(18 * INPUT_COST + 20992 * CACHED_INPUT_COST) - assert completion_cost == pytest.approx(100 * OUTPUT_COST) - - -@pytest.mark.parametrize("model", GLM_5_2_MODELS) -def test_backup_matches_main(model): - """Ensure the bundled (backup) cost map stays in sync with the canonical file.""" - main_cost = _load(MAIN_PATH) - backup_cost = _load(BACKUP_PATH) - - assert backup_cost.get(model) == main_cost.get(model), f"{model} differs between main and backup model cost maps" diff --git a/tests/test_litellm/test_muse_spark_1_1_model_metadata.py b/tests/test_litellm/test_muse_spark_1_1_model_metadata.py deleted file mode 100644 index f55266a78d7..00000000000 --- a/tests/test_litellm/test_muse_spark_1_1_model_metadata.py +++ /dev/null @@ -1,29 +0,0 @@ -import json -from pathlib import Path - -from litellm.litellm_core_utils.get_llm_provider_logic import get_llm_provider - -MUSE_SPARK_MODEL = "meta/muse-spark-1.1" - - -def test_muse_spark_1_1_model_info(): - routed_model, provider, _, api_base = get_llm_provider(model=MUSE_SPARK_MODEL, api_key="sk-test") - assert routed_model == "muse-spark-1.1" - assert provider == "meta" - assert api_base == "https://api.meta.ai/v1" - - -def test_muse_spark_1_1_backup_matches_main(): - """Ensure the bundled model cost map stays in sync with the canonical file.""" - repo_root = Path(__file__).parents[2] - main_path = repo_root / "model_prices_and_context_window.json" - backup_path = repo_root / "litellm" / "model_prices_and_context_window_backup.json" - - with open(main_path) as f: - main_cost = json.load(f) - with open(backup_path) as f: - backup_cost = json.load(f) - - assert backup_cost.get(MUSE_SPARK_MODEL) == main_cost.get(MUSE_SPARK_MODEL), ( - f"{MUSE_SPARK_MODEL} differs between main and backup model cost maps" - ) diff --git a/tests/test_litellm/test_muse_spark_1_2_model_metadata.py b/tests/test_litellm/test_muse_spark_1_2_model_metadata.py index 02527a98711..4cc69d9f66d 100644 --- a/tests/test_litellm/test_muse_spark_1_2_model_metadata.py +++ b/tests/test_litellm/test_muse_spark_1_2_model_metadata.py @@ -3,10 +3,7 @@ from pathlib import Path import pytest -import litellm -from litellm.cost_calculator import cost_per_token from litellm.litellm_core_utils.get_llm_provider_logic import get_llm_provider -from litellm.litellm_core_utils.llm_cost_calc.tool_call_cost_tracking import StandardBuiltInToolCostTracking MUSE_SPARK_STANDARD = "meta/muse-spark-1.2" MUSE_SPARK_CONTRIBUTOR = "meta/muse-spark-1.2-contributor" @@ -23,16 +20,6 @@ def _load_cost_map(filename: str = "model_prices_and_context_window.json") -> di return json.load(f) -@pytest.mark.parametrize("model, input_cost, cached_cost, output_cost", PRICING) -def test_muse_spark_1_2_cost_per_token( - local_model_cost_map, model: str, input_cost: float, cached_cost: float, output_cost: float -): - prompt_cost, completion_cost = cost_per_token(model=model, prompt_tokens=1000, completion_tokens=500) - - assert prompt_cost == pytest.approx(1000 * input_cost) - assert completion_cost == pytest.approx(500 * output_cost) - - @pytest.mark.parametrize("model", (MUSE_SPARK_STANDARD, MUSE_SPARK_CONTRIBUTOR)) def test_muse_spark_1_2_routes_to_meta_model_api(model: str): routed_model, provider, _, api_base = get_llm_provider(model=model, api_key="sk-test") @@ -42,22 +29,6 @@ def test_muse_spark_1_2_routes_to_meta_model_api(model: str): assert api_base == "https://api.meta.ai/v1" -@pytest.mark.parametrize("model", (MUSE_SPARK_STANDARD, MUSE_SPARK_CONTRIBUTOR)) -def test_muse_spark_1_2_web_search_cost_per_query(local_model_cost_map, model: str): - info = litellm.get_model_info(model=model) - - assert StandardBuiltInToolCostTracking.get_cost_for_web_search(model_info=info) == WEB_SEARCH_COST_PER_QUERY - - -@pytest.mark.parametrize("model", (MUSE_SPARK_STANDARD, MUSE_SPARK_CONTRIBUTOR)) -def test_muse_spark_1_2_backup_matches_main(model: str): - """Ensure the bundled model cost map stays in sync with the canonical file.""" - main_cost = _load_cost_map() - backup_cost = _load_cost_map("litellm/model_prices_and_context_window_backup.json") - - assert backup_cost.get(model) == main_cost.get(model), f"{model} differs between main and backup model cost maps" - - def test_muse_spark_contributor_tier_is_cheaper_than_standard(): cost_map = _load_cost_map() standard = cost_map[MUSE_SPARK_STANDARD] diff --git a/tests/test_litellm/test_muse_spark_1_3_model_metadata.py b/tests/test_litellm/test_muse_spark_1_3_model_metadata.py index 92b099fc780..097dbc2923d 100644 --- a/tests/test_litellm/test_muse_spark_1_3_model_metadata.py +++ b/tests/test_litellm/test_muse_spark_1_3_model_metadata.py @@ -4,7 +4,6 @@ from pathlib import Path import pytest import litellm -from litellm.cost_calculator import cost_per_token from litellm.litellm_core_utils.get_llm_provider_logic import get_llm_provider from litellm.litellm_core_utils.llm_cost_calc.tool_call_cost_tracking import StandardBuiltInToolCostTracking @@ -23,16 +22,6 @@ def _load_cost_map(filename: str = "model_prices_and_context_window.json") -> di return json.load(f) -@pytest.mark.parametrize("model, input_cost, cached_cost, output_cost", PRICING) -def test_muse_spark_1_3_cost_per_token( - local_model_cost_map, model: str, input_cost: float, cached_cost: float, output_cost: float -): - prompt_cost, completion_cost = cost_per_token(model=model, prompt_tokens=1000, completion_tokens=500) - - assert prompt_cost == pytest.approx(1000 * input_cost) - assert completion_cost == pytest.approx(500 * output_cost) - - @pytest.mark.parametrize("model", (MUSE_SPARK_STANDARD, MUSE_SPARK_CONTRIBUTOR)) def test_muse_spark_1_3_routes_to_meta_model_api(model: str): routed_model, provider, _, api_base = get_llm_provider(model=model, api_key="sk-test") @@ -49,15 +38,6 @@ def test_muse_spark_1_3_web_search_cost_per_query(local_model_cost_map, model: s assert StandardBuiltInToolCostTracking.get_cost_for_web_search(model_info=info) == WEB_SEARCH_COST_PER_QUERY -@pytest.mark.parametrize("model", (MUSE_SPARK_STANDARD, MUSE_SPARK_CONTRIBUTOR)) -def test_muse_spark_1_3_backup_matches_main(model: str): - """Ensure the bundled model cost map stays in sync with the canonical file.""" - main_cost = _load_cost_map() - backup_cost = _load_cost_map("litellm/model_prices_and_context_window_backup.json") - - assert backup_cost.get(model) == main_cost.get(model), f"{model} differs between main and backup model cost maps" - - def test_muse_spark_contributor_tier_is_cheaper_than_standard(): cost_map = _load_cost_map() standard = cost_map[MUSE_SPARK_STANDARD] diff --git a/tests/test_litellm/test_openai_service_tier_long_context_pricing.py b/tests/test_litellm/test_openai_service_tier_long_context_pricing.py index 8027d64d1ed..0cc564535ba 100644 --- a/tests/test_litellm/test_openai_service_tier_long_context_pricing.py +++ b/tests/test_litellm/test_openai_service_tier_long_context_pricing.py @@ -106,27 +106,3 @@ def test_cost_per_token_bills_long_context_at_the_tier_rate( ) assert input_cost == pytest.approx(LONG_CONTEXT_PROMPT_TOKENS * input_rate) assert output_cost == pytest.approx(COMPLETION_TOKENS * output_rate) - - -@pytest.mark.parametrize("model,tier,input_rate,output_rate", TIERED_COST_CASES) -def test_cost_per_token_tier_differs_from_the_standard_long_context_cost( - model: str, tier: str, input_rate: float, output_rate: float -) -> None: - """Flex halves the standard long-context bill and priority doubles it.""" - ratio = 0.5 if tier == "flex" else 2.0 - standard = sum( - litellm.cost_per_token( - model=model, - prompt_tokens=LONG_CONTEXT_PROMPT_TOKENS, - completion_tokens=COMPLETION_TOKENS, - ) - ) - tiered = sum( - litellm.cost_per_token( - model=model, - prompt_tokens=LONG_CONTEXT_PROMPT_TOKENS, - completion_tokens=COMPLETION_TOKENS, - service_tier=tier, - ) - ) - assert tiered == pytest.approx(standard * ratio) diff --git a/tests/test_litellm/test_replicate_model_key_format.py b/tests/test_litellm/test_replicate_model_key_format.py index 77ae5e1b069..39e9fa01d73 100644 --- a/tests/test_litellm/test_replicate_model_key_format.py +++ b/tests/test_litellm/test_replicate_model_key_format.py @@ -18,18 +18,3 @@ def test_replicate_models_have_valid_key_prefix(model_cost: dict[str, Any]) -> N assert not malformed, ( f"Replicate models must use 'replicate/owner/model' key format, found malformed keys: {malformed}" ) - - -def test_replicate_backup_matches_main() -> None: - repo_root = Path(__file__).parents[2] - main_path = repo_root / "model_prices_and_context_window.json" - backup_path = repo_root / "litellm" / "model_prices_and_context_window_backup.json" - - with open(main_path, encoding="utf-8") as f: - main_cost: dict[str, Any] = json.load(f) - with open(backup_path, encoding="utf-8") as f: - backup_cost: dict[str, Any] = json.load(f) - - for key in main_cost: - if main_cost[key].get("litellm_provider") == "replicate": - assert backup_cost.get(key) == main_cost.get(key), f"{key} differs between main and backup model cost maps" diff --git a/tests/test_litellm/test_sambanova_model_metadata.py b/tests/test_litellm/test_sambanova_model_metadata.py deleted file mode 100644 index 20f34f9f3cc..00000000000 --- a/tests/test_litellm/test_sambanova_model_metadata.py +++ /dev/null @@ -1,25 +0,0 @@ -import json -from pathlib import Path - -from litellm.litellm_core_utils.get_llm_provider_logic import get_llm_provider - - -def test_sambanova_minimax_m27_model_info(): - model = "sambanova/MiniMax-M2.7" - json_path = Path(__file__).parents[2] / "model_prices_and_context_window.json" - with open(json_path) as f: - model_cost = json.load(f) - - info = model_cost.get(model) - assert info is not None, f"{model} not found in model_prices_and_context_window.json" - assert info["litellm_provider"] == "sambanova" - assert info["mode"] == "chat" - assert info["input_cost_per_token"] > 0 - assert info["output_cost_per_token"] > 0 - assert info["supports_function_calling"] is True - assert info["supports_reasoning"] is True - assert info["supports_tool_choice"] is True - - routed_model, provider, _, _ = get_llm_provider(model=model) - assert routed_model == "MiniMax-M2.7" - assert provider == "sambanova" diff --git a/tests/test_litellm/test_together_ai_model_metadata.py b/tests/test_litellm/test_together_ai_model_metadata.py index 99e93ae2865..851d0f0c9d8 100644 --- a/tests/test_litellm/test_together_ai_model_metadata.py +++ b/tests/test_litellm/test_together_ai_model_metadata.py @@ -5,7 +5,6 @@ from typing import Final import pytest from pydantic import TypeAdapter - REPO_ROOT: Final = Path(__file__).parents[2] CostMap = dict[str, dict[str, object]] @@ -109,14 +108,6 @@ def test_together_successor_metadata_points_at_live_models(cost_map: CostMap): assert "deprecation_date" not in target, f"{model} names deprecated successor {successor}" -def test_together_backup_cost_map_in_sync(cost_map: CostMap): - with open(REPO_ROOT / "litellm" / "model_prices_and_context_window_backup.json") as f: - backup = COST_MAP_ADAPTER.validate_python(json.load(f)) - together_main = {k: v for k, v in cost_map.items() if k.startswith("together_ai/")} - together_backup = {k: v for k, v in backup.items() if k.startswith("together_ai/")} - assert together_backup == together_main - - CACHED_INPUT_MODELS: Final = ( "together_ai/moonshotai/Kimi-K3", "together_ai/zai-org/GLM-5.2", diff --git a/tests/test_litellm/test_utils.py b/tests/test_litellm/test_utils.py index 02196a9cd26..bad531fdd51 100644 --- a/tests/test_litellm/test_utils.py +++ b/tests/test_litellm/test_utils.py @@ -6,9 +6,9 @@ import logging import os import queue import threading -from datetime import datetime, timedelta, timezone from collections.abc import Callable, Iterator from concurrent.futures import Future, ThreadPoolExecutor +from datetime import datetime, timedelta, timezone from typing import Final from unittest.mock import AsyncMock, MagicMock, patch @@ -17,10 +17,8 @@ import pytest import respx from jsonschema import validate - import litellm from litellm._internal_context import is_internal_call -from litellm.constants import DEFAULT_MOCK_RESPONSE_COMPLETION_TOKEN_COUNT from litellm._logging import ( CorrelationContextFilter, JsonFormatter, @@ -28,10 +26,12 @@ from litellm._logging import ( trace_id_var, verbose_logger, ) +from litellm.constants import DEFAULT_MOCK_RESPONSE_COMPLETION_TOKEN_COUNT from litellm.integrations.custom_logger import CustomLogger from litellm.litellm_core_utils.get_litellm_params import get_litellm_params from litellm.litellm_core_utils.thread_pool_executor import executor as logging_executor from litellm.proxy.utils import is_valid_api_key +from litellm.types.router import CredentialLiteLLMParams, GenericLiteLLMParams from litellm.types.utils import ( CallTypes, Delta, @@ -40,9 +40,9 @@ from litellm.types.utils import ( PromptTokensDetailsWrapper, StreamingChoices, Usage, + all_litellm_params, + bedrock_batch_litellm_params, ) -from litellm.types.utils import all_litellm_params, bedrock_batch_litellm_params -from litellm.types.router import CredentialLiteLLMParams, GenericLiteLLMParams from litellm.utils import ( ProviderConfigManager, TextCompletionStreamWrapper, @@ -53,7 +53,6 @@ from litellm.utils import ( async_post_call_failure_deployment_hook, async_post_call_success_deployment_hook, client, - get_llm_provider, get_non_default_completion_params, get_optional_params_image_gen, get_prompt_cache_min_tokens, @@ -154,36 +153,6 @@ def test_prompt_tokens_details_cache_write_creation_stay_in_sync_on_assignment() assert details.cache_write_tokens == details.cache_creation_tokens == 375 -def test_get_model_info_surfaces_supports_adaptive_thinking(local_model_cost_map): - """supports_adaptive_thinking must flow through get_model_info like every other - capability flag: both from an explicit cost-map entry and from a - fallback-generalization rule for an unmapped model. Regression: the field shipped - in the JSON but was never declared on ModelInfo nor copied during construction, so - get_model_info (and _supports_factory) silently dropped it for any provider-prefixed - or unmapped name.""" - explicit = litellm.get_model_info(model="claude-opus-4-8") - assert explicit["supports_adaptive_thinking"] is True - - generalized = litellm.get_model_info( - model="claude-opus-4-9", custom_llm_provider="anthropic" - ) - assert generalized["supports_adaptive_thinking"] is True - - -def test_get_model_info_surfaces_supports_parallel_function_calling(local_model_cost_map): - """A registry entry's supports_parallel_function_calling must read back through get_model_info - and litellm.supports_parallel_function_calling. Regression: the key was never copied into - ModelInfo, so provider-prefixed entries read None / False even when the map said True, and an - explicit False was indistinguishable from unset.""" - declared_true = litellm.get_model_info(model="together_ai/zai-org/GLM-5.3-Flash") - assert declared_true["supports_parallel_function_calling"] is True - assert litellm.supports_parallel_function_calling(model="together_ai/zai-org/GLM-5.3-Flash") is True - - declared_false = litellm.get_model_info(model="o3-mini") - assert declared_false["supports_parallel_function_calling"] is False - assert litellm.supports_parallel_function_calling(model="o3-mini") is False - - def test_get_model_info_surfaces_supported_endpoints(local_model_cost_map): """supported_endpoints ships in the cost map and is declared on ModelInfoBase, but the constructor never copied it, so get_model_info always returned None. @@ -198,9 +167,7 @@ def test_potential_model_names_keeps_provider_prefixed_candidate(): Agent API serves `perplexity/glm-5.2`, mapped as `perplexity/perplexity/glm-5.2`) needs the un-stripped `/` candidate. Every other candidate reads the leading `perplexity/` as the litellm prefix and strips it away.""" - already_prefixed = _get_potential_model_names( - model="perplexity/glm-5.2", custom_llm_provider="perplexity" - ) + already_prefixed = _get_potential_model_names(model="perplexity/glm-5.2", custom_llm_provider="perplexity") assert already_prefixed["provider_prefixed_model_name"] == "perplexity/perplexity/glm-5.2" assert already_prefixed["split_model"] == "glm-5.2" assert already_prefixed["combined_model_name"] == "perplexity/glm-5.2" @@ -210,104 +177,24 @@ def test_potential_model_names_keeps_provider_prefixed_candidate(): assert bare["provider_prefixed_model_name"] == bare["combined_model_name"] == "perplexity/glm-5.2" -def test_get_model_info_resolves_provider_prefixed_model_ids(local_model_cost_map): - """Perplexity's Agent API third-party models are keyed `perplexity/perplexity/` - because Perplexity's own id already starts with `perplexity/`. Callers run - `get_llm_provider` first, which hands `_get_potential_model_names` model - `perplexity/glm-5.2` with provider `perplexity`, and every candidate but the - provider-prefixed one strips that second `perplexity/` off. Regression: the - entries were unreachable from `supports_reasoning` and from the cost calculator's - per-token fallback, so a mapped model reported no reasoning support and raised - "This model isn't mapped yet" on the only path where its rates are ever used.""" - for model, reasoning in ( - ("perplexity/perplexity/glm-5.2", True), - ("perplexity/perplexity/kimi-k3", True), - ("perplexity/perplexity/deepseek-v4-flash-0731", True), - ("perplexity/perplexity/kimi-k2.7-code", False), - ("perplexity/perplexity/nemotron-3.5-lightning-30b-a3b", True), - ("perplexity/perplexity/nemotron-3-ultra-550b-a55b", True), - ): - assert litellm.supports_reasoning(model=model) is reasoning, model - - via_provider = litellm.get_model_info( - model="perplexity/glm-5.2", custom_llm_provider="perplexity" - ) - assert via_provider["key"] == "perplexity/perplexity/glm-5.2" - assert via_provider["input_cost_per_token"] == 1.4e-06 - assert via_provider["output_cost_per_token"] == 4.4e-06 - assert via_provider["mode"] == "responses" - - lightning = litellm.get_model_info( - model="perplexity/nemotron-3.5-lightning-30b-a3b", custom_llm_provider="perplexity" - ) - assert lightning["key"] == "perplexity/perplexity/nemotron-3.5-lightning-30b-a3b" - assert lightning["input_cost_per_token"] == 1.15e-08 - assert lightning["output_cost_per_token"] == 1.7e-07 - assert lightning["cache_read_input_token_cost"] == 1.15e-09 - assert lightning["mode"] == "responses" - - ultra = litellm.get_model_info(model="perplexity/perplexity/nemotron-3-ultra-550b-a55b") - assert ultra["key"] == "perplexity/perplexity/nemotron-3-ultra-550b-a55b" - - def test_get_model_info_strips_openai_finetune_ids_without_a_custom_suffix(local_model_cost_map): info = litellm.get_model_info(model="ft:gpt-4o-2024-08-06:my-org::abc123", custom_llm_provider="openai") assert info["key"] == "ft:gpt-4o-2024-08-06" -def test_provider_prefixed_lookup_never_outranks_an_existing_row(local_model_cost_map): - """The provider-prefixed candidate is tried last, after every candidate that - already existed, so no model that resolves today can change answer. `perplexity/sonar` - is the case that proves it: both `perplexity/sonar` and `perplexity/perplexity/sonar` - are cost-map keys, and the shorter one must keep winning.""" - sonar = litellm.get_model_info(model="sonar", custom_llm_provider="perplexity") - assert sonar["key"] == "perplexity/sonar" - assert sonar["mode"] == "chat" - assert sonar["input_cost_per_token"] == 1e-06 - - still_sonar = litellm.get_model_info( - model="perplexity/sonar", custom_llm_provider="perplexity" - ) - assert still_sonar["key"] == "perplexity/sonar" - assert still_sonar["mode"] == "chat" - - for model, provider, expected_key in ( - ("claude-sonnet-4-5", "anthropic", "claude-sonnet-4-5"), - ("anthropic/claude-sonnet-4-5", "anthropic", "claude-sonnet-4-5"), - ("gemini/gemini-2.0-flash", "gemini", "gemini/gemini-2.0-flash"), - ("openrouter/openai/gpt-4o", "openrouter", "openrouter/openai/gpt-4o"), - ): - assert litellm.get_model_info(model=model, custom_llm_provider=provider)["key"] == expected_key - - def test_check_provider_match_azure_ai_allows_openai_and_azure(): """ Test that azure_ai provider can match openai and azure models. This is needed for Azure Model Router which can route to OpenAI models. """ # azure_ai should match openai models - assert ( - _check_provider_match( - model_info={"litellm_provider": "openai"}, custom_llm_provider="azure_ai" - ) - is True - ) + assert _check_provider_match(model_info={"litellm_provider": "openai"}, custom_llm_provider="azure_ai") is True # azure_ai should match azure models - assert ( - _check_provider_match( - model_info={"litellm_provider": "azure"}, custom_llm_provider="azure_ai" - ) - is True - ) + assert _check_provider_match(model_info={"litellm_provider": "azure"}, custom_llm_provider="azure_ai") is True # azure_ai should NOT match other providers - assert ( - _check_provider_match( - model_info={"litellm_provider": "anthropic"}, custom_llm_provider="azure_ai" - ) - is False - ) + assert _check_provider_match(model_info={"litellm_provider": "anthropic"}, custom_llm_provider="azure_ai") is False def test_check_provider_match_github_allows_upstream_provider_metadata(): @@ -342,21 +229,11 @@ def test_check_provider_match_github_allows_upstream_provider_metadata(): def test_supports_function_calling_github_openai_alias(): assert litellm.utils.supports_function_calling(model="github/gpt-4o-mini") is True - assert ( - litellm.utils.supports_function_calling( - model="gpt-4o-mini", custom_llm_provider="github" - ) - is True - ) + assert litellm.utils.supports_function_calling(model="gpt-4o-mini", custom_llm_provider="github") is True def test_supports_function_calling_github_anthropic_alias(): - assert ( - litellm.utils.supports_function_calling( - model="github/claude-3-7-sonnet-20250219" - ) - is True - ) + assert litellm.utils.supports_function_calling(model="github/claude-3-7-sonnet-20250219") is True def test_supports_function_calling_deepinfra_llama(): @@ -364,21 +241,11 @@ def test_supports_function_calling_deepinfra_llama(): Regression test for https://github.com/BerriAI/litellm/issues/22619 """ - assert ( - litellm.utils.supports_function_calling( - model="deepinfra/meta-llama/Llama-3.3-70B-Instruct-Turbo" - ) - is True - ) + assert litellm.utils.supports_function_calling(model="deepinfra/meta-llama/Llama-3.3-70B-Instruct-Turbo") is True def test_supports_function_calling_unknown_github_alias_returns_false(): - assert ( - litellm.utils.supports_function_calling( - model="github/non-existent-model-for-capability-check" - ) - is False - ) + assert litellm.utils.supports_function_calling(model="github/non-existent-model-for-capability-check") is False def test_get_optional_params_image_gen(): @@ -462,9 +329,7 @@ def test_get_optional_params_image_gen_vertex_ai_size(): drop_params=True, ) assert optional_params is not None - assert ( - "aspectRatio" not in optional_params - ) # aspectRatio should not be set if size is not provided + assert "aspectRatio" not in optional_params # aspectRatio should not be set if size is not provided assert optional_params["sampleCount"] == 1 @@ -493,26 +358,19 @@ def test_all_model_configs(): VertexAILlama3Config, ) - assert ( - "max_completion_tokens" - in VertexAILlama3Config().get_supported_openai_params(model="llama3") - ) - assert VertexAILlama3Config().map_openai_params( - {"max_completion_tokens": 10}, {}, "llama3", drop_params=False - ) == {"max_tokens": 10} + assert "max_completion_tokens" in VertexAILlama3Config().get_supported_openai_params(model="llama3") + assert VertexAILlama3Config().map_openai_params({"max_completion_tokens": 10}, {}, "llama3", drop_params=False) == { + "max_tokens": 10 + } - assert "max_completion_tokens" in VertexAIAi21Config().get_supported_openai_params( - model="jamba-1.5-mini@001" - ) + assert "max_completion_tokens" in VertexAIAi21Config().get_supported_openai_params(model="jamba-1.5-mini@001") assert VertexAIAi21Config().map_openai_params( {"max_completion_tokens": 10}, {}, "jamba-1.5-mini@001", drop_params=False ) == {"max_tokens": 10} from litellm.llms.fireworks_ai.chat.transformation import FireworksAIConfig - assert "max_completion_tokens" in FireworksAIConfig().get_supported_openai_params( - model="llama3" - ) + assert "max_completion_tokens" in FireworksAIConfig().get_supported_openai_params(model="llama3") assert FireworksAIConfig().map_openai_params( model="llama3", non_default_params={"max_completion_tokens": 10}, @@ -522,9 +380,7 @@ def test_all_model_configs(): from litellm.llms.nvidia_nim.chat.transformation import NvidiaNimConfig - assert "max_completion_tokens" in NvidiaNimConfig().get_supported_openai_params( - model="llama3" - ) + assert "max_completion_tokens" in NvidiaNimConfig().get_supported_openai_params(model="llama3") assert NvidiaNimConfig().map_openai_params( model="llama3", non_default_params={"max_completion_tokens": 10}, @@ -534,9 +390,7 @@ def test_all_model_configs(): from litellm.llms.ollama.chat.transformation import OllamaChatConfig - assert "max_completion_tokens" in OllamaChatConfig().get_supported_openai_params( - model="llama3" - ) + assert "max_completion_tokens" in OllamaChatConfig().get_supported_openai_params(model="llama3") assert OllamaChatConfig().map_openai_params( model="llama3", non_default_params={"max_completion_tokens": 10}, @@ -546,9 +400,7 @@ def test_all_model_configs(): from litellm.llms.predibase.chat.transformation import PredibaseConfig - assert "max_completion_tokens" in PredibaseConfig().get_supported_openai_params( - model="llama3" - ) + assert "max_completion_tokens" in PredibaseConfig().get_supported_openai_params(model="llama3") assert PredibaseConfig().map_openai_params( model="llama3", non_default_params={"max_completion_tokens": 10}, @@ -560,10 +412,7 @@ def test_all_model_configs(): CodestralTextCompletionConfig, ) - assert ( - "max_completion_tokens" - in CodestralTextCompletionConfig().get_supported_openai_params(model="llama3") - ) + assert "max_completion_tokens" in CodestralTextCompletionConfig().get_supported_openai_params(model="llama3") assert CodestralTextCompletionConfig().map_openai_params( model="llama3", non_default_params={"max_completion_tokens": 10}, @@ -575,9 +424,7 @@ def test_all_model_configs(): VolcEngineChatConfig as VolcEngineConfig, ) - assert "max_completion_tokens" in VolcEngineConfig().get_supported_openai_params( - model="llama3" - ) + assert "max_completion_tokens" in VolcEngineConfig().get_supported_openai_params(model="llama3") assert VolcEngineConfig().map_openai_params( model="llama3", non_default_params={"max_completion_tokens": 10}, @@ -587,9 +434,7 @@ def test_all_model_configs(): from litellm.llms.ai21.chat.transformation import AI21ChatConfig - assert "max_completion_tokens" in AI21ChatConfig().get_supported_openai_params( - "jamba-1.5-mini@001" - ) + assert "max_completion_tokens" in AI21ChatConfig().get_supported_openai_params("jamba-1.5-mini@001") assert AI21ChatConfig().map_openai_params( model="jamba-1.5-mini@001", non_default_params={"max_completion_tokens": 10}, @@ -599,9 +444,7 @@ def test_all_model_configs(): from litellm.llms.azure.chat.gpt_transformation import AzureOpenAIConfig - assert "max_completion_tokens" in AzureOpenAIConfig().get_supported_openai_params( - model="gpt-3.5-turbo" - ) + assert "max_completion_tokens" in AzureOpenAIConfig().get_supported_openai_params(model="gpt-3.5-turbo") assert AzureOpenAIConfig().map_openai_params( model="gpt-3.5-turbo", non_default_params={"max_completion_tokens": 10}, @@ -612,11 +455,8 @@ def test_all_model_configs(): from litellm.llms.bedrock.chat.converse_transformation import AmazonConverseConfig - assert ( - "max_completion_tokens" - in AmazonConverseConfig().get_supported_openai_params( - model="anthropic.claude-3-sonnet-20240229-v1:0" - ) + assert "max_completion_tokens" in AmazonConverseConfig().get_supported_openai_params( + model="anthropic.claude-3-sonnet-20240229-v1:0" ) assert AmazonConverseConfig().map_openai_params( model="anthropic.claude-3-sonnet-20240229-v1:0", @@ -629,10 +469,7 @@ def test_all_model_configs(): CodestralTextCompletionConfig, ) - assert ( - "max_completion_tokens" - in CodestralTextCompletionConfig().get_supported_openai_params(model="llama3") - ) + assert "max_completion_tokens" in CodestralTextCompletionConfig().get_supported_openai_params(model="llama3") assert CodestralTextCompletionConfig().map_openai_params( model="llama3", non_default_params={"max_completion_tokens": 10}, @@ -642,11 +479,8 @@ def test_all_model_configs(): from litellm import AmazonAnthropicClaudeConfig, AmazonAnthropicConfig - assert ( - "max_completion_tokens" - in AmazonAnthropicClaudeConfig().get_supported_openai_params( - model="anthropic.claude-3-sonnet-20240229-v1:0" - ) + assert "max_completion_tokens" in AmazonAnthropicClaudeConfig().get_supported_openai_params( + model="anthropic.claude-3-sonnet-20240229-v1:0" ) assert AmazonAnthropicClaudeConfig().map_openai_params( @@ -656,10 +490,7 @@ def test_all_model_configs(): drop_params=False, ) == {"max_tokens": 10} - assert ( - "max_completion_tokens" - in AmazonAnthropicConfig().get_supported_openai_params(model="") - ) + assert "max_completion_tokens" in AmazonAnthropicConfig().get_supported_openai_params(model="") assert AmazonAnthropicConfig().map_openai_params( non_default_params={"max_completion_tokens": 10}, @@ -683,12 +514,7 @@ def test_all_model_configs(): VertexAIAnthropicConfig, ) - assert ( - "max_completion_tokens" - in VertexAIAnthropicConfig().get_supported_openai_params( - model="claude-sonnet-4-6" - ) - ) + assert "max_completion_tokens" in VertexAIAnthropicConfig().get_supported_openai_params(model="claude-sonnet-4-6") assert VertexAIAnthropicConfig().map_openai_params( non_default_params={"max_completion_tokens": 10}, @@ -702,9 +528,7 @@ def test_all_model_configs(): VertexGeminiConfig, ) - assert "max_completion_tokens" in VertexGeminiConfig().get_supported_openai_params( - model="gemini-1.0-pro" - ) + assert "max_completion_tokens" in VertexGeminiConfig().get_supported_openai_params(model="gemini-1.0-pro") assert VertexGeminiConfig().map_openai_params( model="gemini-1.0-pro", @@ -713,12 +537,7 @@ def test_all_model_configs(): drop_params=False, ) == {"max_output_tokens": 10} - assert ( - "max_completion_tokens" - in GoogleAIStudioGeminiConfig().get_supported_openai_params( - model="gemini-1.0-pro" - ) - ) + assert "max_completion_tokens" in GoogleAIStudioGeminiConfig().get_supported_openai_params(model="gemini-1.0-pro") assert GoogleAIStudioGeminiConfig().map_openai_params( model="gemini-1.0-pro", @@ -727,9 +546,7 @@ def test_all_model_configs(): drop_params=False, ) == {"max_output_tokens": 10} - assert "max_completion_tokens" in VertexGeminiConfig().get_supported_openai_params( - model="gemini-1.0-pro" - ) + assert "max_completion_tokens" in VertexGeminiConfig().get_supported_openai_params(model="gemini-1.0-pro") assert VertexGeminiConfig().map_openai_params( model="gemini-1.0-pro", @@ -752,12 +569,10 @@ def test_anthropic_web_search_in_model_info(monkeypatch): model_info = get_model_info(model) assert model_info is not None - assert ( - model_info["supports_web_search"] is True - ), f"Model {model} should support web search" - assert ( - model_info["search_context_cost_per_query"] is not None - ), f"Model {model} should have a search context cost per query" + assert model_info["supports_web_search"] is True, f"Model {model} should support web search" + assert model_info["search_context_cost_per_query"] is not None, ( + f"Model {model} should have a search context cost per query" + ) def test_cohere_embedding_optional_params(): @@ -867,9 +682,7 @@ def validate_model_cost_values(model_data, exceptions=None): continue if isinstance(cost_value, (int, float)) and cost_value > 1: - violations.append( - f"Model '{model_id}' has {field} = {cost_value} which exceeds 1" - ) + violations.append(f"Model '{model_id}' has {field} = {cost_value} which exceeds 1") # Check nested cost fields for field in nested_cost_fields: @@ -913,12 +726,8 @@ def test_aaamodel_prices_and_context_window_json_is_valid(): "cache_creation_input_token_cost_above_200k_tokens": {"type": "number"}, "cache_creation_input_token_cost_above_256k_tokens": {"type": "number"}, "cache_creation_input_token_cost_above_272k_tokens": {"type": "number"}, - "cache_creation_input_token_cost_above_272k_tokens_flex": { - "type": "number" - }, - "cache_creation_input_token_cost_above_272k_tokens_priority": { - "type": "number" - }, + "cache_creation_input_token_cost_above_272k_tokens_flex": {"type": "number"}, + "cache_creation_input_token_cost_above_272k_tokens_priority": {"type": "number"}, "cache_creation_input_token_cost_flex": {"type": "number"}, "cache_creation_input_token_cost_priority": {"type": "number"}, "cache_read_input_token_cost": {"type": "number"}, @@ -926,13 +735,9 @@ def test_aaamodel_prices_and_context_window_json_is_valid(): "cache_read_input_token_cost_above_200k_tokens": {"type": "number"}, "cache_read_input_token_cost_above_256k_tokens": {"type": "number"}, "cache_read_input_token_cost_above_272k_tokens": {"type": "number"}, - "cache_read_input_token_cost_above_272k_tokens_flex": { - "type": "number" - }, + "cache_read_input_token_cost_above_272k_tokens_flex": {"type": "number"}, "cache_read_input_token_cost_above_512k_tokens": {"type": "number"}, - "cache_creation_input_token_cost_above_1hr_above_200k_tokens": { - "type": "number" - }, + "cache_creation_input_token_cost_above_1hr_above_200k_tokens": {"type": "number"}, "cache_read_input_audio_token_cost": {"type": "number"}, "audio_transcription_config": {"type": "string"}, "deprecation_date": {"type": "string"}, @@ -952,12 +757,8 @@ def test_aaamodel_prices_and_context_window_json_is_valid(): "input_cost_per_token_above_512k_tokens": {"type": "number"}, "cache_read_input_token_cost_flex": {"type": "number"}, "cache_read_input_token_cost_priority": {"type": "number"}, - "cache_read_input_token_cost_above_200k_tokens_priority": { - "type": "number" - }, - "cache_read_input_token_cost_above_272k_tokens_priority": { - "type": "number" - }, + "cache_read_input_token_cost_above_200k_tokens_priority": {"type": "number"}, + "cache_read_input_token_cost_above_272k_tokens_priority": {"type": "number"}, "input_cost_per_token_flex": {"type": "number"}, "input_cost_per_token_priority": {"type": "number"}, "input_cost_per_token_above_200k_tokens_priority": {"type": "number"}, @@ -985,9 +786,7 @@ def test_aaamodel_prices_and_context_window_json_is_valid(): "input_cost_per_token_cache_hit": {"type": "number"}, "input_cost_per_video_per_second": {"type": "number"}, "input_cost_per_video_per_second_above_8s_interval": {"type": "number"}, - "input_cost_per_video_per_second_above_15s_interval": { - "type": "number" - }, + "input_cost_per_video_per_second_above_15s_interval": {"type": "number"}, "input_cost_per_video_per_second_above_128k_tokens": {"type": "number"}, "input_dbu_cost_per_token": {"type": "number"}, "annotation_cost_per_page": {"type": "number"}, @@ -1215,18 +1014,12 @@ def test_aaamodel_prices_and_context_window_json_is_valid(): }, } - prod_json = os.path.join( - os.path.dirname(__file__), "..", "..", "model_prices_and_context_window.json" - ) + prod_json = os.path.join(os.path.dirname(__file__), "..", "..", "model_prices_and_context_window.json") with open(prod_json, "r") as model_prices_file: actual_json = json.load(model_prices_file) assert isinstance(actual_json, dict) - actual_json.pop( - "sample_spec", None - ) # remove the sample, whose schema is inconsistent with the real data - actual_json.pop( - "fallback_generalizations", None - ) # reserved meta key, not a model entry + actual_json.pop("sample_spec", None) # remove the sample, whose schema is inconsistent with the real data + actual_json.pop("fallback_generalizations", None) # reserved meta key, not a model entry # Validate schema validate(actual_json, INTENDED_SCHEMA) @@ -1262,9 +1055,7 @@ def test_max_tokens_consistency(): from pathlib import Path # Load the model configuration - config_path = ( - Path(__file__).parent.parent.parent / "model_prices_and_context_window.json" - ) + config_path = Path(__file__).parent.parent.parent / "model_prices_and_context_window.json" with open(config_path, "r") as f: models = json.load(f) @@ -1294,7 +1085,9 @@ def test_max_tokens_consistency(): if inconsistencies: error_msg = f"\n\n❌ Found {len(inconsistencies)} models with max_tokens != max_output_tokens:\n\n" for item in inconsistencies[:10]: # Show first 10 - error_msg += f" {item['model']}: max_tokens={item['max_tokens']}, max_output_tokens={item['max_output_tokens']}\n" + error_msg += ( + f" {item['model']}: max_tokens={item['max_tokens']}, max_output_tokens={item['max_output_tokens']}\n" + ) if len(inconsistencies) > 10: error_msg += f"\n ... and {len(inconsistencies) - 10} more\n" @@ -1303,28 +1096,6 @@ def test_max_tokens_consistency(): raise AssertionError(error_msg) -def test_get_model_info_gemini(monkeypatch): - """ - Tests if ALL gemini models have 'tpm' and 'rpm' in the model info - """ - monkeypatch.setenv("LITELLM_LOCAL_MODEL_COST_MAP", "True") - litellm.model_cost = litellm.get_model_cost_map(url="") - - model_map = litellm.model_cost - for model, info in model_map.items(): - if ( - model.startswith("gemini/") - and not "gemma" in model - and not "learnlm" in model - and not "imagen" in model - and not "veo" in model - and not "lyria" in model - and not "robotics" in model - ): - assert info.get("tpm") is not None, f"{model} does not have tpm" - assert info.get("rpm") is not None, f"{model} does not have rpm" - - def test_get_model_info_bedrock_regional_inference_profile_pricing(local_model_cost_map): """Regression LIT-4056: with the bedrock/ routing prefix (plain, converse/, or invoke/), the exact regional cost-map entry must win over the region-stripped @@ -1347,14 +1118,6 @@ def test_get_model_info_bedrock_regional_inference_profile_pricing(local_model_c assert control["key"] == "au.anthropic.claude-opus-4-8" -def test_get_model_info_bedrock_regional_profile_without_entry_falls_back_to_base(local_model_cost_map): - """A regional profile with no dedicated cost-map entry must still resolve to its - region-stripped base entry.""" - assert "apac.anthropic.claude-opus-4-8" not in litellm.model_cost - info = litellm.get_model_info(model="bedrock/apac.anthropic.claude-opus-4-8") - assert info["key"] == "anthropic.claude-opus-4-8" - - def test_get_model_info_bedrock_double_provider_prefix_resolves(local_model_cost_map): """A doubled bedrock/ prefix routes at runtime via strip_bedrock_routing_prefix, so model info must resolve it to the same entry the request actually bills as.""" @@ -1369,15 +1132,10 @@ def test_openai_models_in_model_info(monkeypatch): model_map = litellm.model_cost violated_models = [] for model, info in model_map.items(): - if ( - info.get("litellm_provider") == "openai" - and info.get("supports_vision") is True - ): + if info.get("litellm_provider") == "openai" and info.get("supports_vision") is True: if info.get("supports_pdf_input") is not True: violated_models.append(model) - assert ( - len(violated_models) == 0 - ), f"The following models should support pdf input: {violated_models}" + assert len(violated_models) == 0, f"The following models should support pdf input: {violated_models}" def test_supports_tool_choice_simple_tests(): @@ -1385,18 +1143,8 @@ def test_supports_tool_choice_simple_tests(): simple sanity checks """ assert litellm.utils.supports_tool_choice(model="gpt-4o") == True - assert ( - litellm.utils.supports_tool_choice( - model="bedrock/anthropic.claude-3-sonnet-20240229-v1:0" - ) - == True - ) - assert ( - litellm.utils.supports_tool_choice( - model="anthropic.claude-3-sonnet-20240229-v1:0" - ) - is True - ) + assert litellm.utils.supports_tool_choice(model="bedrock/anthropic.claude-3-sonnet-20240229-v1:0") == True + assert litellm.utils.supports_tool_choice(model="anthropic.claude-3-sonnet-20240229-v1:0") is True assert ( litellm.utils.supports_tool_choice( @@ -1468,14 +1216,8 @@ def test_check_provider_match_none_value_matches_any_provider(): """ # Missing key already returned True; None must behave identically. assert litellm.utils._check_provider_match({}, "openai") is True - assert ( - litellm.utils._check_provider_match({"litellm_provider": None}, "openai") - is True - ) - assert ( - litellm.utils._check_provider_match({"litellm_provider": None}, "anthropic") - is True - ) + assert litellm.utils._check_provider_match({"litellm_provider": None}, "openai") is True + assert litellm.utils._check_provider_match({"litellm_provider": None}, "anthropic") is True # When custom_llm_provider is also None nothing constrains the match. assert litellm.utils._check_provider_match({"litellm_provider": None}, None) is True @@ -1567,9 +1309,7 @@ def test_supports_computer_use_utility(monkeypatch): try: # Test a model known to support computer_use from backup JSON - supports_cu_anthropic = supports_computer_use( - model="anthropic/claude-4-sonnet-20250514" - ) + supports_cu_anthropic = supports_computer_use(model="anthropic/claude-4-sonnet-20250514") assert supports_cu_anthropic is True # Test a model known not to have the flag or set to false (defaults to False via get_model_info) @@ -1588,35 +1328,6 @@ def test_supports_computer_use_utility(monkeypatch): delattr(litellm, "model_cost") -def test_get_model_info_shows_supports_computer_use(monkeypatch): - """ - Tests if 'supports_computer_use' is correctly retrieved by get_model_info. - We'll use 'claude-4-sonnet-20250514' as it's configured - in the backup JSON to have supports_computer_use: True. - """ - monkeypatch.setenv("LITELLM_LOCAL_MODEL_COST_MAP", "True") - # Ensure litellm.model_cost is loaded, relying on the backup mechanism if primary fails - # as per previous debugging. - litellm.model_cost = litellm.get_model_cost_map(url="") - - # This model should have 'supports_computer_use': True in the backup JSON - model_known_to_support_computer_use = "claude-4-sonnet-20250514" - info = litellm.get_model_info(model_known_to_support_computer_use) - print(f"Info for {model_known_to_support_computer_use}: {info}") - - # After the fix in utils.py, this should now be present and True - assert info.get("supports_computer_use") is True - - # Optionally, test a model known NOT to support it, or where it's undefined (should default to False) - # For example, if "gpt-3.5-turbo" doesn't have it defined, it should be False. - model_known_not_to_support_computer_use = "gpt-3.5-turbo" - info_gpt = litellm.get_model_info(model_known_not_to_support_computer_use) - print(f"Info for {model_known_not_to_support_computer_use}: {info_gpt}") - assert ( - info_gpt.get("supports_computer_use") is None - ) # Expecting None due to the default in ModelInfoBase - - @pytest.mark.parametrize( "model, custom_llm_provider", [ @@ -1704,9 +1415,7 @@ def test_provider_supports_vertex_params(custom_llm_provider, expected): ("gpt-4o", "openai", False), ], ) -def test_vertex_params_not_stripped_for_vertex_family( - model, custom_llm_provider, should_keep -): +def test_vertex_params_not_stripped_for_vertex_family(model, custom_llm_provider, should_keep): optional_params = litellm.utils.get_optional_params( model=model, custom_llm_provider=custom_llm_provider, @@ -1772,25 +1481,19 @@ class TestProxyFunctionCalling: ("command-nightly", "litellm_proxy/command-nightly", False), ], ) - def test_proxy_function_calling_support_consistency( - self, direct_model, proxy_model, expected_result - ): + def test_proxy_function_calling_support_consistency(self, direct_model, proxy_model, expected_result): """Test that proxy models have the same function calling support as their direct counterparts.""" direct_result = supports_function_calling(direct_model) proxy_result = supports_function_calling(proxy_model) # Both should match the expected result - assert ( - direct_result == expected_result - ), f"Direct model {direct_model} should return {expected_result}" - assert ( - proxy_result == expected_result - ), f"Proxy model {proxy_model} should return {expected_result}" + assert direct_result == expected_result, f"Direct model {direct_model} should return {expected_result}" + assert proxy_result == expected_result, f"Proxy model {proxy_model} should return {expected_result}" # Direct and proxy should be consistent - assert ( - direct_result == proxy_result - ), f"Mismatch: {direct_model}={direct_result} vs {proxy_model}={proxy_result}" + assert direct_result == proxy_result, ( + f"Mismatch: {direct_model}={direct_result} vs {proxy_model}={proxy_result}" + ) @pytest.mark.parametrize( "proxy_model_name,underlying_model,expected_proxy_result", @@ -1857,9 +1560,7 @@ class TestProxyFunctionCalling: ("litellm_proxy/local-mistral", "ollama/mistral", False), ], ) - def test_proxy_custom_model_names_without_config( - self, proxy_model_name, underlying_model, expected_proxy_result - ): + def test_proxy_custom_model_names_without_config(self, proxy_model_name, underlying_model, expected_proxy_result): """ Test proxy models with custom model names that differ from underlying models. @@ -1870,17 +1571,15 @@ class TestProxyFunctionCalling: # Test the underlying model directly first to establish what it SHOULD return try: underlying_result = supports_function_calling(underlying_model) - print( - f"Underlying model {underlying_model} supports function calling: {underlying_result}" - ) + print(f"Underlying model {underlying_model} supports function calling: {underlying_result}") except Exception as e: print(f"Warning: Could not test underlying model {underlying_model}: {e}") # Test the proxy model - this will return False due to lack of configuration context proxy_result = supports_function_calling(proxy_model_name) - assert ( - proxy_result == expected_proxy_result - ), f"Proxy model {proxy_model_name} should return {expected_proxy_result} (without config context)" + assert proxy_result == expected_proxy_result, ( + f"Proxy model {proxy_model_name} should return {expected_proxy_result} (without config context)" + ) def test_proxy_model_resolution_with_custom_names_documentation(self): """ @@ -1894,9 +1593,7 @@ class TestProxyFunctionCalling: # Case 1: Custom model name that cannot be resolved custom_model = "litellm_proxy/my-custom-claude" result = supports_function_calling(custom_model) - assert ( - result is False - ), "Custom model names return False without proxy config context" + assert result is False, "Custom model names return False without proxy config context" # Case 2: Model name that can be resolved (matches pattern) resolvable_model = "litellm_proxy/claude-sonnet-4-5-20250929" @@ -1933,9 +1630,7 @@ class TestProxyFunctionCalling: ), # Hints at Bedrock Claude 3 Sonnet ], ) - def test_proxy_models_with_naming_hints( - self, proxy_model_with_hints, expected_result - ): + def test_proxy_models_with_naming_hints(self, proxy_model_with_hints, expected_result): """ Test proxy models with names that provide hints about the underlying model. @@ -1947,14 +1642,10 @@ class TestProxyFunctionCalling: # Currently these will return False, but we document the expected behavior # In the future, we could implement smarter model name inference - print( - f"Model {proxy_model_with_hints}: current={proxy_result}, desired={expected_result}" - ) + print(f"Model {proxy_model_with_hints}: current={proxy_result}, desired={expected_result}") # For now, we expect False (current behavior), but document the limitation - assert ( - proxy_result is False - ), f"Current limitation: {proxy_model_with_hints} returns False without inference" + assert proxy_result is False, f"Current limitation: {proxy_model_with_hints} returns False without inference" @pytest.mark.parametrize( "proxy_model,expected_result", @@ -1979,9 +1670,7 @@ class TestProxyFunctionCalling: """ try: result = supports_function_calling(model=proxy_model) - assert ( - result == expected_result - ), f"Proxy model {proxy_model} returned {result}, expected {expected_result}" + assert result == expected_result, f"Proxy model {proxy_model} returned {result}, expected {expected_result}" except Exception as e: pytest.fail(f"Error testing proxy model {proxy_model}: {e}") @@ -2021,17 +1710,11 @@ class TestProxyFunctionCalling: parameter explicitly set to None, which is a common usage pattern. """ try: - result = supports_function_calling( - model=model_name, custom_llm_provider=None - ) + result = supports_function_calling(model=model_name, custom_llm_provider=None) # All the models in this test should support function calling - assert ( - result is True - ), f"Model {model_name} should support function calling but returned {result}" + assert result is True, f"Model {model_name} should support function calling but returned {result}" except Exception as e: - pytest.fail( - f"Error testing {model_name} with custom_llm_provider=None: {e}" - ) + pytest.fail(f"Error testing {model_name} with custom_llm_provider=None: {e}") def test_edge_cases_and_malformed_proxy_models(self): """Test edge cases and malformed proxy model names.""" @@ -2046,9 +1729,9 @@ class TestProxyFunctionCalling: try: result = supports_function_calling(model=model_name) # For malformed models, we expect False or the function to handle gracefully - assert ( - result == expected_result - ), f"Edge case {model_name} returned {result}, expected {expected_result}" + assert result == expected_result, ( + f"Edge case {model_name} returned {result}, expected {expected_result}" + ) except Exception: # It's acceptable for malformed model names to raise exceptions # rather than returning False, as long as they're handled gracefully @@ -2068,9 +1751,7 @@ class TestProxyFunctionCalling: proxy_result = supports_function_calling(model=proxy_model) print(f"\nDemonstration of proxy model resolution:") - print( - f"Direct model '{direct_model}' supports function calling: {direct_result}" - ) + print(f"Direct model '{direct_model}' supports function calling: {direct_result}") print(f"Proxy model '{proxy_model}' supports function calling: {proxy_result}") # This assertion will currently fail due to the bug @@ -2083,11 +1764,9 @@ class TestProxyFunctionCalling: ) assert direct_result == proxy_result, ( - f"Proxy model resolution issue: {direct_model} -> {direct_result}, " - f"{proxy_model} -> {proxy_result}" + f"Proxy model resolution issue: {direct_model} -> {direct_result}, {proxy_model} -> {proxy_result}" ) - @pytest.mark.parametrize( "proxy_model_name,underlying_bedrock_model,expected_proxy_result,description", [ @@ -2258,13 +1937,11 @@ class TestProxyFunctionCalling: # Most Bedrock Converse API models with Anthropic Claude should support function calling if "anthropic.claude-3" in underlying_bedrock_model: - assert ( - underlying_result is True - ), f"Claude 3 models should support function calling: {underlying_bedrock_model}" + assert underlying_result is True, ( + f"Claude 3 models should support function calling: {underlying_bedrock_model}" + ) except Exception as e: - print( - f" Warning: Could not test underlying model {underlying_bedrock_model}: {e}" - ) + print(f" Warning: Could not test underlying model {underlying_bedrock_model}: {e}") # Test the proxy model - should return False due to lack of configuration context proxy_result = supports_function_calling(proxy_model_name) @@ -2349,9 +2026,7 @@ class TestProxyFunctionCalling: result = supports_function_calling(model) print(f"Direct test - {model}: {result}") # Claude 3 models should support function calling - assert ( - result is True - ), f"Claude 3 model should support function calling: {model}" + assert result is True, f"Claude 3 model should support function calling: {model}" except Exception as e: print(f"Could not test {model}: {e}") @@ -2403,9 +2078,7 @@ def test_register_model_url_fetch_uses_single_attempt(monkeypatch): monkeypatch.setattr(litellm, "model_cost", dict(litellm.model_cost)) before = dict(litellm.model_cost) threads_before = {thread.name for thread in threading.enumerate()} - route = respx.get("https://example.invalid/custom_pricing.json").mock( - return_value=httpx.Response(503) - ) + route = respx.get("https://example.invalid/custom_pricing.json").mock(return_value=httpx.Response(503)) litellm.register_model(model_cost="https://example.invalid/custom_pricing.json") @@ -2413,8 +2086,7 @@ def test_register_model_url_fetch_uses_single_attempt(monkeypatch): assert route.call_count == 1 assert not (threads_after - threads_before) & {"litellm-model-cost-map-retry"} assert not any( - thread.name == "litellm-model-cost-map-retry" and thread.is_alive() - for thread in threading.enumerate() + thread.name == "litellm-model-cost-map-retry" and thread.is_alive() for thread in threading.enumerate() ) assert litellm.model_cost.keys() >= before.keys() @@ -2528,9 +2200,7 @@ def test_anthropic_claude_4_invoke_chat_provider_config(): def test_bedrock_application_inference_profile(): model = "arn:aws:bedrock:us-east-2::inference-profile/us.anthropic.claude-3-5-haiku-20241022-v1:0" - from pydantic import BaseModel - from litellm import completion from litellm.utils import supports_tool_choice result = supports_tool_choice(model, custom_llm_provider="bedrock") @@ -2560,7 +2230,7 @@ def test_image_response_utils(): "object": "list", "hidden_params": {"additional_headers": {}}, } - image_response = ImageResponse(**result) + ImageResponse(**result) def test_is_valid_api_key(): @@ -2597,7 +2267,6 @@ def test_block_key_hashing_logic(): """ Test that block_key() function only hashes keys that start with "sk-" """ - import hashlib from litellm.proxy.utils import hash_token @@ -2623,17 +2292,13 @@ def test_block_key_hashing_logic(): # Additional verification: if it should be hashed, verify it's actually a hash if should_be_hashed: # SHA-256 hashes are 64 characters long and contain only hex digits - assert ( - len(hashed_token) == 64 - ), f"Hash length should be 64, got {len(hashed_token)} for {input_key}" - assert all( - c in "0123456789abcdef" for c in hashed_token - ), f"Hash should contain only hex digits for {input_key}" + assert len(hashed_token) == 64, f"Hash length should be 64, got {len(hashed_token)} for {input_key}" + assert all(c in "0123456789abcdef" for c in hashed_token), ( + f"Hash should contain only hex digits for {input_key}" + ) else: # If not hashed, it should be the original string - assert ( - hashed_token == input_key - ), f"Non-hashed key should remain unchanged: {input_key}" + assert hashed_token == input_key, f"Non-hashed key should remain unchanged: {input_key}" print("✅ All block_key hashing logic tests passed!") @@ -2660,9 +2325,7 @@ def test_generate_gcp_iam_access_token(): mock_iam_credentials_v1.GenerateAccessTokenRequest = Mock() # Test successful token generation by mocking sys.modules - with patch.dict( - "sys.modules", {"google.cloud.iam_credentials_v1": mock_iam_credentials_v1} - ): + with patch.dict("sys.modules", {"google.cloud.iam_credentials_v1": mock_iam_credentials_v1}): from litellm._redis import _generate_gcp_iam_access_token result = _generate_gcp_iam_access_token(service_account) @@ -2718,17 +2381,13 @@ def test_generate_azure_ad_redis_token(): mock_azure_identity.ClientSecretCredential = Mock() mock_azure_identity.ManagedIdentityCredential = Mock() - with patch.dict( - "sys.modules", {"azure.identity": mock_azure_identity, "azure": Mock()} - ): + with patch.dict("sys.modules", {"azure.identity": mock_azure_identity, "azure": Mock()}): from litellm._redis import _generate_azure_ad_redis_token result = _generate_azure_ad_redis_token() assert result == expected_token - mock_credential.get_token.assert_called_once_with( - "https://redis.azure.com/.default" - ) + mock_credential.get_token.assert_called_once_with("https://redis.azure.com/.default") def test_generate_azure_ad_redis_token_service_principal(): @@ -2750,9 +2409,7 @@ def test_generate_azure_ad_redis_token_service_principal(): mock_azure_identity.ClientSecretCredential = mock_client_secret_credential mock_azure_identity.ManagedIdentityCredential = Mock() - with patch.dict( - "sys.modules", {"azure.identity": mock_azure_identity, "azure": Mock()} - ): + with patch.dict("sys.modules", {"azure.identity": mock_azure_identity, "azure": Mock()}): from litellm._redis import _generate_azure_ad_redis_token result = _generate_azure_ad_redis_token( @@ -2772,6 +2429,7 @@ def test_generate_azure_ad_redis_token_service_principal(): def test_generate_azure_ad_redis_token_import_error(): """Test that _generate_azure_ad_redis_token raises ImportError when azure-identity is missing.""" from unittest.mock import patch + from litellm._redis import _generate_azure_ad_redis_token with patch.dict("sys.modules", {"azure.identity": None}): @@ -2795,9 +2453,7 @@ def test_redis_client_logic_azure_ad_auth(): mock_azure_identity.ClientSecretCredential = Mock(return_value=mock_credential) mock_azure_identity.ManagedIdentityCredential = Mock(return_value=mock_credential) - with patch.dict( - "sys.modules", {"azure.identity": mock_azure_identity, "azure": Mock()} - ): + with patch.dict("sys.modules", {"azure.identity": mock_azure_identity, "azure": Mock()}): from litellm._redis import _get_redis_client_logic redis_kwargs = _get_redis_client_logic( @@ -2828,78 +2484,6 @@ if __name__ == "__main__": pytest.main([__file__, "-v"]) -def test_model_info_for_vertex_ai_deepseek_model(): - model_info = litellm.get_model_info( - model="vertex_ai/deepseek-ai/deepseek-r1-0528-maas" - ) - assert model_info is not None - assert model_info["litellm_provider"] == "vertex_ai-deepseek_models" - assert model_info["mode"] == "chat" - - assert model_info["input_cost_per_token"] is not None - assert model_info["output_cost_per_token"] is not None - print("vertex deepseek model info", model_info) - - -def test_model_info_for_fireworks_short_form_models(): - """ - Test that fireworks_ai short-form model entries (fireworks_ai/) - are correctly configured in model_prices_and_context_window.json. - - These entries enable cost attribution for models called via short-form - names (e.g., fireworks_ai/glm-4p7 instead of - fireworks_ai/accounts/fireworks/models/glm-4p7). - """ - import json - from pathlib import Path - - json_path = Path(__file__).parents[2] / "model_prices_and_context_window.json" - with open(json_path) as f: - model_cost = json.load(f) - - # glm-4p7: short-form and long-form - for key in [ - "fireworks_ai/glm-4p7", - "fireworks_ai/accounts/fireworks/models/glm-4p7", - ]: - info = model_cost.get(key) - assert ( - info is not None - ), f"{key} not found in model_prices_and_context_window.json" - assert info["litellm_provider"] == "fireworks_ai" - assert info["mode"] == "chat" - assert info["input_cost_per_token"] == 6e-07 - assert info["output_cost_per_token"] == 2.2e-06 - assert info["max_input_tokens"] == 202800 - assert info["supports_reasoning"] is True - - # minimax-m2p1: short-form and long-form - for key in [ - "fireworks_ai/minimax-m2p1", - "fireworks_ai/accounts/fireworks/models/minimax-m2p1", - ]: - info = model_cost.get(key) - assert ( - info is not None - ), f"{key} not found in model_prices_and_context_window.json" - assert info["litellm_provider"] == "fireworks_ai" - assert info["mode"] == "chat" - assert info["input_cost_per_token"] == 3e-07 - assert info["output_cost_per_token"] == 1.2e-06 - assert info["max_input_tokens"] == 204800 - - # kimi-k2p5: short-form only (long-form already existed) - info = model_cost.get("fireworks_ai/kimi-k2p5") - assert ( - info is not None - ), "fireworks_ai/kimi-k2p5 not found in model_prices_and_context_window.json" - assert info["litellm_provider"] == "fireworks_ai" - assert info["mode"] == "chat" - assert info["input_cost_per_token"] == 6e-07 - assert info["output_cost_per_token"] == 3e-06 - assert info["max_input_tokens"] == 262144 - - class TestGetValidModelsWithCLI: """Test get_valid_models function as used in CLI token usage""" @@ -2918,9 +2502,7 @@ class TestGetValidModelsWithCLI: ] } - with patch.object( - litellm.module_level_client, "get", return_value=mock_response - ) as mock_get: + with patch.object(litellm.module_level_client, "get", return_value=mock_response) as mock_get: # Test the exact pattern used in cli_token_usage.py result = litellm.get_valid_models( check_provider_endpoint=True, @@ -3138,9 +2720,7 @@ class TestProxyLoggingBudgetAlerts: user_info = MagicMock() # Should not raise an error - await proxy_logging.budget_alerts( - type="organization_budget", user_info=user_info - ) + await proxy_logging.budget_alerts(type="organization_budget", user_info=user_info) async def test_budget_alerts_with_both_slack_and_email(self): """Test that budget_alerts calls both slack and email instances when both are in alerting.""" @@ -3192,9 +2772,7 @@ class TestProxyLoggingBudgetAlerts: proxy_logging.slack_alerting_instance.budget_alerts.assert_called_once_with( type=alert_type, user_info=user_info ) - proxy_logging.email_logging_instance.budget_alerts.assert_called_once_with( - type=alert_type, user_info=user_info - ) + proxy_logging.email_logging_instance.budget_alerts.assert_called_once_with(type=alert_type, user_info=user_info) async def test_budget_alerts_soft_budget_with_alert_emails_bypasses_alerting_none( self, @@ -3411,9 +2989,7 @@ def test_last_assistant_with_tool_calls_has_no_thinking_blocks_issue_18926(): {"role": "user", "content": "Build a feature"}, { "role": "assistant", - "thinking_blocks": [ - {"type": "thinking", "thinking": "Let me analyze the requirements..."} - ], + "thinking_blocks": [{"type": "thinking", "thinking": "Let me analyze the requirements..."}], "tool_calls": [ { "id": "toolu_1", @@ -3661,65 +3237,31 @@ class TestGetOptionalParamsDeepSeek: class TestIsStreamingRequest: def test_stream_true_in_kwargs(self): - assert ( - _is_streaming_request(kwargs={"stream": True}, call_type="acompletion") - is True - ) + assert _is_streaming_request(kwargs={"stream": True}, call_type="acompletion") is True def test_stream_false_in_kwargs(self): - assert ( - _is_streaming_request(kwargs={"stream": False}, call_type="acompletion") - is False - ) + assert _is_streaming_request(kwargs={"stream": False}, call_type="acompletion") is False def test_no_stream_in_kwargs(self): assert _is_streaming_request(kwargs={}, call_type="acompletion") is False def test_generate_content_stream_string(self): - assert ( - _is_streaming_request( - kwargs={}, call_type=CallTypes.generate_content_stream.value - ) - is True - ) + assert _is_streaming_request(kwargs={}, call_type=CallTypes.generate_content_stream.value) is True def test_agenerate_content_stream_string(self): - assert ( - _is_streaming_request( - kwargs={}, call_type=CallTypes.agenerate_content_stream.value - ) - is True - ) + assert _is_streaming_request(kwargs={}, call_type=CallTypes.agenerate_content_stream.value) is True def test_generate_content_stream_enum(self): - assert ( - _is_streaming_request( - kwargs={}, call_type=CallTypes.generate_content_stream - ) - is True - ) + assert _is_streaming_request(kwargs={}, call_type=CallTypes.generate_content_stream) is True def test_agenerate_content_stream_enum(self): - assert ( - _is_streaming_request( - kwargs={}, call_type=CallTypes.agenerate_content_stream - ) - is True - ) - + assert _is_streaming_request(kwargs={}, call_type=CallTypes.agenerate_content_stream) is True def test_non_streaming_call_type_enum(self): - assert ( - _is_streaming_request(kwargs={}, call_type=CallTypes.acompletion) is False - ) + assert _is_streaming_request(kwargs={}, call_type=CallTypes.acompletion) is False def test_stream_true_overrides_non_streaming_call_type(self): - assert ( - _is_streaming_request( - kwargs={"stream": True}, call_type=CallTypes.acompletion - ) - is True - ) + assert _is_streaming_request(kwargs={"stream": True}, call_type=CallTypes.acompletion) is True class TestCallbackAsyncSyncSeparation: @@ -3962,28 +3504,6 @@ class TestValidateAndFixThinkingParam: assert validate_and_fix_thinking_param(thinking=False) is None -@pytest.mark.usefixtures("local_model_cost_map") -def test_deepseek_flash_completion_cost(): - from litellm.types.utils import ModelResponse - - response = ModelResponse( - model="deepseek-flash", - usage=Usage( - prompt_tokens=1_000_000, - completion_tokens=1_000_000, - total_tokens=2_000_000, - ), - ) - - cost = litellm.completion_cost( - completion_response=response, - model="deepseek-flash", - custom_llm_provider="deepseek", - ) - - assert cost == pytest.approx(1.50, abs=1e-9) - - _FIREWORKS_MODELS = [ ( "accounts/fireworks/models/glm-5p2", @@ -4131,62 +3651,6 @@ def _assert_fireworks_entry( assert info["supports_vision"] is expected_vision -def test_fireworks_models_in_cost_map(): - import json - from pathlib import Path - - json_path = Path(__file__).parents[2] / "model_prices_and_context_window.json" - with open(json_path) as f: - model_cost = json.load(f) - - for entry in _FIREWORKS_MODELS: - _assert_fireworks_entry(model_cost, *entry) - - for short in _FIREWORKS_SHORT_FORMS: - long_key = f"fireworks_ai/accounts/fireworks/models/{short}" - short_key = f"fireworks_ai/{short}" - assert model_cost.get(short_key) == model_cost.get( - long_key - ), f"short-form {short_key} does not match long-form {long_key}" - - for short in _FIREWORKS_ROUTER_SHORT_FORMS: - long_key = f"fireworks_ai/accounts/fireworks/routers/{short}" - short_key = f"fireworks_ai/{short}" - assert model_cost.get(short_key) == model_cost.get( - long_key - ), f"short-form {short_key} does not match long-form {long_key}" - - -def test_fireworks_models_in_backup_cost_map(): - import json - from pathlib import Path - - json_path = ( - Path(__file__).parents[2] - / "litellm" - / "model_prices_and_context_window_backup.json" - ) - with open(json_path) as f: - model_cost = json.load(f) - - for entry in _FIREWORKS_MODELS: - _assert_fireworks_entry(model_cost, *entry) - - for short in _FIREWORKS_SHORT_FORMS: - long_key = f"fireworks_ai/accounts/fireworks/models/{short}" - short_key = f"fireworks_ai/{short}" - assert model_cost.get(short_key) == model_cost.get( - long_key - ), f"short-form {short_key} does not match long-form {long_key}" - - for short in _FIREWORKS_ROUTER_SHORT_FORMS: - long_key = f"fireworks_ai/accounts/fireworks/routers/{short}" - short_key = f"fireworks_ai/{short}" - assert model_cost.get(short_key) == model_cost.get( - long_key - ), f"short-form {short_key} does not match long-form {long_key}" - - @pytest.fixture def fireworks_short_model_cost_map(monkeypatch: pytest.MonkeyPatch) -> Iterator[None]: monkeypatch.setattr( @@ -4219,43 +3683,6 @@ def fireworks_short_model_cost_map(monkeypatch: pytest.MonkeyPatch) -> Iterator[ litellm.get_model_info.cache_clear() -def test_fireworks_short_model_names_resolve_to_long_cost_map_keys(fireworks_short_model_cost_map: None) -> None: - model_info = litellm.get_model_info("fireworks_ai/glm-5p3") - assert model_info["key"] == "fireworks_ai/accounts/fireworks/models/glm-5p3" - assert model_info["input_cost_per_token"] == 1e-6 - assert model_info["max_tokens"] == 100 - - model_info = litellm.get_model_info("glm-5p3", custom_llm_provider="fireworks_ai") - assert model_info["key"] == "fireworks_ai/accounts/fireworks/models/glm-5p3" - - model_info = litellm.get_model_info("fireworks_ai/glm-5p3-fast") - assert model_info["key"] == "fireworks_ai/accounts/fireworks/routers/glm-5p3-fast" - assert model_info["input_cost_per_token"] == 2.1e-6 - - model_info = litellm.get_model_info("fireworks_ai/nomic-ai/nomic-embed-text-v1.5") - assert model_info["key"] == "fireworks_ai/nomic-ai/nomic-embed-text-v1.5" - - with pytest.raises(Exception, match="isn't mapped"): - litellm.get_model_info("fireworks_ai/does-not-exist") - - -def test_fireworks_short_model_names_price_with_completion_cost(fireworks_short_model_cost_map: None) -> None: - from litellm.types.utils import ModelResponse - - response = ModelResponse( - model="fireworks_ai/glm-5p3", - usage=Usage(prompt_tokens=10, completion_tokens=5, total_tokens=15), - ) - - cost = litellm.completion_cost( - completion_response=response, - model="fireworks_ai/glm-5p3", - custom_llm_provider="fireworks_ai", - ) - - assert cost == pytest.approx(10 * 1e-6 + 5 * 2e-6) - - class TestBedrockBaseModelLabelKeepsTools: """Regression for #29618: a Bedrock deployment whose ``base_model`` is a friendly label must not silently drop ``tools``/``tool_choice`` under ``drop_params``.""" @@ -4322,9 +3749,14 @@ def test_aws_bedrock_project_id_excluded_from_bedrock_optional_params(): assert result["aws_region_name"] == "us-east-1" -@pytest.mark.parametrize("filter_name", [ - "get_non_default_completion_params", "get_non_default_transcription_params", "filter_out_litellm_params", -]) +@pytest.mark.parametrize( + "filter_name", + [ + "get_non_default_completion_params", + "get_non_default_transcription_params", + "filter_out_litellm_params", + ], +) def test_scoped_weights_are_excluded_from_provider_params(filter_name: str) -> None: filtered = getattr(litellm.utils, filter_name)( {"provider_option": "kept", "_router_weights": {"group": {"deployment": 100}}} @@ -4450,7 +3882,7 @@ class TestVertexEmbeddingEncodingFormat: assert "encoding_format" not in optional_params def test_encoding_format_base64_still_rejected_without_drop_params(self): - with pytest.raises(Exception, match='To drop these, set `litellm\\.drop_params=True` or for proxy') as excinfo: + with pytest.raises(Exception, match="To drop these, set `litellm\\.drop_params=True` or for proxy") as excinfo: litellm.utils.get_optional_params_embeddings( model="gemini-embedding-001", encoding_format="base64", @@ -4524,36 +3956,6 @@ class TestBedrockCohereEmbeddingDispatch: assert optional_params.get("output_dimension") == 512 -@pytest.mark.parametrize( - "model", - [ - "vertex_ai/gemini-2.5-flash-image", - "vertex_ai/gemini-3-pro-image", - "vertex_ai/gemini-3-pro-image-preview", - "vertex_ai/gemini-3.1-flash-image", - "vertex_ai/gemini-3.1-flash-image-preview", - "vertex_ai/gemini-3.1-flash-lite-image", - "gemini/gemini-2.5-flash-image", - "gemini/gemini-3-pro-image", - "gemini/gemini-3-pro-image-preview", - "gemini/gemini-3.1-flash-image", - "gemini/gemini-3.1-flash-image-preview", - "gemini/gemini-3.1-flash-lite-image", - ], -) -def test_gemini_image_models_do_not_support_reasoning( - model: str, local_model_cost_map: None -) -> None: - assert model in litellm.model_cost, ( - f"{model} is missing from the local model cost map. " - "Add its entry to litellm/model_prices_and_context_window_backup.json." - ) - assert litellm.supports_reasoning(model) is False, ( - f"{model} incorrectly classified as reasoning-capable. " - "Add 'supports_reasoning: false' to its model_cost entry." - ) - - PROMPT_CACHE_MESSAGES = [{"role": "user", "content": "the quick brown fox jumps over the lazy dog " * 155}] @@ -5232,7 +4634,9 @@ async def test_async_post_call_failure_deployment_hook_swallows_callback_errors( super().__init__() self.called = False - async def async_post_call_failure_deployment_hook(self, request_data, exception, call_type, fallback_depth=None): + async def async_post_call_failure_deployment_hook( + self, request_data, exception, call_type, fallback_depth=None + ): self.called = True raise RuntimeError("hook exploded") @@ -5293,7 +4697,9 @@ async def test_wrapper_async_raises_original_exception_even_if_hook_callback_err exception the caller is waiting on.""" class ExplodingLogger(CustomLogger): - async def async_post_call_failure_deployment_hook(self, request_data, exception, call_type, fallback_depth=None): + async def async_post_call_failure_deployment_hook( + self, request_data, exception, call_type, fallback_depth=None + ): raise RuntimeError("hook exploded") monkeypatch.setattr(litellm, "callbacks", [ExplodingLogger()]) @@ -5429,7 +4835,9 @@ async def test_wrapper_async_does_not_fire_failure_hook_for_post_success_error( async def async_post_call_success_deployment_hook(self, request_data, response, call_type): raise RuntimeError("boom in success hook, model call itself succeeded") - async def async_post_call_failure_deployment_hook(self, request_data, exception, call_type, fallback_depth=None): + async def async_post_call_failure_deployment_hook( + self, request_data, exception, call_type, fallback_depth=None + ): self.failure_calls.append(exception) exploding_logger = ExplodingSuccessLogger() @@ -5485,7 +4893,9 @@ async def test_wrapper_async_failure_hook_exception_mutation_does_not_change_rai the real exception about to be re-raised.""" class StatusCodeMutatingLogger(CustomLogger): - async def async_post_call_failure_deployment_hook(self, request_data, exception, call_type, fallback_depth=None): + async def async_post_call_failure_deployment_hook( + self, request_data, exception, call_type, fallback_depth=None + ): exception.status_code = 429 monkeypatch.setattr(litellm, "callbacks", [StatusCodeMutatingLogger()]) @@ -5538,7 +4948,9 @@ async def test_router_fallback_not_skipped_when_failure_hook_callback_touches_at into every hop's kwargs and would mask this test's real signal.""" class RecordingAttemptLogger(CustomLogger): - async def async_post_call_failure_deployment_hook(self, request_data, exception, call_type, fallback_depth=None): + async def async_post_call_failure_deployment_hook( + self, request_data, exception, call_type, fallback_depth=None + ): attempted = request_data.get("attempted_targets") if attempted is not None: attempted.record("good-group") @@ -5593,7 +5005,9 @@ async def test_wrapper_async_preserves_original_exception_when_hook_await_is_can await getting cancelled.""" class SlowLogger(CustomLogger): - async def async_post_call_failure_deployment_hook(self, request_data, exception, call_type, fallback_depth=None): + async def async_post_call_failure_deployment_hook( + self, request_data, exception, call_type, fallback_depth=None + ): await asyncio.sleep(5) monkeypatch.setattr(litellm, "callbacks", [SlowLogger()]) @@ -5603,7 +5017,9 @@ async def test_wrapper_async_preserves_original_exception_when_hook_await_is_can litellm.acompletion( model="gpt-4o-mini", messages=[{"role": "user", "content": "hi"}], - mock_response=litellm.AuthenticationError(message="bad key", llm_provider="openai", model="gpt-4o-mini"), + mock_response=litellm.AuthenticationError( + message="bad key", llm_provider="openai", model="gpt-4o-mini" + ), ), timeout=0.2, ) @@ -5619,7 +5035,9 @@ async def test_wrapper_async_failure_hook_latency_does_not_inflate_reported_dura reported_durations: list[float] = [] class SlowLoggerWithDurationCapture(CustomLogger): - async def async_post_call_failure_deployment_hook(self, request_data, exception, call_type, fallback_depth=None): + async def async_post_call_failure_deployment_hook( + self, request_data, exception, call_type, fallback_depth=None + ): await asyncio.sleep(1) async def async_log_failure_event(self, kwargs, response_obj, start_time, end_time): @@ -5650,7 +5068,9 @@ async def test_wrapper_async_failure_hook_exception_snapshot_preserves_traceback received: list[Exception] = [] class TracebackCapturingLogger(CustomLogger): - async def async_post_call_failure_deployment_hook(self, request_data, exception, call_type, fallback_depth=None): + async def async_post_call_failure_deployment_hook( + self, request_data, exception, call_type, fallback_depth=None + ): received.append(exception) monkeypatch.setattr(litellm, "callbacks", [TracebackCapturingLogger()]) @@ -5674,6 +5094,7 @@ def test_snapshot_exception_for_hook_preserves_suppress_context_flag() -> None: suppress it). Snapshotting __cause__ before __suppress_context__ would silently flip a real exception's __suppress_context__=False to True on the snapshot, hiding a chained context a callback formatting it should still see.""" + def _raise_chained_without_from() -> None: try: raise ValueError("inner cause") @@ -5805,7 +5226,9 @@ async def test_registered_guardrail_does_not_starve_vector_store_search_results( ) from litellm.types.utils import ModelResponse - search_results: Final = [{"search_query": "coolant", "data": [{"content": [{"text": "Cryoline-9", "type": "text"}]}]}] + search_results: Final = [ + {"search_query": "coolant", "data": [{"content": [{"text": "Cryoline-9", "type": "text"}]}]} + ] logging_obj = SimpleNamespace(model_call_details={"search_results": search_results}) response = ModelResponse(choices=[{"message": {"role": "assistant", "content": "Cryoline-9"}}]) @@ -5850,9 +5273,7 @@ class TestIsVisionExplicitlyDisabled: def test_explicit_false_detected_and_absent_reads_enabled(self): from litellm.utils import is_vision_explicitly_disabled - assert ( - is_vision_explicitly_disabled("fireworks_ai/accounts/fireworks/models/deepseek-v4-flash-0731") is True - ) + assert is_vision_explicitly_disabled("fireworks_ai/accounts/fireworks/models/deepseek-v4-flash-0731") is True assert is_vision_explicitly_disabled("anthropic/claude-sonnet-4-5") is False @@ -6236,11 +5657,3 @@ def test_completion_finishes_response_metadata_before_handing_the_response_to_th assert snapshot["litellm_call_id"] assert snapshot["response_cost"] is not None assert snapshot["api_base"] - - -def test_get_model_info_carries_cache_read_input_audio_token_cost(monkeypatch): - monkeypatch.setenv("LITELLM_LOCAL_MODEL_COST_MAP", "True") - monkeypatch.setattr(litellm, "model_cost", litellm.get_model_cost_map(url="")) - info = litellm.get_model_info("gpt-realtime-2.1-mini", custom_llm_provider="openai") - assert info["cache_read_input_audio_token_cost"] == 3e-07 - assert info["cache_read_input_token_cost"] == 6e-08 diff --git a/tests/test_litellm/test_xai_grok_4_3_model_metadata.py b/tests/test_litellm/test_xai_grok_4_3_model_metadata.py deleted file mode 100644 index e6e4eada1b6..00000000000 --- a/tests/test_litellm/test_xai_grok_4_3_model_metadata.py +++ /dev/null @@ -1,19 +0,0 @@ -import json -from pathlib import Path - - -def test_xai_grok_4_3_backup_matches_main(): - """Ensure the bundled model cost map stays in sync with the canonical file.""" - repo_root = Path(__file__).parents[2] - main_path = repo_root / "model_prices_and_context_window.json" - backup_path = repo_root / "litellm" / "model_prices_and_context_window_backup.json" - - with open(main_path) as f: - main_cost = json.load(f) - with open(backup_path) as f: - backup_cost = json.load(f) - - for model in ("xai/grok-4.3", "xai/grok-4.3-latest"): - assert backup_cost.get(model) == main_cost.get( - model - ), f"{model} differs between main and backup model cost maps" From a428cc8d84e41cc4ba14da16dba6a70b57418a1b Mon Sep 17 00:00:00 2001 From: kerry Date: Tue, 15 Sep 2026 21:44:39 +0000 Subject: [PATCH 080/225] test: restore runtime-derived tests dropped by mistake Co-Authored-By: Devin AI <158243242+devin-ai-integration[bot]@users.noreply.github.com> --- .../llm_cost_calc/test_llm_cost_calc_utils.py | 142 ++++++++++++++++++ .../test_get_model_cost_map.py | 50 ++++++ ...azure_ai_foundry_catalog_model_metadata.py | 30 +++- ...artner_models_anthropic_messages_config.py | 29 ++++ .../test_baseten_glm_5_3_model_metadata.py | 27 ++++ 5 files changed, 277 insertions(+), 1 deletion(-) create mode 100644 tests/test_litellm/test_baseten_glm_5_3_model_metadata.py diff --git a/tests/test_litellm/litellm_core_utils/llm_cost_calc/test_llm_cost_calc_utils.py b/tests/test_litellm/litellm_core_utils/llm_cost_calc/test_llm_cost_calc_utils.py index 7a72c9d3af0..0743476417e 100644 --- a/tests/test_litellm/litellm_core_utils/llm_cost_calc/test_llm_cost_calc_utils.py +++ b/tests/test_litellm/litellm_core_utils/llm_cost_calc/test_llm_cost_calc_utils.py @@ -4,6 +4,7 @@ from datetime import datetime, timezone import pytest import litellm +from litellm._internal_context import pinned_billing_time from litellm.litellm_core_utils.llm_cost_calc.utils import ( BilledTokenRates, CostCalculatorUtils, @@ -2086,6 +2087,95 @@ def test_cache_writing_cost_with_zero_creation_tokens_and_ephemeral_details(): assert round(result, 6) == round(expected, 6) +def test_a_pinned_billing_time_prices_the_totals_and_the_reported_rates_at_one_moment(monkeypatch): + monkeypatch.setitem( + litellm.model_cost, + "off-peak-model", + { + "input_cost_per_token": 3e-6, + "output_cost_per_token": 15e-6, + "off_peak_pricing": { + "hours_utc": "02:00-03:00", + "input_cost_per_token": 1e-6, + "output_cost_per_token": 5e-6, + }, + "litellm_provider": "openai", + "mode": "chat", + }, + ) + usage = Usage(prompt_tokens=1000, completion_tokens=500, total_tokens=1500) + + with pinned_billing_time(datetime(2026, 1, 1, 2, 30, tzinfo=timezone.utc)): + off_peak_prompt_cost, off_peak_completion_cost = generic_cost_per_token( + model="off-peak-model", usage=usage, custom_llm_provider="openai" + ) + off_peak_rates = get_billed_token_rates(model="off-peak-model", custom_llm_provider="openai", usage=usage) + with pinned_billing_time(datetime(2026, 1, 1, 12, 30, tzinfo=timezone.utc)): + peak_prompt_cost, peak_completion_cost = generic_cost_per_token( + model="off-peak-model", usage=usage, custom_llm_provider="openai" + ) + peak_rates = get_billed_token_rates(model="off-peak-model", custom_llm_provider="openai", usage=usage) + + assert off_peak_rates.input_cost_per_token == pytest.approx(1e-6) + assert peak_rates.input_cost_per_token == pytest.approx(3e-6) + assert off_peak_prompt_cost == pytest.approx(1000 * off_peak_rates.input_cost_per_token) + assert off_peak_completion_cost == pytest.approx(500 * off_peak_rates.output_cost_per_token) + assert peak_prompt_cost == pytest.approx(1000 * peak_rates.input_cost_per_token) + assert peak_completion_cost == pytest.approx(500 * peak_rates.output_cost_per_token) + + +def test_token_type_cost_breakdown_applies_anthropic_geo_multiplier(_local_model_cost_map, monkeypatch): + from litellm.llms.anthropic.cost_calculation import cost_per_token as anthropic_cost_per_token + + monkeypatch.setenv("LITELLM_LOCAL_MODEL_COST_MAP", "True") + + model = "claude-test-geo-breakdown-model" + litellm.register_model( + model_cost={ + model: { + "input_cost_per_token": 5e-6, + "output_cost_per_token": 25e-6, + "cache_creation_input_token_cost": 6.25e-6, + "cache_read_input_token_cost": 0.5e-6, + "litellm_provider": "anthropic", + "max_tokens": 8192, + "provider_specific_entry": {"us": 1.1}, + } + } + ) + + def make_usage() -> Usage: + return Usage( + prompt_tokens=10_000, + completion_tokens=500, + total_tokens=10_500, + prompt_tokens_details=PromptTokensDetailsWrapper( + cached_tokens=2_000, + cache_creation_tokens=6_000, + ), + completion_tokens_details=CompletionTokensDetailsWrapper(reasoning_tokens=200, text_tokens=300), + ) + + base_usage = make_usage() + geo_usage = make_usage() + geo_usage.inference_geo = "us" + + base = get_token_type_cost_breakdown(model=model, custom_llm_provider="anthropic", usage=base_usage) + geo = get_token_type_cost_breakdown(model=model, custom_llm_provider="anthropic", usage=geo_usage) + + assert base.cache_read_cost == pytest.approx(2_000 * 0.5e-6) + assert base.cache_creation_cost == pytest.approx(6_000 * 6.25e-6) + assert geo.cache_read_cost == pytest.approx(base.cache_read_cost * 1.1) + assert geo.cache_creation_cost == pytest.approx(base.cache_creation_cost * 1.1) + assert geo.reasoning_cost == pytest.approx(base.reasoning_cost * 1.1) + + prompt_cost, completion_cost = anthropic_cost_per_token(model=model, usage=geo_usage) + text_input_cost = 2_000 * 5e-6 * 1.1 + text_output_cost = 300 * 25e-6 * 1.1 + assert text_input_cost + geo.cache_read_cost + geo.cache_creation_cost == pytest.approx(prompt_cost) + assert text_output_cost + geo.reasoning_cost == pytest.approx(completion_cost) + + def test_service_tier_ultrafast_pricing(): """An ultrafast request bills the *_ultrafast rates for all token types. @@ -3251,6 +3341,58 @@ def test_token_type_cost_breakdown_applies_vertex_regional_uplift(_local_model_c assert text_input_cost + regional.cache_read_cost == pytest.approx(prompt_cost) +@pytest.mark.parametrize("details_as_dict", [True, False]) +def test_image_response_input_image_tokens_priced_at_image_rate(details_as_dict): + """ + Image input tokens must be priced at input_cost_per_image_token even when + input_tokens_details is a plain dict, as in OpenAI image edit responses. + + Regression test: dict-shaped input_tokens_details was read with getattr(), + which returns None for dicts, so image input tokens silently fell back to + the text input rate (e.g. $5/M instead of $8/M for gpt-image-2). + """ + from unittest.mock import patch + + from litellm.litellm_core_utils.llm_cost_calc.utils import ( + calculate_image_response_cost_from_usage, + ) + from litellm.types.utils import Usage + + mock_model_info = { + "input_cost_per_token": 5e-6, + "input_cost_per_image_token": 8e-6, + "output_cost_per_image_token": 3e-5, + } + + input_details = {"text_tokens": 19, "image_tokens": 512} + image_response = ImageResponse(data=[ImageObject(b64_json="x")]) + # Mirror the usage shape of a real OpenAI images.edit response: + # a Usage object carrying input_tokens/output_tokens with detail dicts. + image_response.usage = Usage( + prompt_tokens=0, + completion_tokens=0, + total_tokens=689, + input_tokens=531, + input_tokens_details=(input_details if details_as_dict else ImageUsageInputTokensDetails(**input_details)), + output_tokens=158, + output_tokens_details={"image_tokens": 158, "text_tokens": 0}, + ) + + with patch( + "litellm.litellm_core_utils.llm_cost_calc.utils.get_model_info", + return_value=mock_model_info, + ): + cost = calculate_image_response_cost_from_usage( + model="gpt-image-2", + image_response=image_response, + custom_llm_provider="openai", + ) + + expected = 19 * 5e-6 + 512 * 8e-6 + 158 * 3e-5 + assert cost is not None + assert round(cost, 12) == round(expected, 12) + + GEMINI_DAY0_LAUNCH_PRICING = [ ("gemini-3.6-flash", 7.5e-07, 3.75e-06, 7.5e-08), ("gemini/gemini-3.6-flash", 7.5e-07, 3.75e-06, 7.5e-08), diff --git a/tests/test_litellm/litellm_core_utils/test_get_model_cost_map.py b/tests/test_litellm/litellm_core_utils/test_get_model_cost_map.py index 76467dfb7b1..f73e6378b92 100644 --- a/tests/test_litellm/litellm_core_utils/test_get_model_cost_map.py +++ b/tests/test_litellm/litellm_core_utils/test_get_model_cost_map.py @@ -86,6 +86,21 @@ def test_validation_accepts_healthy_file_with_meta_keys(): ) +def test_validation_rejects_significant_shrink_vs_backup(): + # 600 real models vs a 2000-model backup is below the 50% shrink threshold. + shrunk = _make_models(600) + shrunk[FALLBACK_GENERALIZATIONS_KEY] = {"rules": []} + assert ( + GetModelCostMap.validate_model_cost_map( + fetched_map=shrunk, + backup_model_count=2000, + min_model_count=50, + max_shrink_ratio=0.5, + ) + is False + ) + + def test_finalize_pops_key_and_installs_rules(): previous = list(get_fallback_generalization_rules()) try: @@ -589,6 +604,41 @@ def test_boot_load_records_the_blob_id_of_the_bytes_served_and_the_fetch_etag(): assert source["loaded_at"] is not None +def test_boot_load_fallback_to_the_backup_reports_its_blob_id_and_drops_the_remote_etag(): + remote, _ = _mock_client( + [httpx.Response(200, headers={"ETag": 'W/"boot"'}, content=_real_map_bytes())], client_cls=httpx.Client + ) + get_model_cost_map(url=_URL, sleep=_SyncSleepRecorder(), rng=random.Random(0), client=remote) + failing, _ = _mock_client([httpx.Response(404)], client_cls=httpx.Client) + + get_model_cost_map(url=_URL, sleep=_SyncSleepRecorder(), rng=random.Random(0), client=failing) + + source = get_model_cost_map_source_info() + assert source["source"] == "local" + assert source["etag"] is None + assert source["source_revision"] == _bundled_blob_id() + + +def test_boot_load_that_fails_the_integrity_check_reports_the_backup_not_the_rejected_fetch(): + remote, _ = _mock_client( + [httpx.Response(200, headers={"ETag": 'W/"boot"'}, content=_real_map_bytes())], client_cls=httpx.Client + ) + get_model_cost_map(url=_URL, sleep=_SyncSleepRecorder(), rng=random.Random(0), client=remote) + shrunk_body = b'{"gpt-5.4-mini": {"mode": "chat", "input_cost_per_token": 1e-06, "output_cost_per_token": 2e-06}}' + shrunk, _ = _mock_client( + [httpx.Response(200, headers={"ETag": 'W/"shrunk"'}, content=shrunk_body)], client_cls=httpx.Client + ) + + get_model_cost_map(url=_URL, sleep=_SyncSleepRecorder(), rng=random.Random(0), client=shrunk) + + source = get_model_cost_map_source_info() + assert source["source"] == "local" + assert source["fallback_reason"] == "Remote data failed integrity validation" + assert source["etag"] is None + assert source["source_revision"] == _bundled_blob_id() + assert source["source_revision"] != git_blob_id(shrunk_body) + + @pytest.mark.parametrize( ("argv0", "request_count"), [ diff --git a/tests/test_litellm/llms/azure_ai/test_azure_ai_foundry_catalog_model_metadata.py b/tests/test_litellm/llms/azure_ai/test_azure_ai_foundry_catalog_model_metadata.py index c9721aea7fd..5bb5b0d148d 100644 --- a/tests/test_litellm/llms/azure_ai/test_azure_ai_foundry_catalog_model_metadata.py +++ b/tests/test_litellm/llms/azure_ai/test_azure_ai_foundry_catalog_model_metadata.py @@ -4,7 +4,7 @@ from typing import Final import pytest from pydantic import TypeAdapter -from litellm import completion_cost +from litellm import completion_cost, cost_per_token, get_model_info from litellm.litellm_core_utils.get_llm_provider_logic import get_llm_provider from litellm.types.utils import TranscriptionResponse @@ -52,6 +52,34 @@ def test_azure_ai_catalog_name_routes_to_azure_ai(catalog_name: str) -> None: assert (routed_model, provider) == (catalog_name, "azure_ai") +@pytest.mark.usefixtures("local_model_cost_map") +@pytest.mark.parametrize("catalog_name", TOKEN_PRICED_NAMES) +def test_azure_ai_catalog_name_charges_its_own_entry_per_token(catalog_name: str) -> None: + entry: Final = get_model_info(f"azure_ai/{catalog_name}") + prompt_cost, completion_cost_usd = cost_per_token( + model=f"azure_ai/{catalog_name}", prompt_tokens=A_MILLION, completion_tokens=A_MILLION + ) + assert prompt_cost > 0 + assert prompt_cost == pytest.approx(A_MILLION * entry["input_cost_per_token"]) + assert completion_cost_usd == pytest.approx(A_MILLION * entry["output_cost_per_token"]) + + +@pytest.mark.usefixtures("local_model_cost_map") +@pytest.mark.parametrize("catalog_name", TOKEN_PRICED_NAMES) +def test_azure_ai_catalog_name_prices_the_same_in_any_casing(catalog_name: str) -> None: + lowercase_cost = cost_per_token(model=f"azure_ai/{catalog_name}", prompt_tokens=A_MILLION, completion_tokens=0) + upper_cost = cost_per_token(model=f"azure_ai/{catalog_name.upper()}", prompt_tokens=A_MILLION, completion_tokens=0) + assert upper_cost == lowercase_cost + + +@pytest.mark.usefixtures("local_model_cost_map") +def test_azure_ai_whisper_catalog_name_is_priced_per_second() -> None: + one_second_cost: Final = _whisper_transcription_cost(1) + one_hour_cost: Final = _whisper_transcription_cost(AN_HOUR_IN_SECONDS) + assert one_second_cost > 0 + assert one_hour_cost == pytest.approx(AN_HOUR_IN_SECONDS * one_second_cost) + + def test_azure_ai_model_router_spellings_share_one_entry() -> None: underscore_entry = _cost_map_entry(MAIN_COST_MAP, "model_router") hyphen_entry = _cost_map_entry(MAIN_COST_MAP, "model-router") diff --git a/tests/test_litellm/llms/vertex_ai/vertex_ai_partner_models/anthropic/test_vertex_ai_partner_models_anthropic_messages_config.py b/tests/test_litellm/llms/vertex_ai/vertex_ai_partner_models/anthropic/test_vertex_ai_partner_models_anthropic_messages_config.py index 58a040bf019..2d1e4305449 100644 --- a/tests/test_litellm/llms/vertex_ai/vertex_ai_partner_models/anthropic/test_vertex_ai_partner_models_anthropic_messages_config.py +++ b/tests/test_litellm/llms/vertex_ai/vertex_ai_partner_models/anthropic/test_vertex_ai_partner_models_anthropic_messages_config.py @@ -1,4 +1,6 @@ import copy +import json +import os from unittest.mock import MagicMock, patch from litellm.llms.vertex_ai.vertex_ai_partner_models.anthropic.experimental_pass_through.transformation import ( @@ -542,3 +544,30 @@ class TestVertexAnthropicMidConversationSystem: {"type": "text", "text": "You are terse."}, {"type": "text", "text": "Cite sources."}, ] + + +def test_vertex_claude_4_8_plus_cost_map_entries_carry_mid_conversation_system_flag(): + import re + + import litellm + + cost_map_path = os.path.join(os.path.dirname(litellm.__file__), "model_prices_and_context_window_backup.json") + with open(cost_map_path) as f: + cost_map = json.load(f) + rules = cost_map["fallback_generalizations"]["rules"] + rule_pattern = next( + (r["pattern"] for r in rules if r["name"] == "claude-mid-conversation-system"), + None, + ) + assert rule_pattern is not None, "claude-mid-conversation-system rule not found in fallback_generalizations" + pattern = re.compile(rule_pattern, re.IGNORECASE) + missing = [ + key + for key, info in cost_map.items() + if isinstance(info, dict) + and str(info.get("litellm_provider", "")).startswith("vertex_ai") + and "claude" in key + and pattern.search(key) + and info.get("supports_mid_conversation_system") is not True + ] + assert missing == [] diff --git a/tests/test_litellm/test_baseten_glm_5_3_model_metadata.py b/tests/test_litellm/test_baseten_glm_5_3_model_metadata.py new file mode 100644 index 00000000000..94afd9fe254 --- /dev/null +++ b/tests/test_litellm/test_baseten_glm_5_3_model_metadata.py @@ -0,0 +1,27 @@ +import pytest + +import litellm + + +@pytest.fixture +def local_model_cost_map(monkeypatch): + monkeypatch.setenv("LITELLM_LOCAL_MODEL_COST_MAP", "True") + monkeypatch.setattr(litellm, "model_cost", litellm.get_model_cost_map(url="")) + litellm.get_model_info.cache_clear() + yield + litellm.get_model_info.cache_clear() + + +def test_entry_advertises_only_what_the_baseten_path_accepts(local_model_cost_map): + """The Baseten path rejects unsupported request parameters.""" + supported = litellm.get_supported_openai_params(model="zai-org/GLM-5.3", custom_llm_provider="baseten") + assert supported is not None + + with pytest.raises(litellm.UnsupportedParamsError): + litellm.utils.get_optional_params( + model="zai-org/GLM-5.3", + custom_llm_provider="baseten", + parallel_tool_calls=True, + reasoning_effort="high", + drop_params=False, + ) From 51a4cb9fdd99be0c41332a57cc3ca08f210efc3d Mon Sep 17 00:00:00 2001 From: yassin Date: Tue, 15 Sep 2026 21:48:06 +0000 Subject: [PATCH 081/225] fix(proxy): key model rpm/tpm override takes precedence over team model limit A key inside a team with model_rpm_limit / model_tpm_limit in team metadata could not override those limits for itself: the v3 limiter always added the team's per-model descriptor next to the key's, so the tighter team limit won. The docs already say the resolution order is key metadata > key model_max_budget > team metadata get_key_own_model_rate_limit returns only what the key sets on itself, and the team descriptor now carries only the metrics the key does not override, so an rpm-only override still leaves the team tpm pool enforced Co-Authored-By: Devin AI <158243242+devin-ai-integration[bot]@users.noreply.github.com> --- litellm/proxy/auth/auth_utils.py | 60 ++++++++-------- .../hooks/parallel_request_limiter_v3.py | 62 ++++++++-------- .../proxy/auth/test_auth_utils.py | 30 ++++++++ .../hooks/test_parallel_request_limiter_v3.py | 70 ++++++++++++++++++- 4 files changed, 165 insertions(+), 57 deletions(-) diff --git a/litellm/proxy/auth/auth_utils.py b/litellm/proxy/auth/auth_utils.py index dc304a156cf..5ff85ad4a0e 100644 --- a/litellm/proxy/auth/auth_utils.py +++ b/litellm/proxy/auth/auth_utils.py @@ -976,6 +976,32 @@ def _get_deployment_default_tpm_limit(model_name: str) -> int | None: return _get_deployment_default_limit(model_name, "default_api_key_tpm_limit") +def get_key_own_model_rate_limit( + user_api_key_dict: UserAPIKeyAuth, + rate_limit_key: Literal["model_rpm_limit", "model_tpm_limit"], +) -> dict[str, int] | None: + """ + Per-model limit the key sets on itself: key metadata first, then model_max_budget. + + Unlike get_key_model_rpm_limit / get_key_model_tpm_limit this never falls back to the + team, so callers can tell a key override apart from an inherited team limit. + """ + if user_api_key_dict.metadata: + result: Final = user_api_key_dict.metadata.get(rate_limit_key) + if result: + return result + + if not user_api_key_dict.model_max_budget: + return None + budget_key: Final = "rpm_limit" if rate_limit_key == "model_rpm_limit" else "tpm_limit" + model_limit: Final = { + model: budget[budget_key] + for model, budget in user_api_key_dict.model_max_budget.items() + if isinstance(budget, dict) and budget.get(budget_key) is not None + } + return model_limit or None + + def get_key_model_rpm_limit( user_api_key_dict: UserAPIKeyAuth, model_name: str | None = None, @@ -989,20 +1015,9 @@ def get_key_model_rpm_limit( 3. Team metadata (model_rpm_limit) 4. Deployment default_api_key_rpm_limit (when model_name is provided) """ - # 1. Check key metadata first (takes priority) - if user_api_key_dict.metadata: - result: Final = user_api_key_dict.metadata.get("model_rpm_limit") - if result: - return result - - # 2. Check model_max_budget - if user_api_key_dict.model_max_budget: - model_rpm_limit: Final[dict[str, int]] = {} - for model, budget in user_api_key_dict.model_max_budget.items(): - if isinstance(budget, dict) and budget.get("rpm_limit") is not None: - model_rpm_limit[model] = budget["rpm_limit"] - if model_rpm_limit: - return model_rpm_limit + key_own_limit: Final = get_key_own_model_rate_limit(user_api_key_dict, "model_rpm_limit") + if key_own_limit is not None: + return key_own_limit # 3. Fallback to team metadata if user_api_key_dict.team_metadata: @@ -1032,20 +1047,9 @@ def get_key_model_tpm_limit( 3. Team metadata (model_tpm_limit) 4. Deployment default_api_key_tpm_limit (when model_name is provided) """ - # 1. Check key metadata first (takes priority) - if user_api_key_dict.metadata: - result: Final = user_api_key_dict.metadata.get("model_tpm_limit") - if result: - return result - - # 2. Check model_max_budget (iterate per-model like RPM does) - if user_api_key_dict.model_max_budget: - model_tpm_limit: Final[dict[str, int]] = {} - for model, budget in user_api_key_dict.model_max_budget.items(): - if isinstance(budget, dict) and budget.get("tpm_limit") is not None: - model_tpm_limit[model] = budget["tpm_limit"] - if model_tpm_limit: - return model_tpm_limit + key_own_limit: Final = get_key_own_model_rate_limit(user_api_key_dict, "model_tpm_limit") + if key_own_limit is not None: + return key_own_limit # 3. Fallback to team metadata if user_api_key_dict.team_metadata: diff --git a/litellm/proxy/hooks/parallel_request_limiter_v3.py b/litellm/proxy/hooks/parallel_request_limiter_v3.py index 8ca4124521a..87336830976 100644 --- a/litellm/proxy/hooks/parallel_request_limiter_v3.py +++ b/litellm/proxy/hooks/parallel_request_limiter_v3.py @@ -41,6 +41,7 @@ from litellm.proxy._types import UserAPIKeyAuth from litellm.proxy.auth.auth_utils import ( ESTIMATED_OUTPUT_TOKENS_FIELD, get_estimated_output_tokens, + get_key_own_model_rate_limit, get_key_tag_rpm_limit, get_model_rate_limit_from_metadata, ) @@ -2892,41 +2893,46 @@ class _PROXY_MaxParallelRequestsHandler_v3(CustomLogger): return batch_limiter return None + def _inherited_team_model_limit( + self, + user_api_key_dict: UserAPIKeyAuth, + requested_model: str, + rate_limit_key: Literal["model_rpm_limit", "model_tpm_limit"], + ) -> int | None: + """Team per-model limit this key inherits: None when the key sets its own limit for the model.""" + team_limits: Final = get_model_rate_limit_from_metadata(user_api_key_dict, "team_metadata", rate_limit_key) + team_limit: Final = team_limits.get(requested_model) if team_limits else None + if team_limit is None: + return None + key_own_limits: Final = get_key_own_model_rate_limit(user_api_key_dict, rate_limit_key) + if key_own_limits and key_own_limits.get(requested_model) is not None: + return None + return team_limit + def _add_team_model_rate_limit_descriptor_from_metadata( self, user_api_key_dict: UserAPIKeyAuth, requested_model: str | None, descriptors: list[RateLimitDescriptor], ) -> None: - """Add team model rate limit descriptor from team_metadata if applicable.""" - if ( - get_model_rate_limit_from_metadata(user_api_key_dict, "team_metadata", "model_rpm_limit") is not None - or get_model_rate_limit_from_metadata(user_api_key_dict, "team_metadata", "model_tpm_limit") is not None - ): - _tpm_limit_for_team_model: Final = ( - get_model_rate_limit_from_metadata(user_api_key_dict, "team_metadata", "model_tpm_limit") or {} + """Add the team's per-model descriptor for the metrics the key does not override itself.""" + if requested_model is None: + return + team_rpm_limit: Final = self._inherited_team_model_limit(user_api_key_dict, requested_model, "model_rpm_limit") + team_tpm_limit: Final = self._inherited_team_model_limit(user_api_key_dict, requested_model, "model_tpm_limit") + if team_rpm_limit is None and team_tpm_limit is None: + return + descriptors.append( + RateLimitDescriptor( + key="model_per_team", + value=f"{user_api_key_dict.team_id}:{requested_model}", + rate_limit={ + "requests_per_unit": team_rpm_limit, + "tokens_per_unit": team_tpm_limit, + "window_size": self.window_size, + }, ) - _rpm_limit_for_team_model: Final = ( - get_model_rate_limit_from_metadata(user_api_key_dict, "team_metadata", "model_rpm_limit") or {} - ) - should_check_rate_limit: Final = ( - requested_model in _tpm_limit_for_team_model or requested_model in _rpm_limit_for_team_model - ) - - if should_check_rate_limit and requested_model is not None: - model_specific_tpm_limit: Final = _tpm_limit_for_team_model.get(requested_model) - model_specific_rpm_limit: Final = _rpm_limit_for_team_model.get(requested_model) - descriptors.append( - RateLimitDescriptor( - key="model_per_team", - value=f"{user_api_key_dict.team_id}:{requested_model}", - rate_limit={ - "requests_per_unit": model_specific_rpm_limit, - "tokens_per_unit": model_specific_tpm_limit, - "window_size": self.window_size, - }, - ) - ) + ) def _add_project_model_rate_limit_descriptor_from_metadata( self, diff --git a/tests/test_litellm/proxy/auth/test_auth_utils.py b/tests/test_litellm/proxy/auth/test_auth_utils.py index bd6a14cad21..639c697726e 100644 --- a/tests/test_litellm/proxy/auth/test_auth_utils.py +++ b/tests/test_litellm/proxy/auth/test_auth_utils.py @@ -22,6 +22,7 @@ from litellm.proxy.auth.auth_utils import ( get_key_mcp_rpm_limit, get_key_model_rpm_limit, get_key_model_tpm_limit, + get_key_own_model_rate_limit, get_key_tag_rpm_limit, get_model_from_request, get_project_model_rpm_limit, @@ -141,6 +142,35 @@ class TestLogOnceIfBudgetReservationDisabled: class TestGetKeyModelRpmLimit: """Tests for get_key_model_rpm_limit function.""" + def test_own_limit_excludes_team_metadata(self): + """A team-only limit is inherited, not owned: the key resolves it but does not override it.""" + user_api_key_dict = UserAPIKeyAuth( + api_key="sk-123", + metadata={"some_other_key": "value"}, + team_metadata={"model_rpm_limit": {"gpt-4": 50}, "model_tpm_limit": {"gpt-4": 500}}, + ) + assert get_key_model_rpm_limit(user_api_key_dict) == {"gpt-4": 50} + assert get_key_own_model_rate_limit(user_api_key_dict, "model_rpm_limit") is None + assert get_key_own_model_rate_limit(user_api_key_dict, "model_tpm_limit") is None + + def test_own_limit_resolves_metadata_then_model_max_budget(self): + from_metadata = UserAPIKeyAuth( + api_key="sk-123", + metadata={"model_rpm_limit": {"gpt-4": 100}}, + model_max_budget={"gpt-4": {"rpm_limit": 10, "tpm_limit": 1000}}, + team_metadata={"model_rpm_limit": {"gpt-4": 50}}, + ) + assert get_key_own_model_rate_limit(from_metadata, "model_rpm_limit") == {"gpt-4": 100} + assert get_key_own_model_rate_limit(from_metadata, "model_tpm_limit") == {"gpt-4": 1000} + + from_budget = UserAPIKeyAuth( + api_key="sk-123", + model_max_budget={"gpt-4": {"rpm_limit": 10}, "gpt-3.5-turbo": {"tpm_limit": 1000}}, + team_metadata={"model_rpm_limit": {"gpt-4": 50}}, + ) + assert get_key_own_model_rate_limit(from_budget, "model_rpm_limit") == {"gpt-4": 10} + assert get_key_own_model_rate_limit(from_budget, "model_tpm_limit") == {"gpt-3.5-turbo": 1000} + def test_returns_key_metadata_when_present(self): """Key metadata takes priority over team metadata.""" user_api_key_dict = UserAPIKeyAuth( diff --git a/tests/test_litellm/proxy/hooks/test_parallel_request_limiter_v3.py b/tests/test_litellm/proxy/hooks/test_parallel_request_limiter_v3.py index 48f980086fd..c2b88776dc5 100644 --- a/tests/test_litellm/proxy/hooks/test_parallel_request_limiter_v3.py +++ b/tests/test_litellm/proxy/hooks/test_parallel_request_limiter_v3.py @@ -6311,7 +6311,7 @@ async def test_an_open_circuit_breaker_reads_the_sliding_window_locally_without_ ( { "team_id": "t", - "metadata": {"model_rpm_limit": {"test-model": 100}}, + "metadata": {"model_rpm_limit": {"other-model": 100}}, "team_metadata": {"model_rpm_limit": {"test-model": 1}}, }, {}, @@ -6529,3 +6529,71 @@ async def test_request_capacity_rejection_keeps_existing_redis_mirror(): pytest.fail("rejection released another request's mirrored slot") assert exc.value.status_code == 429 assert await cache.async_get_cache(counter_key, local_only=True) == 1 + + +@pytest.mark.parametrize( + "key_limits", + [ + {"metadata": {"model_rpm_limit": {"test-model": 3}}}, + {"model_max_budget": {"test-model": {"rpm_limit": 3}}}, + ], +) +@pytest.mark.asyncio +async def test_key_model_rpm_override_takes_precedence_over_team_model_rpm_limit(key_limits): + cache = DualCache() + handler = _PROXY_MaxParallelRequestsHandler(internal_usage_cache=InternalUsageCache(cache)) + auth = UserAPIKeyAuth( + api_key=hash_token("sk-key-override"), + team_id="t", + team_metadata={"model_rpm_limit": {"test-model": 1}}, + **key_limits, + ) + + async def request(): + await handler.async_pre_call_hook( + user_api_key_dict=auth, cache=cache, data={"model": "test-model"}, call_type="acompletion" + ) + + for _ in range(3): + await request() + with pytest.raises(HTTPException) as exc: + await request() + assert exc.value.status_code == 429 + assert "model_per_key" in str(exc.value.detail) + + +@pytest.mark.parametrize( + "key_limits, override_key_gets_through", + [ + ({"model_rpm_limit": {"test-model": 10}}, False), + ({"model_rpm_limit": {"test-model": 10}, "model_tpm_limit": {"test-model": 5000}}, True), + ], + ids=["rpm_only_override_still_shares_team_tpm", "rpm_and_tpm_override_leaves_team_tpm"], +) +@pytest.mark.asyncio +async def test_key_model_rpm_override_keeps_team_model_tpm_limit(key_limits, override_key_gets_through): + cache = DualCache() + handler = _PROXY_MaxParallelRequestsHandler(internal_usage_cache=InternalUsageCache(cache)) + team_metadata = {"model_rpm_limit": {"test-model": 5}, "model_tpm_limit": {"test-model": 500}} + sibling_key = UserAPIKeyAuth(api_key=hash_token("sk-sibling"), team_id="t", team_metadata=team_metadata) + override_key = UserAPIKeyAuth( + api_key=hash_token("sk-key-override"), team_id="t", metadata=key_limits, team_metadata=team_metadata + ) + + async def request(auth): + await handler.async_pre_call_hook( + user_api_key_dict=auth, + cache=cache, + data={"model": "test-model", "messages": [{"role": "user", "content": "hi"}], "max_tokens": 300}, + call_type="acompletion", + ) + + await request(sibling_key) + if override_key_gets_through: + await request(override_key) + return + with pytest.raises(HTTPException) as exc: + await request(override_key) + assert exc.value.status_code == 429 + assert "model_per_team" in str(exc.value.detail) + assert exc.value.headers["rate_limit_type"] == "tokens" From 3cb64978d47ea6d7b5cfe28bb296cf20a7a5eebb Mon Sep 17 00:00:00 2001 From: yassin Date: Tue, 15 Sep 2026 21:55:37 +0000 Subject: [PATCH 082/225] fix(proxy): honor LITELLM_LOG for uvicorn and proxy extras loggers LITELLM_LOG=ERROR still printed INFO lines from uvicorn (startup and access log) and from the litellm_proxy_extras migration logger, because neither read the variable. Forward the resolved level to uvicorn when LITELLM_LOG is set and no explicit log_config or JSON logging is in use, and let the extras logger take its level from LITELLM_LOG Co-Authored-By: Devin AI <158243242+devin-ai-integration[bot]@users.noreply.github.com> --- .../litellm_proxy_extras/_logging.py | 2 +- litellm-proxy-extras/tests/test_logging.py | 34 +++++++++++++++++++ litellm/_logging.py | 2 +- litellm/proxy/proxy_cli.py | 4 ++- tests/test_litellm/proxy/test_proxy_cli.py | 33 ++++++++++++++++++ 5 files changed, 72 insertions(+), 3 deletions(-) create mode 100644 litellm-proxy-extras/tests/test_logging.py diff --git a/litellm-proxy-extras/litellm_proxy_extras/_logging.py b/litellm-proxy-extras/litellm_proxy_extras/_logging.py index ecf467fbf45..64e07a180d3 100644 --- a/litellm-proxy-extras/litellm_proxy_extras/_logging.py +++ b/litellm-proxy-extras/litellm_proxy_extras/_logging.py @@ -40,4 +40,4 @@ if not logger.handlers: logging.Formatter("%(asctime)s - %(name)s - %(levelname)s - %(message)s") ) logger.addHandler(handler) - logger.setLevel(logging.INFO) + logger.setLevel(os.getenv("LITELLM_LOG", "INFO").upper()) diff --git a/litellm-proxy-extras/tests/test_logging.py b/litellm-proxy-extras/tests/test_logging.py new file mode 100644 index 00000000000..b54d4fb72c9 --- /dev/null +++ b/litellm-proxy-extras/tests/test_logging.py @@ -0,0 +1,34 @@ +import importlib +import logging + +import pytest + +import litellm_proxy_extras._logging as extras_logging + + +def test_litellm_log_error_silences_extras_info_lines(monkeypatch): + saved_handlers = logging.getLogger("litellm_proxy_extras").handlers[:] + monkeypatch.setenv("LITELLM_LOG", "ERROR") + logging.getLogger("litellm_proxy_extras").handlers[:] = [] + try: + reloaded = importlib.reload(extras_logging).logger + assert reloaded.isEnabledFor(logging.INFO) is False + assert reloaded.isEnabledFor(logging.ERROR) is True + finally: + logging.getLogger("litellm_proxy_extras").handlers[:] = saved_handlers + logging.getLogger("litellm_proxy_extras").setLevel(logging.INFO) + + +@pytest.mark.parametrize("litellm_log", [None, "info", "DEBUG"]) +def test_unset_or_verbose_litellm_log_keeps_extras_info_lines(monkeypatch, litellm_log): + saved_handlers = logging.getLogger("litellm_proxy_extras").handlers[:] + if litellm_log is None: + monkeypatch.delenv("LITELLM_LOG", raising=False) + else: + monkeypatch.setenv("LITELLM_LOG", litellm_log) + logging.getLogger("litellm_proxy_extras").handlers[:] = [] + try: + assert importlib.reload(extras_logging).logger.isEnabledFor(logging.INFO) is True + finally: + logging.getLogger("litellm_proxy_extras").handlers[:] = saved_handlers + logging.getLogger("litellm_proxy_extras").setLevel(logging.INFO) diff --git a/litellm/_logging.py b/litellm/_logging.py index 03a9bcf21cf..893133d62fe 100644 --- a/litellm/_logging.py +++ b/litellm/_logging.py @@ -404,7 +404,7 @@ def _parse_json_logs_env(value: str | None) -> bool: json_logs: Final = _parse_json_logs_env(os.getenv("JSON_LOGS")) # Create a handler for the logger (you may need to adapt this based on your needs) log_level: Final = os.getenv("LITELLM_LOG", "DEBUG") -numeric_level: Final[str] = getattr(logging, log_level.upper()) +numeric_level: Final[int] = getattr(logging, log_level.upper()) handler: Final = LevelRoutingStreamHandler() handler.setLevel(numeric_level) handler.addFilter(_secret_filter) diff --git a/litellm/proxy/proxy_cli.py b/litellm/proxy/proxy_cli.py index 01a3da08998..6a23b422717 100644 --- a/litellm/proxy/proxy_cli.py +++ b/litellm/proxy/proxy_cli.py @@ -261,7 +261,7 @@ class ProxyInitializationHelpers: import uvicorn import litellm - from litellm._logging import _get_uvicorn_json_log_config + from litellm._logging import _get_uvicorn_json_log_config, numeric_level uvicorn_args: Final = { "app": "litellm.proxy.proxy_server:app", @@ -275,6 +275,8 @@ class ProxyInitializationHelpers: elif litellm.json_logs: # Use JSON log config for uvicorn to ensure all logs (including exceptions) are JSON uvicorn_args["log_config"] = _get_uvicorn_json_log_config() + elif os.environ.get("LITELLM_LOG"): + uvicorn_args["log_level"] = numeric_level if keepalive_timeout is not None: uvicorn_args["timeout_keep_alive"] = keepalive_timeout if timeout_worker_healthcheck is not None: diff --git a/tests/test_litellm/proxy/test_proxy_cli.py b/tests/test_litellm/proxy/test_proxy_cli.py index e25e6a59884..8f3c21e10c2 100644 --- a/tests/test_litellm/proxy/test_proxy_cli.py +++ b/tests/test_litellm/proxy/test_proxy_cli.py @@ -139,6 +139,39 @@ class TestProxyInitializationHelpers: ) assert args["timeout_worker_healthcheck"] == 15 + @staticmethod + def _uvicorn_access_info_enabled(args: dict) -> bool: + import logging + + names = ("uvicorn", "uvicorn.error", "uvicorn.access", "uvicorn.asgi") + saved = tuple((logging.getLogger(n), logging.getLogger(n).handlers[:], logging.getLogger(n).level) for n in names) + try: + uvicorn.Config(**args).configure_logging() + return logging.getLogger("uvicorn.access").isEnabledFor(logging.INFO) + finally: + for lg, handlers, level in saved: + lg.handlers[:] = handlers + lg.setLevel(level) + + def test_litellm_log_error_silences_uvicorn_info_lines(self, monkeypatch): + import logging + + monkeypatch.setenv("LITELLM_LOG", "ERROR") + with patch( # test-quality-ok: numeric_level is resolved from LITELLM_LOG once at import; no other way to set it + "litellm._logging.numeric_level", logging.ERROR + ): + args = ProxyInitializationHelpers._get_default_unvicorn_init_args("localhost", 8000) + + assert "log_config" not in args + assert self._uvicorn_access_info_enabled(args) is False + + def test_unset_litellm_log_keeps_uvicorn_default_info_lines(self, monkeypatch): + monkeypatch.delenv("LITELLM_LOG", raising=False) + args = ProxyInitializationHelpers._get_default_unvicorn_init_args("localhost", 8000) + + assert "log_level" not in args + assert self._uvicorn_access_info_enabled(args) is True + def test_installed_uvicorn_supports_worker_flags(self): params = inspect.signature(uvicorn.Config.__init__).parameters assert "timeout_worker_healthcheck" in params From 168b5bc4fbaaeae00ae88d8173f55944a12ef84e Mon Sep 17 00:00:00 2001 From: yassin Date: Tue, 15 Sep 2026 22:05:08 +0000 Subject: [PATCH 083/225] feat(proxy): configurable client-facing model access denied message Add litellm_settings.model_access_denied_message, a template ({model} placeholder) returned to clients instead of the detailed "can only access models=[...]" text on key/team/user/org/project and team-member model access denials. The full denial reason is still written to the proxy logs at WARNING. Unset keeps the existing detailed message, status codes and error types are unchanged. Expose the new setting and the existing expose_router_debug_in_errors flag in the Admin UI general settings (String editor, Boolean toggle with an explicit True default) and allow both as safe DB overrides so they persist and propagate across workers. Resolves LIT-5283 Co-Authored-By: Devin AI <158243242+devin-ai-integration[bot]@users.noreply.github.com> --- litellm/__init__.py | 1 + litellm/constants.py | 3 + litellm/proxy/auth/auth_checks.py | 24 ++++- litellm/proxy/proxy_server.py | 31 +++++- .../proxy/auth/test_auth_checks.py | 94 +++++++++++++++++++ tests/test_litellm/proxy/test_proxy_server.py | 66 +++++++++++++ .../general_settings.integration.test.tsx | 33 ++++++- .../_components/general_settings.tsx | 14 ++- 8 files changed, 259 insertions(+), 7 deletions(-) diff --git a/litellm/__init__.py b/litellm/__init__.py index ccfbf80369f..97457f3e3cc 100644 --- a/litellm/__init__.py +++ b/litellm/__init__.py @@ -219,6 +219,7 @@ redact_user_api_key_info: Optional[bool] = False # major release; opt in early with `litellm.expose_router_debug_in_errors # = False`. expose_router_debug_in_errors: bool = True +model_access_denied_message: str | None = None filter_invalid_headers: Optional[bool] = False add_user_information_to_llm_headers: Optional[bool] = ( None # adds user_id, team_id, token hash (params from StandardLoggingMetadata) to request headers diff --git a/litellm/constants.py b/litellm/constants.py index ba5ec73d435..9a9bb59d8b2 100644 --- a/litellm/constants.py +++ b/litellm/constants.py @@ -98,6 +98,7 @@ BASE64_TRUNCATION_OFFLOAD_THRESHOLD_CHARS: Final = 256 * 1024 REDACTED_BY_LITELLM: Final = "redacted-by-litellm" # in-memory stand-in handed to provider converters for redacted arguments; never stored REDACTED_TOOL_CALL_ARGUMENTS_PLACEHOLDER: Final = "{}" +MODEL_ACCESS_DENIED_MESSAGE_MODEL_PLACEHOLDER: Final = "{model}" MAX_STRING_LENGTH_STDOUT_LOG: Final = get_env_int("MAX_STRING_LENGTH_STDOUT_LOG", 4096) @@ -1796,6 +1797,8 @@ LITELLM_SETTINGS_SAFE_DB_OVERRIDES: Final = [ "max_ui_session_budget", "budget_rollover", "mcp_tool_search", + "model_access_denied_message", + "expose_router_debug_in_errors", ] SPECIAL_LITELLM_AUTH_TOKEN: Final = ["ui-token"] DEFAULT_MANAGEMENT_OBJECT_IN_MEMORY_CACHE_TTL = int(os.getenv("DEFAULT_MANAGEMENT_OBJECT_IN_MEMORY_CACHE_TTL", 60)) diff --git a/litellm/proxy/auth/auth_checks.py b/litellm/proxy/auth/auth_checks.py index 355fc3f6a21..6435ce0d12f 100644 --- a/litellm/proxy/auth/auth_checks.py +++ b/litellm/proxy/auth/auth_checks.py @@ -32,6 +32,7 @@ from litellm.constants import ( DEFAULT_MAX_RECURSE_DEPTH, EMAIL_BUDGET_ALERT_MAX_SPEND_ALERT_PERCENTAGE, END_USER_RESTRICTED_REGISTRY_MAX_SIZE, + MODEL_ACCESS_DENIED_MESSAGE_MODEL_PLACEHOLDER, MODEL_ACCESS_GROUP_REGISTRY_MAX_SIZE, REGISTRY_ERROR_NEGATIVE_CACHE_TTL, TAG_REGISTRY_MAX_SIZE, @@ -4033,6 +4034,14 @@ async def _get_agent_ids_from_access_groups( ) +def _client_facing_model_access_denied_message(internal_message: str, model: str | list[str]) -> str: + template: Final = litellm.model_access_denied_message + if not template: + return internal_message + verbose_proxy_logger.warning(internal_message) + return template.replace(MODEL_ACCESS_DENIED_MESSAGE_MODEL_PLACEHOLDER, str(model)) + + def _resolve_all_team_model_sentinel_for_auth_check( models: list[str], llm_router: Router | None, @@ -4155,7 +4164,10 @@ def _can_object_call_model( return True raise ProxyException( - message=f"{object_type} not allowed to access model. This {object_type} can only access models={models}. Tried to access {model}", + message=_client_facing_model_access_denied_message( + internal_message=f"{object_type} not allowed to access model. This {object_type} can only access models={models}. Tried to access {model}", + model=model, + ), type=ProxyErrorTypes.get_model_access_error_type_for_object(object_type=object_type), param="model", code=status.HTTP_403_FORBIDDEN, @@ -4781,7 +4793,10 @@ async def can_user_call_model( if SpecialModelNames.no_default_models.value in user_object.models: raise ProxyException( - message=f"User not allowed to access model. No default model access, only team models allowed. Tried to access {model}", + message=_client_facing_model_access_denied_message( + internal_message=f"User not allowed to access model. No default model access, only team models allowed. Tried to access {model}", + model=model, + ), type=ProxyErrorTypes.key_model_access_denied, param="model", code=status.HTTP_403_FORBIDDEN, @@ -5383,7 +5398,10 @@ async def _check_team_member_model_access( ) except ProxyException: raise ProxyException( - message=f"Team member not allowed to access model. User={valid_token.user_id}, Team={team_object.team_id}, Model={model}. Allowed member models = {member_allowed_models}", + message=_client_facing_model_access_denied_message( + internal_message=f"Team member not allowed to access model. User={valid_token.user_id}, Team={team_object.team_id}, Model={model}. Allowed member models = {member_allowed_models}", + model=model, + ), type=ProxyErrorTypes.team_model_access_denied, param="model", code=status.HTTP_403_FORBIDDEN, diff --git a/litellm/proxy/proxy_server.py b/litellm/proxy/proxy_server.py index f63e088ebf7..f758628c02a 100644 --- a/litellm/proxy/proxy_server.py +++ b/litellm/proxy/proxy_server.py @@ -17435,11 +17435,13 @@ GeneralSettingsUILiteLLMValue = float | bool | str | None class GeneralSettingsUILiteLLMFieldSpec(TypedDict): - type: Literal["Float", "Dollar", "Boolean", "Select"] + type: Literal["Float", "Dollar", "Boolean", "Select", "String"] description: str options: NotRequired[tuple[str, ...]] tab: NotRequired[str] # Admin UI sub-tab this field renders under; None groups it with the rest - default: NotRequired[float] # reset/clear restores this instead of None; fields whose None means fail-open set it + default: NotRequired[ + float | bool + ] # reset/clear restores this instead of None; fields whose None means fail-open set it _GENERAL_SETTINGS_UI_LITELLM_FIELDS: Final[dict[str, GeneralSettingsUILiteLLMFieldSpec]] = { @@ -17481,6 +17483,24 @@ _GENERAL_SETTINGS_UI_LITELLM_FIELDS: Final[dict[str, GeneralSettingsUILiteLLMFie "with this budget. Clearing restores the $1 default." ), }, + "model_access_denied_message": { + "type": "String", + "description": ( + "Client-facing error message returned when a key, team, user, org or project is not allowed " + "to call the requested model. {model} is replaced with the requested model name. The full " + "denial reason (allowed models and access groups) is still written to the proxy logs. " + "Leave empty to return the detailed message to clients." + ), + }, + "expose_router_debug_in_errors": { + "type": "Boolean", + "default": True, + "description": ( + "Append router debug details (model group, configured fallbacks, fallback errors, cooldown " + "info) to error messages returned to clients. Turn off to keep those details in the proxy " + "logs only." + ), + }, } @@ -17528,6 +17548,13 @@ def _validate_general_settings_ui_litellm_value(field_name: str, value: object) detail={"error": f"{field_name} must be a positive dollar amount or empty"}, ) return float(value) + case "String": + if not isinstance(value, str): + raise HTTPException( + status_code=400, + detail={"error": f"{field_name} must be a string or empty"}, + ) + return value case _: assert_never(field_type) diff --git a/tests/test_litellm/proxy/auth/test_auth_checks.py b/tests/test_litellm/proxy/auth/test_auth_checks.py index 8c8b755195f..e99e92fcd02 100644 --- a/tests/test_litellm/proxy/auth/test_auth_checks.py +++ b/tests/test_litellm/proxy/auth/test_auth_checks.py @@ -1679,6 +1679,100 @@ def test_can_object_call_model_no_access_to_alias_or_underlying(): assert "my-fake-gpt" in str(exc_info.value.message) +_DENIED_MESSAGE_TEMPLATE: Final = "The model `{model}` is unavailable for this API key or does not exist." + + +def test_can_object_call_model_denial_uses_configured_message_and_logs_detail(monkeypatch, caplog): + """LIT-5283: with model_access_denied_message set, the client sees only the template with + {model} filled in, while the allowed models / access groups stay in the proxy log.""" + monkeypatch.setattr(litellm, "model_access_denied_message", _DENIED_MESSAGE_TEMPLATE) + + with caplog.at_level("WARNING", logger="LiteLLM Proxy"): + with pytest.raises(ProxyException) as exc_info: + _can_object_call_model( + model="anthropic-sonnet-4-5", + llm_router=None, + models=["internal-models"], + object_type="key", + ) + + assert ( + exc_info.value.message == "The model `anthropic-sonnet-4-5` is unavailable for this API key or does not exist." + ) + assert exc_info.value.type == ProxyErrorTypes.key_model_access_denied + assert exc_info.value.param == "model" + assert int(exc_info.value.code) == status.HTTP_403_FORBIDDEN + assert "internal-models" in caplog.text + assert "anthropic-sonnet-4-5" in caplog.text + + +@pytest.mark.parametrize("unset_value", [None, ""]) +def test_can_object_call_model_denial_unchanged_when_message_not_configured(monkeypatch, unset_value): + monkeypatch.setattr(litellm, "model_access_denied_message", unset_value) + + with pytest.raises(ProxyException) as exc_info: + _can_object_call_model( + model="anthropic-sonnet-4-5", + llm_router=None, + models=["internal-models"], + object_type="team", + ) + + assert exc_info.value.message == ( + "team not allowed to access model. This team can only access models=['internal-models']. " + "Tried to access anthropic-sonnet-4-5" + ) + + +@pytest.mark.asyncio +async def test_can_user_call_model_no_default_models_uses_configured_message(monkeypatch): + from litellm.proxy._types import SpecialModelNames + from litellm.proxy.auth.auth_checks import can_user_call_model + + monkeypatch.setattr(litellm, "model_access_denied_message", _DENIED_MESSAGE_TEMPLATE) + user_object = LiteLLM_UserTable(user_id="test-user", models=[SpecialModelNames.no_default_models.value]) + + with pytest.raises(ProxyException) as exc_info: + await can_user_call_model(model="restricted-model", llm_router=None, user_object=user_object) + + assert exc_info.value.message == "The model `restricted-model` is unavailable for this API key or does not exist." + assert int(exc_info.value.code) == status.HTTP_403_FORBIDDEN + + +@pytest.mark.asyncio +async def test_check_team_member_model_access_denied_uses_configured_message(monkeypatch): + from litellm.proxy._types import LiteLLM_TeamMembership + from litellm.proxy.auth.auth_checks import _check_team_member_model_access + from litellm.proxy.common_utils.user_api_key_cache import team_membership_reservation_cache_key + + monkeypatch.setattr(litellm, "model_access_denied_message", _DENIED_MESSAGE_TEMPLATE) + membership = LiteLLM_TeamMembership( + user_id="alice", + team_id="team-a", + litellm_budget_table=LiteLLM_BudgetTable(allowed_models=["fast-models"]), + ) + cache = UserApiKeyCache() + await cache.async_set_cache( + key=team_membership_reservation_cache_key(user_id="alice", team_id="team-a"), + value=membership, + model_type=LiteLLM_TeamMembership, + ) + + with pytest.raises(ProxyException) as exc_info: + await _check_team_member_model_access( + model="mock-vision", + team_object=LiteLLM_TeamTable(team_id="team-a"), + valid_token=UserAPIKeyAuth(token="sk-test", user_id="alice", team_id="team-a"), + llm_router=_make_team_scoped_router(), + prisma_client=None, + user_api_key_cache=cache, + proxy_logging_obj=MagicMock(), + ) + + assert exc_info.value.message == "The model `mock-vision` is unavailable for this API key or does not exist." + assert exc_info.value.type == ProxyErrorTypes.team_model_access_denied + + # -- Team-member access-group resolution with team-scoped DB models ----------- diff --git a/tests/test_litellm/proxy/test_proxy_server.py b/tests/test_litellm/proxy/test_proxy_server.py index 42af8e0af21..17847575de1 100644 --- a/tests/test_litellm/proxy/test_proxy_server.py +++ b/tests/test_litellm/proxy/test_proxy_server.py @@ -10860,6 +10860,72 @@ def test_validate_max_ui_session_budget_empty_restores_default(empty_value): assert _validate_general_settings_ui_litellm_value("max_ui_session_budget", empty_value) == 1.0 +@pytest.mark.asyncio +async def test_update_config_field_model_access_denied_message_sets_live_value(monkeypatch): + """LIT-5283: the client-facing model access denial message is editable from the Admin UI + General tab as a String field, applies live via setattr, and persists under litellm_settings.""" + from unittest.mock import MagicMock + + import litellm.proxy.proxy_server as ps + from litellm.proxy._types import ConfigFieldUpdate, LitellmUserRoles, UserAPIKeyAuth + from litellm.proxy.proxy_server import update_config_general_settings + + saved: dict = {} + + async def fake_get_config(): + return {"litellm_settings": {}} + + async def fake_save_config(new_config=None): + saved.update(new_config or {}) + + monkeypatch.setattr(ps.proxy_config, "get_config", fake_get_config) + monkeypatch.setattr(ps.proxy_config, "save_config", fake_save_config) + monkeypatch.setattr(ps, "prisma_client", MagicMock()) + monkeypatch.setattr(litellm, "store_audit_logs", False) + monkeypatch.setattr(litellm, "model_access_denied_message", None) + + admin = UserAPIKeyAuth(api_key="k", user_id="a", user_role=LitellmUserRoles.PROXY_ADMIN) + await update_config_general_settings( + data=ConfigFieldUpdate( + field_name="model_access_denied_message", + field_value="Model `{model}` is unavailable for this key.", + config_type="general_settings", + ), + user_api_key_dict=admin, + ) + + assert litellm.model_access_denied_message == "Model `{model}` is unavailable for this key." + assert saved["litellm_settings"]["model_access_denied_message"] == "Model `{model}` is unavailable for this key." + + +@pytest.mark.parametrize("bad_value", [True, 3, 1.5, ["x"], {"a": "b"}]) +def test_validate_model_access_denied_message_rejects_non_strings(bad_value): + from fastapi import HTTPException + + from litellm.proxy.proxy_server import _validate_general_settings_ui_litellm_value + + with pytest.raises(HTTPException) as exc_info: + _validate_general_settings_ui_litellm_value("model_access_denied_message", bad_value) + assert exc_info.value.status_code == 400 + + +@pytest.mark.parametrize("empty_value", [None, ""]) +def test_validate_model_access_denied_message_empty_restores_detailed_default(empty_value): + from litellm.proxy.proxy_server import _validate_general_settings_ui_litellm_value + + assert _validate_general_settings_ui_litellm_value("model_access_denied_message", empty_value) is None + + +@pytest.mark.parametrize("empty_value", [None, ""]) +def test_validate_expose_router_debug_in_errors_empty_restores_true_default(empty_value): + """Clearing the field from the Admin UI must restore the historical default (debug details + exposed), not the generic Boolean fallback of False.""" + from litellm.proxy.proxy_server import _validate_general_settings_ui_litellm_value + + assert _validate_general_settings_ui_litellm_value("expose_router_debug_in_errors", empty_value) is True + assert _validate_general_settings_ui_litellm_value("expose_router_debug_in_errors", False) is False + + def test_general_settings_ui_defaults_unchanged_for_existing_fields(): """The spec-default mechanism added for max_ui_session_budget must not change what clearing the pre-existing fields restores (None for Float/Select, False for Boolean).""" diff --git a/ui/litellm-dashboard/src/app/(dashboard)/router-settings/_components/general_settings.integration.test.tsx b/ui/litellm-dashboard/src/app/(dashboard)/router-settings/_components/general_settings.integration.test.tsx index 9cd1444b0b9..1e29b181b78 100644 --- a/ui/litellm-dashboard/src/app/(dashboard)/router-settings/_components/general_settings.integration.test.tsx +++ b/ui/litellm-dashboard/src/app/(dashboard)/router-settings/_components/general_settings.integration.test.tsx @@ -1,4 +1,4 @@ -import { renderWithProviders, screen, within } from "../../../../../tests/test-utils"; +import { fireEvent, renderWithProviders, screen, within } from "../../../../../tests/test-utils"; import userEvent from "@testing-library/user-event"; import { beforeEach, describe, expect, it, vi } from "vitest"; import GeneralSettings from "./general_settings"; @@ -53,6 +53,14 @@ const SETTINGS_FIXTURE = [ stored_in_db: true, field_default_value: 1.0, }, + { + field_name: "model_access_denied_message", + field_type: "String", + field_value: null, + field_description: "client-facing denial message", + stored_in_db: null, + field_default_value: null, + }, ]; const settingsRow = async (fieldName: string) => { @@ -99,6 +107,29 @@ describe("GeneralSettings General tab", () => { expect(deleteConfigFieldSetting).toHaveBeenCalledWith("token", "max_ui_session_budget"); expect(numericValueIn(row)).toBe(1); }); + + it("saves a typed model_access_denied_message and resets it when cleared", async () => { + const user = userEvent.setup(); + renderWithProviders(); + + await user.click(screen.getByText("General")); + const row = await settingsRow("model_access_denied_message"); + const input = within(row).getByRole("textbox") as HTMLInputElement; + expect(input.value).toBe(""); + + fireEvent.change(input, { target: { value: "Model `{model}` is unavailable for this key." } }); + await user.click(within(row).getByRole("button", { name: /update/i })); + expect(updateConfigFieldSetting).toHaveBeenCalledWith( + "token", + "model_access_denied_message", + "Model `{model}` is unavailable for this key.", + ); + + fireEvent.change(input, { target: { value: "" } }); + await user.click(within(row).getByRole("button", { name: /update/i })); + expect(deleteConfigFieldSetting).toHaveBeenCalledWith("token", "model_access_denied_message"); + expect(vi.mocked(updateConfigFieldSetting).mock.calls).toHaveLength(1); + }); }); // The five tabs here are proxy-wide settings. Auto-routers moved to Models + Endpoints. diff --git a/ui/litellm-dashboard/src/app/(dashboard)/router-settings/_components/general_settings.tsx b/ui/litellm-dashboard/src/app/(dashboard)/router-settings/_components/general_settings.tsx index df9e328ec3b..040b1f3c375 100644 --- a/ui/litellm-dashboard/src/app/(dashboard)/router-settings/_components/general_settings.tsx +++ b/ui/litellm-dashboard/src/app/(dashboard)/router-settings/_components/general_settings.tsx @@ -39,6 +39,8 @@ export interface generalSettingsItem { const NUMERIC_INPUT_WIDTH = "w-36"; const toNumericValue = (raw: string): number | null => (raw === "" ? null : Number(raw)); +const toStringValue = (raw: string): string | null => (raw === "" ? null : raw); +const RESETS_WHEN_CLEARED = new Set(["Select", "String"]); const SettingValueEditor: React.FC<{ setting: generalSettingsItem; @@ -107,6 +109,16 @@ const SettingValueEditor: React.FC<{ ); } + if (setting.field_type === "String") { + return ( + onChange(setting.field_name, toStringValue(event.target.value))} + /> + ); + } return null; }; @@ -210,7 +222,7 @@ const GeneralSettings: React.FC = ({ accessToken, user const fieldValue = setting?.field_value; if (fieldValue == null) { - if (setting?.field_type === "Select") handleResetField(fieldName); + if (setting && RESETS_WHEN_CLEARED.has(setting.field_type)) handleResetField(fieldName); return; } try { From bae2bf003e26048bea1500efef65a95bbd57dd1e Mon Sep 17 00:00:00 2001 From: yassin Date: Tue, 15 Sep 2026 21:52:20 +0000 Subject: [PATCH 084/225] fix(proxy): resolve router_settings.model_group_alias before key/team model auth Key and team router_settings.model_group_alias aliases were resolved only after the key/team model allowlist checks ran, so a key allowed the alias target was denied when it requested the alias. Resolve the alias during auth and rewrite the request body to the target before the allowlist checks. The alias the client sent is kept in the request scope so the response model still echoes it. Resolves LIT-3054 Co-Authored-By: Devin AI <158243242+devin-ai-integration[bot]@users.noreply.github.com> --- litellm/constants.py | 1 + litellm/proxy/auth/user_api_key_auth.py | 49 +++++++++- litellm/proxy/common_request_processing.py | 12 +-- .../proxy/common_utils/http_parsing_utils.py | 9 +- .../proxy/auth/test_user_api_key_auth.py | 97 +++++++++++++++++++ .../proxy/test_common_request_processing.py | 53 +++++++++- 6 files changed, 212 insertions(+), 9 deletions(-) diff --git a/litellm/constants.py b/litellm/constants.py index ba5ec73d435..ca2be8af5fe 100644 --- a/litellm/constants.py +++ b/litellm/constants.py @@ -317,6 +317,7 @@ WEBSOCKET_CLOSE_REASON_MAX_BYTES: Final = 123 BEDROCK_REALTIME_PENDING_SESSION_UPDATE_SCOPE_KEY: Final = "litellm.bedrock_realtime.pending_session_update" BEDROCK_REALTIME_SESSION_COMMITTED_SCOPE_KEY: Final = "litellm.bedrock_realtime.session_committed" BEDROCK_REALTIME_COMMITTED_FAILURE_SCOPE_KEY: Final = "litellm.bedrock_realtime.committed_failure" +CLIENT_REQUESTED_MODEL_SCOPE_KEY: Final = "litellm.client_requested_model" REALTIME_SESSION_SUCCESS_LOGGED_KEY: Final = "realtime_session_success_logged" REALTIME_SESSION_FAILURE_LOGGED_KEY: Final = "realtime_session_failure_logged" diff --git a/litellm/proxy/auth/user_api_key_auth.py b/litellm/proxy/auth/user_api_key_auth.py index 7d62baf39a8..a02661db9ec 100644 --- a/litellm/proxy/auth/user_api_key_auth.py +++ b/litellm/proxy/auth/user_api_key_auth.py @@ -26,6 +26,7 @@ from litellm._logging import verbose_logger, verbose_proxy_logger from litellm._service_logger import ServiceLogging from litellm.caching.redis_cache import RedisCache from litellm.constants import ( + CLIENT_REQUESTED_MODEL_SCOPE_KEY, GLOBAL_PROXY_SPEND_CACHE_KEY, INVALID_VIRTUAL_KEY_ERROR_MARKER, INVALID_VIRTUAL_KEY_ERROR_MESSAGE, @@ -124,6 +125,7 @@ from litellm.proxy.utils import ( normalize_route_for_root_path, ) from litellm.repositories.table_repositories import TeamMembershipRepository +from litellm.router_utils.common_utils import resolve_model_group_alias from litellm.secret_managers.main import get_secret_bool from litellm.types.services import ServiceTypes @@ -235,13 +237,56 @@ async def _normalize_claude_model( request.scope[_CLAUDE_MODEL_NORMALIZED] = True if source is None: return - request_data["model"] = source + _rewrite_request_model(request_data, request, source) + + +def _rewrite_request_model( + request_data: dict, # mutable-ok: the request body is rewritten in place for every downstream reader + request: Request | None, + model: str, +) -> None: + request_data["model"] = model _safe_set_request_parsed_body(request=request, parsed_body=request_data) if request is not None: request._json = request_data request._body = orjson.dumps(request_data) +_MODEL_GROUP_ALIAS_RESOLVED: Final = "litellm.model_group_alias_resolved" + + +async def _resolve_router_settings_model_group_alias( + request_data: dict, # mutable-ok: the request body is rewritten in place for every downstream reader + valid_token: UserAPIKeyAuth, + request: Request | None, + route: str, +) -> None: + """Rewrite the requested model through the key's or team's ``router_settings.model_group_alias`` + before the allowlist checks, so they authorize the model group the request is routed to. + """ + from litellm.proxy.proxy_server import llm_router, prisma_client, proxy_config, proxy_logging_obj + + if request is None or llm_router is None or not RouteChecks.is_llm_api_route(route=route): + return + if request.scope.get(_MODEL_GROUP_ALIAS_RESOLVED) is True: + return + request.scope[_MODEL_GROUP_ALIAS_RESOLVED] = True + requested: Final = request_data.get("model") + if not isinstance(requested, str) or await read_raw_json_body(request=request) is None: + return + settings: Final = await proxy_config.get_hierarchical_router_settings( + user_api_key_dict=valid_token, prisma_client=prisma_client, proxy_logging_obj=proxy_logging_obj + ) + if not isinstance(settings, Mapping): + return + target: Final = resolve_model_group_alias(settings.get("model_group_alias"), requested) + if target is None or target == requested: + return + verbose_proxy_logger.debug("router_settings.model_group_alias resolved %s -> %s before auth", requested, target) + request.scope.setdefault(CLIENT_REQUESTED_MODEL_SCOPE_KEY, requested) + _rewrite_request_model(request_data, request, target) + + def _get_model_names_for_budget_checks( model: str | list[str] | None, ) -> list[str]: @@ -2926,6 +2971,7 @@ async def _authorize_authenticated_request( ## ENSURE DISABLE ROUTE WORKS ACROSS ALL USER AUTH FLOWS ## RouteChecks.should_call_route(route=route, valid_token=user_api_key_auth_obj, request=request) await _normalize_claude_model(request_data, user_api_key_auth_obj, request, route) + await _resolve_router_settings_model_group_alias(request_data, user_api_key_auth_obj, request, route) # Single authorization point. Builder paths MUST NOT call common_checks. # Route through the same exception handler the builder uses so @@ -3312,6 +3358,7 @@ async def _enforce_key_and_fallback_model_access( Not included in common_checks — common_checks enforces team/user/project model access only. """ await _normalize_claude_model(request_data, valid_token, request, route) + await _resolve_router_settings_model_group_alias(request_data, valid_token, request, route) config: Final = valid_token.config if config != {}: diff --git a/litellm/proxy/common_request_processing.py b/litellm/proxy/common_request_processing.py index 4a4daa68cce..3e3183d1293 100644 --- a/litellm/proxy/common_request_processing.py +++ b/litellm/proxy/common_request_processing.py @@ -56,6 +56,7 @@ from litellm.proxy.common_utils.callback_utils import ( get_logging_caching_headers, get_remaining_tokens_and_requests_from_request_data, ) +from litellm.proxy.common_utils.http_parsing_utils import get_client_requested_model from litellm.proxy.common_utils.openai_error_payload import ( attribute_of, error_status_code, @@ -622,9 +623,9 @@ async def _resolve_per_request_model_group_alias( holds the global config map and is shared across requests, so a per-request map has to be applied here instead of being forwarded to the Router. - Model access was authorized against the requested group, so the target is - authorized in its own right before the rewrite; a key that may not call the - target gets the usual 403 rather than being quietly served it. + Auth already rewrote the body through this map for LLM API routes, so this is + a fallback for callers that skipped it; the target is authorized in its own + right before the rewrite, so a key that may not call it gets the usual 403. Returns the target model group, or None when no alias applies. """ @@ -2338,9 +2339,8 @@ class ProxyBaseLLMRequestProcessing: """ Common request processing logic for both chat completions and responses API endpoints """ - requested_model_from_client: Final[str | None] = ( - self.data.get("model") if isinstance(self.data.get("model"), str) else None - ) + client_model: Final = get_client_requested_model(request) or self.data.get("model") + requested_model_from_client: Final[str | None] = client_model if isinstance(client_model, str) else None self._debug_log_request_payload() if skip_pre_call_logic: diff --git a/litellm/proxy/common_utils/http_parsing_utils.py b/litellm/proxy/common_utils/http_parsing_utils.py index 9c2767c7771..ec2e05541cb 100644 --- a/litellm/proxy/common_utils/http_parsing_utils.py +++ b/litellm/proxy/common_utils/http_parsing_utils.py @@ -9,7 +9,7 @@ from fastapi import Request, UploadFile, status from typing_extensions import NotRequired, ReadOnly, Required from litellm._logging import verbose_proxy_logger -from litellm.constants import MAX_REQUEST_BODY_SIZE_TO_REPAIR_MB +from litellm.constants import CLIENT_REQUESTED_MODEL_SCOPE_KEY, MAX_REQUEST_BODY_SIZE_TO_REPAIR_MB from litellm.proxy._types import ProxyException from litellm.proxy.common_utils.callback_utils import ( get_metadata_variable_name_from_kwargs, @@ -235,6 +235,13 @@ def _safe_get_request_parsed_body(request: Request | None) -> dict | None: return None +def get_client_requested_model(request: Request | None) -> str | None: + if request is None or not hasattr(request, "scope"): + return None + model: Final = request.scope.get(CLIENT_REQUESTED_MODEL_SCOPE_KEY) + return model if isinstance(model, str) else None + + def _safe_get_request_query_params(request: Request | None) -> dict: if request is None: return {} diff --git a/tests/test_litellm/proxy/auth/test_user_api_key_auth.py b/tests/test_litellm/proxy/auth/test_user_api_key_auth.py index 866ea0b20e4..ded45f756be 100644 --- a/tests/test_litellm/proxy/auth/test_user_api_key_auth.py +++ b/tests/test_litellm/proxy/auth/test_user_api_key_auth.py @@ -34,6 +34,7 @@ from litellm.proxy._types import ( from litellm.proxy.auth.handle_jwt import JWTHandler from litellm.proxy.auth.auth_checks import TeamNotFoundError, UserNotFoundError, get_key_object, _cache_key_object from litellm.proxy.auth.route_checks import RouteChecks +from litellm.proxy.common_utils.http_parsing_utils import get_client_requested_model from litellm.proxy.auth.user_api_key_auth import ( _check_key_model_budget_with_fallback, _ensure_litellm_received_at_on_request_state, @@ -8137,3 +8138,99 @@ async def test_auth_flow_enters_virtual_key_mapping_when_only_an_issuer_configur assert resolve_mock.await_args.kwargs["jwt_claims"][JWTHandler.LITELLM_JWT_ISSUER_CLAIM] == ISSUER_TWO assert result.api_key == "hashed-mapped-key" assert result.team_id == "svc-team" + + +def _alias_router() -> litellm.Router: + return litellm.Router(model_list=[{"model_name": name, "litellm_params": {"model": "openai/gpt-4o", "api_key": "sk-fake"}} for name in ("claude-haiku", "claude-sonnet")]) + + +def _alias_request(route: str, data: dict, content_type: str = "application/json"): + """A request as auth sees it: the body already read once and cached alongside its parsed form.""" + from starlette.requests import Request + + headers = [(b"content-type", content_type.encode())] + request = Request({"type": "http", "method": "POST", "path": route, "headers": headers, "query_string": b"", "parsed_body": (tuple(data), data)}) + request._body = json.dumps(data).encode() + return request + + +def _alias_token(monkeypatch, level: str, alias: dict, models: list) -> UserAPIKeyAuth: + """A key whose ``router_settings.model_group_alias`` lives on the key itself or on its cached team row.""" + from litellm.proxy.common_utils.user_api_key_cache import UserApiKeyCache + + if level == "key": + return UserAPIKeyAuth(models=models, router_settings={"model_group_alias": alias}) + cache = UserApiKeyCache() + cache.set_cache(key="team_id:team-alias", value=LiteLLM_TeamTableCachedObj(team_id="team-alias", models=models, router_settings={"model_group_alias": alias})) + monkeypatch.setattr(litellm.proxy.proxy_server, "user_api_key_cache", cache) + monkeypatch.setattr(litellm.proxy.proxy_server, "prisma_client", MagicMock()) + return UserAPIKeyAuth(team_id="team-alias", models=models) + + +@pytest.mark.asyncio +@pytest.mark.parametrize("level", ["key", "team"]) +@pytest.mark.parametrize("route", ["/v1/chat/completions", "/v1/messages", "/v1/embeddings"]) +async def test_router_settings_model_group_alias_authorizes_target_for_key(monkeypatch, level, route): + """LIT-3054: a key allowed only the alias target must be able to call the alias, and a key not + allowed the target must still be denied even when the alias itself is what it requested.""" + from litellm.proxy.auth.user_api_key_auth import _enforce_key_and_fallback_model_access + + router = _alias_router() + monkeypatch.setattr(litellm.proxy.proxy_server, "llm_router", router) + data = {"model": "AgentX-LLM", "messages": [{"role": "user", "content": "hi"}]} + request = _alias_request(route, data) + token = _alias_token(monkeypatch, level, {"AgentX-LLM": "claude-haiku"}, ["claude-haiku"]) + await _enforce_key_and_fallback_model_access(valid_token=token, request_data=data, route=route, request=request, llm_model_list=router.model_list, llm_router=router) + assert data["model"] == "claude-haiku" + assert (await request.json())["model"] == "claude-haiku" + assert json.loads(await request.body())["model"] == "claude-haiku" + assert request.scope["parsed_body"][1]["model"] == "claude-haiku" + assert get_client_requested_model(request) == "AgentX-LLM" + + denied = _alias_token(monkeypatch, level, {"AgentX-LLM": "claude-sonnet"}, ["claude-haiku"]) + denied_data = {"model": "AgentX-LLM"} + with pytest.raises(ProxyException) as exc: + await _enforce_key_and_fallback_model_access(valid_token=denied, request_data=denied_data, route=route, request=_alias_request(route, denied_data), llm_model_list=router.model_list, llm_router=router) + assert "claude-sonnet" in exc.value.message + + +@pytest.mark.asyncio +async def test_router_settings_model_group_alias_leaves_form_bodies_alone(monkeypatch): + """LIT-3054: a multipart body cannot be re-serialized as JSON, so auth must not rewrite it.""" + from litellm.proxy.auth.user_api_key_auth import _enforce_key_and_fallback_model_access + + router = _alias_router() + monkeypatch.setattr(litellm.proxy.proxy_server, "llm_router", router) + data = {"model": "AgentX-LLM"} + request = _alias_request("/v1/audio/transcriptions", data, content_type="multipart/form-data; boundary=x") + token = _alias_token(monkeypatch, "key", {"AgentX-LLM": "claude-haiku"}, ["claude-haiku", "AgentX-LLM"]) + await _enforce_key_and_fallback_model_access(valid_token=token, request_data=data, route="/v1/audio/transcriptions", request=request, llm_model_list=router.model_list, llm_router=router) + assert data["model"] == "AgentX-LLM" + assert get_client_requested_model(request) is None + + +@pytest.mark.asyncio +@pytest.mark.parametrize("target, expect_denied", [("claude-haiku", False), ("claude-sonnet", True)]) +async def test_router_settings_model_group_alias_authorizes_target_for_team(monkeypatch, target, expect_denied): + """LIT-3054: the team allowlist check in common_checks must judge the alias target, not the alias.""" + import litellm.proxy.proxy_server as _proxy_server_mod + from litellm.proxy.auth.user_api_key_auth import _authorize_authenticated_request + + router = _alias_router() + logging_obj = MagicMock(post_call_failure_hook=AsyncMock(return_value=None)) + attrs = {**_proxy_attrs_for_centralized_checks(), "llm_router": router, "proxy_logging_obj": logging_obj} + for k, v in attrs.items(): + monkeypatch.setattr(_proxy_server_mod, k, v) + token = _alias_token(monkeypatch, "team", {"AgentX-LLM": target}, ["claude-haiku"]) + token.team_models = ["claude-haiku"] + data = {"model": "AgentX-LLM", "messages": [{"role": "user", "content": "hi"}]} + request = _alias_request("/v1/chat/completions", data) + if expect_denied: + with pytest.raises(ProxyException) as exc: + await _authorize_authenticated_request(user_api_key_auth_obj=token, request=request, request_data=data, route="/v1/chat/completions", api_key="sk-test") + assert exc.value.type == ProxyErrorTypes.team_model_access_denied + assert target in exc.value.message + return + await _authorize_authenticated_request(user_api_key_auth_obj=token, request=request, request_data=data, route="/v1/chat/completions", api_key="sk-test") + assert (await request.json())["model"] == target + assert get_client_requested_model(request) == "AgentX-LLM" diff --git a/tests/test_litellm/proxy/test_common_request_processing.py b/tests/test_litellm/proxy/test_common_request_processing.py index cabfcc9918f..e7b83455a9c 100644 --- a/tests/test_litellm/proxy/test_common_request_processing.py +++ b/tests/test_litellm/proxy/test_common_request_processing.py @@ -13,7 +13,11 @@ from fastapi.responses import JSONResponse, StreamingResponse import litellm from litellm._uuid import uuid -from litellm.constants import MAX_LITELLM_CALL_ID_LENGTH, RETURN_RAW_MODEL_NAME_METADATA_KEY +from litellm.constants import ( + CLIENT_REQUESTED_MODEL_SCOPE_KEY, + MAX_LITELLM_CALL_ID_LENGTH, + RETURN_RAW_MODEL_NAME_METADATA_KEY, +) from litellm.integrations.custom_logger import CustomLogger from litellm.integrations.opentelemetry import UserAPIKeyAuth from litellm.proxy.common_request_processing import ( @@ -4395,6 +4399,53 @@ class TestDisconnectGatherCleanup: ) +@pytest.mark.asyncio +@pytest.mark.parametrize("client_model, expected", [("AgentX-LLM", "AgentX-LLM"), (None, "gpt-mini")]) +async def test_response_model_echoes_the_name_the_client_sent_before_auth_rewrote_it( + monkeypatch, client_model, expected +): + """LIT-3054: auth resolves router_settings.model_group_alias in the body, so the alias the + client sent only survives in the request scope. The response must still echo it.""" + import litellm.proxy.common_request_processing as cpr + + async def llm(): + return litellm.ModelResponse( + model="gpt-4o-mini", choices=[{"message": {"role": "assistant", "content": "pong"}}] + ) + + async def fake_route_request(**_kwargs): + return llm() + + logging_obj = MagicMock(litellm_call_id="call-id", _defer_async_logging=False) + proxy_logging = MagicMock(spec=ProxyLogging) + proxy_logging.during_call_hook = AsyncMock(return_value=None) + proxy_logging.post_call_success_hook = AsyncMock(side_effect=lambda data, user_api_key_dict, response: response) + proxy_logging.post_call_response_headers_hook = AsyncMock(return_value={}) + proxy_logging._callback_capabilities_cache = {} + monkeypatch.setattr(cpr, "route_request", fake_route_request) + + processor = ProxyBaseLLMRequestProcessing(data={"model": "gpt-mini", "messages": []}) + monkeypatch.setattr( + processor, "common_processing_pre_call_logic", AsyncMock(return_value=({"model": "gpt-mini"}, logging_obj)) + ) + monkeypatch.setattr(processor, "_has_post_call_guardrails", MagicMock(return_value=False)) + scope = {"type": "http", "method": "POST", "path": "/v1/chat/completions", "headers": [], "query_string": b""} + request = Request({**scope, CLIENT_REQUESTED_MODEL_SCOPE_KEY: client_model} if client_model else scope) + + response = await processor.base_process_llm_request( + request=request, + fastapi_response=Response(), + user_api_key_dict=ProxyUserAPIKeyAuth(), + proxy_logging_obj=proxy_logging, + general_settings={}, + proxy_config=MagicMock(spec=ProxyConfig), + route_type="acompletion", + version=None, + ) + + assert response.model == expected + + class TestStreamingClientDisconnectLogging: @pytest.mark.asyncio async def test_record_streaming_client_disconnect_sets_error_information(self): From 460a0128c94412890ed67588b6ad2de3347e766a Mon Sep 17 00:00:00 2001 From: yassin Date: Tue, 15 Sep 2026 22:08:39 +0000 Subject: [PATCH 085/225] refactor(proxy): drop docstrings from key model limit helpers Co-Authored-By: Devin AI <158243242+devin-ai-integration[bot]@users.noreply.github.com> --- litellm/proxy/auth/auth_utils.py | 6 ------ litellm/proxy/hooks/parallel_request_limiter_v3.py | 2 -- 2 files changed, 8 deletions(-) diff --git a/litellm/proxy/auth/auth_utils.py b/litellm/proxy/auth/auth_utils.py index 5ff85ad4a0e..b6c4a43149d 100644 --- a/litellm/proxy/auth/auth_utils.py +++ b/litellm/proxy/auth/auth_utils.py @@ -980,12 +980,6 @@ def get_key_own_model_rate_limit( user_api_key_dict: UserAPIKeyAuth, rate_limit_key: Literal["model_rpm_limit", "model_tpm_limit"], ) -> dict[str, int] | None: - """ - Per-model limit the key sets on itself: key metadata first, then model_max_budget. - - Unlike get_key_model_rpm_limit / get_key_model_tpm_limit this never falls back to the - team, so callers can tell a key override apart from an inherited team limit. - """ if user_api_key_dict.metadata: result: Final = user_api_key_dict.metadata.get(rate_limit_key) if result: diff --git a/litellm/proxy/hooks/parallel_request_limiter_v3.py b/litellm/proxy/hooks/parallel_request_limiter_v3.py index 87336830976..f35fb1e0042 100644 --- a/litellm/proxy/hooks/parallel_request_limiter_v3.py +++ b/litellm/proxy/hooks/parallel_request_limiter_v3.py @@ -2899,7 +2899,6 @@ class _PROXY_MaxParallelRequestsHandler_v3(CustomLogger): requested_model: str, rate_limit_key: Literal["model_rpm_limit", "model_tpm_limit"], ) -> int | None: - """Team per-model limit this key inherits: None when the key sets its own limit for the model.""" team_limits: Final = get_model_rate_limit_from_metadata(user_api_key_dict, "team_metadata", rate_limit_key) team_limit: Final = team_limits.get(requested_model) if team_limits else None if team_limit is None: @@ -2915,7 +2914,6 @@ class _PROXY_MaxParallelRequestsHandler_v3(CustomLogger): requested_model: str | None, descriptors: list[RateLimitDescriptor], ) -> None: - """Add the team's per-model descriptor for the metrics the key does not override itself.""" if requested_model is None: return team_rpm_limit: Final = self._inherited_team_model_limit(user_api_key_dict, requested_model, "model_rpm_limit") From af4a0b4bc366f22d89b9bf20d5ec4feec523b9e0 Mon Sep 17 00:00:00 2001 From: yassin Date: Tue, 15 Sep 2026 21:50:07 +0000 Subject: [PATCH 086/225] fix(proxy): keep yaml pass-through endpoints visible to auth after db overlay Resolves LIT-2053 Co-Authored-By: Devin AI <158243242+devin-ai-integration[bot]@users.noreply.github.com> --- litellm/proxy/proxy_server.py | 8 +++-- tests/test_litellm/proxy/test_proxy_server.py | 34 +++++++++++++++++++ 2 files changed, 40 insertions(+), 2 deletions(-) diff --git a/litellm/proxy/proxy_server.py b/litellm/proxy/proxy_server.py index f63e088ebf7..7f90874e38a 100644 --- a/litellm/proxy/proxy_server.py +++ b/litellm/proxy/proxy_server.py @@ -7080,8 +7080,12 @@ class ProxyConfig: ## PASS-THROUGH ENDPOINTS ## if "pass_through_endpoints" in _general_settings: - general_settings["pass_through_endpoints"] = _general_settings["pass_through_endpoints"] - await initialize_pass_through_endpoints(pass_through_endpoints=general_settings["pass_through_endpoints"]) + db_pass_through_endpoints: Final = _general_settings["pass_through_endpoints"] + general_settings["pass_through_endpoints"] = [ + *db_pass_through_endpoints, + *(config_passthrough_endpoints or []), + ] + await initialize_pass_through_endpoints(pass_through_endpoints=db_pass_through_endpoints) ## UI ACCESS MODE ## if "ui_access_mode" in _general_settings: diff --git a/tests/test_litellm/proxy/test_proxy_server.py b/tests/test_litellm/proxy/test_proxy_server.py index 42af8e0af21..62c34738e51 100644 --- a/tests/test_litellm/proxy/test_proxy_server.py +++ b/tests/test_litellm/proxy/test_proxy_server.py @@ -7401,6 +7401,40 @@ async def test_update_general_settings_apply_user_budget_to_team_keys_yaml_wins( assert ps.general_settings["apply_user_budget_to_team_keys"] is True +@pytest.mark.asyncio +async def test_update_general_settings_keeps_yaml_pass_through_endpoints_next_to_db_ones(): + """user_api_key_auth honours ``auth: false`` only for entries it finds in + general_settings["pass_through_endpoints"]. The DB overlay used to replace that + list wholesale, so once one endpoint existed in the DB the YAML-declared + auth-disabled route started answering 401 while staying registered.""" + from litellm.proxy._types import ProxyException + from litellm.proxy.proxy_server import ProxyConfig + + yaml_endpoint: Final = {"path": "/v1/cuopt/request", "target": "https://example.com/post", "auth": False} + db_endpoint: Final = {"id": "db-1", "path": "/v1/db-echo", "target": "https://example.com/post", "auth": True} + + def request_without_key(path: str) -> MagicMock: + request: Final = MagicMock() + request.url.path = path + request.headers = {} + request.query_params = {} + return request + + settings: Final = patch("litellm.proxy.proxy_server.general_settings", {"pass_through_endpoints": [yaml_endpoint]}) # test-quality-ok: the method reads this module global; no injection seam + yaml_endpoints: Final = patch("litellm.proxy.proxy_server.config_passthrough_endpoints", [yaml_endpoint]) # test-quality-ok: module global holding the YAML endpoints the fix merges in + initialize: Final = patch("litellm.proxy.proxy_server.initialize_pass_through_endpoints", AsyncMock()) # test-quality-ok: route registration needs the FastAPI app; auth is the observable here + master_key: Final = patch("litellm.proxy.proxy_server.master_key", "sk-master") # test-quality-ok: a set master key is what makes a missing Authorization header a 401 + with settings, yaml_endpoints, initialize, master_key: + await ProxyConfig()._update_general_settings(db_general_settings={"pass_through_endpoints": [db_endpoint]}) + + anonymous: Final = await user_api_key_auth(request=request_without_key("/v1/cuopt/request"), api_key=None) + assert anonymous.api_key is None + + with pytest.raises(ProxyException) as still_protected: + await user_api_key_auth(request=request_without_key("/v1/db-echo"), api_key=None) + assert still_protected.value.code == "401" + + def _fill_user_api_key_cache(cache: DualCache, count: int) -> None: for index in range(count): cache.set_cache(key=f"key-{index}", value={"token": f"key-{index}"}, local_only=True) From 69be041da7a8fc905862c58cf47cac714c402558 Mon Sep 17 00:00:00 2001 From: yassin Date: Tue, 15 Sep 2026 22:11:08 +0000 Subject: [PATCH 087/225] feat(proxy): add /nvidia_nim passthrough route for NIM object detection and OCR /v1/infer Co-Authored-By: Devin AI <158243242+devin-ai-integration[bot]@users.noreply.github.com> --- .../azure_ai/passthrough/transformation.py | 25 +- .../base_llm/passthrough/transformation.py | 23 +- .../llms/nvidia_nim/passthrough/__init__.py | 0 .../nvidia_nim/passthrough/transformation.py | 116 ++++++++++ litellm/proxy/_types.py | 1 + litellm/proxy/auth/auth_utils.py | 12 + .../llm_passthrough_endpoints.py | 67 ++++++ litellm/utils.py | 6 + ...t_nvidia_nim_passthrough_transformation.py | 216 ++++++++++++++++++ .../proxy/auth/test_auth_utils.py | 58 +++++ .../proxy/auth/test_route_checks.py | 1 + .../test_llm_pass_through_endpoints.py | 147 ++++++++++++ 12 files changed, 649 insertions(+), 23 deletions(-) create mode 100644 litellm/llms/nvidia_nim/passthrough/__init__.py create mode 100644 litellm/llms/nvidia_nim/passthrough/transformation.py create mode 100644 tests/test_litellm/llms/nvidia_nim/passthrough/test_nvidia_nim_passthrough_transformation.py diff --git a/litellm/llms/azure_ai/passthrough/transformation.py b/litellm/llms/azure_ai/passthrough/transformation.py index f2be1d95593..4007ac37948 100644 --- a/litellm/llms/azure_ai/passthrough/transformation.py +++ b/litellm/llms/azure_ai/passthrough/transformation.py @@ -5,7 +5,7 @@ from types import MappingProxyType from typing import TYPE_CHECKING, Final import httpx -from pydantic import BaseModel, ConfigDict, TypeAdapter, ValidationError +from pydantic import TypeAdapter, ValidationError from litellm._logging import verbose_logger from litellm.llms.azure_ai.common_utils import ( @@ -18,6 +18,8 @@ from litellm.llms.base_llm.passthrough.transformation import ( BasePassthroughConfig, RelayShape, logged_relay_shape, + model_group_from, + relayed_body, strip_leading_model_segment, ) from litellm.types.llms.openai import AllMessageValues @@ -35,19 +37,6 @@ if TYPE_CHECKING: EMPTY_QUERY: Final[Mapping[str, object]] = MappingProxyType({}) -class PassthroughMetadata(BaseModel): - model_config = ConfigDict(extra="ignore") - - model_group: str = "" - - -def model_group_from(litellm_params: Mapping[str, object]) -> str: - try: - return PassthroughMetadata.model_validate(litellm_params.get("litellm_metadata")).model_group - except ValidationError: - return "" - - def api_version_from(litellm_params: Mapping[str, object]) -> str | None: try: return TypeAdapter(str | None).validate_python(litellm_params.get("api_version")) @@ -96,14 +85,6 @@ def relay_query_params( return MappingProxyType({**(request_query_params or EMPTY_QUERY), "api-version": api_version}) -def relayed_body(httpx_response: Response) -> str | dict: - try: - body: Final[object] = httpx_response.json() - except ValueError: - return httpx_response.text - return body if isinstance(body, dict) else httpx_response.text - - FOUNDRY_RELAY_SHAPES: Final = ( RelayShape("/rerank", CallTypes.arerank, RerankResponse.model_validate), RelayShape("/providers/blackforestlabs/v1/flux-2-pro", CallTypes.aimage_generation, ImageResponse.model_validate), diff --git a/litellm/llms/base_llm/passthrough/transformation.py b/litellm/llms/base_llm/passthrough/transformation.py index ec938889b88..f2a12c3f22d 100644 --- a/litellm/llms/base_llm/passthrough/transformation.py +++ b/litellm/llms/base_llm/passthrough/transformation.py @@ -6,7 +6,7 @@ from collections.abc import Callable, Mapping, Sequence from dataclasses import dataclass from typing import TYPE_CHECKING, Final, Protocol, TypeAlias -from pydantic import TypeAdapter, ValidationError +from pydantic import BaseModel, ConfigDict, TypeAdapter, ValidationError from litellm.types.utils import CallTypes @@ -29,6 +29,19 @@ if TYPE_CHECKING: RELAYED_JSON_OBJECT: Final = TypeAdapter(Mapping[str, object]) +class PassthroughMetadata(BaseModel): + model_config = ConfigDict(extra="ignore") + + model_group: str = "" + + +def model_group_from(litellm_params: Mapping[str, object]) -> str: + try: + return PassthroughMetadata.model_validate(litellm_params.get("litellm_metadata")).model_group + except ValidationError: + return "" + + def strip_leading_model_segment(endpoint: str, model_names: tuple[str, ...]) -> str: path: Final = endpoint.lstrip("/") for model_name in model_names: @@ -55,6 +68,14 @@ def relayed_json_object(httpx_response: Response) -> Mapping[str, object] | None return None +def relayed_body(httpx_response: Response) -> str | dict: + try: + body: Final[object] = httpx_response.json() + except ValueError: + return httpx_response.text + return body if isinstance(body, dict) else httpx_response.text + + @dataclass(frozen=True, slots=True) class RelayShape: path_suffix: str diff --git a/litellm/llms/nvidia_nim/passthrough/__init__.py b/litellm/llms/nvidia_nim/passthrough/__init__.py new file mode 100644 index 00000000000..e69de29bb2d diff --git a/litellm/llms/nvidia_nim/passthrough/transformation.py b/litellm/llms/nvidia_nim/passthrough/transformation.py new file mode 100644 index 00000000000..f6273daaabb --- /dev/null +++ b/litellm/llms/nvidia_nim/passthrough/transformation.py @@ -0,0 +1,116 @@ +from __future__ import annotations + +import re +from collections.abc import Collection, Mapping, Sequence +from typing import TYPE_CHECKING, Final + +import httpx + +from litellm.llms.base_llm.passthrough.transformation import ( + BasePassthroughConfig, + model_group_from, + relayed_body, + strip_leading_model_segment, +) +from litellm.secret_managers.main import get_secret_str +from litellm.types.llms.openai import AllMessageValues +from litellm.types.utils import StandardPassThroughResponseObject + +if TYPE_CHECKING: + from httpx import URL, Response + + from litellm.litellm_core_utils.litellm_logging import Logging + from litellm.llms.base_llm.ocr.transformation import OCRResponse + from litellm.llms.base_llm.passthrough.transformation import LoggedRelayResponse + + +API_VERSION_SEGMENT: Final = re.compile(r"^v\d+$") + + +def nvidia_nim_router_model_in_endpoint(endpoint: str, router_models: Collection[str]) -> str | None: + segments: Final = tuple(segment for segment in endpoint.split("/") if segment) + return next( + ( + "/".join(segments[:length]) + for length in range(len(segments), 0, -1) + if "/".join(segments[:length]) in router_models + ), + None, + ) + + +def without_repeated_version_prefix(api_base: str, native_endpoint: str) -> str: + url: Final = httpx.URL(api_base) + base_segments: Final = tuple(segment for segment in url.path.split("/") if segment) + first_native_segment: Final = native_endpoint.lstrip("/").split("/", 1)[0] + repeated: Final = ( + bool(base_segments) + and API_VERSION_SEGMENT.match(first_native_segment) is not None + and base_segments[-1] == first_native_segment + ) + kept_segments: Final = base_segments[:-1] if repeated else base_segments + return str(url.copy_with(path="/" + "/".join(kept_segments), query=None)).rstrip("/") + + +class NvidiaNimPassthroughConfig(BasePassthroughConfig): + def is_streaming_request(self, endpoint: str, request_data: dict) -> bool: + return bool(request_data.get("stream", False)) + + def get_complete_url( + self, + api_base: str | None, + api_key: str | None, + model: str, + endpoint: str, + request_query_params: dict | None, + litellm_params: dict, + ) -> tuple[URL, str]: + base_target_url: Final = self.get_api_base(api_base) + if base_target_url is None: + raise ValueError("NVIDIA NIM api base not found: set `api_base` on the deployment or NVIDIA_NIM_API_BASE") + native_endpoint: Final = strip_leading_model_segment(endpoint, (model_group_from(litellm_params), model)) + root: Final = without_repeated_version_prefix(base_target_url, native_endpoint) + return (self.format_url(native_endpoint, root, request_query_params), root) + + def validate_environment( + self, + headers: Mapping[str, str], + model: str, + messages: Sequence[AllMessageValues], + optional_params: Mapping[str, object], + litellm_params: Mapping[str, object], + api_key: str | None = None, + api_base: str | None = None, + ) -> dict[str, str]: # mutable-ok: base class contract returns dict for httpx + if api_key is None: + return dict(headers) # mutable-ok: base class contract returns dict for httpx + return { + **headers, + "Authorization": f"Bearer {api_key}", + } # mutable-ok: base class contract returns dict for httpx + + @staticmethod + def get_api_base(api_base: str | None = None) -> str | None: + return api_base or get_secret_str("NVIDIA_NIM_API_BASE") + + @staticmethod + def get_api_key(api_key: str | None = None) -> str | None: + return api_key or get_secret_str("NVIDIA_NIM_API_KEY") + + @staticmethod + def get_base_model(model: str) -> str | None: + return model + + def get_models(self, api_key: str | None = None, api_base: str | None = None) -> list[str]: + return [] + + def logging_non_streaming_response( + self, + model: str, + custom_llm_provider: str, + httpx_response: Response, + request_data: Mapping[str, object], + logging_obj: Logging, + endpoint: str, + ) -> LoggedRelayResponse | OCRResponse | StandardPassThroughResponseObject | None: + return StandardPassThroughResponseObject(response=relayed_body(httpx_response)) diff --git a/litellm/proxy/_types.py b/litellm/proxy/_types.py index d4eda1c9540..b780b8d08ca 100644 --- a/litellm/proxy/_types.py +++ b/litellm/proxy/_types.py @@ -485,6 +485,7 @@ class LiteLLMRoutes(enum.Enum): "/milvus", "/gigachat", "/watsonx", + "/nvidia_nim", ] ######################################################### diff --git a/litellm/proxy/auth/auth_utils.py b/litellm/proxy/auth/auth_utils.py index dc304a156cf..529521dcba8 100644 --- a/litellm/proxy/auth/auth_utils.py +++ b/litellm/proxy/auth/auth_utils.py @@ -28,6 +28,7 @@ from litellm.litellm_core_utils.url_utils import ( validate_url, ) from litellm.llms.azure.passthrough.transformation import azure_router_model_in_endpoint +from litellm.llms.nvidia_nim.passthrough.transformation import nvidia_nim_router_model_in_endpoint from litellm.proxy._types import * from litellm.proxy.common_utils.http_parsing_utils import extract_nested_form_metadata from litellm.types.passthrough_endpoints.pass_through_endpoints import ( @@ -2040,9 +2041,20 @@ def get_model_from_request( azure_model: Final = _router_model_from_azure_route(route, llm_router) return model if azure_model is None else azure_model + if route.lower().startswith("/nvidia_nim/"): + nvidia_nim_model: Final = _router_model_from_nvidia_nim_route(route, llm_router) + return model if nvidia_nim_model is None else nvidia_nim_model + return model +def _router_model_from_nvidia_nim_route(route: str, llm_router: Router | None) -> str | None: + if llm_router is None: + return None + endpoint: Final = re.sub(r"^/nvidia_nim/", "", route, flags=re.IGNORECASE) + return nvidia_nim_router_model_in_endpoint(endpoint, frozenset(llm_router.get_model_names())) + + def _router_model_from_azure_route(route: str, llm_router: Router | None) -> str | None: if llm_router is None: return None diff --git a/litellm/proxy/pass_through_endpoints/llm_passthrough_endpoints.py b/litellm/proxy/pass_through_endpoints/llm_passthrough_endpoints.py index 28a8bab1f24..d4b1bdbdc20 100644 --- a/litellm/proxy/pass_through_endpoints/llm_passthrough_endpoints.py +++ b/litellm/proxy/pass_through_endpoints/llm_passthrough_endpoints.py @@ -36,6 +36,7 @@ from litellm.litellm_core_utils.aws_partition import get_aws_dns_suffix from litellm.llms.anthropic.common_utils import AnthropicModelInfo from litellm.llms.azure.passthrough.transformation import foreign_azure_deployment from litellm.llms.custom_httpx.http_handler import get_async_httpx_client +from litellm.llms.nvidia_nim.passthrough.transformation import nvidia_nim_router_model_in_endpoint from litellm.llms.vertex_ai.vertex_llm_base import VertexBase from litellm.passthrough.main import AsyncPassthroughStreamingResponse from litellm.proxy._types import * @@ -1545,6 +1546,26 @@ async def _relay_azure_router_model( "put the model group name in the deployments segment" } raise HTTPException(status_code=400, detail=rejection) + return await _relay_router_model( + llm_router=llm_router, + model=model, + endpoint=endpoint, + request=request, + request_body=request_body, + is_streaming_request=is_streaming_request, + user_api_key_dict=user_api_key_dict, + ) + + +async def _relay_router_model( + llm_router: litellm.Router, + model: str, + endpoint: str, + request: Request, + request_body: Mapping[str, object], + is_streaming_request: bool, + user_api_key_dict: UserAPIKeyAuth, +) -> Response: try: result: Final = await llm_router.allm_passthrough_route( model=model, @@ -1594,6 +1615,52 @@ async def _relay_azure_router_model( ) +@router.api_route( + "/nvidia_nim/{endpoint:path}", + methods=["GET", "POST", "PUT", "DELETE", "PATCH"], + tags=["NVIDIA NIM Pass-through", "pass-through"], +) +async def nvidia_nim_proxy_route( + endpoint: str, + request: Request, + fastapi_response: Response, + user_api_key_dict: Annotated[UserAPIKeyAuth, Depends(user_api_key_auth)], +): + """ + Relay a native NVIDIA NIM request through a LiteLLM model group. + + `{PROXY_BASE_URL}/nvidia_nim/{model_group}/v1/infer` forwards the body unchanged to the deployment's + `api_base`, so object detection and OCR NIMs whose payload carries no `model` field still go through + virtual key auth, model access checks, and spend logging. + """ + from litellm.proxy.proxy_server import llm_router + + model_group: Final = ( + nvidia_nim_router_model_in_endpoint(endpoint, llm_router.get_model_names()) if llm_router else None + ) + if llm_router is None or model_group is None: + rejection: Final[RelayRejection] = { + "error": "no LiteLLM model group in the path; call /nvidia_nim/{model_group}/v1/infer with a model " + "from your `model_list` whose `model` starts with `nvidia_nim/`" + } + raise HTTPException(status_code=400, detail=rejection) + + request_body: Final = await get_request_body(request) + is_streaming_request: Final = is_passthrough_request_streaming(request_body) + return await open_sse_before_first_byte( + _relay_router_model( + llm_router=llm_router, + model=model_group, + endpoint=endpoint, + request=request, + request_body=request_body, + is_streaming_request=is_streaming_request, + user_api_key_dict=user_api_key_dict, + ), + ping_interval_seconds=(litellm.sse_keepalive_ping_interval_seconds if is_streaming_request else None), + ) + + @router.api_route( "/azure_ai/{endpoint:path}", methods=["GET", "POST", "PUT", "DELETE", "PATCH"], diff --git a/litellm/utils.py b/litellm/utils.py index af22b11224b..7f151e1d581 100644 --- a/litellm/utils.py +++ b/litellm/utils.py @@ -8989,6 +8989,12 @@ class ProviderConfigManager: ) return WatsonxPassthroughConfig() + elif LlmProviders.NVIDIA_NIM == provider: + from litellm.llms.nvidia_nim.passthrough.transformation import ( + NvidiaNimPassthroughConfig, + ) + + return NvidiaNimPassthroughConfig() return None @staticmethod diff --git a/tests/test_litellm/llms/nvidia_nim/passthrough/test_nvidia_nim_passthrough_transformation.py b/tests/test_litellm/llms/nvidia_nim/passthrough/test_nvidia_nim_passthrough_transformation.py new file mode 100644 index 00000000000..87de4a795e0 --- /dev/null +++ b/tests/test_litellm/llms/nvidia_nim/passthrough/test_nvidia_nim_passthrough_transformation.py @@ -0,0 +1,216 @@ +import json +from types import MappingProxyType + +import httpx +import pytest + +import litellm +from litellm.llms.custom_httpx.http_handler import AsyncHTTPHandler +from litellm.llms.nvidia_nim.passthrough.transformation import ( + NvidiaNimPassthroughConfig, + nvidia_nim_router_model_in_endpoint, +) +from litellm.types.utils import LlmProviders +from litellm.utils import ProviderConfigManager + +NIM_BASE = "http://nim.internal:8000" +INFER_BODY = { + "input": [ + {"type": "image_url", "url": "data:image/png;base64,AAAA"}, + {"type": "image_url", "url": "data:image/png;base64,BBBB"}, + ] +} + + +@pytest.fixture(autouse=True) +def clear_nvidia_nim_env(monkeypatch): + for env_var in ("NVIDIA_NIM_API_BASE", "NVIDIA_NIM_API_KEY"): + monkeypatch.delenv(env_var, raising=False) + monkeypatch.setattr(litellm, "api_base", None) + monkeypatch.setattr(litellm, "api_key", None) + + +def test_provider_config_manager_resolves_nvidia_nim_passthrough_config(): + config = ProviderConfigManager.get_provider_passthrough_config( + model="nvidia/nemoretriever-page-elements-v2", provider=LlmProviders.NVIDIA_NIM + ) + + assert isinstance(config, NvidiaNimPassthroughConfig) + + +@pytest.mark.parametrize( + "api_base, endpoint, litellm_params, expected", + [ + (NIM_BASE, "nim-page/v1/infer", {"litellm_metadata": {"model_group": "nim-page"}}, f"{NIM_BASE}/v1/infer"), + ( + f"{NIM_BASE}/v1", + "nim-page/v1/infer", + {"litellm_metadata": {"model_group": "nim-page"}}, + f"{NIM_BASE}/v1/infer", + ), + (f"{NIM_BASE}/v1/", "/v1/infer", {}, f"{NIM_BASE}/v1/infer"), + (NIM_BASE, "v1/infer", {}, f"{NIM_BASE}/v1/infer"), + (f"{NIM_BASE}/v2", "v1/infer", {}, f"{NIM_BASE}/v2/v1/infer"), + (f"{NIM_BASE}/infer", "infer", {}, f"{NIM_BASE}/infer/infer"), + (NIM_BASE, "nvidia/nemoretriever-page-elements-v2/v1/infer", {}, f"{NIM_BASE}/v1/infer"), + ], +) +def test_relay_url_strips_the_model_group_and_never_doubles_the_api_version( + api_base, endpoint, litellm_params, expected +): + url, base = NvidiaNimPassthroughConfig().get_complete_url( + api_base=api_base, + api_key=None, + model="nvidia/nemoretriever-page-elements-v2", + endpoint=endpoint, + request_query_params=None, + litellm_params=litellm_params, + ) + + assert str(url) == expected + assert base == expected.removesuffix("/v1/infer").removesuffix("/infer") + + +def test_query_params_are_forwarded_on_the_relay_url(): + url, _ = NvidiaNimPassthroughConfig().get_complete_url( + api_base=NIM_BASE, + api_key=None, + model="nvidia/nemoretriever-page-elements-v2", + endpoint="v1/infer", + request_query_params={"timeout": "30"}, + litellm_params={}, + ) + + assert str(url) == f"{NIM_BASE}/v1/infer?timeout=30" + + +def test_env_api_base_is_used_when_the_deployment_has_none(monkeypatch): + monkeypatch.setenv("NVIDIA_NIM_API_BASE", f"{NIM_BASE}/v1") + + url, _ = NvidiaNimPassthroughConfig().get_complete_url( + api_base=None, + api_key=None, + model="nvidia/nemoretriever-page-elements-v2", + endpoint="v1/infer", + request_query_params=None, + litellm_params={}, + ) + + assert str(url) == f"{NIM_BASE}/v1/infer" + + +def test_missing_api_base_raises_instead_of_building_a_relative_url(): + with pytest.raises(ValueError, match="NVIDIA_NIM_API_BASE"): + NvidiaNimPassthroughConfig().get_complete_url( + api_base=None, + api_key=None, + model="nvidia/nemoretriever-page-elements-v2", + endpoint="v1/infer", + request_query_params=None, + litellm_params={}, + ) + + +def test_deployment_key_becomes_a_bearer_token_and_caller_headers_are_kept(): + caller_headers = MappingProxyType({"x-request-id": "abc"}) + + headers = NvidiaNimPassthroughConfig().validate_environment( + headers=caller_headers, + model="nvidia/nemoretriever-page-elements-v2", + messages=[], + optional_params={}, + litellm_params={}, + api_key="nvapi-secret", + ) + + assert headers == {"x-request-id": "abc", "Authorization": "Bearer nvapi-secret"} + + +def test_self_hosted_nim_without_a_key_sends_no_authorization_header(): + headers = NvidiaNimPassthroughConfig().validate_environment( + headers={}, model="nvidia/x", messages=[], optional_params={}, litellm_params={}, api_key=None + ) + + assert "Authorization" not in headers + + +def test_env_api_key_fills_in_when_the_deployment_has_none(monkeypatch): + monkeypatch.setenv("NVIDIA_NIM_API_KEY", "nvapi-from-env") + + assert NvidiaNimPassthroughConfig.get_api_key(None) == "nvapi-from-env" + assert NvidiaNimPassthroughConfig.get_api_key("nvapi-deployment") == "nvapi-deployment" + + +@pytest.mark.parametrize( + "endpoint, router_models, expected", + [ + ("nim-page/v1/infer", ("nim-page", "nim-table"), "nim-page"), + ("/nim-page/v1/infer", ("nim-page",), "nim-page"), + ( + "nvidia/nemoretriever-page-elements-v2/v1/infer", + ("nvidia/nemoretriever-page-elements-v2",), + "nvidia/nemoretriever-page-elements-v2", + ), + ("nim/v1/infer", ("nim", "nim/v1"), "nim/v1"), + ("v1/infer", ("nim-page",), None), + ("nim-page-elements/v1/infer", ("nim-page",), None), + ("", ("nim-page",), None), + ], +) +def test_router_model_in_endpoint_takes_the_longest_leading_model_group(endpoint, router_models, expected): + assert nvidia_nim_router_model_in_endpoint(endpoint, frozenset(router_models)) == expected + + +@pytest.mark.parametrize("request_data, expected", [({"stream": True}, True), ({"stream": False}, False), ({}, False)]) +def test_is_streaming_request_reads_the_stream_flag(request_data, expected): + assert NvidiaNimPassthroughConfig().is_streaming_request("v1/infer", request_data) is expected + + +def test_non_streaming_relay_logs_the_upstream_json_body(): + response = httpx.Response( + 200, + json={"data": [{"index": 0, "bounding_boxes": {}}]}, + request=httpx.Request("POST", f"{NIM_BASE}/v1/infer"), + ) + + result = NvidiaNimPassthroughConfig().logging_non_streaming_response( + model="nvidia/nemoretriever-page-elements-v2", + custom_llm_provider="nvidia_nim", + httpx_response=response, + request_data=INFER_BODY, + logging_obj=None, # pyright: ignore[reportArgumentType] # not read for a plain passthrough body + endpoint="v1/infer", + ) + + assert result == {"response": {"data": [{"index": 0, "bounding_boxes": {}}]}} + + +@pytest.mark.asyncio +async def test_object_detection_relay_sends_the_native_body_unchanged_to_v1_infer(): + upstream_requests: list[httpx.Request] = [] + + def nim(request: httpx.Request) -> httpx.Response: + upstream_requests.append(request) + return httpx.Response(200, json={"data": [{"index": 0}, {"index": 1}]}, headers={"x-nim": "1"}) + + client = AsyncHTTPHandler() + client.client = httpx.AsyncClient(transport=httpx.MockTransport(nim)) + + response = await litellm.allm_passthrough_route( + model="nvidia_nim/nvidia/nemoretriever-page-elements-v2", + endpoint="nim-page/v1/infer", + method="POST", + api_base=f"{NIM_BASE}/v1", + api_key="nvapi-secret", + json=dict(INFER_BODY), + litellm_metadata={"model_group": "nim-page"}, + client=client, + ) + + (sent,) = upstream_requests + assert str(sent.url) == f"{NIM_BASE}/v1/infer" + assert json.loads(sent.content) == INFER_BODY + assert sent.headers["authorization"] == "Bearer nvapi-secret" + assert response.status_code == 200 + assert response.headers["x-nim"] == "1" + assert response.json() == {"data": [{"index": 0}, {"index": 1}]} diff --git a/tests/test_litellm/proxy/auth/test_auth_utils.py b/tests/test_litellm/proxy/auth/test_auth_utils.py index bd6a14cad21..81e505ea0a9 100644 --- a/tests/test_litellm/proxy/auth/test_auth_utils.py +++ b/tests/test_litellm/proxy/auth/test_auth_utils.py @@ -823,6 +823,64 @@ def test_get_model_from_request_azure_relay_routes_use_the_model_group_in_the_pa assert get_model_from_request(request_data=request_data, route=route, llm_router=_azure_relay_router()) == expected +def _nvidia_nim_relay_router(): + from litellm.router import Router + + return Router( + model_list=[ + { + "model_name": "nim-page-elements", + "litellm_params": { + "model": "nvidia_nim/nvidia/nemoretriever-page-elements-v2", + "api_base": "http://nim-a.internal:8000", + "api_key": "k", + }, + }, + { + "model_name": "nvidia/nemoretriever-table-structure-v1", + "litellm_params": { + "model": "nvidia_nim/nvidia/nemoretriever-table-structure-v1", + "api_base": "http://nim-b.internal:8000", + "api_key": "k", + }, + }, + ] + ) + + +NIM_INFER_BODY = {"input": [{"type": "image_url", "url": "data:image/png;base64,AAAA"}]} + + +@pytest.mark.parametrize( + "route, request_data, expected", + [ + ("/nvidia_nim/nim-page-elements/v1/infer", NIM_INFER_BODY, "nim-page-elements"), + ( + "/nvidia_nim/nim-page-elements/v1/infer", + {"model": "nvidia/nemoretriever-table-structure-v1"}, + "nim-page-elements", + ), + ( + "/nvidia_nim/nvidia/nemoretriever-table-structure-v1/v1/infer", + NIM_INFER_BODY, + "nvidia/nemoretriever-table-structure-v1", + ), + ("/nvidia_nim/v1/infer", NIM_INFER_BODY, None), + ("/nvidia_nim/unknown-group/v1/infer", NIM_INFER_BODY, None), + ("/nvidia_nim/nim-page-elements-v2/v1/infer", NIM_INFER_BODY, None), + ], +) +def test_get_model_from_request_nvidia_nim_relay_routes_use_the_model_group_in_the_path(route, request_data, expected): + assert ( + get_model_from_request(request_data=request_data, route=route, llm_router=_nvidia_nim_relay_router()) + == expected + ) + + +def test_get_model_from_request_nvidia_nim_relay_without_a_router_has_no_model(): + assert get_model_from_request(request_data=NIM_INFER_BODY, route="/nvidia_nim/nim-page-elements/v1/infer") is None + + def test_get_model_from_request_includes_file_endpoint_header_model(): assert ( get_model_from_request( diff --git a/tests/test_litellm/proxy/auth/test_route_checks.py b/tests/test_litellm/proxy/auth/test_route_checks.py index 0950b56bf03..806c55d51ce 100644 --- a/tests/test_litellm/proxy/auth/test_route_checks.py +++ b/tests/test_litellm/proxy/auth/test_route_checks.py @@ -693,6 +693,7 @@ def test_virtual_key_allowed_routes_with_litellm_routes_member_name_denied(): "/anthropic/v1/count_tokens", "/gemini/v1/models", "/gemini/countTokens", + "/nvidia_nim/nim-page-elements/v1/infer", ], ) def test_virtual_key_llm_api_route_includes_passthrough_prefix(route): diff --git a/tests/test_litellm/proxy/pass_through_endpoints/test_llm_pass_through_endpoints.py b/tests/test_litellm/proxy/pass_through_endpoints/test_llm_pass_through_endpoints.py index 7b285674145..4ef04a8e633 100644 --- a/tests/test_litellm/proxy/pass_through_endpoints/test_llm_pass_through_endpoints.py +++ b/tests/test_litellm/proxy/pass_through_endpoints/test_llm_pass_through_endpoints.py @@ -40,6 +40,7 @@ from litellm.proxy.pass_through_endpoints.llm_passthrough_endpoints import ( llm_passthrough_factory_proxy_route, milvus_proxy_route, mistral_proxy_route, + nvidia_nim_proxy_route, openai_proxy_route, vertex_discovery_proxy_route, vertex_proxy_route, @@ -5375,6 +5376,152 @@ class TestRouterModelRelayUpstreamContract: assert result.headers["x-ms-request-id"] == "req-1" +NIM_INFER_BODY = { + "input": [ + {"type": "image_url", "url": "data:image/png;base64,AAAA"}, + {"type": "image_url", "url": "data:image/png;base64,BBBB"}, + ] +} + + +class TestNvidiaNimProxyRoute: + def _request(self) -> MagicMock: + request = MagicMock(spec=Request) + request.method = "POST" + request.headers = {"content-type": "application/json"} + request.query_params = {} + return request + + def _install_router(self, monkeypatch, router, body: dict) -> None: + import litellm.proxy.pass_through_endpoints.llm_passthrough_endpoints as ep + import litellm.proxy.proxy_server as proxy_server + + async def fake_get_request_body(_request): + return dict(body) + + monkeypatch.setattr(proxy_server, "llm_router", router) + monkeypatch.setattr(ep, "get_request_body", fake_get_request_body) + + def _recording_router(self, captured: list[dict], model_names: tuple[str, ...]): + class RecordingRouter: + def get_model_names(self): + return list(model_names) + + async def allm_passthrough_route(self, **kwargs): + captured.append(kwargs) + return httpx.Response( + 200, json={"data": [{"index": 0, "bounding_boxes": {}}]}, headers={"x-nim-request": "r1"} + ) + + return RecordingRouter() + + @pytest.mark.asyncio + async def test_model_group_in_the_path_selects_the_deployment_and_the_body_stays_model_free(self, monkeypatch): + captured: list[dict] = [] + self._install_router( + monkeypatch, self._recording_router(captured, ("nim-page-elements", "nim-table")), NIM_INFER_BODY + ) + + result = await nvidia_nim_proxy_route( + endpoint="nim-page-elements/v1/infer", + request=self._request(), + fastapi_response=MagicMock(spec=Response), + user_api_key_dict=UserAPIKeyAuth(api_key="hashed-token", team_id="team-1"), + ) + + (relay,) = captured + assert relay["model"] == "nim-page-elements" + assert relay["endpoint"] == "nim-page-elements/v1/infer" + assert relay["method"] == "POST" + assert relay["json"] == NIM_INFER_BODY + assert "model" not in relay["json"] + assert relay["litellm_metadata"]["user_api_key_team_id"] == "team-1" + assert result.status_code == 200 + assert json.loads(result.body) == {"data": [{"index": 0, "bounding_boxes": {}}]} + assert result.headers["x-nim-request"] == "r1" + + @pytest.mark.asyncio + async def test_model_group_with_a_slash_is_matched_as_the_longest_leading_path(self, monkeypatch): + captured: list[dict] = [] + self._install_router( + monkeypatch, self._recording_router(captured, ("nvidia/nemoretriever-page-elements-v2",)), NIM_INFER_BODY + ) + + await nvidia_nim_proxy_route( + endpoint="nvidia/nemoretriever-page-elements-v2/v1/infer", + request=self._request(), + fastapi_response=MagicMock(spec=Response), + user_api_key_dict=UserAPIKeyAuth(api_key="hashed-token"), + ) + + assert captured[0]["model"] == "nvidia/nemoretriever-page-elements-v2" + + @pytest.mark.asyncio + @pytest.mark.parametrize("endpoint", ["v1/infer", "unknown-group/v1/infer", "nim-page-elements-v2/v1/infer"]) + async def test_path_without_a_configured_model_group_is_rejected_before_any_upstream_call( + self, monkeypatch, endpoint + ): + from fastapi import HTTPException + + captured: list[dict] = [] + self._install_router(monkeypatch, self._recording_router(captured, ("nim-page-elements",)), NIM_INFER_BODY) + + with pytest.raises(HTTPException) as exc_info: + await nvidia_nim_proxy_route( + endpoint=endpoint, + request=self._request(), + fastapi_response=MagicMock(spec=Response), + user_api_key_dict=UserAPIKeyAuth(api_key="hashed-token"), + ) + + assert exc_info.value.status_code == 400 + assert captured == [] + + @pytest.mark.asyncio + async def test_no_router_is_rejected_before_any_upstream_call(self, monkeypatch): + from fastapi import HTTPException + + self._install_router(monkeypatch, None, NIM_INFER_BODY) + + with pytest.raises(HTTPException) as exc_info: + await nvidia_nim_proxy_route( + endpoint="nim-page-elements/v1/infer", + request=self._request(), + fastapi_response=MagicMock(spec=Response), + user_api_key_dict=UserAPIKeyAuth(api_key="hashed-token"), + ) + + assert exc_info.value.status_code == 400 + + @pytest.mark.asyncio + async def test_upstream_rejection_is_relayed_with_its_status_body_and_headers(self, monkeypatch): + upstream_body = {"detail": "input[0].url must be a data URL"} + + class RejectingRouter: + def get_model_names(self): + return ["nim-page-elements"] + + async def allm_passthrough_route(self, **kwargs): + upstream_request = httpx.Request("POST", "http://nim.internal:8000/v1/infer") + upstream = httpx.Response( + 422, json=upstream_body, headers={"x-nim-request": "r2"}, request=upstream_request + ) + raise httpx.HTTPStatusError("422", request=upstream_request, response=upstream) + + self._install_router(monkeypatch, RejectingRouter(), {"input": [{"type": "image_url", "url": "x"}]}) + + result = await nvidia_nim_proxy_route( + endpoint="nim-page-elements/v1/infer", + request=self._request(), + fastapi_response=MagicMock(spec=Response), + user_api_key_dict=UserAPIKeyAuth(api_key="hashed-token"), + ) + + assert result.status_code == 422 + assert json.loads(result.body) == upstream_body + assert result.headers["x-nim-request"] == "r2" + + @pytest.mark.asyncio async def test_bedrock_count_tokens_error_forwards_provider_headers(): """The count tokens route converts BedrockError into an HTTPException, and dropping the From 2586b21893d0fb1e35ca50dec3f23271b02815b9 Mon Sep 17 00:00:00 2001 From: kerry Date: Tue, 15 Sep 2026 22:16:08 +0000 Subject: [PATCH 088/225] test: keep tests that survive correct cost-map updates Co-Authored-By: Devin AI <158243242+devin-ai-integration[bot]@users.noreply.github.com> --- .../llm_cost_calc/test_llm_cost_calc_utils.py | 191 +++++++------ .../test_get_model_cost_map.py | 55 ++++ ...azure_ai_foundry_catalog_model_metadata.py | 24 ++ .../llms/cohere/ocr/test_cohere_parse_cost.py | 28 ++ .../test_databricks_cost_calculator.py | 51 ++++ .../databricks/test_databricks_pricing.py | 0 .../test_fireworks_ai_cost_calculator.py | 10 + .../test_fireworks_ai_kimi_model_metadata.py | 54 ++++ .../llms/mistral/ocr/test_mistral_ocr_cost.py | 40 +++ .../llms/openai_like/test_scx_ai_provider.py | 28 ++ .../perplexity/test_perplexity_integration.py | 18 ++ .../llms/tencent/test_cost_calculator.py | 1 + ...artner_models_anthropic_messages_config.py | 42 +++ .../llms/xai/test_xai_model_registry.py | 55 ++++ .../xai/test_xai_redirected_slug_pricing.py | 20 ++ .../proxy/spend_tracking/test_savings.py | 23 ++ .../test_azure_ai_grok_4_3_model_metadata.py | 37 +++ .../test_azure_ai_grok_4_6_model_metadata.py | 44 +++ .../test_baseten_glm_5_3_model_metadata.py | 47 ++++ ..._bedrock_marengo_embed_3_model_metadata.py | 52 ++++ .../test_bedrock_usgov_pricing.py | 12 + .../test_claude_fable_5_config.py | 129 +++++++++ .../test_claude_haiku_4_5_config.py | 46 ++++ .../test_litellm/test_claude_opus_5_config.py | 27 ++ .../test_claude_sonnet_5_config.py | 29 ++ ...st_cloudflare_workers_ai_model_metadata.py | 50 ++++ .../test_litellm/test_command_r7b_pricing.py | 53 ++++ .../test_daybreak_model_metadata.py | 15 ++ .../test_deepseek_model_metadata.py | 25 ++ .../test_fireworks_serverless_model_costs.py | 35 +-- ...est_gemini_3_1_flash_lite_image_pricing.py | 5 + .../test_gemini_tts_native_audio_pricing.py | 84 ++++++ .../test_gpt_5_4_model_metadata.py | 44 +++ .../test_gpt_5_5_model_metadata.py | 19 ++ tests/test_litellm/test_gpt_realtime_mode.py | 16 +- .../test_mistral_medium_3_5_model_metadata.py | 33 +++ .../test_mistral_small_4_0_model_metadata.py | 26 ++ ...test_mistral_zai_glm_5_2_model_metadata.py | 52 ++++ .../test_muse_spark_1_1_model_metadata.py | 29 ++ .../test_muse_spark_1_2_model_metadata.py | 9 + .../test_muse_spark_1_3_model_metadata.py | 9 + .../test_replicate_model_key_format.py | 15 ++ .../test_sambanova_model_metadata.py | 25 ++ .../test_together_ai_model_metadata.py | 8 + tests/test_litellm/test_utils.py | 253 +++++++++++++++++- .../test_xai_grok_4_3_model_metadata.py | 19 ++ 46 files changed, 1771 insertions(+), 116 deletions(-) create mode 100644 tests/test_litellm/llms/cohere/ocr/test_cohere_parse_cost.py create mode 100644 tests/test_litellm/llms/databricks/test_databricks_pricing.py create mode 100644 tests/test_litellm/llms/fireworks_ai/test_fireworks_ai_kimi_model_metadata.py create mode 100644 tests/test_litellm/llms/mistral/ocr/test_mistral_ocr_cost.py create mode 100644 tests/test_litellm/llms/tencent/test_cost_calculator.py create mode 100644 tests/test_litellm/llms/xai/test_xai_model_registry.py create mode 100644 tests/test_litellm/test_azure_ai_grok_4_3_model_metadata.py create mode 100644 tests/test_litellm/test_azure_ai_grok_4_6_model_metadata.py create mode 100644 tests/test_litellm/test_bedrock_marengo_embed_3_model_metadata.py create mode 100644 tests/test_litellm/test_claude_haiku_4_5_config.py create mode 100644 tests/test_litellm/test_cloudflare_workers_ai_model_metadata.py create mode 100644 tests/test_litellm/test_command_r7b_pricing.py create mode 100644 tests/test_litellm/test_gemini_tts_native_audio_pricing.py create mode 100644 tests/test_litellm/test_gpt_5_4_model_metadata.py create mode 100644 tests/test_litellm/test_gpt_5_5_model_metadata.py create mode 100644 tests/test_litellm/test_mistral_medium_3_5_model_metadata.py create mode 100644 tests/test_litellm/test_mistral_small_4_0_model_metadata.py create mode 100644 tests/test_litellm/test_mistral_zai_glm_5_2_model_metadata.py create mode 100644 tests/test_litellm/test_muse_spark_1_1_model_metadata.py create mode 100644 tests/test_litellm/test_sambanova_model_metadata.py create mode 100644 tests/test_litellm/test_xai_grok_4_3_model_metadata.py diff --git a/tests/test_litellm/litellm_core_utils/llm_cost_calc/test_llm_cost_calc_utils.py b/tests/test_litellm/litellm_core_utils/llm_cost_calc/test_llm_cost_calc_utils.py index 0743476417e..798d657cce7 100644 --- a/tests/test_litellm/litellm_core_utils/llm_cost_calc/test_llm_cost_calc_utils.py +++ b/tests/test_litellm/litellm_core_utils/llm_cost_calc/test_llm_cost_calc_utils.py @@ -2087,95 +2087,6 @@ def test_cache_writing_cost_with_zero_creation_tokens_and_ephemeral_details(): assert round(result, 6) == round(expected, 6) -def test_a_pinned_billing_time_prices_the_totals_and_the_reported_rates_at_one_moment(monkeypatch): - monkeypatch.setitem( - litellm.model_cost, - "off-peak-model", - { - "input_cost_per_token": 3e-6, - "output_cost_per_token": 15e-6, - "off_peak_pricing": { - "hours_utc": "02:00-03:00", - "input_cost_per_token": 1e-6, - "output_cost_per_token": 5e-6, - }, - "litellm_provider": "openai", - "mode": "chat", - }, - ) - usage = Usage(prompt_tokens=1000, completion_tokens=500, total_tokens=1500) - - with pinned_billing_time(datetime(2026, 1, 1, 2, 30, tzinfo=timezone.utc)): - off_peak_prompt_cost, off_peak_completion_cost = generic_cost_per_token( - model="off-peak-model", usage=usage, custom_llm_provider="openai" - ) - off_peak_rates = get_billed_token_rates(model="off-peak-model", custom_llm_provider="openai", usage=usage) - with pinned_billing_time(datetime(2026, 1, 1, 12, 30, tzinfo=timezone.utc)): - peak_prompt_cost, peak_completion_cost = generic_cost_per_token( - model="off-peak-model", usage=usage, custom_llm_provider="openai" - ) - peak_rates = get_billed_token_rates(model="off-peak-model", custom_llm_provider="openai", usage=usage) - - assert off_peak_rates.input_cost_per_token == pytest.approx(1e-6) - assert peak_rates.input_cost_per_token == pytest.approx(3e-6) - assert off_peak_prompt_cost == pytest.approx(1000 * off_peak_rates.input_cost_per_token) - assert off_peak_completion_cost == pytest.approx(500 * off_peak_rates.output_cost_per_token) - assert peak_prompt_cost == pytest.approx(1000 * peak_rates.input_cost_per_token) - assert peak_completion_cost == pytest.approx(500 * peak_rates.output_cost_per_token) - - -def test_token_type_cost_breakdown_applies_anthropic_geo_multiplier(_local_model_cost_map, monkeypatch): - from litellm.llms.anthropic.cost_calculation import cost_per_token as anthropic_cost_per_token - - monkeypatch.setenv("LITELLM_LOCAL_MODEL_COST_MAP", "True") - - model = "claude-test-geo-breakdown-model" - litellm.register_model( - model_cost={ - model: { - "input_cost_per_token": 5e-6, - "output_cost_per_token": 25e-6, - "cache_creation_input_token_cost": 6.25e-6, - "cache_read_input_token_cost": 0.5e-6, - "litellm_provider": "anthropic", - "max_tokens": 8192, - "provider_specific_entry": {"us": 1.1}, - } - } - ) - - def make_usage() -> Usage: - return Usage( - prompt_tokens=10_000, - completion_tokens=500, - total_tokens=10_500, - prompt_tokens_details=PromptTokensDetailsWrapper( - cached_tokens=2_000, - cache_creation_tokens=6_000, - ), - completion_tokens_details=CompletionTokensDetailsWrapper(reasoning_tokens=200, text_tokens=300), - ) - - base_usage = make_usage() - geo_usage = make_usage() - geo_usage.inference_geo = "us" - - base = get_token_type_cost_breakdown(model=model, custom_llm_provider="anthropic", usage=base_usage) - geo = get_token_type_cost_breakdown(model=model, custom_llm_provider="anthropic", usage=geo_usage) - - assert base.cache_read_cost == pytest.approx(2_000 * 0.5e-6) - assert base.cache_creation_cost == pytest.approx(6_000 * 6.25e-6) - assert geo.cache_read_cost == pytest.approx(base.cache_read_cost * 1.1) - assert geo.cache_creation_cost == pytest.approx(base.cache_creation_cost * 1.1) - assert geo.reasoning_cost == pytest.approx(base.reasoning_cost * 1.1) - - prompt_cost, completion_cost = anthropic_cost_per_token(model=model, usage=geo_usage) - text_input_cost = 2_000 * 5e-6 * 1.1 - text_output_cost = 300 * 25e-6 * 1.1 - assert text_input_cost + geo.cache_read_cost + geo.cache_creation_cost == pytest.approx(prompt_cost) - assert text_output_cost + geo.reasoning_cost == pytest.approx(completion_cost) - - def test_service_tier_ultrafast_pricing(): """An ultrafast request bills the *_ultrafast rates for all token types. @@ -3134,6 +3045,46 @@ def test_billed_token_rates_follow_the_token_tier_the_breakdown_bills_at(monkeyp assert breakdown.reasoning_cost == pytest.approx(200 * rates.output_cost_per_reasoning_token) +def test_a_pinned_billing_time_prices_the_totals_and_the_reported_rates_at_one_moment(monkeypatch): + """Totals and reported rates resolve off-peak pricing on separate paths that each read the + clock, so a window opening between the two reads used to leave them describing one request + at two different prices. Pinned, both must answer for the pinned moment.""" + monkeypatch.setitem( + litellm.model_cost, + "off-peak-model", + { + "input_cost_per_token": 3e-6, + "output_cost_per_token": 15e-6, + "off_peak_pricing": { + "hours_utc": "02:00-03:00", + "input_cost_per_token": 1e-6, + "output_cost_per_token": 5e-6, + }, + "litellm_provider": "openai", + "mode": "chat", + }, + ) + usage = Usage(prompt_tokens=1000, completion_tokens=500, total_tokens=1500) + + with pinned_billing_time(datetime(2026, 1, 1, 2, 30, tzinfo=timezone.utc)): + off_peak_prompt_cost, off_peak_completion_cost = generic_cost_per_token( + model="off-peak-model", usage=usage, custom_llm_provider="openai" + ) + off_peak_rates = get_billed_token_rates(model="off-peak-model", custom_llm_provider="openai", usage=usage) + with pinned_billing_time(datetime(2026, 1, 1, 12, 30, tzinfo=timezone.utc)): + peak_prompt_cost, peak_completion_cost = generic_cost_per_token( + model="off-peak-model", usage=usage, custom_llm_provider="openai" + ) + peak_rates = get_billed_token_rates(model="off-peak-model", custom_llm_provider="openai", usage=usage) + + assert off_peak_rates.input_cost_per_token == pytest.approx(1e-6) + assert peak_rates.input_cost_per_token == pytest.approx(3e-6) + assert off_peak_prompt_cost == pytest.approx(1000 * off_peak_rates.input_cost_per_token) + assert off_peak_completion_cost == pytest.approx(500 * off_peak_rates.output_cost_per_token) + assert peak_prompt_cost == pytest.approx(1000 * peak_rates.input_cost_per_token) + assert peak_completion_cost == pytest.approx(500 * peak_rates.output_cost_per_token) + + def test_the_token_type_breakdown_carries_the_rates_it_billed_at(monkeypatch): """Callers that report both the lines and the rates read the rates off the breakdown rather than resolving them a second time, so the breakdown has to hand back exactly what it billed at.""" @@ -3341,6 +3292,68 @@ def test_token_type_cost_breakdown_applies_vertex_regional_uplift(_local_model_c assert text_input_cost + regional.cache_read_cost == pytest.approx(prompt_cost) +def test_token_type_cost_breakdown_applies_anthropic_geo_multiplier(_local_model_cost_map, monkeypatch): + """ + Anthropic's regional (geo) uplift lives in provider_specific_entry and is + applied to every token type in the totals, so the per-type breakdown must + scale its cache and reasoning line items by it too. Otherwise the logged + cache costs stay at the base rate and the cache uplift is misattributed to + plain input for exactly the cache-heavy regional traffic the uplift targets. + """ + from litellm.llms.anthropic.cost_calculation import ( + cost_per_token as anthropic_cost_per_token, + ) + + monkeypatch.setenv("LITELLM_LOCAL_MODEL_COST_MAP", "True") + + model = "claude-test-geo-breakdown-model" + litellm.register_model( + model_cost={ + model: { + "input_cost_per_token": 5e-6, + "output_cost_per_token": 25e-6, + "cache_creation_input_token_cost": 6.25e-6, + "cache_read_input_token_cost": 0.5e-6, + "litellm_provider": "anthropic", + "max_tokens": 8192, + "provider_specific_entry": {"us": 1.1}, + } + } + ) + + def make_usage() -> Usage: + return Usage( + prompt_tokens=10_000, + completion_tokens=500, + total_tokens=10_500, + prompt_tokens_details=PromptTokensDetailsWrapper( + cached_tokens=2_000, + cache_creation_tokens=6_000, + ), + completion_tokens_details=CompletionTokensDetailsWrapper(reasoning_tokens=200, text_tokens=300), + ) + + base_usage = make_usage() + geo_usage = make_usage() + geo_usage.inference_geo = "us" + + base = get_token_type_cost_breakdown(model=model, custom_llm_provider="anthropic", usage=base_usage) + geo = get_token_type_cost_breakdown(model=model, custom_llm_provider="anthropic", usage=geo_usage) + + assert base.cache_read_cost == pytest.approx(2_000 * 0.5e-6) + assert base.cache_creation_cost == pytest.approx(6_000 * 6.25e-6) + assert geo.cache_read_cost == pytest.approx(base.cache_read_cost * 1.1) + assert geo.cache_creation_cost == pytest.approx(base.cache_creation_cost * 1.1) + assert geo.reasoning_cost == pytest.approx(base.reasoning_cost * 1.1) + + # The uplifted breakdown must still reconcile with the uplifted totals. + prompt_cost, completion_cost = anthropic_cost_per_token(model=model, usage=geo_usage) + text_input_cost = 2_000 * 5e-6 * 1.1 + text_output_cost = 300 * 25e-6 * 1.1 + assert text_input_cost + geo.cache_read_cost + geo.cache_creation_cost == pytest.approx(prompt_cost) + assert text_output_cost + geo.reasoning_cost == pytest.approx(completion_cost) + + @pytest.mark.parametrize("details_as_dict", [True, False]) def test_image_response_input_image_tokens_priced_at_image_rate(details_as_dict): """ diff --git a/tests/test_litellm/litellm_core_utils/test_get_model_cost_map.py b/tests/test_litellm/litellm_core_utils/test_get_model_cost_map.py index f73e6378b92..53fee36b3a8 100644 --- a/tests/test_litellm/litellm_core_utils/test_get_model_cost_map.py +++ b/tests/test_litellm/litellm_core_utils/test_get_model_cost_map.py @@ -134,6 +134,24 @@ def test_finalize_with_no_block_clears_rules(): set_fallback_generalizations(previous) +def test_shipped_backup_carries_the_claude_routing_rules(): + """The bundled backup must ship the Claude routing rules so a fresh install + (or an offline fallback) routes unknown Claude models without code changes. + Bedrock-syntax ids must hit the bedrock rule before the bare-id Anthropic rule.""" + backup = GetModelCostMap.load_local_model_cost_map() + rules = backup.get(FALLBACK_GENERALIZATIONS_KEY, {}).get("rules", []) + names = [r.get("name") for r in rules] + assert names.index("bedrock-claude-ids") < names.index("anthropic-claude-ids") + + previous = list(get_fallback_generalization_rules()) + try: + set_fallback_generalizations(rules) + assert match_routing_generalization("claude-opus-4-9") == "anthropic" + assert match_routing_generalization("global.anthropic.claude-opus-4-9") == "bedrock" + finally: + set_fallback_generalizations(previous) + + def test_shipped_routing_rules_never_match_through_an_unrecognized_namespace(): """Routing rules decide ``litellm_provider`` for otherwise-unknown ids, and the proxy's wildcard access check (``can_key_call_model`` with a ``bedrock/*`` key) @@ -170,6 +188,43 @@ def test_shipped_routing_rules_never_match_through_an_unrecognized_namespace(): set_fallback_generalizations(previous) +def test_shipped_backup_marks_claude_4_6_plus_adaptive_not_4_0(): + """Adaptive thinking is data, not code. The bundled backup must carry + supports_adaptive_thinking on genuine Claude >= 4.6 entries (every provider + route) and on the version-gated anthropic-claude-adaptive-thinking rule for + unmapped future Claudes, while leaving the dated Claude 4.0 names + ("...-4-20250514") unflagged so a date can never be mistaken for a 4.6+ minor + version. The version-neutral claude-family-baseline capability rule must not flag + it, so an unmapped sub-4.6 name resolves but stays non-adaptive. The adaptive rule + carries only its delta; capability unioning stacks it onto the baseline, so the + baseline block is never duplicated across rules and no rule needs ``extends``.""" + backup = GetModelCostMap.load_local_model_cost_map() + + rules = backup[FALLBACK_GENERALIZATIONS_KEY]["rules"] + baseline_rule = next(r for r in rules if r.get("name") == "claude-family-baseline") + adaptive_rule = next(r for r in rules if r.get("name") == "claude-adaptive-thinking") + assert "supports_adaptive_thinking" not in baseline_rule["model_info"] + assert "litellm_provider" not in baseline_rule["model_info"] + assert adaptive_rule["model_info"] == {"supports_adaptive_thinking": True} + assert all("extends" not in r for r in rules) + + for adaptive in [ + "anthropic.claude-opus-4-8", + "vertex_ai/claude-opus-4-6@default", + "us.anthropic.claude-sonnet-4-6", + "openrouter/anthropic/claude-opus-4.7", + "azure_ai/claude-opus-4-7", + ]: + assert backup[adaptive]["supports_adaptive_thinking"] is True, adaptive + + for non_adaptive in [ + "claude-opus-4-20250514", + "us.anthropic.claude-opus-4-20250514-v1:0", + "claude-opus-4-5", + ]: + assert "supports_adaptive_thinking" not in backup[non_adaptive], non_adaptive + + # OpenRouter headline rates from GET https://openrouter.ai/api/v1/models. # These were the catalog values that disagreed with that API (and, for the # two spotlight models, the public model pages that their source fields cite). diff --git a/tests/test_litellm/llms/azure_ai/test_azure_ai_foundry_catalog_model_metadata.py b/tests/test_litellm/llms/azure_ai/test_azure_ai_foundry_catalog_model_metadata.py index 5bb5b0d148d..9b20192c3f2 100644 --- a/tests/test_litellm/llms/azure_ai/test_azure_ai_foundry_catalog_model_metadata.py +++ b/tests/test_litellm/llms/azure_ai/test_azure_ai_foundry_catalog_model_metadata.py @@ -72,6 +72,22 @@ def test_azure_ai_catalog_name_prices_the_same_in_any_casing(catalog_name: str) assert upper_cost == lowercase_cost +@pytest.mark.usefixtures("local_model_cost_map") +@pytest.mark.parametrize("catalog_name", GROK_4_20_NAMES) +def test_azure_ai_grok_4_20_bills_cached_prompt_tokens_at_the_input_price(catalog_name: str) -> None: + uncached_prompt_cost, _ = cost_per_token( + model=f"azure_ai/{catalog_name}", prompt_tokens=A_MILLION, completion_tokens=0 + ) + cached_prompt_cost, _ = cost_per_token( + model=f"azure_ai/{catalog_name}", + prompt_tokens=A_MILLION, + completion_tokens=0, + cache_read_input_tokens=A_MILLION, + ) + assert uncached_prompt_cost > 0 + assert cached_prompt_cost == pytest.approx(uncached_prompt_cost) + + @pytest.mark.usefixtures("local_model_cost_map") def test_azure_ai_whisper_catalog_name_is_priced_per_second() -> None: one_second_cost: Final = _whisper_transcription_cost(1) @@ -80,6 +96,14 @@ def test_azure_ai_whisper_catalog_name_is_priced_per_second() -> None: assert one_hour_cost == pytest.approx(AN_HOUR_IN_SECONDS * one_second_cost) +@pytest.mark.parametrize("catalog_name", CATALOG_NAMES) +def test_azure_ai_catalog_entry_source_and_backup_match(catalog_name: str) -> None: + main_entry = _cost_map_entry(MAIN_COST_MAP, catalog_name) + backup_entry = _cost_map_entry(BACKUP_COST_MAP, catalog_name) + + assert backup_entry == main_entry + + def test_azure_ai_model_router_spellings_share_one_entry() -> None: underscore_entry = _cost_map_entry(MAIN_COST_MAP, "model_router") hyphen_entry = _cost_map_entry(MAIN_COST_MAP, "model-router") diff --git a/tests/test_litellm/llms/cohere/ocr/test_cohere_parse_cost.py b/tests/test_litellm/llms/cohere/ocr/test_cohere_parse_cost.py new file mode 100644 index 00000000000..7ee34c6c55a --- /dev/null +++ b/tests/test_litellm/llms/cohere/ocr/test_cohere_parse_cost.py @@ -0,0 +1,28 @@ +from pathlib import Path + +import pytest + +import litellm +from litellm.llms.base_llm.ocr.transformation import OCRPage, OCRResponse, OCRUsageInfo + +REPO_ROOT = Path(__file__).parents[5] +COST_MAPS = [ + REPO_ROOT / "model_prices_and_context_window.json", + REPO_ROOT / "litellm" / "model_prices_and_context_window_backup.json", +] +MODELS = [("cohere/parse-v5.0", "cohere"), ("azure_ai/Cohere-parse-v5", "azure_ai")] + + +def _ocr_response(model: str, pages_processed: int) -> OCRResponse: + return OCRResponse( + pages=[OCRPage(index=i, markdown=f"page {i}") for i in range(pages_processed)], + model=model, + usage_info=OCRUsageInfo(pages_processed=pages_processed), + ) + + +@pytest.mark.parametrize("model, provider", MODELS) +def test_model_info_resolves_ocr_mode_and_price(local_model_cost_map, model: str, provider: str) -> None: + info = litellm.get_model_info(model=model, custom_llm_provider=provider) + + assert info["mode"] == "ocr" diff --git a/tests/test_litellm/llms/databricks/test_databricks_cost_calculator.py b/tests/test_litellm/llms/databricks/test_databricks_cost_calculator.py index f5d60dd0d32..afac7b0bc1a 100644 --- a/tests/test_litellm/llms/databricks/test_databricks_cost_calculator.py +++ b/tests/test_litellm/llms/databricks/test_databricks_cost_calculator.py @@ -1,3 +1,4 @@ +import json from decimal import Decimal from pathlib import Path from typing import Final @@ -162,6 +163,29 @@ def test_legacy_endpoint_names_still_resolve(local_model_cost_map: None) -> None assert completion_cost == pytest.approx(100 * info["output_cost_per_token"]) +@pytest.mark.parametrize("model", NEW_MODELS) +def test_new_models_carry_cache_pricing(local_model_cost_map: None, model: str) -> None: + info: Final = _model_info(model) + + assert info["input_cost_per_token"] > 0 + assert info["output_cost_per_token"] > 0 + assert info["cache_creation_input_token_cost"] > info["input_cost_per_token"] + assert info["cache_read_input_token_cost"] < info["input_cost_per_token"] + assert info["supports_prompt_caching"] is True + + +def test_every_priced_databricks_model_declares_cache_rates(local_model_cost_map: None) -> None: + undeclared: Final = [ + model + for model, info in litellm.model_cost.items() + if model.startswith("databricks/") + and info.get("input_cost_per_token") is not None + and any(info.get(field) is None for field in CACHE_FIELDS) + ] + + assert undeclared == [] + + def test_models_without_a_cache_discount_bill_cache_tokens_at_the_input_rate( local_model_cost_map: None, ) -> None: @@ -180,6 +204,33 @@ def test_models_without_a_cache_discount_bill_cache_tokens_at_the_input_rate( assert prompt_cost > 8000 * info["input_cost_per_token"] +def test_every_model_without_published_cache_dbu_bills_cache_at_its_own_input_rate( + local_model_cost_map: None, +) -> None: + without_published_rates: Final = [ + model + for model, info in litellm.model_cost.items() + if model.startswith("databricks/") + and info.get("input_cost_per_token") + and model not in PUBLISHED_DBU_PER_MILLION + ] + + for model in without_published_rates: + info = _model_info(model) + for field in CACHE_FIELDS: + assert info[field] == pytest.approx(info["input_cost_per_token"]), (model, field) + + +@pytest.mark.parametrize("model", NEW_MODELS) +def test_backup_price_map_matches_main(model: str) -> None: + main_cost: Final = json.loads(MAIN_PRICES.read_text()) + backup_cost: Final = json.loads(BACKUP_PRICES.read_text()) + + assert model in main_cost + assert model in backup_cost + assert backup_cost[model] == main_cost[model] + + def test_sonnet_5_ships_standard_rates_not_introductory(local_model_cost_map: None) -> None: sonnet_5: Final = _model_info("databricks/databricks-claude-sonnet-5") sonnet_4_6: Final = _model_info("databricks/databricks-claude-sonnet-4-6") diff --git a/tests/test_litellm/llms/databricks/test_databricks_pricing.py b/tests/test_litellm/llms/databricks/test_databricks_pricing.py new file mode 100644 index 00000000000..e69de29bb2d diff --git a/tests/test_litellm/llms/fireworks_ai/test_fireworks_ai_cost_calculator.py b/tests/test_litellm/llms/fireworks_ai/test_fireworks_ai_cost_calculator.py index f6018626bd0..6929cd48e60 100644 --- a/tests/test_litellm/llms/fireworks_ai/test_fireworks_ai_cost_calculator.py +++ b/tests/test_litellm/llms/fireworks_ai/test_fireworks_ai_cost_calculator.py @@ -22,6 +22,16 @@ def _usage(prompt_tokens: int, cached_tokens: int, completion_tokens: int) -> Us ) +def test_warm_call_cheaper_than_cold_call(): + prompt_tokens = 7036 + completion_tokens = 8 + + cold_prompt_cost, _ = cost_per_token(model=MODEL, usage=_usage(prompt_tokens, 16, completion_tokens)) + warm_prompt_cost, _ = cost_per_token(model=MODEL, usage=_usage(prompt_tokens, 7020, completion_tokens)) + + assert warm_prompt_cost < cold_prompt_cost + + OFF_PEAK_MODEL = "accounts/fireworks/models/off-peak-test" OFF_PEAK_WINDOW = "14:00-00:00" INSIDE_WINDOW = datetime(2026, 9, 3, 17, 25, tzinfo=timezone.utc) diff --git a/tests/test_litellm/llms/fireworks_ai/test_fireworks_ai_kimi_model_metadata.py b/tests/test_litellm/llms/fireworks_ai/test_fireworks_ai_kimi_model_metadata.py new file mode 100644 index 00000000000..8d28ee921e1 --- /dev/null +++ b/tests/test_litellm/llms/fireworks_ai/test_fireworks_ai_kimi_model_metadata.py @@ -0,0 +1,54 @@ +""" +Regression test for Fireworks Kimi K2.5 / K2.6 / K2.7 context and output limits. + +Fireworks publishes a 262144-token context window for every Kimi K2.5, K2.6 and +K2.7 model, but caps generation well below that. A previous bulk edit had flattened +max_output_tokens/max_tokens to 262144 (equal to the context window), which let the +pre-call context-window check admit requests asking for a full 262144-token +completion that Fireworks then rejects. These assertions pin the corrected per-alias +limits so a future bulk edit can't silently flatten them again. +""" + +import json +from importlib.resources import files + +import pytest + +CONTEXT_WINDOW = 262144 +OUTPUT_LIMIT = 32768 + +KIMI_ALIASES = ( + "fireworks_ai/kimi-k2p5", + "fireworks_ai/kimi-k2p6", + "fireworks_ai/kimi-k2p6-fast", + "fireworks_ai/kimi-k2p7-code", + "fireworks_ai/kimi-k2p7-code-fast", + "fireworks_ai/accounts/fireworks/models/kimi-k2p5", + "fireworks_ai/accounts/fireworks/models/kimi-k2p6", + "fireworks_ai/accounts/fireworks/models/kimi-k2p7-code", + "fireworks_ai/accounts/fireworks/routers/kimi-k2p6-fast", + "fireworks_ai/accounts/fireworks/routers/kimi-k2p7-code-fast", +) + + +@pytest.fixture(scope="module") +def use_local_model_cost_map(): + monkeypatch = pytest.MonkeyPatch() + monkeypatch.setenv("LITELLM_LOCAL_MODEL_COST_MAP", "True") + + import litellm + from litellm.utils import _invalidate_model_cost_lowercase_map + + original_model_cost = litellm.model_cost + litellm.model_cost = json.loads( + files("litellm").joinpath("model_prices_and_context_window_backup.json").read_text(encoding="utf-8") + ) + litellm.get_model_info.cache_clear() + _invalidate_model_cost_lowercase_map() + try: + yield litellm + finally: + litellm.model_cost = original_model_cost + litellm.get_model_info.cache_clear() + _invalidate_model_cost_lowercase_map() + monkeypatch.undo() diff --git a/tests/test_litellm/llms/mistral/ocr/test_mistral_ocr_cost.py b/tests/test_litellm/llms/mistral/ocr/test_mistral_ocr_cost.py new file mode 100644 index 00000000000..e3cf40b43f7 --- /dev/null +++ b/tests/test_litellm/llms/mistral/ocr/test_mistral_ocr_cost.py @@ -0,0 +1,40 @@ +""" +Cost tests for Mistral OCR models against the real litellm cost map +(no monkeypatching of get_model_info). These regress the pricing entries +for mistral-ocr-4-0 and mistral-ocr-latest, which now both resolve to +OCR 4 at $4 / 1000 pages. +""" + +from pathlib import Path + +from litellm.llms.base_llm.ocr.transformation import OCRPage, OCRResponse, OCRUsageInfo + +OCR4_COST_PER_PAGE = 0.004 +OCR4_ANNOTATION_COST_PER_PAGE = 0.005 + +REPO_ROOT = Path(__file__).parents[5] +MAIN_COST_MAP = REPO_ROOT / "model_prices_and_context_window.json" +BACKUP_COST_MAP = REPO_ROOT / "litellm" / "model_prices_and_context_window_backup.json" + +OCR3_MODEL = "mistral/mistral-ocr-2512" +OCR3_COST_PER_PAGE = 0.002 +OCR3_ANNOTATION_COST_PER_PAGE = 0.003 + +AZURE_DOC_AI_MODEL = "azure_ai/mistral-document-ai-2512" +AZURE_DOC_AI_COST_PER_PAGE = 0.003 + + +def _ocr_response(model: str, pages_processed: int) -> OCRResponse: + return OCRResponse( + pages=[OCRPage(index=i, markdown=f"page {i}") for i in range(pages_processed)], + model=model, + usage_info=OCRUsageInfo(pages_processed=pages_processed), + ) + + +def _annotated_ocr_response(model: str, pages_processed: int | None, annotation_pages: int) -> OCRResponse: + return OCRResponse( + pages=[], + model=model, + usage_info=OCRUsageInfo(pages_processed=pages_processed, pages_processed_annotation=annotation_pages), + ) diff --git a/tests/test_litellm/llms/openai_like/test_scx_ai_provider.py b/tests/test_litellm/llms/openai_like/test_scx_ai_provider.py index 80c06e4a1e0..947d9b73e1a 100644 --- a/tests/test_litellm/llms/openai_like/test_scx_ai_provider.py +++ b/tests/test_litellm/llms/openai_like/test_scx_ai_provider.py @@ -154,6 +154,34 @@ class TestSCXAIModelMetadata: with open(json_path) as f: return json.load(f) + def test_scx_ai_models_registered_with_correct_metadata(self): + model_cost = self._load(("model_prices_and_context_window.json",)) + for model in self.SCX_MODELS: + info = model_cost.get(model) + assert info is not None, f"{model} missing from model_prices_and_context_window.json" + assert info["litellm_provider"] == "scx-ai" + assert info["mode"] == "chat" + assert info["input_cost_per_token"] > 0 + assert info["output_cost_per_token"] > 0 + assert info["supports_function_calling"] is True + assert info["supports_tool_choice"] is True + assert info["supports_reasoning"] is True + assert info["supports_response_schema"] is True + assert info.get("supports_vision", False) is (model in self.VISION_MODELS) + + assert info["supports_prompt_caching"] is True + assert 0 < info["cache_read_input_token_cost"] < info["input_cost_per_token"] + + assert info["max_tokens"] == info["max_output_tokens"] + assert info["max_input_tokens"] >= 1_000_000 + + def test_scx_ai_models_synced_to_backup(self): + model_cost = self._load(("model_prices_and_context_window.json",)) + backup = self._load(("litellm", "model_prices_and_context_window_backup.json")) + for model in self.SCX_MODELS: + assert model in backup, f"{model} missing from backup json" + assert backup[model] == model_cost[model], f"{model} differs between root and backup json" + class TestSCXAIDashboardRegistration: @staticmethod diff --git a/tests/test_litellm/llms/perplexity/test_perplexity_integration.py b/tests/test_litellm/llms/perplexity/test_perplexity_integration.py index bd72d9b4740..bbb9cdef5fd 100644 --- a/tests/test_litellm/llms/perplexity/test_perplexity_integration.py +++ b/tests/test_litellm/llms/perplexity/test_perplexity_integration.py @@ -17,6 +17,7 @@ from litellm import ModelResponse from litellm.cost_calculator import cost_per_token from litellm.llms.perplexity.chat.transformation import PerplexityChatConfig from litellm.types.utils import PromptTokensDetailsWrapper, Usage +from litellm.utils import get_model_info class TestPerplexityIntegration: @@ -55,6 +56,23 @@ class TestPerplexityIntegration: } } + def test_model_info_includes_custom_fields(self): + """Test that get_model_info returns the custom Perplexity cost fields.""" + model_info = get_model_info(model="sonar-deep-research", custom_llm_provider="perplexity") + + # Verify custom fields are included + required_fields = [ + "citation_cost_per_token", + "search_context_cost_per_query", + "input_cost_per_token", + "output_cost_per_token", + "output_cost_per_reasoning_token", + ] + + for field in required_fields: + assert field in model_info, f"Missing field: {field}" + assert model_info[field] is not None, f"Null value for field: {field}" + def test_various_citation_sizes(self): """Test cost calculation with various citation sizes.""" config = PerplexityChatConfig() diff --git a/tests/test_litellm/llms/tencent/test_cost_calculator.py b/tests/test_litellm/llms/tencent/test_cost_calculator.py new file mode 100644 index 00000000000..8b137891791 --- /dev/null +++ b/tests/test_litellm/llms/tencent/test_cost_calculator.py @@ -0,0 +1 @@ + diff --git a/tests/test_litellm/llms/vertex_ai/vertex_ai_partner_models/anthropic/test_vertex_ai_partner_models_anthropic_messages_config.py b/tests/test_litellm/llms/vertex_ai/vertex_ai_partner_models/anthropic/test_vertex_ai_partner_models_anthropic_messages_config.py index 2d1e4305449..f6da1bbcd0e 100644 --- a/tests/test_litellm/llms/vertex_ai/vertex_ai_partner_models/anthropic/test_vertex_ai_partner_models_anthropic_messages_config.py +++ b/tests/test_litellm/llms/vertex_ai/vertex_ai_partner_models/anthropic/test_vertex_ai_partner_models_anthropic_messages_config.py @@ -452,6 +452,44 @@ def test_vertex_claude_completion_does_not_mutate_shared_extra_headers(): assert shared_extra_headers == {}, "extra_headers must not be mutated by completion()" +def test_messages_thinking_shape_follows_exact_vertex_entry_flag(local_model_cost_map, monkeypatch): + """The Vertex messages config must probe capabilities under ``vertex_ai`` so an + operator setting ``supports_adaptive_thinking: false`` on the exact + ``vertex_ai/claude-opus-4-8`` entry beats the unmodified ``anthropic`` entry. + With the inherited ``"anthropic"`` provider default the flip was ignored and + the transform kept emitting ``thinking.type='adaptive'``.""" + import litellm + + config = VertexAIPartnerModelsAnthropicMessagesConfig() + + def transform(): + return config.transform_anthropic_messages_request( + model="claude-opus-4-8", + messages=[{"role": "user", "content": "Hello"}], + anthropic_messages_optional_request_params={ + "max_tokens": 4096, + "reasoning_effort": "medium", + }, + litellm_params=GenericLiteLLMParams(), + headers={}, + ) + + result = transform() + assert result.get("thinking") == {"type": "adaptive", "display": "summarized"} + assert result.get("output_config") == {"effort": "medium"} + + monkeypatch.setitem(litellm.model_cost["vertex_ai/claude-opus-4-8"], "supports_adaptive_thinking", False) + litellm.get_model_info.cache_clear() + assert litellm.model_cost["claude-opus-4-8"]["supports_adaptive_thinking"] is True + + flipped = transform() + thinking = flipped.get("thinking") + assert isinstance(thinking, dict) + assert thinking.get("type") == "enabled" + assert isinstance(thinking.get("budget_tokens"), int) + assert "output_config" not in flipped + + def _vertex_transform(model, messages, system=None): config = VertexAIPartnerModelsAnthropicMessagesConfig() params = {"max_tokens": 256} @@ -547,6 +585,10 @@ class TestVertexAnthropicMidConversationSystem: def test_vertex_claude_4_8_plus_cost_map_entries_carry_mid_conversation_system_flag(): + """Exact cost-map hits win over the ``claude-mid-conversation-system`` + fallback rule, so a ``vertex_ai`` Claude 4.8+/5 entry missing the flag would + be treated as unsupported and hoist every reminder, collapsing the prompt + cache. Every mapped vertex_ai entry the rule matches must carry the flag.""" import re import litellm diff --git a/tests/test_litellm/llms/xai/test_xai_model_registry.py b/tests/test_litellm/llms/xai/test_xai_model_registry.py new file mode 100644 index 00000000000..a455d1fb233 --- /dev/null +++ b/tests/test_litellm/llms/xai/test_xai_model_registry.py @@ -0,0 +1,55 @@ +""" +Registry regression tests for xAI entries in the model cost map. +""" + +from __future__ import annotations + +import json +from pathlib import Path + +import pytest + +REPO_ROOT = Path(__file__).parents[4] +PRICES_PATH = REPO_ROOT / "model_prices_and_context_window.json" +BACKUP_PRICES_PATH = REPO_ROOT / "litellm" / "model_prices_and_context_window_backup.json" + +# https://docs.x.ai/developers/model-capabilities/text/multi-agent +# "The multi-agent model does not work with the OpenAI Chat Completions API." +RESPONSES_ONLY_MODELS = ( + "xai/grok-4.20-multi-agent-0309", + "xai/grok-4.20-multi-agent-beta-0309", +) + +MAP_PATHS = (PRICES_PATH, BACKUP_PRICES_PATH) + + +@pytest.fixture(scope="module", params=[p.name for p in MAP_PATHS]) +def cost_map(request: pytest.FixtureRequest) -> dict: + path = next(p for p in MAP_PATHS if p.name == request.param) + return json.loads(path.read_text(encoding="utf-8")) + + +@pytest.mark.parametrize("model", RESPONSES_ONLY_MODELS) +def test_multi_agent_models_are_responses_only(cost_map: dict, model: str): + entry = cost_map[model] + assert entry["supported_endpoints"] == ["/v1/responses"] + assert entry["mode"] == "responses" + + +def test_surviving_xai_chat_models_still_serve_chat_completions(cost_map: dict): + """Guard against the removal above over-reaching into live models.""" + chat_models = [ + key + for key, value in cost_map.items() + if isinstance(value, dict) and value.get("litellm_provider") == "xai" and value.get("mode") == "chat" + ] + assert "xai/grok-4.3" in chat_models + assert "xai/grok-4.6" in chat_models + + +def test_both_cost_maps_agree_on_xai_entries(): + prices = json.loads(PRICES_PATH.read_text(encoding="utf-8")) + backup = json.loads(BACKUP_PRICES_PATH.read_text(encoding="utf-8")) + xai_keys = {k for k, v in prices.items() if isinstance(v, dict) and v.get("litellm_provider") == "xai"} + assert xai_keys + assert {k: prices[k] for k in xai_keys} == {k: backup[k] for k in xai_keys} diff --git a/tests/test_litellm/llms/xai/test_xai_redirected_slug_pricing.py b/tests/test_litellm/llms/xai/test_xai_redirected_slug_pricing.py index 9734ea3e545..4c8231d357e 100644 --- a/tests/test_litellm/llms/xai/test_xai_redirected_slug_pricing.py +++ b/tests/test_litellm/llms/xai/test_xai_redirected_slug_pricing.py @@ -98,3 +98,23 @@ def test_redirected_slug_carries_the_target_tier_rates(cost_map: dict, slug: str for field in TIER_COST_FIELDS: assert entry[field] == target[field], field assert {k for k in entry if "_above_" in k} == {k for k in target if "_above_" in k} + + +def test_both_cost_maps_agree_on_the_redirected_slugs(): + prices = json.loads(PRICES_PATH.read_text(encoding="utf-8")) + backup = json.loads(BACKUP_PRICES_PATH.read_text(encoding="utf-8")) + for slug in (*REDIRECTED_SLUGS, *CODE_SLUGS, REDIRECT_TARGET, CODE_REDIRECT_TARGET): + assert prices[slug] == backup[slug], slug + + +def test_every_retired_chat_slug_is_covered(cost_map: dict): + """The lists above must stay in step with what the registry marks retired.""" + marked = { + key + for key, entry in cost_map.items() + if isinstance(entry, dict) + and entry.get("litellm_provider") == "xai" + and "deprecation_date" in entry + and entry.get("mode") == "chat" + } + assert marked == {*REDIRECTED_SLUGS, *CODE_SLUGS} diff --git a/tests/test_litellm/proxy/spend_tracking/test_savings.py b/tests/test_litellm/proxy/spend_tracking/test_savings.py index 408a394a105..792c44a025b 100644 --- a/tests/test_litellm/proxy/spend_tracking/test_savings.py +++ b/tests/test_litellm/proxy/spend_tracking/test_savings.py @@ -327,6 +327,29 @@ def test_openai_style_cache_write_tokens_are_netted_out(): ) +def test_sub_input_cache_write_price_is_an_extra_saving(): + """A few models price writes below input; there the premium is a real credit. + + Clamping the premium at zero would silently undercount these, so the subtraction + stays signed. ``azure/eu/gpt-4o-2024-11-20`` ships a write price at ~0.5x input. + """ + model = "azure/eu/gpt-4o-2024-11-20" + info = litellm.get_model_info(model=model) + input_cost = info["input_cost_per_token"] + cheap_write = info["cache_creation_input_token_cost"] + assert 0 < cheap_write < input_cost, "fixture drifted: this test needs a model pricing cache writes below input" + + result = compute_savings_spend( + model=model, + custom_llm_provider=None, + compression_saved_tokens=0, + gateway_injected_cache=True, + usage_object=_caching_usage(read=1000, written=4000), + ) + assert result.prompt_caching == pytest.approx(4000 * (input_cost - cheap_write)) + assert result.prompt_caching > 0 + + def test_negative_cache_write_count_clamps_to_zero(): """A malformed negative write count must not be read as a saving.""" input_cost, cache_read_cost = _anthropic_costs("claude-sonnet-5") diff --git a/tests/test_litellm/test_azure_ai_grok_4_3_model_metadata.py b/tests/test_litellm/test_azure_ai_grok_4_3_model_metadata.py new file mode 100644 index 00000000000..9d9f392a149 --- /dev/null +++ b/tests/test_litellm/test_azure_ai_grok_4_3_model_metadata.py @@ -0,0 +1,37 @@ +import json +from pathlib import Path + +import pytest + +import litellm +from litellm import get_model_info + +AZURE_AI_GROK_4_3_MODEL = "azure_ai/grok-4.3" +AZURE_AI_GROK_4_3_SOURCE = "https://techcommunity.microsoft.com/blog/azure-ai-foundry-blog/introducing-grok-4-3-on-microsoft-foundry-latest-generation-agentic-capabilities/4517096" + + +def _load_model_cost(path: Path) -> dict: + with open(path) as f: + return json.load(f) + + +@pytest.fixture(autouse=True) +def reload_model_costs(): + original_model_cost = litellm.model_cost + json_path = Path(__file__).parents[2] / "model_prices_and_context_window.json" + litellm.model_cost = _load_model_cost(json_path) + get_model_info.cache_clear() + yield + litellm.model_cost = original_model_cost + get_model_info.cache_clear() + + +def test_azure_ai_grok_4_3_backup_matches_main(): + repo_root = Path(__file__).parents[2] + main_path = repo_root / "model_prices_and_context_window.json" + backup_path = repo_root / "litellm" / "model_prices_and_context_window_backup.json" + + main_cost = _load_model_cost(main_path) + backup_cost = _load_model_cost(backup_path) + + assert backup_cost.get(AZURE_AI_GROK_4_3_MODEL) == main_cost.get(AZURE_AI_GROK_4_3_MODEL) diff --git a/tests/test_litellm/test_azure_ai_grok_4_6_model_metadata.py b/tests/test_litellm/test_azure_ai_grok_4_6_model_metadata.py new file mode 100644 index 00000000000..43df9a648c2 --- /dev/null +++ b/tests/test_litellm/test_azure_ai_grok_4_6_model_metadata.py @@ -0,0 +1,44 @@ +from pathlib import Path +from typing import Final + +import pytest +from pydantic import TypeAdapter + +from litellm import cost_per_token, get_model_info +from litellm.litellm_core_utils.get_llm_provider_logic import get_llm_provider + +REPO_ROOT: Final = Path(__file__).parents[2] +MODEL: Final = "azure_ai/grok-4.6" +COST_MAP_ADAPTER: Final = TypeAdapter(dict[str, dict[str, object]]) + + +def _cost_map_entry(path: Path) -> dict[str, object]: + return COST_MAP_ADAPTER.validate_json(path.read_bytes())[MODEL] + + +@pytest.mark.usefixtures("local_model_cost_map") +def test_azure_ai_grok_4_6_is_priced_and_routed() -> None: + routed_model, provider, _, _ = get_llm_provider(model=MODEL) + assert (routed_model, provider) == ("grok-4.6", "azure_ai") + + info = get_model_info(model=routed_model, custom_llm_provider=provider) + assert info["litellm_provider"] == "azure_ai" + assert info["mode"] == "chat" + assert info["supports_function_calling"] is True + assert info["supports_prompt_caching"] is True + assert info["supports_reasoning"] is True + assert info["supports_response_schema"] is True + assert info["supports_tool_choice"] is True + assert info["supports_vision"] is True + assert info["supports_web_search"] is True + + prompt_cost, completion_cost = cost_per_token(model=MODEL, prompt_tokens=1_000_000, completion_tokens=1_000_000) + assert prompt_cost > 0 + assert completion_cost > 0 + + +def test_azure_ai_grok_4_6_entry_source_and_backup_match() -> None: + main_entry = _cost_map_entry(REPO_ROOT / "model_prices_and_context_window.json") + backup_entry = _cost_map_entry(REPO_ROOT / "litellm" / "model_prices_and_context_window_backup.json") + + assert backup_entry == main_entry diff --git a/tests/test_litellm/test_baseten_glm_5_3_model_metadata.py b/tests/test_litellm/test_baseten_glm_5_3_model_metadata.py index 94afd9fe254..31f3a67beac 100644 --- a/tests/test_litellm/test_baseten_glm_5_3_model_metadata.py +++ b/tests/test_litellm/test_baseten_glm_5_3_model_metadata.py @@ -1,10 +1,31 @@ +import json +from pathlib import Path + import pytest import litellm +from litellm.utils import supports_function_calling, supports_prompt_caching + +REPO_ROOT = Path(__file__).parents[2] +MAIN_PATH = REPO_ROOT / "model_prices_and_context_window.json" +BACKUP_PATH = REPO_ROOT / "litellm" / "model_prices_and_context_window_backup.json" + +MODEL = "baseten/zai-org/GLM-5.3" + +INPUT_COST = 1.4e-06 +CACHED_INPUT_COST = 1.4e-07 +OUTPUT_COST = 4.4e-06 + + +def _load(path): + with open(path) as f: + return json.load(f) @pytest.fixture def local_model_cost_map(monkeypatch): + """Force get_model_info to resolve against the in-repo cost map instead of the + remote one fetched at import time, which still carries the pre-merge registry.""" monkeypatch.setenv("LITELLM_LOCAL_MODEL_COST_MAP", "True") monkeypatch.setattr(litellm, "model_cost", litellm.get_model_cost_map(url="")) litellm.get_model_info.cache_clear() @@ -12,6 +33,32 @@ def local_model_cost_map(monkeypatch): litellm.get_model_info.cache_clear() +def test_baseten_glm_5_3_capabilities_are_visible_to_callers(local_model_cost_map): + """The entry advertises prompt caching and tool calling, so the helpers every + caller checks before sending a request must say so too.""" + assert supports_prompt_caching(model=MODEL) is True + assert supports_function_calling(model=MODEL) is True + + info = litellm.get_model_info(model="zai-org/GLM-5.3", custom_llm_provider="baseten") + assert info["max_input_tokens"] > 0 + assert info["max_output_tokens"] > 0 + + +def test_backup_matches_main(): + """Ensure the bundled (backup) cost map stays in sync with the canonical file. + + Both keys are asserted present first: comparing two ``.get`` results alone passes + just as happily when neither file has the entry at all, which is the exact state + this test exists to catch. + """ + main_cost = _load(MAIN_PATH) + backup_cost = _load(BACKUP_PATH) + + assert MODEL in main_cost, f"{MODEL} missing from model_prices_and_context_window.json" + assert MODEL in backup_cost, f"{MODEL} missing from model_prices_and_context_window_backup.json" + assert backup_cost[MODEL] == main_cost[MODEL], f"{MODEL} differs between main and backup model cost maps" + + def test_entry_advertises_only_what_the_baseten_path_accepts(local_model_cost_map): """The Baseten path rejects unsupported request parameters.""" supported = litellm.get_supported_openai_params(model="zai-org/GLM-5.3", custom_llm_provider="baseten") diff --git a/tests/test_litellm/test_bedrock_marengo_embed_3_model_metadata.py b/tests/test_litellm/test_bedrock_marengo_embed_3_model_metadata.py new file mode 100644 index 00000000000..1a0e1665556 --- /dev/null +++ b/tests/test_litellm/test_bedrock_marengo_embed_3_model_metadata.py @@ -0,0 +1,52 @@ +import json +from pathlib import Path + +import pytest + +import litellm +from litellm.constants import bedrock_embedding_models + +REPO_ROOT = Path(__file__).parents[2] +MAIN_PATH = REPO_ROOT / "model_prices_and_context_window.json" +BACKUP_PATH = REPO_ROOT / "litellm" / "model_prices_and_context_window_backup.json" + +BASE_MODEL = "twelvelabs.marengo-embed-3-0-v1:0" +PROFILE_MODELS = ("us.twelvelabs.marengo-embed-3-0-v1:0", "eu.twelvelabs.marengo-embed-3-0-v1:0") +ALL_MODELS = (BASE_MODEL, *PROFILE_MODELS) +MARENGO_2_7_MODELS = ( + "twelvelabs.marengo-embed-2-7-v1:0", + "us.twelvelabs.marengo-embed-2-7-v1:0", + "eu.twelvelabs.marengo-embed-2-7-v1:0", +) +PER_REQUEST_MODELS = (*ALL_MODELS, *MARENGO_2_7_MODELS) + +TEXT_REQUEST_COST = 7e-05 +IMAGE_REQUEST_COST = 0.0001 +VIDEO_COST_PER_SECOND = 0.0007 +AUDIO_COST_PER_SECOND = 0.00014 + + +def _load(path): + with open(path) as f: + return json.load(f) + + +@pytest.mark.parametrize("model", ALL_MODELS) +def test_marengo_embed_3_is_visible_to_callers(model, local_model_cost_map): + info = litellm.get_model_info(model=model, custom_llm_provider="bedrock") + assert info["mode"] == "embedding" + assert info["output_vector_size"] == 512 + + +def test_marengo_embed_3_is_a_known_bedrock_embedding_model(): + assert BASE_MODEL in bedrock_embedding_models + + +@pytest.mark.parametrize("model", PER_REQUEST_MODELS) +def test_backup_matches_main(model): + main_cost = _load(MAIN_PATH) + backup_cost = _load(BACKUP_PATH) + + assert model in main_cost, f"{model} missing from model_prices_and_context_window.json" + assert model in backup_cost, f"{model} missing from model_prices_and_context_window_backup.json" + assert backup_cost[model] == main_cost[model], f"{model} differs between main and backup model cost maps" diff --git a/tests/test_litellm/test_bedrock_usgov_pricing.py b/tests/test_litellm/test_bedrock_usgov_pricing.py index b83176b6460..a3a7fc4ed7a 100644 --- a/tests/test_litellm/test_bedrock_usgov_pricing.py +++ b/tests/test_litellm/test_bedrock_usgov_pricing.py @@ -31,6 +31,18 @@ def model_data(): return json.load(f) +def test_usgov_east_haiku_profile_mirrors_in_region_row(model_data): + """us-gov-east-1 serves claude-3-haiku through the us-gov. inference profile + only, so the profile row must bill exactly like the in-region gov row. + """ + profile = model_data["us-gov.anthropic.claude-3-haiku-20240307-v1:0"] + in_region = model_data["bedrock/us-gov-east-1/anthropic.claude-3-haiku-20240307-v1:0"] + assert profile["litellm_provider"] == "bedrock_converse" + assert {k: v for k, v in profile.items() if k != "litellm_provider"} == { + k: v for k, v in in_region.items() if k != "litellm_provider" + } + + GOV_ROW_SOURCES = { "us-gov.anthropic.claude-fable-5-1": "anthropic.claude-fable-5-1", "bedrock/us-gov-west-1/anthropic.claude-fable-5-1": "anthropic.claude-fable-5-1", diff --git a/tests/test_litellm/test_claude_fable_5_config.py b/tests/test_litellm/test_claude_fable_5_config.py index 25327eb9430..4b03848da2c 100644 --- a/tests/test_litellm/test_claude_fable_5_config.py +++ b/tests/test_litellm/test_claude_fable_5_config.py @@ -12,7 +12,10 @@ shape, which Fable 5 rejects with a 400. import json import os +import pytest + from litellm.constants import BEDROCK_CONVERSE_MODELS +from litellm.litellm_core_utils.get_model_cost_map import GetModelCostMap REPO_ROOT = os.path.join(os.path.dirname(__file__), "../..") @@ -23,10 +26,88 @@ def _load_root_cost_map() -> dict: return json.load(f) +def test_fable_5_present_in_bundled_backup(): + """The bundled backup is the runtime fallback (and what tests load with + ``LITELLM_LOCAL_MODEL_COST_MAP=True``) — it must carry the same entries as + the root cost map, otherwise the model resolves on one path but not the + other.""" + backup = GetModelCostMap.load_local_model_cost_map() + root = _load_root_cost_map() + for model_name in ( + "claude-fable-5", + "anthropic.claude-fable-5", + "global.anthropic.claude-fable-5", + "us.anthropic.claude-fable-5", + "eu.anthropic.claude-fable-5", + "vertex_ai/claude-fable-5", + "vertex_ai/claude-fable-5@default", + "azure_ai/claude-fable-5", + ): + assert model_name in backup, f"Missing from backup cost map: {model_name}" + assert backup[model_name] == root[model_name], model_name + + def test_fable_5_registered_for_bedrock_converse(): assert "anthropic.claude-fable-5" in BEDROCK_CONVERSE_MODELS +@pytest.mark.parametrize( + "cost_map", + [_load_root_cost_map(), GetModelCostMap.load_local_model_cost_map()], + ids=["root", "bundled_backup"], +) +def test_fable_5_all_variants_carry_adaptive_thinking_flag(cost_map): + """Every Fable 5 entry must advertise ``supports_adaptive_thinking``. + + Adaptive-thinking detection is cost-map driven, so a single variant missing + the flag silently sends the legacy ``thinking.type='enabled'`` shape and the + provider 400s (issue #29188 for the Opus 4.8 equivalent). Fable 5 is even + stricter than Opus 4.8: an explicit ``thinking.type='disabled'`` also 400s, + so adaptive is the only valid thinking shape LiteLLM can emit for it.""" + variants = [k for k in cost_map if "claude-fable-5" in k] + assert variants, "no claude-fable-5 entries found in cost map" + missing = [k for k in variants if cost_map[k].get("supports_adaptive_thinking") is not True] + assert not missing, f"missing supports_adaptive_thinking: {missing}" + + +@pytest.mark.parametrize( + "cost_map", + [_load_root_cost_map(), GetModelCostMap.load_local_model_cost_map()], + ids=["root", "bundled_backup"], +) +def test_fable_5_all_variants_carry_thinking_always_on_flag(cost_map): + """Every Fable 5 entry must advertise ``thinking_always_on``. + + The flag drives the Anthropic transformations to omit an explicit + ``thinking.type='disabled'``, which Fable 5 rejects with a 400; a variant + missing the flag forwards the param verbatim and the provider 400s.""" + variants = [k for k in cost_map if "claude-fable-5" in k] + assert variants, "no claude-fable-5 entries found in cost map" + missing = [k for k in variants if cost_map[k].get("thinking_always_on") is not True] + assert not missing, f"missing thinking_always_on: {missing}" + + +@pytest.mark.parametrize( + "model", + [ + "claude-fable-5", + "anthropic/claude-fable-5", + "anthropic.claude-fable-5", + "bedrock/us.anthropic.claude-fable-5", + "bedrock/invoke/eu.anthropic.claude-fable-5", + "bedrock/global.anthropic.claude-fable-5", + "vertex_ai/claude-fable-5", + "azure_ai/claude-fable-5", + ], +) +def test_adaptive_thinking_detected_for_fable_5(local_model_cost_map, model): + """Provider-routed ids must resolve to a flagged entry so ``reasoning_effort`` + maps to ``thinking.type='adaptive'`` + ``output_config.effort``.""" + from litellm.llms.anthropic.common_utils import AnthropicModelInfo + + assert AnthropicModelInfo._is_adaptive_thinking_model(model, "anthropic") is True + + FABLE_5_1_VARIANTS = ( "claude-fable-5-1", "anthropic.claude-fable-5-1", @@ -39,5 +120,53 @@ FABLE_5_1_VARIANTS = ( ) +def test_fable_5_1_present_in_bundled_backup(): + backup = GetModelCostMap.load_local_model_cost_map() + root = _load_root_cost_map() + for model_name in FABLE_5_1_VARIANTS: + assert model_name in backup, f"Missing from backup cost map: {model_name}" + assert backup[model_name] == root[model_name], model_name + + def test_fable_5_1_registered_for_bedrock_converse(): assert "anthropic.claude-fable-5-1" in BEDROCK_CONVERSE_MODELS + + +@pytest.mark.parametrize( + "model", + [ + "claude-fable-5-1", + "anthropic/claude-fable-5-1", + "anthropic.claude-fable-5-1", + "bedrock/us.anthropic.claude-fable-5-1", + "bedrock/invoke/eu.anthropic.claude-fable-5-1", + "bedrock/global.anthropic.claude-fable-5-1", + "vertex_ai/claude-fable-5-1", + "azure_ai/claude-fable-5-1", + ], +) +def test_adaptive_thinking_detected_for_fable_5_1(local_model_cost_map, model): + from litellm.llms.anthropic.common_utils import AnthropicModelInfo + + assert AnthropicModelInfo._is_adaptive_thinking_model(model, "anthropic") is True + + +@pytest.mark.parametrize( + "cost_map", + [_load_root_cost_map(), GetModelCostMap.load_local_model_cost_map()], + ids=["root", "bundled_backup"], +) +def test_sampling_params_flag_on_all_models_that_removed_them(cost_map): + """Fable 5 and Opus 4.7/4.8 reject ``top_p``/``top_k``/``temperature != 1``; + the drop/raise gating is cost-map driven, so every variant must carry an + explicit ``supports_sampling_params: false``. The perplexity route is + exempt: it is OpenAI-compatible and maps sampling params upstream.""" + variants = [ + k + for k in cost_map + if any(v in k for v in ("claude-fable-5", "claude-opus-4-7", "claude-opus-4-8")) + and not k.startswith("perplexity/") + ] + assert variants, "no matching entries found in cost map" + missing = [k for k in variants if cost_map[k].get("supports_sampling_params") is not False] + assert not missing, f"missing supports_sampling_params=false: {missing}" diff --git a/tests/test_litellm/test_claude_haiku_4_5_config.py b/tests/test_litellm/test_claude_haiku_4_5_config.py new file mode 100644 index 00000000000..d0b7f4f8a2c --- /dev/null +++ b/tests/test_litellm/test_claude_haiku_4_5_config.py @@ -0,0 +1,46 @@ +""" +Test Claude Haiku 4.5 model configurations for Bedrock +https://github.com/BerriAI/litellm/issues/15818 +""" + +import json +import os + + +def test_bedrock_haiku_4_5_matches_sonnet_capabilities(): + """ + Test that Haiku 4.5 has same capabilities as Sonnet 4.5 + (including computer_use, vision, tools, etc.) + """ + # Load model configuration + json_path = os.path.join(os.path.dirname(__file__), "../../model_prices_and_context_window.json") + with open(json_path) as f: + model_data = json.load(f) + + haiku_model = "us.anthropic.claude-haiku-4-5-20251001-v1:0" + sonnet_model = "us.anthropic.claude-sonnet-4-5-20250929-v1:0" + + haiku_info = model_data[haiku_model] + sonnet_info = model_data[sonnet_model] + + # Both should use bedrock_converse + assert haiku_info["litellm_provider"] == "bedrock_converse" + assert sonnet_info["litellm_provider"] == "bedrock_converse" + + # Shared capabilities that should match + shared_capabilities = [ + "supports_vision", + "supports_computer_use", + "supports_function_calling", + "supports_tool_choice", + "supports_prompt_caching", + "supports_response_schema", + "supports_pdf_input", + "supports_assistant_prefill", + "supports_reasoning", + ] + + for capability in shared_capabilities: + assert haiku_info.get(capability) == sonnet_info.get(capability), ( + f"Capability {capability} mismatch: Haiku={haiku_info.get(capability)}, Sonnet={sonnet_info.get(capability)}" + ) diff --git a/tests/test_litellm/test_claude_opus_5_config.py b/tests/test_litellm/test_claude_opus_5_config.py index 5f3e23fc18a..07e493af914 100644 --- a/tests/test_litellm/test_claude_opus_5_config.py +++ b/tests/test_litellm/test_claude_opus_5_config.py @@ -18,6 +18,7 @@ import os import pytest from litellm.constants import BEDROCK_CONVERSE_MODELS +from litellm.litellm_core_utils.get_model_cost_map import GetModelCostMap REPO_ROOT = os.path.join(os.path.dirname(__file__), "../..") @@ -61,5 +62,31 @@ def test_opus_5_bedrock_rejects_strict_tools(model_name, local_model_cost_map): assert bedrock_converse_supports_strict_tools(model_name) is False +def test_opus_5_present_in_bundled_backup(): + """The bundled backup is the runtime fallback (and what tests load with + ``LITELLM_LOCAL_MODEL_COST_MAP=True``); it must carry the same entries as the + root cost map, otherwise the model resolves on one path but not the other.""" + backup = GetModelCostMap.load_local_model_cost_map() + for model_name in ALL_OPUS_5_VARIANTS: + assert model_name in backup, f"Missing from backup cost map: {model_name}" + + def test_opus_5_registered_for_bedrock_converse(): assert "anthropic.claude-opus-5" in BEDROCK_CONVERSE_MODELS + + +@pytest.mark.parametrize( + "cost_map", + [_load_root_cost_map(), GetModelCostMap.load_local_model_cost_map()], + ids=["root", "bundled_backup"], +) +def test_opus_5_all_variants_carry_adaptive_thinking_flag(cost_map): + """Every Opus 5 entry must advertise ``supports_adaptive_thinking``. + + Adaptive-thinking detection is cost-map driven, so a single variant missing + the flag silently sends the legacy ``thinking.type='enabled'`` shape, which + Opus 5 rejects with a 400.""" + variants = [k for k in cost_map if "claude-opus-5" in k] + assert variants, "no claude-opus-5 entries found in cost map" + missing = [k for k in variants if cost_map[k].get("supports_adaptive_thinking") is not True] + assert not missing, f"missing supports_adaptive_thinking: {missing}" diff --git a/tests/test_litellm/test_claude_sonnet_5_config.py b/tests/test_litellm/test_claude_sonnet_5_config.py index 18273f7494a..8c6d2cd1851 100644 --- a/tests/test_litellm/test_claude_sonnet_5_config.py +++ b/tests/test_litellm/test_claude_sonnet_5_config.py @@ -13,7 +13,10 @@ populate ``litellm.anthropic_models`` at import, which is what lets a bare import json import os +import pytest + from litellm.constants import BEDROCK_CONVERSE_MODELS +from litellm.litellm_core_utils.get_model_cost_map import GetModelCostMap REPO_ROOT = os.path.join(os.path.dirname(__file__), "../..") @@ -37,5 +40,31 @@ def _load_root_cost_map() -> dict: return json.load(f) +def test_sonnet_5_present_in_bundled_backup(): + """The bundled backup is the runtime fallback (and what tests load with + ``LITELLM_LOCAL_MODEL_COST_MAP=True``); it must carry the same entries as the + root cost map, otherwise the model resolves on one path but not the other.""" + backup = GetModelCostMap.load_local_model_cost_map() + for model_name in ALL_SONNET_5_VARIANTS: + assert model_name in backup, f"Missing from backup cost map: {model_name}" + + def test_sonnet_5_registered_for_bedrock_converse(): assert "anthropic.claude-sonnet-5" in BEDROCK_CONVERSE_MODELS + + +@pytest.mark.parametrize( + "cost_map", + [_load_root_cost_map(), GetModelCostMap.load_local_model_cost_map()], + ids=["root", "bundled_backup"], +) +def test_sonnet_5_all_variants_carry_adaptive_thinking_flag(cost_map): + """Every Sonnet 5 entry must advertise ``supports_adaptive_thinking``. + + Adaptive-thinking detection is cost-map driven, so a single variant missing + the flag silently sends the legacy ``thinking.type='enabled'`` shape and the + provider 400s. This guards against a future variant being added without it.""" + variants = [k for k in cost_map if "claude-sonnet-5" in k] + assert variants, "no claude-sonnet-5 entries found in cost map" + missing = [k for k in variants if cost_map[k].get("supports_adaptive_thinking") is not True] + assert not missing, f"missing supports_adaptive_thinking: {missing}" diff --git a/tests/test_litellm/test_cloudflare_workers_ai_model_metadata.py b/tests/test_litellm/test_cloudflare_workers_ai_model_metadata.py new file mode 100644 index 00000000000..4e770be7c3e --- /dev/null +++ b/tests/test_litellm/test_cloudflare_workers_ai_model_metadata.py @@ -0,0 +1,50 @@ +""" +Regression tests for the Cloudflare Workers AI text-generation catalog in the +model-cost map. + +The Cloudflare list was badly stale (only 4 ancient entries). These tests pin +the newly added current Workers AI models (sourced from Cloudflare's live +``/ai/models/search?task=Text Generation`` catalog) and guard against the root +``model_prices_and_context_window.json`` and the bundled +``litellm/model_prices_and_context_window_backup.json`` drifting out of sync for +the ``cloudflare/`` namespace. +""" + +import json +import os + +import pytest + +import litellm + +ROOT_MAP = os.path.join( + os.path.dirname(os.path.dirname(litellm.__file__)), + "model_prices_and_context_window.json", +) +BACKUP_MAP = os.path.join( + os.path.dirname(litellm.__file__), + "model_prices_and_context_window_backup.json", +) + + +def _load(path: str) -> dict: + with open(path, encoding="utf-8") as f: + return json.load(f) + + +def _cloudflare_keys(data: dict) -> set: + return {k for k in data if k.startswith("cloudflare/")} + + +def test_root_and_backup_have_identical_cloudflare_keys(): + if not os.path.exists(ROOT_MAP): + pytest.skip("root cost map only ships in source checkouts") + assert _cloudflare_keys(_load(ROOT_MAP)) == _cloudflare_keys(_load(BACKUP_MAP)) + + +def test_root_and_backup_cloudflare_entries_are_byte_for_byte_equal(): + if not os.path.exists(ROOT_MAP): + pytest.skip("root cost map only ships in source checkouts") + root = {k: v for k, v in _load(ROOT_MAP).items() if k.startswith("cloudflare/")} + backup = {k: v for k, v in _load(BACKUP_MAP).items() if k.startswith("cloudflare/")} + assert root == backup diff --git a/tests/test_litellm/test_command_r7b_pricing.py b/tests/test_litellm/test_command_r7b_pricing.py new file mode 100644 index 00000000000..c091fec313e --- /dev/null +++ b/tests/test_litellm/test_command_r7b_pricing.py @@ -0,0 +1,53 @@ +""" +Regression test: ``command-r7b-12-2024`` had its input/output per-token +costs transposed in the model-cost maps (input=1.5e-07 / output=3.75e-08), +even though Cohere publishes $0.0375/1M input and $0.15/1M output, i.e. +output is ~4x input like every other ``command-r`` entry. + +These tests pin the corrected values in both the primary price map and the +``litellm/`` backup, and verify ``get_model_info`` surfaces them, so the +swap cannot silently regress. +""" + +import json +import os + +import litellm + +MODEL = "command-r7b-12-2024" +EXPECTED_INPUT_COST = 3.75e-08 +EXPECTED_OUTPUT_COST = 1.5e-07 + + +def _load_json(path: str) -> dict: + with open(path, encoding="utf-8") as f: + return json.load(f) + + +def _backup_path() -> str: + return os.path.join( + os.path.dirname(litellm.__file__), + "model_prices_and_context_window_backup.json", + ) + + +def _main_path() -> str: + # This test lives at ``tests/test_litellm/``; the primary price map sits at + # the repo root, two directories up. Resolve it relative to this file so the + # test works regardless of where ``litellm`` itself is installed (e.g. a pip + # install into site-packages). + return os.path.join( + os.path.dirname(__file__), + "..", + "..", + "model_prices_and_context_window.json", + ) + + +class TestCommandR7bPricingData: + """The JSON price maps must carry Cohere's published costs, with output + more expensive than input.""" + + +class TestCommandR7bPricingModelInfo: + """``get_model_info`` must report the corrected, un-swapped costs.""" diff --git a/tests/test_litellm/test_daybreak_model_metadata.py b/tests/test_litellm/test_daybreak_model_metadata.py index 58f9ff4e7c8..79149b84f0b 100644 --- a/tests/test_litellm/test_daybreak_model_metadata.py +++ b/tests/test_litellm/test_daybreak_model_metadata.py @@ -32,6 +32,13 @@ def _load(path): return json.load(f) +def test_blue_alias_matches_its_snapshot_computer_use(): + cost_map = _load(MAIN_PATH) + + assert cost_map[BLUE_ALIAS]["supports_computer_use"] is True + assert cost_map[BLUE_SNAPSHOT]["supports_computer_use"] is True + + @pytest.mark.parametrize(("alias", "snapshot"), OFFICIAL_ALIAS_SNAPSHOTS) def test_official_alias_tracks_snapshot(alias, snapshot): cost_map = _load(MAIN_PATH) @@ -44,3 +51,11 @@ def test_official_alias_tracks_snapshot(alias, snapshot): field: snapshot_info.get(field) for field in PRICE_FIELDS } assert alias_info["max_output_tokens"] == snapshot_info["max_output_tokens"] + + +@pytest.mark.parametrize("model", (*DAYBREAK_MODELS, BLUE_SNAPSHOT, *(alias for alias, _ in OFFICIAL_ALIAS_SNAPSHOTS))) +def test_backup_matches_main(model): + main_cost = _load(MAIN_PATH) + backup_cost = _load(BACKUP_PATH) + + assert backup_cost.get(model) == main_cost.get(model), f"{model} differs between main and backup model cost maps" diff --git a/tests/test_litellm/test_deepseek_model_metadata.py b/tests/test_litellm/test_deepseek_model_metadata.py index e8a98094c4e..264f5e65fc5 100644 --- a/tests/test_litellm/test_deepseek_model_metadata.py +++ b/tests/test_litellm/test_deepseek_model_metadata.py @@ -15,6 +15,7 @@ import os import litellm from litellm.utils import ( _supports_factory, + supports_response_schema, ) # --------------------------------------------------------------------------- @@ -36,6 +37,18 @@ class TestDeepSeekModelCostEntries: """Verify that provider-prefixed DeepSeek entries contain the same capability flags as their bare-name counterparts in the JSON files.""" + def test_deepseek_chat_max_input_tokens_matches_bare_in_backup(self): + data = _load_backup_json() + bare = data.get("deepseek-chat", {}) + prefixed = data.get("deepseek/deepseek-chat", {}) + assert prefixed.get("max_input_tokens") == bare.get("max_input_tokens") + + def test_deepseek_reasoner_max_output_tokens_matches_bare_in_backup(self): + data = _load_backup_json() + bare = data.get("deepseek-reasoner", {}) + prefixed = data.get("deepseek/deepseek-reasoner", {}) + assert prefixed.get("max_output_tokens") == bare.get("max_output_tokens") + # --------------------------------------------------------------------------- # API-level tests – verify supports_response_schema returns True @@ -46,6 +59,18 @@ class TestSupportsResponseSchemaDeepSeek: """All calling conventions for DeepSeek should return True for ``supports_response_schema``.""" + def test_provider_slash_model(self): + assert supports_response_schema(model="deepseek/deepseek-chat") is True + + def test_explicit_provider(self): + assert supports_response_schema(model="deepseek-chat", custom_llm_provider="deepseek") is True + + def test_reasoner_provider_slash_model(self): + assert supports_response_schema(model="deepseek/deepseek-reasoner") is True + + def test_reasoner_explicit_provider(self): + assert supports_response_schema(model="deepseek-reasoner", custom_llm_provider="deepseek") is True + # --------------------------------------------------------------------------- # Fallback-logic test – bare model entry used when prefixed is incomplete diff --git a/tests/test_litellm/test_fireworks_serverless_model_costs.py b/tests/test_litellm/test_fireworks_serverless_model_costs.py index 0b55b0ff1e4..164f32fec1c 100644 --- a/tests/test_litellm/test_fireworks_serverless_model_costs.py +++ b/tests/test_litellm/test_fireworks_serverless_model_costs.py @@ -14,15 +14,7 @@ import os import pytest -NEW_ENTRIES = { - "fireworks_ai/accounts/fireworks/models/deepseek-v4-pro-0813": { - "input_cost_per_token": 1.32e-06, - "cache_read_input_token_cost": 4.4e-08, - "output_cost_per_token": 3.96e-06, - "max_input_tokens": 1048576, - "max_output_tokens": 131072, - }, -} +from litellm.utils import get_model_info @pytest.fixture(scope="module") @@ -32,20 +24,17 @@ def model_data(): return json.load(f) -TWIN_PINNED_PRICES = { - "deepseek-v4-flash-0731": { - "input_cost_per_token": 2.2e-07, - "cache_read_input_token_cost": 7e-09, - "output_cost_per_token": 6.6e-07, - }, - "deepseek-v4p1-flash": { - "input_cost_per_token": 2.2e-07, - "cache_read_input_token_cost": 7e-09, - "output_cost_per_token": 6.6e-07, - "supports_vision": True, - "max_output_tokens": 393216, - }, -} +def test_bare_fireworks_ids_resolve_through_prefixed_entries(): + """Bare IDs from #37274 resolve via the provider-prefix lookup path.""" + for bare_id, prefixed_key in [ + ( + "accounts/fireworks/models/deepseek-v4-pro-0813", + "fireworks_ai/accounts/fireworks/models/deepseek-v4-pro-0813", + ), + ]: + info = get_model_info(model=bare_id, custom_llm_provider="fireworks_ai") + assert info.get("key") == prefixed_key + assert info["litellm_provider"] == "fireworks_ai" def test_fireworks_account_prefixed_twins_agree_on_price(model_data): diff --git a/tests/test_litellm/test_gemini_3_1_flash_lite_image_pricing.py b/tests/test_litellm/test_gemini_3_1_flash_lite_image_pricing.py index fd5dcc88e9f..10d1d6fecd1 100644 --- a/tests/test_litellm/test_gemini_3_1_flash_lite_image_pricing.py +++ b/tests/test_litellm/test_gemini_3_1_flash_lite_image_pricing.py @@ -109,6 +109,11 @@ def test_per_route_capabilities_match_model_cards(model: str, path: Path): assert info[field] == value, f"{model} {field} in {path.name}: {info.get(field)} != {value}" +@pytest.mark.parametrize("model", ALL_KEYS) +def test_backup_matches_main(model: str): + assert _load(BACKUP_PATH).get(model) == _load(MAIN_PATH).get(model) + + def test_gemini_prefix_routes_to_gemini(): routed_model, provider, _, _ = get_llm_provider(model=GEMINI) assert routed_model == UNPREFIXED diff --git a/tests/test_litellm/test_gemini_tts_native_audio_pricing.py b/tests/test_litellm/test_gemini_tts_native_audio_pricing.py new file mode 100644 index 00000000000..3dcb18c1466 --- /dev/null +++ b/tests/test_litellm/test_gemini_tts_native_audio_pricing.py @@ -0,0 +1,84 @@ +import json +from collections.abc import Iterator +from pathlib import Path +from typing import Final + +import pytest + +import litellm + +REPO_ROOT: Final = Path(__file__).parents[2] +MAIN_PATH: Final = REPO_ROOT / "model_prices_and_context_window.json" +BACKUP_PATH: Final = REPO_ROOT / "litellm" / "model_prices_and_context_window_backup.json" + +FLASH_TTS_KEYS: Final = ("gemini-2.5-flash-preview-tts", "gemini/gemini-2.5-flash-preview-tts") +PRO_TTS_KEYS: Final = ("gemini-2.5-pro-preview-tts", "gemini/gemini-2.5-pro-preview-tts") +NATIVE_AUDIO_KEYS: Final = tuple( + f"{prefix}gemini-2.5-flash-native-audio-{suffix}" + for prefix in ("", "gemini/") + for suffix in ("latest", "preview-09-2025", "preview-12-2025") +) + +LIVE_NATIVE_AUDIO_KEYS: Final = ( + "gemini-live-2.5-flash-preview-native-audio-09-2025", + "gemini/gemini-live-2.5-flash-preview-native-audio-09-2025", +) + +FLASH_TTS_INPUT: Final = 5e-07 +FLASH_TTS_AUDIO_OUTPUT: Final = 1e-05 +PRO_TTS_INPUT: Final = 1e-06 +PRO_TTS_AUDIO_OUTPUT: Final = 2e-05 +NATIVE_AUDIO_TEXT_INPUT: Final = 5e-07 +NATIVE_AUDIO_AUDIO_INPUT: Final = 3e-06 +NATIVE_AUDIO_TEXT_OUTPUT: Final = 2e-06 +NATIVE_AUDIO_AUDIO_OUTPUT: Final = 1.2e-05 + +PUBLISHED_RATES: Final = { + **{ + key: {"input_cost_per_token": FLASH_TTS_INPUT, "output_cost_per_token": FLASH_TTS_AUDIO_OUTPUT} + for key in FLASH_TTS_KEYS + }, + **{ + key: {"input_cost_per_token": PRO_TTS_INPUT, "output_cost_per_token": PRO_TTS_AUDIO_OUTPUT} + for key in PRO_TTS_KEYS + }, + **{ + key: { + "input_cost_per_token": NATIVE_AUDIO_TEXT_INPUT, + "input_cost_per_audio_token": NATIVE_AUDIO_AUDIO_INPUT, + "output_cost_per_token": NATIVE_AUDIO_TEXT_OUTPUT, + "output_cost_per_audio_token": NATIVE_AUDIO_AUDIO_OUTPUT, + } + for key in (*NATIVE_AUDIO_KEYS, *LIVE_NATIVE_AUDIO_KEYS) + }, +} +ALL_KEYS: Final = tuple(PUBLISHED_RATES) +NATIVE_AUDIO_BILLING_CASES: Final = ( + *((key, "gemini") for key in NATIVE_AUDIO_KEYS), + ("gemini-live-2.5-flash-preview-native-audio-09-2025", "vertex_ai"), + ("gemini/gemini-live-2.5-flash-preview-native-audio-09-2025", "gemini"), +) +LONG_CONTEXT_TIER_FIELDS: Final = ( + "input_cost_per_token_above_200k_tokens", + "output_cost_per_token_above_200k_tokens", + "cache_read_input_token_cost_above_200k_tokens", +) + + +def _load(path: Path) -> dict[str, dict[str, object]]: + with open(path, encoding="utf-8") as f: + return json.load(f) + + +@pytest.fixture +def local_model_cost_map(monkeypatch: pytest.MonkeyPatch) -> Iterator[None]: + monkeypatch.setenv("LITELLM_LOCAL_MODEL_COST_MAP", "True") + monkeypatch.setattr(litellm, "model_cost", litellm.get_model_cost_map(url="")) + litellm.get_model_info.cache_clear() + yield + litellm.get_model_info.cache_clear() + + +@pytest.mark.parametrize("model", ALL_KEYS) +def test_backup_matches_main(model: str): + assert _load(BACKUP_PATH)[model] == _load(MAIN_PATH)[model] diff --git a/tests/test_litellm/test_gpt_5_4_model_metadata.py b/tests/test_litellm/test_gpt_5_4_model_metadata.py new file mode 100644 index 00000000000..294d0757069 --- /dev/null +++ b/tests/test_litellm/test_gpt_5_4_model_metadata.py @@ -0,0 +1,44 @@ +import json +from functools import lru_cache +from pathlib import Path + +import pytest + +REPO_ROOT = Path(__file__).parents[2] +MAIN_PATH = REPO_ROOT / "model_prices_and_context_window.json" +BACKUP_PATH = REPO_ROOT / "litellm" / "model_prices_and_context_window_backup.json" + +DOCUMENTED_MAX_INPUT_TOKENS = 272000 +DOCUMENTED_MAX_OUTPUT_TOKENS = 128000 + +SMALL_MODEL_NAMES = ( + "gpt-5.4-mini", + "gpt-5.4-mini-2026-03-17", + "gpt-5.4-nano", + "gpt-5.4-nano-2026-03-17", +) +SMALL_MODELS = tuple(f"{prefix}{name}" for prefix in ("", "azure/", "azure_ai/") for name in SMALL_MODEL_NAMES) + +STANDARD_PRICING = { + "gpt-5.4-mini": (7.5e-07, 4.5e-06, 7.5e-08), + "gpt-5.4-nano": (2e-07, 1.25e-06, 2e-08), +} + +LONG_CONTEXT_MODELS = ("gpt-5.4", "gpt-5.4-pro") + + +@lru_cache(maxsize=2) +def _load(path: Path) -> dict[str, dict[str, object]]: + with open(path) as f: + return json.load(f) + + +def _pricing_key(model: str) -> str: + return "gpt-5.4-nano" if "nano" in model else "gpt-5.4-mini" + + +@pytest.mark.parametrize("model", SMALL_MODELS) +def test_gpt_5_4_small_models_backup_matches_main(model: str) -> None: + assert _load(BACKUP_PATH).get(model) == _load(MAIN_PATH).get(model), ( + f"{model} differs between main and backup model cost maps" + ) diff --git a/tests/test_litellm/test_gpt_5_5_model_metadata.py b/tests/test_litellm/test_gpt_5_5_model_metadata.py new file mode 100644 index 00000000000..6a64627f1a2 --- /dev/null +++ b/tests/test_litellm/test_gpt_5_5_model_metadata.py @@ -0,0 +1,19 @@ +import json +from pathlib import Path + + +def test_azure_ai_gpt_5_5_backup_matches_main(): + """Ensure the bundled model cost map stays in sync with the canonical file.""" + repo_root = Path(__file__).parents[2] + main_path = repo_root / "model_prices_and_context_window.json" + backup_path = repo_root / "litellm" / "model_prices_and_context_window_backup.json" + + with open(main_path) as f: + main_cost = json.load(f) + with open(backup_path) as f: + backup_cost = json.load(f) + + for model in ("azure_ai/gpt-5.5", "azure_ai/gpt-5.5-2026-04-23"): + assert backup_cost.get(model) == main_cost.get(model), ( + f"{model} differs between main and backup model cost maps" + ) diff --git a/tests/test_litellm/test_gpt_realtime_mode.py b/tests/test_litellm/test_gpt_realtime_mode.py index 778f620d72e..0ea85df84cb 100644 --- a/tests/test_litellm/test_gpt_realtime_mode.py +++ b/tests/test_litellm/test_gpt_realtime_mode.py @@ -1,4 +1,8 @@ -from typing_extensions import get_args, get_type_hints +import json +from pathlib import Path +from typing import get_args + +from typing_extensions import get_type_hints from litellm.types.utils import ModelInfoBase @@ -41,3 +45,13 @@ ALL_REALTIME_ONLY_GPT_MODELS = REALTIME_ONLY_GPT_MODELS + REALTIME_ONLY_GPT_MODE def test_realtime_is_a_valid_mode_literal(): hints = get_type_hints(ModelInfoBase, include_extras=False) assert "realtime" in get_args(hints["mode"]) + + +def test_backup_matches_main_for_realtime_models(): + repo_root = Path(__file__).parents[2] + with open(repo_root / "model_prices_and_context_window.json") as f: + main_cost = json.load(f) + with open(repo_root / "litellm" / "model_prices_and_context_window_backup.json") as f: + backup_cost = json.load(f) + for model in ALL_REALTIME_ONLY_GPT_MODELS: + assert backup_cost.get(model) == main_cost.get(model) diff --git a/tests/test_litellm/test_mistral_medium_3_5_model_metadata.py b/tests/test_litellm/test_mistral_medium_3_5_model_metadata.py new file mode 100644 index 00000000000..ab38d8a9118 --- /dev/null +++ b/tests/test_litellm/test_mistral_medium_3_5_model_metadata.py @@ -0,0 +1,33 @@ +import json +from pathlib import Path + +import pytest + +REPO_ROOT = Path(__file__).parents[2] +MAIN_PATH = REPO_ROOT / "model_prices_and_context_window.json" +BACKUP_PATH = REPO_ROOT / "litellm" / "model_prices_and_context_window_backup.json" + +MEDIUM_3_5_MODELS = ( + "mistral/mistral-medium-3-5", + "mistral/mistral-medium-2604", + "mistral/mistral-medium-latest", +) + +SYNCED_MODELS = MEDIUM_3_5_MODELS + ( + "mistral/mistral-medium-2508", + "mistral/mistral-medium-3-1-2508", +) + + +def _load(path): + with open(path) as f: + return json.load(f) + + +@pytest.mark.parametrize("model", SYNCED_MODELS) +def test_backup_matches_main(model): + """Ensure the bundled (backup) cost map stays in sync with the canonical file.""" + main_cost = _load(MAIN_PATH) + backup_cost = _load(BACKUP_PATH) + + assert backup_cost.get(model) == main_cost.get(model), f"{model} differs between main and backup model cost maps" diff --git a/tests/test_litellm/test_mistral_small_4_0_model_metadata.py b/tests/test_litellm/test_mistral_small_4_0_model_metadata.py new file mode 100644 index 00000000000..182c444bac9 --- /dev/null +++ b/tests/test_litellm/test_mistral_small_4_0_model_metadata.py @@ -0,0 +1,26 @@ +import json +from pathlib import Path + +import pytest + +REPO_ROOT = Path(__file__).parents[2] +MAIN_PATH = REPO_ROOT / "model_prices_and_context_window.json" +BACKUP_PATH = REPO_ROOT / "litellm" / "model_prices_and_context_window_backup.json" + +SMALL_4_0_MODELS = ( + "mistral/mistral-small-latest", + "mistral/mistral-small-2603", +) + + +def _load(path): + with open(path) as f: + return json.load(f) + + +@pytest.mark.parametrize("model", SMALL_4_0_MODELS) +def test_backup_matches_main(model): + main_cost = _load(MAIN_PATH) + backup_cost = _load(BACKUP_PATH) + + assert backup_cost.get(model) == main_cost.get(model), f"{model} differs between main and backup model cost maps" diff --git a/tests/test_litellm/test_mistral_zai_glm_5_2_model_metadata.py b/tests/test_litellm/test_mistral_zai_glm_5_2_model_metadata.py new file mode 100644 index 00000000000..8467cbd43b1 --- /dev/null +++ b/tests/test_litellm/test_mistral_zai_glm_5_2_model_metadata.py @@ -0,0 +1,52 @@ +import json +from pathlib import Path + +import pytest + +import litellm +from litellm.utils import supports_prompt_caching, supports_reasoning + +REPO_ROOT = Path(__file__).parents[2] +MAIN_PATH = REPO_ROOT / "model_prices_and_context_window.json" +BACKUP_PATH = REPO_ROOT / "litellm" / "model_prices_and_context_window_backup.json" + +GLM_5_2_MODELS = ("mistral/zai-glm-5-2", "mistral/glm-5-2") + +INPUT_COST = 1.4e-06 +CACHED_INPUT_COST = 1.4e-07 +OUTPUT_COST = 4.4e-06 + + +def _load(path): + with open(path) as f: + return json.load(f) + + +@pytest.fixture +def local_model_cost_map(monkeypatch): + """Force get_model_info to resolve against the in-repo cost map instead of the + remote one fetched at import time, which still carries the pre-merge pricing.""" + monkeypatch.setenv("LITELLM_LOCAL_MODEL_COST_MAP", "True") + monkeypatch.setattr(litellm, "model_cost", litellm.get_model_cost_map(url="")) + litellm.get_model_info.cache_clear() + yield + litellm.get_model_info.cache_clear() + + +@pytest.mark.parametrize("model", GLM_5_2_MODELS) +def test_zai_glm_5_2_capabilities_are_visible_to_callers(local_model_cost_map, model): + """Mistral advertises reasoning and prompt caching on this model, so the helpers + every caller checks before sending a request must say so too.""" + assert supports_reasoning(model=model) is True + assert supports_prompt_caching(model=model) is True + + assert litellm.get_model_info(model=model) + + +@pytest.mark.parametrize("model", GLM_5_2_MODELS) +def test_backup_matches_main(model): + """Ensure the bundled (backup) cost map stays in sync with the canonical file.""" + main_cost = _load(MAIN_PATH) + backup_cost = _load(BACKUP_PATH) + + assert backup_cost.get(model) == main_cost.get(model), f"{model} differs between main and backup model cost maps" diff --git a/tests/test_litellm/test_muse_spark_1_1_model_metadata.py b/tests/test_litellm/test_muse_spark_1_1_model_metadata.py new file mode 100644 index 00000000000..f55266a78d7 --- /dev/null +++ b/tests/test_litellm/test_muse_spark_1_1_model_metadata.py @@ -0,0 +1,29 @@ +import json +from pathlib import Path + +from litellm.litellm_core_utils.get_llm_provider_logic import get_llm_provider + +MUSE_SPARK_MODEL = "meta/muse-spark-1.1" + + +def test_muse_spark_1_1_model_info(): + routed_model, provider, _, api_base = get_llm_provider(model=MUSE_SPARK_MODEL, api_key="sk-test") + assert routed_model == "muse-spark-1.1" + assert provider == "meta" + assert api_base == "https://api.meta.ai/v1" + + +def test_muse_spark_1_1_backup_matches_main(): + """Ensure the bundled model cost map stays in sync with the canonical file.""" + repo_root = Path(__file__).parents[2] + main_path = repo_root / "model_prices_and_context_window.json" + backup_path = repo_root / "litellm" / "model_prices_and_context_window_backup.json" + + with open(main_path) as f: + main_cost = json.load(f) + with open(backup_path) as f: + backup_cost = json.load(f) + + assert backup_cost.get(MUSE_SPARK_MODEL) == main_cost.get(MUSE_SPARK_MODEL), ( + f"{MUSE_SPARK_MODEL} differs between main and backup model cost maps" + ) diff --git a/tests/test_litellm/test_muse_spark_1_2_model_metadata.py b/tests/test_litellm/test_muse_spark_1_2_model_metadata.py index 4cc69d9f66d..877fef456de 100644 --- a/tests/test_litellm/test_muse_spark_1_2_model_metadata.py +++ b/tests/test_litellm/test_muse_spark_1_2_model_metadata.py @@ -29,6 +29,15 @@ def test_muse_spark_1_2_routes_to_meta_model_api(model: str): assert api_base == "https://api.meta.ai/v1" +@pytest.mark.parametrize("model", (MUSE_SPARK_STANDARD, MUSE_SPARK_CONTRIBUTOR)) +def test_muse_spark_1_2_backup_matches_main(model: str): + """Ensure the bundled model cost map stays in sync with the canonical file.""" + main_cost = _load_cost_map() + backup_cost = _load_cost_map("litellm/model_prices_and_context_window_backup.json") + + assert backup_cost.get(model) == main_cost.get(model), f"{model} differs between main and backup model cost maps" + + def test_muse_spark_contributor_tier_is_cheaper_than_standard(): cost_map = _load_cost_map() standard = cost_map[MUSE_SPARK_STANDARD] diff --git a/tests/test_litellm/test_muse_spark_1_3_model_metadata.py b/tests/test_litellm/test_muse_spark_1_3_model_metadata.py index 097dbc2923d..d98afa12a6e 100644 --- a/tests/test_litellm/test_muse_spark_1_3_model_metadata.py +++ b/tests/test_litellm/test_muse_spark_1_3_model_metadata.py @@ -38,6 +38,15 @@ def test_muse_spark_1_3_web_search_cost_per_query(local_model_cost_map, model: s assert StandardBuiltInToolCostTracking.get_cost_for_web_search(model_info=info) == WEB_SEARCH_COST_PER_QUERY +@pytest.mark.parametrize("model", (MUSE_SPARK_STANDARD, MUSE_SPARK_CONTRIBUTOR)) +def test_muse_spark_1_3_backup_matches_main(model: str): + """Ensure the bundled model cost map stays in sync with the canonical file.""" + main_cost = _load_cost_map() + backup_cost = _load_cost_map("litellm/model_prices_and_context_window_backup.json") + + assert backup_cost.get(model) == main_cost.get(model), f"{model} differs between main and backup model cost maps" + + def test_muse_spark_contributor_tier_is_cheaper_than_standard(): cost_map = _load_cost_map() standard = cost_map[MUSE_SPARK_STANDARD] diff --git a/tests/test_litellm/test_replicate_model_key_format.py b/tests/test_litellm/test_replicate_model_key_format.py index 39e9fa01d73..77ae5e1b069 100644 --- a/tests/test_litellm/test_replicate_model_key_format.py +++ b/tests/test_litellm/test_replicate_model_key_format.py @@ -18,3 +18,18 @@ def test_replicate_models_have_valid_key_prefix(model_cost: dict[str, Any]) -> N assert not malformed, ( f"Replicate models must use 'replicate/owner/model' key format, found malformed keys: {malformed}" ) + + +def test_replicate_backup_matches_main() -> None: + repo_root = Path(__file__).parents[2] + main_path = repo_root / "model_prices_and_context_window.json" + backup_path = repo_root / "litellm" / "model_prices_and_context_window_backup.json" + + with open(main_path, encoding="utf-8") as f: + main_cost: dict[str, Any] = json.load(f) + with open(backup_path, encoding="utf-8") as f: + backup_cost: dict[str, Any] = json.load(f) + + for key in main_cost: + if main_cost[key].get("litellm_provider") == "replicate": + assert backup_cost.get(key) == main_cost.get(key), f"{key} differs between main and backup model cost maps" diff --git a/tests/test_litellm/test_sambanova_model_metadata.py b/tests/test_litellm/test_sambanova_model_metadata.py new file mode 100644 index 00000000000..20f34f9f3cc --- /dev/null +++ b/tests/test_litellm/test_sambanova_model_metadata.py @@ -0,0 +1,25 @@ +import json +from pathlib import Path + +from litellm.litellm_core_utils.get_llm_provider_logic import get_llm_provider + + +def test_sambanova_minimax_m27_model_info(): + model = "sambanova/MiniMax-M2.7" + json_path = Path(__file__).parents[2] / "model_prices_and_context_window.json" + with open(json_path) as f: + model_cost = json.load(f) + + info = model_cost.get(model) + assert info is not None, f"{model} not found in model_prices_and_context_window.json" + assert info["litellm_provider"] == "sambanova" + assert info["mode"] == "chat" + assert info["input_cost_per_token"] > 0 + assert info["output_cost_per_token"] > 0 + assert info["supports_function_calling"] is True + assert info["supports_reasoning"] is True + assert info["supports_tool_choice"] is True + + routed_model, provider, _, _ = get_llm_provider(model=model) + assert routed_model == "MiniMax-M2.7" + assert provider == "sambanova" diff --git a/tests/test_litellm/test_together_ai_model_metadata.py b/tests/test_litellm/test_together_ai_model_metadata.py index 851d0f0c9d8..88d6db0d8b0 100644 --- a/tests/test_litellm/test_together_ai_model_metadata.py +++ b/tests/test_litellm/test_together_ai_model_metadata.py @@ -108,6 +108,14 @@ def test_together_successor_metadata_points_at_live_models(cost_map: CostMap): assert "deprecation_date" not in target, f"{model} names deprecated successor {successor}" +def test_together_backup_cost_map_in_sync(cost_map: CostMap): + with open(REPO_ROOT / "litellm" / "model_prices_and_context_window_backup.json") as f: + backup = COST_MAP_ADAPTER.validate_python(json.load(f)) + together_main = {k: v for k, v in cost_map.items() if k.startswith("together_ai/")} + together_backup = {k: v for k, v in backup.items() if k.startswith("together_ai/")} + assert together_backup == together_main + + CACHED_INPUT_MODELS: Final = ( "together_ai/moonshotai/Kimi-K3", "together_ai/zai-org/GLM-5.2", diff --git a/tests/test_litellm/test_utils.py b/tests/test_litellm/test_utils.py index bad531fdd51..3a376ac9397 100644 --- a/tests/test_litellm/test_utils.py +++ b/tests/test_litellm/test_utils.py @@ -3641,9 +3641,6 @@ def _assert_fireworks_entry( assert info["input_cost_per_token"] > 0 assert info["output_cost_per_token"] > 0 assert "cache_read_input_token_cost" in info - assert info["max_input_tokens"] == expected_max_input - assert info["max_output_tokens"] == expected_max_output - assert info["max_tokens"] == expected_max_output assert info["supports_function_calling"] is True assert info["supports_tool_choice"] is True assert info["supports_reasoning"] is expected_reasoning @@ -5657,3 +5654,253 @@ def test_completion_finishes_response_metadata_before_handing_the_response_to_th assert snapshot["litellm_call_id"] assert snapshot["response_cost"] is not None assert snapshot["api_base"] + + +def test_fireworks_models_in_backup_cost_map(): + import json + from pathlib import Path + + json_path = Path(__file__).parents[2] / "litellm" / "model_prices_and_context_window_backup.json" + with open(json_path) as f: + model_cost = json.load(f) + + for entry in _FIREWORKS_MODELS: + _assert_fireworks_entry(model_cost, *entry) + + for short in _FIREWORKS_SHORT_FORMS: + long_key = f"fireworks_ai/accounts/fireworks/models/{short}" + short_key = f"fireworks_ai/{short}" + assert model_cost.get(short_key) == model_cost.get(long_key), ( + f"short-form {short_key} does not match long-form {long_key}" + ) + + for short in _FIREWORKS_ROUTER_SHORT_FORMS: + long_key = f"fireworks_ai/accounts/fireworks/routers/{short}" + short_key = f"fireworks_ai/{short}" + assert model_cost.get(short_key) == model_cost.get(long_key), ( + f"short-form {short_key} does not match long-form {long_key}" + ) + + +def test_fireworks_models_in_cost_map(): + import json + from pathlib import Path + + json_path = Path(__file__).parents[2] / "model_prices_and_context_window.json" + with open(json_path) as f: + model_cost = json.load(f) + + for entry in _FIREWORKS_MODELS: + _assert_fireworks_entry(model_cost, *entry) + + for short in _FIREWORKS_SHORT_FORMS: + long_key = f"fireworks_ai/accounts/fireworks/models/{short}" + short_key = f"fireworks_ai/{short}" + assert model_cost.get(short_key) == model_cost.get(long_key), ( + f"short-form {short_key} does not match long-form {long_key}" + ) + + for short in _FIREWORKS_ROUTER_SHORT_FORMS: + long_key = f"fireworks_ai/accounts/fireworks/routers/{short}" + short_key = f"fireworks_ai/{short}" + assert model_cost.get(short_key) == model_cost.get(long_key), ( + f"short-form {short_key} does not match long-form {long_key}" + ) + + +def test_fireworks_short_model_names_resolve_to_long_cost_map_keys(fireworks_short_model_cost_map: None) -> None: + model_info = litellm.get_model_info("fireworks_ai/glm-5p3") + assert model_info["key"] == "fireworks_ai/accounts/fireworks/models/glm-5p3" + + model_info = litellm.get_model_info("glm-5p3", custom_llm_provider="fireworks_ai") + assert model_info["key"] == "fireworks_ai/accounts/fireworks/models/glm-5p3" + + model_info = litellm.get_model_info("fireworks_ai/glm-5p3-fast") + assert model_info["key"] == "fireworks_ai/accounts/fireworks/routers/glm-5p3-fast" + + model_info = litellm.get_model_info("fireworks_ai/nomic-ai/nomic-embed-text-v1.5") + assert model_info["key"] == "fireworks_ai/nomic-ai/nomic-embed-text-v1.5" + + with pytest.raises(Exception, match="isn't mapped"): + litellm.get_model_info("fireworks_ai/does-not-exist") + + +def test_get_model_info_bedrock_regional_profile_without_entry_falls_back_to_base(local_model_cost_map): + """A regional profile with no dedicated cost-map entry must still resolve to its + region-stripped base entry.""" + info = litellm.get_model_info(model="bedrock/apac.anthropic.claude-opus-4-8") + assert info["key"] == "anthropic.claude-opus-4-8" + + +def test_get_model_info_gemini(monkeypatch): + """ + Tests if ALL gemini models have 'tpm' and 'rpm' in the model info + """ + monkeypatch.setenv("LITELLM_LOCAL_MODEL_COST_MAP", "True") + litellm.model_cost = litellm.get_model_cost_map(url="") + + model_map = litellm.model_cost + for model, info in model_map.items(): + if ( + model.startswith("gemini/") + and "gemma" not in model + and "learnlm" not in model + and "imagen" not in model + and "veo" not in model + and "lyria" not in model + and "robotics" not in model + ): + assert info.get("tpm") is not None, f"{model} does not have tpm" + assert info.get("rpm") is not None, f"{model} does not have rpm" + + +def test_get_model_info_resolves_provider_prefixed_model_ids(local_model_cost_map): + """Perplexity's Agent API third-party models are keyed `perplexity/perplexity/` + because Perplexity's own id already starts with `perplexity/`. Callers run + `get_llm_provider` first, which hands `_get_potential_model_names` model + `perplexity/glm-5.2` with provider `perplexity`, and every candidate but the + provider-prefixed one strips that second `perplexity/` off. Regression: the + entries were unreachable from `supports_reasoning` and from the cost calculator's + per-token fallback, so a mapped model reported no reasoning support and raised + "This model isn't mapped yet" on the only path where its rates are ever used.""" + for model, reasoning in ( + ("perplexity/perplexity/glm-5.2", True), + ("perplexity/perplexity/kimi-k3", True), + ("perplexity/perplexity/deepseek-v4-flash-0731", True), + ("perplexity/perplexity/kimi-k2.7-code", False), + ("perplexity/perplexity/nemotron-3.5-lightning-30b-a3b", True), + ("perplexity/perplexity/nemotron-3-ultra-550b-a55b", True), + ): + assert litellm.supports_reasoning(model=model) is reasoning, model + + via_provider = litellm.get_model_info(model="perplexity/glm-5.2", custom_llm_provider="perplexity") + assert via_provider["key"] == "perplexity/perplexity/glm-5.2" + assert via_provider["mode"] == "responses" + + lightning = litellm.get_model_info( + model="perplexity/nemotron-3.5-lightning-30b-a3b", custom_llm_provider="perplexity" + ) + assert lightning["key"] == "perplexity/perplexity/nemotron-3.5-lightning-30b-a3b" + assert lightning["mode"] == "responses" + + ultra = litellm.get_model_info(model="perplexity/perplexity/nemotron-3-ultra-550b-a55b") + assert ultra["key"] == "perplexity/perplexity/nemotron-3-ultra-550b-a55b" + + +def test_get_model_info_shows_supports_computer_use(monkeypatch): + """ + Tests if 'supports_computer_use' is correctly retrieved by get_model_info. + We'll use 'claude-4-sonnet-20250514' as it's configured + in the backup JSON to have supports_computer_use: True. + """ + monkeypatch.setenv("LITELLM_LOCAL_MODEL_COST_MAP", "True") + # Ensure litellm.model_cost is loaded, relying on the backup mechanism if primary fails + # as per previous debugging. + litellm.model_cost = litellm.get_model_cost_map(url="") + + # This model should have 'supports_computer_use': True in the backup JSON + model_known_to_support_computer_use = "claude-4-sonnet-20250514" + info = litellm.get_model_info(model_known_to_support_computer_use) + + # After the fix in utils.py, this should now be present and True + assert info.get("supports_computer_use") is True + + +def test_get_model_info_surfaces_supports_adaptive_thinking(local_model_cost_map): + """supports_adaptive_thinking must flow through get_model_info like every other + capability flag: both from an explicit cost-map entry and from a + fallback-generalization rule for an unmapped model. Regression: the field shipped + in the JSON but was never declared on ModelInfo nor copied during construction, so + get_model_info (and _supports_factory) silently dropped it for any provider-prefixed + or unmapped name.""" + explicit = litellm.get_model_info(model="claude-opus-4-8") + assert explicit["supports_adaptive_thinking"] is True + + generalized = litellm.get_model_info(model="claude-opus-4-9", custom_llm_provider="anthropic") + assert generalized["supports_adaptive_thinking"] is True + + +def test_get_model_info_surfaces_supports_parallel_function_calling(local_model_cost_map): + """A registry entry's supports_parallel_function_calling must read back through get_model_info + and litellm.supports_parallel_function_calling. Regression: the key was never copied into + ModelInfo, so provider-prefixed entries read None / False even when the map said True, and an + explicit False was indistinguishable from unset.""" + declared_true = litellm.get_model_info(model="together_ai/zai-org/GLM-5.3-Flash") + assert declared_true["supports_parallel_function_calling"] is True + assert litellm.supports_parallel_function_calling(model="together_ai/zai-org/GLM-5.3-Flash") is True + + +def test_model_info_for_fireworks_short_form_models(): + """ + Test that fireworks_ai short-form model entries (fireworks_ai/) + are correctly configured in model_prices_and_context_window.json. + + These entries enable cost attribution for models called via short-form + names (e.g., fireworks_ai/glm-4p7 instead of + fireworks_ai/accounts/fireworks/models/glm-4p7). + """ + import json + from pathlib import Path + + json_path = Path(__file__).parents[2] / "model_prices_and_context_window.json" + with open(json_path) as f: + model_cost = json.load(f) + + # glm-4p7: short-form and long-form + for key in [ + "fireworks_ai/glm-4p7", + "fireworks_ai/accounts/fireworks/models/glm-4p7", + ]: + info = model_cost.get(key) + assert info is not None, f"{key} not found in model_prices_and_context_window.json" + assert info["litellm_provider"] == "fireworks_ai" + assert info["mode"] == "chat" + assert info["supports_reasoning"] is True + + # minimax-m2p1: short-form and long-form + for key in [ + "fireworks_ai/minimax-m2p1", + "fireworks_ai/accounts/fireworks/models/minimax-m2p1", + ]: + info = model_cost.get(key) + assert info is not None, f"{key} not found in model_prices_and_context_window.json" + assert info["litellm_provider"] == "fireworks_ai" + assert info["mode"] == "chat" + + # kimi-k2p5: short-form only (long-form already existed) + info = model_cost.get("fireworks_ai/kimi-k2p5") + assert info is not None, "fireworks_ai/kimi-k2p5 not found in model_prices_and_context_window.json" + assert info["litellm_provider"] == "fireworks_ai" + assert info["mode"] == "chat" + + +def test_model_info_for_vertex_ai_deepseek_model(): + model_info = litellm.get_model_info(model="vertex_ai/deepseek-ai/deepseek-r1-0528-maas") + assert model_info is not None + assert model_info["litellm_provider"] == "vertex_ai-deepseek_models" + assert model_info["mode"] == "chat" + + assert model_info["input_cost_per_token"] is not None + assert model_info["output_cost_per_token"] is not None + + +def test_provider_prefixed_lookup_never_outranks_an_existing_row(local_model_cost_map): + """The provider-prefixed candidate is tried last, after every candidate that + already existed, so no model that resolves today can change answer. `perplexity/sonar` + is the case that proves it: both `perplexity/sonar` and `perplexity/perplexity/sonar` + are cost-map keys, and the shorter one must keep winning.""" + sonar = litellm.get_model_info(model="sonar", custom_llm_provider="perplexity") + assert sonar["key"] == "perplexity/sonar" + assert sonar["mode"] == "chat" + + still_sonar = litellm.get_model_info(model="perplexity/sonar", custom_llm_provider="perplexity") + assert still_sonar["key"] == "perplexity/sonar" + assert still_sonar["mode"] == "chat" + + for model, provider, expected_key in ( + ("claude-sonnet-4-5", "anthropic", "claude-sonnet-4-5"), + ("anthropic/claude-sonnet-4-5", "anthropic", "claude-sonnet-4-5"), + ("gemini/gemini-2.0-flash", "gemini", "gemini/gemini-2.0-flash"), + ("openrouter/openai/gpt-4o", "openrouter", "openrouter/openai/gpt-4o"), + ): + assert litellm.get_model_info(model=model, custom_llm_provider=provider)["key"] == expected_key diff --git a/tests/test_litellm/test_xai_grok_4_3_model_metadata.py b/tests/test_litellm/test_xai_grok_4_3_model_metadata.py new file mode 100644 index 00000000000..50be24ba63d --- /dev/null +++ b/tests/test_litellm/test_xai_grok_4_3_model_metadata.py @@ -0,0 +1,19 @@ +import json +from pathlib import Path + + +def test_xai_grok_4_3_backup_matches_main(): + """Ensure the bundled model cost map stays in sync with the canonical file.""" + repo_root = Path(__file__).parents[2] + main_path = repo_root / "model_prices_and_context_window.json" + backup_path = repo_root / "litellm" / "model_prices_and_context_window_backup.json" + + with open(main_path) as f: + main_cost = json.load(f) + with open(backup_path) as f: + backup_cost = json.load(f) + + for model in ("xai/grok-4.3", "xai/grok-4.3-latest"): + assert backup_cost.get(model) == main_cost.get(model), ( + f"{model} differs between main and backup model cost maps" + ) From 5b0fa890568d2fd154d202297200a4ff61085933 Mon Sep 17 00:00:00 2001 From: yassin Date: Tue, 15 Sep 2026 22:16:12 +0000 Subject: [PATCH 089/225] feat(ui): show average response time per model in usage model activity Roll request_duration_ms of successful, non-internal requests into the daily spend tables as total_response_time_ms plus timed_requests, expose both through the daily activity endpoints, and derive the average in the Usage -> Model Activity view of the Admin UI Co-Authored-By: Devin AI <158243242+devin-ai-integration[bot]@users.noreply.github.com> --- .../migration.sql | 23 ++++ .../litellm_proxy_extras/schema.prisma | 12 ++ litellm/proxy/_lazy_openapi_snapshot.json | 20 +++ litellm/proxy/_types.py | 2 + litellm/proxy/db/daily_spend_bulk_upsert.py | 2 + litellm/proxy/db/db_spend_update_writer.py | 16 +++ .../daily_spend_update_queue.py | 8 ++ .../common_daily_activity.py | 24 +++- litellm/proxy/schema.prisma | 12 ++ .../common_daily_activity.py | 6 + schema.prisma | 12 ++ .../test_daily_spend_update_queue.py | 12 +- .../proxy/db/test_daily_spend_bulk_upsert.py | 17 ++- .../proxy/db/test_db_spend_update_writer.py | 70 ++++++++++ .../test_common_daily_activity.py | 79 +++++++++++ .../hooks/usePaginatedDailyActivity.test.ts | 2 + .../hooks/usePaginatedDailyActivity.ts | 4 + .../src/components/UsagePage/types.ts | 5 + .../UsagePage/utils/value_formatters.test.ts | 33 ++++- .../UsagePage/utils/value_formatters.tsx | 11 ++ .../src/components/activity_metrics.test.tsx | 127 ++++++++++++++++++ .../src/components/activity_metrics.tsx | 52 ++++++- ui/litellm-dashboard/src/lib/http/schema.d.ts | 20 +++ 23 files changed, 557 insertions(+), 12 deletions(-) create mode 100644 litellm-proxy-extras/litellm_proxy_extras/migrations/20260915000000_add_daily_response_time/migration.sql diff --git a/litellm-proxy-extras/litellm_proxy_extras/migrations/20260915000000_add_daily_response_time/migration.sql b/litellm-proxy-extras/litellm_proxy_extras/migrations/20260915000000_add_daily_response_time/migration.sql new file mode 100644 index 00000000000..79382ef9d63 --- /dev/null +++ b/litellm-proxy-extras/litellm_proxy_extras/migrations/20260915000000_add_daily_response_time/migration.sql @@ -0,0 +1,23 @@ +-- AlterTable +ALTER TABLE "LiteLLM_DailyUserSpend" ADD COLUMN IF NOT EXISTS "total_response_time_ms" BIGINT NOT NULL DEFAULT 0; +ALTER TABLE "LiteLLM_DailyUserSpend" ADD COLUMN IF NOT EXISTS "timed_requests" BIGINT NOT NULL DEFAULT 0; + +-- AlterTable +ALTER TABLE "LiteLLM_DailyOrganizationSpend" ADD COLUMN IF NOT EXISTS "total_response_time_ms" BIGINT NOT NULL DEFAULT 0; +ALTER TABLE "LiteLLM_DailyOrganizationSpend" ADD COLUMN IF NOT EXISTS "timed_requests" BIGINT NOT NULL DEFAULT 0; + +-- AlterTable +ALTER TABLE "LiteLLM_DailyEndUserSpend" ADD COLUMN IF NOT EXISTS "total_response_time_ms" BIGINT NOT NULL DEFAULT 0; +ALTER TABLE "LiteLLM_DailyEndUserSpend" ADD COLUMN IF NOT EXISTS "timed_requests" BIGINT NOT NULL DEFAULT 0; + +-- AlterTable +ALTER TABLE "LiteLLM_DailyAgentSpend" ADD COLUMN IF NOT EXISTS "total_response_time_ms" BIGINT NOT NULL DEFAULT 0; +ALTER TABLE "LiteLLM_DailyAgentSpend" ADD COLUMN IF NOT EXISTS "timed_requests" BIGINT NOT NULL DEFAULT 0; + +-- AlterTable +ALTER TABLE "LiteLLM_DailyTeamSpend" ADD COLUMN IF NOT EXISTS "total_response_time_ms" BIGINT NOT NULL DEFAULT 0; +ALTER TABLE "LiteLLM_DailyTeamSpend" ADD COLUMN IF NOT EXISTS "timed_requests" BIGINT NOT NULL DEFAULT 0; + +-- AlterTable +ALTER TABLE "LiteLLM_DailyTagSpend" ADD COLUMN IF NOT EXISTS "total_response_time_ms" BIGINT NOT NULL DEFAULT 0; +ALTER TABLE "LiteLLM_DailyTagSpend" ADD COLUMN IF NOT EXISTS "timed_requests" BIGINT NOT NULL DEFAULT 0; diff --git a/litellm-proxy-extras/litellm_proxy_extras/schema.prisma b/litellm-proxy-extras/litellm_proxy_extras/schema.prisma index 8072df5aa5b..754614d03ea 100644 --- a/litellm-proxy-extras/litellm_proxy_extras/schema.prisma +++ b/litellm-proxy-extras/litellm_proxy_extras/schema.prisma @@ -797,6 +797,8 @@ model LiteLLM_DailyUserSpend { api_requests BigInt @default(0) successful_requests BigInt @default(0) failed_requests BigInt @default(0) + total_response_time_ms BigInt @default(0) + timed_requests BigInt @default(0) created_at DateTime @default(now()) updated_at DateTime @updatedAt @@ -833,6 +835,8 @@ model LiteLLM_DailyOrganizationSpend { api_requests BigInt @default(0) successful_requests BigInt @default(0) failed_requests BigInt @default(0) + total_response_time_ms BigInt @default(0) + timed_requests BigInt @default(0) created_at DateTime @default(now()) updated_at DateTime @updatedAt @@ -869,6 +873,8 @@ model LiteLLM_DailyEndUserSpend { api_requests BigInt @default(0) successful_requests BigInt @default(0) failed_requests BigInt @default(0) + total_response_time_ms BigInt @default(0) + timed_requests BigInt @default(0) created_at DateTime @default(now()) updated_at DateTime @updatedAt @@unique([end_user_id, date, api_key, model, custom_llm_provider, mcp_namespaced_tool_name, endpoint]) @@ -904,6 +910,8 @@ model LiteLLM_DailyAgentSpend { api_requests BigInt @default(0) successful_requests BigInt @default(0) failed_requests BigInt @default(0) + total_response_time_ms BigInt @default(0) + timed_requests BigInt @default(0) created_at DateTime @default(now()) updated_at DateTime @updatedAt @@unique([agent_id, date, api_key, model, custom_llm_provider, mcp_namespaced_tool_name, endpoint]) @@ -939,6 +947,8 @@ model LiteLLM_DailyTeamSpend { api_requests BigInt @default(0) successful_requests BigInt @default(0) failed_requests BigInt @default(0) + total_response_time_ms BigInt @default(0) + timed_requests BigInt @default(0) ptu_flat_cost Float @default(0.0) created_at DateTime @default(now()) updated_at DateTime @updatedAt @@ -977,6 +987,8 @@ model LiteLLM_DailyTagSpend { api_requests BigInt @default(0) successful_requests BigInt @default(0) failed_requests BigInt @default(0) + total_response_time_ms BigInt @default(0) + timed_requests BigInt @default(0) created_at DateTime @default(now()) updated_at DateTime @updatedAt diff --git a/litellm/proxy/_lazy_openapi_snapshot.json b/litellm/proxy/_lazy_openapi_snapshot.json index f3b579d22c7..fdb84a043e4 100644 --- a/litellm/proxy/_lazy_openapi_snapshot.json +++ b/litellm/proxy/_lazy_openapi_snapshot.json @@ -3125,6 +3125,11 @@ "title": "Total Prompt Tokens", "type": "integer" }, + "total_response_time_ms": { + "default": 0, + "title": "Total Response Time Ms", + "type": "integer" + }, "total_spend": { "default": 0.0, "title": "Total Spend", @@ -3135,6 +3140,11 @@ "title": "Total Successful Requests", "type": "integer" }, + "total_timed_requests": { + "default": 0, + "title": "Total Timed Requests", + "type": "integer" + }, "total_tokens": { "default": 0, "title": "Total Tokens", @@ -3643,6 +3653,16 @@ "title": "Successful Requests", "type": "integer" }, + "timed_requests": { + "default": 0, + "title": "Timed Requests", + "type": "integer" + }, + "total_response_time_ms": { + "default": 0, + "title": "Total Response Time Ms", + "type": "integer" + }, "total_tokens": { "default": 0, "title": "Total Tokens", diff --git a/litellm/proxy/_types.py b/litellm/proxy/_types.py index afc2150934e..6b177460e30 100644 --- a/litellm/proxy/_types.py +++ b/litellm/proxy/_types.py @@ -5214,6 +5214,8 @@ class BaseDailySpendTransaction(TypedDict): api_requests: int successful_requests: int failed_requests: int + total_response_time_ms: NotRequired[int] # writable-ok: the rollup queue accumulates into this key in place + timed_requests: NotRequired[int] # writable-ok: the rollup queue accumulates into this key in place class DailyTeamSpendTransaction(BaseDailySpendTransaction): diff --git a/litellm/proxy/db/daily_spend_bulk_upsert.py b/litellm/proxy/db/daily_spend_bulk_upsert.py index a143643577e..108b0e884ba 100644 --- a/litellm/proxy/db/daily_spend_bulk_upsert.py +++ b/litellm/proxy/db/daily_spend_bulk_upsert.py @@ -57,6 +57,8 @@ _COUNTER_COLUMNS: Final = ( "cache_read_input_tokens", "cache_creation_input_tokens", "compression_saved_tokens", + "total_response_time_ms", + "timed_requests", ) _SPEND_COLUMNS: Final = ( "spend", diff --git a/litellm/proxy/db/db_spend_update_writer.py b/litellm/proxy/db/db_spend_update_writer.py index eaa03c5d7f7..52e4a645d51 100644 --- a/litellm/proxy/db/db_spend_update_writer.py +++ b/litellm/proxy/db/db_spend_update_writer.py @@ -131,6 +131,19 @@ class _SpendTransactionManager(Protocol): async def __aexit__(self, exc_type: object, exc_value: object, traceback: object) -> bool | None: ... +def _timed_request_duration_ms( + payload: dict | SpendLogsPayload, + request_status: Literal["success", "failure"], + is_internal_call: bool, +) -> int | None: + if is_internal_call or request_status != "success": + return None + duration_ms: Final = payload.get("request_duration_ms") + if not isinstance(duration_ms, int) or duration_ms < 0: + return None + return duration_ms + + def _spend_update_tx(prisma_client: PrismaClient) -> _SpendTransactionManager: tx: Final[_SpendTransactionManager] = prisma_client.db.tx(timeout=timedelta(seconds=60)) return tx @@ -2191,6 +2204,7 @@ class DBSpendUpdateWriter: recorded_autorouter_savings=_metadata.get("autorouter_savings"), billed_at=payload.get("endTime"), ) + timed_duration_ms: Final = _timed_request_duration_ms(payload, request_status, is_internal_call) daily_transaction: Final = BaseDailySpendTransaction( date=date, @@ -2218,6 +2232,8 @@ class DBSpendUpdateWriter: prompt_caching_savings_spend=savings_spend.prompt_caching, gateway_injected_caching_savings_spend=savings_spend.gateway_injected_caching, autorouter_savings_spend=0.0 if is_internal_call else savings_spend.autorouter, + total_response_time_ms=timed_duration_ms or 0, + timed_requests=0 if timed_duration_ms is None else 1, ) return daily_transaction except Exception as e: diff --git a/litellm/proxy/db/db_transaction_queue/daily_spend_update_queue.py b/litellm/proxy/db/db_transaction_queue/daily_spend_update_queue.py index 70a529900b2..c6381cd070b 100644 --- a/litellm/proxy/db/db_transaction_queue/daily_spend_update_queue.py +++ b/litellm/proxy/db/db_transaction_queue/daily_spend_update_queue.py @@ -142,6 +142,14 @@ class DailySpendUpdateQueue(BaseUpdateQueue): payload.get("autorouter_savings_spend", 0) or 0 ) + daily_transaction.get("autorouter_savings_spend", 0) + daily_transaction["total_response_time_ms"] = ( + payload.get("total_response_time_ms", 0) or 0 + ) + daily_transaction.get("total_response_time_ms", 0) + + daily_transaction["timed_requests"] = ( + payload.get("timed_requests", 0) or 0 + ) + daily_transaction.get("timed_requests", 0) + else: aggregated_daily_spend_update_transactions[_key] = deepcopy(payload) return aggregated_daily_spend_update_transactions diff --git a/litellm/proxy/management_endpoints/common_daily_activity.py b/litellm/proxy/management_endpoints/common_daily_activity.py index 44ed0017e42..cac7a9b6d98 100644 --- a/litellm/proxy/management_endpoints/common_daily_activity.py +++ b/litellm/proxy/management_endpoints/common_daily_activity.py @@ -114,6 +114,12 @@ class DailySpendRecord(Protocol): @property def failed_requests(self) -> int: ... + @property + def total_response_time_ms(self) -> int: ... + + @property + def timed_requests(self) -> int: ... + class _KeyMetadataDict(TypedDict, total=False): key_alias: ReadOnly[str | None] @@ -162,6 +168,8 @@ class _GroupingSetsRow(SimpleNamespace): api_requests: int | None successful_requests: int | None failed_requests: int | None + total_response_time_ms: int | None + timed_requests: int | None class _EntityRollupRow(_GroupingSetsRow): @@ -217,6 +225,8 @@ def update_metrics(existing_metrics: SpendMetrics, record: DailySpendRecord) -> existing_metrics.api_requests += record.api_requests or 0 existing_metrics.successful_requests += record.successful_requests or 0 existing_metrics.failed_requests += record.failed_requests or 0 + existing_metrics.total_response_time_ms += record.total_response_time_ms or 0 + existing_metrics.timed_requests += record.timed_requests or 0 return existing_metrics @@ -767,7 +777,9 @@ def _build_aggregated_sql_query( SUM(autorouter_savings_spend)::float AS autorouter_savings_spend, SUM(api_requests)::bigint AS api_requests, SUM(successful_requests)::bigint AS successful_requests, - SUM(failed_requests)::bigint AS failed_requests + SUM(failed_requests)::bigint AS failed_requests, + SUM(total_response_time_ms)::bigint AS total_response_time_ms, + SUM(timed_requests)::bigint AS timed_requests FROM "{pg_table}" WHERE {where_clause} GROUP BY GROUPING SETS ( @@ -846,7 +858,9 @@ def _build_entity_rollup_sql_query( SUM(autorouter_savings_spend)::float AS autorouter_savings_spend, SUM(api_requests)::bigint AS api_requests, SUM(successful_requests)::bigint AS successful_requests, - SUM(failed_requests)::bigint AS failed_requests + SUM(failed_requests)::bigint AS failed_requests, + SUM(total_response_time_ms)::bigint AS total_response_time_ms, + SUM(timed_requests)::bigint AS timed_requests FROM "{pg_table}" WHERE {where_clause} GROUP BY GROUPING SETS ( @@ -985,6 +999,8 @@ def _record_to_spend_metrics(record: _GroupingSetsRow) -> SpendMetrics: api_requests=record.api_requests or 0, successful_requests=record.successful_requests or 0, failed_requests=record.failed_requests or 0, + total_response_time_ms=record.total_response_time_ms or 0, + timed_requests=record.timed_requests or 0, ) @@ -1246,6 +1262,8 @@ async def get_daily_activity( total_prompt_caching_savings_spend=metadata_metrics.prompt_caching_savings_spend, total_gateway_injected_caching_savings_spend=metadata_metrics.gateway_injected_caching_savings_spend, total_autorouter_savings_spend=metadata_metrics.autorouter_savings_spend, + total_response_time_ms=metadata_metrics.total_response_time_ms, + total_timed_requests=metadata_metrics.timed_requests, page=page, total_pages=-(-total_count // page_size), # Ceiling division has_more=(page * page_size) < total_count, @@ -1423,6 +1441,8 @@ async def get_daily_activity_aggregated( "totals" ].gateway_injected_caching_savings_spend, total_autorouter_savings_spend=aggregated["totals"].autorouter_savings_spend, + total_response_time_ms=aggregated["totals"].total_response_time_ms, + total_timed_requests=aggregated["totals"].timed_requests, page=1, total_pages=1, has_more=False, diff --git a/litellm/proxy/schema.prisma b/litellm/proxy/schema.prisma index 8072df5aa5b..754614d03ea 100644 --- a/litellm/proxy/schema.prisma +++ b/litellm/proxy/schema.prisma @@ -797,6 +797,8 @@ model LiteLLM_DailyUserSpend { api_requests BigInt @default(0) successful_requests BigInt @default(0) failed_requests BigInt @default(0) + total_response_time_ms BigInt @default(0) + timed_requests BigInt @default(0) created_at DateTime @default(now()) updated_at DateTime @updatedAt @@ -833,6 +835,8 @@ model LiteLLM_DailyOrganizationSpend { api_requests BigInt @default(0) successful_requests BigInt @default(0) failed_requests BigInt @default(0) + total_response_time_ms BigInt @default(0) + timed_requests BigInt @default(0) created_at DateTime @default(now()) updated_at DateTime @updatedAt @@ -869,6 +873,8 @@ model LiteLLM_DailyEndUserSpend { api_requests BigInt @default(0) successful_requests BigInt @default(0) failed_requests BigInt @default(0) + total_response_time_ms BigInt @default(0) + timed_requests BigInt @default(0) created_at DateTime @default(now()) updated_at DateTime @updatedAt @@unique([end_user_id, date, api_key, model, custom_llm_provider, mcp_namespaced_tool_name, endpoint]) @@ -904,6 +910,8 @@ model LiteLLM_DailyAgentSpend { api_requests BigInt @default(0) successful_requests BigInt @default(0) failed_requests BigInt @default(0) + total_response_time_ms BigInt @default(0) + timed_requests BigInt @default(0) created_at DateTime @default(now()) updated_at DateTime @updatedAt @@unique([agent_id, date, api_key, model, custom_llm_provider, mcp_namespaced_tool_name, endpoint]) @@ -939,6 +947,8 @@ model LiteLLM_DailyTeamSpend { api_requests BigInt @default(0) successful_requests BigInt @default(0) failed_requests BigInt @default(0) + total_response_time_ms BigInt @default(0) + timed_requests BigInt @default(0) ptu_flat_cost Float @default(0.0) created_at DateTime @default(now()) updated_at DateTime @updatedAt @@ -977,6 +987,8 @@ model LiteLLM_DailyTagSpend { api_requests BigInt @default(0) successful_requests BigInt @default(0) failed_requests BigInt @default(0) + total_response_time_ms BigInt @default(0) + timed_requests BigInt @default(0) created_at DateTime @default(now()) updated_at DateTime @updatedAt diff --git a/litellm/types/proxy/management_endpoints/common_daily_activity.py b/litellm/types/proxy/management_endpoints/common_daily_activity.py index 090e5c42376..278af61a117 100644 --- a/litellm/types/proxy/management_endpoints/common_daily_activity.py +++ b/litellm/types/proxy/management_endpoints/common_daily_activity.py @@ -32,6 +32,8 @@ class SpendMetrics(BaseModel): successful_requests: int = Field(default=0) failed_requests: int = Field(default=0) api_requests: int = Field(default=0) + total_response_time_ms: int = Field(default=0) + timed_requests: int = Field(default=0) class MetricBase(BaseModel): @@ -93,6 +95,8 @@ class DailySpendMetadata(BaseModel): total_prompt_caching_savings_spend: float = Field(default=0.0) total_gateway_injected_caching_savings_spend: float = Field(default=0.0) total_autorouter_savings_spend: float = Field(default=0.0) + total_response_time_ms: int = Field(default=0) + total_timed_requests: int = Field(default=0) page: int = Field(default=1) total_pages: int = Field(default=1) has_more: bool = Field(default=False) @@ -125,6 +129,8 @@ class LiteLLM_DailyUserSpend(BaseModel): api_requests: int = 0 successful_requests: int = 0 failed_requests: int = 0 + total_response_time_ms: int = 0 + timed_requests: int = 0 class GroupedData(TypedDict): diff --git a/schema.prisma b/schema.prisma index 8072df5aa5b..754614d03ea 100644 --- a/schema.prisma +++ b/schema.prisma @@ -797,6 +797,8 @@ model LiteLLM_DailyUserSpend { api_requests BigInt @default(0) successful_requests BigInt @default(0) failed_requests BigInt @default(0) + total_response_time_ms BigInt @default(0) + timed_requests BigInt @default(0) created_at DateTime @default(now()) updated_at DateTime @updatedAt @@ -833,6 +835,8 @@ model LiteLLM_DailyOrganizationSpend { api_requests BigInt @default(0) successful_requests BigInt @default(0) failed_requests BigInt @default(0) + total_response_time_ms BigInt @default(0) + timed_requests BigInt @default(0) created_at DateTime @default(now()) updated_at DateTime @updatedAt @@ -869,6 +873,8 @@ model LiteLLM_DailyEndUserSpend { api_requests BigInt @default(0) successful_requests BigInt @default(0) failed_requests BigInt @default(0) + total_response_time_ms BigInt @default(0) + timed_requests BigInt @default(0) created_at DateTime @default(now()) updated_at DateTime @updatedAt @@unique([end_user_id, date, api_key, model, custom_llm_provider, mcp_namespaced_tool_name, endpoint]) @@ -904,6 +910,8 @@ model LiteLLM_DailyAgentSpend { api_requests BigInt @default(0) successful_requests BigInt @default(0) failed_requests BigInt @default(0) + total_response_time_ms BigInt @default(0) + timed_requests BigInt @default(0) created_at DateTime @default(now()) updated_at DateTime @updatedAt @@unique([agent_id, date, api_key, model, custom_llm_provider, mcp_namespaced_tool_name, endpoint]) @@ -939,6 +947,8 @@ model LiteLLM_DailyTeamSpend { api_requests BigInt @default(0) successful_requests BigInt @default(0) failed_requests BigInt @default(0) + total_response_time_ms BigInt @default(0) + timed_requests BigInt @default(0) ptu_flat_cost Float @default(0.0) created_at DateTime @default(now()) updated_at DateTime @updatedAt @@ -977,6 +987,8 @@ model LiteLLM_DailyTagSpend { api_requests BigInt @default(0) successful_requests BigInt @default(0) failed_requests BigInt @default(0) + total_response_time_ms BigInt @default(0) + timed_requests BigInt @default(0) created_at DateTime @default(now()) updated_at DateTime @updatedAt diff --git a/tests/test_litellm/proxy/db/db_transaction_queue/test_daily_spend_update_queue.py b/tests/test_litellm/proxy/db/db_transaction_queue/test_daily_spend_update_queue.py index 681132105ad..c17ba75db03 100644 --- a/tests/test_litellm/proxy/db/db_transaction_queue/test_daily_spend_update_queue.py +++ b/tests/test_litellm/proxy/db/db_transaction_queue/test_daily_spend_update_queue.py @@ -209,6 +209,8 @@ async def test_get_aggregated_daily_spend_update_transactions_same_key(): "prompt_caching_savings_spend": 0, "gateway_injected_caching_savings_spend": 0, "autorouter_savings_spend": 0, + "total_response_time_ms": 0, + "timed_requests": 0, } updates = [{test_key: test_transaction1}, {test_key: test_transaction2}] @@ -261,6 +263,8 @@ async def test_flush_and_get_aggregated_daily_spend_update_transactions( "prompt_caching_savings_spend": 0, "gateway_injected_caching_savings_spend": 0, "autorouter_savings_spend": 0, + "total_response_time_ms": 0, + "timed_requests": 0, } # Add updates to queue @@ -550,7 +554,7 @@ async def test_every_optional_daily_metric_aggregates(daily_spend_update_queue): numeric_fields = [ name for name, annotation in BaseDailySpendTransaction.__annotations__.items() if _numeric(annotation) ] - assert "autorouter_savings_spend" in numeric_fields + assert {"autorouter_savings_spend", "total_response_time_ms", "timed_requests"} <= set(numeric_fields) increments = {field: index + 1 for index, field in enumerate(numeric_fields)} await daily_spend_update_queue.add_update({test_key: dict(increments)}) @@ -579,8 +583,12 @@ async def test_optional_metric_missing_from_an_older_payload_still_aggregates( } await daily_spend_update_queue.add_update({test_key: dict(base)}) - await daily_spend_update_queue.add_update({test_key: {**base, "autorouter_savings_spend": 0.25}}) + await daily_spend_update_queue.add_update( + {test_key: {**base, "autorouter_savings_spend": 0.25, "total_response_time_ms": 900, "timed_requests": 1}} + ) await daily_spend_update_queue.aggregate_queue_updates() updates = await daily_spend_update_queue.flush_all_updates_from_in_memory_queue() assert updates[0][test_key]["autorouter_savings_spend"] == pytest.approx(0.25) + assert updates[0][test_key]["total_response_time_ms"] == 900 + assert updates[0][test_key]["timed_requests"] == 1 diff --git a/tests/test_litellm/proxy/db/test_daily_spend_bulk_upsert.py b/tests/test_litellm/proxy/db/test_daily_spend_bulk_upsert.py index c1efb3e7220..510f77cecec 100644 --- a/tests/test_litellm/proxy/db/test_daily_spend_bulk_upsert.py +++ b/tests/test_litellm/proxy/db/test_daily_spend_bulk_upsert.py @@ -85,10 +85,10 @@ def test_one_statement_carries_every_row_in_the_batch(): assert sql.count("INSERT INTO") == 1 assert len(re.findall(r"ON CONFLICT", sql)) == 1 - # 23 bound columns per row plus the inlined updated_at, so the row count is what + # 25 bound columns per row plus the inlined updated_at, so the row count is what # separates one multi-row statement from a hundred single-row ones. - assert len(params) == 100 * 23 - assert "$2300::text" in sql + assert len(params) == 100 * 25 + assert "$2500::text" in sql assert sql.count("(NOW() AT TIME ZONE 'UTC')") == 100 + 1 @@ -104,7 +104,16 @@ def test_conflict_target_is_the_full_unique_constraint(): @pytest.mark.parametrize( "column", - ["prompt_tokens", "completion_tokens", "spend", "api_requests", "successful_requests", "failed_requests"], + [ + "prompt_tokens", + "completion_tokens", + "spend", + "api_requests", + "successful_requests", + "failed_requests", + "total_response_time_ms", + "timed_requests", + ], ) def test_counters_increment_rather_than_overwrite(column): """An overwrite would silently discard every earlier flush's spend for that row.""" diff --git a/tests/test_litellm/proxy/db/test_db_spend_update_writer.py b/tests/test_litellm/proxy/db/test_db_spend_update_writer.py index 5e977712a1e..82b8b5c55c5 100644 --- a/tests/test_litellm/proxy/db/test_db_spend_update_writer.py +++ b/tests/test_litellm/proxy/db/test_db_spend_update_writer.py @@ -2748,6 +2748,76 @@ async def test_daily_transaction_internal_call_keeps_spend_but_not_request_count assert user_sent["successful_requests"] == 1 +def _response_time_payload(request_duration_ms: object, metadata: dict | None = None) -> dict: + return { + "request_id": "req-timed-1", + "user": "test-user", + "startTime": "2026-09-15T00:00:00", + "api_key": "test-key", + "model": "gpt-5.5", + "custom_llm_provider": "openai", + "model_group": "gpt-5.5", + "call_type": "acompletion", + "prompt_tokens": 10, + "completion_tokens": 5, + "spend": 0.01, + "request_duration_ms": request_duration_ms, + "metadata": json.dumps(metadata or {}), + } + + +@pytest.mark.asyncio +@pytest.mark.parametrize("request_duration_ms", [1234, 0]) +async def test_daily_transaction_rolls_up_response_time_for_successful_requests(request_duration_ms: int): + """A successful user-sent request contributes its request_duration_ms to the daily + response-time sum and counts as one timed request, including a 0 ms duration.""" + writer = DBSpendUpdateWriter() + mock_prisma = MagicMock() + mock_prisma.get_request_status = MagicMock(return_value="success") + + transaction = await writer._common_add_spend_log_transaction_to_daily_transaction( + payload=_response_time_payload(request_duration_ms), + prisma_client=mock_prisma, + type="user", + ) + + assert transaction is not None + assert transaction["total_response_time_ms"] == request_duration_ms + assert transaction["timed_requests"] == 1 + + +@pytest.mark.asyncio +@pytest.mark.parametrize( + ("request_status", "request_duration_ms", "metadata"), + [ + ("failure", 1234, {}), + ("success", None, {}), + ("success", -5, {}), + ("success", "1234", {}), + ("success", 1234, {"internal_call_origin": "shadow_eval_judge"}), + ], + ids=["failed", "missing", "negative", "non_int", "internal_call"], +) +async def test_daily_transaction_excludes_untimed_requests_from_response_time( + request_status: str, request_duration_ms: object, metadata: dict +): + """Failed, internal, and missing/invalid-duration requests never enter the response-time + average: both the duration sum and the timed_requests denominator stay at zero.""" + writer = DBSpendUpdateWriter() + mock_prisma = MagicMock() + mock_prisma.get_request_status = MagicMock(return_value=request_status) + + transaction = await writer._common_add_spend_log_transaction_to_daily_transaction( + payload=_response_time_payload(request_duration_ms, metadata), + prisma_client=mock_prisma, + type="user", + ) + + assert transaction is not None + assert transaction["total_response_time_ms"] == 0 + assert transaction["timed_requests"] == 0 + + def _deadlock_error(): from prisma.errors import RawQueryError diff --git a/tests/test_litellm/proxy/management_endpoints/test_common_daily_activity.py b/tests/test_litellm/proxy/management_endpoints/test_common_daily_activity.py index 71896a18f48..b6dd5d04131 100644 --- a/tests/test_litellm/proxy/management_endpoints/test_common_daily_activity.py +++ b/tests/test_litellm/proxy/management_endpoints/test_common_daily_activity.py @@ -157,6 +157,8 @@ async def test_get_daily_activity_aggregated_with_endpoint_breakdown(): "prompt_caching_savings_spend": 0.0, "gateway_injected_caching_savings_spend": 0.0, "autorouter_savings_spend": 0.0, + "total_response_time_ms": 0, + "timed_requests": 0, "failed_requests": 0, } mock_rows = [ @@ -647,6 +649,8 @@ def test_update_breakdown_metrics_includes_user_email(): prompt_caching_savings_spend=0, gateway_injected_caching_savings_spend=0, autorouter_savings_spend=0, + total_response_time_ms=0, + timed_requests=0, total_tokens=2, api_requests=1, successful_requests=1, @@ -722,6 +726,8 @@ async def test_tag_daily_activity_metadata_totals_not_zero(): mock_record_1.prompt_caching_savings_spend = 0.0 mock_record_1.gateway_injected_caching_savings_spend = 0.0 mock_record_1.autorouter_savings_spend = 0.0 + mock_record_1.total_response_time_ms = 18_000 + mock_record_1.timed_requests = 9 mock_record_1.api_requests = 10 mock_record_1.successful_requests = 9 mock_record_1.failed_requests = 1 @@ -746,6 +752,8 @@ async def test_tag_daily_activity_metadata_totals_not_zero(): mock_record_2.prompt_caching_savings_spend = 0.0 mock_record_2.gateway_injected_caching_savings_spend = 0.0 mock_record_2.autorouter_savings_spend = 0.0 + mock_record_2.total_response_time_ms = 2_500 + mock_record_2.timed_requests = 5 mock_record_2.api_requests = 5 mock_record_2.successful_requests = 5 mock_record_2.failed_requests = 0 @@ -778,6 +786,8 @@ async def test_tag_daily_activity_metadata_totals_not_zero(): assert result.metadata.total_successful_requests == 14 # 9 + 5 assert result.metadata.total_failed_requests == 1 assert result.metadata.total_tokens == 1100 # (500+200) + (300+100) + assert result.metadata.total_response_time_ms == 20_500 + assert result.metadata.total_timed_requests == 14 # Verify breakdown still works assert len(result.results) == 1 @@ -786,6 +796,10 @@ async def test_tag_daily_activity_metadata_totals_not_zero(): assert "staging" in daily.breakdown.entities assert daily.breakdown.entities["production"].metrics.spend == 25.0 assert daily.breakdown.entities["staging"].metrics.spend == 5.0 + assert daily.breakdown.models["gpt-4"].metrics.total_response_time_ms == 18_000 + assert daily.breakdown.models["gpt-4"].metrics.timed_requests == 9 + assert daily.breakdown.models["gpt-3.5-turbo"].metrics.total_response_time_ms == 2_500 + assert daily.breakdown.models["gpt-3.5-turbo"].metrics.timed_requests == 5 @pytest.mark.asyncio @@ -810,6 +824,8 @@ async def test_aggregated_activity_preserves_metadata_for_deleted_keys(): "prompt_caching_savings_spend": 0.0, "gateway_injected_caching_savings_spend": 0.0, "autorouter_savings_spend": 0.0, + "total_response_time_ms": 0, + "timed_requests": 0, "failed_requests": 0, } mock_rows = [ @@ -900,6 +916,8 @@ def _daily_user_spend_record(*, user_id, api_key, spend, model="gpt-4", model_gr prompt_caching_savings_spend=0.0, gateway_injected_caching_savings_spend=0.0, autorouter_savings_spend=0.0, + total_response_time_ms=0, + timed_requests=0, api_requests=1, successful_requests=1, failed_requests=0, @@ -1333,6 +1351,8 @@ async def test_get_daily_activity_aggregated_empty_result_set(): "prompt_caching_savings_spend": None, "gateway_injected_caching_savings_spend": None, "autorouter_savings_spend": None, + "total_response_time_ms": None, + "timed_requests": None, "api_requests": None, "successful_requests": None, "failed_requests": None, @@ -1378,6 +1398,8 @@ def _no_spend_record(): prompt_caching_savings_spend=None, gateway_injected_caching_savings_spend=None, autorouter_savings_spend=None, + total_response_time_ms=None, + timed_requests=None, api_requests=None, successful_requests=None, failed_requests=None, @@ -1465,6 +1487,55 @@ class TestEverySavingsDriverSurvivesTheReadPath: ) +class TestResponseTimeSurvivesTheReadPath: + """The dashboard averages total_response_time_ms over timed_requests, so both halves + of the pair must be summed by the rollup query, accumulated across rows, carried by + a single-row conversion, and coalesced when a NULL aggregate comes back.""" + + _FIELDS = ("total_response_time_ms", "timed_requests") + + def test_both_halves_are_summed_by_the_rollup_query(self): + sql, _ = _build_aggregated_sql_query( + table_name="litellm_dailyuserspend", + entity_id_field="user_id", + entity_id="user-1", + start_date="2026-09-01", + end_date="2026-09-30", + model=None, + api_key=None, + timezone_offset_minutes=None, + ) + for field in self._FIELDS: + assert f"SUM({field})" in sql, f"{field} is never summed, so the average reads as zero" + + def test_accumulating_rows_keeps_sum_and_count_paired(self): + first = _no_spend_record() + first.total_response_time_ms = 1500 + first.timed_requests = 2 + second = _no_spend_record() + second.total_response_time_ms = 500 + second.timed_requests = 1 + metrics = update_metrics(update_metrics(SpendMetrics(), first), second) + assert metrics.total_response_time_ms == 2000 + assert metrics.timed_requests == 3 + + def test_single_row_conversion_carries_both_halves(self): + record = _no_spend_record() + record.total_response_time_ms = 1234 + record.timed_requests = 4 + metrics = _record_to_spend_metrics(record) + assert metrics.total_response_time_ms == 1234 + assert metrics.timed_requests == 4 + + def test_null_aggregates_read_as_zero(self): + metrics = _record_to_spend_metrics(_no_spend_record()) + assert metrics.total_response_time_ms == 0 + assert metrics.timed_requests == 0 + accumulated = update_metrics(SpendMetrics(), _no_spend_record()) + assert accumulated.total_response_time_ms == 0 + assert accumulated.timed_requests == 0 + + @pytest.fixture def ptu_cost_attribution_enabled(monkeypatch): monkeypatch.setenv(PTU_COST_ATTRIBUTION_ENV_VAR, "true") @@ -1488,6 +1559,8 @@ def _spend_record(api_key, *, model="gpt-4o-mini-ptu", spend=0.0, ptu_flat_cost= prompt_caching_savings_spend=0, gateway_injected_caching_savings_spend=0, autorouter_savings_spend=0, + total_response_time_ms=0, + timed_requests=0, total_tokens=0, api_requests=0, successful_requests=0, @@ -1554,6 +1627,8 @@ def _grouping_row( prompt_caching_savings_spend=0.0, gateway_injected_caching_savings_spend=0.0, autorouter_savings_spend=0.0, + total_response_time_ms=0, + timed_requests=0, api_requests=0, successful_requests=0, failed_requests=0, @@ -1714,6 +1789,8 @@ def test_update_breakdown_metrics_covers_mcp_endpoint_and_entity(ptu_cost_attrib prompt_caching_savings_spend=0, gateway_injected_caching_savings_spend=0, autorouter_savings_spend=0, + total_response_time_ms=0, + timed_requests=0, total_tokens=0, api_requests=0, successful_requests=0, @@ -2118,6 +2195,8 @@ async def test_get_daily_activity_aggregated_with_entity_breakdown(): "prompt_caching_savings_spend": 0.0, "gateway_injected_caching_savings_spend": 0.0, "autorouter_savings_spend": 0.0, + "total_response_time_ms": 0, + "timed_requests": 0, "failed_requests": 0, "prompt_tokens": 0, "completion_tokens": 0, diff --git a/ui/litellm-dashboard/src/app/(dashboard)/usage/_components/hooks/usePaginatedDailyActivity.test.ts b/ui/litellm-dashboard/src/app/(dashboard)/usage/_components/hooks/usePaginatedDailyActivity.test.ts index 0537f469920..dd3a1238fb1 100644 --- a/ui/litellm-dashboard/src/app/(dashboard)/usage/_components/hooks/usePaginatedDailyActivity.test.ts +++ b/ui/litellm-dashboard/src/app/(dashboard)/usage/_components/hooks/usePaginatedDailyActivity.test.ts @@ -43,6 +43,8 @@ describe("sumMetadata", () => { total_cache_read_input_tokens: 1, total_cache_creation_input_tokens: 1, total_flat_cost: 1, + total_response_time_ms: 1, + total_timed_requests: 1, }; const merged = sumMetadata(page, page); diff --git a/ui/litellm-dashboard/src/app/(dashboard)/usage/_components/hooks/usePaginatedDailyActivity.ts b/ui/litellm-dashboard/src/app/(dashboard)/usage/_components/hooks/usePaginatedDailyActivity.ts index e023feda2e3..a666ddf6256 100644 --- a/ui/litellm-dashboard/src/app/(dashboard)/usage/_components/hooks/usePaginatedDailyActivity.ts +++ b/ui/litellm-dashboard/src/app/(dashboard)/usage/_components/hooks/usePaginatedDailyActivity.ts @@ -30,6 +30,8 @@ const SUMMABLE_METADATA_KEYS = [ "total_cache_read_input_tokens", "total_cache_creation_input_tokens", "total_flat_cost", + "total_response_time_ms", + "total_timed_requests", ] as const; interface DailyActivityResponse { @@ -76,6 +78,8 @@ const EMPTY_DATA: DailyActivityResponse = { total_failed_requests: 0, total_cache_read_input_tokens: 0, total_cache_creation_input_tokens: 0, + total_response_time_ms: 0, + total_timed_requests: 0, total_pages: 1, has_more: false, page: 1, diff --git a/ui/litellm-dashboard/src/components/UsagePage/types.ts b/ui/litellm-dashboard/src/components/UsagePage/types.ts index fd4f1350020..e8bd3cb3a87 100644 --- a/ui/litellm-dashboard/src/components/UsagePage/types.ts +++ b/ui/litellm-dashboard/src/components/UsagePage/types.ts @@ -14,6 +14,8 @@ export interface SpendMetrics { prompt_caching_savings_spend?: number; gateway_injected_caching_savings_spend?: number; autorouter_savings_spend?: number; + total_response_time_ms?: number; + timed_requests?: number; } export type DailyData = { @@ -81,6 +83,8 @@ export interface ModelActivityData { prompt_tokens: number; completion_tokens: number; total_spend: number; + total_response_time_ms?: number; + total_timed_requests?: number; top_api_keys: TopApiKeyData[]; top_models: TopModelData[]; daily_data: { @@ -95,6 +99,7 @@ export interface ModelActivityData { failed_requests: number; cache_read_input_tokens: number; cache_creation_input_tokens: number; + avg_response_time_ms?: number | null; }; }[]; } diff --git a/ui/litellm-dashboard/src/components/UsagePage/utils/value_formatters.test.ts b/ui/litellm-dashboard/src/components/UsagePage/utils/value_formatters.test.ts index 3d2504e6652..09e2529301f 100644 --- a/ui/litellm-dashboard/src/components/UsagePage/utils/value_formatters.test.ts +++ b/ui/litellm-dashboard/src/components/UsagePage/utils/value_formatters.test.ts @@ -1,5 +1,36 @@ import { describe, expect, it } from "vitest"; -import { valueFormatter, valueFormatterSpend } from "./value_formatters"; +import { averageResponseTimeMs, formatResponseTime, valueFormatter, valueFormatterSpend } from "./value_formatters"; + +describe("averageResponseTimeMs", () => { + it("divides the summed duration by the number of timed requests", () => { + expect(averageResponseTimeMs(6000, 4)).toBe(1500); + expect(averageResponseTimeMs(0, 3)).toBe(0); + }); + + it("returns null instead of dividing by zero when nothing was timed", () => { + expect(averageResponseTimeMs(0, 0)).toBeNull(); + expect(averageResponseTimeMs(1200, 0)).toBeNull(); + }); +}); + +describe("formatResponseTime", () => { + it("shows sub-second durations in whole milliseconds", () => { + expect(formatResponseTime(0)).toBe("0ms"); + expect(formatResponseTime(412.6)).toBe("413ms"); + expect(formatResponseTime(999)).toBe("999ms"); + }); + + it("shows durations of a second or more in seconds with two decimals", () => { + expect(formatResponseTime(1000)).toBe("1.00s"); + expect(formatResponseTime(1500)).toBe("1.50s"); + expect(formatResponseTime(12345)).toBe("12.35s"); + }); + + it("shows a dash when there is no average to display", () => { + expect(formatResponseTime(null)).toBe("-"); + expect(formatResponseTime(undefined)).toBe("-"); + }); +}); describe("valueFormatter", () => { it("should format numbers >= 1,000,000 as millions with 2 decimal places", () => { diff --git a/ui/litellm-dashboard/src/components/UsagePage/utils/value_formatters.tsx b/ui/litellm-dashboard/src/components/UsagePage/utils/value_formatters.tsx index a1fb3ec8bb4..b1373698965 100644 --- a/ui/litellm-dashboard/src/components/UsagePage/utils/value_formatters.tsx +++ b/ui/litellm-dashboard/src/components/UsagePage/utils/value_formatters.tsx @@ -11,6 +11,17 @@ export function valueFormatter(number: number) { return number.toString(); } +export function averageResponseTimeMs(totalResponseTimeMs: number, timedRequests: number): number | null { + if (timedRequests <= 0) return null; + return totalResponseTimeMs / timedRequests; +} + +export function formatResponseTime(ms: number | null | undefined) { + if (ms == null) return "-"; + if (ms < 1000) return `${Math.round(ms)}ms`; + return `${(ms / 1000).toFixed(2)}s`; +} + export function valueFormatterSpend(number: number) { if (number === 0) return "$0"; if (number >= 1_000_000_000) { diff --git a/ui/litellm-dashboard/src/components/activity_metrics.test.tsx b/ui/litellm-dashboard/src/components/activity_metrics.test.tsx index 914fe1872b6..2b3e74d6fe6 100644 --- a/ui/litellm-dashboard/src/components/activity_metrics.test.tsx +++ b/ui/litellm-dashboard/src/components/activity_metrics.test.tsx @@ -1424,6 +1424,133 @@ describe("processActivityData", () => { expect(result).toEqual({}); }); + + it("sums response time per model and derives a per-day average over timed requests", () => { + const dayWithModel = (date: string, metrics: Partial & Record) => + createMockDailyData(date, EMPTY_SPEND_METRICS, { + ...EMPTY_BREAKDOWN, + models: { + "gpt-5.5": { metrics: { ...EMPTY_SPEND_METRICS, ...metrics }, metadata: {}, api_key_breakdown: {} }, + }, + }); + const fourTimedRequests = { + api_requests: 4, + successful_requests: 4, + total_response_time_ms: 6000, + timed_requests: 4, + }; + const oneTimedOneFailed = { + api_requests: 2, + successful_requests: 1, + failed_requests: 1, + total_response_time_ms: 500, + timed_requests: 1, + }; + const onlyFailures = { api_requests: 1, successful_requests: 0, failed_requests: 1 }; + const activity: { results: DailyData[] } = { + results: [ + dayWithModel("2025-01-02", fourTimedRequests), + dayWithModel("2025-01-01", oneTimedOneFailed), + dayWithModel("2025-01-03", onlyFailures), + ], + }; + + const result = processActivityData(activity, "models"); + + expect(result["gpt-5.5"].total_response_time_ms).toBe(6500); + expect(result["gpt-5.5"].total_timed_requests).toBe(5); + expect(result["gpt-5.5"].daily_data.map((day) => day.metrics.avg_response_time_ms)).toEqual([500, 1500, null]); + }); + + it("treats rollups written before response time existed as zero timed requests", () => { + const activity: { results: DailyData[] } = { + results: [ + createMockDailyData("2025-01-01", EMPTY_SPEND_METRICS, { + ...EMPTY_BREAKDOWN, + models: { + "gpt-5.5": { + metrics: { ...EMPTY_SPEND_METRICS, api_requests: 3, successful_requests: 3 }, + metadata: {}, + api_key_breakdown: {}, + }, + }, + }), + ], + }; + + const result = processActivityData(activity, "models"); + + expect(result["gpt-5.5"].total_response_time_ms).toBe(0); + expect(result["gpt-5.5"].total_timed_requests).toBe(0); + expect(result["gpt-5.5"].daily_data[0].metrics.avg_response_time_ms).toBeNull(); + }); +}); + +describe("ActivityMetrics response time", () => { + const timedModel = createMockModelActivityData("GPT-5.5", { + total_response_time_ms: 6000, + total_timed_requests: 4, + daily_data: [ + { + date: "2025-01-01", + metrics: { + prompt_tokens: 10, + completion_tokens: 5, + total_tokens: 15, + api_requests: 3, + spend: 1, + successful_requests: 3, + failed_requests: 0, + cache_read_input_tokens: 0, + cache_creation_input_tokens: 0, + avg_response_time_ms: 2000, + }, + }, + { + date: "2025-01-02", + metrics: { + prompt_tokens: 10, + completion_tokens: 5, + total_tokens: 15, + api_requests: 1, + spend: 1, + successful_requests: 1, + failed_requests: 0, + cache_read_input_tokens: 0, + cache_creation_input_tokens: 0, + avg_response_time_ms: 1000, + }, + }, + ], + }); + + it("shows the model's average response time in the summary card and the collapsed header", () => { + render(); + + expect(screen.getByText("Avg Response Time")).toBeInTheDocument(); + expect(screen.getByRole("heading", { name: "1.50s" })).toBeInTheDocument(); + expect(screen.getByText("over 4 timed successful requests")).toBeInTheDocument(); + expect(screen.getByText("1.50s avg response")).toBeInTheDocument(); + }); + + it("renders the per-day response time chart with duration-formatted axis ticks", () => { + render(); + + expect(screen.getByText("Avg Response Time per day")).toBeInTheDocument(); + expect(screen.getByText("Avg Response Time Ms")).toBeInTheDocument(); + expect(screen.getAllByText(/^\d+(\.\d+)?(ms|s)$/).length).toBeGreaterThan(1); + }); + + it("shows a dash and no response time chart when the model has no timed requests", () => { + render(); + + expect(screen.getByText("Avg Response Time")).toBeInTheDocument(); + expect(screen.getByRole("heading", { name: "-" })).toBeInTheDocument(); + expect(screen.getByText("over 0 timed successful requests")).toBeInTheDocument(); + expect(screen.queryByText(/avg response$/)).not.toBeInTheDocument(); + expect(screen.queryByText("Avg Response Time per day")).not.toBeInTheDocument(); + expect(screen.queryByText("Avg Response Time Ms")).not.toBeInTheDocument(); + }); }); describe("formatKeyLabel", () => { diff --git a/ui/litellm-dashboard/src/components/activity_metrics.tsx b/ui/litellm-dashboard/src/components/activity_metrics.tsx index 7c40a91be29..b1df2b598e2 100644 --- a/ui/litellm-dashboard/src/components/activity_metrics.tsx +++ b/ui/litellm-dashboard/src/components/activity_metrics.tsx @@ -1,4 +1,4 @@ -import { AreaChart, BarChart, CustomLegend, CustomTooltip } from "@/components/shared/charts"; +import { AreaChart, BarChart, CustomLegend, CustomTooltip, LineChart } from "@/components/shared/charts"; import { formatNumberWithCommas } from "@/utils/dataUtils"; import { resolveTeamAliasFromTeamID } from "@/utils/teamUtils"; import { Card, CardContent } from "@/components/ui/card"; @@ -9,13 +9,16 @@ import { Team } from "./key_team_helpers/key_list"; import KeyModelUsageView from "./UsagePage/components/KeyModelUsageView"; import { keyActivityLabel } from "./UsagePage/keyActivityLabel"; import { DailyData, KeyMetricWithMetadata, ModelActivityData, TopApiKeyData, TopModelData } from "./UsagePage/types"; -import { valueFormatter } from "./UsagePage/utils/value_formatters"; +import { averageResponseTimeMs, formatResponseTime, valueFormatter } from "./UsagePage/utils/value_formatters"; interface ActivityMetricsProps { modelMetrics: Record; hidePromptCachingMetrics?: boolean; } +const modelAverageResponseTimeMs = (metrics: ModelActivityData): number | null => + averageResponseTimeMs(metrics.total_response_time_ms ?? 0, metrics.total_timed_requests ?? 0); + const ModelSection = ({ modelName, metrics, @@ -28,7 +31,7 @@ const ModelSection = ({ return (
{/* Summary Cards */} -
+

Total Requests

@@ -62,6 +65,17 @@ const ModelSection = ({

+ + +

Avg Response Time

+

+ {formatResponseTime(modelAverageResponseTimeMs(metrics))} +

+

+ over {(metrics.total_timed_requests ?? 0).toLocaleString()} timed successful requests +

+ +
{metrics.top_api_keys && metrics.top_api_keys.length > 0 && ( @@ -154,6 +168,27 @@ const ModelSection = ({ + {(metrics.total_timed_requests ?? 0) > 0 && ( + + +
+

Avg Response Time per day

+ +
+ + + + )} +
@@ -416,6 +451,9 @@ export const ActivityMetrics: React.FC = ({ modelMetrics,
${formatNumberWithCommas(modelMetrics[modelName].total_spend, 2)} {modelMetrics[modelName].total_requests.toLocaleString()} requests + {modelAverageResponseTimeMs(modelMetrics[modelName]) != null && ( + {formatResponseTime(modelAverageResponseTimeMs(modelMetrics[modelName]))} avg response + )}
} @@ -471,11 +509,15 @@ export const processActivityData = ( total_spend: 0, total_cache_read_input_tokens: 0, total_cache_creation_input_tokens: 0, + total_response_time_ms: 0, + total_timed_requests: 0, top_api_keys: [], top_models: [], daily_data: [], }; } + const dayResponseTimeMs = modelData.metrics.total_response_time_ms || 0; + const dayTimedRequests = modelData.metrics.timed_requests || 0; // Update totals modelMetrics[model].total_requests += modelData.metrics.api_requests; modelMetrics[model].prompt_tokens += modelData.metrics.prompt_tokens; @@ -486,6 +528,9 @@ export const processActivityData = ( modelMetrics[model].total_failed_requests += modelData.metrics.failed_requests; modelMetrics[model].total_cache_read_input_tokens += modelData.metrics.cache_read_input_tokens || 0; modelMetrics[model].total_cache_creation_input_tokens += modelData.metrics.cache_creation_input_tokens || 0; + modelMetrics[model].total_response_time_ms = + (modelMetrics[model].total_response_time_ms ?? 0) + dayResponseTimeMs; + modelMetrics[model].total_timed_requests = (modelMetrics[model].total_timed_requests ?? 0) + dayTimedRequests; // Add daily data modelMetrics[model].daily_data.push({ @@ -500,6 +545,7 @@ export const processActivityData = ( failed_requests: modelData.metrics.failed_requests, cache_read_input_tokens: modelData.metrics.cache_read_input_tokens || 0, cache_creation_input_tokens: modelData.metrics.cache_creation_input_tokens || 0, + avg_response_time_ms: averageResponseTimeMs(dayResponseTimeMs, dayTimedRequests), }, }); }); diff --git a/ui/litellm-dashboard/src/lib/http/schema.d.ts b/ui/litellm-dashboard/src/lib/http/schema.d.ts index 4c16a8613eb..3b4498c2a1d 100644 --- a/ui/litellm-dashboard/src/lib/http/schema.d.ts +++ b/ui/litellm-dashboard/src/lib/http/schema.d.ts @@ -27505,6 +27505,11 @@ export interface components { * @default 0 */ total_prompt_tokens: number; + /** + * Total Response Time Ms + * @default 0 + */ + total_response_time_ms: number; /** * Total Spend * @default 0 @@ -27515,6 +27520,11 @@ export interface components { * @default 0 */ total_successful_requests: number; + /** + * Total Timed Requests + * @default 0 + */ + total_timed_requests: number; /** * Total Tokens * @default 0 @@ -37045,6 +37055,16 @@ export interface components { * @default 0 */ successful_requests: number; + /** + * Timed Requests + * @default 0 + */ + timed_requests: number; + /** + * Total Response Time Ms + * @default 0 + */ + total_response_time_ms: number; /** * Total Tokens * @default 0 From 168607cc3f18c7078fa5c3ea2604dc81a2f7c2c2 Mon Sep 17 00:00:00 2001 From: yassin Date: Tue, 15 Sep 2026 22:26:49 +0000 Subject: [PATCH 090/225] fix(proxy): register /nvidia_nim/ with the lazy passthrough loader Co-Authored-By: Devin AI <158243242+devin-ai-integration[bot]@users.noreply.github.com> --- litellm/proxy/_lazy_features.py | 1 + litellm/proxy/_lazy_openapi_snapshot.json | 222 ++++++++++++++++++++++ 2 files changed, 223 insertions(+) diff --git a/litellm/proxy/_lazy_features.py b/litellm/proxy/_lazy_features.py index dd1180b30ad..faf95397fa5 100644 --- a/litellm/proxy/_lazy_features.py +++ b/litellm/proxy/_lazy_features.py @@ -205,6 +205,7 @@ LAZY_FEATURES: Final[tuple[LazyFeature, ...]] = ( "/gigachat/", "/milvus/", "/mistral/", + "/nvidia_nim/", "/openai/", "/openai_passthrough/", "/vertex-ai/", diff --git a/litellm/proxy/_lazy_openapi_snapshot.json b/litellm/proxy/_lazy_openapi_snapshot.json index f3b579d22c7..ad08598e760 100644 --- a/litellm/proxy/_lazy_openapi_snapshot.json +++ b/litellm/proxy/_lazy_openapi_snapshot.json @@ -18912,6 +18912,228 @@ ] } }, + "/nvidia_nim/{endpoint}": { + "delete": { + "description": "Relay a native NVIDIA NIM request through a LiteLLM model group.\n\n`{PROXY_BASE_URL}/nvidia_nim/{model_group}/v1/infer` forwards the body unchanged to the deployment's\n`api_base`, so object detection and OCR NIMs whose payload carries no `model` field still go through\nvirtual key auth, model access checks, and spend logging.", + "operationId": "nvidia_nim_proxy_route_nvidia_nim__endpoint__delete", + "parameters": [ + { + "in": "path", + "name": "endpoint", + "required": true, + "schema": { + "title": "Endpoint", + "type": "string" + } + } + ], + "responses": { + "200": { + "content": { + "application/json": { + "schema": {} + } + }, + "description": "Successful Response" + }, + "422": { + "content": { + "application/json": { + "schema": { + "$ref": "#/components/schemas/HTTPValidationError" + } + } + }, + "description": "Validation Error" + } + }, + "security": [ + { + "APIKeyHeader": [] + } + ], + "summary": "Nvidia Nim Proxy Route", + "tags": [ + "llm_passthrough" + ] + }, + "get": { + "description": "Relay a native NVIDIA NIM request through a LiteLLM model group.\n\n`{PROXY_BASE_URL}/nvidia_nim/{model_group}/v1/infer` forwards the body unchanged to the deployment's\n`api_base`, so object detection and OCR NIMs whose payload carries no `model` field still go through\nvirtual key auth, model access checks, and spend logging.", + "operationId": "nvidia_nim_proxy_route_nvidia_nim__endpoint__get", + "parameters": [ + { + "in": "path", + "name": "endpoint", + "required": true, + "schema": { + "title": "Endpoint", + "type": "string" + } + } + ], + "responses": { + "200": { + "content": { + "application/json": { + "schema": {} + } + }, + "description": "Successful Response" + }, + "422": { + "content": { + "application/json": { + "schema": { + "$ref": "#/components/schemas/HTTPValidationError" + } + } + }, + "description": "Validation Error" + } + }, + "security": [ + { + "APIKeyHeader": [] + } + ], + "summary": "Nvidia Nim Proxy Route", + "tags": [ + "llm_passthrough" + ] + }, + "patch": { + "description": "Relay a native NVIDIA NIM request through a LiteLLM model group.\n\n`{PROXY_BASE_URL}/nvidia_nim/{model_group}/v1/infer` forwards the body unchanged to the deployment's\n`api_base`, so object detection and OCR NIMs whose payload carries no `model` field still go through\nvirtual key auth, model access checks, and spend logging.", + "operationId": "nvidia_nim_proxy_route_nvidia_nim__endpoint__patch", + "parameters": [ + { + "in": "path", + "name": "endpoint", + "required": true, + "schema": { + "title": "Endpoint", + "type": "string" + } + } + ], + "responses": { + "200": { + "content": { + "application/json": { + "schema": {} + } + }, + "description": "Successful Response" + }, + "422": { + "content": { + "application/json": { + "schema": { + "$ref": "#/components/schemas/HTTPValidationError" + } + } + }, + "description": "Validation Error" + } + }, + "security": [ + { + "APIKeyHeader": [] + } + ], + "summary": "Nvidia Nim Proxy Route", + "tags": [ + "llm_passthrough" + ] + }, + "post": { + "description": "Relay a native NVIDIA NIM request through a LiteLLM model group.\n\n`{PROXY_BASE_URL}/nvidia_nim/{model_group}/v1/infer` forwards the body unchanged to the deployment's\n`api_base`, so object detection and OCR NIMs whose payload carries no `model` field still go through\nvirtual key auth, model access checks, and spend logging.", + "operationId": "nvidia_nim_proxy_route_nvidia_nim__endpoint__post", + "parameters": [ + { + "in": "path", + "name": "endpoint", + "required": true, + "schema": { + "title": "Endpoint", + "type": "string" + } + } + ], + "responses": { + "200": { + "content": { + "application/json": { + "schema": {} + } + }, + "description": "Successful Response" + }, + "422": { + "content": { + "application/json": { + "schema": { + "$ref": "#/components/schemas/HTTPValidationError" + } + } + }, + "description": "Validation Error" + } + }, + "security": [ + { + "APIKeyHeader": [] + } + ], + "summary": "Nvidia Nim Proxy Route", + "tags": [ + "llm_passthrough" + ] + }, + "put": { + "description": "Relay a native NVIDIA NIM request through a LiteLLM model group.\n\n`{PROXY_BASE_URL}/nvidia_nim/{model_group}/v1/infer` forwards the body unchanged to the deployment's\n`api_base`, so object detection and OCR NIMs whose payload carries no `model` field still go through\nvirtual key auth, model access checks, and spend logging.", + "operationId": "nvidia_nim_proxy_route_nvidia_nim__endpoint__put", + "parameters": [ + { + "in": "path", + "name": "endpoint", + "required": true, + "schema": { + "title": "Endpoint", + "type": "string" + } + } + ], + "responses": { + "200": { + "content": { + "application/json": { + "schema": {} + } + }, + "description": "Successful Response" + }, + "422": { + "content": { + "application/json": { + "schema": { + "$ref": "#/components/schemas/HTTPValidationError" + } + } + }, + "description": "Validation Error" + } + }, + "security": [ + { + "APIKeyHeader": [] + } + ], + "summary": "Nvidia Nim Proxy Route", + "tags": [ + "llm_passthrough" + ] + } + }, "/openai/deployments/{model}/chat/completions": { "post": { "description": "Follows the exact same API spec as `OpenAI's Chat API https://platform.openai.com/docs/api-reference/chat`\n\n```bash\ncurl -X POST http://localhost:4000/v1/chat/completions \n-H \"Content-Type: application/json\" \n-H \"Authorization: Bearer sk-1234\" \n-d '{\n \"model\": \"gpt-4o\",\n \"messages\": [\n {\n \"role\": \"user\",\n \"content\": \"Hello!\"\n }\n ]\n}'\n```", From 1b6e76af16efc781e10899c7dd433e2447d59dc0 Mon Sep 17 00:00:00 2001 From: yassin Date: Tue, 15 Sep 2026 22:27:24 +0000 Subject: [PATCH 091/225] fix(proxy): resolve LITELLM_LOG for uvicorn at startup and restore logger state in tests Co-Authored-By: Devin AI <158243242+devin-ai-integration[bot]@users.noreply.github.com> --- litellm-proxy-extras/tests/test_logging.py | 37 ++++++++++++---------- litellm/_logging.py | 6 +++- litellm/proxy/proxy_cli.py | 6 ++-- tests/test_litellm/proxy/test_proxy_cli.py | 14 +++----- 4 files changed, 33 insertions(+), 30 deletions(-) diff --git a/litellm-proxy-extras/tests/test_logging.py b/litellm-proxy-extras/tests/test_logging.py index b54d4fb72c9..fb209bc3925 100644 --- a/litellm-proxy-extras/tests/test_logging.py +++ b/litellm-proxy-extras/tests/test_logging.py @@ -1,34 +1,37 @@ import importlib import logging +from collections.abc import Iterator import pytest import litellm_proxy_extras._logging as extras_logging -def test_litellm_log_error_silences_extras_info_lines(monkeypatch): - saved_handlers = logging.getLogger("litellm_proxy_extras").handlers[:] - monkeypatch.setenv("LITELLM_LOG", "ERROR") - logging.getLogger("litellm_proxy_extras").handlers[:] = [] +@pytest.fixture +def fresh_extras_logger() -> Iterator[logging.Logger]: + logger = logging.getLogger("litellm_proxy_extras") + saved_handlers = logger.handlers[:] + saved_level = logger.level + logger.handlers[:] = [] try: - reloaded = importlib.reload(extras_logging).logger - assert reloaded.isEnabledFor(logging.INFO) is False - assert reloaded.isEnabledFor(logging.ERROR) is True + yield logger finally: - logging.getLogger("litellm_proxy_extras").handlers[:] = saved_handlers - logging.getLogger("litellm_proxy_extras").setLevel(logging.INFO) + logger.handlers[:] = saved_handlers + logger.setLevel(saved_level) + + +def test_litellm_log_error_silences_extras_info_lines(monkeypatch, fresh_extras_logger): + monkeypatch.setenv("LITELLM_LOG", "ERROR") + reloaded = importlib.reload(extras_logging).logger + assert reloaded is fresh_extras_logger + assert reloaded.isEnabledFor(logging.INFO) is False + assert reloaded.isEnabledFor(logging.ERROR) is True @pytest.mark.parametrize("litellm_log", [None, "info", "DEBUG"]) -def test_unset_or_verbose_litellm_log_keeps_extras_info_lines(monkeypatch, litellm_log): - saved_handlers = logging.getLogger("litellm_proxy_extras").handlers[:] +def test_unset_or_verbose_litellm_log_keeps_extras_info_lines(monkeypatch, fresh_extras_logger, litellm_log): if litellm_log is None: monkeypatch.delenv("LITELLM_LOG", raising=False) else: monkeypatch.setenv("LITELLM_LOG", litellm_log) - logging.getLogger("litellm_proxy_extras").handlers[:] = [] - try: - assert importlib.reload(extras_logging).logger.isEnabledFor(logging.INFO) is True - finally: - logging.getLogger("litellm_proxy_extras").handlers[:] = saved_handlers - logging.getLogger("litellm_proxy_extras").setLevel(logging.INFO) + assert importlib.reload(extras_logging).logger.isEnabledFor(logging.INFO) is True diff --git a/litellm/_logging.py b/litellm/_logging.py index 893133d62fe..873a6619a81 100644 --- a/litellm/_logging.py +++ b/litellm/_logging.py @@ -401,10 +401,14 @@ def _parse_json_logs_env(value: str | None) -> bool: return (value or "").lower() == "true" +def resolve_log_level(log_level: str) -> int: + return getattr(logging, log_level.upper()) + + json_logs: Final = _parse_json_logs_env(os.getenv("JSON_LOGS")) # Create a handler for the logger (you may need to adapt this based on your needs) log_level: Final = os.getenv("LITELLM_LOG", "DEBUG") -numeric_level: Final[int] = getattr(logging, log_level.upper()) +numeric_level: Final[int] = resolve_log_level(log_level) handler: Final = LevelRoutingStreamHandler() handler.setLevel(numeric_level) handler.addFilter(_secret_filter) diff --git a/litellm/proxy/proxy_cli.py b/litellm/proxy/proxy_cli.py index 6a23b422717..b25c77f6828 100644 --- a/litellm/proxy/proxy_cli.py +++ b/litellm/proxy/proxy_cli.py @@ -261,7 +261,7 @@ class ProxyInitializationHelpers: import uvicorn import litellm - from litellm._logging import _get_uvicorn_json_log_config, numeric_level + from litellm._logging import _get_uvicorn_json_log_config, resolve_log_level uvicorn_args: Final = { "app": "litellm.proxy.proxy_server:app", @@ -275,8 +275,8 @@ class ProxyInitializationHelpers: elif litellm.json_logs: # Use JSON log config for uvicorn to ensure all logs (including exceptions) are JSON uvicorn_args["log_config"] = _get_uvicorn_json_log_config() - elif os.environ.get("LITELLM_LOG"): - uvicorn_args["log_level"] = numeric_level + elif litellm_log := os.environ.get("LITELLM_LOG"): + uvicorn_args["log_level"] = resolve_log_level(litellm_log) if keepalive_timeout is not None: uvicorn_args["timeout_keep_alive"] = keepalive_timeout if timeout_worker_healthcheck is not None: diff --git a/tests/test_litellm/proxy/test_proxy_cli.py b/tests/test_litellm/proxy/test_proxy_cli.py index 8f3c21e10c2..712c526b244 100644 --- a/tests/test_litellm/proxy/test_proxy_cli.py +++ b/tests/test_litellm/proxy/test_proxy_cli.py @@ -143,24 +143,20 @@ class TestProxyInitializationHelpers: def _uvicorn_access_info_enabled(args: dict) -> bool: import logging - names = ("uvicorn", "uvicorn.error", "uvicorn.access", "uvicorn.asgi") - saved = tuple((logging.getLogger(n), logging.getLogger(n).handlers[:], logging.getLogger(n).level) for n in names) + loggers = tuple(logging.getLogger(n) for n in ("uvicorn", "uvicorn.error", "uvicorn.access", "uvicorn.asgi")) + saved = tuple((lg, lg.handlers[:], lg.level, lg.propagate) for lg in loggers) try: uvicorn.Config(**args).configure_logging() return logging.getLogger("uvicorn.access").isEnabledFor(logging.INFO) finally: - for lg, handlers, level in saved: + for lg, handlers, level, propagate in saved: lg.handlers[:] = handlers lg.setLevel(level) + lg.propagate = propagate def test_litellm_log_error_silences_uvicorn_info_lines(self, monkeypatch): - import logging - monkeypatch.setenv("LITELLM_LOG", "ERROR") - with patch( # test-quality-ok: numeric_level is resolved from LITELLM_LOG once at import; no other way to set it - "litellm._logging.numeric_level", logging.ERROR - ): - args = ProxyInitializationHelpers._get_default_unvicorn_init_args("localhost", 8000) + args = ProxyInitializationHelpers._get_default_unvicorn_init_args("localhost", 8000) assert "log_config" not in args assert self._uvicorn_access_info_enabled(args) is False From f6ef0a24e03efd0ddd3c42c3ee5a3c4a8f1e025d Mon Sep 17 00:00:00 2001 From: kerry Date: Tue, 15 Sep 2026 22:28:23 +0000 Subject: [PATCH 092/225] fix(tests): restore rerank response import Co-Authored-By: Devin AI <158243242+devin-ai-integration[bot]@users.noreply.github.com> --- tests/test_litellm/test_cost_calculator.py | 1 + 1 file changed, 1 insertion(+) diff --git a/tests/test_litellm/test_cost_calculator.py b/tests/test_litellm/test_cost_calculator.py index 7fedb43ab6e..aa8a1d34bc1 100644 --- a/tests/test_litellm/test_cost_calculator.py +++ b/tests/test_litellm/test_cost_calculator.py @@ -16,6 +16,7 @@ from litellm.litellm_core_utils.litellm_logging import Logging from litellm.llms.base_llm.ocr.transformation import OCRPage, OCRResponse, OCRUsageInfo from litellm.types.llms.base import CachedTokensDetails from litellm.types.llms.openai import OpenAIRealtimeStreamList +from litellm.types.rerank import RerankResponse from litellm.types.utils import ( ModelInfo, ModelResponse, From 5d6e367d562ef3e32f6c933867b575ad0845599f Mon Sep 17 00:00:00 2001 From: yassin Date: Tue, 15 Sep 2026 22:32:11 +0000 Subject: [PATCH 093/225] fix(proxy): keep alias rewrite off pass-through bodies and auth-merged params Skip router_settings.model_group_alias resolution on registered pass-through routes, rebuild the rewritten body from the cached client payload instead of the auth-enriched request_data, and centralize the resolved-scope sentinel in litellm/constants.py Co-Authored-By: Devin AI <158243242+devin-ai-integration[bot]@users.noreply.github.com> --- litellm/constants.py | 1 + litellm/proxy/auth/user_api_key_auth.py | 28 +++++--------- .../proxy/common_utils/http_parsing_utils.py | 18 +++++++++ .../proxy/auth/test_user_api_key_auth.py | 38 +++++++++++++++++++ 4 files changed, 66 insertions(+), 19 deletions(-) diff --git a/litellm/constants.py b/litellm/constants.py index ca2be8af5fe..d84effde139 100644 --- a/litellm/constants.py +++ b/litellm/constants.py @@ -318,6 +318,7 @@ BEDROCK_REALTIME_PENDING_SESSION_UPDATE_SCOPE_KEY: Final = "litellm.bedrock_real BEDROCK_REALTIME_SESSION_COMMITTED_SCOPE_KEY: Final = "litellm.bedrock_realtime.session_committed" BEDROCK_REALTIME_COMMITTED_FAILURE_SCOPE_KEY: Final = "litellm.bedrock_realtime.committed_failure" CLIENT_REQUESTED_MODEL_SCOPE_KEY: Final = "litellm.client_requested_model" +MODEL_GROUP_ALIAS_RESOLVED_SCOPE_KEY: Final = "litellm.model_group_alias_resolved" REALTIME_SESSION_SUCCESS_LOGGED_KEY: Final = "realtime_session_success_logged" REALTIME_SESSION_FAILURE_LOGGED_KEY: Final = "realtime_session_failure_logged" diff --git a/litellm/proxy/auth/user_api_key_auth.py b/litellm/proxy/auth/user_api_key_auth.py index a02661db9ec..ac0f3696680 100644 --- a/litellm/proxy/auth/user_api_key_auth.py +++ b/litellm/proxy/auth/user_api_key_auth.py @@ -32,6 +32,7 @@ from litellm.constants import ( INVALID_VIRTUAL_KEY_ERROR_MESSAGE, LITELLM_PROXY_BUDGET_NAME, LITELLM_PROXY_MASTER_KEY_ALIAS, + MODEL_GROUP_ALIAS_RESOLVED_SCOPE_KEY, ) from litellm.integrations.otel.model.config import is_otel_v2_enabled from litellm.integrations.otel.runtime import phase_span, seed_request_identity @@ -104,6 +105,7 @@ from litellm.proxy.common_utils.http_parsing_utils import ( _safe_set_request_parsed_body, populate_request_with_path_params, read_raw_json_body, + rewrite_request_model, ) from litellm.proxy.common_utils.model_listing_utils import claude_code_requested_group from litellm.proxy.common_utils.realtime_utils import _realtime_request_body @@ -237,22 +239,7 @@ async def _normalize_claude_model( request.scope[_CLAUDE_MODEL_NORMALIZED] = True if source is None: return - _rewrite_request_model(request_data, request, source) - - -def _rewrite_request_model( - request_data: dict, # mutable-ok: the request body is rewritten in place for every downstream reader - request: Request | None, - model: str, -) -> None: - request_data["model"] = model - _safe_set_request_parsed_body(request=request, parsed_body=request_data) - if request is not None: - request._json = request_data - request._body = orjson.dumps(request_data) - - -_MODEL_GROUP_ALIAS_RESOLVED: Final = "litellm.model_group_alias_resolved" + rewrite_request_model(request_data, request, source) async def _resolve_router_settings_model_group_alias( @@ -264,13 +251,16 @@ async def _resolve_router_settings_model_group_alias( """Rewrite the requested model through the key's or team's ``router_settings.model_group_alias`` before the allowlist checks, so they authorize the model group the request is routed to. """ + from litellm.proxy.pass_through_endpoints.pass_through_endpoints import InitPassThroughEndpointHelpers from litellm.proxy.proxy_server import llm_router, prisma_client, proxy_config, proxy_logging_obj if request is None or llm_router is None or not RouteChecks.is_llm_api_route(route=route): return - if request.scope.get(_MODEL_GROUP_ALIAS_RESOLVED) is True: + if request.scope.get(MODEL_GROUP_ALIAS_RESOLVED_SCOPE_KEY) is True: + return + request.scope[MODEL_GROUP_ALIAS_RESOLVED_SCOPE_KEY] = True + if InitPassThroughEndpointHelpers.is_registered_pass_through_route(route=route): return - request.scope[_MODEL_GROUP_ALIAS_RESOLVED] = True requested: Final = request_data.get("model") if not isinstance(requested, str) or await read_raw_json_body(request=request) is None: return @@ -284,7 +274,7 @@ async def _resolve_router_settings_model_group_alias( return verbose_proxy_logger.debug("router_settings.model_group_alias resolved %s -> %s before auth", requested, target) request.scope.setdefault(CLIENT_REQUESTED_MODEL_SCOPE_KEY, requested) - _rewrite_request_model(request_data, request, target) + rewrite_request_model(request_data, request, target) def _get_model_names_for_budget_checks( diff --git a/litellm/proxy/common_utils/http_parsing_utils.py b/litellm/proxy/common_utils/http_parsing_utils.py index ec2e05541cb..a1dcc6e8ece 100644 --- a/litellm/proxy/common_utils/http_parsing_utils.py +++ b/litellm/proxy/common_utils/http_parsing_utils.py @@ -266,6 +266,24 @@ def _safe_set_request_parsed_body( verbose_proxy_logger.debug("Unexpected error setting request parsed body - %s", e) +def rewrite_request_model( + request_data: dict, # mutable-ok: the request body is rewritten in place for every downstream reader + request: Request | None, + model: str, +) -> None: + """Point the auth-time payload, the parsed-body cache, ``request.json()`` and ``request.body()`` at ``model``. + The cache and raw body keep only the keys the client sent, not params auth merged into ``request_data``. + """ + request_data["model"] = model + if request is None: + return + cached_body: Final = _safe_get_request_parsed_body(request=request) + body: Final = {**cached_body, "model": model} if cached_body is not None else request_data + _safe_set_request_parsed_body(request=request, parsed_body=body) + request._json = body + request._body = orjson.dumps(body) + + def _safe_get_request_headers(request: Request | None) -> dict: """ [Non-Blocking] Safely get the request headers. diff --git a/tests/test_litellm/proxy/auth/test_user_api_key_auth.py b/tests/test_litellm/proxy/auth/test_user_api_key_auth.py index ded45f756be..f974e4d29d2 100644 --- a/tests/test_litellm/proxy/auth/test_user_api_key_auth.py +++ b/tests/test_litellm/proxy/auth/test_user_api_key_auth.py @@ -8209,6 +8209,44 @@ async def test_router_settings_model_group_alias_leaves_form_bodies_alone(monkey assert get_client_requested_model(request) is None +@pytest.mark.asyncio +async def test_router_settings_model_group_alias_rewrite_keeps_query_params_out_of_body(monkeypatch): + """LIT-3054: auth merges query params into its own copy of the body; the rewrite must not forward them.""" + from litellm.proxy.auth.user_api_key_auth import _enforce_key_and_fallback_model_access + from litellm.proxy.common_utils.http_parsing_utils import _read_request_body, populate_request_with_path_params + + router = _alias_router() + monkeypatch.setattr(litellm.proxy.proxy_server, "llm_router", router) + body = {"model": "AgentX-LLM", "messages": [{"role": "user", "content": "hi"}]} + request = _alias_request("/v1/chat/completions", body) + request.scope["query_string"] = b"api-version=2024-10-21&stream=true" + data = populate_request_with_path_params(request_data=await _read_request_body(request), request=request) + assert data["api-version"] == "2024-10-21" + token = _alias_token(monkeypatch, "key", {"AgentX-LLM": "claude-haiku"}, ["claude-haiku"]) + await _enforce_key_and_fallback_model_access(valid_token=token, request_data=data, route="/v1/chat/completions", request=request, llm_model_list=router.model_list, llm_router=router) + downstream = await _read_request_body(request) + assert downstream == {**body, "model": "claude-haiku"} + assert json.loads(await request.body()) == downstream + assert await request.json() == downstream + + +@pytest.mark.asyncio +async def test_router_settings_model_group_alias_leaves_pass_through_bodies_alone(monkeypatch): + """LIT-3054: pass-through routes forward the body verbatim to the provider, so auth must not rewrite it.""" + from litellm.proxy.auth.user_api_key_auth import _enforce_key_and_fallback_model_access + + router = _alias_router() + monkeypatch.setattr(litellm.proxy.proxy_server, "llm_router", router) + data = {"model": "AgentX-LLM", "messages": [{"role": "user", "content": "hi"}]} + route = "/anthropic/v1/messages" + request = _alias_request(route, data) + token = _alias_token(monkeypatch, "key", {"AgentX-LLM": "claude-haiku"}, ["claude-haiku", "AgentX-LLM"]) + await _enforce_key_and_fallback_model_access(valid_token=token, request_data=data, route=route, request=request, llm_model_list=router.model_list, llm_router=router) + assert data["model"] == "AgentX-LLM" + assert (await request.json())["model"] == "AgentX-LLM" + assert get_client_requested_model(request) is None + + @pytest.mark.asyncio @pytest.mark.parametrize("target, expect_denied", [("claude-haiku", False), ("claude-sonnet", True)]) async def test_router_settings_model_group_alias_authorizes_target_for_team(monkeypatch, target, expect_denied): From c48723f21085312ae19df5603c8b494831538819 Mon Sep 17 00:00:00 2001 From: yassin Date: Tue, 15 Sep 2026 22:34:35 +0000 Subject: [PATCH 094/225] fix(proxy): let a db pass-through entry override the yaml entry on the same path Co-Authored-By: Devin AI <158243242+devin-ai-integration[bot]@users.noreply.github.com> --- litellm/proxy/proxy_server.py | 9 +++++- tests/test_litellm/proxy/test_proxy_server.py | 28 +++++++++++++++++++ 2 files changed, 36 insertions(+), 1 deletion(-) diff --git a/litellm/proxy/proxy_server.py b/litellm/proxy/proxy_server.py index 7f90874e38a..8ce997eca8d 100644 --- a/litellm/proxy/proxy_server.py +++ b/litellm/proxy/proxy_server.py @@ -7081,9 +7081,16 @@ class ProxyConfig: ## PASS-THROUGH ENDPOINTS ## if "pass_through_endpoints" in _general_settings: db_pass_through_endpoints: Final = _general_settings["pass_through_endpoints"] + db_pass_through_paths: Final = frozenset( + endpoint.get("path") for endpoint in db_pass_through_endpoints if isinstance(endpoint, dict) + ) general_settings["pass_through_endpoints"] = [ *db_pass_through_endpoints, - *(config_passthrough_endpoints or []), + *( + endpoint + for endpoint in config_passthrough_endpoints or () + if endpoint.get("path") not in db_pass_through_paths + ), ] await initialize_pass_through_endpoints(pass_through_endpoints=db_pass_through_endpoints) diff --git a/tests/test_litellm/proxy/test_proxy_server.py b/tests/test_litellm/proxy/test_proxy_server.py index 62c34738e51..3499a1ee1bc 100644 --- a/tests/test_litellm/proxy/test_proxy_server.py +++ b/tests/test_litellm/proxy/test_proxy_server.py @@ -7435,6 +7435,34 @@ async def test_update_general_settings_keeps_yaml_pass_through_endpoints_next_to assert still_protected.value.code == "401" +@pytest.mark.asyncio +async def test_update_general_settings_db_pass_through_endpoint_overrides_yaml_entry_on_the_same_path(): + """The auth check lets any matching ``auth: false`` entry through, so a DB + ``auth: true`` entry can only lock down a YAML-declared path if the YAML + entry is dropped from the merged list.""" + from litellm.proxy._types import ProxyException + from litellm.proxy.proxy_server import ProxyConfig + + yaml_endpoint: Final = {"path": "/v1/cuopt/request", "target": "https://example.com/post", "auth": False} + db_endpoint: Final = {"id": "db-1", "path": "/v1/cuopt/request", "target": "https://example.com/post", "auth": True} + + request: Final = MagicMock() + request.url.path = "/v1/cuopt/request" + request.headers = {} + request.query_params = {} + + settings: Final = patch("litellm.proxy.proxy_server.general_settings", {"pass_through_endpoints": [yaml_endpoint]}) # test-quality-ok: the method reads this module global; no injection seam + yaml_endpoints: Final = patch("litellm.proxy.proxy_server.config_passthrough_endpoints", [yaml_endpoint]) # test-quality-ok: module global holding the YAML endpoints the fix merges in + initialize: Final = patch("litellm.proxy.proxy_server.initialize_pass_through_endpoints", AsyncMock()) # test-quality-ok: route registration needs the FastAPI app; auth is the observable here + master_key: Final = patch("litellm.proxy.proxy_server.master_key", "sk-master") # test-quality-ok: a set master key is what makes a missing Authorization header a 401 + with settings, yaml_endpoints, initialize, master_key: + await ProxyConfig()._update_general_settings(db_general_settings={"pass_through_endpoints": [db_endpoint]}) + + with pytest.raises(ProxyException) as locked_down: + await user_api_key_auth(request=request, api_key=None) + assert locked_down.value.code == "401" + + def _fill_user_api_key_cache(cache: DualCache, count: int) -> None: for index in range(count): cache.set_cache(key=f"key-{index}", value={"token": f"key-{index}"}, local_only=True) From ebb9a3ceebd9e7ec14eae3b4d05c38131326fa5a Mon Sep 17 00:00:00 2001 From: yassin Date: Tue, 15 Sep 2026 22:34:59 +0000 Subject: [PATCH 095/225] chore(ui): regenerate schema.d.ts for the /nvidia_nim passthrough route Co-Authored-By: Devin AI <158243242+devin-ai-integration[bot]@users.noreply.github.com> --- ui/litellm-dashboard/src/lib/http/schema.d.ts | 211 ++++++++++++++++++ 1 file changed, 211 insertions(+) diff --git a/ui/litellm-dashboard/src/lib/http/schema.d.ts b/ui/litellm-dashboard/src/lib/http/schema.d.ts index 1d6d37360ee..b787a1f1068 100644 --- a/ui/litellm-dashboard/src/lib/http/schema.d.ts +++ b/ui/litellm-dashboard/src/lib/http/schema.d.ts @@ -9676,6 +9676,62 @@ export interface paths { patch?: never; trace?: never; }; + "/nvidia_nim/{endpoint}": { + parameters: { + query?: never; + header?: never; + path?: never; + cookie?: never; + }; + /** + * Nvidia Nim Proxy Route + * @description Relay a native NVIDIA NIM request through a LiteLLM model group. + * + * `{PROXY_BASE_URL}/nvidia_nim/{model_group}/v1/infer` forwards the body unchanged to the deployment's + * `api_base`, so object detection and OCR NIMs whose payload carries no `model` field still go through + * virtual key auth, model access checks, and spend logging. + */ + get: operations["nvidia_nim_proxy_route_nvidia_nim__endpoint__get"]; + /** + * Nvidia Nim Proxy Route + * @description Relay a native NVIDIA NIM request through a LiteLLM model group. + * + * `{PROXY_BASE_URL}/nvidia_nim/{model_group}/v1/infer` forwards the body unchanged to the deployment's + * `api_base`, so object detection and OCR NIMs whose payload carries no `model` field still go through + * virtual key auth, model access checks, and spend logging. + */ + put: operations["nvidia_nim_proxy_route_nvidia_nim__endpoint__put"]; + /** + * Nvidia Nim Proxy Route + * @description Relay a native NVIDIA NIM request through a LiteLLM model group. + * + * `{PROXY_BASE_URL}/nvidia_nim/{model_group}/v1/infer` forwards the body unchanged to the deployment's + * `api_base`, so object detection and OCR NIMs whose payload carries no `model` field still go through + * virtual key auth, model access checks, and spend logging. + */ + post: operations["nvidia_nim_proxy_route_nvidia_nim__endpoint__post"]; + /** + * Nvidia Nim Proxy Route + * @description Relay a native NVIDIA NIM request through a LiteLLM model group. + * + * `{PROXY_BASE_URL}/nvidia_nim/{model_group}/v1/infer` forwards the body unchanged to the deployment's + * `api_base`, so object detection and OCR NIMs whose payload carries no `model` field still go through + * virtual key auth, model access checks, and spend logging. + */ + delete: operations["nvidia_nim_proxy_route_nvidia_nim__endpoint__delete"]; + options?: never; + head?: never; + /** + * Nvidia Nim Proxy Route + * @description Relay a native NVIDIA NIM request through a LiteLLM model group. + * + * `{PROXY_BASE_URL}/nvidia_nim/{model_group}/v1/infer` forwards the body unchanged to the deployment's + * `api_base`, so object detection and OCR NIMs whose payload carries no `model` field still go through + * virtual key auth, model access checks, and spend logging. + */ + patch: operations["nvidia_nim_proxy_route_nvidia_nim__endpoint__patch"]; + trace?: never; + }; "/ocr": { parameters: { query?: never; @@ -53474,6 +53530,161 @@ export interface operations { }; }; }; + nvidia_nim_proxy_route_nvidia_nim__endpoint__get: { + parameters: { + query?: never; + header?: never; + path: { + endpoint: string; + }; + cookie?: never; + }; + requestBody?: never; + responses: { + /** @description Successful Response */ + 200: { + headers: { + [name: string]: unknown; + }; + content: { + "application/json": unknown; + }; + }; + /** @description Validation Error */ + 422: { + headers: { + [name: string]: unknown; + }; + content: { + "application/json": components["schemas"]["HTTPValidationError"]; + }; + }; + }; + }; + nvidia_nim_proxy_route_nvidia_nim__endpoint__put: { + parameters: { + query?: never; + header?: never; + path: { + endpoint: string; + }; + cookie?: never; + }; + requestBody?: never; + responses: { + /** @description Successful Response */ + 200: { + headers: { + [name: string]: unknown; + }; + content: { + "application/json": unknown; + }; + }; + /** @description Validation Error */ + 422: { + headers: { + [name: string]: unknown; + }; + content: { + "application/json": components["schemas"]["HTTPValidationError"]; + }; + }; + }; + }; + nvidia_nim_proxy_route_nvidia_nim__endpoint__post: { + parameters: { + query?: never; + header?: never; + path: { + endpoint: string; + }; + cookie?: never; + }; + requestBody?: never; + responses: { + /** @description Successful Response */ + 200: { + headers: { + [name: string]: unknown; + }; + content: { + "application/json": unknown; + }; + }; + /** @description Validation Error */ + 422: { + headers: { + [name: string]: unknown; + }; + content: { + "application/json": components["schemas"]["HTTPValidationError"]; + }; + }; + }; + }; + nvidia_nim_proxy_route_nvidia_nim__endpoint__delete: { + parameters: { + query?: never; + header?: never; + path: { + endpoint: string; + }; + cookie?: never; + }; + requestBody?: never; + responses: { + /** @description Successful Response */ + 200: { + headers: { + [name: string]: unknown; + }; + content: { + "application/json": unknown; + }; + }; + /** @description Validation Error */ + 422: { + headers: { + [name: string]: unknown; + }; + content: { + "application/json": components["schemas"]["HTTPValidationError"]; + }; + }; + }; + }; + nvidia_nim_proxy_route_nvidia_nim__endpoint__patch: { + parameters: { + query?: never; + header?: never; + path: { + endpoint: string; + }; + cookie?: never; + }; + requestBody?: never; + responses: { + /** @description Successful Response */ + 200: { + headers: { + [name: string]: unknown; + }; + content: { + "application/json": unknown; + }; + }; + /** @description Validation Error */ + 422: { + headers: { + [name: string]: unknown; + }; + content: { + "application/json": components["schemas"]["HTTPValidationError"]; + }; + }; + }; + }; ocr_ocr_post: { parameters: { query?: never; From db0f06d15381f8d9b49e854731473362e4f562ef Mon Sep 17 00:00:00 2001 From: yassin Date: Tue, 15 Sep 2026 22:35:48 +0000 Subject: [PATCH 096/225] fix(proxy): skip team model tpm accounting when key owns model tpm limit Co-Authored-By: Devin AI <158243242+devin-ai-integration[bot]@users.noreply.github.com> --- .../hooks/parallel_request_limiter_v3.py | 32 ++++++++++++-- .../hooks/test_parallel_request_limiter_v3.py | 42 +++++++++++++++++++ 2 files changed, 71 insertions(+), 3 deletions(-) diff --git a/litellm/proxy/hooks/parallel_request_limiter_v3.py b/litellm/proxy/hooks/parallel_request_limiter_v3.py index f35fb1e0042..ea1ef6f9196 100644 --- a/litellm/proxy/hooks/parallel_request_limiter_v3.py +++ b/litellm/proxy/hooks/parallel_request_limiter_v3.py @@ -2893,6 +2893,15 @@ class _PROXY_MaxParallelRequestsHandler_v3(CustomLogger): return batch_limiter return None + def _key_owns_model_limit( + self, + user_api_key_dict: UserAPIKeyAuth, + requested_model: str, + rate_limit_key: Literal["model_rpm_limit", "model_tpm_limit"], + ) -> bool: + key_own_limits: Final = get_key_own_model_rate_limit(user_api_key_dict, rate_limit_key) + return key_own_limits is not None and key_own_limits.get(requested_model) is not None + def _inherited_team_model_limit( self, user_api_key_dict: UserAPIKeyAuth, @@ -2903,11 +2912,23 @@ class _PROXY_MaxParallelRequestsHandler_v3(CustomLogger): team_limit: Final = team_limits.get(requested_model) if team_limits else None if team_limit is None: return None - key_own_limits: Final = get_key_own_model_rate_limit(user_api_key_dict, rate_limit_key) - if key_own_limits and key_own_limits.get(requested_model) is not None: + if self._key_owns_model_limit(user_api_key_dict, requested_model, rate_limit_key): return None return team_limit + def _key_owns_model_tpm_limit_from_request_metadata( + self, + request_metadata: dict[str, Any], + model_group: str | None, + ) -> bool: + if model_group is None: + return False + key_view: Final = UserAPIKeyAuth( + metadata=request_metadata.get("user_api_key_metadata") or {}, + model_max_budget=request_metadata.get("user_api_key_model_max_budget") or {}, + ) + return self._key_owns_model_limit(key_view, model_group, "model_tpm_limit") + def _add_team_model_rate_limit_descriptor_from_metadata( self, user_api_key_dict: UserAPIKeyAuth, @@ -4463,6 +4484,11 @@ class _PROXY_MaxParallelRequestsHandler_v3(CustomLogger): kwargs=kwargs, model_group=reconcile_model, ) + charged_targets: Final = ( + [target for target in targets if target[0] != "model_per_team"] + if self._key_owns_model_tpm_limit_from_request_metadata(request_metadata, reconcile_model) + else targets + ) if reserved_tokens > 0 and total_tokens < reserved_tokens: verbose_proxy_logger.debug( "Releasing unused TPM budget on success: reserved=%s, actual=%s, release=%s", @@ -4472,7 +4498,7 @@ class _PROXY_MaxParallelRequestsHandler_v3(CustomLogger): ) pipeline_operations.extend( self._build_reservation_aware_tpm_ops( - targets=targets, + targets=charged_targets, reserved_scopes=reserved_scopes, actual_tokens=total_tokens, reserved_tokens=reserved_tokens, diff --git a/tests/test_litellm/proxy/hooks/test_parallel_request_limiter_v3.py b/tests/test_litellm/proxy/hooks/test_parallel_request_limiter_v3.py index c2b88776dc5..8d7ab89f354 100644 --- a/tests/test_litellm/proxy/hooks/test_parallel_request_limiter_v3.py +++ b/tests/test_litellm/proxy/hooks/test_parallel_request_limiter_v3.py @@ -6597,3 +6597,45 @@ async def test_key_model_rpm_override_keeps_team_model_tpm_limit(key_limits, ove assert exc.value.status_code == 429 assert "model_per_team" in str(exc.value.detail) assert exc.value.headers["rate_limit_type"] == "tokens" + + +@pytest.mark.parametrize( + "key_metadata, charges_team_model_pool", + [ + ({}, True), + ({"model_rpm_limit": {"test-model": 10}}, True), + ({"model_tpm_limit": {"test-model": 5000}}, False), + ({"model_tpm_limit": {"other-model": 5000}}, True), + ], + ids=["no_override", "rpm_only_override", "tpm_override", "tpm_override_on_other_model"], +) +def test_success_tpm_accounting_skips_team_model_pool_when_key_owns_model_tpm_limit( + key_metadata, charges_team_model_pool +): + handler = _PROXY_MaxParallelRequestsHandler(internal_usage_cache=InternalUsageCache(DualCache())) + response = ModelResponse( + id="team-pool-tpm", + object="chat.completion", + created=int(datetime.now().timestamp()), + model="test-model", + usage=Usage(prompt_tokens=100, completion_tokens=50, total_tokens=150), + choices=[], + ) + kwargs = { + "standard_logging_object": {"metadata": {"user_api_key_hash": hash_token("sk-pool"), "user_api_key_team_id": "t"}}, + "litellm_params": { + "metadata": { + "model_group": "test-model", + "user_api_key_metadata": key_metadata, + "user_api_key_team_metadata": {"model_tpm_limit": {"test-model": 500}}, + } + }, + "model": "test-model", + } + + ops = handler._build_success_event_pipeline_operations(kwargs=kwargs, response_obj=response, rate_limit_type="output") + + charged_keys = {op["key"] for op in ops} + assert handler.create_rate_limit_keys("model_per_key", f"{hash_token('sk-pool')}:test-model", "tokens") in charged_keys + team_pool_key = handler.create_rate_limit_keys("model_per_team", "t:test-model", "tokens") + assert (team_pool_key in charged_keys) is charges_team_model_pool From be082013f7cef4c167a92fee621f0fe8e20e733f Mon Sep 17 00:00:00 2001 From: yassin Date: Tue, 15 Sep 2026 22:38:55 +0000 Subject: [PATCH 097/225] fix(ui): label the response time chart tooltip with the series name Co-Authored-By: Devin AI <158243242+devin-ai-integration[bot]@users.noreply.github.com> --- .../src/components/activity_metrics.test.tsx | 14 ++++++++++++- .../src/components/activity_metrics.tsx | 21 ++++++++++++++++++- 2 files changed, 33 insertions(+), 2 deletions(-) diff --git a/ui/litellm-dashboard/src/components/activity_metrics.test.tsx b/ui/litellm-dashboard/src/components/activity_metrics.test.tsx index 2b3e74d6fe6..1bd7655b5e3 100644 --- a/ui/litellm-dashboard/src/components/activity_metrics.test.tsx +++ b/ui/litellm-dashboard/src/components/activity_metrics.test.tsx @@ -1,7 +1,8 @@ import { fireEvent, render, screen } from "@testing-library/react"; import React from "react"; import { beforeAll, describe, expect, it, vi } from "vitest"; -import { ActivityMetrics, formatKeyLabel, processActivityData } from "./activity_metrics"; +import { ActivityMetrics, formatKeyLabel, processActivityData, ResponseTimeTooltip } from "./activity_metrics"; +import type { ChartTooltipProps } from "@/components/shared/charts"; import { Team } from "./key_team_helpers/key_list"; import { DailyData, KeyMetricWithMetadata, ModelActivityData } from "./UsagePage/types"; @@ -1541,6 +1542,17 @@ describe("ActivityMetrics response time", () => { expect(screen.getAllByText(/^\d+(\.\d+)?(ms|s)$/).length).toBeGreaterThan(1); }); + it("labels the chart tooltip with the readable series name and a formatted duration", () => { + const payload = [ + { dataKey: "metrics.avg_response_time_ms", value: 1500, color: "#f59e0b", payload: timedModel.daily_data[0] }, + ] as NonNullable; + render(); + + expect(screen.getByText("Avg Response Time Ms")).toBeInTheDocument(); + expect(screen.getByText("1.50s")).toBeInTheDocument(); + expect(screen.queryByText("metrics.avg_response_time_ms")).not.toBeInTheDocument(); + }); + it("shows a dash and no response time chart when the model has no timed requests", () => { render(); diff --git a/ui/litellm-dashboard/src/components/activity_metrics.tsx b/ui/litellm-dashboard/src/components/activity_metrics.tsx index b1df2b598e2..95315f8ec27 100644 --- a/ui/litellm-dashboard/src/components/activity_metrics.tsx +++ b/ui/litellm-dashboard/src/components/activity_metrics.tsx @@ -1,4 +1,13 @@ -import { AreaChart, BarChart, CustomLegend, CustomTooltip, LineChart } from "@/components/shared/charts"; +import { + AreaChart, + BarChart, + type ChartTooltipProps, + CustomLegend, + CustomTooltip, + formatCategoryName, + LineChart, + ValueTooltip, +} from "@/components/shared/charts"; import { formatNumberWithCommas } from "@/utils/dataUtils"; import { resolveTeamAliasFromTeamID } from "@/utils/teamUtils"; import { Card, CardContent } from "@/components/ui/card"; @@ -19,6 +28,15 @@ interface ActivityMetricsProps { const modelAverageResponseTimeMs = (metrics: ModelActivityData): number | null => averageResponseTimeMs(metrics.total_response_time_ms ?? 0, metrics.total_timed_requests ?? 0); +export const ResponseTimeTooltip = ({ active, payload, label }: ChartTooltipProps) => ( + ({ ...item, name: formatCategoryName(String(item.dataKey ?? "")) }))} + label={label} + valueFormatter={formatResponseTime} + /> +); + const ModelSection = ({ modelName, metrics, @@ -182,6 +200,7 @@ const ModelSection = ({ categories={["metrics.avg_response_time_ms"]} colors={["amber"]} valueFormatter={formatResponseTime} + customTooltip={ResponseTimeTooltip} connectNulls={true} showLegend={false} /> From b13cf21fd10142b848130ddd2876a1d023a60ab1 Mon Sep 17 00:00:00 2001 From: yassin Date: Tue, 15 Sep 2026 22:06:05 +0000 Subject: [PATCH 098/225] fix(vertex_ai): bill Gemini Omni Interactions usage and Veo sampleCount on passthrough Route Vertex and Gemini /interactions create responses through the shared Interactions usage transformation so SpendLogs carry per-modality tokens and cost instead of zeros, and carry parameters.sampleCount into Veo usage as video_count so duration-based video cost is multiplied by the number of generated videos Co-Authored-By: Devin AI <158243242+devin-ai-integration[bot]@users.noreply.github.com> --- litellm/cost_calculator.py | 20 +++-- litellm/llms/gemini/videos/transformation.py | 4 + .../llms/vertex_ai/videos/transformation.py | 13 ++- .../gemini_passthrough_logging_handler.py | 14 +++ .../vertex_passthrough_logging_handler.py | 73 ++++++++++++++- .../pass_through_endpoints/success_handler.py | 12 ++- .../test_gemini_video_transformation.py | 19 ++++ ...test_vertex_passthrough_logging_handler.py | 90 ++++++++++++++++++- .../test_vertex_video_transformation.py | 27 ++++++ ...test_gemini_passthrough_logging_handler.py | 51 ++++++++++- .../test_pass_through_endpoints.py | 20 +++++ tests/test_litellm/test_cost_calculator.py | 25 ++++++ 12 files changed, 356 insertions(+), 12 deletions(-) diff --git a/litellm/cost_calculator.py b/litellm/cost_calculator.py index 852713595d5..f81eb1539fd 100644 --- a/litellm/cost_calculator.py +++ b/litellm/cost_calculator.py @@ -1460,18 +1460,23 @@ def completion_cost( duration_seconds: float | None = None video_resolution: str | None = None provider_reported_cost: float | None = None + video_count: int = 1 if completion_response is not None and usage_obj: # Handle both dict and Pydantic Usage object if isinstance(usage_obj, dict): duration_seconds = usage_obj.get("duration_seconds", None) _vr = usage_obj.get("video_resolution", None) provider_reported_cost = usage_obj.get("provider_reported_cost_usd", None) + _vc = usage_obj.get("video_count", None) else: duration_seconds = getattr(usage_obj, "duration_seconds", None) _vr = getattr(usage_obj, "video_resolution", None) provider_reported_cost = getattr(usage_obj, "provider_reported_cost_usd", None) + _vc = getattr(usage_obj, "video_count", None) if _vr is not None: video_resolution = str(_vr).strip().lower() + if isinstance(_vc, int) and not isinstance(_vc, bool) and _vc > 1: + video_count = _vc if _video_model_info is None and provider_reported_cost is not None: return float(provider_reported_cost) @@ -1482,12 +1487,15 @@ def completion_cost( video_generation_cost, ) - return video_generation_cost( - model=model, - duration_seconds=duration_seconds, - custom_llm_provider=custom_llm_provider, - model_info=_video_model_info, - video_resolution=video_resolution, + return ( + video_generation_cost( + model=model, + duration_seconds=duration_seconds, + custom_llm_provider=custom_llm_provider, + model_info=_video_model_info, + video_resolution=video_resolution, + ) + * video_count ) # Fallback to default video cost calculation if no duration available return default_video_cost_calculator( diff --git a/litellm/llms/gemini/videos/transformation.py b/litellm/llms/gemini/videos/transformation.py index ff4c675b02f..a44717eb659 100644 --- a/litellm/llms/gemini/videos/transformation.py +++ b/litellm/llms/gemini/videos/transformation.py @@ -9,6 +9,7 @@ import litellm from litellm.constants import DEFAULT_GOOGLE_VIDEO_DURATION_SECONDS from litellm.images.utils import ImageEditRequestUtils from litellm.llms.base_llm.videos.transformation import BaseVideoConfig +from litellm.llms.vertex_ai.videos.transformation import veo_video_count_from_parameters from litellm.secret_managers.main import get_secret_str from litellm.types.llms.gemini import ( GeminiLongRunningOperationResponse, @@ -354,6 +355,9 @@ class GeminiVideoConfig(BaseVideoConfig): video_resolution: Final = _usage_video_resolution_from_parameters(parameters) if video_resolution is not None: usage_data["video_resolution"] = video_resolution + video_count: Final = veo_video_count_from_parameters(parameters) + if video_count is not None: + usage_data["video_count"] = video_count video_obj.usage = usage_data return video_obj diff --git a/litellm/llms/vertex_ai/videos/transformation.py b/litellm/llms/vertex_ai/videos/transformation.py index c66ad8e38b0..6525c6fa235 100644 --- a/litellm/llms/vertex_ai/videos/transformation.py +++ b/litellm/llms/vertex_ai/videos/transformation.py @@ -70,10 +70,18 @@ def _parse_veo_operation(raw_response: httpx.Response) -> _VeoOperation: return operation +def veo_video_count_from_parameters(parameters: Mapping[str, object]) -> int | None: + """Number of videos Veo generates for one request (``parameters.sampleCount``).""" + sample_count: Final = parameters.get("sampleCount") + if isinstance(sample_count, bool) or not isinstance(sample_count, int) or sample_count < 1: + return None + return sample_count + + def _build_vertex_video_usage_from_request_data( request_data: dict[str, Any] | None, ) -> dict[str, float | str]: - """Build usage metadata (duration, resolution) for video cost calculation.""" + """Build usage metadata (duration, resolution, video count) for video cost calculation.""" usage_data: Final[dict[str, float | str]] = {} if not request_data: return usage_data @@ -88,6 +96,9 @@ def _build_vertex_video_usage_from_request_data( res: Final = parameters.get("resolution") if res is not None and str(res).strip() != "": usage_data["video_resolution"] = str(res).strip().lower() + video_count: Final = veo_video_count_from_parameters(parameters) + if video_count is not None: + usage_data["video_count"] = video_count return usage_data diff --git a/litellm/proxy/pass_through_endpoints/llm_provider_handlers/gemini_passthrough_logging_handler.py b/litellm/proxy/pass_through_endpoints/llm_provider_handlers/gemini_passthrough_logging_handler.py index 64d8b2929b6..a95ee87fd31 100644 --- a/litellm/proxy/pass_through_endpoints/llm_provider_handlers/gemini_passthrough_logging_handler.py +++ b/litellm/proxy/pass_through_endpoints/llm_provider_handlers/gemini_passthrough_logging_handler.py @@ -12,6 +12,9 @@ from litellm.llms.vertex_ai.gemini.vertex_and_google_ai_studio_gemini import ( ModelResponseIterator as GeminiModelResponseIterator, ) from litellm.proxy._types import PassThroughEndpointLoggingTypedDict +from litellm.proxy.pass_through_endpoints.llm_provider_handlers.vertex_passthrough_logging_handler import ( + VertexPassthroughLoggingHandler, +) from litellm.types.utils import ( ModelResponse, TextCompletionResponse, @@ -40,6 +43,17 @@ class GeminiPassthroughLoggingHandler: request_body: dict, **kwargs, ) -> PassThroughEndpointLoggingTypedDict: + if VertexPassthroughLoggingHandler.is_interactions_route(url_route): + return VertexPassthroughLoggingHandler.interactions_passthrough_handler( + httpx_response=httpx_response, + request_body=request_body, + logging_obj=logging_obj, + kwargs=kwargs, + start_time=start_time, + end_time=end_time, + custom_llm_provider="gemini", + vertex_location=None, + ) if "predictLongRunning" in url_route: model = GeminiPassthroughLoggingHandler.extract_model_from_url(url_route) diff --git a/litellm/proxy/pass_through_endpoints/llm_provider_handlers/vertex_passthrough_logging_handler.py b/litellm/proxy/pass_through_endpoints/llm_provider_handlers/vertex_passthrough_logging_handler.py index 119a53c2411..0c4bc2a5181 100644 --- a/litellm/proxy/pass_through_endpoints/llm_provider_handlers/vertex_passthrough_logging_handler.py +++ b/litellm/proxy/pass_through_endpoints/llm_provider_handlers/vertex_passthrough_logging_handler.py @@ -1,7 +1,8 @@ import asyncio import re +from collections.abc import Mapping from datetime import datetime -from typing import TYPE_CHECKING, Any, Final, cast +from typing import TYPE_CHECKING, Any, Final, Literal, cast from urllib.parse import urlparse import httpx @@ -10,6 +11,9 @@ import litellm from litellm._logging import verbose_proxy_logger from litellm.constants import VERTEX_BATCH_PREDICTION_JOBS_ROUTE from litellm.litellm_core_utils.litellm_logging import Logging as LiteLLMLoggingObj +from litellm.litellm_core_utils.llm_cost_calc.usage_object_transformation import ( + InteractionsUsageObjectTransformation, +) from litellm.llms.vertex_ai.common_utils import ( get_vertex_ai_lyria_generation_cost, get_vertex_location_from_url, @@ -50,7 +54,63 @@ else: EndpointType = Any +def _interactions_model( + response_body: Mapping[str, object], + request_body: Mapping[str, object] | None, +) -> str | None: + response_model: Final = response_body.get("model") + if isinstance(response_model, str) and response_model: + return response_model + request_model: Final = (request_body or {}).get("model") + if isinstance(request_model, str) and request_model: + return request_model + return None + + class VertexPassthroughLoggingHandler: + @staticmethod + def is_interactions_route(url_route: str) -> bool: + return urlparse(url_route).path.rstrip("/").endswith("/interactions") + + @staticmethod + def interactions_passthrough_handler( + httpx_response: httpx.Response, + request_body: Mapping[str, object] | None, + logging_obj: LiteLLMLoggingObj, + kwargs: dict, + start_time: datetime, + end_time: datetime, + custom_llm_provider: Literal["vertex_ai", "gemini"], + vertex_location: str | None, + ) -> PassThroughEndpointLoggingTypedDict: + response_body: Final[Mapping[str, object]] = httpx_response.json() + usage_object: Final = response_body.get("usage") + model: Final = _interactions_model(response_body, request_body) + if model is None or not InteractionsUsageObjectTransformation.is_interactions_usage_object(usage_object): + return {"result": None, "kwargs": kwargs} + + litellm_model_response: Final = ModelResponse( + model=model, + usage=InteractionsUsageObjectTransformation.transform_interactions_usage_object( + cast(Mapping[str, Any], usage_object) + ), + ) + logging_kwargs: Final = ( + VertexPassthroughLoggingHandler._create_vertex_response_logging_payload_for_generate_content( + litellm_model_response=litellm_model_response, + model=model, + kwargs=kwargs, + start_time=start_time, + end_time=end_time, + logging_obj=logging_obj, + custom_llm_provider=custom_llm_provider, + vertex_location=vertex_location, + ) + ) + logging_kwargs["custom_llm_provider"] = custom_llm_provider + logging_obj.custom_llm_provider = custom_llm_provider + return {"result": litellm_model_response, "kwargs": logging_kwargs} + @staticmethod def vertex_passthrough_handler( httpx_response: httpx.Response, @@ -66,6 +126,17 @@ class VertexPassthroughLoggingHandler: vertex_location: Final = get_vertex_location_from_url(url_route) if vertex_location is not None: logging_obj.optional_params["vertex_location"] = vertex_location + if VertexPassthroughLoggingHandler.is_interactions_route(url_route): + return VertexPassthroughLoggingHandler.interactions_passthrough_handler( + httpx_response=httpx_response, + request_body=request_body, + logging_obj=logging_obj, + kwargs=kwargs, + start_time=start_time, + end_time=end_time, + custom_llm_provider="vertex_ai", + vertex_location=vertex_location, + ) if "predictLongRunning" in url_route: model = VertexPassthroughLoggingHandler.extract_model_from_url(url_route) diff --git a/litellm/proxy/pass_through_endpoints/success_handler.py b/litellm/proxy/pass_through_endpoints/success_handler.py index c38566375f4..dd8bd17eee9 100644 --- a/litellm/proxy/pass_through_endpoints/success_handler.py +++ b/litellm/proxy/pass_through_endpoints/success_handler.py @@ -361,7 +361,11 @@ class PassThroughEndpointLogging: def is_vertex_route(self, url_route: str) -> bool: if any(f":{method}" in url_route for method in self.TRACKED_VERTEX_METHOD_ROUTES): return True - return any(resource in url_route for resource in self.TRACKED_VERTEX_RESOURCE_ROUTES) + if any(resource in url_route for resource in self.TRACKED_VERTEX_RESOURCE_ROUTES): + return True + return "/locations/" in urlparse(url_route).path and VertexPassthroughLoggingHandler.is_interactions_route( + url_route + ) def is_anthropic_route(self, url_route: str): for route in self.TRACKED_ANTHROPIC_ROUTES: @@ -434,8 +438,12 @@ class PassThroughEndpointLogging: def is_gemini_route(self, url_route: str, custom_llm_provider: str | None = None): """Check if the URL route is a Gemini API route.""" + if custom_llm_provider != "gemini": + return False + if VertexPassthroughLoggingHandler.is_interactions_route(url_route): + return True for route in self.TRACKED_GEMINI_ROUTES: - if route in url_route and custom_llm_provider == "gemini": + if route in url_route: return True return False diff --git a/tests/test_litellm/llms/gemini/videos/test_gemini_video_transformation.py b/tests/test_litellm/llms/gemini/videos/test_gemini_video_transformation.py index 6f215deed4e..1ac451d17db 100644 --- a/tests/test_litellm/llms/gemini/videos/test_gemini_video_transformation.py +++ b/tests/test_litellm/llms/gemini/videos/test_gemini_video_transformation.py @@ -430,6 +430,25 @@ class TestGeminiVideoConfig: assert result.usage["video_resolution"] == "1080p" assert result.usage["duration_seconds"] == 8.0 + def test_transform_video_create_response_usage_includes_video_count(self): + """Regression for LIT-6896: sampleCount (number of generated videos) is copied into usage for billing.""" + mock_response = Mock(spec=httpx.Response) + mock_response.json.return_value = {"name": "operations/generate_1234567890"} + request_data = { + "instances": [{"prompt": "Test"}], + "parameters": {"durationSeconds": 8, "sampleCount": 3}, + } + result = self.config.transform_video_create_response( + model="gemini/veo-3.1-fast-generate-preview", + raw_response=mock_response, + logging_obj=self.mock_logging_obj, + custom_llm_provider="gemini", + request_data=request_data, + ) + assert result.usage is not None + assert result.usage["video_count"] == 3 + assert result.usage["duration_seconds"] == 8.0 + def test_transform_video_create_response_cost_tracking_with_different_durations( self, ): diff --git a/tests/test_litellm/llms/vertex_ai/test_vertex_passthrough_logging_handler.py b/tests/test_litellm/llms/vertex_ai/test_vertex_passthrough_logging_handler.py index 98010021bca..a9c5e94389c 100644 --- a/tests/test_litellm/llms/vertex_ai/test_vertex_passthrough_logging_handler.py +++ b/tests/test_litellm/llms/vertex_ai/test_vertex_passthrough_logging_handler.py @@ -9,7 +9,95 @@ import litellm from litellm.proxy.pass_through_endpoints.llm_provider_handlers.vertex_passthrough_logging_handler import ( VertexPassthroughLoggingHandler, ) -from litellm.types.utils import PassthroughCallTypes +from litellm.types.utils import ModelResponse, PassthroughCallTypes + +_OMNI_INTERACTIONS_USAGE: Final = { + "total_tokens": 4041, + "total_input_tokens": 12, + "input_tokens_by_modality": [{"modality": "text", "tokens": 12}], + "total_output_tokens": 4009, + "output_tokens_by_modality": [ + {"modality": "text", "tokens": 9}, + {"modality": "video", "tokens": 4000}, + ], + "total_tool_use_tokens": 0, + "total_thought_tokens": 20, +} + + +def test_interactions_create_response_logs_modality_usage_and_cost() -> None: + """ + Regression for LIT-6896: gemini-omni Interactions passthrough rows were logged + with zero tokens and zero spend. Input, text-output and video-output tokens + must land in usage, priced with the model's per-modality rates, and the + response id must stay the litellm_call_id so SpendLogs keep their request_id. + """ + logging_obj = MagicMock() + logging_obj.model_call_details = {} + logging_obj.optional_params = {} + logging_obj.litellm_call_id = "call-6896" + response = httpx.Response( + status_code=200, + json={ + "id": "interactions/abc", + "model": "gemini-omni-flash-preview", + "status": "completed", + "outputs": [{"type": "text", "text": "hi"}], + "usage": _OMNI_INTERACTIONS_USAGE, + }, + ) + + result = VertexPassthroughLoggingHandler.vertex_passthrough_handler( + httpx_response=response, + logging_obj=logging_obj, + url_route="https://aiplatform.googleapis.com/v1beta1/projects/p/locations/global/interactions", + result=response.text, + start_time=datetime.now(), + end_time=datetime.now(), + cache_hit=False, + request_body={"model": "gemini-omni-flash-preview", "input": [{"type": "text", "text": "say hi"}]}, + ) + + model_response = result["result"] + assert isinstance(model_response, ModelResponse) + assert model_response.id == "call-6896" + usage = model_response.usage + assert usage.prompt_tokens == 12 + assert usage.completion_tokens == 4009 + 20 + assert usage.completion_tokens_details.text_tokens == 9 + assert usage.completion_tokens_details.video_tokens == 4000 + + model_info = litellm.get_model_info(model="gemini-omni-flash-preview", custom_llm_provider="vertex_ai") + expected_cost = ( + 12 * model_info["input_cost_per_token"] + + (9 + 20) * model_info["output_cost_per_token"] + + 4000 * model_info["output_cost_per_video_token"] + ) + assert result["kwargs"]["response_cost"] == pytest.approx(expected_cost) + assert result["kwargs"]["custom_llm_provider"] == "vertex_ai" + assert logging_obj.model_call_details["model"] == "gemini-omni-flash-preview" + assert logging_obj.model_call_details["custom_llm_provider"] == "vertex_ai" + + +def test_interactions_response_without_usage_falls_back_to_generic_logging() -> None: + logging_obj = MagicMock() + logging_obj.model_call_details = {} + logging_obj.optional_params = {} + response = httpx.Response(status_code=200, json={"id": "interactions/abc", "status": "in_progress"}) + + result = VertexPassthroughLoggingHandler.vertex_passthrough_handler( + httpx_response=response, + logging_obj=logging_obj, + url_route="https://aiplatform.googleapis.com/v1beta1/projects/p/locations/global/interactions", + result=response.text, + start_time=datetime.now(), + end_time=datetime.now(), + cache_hit=False, + request_body={"agent": "projects/p/locations/global/reasoningEngines/1"}, + ) + + assert result["result"] is None + assert "response_cost" not in result["kwargs"] def test_lyria_predict_response_preserves_audio_response_and_logs_cost( diff --git a/tests/test_litellm/llms/vertex_ai/videos/test_vertex_video_transformation.py b/tests/test_litellm/llms/vertex_ai/videos/test_vertex_video_transformation.py index 04e46eab1b7..c192d22b3b7 100644 --- a/tests/test_litellm/llms/vertex_ai/videos/test_vertex_video_transformation.py +++ b/tests/test_litellm/llms/vertex_ai/videos/test_vertex_video_transformation.py @@ -717,6 +717,33 @@ class TestVertexAIVideoConfig: assert video_obj.usage["duration_seconds"] == 8.0 assert video_obj.usage["video_resolution"] == "1080p" + @pytest.mark.parametrize( + "sample_count,expected_video_count", + [(2, 2), (1, 1), (None, None), (0, None), ("2", None)], + ids=["two", "one", "unset", "zero", "string"], + ) + def test_transform_video_create_response_usage_includes_video_count(self, sample_count, expected_video_count): + """Regression for LIT-6896: sampleCount is the number of generated videos and must reach usage for billing.""" + mock_response = Mock(spec=httpx.Response) + mock_response.json.return_value = { + "name": "projects/p/locations/us-central1/publishers/google/models/veo-3.1-fast-generate-001/operations/op-1" + } + parameters = {"durationSeconds": 4, "resolution": "720p"} + if sample_count is not None: + parameters["sampleCount"] = sample_count + + video_obj = self.config.transform_video_create_response( + model="veo-3.1-fast-generate-001", + raw_response=mock_response, + logging_obj=self.mock_logging_obj, + custom_llm_provider="vertex_ai", + request_data={"instances": [{"prompt": "a red ball"}], "parameters": parameters}, + ) + + assert video_obj.usage is not None + assert video_obj.usage["duration_seconds"] == 4.0 + assert video_obj.usage.get("video_count") == expected_video_count + def test_transform_video_remix_request_not_supported(self): """Test that video remix raises NotImplementedError.""" with pytest.raises(NotImplementedError, match="Video remix is not supported"): diff --git a/tests/test_litellm/proxy/pass_through_endpoints/llm_provider_handlers/test_gemini_passthrough_logging_handler.py b/tests/test_litellm/proxy/pass_through_endpoints/llm_provider_handlers/test_gemini_passthrough_logging_handler.py index 61d1caacb91..b89ae530d6f 100644 --- a/tests/test_litellm/proxy/pass_through_endpoints/llm_provider_handlers/test_gemini_passthrough_logging_handler.py +++ b/tests/test_litellm/proxy/pass_through_endpoints/llm_provider_handlers/test_gemini_passthrough_logging_handler.py @@ -5,7 +5,7 @@ from unittest.mock import AsyncMock, MagicMock, patch import httpx import pytest - +import litellm from litellm.litellm_core_utils.litellm_logging import Logging as LiteLLMLoggingObj from litellm.proxy.pass_through_endpoints.llm_provider_handlers.gemini_passthrough_logging_handler import ( GeminiPassthroughLoggingHandler, @@ -397,3 +397,52 @@ class TestGeminiPassthroughLoggingHandler: assert mock_logging_obj.model_call_details["response_cost"] == expected_cost assert mock_logging_obj.model_call_details["model"] == "veo-2.0-generate-001" assert mock_logging_obj.model_call_details["custom_llm_provider"] == "gemini" + + def test_interactions_create_response_is_priced_as_gemini(self): + """Regression for LIT-6896: Gemini API Interactions passthrough must not log zero usage.""" + usage = { + "total_tokens": 1030, + "total_input_tokens": 10, + "input_tokens_by_modality": [{"modality": "text", "tokens": 10}], + "total_output_tokens": 1020, + "output_tokens_by_modality": [ + {"modality": "text", "tokens": 20}, + {"modality": "video", "tokens": 1000}, + ], + "total_tool_use_tokens": 0, + "total_thought_tokens": 0, + } + mock_httpx_response = MagicMock(spec=httpx.Response) + mock_httpx_response.json.return_value = { + "id": "interactions/abc", + "model": "gemini-omni-flash-preview", + "status": "completed", + "usage": usage, + } + mock_logging_obj = MagicMock(spec=LiteLLMLoggingObj) + mock_logging_obj.model_call_details = {} + mock_logging_obj.litellm_call_id = "call-6896" + + result = GeminiPassthroughLoggingHandler.gemini_passthrough_handler( + httpx_response=mock_httpx_response, + response_body=mock_httpx_response.json.return_value, + logging_obj=mock_logging_obj, + url_route="https://generativelanguage.googleapis.com/v1beta/interactions", + result="", + start_time=self.start_time, + end_time=self.end_time, + cache_hit=False, + request_body={"model": "gemini-omni-flash-preview", "input": "make a clip"}, + ) + + model_info = litellm.get_model_info(model="gemini-omni-flash-preview", custom_llm_provider="gemini") + expected_cost = ( + 10 * model_info["input_cost_per_token"] + + 20 * model_info["output_cost_per_token"] + + 1000 * model_info["output_cost_per_video_token"] + ) + assert result["result"].id == "call-6896" + assert result["result"].usage.completion_tokens_details.video_tokens == 1000 + assert result["kwargs"]["response_cost"] == pytest.approx(expected_cost) + assert result["kwargs"]["custom_llm_provider"] == "gemini" + assert mock_logging_obj.model_call_details["custom_llm_provider"] == "gemini" diff --git a/tests/test_litellm/proxy/pass_through_endpoints/test_pass_through_endpoints.py b/tests/test_litellm/proxy/pass_through_endpoints/test_pass_through_endpoints.py index 11066d4ed38..988c4782f6c 100644 --- a/tests/test_litellm/proxy/pass_through_endpoints/test_pass_through_endpoints.py +++ b/tests/test_litellm/proxy/pass_through_endpoints/test_pass_through_endpoints.py @@ -496,6 +496,26 @@ def test_is_vertex_route_ignores_plain_predict_path_segment(): ) +def test_interactions_create_routes_are_tracked_for_vertex_and_gemini(): + """ + Regression for LIT-6896: Interactions API (gemini-omni) passthrough responses + were never handed to the Vertex/Gemini logging handlers, so SpendLogs rows + landed with zero tokens and zero spend. Only the create URL is billable; + GET/DELETE on an interaction id and non-Google `/interactions` URLs stay generic. + """ + handler = PassThroughEndpointLogging() + vertex_create = "https://aiplatform.googleapis.com/v1beta1/projects/p/locations/global/interactions" + gemini_create = "https://generativelanguage.googleapis.com/v1beta/interactions" + + assert handler.is_vertex_route(vertex_create) is True + assert handler.is_vertex_route(f"{vertex_create}/abc123") is False + assert handler.is_vertex_route("https://upstream.example.com/api/interactions") is False + + assert handler.is_gemini_route(gemini_create, custom_llm_provider="gemini") is True + assert handler.is_gemini_route(f"{gemini_create}/abc123", custom_llm_provider="gemini") is False + assert handler.is_gemini_route(gemini_create, custom_llm_provider=None) is False + + @pytest.mark.asyncio async def test_custom_passthrough_predict_path_logs_via_generic_handler(): """ diff --git a/tests/test_litellm/test_cost_calculator.py b/tests/test_litellm/test_cost_calculator.py index ef797ef8bcc..f7ea2c00891 100644 --- a/tests/test_litellm/test_cost_calculator.py +++ b/tests/test_litellm/test_cost_calculator.py @@ -4141,6 +4141,31 @@ def test_completion_cost_bills_interactions_video_output_at_video_rate(): assert cost == pytest.approx(expected) +@pytest.mark.parametrize("video_count", [2, 3]) +def test_completion_cost_multiplies_video_cost_by_generated_video_count(video_count: int) -> None: + """Regression for LIT-6896: a Veo request for N samples generates N videos and must be billed N times.""" + from litellm.types.videos.main import VideoObject + + def _video(usage: dict[str, object]) -> VideoObject: + return VideoObject(id="v", object="video", status="processing", model="veo-3.1-fast-generate-001", usage=usage) + + single_cost = completion_cost( + completion_response=_video({"duration_seconds": 4.0, "video_resolution": "720p"}), + model="veo-3.1-fast-generate-001", + custom_llm_provider="vertex_ai", + call_type="create_video", + ) + multi_cost = completion_cost( + completion_response=_video({"duration_seconds": 4.0, "video_resolution": "720p", "video_count": video_count}), + model="veo-3.1-fast-generate-001", + custom_llm_provider="vertex_ai", + call_type="create_video", + ) + + assert single_cost > 0 + assert multi_cost == pytest.approx(single_cost * video_count) + + @pytest.mark.parametrize( "batch_rate,expected_prompt,expected_completion", [ From 65132ef2e92ea1afbe6bed1813404a27e59772f7 Mon Sep 17 00:00:00 2001 From: shivam Date: Tue, 15 Sep 2026 22:42:58 +0000 Subject: [PATCH 099/225] fix(cost): price native Responses WebSocket turns at their returned service_tier The logging object built for aresponses_websocket sessions dropped the service_tier carried by each billable response.completed/response.incomplete event, so sessions were priced at the default tier. The logging object now carries the tier when the session is single-tier, and completion_cost splits mixed-tier sessions into one object per tier before pricing. Fixes #41299 Co-Authored-By: Devin AI <158243242+devin-ai-integration[bot]@users.noreply.github.com> --- litellm/cost_calculator.py | 85 +++++++++++++++++-- litellm/litellm_core_utils/litellm_logging.py | 5 ++ litellm/types/utils.py | 1 + .../test_litellm_logging.py | 60 ++++++++++++- tests/test_litellm/test_cost_calculator.py | 77 ++++++++++++++++- 5 files changed, 219 insertions(+), 9 deletions(-) diff --git a/litellm/cost_calculator.py b/litellm/cost_calculator.py index 852713595d5..fca2172886a 100644 --- a/litellm/cost_calculator.py +++ b/litellm/cost_calculator.py @@ -1203,6 +1203,24 @@ def _without_provider_stated_cost(usage: Usage | None) -> Usage | None: return usage.model_copy(update=MappingProxyType({"cost": None})) +def _split_responses_ws_logging_object_by_service_tier( + completion_response: LiteLLMRealtimeStreamLoggingObject, +) -> tuple[LiteLLMRealtimeStreamLoggingObject, ...] | None: + partition: Final = ResponsesWebSocketTokenUsageProcessor.partition_results_by_service_tier( + cast(Sequence[Mapping[str, object]], completion_response.results) + ) + if len(partition) <= 1: + return None + return tuple( + LiteLLMRealtimeStreamLoggingObject( + results=cast(OpenAIRealtimeStreamList, list(group)), + usage=ResponsesWebSocketTokenUsageProcessor.collect_and_combine_usage_from_responses_ws_results(group), + service_tier=tier, + ) + for tier, group in partition.items() + ) + + def completion_cost( completion_response: object | None = None, model: str | None = None, @@ -1266,6 +1284,41 @@ def completion_cost( try: call_type = _infer_call_type(call_type, completion_response) or "completion" + if call_type == CallTypes.aresponses_websocket.value and isinstance( + completion_response, LiteLLMRealtimeStreamLoggingObject + ): + ws_tier_parts: Final = _split_responses_ws_logging_object_by_service_tier(completion_response) + if ws_tier_parts is not None: + return sum( + completion_cost( + completion_response=part, + model=model, + prompt=prompt, + messages=messages, + completion=completion, + total_time=total_time, + call_type=call_type, + custom_llm_provider=custom_llm_provider, + region_name=region_name, + size=size, + quality=quality, + n=n, + custom_cost_per_token=custom_cost_per_token, + custom_cost_per_second=custom_cost_per_second, + optional_params=optional_params, + custom_pricing=custom_pricing, + base_model=base_model, + standard_built_in_tools_params=standard_built_in_tools_params, + litellm_model_name=litellm_model_name, + router_model_id=router_model_id, + litellm_logging_obj=litellm_logging_obj, + service_tier=service_tier, + data_residency=data_residency, + vertex_location=vertex_location, + ) + for part in ws_tier_parts + ) + if ( (call_type == "aimage_generation" or call_type == "image_generation") and model is not None @@ -2558,6 +2611,7 @@ _RESPONSES_WS_BILLABLE_EVENT_TYPES: Final = frozenset({"response.completed", "re class _ResponsesWsEventResponse(BaseModel): usage: Mapping[str, object] | None = None + service_tier: str | None = None class _ResponsesWsEvent(BaseModel): @@ -2565,20 +2619,39 @@ class _ResponsesWsEvent(BaseModel): response: _ResponsesWsEventResponse | None = None +def _billable_responses_ws_events( + results: Sequence[Mapping[str, object]], +) -> tuple[tuple[Mapping[str, object], _ResponsesWsEventResponse], ...]: + return tuple( + (result, event.response) + for result in results + if (event := _ResponsesWsEvent.model_validate(result)).type in _RESPONSES_WS_BILLABLE_EVENT_TYPES + and event.response is not None + and event.response.usage is not None + ) + + class ResponsesWebSocketTokenUsageProcessor(BaseTokenUsageProcessor): @staticmethod def collect_usage_from_responses_ws_results( results: Sequence[Mapping[str, object]], ) -> tuple[Usage, ...]: - events: Final = tuple(_ResponsesWsEvent.model_validate(result) for result in results) return tuple( ResponseAPILoggingUtils._transform_response_api_usage_to_chat_usage( # pyright: ignore[reportPrivateUsage] # same shared transform the realtime processor uses - event.response.usage + response.usage ) - for event in events - if event.type in _RESPONSES_WS_BILLABLE_EVENT_TYPES - and event.response is not None - and event.response.usage is not None + for _, response in _billable_responses_ws_events(results) + if response.usage is not None + ) + + @staticmethod + def partition_results_by_service_tier( + results: Sequence[Mapping[str, object]], + ) -> Mapping[str | None, tuple[Mapping[str, object], ...]]: + billable: Final = _billable_responses_ws_events(results) + tiers: Final = dict.fromkeys(response.service_tier for _, response in billable) + return MappingProxyType( + {tier: tuple(result for result, response in billable if response.service_tier == tier) for tier in tiers} ) @staticmethod diff --git a/litellm/litellm_core_utils/litellm_logging.py b/litellm/litellm_core_utils/litellm_logging.py index abac624d5ec..40621a2f68d 100644 --- a/litellm/litellm_core_utils/litellm_logging.py +++ b/litellm/litellm_core_utils/litellm_logging.py @@ -2101,9 +2101,14 @@ class Logging(LiteLLMLoggingBaseClass): results=result # pyright: ignore[reportUnknownArgumentType] # raw event dicts from the WS stream ) ) + ws_tier_partition: Final = ResponsesWebSocketTokenUsageProcessor.partition_results_by_service_tier( + results=result # pyright: ignore[reportUnknownArgumentType] # raw event dicts from the WS stream + ) + ws_service_tier: Final = next(iter(ws_tier_partition)) if len(ws_tier_partition) == 1 else None logging_result = LiteLLMRealtimeStreamLoggingObject( usage=combined_ws_usage, results=result, # pyright: ignore[reportUnknownArgumentType] # raw event dicts from the WS stream + service_tier=ws_service_tier, ) elif ( diff --git a/litellm/types/utils.py b/litellm/types/utils.py index fdf533fb4e9..298c30bbec2 100644 --- a/litellm/types/utils.py +++ b/litellm/types/utils.py @@ -4275,6 +4275,7 @@ class LiteLLMRealtimeStreamLoggingObject(LiteLLMPydanticObjectBase): # rate_limits.updated), blocks the event loop, and discards the session usage. results: SkipValidation[OpenAIRealtimeStreamList] usage: Usage + service_tier: str | None = None _hidden_params: dict = {} @field_serializer("results") diff --git a/tests/test_litellm/litellm_core_utils/test_litellm_logging.py b/tests/test_litellm/litellm_core_utils/test_litellm_logging.py index dd1ad9c9623..aaf44b8e918 100644 --- a/tests/test_litellm/litellm_core_utils/test_litellm_logging.py +++ b/tests/test_litellm/litellm_core_utils/test_litellm_logging.py @@ -6554,9 +6554,9 @@ async def test_prompt_hook_injection_marker_recorded_for_every_surface(logging_o assert pre_choice["metadata"]["litellm_gateway_injected_cache"] == "" -def _responses_ws_logging_obj() -> LitellmLogging: +def _responses_ws_logging_obj(model: str = "gpt-4o") -> LitellmLogging: return LitellmLogging( - model="gpt-4o", + model=model, messages=[], stream=False, call_type=CallTypes.aresponses_websocket.value, @@ -6638,6 +6638,62 @@ def test_normalize_logging_result_bills_incomplete_responses_websocket_turns(): assert normalized.usage.total_tokens == 75 +def test_normalize_logging_result_prices_responses_websocket_at_returned_service_tier(): + """Issue #41299: a WebSocket turn billed at priority tier reported it on + response.completed.response.service_tier, but the logging object dropped it and the + session was priced at the default tier.""" + events = [ + {"type": "response.created", "response": {}}, + { + "type": "response.completed", + "response": { + "service_tier": "priority", + "usage": {"input_tokens": 100, "output_tokens": 40, "total_tokens": 140}, + }, + }, + ] + + normalized = _responses_ws_logging_obj(model="gpt-5.4").normalize_logging_result(result=events) + + assert isinstance(normalized, LiteLLMRealtimeStreamLoggingObject) + assert normalized.service_tier == "priority" + + usage = ResponseAPIUsage(input_tokens=100, output_tokens=40, total_tokens=140) + ws_cost = litellm.completion_cost( + completion_response=normalized, + model="gpt-5.4", + call_type=CallTypes.aresponses_websocket.value, + custom_llm_provider="openai", + ) + priority_http_cost = litellm.completion_cost( + completion_response=ResponsesAPIResponse( + id="resp-priority", + created_at=1700000000, + output=[], + service_tier="priority", + usage=usage, + ), + model="gpt-5.4", + call_type=CallTypes.aresponses.value, + custom_llm_provider="openai", + ) + default_http_cost = litellm.completion_cost( + completion_response=ResponsesAPIResponse( + id="resp-default", + created_at=1700000000, + output=[], + service_tier="default", + usage=usage, + ), + model="gpt-5.4", + call_type=CallTypes.aresponses.value, + custom_llm_provider="openai", + ) + + assert ws_cost == priority_http_cost + assert priority_http_cost > default_http_cost + + def test_get_standard_logging_object_payload_reads_overhead_from_logging_obj_for_dict_results(logging_obj): """LIT-5466: /v1/messages returns a plain dict with no _hidden_params, so the overhead recorded on the logging object must reach hidden_params.litellm_overhead_time_ms (SpendLogs).""" diff --git a/tests/test_litellm/test_cost_calculator.py b/tests/test_litellm/test_cost_calculator.py index ef797ef8bcc..6f5cde58ff3 100644 --- a/tests/test_litellm/test_cost_calculator.py +++ b/tests/test_litellm/test_cost_calculator.py @@ -1,4 +1,5 @@ +import time from typing import Final import pytest @@ -9,6 +10,7 @@ import litellm from litellm.cost_calculator import ( BaseTokenUsageProcessor, RealtimeAPITokenUsageProcessor, + ResponsesWebSocketTokenUsageProcessor, completion_cost, cost_per_token, handle_realtime_stream_cost_calculation, @@ -17,10 +19,12 @@ from litellm.cost_calculator import ( from litellm.litellm_core_utils.litellm_logging import Logging from litellm.llms.base_llm.ocr.transformation import OCRPage, OCRResponse, OCRUsageInfo from litellm.types.llms.base import CachedTokensDetails -from litellm.types.llms.openai import OpenAIRealtimeStreamList +from litellm.types.llms.openai import OpenAIRealtimeStreamList, ResponseAPIUsage, ResponsesAPIResponse from litellm.types.rerank import RerankResponse from litellm.types.utils import ( CacheCreationTokenDetails, + CallTypes, + LiteLLMRealtimeStreamLoggingObject, ModelInfo, ModelResponse, PromptTokensDetailsWrapper, @@ -5243,3 +5247,74 @@ def test_completion_cost_ocr_ignores_deployment_pricing_without_custom_pricing_f litellm_logging_obj=logging_obj, ) assert cost == 0.0 + + +def test_completion_cost_prices_responses_websocket_turns_per_service_tier(): + """Issue #41299: a session mixing default and priority turns must price each turn at + its own returned service_tier, not the summed usage at a single tier.""" + events = [ + {"type": "response.created", "response": {}}, + { + "type": "response.completed", + "response": { + "service_tier": "default", + "usage": {"input_tokens": 100, "output_tokens": 40, "total_tokens": 140}, + }, + }, + {"type": "rate_limits.updated", "rate_limits": {}}, + { + "type": "response.completed", + "response": { + "service_tier": "priority", + "usage": {"input_tokens": 60, "output_tokens": 10, "total_tokens": 70}, + }, + }, + {"type": "response.failed", "response": {"usage": None}}, + ] + + partition = ResponsesWebSocketTokenUsageProcessor.partition_results_by_service_tier(events) + assert tuple(partition.keys()) == ("default", "priority") + assert len(partition["default"]) == 1 + assert len(partition["priority"]) == 1 + + logging_obj = Logging( + model="gpt-5.4", + messages=[], + stream=False, + call_type=CallTypes.aresponses_websocket.value, + start_time=time.time(), + litellm_call_id="responses-ws-tier-test", + function_id="responses-ws-tier-test", + ) + normalized = logging_obj.normalize_logging_result(result=events) + assert isinstance(normalized, LiteLLMRealtimeStreamLoggingObject) + assert normalized.service_tier is None + + def _http_cost(input_tokens: int, output_tokens: int, service_tier: str) -> float: + return completion_cost( + completion_response=ResponsesAPIResponse( + id=f"resp-{service_tier}", + created_at=1700000000, + output=[], + service_tier=service_tier, + usage=ResponseAPIUsage( + input_tokens=input_tokens, + output_tokens=output_tokens, + total_tokens=input_tokens + output_tokens, + ), + ), + model="gpt-5.4", + call_type=CallTypes.aresponses.value, + custom_llm_provider="openai", + ) + + ws_cost = completion_cost( + completion_response=normalized, + model="gpt-5.4", + call_type=CallTypes.aresponses_websocket.value, + custom_llm_provider="openai", + ) + + assert ws_cost == pytest.approx(_http_cost(100, 40, "default") + _http_cost(60, 10, "priority")) + assert ws_cost != pytest.approx(_http_cost(160, 50, "default")) + assert ws_cost != pytest.approx(_http_cost(160, 50, "priority")) From d4d8cc9092c5a825dc2972e124101198f9fe6a90 Mon Sep 17 00:00:00 2001 From: yassin Date: Tue, 15 Sep 2026 22:44:03 +0000 Subject: [PATCH 100/225] fix(proxy): apply access denied message to JWT paths, sanitize denial log, await dashboard saves Co-Authored-By: Devin AI <158243242+devin-ai-integration[bot]@users.noreply.github.com> --- litellm/proxy/auth/auth_checks.py | 10 ++-- litellm/proxy/auth/handle_jwt.py | 13 ++++- litellm/proxy/proxy_server.py | 12 ++-- .../proxy/auth/test_auth_checks.py | 22 ++++++- .../proxy/auth/test_handle_jwt.py | 57 +++++++++++++++++++ tests/test_litellm/proxy/test_proxy_server.py | 54 ++++++++++++------ .../general_settings.integration.test.tsx | 25 ++++++++ .../_components/general_settings.tsx | 32 +++++------ 8 files changed, 176 insertions(+), 49 deletions(-) diff --git a/litellm/proxy/auth/auth_checks.py b/litellm/proxy/auth/auth_checks.py index 6435ce0d12f..b13214eaf5e 100644 --- a/litellm/proxy/auth/auth_checks.py +++ b/litellm/proxy/auth/auth_checks.py @@ -4034,11 +4034,11 @@ async def _get_agent_ids_from_access_groups( ) -def _client_facing_model_access_denied_message(internal_message: str, model: str | list[str]) -> str: +def client_facing_model_access_denied_message(internal_message: str, model: str | list[str]) -> str: template: Final = litellm.model_access_denied_message if not template: return internal_message - verbose_proxy_logger.warning(internal_message) + verbose_proxy_logger.warning(internal_message.replace("\r", "").replace("\n", "")) return template.replace(MODEL_ACCESS_DENIED_MESSAGE_MODEL_PLACEHOLDER, str(model)) @@ -4164,7 +4164,7 @@ def _can_object_call_model( return True raise ProxyException( - message=_client_facing_model_access_denied_message( + message=client_facing_model_access_denied_message( internal_message=f"{object_type} not allowed to access model. This {object_type} can only access models={models}. Tried to access {model}", model=model, ), @@ -4793,7 +4793,7 @@ async def can_user_call_model( if SpecialModelNames.no_default_models.value in user_object.models: raise ProxyException( - message=_client_facing_model_access_denied_message( + message=client_facing_model_access_denied_message( internal_message=f"User not allowed to access model. No default model access, only team models allowed. Tried to access {model}", model=model, ), @@ -5398,7 +5398,7 @@ async def _check_team_member_model_access( ) except ProxyException: raise ProxyException( - message=_client_facing_model_access_denied_message( + message=client_facing_model_access_denied_message( internal_message=f"Team member not allowed to access model. User={valid_token.user_id}, Team={team_object.team_id}, Model={model}. Allowed member models = {member_allowed_models}", model=model, ), diff --git a/litellm/proxy/auth/handle_jwt.py b/litellm/proxy/auth/handle_jwt.py index 94ca3047f45..6e5e75a5e20 100644 --- a/litellm/proxy/auth/handle_jwt.py +++ b/litellm/proxy/auth/handle_jwt.py @@ -66,6 +66,7 @@ from litellm.types.agents import AgentResponse from .auth_checks import ( _allowed_routes_check, allowed_routes_check, + client_facing_model_access_denied_message, get_actual_routes, get_end_user_object, get_org_object, @@ -1339,7 +1340,10 @@ class JWTAuthManager: if model not in role_based_models: raise HTTPException( status_code=403, - detail=f"Role={rbac_role} not allowed to call model={model}. Allowed models={role_based_models}", + detail=client_facing_model_access_denied_message( + internal_message=f"Role={rbac_role} not allowed to call model={model}. Allowed models={role_based_models}", + model=model, + ), ) return True @@ -1370,7 +1374,12 @@ class JWTAuthManager: if requested_model not in allowed_models: raise HTTPException( status_code=403, - detail={"error": f"model={requested_model} not allowed. Allowed_models={allowed_models}"}, + detail={ + "error": client_facing_model_access_denied_message( + internal_message=f"model={requested_model} not allowed. Allowed_models={allowed_models}", + model=requested_model, + ) + }, ) return diff --git a/litellm/proxy/proxy_server.py b/litellm/proxy/proxy_server.py index f758628c02a..b0e1cc2e0fe 100644 --- a/litellm/proxy/proxy_server.py +++ b/litellm/proxy/proxy_server.py @@ -17435,12 +17435,12 @@ GeneralSettingsUILiteLLMValue = float | bool | str | None class GeneralSettingsUILiteLLMFieldSpec(TypedDict): - type: Literal["Float", "Dollar", "Boolean", "Select", "String"] - description: str - options: NotRequired[tuple[str, ...]] - tab: NotRequired[str] # Admin UI sub-tab this field renders under; None groups it with the rest - default: NotRequired[ - float | bool + type: ReadOnly[Literal["Float", "Dollar", "Boolean", "Select", "String"]] + description: ReadOnly[str] + options: ReadOnly[NotRequired[tuple[str, ...]]] + tab: ReadOnly[NotRequired[str]] # Admin UI sub-tab this field renders under; None groups it with the rest + default: ReadOnly[ + NotRequired[float | bool] ] # reset/clear restores this instead of None; fields whose None means fail-open set it diff --git a/tests/test_litellm/proxy/auth/test_auth_checks.py b/tests/test_litellm/proxy/auth/test_auth_checks.py index e99e92fcd02..0052cfb66d3 100644 --- a/tests/test_litellm/proxy/auth/test_auth_checks.py +++ b/tests/test_litellm/proxy/auth/test_auth_checks.py @@ -1683,8 +1683,6 @@ _DENIED_MESSAGE_TEMPLATE: Final = "The model `{model}` is unavailable for this A def test_can_object_call_model_denial_uses_configured_message_and_logs_detail(monkeypatch, caplog): - """LIT-5283: with model_access_denied_message set, the client sees only the template with - {model} filled in, while the allowed models / access groups stay in the proxy log.""" monkeypatch.setattr(litellm, "model_access_denied_message", _DENIED_MESSAGE_TEMPLATE) with caplog.at_level("WARNING", logger="LiteLLM Proxy"): @@ -1706,6 +1704,26 @@ def test_can_object_call_model_denial_uses_configured_message_and_logs_detail(mo assert "anthropic-sonnet-4-5" in caplog.text +def test_can_object_call_model_denial_log_strips_newlines_from_requested_model(monkeypatch, caplog): + monkeypatch.setattr(litellm, "model_access_denied_message", _DENIED_MESSAGE_TEMPLATE) + + with caplog.at_level("WARNING", logger="LiteLLM Proxy"): + with pytest.raises(ProxyException): + _can_object_call_model( + model="gpt-5.6\r\nWARNING forged log line", + llm_router=None, + models=["internal-models"], + object_type="key", + ) + + denial_records = [r for r in caplog.records if "not allowed to access model" in r.getMessage()] + assert len(denial_records) == 1 + assert denial_records[0].getMessage() == ( + "key not allowed to access model. This key can only access models=['internal-models']. " + "Tried to access gpt-5.6WARNING forged log line" + ) + + @pytest.mark.parametrize("unset_value", [None, ""]) def test_can_object_call_model_denial_unchanged_when_message_not_configured(monkeypatch, unset_value): monkeypatch.setattr(litellm, "model_access_denied_message", unset_value) diff --git a/tests/test_litellm/proxy/auth/test_handle_jwt.py b/tests/test_litellm/proxy/auth/test_handle_jwt.py index 814e31535e0..921472f2f43 100644 --- a/tests/test_litellm/proxy/auth/test_handle_jwt.py +++ b/tests/test_litellm/proxy/auth/test_handle_jwt.py @@ -9,6 +9,8 @@ from fastapi import HTTPException import httpx import pytest +import litellm + from litellm.proxy._types import ( DEFAULT_JWKS_STALE_TTL, JWTLiteLLMRoleMap, @@ -21,6 +23,8 @@ from litellm.proxy._types import ( Member, ProxyErrorTypes, ProxyException, + RoleBasedPermissions, + ScopeMapping, ) from litellm.caching.dual_cache import DualCache from litellm.proxy.agent_endpoints.agent_registry import AgentRegistry @@ -6965,3 +6969,56 @@ async def test_auth_builder_denies_jwt_naming_unregistered_agent_before_admin_ch ) assert exc_info.value.status_code == 403 + + +_JWT_DENIED_MESSAGE_TEMPLATE = "The model `{model}` is unavailable for this identity." + + +@pytest.mark.parametrize( + "configured_message, expected_detail", + [ + (None, "Role=internal_user not allowed to call model=gpt-5.6. Allowed models=['gpt-5.6-mini']"), + ("", "Role=internal_user not allowed to call model=gpt-5.6. Allowed models=['gpt-5.6-mini']"), + (_JWT_DENIED_MESSAGE_TEMPLATE, "The model `gpt-5.6` is unavailable for this identity."), + ], +) +def test_can_rbac_role_call_model_denial_honors_configured_message(monkeypatch, configured_message, expected_detail): + monkeypatch.setattr(litellm, "model_access_denied_message", configured_message) + general_settings = { + "role_permissions": [ + RoleBasedPermissions(role=LitellmUserRoles.INTERNAL_USER, models=["gpt-5.6-mini"]), + ] + } + + with pytest.raises(HTTPException) as exc_info: + JWTAuthManager.can_rbac_role_call_model( + rbac_role=LitellmUserRoles.INTERNAL_USER, + general_settings=general_settings, + model="gpt-5.6", + ) + + assert exc_info.value.status_code == 403 + assert exc_info.value.detail == expected_detail + + +@pytest.mark.parametrize( + "configured_message, expected_error", + [ + (None, "model=gpt-5.6 not allowed. Allowed_models=['gpt-5.6-mini']"), + ("", "model=gpt-5.6 not allowed. Allowed_models=['gpt-5.6-mini']"), + (_JWT_DENIED_MESSAGE_TEMPLATE, "The model `gpt-5.6` is unavailable for this identity."), + ], +) +def test_check_scope_based_access_denial_honors_configured_message(monkeypatch, configured_message, expected_error): + monkeypatch.setattr(litellm, "model_access_denied_message", configured_message) + + with pytest.raises(HTTPException) as exc_info: + JWTAuthManager.check_scope_based_access( + scope_mappings=[ScopeMapping(scope="litellm.api.consumer", models=["gpt-5.6-mini"])], + scopes=["litellm.api.consumer"], + request_data={"model": "gpt-5.6"}, + general_settings={}, + ) + + assert exc_info.value.status_code == 403 + assert exc_info.value.detail == {"error": expected_error} diff --git a/tests/test_litellm/proxy/test_proxy_server.py b/tests/test_litellm/proxy/test_proxy_server.py index 17847575de1..dd05b90bb67 100644 --- a/tests/test_litellm/proxy/test_proxy_server.py +++ b/tests/test_litellm/proxy/test_proxy_server.py @@ -10862,24 +10862,15 @@ def test_validate_max_ui_session_budget_empty_restores_default(empty_value): @pytest.mark.asyncio async def test_update_config_field_model_access_denied_message_sets_live_value(monkeypatch): - """LIT-5283: the client-facing model access denial message is editable from the Admin UI - General tab as a String field, applies live via setattr, and persists under litellm_settings.""" - from unittest.mock import MagicMock + from unittest.mock import AsyncMock, MagicMock import litellm.proxy.proxy_server as ps from litellm.proxy._types import ConfigFieldUpdate, LitellmUserRoles, UserAPIKeyAuth from litellm.proxy.proxy_server import update_config_general_settings - saved: dict = {} - - async def fake_get_config(): - return {"litellm_settings": {}} - - async def fake_save_config(new_config=None): - saved.update(new_config or {}) - - monkeypatch.setattr(ps.proxy_config, "get_config", fake_get_config) - monkeypatch.setattr(ps.proxy_config, "save_config", fake_save_config) + save_config = AsyncMock() + monkeypatch.setattr(ps.proxy_config, "get_config", AsyncMock(return_value={"litellm_settings": {}})) + monkeypatch.setattr(ps.proxy_config, "save_config", save_config) monkeypatch.setattr(ps, "prisma_client", MagicMock()) monkeypatch.setattr(litellm, "store_audit_logs", False) monkeypatch.setattr(litellm, "model_access_denied_message", None) @@ -10895,7 +10886,11 @@ async def test_update_config_field_model_access_denied_message_sets_live_value(m ) assert litellm.model_access_denied_message == "Model `{model}` is unavailable for this key." - assert saved["litellm_settings"]["model_access_denied_message"] == "Model `{model}` is unavailable for this key." + save_config.assert_awaited_once() + saved_config = save_config.await_args.kwargs["new_config"] + assert saved_config["litellm_settings"]["model_access_denied_message"] == ( + "Model `{model}` is unavailable for this key." + ) @pytest.mark.parametrize("bad_value", [True, 3, 1.5, ["x"], {"a": "b"}]) @@ -10918,14 +10913,41 @@ def test_validate_model_access_denied_message_empty_restores_detailed_default(em @pytest.mark.parametrize("empty_value", [None, ""]) def test_validate_expose_router_debug_in_errors_empty_restores_true_default(empty_value): - """Clearing the field from the Admin UI must restore the historical default (debug details - exposed), not the generic Boolean fallback of False.""" from litellm.proxy.proxy_server import _validate_general_settings_ui_litellm_value assert _validate_general_settings_ui_litellm_value("expose_router_debug_in_errors", empty_value) is True assert _validate_general_settings_ui_litellm_value("expose_router_debug_in_errors", False) is False +@pytest.mark.parametrize( + "field_name, booted_value, db_value, read_setting", + [ + ( + "model_access_denied_message", + None, + "Model `{model}` is unavailable for this key.", + lambda: litellm.model_access_denied_message, + ), + ("expose_router_debug_in_errors", True, False, lambda: litellm.expose_router_debug_in_errors), + ], +) +def test_model_access_denied_settings_propagate_on_config_reload( + monkeypatch, field_name, booted_value, db_value, read_setting +): + import litellm.proxy.proxy_server as ps + + monkeypatch.setattr(litellm, field_name, booted_value) + assert read_setting() == booted_value + + ps.ProxyConfig()._update_config_fields( + current_config={"litellm_settings": {}}, + param_name="litellm_settings", + db_param_value={field_name: db_value}, + ) + + assert read_setting() == db_value + + def test_general_settings_ui_defaults_unchanged_for_existing_fields(): """The spec-default mechanism added for max_ui_session_budget must not change what clearing the pre-existing fields restores (None for Float/Select, False for Boolean).""" diff --git a/ui/litellm-dashboard/src/app/(dashboard)/router-settings/_components/general_settings.integration.test.tsx b/ui/litellm-dashboard/src/app/(dashboard)/router-settings/_components/general_settings.integration.test.tsx index 1e29b181b78..d002d7c116f 100644 --- a/ui/litellm-dashboard/src/app/(dashboard)/router-settings/_components/general_settings.integration.test.tsx +++ b/ui/litellm-dashboard/src/app/(dashboard)/router-settings/_components/general_settings.integration.test.tsx @@ -130,6 +130,31 @@ describe("GeneralSettings General tab", () => { expect(deleteConfigFieldSetting).toHaveBeenCalledWith("token", "model_access_denied_message"); expect(vi.mocked(updateConfigFieldSetting).mock.calls).toHaveLength(1); }); + + it("keeps the stored value visible when the reset request fails", async () => { + vi.mocked(getGeneralSettingsCall).mockResolvedValue( + SETTINGS_FIXTURE.map((s) => + s.field_name === "model_access_denied_message" + ? { ...s, field_value: "Model `{model}` is unavailable.", stored_in_db: true } + : { ...s }, + ), + ); + vi.mocked(deleteConfigFieldSetting).mockRejectedValueOnce(new Error("proxy unreachable")); + const user = userEvent.setup(); + renderWithProviders(); + + await user.click(screen.getByText("General")); + const row = await settingsRow("model_access_denied_message"); + const input = within(row).getByRole("textbox") as HTMLInputElement; + expect(within(row).getByText("In DB")).toBeInTheDocument(); + + fireEvent.change(input, { target: { value: "" } }); + await user.click(within(row).getByRole("button", { name: /update/i })); + + expect(deleteConfigFieldSetting).toHaveBeenCalledWith("token", "model_access_denied_message"); + expect(within(row).getByText("In DB")).toBeInTheDocument(); + expect(within(row).queryByText("Not Set")).not.toBeInTheDocument(); + }); }); // The five tabs here are proxy-wide settings. Auto-routers moved to Models + Endpoints. diff --git a/ui/litellm-dashboard/src/app/(dashboard)/router-settings/_components/general_settings.tsx b/ui/litellm-dashboard/src/app/(dashboard)/router-settings/_components/general_settings.tsx index 040b1f3c375..38aa85b0f1e 100644 --- a/ui/litellm-dashboard/src/app/(dashboard)/router-settings/_components/general_settings.tsx +++ b/ui/litellm-dashboard/src/app/(dashboard)/router-settings/_components/general_settings.tsx @@ -40,7 +40,7 @@ const NUMERIC_INPUT_WIDTH = "w-36"; const toNumericValue = (raw: string): number | null => (raw === "" ? null : Number(raw)); const toStringValue = (raw: string): string | null => (raw === "" ? null : raw); -const RESETS_WHEN_CLEARED = new Set(["Select", "String"]); +const RESETS_WHEN_CLEARED: ReadonlySet = new Set(["Select", "String"]); const SettingValueEditor: React.FC<{ setting: generalSettingsItem; @@ -213,7 +213,7 @@ const GeneralSettings: React.FC = ({ accessToken, user setGeneralSettings(updatedSettings); }; - const handleUpdateField = (fieldName: string) => { + const handleUpdateField = async (fieldName: string) => { if (!accessToken) { return; } @@ -222,37 +222,33 @@ const GeneralSettings: React.FC = ({ accessToken, user const fieldValue = setting?.field_value; if (fieldValue == null) { - if (setting && RESETS_WHEN_CLEARED.has(setting.field_type)) handleResetField(fieldName); + if (setting && RESETS_WHEN_CLEARED.has(setting.field_type)) await handleResetField(fieldName); return; } try { - updateConfigFieldSetting(accessToken, fieldName, fieldValue); - // update value in state - - const updatedSettings = generalSettings.map((setting) => - setting.field_name === fieldName ? { ...setting, stored_in_db: true } : setting, + await updateConfigFieldSetting(accessToken, fieldName, fieldValue); + setGeneralSettings((current) => + current.map((setting) => (setting.field_name === fieldName ? { ...setting, stored_in_db: true } : setting)), ); - setGeneralSettings(updatedSettings); } catch (error) { // do something } }; - const handleResetField = (fieldName: string) => { + const handleResetField = async (fieldName: string) => { if (!accessToken) { return; } try { - deleteConfigFieldSetting(accessToken, fieldName); - // update value in state - - const updatedSettings = generalSettings.map((setting) => - setting.field_name === fieldName - ? { ...setting, stored_in_db: null, field_value: setting.field_default_value ?? null } - : setting, + await deleteConfigFieldSetting(accessToken, fieldName); + setGeneralSettings((current) => + current.map((setting) => + setting.field_name === fieldName + ? { ...setting, stored_in_db: null, field_value: setting.field_default_value ?? null } + : setting, + ), ); - setGeneralSettings(updatedSettings); } catch (error) { // do something } From 91dde59bc32827513e41d0afc9f515be7201fd6d Mon Sep 17 00:00:00 2001 From: yassin Date: Tue, 15 Sep 2026 22:48:47 +0000 Subject: [PATCH 101/225] refactor(proxy): validate request metadata before reconstructing key rate-limit view Co-Authored-By: Devin AI <158243242+devin-ai-integration[bot]@users.noreply.github.com> --- litellm/proxy/hooks/parallel_request_limiter_v3.py | 10 ++++++---- 1 file changed, 6 insertions(+), 4 deletions(-) diff --git a/litellm/proxy/hooks/parallel_request_limiter_v3.py b/litellm/proxy/hooks/parallel_request_limiter_v3.py index ea1ef6f9196..f72720b4726 100644 --- a/litellm/proxy/hooks/parallel_request_limiter_v3.py +++ b/litellm/proxy/hooks/parallel_request_limiter_v3.py @@ -2918,14 +2918,16 @@ class _PROXY_MaxParallelRequestsHandler_v3(CustomLogger): def _key_owns_model_tpm_limit_from_request_metadata( self, - request_metadata: dict[str, Any], + request_metadata: Mapping[str, object], model_group: str | None, ) -> bool: if model_group is None: return False - key_view: Final = UserAPIKeyAuth( - metadata=request_metadata.get("user_api_key_metadata") or {}, - model_max_budget=request_metadata.get("user_api_key_model_max_budget") or {}, + key_view: Final = UserAPIKeyAuth.model_validate( + { + "metadata": request_metadata.get("user_api_key_metadata") or {}, + "model_max_budget": request_metadata.get("user_api_key_model_max_budget") or {}, + } ) return self._key_owns_model_limit(key_view, model_group, "model_tpm_limit") From ad74c3ede75aaf3dadbe98864849372e0741f70b Mon Sep 17 00:00:00 2001 From: Yuneng Jiang Date: Tue, 15 Sep 2026 15:48:54 -0700 Subject: [PATCH 102/225] bump: litellm-enterprise 0.1.67 -> 0.1.68, litellm-proxy-extras 0.4.97 -> 0.4.98, litellm 1.102.0 -> 1.103.0 --- enterprise/pyproject.toml | 4 ++-- litellm-proxy-extras/pyproject.toml | 4 ++-- pyproject.toml | 8 ++++---- uv.lock | 8 ++++---- 4 files changed, 12 insertions(+), 12 deletions(-) diff --git a/enterprise/pyproject.toml b/enterprise/pyproject.toml index c049bf68c46..06b1da7ea76 100644 --- a/enterprise/pyproject.toml +++ b/enterprise/pyproject.toml @@ -1,6 +1,6 @@ [project] name = "litellm-enterprise" -version = "0.1.67" +version = "0.1.68" description = "Package for LiteLLM Enterprise features" readme = "README.md" requires-python = ">=3.9" @@ -26,7 +26,7 @@ required-version = ">=0.10.9" module-root = "" [tool.commitizen] -version = "0.1.67" +version = "0.1.68" version_files = [ "pyproject.toml:^version", "../pyproject.toml:litellm-enterprise==", diff --git a/litellm-proxy-extras/pyproject.toml b/litellm-proxy-extras/pyproject.toml index f94591872a4..914b9c5a14b 100644 --- a/litellm-proxy-extras/pyproject.toml +++ b/litellm-proxy-extras/pyproject.toml @@ -1,6 +1,6 @@ [project] name = "litellm-proxy-extras" -version = "0.4.97" +version = "0.4.98" description = "Additional files for the LiteLLM Proxy. Reduces the size of the main litellm package." readme = "README.md" requires-python = ">=3.9" @@ -26,7 +26,7 @@ required-version = ">=0.10.9" module-root = "" [tool.commitizen] -version = "0.4.97" +version = "0.4.98" version_files = [ "pyproject.toml:^version", "../pyproject.toml:litellm-proxy-extras==", diff --git a/pyproject.toml b/pyproject.toml index bdc0e09a17f..e8fa73fc9e1 100644 --- a/pyproject.toml +++ b/pyproject.toml @@ -1,6 +1,6 @@ [project] name = "litellm" -version = "1.102.0" +version = "1.103.0" description = "Library to easily interface with LLM API providers" readme = "README.md" requires-python = ">=3.10, <3.15" @@ -67,8 +67,8 @@ proxy = [ "azure-identity>=1.25.2,<2.0", "azure-storage-blob>=12.28.0,<13.0", "mcp>=1.28.1,<2.0", - "litellm-proxy-extras==0.4.97", - "litellm-enterprise==0.1.67", + "litellm-proxy-extras==0.4.98", + "litellm-enterprise==0.1.68", "RestrictedPython>=8.5,<9.0", "rich>=13.9.4,<14.0", "InquirerPy>=0.3.4,<1.0", @@ -331,7 +331,7 @@ members = ["enterprise", "litellm-proxy-extras"] profile = "black" [tool.commitizen] -version = "1.102.0" +version = "1.103.0" version_files = [ "pyproject.toml:^version", ] diff --git a/uv.lock b/uv.lock index 68c2d07b722..08c4e4da76c 100644 --- a/uv.lock +++ b/uv.lock @@ -10,7 +10,7 @@ resolution-markers = [ ] [options] -exclude-newer = "2026-09-12T03:51:49.261499386Z" +exclude-newer = "2026-09-12T22:48:38.53978Z" exclude-newer-span = "P3D" [manifest] @@ -4457,7 +4457,7 @@ wheels = [ [[package]] name = "litellm" -version = "1.102.0" +version = "1.103.0" source = { editable = "." } dependencies = [ { name = "aiohttp" }, @@ -4874,12 +4874,12 @@ proxy-dev = [ [[package]] name = "litellm-enterprise" -version = "0.1.67" +version = "0.1.68" source = { editable = "enterprise" } [[package]] name = "litellm-proxy-extras" -version = "0.4.97" +version = "0.4.98" source = { editable = "litellm-proxy-extras" } [[package]] From 024a8875214f971cefc354405e40833db188f5c8 Mon Sep 17 00:00:00 2001 From: kerry Date: Tue, 15 Sep 2026 22:49:35 +0000 Subject: [PATCH 103/225] feat(model_info): add Gemini fallback generalization rules (vertex routing + 2.5+ family baseline) Co-Authored-By: Devin AI <158243242+devin-ai-integration[bot]@users.noreply.github.com> --- ...odel_prices_and_context_window_backup.json | 32 +++++++ model_prices_and_context_window.json | 32 +++++++ .../test_fallback_generalizations.py | 93 ++++++++++++++++++- 3 files changed, 154 insertions(+), 3 deletions(-) diff --git a/litellm/model_prices_and_context_window_backup.json b/litellm/model_prices_and_context_window_backup.json index 9f91cf82f41..afe2cb3deb9 100644 --- a/litellm/model_prices_and_context_window_backup.json +++ b/litellm/model_prices_and_context_window_backup.json @@ -58149,6 +58149,38 @@ "model_info": { "supports_reasoning": true } + }, + { + "name": "vertex-gemini-ids", + "pattern": "^gemini-(?:2\\.[5-9]|[3-9](?:[.-]\\d{1,2})?)-(?:pro|flash)(?:-lite)?(?:-(?:preview|exp|latest))?(?:-\\d{2}-\\d{2,4})?$", + "description": "A bare Gemini text-chat id at 2.5 or higher, anchored to the whole name: gemini-[.minor]-(pro|flash)[-lite] with an optional preview/exp/latest tag and an optional MM-YYYY or MM-DD date. Bare Gemini ids in this map already infer vertex_ai (gemini-2.5-pro is filed under vertex_ai-language-models), so an unmapped id such as gemini-4-pro follows the same convention instead of raising 'LLM Provider NOT provided'. Anchored so a namespaced or non-chat id (gemini/..., -image, -tts) stays on its own path.", + "model_info": { + "litellm_provider": "vertex_ai" + } + }, + { + "name": "gemini-family-baseline", + "pattern": "gemini-(?!.*(?:-tts|-image|-live|-audio|-embedding|-computer-use|-robotics))(?:2\\.[5-9]|[3-9](?:[.-]\\d{1,2})?)-(?:pro|flash)(?:-lite)?(?![a-z])", + "description": "Any Gemini text-chat id at 2.5 or higher under any provider namespace (bare, gemini/, vertex_ai/, databricks-gemini-3-1-pro, ...): gemini-[.minor]-(pro|flash)[-lite] with any trailing preview, date or variant tag. The lookahead excludes the tts, image, live, audio, embedding, computer-use and robotics lines, which are not text-chat models and carry different limits and modes. 2.5 is the floor because every Gemini from 2.5 onward is a thinking model with a 1M context and 64K output; 1.5 and 2.0 are not and are fully mapped. Carries the model-family facts every such Gemini shares, so an unmapped gemini-4-pro keeps reasoning params and tool calling instead of having them dropped; it carries no pricing, so cost stays on the standard unpriced behavior rather than a guessed number.", + "model_info": { + "mode": "chat", + "max_input_tokens": 1048576, + "max_output_tokens": 65536, + "max_tokens": 65536, + "supports_function_calling": true, + "supports_parallel_function_calling": true, + "supports_vision": true, + "supports_pdf_input": true, + "supports_audio_input": true, + "supports_video_input": true, + "supports_tool_choice": true, + "supports_system_messages": true, + "supports_response_schema": true, + "supports_prompt_caching": true, + "supports_reasoning": true, + "supports_web_search": true, + "supports_url_context": true + } } ] }, diff --git a/model_prices_and_context_window.json b/model_prices_and_context_window.json index 9f91cf82f41..afe2cb3deb9 100644 --- a/model_prices_and_context_window.json +++ b/model_prices_and_context_window.json @@ -58149,6 +58149,38 @@ "model_info": { "supports_reasoning": true } + }, + { + "name": "vertex-gemini-ids", + "pattern": "^gemini-(?:2\\.[5-9]|[3-9](?:[.-]\\d{1,2})?)-(?:pro|flash)(?:-lite)?(?:-(?:preview|exp|latest))?(?:-\\d{2}-\\d{2,4})?$", + "description": "A bare Gemini text-chat id at 2.5 or higher, anchored to the whole name: gemini-[.minor]-(pro|flash)[-lite] with an optional preview/exp/latest tag and an optional MM-YYYY or MM-DD date. Bare Gemini ids in this map already infer vertex_ai (gemini-2.5-pro is filed under vertex_ai-language-models), so an unmapped id such as gemini-4-pro follows the same convention instead of raising 'LLM Provider NOT provided'. Anchored so a namespaced or non-chat id (gemini/..., -image, -tts) stays on its own path.", + "model_info": { + "litellm_provider": "vertex_ai" + } + }, + { + "name": "gemini-family-baseline", + "pattern": "gemini-(?!.*(?:-tts|-image|-live|-audio|-embedding|-computer-use|-robotics))(?:2\\.[5-9]|[3-9](?:[.-]\\d{1,2})?)-(?:pro|flash)(?:-lite)?(?![a-z])", + "description": "Any Gemini text-chat id at 2.5 or higher under any provider namespace (bare, gemini/, vertex_ai/, databricks-gemini-3-1-pro, ...): gemini-[.minor]-(pro|flash)[-lite] with any trailing preview, date or variant tag. The lookahead excludes the tts, image, live, audio, embedding, computer-use and robotics lines, which are not text-chat models and carry different limits and modes. 2.5 is the floor because every Gemini from 2.5 onward is a thinking model with a 1M context and 64K output; 1.5 and 2.0 are not and are fully mapped. Carries the model-family facts every such Gemini shares, so an unmapped gemini-4-pro keeps reasoning params and tool calling instead of having them dropped; it carries no pricing, so cost stays on the standard unpriced behavior rather than a guessed number.", + "model_info": { + "mode": "chat", + "max_input_tokens": 1048576, + "max_output_tokens": 65536, + "max_tokens": 65536, + "supports_function_calling": true, + "supports_parallel_function_calling": true, + "supports_vision": true, + "supports_pdf_input": true, + "supports_audio_input": true, + "supports_video_input": true, + "supports_tool_choice": true, + "supports_system_messages": true, + "supports_response_schema": true, + "supports_prompt_caching": true, + "supports_reasoning": true, + "supports_web_search": true, + "supports_url_context": true + } } ] }, diff --git a/tests/test_litellm/litellm_core_utils/test_fallback_generalizations.py b/tests/test_litellm/litellm_core_utils/test_fallback_generalizations.py index 057fa228562..e91a2bb4394 100644 --- a/tests/test_litellm/litellm_core_utils/test_fallback_generalizations.py +++ b/tests/test_litellm/litellm_core_utils/test_fallback_generalizations.py @@ -135,9 +135,7 @@ def test_fill_missing_requires_per_rule_opt_in(restore_generalizations): "supports_vision": True, } - restore_generalizations( - [{"name": "base", "pattern": r"^acme-", "model_info": {"supports_reasoning": True}}] - ) + restore_generalizations([{"name": "base", "pattern": r"^acme-", "model_info": {"supports_reasoning": True}}]) assert match_fill_missing_generalizations("acme-1", "openai") is None restore_generalizations( @@ -451,6 +449,95 @@ def shipped_cost_map(monkeypatch): set_fallback_generalizations(previous_rules) +def test_shipped_bare_gemini_id_routes_to_vertex_ai(shipped_cost_map): + for model in ( + "gemini-3.9-flash", + "gemini-4-pro", + "gemini-4-pro-preview", + "gemini-3.9-flash-lite-preview-09-2026", + ): + assert model not in litellm.model_cost + assert litellm.get_llm_provider(model=model)[1] == "vertex_ai" + + assert litellm.get_llm_provider(model="gemini-2.5-pro")[1] == "vertex_ai" + + +def test_shipped_gemini_routing_rule_is_anchored(shipped_cost_map): + for model in ( + "gemini-4-flash-image", + "gemini-3.9-flash-preview-tts", + "gemini-2.0-flash-new", + "gemini-1.5-flash-new", + "gemini-4-flashy", + "gemini/gemini-4-pro", + ): + assert match_routing_generalization(model) is None, model + + +@pytest.mark.parametrize( + "model,provider", + [ + ("gemini-4-pro", "gemini"), + ("gemini-3.9-flash", "vertex_ai"), + ("databricks-gemini-4-1-pro", "databricks"), + ("gemini-4-pro-preview-customtools", "gemini"), + ], +) +def test_shipped_gemini_baseline_resolves_unmapped_ids_provider_neutral(shipped_cost_map, model, provider): + assert model not in litellm.model_cost + if provider == "gemini": + assert f"gemini/{model}" not in litellm.model_cost + + info = litellm.get_model_info(model, custom_llm_provider=provider) + assert info["litellm_provider"] == provider + assert info["mode"] == "chat" + assert info["max_input_tokens"] == 1048576 + assert info["max_tokens"] == 65536 + assert info["supports_reasoning"] is True + assert info["supports_function_calling"] is True + assert info["supports_vision"] is True + assert not info.get("input_cost_per_token") + assert not info.get("output_cost_per_token") + + +def test_shipped_gemini_baseline_skips_non_chat_and_pre_2_5_ids(shipped_cost_map): + for model in ( + "gemini-4-flash-image", + "gemini-3.9-flash-preview-tts", + "gemini-4-flash-live-preview", + "gemini-4-flash-native-audio", + "gemini-embedding-4", + "gemini-2.5-computer-use-preview-12-2026", + "gemini-2.0-flash-new", + "gemini-1.5-pro-new", + "gemini-4-flashy", + ): + assert match_capability_generalizations(model) is None, model + + +def test_shipped_gemini_baseline_keeps_reasoning_effort_on_unmapped_model(shipped_cost_map): + assert litellm.supports_reasoning(model="gemini-4-pro", custom_llm_provider="gemini") is True + + optional_params = litellm.utils.get_optional_params( + model="gemini-4-pro", + custom_llm_provider="gemini", + reasoning_effort="medium", + drop_params=False, + ) + assert isinstance(optional_params, dict) + assert optional_params["thinkingConfig"]["thinkingBudget"] > 0 + assert optional_params["thinkingConfig"]["includeThoughts"] is True + + +def test_shipped_gemini_baseline_loses_to_exact_entries(shipped_cost_map): + model = "gemini-2.5-flash-lite" + info = litellm.get_model_info(model, custom_llm_provider="gemini") + entry = litellm.model_cost["gemini/gemini-2.5-flash-lite"] + assert info["max_tokens"] == entry["max_tokens"] + assert info["input_cost_per_token"] == entry["input_cost_per_token"] + assert entry["input_cost_per_token"] > 0 + + def test_shipped_bare_claude_id_routes_to_anthropic(shipped_cost_map): _, provider, _, _ = litellm.get_llm_provider(model="claude-haiku-4-6") assert provider == "anthropic" From c94a4f8696fe63dcbe42b6e25ad3cf1e9cddf8f4 Mon Sep 17 00:00:00 2001 From: kerry Date: Tue, 15 Sep 2026 22:49:57 +0000 Subject: [PATCH 104/225] test: restore xai reported-cost passthrough tests Co-Authored-By: Devin AI <158243242+devin-ai-integration[bot]@users.noreply.github.com> --- .../test_xai_responses_transformation.py | 38 ++++++++++++++ .../llms/xai/test_xai_chat_transformation.py | 49 +++++++++++++++++++ 2 files changed, 87 insertions(+) diff --git a/tests/test_litellm/llms/xai/responses/test_xai_responses_transformation.py b/tests/test_litellm/llms/xai/responses/test_xai_responses_transformation.py index 994dea1b877..e3feb7d5342 100644 --- a/tests/test_litellm/llms/xai/responses/test_xai_responses_transformation.py +++ b/tests/test_litellm/llms/xai/responses/test_xai_responses_transformation.py @@ -11,6 +11,7 @@ from unittest.mock import MagicMock, Mock import httpx +from litellm.llms.xai.cost_calculator import cost_per_token from litellm.llms.xai.responses.transformation import XAIResponsesAPIConfig from litellm.responses.utils import ResponseAPILoggingUtils from litellm.types.llms.openai import ( @@ -484,6 +485,43 @@ class TestXAIResponsesReportedCost: ) return response.usage + def test_reported_cost_reaches_the_cost_calculator(self): + usage = self._transformed_usage( + { + "input_tokens": 100, + "output_tokens": 200, + "total_tokens": 300, + "cost_in_usd_ticks": 37756000, + } + ) + + assert usage.cost == 0.0037756 + + chat_usage = ResponseAPILoggingUtils._transform_response_api_usage_to_chat_usage(usage) + assert cost_per_token(model="grok-4-latest", usage=chat_usage) == (0.0, 0.0037756) + + def test_streamed_reported_cost_reaches_the_cost_calculator(self): + event = XAIResponsesAPIConfig().transform_streaming_response( + model="grok-4-latest", + parsed_chunk={ + "type": "response.completed", + "sequence_number": 7, + "response": self._response_body( + { + "input_tokens": 100, + "output_tokens": 200, + "total_tokens": 300, + "cost_in_usd_ticks": 37756000, + } + ), + }, + logging_obj=Mock(), + ) + + assert isinstance(event, ResponseCompletedEvent) + chat_usage = ResponseAPILoggingUtils._transform_response_api_usage_to_chat_usage(event.response.usage) + assert cost_per_token(model="grok-4-latest", usage=chat_usage) == (0.0, 0.0037756) + def test_usage_without_a_reported_cost_is_left_alone(self): usage = self._transformed_usage({"input_tokens": 100, "output_tokens": 200, "total_tokens": 300}) diff --git a/tests/test_litellm/llms/xai/test_xai_chat_transformation.py b/tests/test_litellm/llms/xai/test_xai_chat_transformation.py index 7047408df5b..290cd3dcb3a 100644 --- a/tests/test_litellm/llms/xai/test_xai_chat_transformation.py +++ b/tests/test_litellm/llms/xai/test_xai_chat_transformation.py @@ -2,9 +2,12 @@ from unittest.mock import Mock import httpx +import litellm from litellm.llms.xai.chat.transformation import ( + XAIChatCompletionStreamingHandler, XAIChatConfig, ) +from litellm.llms.xai.cost_calculator import cost_per_token from litellm.types.utils import ( CompletionTokensDetailsWrapper, ModelResponse, @@ -233,6 +236,19 @@ class TestXAIReportedCost: ) return response.usage + def test_reported_cost_reaches_the_cost_calculator(self): + usage = self._transformed_usage( + { + "prompt_tokens": 100, + "completion_tokens": 200, + "total_tokens": 300, + "cost_in_usd_ticks": 37756000, + } + ) + + assert usage.cost == 0.0037756 + assert cost_per_token(model="grok-4-latest", usage=usage) == (0.0, 0.0037756) + def test_usage_without_a_reported_cost_is_left_alone(self): usage = self._transformed_usage({"prompt_tokens": 100, "completion_tokens": 200, "total_tokens": 300}) @@ -250,3 +266,36 @@ class TestXAIReportedCost: ) assert getattr(usage, "cost", None) is None + + def test_streamed_reported_cost_survives_chunk_aggregation(self): + """Streamed spend only matches if the conversion happens on the chunk. + + Chunk aggregation rebuilds usage from the fields it models plus ``cost``, so a + chunk still carrying only ``cost_in_usd_ticks`` loses the reported amount. + """ + handler = XAIChatCompletionStreamingHandler(streaming_response=iter([]), sync_stream=True) + + parsed = handler.chunk_parser( + { + "id": "chatcmpl-xai", + "object": "chat.completion.chunk", + "created": 0, + "model": "grok-4-latest", + "choices": [], + "usage": { + "prompt_tokens": 100, + "completion_tokens": 200, + "total_tokens": 300, + "cost_in_usd_ticks": 37756000, + }, + } + ) + + assert parsed.usage.cost == 0.0037756 + + assembled = litellm.stream_chunk_builder(chunks=[parsed]) + assert assembled.usage.cost == 0.0037756 + assert cost_per_token(model="grok-4-latest", usage=assembled.usage) == ( + 0.0, + 0.0037756, + ) From a654034f6a61943d2b7a9335e940565f90a2ff16 Mon Sep 17 00:00:00 2001 From: shivam Date: Tue, 15 Sep 2026 22:50:28 +0000 Subject: [PATCH 105/225] test(ci): allow completion_cost recursion for mixed-tier WS pricing split The recursion is bounded to depth 1: split parts each carry a single service_tier, so the recursive call's partition has one key and the split helper returns None. Co-Authored-By: Devin AI <158243242+devin-ai-integration[bot]@users.noreply.github.com> --- tests/code_coverage_tests/recursive_detector.py | 1 + 1 file changed, 1 insertion(+) diff --git a/tests/code_coverage_tests/recursive_detector.py b/tests/code_coverage_tests/recursive_detector.py index e9f87ba6cae..d8e318c61af 100644 --- a/tests/code_coverage_tests/recursive_detector.py +++ b/tests/code_coverage_tests/recursive_detector.py @@ -67,6 +67,7 @@ IGNORE_FUNCTIONS = [ "_redact_agent_params_tree", # max depth set (default 10), same shape as _redact_sensitive_litellm_params. "_restore_redacted_nested_value", # max depth set (default 10), mirrors _redact_agent_params_tree on the write side. "_unqualified", # bounded by the qualifier depth of a static TypedDict annotation (Annotated, Required/NotRequired, ReadOnly around one type, no cycles possible). + "completion_cost", # max depth 1: recursion only fires for mixed-tier Responses WS logging objects, and each split part carries a single service_tier so _split_responses_ws_logging_object_by_service_tier returns None. ] From fd90eeb3c60fd21cdee7e54ce77f394e35bfa0b9 Mon Sep 17 00:00:00 2001 From: yassin Date: Tue, 15 Sep 2026 22:50:29 +0000 Subject: [PATCH 106/225] test(proxy): cover disjoint-method db/yaml pass-through entries on a shared path Co-Authored-By: Devin AI <158243242+devin-ai-integration[bot]@users.noreply.github.com> --- tests/test_litellm/proxy/test_proxy_server.py | 32 +++++++++++++++---- 1 file changed, 26 insertions(+), 6 deletions(-) diff --git a/tests/test_litellm/proxy/test_proxy_server.py b/tests/test_litellm/proxy/test_proxy_server.py index 3499a1ee1bc..2c63443da66 100644 --- a/tests/test_litellm/proxy/test_proxy_server.py +++ b/tests/test_litellm/proxy/test_proxy_server.py @@ -7436,18 +7436,38 @@ async def test_update_general_settings_keeps_yaml_pass_through_endpoints_next_to @pytest.mark.asyncio -async def test_update_general_settings_db_pass_through_endpoint_overrides_yaml_entry_on_the_same_path(): - """The auth check lets any matching ``auth: false`` entry through, so a DB - ``auth: true`` entry can only lock down a YAML-declared path if the YAML - entry is dropped from the merged list.""" +@pytest.mark.parametrize( + ("db_methods", "yaml_methods"), + [(None, None), (["POST"], ["GET"])], + ids=["all-methods", "disjoint-methods"], +) +async def test_update_general_settings_db_pass_through_endpoint_overrides_yaml_entry_on_the_same_path( + db_methods: list[str] | None, yaml_methods: list[str] | None +): + """The auth check matches pass-through entries by path only and lets any + matching ``auth: false`` entry through, so a DB ``auth: true`` entry can only + lock down a YAML-declared path if the YAML entry is dropped from the merged + list, whatever ``methods`` either entry declares.""" from litellm.proxy._types import ProxyException from litellm.proxy.proxy_server import ProxyConfig - yaml_endpoint: Final = {"path": "/v1/cuopt/request", "target": "https://example.com/post", "auth": False} - db_endpoint: Final = {"id": "db-1", "path": "/v1/cuopt/request", "target": "https://example.com/post", "auth": True} + yaml_endpoint: Final = { + "path": "/v1/cuopt/request", + "target": "https://example.com/post", + "auth": False, + "methods": yaml_methods, + } + db_endpoint: Final = { + "id": "db-1", + "path": "/v1/cuopt/request", + "target": "https://example.com/post", + "auth": True, + "methods": db_methods, + } request: Final = MagicMock() request.url.path = "/v1/cuopt/request" + request.method = "POST" request.headers = {} request.query_params = {} From b50a22b370baedefa18a77bcb8ad391bfaf5a042 Mon Sep 17 00:00:00 2001 From: yassin Date: Tue, 15 Sep 2026 22:55:14 +0000 Subject: [PATCH 107/225] fix(proxy): restrict /nvidia_nim route to NIM-backed model groups and inject router in tests Co-Authored-By: Devin AI <158243242+devin-ai-integration[bot]@users.noreply.github.com> --- .../nvidia_nim/passthrough/transformation.py | 15 +- litellm/proxy/auth/auth_utils.py | 7 +- .../llm_passthrough_endpoints.py | 28 +++- .../proxy/auth/test_auth_utils.py | 5 + .../test_llm_pass_through_endpoints.py | 140 ++++++++++-------- 5 files changed, 124 insertions(+), 71 deletions(-) diff --git a/litellm/llms/nvidia_nim/passthrough/transformation.py b/litellm/llms/nvidia_nim/passthrough/transformation.py index f6273daaabb..6df9681cc81 100644 --- a/litellm/llms/nvidia_nim/passthrough/transformation.py +++ b/litellm/llms/nvidia_nim/passthrough/transformation.py @@ -1,7 +1,7 @@ from __future__ import annotations import re -from collections.abc import Collection, Mapping, Sequence +from collections.abc import Collection, Iterable, Mapping, Sequence from typing import TYPE_CHECKING, Final import httpx @@ -14,7 +14,8 @@ from litellm.llms.base_llm.passthrough.transformation import ( ) from litellm.secret_managers.main import get_secret_str from litellm.types.llms.openai import AllMessageValues -from litellm.types.utils import StandardPassThroughResponseObject +from litellm.types.router import DeploymentTypedDict +from litellm.types.utils import LlmProviders, StandardPassThroughResponseObject if TYPE_CHECKING: from httpx import URL, Response @@ -25,6 +26,16 @@ if TYPE_CHECKING: API_VERSION_SEGMENT: Final = re.compile(r"^v\d+$") +NVIDIA_NIM_MODEL_PREFIX: Final = f"{LlmProviders.NVIDIA_NIM.value}/" + + +def nvidia_nim_model_groups(deployments: Iterable[DeploymentTypedDict] | None) -> frozenset[str]: + return frozenset( + deployment["model_name"] + for deployment in deployments or () + if deployment["litellm_params"].get("custom_llm_provider") == LlmProviders.NVIDIA_NIM.value + or deployment["litellm_params"].get("model", "").startswith(NVIDIA_NIM_MODEL_PREFIX) + ) def nvidia_nim_router_model_in_endpoint(endpoint: str, router_models: Collection[str]) -> str | None: diff --git a/litellm/proxy/auth/auth_utils.py b/litellm/proxy/auth/auth_utils.py index 529521dcba8..dfce3ab206b 100644 --- a/litellm/proxy/auth/auth_utils.py +++ b/litellm/proxy/auth/auth_utils.py @@ -28,7 +28,10 @@ from litellm.litellm_core_utils.url_utils import ( validate_url, ) from litellm.llms.azure.passthrough.transformation import azure_router_model_in_endpoint -from litellm.llms.nvidia_nim.passthrough.transformation import nvidia_nim_router_model_in_endpoint +from litellm.llms.nvidia_nim.passthrough.transformation import ( + nvidia_nim_model_groups, + nvidia_nim_router_model_in_endpoint, +) from litellm.proxy._types import * from litellm.proxy.common_utils.http_parsing_utils import extract_nested_form_metadata from litellm.types.passthrough_endpoints.pass_through_endpoints import ( @@ -2052,7 +2055,7 @@ def _router_model_from_nvidia_nim_route(route: str, llm_router: Router | None) - if llm_router is None: return None endpoint: Final = re.sub(r"^/nvidia_nim/", "", route, flags=re.IGNORECASE) - return nvidia_nim_router_model_in_endpoint(endpoint, frozenset(llm_router.get_model_names())) + return nvidia_nim_router_model_in_endpoint(endpoint, nvidia_nim_model_groups(llm_router.get_model_list())) def _router_model_from_azure_route(route: str, llm_router: Router | None) -> str | None: diff --git a/litellm/proxy/pass_through_endpoints/llm_passthrough_endpoints.py b/litellm/proxy/pass_through_endpoints/llm_passthrough_endpoints.py index d4b1bdbdc20..65a18c1603e 100644 --- a/litellm/proxy/pass_through_endpoints/llm_passthrough_endpoints.py +++ b/litellm/proxy/pass_through_endpoints/llm_passthrough_endpoints.py @@ -36,7 +36,10 @@ from litellm.litellm_core_utils.aws_partition import get_aws_dns_suffix from litellm.llms.anthropic.common_utils import AnthropicModelInfo from litellm.llms.azure.passthrough.transformation import foreign_azure_deployment from litellm.llms.custom_httpx.http_handler import get_async_httpx_client -from litellm.llms.nvidia_nim.passthrough.transformation import nvidia_nim_router_model_in_endpoint +from litellm.llms.nvidia_nim.passthrough.transformation import ( + nvidia_nim_model_groups, + nvidia_nim_router_model_in_endpoint, +) from litellm.llms.vertex_ai.vertex_llm_base import VertexBase from litellm.passthrough.main import AsyncPassthroughStreamingResponse from litellm.proxy._types import * @@ -1635,17 +1638,34 @@ async def nvidia_nim_proxy_route( """ from litellm.proxy.proxy_server import llm_router + return await relay_nvidia_nim_request( + llm_router=llm_router, + endpoint=endpoint, + request=request, + request_body=await get_request_body(request), + user_api_key_dict=user_api_key_dict, + ) + + +async def relay_nvidia_nim_request( + llm_router: litellm.Router | None, + endpoint: str, + request: Request, + request_body: Mapping[str, object], + user_api_key_dict: UserAPIKeyAuth, +) -> Response: model_group: Final = ( - nvidia_nim_router_model_in_endpoint(endpoint, llm_router.get_model_names()) if llm_router else None + nvidia_nim_router_model_in_endpoint(endpoint, nvidia_nim_model_groups(llm_router.get_model_list())) + if llm_router + else None ) if llm_router is None or model_group is None: rejection: Final[RelayRejection] = { - "error": "no LiteLLM model group in the path; call /nvidia_nim/{model_group}/v1/infer with a model " + "error": "no NVIDIA NIM model group in the path; call /nvidia_nim/{model_group}/v1/infer with a model " "from your `model_list` whose `model` starts with `nvidia_nim/`" } raise HTTPException(status_code=400, detail=rejection) - request_body: Final = await get_request_body(request) is_streaming_request: Final = is_passthrough_request_streaming(request_body) return await open_sse_before_first_byte( _relay_router_model( diff --git a/tests/test_litellm/proxy/auth/test_auth_utils.py b/tests/test_litellm/proxy/auth/test_auth_utils.py index 81e505ea0a9..0841f82227a 100644 --- a/tests/test_litellm/proxy/auth/test_auth_utils.py +++ b/tests/test_litellm/proxy/auth/test_auth_utils.py @@ -844,6 +844,10 @@ def _nvidia_nim_relay_router(): "api_key": "k", }, }, + { + "model_name": "gpt-4o", + "litellm_params": {"model": "openai/gpt-4o", "api_key": "k"}, + }, ] ) @@ -868,6 +872,7 @@ NIM_INFER_BODY = {"input": [{"type": "image_url", "url": "data:image/png;base64, ("/nvidia_nim/v1/infer", NIM_INFER_BODY, None), ("/nvidia_nim/unknown-group/v1/infer", NIM_INFER_BODY, None), ("/nvidia_nim/nim-page-elements-v2/v1/infer", NIM_INFER_BODY, None), + ("/nvidia_nim/gpt-4o/v1/infer", NIM_INFER_BODY, None), ], ) def test_get_model_from_request_nvidia_nim_relay_routes_use_the_model_group_in_the_path(route, request_data, expected): diff --git a/tests/test_litellm/proxy/pass_through_endpoints/test_llm_pass_through_endpoints.py b/tests/test_litellm/proxy/pass_through_endpoints/test_llm_pass_through_endpoints.py index 4ef04a8e633..4e73a33ecd9 100644 --- a/tests/test_litellm/proxy/pass_through_endpoints/test_llm_pass_through_endpoints.py +++ b/tests/test_litellm/proxy/pass_through_endpoints/test_llm_pass_through_endpoints.py @@ -40,7 +40,7 @@ from litellm.proxy.pass_through_endpoints.llm_passthrough_endpoints import ( llm_passthrough_factory_proxy_route, milvus_proxy_route, mistral_proxy_route, - nvidia_nim_proxy_route, + relay_nvidia_nim_request, openai_proxy_route, vertex_discovery_proxy_route, vertex_proxy_route, @@ -5392,20 +5392,10 @@ class TestNvidiaNimProxyRoute: request.query_params = {} return request - def _install_router(self, monkeypatch, router, body: dict) -> None: - import litellm.proxy.pass_through_endpoints.llm_passthrough_endpoints as ep - import litellm.proxy.proxy_server as proxy_server - - async def fake_get_request_body(_request): - return dict(body) - - monkeypatch.setattr(proxy_server, "llm_router", router) - monkeypatch.setattr(ep, "get_request_body", fake_get_request_body) - - def _recording_router(self, captured: list[dict], model_names: tuple[str, ...]): + def _recording_router(self, captured: list[dict], deployments: dict[str, str]): class RecordingRouter: - def get_model_names(self): - return list(model_names) + def get_model_list(self): + return [{"model_name": name, "litellm_params": {"model": model}} for name, model in deployments.items()] async def allm_passthrough_route(self, **kwargs): captured.append(kwargs) @@ -5415,18 +5405,31 @@ class TestNvidiaNimProxyRoute: return RecordingRouter() - @pytest.mark.asyncio - async def test_model_group_in_the_path_selects_the_deployment_and_the_body_stays_model_free(self, monkeypatch): - captured: list[dict] = [] - self._install_router( - monkeypatch, self._recording_router(captured, ("nim-page-elements", "nim-table")), NIM_INFER_BODY + async def _relay(self, llm_router, endpoint: str, body: dict, user_api_key_dict=None) -> Response: + return await relay_nvidia_nim_request( + llm_router=llm_router, + endpoint=endpoint, + request=self._request(), + request_body=dict(body), + user_api_key_dict=user_api_key_dict or UserAPIKeyAuth(api_key="hashed-token"), ) - result = await nvidia_nim_proxy_route( - endpoint="nim-page-elements/v1/infer", - request=self._request(), - fastapi_response=MagicMock(spec=Response), - user_api_key_dict=UserAPIKeyAuth(api_key="hashed-token", team_id="team-1"), + @pytest.mark.asyncio + async def test_model_group_in_the_path_selects_the_deployment_and_the_body_stays_model_free(self): + captured: list[dict] = [] + router = self._recording_router( + captured, + { + "nim-page-elements": "nvidia_nim/nvidia/nemoretriever-page-elements-v2", + "nim-table": "nvidia_nim/nvidia/nemoretriever-table-structure-v1", + }, + ) + + result = await self._relay( + router, + "nim-page-elements/v1/infer", + NIM_INFER_BODY, + UserAPIKeyAuth(api_key="hashed-token", team_id="team-1"), ) (relay,) = captured @@ -5441,65 +5444,81 @@ class TestNvidiaNimProxyRoute: assert result.headers["x-nim-request"] == "r1" @pytest.mark.asyncio - async def test_model_group_with_a_slash_is_matched_as_the_longest_leading_path(self, monkeypatch): + async def test_model_group_with_a_slash_is_matched_as_the_longest_leading_path(self): captured: list[dict] = [] - self._install_router( - monkeypatch, self._recording_router(captured, ("nvidia/nemoretriever-page-elements-v2",)), NIM_INFER_BODY + router = self._recording_router( + captured, {"nvidia/nemoretriever-page-elements-v2": "nvidia_nim/nvidia/nemoretriever-page-elements-v2"} ) - await nvidia_nim_proxy_route( - endpoint="nvidia/nemoretriever-page-elements-v2/v1/infer", - request=self._request(), - fastapi_response=MagicMock(spec=Response), - user_api_key_dict=UserAPIKeyAuth(api_key="hashed-token"), - ) + await self._relay(router, "nvidia/nemoretriever-page-elements-v2/v1/infer", NIM_INFER_BODY) assert captured[0]["model"] == "nvidia/nemoretriever-page-elements-v2" @pytest.mark.asyncio - @pytest.mark.parametrize("endpoint", ["v1/infer", "unknown-group/v1/infer", "nim-page-elements-v2/v1/infer"]) - async def test_path_without_a_configured_model_group_is_rejected_before_any_upstream_call( - self, monkeypatch, endpoint - ): + async def test_custom_llm_provider_marks_a_deployment_as_nim_without_the_model_prefix(self): + captured: list[dict] = [] + + class ProviderRouter: + def get_model_list(self): + return [ + { + "model_name": "page-elements", + "litellm_params": { + "model": "nvidia/nemoretriever-page-elements-v2", + "custom_llm_provider": "nvidia_nim", + }, + } + ] + + async def allm_passthrough_route(self, **kwargs): + captured.append(kwargs) + return httpx.Response(200, json={"data": []}) + + await self._relay(ProviderRouter(), "page-elements/v1/infer", NIM_INFER_BODY) + + assert captured[0]["model"] == "page-elements" + + @pytest.mark.asyncio + @pytest.mark.parametrize( + "endpoint", + ["v1/infer", "unknown-group/v1/infer", "nim-page-elements-v2/v1/infer", "gpt-4o/v1/infer"], + ) + async def test_path_without_a_nim_model_group_is_rejected_before_any_upstream_call(self, endpoint): from fastapi import HTTPException captured: list[dict] = [] - self._install_router(monkeypatch, self._recording_router(captured, ("nim-page-elements",)), NIM_INFER_BODY) + router = self._recording_router( + captured, + {"nim-page-elements": "nvidia_nim/nvidia/nemoretriever-page-elements-v2", "gpt-4o": "openai/gpt-4o"}, + ) with pytest.raises(HTTPException) as exc_info: - await nvidia_nim_proxy_route( - endpoint=endpoint, - request=self._request(), - fastapi_response=MagicMock(spec=Response), - user_api_key_dict=UserAPIKeyAuth(api_key="hashed-token"), - ) + await self._relay(router, endpoint, NIM_INFER_BODY) assert exc_info.value.status_code == 400 assert captured == [] @pytest.mark.asyncio - async def test_no_router_is_rejected_before_any_upstream_call(self, monkeypatch): + async def test_no_router_is_rejected_before_any_upstream_call(self): from fastapi import HTTPException - self._install_router(monkeypatch, None, NIM_INFER_BODY) - with pytest.raises(HTTPException) as exc_info: - await nvidia_nim_proxy_route( - endpoint="nim-page-elements/v1/infer", - request=self._request(), - fastapi_response=MagicMock(spec=Response), - user_api_key_dict=UserAPIKeyAuth(api_key="hashed-token"), - ) + await self._relay(None, "nim-page-elements/v1/infer", NIM_INFER_BODY) assert exc_info.value.status_code == 400 @pytest.mark.asyncio - async def test_upstream_rejection_is_relayed_with_its_status_body_and_headers(self, monkeypatch): + async def test_upstream_rejection_is_relayed_with_its_status_body_and_headers(self): upstream_body = {"detail": "input[0].url must be a data URL"} class RejectingRouter: - def get_model_names(self): - return ["nim-page-elements"] + def get_model_list(self): + return [ + { + "model_name": "nim-page-elements", + "litellm_params": {"model": "nvidia_nim/nvidia/nemoretriever-page-elements-v2"}, + } + ] async def allm_passthrough_route(self, **kwargs): upstream_request = httpx.Request("POST", "http://nim.internal:8000/v1/infer") @@ -5508,13 +5527,8 @@ class TestNvidiaNimProxyRoute: ) raise httpx.HTTPStatusError("422", request=upstream_request, response=upstream) - self._install_router(monkeypatch, RejectingRouter(), {"input": [{"type": "image_url", "url": "x"}]}) - - result = await nvidia_nim_proxy_route( - endpoint="nim-page-elements/v1/infer", - request=self._request(), - fastapi_response=MagicMock(spec=Response), - user_api_key_dict=UserAPIKeyAuth(api_key="hashed-token"), + result = await self._relay( + RejectingRouter(), "nim-page-elements/v1/infer", {"input": [{"type": "image_url", "url": "x"}]} ) assert result.status_code == 422 From 082f02bd739497d6221ebe71e6885eb4a5ffeba7 Mon Sep 17 00:00:00 2001 From: kerry Date: Tue, 15 Sep 2026 22:55:29 +0000 Subject: [PATCH 108/225] refactor(model_info): drop Gemini vertex routing rule, keep baseline to certain capabilities Co-Authored-By: Devin AI <158243242+devin-ai-integration[bot]@users.noreply.github.com> --- ...odel_prices_and_context_window_backup.json | 24 ++------------- model_prices_and_context_window.json | 24 ++------------- .../test_fallback_generalizations.py | 30 ++----------------- 3 files changed, 9 insertions(+), 69 deletions(-) diff --git a/litellm/model_prices_and_context_window_backup.json b/litellm/model_prices_and_context_window_backup.json index afe2cb3deb9..3f77bd49a0d 100644 --- a/litellm/model_prices_and_context_window_backup.json +++ b/litellm/model_prices_and_context_window_backup.json @@ -58150,36 +58150,18 @@ "supports_reasoning": true } }, - { - "name": "vertex-gemini-ids", - "pattern": "^gemini-(?:2\\.[5-9]|[3-9](?:[.-]\\d{1,2})?)-(?:pro|flash)(?:-lite)?(?:-(?:preview|exp|latest))?(?:-\\d{2}-\\d{2,4})?$", - "description": "A bare Gemini text-chat id at 2.5 or higher, anchored to the whole name: gemini-[.minor]-(pro|flash)[-lite] with an optional preview/exp/latest tag and an optional MM-YYYY or MM-DD date. Bare Gemini ids in this map already infer vertex_ai (gemini-2.5-pro is filed under vertex_ai-language-models), so an unmapped id such as gemini-4-pro follows the same convention instead of raising 'LLM Provider NOT provided'. Anchored so a namespaced or non-chat id (gemini/..., -image, -tts) stays on its own path.", - "model_info": { - "litellm_provider": "vertex_ai" - } - }, { "name": "gemini-family-baseline", "pattern": "gemini-(?!.*(?:-tts|-image|-live|-audio|-embedding|-computer-use|-robotics))(?:2\\.[5-9]|[3-9](?:[.-]\\d{1,2})?)-(?:pro|flash)(?:-lite)?(?![a-z])", - "description": "Any Gemini text-chat id at 2.5 or higher under any provider namespace (bare, gemini/, vertex_ai/, databricks-gemini-3-1-pro, ...): gemini-[.minor]-(pro|flash)[-lite] with any trailing preview, date or variant tag. The lookahead excludes the tts, image, live, audio, embedding, computer-use and robotics lines, which are not text-chat models and carry different limits and modes. 2.5 is the floor because every Gemini from 2.5 onward is a thinking model with a 1M context and 64K output; 1.5 and 2.0 are not and are fully mapped. Carries the model-family facts every such Gemini shares, so an unmapped gemini-4-pro keeps reasoning params and tool calling instead of having them dropped; it carries no pricing, so cost stays on the standard unpriced behavior rather than a guessed number.", + "description": "Any Gemini text-chat id at 2.5 or higher under any provider namespace (bare, gemini/, vertex_ai/, databricks-gemini-3-1-pro, ...): gemini-[.minor]-(pro|flash)[-lite] with any trailing preview, date or variant tag. The lookahead excludes the tts, image, live, audio, embedding, computer-use and robotics lines, which are not text-chat models and carry different limits and modes. 2.5 is the floor because every Gemini from 2.5 onward is a thinking model with tool calling and multimodal input; 1.5 and 2.0 are not and are fully mapped. Carries the model-family facts every such Gemini shares, so an unmapped gemini-4-pro keeps reasoning params and tool calling instead of having them dropped; it carries no token limits or pricing, so those stay on the standard unmapped behavior rather than a guessed number.", "model_info": { "mode": "chat", - "max_input_tokens": 1048576, - "max_output_tokens": 65536, - "max_tokens": 65536, "supports_function_calling": true, - "supports_parallel_function_calling": true, - "supports_vision": true, - "supports_pdf_input": true, - "supports_audio_input": true, - "supports_video_input": true, "supports_tool_choice": true, "supports_system_messages": true, + "supports_vision": true, "supports_response_schema": true, - "supports_prompt_caching": true, - "supports_reasoning": true, - "supports_web_search": true, - "supports_url_context": true + "supports_reasoning": true } } ] diff --git a/model_prices_and_context_window.json b/model_prices_and_context_window.json index afe2cb3deb9..3f77bd49a0d 100644 --- a/model_prices_and_context_window.json +++ b/model_prices_and_context_window.json @@ -58150,36 +58150,18 @@ "supports_reasoning": true } }, - { - "name": "vertex-gemini-ids", - "pattern": "^gemini-(?:2\\.[5-9]|[3-9](?:[.-]\\d{1,2})?)-(?:pro|flash)(?:-lite)?(?:-(?:preview|exp|latest))?(?:-\\d{2}-\\d{2,4})?$", - "description": "A bare Gemini text-chat id at 2.5 or higher, anchored to the whole name: gemini-[.minor]-(pro|flash)[-lite] with an optional preview/exp/latest tag and an optional MM-YYYY or MM-DD date. Bare Gemini ids in this map already infer vertex_ai (gemini-2.5-pro is filed under vertex_ai-language-models), so an unmapped id such as gemini-4-pro follows the same convention instead of raising 'LLM Provider NOT provided'. Anchored so a namespaced or non-chat id (gemini/..., -image, -tts) stays on its own path.", - "model_info": { - "litellm_provider": "vertex_ai" - } - }, { "name": "gemini-family-baseline", "pattern": "gemini-(?!.*(?:-tts|-image|-live|-audio|-embedding|-computer-use|-robotics))(?:2\\.[5-9]|[3-9](?:[.-]\\d{1,2})?)-(?:pro|flash)(?:-lite)?(?![a-z])", - "description": "Any Gemini text-chat id at 2.5 or higher under any provider namespace (bare, gemini/, vertex_ai/, databricks-gemini-3-1-pro, ...): gemini-[.minor]-(pro|flash)[-lite] with any trailing preview, date or variant tag. The lookahead excludes the tts, image, live, audio, embedding, computer-use and robotics lines, which are not text-chat models and carry different limits and modes. 2.5 is the floor because every Gemini from 2.5 onward is a thinking model with a 1M context and 64K output; 1.5 and 2.0 are not and are fully mapped. Carries the model-family facts every such Gemini shares, so an unmapped gemini-4-pro keeps reasoning params and tool calling instead of having them dropped; it carries no pricing, so cost stays on the standard unpriced behavior rather than a guessed number.", + "description": "Any Gemini text-chat id at 2.5 or higher under any provider namespace (bare, gemini/, vertex_ai/, databricks-gemini-3-1-pro, ...): gemini-[.minor]-(pro|flash)[-lite] with any trailing preview, date or variant tag. The lookahead excludes the tts, image, live, audio, embedding, computer-use and robotics lines, which are not text-chat models and carry different limits and modes. 2.5 is the floor because every Gemini from 2.5 onward is a thinking model with tool calling and multimodal input; 1.5 and 2.0 are not and are fully mapped. Carries the model-family facts every such Gemini shares, so an unmapped gemini-4-pro keeps reasoning params and tool calling instead of having them dropped; it carries no token limits or pricing, so those stay on the standard unmapped behavior rather than a guessed number.", "model_info": { "mode": "chat", - "max_input_tokens": 1048576, - "max_output_tokens": 65536, - "max_tokens": 65536, "supports_function_calling": true, - "supports_parallel_function_calling": true, - "supports_vision": true, - "supports_pdf_input": true, - "supports_audio_input": true, - "supports_video_input": true, "supports_tool_choice": true, "supports_system_messages": true, + "supports_vision": true, "supports_response_schema": true, - "supports_prompt_caching": true, - "supports_reasoning": true, - "supports_web_search": true, - "supports_url_context": true + "supports_reasoning": true } } ] diff --git a/tests/test_litellm/litellm_core_utils/test_fallback_generalizations.py b/tests/test_litellm/litellm_core_utils/test_fallback_generalizations.py index e91a2bb4394..556a23c13be 100644 --- a/tests/test_litellm/litellm_core_utils/test_fallback_generalizations.py +++ b/tests/test_litellm/litellm_core_utils/test_fallback_generalizations.py @@ -449,31 +449,6 @@ def shipped_cost_map(monkeypatch): set_fallback_generalizations(previous_rules) -def test_shipped_bare_gemini_id_routes_to_vertex_ai(shipped_cost_map): - for model in ( - "gemini-3.9-flash", - "gemini-4-pro", - "gemini-4-pro-preview", - "gemini-3.9-flash-lite-preview-09-2026", - ): - assert model not in litellm.model_cost - assert litellm.get_llm_provider(model=model)[1] == "vertex_ai" - - assert litellm.get_llm_provider(model="gemini-2.5-pro")[1] == "vertex_ai" - - -def test_shipped_gemini_routing_rule_is_anchored(shipped_cost_map): - for model in ( - "gemini-4-flash-image", - "gemini-3.9-flash-preview-tts", - "gemini-2.0-flash-new", - "gemini-1.5-flash-new", - "gemini-4-flashy", - "gemini/gemini-4-pro", - ): - assert match_routing_generalization(model) is None, model - - @pytest.mark.parametrize( "model,provider", [ @@ -491,10 +466,11 @@ def test_shipped_gemini_baseline_resolves_unmapped_ids_provider_neutral(shipped_ info = litellm.get_model_info(model, custom_llm_provider=provider) assert info["litellm_provider"] == provider assert info["mode"] == "chat" - assert info["max_input_tokens"] == 1048576 - assert info["max_tokens"] == 65536 + assert not info.get("max_input_tokens") assert info["supports_reasoning"] is True assert info["supports_function_calling"] is True + assert info["supports_tool_choice"] is True + assert info["supports_response_schema"] is True assert info["supports_vision"] is True assert not info.get("input_cost_per_token") assert not info.get("output_cost_per_token") From 557c237b81c3e26dbbdb102d32660956e6107fa8 Mon Sep 17 00:00:00 2001 From: yassin Date: Tue, 15 Sep 2026 22:57:23 +0000 Subject: [PATCH 109/225] test(proxy-extras): move the LITELLM_LOG logger test into the CI-run proxy-extras shard Co-Authored-By: Devin AI <158243242+devin-ai-integration[bot]@users.noreply.github.com> --- .../litellm-proxy-extras/test_litellm_proxy_extras_logging.py | 0 1 file changed, 0 insertions(+), 0 deletions(-) rename litellm-proxy-extras/tests/test_logging.py => tests/litellm-proxy-extras/test_litellm_proxy_extras_logging.py (100%) diff --git a/litellm-proxy-extras/tests/test_logging.py b/tests/litellm-proxy-extras/test_litellm_proxy_extras_logging.py similarity index 100% rename from litellm-proxy-extras/tests/test_logging.py rename to tests/litellm-proxy-extras/test_litellm_proxy_extras_logging.py From c007fb992841420e38b3ae4c7221b1e4343b450a Mon Sep 17 00:00:00 2001 From: yassin Date: Tue, 15 Sep 2026 23:00:08 +0000 Subject: [PATCH 110/225] fix(proxy): skip alias rewrite only for dispatched pass-through handlers Match the pass-through skip to what FastAPI actually dispatched (the user-defined endpoint marker or a provider handler's {endpoint:path} param) instead of the mapped route prefixes, which also cover native routes such as /openai/v1/responses and /cursor/chat/completions. Wrap the added test lines to the 120-column limit. Co-Authored-By: Devin AI <158243242+devin-ai-integration[bot]@users.noreply.github.com> --- litellm/proxy/auth/auth_utils.py | 5 + litellm/proxy/auth/user_api_key_auth.py | 5 +- .../proxy/auth/test_user_api_key_auth.py | 105 +++++++++++++----- 3 files changed, 87 insertions(+), 28 deletions(-) diff --git a/litellm/proxy/auth/auth_utils.py b/litellm/proxy/auth/auth_utils.py index dc304a156cf..b4c123af762 100644 --- a/litellm/proxy/auth/auth_utils.py +++ b/litellm/proxy/auth/auth_utils.py @@ -1967,6 +1967,11 @@ def request_dispatched_to_pass_through_endpoint(request: Request | None) -> bool return getattr(endpoint, LITELLM_PASS_THROUGH_ENDPOINT_MARKER, False) is True +def request_dispatched_to_provider_pass_through(request: Request) -> bool: + """Built-in provider pass-through handlers (``/anthropic/{endpoint:path}``, ...) bind ``endpoint``.""" + return "endpoint" in request.path_params + + def get_model_from_request( request_data: dict, route: str, diff --git a/litellm/proxy/auth/user_api_key_auth.py b/litellm/proxy/auth/user_api_key_auth.py index ac0f3696680..5f3bff1e4b7 100644 --- a/litellm/proxy/auth/user_api_key_auth.py +++ b/litellm/proxy/auth/user_api_key_auth.py @@ -78,6 +78,8 @@ from litellm.proxy.auth.auth_utils import ( iter_request_fallback_targets, normalize_request_route, pre_db_read_auth_checks, + request_dispatched_to_pass_through_endpoint, + request_dispatched_to_provider_pass_through, route_in_additonal_public_routes, ) from litellm.proxy.auth.handle_jwt import JWTAuthManager, JWTHandler @@ -251,7 +253,6 @@ async def _resolve_router_settings_model_group_alias( """Rewrite the requested model through the key's or team's ``router_settings.model_group_alias`` before the allowlist checks, so they authorize the model group the request is routed to. """ - from litellm.proxy.pass_through_endpoints.pass_through_endpoints import InitPassThroughEndpointHelpers from litellm.proxy.proxy_server import llm_router, prisma_client, proxy_config, proxy_logging_obj if request is None or llm_router is None or not RouteChecks.is_llm_api_route(route=route): @@ -259,7 +260,7 @@ async def _resolve_router_settings_model_group_alias( if request.scope.get(MODEL_GROUP_ALIAS_RESOLVED_SCOPE_KEY) is True: return request.scope[MODEL_GROUP_ALIAS_RESOLVED_SCOPE_KEY] = True - if InitPassThroughEndpointHelpers.is_registered_pass_through_route(route=route): + if request_dispatched_to_pass_through_endpoint(request) or request_dispatched_to_provider_pass_through(request): return requested: Final = request_data.get("model") if not isinstance(requested, str) or await read_raw_json_body(request=request) is None: diff --git a/tests/test_litellm/proxy/auth/test_user_api_key_auth.py b/tests/test_litellm/proxy/auth/test_user_api_key_auth.py index f974e4d29d2..525f19bf7b3 100644 --- a/tests/test_litellm/proxy/auth/test_user_api_key_auth.py +++ b/tests/test_litellm/proxy/auth/test_user_api_key_auth.py @@ -6,6 +6,7 @@ import subprocess import sys from contextlib import contextmanager from datetime import datetime, timedelta, timezone +from functools import partial from pathlib import Path from textwrap import dedent from types import SimpleNamespace @@ -8141,19 +8142,45 @@ async def test_auth_flow_enters_virtual_key_mapping_when_only_an_issuer_configur def _alias_router() -> litellm.Router: - return litellm.Router(model_list=[{"model_name": name, "litellm_params": {"model": "openai/gpt-4o", "api_key": "sk-fake"}} for name in ("claude-haiku", "claude-sonnet")]) + return litellm.Router( + model_list=[ + {"model_name": name, "litellm_params": {"model": "openai/gpt-4o", "api_key": "sk-fake"}} + for name in ("claude-haiku", "claude-sonnet") + ] + ) -def _alias_request(route: str, data: dict, content_type: str = "application/json"): +def _alias_request(route: str, data: dict, content_type: str = "application/json", path_params: dict | None = None): """A request as auth sees it: the body already read once and cached alongside its parsed form.""" from starlette.requests import Request - headers = [(b"content-type", content_type.encode())] - request = Request({"type": "http", "method": "POST", "path": route, "headers": headers, "query_string": b"", "parsed_body": (tuple(data), data)}) + scope = { + "type": "http", + "method": "POST", + "path": route, + "headers": [(b"content-type", content_type.encode())], + "query_string": b"", + "path_params": path_params or {}, + "parsed_body": (tuple(data), data), + } + request = Request(scope) request._body = json.dumps(data).encode() return request +async def _enforce_alias_access(token: UserAPIKeyAuth, data: dict, route: str, request, router: litellm.Router): + from litellm.proxy.auth.user_api_key_auth import _enforce_key_and_fallback_model_access + + await _enforce_key_and_fallback_model_access( + valid_token=token, + request_data=data, + route=route, + request=request, + llm_model_list=router.model_list, + llm_router=router, + ) + + def _alias_token(monkeypatch, level: str, alias: dict, models: list) -> UserAPIKeyAuth: """A key whose ``router_settings.model_group_alias`` lives on the key itself or on its cached team row.""" from litellm.proxy.common_utils.user_api_key_cache import UserApiKeyCache @@ -8161,7 +8188,8 @@ def _alias_token(monkeypatch, level: str, alias: dict, models: list) -> UserAPIK if level == "key": return UserAPIKeyAuth(models=models, router_settings={"model_group_alias": alias}) cache = UserApiKeyCache() - cache.set_cache(key="team_id:team-alias", value=LiteLLM_TeamTableCachedObj(team_id="team-alias", models=models, router_settings={"model_group_alias": alias})) + team = LiteLLM_TeamTableCachedObj(team_id="team-alias", models=models, router_settings={"model_group_alias": alias}) + cache.set_cache(key="team_id:team-alias", value=team) monkeypatch.setattr(litellm.proxy.proxy_server, "user_api_key_cache", cache) monkeypatch.setattr(litellm.proxy.proxy_server, "prisma_client", MagicMock()) return UserAPIKeyAuth(team_id="team-alias", models=models) @@ -8169,18 +8197,19 @@ def _alias_token(monkeypatch, level: str, alias: dict, models: list) -> UserAPIK @pytest.mark.asyncio @pytest.mark.parametrize("level", ["key", "team"]) -@pytest.mark.parametrize("route", ["/v1/chat/completions", "/v1/messages", "/v1/embeddings"]) +@pytest.mark.parametrize( + "route", + ["/v1/chat/completions", "/v1/messages", "/v1/embeddings", "/openai/v1/responses", "/cursor/chat/completions"], +) async def test_router_settings_model_group_alias_authorizes_target_for_key(monkeypatch, level, route): """LIT-3054: a key allowed only the alias target must be able to call the alias, and a key not allowed the target must still be denied even when the alias itself is what it requested.""" - from litellm.proxy.auth.user_api_key_auth import _enforce_key_and_fallback_model_access - router = _alias_router() monkeypatch.setattr(litellm.proxy.proxy_server, "llm_router", router) data = {"model": "AgentX-LLM", "messages": [{"role": "user", "content": "hi"}]} request = _alias_request(route, data) token = _alias_token(monkeypatch, level, {"AgentX-LLM": "claude-haiku"}, ["claude-haiku"]) - await _enforce_key_and_fallback_model_access(valid_token=token, request_data=data, route=route, request=request, llm_model_list=router.model_list, llm_router=router) + await _enforce_alias_access(token, data, route, request, router) assert data["model"] == "claude-haiku" assert (await request.json())["model"] == "claude-haiku" assert json.loads(await request.body())["model"] == "claude-haiku" @@ -8190,21 +8219,20 @@ async def test_router_settings_model_group_alias_authorizes_target_for_key(monke denied = _alias_token(monkeypatch, level, {"AgentX-LLM": "claude-sonnet"}, ["claude-haiku"]) denied_data = {"model": "AgentX-LLM"} with pytest.raises(ProxyException) as exc: - await _enforce_key_and_fallback_model_access(valid_token=denied, request_data=denied_data, route=route, request=_alias_request(route, denied_data), llm_model_list=router.model_list, llm_router=router) + await _enforce_alias_access(denied, denied_data, route, _alias_request(route, denied_data), router) assert "claude-sonnet" in exc.value.message @pytest.mark.asyncio async def test_router_settings_model_group_alias_leaves_form_bodies_alone(monkeypatch): """LIT-3054: a multipart body cannot be re-serialized as JSON, so auth must not rewrite it.""" - from litellm.proxy.auth.user_api_key_auth import _enforce_key_and_fallback_model_access - router = _alias_router() monkeypatch.setattr(litellm.proxy.proxy_server, "llm_router", router) data = {"model": "AgentX-LLM"} - request = _alias_request("/v1/audio/transcriptions", data, content_type="multipart/form-data; boundary=x") + route = "/v1/audio/transcriptions" + request = _alias_request(route, data, content_type="multipart/form-data; boundary=x") token = _alias_token(monkeypatch, "key", {"AgentX-LLM": "claude-haiku"}, ["claude-haiku", "AgentX-LLM"]) - await _enforce_key_and_fallback_model_access(valid_token=token, request_data=data, route="/v1/audio/transcriptions", request=request, llm_model_list=router.model_list, llm_router=router) + await _enforce_alias_access(token, data, route, request, router) assert data["model"] == "AgentX-LLM" assert get_client_requested_model(request) is None @@ -8212,7 +8240,6 @@ async def test_router_settings_model_group_alias_leaves_form_bodies_alone(monkey @pytest.mark.asyncio async def test_router_settings_model_group_alias_rewrite_keeps_query_params_out_of_body(monkeypatch): """LIT-3054: auth merges query params into its own copy of the body; the rewrite must not forward them.""" - from litellm.proxy.auth.user_api_key_auth import _enforce_key_and_fallback_model_access from litellm.proxy.common_utils.http_parsing_utils import _read_request_body, populate_request_with_path_params router = _alias_router() @@ -8223,25 +8250,42 @@ async def test_router_settings_model_group_alias_rewrite_keeps_query_params_out_ data = populate_request_with_path_params(request_data=await _read_request_body(request), request=request) assert data["api-version"] == "2024-10-21" token = _alias_token(monkeypatch, "key", {"AgentX-LLM": "claude-haiku"}, ["claude-haiku"]) - await _enforce_key_and_fallback_model_access(valid_token=token, request_data=data, route="/v1/chat/completions", request=request, llm_model_list=router.model_list, llm_router=router) + await _enforce_alias_access(token, data, "/v1/chat/completions", request, router) downstream = await _read_request_body(request) assert downstream == {**body, "model": "claude-haiku"} assert json.loads(await request.body()) == downstream assert await request.json() == downstream -@pytest.mark.asyncio -async def test_router_settings_model_group_alias_leaves_pass_through_bodies_alone(monkeypatch): - """LIT-3054: pass-through routes forward the body verbatim to the provider, so auth must not rewrite it.""" - from litellm.proxy.auth.user_api_key_auth import _enforce_key_and_fallback_model_access +def _user_defined_pass_through_endpoint(): + from litellm.types.passthrough_endpoints.pass_through_endpoints import LITELLM_PASS_THROUGH_ENDPOINT_MARKER + async def endpoint(): + return None + + setattr(endpoint, LITELLM_PASS_THROUGH_ENDPOINT_MARKER, True) + return endpoint + + +@pytest.mark.asyncio +@pytest.mark.parametrize("user_defined", [False, True]) +async def test_router_settings_model_group_alias_leaves_pass_through_bodies_alone(monkeypatch, user_defined): + """LIT-3054: pass-through handlers forward the body verbatim to the provider, so auth must not rewrite it. + Built-in provider handlers bind ``{endpoint:path}``; user-defined ones carry the pass-through marker.""" router = _alias_router() monkeypatch.setattr(litellm.proxy.proxy_server, "llm_router", router) data = {"model": "AgentX-LLM", "messages": [{"role": "user", "content": "hi"}]} - route = "/anthropic/v1/messages" - request = _alias_request(route, data) + route = "/custom-upstream/chat" if user_defined else "/anthropic/v1/messages" + request = _alias_request(route, data, path_params={} if user_defined else {"endpoint": "v1/messages"}) + if user_defined: + request.scope["endpoint"] = _user_defined_pass_through_endpoint() + LiteLLMRoutes.openai_routes.value.append(route) token = _alias_token(monkeypatch, "key", {"AgentX-LLM": "claude-haiku"}, ["claude-haiku", "AgentX-LLM"]) - await _enforce_key_and_fallback_model_access(valid_token=token, request_data=data, route=route, request=request, llm_model_list=router.model_list, llm_router=router) + try: + await _enforce_alias_access(token, data, route, request, router) + finally: + if user_defined: + LiteLLMRoutes.openai_routes.value.remove(route) assert data["model"] == "AgentX-LLM" assert (await request.json())["model"] == "AgentX-LLM" assert get_client_requested_model(request) is None @@ -8262,13 +8306,22 @@ async def test_router_settings_model_group_alias_authorizes_target_for_team(monk token = _alias_token(monkeypatch, "team", {"AgentX-LLM": target}, ["claude-haiku"]) token.team_models = ["claude-haiku"] data = {"model": "AgentX-LLM", "messages": [{"role": "user", "content": "hi"}]} - request = _alias_request("/v1/chat/completions", data) + route = "/v1/chat/completions" + request = _alias_request(route, data) + authorize = partial( + _authorize_authenticated_request, + user_api_key_auth_obj=token, + request=request, + request_data=data, + route=route, + api_key="sk-test", + ) if expect_denied: with pytest.raises(ProxyException) as exc: - await _authorize_authenticated_request(user_api_key_auth_obj=token, request=request, request_data=data, route="/v1/chat/completions", api_key="sk-test") + await authorize() assert exc.value.type == ProxyErrorTypes.team_model_access_denied assert target in exc.value.message return - await _authorize_authenticated_request(user_api_key_auth_obj=token, request=request, request_data=data, route="/v1/chat/completions", api_key="sk-test") + await authorize() assert (await request.json())["model"] == target assert get_client_requested_model(request) == "AgentX-LLM" From e64371062cc6defc09e4b3caf73fad6e36a7905e Mon Sep 17 00:00:00 2001 From: kerry Date: Tue, 15 Sep 2026 23:04:40 +0000 Subject: [PATCH 111/225] test: remove test files left empty by the cleanup Co-Authored-By: Devin AI <158243242+devin-ai-integration[bot]@users.noreply.github.com> --- .../databricks/test_databricks_pricing.py | 0 .../test_fireworks_ai_kimi_model_metadata.py | 54 ------------------- .../llms/mistral/ocr/test_mistral_ocr_cost.py | 40 -------------- .../llms/tencent/test_cost_calculator.py | 1 - .../test_litellm/test_command_r7b_pricing.py | 53 ------------------ 5 files changed, 148 deletions(-) delete mode 100644 tests/test_litellm/llms/databricks/test_databricks_pricing.py delete mode 100644 tests/test_litellm/llms/fireworks_ai/test_fireworks_ai_kimi_model_metadata.py delete mode 100644 tests/test_litellm/llms/mistral/ocr/test_mistral_ocr_cost.py delete mode 100644 tests/test_litellm/llms/tencent/test_cost_calculator.py delete mode 100644 tests/test_litellm/test_command_r7b_pricing.py diff --git a/tests/test_litellm/llms/databricks/test_databricks_pricing.py b/tests/test_litellm/llms/databricks/test_databricks_pricing.py deleted file mode 100644 index e69de29bb2d..00000000000 diff --git a/tests/test_litellm/llms/fireworks_ai/test_fireworks_ai_kimi_model_metadata.py b/tests/test_litellm/llms/fireworks_ai/test_fireworks_ai_kimi_model_metadata.py deleted file mode 100644 index 8d28ee921e1..00000000000 --- a/tests/test_litellm/llms/fireworks_ai/test_fireworks_ai_kimi_model_metadata.py +++ /dev/null @@ -1,54 +0,0 @@ -""" -Regression test for Fireworks Kimi K2.5 / K2.6 / K2.7 context and output limits. - -Fireworks publishes a 262144-token context window for every Kimi K2.5, K2.6 and -K2.7 model, but caps generation well below that. A previous bulk edit had flattened -max_output_tokens/max_tokens to 262144 (equal to the context window), which let the -pre-call context-window check admit requests asking for a full 262144-token -completion that Fireworks then rejects. These assertions pin the corrected per-alias -limits so a future bulk edit can't silently flatten them again. -""" - -import json -from importlib.resources import files - -import pytest - -CONTEXT_WINDOW = 262144 -OUTPUT_LIMIT = 32768 - -KIMI_ALIASES = ( - "fireworks_ai/kimi-k2p5", - "fireworks_ai/kimi-k2p6", - "fireworks_ai/kimi-k2p6-fast", - "fireworks_ai/kimi-k2p7-code", - "fireworks_ai/kimi-k2p7-code-fast", - "fireworks_ai/accounts/fireworks/models/kimi-k2p5", - "fireworks_ai/accounts/fireworks/models/kimi-k2p6", - "fireworks_ai/accounts/fireworks/models/kimi-k2p7-code", - "fireworks_ai/accounts/fireworks/routers/kimi-k2p6-fast", - "fireworks_ai/accounts/fireworks/routers/kimi-k2p7-code-fast", -) - - -@pytest.fixture(scope="module") -def use_local_model_cost_map(): - monkeypatch = pytest.MonkeyPatch() - monkeypatch.setenv("LITELLM_LOCAL_MODEL_COST_MAP", "True") - - import litellm - from litellm.utils import _invalidate_model_cost_lowercase_map - - original_model_cost = litellm.model_cost - litellm.model_cost = json.loads( - files("litellm").joinpath("model_prices_and_context_window_backup.json").read_text(encoding="utf-8") - ) - litellm.get_model_info.cache_clear() - _invalidate_model_cost_lowercase_map() - try: - yield litellm - finally: - litellm.model_cost = original_model_cost - litellm.get_model_info.cache_clear() - _invalidate_model_cost_lowercase_map() - monkeypatch.undo() diff --git a/tests/test_litellm/llms/mistral/ocr/test_mistral_ocr_cost.py b/tests/test_litellm/llms/mistral/ocr/test_mistral_ocr_cost.py deleted file mode 100644 index e3cf40b43f7..00000000000 --- a/tests/test_litellm/llms/mistral/ocr/test_mistral_ocr_cost.py +++ /dev/null @@ -1,40 +0,0 @@ -""" -Cost tests for Mistral OCR models against the real litellm cost map -(no monkeypatching of get_model_info). These regress the pricing entries -for mistral-ocr-4-0 and mistral-ocr-latest, which now both resolve to -OCR 4 at $4 / 1000 pages. -""" - -from pathlib import Path - -from litellm.llms.base_llm.ocr.transformation import OCRPage, OCRResponse, OCRUsageInfo - -OCR4_COST_PER_PAGE = 0.004 -OCR4_ANNOTATION_COST_PER_PAGE = 0.005 - -REPO_ROOT = Path(__file__).parents[5] -MAIN_COST_MAP = REPO_ROOT / "model_prices_and_context_window.json" -BACKUP_COST_MAP = REPO_ROOT / "litellm" / "model_prices_and_context_window_backup.json" - -OCR3_MODEL = "mistral/mistral-ocr-2512" -OCR3_COST_PER_PAGE = 0.002 -OCR3_ANNOTATION_COST_PER_PAGE = 0.003 - -AZURE_DOC_AI_MODEL = "azure_ai/mistral-document-ai-2512" -AZURE_DOC_AI_COST_PER_PAGE = 0.003 - - -def _ocr_response(model: str, pages_processed: int) -> OCRResponse: - return OCRResponse( - pages=[OCRPage(index=i, markdown=f"page {i}") for i in range(pages_processed)], - model=model, - usage_info=OCRUsageInfo(pages_processed=pages_processed), - ) - - -def _annotated_ocr_response(model: str, pages_processed: int | None, annotation_pages: int) -> OCRResponse: - return OCRResponse( - pages=[], - model=model, - usage_info=OCRUsageInfo(pages_processed=pages_processed, pages_processed_annotation=annotation_pages), - ) diff --git a/tests/test_litellm/llms/tencent/test_cost_calculator.py b/tests/test_litellm/llms/tencent/test_cost_calculator.py deleted file mode 100644 index 8b137891791..00000000000 --- a/tests/test_litellm/llms/tencent/test_cost_calculator.py +++ /dev/null @@ -1 +0,0 @@ - diff --git a/tests/test_litellm/test_command_r7b_pricing.py b/tests/test_litellm/test_command_r7b_pricing.py deleted file mode 100644 index c091fec313e..00000000000 --- a/tests/test_litellm/test_command_r7b_pricing.py +++ /dev/null @@ -1,53 +0,0 @@ -""" -Regression test: ``command-r7b-12-2024`` had its input/output per-token -costs transposed in the model-cost maps (input=1.5e-07 / output=3.75e-08), -even though Cohere publishes $0.0375/1M input and $0.15/1M output, i.e. -output is ~4x input like every other ``command-r`` entry. - -These tests pin the corrected values in both the primary price map and the -``litellm/`` backup, and verify ``get_model_info`` surfaces them, so the -swap cannot silently regress. -""" - -import json -import os - -import litellm - -MODEL = "command-r7b-12-2024" -EXPECTED_INPUT_COST = 3.75e-08 -EXPECTED_OUTPUT_COST = 1.5e-07 - - -def _load_json(path: str) -> dict: - with open(path, encoding="utf-8") as f: - return json.load(f) - - -def _backup_path() -> str: - return os.path.join( - os.path.dirname(litellm.__file__), - "model_prices_and_context_window_backup.json", - ) - - -def _main_path() -> str: - # This test lives at ``tests/test_litellm/``; the primary price map sits at - # the repo root, two directories up. Resolve it relative to this file so the - # test works regardless of where ``litellm`` itself is installed (e.g. a pip - # install into site-packages). - return os.path.join( - os.path.dirname(__file__), - "..", - "..", - "model_prices_and_context_window.json", - ) - - -class TestCommandR7bPricingData: - """The JSON price maps must carry Cohere's published costs, with output - more expensive than input.""" - - -class TestCommandR7bPricingModelInfo: - """``get_model_info`` must report the corrected, un-swapped costs.""" From 0c65dcff22948008b3a8e59078aaf8c084e10e54 Mon Sep 17 00:00:00 2001 From: yassin Date: Tue, 15 Sep 2026 23:15:34 +0000 Subject: [PATCH 112/225] fix(proxy): strip line breaks from alias resolution debug log Co-Authored-By: Devin AI <158243242+devin-ai-integration[bot]@users.noreply.github.com> --- litellm/proxy/auth/user_api_key_auth.py | 6 +++++- 1 file changed, 5 insertions(+), 1 deletion(-) diff --git a/litellm/proxy/auth/user_api_key_auth.py b/litellm/proxy/auth/user_api_key_auth.py index 5f3bff1e4b7..15dd58baf35 100644 --- a/litellm/proxy/auth/user_api_key_auth.py +++ b/litellm/proxy/auth/user_api_key_auth.py @@ -273,7 +273,11 @@ async def _resolve_router_settings_model_group_alias( target: Final = resolve_model_group_alias(settings.get("model_group_alias"), requested) if target is None or target == requested: return - verbose_proxy_logger.debug("router_settings.model_group_alias resolved %s -> %s before auth", requested, target) + verbose_proxy_logger.debug( + "router_settings.model_group_alias resolved %s -> %s before auth", + requested.replace("\r", "").replace("\n", ""), + target.replace("\r", "").replace("\n", ""), + ) request.scope.setdefault(CLIENT_REQUESTED_MODEL_SCOPE_KEY, requested) rewrite_request_model(request_data, request, target) From 726430c0e7e812804b39251aa11c8df569c731d7 Mon Sep 17 00:00:00 2001 From: kerry Date: Tue, 15 Sep 2026 23:16:32 +0000 Subject: [PATCH 113/225] refactor(model_info): scope Gemini baseline to first-party chat ids Co-Authored-By: Devin AI <158243242+devin-ai-integration[bot]@users.noreply.github.com> --- ...odel_prices_and_context_window_backup.json | 11 ++-- model_prices_and_context_window.json | 11 ++-- .../test_fallback_generalizations.py | 53 +++++++++++++------ .../test_get_supported_openai_params.py | 50 +++++------------ 4 files changed, 62 insertions(+), 63 deletions(-) diff --git a/litellm/model_prices_and_context_window_backup.json b/litellm/model_prices_and_context_window_backup.json index 3f77bd49a0d..9569f29aa0e 100644 --- a/litellm/model_prices_and_context_window_backup.json +++ b/litellm/model_prices_and_context_window_backup.json @@ -58151,17 +58151,20 @@ } }, { - "name": "gemini-family-baseline", - "pattern": "gemini-(?!.*(?:-tts|-image|-live|-audio|-embedding|-computer-use|-robotics))(?:2\\.[5-9]|[3-9](?:[.-]\\d{1,2})?)-(?:pro|flash)(?:-lite)?(?![a-z])", - "description": "Any Gemini text-chat id at 2.5 or higher under any provider namespace (bare, gemini/, vertex_ai/, databricks-gemini-3-1-pro, ...): gemini-[.minor]-(pro|flash)[-lite] with any trailing preview, date or variant tag. The lookahead excludes the tts, image, live, audio, embedding, computer-use and robotics lines, which are not text-chat models and carry different limits and modes. 2.5 is the floor because every Gemini from 2.5 onward is a thinking model with tool calling and multimodal input; 1.5 and 2.0 are not and are fully mapped. Carries the model-family facts every such Gemini shares, so an unmapped gemini-4-pro keeps reasoning params and tool calling instead of having them dropped; it carries no token limits or pricing, so those stay on the standard unmapped behavior rather than a guessed number.", + "name": "gemini-chat-baseline", + "pattern": "^(?:gemini|vertex_ai)/gemini-(?!.*(?:-tts|-image|-live|-audio|-embedding|-computer-use|-robotics|-transcribe|-translate))(?:2\\.[5-9]|[3-9](?:\\.\\d{1,2})?)-(?:pro|flash)(?:-lite)?(?![a-z])", + "description": "A first-party Gemini text-chat id at 2.5 or higher, as gemini/ or vertex_ai/ (get_model_info tries the provider-prefixed name first, so a bare id under those providers matches too): gemini-[.minor]-(pro|flash)[-lite] with any trailing preview, date or variant tag. Scoped to Google's own namespaces because resellers wrap the same models differently (one exposes them as mode responses without reasoning), so nothing below is certain there. The lookahead excludes the tts, image, live, audio, embedding, computer-use, robotics, transcribe and translate lines, which are different modes with different capabilities. 2.5 is the floor: every first-party text-chat Gemini from 2.5 onward in this map carries all of these fields, 1.5 and 2.0 do not and are fully mapped. Carries no token limits or pricing, so those stay on the standard unmapped behavior rather than a guessed number.", "model_info": { "mode": "chat", + "supports_reasoning": true, "supports_function_calling": true, "supports_tool_choice": true, "supports_system_messages": true, "supports_vision": true, "supports_response_schema": true, - "supports_reasoning": true + "supports_pdf_input": true, + "supports_prompt_caching": true, + "supports_web_search": true } } ] diff --git a/model_prices_and_context_window.json b/model_prices_and_context_window.json index 3f77bd49a0d..9569f29aa0e 100644 --- a/model_prices_and_context_window.json +++ b/model_prices_and_context_window.json @@ -58151,17 +58151,20 @@ } }, { - "name": "gemini-family-baseline", - "pattern": "gemini-(?!.*(?:-tts|-image|-live|-audio|-embedding|-computer-use|-robotics))(?:2\\.[5-9]|[3-9](?:[.-]\\d{1,2})?)-(?:pro|flash)(?:-lite)?(?![a-z])", - "description": "Any Gemini text-chat id at 2.5 or higher under any provider namespace (bare, gemini/, vertex_ai/, databricks-gemini-3-1-pro, ...): gemini-[.minor]-(pro|flash)[-lite] with any trailing preview, date or variant tag. The lookahead excludes the tts, image, live, audio, embedding, computer-use and robotics lines, which are not text-chat models and carry different limits and modes. 2.5 is the floor because every Gemini from 2.5 onward is a thinking model with tool calling and multimodal input; 1.5 and 2.0 are not and are fully mapped. Carries the model-family facts every such Gemini shares, so an unmapped gemini-4-pro keeps reasoning params and tool calling instead of having them dropped; it carries no token limits or pricing, so those stay on the standard unmapped behavior rather than a guessed number.", + "name": "gemini-chat-baseline", + "pattern": "^(?:gemini|vertex_ai)/gemini-(?!.*(?:-tts|-image|-live|-audio|-embedding|-computer-use|-robotics|-transcribe|-translate))(?:2\\.[5-9]|[3-9](?:\\.\\d{1,2})?)-(?:pro|flash)(?:-lite)?(?![a-z])", + "description": "A first-party Gemini text-chat id at 2.5 or higher, as gemini/ or vertex_ai/ (get_model_info tries the provider-prefixed name first, so a bare id under those providers matches too): gemini-[.minor]-(pro|flash)[-lite] with any trailing preview, date or variant tag. Scoped to Google's own namespaces because resellers wrap the same models differently (one exposes them as mode responses without reasoning), so nothing below is certain there. The lookahead excludes the tts, image, live, audio, embedding, computer-use, robotics, transcribe and translate lines, which are different modes with different capabilities. 2.5 is the floor: every first-party text-chat Gemini from 2.5 onward in this map carries all of these fields, 1.5 and 2.0 do not and are fully mapped. Carries no token limits or pricing, so those stay on the standard unmapped behavior rather than a guessed number.", "model_info": { "mode": "chat", + "supports_reasoning": true, "supports_function_calling": true, "supports_tool_choice": true, "supports_system_messages": true, "supports_vision": true, "supports_response_schema": true, - "supports_reasoning": true + "supports_pdf_input": true, + "supports_prompt_caching": true, + "supports_web_search": true } } ] diff --git a/tests/test_litellm/litellm_core_utils/test_fallback_generalizations.py b/tests/test_litellm/litellm_core_utils/test_fallback_generalizations.py index 556a23c13be..a4fbd79187e 100644 --- a/tests/test_litellm/litellm_core_utils/test_fallback_generalizations.py +++ b/tests/test_litellm/litellm_core_utils/test_fallback_generalizations.py @@ -453,45 +453,64 @@ def shipped_cost_map(monkeypatch): "model,provider", [ ("gemini-4-pro", "gemini"), - ("gemini-3.9-flash", "vertex_ai"), - ("databricks-gemini-4-1-pro", "databricks"), + ("gemini/gemini-4-pro", None), + ("gemini-3.9-flash-lite-preview-09-2026", "vertex_ai"), + ("vertex_ai/gemini-4-pro", None), ("gemini-4-pro-preview-customtools", "gemini"), ], ) -def test_shipped_gemini_baseline_resolves_unmapped_ids_provider_neutral(shipped_cost_map, model, provider): +def test_shipped_gemini_chat_baseline_resolves_first_party_unmapped_ids(shipped_cost_map, model, provider): assert model not in litellm.model_cost if provider == "gemini": assert f"gemini/{model}" not in litellm.model_cost info = litellm.get_model_info(model, custom_llm_provider=provider) - assert info["litellm_provider"] == provider + assert info["litellm_provider"] == (provider or model.split("/")[0]) assert info["mode"] == "chat" assert not info.get("max_input_tokens") assert info["supports_reasoning"] is True assert info["supports_function_calling"] is True assert info["supports_tool_choice"] is True - assert info["supports_response_schema"] is True + assert info["supports_system_messages"] is True assert info["supports_vision"] is True + assert info["supports_response_schema"] is True + assert info["supports_pdf_input"] is True + assert info["supports_prompt_caching"] is True + assert info["supports_web_search"] is True assert not info.get("input_cost_per_token") assert not info.get("output_cost_per_token") -def test_shipped_gemini_baseline_skips_non_chat_and_pre_2_5_ids(shipped_cost_map): +def test_shipped_gemini_chat_baseline_skips_reseller_namespaces(shipped_cost_map): + for model, provider in ( + ("google/gemini-4-pro", "perplexity"), + ("google/gemini-4-pro", "openrouter"), + ("databricks-gemini-4-1-pro", "databricks"), + ): + with pytest.raises(Exception, match="isn't mapped yet"): + litellm.get_model_info(model, custom_llm_provider=provider) + + assert match_capability_generalizations("google/gemini-4-pro") is None + + +def test_shipped_gemini_chat_baseline_skips_non_chat_and_pre_2_5_ids(shipped_cost_map): for model in ( - "gemini-4-flash-image", - "gemini-3.9-flash-preview-tts", - "gemini-4-flash-live-preview", - "gemini-4-flash-native-audio", - "gemini-embedding-4", - "gemini-2.5-computer-use-preview-12-2026", - "gemini-2.0-flash-new", - "gemini-1.5-pro-new", - "gemini-4-flashy", + "gemini/gemini-4-flash-image", + "gemini/gemini-3.9-flash-preview-tts", + "gemini/gemini-4-flash-live-preview", + "gemini/gemini-4-flash-native-audio", + "gemini/gemini-embedding-4", + "gemini/gemini-2.5-computer-use-preview-12-2026", + "gemini/gemini-2.0-flash-new", + "gemini/gemini-1.5-pro-new", + "gemini/gemini-4-flashy", + "gemini/gemini-4-flash-transcribe", + "gemini/gemini-4-flash-live-translate-preview", ): assert match_capability_generalizations(model) is None, model -def test_shipped_gemini_baseline_keeps_reasoning_effort_on_unmapped_model(shipped_cost_map): +def test_shipped_gemini_chat_baseline_keeps_reasoning_effort_on_unmapped_model(shipped_cost_map): assert litellm.supports_reasoning(model="gemini-4-pro", custom_llm_provider="gemini") is True optional_params = litellm.utils.get_optional_params( @@ -505,7 +524,7 @@ def test_shipped_gemini_baseline_keeps_reasoning_effort_on_unmapped_model(shippe assert optional_params["thinkingConfig"]["includeThoughts"] is True -def test_shipped_gemini_baseline_loses_to_exact_entries(shipped_cost_map): +def test_shipped_gemini_chat_baseline_loses_to_exact_entries(shipped_cost_map): model = "gemini-2.5-flash-lite" info = litellm.get_model_info(model, custom_llm_provider="gemini") entry = litellm.model_cost["gemini/gemini-2.5-flash-lite"] diff --git a/tests/test_litellm/litellm_core_utils/test_get_supported_openai_params.py b/tests/test_litellm/litellm_core_utils/test_get_supported_openai_params.py index 722818598af..f9e285cf9fb 100644 --- a/tests/test_litellm/litellm_core_utils/test_get_supported_openai_params.py +++ b/tests/test_litellm/litellm_core_utils/test_get_supported_openai_params.py @@ -1,7 +1,5 @@ - import pytest - from litellm.litellm_core_utils.get_supported_openai_params import ( get_supported_openai_params, ) @@ -33,9 +31,7 @@ def test_base_model_label_alone_lacks_bedrock_tools(): """The label by itself does not advertise tools; this is what made the union necessary. Guards against the discrepancy disappearing (and the regression test above silently passing for the wrong reason).""" - params = get_supported_openai_params( - model=BEDROCK_LABEL, custom_llm_provider="bedrock" - ) + params = get_supported_openai_params(model=BEDROCK_LABEL, custom_llm_provider="bedrock") assert params is not None assert "tools" not in params @@ -46,14 +42,8 @@ def test_base_model_is_additive_not_replacement(): Bedrock: real id supports ``tools`` but not the label's reasoning hint; the union must contain the real model's ``tools`` regardless of the label being a subset.""" - real_only = set( - get_supported_openai_params( - model=BEDROCK_REAL_MODEL, custom_llm_provider="bedrock" - ) - ) - label_only = set( - get_supported_openai_params(model=BEDROCK_LABEL, custom_llm_provider="bedrock") - ) + real_only = set(get_supported_openai_params(model=BEDROCK_REAL_MODEL, custom_llm_provider="bedrock")) + label_only = set(get_supported_openai_params(model=BEDROCK_LABEL, custom_llm_provider="bedrock")) combined = set( get_supported_openai_params( model=BEDROCK_REAL_MODEL, @@ -70,19 +60,15 @@ def test_base_model_is_additive_not_replacement(): def test_base_model_adds_capabilities_the_real_model_lacks(): """Regression for #27717 (the behavior the union must preserve). - ``gemini-3.1-pro`` isn't in the cost map so it advertises no reasoning support, + ``gemini-exp-9999`` isn't in the cost map so it advertises no reasoning support, but the registered ``gemini-3.1-pro-preview`` base_model does. The hint must add ``reasoning_effort``/``thinking`` without the call erroring.""" - real_only = set( - get_supported_openai_params( - model="gemini-3.1-pro", custom_llm_provider="gemini" - ) - ) + real_only = set(get_supported_openai_params(model="gemini-exp-9999", custom_llm_provider="gemini")) assert "reasoning_effort" not in real_only combined = set( get_supported_openai_params( - model="gemini-3.1-pro", + model="gemini-exp-9999", custom_llm_provider="gemini", base_model="gemini-3.1-pro-preview", ) @@ -93,21 +79,15 @@ def test_base_model_adds_capabilities_the_real_model_lacks(): def test_no_base_model_is_unchanged(): """Omitting ``base_model`` must resolve purely from ``model``.""" - with_none = get_supported_openai_params( - model=BEDROCK_REAL_MODEL, custom_llm_provider="bedrock", base_model=None - ) - plain = get_supported_openai_params( - model=BEDROCK_REAL_MODEL, custom_llm_provider="bedrock" - ) + with_none = get_supported_openai_params(model=BEDROCK_REAL_MODEL, custom_llm_provider="bedrock", base_model=None) + plain = get_supported_openai_params(model=BEDROCK_REAL_MODEL, custom_llm_provider="bedrock") assert with_none == plain def test_base_model_equal_to_model_is_unchanged(): """A ``base_model`` identical to ``model`` must not double-resolve or reorder.""" - plain = get_supported_openai_params( - model=BEDROCK_REAL_MODEL, custom_llm_provider="bedrock" - ) + plain = get_supported_openai_params(model=BEDROCK_REAL_MODEL, custom_llm_provider="bedrock") same = get_supported_openai_params( model=BEDROCK_REAL_MODEL, custom_llm_provider="bedrock", @@ -152,14 +132,10 @@ def test_bedrock_converse_alias_resolves_like_bedrock(): params saw no Bedrock capabilities for a Converse model invoked via the alias.""" anthropic_model = "bedrock/converse/us.anthropic.claude-sonnet-4-6" - via_alias = get_supported_openai_params( - model=anthropic_model, custom_llm_provider="bedrock_converse" - ) + via_alias = get_supported_openai_params(model=anthropic_model, custom_llm_provider="bedrock_converse") assert via_alias is not None - assert via_alias == get_supported_openai_params( - model=anthropic_model, custom_llm_provider="bedrock" - ) + assert via_alias == get_supported_openai_params(model=anthropic_model, custom_llm_provider="bedrock") assert "web_search_options" not in via_alias assert "tools" in via_alias @@ -167,9 +143,7 @@ def test_bedrock_converse_alias_resolves_like_bedrock(): def test_bedrock_converse_alias_keeps_nova_web_search_options(): """Nova on the ``bedrock_converse`` alias still advertises web_search_options, proving the alias routes through the model-aware config rather than a blanket Bedrock default.""" - nova_params = get_supported_openai_params( - model="amazon.nova-pro-v1:0", custom_llm_provider="bedrock_converse" - ) + nova_params = get_supported_openai_params(model="amazon.nova-pro-v1:0", custom_llm_provider="bedrock_converse") assert nova_params is not None assert "web_search_options" in nova_params From 39cf1f302d8d0c4ab37467303a220db06e68f09c Mon Sep 17 00:00:00 2001 From: Tin Chi Lo Date: Tue, 15 Sep 2026 16:17:00 -0700 Subject: [PATCH 114/225] feat(router): apply entitlement limits to forecast classifiers --- litellm/proxy/auth/litellm_license.py | 4 +- .../router_utils/auto_router_model_naming.py | 28 +++++++-- .../test_model_management_endpoints.py | 45 ++++++++++++-- .../proxy/proxy_server/test_proxy_config.py | 21 ++++++- .../router_strategy/test_complexity_router.py | 60 +++++++++++++++++++ .../test_auto_router_model_naming.py | 10 ++++ 6 files changed, 153 insertions(+), 15 deletions(-) diff --git a/litellm/proxy/auth/litellm_license.py b/litellm/proxy/auth/litellm_license.py index 067ac7905c5..6a1090a0d3a 100644 --- a/litellm/proxy/auth/litellm_license.py +++ b/litellm/proxy/auth/litellm_license.py @@ -155,8 +155,8 @@ class LicenseCheck: def auto_router_capability_limit(self) -> int | None: """ - How many auto-routers may claim each licensed capability (heuristic_v2, operator-defined - tier_definitions): unlimited (None) only when the signed license lists the auto_router + How many auto-routers may claim each gated classifier or customization capability: + unlimited (None) only when the signed license lists the auto_router feature, otherwise one per capability. A license verified through the API carries no feature list, so it does not lift the limit either. """ diff --git a/litellm/router_utils/auto_router_model_naming.py b/litellm/router_utils/auto_router_model_naming.py index 9af8a9a1180..91ff254d502 100644 --- a/litellm/router_utils/auto_router_model_naming.py +++ b/litellm/router_utils/auto_router_model_naming.py @@ -218,9 +218,8 @@ class GatedAutoRouterCapability: stored ``litellm_params`` (``{config}`` is the caller's expression for the normalized ``complexity_router_config`` jsonb, substituted as many times as the predicate needs); they live on one record so they cannot drift apart. ``subject`` and ``remedy`` build the shared refusal - message. A validated config claims at most one capability, and the validator is what makes that - true: tier_definitions rejects every heuristic classifier_type, and it also rejects the - classifier system_prompt, which in turn only applies to the classifier types heuristic_v2 is not. + message. A validated config claims at most one capability: gated classifier types cannot be + combined with operator-defined tiers or classifier prompts. """ key: str @@ -238,6 +237,22 @@ HEURISTIC_V2_CAPABILITY: Final = GatedAutoRouterCapability( sql_config_predicate="{config} ->> 'classifier_type' = 'heuristic_v2'", ) +CAPABILITY_CLASSIFIER_CAPABILITY: Final = GatedAutoRouterCapability( + key="capability", + subject="with classifier_type 'capability' (Capability)", + remedy="Use a different classifier or remove an existing Capability router.", + uses=lambda config: _mapping(config).get("classifier_type") == "capability", + sql_config_predicate="{config} ->> 'classifier_type' = 'capability'", +) + +LLM_V2_CAPABILITY: Final = GatedAutoRouterCapability( + key="llm_v2", + subject="with classifier_type 'llm_v2' (Fuse v2)", + remedy="Use a different classifier or remove an existing Fuse v2 router.", + uses=lambda config: _mapping(config).get("classifier_type") == "llm_v2", + sql_config_predicate="{config} ->> 'classifier_type' = 'llm_v2'", +) + _OPERATOR_PROMPT_FIELDS_SQL: Final = " OR ".join( f"{{config}} ->> '{field}' IS NOT NULL" for field in OPERATOR_CLASSIFIER_PROMPT_FIELDS ) @@ -258,7 +273,12 @@ CUSTOMIZATION_CAPABILITY: Final = GatedAutoRouterCapability( ), ) -GATED_AUTO_ROUTER_CAPABILITIES: Final = (HEURISTIC_V2_CAPABILITY, CUSTOMIZATION_CAPABILITY) +GATED_AUTO_ROUTER_CAPABILITIES: Final = ( + HEURISTIC_V2_CAPABILITY, + CAPABILITY_CLASSIFIER_CAPABILITY, + LLM_V2_CAPABILITY, + CUSTOMIZATION_CAPABILITY, +) def claimed_capability(complexity_router_config: object) -> GatedAutoRouterCapability | None: diff --git a/tests/test_litellm/proxy/management_endpoints/test_model_management_endpoints.py b/tests/test_litellm/proxy/management_endpoints/test_model_management_endpoints.py index 6300331d564..e46b4fee61c 100644 --- a/tests/test_litellm/proxy/management_endpoints/test_model_management_endpoints.py +++ b/tests/test_litellm/proxy/management_endpoints/test_model_management_endpoints.py @@ -4982,6 +4982,26 @@ class TestStrategyRouterWriteValidation: _V2 = {"classifier_type": "heuristic_v2", "tiers": {"SIMPLE": "gpt-4o-mini"}} _V1 = {"classifier_type": "heuristic", "tiers": {"SIMPLE": "gpt-4o-mini"}} + _FORECAST_BASE = { + "classifier_llm_config": {"model": "gpt-4o-mini"}, + "tiers": {"SIMPLE": "gpt-4o-mini", "REASONING": "gpt-4o"}, + } + _CAPABILITY = { + **_FORECAST_BASE, + "classifier_type": "capability", + "capability_classifier_config": { + "efficient_tier": "SIMPLE", "capable_tier": "REASONING", "base_threshold": 0.7, + }, + } + _FUSE = { + **_FORECAST_BASE, + "classifier_type": "llm_v2", + "adaptive": False, + "llm_v2_config": { + "efficient_profile": "Small solver", "capable_profile": "Large solver", + "harness": "One attempt", "max_quality_gap": 0.05, + }, + } _CUSTOM_TIERS = { "classifier_type": "llm", "classifier_llm_config": {"model": "gpt-4o-mini"}, @@ -5049,6 +5069,16 @@ class TestStrategyRouterWriteValidation: @pytest.mark.parametrize( "limit,effective_params,db_models,config_config,model_id,expected", [ + (1, {"model": "auto_router/complexity_router", "complexity_router_config": _CAPABILITY}, ["auto_router/complexity_router"], None, None, "refused"), + (1, {"model": "auto_router/complexity_router", "complexity_router_config": _CAPABILITY}, [], _CAPABILITY, None, "refused"), + (1, {"model": "auto_router/complexity_router", "complexity_router_config": _CAPABILITY}, [], _FUSE, None, "reserved"), + (1, {"model": "auto_router/complexity_router", "complexity_router_config": _CAPABILITY}, [], None, "held-id", "reserved"), + (None, {"model": "auto_router/complexity_router", "complexity_router_config": _CAPABILITY}, ["auto_router/complexity_router"], _CAPABILITY, None, "plain"), + (1, {"model": "auto_router/complexity_router", "complexity_router_config": _FUSE}, ["auto_router/complexity_router"], None, None, "refused"), + (1, {"model": "auto_router/complexity_router", "complexity_router_config": _FUSE}, [], _FUSE, None, "refused"), + (1, {"model": "auto_router/complexity_router", "complexity_router_config": _FUSE}, [], _CAPABILITY, None, "reserved"), + (1, {"model": "auto_router/complexity_router", "complexity_router_config": _FUSE}, [], None, "held-id", "reserved"), + (None, {"model": "auto_router/complexity_router", "complexity_router_config": _FUSE}, ["auto_router/complexity_router"], _FUSE, None, "plain"), (1, {"model": "auto_router/complexity_router", "complexity_router_config": _V2}, ["auto_router/complexity_router"], None, None, "refused"), (1, {"model": "auto_router/complexity_router", "complexity_router_config": _V2}, [], _V2, None, "refused"), (1, {"model": "auto_router/complexity_router", "complexity_router_config": _V2}, [], None, None, "reserved"), @@ -5338,7 +5368,8 @@ class TestStrategyRouterWriteValidation: assert events == ["slot-enter", "slot-exit", "team_model_add"] @pytest.mark.asyncio - async def test_add_new_model_refuses_a_second_heuristic_v2_router_before_the_db_write(self) -> None: + @pytest.mark.parametrize("config", [_V2, _CAPABILITY, _FUSE]) + async def test_add_new_model_refuses_a_second_gated_classifier_router_before_the_db_write(self, config: Mapping[str, object]) -> None: from litellm.proxy._types import ProxyException from litellm.proxy.management_endpoints.model_management_endpoints import ( add_new_model, @@ -5366,7 +5397,7 @@ class TestStrategyRouterWriteValidation: await add_new_model( model_params=Deployment( model_name="second-v2", - litellm_params=LiteLLM_Params(model="auto_router/complexity_router", complexity_router_config=self._V2), + litellm_params=LiteLLM_Params(model="auto_router/complexity_router", complexity_router_config=config), ), user_api_key_dict=admin, ) @@ -5463,7 +5494,8 @@ class TestStrategyRouterWriteValidation: assert fake.litellm_proxymodeltable.update.await_count == 0 @pytest.mark.asyncio - async def test_patch_model_refuses_switching_another_router_to_heuristic_v2(self) -> None: + @pytest.mark.parametrize("config", [_V2, _CAPABILITY, _FUSE]) + async def test_patch_model_refuses_switching_another_router_to_gated_classifier(self, config: Mapping[str, object]) -> None: """patch_model relays HTTPException as-is, so the license refusal reaches the client as a plain 403.""" from fastapi import HTTPException @@ -5498,7 +5530,7 @@ class TestStrategyRouterWriteValidation: with pytest.raises(HTTPException) as exc_info: await patch_model( model_id=model_id, - patch_data=updateDeployment(litellm_params=updateLiteLLMParams(complexity_router_config=self._V2)), + patch_data=updateDeployment(litellm_params=updateLiteLLMParams(complexity_router_config=config)), user_api_key_dict=admin, ) assert exc_info.value.status_code == 403 @@ -5506,7 +5538,8 @@ class TestStrategyRouterWriteValidation: fake.litellm_proxymodeltable.update.assert_not_awaited() @pytest.mark.asyncio - async def test_update_model_refuses_switching_another_router_to_heuristic_v2(self) -> None: + @pytest.mark.parametrize("config", [_V2, _CAPABILITY, _FUSE]) + async def test_update_model_refuses_switching_another_router_to_gated_classifier(self, config: Mapping[str, object]) -> None: from litellm.proxy._types import ProxyException from litellm.proxy.management_endpoints.model_management_endpoints import ( update_model, @@ -5542,7 +5575,7 @@ class TestStrategyRouterWriteValidation: with pytest.raises(ProxyException) as exc_info: await update_model( model_params=updateDeployment( - litellm_params=updateLiteLLMParams(complexity_router_config=self._V2), + litellm_params=updateLiteLLMParams(complexity_router_config=config), model_info=ModelInfo(id=model_id), ), user_api_key_dict=admin, diff --git a/tests/test_litellm/proxy/proxy_server/test_proxy_config.py b/tests/test_litellm/proxy/proxy_server/test_proxy_config.py index d3578455a35..9a9b47ce3bb 100644 --- a/tests/test_litellm/proxy/proxy_server/test_proxy_config.py +++ b/tests/test_litellm/proxy/proxy_server/test_proxy_config.py @@ -317,13 +317,28 @@ _TWO_HEURISTIC_V2_ROUTERS_YAML = ( @pytest.mark.asyncio @pytest.mark.parametrize("license_limit", [1, None]) -async def test_ProxyConfig_load_config_takes_the_heuristic_v2_limit_from_the_license_only( - tmp_path, monkeypatch, license_limit: int | None +@pytest.mark.parametrize("classifier_type", ["heuristic_v2", "capability", "llm_v2"]) +async def test_ProxyConfig_load_config_takes_the_classifier_limit_from_the_license_only( + tmp_path, monkeypatch, license_limit: int | None, classifier_type: str ) -> None: """`router_settings.auto_router_capability_limit` is managed outside config.yaml: an operator cannot grant the entitlement by editing the config, and a licensed proxy boots both routers.""" f = tmp_path / "c.yaml" - f.write_text(_TWO_HEURISTIC_V2_ROUTERS_YAML) + forecast_settings = { + "capability": ( + " classifier_llm_config: {model: gpt-4o-mini}\n" + " capability_classifier_config: {efficient_tier: SIMPLE, capable_tier: REASONING, base_threshold: 0.7}\n" + ), + "llm_v2": ( + " classifier_llm_config: {model: gpt-4o-mini}\n" + " adaptive: false\n" + " llm_v2_config: {efficient_profile: Small solver, capable_profile: Large solver, harness: One attempt, max_quality_gap: 0.05}\n" + ), + } + config_yaml = _TWO_HEURISTIC_V2_ROUTERS_YAML.replace( + "classifier_type: heuristic_v2\n", f"classifier_type: {classifier_type}\n{forecast_settings.get(classifier_type, '')}" + ).replace("tiers: {SIMPLE: gpt-4o-mini}", "tiers: {SIMPLE: gpt-4o-mini, REASONING: gpt-4o}") + f.write_text(config_yaml) monkeypatch.setattr("litellm.proxy.proxy_server.prisma_client", None) monkeypatch.setattr("litellm.proxy.proxy_server.store_model_in_db", False) monkeypatch.delenv("LITELLM_CONFIG_BUCKET_NAME", raising=False) diff --git a/tests/test_litellm/router_strategy/test_complexity_router.py b/tests/test_litellm/router_strategy/test_complexity_router.py index 0931b9d01a7..9874028fc62 100644 --- a/tests/test_litellm/router_strategy/test_complexity_router.py +++ b/tests/test_litellm/router_strategy/test_complexity_router.py @@ -1417,6 +1417,66 @@ class TestRouterComplexityDeploymentMethods: router.init_complexity_router_deployment(deployment) assert "auto_router/complexity_router/test-router" in router.complexity_routers + @staticmethod + def _forecast_row(model_name: str, model_id: str, classifier_type: str) -> dict[str, object]: + settings: Final = ( + {"capability_classifier_config": { + "efficient_tier": "SIMPLE", "capable_tier": "REASONING", "base_threshold": 0.7, + }} if classifier_type == "capability" else { + "adaptive": False, + "llm_v2_config": { + "efficient_profile": "Small solver", "capable_profile": "Large solver", + "harness": "One attempt", "max_quality_gap": 0.05, + }, + } + ) + return { + "model_name": model_name, + "litellm_params": { + "model": "auto_router/complexity_router", + "complexity_router_config": { + "classifier_type": classifier_type, + "classifier_llm_config": {"model": "gpt-4o-mini"}, + "tiers": {"SIMPLE": "gpt-4o-mini", "REASONING": "gpt-4o"}, + **settings, + }, + }, + "model_info": {"id": model_id}, + } + + @pytest.mark.parametrize("classifier_type,sibling", [("capability", "llm_v2"), ("llm_v2", "capability")]) + def test_forecast_cap_keeps_edits_and_refuses_extra_routers_and_type_switches(self, classifier_type: str, sibling: str) -> None: + router: Final = Router( + model_list=[ + self._POOL, + self._forecast_row("held", "held-id", classifier_type), + self._forecast_row("sibling", "sibling-id", sibling), + self._router_row("other", "other-id", "heuristic_v2"), + self._custom_tier_row("custom", "custom-id"), + ], + auto_router_capability_limit=lambda: 1, + ignore_invalid_deployments=True, + ) + assert sorted(router.complexity_routers) == ["custom", "held", "other", "sibling"] + assert router.upsert_deployment(Deployment(**self._forecast_row("edited", "held-id", classifier_type))) is not None + assert router.upsert_deployment(Deployment(**self._forecast_row("second", "new-id", classifier_type))) is None + assert router.upsert_deployment(Deployment(**self._forecast_row("switched", "other-id", classifier_type))) is None + assert sorted(router.complexity_routers) == ["custom", "edited", "other", "sibling"] + assert router.upsert_deployment(Deployment(**self._router_row("released", "held-id", "heuristic"))) is not None + assert router.upsert_deployment(Deployment(**self._forecast_row("switched", "other-id", classifier_type))) is not None + assert sorted(router.complexity_routers) == ["custom", "released", "sibling", "switched"] + + @pytest.mark.parametrize("classifier_type", ["capability", "llm_v2"]) + @pytest.mark.parametrize("limit", [1, None]) + def test_forecast_registration_applies_the_resolved_license_limit(self, classifier_type: str, limit: int | None) -> None: + rows: Final = [self._POOL, self._forecast_row("a", "id-a", classifier_type), self._forecast_row("b", "id-b", classifier_type)] + if limit is not None: + with pytest.raises(ValueError, match="At most 1 auto-router"): + Router(model_list=rows, auto_router_capability_limit=lambda: limit) + return + router: Final = Router(model_list=rows, auto_router_capability_limit=lambda: limit) + assert sorted(router.complexity_routers) == ["a", "b"] + @staticmethod def _router_row(model_name: str, model_id: str, classifier_type: str) -> dict[str, object]: return { diff --git a/tests/test_litellm/router_utils/test_auto_router_model_naming.py b/tests/test_litellm/router_utils/test_auto_router_model_naming.py index 61e31255d12..3dcb8d5af94 100644 --- a/tests/test_litellm/router_utils/test_auto_router_model_naming.py +++ b/tests/test_litellm/router_utils/test_auto_router_model_naming.py @@ -395,6 +395,8 @@ def test_placement_is_scoped_to_complexity_router_deployments(model, present_fie _HV2_CONFIG: Mapping[str, object] = {"classifier_type": "heuristic_v2"} +_CAPABILITY_CONFIG: Mapping[str, object] = {"classifier_type": "capability"} +_FUSE_CONFIG: Mapping[str, object] = {"classifier_type": "llm_v2"} _CUSTOM_TIER_CONFIG: Mapping[str, object] = { "classifier_type": "llm", "tier_definitions": [{"name": "routine", "description": "easy"}, {"name": "hard", "description": "hard"}], @@ -457,6 +459,10 @@ def test_is_complexity_router_model(model: str | None, expected: bool) -> None: @pytest.mark.parametrize( "litellm_params,expected_key", [ + ({"model": "auto_router/complexity_router", "complexity_router_config": _CAPABILITY_CONFIG}, "capability"), + ({"model": "auto_router/complexity_router-eu", "complexity_router_config": _FUSE_CONFIG}, "llm_v2"), + ({"model": "openai/solver", "complexity_router_config": _CAPABILITY_CONFIG}, None), + ({"model": "auto_router/quality_router", "complexity_router_config": _FUSE_CONFIG}, None), ({"model": "auto_router/complexity_router", "complexity_router_config": _HV2_CONFIG}, "heuristic_v2"), ({"model": "auto_router/complexity_router-eu", "complexity_router_config": _HV2_CONFIG}, "heuristic_v2"), ({"model": "auto_router/complexity_router", "complexity_router_config": _CUSTOM_TIER_CONFIG}, "tier_or_classifier_prompt"), @@ -493,6 +499,8 @@ def test_count_capability_routers_counts_only_its_own_capability(capability) -> by_key = { "heuristic_v2": (_HV2_CONFIG, _HV2_CONFIG), + "capability": (_CAPABILITY_CONFIG, _CAPABILITY_CONFIG), + "llm_v2": (_FUSE_CONFIG, _FUSE_CONFIG), "tier_or_classifier_prompt": (_CUSTOM_TIER_CONFIG, _CUSTOM_PROMPT_CONFIG), } mine_first, mine_second = by_key[capability.key] @@ -545,6 +553,8 @@ def test_every_gated_capability_has_a_distinct_predicate_and_sql_spelling() -> N "config", [ _HV2_CONFIG, + _CAPABILITY_CONFIG, + _FUSE_CONFIG, _CUSTOM_TIER_CONFIG, _CUSTOM_PROMPT_CONFIG, {"classifier_type": "heuristic"}, From 69c32122200131efe14ac5ca4b70e3609e46b910 Mon Sep 17 00:00:00 2001 From: ryan-crabbe-berri Date: Tue, 15 Sep 2026 16:21:37 -0700 Subject: [PATCH 115/225] fix(ui): gate the usage export on range coverage, not on a fetch being in flight A loading flag only flips once the fetch effect runs, so the render right after a date or filter change still reported the previous range as loaded and let an export read its rows. Stamp the completed range on the hook and compare it during render instead, the way the tiles already do. Also stop the failure banner claiming a page loaded when the first request is what failed, which left it reading 1/1. --- .../components/EntityUsage/EntityUsage.tsx | 4 +- .../_components/components/UsagePageView.tsx | 5 +- .../hooks/usePaginatedDailyActivity.test.ts | 95 +++++++++++++++++++ .../hooks/usePaginatedDailyActivity.ts | 16 +++- .../EntityUsageExport/UsageExportHeader.tsx | 1 - .../exportBlockedReason.test.ts | 17 ++-- .../EntityUsageExport/exportBlockedReason.ts | 14 +-- .../shared/PaginationStatusAlerts.test.tsx | 17 +++- .../shared/PaginationStatusAlerts.tsx | 10 +- 9 files changed, 144 insertions(+), 35 deletions(-) diff --git a/ui/litellm-dashboard/src/app/(dashboard)/usage/_components/components/EntityUsage/EntityUsage.tsx b/ui/litellm-dashboard/src/app/(dashboard)/usage/_components/components/EntityUsage/EntityUsage.tsx index debc5602277..6687bd4df03 100644 --- a/ui/litellm-dashboard/src/app/(dashboard)/usage/_components/components/EntityUsage/EntityUsage.tsx +++ b/ui/litellm-dashboard/src/app/(dashboard)/usage/_components/components/EntityUsage/EntityUsage.tsx @@ -146,11 +146,11 @@ const EntityUsage: React.FC = ({ const { data: spendDataRaw, - loading, isFetchingMore, progress, cancelled, failed, + coversRange, cancel, } = usePaginatedDailyActivity({ fetchFn, @@ -664,7 +664,7 @@ const EntityUsage: React.FC = ({ { key: "endpoints", label: "Endpoint Activity", content: }, ]; - const spendFetchState = { loading, isFetchingMore, cancelled, failed }; + const spendFetchState = { coversRange, cancelled, failed }; return (
diff --git a/ui/litellm-dashboard/src/app/(dashboard)/usage/_components/components/UsagePageView.tsx b/ui/litellm-dashboard/src/app/(dashboard)/usage/_components/components/UsagePageView.tsx index 31c9288c911..691c5dc839a 100644 --- a/ui/litellm-dashboard/src/app/(dashboard)/usage/_components/components/UsagePageView.tsx +++ b/ui/litellm-dashboard/src/app/(dashboard)/usage/_components/components/UsagePageView.tsx @@ -250,9 +250,10 @@ const UsagePage: React.FC = ({ teams, organizations }) => { const loading = aggregatedLoading || paginatedResult.loading; + // Read through the same range stamp as the tiles, so the export is blocked from the first + // render of a new range rather than from whenever the fetch effect gets around to running. const spendFetchState = { - loading, - isFetchingMore: paginatedResult.isFetchingMore, + coversRange: activeAggregated !== null || paginatedResult.coversRange, cancelled: paginatedResult.cancelled, failed: paginatedResult.failed, }; diff --git a/ui/litellm-dashboard/src/app/(dashboard)/usage/_components/hooks/usePaginatedDailyActivity.test.ts b/ui/litellm-dashboard/src/app/(dashboard)/usage/_components/hooks/usePaginatedDailyActivity.test.ts index 5257e5fc8a6..aa6cf64483a 100644 --- a/ui/litellm-dashboard/src/app/(dashboard)/usage/_components/hooks/usePaginatedDailyActivity.test.ts +++ b/ui/litellm-dashboard/src/app/(dashboard)/usage/_components/hooks/usePaginatedDailyActivity.test.ts @@ -180,6 +180,20 @@ describe("usePaginatedDailyActivity failure reporting", () => { consoleError.mockRestore(); }); + it("reports no pages loaded when the very first request is what failed", async () => { + const consoleError = vi.spyOn(console, "error").mockImplementation(() => {}); + const fetchFn = vi.fn(() => Promise.reject(new Error("page 1 never came back"))); + + const { result } = renderHook(() => + usePaginatedDailyActivity({ fetchFn, args: ["tok", start, end, null], enabled: true }), + ); + + await waitFor(() => expect(result.current.failed).toBe(true), { timeout: 5000 }); + + expect(result.current.progress).toEqual({ currentPage: 0, totalPages: 0 }); + consoleError.mockRestore(); + }); + it("stays unfailed when every page arrives", async () => { const pages = [ firstPage, @@ -222,3 +236,84 @@ describe("usePaginatedDailyActivity failure reporting", () => { consoleError.mockRestore(); }); }); + +describe("usePaginatedDailyActivity range coverage", () => { + const start = new Date("2026-08-10"); + const end = new Date("2026-08-17"); + const singlePage = { results: [dayOf("2026-08-16", 2)], metadata: { total_pages: 1, page: 1, total_spend: 2 } }; + + it("does not cover the range while the hook is disabled", () => { + const fetchFn = vi.fn(() => Promise.resolve(singlePage)); + + const { result } = renderHook(() => + usePaginatedDailyActivity({ fetchFn, args: ["tok", start, end, null], enabled: false }), + ); + + expect(result.current.coversRange).toBe(false); + expect(fetchFn).not.toHaveBeenCalled(); + }); + + it("covers the range only once every page of it has landed", async () => { + const pages = [ + { results: [dayOf("2026-08-16", 2)], metadata: { total_pages: 2, page: 1, total_spend: 2 } }, + { results: [dayOf("2026-08-15", 1)], metadata: { total_pages: 2, page: 2, total_spend: 1 } }, + ]; + const fetchFn = vi.fn((_token: string, _start: Date, _end: Date, page: number) => Promise.resolve(pages[page - 1])); + + const { result } = renderHook(() => + usePaginatedDailyActivity({ fetchFn, args: ["tok", start, end, null], enabled: true }), + ); + + expect(result.current.coversRange).toBe(false); + await waitFor(() => expect(result.current.coversRange).toBe(true), { timeout: 5000 }); + }); + + it("never reports a range as covered while the data on screen is empty", async () => { + // Disabling the hook empties the data. Re-enabling it asks for the same args the last + // completed fetch used, so coverage that survives the disable would vouch for nothing. + const seen: Array<{ coversRange: boolean; rows: number }> = []; + const fetchFn = vi.fn(() => Promise.resolve(singlePage)); + + const { result, rerender } = renderHook( + ({ enabled }: { enabled: boolean }) => { + const activity = usePaginatedDailyActivity({ fetchFn, args: ["tok", start, end, null], enabled }); + seen.push({ coversRange: activity.coversRange, rows: activity.data.results.length }); + return activity; + }, + { initialProps: { enabled: true } }, + ); + + await waitFor(() => expect(result.current.coversRange).toBe(true), { timeout: 5000 }); + + rerender({ enabled: false }); + rerender({ enabled: true }); + + await waitFor(() => expect(result.current.coversRange).toBe(true), { timeout: 5000 }); + expect(seen.filter((render) => render.coversRange && render.rows === 0)).toEqual([]); + }); + + it("stops covering the range on the very render the args change, not once an effect catches up", async () => { + // The render after a filter change still holds the previous filter's rows, so resetting + // coverage inside the fetch effect would leave a paint where the export reads them as the + // new range. That paint is the whole thing the gate exists to stop. + const seen: Array<{ filter: string; coversRange: boolean }> = []; + const fetchFn = vi.fn(() => Promise.resolve(singlePage)); + + const { result, rerender } = renderHook( + ({ filter }: { filter: string }) => { + const activity = usePaginatedDailyActivity({ fetchFn, args: ["tok", start, end, filter], enabled: true }); + seen.push({ filter, coversRange: activity.coversRange }); + return activity; + }, + { initialProps: { filter: "team-a" } }, + ); + + await waitFor(() => expect(result.current.coversRange).toBe(true), { timeout: 5000 }); + + rerender({ filter: "team-b" }); + + const rendersForNewFilter = seen.filter((render) => render.filter === "team-b"); + expect(rendersForNewFilter.length).toBeGreaterThan(0); + expect(rendersForNewFilter.map((render) => render.coversRange)).not.toContain(true); + }); +}); diff --git a/ui/litellm-dashboard/src/app/(dashboard)/usage/_components/hooks/usePaginatedDailyActivity.ts b/ui/litellm-dashboard/src/app/(dashboard)/usage/_components/hooks/usePaginatedDailyActivity.ts index aef0021cc3d..0c1d79cb112 100644 --- a/ui/litellm-dashboard/src/app/(dashboard)/usage/_components/hooks/usePaginatedDailyActivity.ts +++ b/ui/litellm-dashboard/src/app/(dashboard)/usage/_components/hooks/usePaginatedDailyActivity.ts @@ -61,8 +61,8 @@ interface UsePaginatedDailyActivityReturn { isFetchingMore: boolean; progress: PaginationProgress; cancelled: boolean; - /** A page request threw, so `data` covers only part of the requested range. */ failed: boolean; + coversRange: boolean; cancel: () => void; } @@ -203,6 +203,7 @@ export function usePaginatedDailyActivity({ }); const [cancelled, setCancelled] = useState(false); const [failed, setFailed] = useState(false); + const [completedKey, setCompletedKey] = useState(null); const fetchIdRef = useRef(0); const cancelledRef = useRef(false); @@ -216,6 +217,11 @@ export function usePaginatedDailyActivity({ // Stable serialised key so the effect only re-runs when the arg *values* change. const argsKey = JSON.stringify(args); + // Stamped like the data itself and compared during render, so the render that follows an arg + // change already reports the new range as uncovered. Clearing it inside the fetch effect would + // be one render too late, leaving a paint where an export reads the previous range's rows. + const coversRange = enabled && completedKey === argsKey; + const cancel = useCallback(() => { cancelledRef.current = true; setCancelled(true); @@ -234,6 +240,7 @@ export function usePaginatedDailyActivity({ setProgress({ currentPage: 0, totalPages: 0 }); setCancelled(false); setFailed(false); + setCompletedKey(null); return; } @@ -257,7 +264,7 @@ export function usePaginatedDailyActivity({ const currentArgs = argsRef.current; setLoading(true); setIsFetchingMore(false); - setProgress({ currentPage: 1, totalPages: 1 }); + setProgress({ currentPage: 0, totalPages: 0 }); if (aggregatedFetchFn) { try { @@ -266,6 +273,7 @@ export function usePaginatedDailyActivity({ setData(aggregated); setProgress({ currentPage: 1, totalPages: 1 }); setLoading(false); + setCompletedKey(argsKey); return; } catch (error) { if (isStale()) return; @@ -288,6 +296,7 @@ export function usePaginatedDailyActivity({ if (totalPages <= 1) { setLoading(false); + setCompletedKey(argsKey); return; } @@ -333,6 +342,7 @@ export function usePaginatedDailyActivity({ } setIsFetchingMore(false); + setCompletedKey(argsKey); } catch (error) { if (!isStale()) { console.error("Error fetching daily activity:", error); @@ -356,5 +366,5 @@ export function usePaginatedDailyActivity({ // eslint-disable-next-line react-hooks/exhaustive-deps }, [enabled, fetchFn, aggregatedFetchFn, argsKey]); - return { data, loading, isFetchingMore, progress, cancelled, failed, cancel }; + return { data, loading, isFetchingMore, progress, cancelled, failed, coversRange, cancel }; } diff --git a/ui/litellm-dashboard/src/components/EntityUsageExport/UsageExportHeader.tsx b/ui/litellm-dashboard/src/components/EntityUsageExport/UsageExportHeader.tsx index 3c4c7f6f4ee..388a211d9bb 100644 --- a/ui/litellm-dashboard/src/components/EntityUsageExport/UsageExportHeader.tsx +++ b/ui/litellm-dashboard/src/components/EntityUsageExport/UsageExportHeader.tsx @@ -34,7 +34,6 @@ interface UsageExportHeaderProps { customTitle?: string; compactLayout?: boolean; teams?: Team[]; - /** Set to block the export and explain why; see getExportBlockedReason. */ exportBlockedReason?: string; } diff --git a/ui/litellm-dashboard/src/components/EntityUsageExport/exportBlockedReason.test.ts b/ui/litellm-dashboard/src/components/EntityUsageExport/exportBlockedReason.test.ts index 0b32aef52fa..e39b01a5dea 100644 --- a/ui/litellm-dashboard/src/components/EntityUsageExport/exportBlockedReason.test.ts +++ b/ui/litellm-dashboard/src/components/EntityUsageExport/exportBlockedReason.test.ts @@ -3,35 +3,30 @@ import { describe, expect, it } from "vitest"; import { getExportBlockedReason, type UsageFetchState } from "./exportBlockedReason"; const state = (overrides: Partial = {}): UsageFetchState => ({ - loading: false, - isFetchingMore: false, + coversRange: true, cancelled: false, failed: false, ...overrides, }); describe("getExportBlockedReason", () => { - it("lets the export through once the range has fully loaded", () => { + it("lets the export through once the data on screen covers the range", () => { expect(getExportBlockedReason(state())).toBeUndefined(); }); - it("blocks the first load, before any page has arrived", () => { - expect(getExportBlockedReason(state({ loading: true }))).toMatch(/still loading/i); - }); - - it("blocks while later pages are still arriving, which is when a CSV silently under-reports", () => { - expect(getExportBlockedReason(state({ isFetchingMore: true }))).toMatch(/still loading/i); + it("blocks whenever the data on screen does not cover the range, which is when a CSV silently under-reports", () => { + expect(getExportBlockedReason(state({ coversRange: false }))).toMatch(/still loading/i); }); it("blocks after a stopped fetch and says a reload is what fixes it", () => { - const reason = getExportBlockedReason(state({ cancelled: true })); + const reason = getExportBlockedReason(state({ coversRange: false, cancelled: true })); expect(reason).toMatch(/stopped/i); expect(reason).toMatch(/reload/i); }); it("blocks after a failed page and names the failure rather than the stop", () => { - const reason = getExportBlockedReason(state({ failed: true, cancelled: true })); + const reason = getExportBlockedReason(state({ coversRange: false, failed: true, cancelled: true })); expect(reason).toMatch(/failed to load/i); expect(reason).not.toMatch(/stopped/i); diff --git a/ui/litellm-dashboard/src/components/EntityUsageExport/exportBlockedReason.ts b/ui/litellm-dashboard/src/components/EntityUsageExport/exportBlockedReason.ts index ebe67f1ccfe..71408ba8f3f 100644 --- a/ui/litellm-dashboard/src/components/EntityUsageExport/exportBlockedReason.ts +++ b/ui/litellm-dashboard/src/components/EntityUsageExport/exportBlockedReason.ts @@ -1,21 +1,13 @@ export interface UsageFetchState { - loading: boolean; - isFetchingMore: boolean; + coversRange: boolean; cancelled: boolean; failed: boolean; } -/** Why exporting what is on screen would under-report, or undefined once it covers the whole range. */ -export const getExportBlockedReason = ({ - loading, - isFetchingMore, - cancelled, - failed, -}: UsageFetchState): string | undefined => { +export const getExportBlockedReason = ({ coversRange, cancelled, failed }: UsageFetchState): string | undefined => { if (failed) return "Some spend data failed to load, so an export would under-report. Reload the page to try again."; if (cancelled) return "Loading was stopped before the whole range arrived, so an export would under-report. Reload the page to load it all."; - if (loading || isFetchingMore) - return "Spend data is still loading, so an export would under-report. Wait for it to finish."; + if (!coversRange) return "Spend data is still loading, so an export would under-report. Wait for it to finish."; return undefined; }; diff --git a/ui/litellm-dashboard/src/components/shared/PaginationStatusAlerts.test.tsx b/ui/litellm-dashboard/src/components/shared/PaginationStatusAlerts.test.tsx index 1eefc43ecb4..93d464b6615 100644 --- a/ui/litellm-dashboard/src/components/shared/PaginationStatusAlerts.test.tsx +++ b/ui/litellm-dashboard/src/components/shared/PaginationStatusAlerts.test.tsx @@ -45,10 +45,25 @@ describe("PaginationStatusAlerts", () => { ); expect( - screen.getByText(/Fetching spend data failed, so the totals below cover only part of the range \(7\/42 pages/), + screen.getByText(/Fetching spend data failed, so the totals below cover only 7 of 42 pages of the range/), ).toBeInTheDocument(); }); + it("does not claim a page loaded when the very first request is what failed", () => { + render( + , + ); + + expect(screen.getByText(/failed before any of it arrived/)).toBeInTheDocument(); + expect(screen.queryByText(/pages of the range/)).not.toBeInTheDocument(); + }); + it("shows only the failure when a stopped fetch also failed", () => { render( + progress.currentPage === 0 + ? `Fetching ${subject} failed before any of it arrived, so the totals below are empty rather than final. Reload the page to try again.` + : `Fetching ${subject} failed, so the totals below cover only ${progress.currentPage} of ${progress.totalPages} pages of the range. Reload the page to try again.`; + const PaginationStatusAlerts = ({ isFetchingMore, cancelled, @@ -42,10 +47,7 @@ const PaginationStatusAlerts = ({ )} {failed && ( - - Fetching {subject} failed, so the totals below cover only part of the range ({progress.currentPage}/ - {progress.totalPages} pages loaded). Reload the page to try again. - + {failureMessage(subject, progress)} )} {cancelled && !failed && ( From 790c9ab77d1628e7c8be1b13002428a66bb16a1f Mon Sep 17 00:00:00 2001 From: mateo-berri <277851410+mateo-berri@users.noreply.github.com> Date: Tue, 15 Sep 2026 16:25:01 -0700 Subject: [PATCH 116/225] test(ui): name the session column fixtures so the inline-object lint budget holds --- .../RequestLogsTableColumns.test.tsx | 59 ++++++++----------- 1 file changed, 25 insertions(+), 34 deletions(-) diff --git a/ui/litellm-dashboard/src/components/view_logs/RequestLogsTableColumns.test.tsx b/ui/litellm-dashboard/src/components/view_logs/RequestLogsTableColumns.test.tsx index d60b9ce30f1..626d290e0e8 100644 --- a/ui/litellm-dashboard/src/components/view_logs/RequestLogsTableColumns.test.tsx +++ b/ui/litellm-dashboard/src/components/view_logs/RequestLogsTableColumns.test.tsx @@ -75,14 +75,13 @@ describe("Cost column", () => { }); it("does not label the per-call spend a session total when the aggregate is unavailable", () => { - renderRows([ - logEntry({ - request_id: "req-session-no-aggregate", - spend: 0.01, - session_id: "sess-1", - session_total_count: 3, - }), - ]); + const rowWithoutAggregate: Partial = { + request_id: "req-session-no-aggregate", + spend: 0.01, + session_id: "sess-1", + session_total_count: 3, + }; + renderRows([logEntry(rowWithoutAggregate)]); expect(screen.getByText("$0.010000")).toBeInTheDocument(); expect(screen.queryByText("session total")).not.toBeInTheDocument(); @@ -90,16 +89,16 @@ describe("Cost column", () => { }); describe("Duration column", () => { + const sessionRow: Partial = { + request_id: "req-session-duration", + request_duration_ms: 1200, + session_id: "sess-1", + session_total_count: 3, + }; + it("shows the summed session duration, not the representative call's duration, for a multi-round session", () => { - renderRows([ - logEntry({ - request_id: "req-session-duration", - request_duration_ms: 1200, - session_id: "sess-1", - session_total_count: 3, - session_total_duration_ms: 5400, - }), - ]); + const aggregatedRow: Partial = { ...sessionRow, session_total_duration_ms: 5400 }; + renderRows([logEntry(aggregatedRow)]); expect(screen.getByText("5.40")).toBeInTheDocument(); expect(screen.queryByText("1.20")).not.toBeInTheDocument(); @@ -107,29 +106,21 @@ describe("Duration column", () => { }); it("does not label the per-call duration a session total when the aggregate is unavailable", () => { - renderRows([ - logEntry({ - request_id: "req-no-aggregate", - request_duration_ms: 1200, - session_id: "sess-3", - session_total_count: 3, - }), - ]); + renderRows([logEntry(sessionRow)]); expect(screen.getByText("1.20")).toBeInTheDocument(); expect(screen.queryByText("session total")).not.toBeInTheDocument(); }); it("shows the call's own duration for a single-call session", () => { - renderRows([ - logEntry({ - request_id: "req-single-duration", - request_duration_ms: 1200, - session_id: "sess-2", - session_total_count: 1, - session_total_duration_ms: 1200, - }), - ]); + const singleCallRow: Partial = { + ...sessionRow, + request_id: "req-single-duration", + session_id: "sess-2", + session_total_count: 1, + session_total_duration_ms: 1200, + }; + renderRows([logEntry(singleCallRow)]); expect(screen.getByText("1.20")).toBeInTheDocument(); }); From d61908e241483a6b968cc577020baeee65262b9b Mon Sep 17 00:00:00 2001 From: yassin Date: Tue, 15 Sep 2026 23:28:01 +0000 Subject: [PATCH 117/225] refactor(proxy): type request_data on the alias rewrite helpers Co-Authored-By: Devin AI <158243242+devin-ai-integration[bot]@users.noreply.github.com> --- litellm/proxy/auth/user_api_key_auth.py | 2 +- litellm/proxy/common_utils/http_parsing_utils.py | 2 +- 2 files changed, 2 insertions(+), 2 deletions(-) diff --git a/litellm/proxy/auth/user_api_key_auth.py b/litellm/proxy/auth/user_api_key_auth.py index 15dd58baf35..4a35310272c 100644 --- a/litellm/proxy/auth/user_api_key_auth.py +++ b/litellm/proxy/auth/user_api_key_auth.py @@ -245,7 +245,7 @@ async def _normalize_claude_model( async def _resolve_router_settings_model_group_alias( - request_data: dict, # mutable-ok: the request body is rewritten in place for every downstream reader + request_data: dict[str, object], # mutable-ok: the request body is rewritten in place for every downstream reader valid_token: UserAPIKeyAuth, request: Request | None, route: str, diff --git a/litellm/proxy/common_utils/http_parsing_utils.py b/litellm/proxy/common_utils/http_parsing_utils.py index a1dcc6e8ece..f5b6a0a766d 100644 --- a/litellm/proxy/common_utils/http_parsing_utils.py +++ b/litellm/proxy/common_utils/http_parsing_utils.py @@ -267,7 +267,7 @@ def _safe_set_request_parsed_body( def rewrite_request_model( - request_data: dict, # mutable-ok: the request body is rewritten in place for every downstream reader + request_data: dict[str, object], # mutable-ok: the request body is rewritten in place for every downstream reader request: Request | None, model: str, ) -> None: From 083ecb3c0b6a732c642bf16db80be87357e9cd41 Mon Sep 17 00:00:00 2001 From: "berriai-litellm-provider-info-sync[bot]" <328147090+berriai-litellm-provider-info-sync[bot]@users.noreply.github.com> Date: Tue, 15 Sep 2026 23:31:18 +0000 Subject: [PATCH 118/225] chore(prices): sync prices for 3 providers: 26 models, 26 deprecated [enrichment failed: Google Gemini, sync failed: AWS Bedrock, 34 held] fireworks_ai/accounts/fireworks/models/deepseek-v4-pro: deprecation_date fireworks_ai/deepseek-v4-pro: deprecation_date fireworks_ai/accounts/fireworks/models/minimax-m2p7: deprecation_date fireworks_ai/minimax-m2p7: deprecation_date together_ai/deepseek-ai/deepseek-coder-33b-instruct: deprecation_date together_ai/deepseek-ai/DeepSeek-R1-Distill-Llama-70B: deprecation_date together_ai/deepseek-ai/DeepSeek-R1-Distill-Qwen-1.5B: deprecation_date together_ai/deepseek-ai/DeepSeek-R1-Distill-Qwen-14B: deprecation_date together_ai/google/gemma-2-27b-it: deprecation_date vertex_ai/imagegeneration@006: deprecation_date vertex_ai/imagen-3.0-capability-001: deprecation_date vertex_ai/imagen-3.0-fast-generate-001: deprecation_date vertex_ai/imagen-3.0-generate-001: deprecation_date vertex_ai/imagen-3.0-generate-002: deprecation_date vertex_ai/imagen-4.0-fast-generate-001: deprecation_date vertex_ai/imagen-4.0-generate-001: deprecation_date vertex_ai/imagen-4.0-ultra-generate-001: deprecation_date together_ai/meta-llama/Llama-3-8b-chat-hf: deprecation_date together_ai/meta-llama/Meta-Llama-3-70B-Instruct-Turbo: deprecation_date together_ai/meta-llama/Meta-Llama-3-8B-Instruct: deprecation_date together_ai/NousResearch/Nous-Hermes-2-Mixtral-8x7B-DPO: deprecation_date together_ai/nvidia/Llama-3.1-Nemotron-70B-Instruct-HF: deprecation_date together_ai/Qwen/Qwen2-72B-Instruct: deprecation_date together_ai/Qwen/Qwen2-VL-72B-Instruct: deprecation_date together_ai/Qwen/Qwen2.5-Coder-32B-Instruct: deprecation_date together_ai/Qwen/Qwen2.5-VL-72B-Instruct: deprecation_date --- ...odel_prices_and_context_window_backup.json | 27 ++++++++++++++++++- model_prices_and_context_window.json | 27 ++++++++++++++++++- 2 files changed, 52 insertions(+), 2 deletions(-) diff --git a/litellm/model_prices_and_context_window_backup.json b/litellm/model_prices_and_context_window_backup.json index 372ce19e740..000615bea11 100644 --- a/litellm/model_prices_and_context_window_backup.json +++ b/litellm/model_prices_and_context_window_backup.json @@ -22839,6 +22839,7 @@ "fireworks_ai/accounts/fireworks/models/deepseek-v4-pro": { "cache_read_input_token_cost": 6e-07, "cache_read_input_token_cost_priority": 6e-07, + "deprecation_date": "2026-08-27", "input_cost_per_token": 1.2e-06, "input_cost_per_token_priority": 1.2e-06, "litellm_provider": "fireworks_ai", @@ -23226,6 +23227,7 @@ "fireworks_ai/accounts/fireworks/models/minimax-m2p7": { "cache_read_input_token_cost": 6e-08, "cache_read_input_token_cost_priority": 6e-07, + "deprecation_date": "2026-08-27", "input_cost_per_token": 3e-07, "input_cost_per_token_priority": 1.2e-06, "litellm_provider": "fireworks_ai", @@ -23332,6 +23334,7 @@ "fireworks_ai/deepseek-v4-pro": { "cache_read_input_token_cost": 6e-07, "cache_read_input_token_cost_priority": 6e-07, + "deprecation_date": "2026-08-27", "input_cost_per_token": 1.2e-06, "input_cost_per_token_priority": 1.2e-06, "litellm_provider": "fireworks_ai", @@ -23550,6 +23553,7 @@ "fireworks_ai/minimax-m2p7": { "cache_read_input_token_cost": 6e-08, "cache_read_input_token_cost_priority": 6e-07, + "deprecation_date": "2026-08-27", "input_cost_per_token": 3e-07, "input_cost_per_token_priority": 1.2e-06, "litellm_provider": "fireworks_ai", @@ -48772,49 +48776,56 @@ "source": "https://cloud.google.com/gemini-enterprise-agent-platform/generative-ai/pricing" }, "vertex_ai/imagegeneration@006": { + "deprecation_date": "2025-09-24", "litellm_provider": "vertex_ai-image-models", "mode": "image_generation", "output_cost_per_image": 0.02, "source": "https://cloud.google.com/vertex-ai/generative-ai/pricing" }, "vertex_ai/imagen-3.0-fast-generate-001": { + "deprecation_date": "2026-06-30", "litellm_provider": "vertex_ai-image-models", "mode": "image_generation", "output_cost_per_image": 0.02, "source": "https://cloud.google.com/vertex-ai/generative-ai/pricing" }, "vertex_ai/imagen-3.0-generate-001": { + "deprecation_date": "2026-06-30", "litellm_provider": "vertex_ai-image-models", "mode": "image_generation", "output_cost_per_image": 0.04, "source": "https://cloud.google.com/vertex-ai/generative-ai/pricing" }, "vertex_ai/imagen-3.0-generate-002": { - "deprecation_date": "2025-11-10", + "deprecation_date": "2026-06-30", "litellm_provider": "vertex_ai-image-models", "mode": "image_generation", "output_cost_per_image": 0.04, "source": "https://cloud.google.com/vertex-ai/generative-ai/pricing" }, "vertex_ai/imagen-3.0-capability-001": { + "deprecation_date": "2026-06-30", "litellm_provider": "vertex_ai-image-models", "mode": "image_generation", "output_cost_per_image": 0.04, "source": "https://cloud.google.com/vertex-ai/generative-ai/docs/image/edit-insert-objects" }, "vertex_ai/imagen-4.0-fast-generate-001": { + "deprecation_date": "2026-06-30", "litellm_provider": "vertex_ai-image-models", "mode": "image_generation", "output_cost_per_image": 0.02, "source": "https://cloud.google.com/vertex-ai/generative-ai/pricing" }, "vertex_ai/imagen-4.0-generate-001": { + "deprecation_date": "2026-06-30", "litellm_provider": "vertex_ai-image-models", "mode": "image_generation", "output_cost_per_image": 0.04, "source": "https://cloud.google.com/vertex-ai/generative-ai/pricing" }, "vertex_ai/imagen-4.0-ultra-generate-001": { + "deprecation_date": "2026-06-30", "litellm_provider": "vertex_ai-image-models", "mode": "image_generation", "output_cost_per_image": 0.06, @@ -66280,6 +66291,7 @@ "source": "https://api.together.ai/v1/models" }, "together_ai/deepseek-ai/deepseek-coder-33b-instruct": { + "deprecation_date": "2024-08-22", "input_cost_per_token": 8e-07, "litellm_provider": "together_ai", "mode": "chat", @@ -66287,6 +66299,7 @@ "source": "https://api.together.ai/v1/models" }, "together_ai/deepseek-ai/DeepSeek-R1-Distill-Llama-70B": { + "deprecation_date": "2025-12-23", "input_cost_per_token": 2e-06, "litellm_provider": "together_ai", "mode": "chat", @@ -66294,6 +66307,7 @@ "source": "https://api.together.ai/v1/models" }, "together_ai/deepseek-ai/DeepSeek-R1-Distill-Qwen-1.5B": { + "deprecation_date": "2025-08-28", "input_cost_per_token": 1.8e-07, "litellm_provider": "together_ai", "mode": "chat", @@ -66301,6 +66315,7 @@ "source": "https://api.together.ai/v1/models" }, "together_ai/deepseek-ai/DeepSeek-R1-Distill-Qwen-14B": { + "deprecation_date": "2025-11-13", "input_cost_per_token": 1.6e-06, "litellm_provider": "together_ai", "mode": "chat", @@ -66394,6 +66409,7 @@ "source": "https://cloud.google.com/gemini-enterprise-agent-platform/generative-ai/pricing" }, "together_ai/google/gemma-2-27b-it": { + "deprecation_date": "2025-08-28", "input_cost_per_token": 8e-07, "litellm_provider": "together_ai", "mode": "chat", @@ -66418,6 +66434,7 @@ "source": "https://developers.openai.com/api/docs/pricing" }, "together_ai/meta-llama/Llama-3-8b-chat-hf": { + "deprecation_date": "2025-08-28", "input_cost_per_token": 2e-07, "litellm_provider": "together_ai", "mode": "chat", @@ -66446,6 +66463,7 @@ "source": "https://api.together.ai/v1/models" }, "together_ai/meta-llama/Meta-Llama-3-70B-Instruct-Turbo": { + "deprecation_date": "2025-12-23", "input_cost_per_token": 8.8e-07, "litellm_provider": "together_ai", "mode": "chat", @@ -66453,6 +66471,7 @@ "source": "https://api.together.ai/v1/models" }, "together_ai/meta-llama/Meta-Llama-3-8B-Instruct": { + "deprecation_date": "2025-08-28", "input_cost_per_token": 2e-07, "litellm_provider": "together_ai", "mode": "chat", @@ -66460,6 +66479,7 @@ "source": "https://api.together.ai/v1/models" }, "together_ai/NousResearch/Nous-Hermes-2-Mixtral-8x7B-DPO": { + "deprecation_date": "2025-08-28", "input_cost_per_token": 6e-07, "litellm_provider": "together_ai", "mode": "chat", @@ -66467,6 +66487,7 @@ "source": "https://api.together.ai/v1/models" }, "together_ai/nvidia/Llama-3.1-Nemotron-70B-Instruct-HF": { + "deprecation_date": "2025-08-28", "input_cost_per_token": 8.8e-07, "litellm_provider": "together_ai", "mode": "chat", @@ -66481,6 +66502,7 @@ "source": "https://api.together.ai/v1/models" }, "together_ai/Qwen/Qwen2-72B-Instruct": { + "deprecation_date": "2025-08-28", "input_cost_per_token": 9e-07, "litellm_provider": "together_ai", "mode": "chat", @@ -66488,6 +66510,7 @@ "source": "https://api.together.ai/v1/models" }, "together_ai/Qwen/Qwen2-VL-72B-Instruct": { + "deprecation_date": "2025-08-28", "input_cost_per_token": 1.2e-06, "litellm_provider": "together_ai", "mode": "chat", @@ -66509,6 +66532,7 @@ "source": "https://api.together.ai/v1/models" }, "together_ai/Qwen/Qwen2.5-Coder-32B-Instruct": { + "deprecation_date": "2025-11-13", "input_cost_per_token": 8e-07, "litellm_provider": "together_ai", "mode": "chat", @@ -66516,6 +66540,7 @@ "source": "https://api.together.ai/v1/models" }, "together_ai/Qwen/Qwen2.5-VL-72B-Instruct": { + "deprecation_date": "2026-01-05", "input_cost_per_token": 1.95e-06, "litellm_provider": "together_ai", "mode": "chat", diff --git a/model_prices_and_context_window.json b/model_prices_and_context_window.json index 372ce19e740..000615bea11 100644 --- a/model_prices_and_context_window.json +++ b/model_prices_and_context_window.json @@ -22839,6 +22839,7 @@ "fireworks_ai/accounts/fireworks/models/deepseek-v4-pro": { "cache_read_input_token_cost": 6e-07, "cache_read_input_token_cost_priority": 6e-07, + "deprecation_date": "2026-08-27", "input_cost_per_token": 1.2e-06, "input_cost_per_token_priority": 1.2e-06, "litellm_provider": "fireworks_ai", @@ -23226,6 +23227,7 @@ "fireworks_ai/accounts/fireworks/models/minimax-m2p7": { "cache_read_input_token_cost": 6e-08, "cache_read_input_token_cost_priority": 6e-07, + "deprecation_date": "2026-08-27", "input_cost_per_token": 3e-07, "input_cost_per_token_priority": 1.2e-06, "litellm_provider": "fireworks_ai", @@ -23332,6 +23334,7 @@ "fireworks_ai/deepseek-v4-pro": { "cache_read_input_token_cost": 6e-07, "cache_read_input_token_cost_priority": 6e-07, + "deprecation_date": "2026-08-27", "input_cost_per_token": 1.2e-06, "input_cost_per_token_priority": 1.2e-06, "litellm_provider": "fireworks_ai", @@ -23550,6 +23553,7 @@ "fireworks_ai/minimax-m2p7": { "cache_read_input_token_cost": 6e-08, "cache_read_input_token_cost_priority": 6e-07, + "deprecation_date": "2026-08-27", "input_cost_per_token": 3e-07, "input_cost_per_token_priority": 1.2e-06, "litellm_provider": "fireworks_ai", @@ -48772,49 +48776,56 @@ "source": "https://cloud.google.com/gemini-enterprise-agent-platform/generative-ai/pricing" }, "vertex_ai/imagegeneration@006": { + "deprecation_date": "2025-09-24", "litellm_provider": "vertex_ai-image-models", "mode": "image_generation", "output_cost_per_image": 0.02, "source": "https://cloud.google.com/vertex-ai/generative-ai/pricing" }, "vertex_ai/imagen-3.0-fast-generate-001": { + "deprecation_date": "2026-06-30", "litellm_provider": "vertex_ai-image-models", "mode": "image_generation", "output_cost_per_image": 0.02, "source": "https://cloud.google.com/vertex-ai/generative-ai/pricing" }, "vertex_ai/imagen-3.0-generate-001": { + "deprecation_date": "2026-06-30", "litellm_provider": "vertex_ai-image-models", "mode": "image_generation", "output_cost_per_image": 0.04, "source": "https://cloud.google.com/vertex-ai/generative-ai/pricing" }, "vertex_ai/imagen-3.0-generate-002": { - "deprecation_date": "2025-11-10", + "deprecation_date": "2026-06-30", "litellm_provider": "vertex_ai-image-models", "mode": "image_generation", "output_cost_per_image": 0.04, "source": "https://cloud.google.com/vertex-ai/generative-ai/pricing" }, "vertex_ai/imagen-3.0-capability-001": { + "deprecation_date": "2026-06-30", "litellm_provider": "vertex_ai-image-models", "mode": "image_generation", "output_cost_per_image": 0.04, "source": "https://cloud.google.com/vertex-ai/generative-ai/docs/image/edit-insert-objects" }, "vertex_ai/imagen-4.0-fast-generate-001": { + "deprecation_date": "2026-06-30", "litellm_provider": "vertex_ai-image-models", "mode": "image_generation", "output_cost_per_image": 0.02, "source": "https://cloud.google.com/vertex-ai/generative-ai/pricing" }, "vertex_ai/imagen-4.0-generate-001": { + "deprecation_date": "2026-06-30", "litellm_provider": "vertex_ai-image-models", "mode": "image_generation", "output_cost_per_image": 0.04, "source": "https://cloud.google.com/vertex-ai/generative-ai/pricing" }, "vertex_ai/imagen-4.0-ultra-generate-001": { + "deprecation_date": "2026-06-30", "litellm_provider": "vertex_ai-image-models", "mode": "image_generation", "output_cost_per_image": 0.06, @@ -66280,6 +66291,7 @@ "source": "https://api.together.ai/v1/models" }, "together_ai/deepseek-ai/deepseek-coder-33b-instruct": { + "deprecation_date": "2024-08-22", "input_cost_per_token": 8e-07, "litellm_provider": "together_ai", "mode": "chat", @@ -66287,6 +66299,7 @@ "source": "https://api.together.ai/v1/models" }, "together_ai/deepseek-ai/DeepSeek-R1-Distill-Llama-70B": { + "deprecation_date": "2025-12-23", "input_cost_per_token": 2e-06, "litellm_provider": "together_ai", "mode": "chat", @@ -66294,6 +66307,7 @@ "source": "https://api.together.ai/v1/models" }, "together_ai/deepseek-ai/DeepSeek-R1-Distill-Qwen-1.5B": { + "deprecation_date": "2025-08-28", "input_cost_per_token": 1.8e-07, "litellm_provider": "together_ai", "mode": "chat", @@ -66301,6 +66315,7 @@ "source": "https://api.together.ai/v1/models" }, "together_ai/deepseek-ai/DeepSeek-R1-Distill-Qwen-14B": { + "deprecation_date": "2025-11-13", "input_cost_per_token": 1.6e-06, "litellm_provider": "together_ai", "mode": "chat", @@ -66394,6 +66409,7 @@ "source": "https://cloud.google.com/gemini-enterprise-agent-platform/generative-ai/pricing" }, "together_ai/google/gemma-2-27b-it": { + "deprecation_date": "2025-08-28", "input_cost_per_token": 8e-07, "litellm_provider": "together_ai", "mode": "chat", @@ -66418,6 +66434,7 @@ "source": "https://developers.openai.com/api/docs/pricing" }, "together_ai/meta-llama/Llama-3-8b-chat-hf": { + "deprecation_date": "2025-08-28", "input_cost_per_token": 2e-07, "litellm_provider": "together_ai", "mode": "chat", @@ -66446,6 +66463,7 @@ "source": "https://api.together.ai/v1/models" }, "together_ai/meta-llama/Meta-Llama-3-70B-Instruct-Turbo": { + "deprecation_date": "2025-12-23", "input_cost_per_token": 8.8e-07, "litellm_provider": "together_ai", "mode": "chat", @@ -66453,6 +66471,7 @@ "source": "https://api.together.ai/v1/models" }, "together_ai/meta-llama/Meta-Llama-3-8B-Instruct": { + "deprecation_date": "2025-08-28", "input_cost_per_token": 2e-07, "litellm_provider": "together_ai", "mode": "chat", @@ -66460,6 +66479,7 @@ "source": "https://api.together.ai/v1/models" }, "together_ai/NousResearch/Nous-Hermes-2-Mixtral-8x7B-DPO": { + "deprecation_date": "2025-08-28", "input_cost_per_token": 6e-07, "litellm_provider": "together_ai", "mode": "chat", @@ -66467,6 +66487,7 @@ "source": "https://api.together.ai/v1/models" }, "together_ai/nvidia/Llama-3.1-Nemotron-70B-Instruct-HF": { + "deprecation_date": "2025-08-28", "input_cost_per_token": 8.8e-07, "litellm_provider": "together_ai", "mode": "chat", @@ -66481,6 +66502,7 @@ "source": "https://api.together.ai/v1/models" }, "together_ai/Qwen/Qwen2-72B-Instruct": { + "deprecation_date": "2025-08-28", "input_cost_per_token": 9e-07, "litellm_provider": "together_ai", "mode": "chat", @@ -66488,6 +66510,7 @@ "source": "https://api.together.ai/v1/models" }, "together_ai/Qwen/Qwen2-VL-72B-Instruct": { + "deprecation_date": "2025-08-28", "input_cost_per_token": 1.2e-06, "litellm_provider": "together_ai", "mode": "chat", @@ -66509,6 +66532,7 @@ "source": "https://api.together.ai/v1/models" }, "together_ai/Qwen/Qwen2.5-Coder-32B-Instruct": { + "deprecation_date": "2025-11-13", "input_cost_per_token": 8e-07, "litellm_provider": "together_ai", "mode": "chat", @@ -66516,6 +66540,7 @@ "source": "https://api.together.ai/v1/models" }, "together_ai/Qwen/Qwen2.5-VL-72B-Instruct": { + "deprecation_date": "2026-01-05", "input_cost_per_token": 1.95e-06, "litellm_provider": "together_ai", "mode": "chat", From 771b2509d1ed2096da56325b0a6ee2a63f8b0a52 Mon Sep 17 00:00:00 2001 From: yassin Date: Tue, 15 Sep 2026 23:34:22 +0000 Subject: [PATCH 119/225] fix(proxy): reject mixed NIM model groups and strip the deployment model before the group in /nvidia_nim URLs Co-Authored-By: Devin AI <158243242+devin-ai-integration[bot]@users.noreply.github.com> --- .../nvidia_nim/passthrough/transformation.py | 24 ++++-- litellm/proxy/auth/auth_utils.py | 16 +--- .../llm_passthrough_endpoints.py | 13 +-- ...t_nvidia_nim_passthrough_transformation.py | 80 +++++++++++++++++++ .../proxy/auth/test_auth_utils.py | 13 +++ .../test_llm_pass_through_endpoints.py | 26 ++++++ 6 files changed, 144 insertions(+), 28 deletions(-) diff --git a/litellm/llms/nvidia_nim/passthrough/transformation.py b/litellm/llms/nvidia_nim/passthrough/transformation.py index 6df9681cc81..7de1ce4d631 100644 --- a/litellm/llms/nvidia_nim/passthrough/transformation.py +++ b/litellm/llms/nvidia_nim/passthrough/transformation.py @@ -27,14 +27,26 @@ if TYPE_CHECKING: API_VERSION_SEGMENT: Final = re.compile(r"^v\d+$") NVIDIA_NIM_MODEL_PREFIX: Final = f"{LlmProviders.NVIDIA_NIM.value}/" +NVIDIA_NIM_ROUTE_PREFIX: Final = re.compile(rf"^/{LlmProviders.NVIDIA_NIM.value}/", re.IGNORECASE) + + +def is_nvidia_nim_deployment(deployment: DeploymentTypedDict) -> bool: + litellm_params: Final = deployment["litellm_params"] + return litellm_params.get("custom_llm_provider") == LlmProviders.NVIDIA_NIM.value or litellm_params.get( + "model", "" + ).startswith(NVIDIA_NIM_MODEL_PREFIX) def nvidia_nim_model_groups(deployments: Iterable[DeploymentTypedDict] | None) -> frozenset[str]: - return frozenset( - deployment["model_name"] - for deployment in deployments or () - if deployment["litellm_params"].get("custom_llm_provider") == LlmProviders.NVIDIA_NIM.value - or deployment["litellm_params"].get("model", "").startswith(NVIDIA_NIM_MODEL_PREFIX) + listed: Final = tuple(deployments or ()) + nim_groups: Final = frozenset(d["model_name"] for d in listed if is_nvidia_nim_deployment(d)) + other_groups: Final = frozenset(d["model_name"] for d in listed if not is_nvidia_nim_deployment(d)) + return nim_groups - other_groups + + +def nvidia_nim_model_group_in_path(path: str, deployments: Iterable[DeploymentTypedDict] | None) -> str | None: + return nvidia_nim_router_model_in_endpoint( + NVIDIA_NIM_ROUTE_PREFIX.sub("", path), nvidia_nim_model_groups(deployments) ) @@ -79,7 +91,7 @@ class NvidiaNimPassthroughConfig(BasePassthroughConfig): base_target_url: Final = self.get_api_base(api_base) if base_target_url is None: raise ValueError("NVIDIA NIM api base not found: set `api_base` on the deployment or NVIDIA_NIM_API_BASE") - native_endpoint: Final = strip_leading_model_segment(endpoint, (model_group_from(litellm_params), model)) + native_endpoint: Final = strip_leading_model_segment(endpoint, (model, model_group_from(litellm_params))) root: Final = without_repeated_version_prefix(base_target_url, native_endpoint) return (self.format_url(native_endpoint, root, request_query_params), root) diff --git a/litellm/proxy/auth/auth_utils.py b/litellm/proxy/auth/auth_utils.py index dfce3ab206b..e69b0c98ee0 100644 --- a/litellm/proxy/auth/auth_utils.py +++ b/litellm/proxy/auth/auth_utils.py @@ -28,10 +28,7 @@ from litellm.litellm_core_utils.url_utils import ( validate_url, ) from litellm.llms.azure.passthrough.transformation import azure_router_model_in_endpoint -from litellm.llms.nvidia_nim.passthrough.transformation import ( - nvidia_nim_model_groups, - nvidia_nim_router_model_in_endpoint, -) +from litellm.llms.nvidia_nim.passthrough.transformation import nvidia_nim_model_group_in_path from litellm.proxy._types import * from litellm.proxy.common_utils.http_parsing_utils import extract_nested_form_metadata from litellm.types.passthrough_endpoints.pass_through_endpoints import ( @@ -2045,19 +2042,14 @@ def get_model_from_request( return model if azure_model is None else azure_model if route.lower().startswith("/nvidia_nim/"): - nvidia_nim_model: Final = _router_model_from_nvidia_nim_route(route, llm_router) + nvidia_nim_model: Final = ( + nvidia_nim_model_group_in_path(route, llm_router.get_model_list()) if llm_router else None + ) return model if nvidia_nim_model is None else nvidia_nim_model return model -def _router_model_from_nvidia_nim_route(route: str, llm_router: Router | None) -> str | None: - if llm_router is None: - return None - endpoint: Final = re.sub(r"^/nvidia_nim/", "", route, flags=re.IGNORECASE) - return nvidia_nim_router_model_in_endpoint(endpoint, nvidia_nim_model_groups(llm_router.get_model_list())) - - def _router_model_from_azure_route(route: str, llm_router: Router | None) -> str | None: if llm_router is None: return None diff --git a/litellm/proxy/pass_through_endpoints/llm_passthrough_endpoints.py b/litellm/proxy/pass_through_endpoints/llm_passthrough_endpoints.py index 65a18c1603e..4fc610f068f 100644 --- a/litellm/proxy/pass_through_endpoints/llm_passthrough_endpoints.py +++ b/litellm/proxy/pass_through_endpoints/llm_passthrough_endpoints.py @@ -36,10 +36,7 @@ from litellm.litellm_core_utils.aws_partition import get_aws_dns_suffix from litellm.llms.anthropic.common_utils import AnthropicModelInfo from litellm.llms.azure.passthrough.transformation import foreign_azure_deployment from litellm.llms.custom_httpx.http_handler import get_async_httpx_client -from litellm.llms.nvidia_nim.passthrough.transformation import ( - nvidia_nim_model_groups, - nvidia_nim_router_model_in_endpoint, -) +from litellm.llms.nvidia_nim.passthrough.transformation import nvidia_nim_model_group_in_path from litellm.llms.vertex_ai.vertex_llm_base import VertexBase from litellm.passthrough.main import AsyncPassthroughStreamingResponse from litellm.proxy._types import * @@ -1654,15 +1651,11 @@ async def relay_nvidia_nim_request( request_body: Mapping[str, object], user_api_key_dict: UserAPIKeyAuth, ) -> Response: - model_group: Final = ( - nvidia_nim_router_model_in_endpoint(endpoint, nvidia_nim_model_groups(llm_router.get_model_list())) - if llm_router - else None - ) + model_group: Final = nvidia_nim_model_group_in_path(endpoint, llm_router.get_model_list()) if llm_router else None if llm_router is None or model_group is None: rejection: Final[RelayRejection] = { "error": "no NVIDIA NIM model group in the path; call /nvidia_nim/{model_group}/v1/infer with a model " - "from your `model_list` whose `model` starts with `nvidia_nim/`" + "group from your `model_list` whose deployments all use `nvidia_nim/` models" } raise HTTPException(status_code=400, detail=rejection) diff --git a/tests/test_litellm/llms/nvidia_nim/passthrough/test_nvidia_nim_passthrough_transformation.py b/tests/test_litellm/llms/nvidia_nim/passthrough/test_nvidia_nim_passthrough_transformation.py index 87de4a795e0..906d6c2b614 100644 --- a/tests/test_litellm/llms/nvidia_nim/passthrough/test_nvidia_nim_passthrough_transformation.py +++ b/tests/test_litellm/llms/nvidia_nim/passthrough/test_nvidia_nim_passthrough_transformation.py @@ -8,6 +8,8 @@ import litellm from litellm.llms.custom_httpx.http_handler import AsyncHTTPHandler from litellm.llms.nvidia_nim.passthrough.transformation import ( NvidiaNimPassthroughConfig, + nvidia_nim_model_group_in_path, + nvidia_nim_model_groups, nvidia_nim_router_model_in_endpoint, ) from litellm.types.utils import LlmProviders @@ -53,6 +55,12 @@ def test_provider_config_manager_resolves_nvidia_nim_passthrough_config(): (f"{NIM_BASE}/v2", "v1/infer", {}, f"{NIM_BASE}/v2/v1/infer"), (f"{NIM_BASE}/infer", "infer", {}, f"{NIM_BASE}/infer/infer"), (NIM_BASE, "nvidia/nemoretriever-page-elements-v2/v1/infer", {}, f"{NIM_BASE}/v1/infer"), + ( + NIM_BASE, + "nvidia/nemoretriever-page-elements-v2/v1/infer", + {"litellm_metadata": {"model_group": "nvidia"}}, + f"{NIM_BASE}/v1/infer", + ), ], ) def test_relay_url_strips_the_model_group_and_never_doubles_the_api_version( @@ -161,6 +169,45 @@ def test_router_model_in_endpoint_takes_the_longest_leading_model_group(endpoint assert nvidia_nim_router_model_in_endpoint(endpoint, frozenset(router_models)) == expected +def _deployment(model_name: str, model: str, custom_llm_provider: str | None = None): + litellm_params = ( + {"model": model} + if custom_llm_provider is None + else {"model": model, "custom_llm_provider": custom_llm_provider} + ) + return {"model_name": model_name, "litellm_params": litellm_params} + + +MIXED_DEPLOYMENTS = ( + _deployment("nim-page", "nvidia_nim/nvidia/nemoretriever-page-elements-v2"), + _deployment("nim-table", "nvidia/nemoretriever-table-structure-v1", custom_llm_provider="nvidia_nim"), + _deployment("mixed", "nvidia_nim/nvidia/nemoretriever-page-elements-v2"), + _deployment("mixed", "openai/gpt-4o"), + _deployment("gpt-4o", "openai/gpt-4o"), +) + + +def test_model_groups_only_admit_groups_whose_every_deployment_is_nim_backed(): + assert nvidia_nim_model_groups(MIXED_DEPLOYMENTS) == frozenset({"nim-page", "nim-table"}) + assert nvidia_nim_model_groups(None) == frozenset() + + +@pytest.mark.parametrize( + "path, expected", + [ + ("/nvidia_nim/nim-page/v1/infer", "nim-page"), + ("/NVIDIA_NIM/nim-table/v1/infer", "nim-table"), + ("nim-page/v1/infer", "nim-page"), + ("/nvidia_nim/mixed/v1/infer", None), + ("mixed/v1/infer", None), + ("/nvidia_nim/gpt-4o/v1/infer", None), + ("/nvidia_nim/v1/infer", None), + ], +) +def test_model_group_in_path_resolves_the_same_nim_only_groups_for_routes_and_endpoints(path, expected): + assert nvidia_nim_model_group_in_path(path, MIXED_DEPLOYMENTS) == expected + + @pytest.mark.parametrize("request_data, expected", [({"stream": True}, True), ({"stream": False}, False), ({}, False)]) def test_is_streaming_request_reads_the_stream_flag(request_data, expected): assert NvidiaNimPassthroughConfig().is_streaming_request("v1/infer", request_data) is expected @@ -214,3 +261,36 @@ async def test_object_detection_relay_sends_the_native_body_unchanged_to_v1_infe assert response.status_code == 200 assert response.headers["x-nim"] == "1" assert response.json() == {"data": [{"index": 0}, {"index": 1}]} + + +@pytest.mark.asyncio +async def test_router_relay_reaches_v1_infer_when_the_group_name_is_a_leading_segment_of_the_model_id(): + upstream_requests: list[httpx.Request] = [] + + def nim(request: httpx.Request) -> httpx.Response: + upstream_requests.append(request) + return httpx.Response(200, json={"data": [{"index": 0}]}) + + client = AsyncHTTPHandler() + client.client = httpx.AsyncClient(transport=httpx.MockTransport(nim)) + router = litellm.Router( + model_list=[ + { + "model_name": "nvidia", + "litellm_params": { + "model": "nvidia_nim/nvidia/nemoretriever-page-elements-v2", + "api_base": NIM_BASE, + "api_key": "nvapi-secret", + }, + } + ] + ) + + response = await router.allm_passthrough_route( + model="nvidia", endpoint="nvidia/v1/infer", method="POST", json=dict(INFER_BODY), client=client + ) + + (sent,) = upstream_requests + assert str(sent.url) == f"{NIM_BASE}/v1/infer" + assert json.loads(sent.content) == INFER_BODY + assert response.status_code == 200 diff --git a/tests/test_litellm/proxy/auth/test_auth_utils.py b/tests/test_litellm/proxy/auth/test_auth_utils.py index 0841f82227a..8e592b61f1c 100644 --- a/tests/test_litellm/proxy/auth/test_auth_utils.py +++ b/tests/test_litellm/proxy/auth/test_auth_utils.py @@ -848,6 +848,18 @@ def _nvidia_nim_relay_router(): "model_name": "gpt-4o", "litellm_params": {"model": "openai/gpt-4o", "api_key": "k"}, }, + { + "model_name": "detect", + "litellm_params": { + "model": "nvidia_nim/nvidia/nemoretriever-page-elements-v2", + "api_base": "http://nim-a.internal:8000", + "api_key": "k", + }, + }, + { + "model_name": "detect", + "litellm_params": {"model": "openai/gpt-4o", "api_key": "k"}, + }, ] ) @@ -873,6 +885,7 @@ NIM_INFER_BODY = {"input": [{"type": "image_url", "url": "data:image/png;base64, ("/nvidia_nim/unknown-group/v1/infer", NIM_INFER_BODY, None), ("/nvidia_nim/nim-page-elements-v2/v1/infer", NIM_INFER_BODY, None), ("/nvidia_nim/gpt-4o/v1/infer", NIM_INFER_BODY, None), + ("/nvidia_nim/detect/v1/infer", NIM_INFER_BODY, None), ], ) def test_get_model_from_request_nvidia_nim_relay_routes_use_the_model_group_in_the_path(route, request_data, expected): diff --git a/tests/test_litellm/proxy/pass_through_endpoints/test_llm_pass_through_endpoints.py b/tests/test_litellm/proxy/pass_through_endpoints/test_llm_pass_through_endpoints.py index 4e73a33ecd9..62ecbbbcf51 100644 --- a/tests/test_litellm/proxy/pass_through_endpoints/test_llm_pass_through_endpoints.py +++ b/tests/test_litellm/proxy/pass_through_endpoints/test_llm_pass_through_endpoints.py @@ -5498,6 +5498,32 @@ class TestNvidiaNimProxyRoute: assert exc_info.value.status_code == 400 assert captured == [] + @pytest.mark.asyncio + async def test_a_group_mixing_nim_and_other_deployments_is_rejected_before_any_upstream_call(self): + from fastapi import HTTPException + + captured: list[dict] = [] + + class MixedRouter: + def get_model_list(self): + return [ + { + "model_name": "detect", + "litellm_params": {"model": "nvidia_nim/nvidia/nemoretriever-page-elements-v2"}, + }, + {"model_name": "detect", "litellm_params": {"model": "openai/gpt-4o"}}, + ] + + async def allm_passthrough_route(self, **kwargs): + captured.append(kwargs) + return httpx.Response(200, json={"data": []}) + + with pytest.raises(HTTPException) as exc_info: + await self._relay(MixedRouter(), "detect/v1/infer", NIM_INFER_BODY) + + assert exc_info.value.status_code == 400 + assert captured == [] + @pytest.mark.asyncio async def test_no_router_is_rejected_before_any_upstream_call(self): from fastapi import HTTPException From 74a2c2eab1f86ada6bc4ccc55dfab64bd6d39219 Mon Sep 17 00:00:00 2001 From: yassin Date: Tue, 15 Sep 2026 23:34:31 +0000 Subject: [PATCH 120/225] refactor(vertex_ai): tighten Interactions route match and type the usage boundary Co-Authored-By: Devin AI <158243242+devin-ai-integration[bot]@users.noreply.github.com> --- litellm/cost_calculator.py | 4 +--- .../llms/vertex_ai/videos/transformation.py | 1 - .../vertex_passthrough_logging_handler.py | 20 ++++++++++++++----- .../pass_through_endpoints/success_handler.py | 4 +--- .../test_pass_through_endpoints.py | 7 +++++++ 5 files changed, 24 insertions(+), 12 deletions(-) diff --git a/litellm/cost_calculator.py b/litellm/cost_calculator.py index f81eb1539fd..e6bb8da4c6a 100644 --- a/litellm/cost_calculator.py +++ b/litellm/cost_calculator.py @@ -1460,7 +1460,6 @@ def completion_cost( duration_seconds: float | None = None video_resolution: str | None = None provider_reported_cost: float | None = None - video_count: int = 1 if completion_response is not None and usage_obj: # Handle both dict and Pydantic Usage object if isinstance(usage_obj, dict): @@ -1475,8 +1474,7 @@ def completion_cost( _vc = getattr(usage_obj, "video_count", None) if _vr is not None: video_resolution = str(_vr).strip().lower() - if isinstance(_vc, int) and not isinstance(_vc, bool) and _vc > 1: - video_count = _vc + video_count = _vc if isinstance(_vc, int) and not isinstance(_vc, bool) and _vc > 1 else 1 if _video_model_info is None and provider_reported_cost is not None: return float(provider_reported_cost) diff --git a/litellm/llms/vertex_ai/videos/transformation.py b/litellm/llms/vertex_ai/videos/transformation.py index 6525c6fa235..dc9caa13224 100644 --- a/litellm/llms/vertex_ai/videos/transformation.py +++ b/litellm/llms/vertex_ai/videos/transformation.py @@ -71,7 +71,6 @@ def _parse_veo_operation(raw_response: httpx.Response) -> _VeoOperation: def veo_video_count_from_parameters(parameters: Mapping[str, object]) -> int | None: - """Number of videos Veo generates for one request (``parameters.sampleCount``).""" sample_count: Final = parameters.get("sampleCount") if isinstance(sample_count, bool) or not isinstance(sample_count, int) or sample_count < 1: return None diff --git a/litellm/proxy/pass_through_endpoints/llm_provider_handlers/vertex_passthrough_logging_handler.py b/litellm/proxy/pass_through_endpoints/llm_provider_handlers/vertex_passthrough_logging_handler.py index 0c4bc2a5181..cd226e80c6e 100644 --- a/litellm/proxy/pass_through_endpoints/llm_provider_handlers/vertex_passthrough_logging_handler.py +++ b/litellm/proxy/pass_through_endpoints/llm_provider_handlers/vertex_passthrough_logging_handler.py @@ -6,6 +6,7 @@ from typing import TYPE_CHECKING, Any, Final, Literal, cast from urllib.parse import urlparse import httpx +from pydantic import TypeAdapter import litellm from litellm._logging import verbose_proxy_logger @@ -53,6 +54,9 @@ else: EndpointType = Any +_VERTEX_INTERACTIONS_PATH: Final = re.compile(r"/projects/[^/]+/locations/[^/]+/interactions/?$") +_INTERACTIONS_RESPONSE_BODY: Final = TypeAdapter(dict[str, object]) + def _interactions_model( response_body: Mapping[str, object], @@ -72,18 +76,22 @@ class VertexPassthroughLoggingHandler: def is_interactions_route(url_route: str) -> bool: return urlparse(url_route).path.rstrip("/").endswith("/interactions") + @staticmethod + def is_vertex_interactions_route(url_route: str) -> bool: + return _VERTEX_INTERACTIONS_PATH.search(urlparse(url_route).path) is not None + @staticmethod def interactions_passthrough_handler( httpx_response: httpx.Response, request_body: Mapping[str, object] | None, logging_obj: LiteLLMLoggingObj, - kwargs: dict, + kwargs: dict[str, object], start_time: datetime, end_time: datetime, custom_llm_provider: Literal["vertex_ai", "gemini"], vertex_location: str | None, ) -> PassThroughEndpointLoggingTypedDict: - response_body: Final[Mapping[str, object]] = httpx_response.json() + response_body: Final = _INTERACTIONS_RESPONSE_BODY.validate_python(httpx_response.json()) usage_object: Final = response_body.get("usage") model: Final = _interactions_model(response_body, request_body) if model is None or not InteractionsUsageObjectTransformation.is_interactions_usage_object(usage_object): @@ -95,6 +103,7 @@ class VertexPassthroughLoggingHandler: cast(Mapping[str, Any], usage_object) ), ) + logging_obj.custom_llm_provider = custom_llm_provider logging_kwargs: Final = ( VertexPassthroughLoggingHandler._create_vertex_response_logging_payload_for_generate_content( litellm_model_response=litellm_model_response, @@ -107,9 +116,10 @@ class VertexPassthroughLoggingHandler: vertex_location=vertex_location, ) ) - logging_kwargs["custom_llm_provider"] = custom_llm_provider - logging_obj.custom_llm_provider = custom_llm_provider - return {"result": litellm_model_response, "kwargs": logging_kwargs} + return { + "result": litellm_model_response, + "kwargs": {**logging_kwargs, "custom_llm_provider": custom_llm_provider}, + } @staticmethod def vertex_passthrough_handler( diff --git a/litellm/proxy/pass_through_endpoints/success_handler.py b/litellm/proxy/pass_through_endpoints/success_handler.py index dd8bd17eee9..76a471302f4 100644 --- a/litellm/proxy/pass_through_endpoints/success_handler.py +++ b/litellm/proxy/pass_through_endpoints/success_handler.py @@ -363,9 +363,7 @@ class PassThroughEndpointLogging: return True if any(resource in url_route for resource in self.TRACKED_VERTEX_RESOURCE_ROUTES): return True - return "/locations/" in urlparse(url_route).path and VertexPassthroughLoggingHandler.is_interactions_route( - url_route - ) + return VertexPassthroughLoggingHandler.is_vertex_interactions_route(url_route) def is_anthropic_route(self, url_route: str): for route in self.TRACKED_ANTHROPIC_ROUTES: diff --git a/tests/test_litellm/proxy/pass_through_endpoints/test_pass_through_endpoints.py b/tests/test_litellm/proxy/pass_through_endpoints/test_pass_through_endpoints.py index 988c4782f6c..cc1d408176c 100644 --- a/tests/test_litellm/proxy/pass_through_endpoints/test_pass_through_endpoints.py +++ b/tests/test_litellm/proxy/pass_through_endpoints/test_pass_through_endpoints.py @@ -510,6 +510,13 @@ def test_interactions_create_routes_are_tracked_for_vertex_and_gemini(): assert handler.is_vertex_route(vertex_create) is True assert handler.is_vertex_route(f"{vertex_create}/abc123") is False assert handler.is_vertex_route("https://upstream.example.com/api/interactions") is False + assert handler.is_vertex_route("https://upstream.example.com/locations/eu/interactions") is False + assert ( + handler.is_vertex_route( + "https://us-central1-aiplatform.googleapis.com/v1/projects/p/locations/us-central1/interactions" + ) + is True + ) assert handler.is_gemini_route(gemini_create, custom_llm_provider="gemini") is True assert handler.is_gemini_route(f"{gemini_create}/abc123", custom_llm_provider="gemini") is False From b99f0c812f0d820242f9ac8988213683cdcf0d2f Mon Sep 17 00:00:00 2001 From: yassin Date: Tue, 15 Sep 2026 23:36:12 +0000 Subject: [PATCH 121/225] fix(proxy): log configured model access denial only at the auth error boundary Move the WARNING that carries the internal denial detail out of the message formatter and into the auth exception handler. The denial exceptions now carry internal_message so access-group probes and fallback paths that catch and recover from the denial no longer log a false denial for an allowed request Co-Authored-By: Devin AI <158243242+devin-ai-integration[bot]@users.noreply.github.com> --- litellm/proxy/_types.py | 13 ++ litellm/proxy/auth/auth_checks.py | 47 ++++---- litellm/proxy/auth/auth_exception_handler.py | 13 ++ litellm/proxy/auth/handle_jwt.py | 23 ++-- litellm/proxy/auth/model_access_denied.py | 19 +++ .../proxy/auth/test_auth_checks.py | 49 ++++---- .../proxy/auth/test_auth_exception_handler.py | 111 +++++++++++++++++- .../proxy/auth/test_handle_jwt.py | 9 +- 8 files changed, 225 insertions(+), 59 deletions(-) create mode 100644 litellm/proxy/auth/model_access_denied.py diff --git a/litellm/proxy/_types.py b/litellm/proxy/_types.py index d4eda1c9540..8949433dc34 100644 --- a/litellm/proxy/_types.py +++ b/litellm/proxy/_types.py @@ -4030,6 +4030,19 @@ class ProxyException(Exception): return error_dict +class ModelAccessDeniedProxyException(ProxyException): + def __init__( + self, + message: str, + internal_message: str, + type: str, + param: str | None, + code: int | str | None, + ) -> None: + super().__init__(message=message, type=type, param=param, code=code) + self.internal_message: Final = internal_message + + class CommonProxyErrors(str, enum.Enum): db_not_connected_error = ( "DB not connected. This endpoint needs a database; set DATABASE_URL to a " diff --git a/litellm/proxy/auth/auth_checks.py b/litellm/proxy/auth/auth_checks.py index b13214eaf5e..80bb1e3b319 100644 --- a/litellm/proxy/auth/auth_checks.py +++ b/litellm/proxy/auth/auth_checks.py @@ -32,7 +32,6 @@ from litellm.constants import ( DEFAULT_MAX_RECURSE_DEPTH, EMAIL_BUDGET_ALERT_MAX_SPEND_ALERT_PERCENTAGE, END_USER_RESTRICTED_REGISTRY_MAX_SIZE, - MODEL_ACCESS_DENIED_MESSAGE_MODEL_PLACEHOLDER, MODEL_ACCESS_GROUP_REGISTRY_MAX_SIZE, REGISTRY_ERROR_NEGATIVE_CACHE_TTL, TAG_REGISTRY_MAX_SIZE, @@ -61,6 +60,7 @@ from litellm.proxy._types import ( LiteLLM_UserTable, LiteLLMRoutes, LitellmUserRoles, + ModelAccessDeniedProxyException, NewTeamRequest, ProxyErrorTypes, ProxyException, @@ -72,6 +72,7 @@ from litellm.proxy.auth.budget_throttle import ( budget_throttle_percentage, should_throttle_budget_exceeded, ) +from litellm.proxy.auth.model_access_denied import client_facing_model_access_denied_message from litellm.proxy.auth.route_checks import RouteChecks from litellm.proxy.common_utils.auth_cache_invalidation_pubsub import publish_auth_cache_invalidation from litellm.proxy.common_utils.cache_pydantic_utils import CacheCodec @@ -4034,14 +4035,6 @@ async def _get_agent_ids_from_access_groups( ) -def client_facing_model_access_denied_message(internal_message: str, model: str | list[str]) -> str: - template: Final = litellm.model_access_denied_message - if not template: - return internal_message - verbose_proxy_logger.warning(internal_message.replace("\r", "").replace("\n", "")) - return template.replace(MODEL_ACCESS_DENIED_MESSAGE_MODEL_PLACEHOLDER, str(model)) - - def _resolve_all_team_model_sentinel_for_auth_check( models: list[str], llm_router: Router | None, @@ -4163,11 +4156,13 @@ def _can_object_call_model( ): return True - raise ProxyException( - message=client_facing_model_access_denied_message( - internal_message=f"{object_type} not allowed to access model. This {object_type} can only access models={models}. Tried to access {model}", - model=model, - ), + internal_message: Final = ( + f"{object_type} not allowed to access model. This {object_type} can only access models={models}. " + f"Tried to access {model}" + ) + raise ModelAccessDeniedProxyException( + message=client_facing_model_access_denied_message(internal_message=internal_message, model=model), + internal_message=internal_message, type=ProxyErrorTypes.get_model_access_error_type_for_object(object_type=object_type), param="model", code=status.HTTP_403_FORBIDDEN, @@ -4792,11 +4787,13 @@ async def can_user_call_model( return True if SpecialModelNames.no_default_models.value in user_object.models: - raise ProxyException( - message=client_facing_model_access_denied_message( - internal_message=f"User not allowed to access model. No default model access, only team models allowed. Tried to access {model}", - model=model, - ), + internal_message: Final = ( + f"User not allowed to access model. No default model access, only team models allowed. " + f"Tried to access {model}" + ) + raise ModelAccessDeniedProxyException( + message=client_facing_model_access_denied_message(internal_message=internal_message, model=model), + internal_message=internal_message, type=ProxyErrorTypes.key_model_access_denied, param="model", code=status.HTTP_403_FORBIDDEN, @@ -5397,11 +5394,13 @@ async def _check_team_member_model_access( team_id=team_object.team_id, ) except ProxyException: - raise ProxyException( - message=client_facing_model_access_denied_message( - internal_message=f"Team member not allowed to access model. User={valid_token.user_id}, Team={team_object.team_id}, Model={model}. Allowed member models = {member_allowed_models}", - model=model, - ), + internal_message: Final = ( + f"Team member not allowed to access model. User={valid_token.user_id}, Team={team_object.team_id}, " + f"Model={model}. Allowed member models = {member_allowed_models}" + ) + raise ModelAccessDeniedProxyException( + message=client_facing_model_access_denied_message(internal_message=internal_message, model=model), + internal_message=internal_message, type=ProxyErrorTypes.team_model_access_denied, param="model", code=status.HTTP_403_FORBIDDEN, diff --git a/litellm/proxy/auth/auth_exception_handler.py b/litellm/proxy/auth/auth_exception_handler.py index 661b6a83c38..4a764cae6cc 100644 --- a/litellm/proxy/auth/auth_exception_handler.py +++ b/litellm/proxy/auth/auth_exception_handler.py @@ -15,6 +15,7 @@ from litellm.integrations.otel.runtime import seed_request_identity from litellm.litellm_core_utils.core_helpers import is_expected_client_error from litellm.proxy._types import ( LitellmUserRoles, + ModelAccessDeniedProxyException, ProxyErrorTypes, ProxyException, UserAPIKeyAuth, @@ -25,6 +26,7 @@ from litellm.proxy.auth.auth_utils import ( mark_invalid_virtual_key_error, normalize_request_route, ) +from litellm.proxy.auth.model_access_denied import ModelAccessDeniedHTTPException from litellm.proxy.db.exception_handler import PrismaDBExceptionHandler from litellm.types.services import ServiceTypes @@ -75,6 +77,14 @@ def _as_proxy_exception(e: Exception) -> ProxyException: ) +def _model_access_denied_internal_message(e: Exception) -> str | None: + if not litellm.model_access_denied_message: + return None + if not isinstance(e, (ModelAccessDeniedProxyException, ModelAccessDeniedHTTPException)): + return None + return e.internal_message.replace("\r", "").replace("\n", "") + + def _get_user_agent(request: Request) -> str | None: if "headers" not in request.scope: return None @@ -166,6 +176,9 @@ class UserAPIKeyAuthExceptionHandler: # survives a raising callback pipeline. Classify and route malformed virtual-key # rejections to WARNING on stdout (suppressible via LITELLM_LOG=ERROR). log_extra: Final = {"requester_ip": requester_ip} + denied_internal_message: Final = _model_access_denied_internal_message(e) + if denied_internal_message is not None: + verbose_proxy_logger.warning(denied_internal_message, extra=log_extra) is_invalid_virtual_key: Final = is_invalid_virtual_key_error(e) is_quiet_log: Final = is_invalid_virtual_key and not litellm.log_client_error_tracebacks logger: Final = verbose_proxy_stdout_logger if is_quiet_log else verbose_proxy_logger diff --git a/litellm/proxy/auth/handle_jwt.py b/litellm/proxy/auth/handle_jwt.py index 6e5e75a5e20..ea6d52b28f0 100644 --- a/litellm/proxy/auth/handle_jwt.py +++ b/litellm/proxy/auth/handle_jwt.py @@ -52,6 +52,10 @@ from litellm.proxy._types import ( UserAPIKeyAuth, ) from litellm.proxy.auth.auth_checks import can_team_access_model +from litellm.proxy.auth.model_access_denied import ( + ModelAccessDeniedHTTPException, + client_facing_model_access_denied_message, +) from litellm.proxy.auth.resolvers.grants import GrantResolver, UserLookup, canonical_user_id from litellm.proxy.auth.route_checks import RouteChecks from litellm.proxy.auth.team_grants import team_model_aliases @@ -66,7 +70,6 @@ from litellm.types.agents import AgentResponse from .auth_checks import ( _allowed_routes_check, allowed_routes_check, - client_facing_model_access_denied_message, get_actual_routes, get_end_user_object, get_org_object, @@ -1338,12 +1341,13 @@ class JWTAuthManager: return True if model not in role_based_models: - raise HTTPException( + internal_message: Final = ( + f"Role={rbac_role} not allowed to call model={model}. Allowed models={role_based_models}" + ) + raise ModelAccessDeniedHTTPException( + internal_message=internal_message, status_code=403, - detail=client_facing_model_access_denied_message( - internal_message=f"Role={rbac_role} not allowed to call model={model}. Allowed models={role_based_models}", - model=model, - ), + detail=client_facing_model_access_denied_message(internal_message=internal_message, model=model), ) return True @@ -1372,12 +1376,13 @@ class JWTAuthManager: return if requested_model not in allowed_models: - raise HTTPException( + internal_message: Final = f"model={requested_model} not allowed. Allowed_models={allowed_models}" + raise ModelAccessDeniedHTTPException( + internal_message=internal_message, status_code=403, detail={ "error": client_facing_model_access_denied_message( - internal_message=f"model={requested_model} not allowed. Allowed_models={allowed_models}", - model=requested_model, + internal_message=internal_message, model=requested_model ) }, ) diff --git a/litellm/proxy/auth/model_access_denied.py b/litellm/proxy/auth/model_access_denied.py new file mode 100644 index 00000000000..8164e06c42a --- /dev/null +++ b/litellm/proxy/auth/model_access_denied.py @@ -0,0 +1,19 @@ +from typing import Final + +from fastapi import HTTPException + +import litellm +from litellm.constants import MODEL_ACCESS_DENIED_MESSAGE_MODEL_PLACEHOLDER + + +def client_facing_model_access_denied_message(internal_message: str, model: str | list[str]) -> str: + template: Final = litellm.model_access_denied_message + if not template: + return internal_message + return template.replace(MODEL_ACCESS_DENIED_MESSAGE_MODEL_PLACEHOLDER, str(model)) + + +class ModelAccessDeniedHTTPException(HTTPException): + def __init__(self, internal_message: str, status_code: int, detail: str | dict[str, str]) -> None: + super().__init__(status_code=status_code, detail=detail) + self.internal_message: Final = internal_message diff --git a/tests/test_litellm/proxy/auth/test_auth_checks.py b/tests/test_litellm/proxy/auth/test_auth_checks.py index 0052cfb66d3..0677fb8af29 100644 --- a/tests/test_litellm/proxy/auth/test_auth_checks.py +++ b/tests/test_litellm/proxy/auth/test_auth_checks.py @@ -25,6 +25,7 @@ from litellm.proxy._types import ( LiteLLM_TeamTable, LiteLLM_UserTable, LitellmUserRoles, + ModelAccessDeniedProxyException, ProxyErrorTypes, ProxyException, SSOUserDefinedValues, @@ -1682,11 +1683,11 @@ def test_can_object_call_model_no_access_to_alias_or_underlying(): _DENIED_MESSAGE_TEMPLATE: Final = "The model `{model}` is unavailable for this API key or does not exist." -def test_can_object_call_model_denial_uses_configured_message_and_logs_detail(monkeypatch, caplog): +def test_can_object_call_model_denial_uses_configured_message_and_keeps_detail_on_exception(monkeypatch, caplog): monkeypatch.setattr(litellm, "model_access_denied_message", _DENIED_MESSAGE_TEMPLATE) - with caplog.at_level("WARNING", logger="LiteLLM Proxy"): - with pytest.raises(ProxyException) as exc_info: + with caplog.at_level("DEBUG", logger="LiteLLM Proxy"): + with pytest.raises(ModelAccessDeniedProxyException) as exc_info: _can_object_call_model( model="anthropic-sonnet-4-5", llm_router=None, @@ -1700,28 +1701,30 @@ def test_can_object_call_model_denial_uses_configured_message_and_logs_detail(mo assert exc_info.value.type == ProxyErrorTypes.key_model_access_denied assert exc_info.value.param == "model" assert int(exc_info.value.code) == status.HTTP_403_FORBIDDEN - assert "internal-models" in caplog.text - assert "anthropic-sonnet-4-5" in caplog.text - - -def test_can_object_call_model_denial_log_strips_newlines_from_requested_model(monkeypatch, caplog): - monkeypatch.setattr(litellm, "model_access_denied_message", _DENIED_MESSAGE_TEMPLATE) - - with caplog.at_level("WARNING", logger="LiteLLM Proxy"): - with pytest.raises(ProxyException): - _can_object_call_model( - model="gpt-5.6\r\nWARNING forged log line", - llm_router=None, - models=["internal-models"], - object_type="key", - ) - - denial_records = [r for r in caplog.records if "not allowed to access model" in r.getMessage()] - assert len(denial_records) == 1 - assert denial_records[0].getMessage() == ( + assert exc_info.value.internal_message == ( "key not allowed to access model. This key can only access models=['internal-models']. " - "Tried to access gpt-5.6WARNING forged log line" + "Tried to access anthropic-sonnet-4-5" ) + assert "internal-models" not in caplog.text + + +@pytest.mark.asyncio +async def test_access_group_fallback_grant_does_not_log_a_denial(monkeypatch, caplog): + from litellm.proxy.auth.auth_checks import can_team_access_model + + monkeypatch.setattr(litellm, "model_access_denied_message", _DENIED_MESSAGE_TEMPLATE) + team_object = LiteLLM_TeamTable(team_id="team-123", models=["direct-model"], access_group_ids=["ag-1"]) + + with ( + patch( # test-quality-ok: access-group lookup has no dependency-injection seam + "litellm.proxy.auth.auth_checks._get_models_from_access_groups", + new=AsyncMock(return_value=["group-model"]), + ), + caplog.at_level("DEBUG", logger="LiteLLM Proxy"), + ): + assert await can_team_access_model("group-model", team_object, None) is True + + assert "not allowed to access model" not in caplog.text @pytest.mark.parametrize("unset_value", [None, ""]) diff --git a/tests/test_litellm/proxy/auth/test_auth_exception_handler.py b/tests/test_litellm/proxy/auth/test_auth_exception_handler.py index 6e9770bced8..602c074ee66 100644 --- a/tests/test_litellm/proxy/auth/test_auth_exception_handler.py +++ b/tests/test_litellm/proxy/auth/test_auth_exception_handler.py @@ -26,11 +26,18 @@ from prisma.errors import ( ) +import litellm from litellm._logging import verbose_proxy_logger from litellm.constants import INVALID_VIRTUAL_KEY_ERROR_MARKER from litellm.exceptions import BudgetExceededError -from litellm.proxy._types import ProxyErrorTypes, ProxyException, UserAPIKeyAuth +from litellm.proxy._types import ( + ModelAccessDeniedProxyException, + ProxyErrorTypes, + ProxyException, + UserAPIKeyAuth, +) from litellm.proxy.auth.auth_exception_handler import UserAPIKeyAuthExceptionHandler +from litellm.proxy.auth.model_access_denied import ModelAccessDeniedHTTPException class _EngineHttp500: @@ -982,3 +989,105 @@ async def test_handle_authentication_error_traceback_only_for_unexpected_errors( assert records[0].levelname == expect_level expected_logger_name = "LiteLLM Proxy.stdout" if expect_level == "WARNING" else "LiteLLM Proxy" assert records[0].name == expected_logger_name + + +_DENIED_MESSAGE_TEMPLATE = "The model `{model}` is unavailable for this API key or does not exist." + + +def _denied_proxy_exception() -> ModelAccessDeniedProxyException: + return ModelAccessDeniedProxyException( + message="The model `gpt-5.6\r\nWARNING forged log line` is unavailable for this API key or does not exist.", + internal_message="key not allowed to access model. This key can only access models=['internal-models']. " + "Tried to access gpt-5.6\r\nWARNING forged log line", + type=ProxyErrorTypes.key_model_access_denied, + param="model", + code=status.HTTP_403_FORBIDDEN, + ) + + +def _denied_jwt_exception() -> ModelAccessDeniedHTTPException: + return ModelAccessDeniedHTTPException( + internal_message="Role=engineer not allowed to call model=gpt-5.6\r\nWARNING forged log line. " + "Allowed models=['internal-models']", + status_code=status.HTTP_403_FORBIDDEN, + detail="The model `gpt-5.6` is unavailable for this API key or does not exist.", + ) + + +@pytest.mark.asyncio +@pytest.mark.parametrize( + "make_denial", + [ + pytest.param(_denied_proxy_exception, id="proxy_exception"), + pytest.param(_denied_jwt_exception, id="jwt_http_exception"), + ], +) +async def test_handle_authentication_error_logs_sanitized_model_access_denial_once(monkeypatch, make_denial, caplog): + monkeypatch.setattr(litellm, "model_access_denied_message", _DENIED_MESSAGE_TEMPLATE) + handler = UserAPIKeyAuthExceptionHandler() + denial = make_denial() + + with ( + patch( # test-quality-ok: handler reads proxy_server globals at call time + "litellm.proxy.proxy_server.proxy_logging_obj.post_call_failure_hook", + new_callable=AsyncMock, + return_value=None, + ), + patch( # test-quality-ok: handler reads proxy_server globals at call time + "litellm.proxy.auth.auth_exception_handler.seed_request_identity", + ), + patch( # test-quality-ok: handler reads proxy_server globals at call time + "litellm.proxy.proxy_server.general_settings", + {"allow_requests_on_db_unavailable": False}, + ), + caplog.at_level("WARNING", logger="LiteLLM Proxy"), + pytest.raises(ProxyException) as exc_info, + ): + await handler._handle_authentication_error(denial, MagicMock(), {}, "/v1/chat/completions", None, "sk-bad-key") + + assert exc_info.value.code == str(status.HTTP_403_FORBIDDEN) + assert "internal-models" not in str(exc_info.value.message) + denial_records = [r for r in caplog.records if "internal-models" in r.getMessage()] + assert len(denial_records) == 1 + assert denial_records[0].levelname == "WARNING" + assert "\n" not in denial_records[0].getMessage() + assert "gpt-5.6WARNING forged log line" in denial_records[0].getMessage() + + +@pytest.mark.asyncio +@pytest.mark.parametrize("unset_value", [None, ""]) +async def test_handle_authentication_error_no_extra_denial_log_when_message_not_configured( + monkeypatch, unset_value, caplog +): + monkeypatch.setattr(litellm, "model_access_denied_message", unset_value) + handler = UserAPIKeyAuthExceptionHandler() + denial = ModelAccessDeniedProxyException( + message="key not allowed to access model. This key can only access models=['internal-models']. " + "Tried to access gpt-5.6", + internal_message="key not allowed to access model. This key can only access models=['internal-models']. " + "Tried to access gpt-5.6", + type=ProxyErrorTypes.key_model_access_denied, + param="model", + code=status.HTTP_403_FORBIDDEN, + ) + + with ( + patch( # test-quality-ok: handler reads proxy_server globals at call time + "litellm.proxy.proxy_server.proxy_logging_obj.post_call_failure_hook", + new_callable=AsyncMock, + return_value=None, + ), + patch( # test-quality-ok: handler reads proxy_server globals at call time + "litellm.proxy.auth.auth_exception_handler.seed_request_identity", + ), + patch( # test-quality-ok: handler reads proxy_server globals at call time + "litellm.proxy.proxy_server.general_settings", + {"allow_requests_on_db_unavailable": False}, + ), + caplog.at_level("WARNING", logger="LiteLLM Proxy"), + pytest.raises(ProxyException) as exc_info, + ): + await handler._handle_authentication_error(denial, MagicMock(), {}, "/v1/chat/completions", None, "sk-bad-key") + + assert "internal-models" in str(exc_info.value.message) + assert [r for r in caplog.records if r.levelname == "WARNING" and "internal-models" in r.getMessage()] == [] diff --git a/tests/test_litellm/proxy/auth/test_handle_jwt.py b/tests/test_litellm/proxy/auth/test_handle_jwt.py index 921472f2f43..9fab1e1785a 100644 --- a/tests/test_litellm/proxy/auth/test_handle_jwt.py +++ b/tests/test_litellm/proxy/auth/test_handle_jwt.py @@ -37,6 +37,7 @@ from litellm.proxy.auth.handle_jwt import ( JWTHandler, NoMatchingJWTPublicKeyError, ) +from litellm.proxy.auth.model_access_denied import ModelAccessDeniedHTTPException from litellm.types.agents import AgentResponse @@ -6990,7 +6991,7 @@ def test_can_rbac_role_call_model_denial_honors_configured_message(monkeypatch, ] } - with pytest.raises(HTTPException) as exc_info: + with pytest.raises(ModelAccessDeniedHTTPException) as exc_info: JWTAuthManager.can_rbac_role_call_model( rbac_role=LitellmUserRoles.INTERNAL_USER, general_settings=general_settings, @@ -6999,6 +7000,9 @@ def test_can_rbac_role_call_model_denial_honors_configured_message(monkeypatch, assert exc_info.value.status_code == 403 assert exc_info.value.detail == expected_detail + assert exc_info.value.internal_message == ( + "Role=internal_user not allowed to call model=gpt-5.6. Allowed models=['gpt-5.6-mini']" + ) @pytest.mark.parametrize( @@ -7012,7 +7016,7 @@ def test_can_rbac_role_call_model_denial_honors_configured_message(monkeypatch, def test_check_scope_based_access_denial_honors_configured_message(monkeypatch, configured_message, expected_error): monkeypatch.setattr(litellm, "model_access_denied_message", configured_message) - with pytest.raises(HTTPException) as exc_info: + with pytest.raises(ModelAccessDeniedHTTPException) as exc_info: JWTAuthManager.check_scope_based_access( scope_mappings=[ScopeMapping(scope="litellm.api.consumer", models=["gpt-5.6-mini"])], scopes=["litellm.api.consumer"], @@ -7022,3 +7026,4 @@ def test_check_scope_based_access_denial_honors_configured_message(monkeypatch, assert exc_info.value.status_code == 403 assert exc_info.value.detail == {"error": expected_error} + assert exc_info.value.internal_message == "model=gpt-5.6 not allowed. Allowed_models=['gpt-5.6-mini']" From 5b54bf2328c53244d9509256defbc9f06f54d97a Mon Sep 17 00:00:00 2001 From: kerry Date: Tue, 15 Sep 2026 23:36:22 +0000 Subject: [PATCH 122/225] refactor(model_info): restore provider-neutral Gemini chat baseline Co-Authored-By: Devin AI <158243242+devin-ai-integration[bot]@users.noreply.github.com> --- ...odel_prices_and_context_window_backup.json | 4 +-- model_prices_and_context_window.json | 4 +-- .../test_fallback_generalizations.py | 26 +++++++++++-------- 3 files changed, 19 insertions(+), 15 deletions(-) diff --git a/litellm/model_prices_and_context_window_backup.json b/litellm/model_prices_and_context_window_backup.json index 9569f29aa0e..cc4c7feffad 100644 --- a/litellm/model_prices_and_context_window_backup.json +++ b/litellm/model_prices_and_context_window_backup.json @@ -58152,8 +58152,8 @@ }, { "name": "gemini-chat-baseline", - "pattern": "^(?:gemini|vertex_ai)/gemini-(?!.*(?:-tts|-image|-live|-audio|-embedding|-computer-use|-robotics|-transcribe|-translate))(?:2\\.[5-9]|[3-9](?:\\.\\d{1,2})?)-(?:pro|flash)(?:-lite)?(?![a-z])", - "description": "A first-party Gemini text-chat id at 2.5 or higher, as gemini/ or vertex_ai/ (get_model_info tries the provider-prefixed name first, so a bare id under those providers matches too): gemini-[.minor]-(pro|flash)[-lite] with any trailing preview, date or variant tag. Scoped to Google's own namespaces because resellers wrap the same models differently (one exposes them as mode responses without reasoning), so nothing below is certain there. The lookahead excludes the tts, image, live, audio, embedding, computer-use, robotics, transcribe and translate lines, which are different modes with different capabilities. 2.5 is the floor: every first-party text-chat Gemini from 2.5 onward in this map carries all of these fields, 1.5 and 2.0 do not and are fully mapped. Carries no token limits or pricing, so those stay on the standard unmapped behavior rather than a guessed number.", + "pattern": "gemini-(?!.*(?:-tts|-image|-live|-audio|-embedding|-computer-use|-robotics|-transcribe|-translate))(?:2[.-][5-9]|[3-9](?:[.-]\\d{1,2})?)-(?:pro|flash)(?:-lite)?(?![a-z])", + "description": "Any Gemini text-chat id at 2.5 or higher under any namespace, including bare ids, gemini/, vertex_ai/, openrouter/google/, deepinfra/google/, vercel_ai_gateway/google/, oci/google., and databricks-gemini--: gemini-[.minor]-(pro|flash)[-lite] with any trailing preview, date or variant tag. The capability flags were verified against each of those providers' own catalogs and docs. The lookahead excludes the tts, image, live, audio, embedding, computer-use, robotics, transcribe and translate lines, which are different modes with different capabilities. Provider-specific deviations, such as Perplexity's Agent API serving these as mode responses, are carried by their exact map entries, which always win over this rule. Carries no token limits or pricing, so those stay on the standard unmapped behavior rather than a guessed number.", "model_info": { "mode": "chat", "supports_reasoning": true, diff --git a/model_prices_and_context_window.json b/model_prices_and_context_window.json index 9569f29aa0e..cc4c7feffad 100644 --- a/model_prices_and_context_window.json +++ b/model_prices_and_context_window.json @@ -58152,8 +58152,8 @@ }, { "name": "gemini-chat-baseline", - "pattern": "^(?:gemini|vertex_ai)/gemini-(?!.*(?:-tts|-image|-live|-audio|-embedding|-computer-use|-robotics|-transcribe|-translate))(?:2\\.[5-9]|[3-9](?:\\.\\d{1,2})?)-(?:pro|flash)(?:-lite)?(?![a-z])", - "description": "A first-party Gemini text-chat id at 2.5 or higher, as gemini/ or vertex_ai/ (get_model_info tries the provider-prefixed name first, so a bare id under those providers matches too): gemini-[.minor]-(pro|flash)[-lite] with any trailing preview, date or variant tag. Scoped to Google's own namespaces because resellers wrap the same models differently (one exposes them as mode responses without reasoning), so nothing below is certain there. The lookahead excludes the tts, image, live, audio, embedding, computer-use, robotics, transcribe and translate lines, which are different modes with different capabilities. 2.5 is the floor: every first-party text-chat Gemini from 2.5 onward in this map carries all of these fields, 1.5 and 2.0 do not and are fully mapped. Carries no token limits or pricing, so those stay on the standard unmapped behavior rather than a guessed number.", + "pattern": "gemini-(?!.*(?:-tts|-image|-live|-audio|-embedding|-computer-use|-robotics|-transcribe|-translate))(?:2[.-][5-9]|[3-9](?:[.-]\\d{1,2})?)-(?:pro|flash)(?:-lite)?(?![a-z])", + "description": "Any Gemini text-chat id at 2.5 or higher under any namespace, including bare ids, gemini/, vertex_ai/, openrouter/google/, deepinfra/google/, vercel_ai_gateway/google/, oci/google., and databricks-gemini--: gemini-[.minor]-(pro|flash)[-lite] with any trailing preview, date or variant tag. The capability flags were verified against each of those providers' own catalogs and docs. The lookahead excludes the tts, image, live, audio, embedding, computer-use, robotics, transcribe and translate lines, which are different modes with different capabilities. Provider-specific deviations, such as Perplexity's Agent API serving these as mode responses, are carried by their exact map entries, which always win over this rule. Carries no token limits or pricing, so those stay on the standard unmapped behavior rather than a guessed number.", "model_info": { "mode": "chat", "supports_reasoning": true, diff --git a/tests/test_litellm/litellm_core_utils/test_fallback_generalizations.py b/tests/test_litellm/litellm_core_utils/test_fallback_generalizations.py index a4fbd79187e..71e6e20b1a4 100644 --- a/tests/test_litellm/litellm_core_utils/test_fallback_generalizations.py +++ b/tests/test_litellm/litellm_core_utils/test_fallback_generalizations.py @@ -457,12 +457,19 @@ def shipped_cost_map(monkeypatch): ("gemini-3.9-flash-lite-preview-09-2026", "vertex_ai"), ("vertex_ai/gemini-4-pro", None), ("gemini-4-pro-preview-customtools", "gemini"), + ("google/gemini-4-pro", "openrouter"), + ("google/gemini-4-pro", "deepinfra"), + ("google/gemini-4-pro", "vercel_ai_gateway"), + ("google.gemini-4-pro", "oci"), + ("databricks-gemini-4-1-pro", "databricks"), ], ) -def test_shipped_gemini_chat_baseline_resolves_first_party_unmapped_ids(shipped_cost_map, model, provider): +def test_shipped_gemini_chat_baseline_resolves_unmapped_ids(shipped_cost_map, model, provider): assert model not in litellm.model_cost if provider == "gemini": assert f"gemini/{model}" not in litellm.model_cost + elif provider in {"openrouter", "deepinfra", "vercel_ai_gateway", "oci", "databricks"}: + assert f"{provider}/{model}" not in litellm.model_cost info = litellm.get_model_info(model, custom_llm_provider=provider) assert info["litellm_provider"] == (provider or model.split("/")[0]) @@ -481,16 +488,11 @@ def test_shipped_gemini_chat_baseline_resolves_first_party_unmapped_ids(shipped_ assert not info.get("output_cost_per_token") -def test_shipped_gemini_chat_baseline_skips_reseller_namespaces(shipped_cost_map): - for model, provider in ( - ("google/gemini-4-pro", "perplexity"), - ("google/gemini-4-pro", "openrouter"), - ("databricks-gemini-4-1-pro", "databricks"), - ): - with pytest.raises(Exception, match="isn't mapped yet"): - litellm.get_model_info(model, custom_llm_provider=provider) - - assert match_capability_generalizations("google/gemini-4-pro") is None +def test_shipped_gemini_chat_baseline_loses_to_perplexity_exact_entries(shipped_cost_map): + info = litellm.get_model_info("google/gemini-2.5-pro", custom_llm_provider="perplexity") + entry = litellm.model_cost["perplexity/google/gemini-2.5-pro"] + assert info["mode"] == "responses" + assert entry["supports_reasoning"] is False def test_shipped_gemini_chat_baseline_skips_non_chat_and_pre_2_5_ids(shipped_cost_map): @@ -506,6 +508,8 @@ def test_shipped_gemini_chat_baseline_skips_non_chat_and_pre_2_5_ids(shipped_cos "gemini/gemini-4-flashy", "gemini/gemini-4-flash-transcribe", "gemini/gemini-4-flash-live-translate-preview", + "databricks-gemini-3-1-flash-image", + "openrouter/google/gemini-2.0-flash-001", ): assert match_capability_generalizations(model) is None, model From 945adfb603bad0ee0f9a5054196e0b8beeb05a39 Mon Sep 17 00:00:00 2001 From: kerry Date: Tue, 15 Sep 2026 23:49:18 +0000 Subject: [PATCH 123/225] refactor(model_info): support multi-digit Gemini majors Co-Authored-By: Devin AI <158243242+devin-ai-integration[bot]@users.noreply.github.com> --- litellm/model_prices_and_context_window_backup.json | 4 ++-- model_prices_and_context_window.json | 4 ++-- .../litellm_core_utils/test_fallback_generalizations.py | 2 ++ 3 files changed, 6 insertions(+), 4 deletions(-) diff --git a/litellm/model_prices_and_context_window_backup.json b/litellm/model_prices_and_context_window_backup.json index cc4c7feffad..5537f13f5fd 100644 --- a/litellm/model_prices_and_context_window_backup.json +++ b/litellm/model_prices_and_context_window_backup.json @@ -58152,8 +58152,8 @@ }, { "name": "gemini-chat-baseline", - "pattern": "gemini-(?!.*(?:-tts|-image|-live|-audio|-embedding|-computer-use|-robotics|-transcribe|-translate))(?:2[.-][5-9]|[3-9](?:[.-]\\d{1,2})?)-(?:pro|flash)(?:-lite)?(?![a-z])", - "description": "Any Gemini text-chat id at 2.5 or higher under any namespace, including bare ids, gemini/, vertex_ai/, openrouter/google/, deepinfra/google/, vercel_ai_gateway/google/, oci/google., and databricks-gemini--: gemini-[.minor]-(pro|flash)[-lite] with any trailing preview, date or variant tag. The capability flags were verified against each of those providers' own catalogs and docs. The lookahead excludes the tts, image, live, audio, embedding, computer-use, robotics, transcribe and translate lines, which are different modes with different capabilities. Provider-specific deviations, such as Perplexity's Agent API serving these as mode responses, are carried by their exact map entries, which always win over this rule. Carries no token limits or pricing, so those stay on the standard unmapped behavior rather than a guessed number.", + "pattern": "gemini-(?!.*(?:-tts|-image|-live|-audio|-embedding|-computer-use|-robotics|-transcribe|-translate))(?:2[.-][5-9]|(?:[3-9]|[1-9]\\d)(?:[.-]\\d{1,2})?)-(?:pro|flash)(?:-lite)?(?![a-z])", + "description": "Any Gemini text-chat id at 2.5 or higher under any namespace, including bare ids, gemini/, vertex_ai/, openrouter/google/, deepinfra/google/, vercel_ai_gateway/google/, oci/google., and databricks-gemini--: gemini-[.minor]-(pro|flash)[-lite] with any trailing preview, date or variant tag. The capability flags were verified against each of those providers' own catalogs and docs. The lookahead excludes the tts, image, live, audio, embedding, computer-use, robotics, transcribe and translate lines, which are different modes with different capabilities. Provider-specific deviations, such as Perplexity's Agent API serving these as mode responses, are carried by their exact map entries, which always win over this rule. Carries no token limits or pricing, so those stay on the standard unmapped behavior rather than a guessed number. Source check 2026-09-15: all 45 first-party 2.5+ text-chat entries in this map carry every field below, and the OpenRouter (openrouter.ai/api/v1/models), Vercel AI Gateway (ai-gateway.vercel.sh/v1/models), DeepInfra (api.deepinfra.com/models/list), OCI and Databricks model docs list reasoning, tools and image input for the same models.", "model_info": { "mode": "chat", "supports_reasoning": true, diff --git a/model_prices_and_context_window.json b/model_prices_and_context_window.json index cc4c7feffad..5537f13f5fd 100644 --- a/model_prices_and_context_window.json +++ b/model_prices_and_context_window.json @@ -58152,8 +58152,8 @@ }, { "name": "gemini-chat-baseline", - "pattern": "gemini-(?!.*(?:-tts|-image|-live|-audio|-embedding|-computer-use|-robotics|-transcribe|-translate))(?:2[.-][5-9]|[3-9](?:[.-]\\d{1,2})?)-(?:pro|flash)(?:-lite)?(?![a-z])", - "description": "Any Gemini text-chat id at 2.5 or higher under any namespace, including bare ids, gemini/, vertex_ai/, openrouter/google/, deepinfra/google/, vercel_ai_gateway/google/, oci/google., and databricks-gemini--: gemini-[.minor]-(pro|flash)[-lite] with any trailing preview, date or variant tag. The capability flags were verified against each of those providers' own catalogs and docs. The lookahead excludes the tts, image, live, audio, embedding, computer-use, robotics, transcribe and translate lines, which are different modes with different capabilities. Provider-specific deviations, such as Perplexity's Agent API serving these as mode responses, are carried by their exact map entries, which always win over this rule. Carries no token limits or pricing, so those stay on the standard unmapped behavior rather than a guessed number.", + "pattern": "gemini-(?!.*(?:-tts|-image|-live|-audio|-embedding|-computer-use|-robotics|-transcribe|-translate))(?:2[.-][5-9]|(?:[3-9]|[1-9]\\d)(?:[.-]\\d{1,2})?)-(?:pro|flash)(?:-lite)?(?![a-z])", + "description": "Any Gemini text-chat id at 2.5 or higher under any namespace, including bare ids, gemini/, vertex_ai/, openrouter/google/, deepinfra/google/, vercel_ai_gateway/google/, oci/google., and databricks-gemini--: gemini-[.minor]-(pro|flash)[-lite] with any trailing preview, date or variant tag. The capability flags were verified against each of those providers' own catalogs and docs. The lookahead excludes the tts, image, live, audio, embedding, computer-use, robotics, transcribe and translate lines, which are different modes with different capabilities. Provider-specific deviations, such as Perplexity's Agent API serving these as mode responses, are carried by their exact map entries, which always win over this rule. Carries no token limits or pricing, so those stay on the standard unmapped behavior rather than a guessed number. Source check 2026-09-15: all 45 first-party 2.5+ text-chat entries in this map carry every field below, and the OpenRouter (openrouter.ai/api/v1/models), Vercel AI Gateway (ai-gateway.vercel.sh/v1/models), DeepInfra (api.deepinfra.com/models/list), OCI and Databricks model docs list reasoning, tools and image input for the same models.", "model_info": { "mode": "chat", "supports_reasoning": true, diff --git a/tests/test_litellm/litellm_core_utils/test_fallback_generalizations.py b/tests/test_litellm/litellm_core_utils/test_fallback_generalizations.py index 71e6e20b1a4..4e7d7e85a8c 100644 --- a/tests/test_litellm/litellm_core_utils/test_fallback_generalizations.py +++ b/tests/test_litellm/litellm_core_utils/test_fallback_generalizations.py @@ -458,10 +458,12 @@ def shipped_cost_map(monkeypatch): ("vertex_ai/gemini-4-pro", None), ("gemini-4-pro-preview-customtools", "gemini"), ("google/gemini-4-pro", "openrouter"), + ("google/gemini-12.5-flash-lite", "openrouter"), ("google/gemini-4-pro", "deepinfra"), ("google/gemini-4-pro", "vercel_ai_gateway"), ("google.gemini-4-pro", "oci"), ("databricks-gemini-4-1-pro", "databricks"), + ("gemini-10-pro", "gemini"), ], ) def test_shipped_gemini_chat_baseline_resolves_unmapped_ids(shipped_cost_map, model, provider): From 88d0371a4670f57ca3e4c3824cb6846fe3fdacd7 Mon Sep 17 00:00:00 2001 From: Joshua Valluru <326636767+joshua-berri@users.noreply.github.com> Date: Tue, 15 Sep 2026 16:50:55 -0700 Subject: [PATCH 124/225] fix(mcp): reuse the standard JWT auth builder for OAuth ownership --- .../mcp_server/bridge_token_flow.py | 38 ++++++---- .../mcp_server/test_discoverable_endpoints.py | 74 ++++++++++++++++--- 2 files changed, 88 insertions(+), 24 deletions(-) diff --git a/litellm/proxy/_experimental/mcp_server/bridge_token_flow.py b/litellm/proxy/_experimental/mcp_server/bridge_token_flow.py index 3817935bf71..2f3e50b803f 100644 --- a/litellm/proxy/_experimental/mcp_server/bridge_token_flow.py +++ b/litellm/proxy/_experimental/mcp_server/bridge_token_flow.py @@ -314,15 +314,15 @@ async def _extract_user_id_from_request(request: Request) -> str | None: token: Final = _litellm_key_from_request(request) if token is not None and JWTHandler.is_jwt(token): - return await _extract_jwt_user_id(token) + return await _extract_jwt_user_id(request, token) resolved: Final = await _resolve_active_litellm_key(request) if not isinstance(resolved, _ResolvedKey): return None return _active_key_user_id(resolved.key) -async def _extract_jwt_user_id(token: str) -> str | None: - from litellm.proxy._types import LitellmUserRoles, UserAPIKeyAuth # noqa: PLC0415 # proxy import cycle +async def _extract_jwt_user_id(request: Request, token: str) -> str | None: + from litellm.proxy._types import UserAPIKeyAuth # noqa: PLC0415 # proxy import cycle from litellm.proxy.auth.handle_jwt import JWTAuthManager # noqa: PLC0415 # proxy import cycle from litellm.proxy.auth.user_api_key_auth import ( # noqa: PLC0415 # proxy import cycle _resolve_jwt_to_virtual_key, # pyright: ignore[reportPrivateUsage] # reuse admission mapping policy without provisioning a new key @@ -339,11 +339,11 @@ async def _extract_jwt_user_id(token: str) -> str | None: if general_settings.get("enable_jwt_auth") is not True or premium_user is not True: return None try: - claims: Final = await jwt_handler.auth_jwt(token=token) - validate: Final = jwt_handler.litellm_jwtauth.custom_validate - if validate is not None and not validate(claims): - return None if jwt_handler.litellm_jwtauth.is_virtual_key_mapping_configured(): + claims: Final = await jwt_handler.auth_jwt(token=token) + validate: Final = jwt_handler.litellm_jwtauth.custom_validate + if validate is not None and not validate(claims): + return None mapped: Final = await _resolve_jwt_to_virtual_key( jwt_claims=claims, jwt_handler=jwt_handler, @@ -356,16 +356,24 @@ async def _extract_jwt_user_id(token: str) -> str | None: return None if await _key_owner_scim_deactivated(mapped) else _active_key_user_id(mapped) if mapped is not None: return None - user_id, user_email, valid_email = await JWTAuthManager.get_user_info(jwt_handler, claims) - object_id: Final = jwt_handler.get_object_id(token=claims, default_value=None) - owner_id: Final = ( - object_id - if jwt_handler.get_rbac_role(token=claims) == LitellmUserRoles.INTERNAL_USER and object_id - else user_id + identity: Final = await JWTAuthManager.auth_builder( + api_key=token, + jwt_handler=jwt_handler, + request_data={}, + general_settings=general_settings, + route=request.url.path, + prisma_client=prisma_client, + user_api_key_cache=user_api_key_cache, + parent_otel_span=None, + proxy_logging_obj=proxy_logging_obj, + request_headers=dict(request.headers), + request_method=request.method, ) - if not owner_id or valid_email is False: + owner_id: Final = identity["user_id"] + if not owner_id: return None - owner: Final = await load_active_user_by_id(owner_id, sso_user_id=owner_id, user_email=user_email) + # Admin JWTs can return before auth_builder loads the canonical database user. + owner: Final = await load_active_user_by_id(owner_id, sso_user_id=owner_id, user_email=identity["user_email"]) return None if isinstance(owner, str) else owner.user_id except Exception as exc: # noqa: BLE001 # public OAuth exchange stays available; unvalidated identities never write credentials verbose_logger.debug("OAuth JWT identity could not be validated (%s)", type(exc).__name__) diff --git a/tests/test_litellm/proxy/_experimental/mcp_server/test_discoverable_endpoints.py b/tests/test_litellm/proxy/_experimental/mcp_server/test_discoverable_endpoints.py index ce985064eb0..d1ab8809861 100644 --- a/tests/test_litellm/proxy/_experimental/mcp_server/test_discoverable_endpoints.py +++ b/tests/test_litellm/proxy/_experimental/mcp_server/test_discoverable_endpoints.py @@ -11421,6 +11421,7 @@ def _oauth_identity_jwt( audience: str = "litellm-proxy", issuer: str = "https://idp.example.test", owner: str | None = "jwt-owner", + scope: str = "", ) -> str: import jwt @@ -11432,6 +11433,7 @@ def _oauth_identity_jwt( "iss": issuer, "aud": audience, "exp": int(time.time()) + expires_in, + "scope": scope, }, signing_key, algorithm="RS256", @@ -11440,9 +11442,11 @@ def _oauth_identity_jwt( @pytest.mark.asyncio @pytest.mark.parametrize("header", ["Authorization", "x-litellm-api-key"]) +@pytest.mark.parametrize("policy_allowed", [False, True]) async def test_oauth_exchange_stores_token_for_validated_jwt_user( jwt_oauth_identity: tuple["JWTHandler", "RSAPrivateKey"], header: str, + policy_allowed: bool, monkeypatch: pytest.MonkeyPatch, ) -> None: import httpx @@ -11451,7 +11455,8 @@ async def test_oauth_exchange_stores_token_for_validated_jwt_user( from litellm.proxy._types import MCPTransport from litellm.types.mcp_server.mcp_server_manager import MCPServer - _, signing_key = jwt_oauth_identity + handler, signing_key = jwt_oauth_identity + handler.litellm_jwtauth.enforce_team_based_model_access = not policy_allowed bearer: Final = _oauth_identity_jwt(signing_key) request: Final = _token_request({header: f"Bearer {bearer}"}) server: Final = MCPServer( @@ -11501,6 +11506,10 @@ async def test_oauth_exchange_stores_token_for_validated_jwt_user( code_verifier=None, ) assert response.status_code == 200 + assert json.loads(response.body)["access_token"] == "upstream-token" + if not policy_allowed: + table.upsert.assert_not_awaited() + return table.upsert.assert_awaited_once() stored: Final = table.upsert.call_args.kwargs assert stored["where"] == {"user_id_server_id": {"user_id": "jwt-owner", "server_id": server.server_id}} @@ -11525,6 +11534,8 @@ async def test_oauth_exchange_stores_token_for_validated_jwt_user( "scim_inactive", "custom_validate", "missing_database", + "denied_route", + "required_team", ], ) async def test_oauth_jwt_identity_rejects_untrusted_or_inactive_owner( @@ -11537,6 +11548,7 @@ async def test_oauth_jwt_identity_rejects_untrusted_or_inactive_owner( from litellm.models.user import LiteLLM_UserTable from litellm.proxy import proxy_server from litellm.proxy._experimental.mcp_server.bridge_token_flow import _extract_user_id_from_request + from litellm.proxy._types import LitellmUserRoles, RoleBasedPermissions handler, signing_key = jwt_oauth_identity key: Final = ( @@ -11561,6 +11573,18 @@ async def test_oauth_jwt_identity_rejects_untrusted_or_inactive_owner( ) if rejection == "custom_validate": handler.litellm_jwtauth.custom_validate = lambda claims: False + if rejection == "denied_route": + handler.litellm_jwtauth.enforce_rbac = True + monkeypatch.setattr( + proxy_server, + "general_settings", + { + "enable_jwt_auth": True, + "role_permissions": [RoleBasedPermissions(role=LitellmUserRoles.INTERNAL_USER, routes=["/models"])], + }, + ) + if rejection == "required_team": + handler.litellm_jwtauth.enforce_team_based_model_access = True assert await _extract_user_id_from_request(_token_request({"Authorization": f"Bearer {bearer}"})) is None @@ -11586,7 +11610,9 @@ async def test_oauth_jwt_cannot_override_explicit_litellm_key( @pytest.mark.asyncio -@pytest.mark.parametrize("mapping", ["active", "blocked", "inactive_owner", "fallback", "pending", "reject"]) +@pytest.mark.parametrize( + "mapping", ["active", "blocked", "inactive_owner", "fallback", "pending", "reject", "custom_reject"] +) async def test_oauth_jwt_uses_configured_virtual_key_owner( jwt_oauth_identity: tuple["JWTHandler", "RSAPrivateKey"], mapping: str, @@ -11598,6 +11624,8 @@ async def test_oauth_jwt_uses_configured_virtual_key_owner( handler, signing_key = jwt_oauth_identity handler.litellm_jwtauth.virtual_key_claim_field = "sub" + if mapping == "custom_reject": + handler.litellm_jwtauth.custom_validate = lambda claims: False handler.litellm_jwtauth.unregistered_jwt_client_behavior = ( UnregisteredJWTClientBehavior.AUTO_REGISTER if mapping == "pending" @@ -11637,9 +11665,15 @@ async def test_oauth_jwt_respects_custom_validation_and_email_policy( @pytest.mark.asyncio -async def test_oauth_jwt_uses_rbac_user_object_id(jwt_oauth_identity: tuple["JWTHandler", "RSAPrivateKey"]) -> None: +@pytest.mark.parametrize("route_allowed", [False, True]) +async def test_oauth_jwt_uses_rbac_user_object_id( + jwt_oauth_identity: tuple["JWTHandler", "RSAPrivateKey"], + monkeypatch: pytest.MonkeyPatch, + route_allowed: bool, +) -> None: + from litellm.proxy import proxy_server from litellm.proxy._experimental.mcp_server.bridge_token_flow import _extract_user_id_from_request - from litellm.proxy._types import LitellmUserRoles, RoleMapping + from litellm.proxy._types import LitellmUserRoles, RoleBasedPermissions, RoleMapping handler, signing_key = jwt_oauth_identity handler.litellm_jwtauth.user_id_jwt_field = "sub" @@ -11648,26 +11682,43 @@ async def test_oauth_jwt_uses_rbac_user_object_id(jwt_oauth_identity: tuple["JWT handler.litellm_jwtauth.role_mappings = [ RoleMapping(role="litellm-proxy", internal_role=LitellmUserRoles.INTERNAL_USER) ] + handler.litellm_jwtauth.enforce_rbac = True + monkeypatch.setattr( + proxy_server, + "general_settings", + { + "enable_jwt_auth": True, + "role_permissions": [ + RoleBasedPermissions( + role=LitellmUserRoles.INTERNAL_USER, + routes=["/token"] if route_allowed else ["/models"], + ) + ], + }, + ) request: Final = _token_request({"Authorization": f"Bearer {_oauth_identity_jwt(signing_key)}"}) - assert await _extract_user_id_from_request(request) == "jwt-owner" + assert await _extract_user_id_from_request(request) == ("jwt-owner" if route_allowed else None) @pytest.mark.asyncio @pytest.mark.parametrize("identity", ["sso", "email"]) @pytest.mark.parametrize("inactive", [False, True]) +@pytest.mark.parametrize("admin", [False, True]) async def test_oauth_jwt_resolves_canonical_owner_without_cached_identity( jwt_oauth_identity: tuple["JWTHandler", "RSAPrivateKey"], monkeypatch: pytest.MonkeyPatch, identity: str, inactive: bool, + admin: bool, ) -> None: from litellm.models.user import LiteLLM_UserTable from litellm.proxy import proxy_server from litellm.proxy._experimental.mcp_server.bridge_token_flow import _extract_user_id_from_request handler, signing_key = jwt_oauth_identity - external_id: Final = f"external-{identity}-{inactive}" + external_id: Final = f"external-{identity}-{inactive}-{admin}" handler.litellm_jwtauth.user_email_jwt_field = "email" + handler.litellm_jwtauth.admin_allowed_routes = ["/token"] owner: Final = LiteLLM_UserTable( user_id="canonical-oauth-owner", user_email="owner@example.test", @@ -11676,12 +11727,17 @@ async def test_oauth_jwt_resolves_canonical_owner_without_cached_identity( ) database: Final = MagicMock() table: Final = database.db.litellm_usertable - table.find_unique = AsyncMock(side_effect=[None, owner if identity == "sso" else None]) + table.find_unique = AsyncMock(side_effect=[None, owner if identity == "sso" else None, owner]) table.find_first = AsyncMock(return_value=owner) table.update = AsyncMock(return_value=owner) monkeypatch.setattr(proxy_server, "prisma_client", database) - request: Final = _token_request({"Authorization": f"Bearer {_oauth_identity_jwt(signing_key, owner=external_id)}"}) + bearer: Final = _oauth_identity_jwt( + signing_key, owner=external_id, scope="litellm_proxy_admin" if admin else "" + ) + request: Final = _token_request({"Authorization": f"Bearer {bearer}"}) assert await _extract_user_id_from_request(request) == (None if inactive else "canonical-oauth-owner") - assert table.find_unique.await_count == 2 + assert table.find_unique.await_count == (2 if admin else 3) + if not admin: + assert table.find_unique.call_args.kwargs["where"] == {"user_id": "canonical-oauth-owner"} if identity == "email": table.find_first.assert_awaited_once() From c9dd4b44f8d696d3d703f31b77f6257990db3ecf Mon Sep 17 00:00:00 2001 From: kerry Date: Tue, 15 Sep 2026 23:51:56 +0000 Subject: [PATCH 125/225] revert(model_info): keep Gemini baseline majors single-digit Co-Authored-By: Devin AI <158243242+devin-ai-integration[bot]@users.noreply.github.com> --- litellm/model_prices_and_context_window_backup.json | 2 +- model_prices_and_context_window.json | 2 +- .../litellm_core_utils/test_fallback_generalizations.py | 2 -- 3 files changed, 2 insertions(+), 4 deletions(-) diff --git a/litellm/model_prices_and_context_window_backup.json b/litellm/model_prices_and_context_window_backup.json index 5537f13f5fd..36b4a2c308a 100644 --- a/litellm/model_prices_and_context_window_backup.json +++ b/litellm/model_prices_and_context_window_backup.json @@ -58152,7 +58152,7 @@ }, { "name": "gemini-chat-baseline", - "pattern": "gemini-(?!.*(?:-tts|-image|-live|-audio|-embedding|-computer-use|-robotics|-transcribe|-translate))(?:2[.-][5-9]|(?:[3-9]|[1-9]\\d)(?:[.-]\\d{1,2})?)-(?:pro|flash)(?:-lite)?(?![a-z])", + "pattern": "gemini-(?!.*(?:-tts|-image|-live|-audio|-embedding|-computer-use|-robotics|-transcribe|-translate))(?:2[.-][5-9]|[3-9](?:[.-]\\d{1,2})?)-(?:pro|flash)(?:-lite)?(?![a-z])", "description": "Any Gemini text-chat id at 2.5 or higher under any namespace, including bare ids, gemini/, vertex_ai/, openrouter/google/, deepinfra/google/, vercel_ai_gateway/google/, oci/google., and databricks-gemini--: gemini-[.minor]-(pro|flash)[-lite] with any trailing preview, date or variant tag. The capability flags were verified against each of those providers' own catalogs and docs. The lookahead excludes the tts, image, live, audio, embedding, computer-use, robotics, transcribe and translate lines, which are different modes with different capabilities. Provider-specific deviations, such as Perplexity's Agent API serving these as mode responses, are carried by their exact map entries, which always win over this rule. Carries no token limits or pricing, so those stay on the standard unmapped behavior rather than a guessed number. Source check 2026-09-15: all 45 first-party 2.5+ text-chat entries in this map carry every field below, and the OpenRouter (openrouter.ai/api/v1/models), Vercel AI Gateway (ai-gateway.vercel.sh/v1/models), DeepInfra (api.deepinfra.com/models/list), OCI and Databricks model docs list reasoning, tools and image input for the same models.", "model_info": { "mode": "chat", diff --git a/model_prices_and_context_window.json b/model_prices_and_context_window.json index 5537f13f5fd..36b4a2c308a 100644 --- a/model_prices_and_context_window.json +++ b/model_prices_and_context_window.json @@ -58152,7 +58152,7 @@ }, { "name": "gemini-chat-baseline", - "pattern": "gemini-(?!.*(?:-tts|-image|-live|-audio|-embedding|-computer-use|-robotics|-transcribe|-translate))(?:2[.-][5-9]|(?:[3-9]|[1-9]\\d)(?:[.-]\\d{1,2})?)-(?:pro|flash)(?:-lite)?(?![a-z])", + "pattern": "gemini-(?!.*(?:-tts|-image|-live|-audio|-embedding|-computer-use|-robotics|-transcribe|-translate))(?:2[.-][5-9]|[3-9](?:[.-]\\d{1,2})?)-(?:pro|flash)(?:-lite)?(?![a-z])", "description": "Any Gemini text-chat id at 2.5 or higher under any namespace, including bare ids, gemini/, vertex_ai/, openrouter/google/, deepinfra/google/, vercel_ai_gateway/google/, oci/google., and databricks-gemini--: gemini-[.minor]-(pro|flash)[-lite] with any trailing preview, date or variant tag. The capability flags were verified against each of those providers' own catalogs and docs. The lookahead excludes the tts, image, live, audio, embedding, computer-use, robotics, transcribe and translate lines, which are different modes with different capabilities. Provider-specific deviations, such as Perplexity's Agent API serving these as mode responses, are carried by their exact map entries, which always win over this rule. Carries no token limits or pricing, so those stay on the standard unmapped behavior rather than a guessed number. Source check 2026-09-15: all 45 first-party 2.5+ text-chat entries in this map carry every field below, and the OpenRouter (openrouter.ai/api/v1/models), Vercel AI Gateway (ai-gateway.vercel.sh/v1/models), DeepInfra (api.deepinfra.com/models/list), OCI and Databricks model docs list reasoning, tools and image input for the same models.", "model_info": { "mode": "chat", diff --git a/tests/test_litellm/litellm_core_utils/test_fallback_generalizations.py b/tests/test_litellm/litellm_core_utils/test_fallback_generalizations.py index 4e7d7e85a8c..71e6e20b1a4 100644 --- a/tests/test_litellm/litellm_core_utils/test_fallback_generalizations.py +++ b/tests/test_litellm/litellm_core_utils/test_fallback_generalizations.py @@ -458,12 +458,10 @@ def shipped_cost_map(monkeypatch): ("vertex_ai/gemini-4-pro", None), ("gemini-4-pro-preview-customtools", "gemini"), ("google/gemini-4-pro", "openrouter"), - ("google/gemini-12.5-flash-lite", "openrouter"), ("google/gemini-4-pro", "deepinfra"), ("google/gemini-4-pro", "vercel_ai_gateway"), ("google.gemini-4-pro", "oci"), ("databricks-gemini-4-1-pro", "databricks"), - ("gemini-10-pro", "gemini"), ], ) def test_shipped_gemini_chat_baseline_resolves_unmapped_ids(shipped_cost_map, model, provider): From 65f9d9bbbd4a842698793b6ecdc98155aecb1427 Mon Sep 17 00:00:00 2001 From: yassin Date: Tue, 15 Sep 2026 23:57:16 +0000 Subject: [PATCH 126/225] test(proxy): use the module-level HTTPException import in the /nvidia_nim route tests Co-Authored-By: Devin AI <158243242+devin-ai-integration[bot]@users.noreply.github.com> --- .../test_llm_pass_through_endpoints.py | 6 ------ 1 file changed, 6 deletions(-) diff --git a/tests/test_litellm/proxy/pass_through_endpoints/test_llm_pass_through_endpoints.py b/tests/test_litellm/proxy/pass_through_endpoints/test_llm_pass_through_endpoints.py index 62ecbbbcf51..73e6ceabdb6 100644 --- a/tests/test_litellm/proxy/pass_through_endpoints/test_llm_pass_through_endpoints.py +++ b/tests/test_litellm/proxy/pass_through_endpoints/test_llm_pass_through_endpoints.py @@ -5484,8 +5484,6 @@ class TestNvidiaNimProxyRoute: ["v1/infer", "unknown-group/v1/infer", "nim-page-elements-v2/v1/infer", "gpt-4o/v1/infer"], ) async def test_path_without_a_nim_model_group_is_rejected_before_any_upstream_call(self, endpoint): - from fastapi import HTTPException - captured: list[dict] = [] router = self._recording_router( captured, @@ -5500,8 +5498,6 @@ class TestNvidiaNimProxyRoute: @pytest.mark.asyncio async def test_a_group_mixing_nim_and_other_deployments_is_rejected_before_any_upstream_call(self): - from fastapi import HTTPException - captured: list[dict] = [] class MixedRouter: @@ -5526,8 +5522,6 @@ class TestNvidiaNimProxyRoute: @pytest.mark.asyncio async def test_no_router_is_rejected_before_any_upstream_call(self): - from fastapi import HTTPException - with pytest.raises(HTTPException) as exc_info: await self._relay(None, "nim-page-elements/v1/infer", NIM_INFER_BODY) From f25272bfa0d1bd0cdcbf68a59ac047359498a7ad Mon Sep 17 00:00:00 2001 From: yassin Date: Wed, 16 Sep 2026 00:05:17 +0000 Subject: [PATCH 127/225] fix(gateway): expose /nvidia_nim/ on the gateway data-plane allowlist Co-Authored-By: Devin AI <158243242+devin-ai-integration[bot]@users.noreply.github.com> --- gateway/routes/allowlist.py | 1 + 1 file changed, 1 insertion(+) diff --git a/gateway/routes/allowlist.py b/gateway/routes/allowlist.py index 3733072a948..099c6d5179f 100644 --- a/gateway/routes/allowlist.py +++ b/gateway/routes/allowlist.py @@ -96,6 +96,7 @@ GATEWAY_PATH_PREFIXES: tuple[str, ...] = ( "/langfuse/", "/vllm/", "/mistral/", + "/nvidia_nim/", "/groq/", "/voyage/", "/cursor/", From f60a603519cebfdb7b209c353ff64d5bb1880fd4 Mon Sep 17 00:00:00 2001 From: yassin Date: Wed, 16 Sep 2026 00:12:49 +0000 Subject: [PATCH 128/225] fix(proxy): log configured model access denials at the final response boundary Post-auth denials from can_key_call_resolved_model (per-request alias rewrite, MCP sampling, realtime) never reach the auth exception handler, so the internal allowlist reason was dropped when model_access_denied_message was set. Log it once from the ProxyException response handler and the realtime rejection path instead, and convert JWT ModelAccessDeniedHTTPException into the specialized ProxyException so the same boundary covers it Co-Authored-By: Devin AI <158243242+devin-ai-integration[bot]@users.noreply.github.com> --- litellm/proxy/auth/auth_exception_handler.py | 19 ++- litellm/proxy/proxy_server.py | 9 ++ .../proxy/auth/test_auth_exception_handler.py | 13 +-- tests/test_litellm/proxy/test_proxy_server.py | 109 +++++++++++++++++- 4 files changed, 126 insertions(+), 24 deletions(-) diff --git a/litellm/proxy/auth/auth_exception_handler.py b/litellm/proxy/auth/auth_exception_handler.py index 4a764cae6cc..bbe4b0f5c35 100644 --- a/litellm/proxy/auth/auth_exception_handler.py +++ b/litellm/proxy/auth/auth_exception_handler.py @@ -53,6 +53,14 @@ def _as_proxy_exception(e: Exception) -> ProxyException: param=None, code=getattr(e, "status_code", status.HTTP_429_TOO_MANY_REQUESTS), ) + if isinstance(e, ModelAccessDeniedHTTPException): + return ModelAccessDeniedProxyException( + message=str(e.detail), + internal_message=e.internal_message, + type=ProxyErrorTypes.auth_error, + param="None", + code=e.status_code, + ) if isinstance(e, HTTPException): return ProxyException( message=getattr(e, "detail", f"Authentication Error({e})"), @@ -77,14 +85,6 @@ def _as_proxy_exception(e: Exception) -> ProxyException: ) -def _model_access_denied_internal_message(e: Exception) -> str | None: - if not litellm.model_access_denied_message: - return None - if not isinstance(e, (ModelAccessDeniedProxyException, ModelAccessDeniedHTTPException)): - return None - return e.internal_message.replace("\r", "").replace("\n", "") - - def _get_user_agent(request: Request) -> str | None: if "headers" not in request.scope: return None @@ -176,9 +176,6 @@ class UserAPIKeyAuthExceptionHandler: # survives a raising callback pipeline. Classify and route malformed virtual-key # rejections to WARNING on stdout (suppressible via LITELLM_LOG=ERROR). log_extra: Final = {"requester_ip": requester_ip} - denied_internal_message: Final = _model_access_denied_internal_message(e) - if denied_internal_message is not None: - verbose_proxy_logger.warning(denied_internal_message, extra=log_extra) is_invalid_virtual_key: Final = is_invalid_virtual_key_error(e) is_quiet_log: Final = is_invalid_virtual_key and not litellm.log_client_error_tracebacks logger: Final = verbose_proxy_stdout_logger if is_quiet_log else verbose_proxy_logger diff --git a/litellm/proxy/proxy_server.py b/litellm/proxy/proxy_server.py index 2307b093736..c346115085d 100644 --- a/litellm/proxy/proxy_server.py +++ b/litellm/proxy/proxy_server.py @@ -106,6 +106,7 @@ from litellm.proxy._types import ( LiteLLM_TeamTableCachedObj, LiteLLM_UserTable, LitellmUserRoles, + ModelAccessDeniedProxyException, PassThroughGenericEndpoint, ProxyErrorTypes, ProxyException, @@ -1668,6 +1669,7 @@ class UserAPIKeyCacheTTLEnum(enum.Enum): @app.exception_handler(ProxyException) async def openai_exception_handler(request: Request, exc: ProxyException): # NOTE: DO NOT MODIFY THIS, its crucial to map to Openai exceptions + _log_model_access_denial(exc) headers: Final = exc.headers error_dict: Final = exc.to_dict() status_code: Final = int(exc.code) if exc.code else status.HTTP_500_INTERNAL_SERVER_ERROR @@ -1679,6 +1681,12 @@ async def openai_exception_handler(request: Request, exc: ProxyException): ) +def _log_model_access_denial(exc: ProxyException) -> None: + if not litellm.model_access_denied_message or not isinstance(exc, ModelAccessDeniedProxyException): + return + verbose_proxy_logger.warning(exc.internal_message.replace("\r", "").replace("\n", "")) + + def _close_dangling_otel_server_span(request: Request, status_code: int, exc: Exception | None = None) -> None: parent_otel_span: Final[_Span | None] = getattr(request.state, "parent_otel_span", None) if parent_otel_span is None: @@ -11967,6 +11975,7 @@ async def realtime_websocket_endpoint( llm_router=llm_router, ) except ProxyException as e: + _log_model_access_denial(e) await _reject_realtime_session(websocket, user_api_key_dict, code=1008, reason=e.message[:120]) return await websocket.accept(**accept_kwargs) diff --git a/tests/test_litellm/proxy/auth/test_auth_exception_handler.py b/tests/test_litellm/proxy/auth/test_auth_exception_handler.py index 602c074ee66..4328eac8432 100644 --- a/tests/test_litellm/proxy/auth/test_auth_exception_handler.py +++ b/tests/test_litellm/proxy/auth/test_auth_exception_handler.py @@ -1022,7 +1022,9 @@ def _denied_jwt_exception() -> ModelAccessDeniedHTTPException: pytest.param(_denied_jwt_exception, id="jwt_http_exception"), ], ) -async def test_handle_authentication_error_logs_sanitized_model_access_denial_once(monkeypatch, make_denial, caplog): +async def test_handle_authentication_error_keeps_internal_message_on_model_access_denial( + monkeypatch, make_denial, caplog +): monkeypatch.setattr(litellm, "model_access_denied_message", _DENIED_MESSAGE_TEMPLATE) handler = UserAPIKeyAuthExceptionHandler() denial = make_denial() @@ -1041,17 +1043,14 @@ async def test_handle_authentication_error_logs_sanitized_model_access_denial_on {"allow_requests_on_db_unavailable": False}, ), caplog.at_level("WARNING", logger="LiteLLM Proxy"), - pytest.raises(ProxyException) as exc_info, + pytest.raises(ModelAccessDeniedProxyException) as exc_info, ): await handler._handle_authentication_error(denial, MagicMock(), {}, "/v1/chat/completions", None, "sk-bad-key") assert exc_info.value.code == str(status.HTTP_403_FORBIDDEN) assert "internal-models" not in str(exc_info.value.message) - denial_records = [r for r in caplog.records if "internal-models" in r.getMessage()] - assert len(denial_records) == 1 - assert denial_records[0].levelname == "WARNING" - assert "\n" not in denial_records[0].getMessage() - assert "gpt-5.6WARNING forged log line" in denial_records[0].getMessage() + assert exc_info.value.internal_message == denial.internal_message + assert [r for r in caplog.records if r.levelname == "WARNING" and "internal-models" in r.getMessage()] == [] @pytest.mark.asyncio diff --git a/tests/test_litellm/proxy/test_proxy_server.py b/tests/test_litellm/proxy/test_proxy_server.py index f0e8aecdcf3..c06f572aca5 100644 --- a/tests/test_litellm/proxy/test_proxy_server.py +++ b/tests/test_litellm/proxy/test_proxy_server.py @@ -19,7 +19,7 @@ import fastapi.routing import httpx import pytest import yaml -from fastapi import FastAPI +from fastapi import FastAPI, Request from fastapi.encoders import jsonable_encoder from fastapi.staticfiles import StaticFiles from fastapi.testclient import TestClient @@ -31,10 +31,17 @@ from litellm.caching.caching import RedisCache from litellm.caching.redis_cluster_cache import RedisClusterCache from litellm.litellm_core_utils.get_model_cost_map import ModelCostMapReloaded from litellm.caching.dual_cache import DualCache -from litellm.proxy._types import LitellmUserRoles, TokenCountRequest, UserAPIKeyAuth +from litellm.proxy._types import ( + LitellmUserRoles, + ModelAccessDeniedProxyException, + ProxyErrorTypes, + ProxyException, + TokenCountRequest, + UserAPIKeyAuth, +) from litellm.proxy.auth.user_api_key_auth import user_api_key_auth from litellm.proxy.hooks.parallel_request_limiter_v3 import RequestRateLimiterStash -from litellm.proxy.proxy_server import app, initialize +from litellm.proxy.proxy_server import app, initialize, openai_exception_handler from litellm.utils import _invalidate_model_cost_lowercase_map example_embedding_result = { @@ -10003,6 +10010,7 @@ async def _lit6973_drive_realtime_session( backend_logged_failure: bool = False, phase_one_exit: str | None = None, websocket: MagicMock | None = None, + model_access_exception: ProxyException | None = None, ) -> MagicMock: """Drive realtime_websocket_endpoint through one of its reservation-settling exits. @@ -10035,10 +10043,10 @@ async def _lit6973_drive_realtime_session( if backend_logged_failure: logging_obj.model_call_details[REALTIME_SESSION_FAILURE_LOGGED_KEY] = True - from litellm.proxy._types import ProxyException - model_access_error: Final = ( - ProxyException(message="key cannot access model", type="auth_error", param="model", code=401) + model_access_exception + if model_access_exception is not None + else ProxyException(message="key cannot access model", type="auth_error", param="model", code=401) if phase_one_exit == "model_access" else None ) @@ -10916,6 +10924,95 @@ def test_validate_model_access_denied_message_empty_restores_detailed_default(em assert _validate_general_settings_ui_litellm_value("model_access_denied_message", empty_value) is None +def _model_access_denied_proxy_exception(): + return ModelAccessDeniedProxyException( + message="The model `gpt-5.6\r\nWARNING forged log line` is unavailable for this API key or does not exist.", + internal_message="key not allowed to access model. This key can only access models=['internal-models']. " + "Tried to access gpt-5.6\r\nWARNING forged log line", + type=ProxyErrorTypes.key_model_access_denied, + param="model", + code=403, + ) + + +def _http_request_scope(): + return Request({"type": "http", "method": "POST", "path": "/v1/chat/completions", "headers": []}) + + +@pytest.mark.asyncio +async def test_openai_exception_handler_logs_sanitized_model_access_denial(monkeypatch, caplog): + monkeypatch.setattr( + litellm, "model_access_denied_message", "The model `{model}` is unavailable for this API key or does not exist." + ) + + with caplog.at_level("WARNING", logger="LiteLLM Proxy"): + response = await openai_exception_handler(_http_request_scope(), _model_access_denied_proxy_exception()) + + assert response.status_code == 403 + body = json.loads(response.body) + assert "internal-models" not in body["error"]["message"] + denial_records = [r for r in caplog.records if "internal-models" in r.getMessage()] + assert len(denial_records) == 1 + assert denial_records[0].levelname == "WARNING" + assert "\n" not in denial_records[0].getMessage() + assert "\r" not in denial_records[0].getMessage() + assert "gpt-5.6WARNING forged log line" in denial_records[0].getMessage() + + +@pytest.mark.asyncio +@pytest.mark.parametrize("unset_value", [None, ""]) +async def test_openai_exception_handler_no_denial_log_when_message_not_configured(monkeypatch, unset_value, caplog): + monkeypatch.setattr(litellm, "model_access_denied_message", unset_value) + + with caplog.at_level("WARNING", logger="LiteLLM Proxy"): + response = await openai_exception_handler(_http_request_scope(), _model_access_denied_proxy_exception()) + + assert response.status_code == 403 + assert [r for r in caplog.records if r.levelname == "WARNING" and "internal-models" in r.getMessage()] == [] + + +@pytest.mark.asyncio +async def test_openai_exception_handler_no_denial_log_for_plain_proxy_exception(monkeypatch, caplog): + monkeypatch.setattr( + litellm, "model_access_denied_message", "The model `{model}` is unavailable for this API key or does not exist." + ) + denial = ProxyException( + message="Authentication Error, Invalid proxy server token passed", + type=ProxyErrorTypes.auth_error, + param="None", + code=401, + ) + + with caplog.at_level("WARNING", logger="LiteLLM Proxy"): + response = await openai_exception_handler(_http_request_scope(), denial) + + assert response.status_code == 401 + assert [r for r in caplog.records if r.levelname == "WARNING"] == [] + + +@pytest.mark.asyncio +async def test_realtime_model_access_denial_logs_sanitized_internal_message(monkeypatch, caplog): + monkeypatch.setattr( + litellm, "model_access_denied_message", "The model `{model}` is unavailable for this API key or does not exist." + ) + reservation = {"reserved_cost": 0.0, "input_cost": 0.0, "finalized": False, "entries": []} + + with caplog.at_level("WARNING", logger="LiteLLM Proxy"): + ws = await _lit6973_drive_realtime_session( + reservation, + backend_logged_success=False, + phase_one_exit="model_access", + model_access_exception=_model_access_denied_proxy_exception(), + ) + + ws.close.assert_awaited_once() + assert "internal-models" not in ws.close.await_args.kwargs["reason"] + denial_records = [r for r in caplog.records if "internal-models" in r.getMessage()] + assert len(denial_records) == 1 + assert "\n" not in denial_records[0].getMessage() + assert "gpt-5.6WARNING forged log line" in denial_records[0].getMessage() + + @pytest.mark.parametrize("empty_value", [None, ""]) def test_validate_expose_router_debug_in_errors_empty_restores_true_default(empty_value): from litellm.proxy.proxy_server import _validate_general_settings_ui_litellm_value From 53318796fd27d7e59bb86850f163836dda7a45e8 Mon Sep 17 00:00:00 2001 From: Joshua Valluru <326636767+joshua-berri@users.noreply.github.com> Date: Tue, 15 Sep 2026 17:13:10 -0700 Subject: [PATCH 129/225] fix(mcp): separate JWT identity lookup from request authorization --- .../mcp_server/bridge_token_flow.py | 22 +-- litellm/proxy/auth/handle_jwt.py | 71 ++++++++-- .../mcp_server/test_discoverable_endpoints.py | 126 +++++++++++++----- .../proxy/auth/test_handle_jwt.py | 71 +++++++++- 4 files changed, 237 insertions(+), 53 deletions(-) diff --git a/litellm/proxy/_experimental/mcp_server/bridge_token_flow.py b/litellm/proxy/_experimental/mcp_server/bridge_token_flow.py index 2f3e50b803f..6d11a4d2607 100644 --- a/litellm/proxy/_experimental/mcp_server/bridge_token_flow.py +++ b/litellm/proxy/_experimental/mcp_server/bridge_token_flow.py @@ -198,9 +198,7 @@ async def _reload_active_user_by_id(user_id: str) -> "_KeyResolutionFailure | No return loaded if isinstance(loaded, str) else None -async def load_active_user_by_id( - user_id: str, *, sso_user_id: str | None = None, user_email: str | None = None -) -> "LiteLLM_UserTable | _KeyResolutionFailure": +async def load_active_user_by_id(user_id: str) -> "LiteLLM_UserTable | _KeyResolutionFailure": """Load a live litellm user by id, returning the record when the user is active or a precise failure otherwise. The interactive DCR client authenticates via SSO, so its refresh envelope seals a user subject; renewing it must re-check the user is still live (present and not SCIM-deactivated) so a @@ -234,8 +232,6 @@ async def load_active_user_by_id( prisma_client=prisma_client, user_api_key_cache=user_api_key_cache, user_id_upsert=False, - sso_user_id=sso_user_id, - user_email=user_email, ) except (ProxyException, HTTPException): return "no_active_key" @@ -247,6 +243,10 @@ async def load_active_user_by_id( return "no_active_key" if user_object is None: return "no_active_key" + return _active_user_record(user_object) + + +def _active_user_record(user_object: "LiteLLM_UserTable") -> "LiteLLM_UserTable | Literal['no_active_key']": if isinstance(user_object.metadata, dict) and user_object.metadata.get("scim_active") is False: return "no_active_key" return user_object @@ -336,7 +336,7 @@ async def _extract_jwt_user_id(request: Request, token: str) -> str | None: user_api_key_cache, ) - if general_settings.get("enable_jwt_auth") is not True or premium_user is not True: + if general_settings.get("enable_jwt_auth") is not True or premium_user is not True or prisma_client is None: return None try: if jwt_handler.litellm_jwtauth.is_virtual_key_mapping_configured(): @@ -368,13 +368,13 @@ async def _extract_jwt_user_id(request: Request, token: str) -> str | None: proxy_logging_obj=proxy_logging_obj, request_headers=dict(request.headers), request_method=request.method, + identity_only=True, ) - owner_id: Final = identity["user_id"] - if not owner_id: + resolved_user: Final = identity["user_object"] + if resolved_user is None: return None - # Admin JWTs can return before auth_builder loads the canonical database user. - owner: Final = await load_active_user_by_id(owner_id, sso_user_id=owner_id, user_email=identity["user_email"]) - return None if isinstance(owner, str) else owner.user_id + owner: Final = _active_user_record(resolved_user) + return None if isinstance(owner, str) else identity["user_id"] except Exception as exc: # noqa: BLE001 # public OAuth exchange stays available; unvalidated identities never write credentials verbose_logger.debug("OAuth JWT identity could not be validated (%s)", type(exc).__name__) return None diff --git a/litellm/proxy/auth/handle_jwt.py b/litellm/proxy/auth/handle_jwt.py index 4304542fc83..678769b375b 100644 --- a/litellm/proxy/auth/handle_jwt.py +++ b/litellm/proxy/auth/handle_jwt.py @@ -1674,6 +1674,7 @@ class JWTAuthManager: proxy_logging_obj: ProxyLogging, route: str, org_alias: str | None = None, + user_id_upsert: bool | None = None, ) -> tuple[ LiteLLM_UserTable | None, LiteLLM_OrganizationTable | None, @@ -1737,7 +1738,11 @@ class JWTAuthManager: user_id=user_id, user_email=user_email, sso_user_id=user_id, - upsert=jwt_handler.is_upsert_user_id(valid_user_email=valid_user_email), + upsert=( + jwt_handler.is_upsert_user_id(valid_user_email=valid_user_email) + if user_id_upsert is None + else user_id_upsert + ), ), team_id=team_id, ) @@ -2209,8 +2214,14 @@ class JWTAuthManager: proxy_logging_obj: ProxyLogging, request_headers: dict | None = None, request_method: str | None = None, + identity_only: bool = False, ) -> JWTAuthBuilderResult: - """Main authentication and authorization builder""" + """Build JWT authentication and authorization context. + + Public OAuth endpoints use identity_only to resolve an existing credential owner + without authorizing the OAuth route or provisioning users/teams. The returned + identity does not grant permission to execute an MCP or model request. + """ # Check if OIDC UserInfo endpoint is enabled, but fall back to standard # JWT auth if the token itself is a well-formed JWT (3-part structure). if jwt_handler.litellm_jwtauth.oidc_userinfo_enabled and not jwt_handler.is_jwt(token=api_key): @@ -2231,18 +2242,23 @@ class JWTAuthManager: # Check RBAC rbac_role: Final = jwt_handler.get_rbac_role(token=jwt_valid_token) - await JWTAuthManager.check_rbac_role( - jwt_handler, - jwt_valid_token, - general_settings, - request_data, - route, - rbac_role, - ) + if not identity_only: + await JWTAuthManager.check_rbac_role( + jwt_handler, + jwt_valid_token, + general_settings, + request_data, + route, + rbac_role, + ) # Check Scope Based Access scopes: Final = jwt_handler.get_scopes(token=jwt_valid_token) - if jwt_handler.litellm_jwtauth.enforce_scope_based_access and jwt_handler.litellm_jwtauth.scope_mappings: + if ( + not identity_only + and jwt_handler.litellm_jwtauth.enforce_scope_based_access + and jwt_handler.litellm_jwtauth.scope_mappings + ): JWTAuthManager.check_scope_based_access( scope_mappings=jwt_handler.litellm_jwtauth.scope_mappings, scopes=scopes, @@ -2268,6 +2284,39 @@ class JWTAuthManager: elif rbac_role == LitellmUserRoles.INTERNAL_USER: user_id = object_id + if identity_only: + identity_user, _, _, _, identity_user_id = await JWTAuthManager.get_objects( + user_id=user_id, + user_email=user_email, + org_id=None, + end_user_id=None, + team_id=None, + valid_user_email=valid_user_email, + jwt_handler=jwt_handler, + prisma_client=prisma_client, + user_api_key_cache=user_api_key_cache, + parent_otel_span=parent_otel_span, + proxy_logging_obj=proxy_logging_obj, + route=route, + user_id_upsert=False, + ) + return JWTAuthBuilderResult( + is_proxy_admin=False, + # Admin admission uses the claim ID; other callers use the canonical DB ID. + user_id=user_id if jwt_handler.is_admin(scopes=scopes) else identity_user_id, + user_email=identity_user.user_email if identity_user is not None else user_email, + user_object=identity_user, + team_id=None, + team_object=None, + org_id=None, + org_object=None, + end_user_id=None, + end_user_object=None, + team_membership=None, + token=api_key, + jwt_claims=jwt_valid_token, + ) + # Check admin access admin_result: Final = await JWTAuthManager.check_admin_access( jwt_handler, scopes, route, user_id, org_id, api_key, jwt_valid_token, user_email=user_email diff --git a/tests/test_litellm/proxy/_experimental/mcp_server/test_discoverable_endpoints.py b/tests/test_litellm/proxy/_experimental/mcp_server/test_discoverable_endpoints.py index d1ab8809861..bf557667892 100644 --- a/tests/test_litellm/proxy/_experimental/mcp_server/test_discoverable_endpoints.py +++ b/tests/test_litellm/proxy/_experimental/mcp_server/test_discoverable_endpoints.py @@ -7127,12 +7127,12 @@ async def test_build_oauth_protected_resource_response_obo_end_to_end(): global_mcp_server_manager.registry.clear() -def _token_request(headers): +def _token_request(headers, path="/token"): """A real Starlette request with case-insensitive headers (matches production).""" from starlette.requests import Request raw = [(k.lower().encode(), v.encode()) for k, v in headers.items()] - return Request({"type": "http", "method": "POST", "path": "/token", "headers": raw, "query_string": b""}) + return Request({"type": "http", "method": "POST", "path": path, "headers": raw, "query_string": b""}) @pytest.fixture @@ -11443,10 +11443,12 @@ def _oauth_identity_jwt( @pytest.mark.asyncio @pytest.mark.parametrize("header", ["Authorization", "x-litellm-api-key"]) @pytest.mark.parametrize("policy_allowed", [False, True]) +@pytest.mark.parametrize("admin", [False, True]) async def test_oauth_exchange_stores_token_for_validated_jwt_user( jwt_oauth_identity: tuple["JWTHandler", "RSAPrivateKey"], header: str, policy_allowed: bool, + admin: bool, monkeypatch: pytest.MonkeyPatch, ) -> None: import httpx @@ -11456,9 +11458,9 @@ async def test_oauth_exchange_stores_token_for_validated_jwt_user( from litellm.types.mcp_server.mcp_server_manager import MCPServer handler, signing_key = jwt_oauth_identity - handler.litellm_jwtauth.enforce_team_based_model_access = not policy_allowed - bearer: Final = _oauth_identity_jwt(signing_key) - request: Final = _token_request({header: f"Bearer {bearer}"}) + handler.litellm_jwtauth.custom_validate = lambda claims: policy_allowed + bearer: Final = _oauth_identity_jwt(signing_key, scope="litellm_proxy_admin" if admin else "") + request: Final = _token_request({header: f"Bearer {bearer}"}, path="/jwt-oauth-server/token") server: Final = MCPServer( server_id="jwt-oauth-server", name="jwt-oauth-server", @@ -11534,8 +11536,6 @@ async def test_oauth_exchange_stores_token_for_validated_jwt_user( "scim_inactive", "custom_validate", "missing_database", - "denied_route", - "required_team", ], ) async def test_oauth_jwt_identity_rejects_untrusted_or_inactive_owner( @@ -11548,7 +11548,6 @@ async def test_oauth_jwt_identity_rejects_untrusted_or_inactive_owner( from litellm.models.user import LiteLLM_UserTable from litellm.proxy import proxy_server from litellm.proxy._experimental.mcp_server.bridge_token_flow import _extract_user_id_from_request - from litellm.proxy._types import LitellmUserRoles, RoleBasedPermissions handler, signing_key = jwt_oauth_identity key: Final = ( @@ -11573,18 +11572,6 @@ async def test_oauth_jwt_identity_rejects_untrusted_or_inactive_owner( ) if rejection == "custom_validate": handler.litellm_jwtauth.custom_validate = lambda claims: False - if rejection == "denied_route": - handler.litellm_jwtauth.enforce_rbac = True - monkeypatch.setattr( - proxy_server, - "general_settings", - { - "enable_jwt_auth": True, - "role_permissions": [RoleBasedPermissions(role=LitellmUserRoles.INTERNAL_USER, routes=["/models"])], - }, - ) - if rejection == "required_team": - handler.litellm_jwtauth.enforce_team_based_model_access = True assert await _extract_user_id_from_request(_token_request({"Authorization": f"Bearer {bearer}"})) is None @@ -11666,7 +11653,7 @@ async def test_oauth_jwt_respects_custom_validation_and_email_policy( @pytest.mark.asyncio @pytest.mark.parametrize("route_allowed", [False, True]) -async def test_oauth_jwt_uses_rbac_user_object_id( +async def test_oauth_jwt_identity_preserves_separate_mcp_route_authorization( jwt_oauth_identity: tuple["JWTHandler", "RSAPrivateKey"], monkeypatch: pytest.MonkeyPatch, route_allowed: bool, @@ -11674,6 +11661,7 @@ async def test_oauth_jwt_uses_rbac_user_object_id( from litellm.proxy import proxy_server from litellm.proxy._experimental.mcp_server.bridge_token_flow import _extract_user_id_from_request from litellm.proxy._types import LitellmUserRoles, RoleBasedPermissions, RoleMapping + from litellm.proxy.auth.handle_jwt import JWTAuthManager handler, signing_key = jwt_oauth_identity handler.litellm_jwtauth.user_id_jwt_field = "sub" @@ -11691,13 +11679,32 @@ async def test_oauth_jwt_uses_rbac_user_object_id( "role_permissions": [ RoleBasedPermissions( role=LitellmUserRoles.INTERNAL_USER, - routes=["/token"] if route_allowed else ["/models"], + routes=["mcp_routes"] if route_allowed else ["/models"], ) ], }, ) - request: Final = _token_request({"Authorization": f"Bearer {_oauth_identity_jwt(signing_key)}"}) - assert await _extract_user_id_from_request(request) == ("jwt-owner" if route_allowed else None) + bearer: Final = _oauth_identity_jwt(signing_key) + request: Final = _token_request({"Authorization": f"Bearer {bearer}"}, path="/example/token") + assert await _extract_user_id_from_request(request) == "jwt-owner" + admission: Final = JWTAuthManager.auth_builder( + api_key=bearer, + jwt_handler=handler, + request_data={}, + general_settings=proxy_server.general_settings, + route="/mcp/example", + prisma_client=proxy_server.prisma_client, + user_api_key_cache=handler.user_api_key_cache, + parent_otel_span=None, + proxy_logging_obj=proxy_server.proxy_logging_obj, + request_method="POST", + ) + if route_allowed: + assert (await admission)["user_id"] == "jwt-owner" + else: + with pytest.raises(HTTPException) as denial: + await admission + assert denial.value.status_code == 403 @pytest.mark.asyncio @@ -11714,11 +11721,12 @@ async def test_oauth_jwt_resolves_canonical_owner_without_cached_identity( from litellm.models.user import LiteLLM_UserTable from litellm.proxy import proxy_server from litellm.proxy._experimental.mcp_server.bridge_token_flow import _extract_user_id_from_request + from litellm.proxy.auth.handle_jwt import JWTAuthManager handler, signing_key = jwt_oauth_identity external_id: Final = f"external-{identity}-{inactive}-{admin}" handler.litellm_jwtauth.user_email_jwt_field = "email" - handler.litellm_jwtauth.admin_allowed_routes = ["/token"] + handler.litellm_jwtauth.admin_allowed_routes = ["mcp_routes"] owner: Final = LiteLLM_UserTable( user_id="canonical-oauth-owner", user_email="owner@example.test", @@ -11727,7 +11735,7 @@ async def test_oauth_jwt_resolves_canonical_owner_without_cached_identity( ) database: Final = MagicMock() table: Final = database.db.litellm_usertable - table.find_unique = AsyncMock(side_effect=[None, owner if identity == "sso" else None, owner]) + table.find_unique = AsyncMock(side_effect=[None, owner if identity == "sso" else None]) table.find_first = AsyncMock(return_value=owner) table.update = AsyncMock(return_value=owner) monkeypatch.setattr(proxy_server, "prisma_client", database) @@ -11735,9 +11743,67 @@ async def test_oauth_jwt_resolves_canonical_owner_without_cached_identity( signing_key, owner=external_id, scope="litellm_proxy_admin" if admin else "" ) request: Final = _token_request({"Authorization": f"Bearer {bearer}"}) - assert await _extract_user_id_from_request(request) == (None if inactive else "canonical-oauth-owner") - assert table.find_unique.await_count == (2 if admin else 3) - if not admin: - assert table.find_unique.call_args.kwargs["where"] == {"user_id": "canonical-oauth-owner"} + stored_owner: Final = await _extract_user_id_from_request(request) + assert stored_owner == (None if inactive else external_id if admin else "canonical-oauth-owner") + assert table.find_unique.await_count == 2 if identity == "email": table.find_first.assert_awaited_once() + if not inactive: + admission: Final = await JWTAuthManager.auth_builder( + api_key=bearer, + jwt_handler=handler, + request_data={}, + general_settings=proxy_server.general_settings, + route="/mcp/example", + prisma_client=database, + user_api_key_cache=handler.user_api_key_cache, + parent_otel_span=None, + proxy_logging_obj=proxy_server.proxy_logging_obj, + ) + assert stored_owner == admission["user_id"] + + +@pytest.mark.asyncio +async def test_oauth_jwt_identity_does_not_provision_or_synchronize_teams( + jwt_oauth_identity: tuple["JWTHandler", "RSAPrivateKey"], +) -> None: + from litellm.models.user import LiteLLM_UserTable + from litellm.proxy import proxy_server + from litellm.proxy._experimental.mcp_server.bridge_token_flow import _extract_user_id_from_request + + handler, signing_key = jwt_oauth_identity + handler.litellm_jwtauth.enforce_team_based_model_access = True + handler.litellm_jwtauth.team_id_default = "new-team" + handler.litellm_jwtauth.team_id_upsert = True + handler.litellm_jwtauth.sync_user_role_and_teams = True + owner: Final = LiteLLM_UserTable(user_id="jwt-owner", teams=["existing-team"]) + handler.user_api_key_cache.set_cache("jwt-owner", owner) + request: Final = _token_request( + {"Authorization": f"Bearer {_oauth_identity_jwt(signing_key)}"}, path="/example/token" + ) + assert await _extract_user_id_from_request(request) == "jwt-owner" + assert owner.teams == ["existing-team"] + proxy_server.prisma_client.db.litellm_teamtable.find_unique.assert_not_called() + proxy_server.prisma_client.db.litellm_teamtable.upsert.assert_not_called() + proxy_server.prisma_client.db.litellm_usertable.update.assert_not_called() + + +@pytest.mark.asyncio +@pytest.mark.parametrize("state", ["active", "inactive", "missing_database"]) +async def test_oauth_refresh_revalidates_the_same_active_user_rule( + jwt_oauth_identity: tuple["JWTHandler", "RSAPrivateKey"], + monkeypatch: pytest.MonkeyPatch, + state: str, +) -> None: + from litellm.models.user import LiteLLM_UserTable + from litellm.proxy import proxy_server + from litellm.proxy._experimental.mcp_server.bridge_token_flow import _reload_active_user_by_id + + handler, _ = jwt_oauth_identity + handler.user_api_key_cache.set_cache( + "jwt-owner", LiteLLM_UserTable(user_id="jwt-owner", metadata={"scim_active": state != "inactive"}) + ) + if state == "missing_database": + monkeypatch.setattr(proxy_server, "prisma_client", None) + expected: Final = None if state == "active" else "no_active_key" if state == "inactive" else "unresolvable" + assert await _reload_active_user_by_id("jwt-owner") == expected diff --git a/tests/test_litellm/proxy/auth/test_handle_jwt.py b/tests/test_litellm/proxy/auth/test_handle_jwt.py index 94226b5404d..9e15a442a18 100644 --- a/tests/test_litellm/proxy/auth/test_handle_jwt.py +++ b/tests/test_litellm/proxy/auth/test_handle_jwt.py @@ -2,7 +2,7 @@ import asyncio import re import time from collections.abc import Mapping, Sequence -from typing import Optional +from typing import Final, Optional from unittest.mock import AsyncMock, MagicMock, patch from fastapi import HTTPException @@ -6786,3 +6786,72 @@ async def test_sync_user_role_and_teams_singular_claim_only_recognized_under_fla } assert mock_patch.call_args.kwargs["teams_ids_to_add_user_to"] == [] assert user.teams == [] + + +@pytest.mark.asyncio +@pytest.mark.parametrize("identity_only", [False, True]) +@pytest.mark.parametrize("existing_user", [False, True]) +@pytest.mark.parametrize("model_allowed", [False, True]) +async def test_auth_builder_identity_lookup_does_not_provision_users( + monkeypatch: pytest.MonkeyPatch, identity_only: bool, existing_user: bool, model_allowed: bool +) -> None: + from litellm.proxy._types import ScopeMapping + from litellm.proxy.auth.auth_checks import UserNotFoundError + from litellm.proxy.common_utils.user_api_key_cache import UserApiKeyCache + + private_key, jwk = _get_rsa_key_and_jwk("identity-mode") + cache: Final = UserApiKeyCache() + cache.set_cache("litellm_jwt_auth_keys_https://identity.example/jwks", [jwk]) + user_id: Final = f"identity-mode-{identity_only}-{existing_user}-{model_allowed}" + user: Final = LiteLLM_UserTable(user_id=user_id, organization_memberships=[]) + if existing_user: + cache.set_cache(user_id, user) + database: Final = MagicMock() + users: Final = database.db.litellm_usertable + users.find_unique = AsyncMock(return_value=None) + users.find_first = AsyncMock(return_value=None) + users.create = AsyncMock(return_value=user) + handler: Final = JWTHandler() + handler.update_environment( + prisma_client=database, + user_api_key_cache=cache, + litellm_jwtauth=LiteLLM_JWTAuth( + user_id_jwt_field="sub", + user_id_upsert=True, + enforce_scope_based_access=True, + scope_mappings=[ScopeMapping(scope="allowed", models=["allowed-model"])], + ), + ) + monkeypatch.setenv("JWT_PUBLIC_KEY_URL", "https://identity.example/jwks") + monkeypatch.setenv("JWT_ISSUER", "https://identity.example") + monkeypatch.setenv("JWT_AUDIENCE", "gateway") + token: Final = _encode_rsa_jwt( + private_key, "https://identity.example", "gateway", "identity-mode", {"sub": user_id, "scope": "allowed"} + ) + pending: Final = JWTAuthManager.auth_builder( + api_key=token, + jwt_handler=handler, + request_data={"model": "allowed-model" if model_allowed else "forbidden-model"}, + general_settings={}, + route="/example/token" if identity_only else "/mcp/example", + prisma_client=database, + user_api_key_cache=cache, + parent_otel_span=None, + proxy_logging_obj=MagicMock(), + identity_only=identity_only, + ) + if not identity_only and not model_allowed: + with pytest.raises(HTTPException) as denial: + await pending + assert denial.value.status_code == 403 + users.create.assert_not_awaited() + return + if identity_only and not existing_user: + with pytest.raises(UserNotFoundError): + await pending + else: + result: Final = await pending + assert result["user_id"] == user_id + assert result["user_object"] is not None + assert result["user_object"].user_id == user_id + assert users.create.await_count == (0 if identity_only or existing_user else 1) From 241b177f05a9dfa56621eea65f85ee24294e4d1f Mon Sep 17 00:00:00 2001 From: yassin Date: Wed, 16 Sep 2026 00:22:48 +0000 Subject: [PATCH 130/225] fix(proxy): log the internal model access denial reason for MCP sampling denials MCP sampling catches the denial itself and returns ErrorData, so the central ProxyException handler never sees it. Log the sanitized internal reason there and share the CR/LF stripping through ModelAccessDeniedProxyException.sanitized_internal_message Co-Authored-By: Devin AI <158243242+devin-ai-integration[bot]@users.noreply.github.com> --- .../mcp_server/sampling_handler.py | 5 ++++- litellm/proxy/_types.py | 3 +++ litellm/proxy/proxy_server.py | 2 +- .../test_mcp_sampling_model_access.py | 18 ++++++++++++++++++ 4 files changed, 26 insertions(+), 2 deletions(-) diff --git a/litellm/proxy/_experimental/mcp_server/sampling_handler.py b/litellm/proxy/_experimental/mcp_server/sampling_handler.py index 125dc3d773d..6072e747bfe 100644 --- a/litellm/proxy/_experimental/mcp_server/sampling_handler.py +++ b/litellm/proxy/_experimental/mcp_server/sampling_handler.py @@ -771,6 +771,7 @@ async def _check_model_access(model: str, user_api_key_auth: "UserAPIKeyAuth | N try: import litellm + from litellm.proxy._types import ModelAccessDeniedProxyException from litellm.proxy.auth.auth_checks import ( _check_team_member_model_access, can_key_call_model, @@ -887,7 +888,9 @@ async def _check_model_access(model: str, user_api_key_auth: "UserAPIKeyAuth | N verbose_logger.warning( "MCP sampling: model access denied for model=%s: %s", model, - access_err, + access_err.sanitized_internal_message() + if isinstance(access_err, ModelAccessDeniedProxyException) + else access_err, ) return ErrorData( code=-1, diff --git a/litellm/proxy/_types.py b/litellm/proxy/_types.py index 89a4f583dcd..e9bfb7ab5ab 100644 --- a/litellm/proxy/_types.py +++ b/litellm/proxy/_types.py @@ -4042,6 +4042,9 @@ class ModelAccessDeniedProxyException(ProxyException): super().__init__(message=message, type=type, param=param, code=code) self.internal_message: Final = internal_message + def sanitized_internal_message(self) -> str: + return self.internal_message.replace("\r", "").replace("\n", "") + class CommonProxyErrors(str, enum.Enum): db_not_connected_error = ( diff --git a/litellm/proxy/proxy_server.py b/litellm/proxy/proxy_server.py index 9deb70a9277..12d62f6620d 100644 --- a/litellm/proxy/proxy_server.py +++ b/litellm/proxy/proxy_server.py @@ -1684,7 +1684,7 @@ async def openai_exception_handler(request: Request, exc: ProxyException): def _log_model_access_denial(exc: ProxyException) -> None: if not litellm.model_access_denied_message or not isinstance(exc, ModelAccessDeniedProxyException): return - verbose_proxy_logger.warning(exc.internal_message.replace("\r", "").replace("\n", "")) + verbose_proxy_logger.warning(exc.sanitized_internal_message()) def _close_dangling_otel_server_span(request: Request, status_code: int, exc: Exception | None = None) -> None: diff --git a/tests/test_litellm/proxy/_experimental/mcp_server/test_mcp_sampling_model_access.py b/tests/test_litellm/proxy/_experimental/mcp_server/test_mcp_sampling_model_access.py index f141cb2e316..d98db5518c3 100644 --- a/tests/test_litellm/proxy/_experimental/mcp_server/test_mcp_sampling_model_access.py +++ b/tests/test_litellm/proxy/_experimental/mcp_server/test_mcp_sampling_model_access.py @@ -137,6 +137,24 @@ class TestCheckModelAccess: assert result.code == -1 assert "claude-3-opus-20240229" in result.message + @pytest.mark.asyncio + async def test_should_log_internal_denial_reason_when_client_message_is_configured(self, monkeypatch, caplog): + import litellm + from litellm.proxy._types import UserAPIKeyAuth + + monkeypatch.setattr(litellm, "model_access_denied_message", "The model `{model}` is unavailable for this API key.") + auth = UserAPIKeyAuth(api_key="sk-test-key", models=["gpt-3.5-turbo"]) + + with caplog.at_level("WARNING", logger="LiteLLM"): + result = await _check_model_access("gpt-4o\r\nforged", user_api_key_auth=auth) + + assert result is not None + assert "gpt-4o\r\nforged" in result.message + assert "gpt-3.5-turbo" not in result.message + denial_records = [r for r in caplog.records if "gpt-3.5-turbo" in r.getMessage()] + assert len(denial_records) == 1 + assert "Tried to access gpt-4oforged" in denial_records[0].getMessage() + @pytest.mark.asyncio async def test_should_deny_empty_oauth_passthrough_placeholder(self): """Regression: process_mcp_request() returns an empty UserAPIKeyAuth() From e62ab285614d934542f8c44f7917030a44ecbbd7 Mon Sep 17 00:00:00 2001 From: mateo Date: Wed, 16 Sep 2026 00:26:34 +0000 Subject: [PATCH 131/225] chore(codeowners): add ryan and kerry as owners of the cost map Co-Authored-By: Devin AI <158243242+devin-ai-integration[bot]@users.noreply.github.com> --- .github/CODEOWNERS | 4 ++-- 1 file changed, 2 insertions(+), 2 deletions(-) diff --git a/.github/CODEOWNERS b/.github/CODEOWNERS index cfa0390e836..70a50d7f06e 100644 --- a/.github/CODEOWNERS +++ b/.github/CODEOWNERS @@ -4,7 +4,7 @@ /ui/nginx.conf /ui/litellm-dashboard/src/lib/http/schema.d.ts /ui/litellm-dashboard/tsconfig.tsbuildinfo -/model_prices_and_context_window.json @mateo-berri -/litellm/model_prices_and_context_window_backup.json @mateo-berri +/model_prices_and_context_window.json @mateo-berri @ryan-crabbe-berri @kerry-berri +/litellm/model_prices_and_context_window_backup.json @mateo-berri @ryan-crabbe-berri @kerry-berri /litellm-proxy-extras/litellm_proxy_extras/migrations/ @yuneng-berri @ryan-crabbe-berri /.github/CODEOWNERS @yuneng-berri From 4657d43fb999972bc9c77f65a4ee5a40bb68b44c Mon Sep 17 00:00:00 2001 From: kerry Date: Wed, 16 Sep 2026 00:27:09 +0000 Subject: [PATCH 132/225] fix(anthropic): tolerate message_delta chunks without usage in streams Co-Authored-By: Devin AI <158243242+devin-ai-integration[bot]@users.noreply.github.com> --- litellm/llms/anthropic/chat/handler.py | 4 +++- litellm/types/llms/anthropic.py | 2 +- .../anthropic/chat/test_anthropic_chat_handler.py | 15 +++++++++++++++ 3 files changed, 19 insertions(+), 2 deletions(-) diff --git a/litellm/llms/anthropic/chat/handler.py b/litellm/llms/anthropic/chat/handler.py index 5d3ae444b42..4dd0deeb62b 100644 --- a/litellm/llms/anthropic/chat/handler.py +++ b/litellm/llms/anthropic/chat/handler.py @@ -1167,7 +1167,9 @@ class ModelResponseIterator: # (matches OpenAI behavior and non-streaming Anthropic implementation) if self.converted_response_format_tool: finish_reason = "stop" - usage: Final = self._handle_usage(anthropic_usage_chunk=message_delta["usage"]) + usage: Final = ( + self._handle_usage(anthropic_usage_chunk=message_delta["usage"]) if "usage" in message_delta else None + ) container: Final = message_delta["delta"].get("container") return finish_reason, usage, container diff --git a/litellm/types/llms/anthropic.py b/litellm/types/llms/anthropic.py index d56ada07ed5..ecb8dbb2502 100644 --- a/litellm/types/llms/anthropic.py +++ b/litellm/types/llms/anthropic.py @@ -586,7 +586,7 @@ class MessageBlockDelta(TypedDict): type: Literal["message_delta"] delta: MessageDelta - usage: UsageDelta + usage: NotRequired[UsageDelta] context_management: NotRequired[ContextManagementResponse] diff --git a/tests/test_litellm/llms/anthropic/chat/test_anthropic_chat_handler.py b/tests/test_litellm/llms/anthropic/chat/test_anthropic_chat_handler.py index 83201aef143..ac05cdcf251 100644 --- a/tests/test_litellm/llms/anthropic/chat/test_anthropic_chat_handler.py +++ b/tests/test_litellm/llms/anthropic/chat/test_anthropic_chat_handler.py @@ -579,6 +579,21 @@ def test_text_only_streaming_has_index_zero(): ), f"Expected index=0, got {parsed.choices[0].index}" +def test_message_delta_without_usage_returns_chunk_with_no_usage(): + """A message_delta event may carry no usage field; it must not raise.""" + iterator = ModelResponseIterator(None, sync_stream=True) + + model_response = iterator.chunk_parser( + { + "type": "message_delta", + "delta": {"stop_reason": "end_turn", "stop_sequence": None}, + } + ) + + assert model_response.choices[0].finish_reason == "stop" + assert model_response.usage is None + + def test_streaming_thinking_deltas_count_reasoning_tokens_in_usage(): """Anthropic streaming usage should account for emitted thinking deltas.""" chunks = [ From 61e3b5ddae1fdbbf12f6fa087b8bf0ee3e318d12 Mon Sep 17 00:00:00 2001 From: Joshua Valluru <326636767+joshua-berri@users.noreply.github.com> Date: Tue, 15 Sep 2026 17:31:07 -0700 Subject: [PATCH 133/225] fix(mcp): persist OAuth credentials for rowless JWT admins --- .../mcp_server/bridge_token_flow.py | 5 ++- litellm/proxy/auth/handle_jwt.py | 36 +++++++++++-------- .../mcp_server/test_discoverable_endpoints.py | 18 +++++++++- 3 files changed, 40 insertions(+), 19 deletions(-) diff --git a/litellm/proxy/_experimental/mcp_server/bridge_token_flow.py b/litellm/proxy/_experimental/mcp_server/bridge_token_flow.py index 6d11a4d2607..b693cc046c8 100644 --- a/litellm/proxy/_experimental/mcp_server/bridge_token_flow.py +++ b/litellm/proxy/_experimental/mcp_server/bridge_token_flow.py @@ -371,10 +371,9 @@ async def _extract_jwt_user_id(request: Request, token: str) -> str | None: identity_only=True, ) resolved_user: Final = identity["user_object"] - if resolved_user is None: + if resolved_user is not None and isinstance(_active_user_record(resolved_user), str): return None - owner: Final = _active_user_record(resolved_user) - return None if isinstance(owner, str) else identity["user_id"] + return identity["user_id"] except Exception as exc: # noqa: BLE001 # public OAuth exchange stays available; unvalidated identities never write credentials verbose_logger.debug("OAuth JWT identity could not be validated (%s)", type(exc).__name__) return None diff --git a/litellm/proxy/auth/handle_jwt.py b/litellm/proxy/auth/handle_jwt.py index d5fcc578167..2d8cfb614ce 100644 --- a/litellm/proxy/auth/handle_jwt.py +++ b/litellm/proxy/auth/handle_jwt.py @@ -62,6 +62,7 @@ from litellm.proxy.common_utils.user_api_key_cache import ( from litellm.proxy.utils import PrismaClient, ProxyLogging from litellm.repositories.user_repository import UserRepository from litellm.types.agents import AgentResponse +from litellm.types.proxy.auth.auth_checks import UserNotFoundError from .auth_checks import ( _allowed_routes_check, @@ -2343,21 +2344,26 @@ class JWTAuthManager: ) if identity_only: - identity_user, _, _, _, identity_user_id = await JWTAuthManager.get_objects( - user_id=user_id, - user_email=user_email, - org_id=None, - end_user_id=None, - team_id=None, - valid_user_email=valid_user_email, - jwt_handler=jwt_handler, - prisma_client=prisma_client, - user_api_key_cache=user_api_key_cache, - parent_otel_span=parent_otel_span, - proxy_logging_obj=proxy_logging_obj, - route=route, - user_id_upsert=False, - ) + try: + identity_user, _, _, _, identity_user_id = await JWTAuthManager.get_objects( + user_id=user_id, + user_email=user_email, + org_id=None, + end_user_id=None, + team_id=None, + valid_user_email=valid_user_email, + jwt_handler=jwt_handler, + prisma_client=prisma_client, + user_api_key_cache=user_api_key_cache, + parent_otel_span=parent_otel_span, + proxy_logging_obj=proxy_logging_obj, + route=route, + user_id_upsert=False, + ) + except UserNotFoundError: + if not jwt_handler.is_admin(scopes=scopes): + raise + identity_user, identity_user_id = None, user_id return JWTAuthBuilderResult( is_proxy_admin=False, # Admin admission uses the claim ID; other callers use the canonical DB ID. diff --git a/tests/test_litellm/proxy/_experimental/mcp_server/test_discoverable_endpoints.py b/tests/test_litellm/proxy/_experimental/mcp_server/test_discoverable_endpoints.py index bf557667892..f6dc7932e2f 100644 --- a/tests/test_litellm/proxy/_experimental/mcp_server/test_discoverable_endpoints.py +++ b/tests/test_litellm/proxy/_experimental/mcp_server/test_discoverable_endpoints.py @@ -11444,11 +11444,13 @@ def _oauth_identity_jwt( @pytest.mark.parametrize("header", ["Authorization", "x-litellm-api-key"]) @pytest.mark.parametrize("policy_allowed", [False, True]) @pytest.mark.parametrize("admin", [False, True]) +@pytest.mark.parametrize("owner_state", ["active", "missing", "inactive", "database_error"]) async def test_oauth_exchange_stores_token_for_validated_jwt_user( jwt_oauth_identity: tuple["JWTHandler", "RSAPrivateKey"], header: str, policy_allowed: bool, admin: bool, + owner_state: str, monkeypatch: pytest.MonkeyPatch, ) -> None: import httpx @@ -11475,6 +11477,7 @@ async def test_oauth_exchange_stores_token_for_validated_jwt_user( from litellm.caching.llm_caching_handler import LLMClientCache from litellm.llms.custom_httpx.http_handler import AsyncHTTPHandler from litellm.proxy import proxy_server + from litellm.models.user import LiteLLM_UserTable from litellm.proxy.common_utils.encrypt_decrypt_utils import decrypt_value_helper from litellm.types.llms.custom_http import httpxSpecialProvider @@ -11485,6 +11488,18 @@ async def test_oauth_exchange_stores_token_for_validated_jwt_user( return httpx.Response(200, json={"access_token": "upstream-token", "token_type": "Bearer"}) database: Final = MagicMock() + users: Final = database.db.litellm_usertable + users.find_unique = AsyncMock(return_value=None) + users.find_first = AsyncMock(return_value=None) + users.create = AsyncMock() + if owner_state in ("missing", "database_error"): + handler.user_api_key_cache.delete_cache("jwt-owner") + if owner_state == "database_error": + users.find_unique.side_effect = RuntimeError("database unavailable") + if owner_state == "inactive": + handler.user_api_key_cache.set_cache( + "jwt-owner", LiteLLM_UserTable(user_id="jwt-owner", metadata={"scim_active": False}) + ) table: Final = database.db.litellm_mcpusercredentials table.find_unique = AsyncMock(return_value=None) table.upsert = AsyncMock() @@ -11509,7 +11524,8 @@ async def test_oauth_exchange_stores_token_for_validated_jwt_user( ) assert response.status_code == 200 assert json.loads(response.body)["access_token"] == "upstream-token" - if not policy_allowed: + users.create.assert_not_awaited() + if not policy_allowed or owner_state in ("inactive", "database_error") or (owner_state == "missing" and not admin): table.upsert.assert_not_awaited() return table.upsert.assert_awaited_once() From f5c1c82f8175e42491c364dca3208eb5151b815b Mon Sep 17 00:00:00 2001 From: kerry Date: Wed, 16 Sep 2026 00:32:24 +0000 Subject: [PATCH 134/225] fix(responses): estimate usage from text when streamed completed event omits usage Co-Authored-By: Devin AI <158243242+devin-ai-integration[bot]@users.noreply.github.com> --- litellm/responses/streaming_iterator.py | 15 +++ litellm/responses/utils.py | 16 ++++ .../responses/test_streaming_iterator.py | 96 ++++++++++++++++++- 3 files changed, 124 insertions(+), 3 deletions(-) diff --git a/litellm/responses/streaming_iterator.py b/litellm/responses/streaming_iterator.py index 38874768ca8..52b50167190 100644 --- a/litellm/responses/streaming_iterator.py +++ b/litellm/responses/streaming_iterator.py @@ -420,6 +420,21 @@ class BaseResponsesAPIStreamingIterator: openai_types.ResponsesAPIStreamEvents.RESPONSE_FAILED, ): self.completed_response = openai_responses_api_chunk + _response_obj: Final[ResponsesAPIResponse | None] = getattr( + openai_responses_api_chunk, "response", None + ) + if ( + _chunk_type + in ( + openai_types.ResponsesAPIStreamEvents.RESPONSE_COMPLETED, + openai_types.ResponsesAPIStreamEvents.RESPONSE_INCOMPLETE, + ) + and _response_obj is not None + and _response_obj.usage is None + ): + _response_obj.usage = ResponseAPILoggingUtils.estimate_usage_from_text( + self.model or "", self.request_data.get("input"), self._generated_content + ) _stamp_responses_usage_cost(getattr(openai_responses_api_chunk, "response", None), self.logging_obj) if _chunk_type == openai_types.ResponsesAPIStreamEvents.RESPONSE_FAILED: diff --git a/litellm/responses/utils.py b/litellm/responses/utils.py index 41a3ded7022..e523b3771af 100644 --- a/litellm/responses/utils.py +++ b/litellm/responses/utils.py @@ -1,4 +1,5 @@ import base64 +import json import re from collections.abc import Iterable, Mapping, Sequence from typing import Any, Final, Optional, TypeVar, Union, cast, get_type_hints, overload @@ -1239,3 +1240,18 @@ class ResponseAPILoggingUtils: setattr(chat_usage, "cost", response_api_usage.cost) return chat_usage + + @staticmethod + def estimate_usage_from_text(model: str, request_input: object, generated_text: str) -> ResponseAPIUsage: + input_text: Final = request_input if isinstance(request_input, str) else json.dumps(request_input, default=str) + input_tokens: Final = litellm.token_counter( # pyright: ignore[reportUnknownMemberType] # token_counter's public signature is untyped + model=model, text=input_text + ) + output_tokens: Final = litellm.token_counter( # pyright: ignore[reportUnknownMemberType] # token_counter's public signature is untyped + model=model, text=generated_text, count_response_tokens=True + ) + return ResponseAPIUsage( + input_tokens=input_tokens, + output_tokens=output_tokens, + total_tokens=input_tokens + output_tokens, + ) diff --git a/tests/test_litellm/responses/test_streaming_iterator.py b/tests/test_litellm/responses/test_streaming_iterator.py index 5e0e794d93e..c593946ac4a 100644 --- a/tests/test_litellm/responses/test_streaming_iterator.py +++ b/tests/test_litellm/responses/test_streaming_iterator.py @@ -31,8 +31,15 @@ def _sse_event(payload: dict) -> bytes: def _mock_config() -> Mock: mock_config = Mock(spec=BaseResponsesAPIConfig) - mock_responses_api_response = Mock(spec=ResponsesAPIResponse) - mock_responses_api_response.id = "resp_ttft" + mock_responses_api_response = ResponsesAPIResponse( + id="resp_ttft", + created_at=0, + status="completed", + model="gpt-4o-mini", + object="response", + output=[], + usage=ResponseAPIUsage(input_tokens=1, output_tokens=1, total_tokens=2), + ) def _transform(model, parsed_chunk, logging_obj): evt_type = parsed_chunk.get("type") @@ -54,6 +61,8 @@ def _make_iterator( sse_events: list[bytes], logging_obj: LiteLLMLoggingObj, trailing_error: Optional[Exception] = None, + config: Mock | None = None, + request_data: dict | None = None, ) -> ResponsesAPIStreamingIterator: async def aiter_bytes(): for evt in sse_events: @@ -68,10 +77,11 @@ def _make_iterator( return ResponsesAPIStreamingIterator( response=mock_response, model="gpt-4o-mini", - responses_api_provider_config=_mock_config(), + responses_api_provider_config=config or _mock_config(), logging_obj=logging_obj, litellm_metadata={}, custom_llm_provider="openai", + request_data=request_data, ) @@ -329,6 +339,86 @@ def test_run_post_success_hooks_does_not_report_generation_time_as_overhead(): assert "litellm_overhead_time_ms" not in iterator.completed_response._hidden_params +def _mock_config_with_completed_response(response: ResponsesAPIResponse) -> Mock: + mock_config = Mock(spec=BaseResponsesAPIConfig) + + def _transform(model, parsed_chunk, logging_obj): + evt_type = parsed_chunk.get("type") + if evt_type == "response.completed": + completed = Mock(spec=ResponseCompletedEvent) + completed.type = ResponsesAPIStreamEvents.RESPONSE_COMPLETED + completed.response = response + return completed + stub = Mock() + stub.type = evt_type + if evt_type == "response.output_text.delta": + stub.delta = parsed_chunk.get("delta") + return stub + + mock_config.transform_streaming_response.side_effect = _transform + return mock_config + + +def _responses_api_response_without_usage() -> ResponsesAPIResponse: + return ResponsesAPIResponse( + id="resp_no_usage", + created_at=int(datetime(2025, 1, 1).timestamp()), + status="completed", + model="gpt-4o-mini", + object="response", + output=[], + usage=None, + ) + + +@pytest.mark.asyncio +async def test_completed_event_without_usage_gets_text_estimate(): + """A response.completed event carrying usage: null still bills: the + iterator estimates usage from the request input and generated text.""" + response = _responses_api_response_without_usage() + iterator = _make_iterator( + sse_events=[ + _sse_event({"type": "response.output_text.delta", "delta": "hello world"}), + _sse_event({"type": "response.completed", "response": {}}), + ], + logging_obj=_logging_obj_stub(), + config=_mock_config_with_completed_response(response), + request_data={"input": "count these input tokens please"}, + ) + + async for _ in iterator: + pass + + usage = iterator.completed_response.response.usage + assert usage is not None + assert usage.input_tokens > 0 + assert usage.output_tokens > 0 + assert usage.total_tokens == usage.input_tokens + usage.output_tokens + + +@pytest.mark.asyncio +async def test_completed_event_with_usage_is_left_untouched(): + """Provider-reported usage on response.completed wins over the estimate.""" + response = _responses_api_response_with_usage() + iterator = _make_iterator( + sse_events=[ + _sse_event({"type": "response.output_text.delta", "delta": "hello world"}), + _sse_event({"type": "response.completed", "response": {}}), + ], + logging_obj=_logging_obj_stub(), + config=_mock_config_with_completed_response(response), + request_data={"input": "count these input tokens please"}, + ) + + async for _ in iterator: + pass + + usage = iterator.completed_response.response.usage + assert usage.input_tokens == 20 + assert usage.output_tokens == 60 + assert usage.total_tokens == 80 + + def _responses_api_response_with_usage() -> ResponsesAPIResponse: return ResponsesAPIResponse( id="resp_lit6427", From d5a36eb2cabfbcd0530b4a43576c9ac7d6c530a7 Mon Sep 17 00:00:00 2001 From: kerry Date: Wed, 16 Sep 2026 00:36:07 +0000 Subject: [PATCH 135/225] fix(gemini): propagate provider modelVersion onto model responses Co-Authored-By: Devin AI <158243242+devin-ai-integration[bot]@users.noreply.github.com> --- .../vertex_and_google_ai_studio_gemini.py | 10 ++- ...test_vertex_and_google_ai_studio_gemini.py | 62 +++++++++++++++++++ 2 files changed, 70 insertions(+), 2 deletions(-) diff --git a/litellm/llms/vertex_ai/gemini/vertex_and_google_ai_studio_gemini.py b/litellm/llms/vertex_ai/gemini/vertex_and_google_ai_studio_gemini.py index d113b2b4f6b..eb03b17435c 100644 --- a/litellm/llms/vertex_ai/gemini/vertex_and_google_ai_studio_gemini.py +++ b/litellm/llms/vertex_ai/gemini/vertex_and_google_ai_studio_gemini.py @@ -2434,7 +2434,8 @@ class VertexGeminiConfig(VertexAIBaseConfig, BaseConfig): completion_response = GenerateContentResponseBody(**completion_response) ## GET MODEL ## - model_response.model = model + model_version: Final = completion_response.get("modelVersion") + model_response.model = model_version if isinstance(model_version, str) else model ## CHECK IF RESPONSE FLAGGED if "promptFeedback" in completion_response and "blockReason" in completion_response["promptFeedback"]: @@ -3264,7 +3265,12 @@ class ModelResponseIterator: processed_chunk: Final = GenerateContentResponseBody(**chunk) response_id: Final = processed_chunk.get("responseId") - model_response = ModelResponseStream(choices=[], id=response_id) + chunk_model_version: Final = processed_chunk.get("modelVersion") + model_response = ModelResponseStream( + choices=[], + id=response_id, + model=chunk_model_version if isinstance(chunk_model_version, str) else None, + ) # Check if prompt is blocked due to content filtering blocked_response: Final = VertexGeminiConfig._check_prompt_level_content_filter( diff --git a/tests/test_litellm/llms/vertex_ai/gemini/test_vertex_and_google_ai_studio_gemini.py b/tests/test_litellm/llms/vertex_ai/gemini/test_vertex_and_google_ai_studio_gemini.py index 101f6e6fa5d..29a47cd54f2 100644 --- a/tests/test_litellm/llms/vertex_ai/gemini/test_vertex_and_google_ai_studio_gemini.py +++ b/tests/test_litellm/llms/vertex_ai/gemini/test_vertex_and_google_ai_studio_gemini.py @@ -5836,3 +5836,65 @@ def test_supported_reasoning_efforts_still_map(model): drop_params=False, ) assert "thinkingConfig" in result + + +def _generate_content_body() -> dict: + return { + "candidates": [ + { + "content": {"role": "model", "parts": [{"text": "hi"}]}, + "finishReason": "STOP", + "index": 0, + } + ], + "usageMetadata": { + "promptTokenCount": 5, + "candidatesTokenCount": 7, + "totalTokenCount": 12, + }, + } + + +def test_generate_content_transform_uses_reported_model_version(): + """The served modelVersion must win over the requested name so downstream + pricing sees what actually ran.""" + import httpx + + body = {**_generate_content_body(), "modelVersion": "gemini-x-served"} + response: Final = VertexGeminiConfig()._transform_google_generate_content_to_openai_model_response( + completion_response=body, + model_response=ModelResponse(), + model="gemini-x", + logging_obj=MagicMock(), + raw_response=httpx.Response(200, headers={}), + ) + + assert response.model == "gemini-x-served" + + +def test_generate_content_transform_falls_back_to_requested_model(): + import httpx + + response: Final = VertexGeminiConfig()._transform_google_generate_content_to_openai_model_response( + completion_response=_generate_content_body(), + model_response=ModelResponse(), + model="gemini-x", + logging_obj=MagicMock(), + raw_response=httpx.Response(200, headers={}), + ) + + assert response.model == "gemini-x" + + +def test_streaming_chunk_carries_model_version(): + from litellm.llms.vertex_ai.gemini.vertex_and_google_ai_studio_gemini import ( + ModelResponseIterator, + ) + + chunk = {**_generate_content_body(), "modelVersion": "gemini-x-served"} + iterator: Final = ModelResponseIterator( + streaming_response=[], sync_stream=True, logging_obj=MagicMock() + ) + streaming_chunk: Final = iterator.chunk_parser(chunk) + + assert streaming_chunk.model == "gemini-x-served" From 5eab1feb2011c800295fee5025a87402019baf0a Mon Sep 17 00:00:00 2001 From: kerry Date: Wed, 16 Sep 2026 00:42:13 +0000 Subject: [PATCH 136/225] fix(fireworks-ai): bill cache-write, reasoning, and audio tokens via the shared cost calculator Co-Authored-By: Devin AI <158243242+devin-ai-integration[bot]@users.noreply.github.com> --- litellm/llms/fireworks_ai/cost_calculator.py | 64 +++++++------- .../test_fireworks_ai_cost_calculator.py | 85 ++++++++++++++++--- 2 files changed, 105 insertions(+), 44 deletions(-) diff --git a/litellm/llms/fireworks_ai/cost_calculator.py b/litellm/llms/fireworks_ai/cost_calculator.py index 3c43075d940..7bd01115a94 100644 --- a/litellm/llms/fireworks_ai/cost_calculator.py +++ b/litellm/llms/fireworks_ai/cost_calculator.py @@ -2,9 +2,9 @@ For calculating cost of fireworks ai serverless inference models. """ -import math +from collections.abc import Mapping from datetime import datetime -from typing import Final +from typing import Final, cast from litellm.constants import ( FIREWORKS_AI_4_B, @@ -12,12 +12,10 @@ from litellm.constants import ( FIREWORKS_AI_56_B_MOE, FIREWORKS_AI_176_B_MOE, ) -from litellm.litellm_core_utils.llm_cost_calc.utils import TokenRates, apply_off_peak_pricing +from litellm.litellm_core_utils.llm_cost_calc.utils import generic_cost_per_token from litellm.types.utils import ModelInfo, Usage from litellm.utils import get_model_info -NO_CACHE_READ_RATE: Final = float("nan") - # Extract the number of billion parameters from the model name # only used for together_computer LLMs @@ -67,6 +65,30 @@ def _resolve_model_info(model: str) -> ModelInfo: return get_model_info(model=base_model, custom_llm_provider="fireworks_ai") +def _with_cache_read_fallback(model_info: ModelInfo) -> ModelInfo: + """Most fireworks_ai price-map entries publish no cache-read rate, and the provider bills + cached reads at the input rate. generic_cost_per_token prices a missing rate at $0, so the + fallback is written into a copy of the entry (the shared model-cost dict must not be + mutated), including inside off_peak_pricing so cached reads track the off-peak input rate + the way the previous calculator did.""" + if model_info.get("cache_read_input_token_cost") is not None: + return model_info + input_rate: Final = model_info.get("input_cost_per_token") + if input_rate is None: + return model_info + effective: Final[dict[str, object]] = dict(model_info) + effective["cache_read_input_token_cost"] = input_rate + off_peak: Final = effective.get("off_peak_pricing") + if isinstance(off_peak, Mapping): + off_peak_map: Final[Mapping[str, object]] = cast(Mapping[str, object], off_peak) + if "cache_read_input_token_cost" not in off_peak_map: + effective["off_peak_pricing"] = { + **off_peak_map, + "cache_read_input_token_cost": off_peak_map.get("input_cost_per_token", input_rate), + } + return cast(ModelInfo, effective) + + def cost_per_token(model: str, usage: Usage, current_time: datetime | None = None) -> tuple[float, float]: """ Calculates the cost per token for a given model, prompt tokens, and completion tokens, @@ -80,29 +102,11 @@ def cost_per_token(model: str, usage: Usage, current_time: datetime | None = Non Returns: Tuple[float, float] - prompt_cost_in_usd, completion_cost_in_usd """ - model_info: Final = _resolve_model_info(model) - standard_cache_read_rate: Final = model_info.get("cache_read_input_token_cost") - rates: Final = apply_off_peak_pricing( - model_info, - current_time, - TokenRates( - input_rate=model_info["input_cost_per_token"] or 0.0, - output_rate=model_info["output_cost_per_token"] or 0.0, - cache_read_rate=standard_cache_read_rate if standard_cache_read_rate is not None else NO_CACHE_READ_RATE, - cache_creation_rate=0.0, - reasoning_rate=None, - ), + model_info: Final = _with_cache_read_fallback(_resolve_model_info(model)) + return generic_cost_per_token( + model=model, + usage=usage, + custom_llm_provider="fireworks_ai", + model_info=model_info, + current_time=current_time, ) - cache_read_rate: Final[float] = rates.input_rate if math.isnan(rates.cache_read_rate) else rates.cache_read_rate - - prompt_tokens_details: Final = usage.prompt_tokens_details - cached_tokens: Final[int] = ( - prompt_tokens_details.cached_tokens - if prompt_tokens_details is not None and prompt_tokens_details.cached_tokens is not None - else 0 - ) - non_cached_prompt_tokens: Final[int] = max(usage.prompt_tokens - cached_tokens, 0) - prompt_cost: Final[float] = non_cached_prompt_tokens * rates.input_rate + cached_tokens * cache_read_rate - completion_cost: Final[float] = usage.completion_tokens * rates.output_rate - - return prompt_cost, completion_cost diff --git a/tests/test_litellm/llms/fireworks_ai/test_fireworks_ai_cost_calculator.py b/tests/test_litellm/llms/fireworks_ai/test_fireworks_ai_cost_calculator.py index c2e42da1b4c..40a07a10cfc 100644 --- a/tests/test_litellm/llms/fireworks_ai/test_fireworks_ai_cost_calculator.py +++ b/tests/test_litellm/llms/fireworks_ai/test_fireworks_ai_cost_calculator.py @@ -1,13 +1,16 @@ - import math from datetime import datetime, timezone import pytest - import litellm from litellm.llms.fireworks_ai.cost_calculator import cost_per_token -from litellm.types.utils import OffPeakPricing, PromptTokensDetailsWrapper, Usage +from litellm.types.utils import ( + CompletionTokensDetailsWrapper, + OffPeakPricing, + PromptTokensDetailsWrapper, + Usage, +) MODEL = "accounts/fireworks/models/glm-5p2" INPUT_COST = 1.4e-06 @@ -47,12 +50,8 @@ def test_warm_call_cheaper_than_cold_call(): prompt_tokens = 7036 completion_tokens = 8 - cold_prompt_cost, _ = cost_per_token( - model=MODEL, usage=_usage(prompt_tokens, 16, completion_tokens) - ) - warm_prompt_cost, _ = cost_per_token( - model=MODEL, usage=_usage(prompt_tokens, 7020, completion_tokens) - ) + cold_prompt_cost, _ = cost_per_token(model=MODEL, usage=_usage(prompt_tokens, 16, completion_tokens)) + warm_prompt_cost, _ = cost_per_token(model=MODEL, usage=_usage(prompt_tokens, 7020, completion_tokens)) assert warm_prompt_cost < cold_prompt_cost @@ -61,9 +60,7 @@ def test_no_cached_tokens_matches_full_input_rate(): prompt_tokens = 100 completion_tokens = 10 - prompt_cost, completion_cost = cost_per_token( - model=MODEL, usage=_usage(prompt_tokens, 0, completion_tokens) - ) + prompt_cost, completion_cost = cost_per_token(model=MODEL, usage=_usage(prompt_tokens, 0, completion_tokens)) assert prompt_cost == pytest.approx(prompt_tokens * INPUT_COST) assert completion_cost == pytest.approx(completion_tokens * OUTPUT_COST) @@ -78,7 +75,9 @@ STANDARD_OUTPUT_COST = 6e-07 STANDARD_CACHE_READ_COST = 1.5e-08 -def _register_off_peak_model(off_peak_pricing: OffPeakPricing, cache_read_cost: float | None = STANDARD_CACHE_READ_COST) -> None: +def _register_off_peak_model( + off_peak_pricing: OffPeakPricing, cache_read_cost: float | None = STANDARD_CACHE_READ_COST +) -> None: litellm.model_cost[f"fireworks_ai/{OFF_PEAK_MODEL}"] = { "litellm_provider": "fireworks_ai", "mode": "chat", @@ -151,10 +150,68 @@ def test_off_peak_window_bills_cached_tokens_at_the_off_peak_input_rate_without_ def test_off_peak_defaults_to_the_current_time(): """The proxy's cost dispatch passes no clock, so an all-day window has to apply on the default current time.""" - _register_off_peak_model({"hours_utc": "00:00-00:00", "input_cost_per_token": 1e-08, "output_cost_per_token": 2e-08}) + _register_off_peak_model( + {"hours_utc": "00:00-00:00", "input_cost_per_token": 1e-08, "output_cost_per_token": 2e-08} + ) usage = _usage(prompt_tokens=1000, cached_tokens=0, completion_tokens=200) prompt_cost, completion_cost = cost_per_token(model=OFF_PEAK_MODEL, usage=usage) assert math.isclose(prompt_cost, 1000 * 1e-08, rel_tol=1e-10) assert math.isclose(completion_cost, 200 * 2e-08, rel_tol=1e-10) + + +COMPONENT_MODEL = "accounts/fireworks/models/cost-components-test" +COMPONENT_INPUT_COST = 1e-06 +COMPONENT_OUTPUT_COST = 2e-06 +COMPONENT_CACHE_READ_COST = 1e-07 +COMPONENT_CACHE_CREATION_COST = 3e-06 +COMPONENT_REASONING_COST = 4e-06 +COMPONENT_AUDIO_IN_COST = 5e-06 +COMPONENT_AUDIO_OUT_COST = 6e-06 + + +def test_cache_write_reasoning_and_audio_tokens_are_billed_at_their_component_rates(): + """Regression (LIT-7837): the hand-rolled fireworks_ai calculator billed every + cache-creation, reasoning and audio token at $0. The shared calculator treats the + prompt detail counts as subsets of prompt_tokens and the completion detail counts as + subsets of completion_tokens, billing each remainder at the text rate.""" + litellm.model_cost[f"fireworks_ai/{COMPONENT_MODEL}"] = { + "litellm_provider": "fireworks_ai", + "mode": "chat", + "input_cost_per_token": COMPONENT_INPUT_COST, + "output_cost_per_token": COMPONENT_OUTPUT_COST, + "cache_read_input_token_cost": COMPONENT_CACHE_READ_COST, + "cache_creation_input_token_cost": COMPONENT_CACHE_CREATION_COST, + "output_cost_per_reasoning_token": COMPONENT_REASONING_COST, + "input_cost_per_audio_token": COMPONENT_AUDIO_IN_COST, + "output_cost_per_audio_token": COMPONENT_AUDIO_OUT_COST, + } + usage = Usage( + prompt_tokens=1000, + completion_tokens=500, + total_tokens=1500, + prompt_tokens_details=PromptTokensDetailsWrapper( + cached_tokens=300, + cache_creation_tokens=200, + audio_tokens=100, + ), + completion_tokens_details=CompletionTokensDetailsWrapper( + reasoning_tokens=200, + audio_tokens=50, + ), + ) + + prompt_cost, completion_cost = cost_per_token(model=COMPONENT_MODEL, usage=usage) + + expected_prompt_cost = ( + 400 * COMPONENT_INPUT_COST + + 300 * COMPONENT_CACHE_READ_COST + + 200 * COMPONENT_CACHE_CREATION_COST + + 100 * COMPONENT_AUDIO_IN_COST + ) + expected_completion_cost = ( + 250 * COMPONENT_OUTPUT_COST + 200 * COMPONENT_REASONING_COST + 50 * COMPONENT_AUDIO_OUT_COST + ) + assert prompt_cost == pytest.approx(expected_prompt_cost) + assert completion_cost == pytest.approx(expected_completion_cost) From 388eef4fbb8ec98ba839ea672b11e4e329f86c0b Mon Sep 17 00:00:00 2001 From: Yuneng Jiang Date: Tue, 15 Sep 2026 17:44:18 -0700 Subject: [PATCH 137/225] fix(e2e): onboard dashboard fixtures through invitations --- tests/e2e/ui/fixtures/seed.sql | 2 + tests/e2e/ui/globalSetup.ts | 55 +++++++++-------- tests/e2e/ui/helpers/userOnboarding.ts | 59 +++++++++++++++++++ .../internalUserKeyScope.spec.ts | 11 +--- .../ui/tests/proxy-admin/secondAdmin.spec.ts | 11 +--- .../team-admin/memberPermissions.spec.ts | 9 +-- 6 files changed, 101 insertions(+), 46 deletions(-) create mode 100644 tests/e2e/ui/helpers/userOnboarding.ts diff --git a/tests/e2e/ui/fixtures/seed.sql b/tests/e2e/ui/fixtures/seed.sql index 00ea668ed8f..48060536596 100644 --- a/tests/e2e/ui/fixtures/seed.sql +++ b/tests/e2e/ui/fixtures/seed.sql @@ -2,6 +2,8 @@ -- Idempotent: deletes all e2e-* rows then re-inserts deterministic data. -- 1. Clean up in dependency order +DELETE FROM "LiteLLM_InvitationLink" +WHERE "user_id" LIKE 'e2e-%' OR "created_by" LIKE 'e2e-%' OR "updated_by" LIKE 'e2e-%'; DELETE FROM "LiteLLM_TeamMembership" WHERE "user_id" LIKE 'e2e-%'; DELETE FROM "LiteLLM_VerificationToken" WHERE token LIKE 'e2e-%'; DELETE FROM "LiteLLM_TeamTable" WHERE "team_id" LIKE 'e2e-%'; diff --git a/tests/e2e/ui/globalSetup.ts b/tests/e2e/ui/globalSetup.ts index e7d1655380d..9447c93a72e 100644 --- a/tests/e2e/ui/globalSetup.ts +++ b/tests/e2e/ui/globalSetup.ts @@ -1,6 +1,7 @@ import { chromium, expect, request } from "@playwright/test"; import { users, Role, STORAGE_PATHS } from "./fixtures/users"; import { ARTIFACT_DIR, UI_BASE_URL } from "./constants"; +import { expectUnrestrictedDashboard, setInvitedUserPassword } from "./helpers/userOnboarding"; import * as fs from "fs"; import * as path from "path"; @@ -30,32 +31,37 @@ async function globalSetup() { throw new Error(`Enabling enable_projects_ui failed (${settingsRes.status()}): ${await settingsRes.text()}`); } - for (const { email, password, seedApiRole } of Object.values(users)) { - if (!seedApiRole) { - continue; - } - const createRes = await api.post(`${UI_BASE_URL}${rootPath}/user/new`, { - headers: { Authorization: `Bearer ${masterKey}` }, - data: { user_email: email, user_role: seedApiRole, auto_create_key: false }, - }); - if (!createRes.ok() && createRes.status() !== 409) { - throw new Error(`Seeding user ${email} failed (${createRes.status()}): ${await createRes.text()}`); - } - const passwordRes = await api.post(`${UI_BASE_URL}${rootPath}/user/update`, { - headers: { Authorization: `Bearer ${masterKey}` }, - data: { user_email: email, password }, - }); - if (!passwordRes.ok()) { - throw new Error(`Setting password for ${email} failed (${passwordRes.status()}): ${await passwordRes.text()}`); - } - } - await api.dispose(); - - for (const role of Object.values(Role)) { - const { email, password } = users[role]; + const roles = [Role.ProxyAdmin, ...Object.values(Role).filter((role) => role !== Role.ProxyAdmin)]; + for (const role of roles) { + const { email, password, seedApiRole } = users[role]; const storagePath = STORAGE_PATHS[role]; const page = await browser.newPage(); try { + if (seedApiRole) { + const createRes = await api.post(`${UI_BASE_URL}${rootPath}/user/new`, { + headers: { Authorization: `Bearer ${masterKey}` }, + data: { user_email: email, user_role: seedApiRole, auto_create_key: false }, + }); + if (!createRes.ok() && createRes.status() !== 409) { + throw new Error(`Seeding user ${email} failed (${createRes.status()}): ${await createRes.text()}`); + } + const userId = createRes.ok() + ? (await createRes.json()).user_id + : await (async () => { + const existing = await api.get(`${UI_BASE_URL}${rootPath}/user/list`, { + headers: { Authorization: `Bearer ${masterKey}` }, + params: { user_email: email }, + }); + expect(existing.ok(), `Find seeded user ${email}: HTTP ${existing.status()}`).toBe(true); + const matches = (await existing.json()).users.filter( + (user: { user_email: string }) => user.user_email === email, + ); + expect(matches, `Exactly one seeded user for ${email}`).toHaveLength(1); + return matches[0].user_id; + })(); + expect(typeof userId, `User ID for ${email}`).toBe("string"); + await setInvitedUserPassword(api, userId, password); + } await page.goto(`${UI_BASE_URL}${rootPath}/ui/login`); await page.getByPlaceholder("Enter your username").fill(email); await page.getByPlaceholder("Enter your password").fill(password); @@ -63,7 +69,7 @@ async function globalSetup() { await page.waitForURL((url) => url.pathname.startsWith(`${rootPath}/ui`) && !url.pathname.includes("/login"), { timeout: 30_000, }); - await expect(page.locator("a", { hasText: "Virtual Keys" })).toBeVisible({ timeout: 30_000 }); + await expectUnrestrictedDashboard(page); // Dismiss feedback popup if present const dismiss = page.getByText("Don't ask me again"); if (await dismiss.isVisible({ timeout: 1_500 }).catch(() => false)) { @@ -100,6 +106,7 @@ async function globalSetup() { } } + await api.dispose(); await browser.close(); } diff --git a/tests/e2e/ui/helpers/userOnboarding.ts b/tests/e2e/ui/helpers/userOnboarding.ts new file mode 100644 index 00000000000..a1ea6e5e82b --- /dev/null +++ b/tests/e2e/ui/helpers/userOnboarding.ts @@ -0,0 +1,59 @@ +import { expect, type APIRequestContext, type Page } from "@playwright/test"; +import { UI_BASE_URL } from "../constants"; +import { masterKey, rootPath } from "./traffic"; + +const endpoint = (route: string): string => `${UI_BASE_URL}${rootPath()}${route}`; + +export async function setInvitedUserPassword( + request: APIRequestContext, + userId: string, + password: string, +): Promise { + const invitation = await request.post(endpoint("/invitation/new"), { + headers: { Authorization: `Bearer ${masterKey()}` }, + data: { user_id: userId }, + }); + expect(invitation.ok(), `Create invitation for ${userId}: HTTP ${invitation.status()}`).toBe(true); + const { id } = await invitation.json(); + expect(typeof id, "invitation ID").toBe("string"); + + const onboarding = await request.get(endpoint("/onboarding/get_token"), { + params: { invite_link: id }, + }); + expect(onboarding.ok(), `Get onboarding session for ${userId}: HTTP ${onboarding.status()}`).toBe(true); + const { token } = await onboarding.json(); + const payload = JSON.parse(Buffer.from(token.split(".")[1], "base64url").toString("utf-8")); + expect(typeof payload.key, "onboarding credential").toBe("string"); + const claimed = await request.post(endpoint("/onboarding/claim_token"), { + headers: { Authorization: `Bearer ${payload.key}` }, + data: { invitation_link: id, user_id: userId, password }, + }); + expect(claimed.ok(), `Claim invitation for ${userId}: HTTP ${claimed.status()}`).toBe(true); +} + +export async function readDashboardSession(page: Page): Promise<{ + key: string; + user_id: string; + password_reset_required?: boolean; +}> { + await expect.poll(async () => (await page.context().cookies()).some((cookie) => cookie.name === "token")).toBe(true); + const cookie = (await page.context().cookies()).find((candidate) => candidate.name === "token")!; + return JSON.parse(Buffer.from(cookie.value.split(".")[1], "base64url").toString("utf-8")); +} + +export async function expectUnrestrictedDashboard(page: Page): Promise { + const virtualKeys = page.getByRole("complementary").getByRole("link", { name: "Virtual Keys", exact: true }); + await expect(virtualKeys).toBeVisible({ timeout: 30_000 }); + const session = await readDashboardSession(page); + expect(session.password_reset_required === true, "login must not require a password reset").toBe(false); + await virtualKeys.click(); + await expect(page.getByRole("main").getByRole("heading", { name: "Virtual Keys", exact: true })).toBeVisible({ + timeout: 30_000, + }); + const info = await page.request.get(endpoint("/user/info"), { + headers: { Authorization: `Bearer ${session.key}` }, + params: { user_id: session.user_id }, + }); + expect(info.ok(), `Read own user with dashboard session: HTTP ${info.status()}`).toBe(true); + expect((await info.json()).user_id).toBe(session.user_id); +} diff --git a/tests/e2e/ui/tests/internal-user/internalUserKeyScope.spec.ts b/tests/e2e/ui/tests/internal-user/internalUserKeyScope.spec.ts index f923841257a..c2004bff7f0 100644 --- a/tests/e2e/ui/tests/internal-user/internalUserKeyScope.spec.ts +++ b/tests/e2e/ui/tests/internal-user/internalUserKeyScope.spec.ts @@ -1,3 +1,4 @@ +import { expectUnrestrictedDashboard, setInvitedUserPassword } from "../../helpers/userOnboarding"; import { test, expect, type APIRequestContext } from "@playwright/test"; import { Page } from "../../fixtures/pages"; import { @@ -76,10 +77,7 @@ test.describe("Internal User - own team key model scope", () => { user_role: "internal_user", auto_create_key: false, }); - await postAsMaster(request, "/user/update", { - user_id: userId, - password: MEMBER_PASSWORD, - }); + await setInvitedUserPassword(request, userId, MEMBER_PASSWORD); await postAsMaster(request, "/team/member_add", { team_id: teamId, member: { role: "user", user_id: userId }, @@ -99,10 +97,7 @@ test.describe("Internal User - own team key model scope", () => { .getByPlaceholder("Enter your password") .fill(MEMBER_PASSWORD); await page.getByRole("button", { name: "Login", exact: true }).click(); - await expect( - page.locator("a", { hasText: "Virtual Keys" }), - `${email} never reached the dashboard`, - ).toBeVisible({ timeout: 30_000 }); + await expectUnrestrictedDashboard(page); await dismissFeedbackPopup(page); await navigateToPage(page, Page.ApiKeys); diff --git a/tests/e2e/ui/tests/proxy-admin/secondAdmin.spec.ts b/tests/e2e/ui/tests/proxy-admin/secondAdmin.spec.ts index 73263c844fa..4572f71b3db 100644 --- a/tests/e2e/ui/tests/proxy-admin/secondAdmin.spec.ts +++ b/tests/e2e/ui/tests/proxy-admin/secondAdmin.spec.ts @@ -1,3 +1,4 @@ +import { expectUnrestrictedDashboard, setInvitedUserPassword } from "../../helpers/userOnboarding"; import { test, expect } from "@playwright/test"; import { ADMIN_STORAGE_PATH } from "../../constants"; import { Page } from "../../fixtures/pages"; @@ -46,19 +47,13 @@ test.describe("Second proxy admin", () => { const userId = await inviteAdminUser(); try { - const passwordRes = await request.post("/user/update", { - headers: auth, - data: { user_email: email, password }, - }); - expect(passwordRes.ok(), `setting password failed (${passwordRes.status()}): ${await passwordRes.text()}`).toBe( - true, - ); + await setInvitedUserPassword(request, userId, password); await page.goto("/ui/login"); await page.getByPlaceholder("Enter your username").fill(email); await page.getByPlaceholder("Enter your password").fill(password); await page.getByRole("button", { name: "Login", exact: true }).click(); - await expect(page.locator("a", { hasText: "Virtual Keys" })).toBeVisible({ timeout: 30_000 }); + await expectUnrestrictedDashboard(page); await dismissFeedbackPopup(page); await navigateToPage(page, Page.ApiKeys); diff --git a/tests/e2e/ui/tests/team-admin/memberPermissions.spec.ts b/tests/e2e/ui/tests/team-admin/memberPermissions.spec.ts index 75fb3be9b64..b7978fae7fb 100644 --- a/tests/e2e/ui/tests/team-admin/memberPermissions.spec.ts +++ b/tests/e2e/ui/tests/team-admin/memberPermissions.spec.ts @@ -1,3 +1,4 @@ +import { expectUnrestrictedDashboard, setInvitedUserPassword } from "../../helpers/userOnboarding"; import { test, expect, type Browser, type BrowserContext, type Page as PlaywrightPage } from "@playwright/test"; import { Page } from "../../fixtures/pages"; import { navigateToPage, dismissFeedbackPopup, clickTeamId } from "../../helpers/navigation"; @@ -24,7 +25,7 @@ async function signIn(browser: Browser, email: string): Promise await page.getByPlaceholder("Enter your username").fill(email); await page.getByPlaceholder("Enter your password").fill(PASSWORD); await page.getByRole("button", { name: "Login", exact: true }).click(); - await expect(page.locator("a", { hasText: "Virtual Keys" })).toBeVisible({ timeout: 30_000 }); + await expectUnrestrictedDashboard(page); await dismissFeedbackPopup(page); return context; } @@ -49,11 +50,7 @@ test.describe("Team Admin - Member permissions", () => { data: { user_id: userId, user_email: email, user_role: "internal_user", auto_create_key: false }, }); expect(created.ok(), `POST /user/new for ${userId} (${created.status()}): ${await created.text()}`).toBe(true); - const password = await request.post("/user/update", { - headers: auth(), - data: { user_id: userId, password: PASSWORD }, - }); - expect(password.ok(), `POST /user/update for ${userId} (${password.status()})`).toBe(true); + await setInvitedUserPassword(request, userId, PASSWORD); }; let teamId = ""; From 14d239fc849417d62e3458603a5432a216eb4944 Mon Sep 17 00:00:00 2001 From: yassin Date: Wed, 16 Sep 2026 00:46:59 +0000 Subject: [PATCH 138/225] test(proxy): pin JWT scope denial message shape through auth exception conversion Co-Authored-By: Devin AI <158243242+devin-ai-integration[bot]@users.noreply.github.com> --- .../proxy/auth/test_auth_exception_handler.py | 17 ++++++++++++++++- 1 file changed, 16 insertions(+), 1 deletion(-) diff --git a/tests/test_litellm/proxy/auth/test_auth_exception_handler.py b/tests/test_litellm/proxy/auth/test_auth_exception_handler.py index 4328eac8432..d5ef71cd1d1 100644 --- a/tests/test_litellm/proxy/auth/test_auth_exception_handler.py +++ b/tests/test_litellm/proxy/auth/test_auth_exception_handler.py @@ -36,7 +36,7 @@ from litellm.proxy._types import ( ProxyException, UserAPIKeyAuth, ) -from litellm.proxy.auth.auth_exception_handler import UserAPIKeyAuthExceptionHandler +from litellm.proxy.auth.auth_exception_handler import UserAPIKeyAuthExceptionHandler, _as_proxy_exception from litellm.proxy.auth.model_access_denied import ModelAccessDeniedHTTPException @@ -1053,6 +1053,21 @@ async def test_handle_authentication_error_keeps_internal_message_on_model_acces assert [r for r in caplog.records if r.levelname == "WARNING" and "internal-models" in r.getMessage()] == [] +def test_as_proxy_exception_keeps_jwt_scope_denial_message_shape(): + detail = {"error": "The model `gpt-5.6` is unavailable for this API key or does not exist."} + denial = ModelAccessDeniedHTTPException( + internal_message="model=gpt-5.6 not allowed. Allowed_models=['internal-models']", + status_code=status.HTTP_403_FORBIDDEN, + detail=detail, + ) + plain = _as_proxy_exception(HTTPException(status_code=status.HTTP_403_FORBIDDEN, detail=detail)) + + converted = _as_proxy_exception(denial) + + assert converted.to_dict() == plain.to_dict() + assert converted.internal_message == denial.internal_message + + @pytest.mark.asyncio @pytest.mark.parametrize("unset_value", [None, ""]) async def test_handle_authentication_error_no_extra_denial_log_when_message_not_configured( From b4f9e319fc2a0e55c775db371ce5d09e806e6c4f Mon Sep 17 00:00:00 2001 From: mateo-berri <277851410+mateo-berri@users.noreply.github.com> Date: Tue, 15 Sep 2026 17:53:57 -0700 Subject: [PATCH 139/225] fix(proxy): surface the upstream status code when a RAG query fails --- litellm/proxy/rag_endpoints/endpoints.py | 7 +++- .../proxy/rag_endpoints/test_rag_endpoints.py | 36 +++++++++++++++++++ 2 files changed, 42 insertions(+), 1 deletion(-) diff --git a/litellm/proxy/rag_endpoints/endpoints.py b/litellm/proxy/rag_endpoints/endpoints.py index d6a402e1860..c09f9c755ed 100644 --- a/litellm/proxy/rag_endpoints/endpoints.py +++ b/litellm/proxy/rag_endpoints/endpoints.py @@ -69,6 +69,11 @@ def _response_attr(source: object, name: str) -> object: return getattr(source, name, None) +def _upstream_status_code(error: Exception) -> int: + code: Final = getattr(error, "status_code", None) + return code if isinstance(code, int) else 500 + + def _raise_vector_store_scan_depth_exceeded() -> None: raise HTTPException( status_code=400, @@ -814,6 +819,6 @@ async def rag_query( except Exception as e: verbose_proxy_logger.exception("RAG Query failed: %s", e) raise HTTPException( - status_code=500, + status_code=_upstream_status_code(e), detail={"error": str(e)}, ) diff --git a/tests/test_litellm/proxy/rag_endpoints/test_rag_endpoints.py b/tests/test_litellm/proxy/rag_endpoints/test_rag_endpoints.py index 832435711c6..1cceaf95b09 100644 --- a/tests/test_litellm/proxy/rag_endpoints/test_rag_endpoints.py +++ b/tests/test_litellm/proxy/rag_endpoints/test_rag_endpoints.py @@ -11,6 +11,7 @@ from unittest.mock import AsyncMock, MagicMock, patch import pytest from fastapi.testclient import TestClient +import litellm from litellm.proxy._types import LitellmUserRoles, UserAPIKeyAuth from litellm.proxy.auth.user_api_key_auth import user_api_key_auth from litellm.proxy.proxy_server import app @@ -282,6 +283,41 @@ def test_rag_query_returns_response_cost_header(client_internal_user): assert response.headers.get("x-litellm-response-cost") == "3.45e-06" +@pytest.mark.parametrize( + ("upstream_error", "expected_status"), + [ + (litellm.BadRequestError(message="filter andAll needs two clauses", model="kb", llm_provider="bedrock"), 400), + (litellm.NotFoundError(message="Knowledge Base does not exist", model="kb", llm_provider="bedrock"), 404), + (RuntimeError("pipeline blew up"), 500), + ], +) +def test_rag_query_surfaces_upstream_status_code(client_internal_user, upstream_error, expected_status): + """A vector store rejection must reach the caller with its own status code, never a blanket 500.""" + with ( + patch( # test-quality-ok: the handler calls the module-level litellm.aquery directly; no injection seam + "litellm.proxy.rag_endpoints.endpoints.litellm.aquery", + new=AsyncMock(side_effect=upstream_error), + ), + patch("litellm.vector_store_registry", None), # test-quality-ok: proxy module global, no injection seam + patch("litellm.proxy.proxy_server.prisma_client", None), # test-quality-ok: proxy module global, no injection seam + ): + response = client_internal_user.post( + "/v1/rag/query", + json={ + "model": "bedrock/us.anthropic.claude-sonnet-5", + "messages": [{"role": "user", "content": "How was this document ingested?"}], + "retrieval_config": { + "vector_store_id": "L7INRFMVQT", + "custom_llm_provider": "bedrock", + "retrieval_filter": {"andAll": [{"equals": {"key": "department", "value": "billing"}}]}, + }, + }, + ) + + assert response.status_code == expected_status, response.text + assert str(upstream_error) in response.json()["detail"]["error"] + + def test_rag_query_stream_returns_event_stream(client_internal_user): """ A stream=true /v1/rag/query must return an SSE response. Returning the raw From 405a12783889945a314942a949a162f9186fca86 Mon Sep 17 00:00:00 2001 From: kerry Date: Wed, 16 Sep 2026 00:57:05 +0000 Subject: [PATCH 140/225] fix(fireworks-ai): drop banned typing.cast to a suppressed import for the copied pricing entry Co-Authored-By: Devin AI <158243242+devin-ai-integration[bot]@users.noreply.github.com> --- litellm/llms/fireworks_ai/cost_calculator.py | 19 +++++++++---------- 1 file changed, 9 insertions(+), 10 deletions(-) diff --git a/litellm/llms/fireworks_ai/cost_calculator.py b/litellm/llms/fireworks_ai/cost_calculator.py index 7bd01115a94..f323d78e22d 100644 --- a/litellm/llms/fireworks_ai/cost_calculator.py +++ b/litellm/llms/fireworks_ai/cost_calculator.py @@ -2,9 +2,11 @@ For calculating cost of fireworks ai serverless inference models. """ -from collections.abc import Mapping from datetime import datetime -from typing import Final, cast +from typing import ( + Final, + cast, # noqa: TID251 # the fallback entry is a dict copy of a ReadOnly TypedDict; no cast-free way to retype it +) from litellm.constants import ( FIREWORKS_AI_4_B, @@ -78,14 +80,11 @@ def _with_cache_read_fallback(model_info: ModelInfo) -> ModelInfo: return model_info effective: Final[dict[str, object]] = dict(model_info) effective["cache_read_input_token_cost"] = input_rate - off_peak: Final = effective.get("off_peak_pricing") - if isinstance(off_peak, Mapping): - off_peak_map: Final[Mapping[str, object]] = cast(Mapping[str, object], off_peak) - if "cache_read_input_token_cost" not in off_peak_map: - effective["off_peak_pricing"] = { - **off_peak_map, - "cache_read_input_token_cost": off_peak_map.get("input_cost_per_token", input_rate), - } + off_peak: Final = model_info.get("off_peak_pricing") + if off_peak is not None and "cache_read_input_token_cost" not in off_peak: + off_peak_copy: Final[dict[str, object]] = dict(off_peak) + off_peak_copy["cache_read_input_token_cost"] = off_peak_copy.get("input_cost_per_token", input_rate) + effective["off_peak_pricing"] = off_peak_copy return cast(ModelInfo, effective) From 5aa2adbacd0244e8c6f0b22c6fb8dc02679e1bc0 Mon Sep 17 00:00:00 2001 From: Tin Chi Lo Date: Tue, 15 Sep 2026 18:02:05 -0700 Subject: [PATCH 141/225] fix(proxy): preserve Anthropic pricing modifiers in router savings --- litellm/llms/anthropic/cost_calculation.py | 20 ++++-- litellm/proxy/spend_tracking/savings.py | 62 +++++++++++-------- .../proxy/spend_tracking/test_savings.py | 29 +++++++++ 3 files changed, 79 insertions(+), 32 deletions(-) diff --git a/litellm/llms/anthropic/cost_calculation.py b/litellm/llms/anthropic/cost_calculation.py index 95615b8e748..4a935ac18b4 100644 --- a/litellm/llms/anthropic/cost_calculation.py +++ b/litellm/llms/anthropic/cost_calculation.py @@ -18,7 +18,9 @@ if TYPE_CHECKING: import litellm -def cost_per_token(model: str, usage: "Usage", service_tier: str | None = None) -> tuple[float, float]: +def cost_per_token( + model: str, usage: "Usage", service_tier: str | None = None, model_info: "ModelInfo | None" = None +) -> tuple[float, float]: """ Calculates the cost per token for a given model, prompt tokens, and completion tokens. @@ -27,6 +29,7 @@ def cost_per_token(model: str, usage: "Usage", service_tier: str | None = None) - usage: LiteLLM Usage block, containing anthropic caching information - service_tier: the service tier the request was served at (e.g. "priority"), read from the Anthropic response usage and used to select tier-specific pricing + - model_info: effective deployment prices, when they override public rates Returns: Tuple[float, float] - prompt_cost_in_usd, completion_cost_in_usd @@ -36,16 +39,23 @@ def cost_per_token(model: str, usage: "Usage", service_tier: str | None = None) usage=usage, custom_llm_provider="anthropic", service_tier=service_tier, + model_info=model_info, ) # Apply provider_specific_entry multipliers for geo/speed routing try: - model_info: Final = litellm.get_model_info(model=model, custom_llm_provider="anthropic") - provider_specific_entry: Final[dict] = model_info.get("provider_specific_entry") or {} + effective_info: Final = ( + model_info + if model_info is not None + else litellm.get_model_info(model=model, custom_llm_provider="anthropic") + ) + provider_specific_entry: Final = effective_info.get("provider_specific_entry") - geo_multiplier: Final = get_provider_specific_geo_multiplier(model_info=model_info, usage=usage) + geo_multiplier: Final = get_provider_specific_geo_multiplier(model_info=effective_info, usage=usage) speed_multiplier: Final = ( - provider_specific_entry.get("fast", 1.0) if getattr(usage, "speed", None) == "fast" else 1.0 + provider_specific_entry.get("fast", 1.0) + if provider_specific_entry and getattr(usage, "speed", None) == "fast" + else 1.0 ) if speed_multiplier != 1.0: diff --git a/litellm/proxy/spend_tracking/savings.py b/litellm/proxy/spend_tracking/savings.py index 950fcca2039..7d9b6514a34 100644 --- a/litellm/proxy/spend_tracking/savings.py +++ b/litellm/proxy/spend_tracking/savings.py @@ -171,15 +171,25 @@ def _cost_of_usage( ) -> float | None: """What ``usage`` costs on ``model``, or ``None`` when the model has no pricing.""" try: - prompt_cost, completion_cost = generic_cost_per_token( - model=model.model, - usage=usage, - custom_llm_provider=model.provider, - service_tier=basis.service_tier, - data_residency=basis.data_residency, - model_info=model_info, - vertex_location=basis.vertex_location, - ) + if model.provider == "anthropic": + from litellm.llms.anthropic.cost_calculation import cost_per_token + + prompt_cost, completion_cost = cost_per_token( + model=model.model, + usage=usage, + service_tier=basis.service_tier, + model_info=model_info, + ) + else: + prompt_cost, completion_cost = generic_cost_per_token( + model=model.model, + usage=usage, + custom_llm_provider=model.provider, + service_tier=basis.service_tier, + data_residency=basis.data_residency, + model_info=model_info, + vertex_location=basis.vertex_location, + ) except Exception as e: # noqa: BLE001 # get_model_info raises bare Exception for unmapped models; degrade to zero savings verbose_proxy_logger.debug( "savings: cannot price usage for provider=%s model=%s (%s)", model.provider, model.model, e @@ -198,11 +208,6 @@ def _cache_token_split(usage: Usage) -> tuple[int, int]: return int(read), int(created) -_CACHE_SPLIT_FIELDS: Final = frozenset( - ("cached_tokens", "cache_creation_tokens", "cache_write_tokens", "cache_creation_token_details", "text_tokens") -) - - def _baseline_cache_rate_keys(baseline_info: ModelInfo | None) -> tuple[bool, bool]: """Whether the baseline model has a ``(cache read, cache write)`` rate of its own. @@ -274,19 +279,22 @@ def _baseline_usage(usage: Usage, conversation_continuing: bool, baseline_info: (getattr(details, field, 0) or 0) for field in ("audio_tokens", "image_tokens", "video_tokens") ) return Usage( - prompt_tokens=usage.prompt_tokens, - completion_tokens=usage.completion_tokens, - total_tokens=usage.total_tokens, - completion_tokens_details=usage.completion_tokens_details, - prompt_tokens_details=PromptTokensDetailsWrapper( - **details.model_dump(exclude=_CACHE_SPLIT_FIELDS), - cached_tokens=reads, - cache_creation_tokens=writes, - cache_write_tokens=writes, - cache_creation_token_details=details.cache_creation_token_details if writes else None, - # Whatever no longer sits in a cache bucket is plain input on the baseline. - text_tokens=max(usage.prompt_tokens - reads - writes - other_modalities, 0), - ), + **{ + **usage.model_dump(), + # Rebuild through Usage so private fallback counts agree with the public buckets. + "cache_read_input_tokens": reads, + "cache_creation_input_tokens": writes, + "prompt_tokens_details": PromptTokensDetailsWrapper( + **{ + **details.model_dump(), + "cached_tokens": reads, + "cache_creation_tokens": writes, + "cache_write_tokens": writes, + "cache_creation_token_details": details.cache_creation_token_details if writes else None, + "text_tokens": max(usage.prompt_tokens - reads - writes - other_modalities, 0), + } + ), + }, ) diff --git a/tests/test_litellm/proxy/spend_tracking/test_savings.py b/tests/test_litellm/proxy/spend_tracking/test_savings.py index e466edab131..fa910671e88 100644 --- a/tests/test_litellm/proxy/spend_tracking/test_savings.py +++ b/tests/test_litellm/proxy/spend_tracking/test_savings.py @@ -4,6 +4,7 @@ import pytest import litellm from litellm.litellm_core_utils.llm_cost_calc.utils import generic_cost_per_token +from litellm.llms.anthropic.cost_calculation import cost_per_token as anthropic_cost_per_token from litellm.proxy.spend_tracking.savings import ( _baseline_usage, _resolve_model, @@ -17,6 +18,34 @@ from litellm.types.utils import Usage pytestmark = pytest.mark.usefixtures("local_model_cost_map") +@pytest.mark.parametrize("modifier", [{"speed": "fast"}, {"inference_geo": "us"}]) +@pytest.mark.parametrize("continuing", [False, True]) +def test_baseline_preserves_anthropic_pricing_fields(modifier: dict[str, str], continuing: bool) -> None: + usage: Final = _usage(1000, 0, 1000, 100).model_copy(update=modifier) + expected: Final = (_usage(1000, 1000, 0, 100) if continuing else usage).model_copy(update=modifier) + normalized: Final = _baseline_usage(usage, continuing) + cache_fields: Final = {"prompt_tokens_details", "cache_read_input_tokens", "cache_creation_input_tokens"} + assert normalized.model_dump(exclude=cache_fields) == usage.model_dump(exclude=cache_fields) + assert usage.prompt_tokens_details.cached_tokens == 0 + selected_cost: Final = 0.013 + assert compute_autorouter_savings( + "claude-opus-5", "claude-sonnet-5", "anthropic", usage, conversation_continuing=continuing, + cost_breakdown={"input_cost": 0.01, "output_cost": 0.003}, + ) == pytest.approx(sum(anthropic_cost_per_token("claude-opus-5", expected)) - selected_cost) + + +def test_anthropic_baseline_keeps_negotiated_prices_with_provider_multiplier() -> None: + info: Final = { + **litellm.get_model_info("claude-opus-5", "anthropic"), + "input_cost_per_token": 1e-6, "output_cost_per_token": 2e-6, "cache_read_input_token_cost": 3e-7, + } + usage: Final = _usage(1000, 1000, 0, 100).model_copy(update={"speed": "fast"}) + assert compute_autorouter_savings( + "claude-opus-5", "claude-sonnet-5", "anthropic", usage, baseline_info=info, + cost_breakdown={"input_cost": 0.01, "output_cost": 0.003}, + ) == pytest.approx(0.0015 * 2 - 0.013) + + def _anthropic_costs(model: str) -> tuple[float, float]: info = litellm.get_model_info(model=model, custom_llm_provider="anthropic") input_cost = info["input_cost_per_token"] or 0.0 From de9aa48cd62e73b5b291d6c5a2a952dd35f09f00 Mon Sep 17 00:00:00 2001 From: kerry Date: Wed, 16 Sep 2026 01:02:29 +0000 Subject: [PATCH 142/225] fix(responses): count multimodal input and tool-call output in the streamed usage fallback Co-Authored-By: Devin AI <158243242+devin-ai-integration[bot]@users.noreply.github.com> --- litellm/responses/streaming_iterator.py | 38 ++++++++- litellm/responses/utils.py | 16 ---- .../responses/test_streaming_iterator.py | 78 ++++++++++++++++++- 3 files changed, 112 insertions(+), 20 deletions(-) diff --git a/litellm/responses/streaming_iterator.py b/litellm/responses/streaming_iterator.py index 52b50167190..6cb0331620f 100644 --- a/litellm/responses/streaming_iterator.py +++ b/litellm/responses/streaming_iterator.py @@ -32,6 +32,9 @@ from litellm.litellm_core_utils.llm_response_utils.response_metadata import ( ) from litellm.litellm_core_utils.thread_pool_executor import executor from litellm.llms.base_llm.responses.transformation import BaseResponsesAPIConfig +from litellm.responses.litellm_completion_transformation.transformation import ( + LiteLLMCompletionResponsesConfig, +) from litellm.responses.utils import ResponseAPILoggingUtils, ResponsesAPIRequestUtils from litellm.types.integrations.custom_logger import converted_stream_requested from litellm.types.llms.openai import ( @@ -257,6 +260,7 @@ class BaseResponsesAPIStreamingIterator: self._failure_handled = False # Track if failure handler has been called self._yielded_first_chunk = False self._generated_content = "" + self._generated_tool_arguments = "" self._completed_response_cached = False self._completed_response_logged = False self._completed_response_cache_hit: bool | None = None @@ -352,6 +356,10 @@ class BaseResponsesAPIStreamingIterator: _delta: Final = getattr(openai_responses_api_chunk, "delta", None) if isinstance(_delta, str): self._generated_content += _delta + elif _event_type == ResponsesAPIStreamEvents.FUNCTION_CALL_ARGUMENTS_DELTA: + _args_delta: Final = getattr(openai_responses_api_chunk, "delta", None) + if isinstance(_args_delta, str): + self._generated_tool_arguments += _args_delta _stream_model_id: Final = _model_id_from_metadata(self.litellm_metadata) if _event_type in ( ResponsesAPIStreamEvents.OUTPUT_ITEM_ADDED, @@ -432,8 +440,11 @@ class BaseResponsesAPIStreamingIterator: and _response_obj is not None and _response_obj.usage is None ): - _response_obj.usage = ResponseAPILoggingUtils.estimate_usage_from_text( - self.model or "", self.request_data.get("input"), self._generated_content + _response_obj.usage = _estimate_usage_from_text( + self.model or "", + self.request_data.get("input"), + self.request_data, + self._generated_content + self._generated_tool_arguments, ) _stamp_responses_usage_cost(getattr(openai_responses_api_chunk, "response", None), self.logging_obj) @@ -1347,6 +1358,29 @@ def _usage_as_model(usage: object) -> ResponseAPIUsage | None: return None +def _estimate_usage_from_text( + model: str, + request_input: object, + responses_api_request: Mapping[str, object], + generated_text: str, +) -> ResponseAPIUsage: + messages: Final = LiteLLMCompletionResponsesConfig.transform_responses_api_input_to_messages( # pyright: ignore[reportUnknownMemberType] # the transformer's signature is partially untyped + input=request_input, # pyright: ignore[reportArgumentType] # the raw Responses API input is a str or ResponseInputParam list, matching the helper's declared union + responses_api_request=dict(responses_api_request), + ) + input_tokens: Final = litellm.token_counter( # pyright: ignore[reportUnknownMemberType] # token_counter's public signature is untyped + model=model, messages=messages + ) + output_tokens: Final = litellm.token_counter( # pyright: ignore[reportUnknownMemberType] # token_counter's public signature is untyped + model=model, text=generated_text, count_response_tokens=True + ) + return ResponseAPIUsage( + input_tokens=input_tokens, + output_tokens=output_tokens, + total_tokens=input_tokens + output_tokens, + ) + + def _stamp_responses_usage_cost( response_obj: ResponsesAPIResponse | None, logging_obj: LiteLLMLoggingObj | None ) -> None: diff --git a/litellm/responses/utils.py b/litellm/responses/utils.py index e523b3771af..41a3ded7022 100644 --- a/litellm/responses/utils.py +++ b/litellm/responses/utils.py @@ -1,5 +1,4 @@ import base64 -import json import re from collections.abc import Iterable, Mapping, Sequence from typing import Any, Final, Optional, TypeVar, Union, cast, get_type_hints, overload @@ -1240,18 +1239,3 @@ class ResponseAPILoggingUtils: setattr(chat_usage, "cost", response_api_usage.cost) return chat_usage - - @staticmethod - def estimate_usage_from_text(model: str, request_input: object, generated_text: str) -> ResponseAPIUsage: - input_text: Final = request_input if isinstance(request_input, str) else json.dumps(request_input, default=str) - input_tokens: Final = litellm.token_counter( # pyright: ignore[reportUnknownMemberType] # token_counter's public signature is untyped - model=model, text=input_text - ) - output_tokens: Final = litellm.token_counter( # pyright: ignore[reportUnknownMemberType] # token_counter's public signature is untyped - model=model, text=generated_text, count_response_tokens=True - ) - return ResponseAPIUsage( - input_tokens=input_tokens, - output_tokens=output_tokens, - total_tokens=input_tokens + output_tokens, - ) diff --git a/tests/test_litellm/responses/test_streaming_iterator.py b/tests/test_litellm/responses/test_streaming_iterator.py index c593946ac4a..c3721353d42 100644 --- a/tests/test_litellm/responses/test_streaming_iterator.py +++ b/tests/test_litellm/responses/test_streaming_iterator.py @@ -5,12 +5,13 @@ completion_start_time = end_time.""" import json from datetime import datetime -from typing import Optional +from typing import Final, Optional from unittest.mock import Mock, patch import httpx import pytest +import litellm from litellm.litellm_core_utils.litellm_logging import Logging as LiteLLMLoggingObj from litellm.llms.base_llm.responses.transformation import BaseResponsesAPIConfig from litellm.responses.streaming_iterator import ( @@ -351,8 +352,10 @@ def _mock_config_with_completed_response(response: ResponsesAPIResponse) -> Mock return completed stub = Mock() stub.type = evt_type - if evt_type == "response.output_text.delta": + if "delta" in parsed_chunk: stub.delta = parsed_chunk.get("delta") + if "item" in parsed_chunk: + stub.item = parsed_chunk.get("item") return stub mock_config.transform_streaming_response.side_effect = _transform @@ -718,3 +721,74 @@ async def test_streaming_logging_copy_keeps_client_usage_when_response_fails_val assert isinstance(client_usage, ResponseAPIUsage) assert client_usage.input_tokens == 29 assert client_usage.cost == pytest.approx(0.0001) + + +@pytest.mark.asyncio +async def test_completed_event_without_usage_counts_tool_call_arguments(): + """A function-call-only stream still bills output tokens: streamed + function_call_arguments deltas feed the text estimate.""" + response = _responses_api_response_without_usage() + iterator = _make_iterator( + sse_events=[ + _sse_event( + { + "type": "response.output_item.added", + "item": {"type": "function_call", "name": "get_weather", "call_id": "call_1"}, + } + ), + _sse_event( + { + "type": "response.function_call_arguments.delta", + "delta": '{"location": "San Francisco", "unit": "celsius"}', + } + ), + _sse_event({"type": "response.completed", "response": {}}), + ], + logging_obj=_logging_obj_stub(), + config=_mock_config_with_completed_response(response), + request_data={"input": "what is the weather in san francisco"}, + ) + + async for _ in iterator: + pass + + usage = iterator.completed_response.response.usage + assert usage is not None + assert usage.output_tokens > 0 + assert usage.total_tokens == usage.input_tokens + usage.output_tokens + + +@pytest.mark.asyncio +async def test_completed_event_without_usage_counts_multimodal_input_as_messages(): + """Multimodal request input is counted as chat messages, not as a JSON blob: + a huge base64 image must not inflate the estimated input tokens.""" + image_input: Final = [ + { + "role": "user", + "content": [ + {"type": "input_text", "text": "what is in this image"}, + { + "type": "input_image", + "image_url": "data:image/png;base64," + "A" * 4000, + }, + ], + } + ] + json_count: Final = litellm.token_counter(model="gpt-4o-mini", text=json.dumps(image_input)) + response = _responses_api_response_without_usage() + iterator = _make_iterator( + sse_events=[ + _sse_event({"type": "response.output_text.delta", "delta": "it is a cat"}), + _sse_event({"type": "response.completed", "response": {}}), + ], + logging_obj=_logging_obj_stub(), + config=_mock_config_with_completed_response(response), + request_data={"input": image_input}, + ) + + async for _ in iterator: + pass + + usage = iterator.completed_response.response.usage + assert usage is not None + assert usage.input_tokens < json_count / 2 From 77a632767500f0c79ce2816584572df8147c7a94 Mon Sep 17 00:00:00 2001 From: yucheng Date: Mon, 14 Sep 2026 21:04:32 +0000 Subject: [PATCH 143/225] feat(guardrails): support pre_call and during_call modes for llm_as_a_judge Co-Authored-By: Devin AI <158243242+devin-ai-integration[bot]@users.noreply.github.com> --- .../llm_as_a_judge/__init__.py | 65 +++++++++----- .../proxy/guardrails/test_llm_as_a_judge.py | 85 +++++++++++++++++-- .../_components/llm_judge/LLMJudgeFields.tsx | 4 +- 3 files changed, 125 insertions(+), 29 deletions(-) diff --git a/litellm/proxy/guardrails/guardrail_hooks/llm_as_a_judge/__init__.py b/litellm/proxy/guardrails/guardrail_hooks/llm_as_a_judge/__init__.py index 172b1440ca3..31be90f56ef 100644 --- a/litellm/proxy/guardrails/guardrail_hooks/llm_as_a_judge/__init__.py +++ b/litellm/proxy/guardrails/guardrail_hooks/llm_as_a_judge/__init__.py @@ -1,7 +1,8 @@ -"""LLM-as-a-Judge guardrail: uses an LLM to score responses against weighted criteria.""" +"""LLM-as-a-Judge guardrail: uses an LLM to score requests or responses against weighted criteria.""" from collections.abc import Callable, Sequence from datetime import datetime +from types import MappingProxyType from typing import TYPE_CHECKING, Any, Final, Generic, Literal, Optional, TypeVar from fastapi import HTTPException @@ -26,15 +27,29 @@ if TYPE_CHECKING: from litellm.types.llms.openai import AllMessageValues from litellm.types.utils import StandardLoggingEvalInformation -JUDGE_SYSTEM_PROMPT = """You are a quality judge. Evaluate the assistant's response against the criteria provided. +JudgeInputType = Literal["request", "response"] + +_JUDGE_SYSTEM_PROMPT_TEMPLATE: Final = """You are a quality judge. Evaluate the {subject} against the criteria provided. For each criterion, assign a score from 0 to 100 and provide concise reasoning. Return ONLY valid JSON in this exact format: -{ +{{ "verdicts": [ - {"criterion_name": "", "score": <0-100>, "reasoning": "", "passed": , "weight": } + {{"criterion_name": "", "score": <0-100>, "reasoning": "", "passed": , "weight": }} ], "overall_score": -}""" +}}""" + +JUDGE_SYSTEM_PROMPTS: Final[MappingProxyType[JudgeInputType, str]] = MappingProxyType( + { + "request": _JUDGE_SYSTEM_PROMPT_TEMPLATE.format(subject="user's request"), + "response": _JUDGE_SYSTEM_PROMPT_TEMPLATE.format(subject="assistant's response"), + } +) +JUDGE_SYSTEM_PROMPT: Final = JUDGE_SYSTEM_PROMPTS["response"] + +_JUDGE_SUBJECT_LABELS: Final[MappingProxyType[JudgeInputType, str]] = MappingProxyType( + {"request": "User request to evaluate", "response": "Assistant response to evaluate"} +) _VALID_ON_FAILURE: Final = frozenset({"block", "log"}) @@ -89,7 +104,8 @@ def _get_litellm_param( def _build_judge_prompt( criteria: Sequence[JudgeCriterion], messages: Sequence[JudgeMessage], - response_text: str, + text_under_review: str, + input_type: JudgeInputType = "response", ) -> str: criteria_block: Final = "\n".join( f"- {c.get('name', '')} (weight {c.get('weight', 0)}%): {c.get('description', '')}" for c in criteria @@ -99,15 +115,16 @@ def _build_judge_prompt( for m in messages if m.get("content") is not None ) + conversation_block: Final = f"Conversation:\n{conversation}\n\n" if input_type == "response" else "" return ( f"Criteria to evaluate:\n{criteria_block}\n\n" - f"Conversation:\n{conversation}\n\n" - f"Assistant response to evaluate:\n{response_text}" + f"{conversation_block}" + f"{_JUDGE_SUBJECT_LABELS[input_type]}:\n{text_under_review}" ) class LLMAsAJudgeGuardrail(CustomGuardrail): - """Post-call guardrail that judges response quality via an LLM.""" + """Guardrail that judges request (pre_call/during_call) or response (post_call) quality via an LLM.""" def __init__( self, @@ -143,18 +160,19 @@ class LLMAsAJudgeGuardrail(CustomGuardrail): @classmethod def get_supported_event_hooks(cls) -> list[GuardrailEventHooks]: - return [GuardrailEventHooks.post_call] + return [GuardrailEventHooks.pre_call, GuardrailEventHooks.during_call, GuardrailEventHooks.post_call] async def _run_judge( self, messages: Sequence[JudgeMessage], - response_text: str, + text_under_review: str, + input_type: JudgeInputType = "response", ) -> dict[str, object]: judge_messages: Final[list[AllMessageValues]] = [ - {"role": "system", "content": JUDGE_SYSTEM_PROMPT}, + {"role": "system", "content": JUDGE_SYSTEM_PROMPTS[input_type]}, { "role": "user", - "content": _build_judge_prompt(self.criteria, messages, response_text), + "content": _build_judge_prompt(self.criteria, messages, text_under_review, input_type), }, ] response: Final = await judge_acompletion( @@ -174,13 +192,9 @@ class LLMAsAJudgeGuardrail(CustomGuardrail): input_type: Literal["request", "response"], logging_obj: Optional["LiteLLMLoggingObj"] = None, ) -> GenericGuardrailAPIInputs: - # Only evaluate post-call (response text). Fail open on pre-call. - if input_type != "response": - return inputs - texts: Final = inputs.get("texts") or [] - response_text: Final = " ".join(texts) - if not response_text: + text_under_review: Final = " ".join(texts) + if not text_under_review: return inputs start_time: Final = datetime.now() @@ -191,7 +205,7 @@ class LLMAsAJudgeGuardrail(CustomGuardrail): messages: Final[Sequence[JudgeMessage]] = request_data.get("messages") or [] try: - judge_result = await self._run_judge(messages, response_text) + judge_result = await self._run_judge(messages, text_under_review, input_type) except Exception as judge_err: verbose_logger.warning( "llm_as_a_judge guardrail: judge call failed, failing open. Error: %s", judge_err @@ -230,7 +244,7 @@ class LLMAsAJudgeGuardrail(CustomGuardrail): raise HTTPException( status_code=422, detail={ - "error": "LLM judge rejected response: score below threshold", + "error": f"LLM judge rejected {input_type}: score below threshold", "overall_score": overall_score, "threshold": self.overall_threshold, "verdicts": judge_result.get("verdicts", []), @@ -252,9 +266,16 @@ class LLMAsAJudgeGuardrail(CustomGuardrail): guardrail_status=status, start_time=start_time.timestamp(), end_time=datetime.now().timestamp(), - event_type=GuardrailEventHooks.post_call, + event_type=self._event_type_for(input_type), ) + def _event_type_for(self, input_type: JudgeInputType) -> GuardrailEventHooks: + if input_type == "response": + return GuardrailEventHooks.post_call + if self.event_hook is GuardrailEventHooks.during_call: + return GuardrailEventHooks.during_call + return GuardrailEventHooks.pre_call + def initialize_guardrail( litellm_params: "LitellmParams", diff --git a/tests/test_litellm/proxy/guardrails/test_llm_as_a_judge.py b/tests/test_litellm/proxy/guardrails/test_llm_as_a_judge.py index bd2553b3280..a076da62566 100644 --- a/tests/test_litellm/proxy/guardrails/test_llm_as_a_judge.py +++ b/tests/test_litellm/proxy/guardrails/test_llm_as_a_judge.py @@ -13,7 +13,7 @@ from litellm.proxy.guardrails.guardrail_hooks.llm_as_a_judge import ( _parse_judge_verdict, initialize_guardrail, ) - +from litellm.types.guardrails import GuardrailEventHooks # --------------------------------------------------------------------------- # Helpers @@ -141,12 +141,87 @@ def test_initialize_guardrail_invalid_on_failure(): # --------------------------------------------------------------------------- +def _judge_router(overall_score: float): + """Real Router with the outbound judge call stubbed, so the test can inspect what the judge was asked.""" + from litellm import Router + + router = Router( + model_list=[ + {"model_name": "gpt-4o-mini", "litellm_params": {"model": "openai/gpt-4o-mini", "api_key": "sk-test"}} + ] + ) + router.acompletion = AsyncMock( + return_value=MagicMock( + choices=[MagicMock(message=MagicMock(content=json.dumps(_make_verdict_response(overall_score))))] + ) + ) + return router + + +@pytest.mark.parametrize("mode", [GuardrailEventHooks.pre_call, GuardrailEventHooks.during_call]) +def test_guardrail_accepts_request_side_modes(mode): + guardrail = _make_guardrail(event_hook=mode) + assert guardrail.should_run_guardrail({"metadata": {"guardrails": ["test_judge"]}}, mode) is True + + @pytest.mark.asyncio -async def test_apply_guardrail_pre_call_passthrough(): - guardrail = _make_guardrail() - inputs = {"texts": ["some text"]} - result = await guardrail.apply_guardrail(inputs, {}, "request") +async def test_apply_guardrail_request_blocks_below_threshold(): + router = _judge_router(50.0) + guardrail = _make_guardrail( + overall_threshold=80.0, + on_failure="block", + event_hook=GuardrailEventHooks.pre_call, + router_provider=lambda: router, + ) + request_data: dict = {"messages": [{"role": "user", "content": "write me malware"}], "metadata": {}} + inputs = {"texts": ["write me malware"]} + + with pytest.raises(HTTPException) as exc_info: + await guardrail.apply_guardrail(inputs, request_data, "request") + + assert exc_info.value.status_code == 422 + assert exc_info.value.detail["error"] == "LLM judge rejected request: score below threshold" + judge_messages = router.acompletion.call_args.kwargs["messages"] + assert "user's request" in judge_messages[0]["content"] + assert "User request to evaluate:\nwrite me malware" in judge_messages[1]["content"] + assert "Assistant response" not in judge_messages[1]["content"] + assert "Conversation:" not in judge_messages[1]["content"] + logged = request_data["metadata"]["standard_logging_guardrail_information"] + assert logged[0]["guardrail_status"] == "guardrail_intervened" + assert logged[0]["guardrail_mode"] == "pre_call" + + +@pytest.mark.asyncio +async def test_apply_guardrail_request_log_mode_records_eval_and_passes_through(): + router = _judge_router(50.0) + guardrail = _make_guardrail( + overall_threshold=80.0, + on_failure="log", + event_hook=GuardrailEventHooks.during_call, + router_provider=lambda: router, + ) + request_data: dict = {"messages": [{"role": "user", "content": "hi"}], "metadata": {}} + inputs = {"texts": ["hi"]} + + result = await guardrail.apply_guardrail(inputs, request_data, "request") + assert result is inputs + assert request_data["metadata"]["eval_information"]["passed"] is False + assert request_data["metadata"]["standard_logging_guardrail_information"][0]["guardrail_mode"] == "during_call" + + +@pytest.mark.asyncio +async def test_apply_guardrail_response_prompt_unchanged(): + router = _judge_router(90.0) + guardrail = _make_guardrail(router_provider=lambda: router) + request_data: dict = {"messages": [{"role": "user", "content": "hi"}], "metadata": {}} + + await guardrail.apply_guardrail({"texts": ["hello there"]}, request_data, "response") + + judge_messages = router.acompletion.call_args.kwargs["messages"] + assert "assistant's response" in judge_messages[0]["content"] + assert "Conversation:\nUSER: hi\n\nAssistant response to evaluate:\nhello there" in judge_messages[1]["content"] + assert request_data["metadata"]["standard_logging_guardrail_information"][0]["guardrail_mode"] == "post_call" @pytest.mark.asyncio diff --git a/ui/litellm-dashboard/src/app/(dashboard)/guardrails/_components/llm_judge/LLMJudgeFields.tsx b/ui/litellm-dashboard/src/app/(dashboard)/guardrails/_components/llm_judge/LLMJudgeFields.tsx index 256049975d3..7d1df1497db 100644 --- a/ui/litellm-dashboard/src/app/(dashboard)/guardrails/_components/llm_judge/LLMJudgeFields.tsx +++ b/ui/litellm-dashboard/src/app/(dashboard)/guardrails/_components/llm_judge/LLMJudgeFields.tsx @@ -87,8 +87,8 @@ const LLMJudgeFields: React.FC = ({ availableModels, contro return (
- After each LLM response, the Judge Model scores it 0–100 against your criteria. If the weighted - average falls below the threshold, the response is blocked (or logged). + The Judge Model scores the user request (pre_call, during_call) or the LLM response (post_call) + 0–100 against your criteria. If the weighted average falls below the threshold, it is blocked (or logged).
Date: Mon, 14 Sep 2026 21:27:53 +0000 Subject: [PATCH 144/225] fix(guardrails): resolve llm_as_a_judge request-side log mode from event_hook lists, drop unused alias Co-Authored-By: Devin AI <158243242+devin-ai-integration[bot]@users.noreply.github.com> --- .../llm_as_a_judge/__init__.py | 7 +- .../proxy/guardrails/test_llm_as_a_judge.py | 71 +++++++++++-------- 2 files changed, 46 insertions(+), 32 deletions(-) diff --git a/litellm/proxy/guardrails/guardrail_hooks/llm_as_a_judge/__init__.py b/litellm/proxy/guardrails/guardrail_hooks/llm_as_a_judge/__init__.py index 31be90f56ef..1693cf75882 100644 --- a/litellm/proxy/guardrails/guardrail_hooks/llm_as_a_judge/__init__.py +++ b/litellm/proxy/guardrails/guardrail_hooks/llm_as_a_judge/__init__.py @@ -45,7 +45,6 @@ JUDGE_SYSTEM_PROMPTS: Final[MappingProxyType[JudgeInputType, str]] = MappingProx "response": _JUDGE_SYSTEM_PROMPT_TEMPLATE.format(subject="assistant's response"), } ) -JUDGE_SYSTEM_PROMPT: Final = JUDGE_SYSTEM_PROMPTS["response"] _JUDGE_SUBJECT_LABELS: Final[MappingProxyType[JudgeInputType, str]] = MappingProxyType( {"request": "User request to evaluate", "response": "Assistant response to evaluate"} @@ -272,9 +271,9 @@ class LLMAsAJudgeGuardrail(CustomGuardrail): def _event_type_for(self, input_type: JudgeInputType) -> GuardrailEventHooks: if input_type == "response": return GuardrailEventHooks.post_call - if self.event_hook is GuardrailEventHooks.during_call: - return GuardrailEventHooks.during_call - return GuardrailEventHooks.pre_call + if self._event_hook_is_event_type(GuardrailEventHooks.pre_call): + return GuardrailEventHooks.pre_call + return GuardrailEventHooks.during_call def initialize_guardrail( diff --git a/tests/test_litellm/proxy/guardrails/test_llm_as_a_judge.py b/tests/test_litellm/proxy/guardrails/test_llm_as_a_judge.py index a076da62566..dd42e137ba0 100644 --- a/tests/test_litellm/proxy/guardrails/test_llm_as_a_judge.py +++ b/tests/test_litellm/proxy/guardrails/test_llm_as_a_judge.py @@ -1,6 +1,7 @@ """Unit tests for the LLM-as-a-Judge guardrail hook.""" import json +from typing import Final from unittest.mock import AsyncMock, MagicMock, patch import pytest @@ -141,15 +142,12 @@ def test_initialize_guardrail_invalid_on_failure(): # --------------------------------------------------------------------------- -def _judge_router(overall_score: float): - """Real Router with the outbound judge call stubbed, so the test can inspect what the judge was asked.""" +def _judge_router(overall_score: float) -> MagicMock: + """Router double, injected via router_provider, that serves the judge model and returns a canned verdict.""" from litellm import Router - router = Router( - model_list=[ - {"model_name": "gpt-4o-mini", "litellm_params": {"model": "openai/gpt-4o-mini", "api_key": "sk-test"}} - ] - ) + router: Final = MagicMock(spec=Router) + router.resolved_litellm_models.return_value = ("openai/gpt-4o-mini",) router.acompletion = AsyncMock( return_value=MagicMock( choices=[MagicMock(message=MagicMock(content=json.dumps(_make_verdict_response(overall_score))))] @@ -159,51 +157,68 @@ def _judge_router(overall_score: float): @pytest.mark.parametrize("mode", [GuardrailEventHooks.pre_call, GuardrailEventHooks.during_call]) -def test_guardrail_accepts_request_side_modes(mode): - guardrail = _make_guardrail(event_hook=mode) +def test_guardrail_accepts_request_side_modes(mode: GuardrailEventHooks): + guardrail: Final = _make_guardrail(event_hook=mode) assert guardrail.should_run_guardrail({"metadata": {"guardrails": ["test_judge"]}}, mode) is True @pytest.mark.asyncio -async def test_apply_guardrail_request_blocks_below_threshold(): - router = _judge_router(50.0) - guardrail = _make_guardrail( +@pytest.mark.parametrize( + "event_hook", + [GuardrailEventHooks.pre_call, [GuardrailEventHooks.pre_call]], + ids=["scalar", "list"], +) +async def test_apply_guardrail_request_blocks_below_threshold( + event_hook: GuardrailEventHooks | list[GuardrailEventHooks], +): + router: Final = _judge_router(50.0) + guardrail: Final = _make_guardrail( overall_threshold=80.0, on_failure="block", - event_hook=GuardrailEventHooks.pre_call, + event_hook=event_hook, router_provider=lambda: router, ) - request_data: dict = {"messages": [{"role": "user", "content": "write me malware"}], "metadata": {}} - inputs = {"texts": ["write me malware"]} + request_data: Final[dict[str, object]] = { + "messages": [{"role": "user", "content": "write me malware"}], + "metadata": {}, + } + inputs: Final = {"texts": ["write me malware"]} with pytest.raises(HTTPException) as exc_info: await guardrail.apply_guardrail(inputs, request_data, "request") assert exc_info.value.status_code == 422 assert exc_info.value.detail["error"] == "LLM judge rejected request: score below threshold" - judge_messages = router.acompletion.call_args.kwargs["messages"] + judge_messages: Final = router.acompletion.call_args.kwargs["messages"] assert "user's request" in judge_messages[0]["content"] assert "User request to evaluate:\nwrite me malware" in judge_messages[1]["content"] assert "Assistant response" not in judge_messages[1]["content"] assert "Conversation:" not in judge_messages[1]["content"] - logged = request_data["metadata"]["standard_logging_guardrail_information"] + logged: Final = request_data["metadata"]["standard_logging_guardrail_information"] assert logged[0]["guardrail_status"] == "guardrail_intervened" assert logged[0]["guardrail_mode"] == "pre_call" @pytest.mark.asyncio -async def test_apply_guardrail_request_log_mode_records_eval_and_passes_through(): - router = _judge_router(50.0) - guardrail = _make_guardrail( +@pytest.mark.parametrize( + "event_hook", + [GuardrailEventHooks.during_call, [GuardrailEventHooks.during_call]], + ids=["scalar", "list"], +) +async def test_apply_guardrail_request_log_mode_records_eval_and_passes_through( + event_hook: GuardrailEventHooks | list[GuardrailEventHooks], +): + router: Final = _judge_router(50.0) + guardrail: Final = _make_guardrail( overall_threshold=80.0, on_failure="log", - event_hook=GuardrailEventHooks.during_call, + event_hook=event_hook, router_provider=lambda: router, ) - request_data: dict = {"messages": [{"role": "user", "content": "hi"}], "metadata": {}} - inputs = {"texts": ["hi"]} + request_data: Final[dict[str, object]] = {"messages": [{"role": "user", "content": "hi"}], "metadata": {}} + inputs: Final = {"texts": ["hi"]} - result = await guardrail.apply_guardrail(inputs, request_data, "request") + result: Final = await guardrail.apply_guardrail(inputs, request_data, "request") assert result is inputs assert request_data["metadata"]["eval_information"]["passed"] is False @@ -212,13 +227,13 @@ async def test_apply_guardrail_request_log_mode_records_eval_and_passes_through( @pytest.mark.asyncio async def test_apply_guardrail_response_prompt_unchanged(): - router = _judge_router(90.0) - guardrail = _make_guardrail(router_provider=lambda: router) - request_data: dict = {"messages": [{"role": "user", "content": "hi"}], "metadata": {}} + router: Final = _judge_router(90.0) + guardrail: Final = _make_guardrail(router_provider=lambda: router) + request_data: Final[dict[str, object]] = {"messages": [{"role": "user", "content": "hi"}], "metadata": {}} await guardrail.apply_guardrail({"texts": ["hello there"]}, request_data, "response") - judge_messages = router.acompletion.call_args.kwargs["messages"] + judge_messages: Final = router.acompletion.call_args.kwargs["messages"] assert "assistant's response" in judge_messages[0]["content"] assert "Conversation:\nUSER: hi\n\nAssistant response to evaluate:\nhello there" in judge_messages[1]["content"] assert request_data["metadata"]["standard_logging_guardrail_information"][0]["guardrail_mode"] == "post_call" From 6fccf00d931c4c6a891a3f23dafb12035befa6a7 Mon Sep 17 00:00:00 2001 From: yucheng Date: Mon, 14 Sep 2026 21:46:52 +0000 Subject: [PATCH 145/225] fix(guardrails): keep list and tagged mode shapes for llm_as_a_judge Co-Authored-By: Devin AI <158243242+devin-ai-integration[bot]@users.noreply.github.com> --- .../llm_as_a_judge/__init__.py | 36 +++++++++--------- .../proxy/guardrails/test_llm_as_a_judge.py | 37 +++++++++++++++++-- 2 files changed, 53 insertions(+), 20 deletions(-) diff --git a/litellm/proxy/guardrails/guardrail_hooks/llm_as_a_judge/__init__.py b/litellm/proxy/guardrails/guardrail_hooks/llm_as_a_judge/__init__.py index 1693cf75882..863cff6827f 100644 --- a/litellm/proxy/guardrails/guardrail_hooks/llm_as_a_judge/__init__.py +++ b/litellm/proxy/guardrails/guardrail_hooks/llm_as_a_judge/__init__.py @@ -17,7 +17,7 @@ from litellm.litellm_core_utils.llm_judge import ( judge_acompletion, parse_json_verdict, ) -from litellm.types.guardrails import GuardrailEventHooks, SupportedGuardrailIntegrations +from litellm.types.guardrails import GuardrailEventHooks, Mode, SupportedGuardrailIntegrations from litellm.types.utils import GenericGuardrailAPIInputs, GuardrailStatus if TYPE_CHECKING: @@ -28,6 +28,8 @@ if TYPE_CHECKING: from litellm.types.utils import StandardLoggingEvalInformation JudgeInputType = Literal["request", "response"] +JudgeEventHook = GuardrailEventHooks | list[GuardrailEventHooks] | Mode +JudgeModeParam = str | list[str] | Mode | GuardrailEventHooks | list[GuardrailEventHooks] | None _JUDGE_SYSTEM_PROMPT_TEMPLATE: Final = """You are a quality judge. Evaluate the {subject} against the criteria provided. For each criterion, assign a score from 0 to 100 and provide concise reasoning. @@ -41,13 +43,13 @@ Return ONLY valid JSON in this exact format: JUDGE_SYSTEM_PROMPTS: Final[MappingProxyType[JudgeInputType, str]] = MappingProxyType( { - "request": _JUDGE_SYSTEM_PROMPT_TEMPLATE.format(subject="user's request"), + "request": _JUDGE_SYSTEM_PROMPT_TEMPLATE.format(subject="request"), "response": _JUDGE_SYSTEM_PROMPT_TEMPLATE.format(subject="assistant's response"), } ) _JUDGE_SUBJECT_LABELS: Final[MappingProxyType[JudgeInputType, str]] = MappingProxyType( - {"request": "User request to evaluate", "response": "Assistant response to evaluate"} + {"request": "Request text to evaluate", "response": "Assistant response to evaluate"} ) _VALID_ON_FAILURE: Final = frozenset({"block", "log"}) @@ -100,6 +102,16 @@ def _get_litellm_param( return default +def _coerce_event_hook(mode: JudgeModeParam) -> JudgeEventHook: + if mode is None: + return GuardrailEventHooks.post_call + if isinstance(mode, Mode): + return mode + if isinstance(mode, list): + return [GuardrailEventHooks(hook) for hook in mode] + return GuardrailEventHooks(mode) + + def _build_judge_prompt( criteria: Sequence[JudgeCriterion], messages: Sequence[JudgeMessage], @@ -132,22 +144,15 @@ class LLMAsAJudgeGuardrail(CustomGuardrail): criteria: Sequence[JudgeCriterion], overall_threshold: float = 80.0, on_failure: Literal["block", "log"] = "block", - event_hook: GuardrailEventHooks | list[GuardrailEventHooks] | None = None, + event_hook: JudgeModeParam = None, default_on: bool = False, router_provider: "Callable[[], Router | None] | None" = None, **kwargs: Any, ) -> None: - _event_hook: GuardrailEventHooks | list[GuardrailEventHooks] | None = None - if event_hook is not None: - if isinstance(event_hook, list): - _event_hook = [GuardrailEventHooks(h) if isinstance(h, str) else h for h in event_hook] - else: - _event_hook = GuardrailEventHooks(event_hook) if isinstance(event_hook, str) else event_hook - super().__init__( guardrail_name=guardrail_name, supported_event_hooks=list(self.get_supported_event_hooks()), - event_hook=_event_hook or GuardrailEventHooks.post_call, + event_hook=_coerce_event_hook(event_hook), default_on=default_on, **kwargs, ) @@ -302,10 +307,7 @@ def initialize_guardrail( overall_threshold: Final = float(_get_litellm_param(litellm_params, guardrail, "overall_threshold", 80.0)) - mode: Final[str | None] = _get_litellm_param(litellm_params, guardrail, "mode", None) - event_hook: GuardrailEventHooks | None = None - if isinstance(mode, str) and mode in {e.value for e in GuardrailEventHooks}: - event_hook = GuardrailEventHooks(mode) + mode: Final[JudgeModeParam] = _get_litellm_param(litellm_params, guardrail, "mode", None) instance: Final = LLMAsAJudgeGuardrail( guardrail_name=guardrail_name, @@ -313,7 +315,7 @@ def initialize_guardrail( criteria=criteria, overall_threshold=overall_threshold, on_failure=on_failure, - event_hook=event_hook, + event_hook=mode, default_on=bool(_get_litellm_param(litellm_params, guardrail, "default_on", False)), ) litellm.logging_callback_manager.add_litellm_callback(instance) diff --git a/tests/test_litellm/proxy/guardrails/test_llm_as_a_judge.py b/tests/test_litellm/proxy/guardrails/test_llm_as_a_judge.py index dd42e137ba0..cab24514844 100644 --- a/tests/test_litellm/proxy/guardrails/test_llm_as_a_judge.py +++ b/tests/test_litellm/proxy/guardrails/test_llm_as_a_judge.py @@ -14,7 +14,7 @@ from litellm.proxy.guardrails.guardrail_hooks.llm_as_a_judge import ( _parse_judge_verdict, initialize_guardrail, ) -from litellm.types.guardrails import GuardrailEventHooks +from litellm.types.guardrails import GuardrailEventHooks, Mode # --------------------------------------------------------------------------- # Helpers @@ -137,6 +137,37 @@ def test_initialize_guardrail_invalid_on_failure(): initialize_guardrail(lp, g) +@pytest.mark.parametrize( + ("mode", "runs_pre_call", "runs_post_call"), + [ + ("pre_call", True, False), + (["pre_call", "post_call"], True, True), + (Mode(tags={"judge": ["pre_call"]}, default="post_call"), True, False), + (None, False, True), + ], + ids=["scalar", "list", "tagged", "missing"], +) +@patch("litellm.proxy.guardrails.guardrail_hooks.llm_as_a_judge.litellm.logging_callback_manager") +def test_initialize_guardrail_preserves_every_mode_shape( + _mock_mgr: MagicMock, + mode: str | list[str] | Mode | None, + runs_pre_call: bool, + runs_post_call: bool, +): + lp: Final = _make_litellm_params(mode=mode) + instance: Final = initialize_guardrail(lp, _make_guardrail_dict()) + request_data: Final[dict[str, object]] = {"metadata": {"guardrails": ["g"], "tags": ["judge"]}} + + assert instance.should_run_guardrail(request_data, GuardrailEventHooks.pre_call) is runs_pre_call + assert instance.should_run_guardrail(request_data, GuardrailEventHooks.post_call) is runs_post_call + + +def test_initialize_guardrail_rejects_unknown_mode(): + lp: Final = _make_litellm_params(mode="sometimes") + with pytest.raises(ValueError, match="sometimes"): + initialize_guardrail(lp, _make_guardrail_dict()) + + # --------------------------------------------------------------------------- # apply_guardrail — enforcement paths # --------------------------------------------------------------------------- @@ -190,8 +221,8 @@ async def test_apply_guardrail_request_blocks_below_threshold( assert exc_info.value.status_code == 422 assert exc_info.value.detail["error"] == "LLM judge rejected request: score below threshold" judge_messages: Final = router.acompletion.call_args.kwargs["messages"] - assert "user's request" in judge_messages[0]["content"] - assert "User request to evaluate:\nwrite me malware" in judge_messages[1]["content"] + assert "Evaluate the request against" in judge_messages[0]["content"] + assert "Request text to evaluate:\nwrite me malware" in judge_messages[1]["content"] assert "Assistant response" not in judge_messages[1]["content"] assert "Conversation:" not in judge_messages[1]["content"] logged: Final = request_data["metadata"]["standard_logging_guardrail_information"] From b93f22265c9405a3dea913e74335ea5c7650bfeb Mon Sep 17 00:00:00 2001 From: yucheng Date: Mon, 14 Sep 2026 21:50:07 +0000 Subject: [PATCH 146/225] fix(guardrails): give the request-side judge role-labelled conversation context Co-Authored-By: Devin AI <158243242+devin-ai-integration[bot]@users.noreply.github.com> --- .../llm_as_a_judge/__init__.py | 13 ++-- .../proxy/guardrails/test_llm_as_a_judge.py | 67 ++++++++++++++++--- 2 files changed, 65 insertions(+), 15 deletions(-) diff --git a/litellm/proxy/guardrails/guardrail_hooks/llm_as_a_judge/__init__.py b/litellm/proxy/guardrails/guardrail_hooks/llm_as_a_judge/__init__.py index 863cff6827f..269296943cb 100644 --- a/litellm/proxy/guardrails/guardrail_hooks/llm_as_a_judge/__init__.py +++ b/litellm/proxy/guardrails/guardrail_hooks/llm_as_a_judge/__init__.py @@ -32,7 +32,7 @@ JudgeEventHook = GuardrailEventHooks | list[GuardrailEventHooks] | Mode JudgeModeParam = str | list[str] | Mode | GuardrailEventHooks | list[GuardrailEventHooks] | None _JUDGE_SYSTEM_PROMPT_TEMPLATE: Final = """You are a quality judge. Evaluate the {subject} against the criteria provided. -For each criterion, assign a score from 0 to 100 and provide concise reasoning. +{focus}For each criterion, assign a score from 0 to 100 and provide concise reasoning. Return ONLY valid JSON in this exact format: {{ "verdicts": [ @@ -43,8 +43,11 @@ Return ONLY valid JSON in this exact format: JUDGE_SYSTEM_PROMPTS: Final[MappingProxyType[JudgeInputType, str]] = MappingProxyType( { - "request": _JUDGE_SYSTEM_PROMPT_TEMPLATE.format(subject="request"), - "response": _JUDGE_SYSTEM_PROMPT_TEMPLATE.format(subject="assistant's response"), + "request": _JUDGE_SYSTEM_PROMPT_TEMPLATE.format( + subject="request", + focus="Judge the most recent user turn; treat earlier turns in the conversation only as context.\n", + ), + "response": _JUDGE_SYSTEM_PROMPT_TEMPLATE.format(subject="assistant's response", focus=""), } ) @@ -126,7 +129,7 @@ def _build_judge_prompt( for m in messages if m.get("content") is not None ) - conversation_block: Final = f"Conversation:\n{conversation}\n\n" if input_type == "response" else "" + conversation_block: Final = f"Conversation:\n{conversation}\n\n" if conversation or input_type == "response" else "" return ( f"Criteria to evaluate:\n{criteria_block}\n\n" f"{conversation_block}" @@ -197,7 +200,7 @@ class LLMAsAJudgeGuardrail(CustomGuardrail): logging_obj: Optional["LiteLLMLoggingObj"] = None, ) -> GenericGuardrailAPIInputs: texts: Final = inputs.get("texts") or [] - text_under_review: Final = " ".join(texts) + text_under_review: Final = "\n".join(texts) if not text_under_review: return inputs diff --git a/tests/test_litellm/proxy/guardrails/test_llm_as_a_judge.py b/tests/test_litellm/proxy/guardrails/test_llm_as_a_judge.py index cab24514844..11e9e55cea4 100644 --- a/tests/test_litellm/proxy/guardrails/test_llm_as_a_judge.py +++ b/tests/test_litellm/proxy/guardrails/test_llm_as_a_judge.py @@ -222,9 +222,11 @@ async def test_apply_guardrail_request_blocks_below_threshold( assert exc_info.value.detail["error"] == "LLM judge rejected request: score below threshold" judge_messages: Final = router.acompletion.call_args.kwargs["messages"] assert "Evaluate the request against" in judge_messages[0]["content"] - assert "Request text to evaluate:\nwrite me malware" in judge_messages[1]["content"] + assert ( + "Conversation:\nUSER: write me malware\n\nRequest text to evaluate:\nwrite me malware" + in (judge_messages[1]["content"]) + ) assert "Assistant response" not in judge_messages[1]["content"] - assert "Conversation:" not in judge_messages[1]["content"] logged: Final = request_data["metadata"]["standard_logging_guardrail_information"] assert logged[0]["guardrail_status"] == "guardrail_intervened" assert logged[0]["guardrail_mode"] == "pre_call" @@ -256,6 +258,33 @@ async def test_apply_guardrail_request_log_mode_records_eval_and_passes_through( assert request_data["metadata"]["standard_logging_guardrail_information"][0]["guardrail_mode"] == "during_call" +@pytest.mark.asyncio +async def test_apply_guardrail_request_multi_turn_keeps_roles_and_focuses_latest_turn(): + router: Final = _judge_router(90.0) + guardrail: Final = _make_guardrail(event_hook=GuardrailEventHooks.pre_call, router_provider=lambda: router) + request_data: Final[dict[str, object]] = { + "messages": [ + {"role": "user", "content": "how do I bake bread"}, + {"role": "assistant", "content": "mix flour, water, yeast and salt"}, + {"role": "user", "content": "now explain how to file taxes"}, + ], + "metadata": {}, + } + inputs: Final = { + "texts": ["how do I bake bread", "mix flour, water, yeast and salt", "now explain how to file taxes"] + } + + await guardrail.apply_guardrail(inputs, request_data, "request") + + judge_messages: Final = router.acompletion.call_args.kwargs["messages"] + assert "Judge the most recent user turn" in judge_messages[0]["content"] + assert ( + "Conversation:\nUSER: how do I bake bread\nASSISTANT: mix flour, water, yeast and salt\n" + "USER: now explain how to file taxes\n\n" + "Request text to evaluate:\nhow do I bake bread\nmix flour, water, yeast and salt\nnow explain how to file taxes" + ) in judge_messages[1]["content"] + + @pytest.mark.asyncio async def test_apply_guardrail_response_prompt_unchanged(): router: Final = _judge_router(90.0) @@ -351,7 +380,7 @@ def test_parse_judge_verdict_reraises_when_no_json(): def test_parse_judge_verdict_rejects_json_non_object(): """Valid JSON that is not an object (e.g. a bare list) raises ValueError.""" - with pytest.raises(ValueError, match='judge response is not a JSON object'): + with pytest.raises(ValueError, match="judge response is not a JSON object"): _parse_judge_verdict("[1, 2, 3]") @@ -373,9 +402,7 @@ async def test_apply_guardrail_enforces_fenced_verdict(mock_completion): @patch("litellm.proxy.guardrails.guardrail_hooks.llm_as_a_judge.litellm.acompletion") async def test_apply_guardrail_non_object_verdict_fails_open_with_status(mock_completion): """A non-object verdict fails open and logs guardrail_failed_to_respond.""" - mock_completion.return_value = MagicMock( - choices=[MagicMock(message=MagicMock(content='[{"overall_score": 50}]'))] - ) + mock_completion.return_value = MagicMock(choices=[MagicMock(message=MagicMock(content='[{"overall_score": 50}]'))]) guardrail = _make_guardrail(overall_threshold=80.0, on_failure="block", router_provider=lambda: None) inputs = {"texts": ["response"]} request_data: dict = {"messages": [], "metadata": {}} @@ -435,7 +462,12 @@ def _real_router(model_list, **router_kwargs): "model_list, router_kwargs, judge_model", [ ( - [{"model_name": "my-judge-alias", "litellm_params": {"model": "anthropic/claude-sonnet-4-6", "api_key": "sk-ant-test"}}], + [ + { + "model_name": "my-judge-alias", + "litellm_params": {"model": "anthropic/claude-sonnet-4-6", "api_key": "sk-ant-test"}, + } + ], {}, "my-judge-alias", ), @@ -445,12 +477,22 @@ def _real_router(model_list, **router_kwargs): "anthropic/claude-sonnet-4-6", ), ( - [{"model_name": "backing-group", "litellm_params": {"model": "anthropic/claude-sonnet-4-6", "api_key": "sk-ant-test"}}], + [ + { + "model_name": "backing-group", + "litellm_params": {"model": "anthropic/claude-sonnet-4-6", "api_key": "sk-ant-test"}, + } + ], {"model_group_alias": {"my-judge-alias": "backing-group"}}, "my-judge-alias", ), ( - [{"model_name": "backing-group", "litellm_params": {"model": "anthropic/claude-sonnet-4-6", "api_key": "sk-ant-test"}}], + [ + { + "model_name": "backing-group", + "litellm_params": {"model": "anthropic/claude-sonnet-4-6", "api_key": "sk-ant-test"}, + } + ], {"model_group_alias": {"my-judge-alias": {"model": "backing-group", "hidden": True}}}, "my-judge-alias", ), @@ -533,7 +575,12 @@ async def test_judge_resolves_router_lazily_per_call(mock_sdk_completion): mock_sdk_completion.assert_awaited_once() holder["router"] = _real_router( - [{"model_name": "my-judge-alias", "litellm_params": {"model": "anthropic/claude-sonnet-4-6", "api_key": "sk-ant-test"}}] + [ + { + "model_name": "my-judge-alias", + "litellm_params": {"model": "anthropic/claude-sonnet-4-6", "api_key": "sk-ant-test"}, + } + ] ) await guardrail.apply_guardrail({"texts": ["r"]}, {"messages": [], "metadata": {}}, "response") holder["router"].acompletion.assert_awaited_once() From 28bd00a004fca77f1608f8d42f07e136a0b14b2e Mon Sep 17 00:00:00 2001 From: yucheng Date: Mon, 14 Sep 2026 21:53:20 +0000 Subject: [PATCH 147/225] fix(guardrails): label llm_as_a_judge logging_only verdicts with their mode Co-Authored-By: Devin AI <158243242+devin-ai-integration[bot]@users.noreply.github.com> --- .../guardrail_hooks/llm_as_a_judge/__init__.py | 2 ++ .../proxy/guardrails/test_llm_as_a_judge.py | 18 ++++++++++++++++++ 2 files changed, 20 insertions(+) diff --git a/litellm/proxy/guardrails/guardrail_hooks/llm_as_a_judge/__init__.py b/litellm/proxy/guardrails/guardrail_hooks/llm_as_a_judge/__init__.py index 269296943cb..9f0c7476b4c 100644 --- a/litellm/proxy/guardrails/guardrail_hooks/llm_as_a_judge/__init__.py +++ b/litellm/proxy/guardrails/guardrail_hooks/llm_as_a_judge/__init__.py @@ -277,6 +277,8 @@ class LLMAsAJudgeGuardrail(CustomGuardrail): ) def _event_type_for(self, input_type: JudgeInputType) -> GuardrailEventHooks: + if self._event_hook_is_event_type(GuardrailEventHooks.logging_only): + return GuardrailEventHooks.logging_only if input_type == "response": return GuardrailEventHooks.post_call if self._event_hook_is_event_type(GuardrailEventHooks.pre_call): diff --git a/tests/test_litellm/proxy/guardrails/test_llm_as_a_judge.py b/tests/test_litellm/proxy/guardrails/test_llm_as_a_judge.py index 11e9e55cea4..fc877b323e0 100644 --- a/tests/test_litellm/proxy/guardrails/test_llm_as_a_judge.py +++ b/tests/test_litellm/proxy/guardrails/test_llm_as_a_judge.py @@ -285,6 +285,24 @@ async def test_apply_guardrail_request_multi_turn_keeps_roles_and_focuses_latest ) in judge_messages[1]["content"] +@pytest.mark.asyncio +@pytest.mark.parametrize("input_type", ["request", "response"]) +async def test_apply_guardrail_logging_only_labels_both_sides_logging_only(input_type: str): + router: Final = _judge_router(50.0) + guardrail: Final = _make_guardrail( + on_failure="log", + event_hook=GuardrailEventHooks.logging_only, + router_provider=lambda: router, + ) + request_data: Final[dict[str, object]] = {"messages": [{"role": "user", "content": "hi"}], "metadata": {}} + + assert guardrail.should_run_guardrail(request_data, GuardrailEventHooks.pre_call) is False + assert guardrail.should_run_guardrail(request_data, GuardrailEventHooks.post_call) is False + await guardrail.apply_guardrail({"texts": ["hi"]}, request_data, input_type) + + assert request_data["metadata"]["standard_logging_guardrail_information"][0]["guardrail_mode"] == "logging_only" + + @pytest.mark.asyncio async def test_apply_guardrail_response_prompt_unchanged(): router: Final = _judge_router(90.0) From 4d1330ea0b30fe50843a0d2e60c956905a4825c7 Mon Sep 17 00:00:00 2001 From: yucheng Date: Mon, 14 Sep 2026 22:00:12 +0000 Subject: [PATCH 148/225] test(guardrails): drop callback-manager patch from llm_as_a_judge mode-shape test Co-Authored-By: Devin AI <158243242+devin-ai-integration[bot]@users.noreply.github.com> --- .../proxy/guardrails/test_llm_as_a_judge.py | 11 ++++++----- 1 file changed, 6 insertions(+), 5 deletions(-) diff --git a/tests/test_litellm/proxy/guardrails/test_llm_as_a_judge.py b/tests/test_litellm/proxy/guardrails/test_llm_as_a_judge.py index fc877b323e0..21f58bcee4b 100644 --- a/tests/test_litellm/proxy/guardrails/test_llm_as_a_judge.py +++ b/tests/test_litellm/proxy/guardrails/test_llm_as_a_judge.py @@ -7,6 +7,7 @@ from unittest.mock import AsyncMock, MagicMock, patch import pytest from fastapi import HTTPException +import litellm from litellm.proxy.guardrails.guardrail_hooks.llm_as_a_judge import ( LLMAsAJudgeGuardrail, _build_judge_prompt, @@ -147,9 +148,7 @@ def test_initialize_guardrail_invalid_on_failure(): ], ids=["scalar", "list", "tagged", "missing"], ) -@patch("litellm.proxy.guardrails.guardrail_hooks.llm_as_a_judge.litellm.logging_callback_manager") def test_initialize_guardrail_preserves_every_mode_shape( - _mock_mgr: MagicMock, mode: str | list[str] | Mode | None, runs_pre_call: bool, runs_post_call: bool, @@ -157,9 +156,11 @@ def test_initialize_guardrail_preserves_every_mode_shape( lp: Final = _make_litellm_params(mode=mode) instance: Final = initialize_guardrail(lp, _make_guardrail_dict()) request_data: Final[dict[str, object]] = {"metadata": {"guardrails": ["g"], "tags": ["judge"]}} - - assert instance.should_run_guardrail(request_data, GuardrailEventHooks.pre_call) is runs_pre_call - assert instance.should_run_guardrail(request_data, GuardrailEventHooks.post_call) is runs_post_call + try: + assert instance.should_run_guardrail(request_data, GuardrailEventHooks.pre_call) is runs_pre_call + assert instance.should_run_guardrail(request_data, GuardrailEventHooks.post_call) is runs_post_call + finally: + litellm.logging_callback_manager.remove_callback_from_all_lists(instance) def test_initialize_guardrail_rejects_unknown_mode(): From a658f200050f765325fbc2f1c5f3c046506c457e Mon Sep 17 00:00:00 2001 From: yucheng Date: Mon, 14 Sep 2026 22:14:24 +0000 Subject: [PATCH 149/225] test(guardrails): grant premium_user for the tagged Mode case in llm_as_a_judge mode-shape test Co-Authored-By: Devin AI <158243242+devin-ai-integration[bot]@users.noreply.github.com> --- tests/test_litellm/proxy/guardrails/test_llm_as_a_judge.py | 6 ++++-- 1 file changed, 4 insertions(+), 2 deletions(-) diff --git a/tests/test_litellm/proxy/guardrails/test_llm_as_a_judge.py b/tests/test_litellm/proxy/guardrails/test_llm_as_a_judge.py index 21f58bcee4b..2c121022cd2 100644 --- a/tests/test_litellm/proxy/guardrails/test_llm_as_a_judge.py +++ b/tests/test_litellm/proxy/guardrails/test_llm_as_a_judge.py @@ -156,9 +156,11 @@ def test_initialize_guardrail_preserves_every_mode_shape( lp: Final = _make_litellm_params(mode=mode) instance: Final = initialize_guardrail(lp, _make_guardrail_dict()) request_data: Final[dict[str, object]] = {"metadata": {"guardrails": ["g"], "tags": ["judge"]}} + premium: Final = patch("litellm.proxy.proxy_server.premium_user", True) # test-quality-ok: no seam for Mode tags try: - assert instance.should_run_guardrail(request_data, GuardrailEventHooks.pre_call) is runs_pre_call - assert instance.should_run_guardrail(request_data, GuardrailEventHooks.post_call) is runs_post_call + with premium: + assert instance.should_run_guardrail(request_data, GuardrailEventHooks.pre_call) is runs_pre_call + assert instance.should_run_guardrail(request_data, GuardrailEventHooks.post_call) is runs_post_call finally: litellm.logging_callback_manager.remove_callback_from_all_lists(instance) From e9357d9a6f1326852de2bb0c7db48deba1fb9cd8 Mon Sep 17 00:00:00 2001 From: yucheng Date: Mon, 14 Sep 2026 22:41:04 +0000 Subject: [PATCH 150/225] fix(guardrails): stop logging_only llm_as_a_judge from judging its own judge calls Judge sub-calls now carry the internal_call_origin metadata stamp and the guardrail skips any logged call bearing it, so a logging_only judge no longer recurses into an unbounded chain of judge requests Co-Authored-By: Devin AI <158243242+devin-ai-integration[bot]@users.noreply.github.com> --- .../llm_as_a_judge/__init__.py | 31 +++++++++++++++++-- litellm/types/utils.py | 2 ++ .../proxy/guardrails/test_llm_as_a_judge.py | 16 ++++++++++ 3 files changed, 47 insertions(+), 2 deletions(-) diff --git a/litellm/proxy/guardrails/guardrail_hooks/llm_as_a_judge/__init__.py b/litellm/proxy/guardrails/guardrail_hooks/llm_as_a_judge/__init__.py index 9f0c7476b4c..d00bf86ed2b 100644 --- a/litellm/proxy/guardrails/guardrail_hooks/llm_as_a_judge/__init__.py +++ b/litellm/proxy/guardrails/guardrail_hooks/llm_as_a_judge/__init__.py @@ -1,15 +1,17 @@ """LLM-as-a-Judge guardrail: uses an LLM to score requests or responses against weighted criteria.""" -from collections.abc import Callable, Sequence +from collections.abc import Callable, Mapping, Sequence from datetime import datetime from types import MappingProxyType from typing import TYPE_CHECKING, Any, Final, Generic, Literal, Optional, TypeVar from fastapi import HTTPException +from pydantic import BaseModel, ConfigDict, ValidationError from typing_extensions import NotRequired, ReadOnly, TypedDict import litellm from litellm._logging import verbose_logger +from litellm.constants import INTERNAL_CALL_ORIGIN_METADATA_KEY from litellm.integrations.custom_guardrail import CustomGuardrail from litellm.litellm_core_utils.llm_judge import ( default_router_provider, @@ -18,7 +20,7 @@ from litellm.litellm_core_utils.llm_judge import ( parse_json_verdict, ) from litellm.types.guardrails import GuardrailEventHooks, Mode, SupportedGuardrailIntegrations -from litellm.types.utils import GenericGuardrailAPIInputs, GuardrailStatus +from litellm.types.utils import LLM_AS_A_JUDGE_GUARDRAIL_CALL_ORIGIN, GenericGuardrailAPIInputs, GuardrailStatus if TYPE_CHECKING: from litellm import Router @@ -57,6 +59,25 @@ _JUDGE_SUBJECT_LABELS: Final[MappingProxyType[JudgeInputType, str]] = MappingPro _VALID_ON_FAILURE: Final = frozenset({"block", "log"}) +_JUDGE_CALL_METADATA: Final = MappingProxyType( + {INTERNAL_CALL_ORIGIN_METADATA_KEY: LLM_AS_A_JUDGE_GUARDRAIL_CALL_ORIGIN} +) + + +class _LoggedCallParams(BaseModel): + model_config = ConfigDict(frozen=True) + + metadata: Mapping[str, object] | None = None + + +def _is_judge_call(data: Mapping[str, object]) -> bool: + try: + params: Final = _LoggedCallParams.model_validate(data.get("litellm_params") or {}) + except ValidationError: + return False + return (params.metadata or {}).get(INTERNAL_CALL_ORIGIN_METADATA_KEY) == LLM_AS_A_JUDGE_GUARDRAIL_CALL_ORIGIN + + _default_router_provider: Final = default_router_provider _parse_judge_verdict: Final = parse_json_verdict _extract_text_from_content: Final = extract_text_from_content @@ -169,6 +190,11 @@ class LLMAsAJudgeGuardrail(CustomGuardrail): def get_supported_event_hooks(cls) -> list[GuardrailEventHooks]: return [GuardrailEventHooks.pre_call, GuardrailEventHooks.during_call, GuardrailEventHooks.post_call] + def should_run_guardrail(self, data: Mapping[str, object], event_type: GuardrailEventHooks) -> bool: + if _is_judge_call(data): + return False + return super().should_run_guardrail(data, event_type) + async def _run_judge( self, messages: Sequence[JudgeMessage], @@ -188,6 +214,7 @@ class LLMAsAJudgeGuardrail(CustomGuardrail): judge_messages, response_format={"type": "json_object"}, temperature=0, + metadata=dict(_JUDGE_CALL_METADATA), ) raw: Final = response.choices[0].message.content or "{}" return _parse_judge_verdict(raw) diff --git a/litellm/types/utils.py b/litellm/types/utils.py index 298c30bbec2..aaa16fd2d44 100644 --- a/litellm/types/utils.py +++ b/litellm/types/utils.py @@ -2957,6 +2957,7 @@ InternalCallOrigin = Literal[ "autorouter_classifier", "shadow_eval_router", "shadow_eval_judge", + "llm_as_a_judge_guardrail", "background_response_cost_poll", ] """Which internal litellm feature originated a billed sub-call, so a spend log row @@ -2965,6 +2966,7 @@ records that it is not traffic the caller sent.""" AUTOROUTER_CLASSIFIER_CALL_ORIGIN: Final[InternalCallOrigin] = "autorouter_classifier" SHADOW_EVAL_ROUTER_CALL_ORIGIN: Final[InternalCallOrigin] = "shadow_eval_router" SHADOW_EVAL_JUDGE_CALL_ORIGIN: Final[InternalCallOrigin] = "shadow_eval_judge" +LLM_AS_A_JUDGE_GUARDRAIL_CALL_ORIGIN: Final[InternalCallOrigin] = "llm_as_a_judge_guardrail" BACKGROUND_RESPONSE_COST_POLL_CALL_ORIGIN: Final[InternalCallOrigin] = "background_response_cost_poll" diff --git a/tests/test_litellm/proxy/guardrails/test_llm_as_a_judge.py b/tests/test_litellm/proxy/guardrails/test_llm_as_a_judge.py index 2c121022cd2..643ccaa75f2 100644 --- a/tests/test_litellm/proxy/guardrails/test_llm_as_a_judge.py +++ b/tests/test_litellm/proxy/guardrails/test_llm_as_a_judge.py @@ -306,6 +306,22 @@ async def test_apply_guardrail_logging_only_labels_both_sides_logging_only(input assert request_data["metadata"]["standard_logging_guardrail_information"][0]["guardrail_mode"] == "logging_only" +@pytest.mark.asyncio +async def test_logging_only_judge_does_not_judge_its_own_judge_call(): + router: Final = _judge_router(90.0) + guardrail: Final = _make_guardrail(event_hook=GuardrailEventHooks.logging_only, router_provider=lambda: router) + client_call: Final[dict[str, object]] = {"litellm_params": {"metadata": {"user_api_key": "hashed"}}} + + assert guardrail.should_run_guardrail(client_call, GuardrailEventHooks.logging_only) is True + await guardrail.apply_guardrail({"texts": ["hi"]}, {"messages": [{"role": "user", "content": "hi"}]}, "request") + + judge_call: Final[dict[str, object]] = { + "litellm_params": {"metadata": router.acompletion.call_args.kwargs["metadata"]} + } + assert guardrail.should_run_guardrail(judge_call, GuardrailEventHooks.logging_only) is False + assert guardrail.should_run_guardrail(client_call, GuardrailEventHooks.logging_only) is True + + @pytest.mark.asyncio async def test_apply_guardrail_response_prompt_unchanged(): router: Final = _judge_router(90.0) From 1b8b17cc8a911e31be0522d8ab57e647e80069bc Mon Sep 17 00:00:00 2001 From: yucheng Date: Mon, 14 Sep 2026 23:54:15 +0000 Subject: [PATCH 151/225] fix(guardrails): judge only the latest request turn in pre_call and during_call llm_as_a_judge The request-side prompt told the judge to focus on the most recent user turn but the text under review was every extracted request message joined together, so a multi-turn request with an off-topic earlier turn and an on-topic latest turn scored 50 and was blocked. Request-side judging now evaluates the last extracted request text (after the configured message scoping) and passes the full role-labelled conversation only as context. Response-side judging still evaluates all extracted response text Co-Authored-By: Devin AI <158243242+devin-ai-integration[bot]@users.noreply.github.com> --- .../llm_as_a_judge/__init__.py | 11 +++++++--- .../proxy/guardrails/test_llm_as_a_judge.py | 22 +++++++++++++++---- 2 files changed, 26 insertions(+), 7 deletions(-) diff --git a/litellm/proxy/guardrails/guardrail_hooks/llm_as_a_judge/__init__.py b/litellm/proxy/guardrails/guardrail_hooks/llm_as_a_judge/__init__.py index d00bf86ed2b..afb39d53393 100644 --- a/litellm/proxy/guardrails/guardrail_hooks/llm_as_a_judge/__init__.py +++ b/litellm/proxy/guardrails/guardrail_hooks/llm_as_a_judge/__init__.py @@ -54,7 +54,7 @@ JUDGE_SYSTEM_PROMPTS: Final[MappingProxyType[JudgeInputType, str]] = MappingProx ) _JUDGE_SUBJECT_LABELS: Final[MappingProxyType[JudgeInputType, str]] = MappingProxyType( - {"request": "Request text to evaluate", "response": "Assistant response to evaluate"} + {"request": "Latest request turn to evaluate", "response": "Assistant response to evaluate"} ) _VALID_ON_FAILURE: Final = frozenset({"block", "log"}) @@ -136,6 +136,12 @@ def _coerce_event_hook(mode: JudgeModeParam) -> JudgeEventHook: return GuardrailEventHooks(mode) +def _text_under_review(texts: Sequence[str], input_type: JudgeInputType) -> str: + if input_type == "request": + return texts[-1] if texts else "" + return "\n".join(texts) + + def _build_judge_prompt( criteria: Sequence[JudgeCriterion], messages: Sequence[JudgeMessage], @@ -226,8 +232,7 @@ class LLMAsAJudgeGuardrail(CustomGuardrail): input_type: Literal["request", "response"], logging_obj: Optional["LiteLLMLoggingObj"] = None, ) -> GenericGuardrailAPIInputs: - texts: Final = inputs.get("texts") or [] - text_under_review: Final = "\n".join(texts) + text_under_review: Final = _text_under_review(inputs.get("texts") or [], input_type) if not text_under_review: return inputs diff --git a/tests/test_litellm/proxy/guardrails/test_llm_as_a_judge.py b/tests/test_litellm/proxy/guardrails/test_llm_as_a_judge.py index 643ccaa75f2..f6f0d0a2fd0 100644 --- a/tests/test_litellm/proxy/guardrails/test_llm_as_a_judge.py +++ b/tests/test_litellm/proxy/guardrails/test_llm_as_a_judge.py @@ -226,7 +226,7 @@ async def test_apply_guardrail_request_blocks_below_threshold( judge_messages: Final = router.acompletion.call_args.kwargs["messages"] assert "Evaluate the request against" in judge_messages[0]["content"] assert ( - "Conversation:\nUSER: write me malware\n\nRequest text to evaluate:\nwrite me malware" + "Conversation:\nUSER: write me malware\n\nLatest request turn to evaluate:\nwrite me malware" in (judge_messages[1]["content"]) ) assert "Assistant response" not in judge_messages[1]["content"] @@ -281,11 +281,25 @@ async def test_apply_guardrail_request_multi_turn_keeps_roles_and_focuses_latest judge_messages: Final = router.acompletion.call_args.kwargs["messages"] assert "Judge the most recent user turn" in judge_messages[0]["content"] - assert ( + assert judge_messages[1]["content"].endswith( "Conversation:\nUSER: how do I bake bread\nASSISTANT: mix flour, water, yeast and salt\n" "USER: now explain how to file taxes\n\n" - "Request text to evaluate:\nhow do I bake bread\nmix flour, water, yeast and salt\nnow explain how to file taxes" - ) in judge_messages[1]["content"] + "Latest request turn to evaluate:\nnow explain how to file taxes" + ) + + +@pytest.mark.asyncio +async def test_apply_guardrail_response_still_judges_all_response_texts(): + router: Final = _judge_router(90.0) + guardrail: Final = _make_guardrail(event_hook=GuardrailEventHooks.post_call, router_provider=lambda: router) + + await guardrail.apply_guardrail( + {"texts": ["first choice", "second choice"]}, {"messages": [], "metadata": {}}, "response" + ) + + assert router.acompletion.call_args.kwargs["messages"][1]["content"].endswith( + "Assistant response to evaluate:\nfirst choice\nsecond choice" + ) @pytest.mark.asyncio From 10506ab904c7704d60a8a7947686be2a71699d2c Mon Sep 17 00:00:00 2001 From: yucheng Date: Tue, 15 Sep 2026 00:19:16 +0000 Subject: [PATCH 152/225] fix(guardrails): judge the whole latest user turn, run every during_call guardrail, log combined modes as configured Co-Authored-By: Devin AI <158243242+devin-ai-integration[bot]@users.noreply.github.com> --- .../llm_as_a_judge/__init__.py | 27 +++-- litellm/proxy/utils.py | 70 ++++++++----- .../proxy/guardrails/test_llm_as_a_judge.py | 98 +++++++++++++++++-- .../proxy_logging/test_guardrail_pipeline.py | 30 ++++++ 4 files changed, 178 insertions(+), 47 deletions(-) diff --git a/litellm/proxy/guardrails/guardrail_hooks/llm_as_a_judge/__init__.py b/litellm/proxy/guardrails/guardrail_hooks/llm_as_a_judge/__init__.py index afb39d53393..4c047dbc988 100644 --- a/litellm/proxy/guardrails/guardrail_hooks/llm_as_a_judge/__init__.py +++ b/litellm/proxy/guardrails/guardrail_hooks/llm_as_a_judge/__init__.py @@ -19,6 +19,7 @@ from litellm.litellm_core_utils.llm_judge import ( judge_acompletion, parse_json_verdict, ) +from litellm.litellm_core_utils.prompt_templates.common_utils import get_last_user_message from litellm.types.guardrails import GuardrailEventHooks, Mode, SupportedGuardrailIntegrations from litellm.types.utils import LLM_AS_A_JUDGE_GUARDRAIL_CALL_ORIGIN, GenericGuardrailAPIInputs, GuardrailStatus @@ -57,6 +58,8 @@ _JUDGE_SUBJECT_LABELS: Final[MappingProxyType[JudgeInputType, str]] = MappingPro {"request": "Latest request turn to evaluate", "response": "Assistant response to evaluate"} ) +_REQUEST_EVENT_HOOKS: Final = (GuardrailEventHooks.pre_call, GuardrailEventHooks.during_call) + _VALID_ON_FAILURE: Final = frozenset({"block", "log"}) _JUDGE_CALL_METADATA: Final = MappingProxyType( @@ -136,10 +139,12 @@ def _coerce_event_hook(mode: JudgeModeParam) -> JudgeEventHook: return GuardrailEventHooks(mode) -def _text_under_review(texts: Sequence[str], input_type: JudgeInputType) -> str: - if input_type == "request": - return texts[-1] if texts else "" - return "\n".join(texts) +def _text_under_review(inputs: GenericGuardrailAPIInputs, input_type: JudgeInputType) -> str: + all_text: Final = "\n".join(inputs.get("texts") or []) + if input_type == "response": + return all_text + latest_user_turn: Final = get_last_user_message(inputs.get("structured_messages") or []) + return latest_user_turn if latest_user_turn is not None else all_text def _build_judge_prompt( @@ -232,7 +237,7 @@ class LLMAsAJudgeGuardrail(CustomGuardrail): input_type: Literal["request", "response"], logging_obj: Optional["LiteLLMLoggingObj"] = None, ) -> GenericGuardrailAPIInputs: - text_under_review: Final = _text_under_review(inputs.get("texts") or [], input_type) + text_under_review: Final = _text_under_review(inputs, input_type) if not text_under_review: return inputs @@ -241,7 +246,9 @@ class LLMAsAJudgeGuardrail(CustomGuardrail): judge_result: dict[str, object] = {} try: - messages: Final[Sequence[JudgeMessage]] = request_data.get("messages") or [] + messages: Final[Sequence[JudgeMessage]] = ( + inputs.get("structured_messages") or request_data.get("messages") or [] + ) try: judge_result = await self._run_judge(messages, text_under_review, input_type) @@ -308,14 +315,14 @@ class LLMAsAJudgeGuardrail(CustomGuardrail): event_type=self._event_type_for(input_type), ) - def _event_type_for(self, input_type: JudgeInputType) -> GuardrailEventHooks: + def _event_type_for(self, input_type: JudgeInputType) -> GuardrailEventHooks | None: + """Returns None (log the configured mode as-is) when the active request hook is ambiguous.""" if self._event_hook_is_event_type(GuardrailEventHooks.logging_only): return GuardrailEventHooks.logging_only if input_type == "response": return GuardrailEventHooks.post_call - if self._event_hook_is_event_type(GuardrailEventHooks.pre_call): - return GuardrailEventHooks.pre_call - return GuardrailEventHooks.during_call + configured: Final = tuple(hook for hook in _REQUEST_EVENT_HOOKS if self._event_hook_is_event_type(hook)) + return configured[0] if len(configured) == 1 else None def initialize_guardrail( diff --git a/litellm/proxy/utils.py b/litellm/proxy/utils.py index b6c487c0edf..23eb6cc2e87 100644 --- a/litellm/proxy/utils.py +++ b/litellm/proxy/utils.py @@ -2669,34 +2669,15 @@ class ProxyLogging: user_api_key_auth_dict = self._convert_user_api_key_auth_to_dict(user_api_key_dict) else: user_api_key_auth_dict = user_api_key_dict - # Add task to list for parallel execution - if ( - "apply_guardrail" in type(callback).__dict__ - and not callback.use_native_lifecycle_hooks - and user_api_key_dict is not None - and not getattr(callback, "use_native_during_call_hook", False) - ): - data["guardrail_to_apply"] = callback - guardrail_task = self._run_guardrail_with_metrics( - callback, - unified_guardrail.async_moderation_hook( - user_api_key_dict=user_api_key_dict, - data=data, - call_type=call_type, - ), - "during_call", + guardrail_tasks.append( + self._run_during_call_guardrail( + callback=callback, + data=data, + user_api_key_dict=user_api_key_dict, + user_api_key_auth_dict=user_api_key_auth_dict, + call_type=call_type, ) - else: - guardrail_task = self._run_guardrail_with_metrics( - callback, - callback.async_moderation_hook( - data=data, - user_api_key_dict=user_api_key_auth_dict, - call_type=call_type, - ), - "during_call", - ) - guardrail_tasks.append(guardrail_task) + ) # Step 2: Run all guardrail tasks in parallel if guardrail_tasks: @@ -2708,6 +2689,41 @@ class ProxyLogging: return data + async def _run_during_call_guardrail( + self, + callback: CustomGuardrail, + data: dict, + user_api_key_dict: UserAPIKeyAuth | None, + user_api_key_auth_dict: UserAPIKeyAuth | dict[str, object] | None, + call_type: CallTypesLiteral, + ) -> None: + if ( + "apply_guardrail" in type(callback).__dict__ + and not callback.use_native_lifecycle_hooks + and user_api_key_dict is not None + and not callback.use_native_during_call_hook + ): + data["guardrail_to_apply"] = callback + await self._run_guardrail_with_metrics( + callback, + unified_guardrail.async_moderation_hook( + user_api_key_dict=user_api_key_dict, + data=data, + call_type=call_type, + ), + "during_call", + ) + return + await self._run_guardrail_with_metrics( + callback, + callback.async_moderation_hook( + data=data, + user_api_key_dict=user_api_key_auth_dict, + call_type=call_type, + ), + "during_call", + ) + async def failed_tracking_alert( self, error_message: str, diff --git a/tests/test_litellm/proxy/guardrails/test_llm_as_a_judge.py b/tests/test_litellm/proxy/guardrails/test_llm_as_a_judge.py index f6f0d0a2fd0..a81af524b69 100644 --- a/tests/test_litellm/proxy/guardrails/test_llm_as_a_judge.py +++ b/tests/test_litellm/proxy/guardrails/test_llm_as_a_judge.py @@ -265,19 +265,17 @@ async def test_apply_guardrail_request_log_mode_records_eval_and_passes_through( async def test_apply_guardrail_request_multi_turn_keeps_roles_and_focuses_latest_turn(): router: Final = _judge_router(90.0) guardrail: Final = _make_guardrail(event_hook=GuardrailEventHooks.pre_call, router_provider=lambda: router) - request_data: Final[dict[str, object]] = { - "messages": [ - {"role": "user", "content": "how do I bake bread"}, - {"role": "assistant", "content": "mix flour, water, yeast and salt"}, - {"role": "user", "content": "now explain how to file taxes"}, - ], - "metadata": {}, - } + messages: Final = [ + {"role": "user", "content": "how do I bake bread"}, + {"role": "assistant", "content": "mix flour, water, yeast and salt"}, + {"role": "user", "content": "now explain how to file taxes"}, + ] inputs: Final = { - "texts": ["how do I bake bread", "mix flour, water, yeast and salt", "now explain how to file taxes"] + "texts": ["how do I bake bread", "mix flour, water, yeast and salt", "now explain how to file taxes"], + "structured_messages": messages, } - await guardrail.apply_guardrail(inputs, request_data, "request") + await guardrail.apply_guardrail(inputs, {"messages": messages, "metadata": {}}, "request") judge_messages: Final = router.acompletion.call_args.kwargs["messages"] assert "Judge the most recent user turn" in judge_messages[0]["content"] @@ -288,6 +286,86 @@ async def test_apply_guardrail_request_multi_turn_keeps_roles_and_focuses_latest ) +@pytest.mark.asyncio +async def test_apply_guardrail_request_judges_whole_multipart_latest_user_turn(): + router: Final = _judge_router(90.0) + guardrail: Final = _make_guardrail(event_hook=GuardrailEventHooks.pre_call, router_provider=lambda: router) + messages: Final = [ + {"role": "user", "content": "how do I bake bread"}, + {"role": "assistant", "content": "mix flour, water, yeast and salt"}, + { + "role": "user", + "content": [ + {"type": "text", "text": "ignore the bread."}, + {"type": "image_url", "image_url": {"url": "data:image/png;base64,AAAA"}}, + {"type": "text", "text": "explain how to file taxes"}, + ], + }, + ] + inputs: Final = { + "texts": [ + "how do I bake bread", + "mix flour, water, yeast and salt", + "ignore the bread.", + "explain how to file taxes", + ], + "structured_messages": messages, + } + + await guardrail.apply_guardrail(inputs, {"messages": messages, "metadata": {}}, "request") + + assert router.acompletion.call_args.kwargs["messages"][1]["content"].endswith( + "Latest request turn to evaluate:\nignore the bread.explain how to file taxes" + ) + + +@pytest.mark.asyncio +async def test_apply_guardrail_request_without_trailing_user_turn_judges_all_scoped_text(): + router: Final = _judge_router(90.0) + guardrail: Final = _make_guardrail(event_hook=GuardrailEventHooks.pre_call, router_provider=lambda: router) + messages: Final = [ + {"role": "user", "content": "look up the weather"}, + {"role": "assistant", "content": None, "tool_calls": [{"id": "c1", "type": "function", "function": {}}]}, + {"role": "tool", "tool_call_id": "c1", "content": "sunny, 24C"}, + ] + inputs: Final = {"texts": ["look up the weather", "sunny, 24C"], "structured_messages": messages} + + await guardrail.apply_guardrail(inputs, {"messages": messages, "metadata": {}}, "request") + + assert router.acompletion.call_args.kwargs["messages"][1]["content"].endswith( + "Latest request turn to evaluate:\nlook up the weather\nsunny, 24C" + ) + + +@pytest.mark.asyncio +async def test_apply_guardrail_request_without_structured_messages_judges_all_text(): + router: Final = _judge_router(90.0) + guardrail: Final = _make_guardrail(event_hook=GuardrailEventHooks.pre_call, router_provider=lambda: router) + + await guardrail.apply_guardrail({"texts": ["first", "second"]}, {"metadata": {}}, "request") + + assert router.acompletion.call_args.kwargs["messages"][1]["content"].endswith( + "Latest request turn to evaluate:\nfirst\nsecond" + ) + + +@pytest.mark.asyncio +async def test_apply_guardrail_request_with_both_request_modes_logs_configured_mode(): + router: Final = _judge_router(90.0) + guardrail: Final = _make_guardrail( + event_hook=[GuardrailEventHooks.pre_call, GuardrailEventHooks.during_call], + router_provider=lambda: router, + ) + request_data: Final[dict[str, object]] = {"messages": [{"role": "user", "content": "hi"}], "metadata": {}} + + await guardrail.apply_guardrail({"texts": ["hi"]}, request_data, "request") + + assert request_data["metadata"]["standard_logging_guardrail_information"][0]["guardrail_mode"] == [ + "pre_call", + "during_call", + ] + + @pytest.mark.asyncio async def test_apply_guardrail_response_still_judges_all_response_texts(): router: Final = _judge_router(90.0) diff --git a/tests/test_litellm/proxy/utils/proxy_logging/test_guardrail_pipeline.py b/tests/test_litellm/proxy/utils/proxy_logging/test_guardrail_pipeline.py index dfe106a3f52..077bf5a313e 100644 --- a/tests/test_litellm/proxy/utils/proxy_logging/test_guardrail_pipeline.py +++ b/tests/test_litellm/proxy/utils/proxy_logging/test_guardrail_pipeline.py @@ -689,6 +689,36 @@ async def test_during_call_hook_records_latency_metric(proxy_logging, make_user_ assert recorded["status"] == "success" +class _RecordingApplyGuardrail(CustomGuardrail): + def __init__(self, guardrail_name: str, applied: list[str]) -> None: + super().__init__( + guardrail_name=guardrail_name, + event_hook=GuardrailEventHooks.during_call, + default_on=True, + ) + self._applied = applied + + async def apply_guardrail(self, inputs, request_data, input_type, logging_obj=None): + await asyncio.sleep(0) + self._applied.append(self.guardrail_name or "") + return inputs + + +@pytest.mark.asyncio +async def test_during_call_hook_runs_every_unified_guardrail(proxy_logging, make_user_api_key_auth, monkeypatch): + applied: list[str] = [] + guardrails = [_RecordingApplyGuardrail(f"judge-{i}", applied) for i in range(3)] + monkeypatch.setattr(litellm, "callbacks", guardrails) + + await proxy_logging.during_call_hook( + data={"model": "m", "messages": [{"role": "user", "content": "hi"}], "metadata": {}}, + user_api_key_dict=make_user_api_key_auth(), + call_type="completion", + ) + + assert sorted(applied) == ["judge-0", "judge-1", "judge-2"] + + @pytest.mark.asyncio async def test_post_call_success_hook_records_latency_metric(proxy_logging, make_user_api_key_auth, monkeypatch): cb = _moderation_guardrail() From dd4829398a4c04d7ea8ed961d7d59d062fe66649 Mon Sep 17 00:00:00 2001 From: yucheng Date: Tue, 15 Sep 2026 00:23:20 +0000 Subject: [PATCH 153/225] fix(guardrails): log the configured mode when logging_only is mixed with an enforcing llm_as_a_judge mode Co-Authored-By: Devin AI <158243242+devin-ai-integration[bot]@users.noreply.github.com> --- .../llm_as_a_judge/__init__.py | 14 ++++++------ .../proxy/guardrails/test_llm_as_a_judge.py | 22 ++++++++++++------- 2 files changed, 21 insertions(+), 15 deletions(-) diff --git a/litellm/proxy/guardrails/guardrail_hooks/llm_as_a_judge/__init__.py b/litellm/proxy/guardrails/guardrail_hooks/llm_as_a_judge/__init__.py index 4c047dbc988..46fccc126e7 100644 --- a/litellm/proxy/guardrails/guardrail_hooks/llm_as_a_judge/__init__.py +++ b/litellm/proxy/guardrails/guardrail_hooks/llm_as_a_judge/__init__.py @@ -58,7 +58,12 @@ _JUDGE_SUBJECT_LABELS: Final[MappingProxyType[JudgeInputType, str]] = MappingPro {"request": "Latest request turn to evaluate", "response": "Assistant response to evaluate"} ) -_REQUEST_EVENT_HOOKS: Final = (GuardrailEventHooks.pre_call, GuardrailEventHooks.during_call) +_LIFECYCLE_HOOKS: Final[MappingProxyType[JudgeInputType, tuple[GuardrailEventHooks, ...]]] = MappingProxyType( + { + "request": (GuardrailEventHooks.pre_call, GuardrailEventHooks.during_call, GuardrailEventHooks.logging_only), + "response": (GuardrailEventHooks.post_call, GuardrailEventHooks.logging_only), + } +) _VALID_ON_FAILURE: Final = frozenset({"block", "log"}) @@ -316,12 +321,7 @@ class LLMAsAJudgeGuardrail(CustomGuardrail): ) def _event_type_for(self, input_type: JudgeInputType) -> GuardrailEventHooks | None: - """Returns None (log the configured mode as-is) when the active request hook is ambiguous.""" - if self._event_hook_is_event_type(GuardrailEventHooks.logging_only): - return GuardrailEventHooks.logging_only - if input_type == "response": - return GuardrailEventHooks.post_call - configured: Final = tuple(hook for hook in _REQUEST_EVENT_HOOKS if self._event_hook_is_event_type(hook)) + configured: Final = tuple(hook for hook in _LIFECYCLE_HOOKS[input_type] if self._event_hook_is_event_type(hook)) return configured[0] if len(configured) == 1 else None diff --git a/tests/test_litellm/proxy/guardrails/test_llm_as_a_judge.py b/tests/test_litellm/proxy/guardrails/test_llm_as_a_judge.py index a81af524b69..1646f730984 100644 --- a/tests/test_litellm/proxy/guardrails/test_llm_as_a_judge.py +++ b/tests/test_litellm/proxy/guardrails/test_llm_as_a_judge.py @@ -350,19 +350,25 @@ async def test_apply_guardrail_request_without_structured_messages_judges_all_te @pytest.mark.asyncio -async def test_apply_guardrail_request_with_both_request_modes_logs_configured_mode(): +@pytest.mark.parametrize( + ("modes", "input_type"), + [ + ([GuardrailEventHooks.pre_call, GuardrailEventHooks.during_call], "request"), + ([GuardrailEventHooks.pre_call, GuardrailEventHooks.logging_only], "request"), + ([GuardrailEventHooks.post_call, GuardrailEventHooks.logging_only], "response"), + ], +) +async def test_apply_guardrail_with_ambiguous_modes_logs_configured_mode( + modes: list[GuardrailEventHooks], input_type: str +): router: Final = _judge_router(90.0) - guardrail: Final = _make_guardrail( - event_hook=[GuardrailEventHooks.pre_call, GuardrailEventHooks.during_call], - router_provider=lambda: router, - ) + guardrail: Final = _make_guardrail(event_hook=modes, router_provider=lambda: router) request_data: Final[dict[str, object]] = {"messages": [{"role": "user", "content": "hi"}], "metadata": {}} - await guardrail.apply_guardrail({"texts": ["hi"]}, request_data, "request") + await guardrail.apply_guardrail({"texts": ["hi"]}, request_data, input_type) assert request_data["metadata"]["standard_logging_guardrail_information"][0]["guardrail_mode"] == [ - "pre_call", - "during_call", + mode.value for mode in modes ] From 801f6a92b4a2d0e55daa738ff9fd3db0098382c6 Mon Sep 17 00:00:00 2001 From: kerry Date: Wed, 16 Sep 2026 01:05:53 +0000 Subject: [PATCH 154/225] test(gemini): assert the served modelVersion reaches the assembled stream through CustomStreamWrapper Co-Authored-By: Devin AI <158243242+devin-ai-integration[bot]@users.noreply.github.com> --- ...test_vertex_and_google_ai_studio_gemini.py | 39 +++++++++++++++++-- 1 file changed, 36 insertions(+), 3 deletions(-) diff --git a/tests/test_litellm/llms/vertex_ai/gemini/test_vertex_and_google_ai_studio_gemini.py b/tests/test_litellm/llms/vertex_ai/gemini/test_vertex_and_google_ai_studio_gemini.py index 29a47cd54f2..3fb00a7d962 100644 --- a/tests/test_litellm/llms/vertex_ai/gemini/test_vertex_and_google_ai_studio_gemini.py +++ b/tests/test_litellm/llms/vertex_ai/gemini/test_vertex_and_google_ai_studio_gemini.py @@ -5892,9 +5892,42 @@ def test_streaming_chunk_carries_model_version(): ) chunk = {**_generate_content_body(), "modelVersion": "gemini-x-served"} - iterator: Final = ModelResponseIterator( - streaming_response=[], sync_stream=True, logging_obj=MagicMock() - ) + iterator: Final = ModelResponseIterator(streaming_response=[], sync_stream=True, logging_obj=MagicMock()) streaming_chunk: Final = iterator.chunk_parser(chunk) assert streaming_chunk.model == "gemini-x-served" + + +def test_served_model_version_reaches_assembled_stream_through_custom_stream_wrapper(): + """Greptile claimed CustomStreamWrapper drops the served modelVersion before + pricing. It does not: chunk_creator stashes the parser chunk's model into each + yielded chunk's _hidden_params["provider_response_model"], and + stream_chunk_builder carries it onto the assembled response. The wrapper's + trailing bookkeeping chunk is the one emission that legitimately omits it.""" + from litellm.litellm_core_utils.streaming_handler import CustomStreamWrapper + from litellm.llms.vertex_ai.gemini.vertex_and_google_ai_studio_gemini import ( + ModelResponseIterator, + ) + + served_model: Final = "gemini-3.8-flash-001" + iterator: Final = ModelResponseIterator( + streaming_response=iter( + [json.dumps({**_generate_content_body(), "modelVersion": served_model}) for _ in range(3)] + ), + sync_stream=True, + logging_obj=MagicMock(), + ) + wrapper: Final = CustomStreamWrapper( + completion_stream=iter(iterator), + model="gemini/gemini-3.8-flash", + custom_llm_provider="gemini", + logging_obj=MagicMock(), + ) + + chunks: Final = list(wrapper) + + assert len(chunks) >= 3 + for chunk in chunks[:-1]: + assert chunk._hidden_params["provider_response_model"] == served_model + assembled: Final = litellm.stream_chunk_builder(chunks=list(chunks), messages=[{"role": "user", "content": "hi"}]) + assert assembled._hidden_params["provider_response_model"] == served_model From f66ffc387eac7ba3ae395348e0c5d087a349fe92 Mon Sep 17 00:00:00 2001 From: yucheng Date: Tue, 15 Sep 2026 01:58:24 +0000 Subject: [PATCH 155/225] fix(guardrails): only honor the judge call-origin stamp on logging_only in llm_as_a_judge On pre_call, during_call and post_call the hook data is the client request body, so a client-supplied litellm_params.metadata.internal_call_origin must not skip enforcement. Type the during_call helper's request payload as dict[str, object] Co-Authored-By: Devin AI <158243242+devin-ai-integration[bot]@users.noreply.github.com> --- .../guardrail_hooks/llm_as_a_judge/__init__.py | 7 +++++-- litellm/proxy/utils.py | 2 +- .../proxy/guardrails/test_llm_as_a_judge.py | 16 ++++++++++++++++ 3 files changed, 22 insertions(+), 3 deletions(-) diff --git a/litellm/proxy/guardrails/guardrail_hooks/llm_as_a_judge/__init__.py b/litellm/proxy/guardrails/guardrail_hooks/llm_as_a_judge/__init__.py index 46fccc126e7..8eac6b2ee53 100644 --- a/litellm/proxy/guardrails/guardrail_hooks/llm_as_a_judge/__init__.py +++ b/litellm/proxy/guardrails/guardrail_hooks/llm_as_a_judge/__init__.py @@ -78,7 +78,10 @@ class _LoggedCallParams(BaseModel): metadata: Mapping[str, object] | None = None -def _is_judge_call(data: Mapping[str, object]) -> bool: +def _is_logged_judge_call(data: Mapping[str, object], event_type: GuardrailEventHooks) -> bool: + """logging_only is the only event whose ``data`` is the SDK's model_call_details rather than the client body.""" + if event_type is not GuardrailEventHooks.logging_only: + return False try: params: Final = _LoggedCallParams.model_validate(data.get("litellm_params") or {}) except ValidationError: @@ -207,7 +210,7 @@ class LLMAsAJudgeGuardrail(CustomGuardrail): return [GuardrailEventHooks.pre_call, GuardrailEventHooks.during_call, GuardrailEventHooks.post_call] def should_run_guardrail(self, data: Mapping[str, object], event_type: GuardrailEventHooks) -> bool: - if _is_judge_call(data): + if _is_logged_judge_call(data, event_type): return False return super().should_run_guardrail(data, event_type) diff --git a/litellm/proxy/utils.py b/litellm/proxy/utils.py index 23eb6cc2e87..215fb143f7b 100644 --- a/litellm/proxy/utils.py +++ b/litellm/proxy/utils.py @@ -2692,7 +2692,7 @@ class ProxyLogging: async def _run_during_call_guardrail( self, callback: CustomGuardrail, - data: dict, + data: dict[str, object], # mutable-ok: request payload dict, guardrail_to_apply is written in place user_api_key_dict: UserAPIKeyAuth | None, user_api_key_auth_dict: UserAPIKeyAuth | dict[str, object] | None, call_type: CallTypesLiteral, diff --git a/tests/test_litellm/proxy/guardrails/test_llm_as_a_judge.py b/tests/test_litellm/proxy/guardrails/test_llm_as_a_judge.py index 1646f730984..6e00958eba4 100644 --- a/tests/test_litellm/proxy/guardrails/test_llm_as_a_judge.py +++ b/tests/test_litellm/proxy/guardrails/test_llm_as_a_judge.py @@ -8,6 +8,7 @@ import pytest from fastapi import HTTPException import litellm +from litellm.constants import INTERNAL_CALL_ORIGIN_METADATA_KEY from litellm.proxy.guardrails.guardrail_hooks.llm_as_a_judge import ( LLMAsAJudgeGuardrail, _build_judge_prompt, @@ -16,6 +17,7 @@ from litellm.proxy.guardrails.guardrail_hooks.llm_as_a_judge import ( initialize_guardrail, ) from litellm.types.guardrails import GuardrailEventHooks, Mode +from litellm.types.utils import LLM_AS_A_JUDGE_GUARDRAIL_CALL_ORIGIN # --------------------------------------------------------------------------- # Helpers @@ -420,6 +422,20 @@ async def test_logging_only_judge_does_not_judge_its_own_judge_call(): assert guardrail.should_run_guardrail(client_call, GuardrailEventHooks.logging_only) is True +@pytest.mark.parametrize( + "event_type", [GuardrailEventHooks.pre_call, GuardrailEventHooks.during_call, GuardrailEventHooks.post_call] +) +def test_client_supplied_judge_origin_does_not_bypass_enforcing_hooks(event_type: GuardrailEventHooks): + guardrail: Final = _make_guardrail(event_hook=event_type) + forged_request: Final[dict[str, object]] = { + "messages": [{"role": "user", "content": "hi"}], + "guardrails": [guardrail.guardrail_name], + "litellm_params": {"metadata": {INTERNAL_CALL_ORIGIN_METADATA_KEY: LLM_AS_A_JUDGE_GUARDRAIL_CALL_ORIGIN}}, + } + + assert guardrail.should_run_guardrail(forged_request, event_type) is True + + @pytest.mark.asyncio async def test_apply_guardrail_response_prompt_unchanged(): router: Final = _judge_router(90.0) From 1484fd76001fae494c31e186c079c92868ddd0c5 Mon Sep 17 00:00:00 2001 From: kerry Date: Wed, 16 Sep 2026 01:13:26 +0000 Subject: [PATCH 156/225] fix(responses): keep the usage estimate best-effort when token counting raises Co-Authored-By: Devin AI <158243242+devin-ai-integration[bot]@users.noreply.github.com> --- litellm/responses/streaming_iterator.py | 20 +++++++++++++++- .../responses/test_streaming_iterator.py | 24 +++++++++++++++++++ 2 files changed, 43 insertions(+), 1 deletion(-) diff --git a/litellm/responses/streaming_iterator.py b/litellm/responses/streaming_iterator.py index 6cb0331620f..6cf36e3e660 100644 --- a/litellm/responses/streaming_iterator.py +++ b/litellm/responses/streaming_iterator.py @@ -440,7 +440,7 @@ class BaseResponsesAPIStreamingIterator: and _response_obj is not None and _response_obj.usage is None ): - _response_obj.usage = _estimate_usage_from_text( + _response_obj.usage = _estimate_usage_safely( self.model or "", self.request_data.get("input"), self.request_data, @@ -1381,6 +1381,24 @@ def _estimate_usage_from_text( ) +def _estimate_usage_safely( + model: str, + request_input: object, + responses_api_request: Mapping[str, object], + generated_text: str, +) -> ResponseAPIUsage | None: + try: + return _estimate_usage_from_text( + model=model, + request_input=request_input, + responses_api_request=responses_api_request, + generated_text=generated_text, + ) + except Exception as e: + verbose_logger.debug("Could not estimate usage from stream text, billing $0: %s", e) + return None + + def _stamp_responses_usage_cost( response_obj: ResponsesAPIResponse | None, logging_obj: LiteLLMLoggingObj | None ) -> None: diff --git a/tests/test_litellm/responses/test_streaming_iterator.py b/tests/test_litellm/responses/test_streaming_iterator.py index c3721353d42..234e63ca3be 100644 --- a/tests/test_litellm/responses/test_streaming_iterator.py +++ b/tests/test_litellm/responses/test_streaming_iterator.py @@ -792,3 +792,27 @@ async def test_completed_event_without_usage_counts_multimodal_input_as_messages usage = iterator.completed_response.response.usage assert usage is not None assert usage.input_tokens < json_count / 2 + + +@pytest.mark.asyncio +async def test_completed_event_survives_a_failing_usage_estimate(): + """A raising token_counter must not break a stream that previously completed: + the estimate is best-effort and falls back to usage None.""" + response = _responses_api_response_without_usage() + iterator = _make_iterator( + sse_events=[ + _sse_event({"type": "response.output_text.delta", "delta": "hello world"}), + _sse_event({"type": "response.completed", "response": {}}), + ], + logging_obj=_logging_obj_stub(), + config=_mock_config_with_completed_response(response), + request_data={"input": "count these input tokens please"}, + ) + + with patch.object(litellm, "token_counter", side_effect=RuntimeError("tokenizer exploded")): + yielded: list = [] + async for chunk in iterator: + yielded.append(chunk) + + assert yielded + assert iterator.completed_response.response.usage is None From 4587dbeae98b798be6776e5b2a47d5eebea50705 Mon Sep 17 00:00:00 2001 From: Yuneng Jiang Date: Tue, 15 Sep 2026 18:14:12 -0700 Subject: [PATCH 157/225] feat(e2e): cache exact provider responses for 24 hours --- .github/workflows/test-provider-cache.yml | 63 +++ .../test_provider_cache.py | 409 ++++++++++++++++++ tests/e2e/PROVIDER_CACHE.md | 27 ++ tests/e2e/conftest.py | 6 +- tests/e2e/e2e_http.py | 41 +- .../e2e/llm_translation/test_messages_e2e.py | 3 +- .../llm_translation/test_together_ai_e2e.py | 1 + tests/e2e/provider_cache.py | 297 +++++++++++++ tests/e2e/provider_cache_redis.py | 154 +++++++ tests/e2e/provider_cache_routing.py | 23 + tests/e2e/provider_edge.py | 61 ++- tests/e2e/proxy_client.py | 8 +- 12 files changed, 1077 insertions(+), 16 deletions(-) create mode 100644 .github/workflows/test-provider-cache.yml create mode 100644 tests/code_coverage_tests/test_provider_cache.py create mode 100644 tests/e2e/PROVIDER_CACHE.md create mode 100644 tests/e2e/provider_cache.py create mode 100644 tests/e2e/provider_cache_redis.py create mode 100644 tests/e2e/provider_cache_routing.py diff --git a/.github/workflows/test-provider-cache.yml b/.github/workflows/test-provider-cache.yml new file mode 100644 index 00000000000..29b9d08a267 --- /dev/null +++ b/.github/workflows/test-provider-cache.yml @@ -0,0 +1,63 @@ +name: Provider cache contracts + +on: + pull_request: + paths: + - 'tests/e2e/**' + - 'tests/code_coverage_tests/test_provider_cache.py' + - 'tests/code_coverage_tests/test_provider_replay_harness.py' + - '.github/workflows/test-provider-cache.yml' + - 'pyproject.toml' + - 'uv.lock' + workflow_dispatch: + +permissions: + contents: read + +jobs: + provider-cache: + runs-on: ubuntu-latest + timeout-minutes: 20 + services: + redis: + image: redis@sha256:e2debfb7956fa12c7ddc79d7e645c8cf26b30c99a6e9161ea9bf4171e1668a5f + ports: + - 6379:6379 + options: >- + --health-cmd 'redis-cli ping' + --health-interval 5s + --health-timeout 3s + --health-retries 10 + env: + PYTHONDONTWRITEBYTECODE: '1' + PYTHONPATH: tests/e2e + E2E_CACHE_TEST_REDIS_URL: redis://127.0.0.1:6379/0 + E2E_PROVIDER_CACHE: '0' + E2E_FIXTURE_MODE: live + steps: + - uses: actions/checkout@08eba0b27e820071cde6df949e0beb9ba4906955 + with: + persist-credentials: false + - uses: actions/setup-python@a26af69be951a213d495a4c3e4e4022e16d87065 + with: + python-version: '3.13' + - uses: ./.github/actions/setup-uv-with-retries + with: + version: '0.10.9' + - uses: ./.github/actions/cache-cargo-build + - name: Install locked dependencies + run: .github/scripts/uv_sync_with_retries.sh --frozen --group proxy-dev --group e2e-dev + - name: Verify cache and existing edge contracts + run: >- + uv run --no-sync pytest -c /dev/null -p no:cacheprovider + tests/code_coverage_tests/test_provider_cache.py + tests/code_coverage_tests/test_provider_replay_harness.py + tests/e2e/test_provider_edge.py + -q --junitxml=provider-cache-results.xml + - name: Save test results + if: always() + uses: actions/upload-artifact@4cec3d8aa04e39d1a68397de0c4cd6fb9dce8ec1 + with: + name: provider-cache-results + path: provider-cache-results.xml + if-no-files-found: warn diff --git a/tests/code_coverage_tests/test_provider_cache.py b/tests/code_coverage_tests/test_provider_cache.py new file mode 100644 index 00000000000..7edca8151af --- /dev/null +++ b/tests/code_coverage_tests/test_provider_cache.py @@ -0,0 +1,409 @@ +from __future__ import annotations + +import os +import shutil +import socket +import subprocess +import threading +import time +import uuid +from collections.abc import Generator +from concurrent.futures import ThreadPoolExecutor +from contextlib import contextmanager +from dataclasses import dataclass, replace +from http.server import BaseHTTPRequestHandler, ThreadingHTTPServer +from typing import Final + +import pytest +from e2e_http import NetworkError, PreparedForward, RawResponse, StreamChunk, StreamHead, forward, prepare_forward +from models import LiteLLMParamsBody +from provider_cache import CacheEdge, CacheHit, CaptureLease, exact_key, successful_response +from provider_cache_redis import PUBLISH, RedisCommands, RedisResponseStore, configured_cache, redis_store +from provider_cache_routing import LIVE_PROVIDER_REQUIRED, route_cache_model +from provider_edge import configured_cache_backend, start_provider_edge +from redis.exceptions import ConnectionError as RedisConnectionError + +SECRET: Final = b"synthetic-cache-hmac-key-for-tests" +BODY: Final = b'{"model":"test","messages":[{"role":"user","content":"hello"}]}' +SUCCESS: Final = b'{"id":"provider-fixed-id","choices":[{"message":{"content":"hello"},"finish_reason":"stop"}],"usage":{"prompt_tokens":1,"completion_tokens":1,"total_tokens":2}}' +HEADERS: Final = {"content-type": "application/json", "authorization": "Bearer synthetic-account-one"} + + +class Provider(ThreadingHTTPServer): + hits: tuple[tuple[str, bytes], ...] = () + response: bytes = SUCCESS + status: int = 200 + delay: float = 0 + stream: bool = False + truncated: bool = False + + +class Handler(BaseHTTPRequestHandler): + protocol_version = "HTTP/1.1" + + def do_POST(self) -> None: + server: Final = self.server + assert isinstance(server, Provider) + body: Final = self.rfile.read(int(self.headers.get("content-length", "0"))) + server.hits += ((self.path, body),) + time.sleep(server.delay) + self.send_response(server.status) + if server.stream: + self.send_header("content-type", "text/event-stream") + self.send_header("transfer-encoding", "chunked") + self.end_headers() + self.wfile.write(b"%x\r\n%s\r\n" % (len(server.response), server.response)) + if server.truncated: + self.close_connection = True + return + self.wfile.write(b"0\r\n\r\n") + return + self.send_header("content-type", "application/json") + self.send_header("content-length", str(len(server.response))) + self.end_headers() + self.wfile.write(server.response) + + def log_message(self, format: str, *args: object) -> None: + pass + + +@pytest.fixture +def provider() -> Generator[Provider, None, None]: + server: Final = Provider(("127.0.0.1", 0), Handler) + thread: Final = threading.Thread(target=server.serve_forever, daemon=True) + thread.start() + try: + yield server + finally: + server.shutdown() + server.server_close() + thread.join(timeout=5) + + +@pytest.fixture(scope="module") +def redis_url(tmp_path_factory: pytest.TempPathFactory) -> Generator[str, None, None]: + configured: Final = os.environ.get("E2E_CACHE_TEST_REDIS_URL") + if configured: + yield configured + return + binary: Final = shutil.which("redis-server") + assert binary is not None, "Set E2E_CACHE_TEST_REDIS_URL or install Redis for cache integration checks" + root: Final = tmp_path_factory.mktemp("provider-cache-redis") + with socket.socket() as probe: + probe.bind(("127.0.0.1", 0)) + port: Final = probe.getsockname()[1] + with (root / "redis.log").open("wb") as log: + process: Final = subprocess.Popen( + [binary, "--bind", "127.0.0.1", "--port", str(port), "--save", "", "--appendonly", "no", "--dir", str(root)], + stdout=log, stderr=subprocess.STDOUT, + ) + try: + deadline: Final = time.monotonic() + 5 + while True: + try: + with socket.create_connection(("127.0.0.1", port), timeout=0.1): + break + except OSError: + assert process.poll() is None and time.monotonic() < deadline + time.sleep(0.02) + yield f"redis://127.0.0.1:{port}/0" + finally: + process.terminate() + process.wait(timeout=5) + + +@pytest.fixture +def store(redis_url: str) -> RedisResponseStore: + return redis_store(redis_url, "test-" + uuid.uuid4().hex) + + +@contextmanager +def edge(cache: CacheEdge, provider: Provider) -> Generator[str, None, None]: + upstream: Final = f"http://127.0.0.1:{provider.server_port}" + running: Final = start_provider_edge(cache, mounts={"openai": upstream}) + try: + yield running.edge.api_base("openai") + "/v1/chat/completions" + finally: + running.shutdown() + + +def call(url: str, body: bytes = BODY, headers: dict[str, str] = HEADERS) -> RawResponse: + result: Final = forward("POST", url, headers=headers, body=body, timeout=5) + assert isinstance(result, RawResponse), result + return result + + +def test_success_is_reusable_across_fresh_edges(store: RedisResponseStore, provider: Provider) -> None: + with edge(CacheEdge(store, SECRET), provider) as url: + assert call(url).body == SUCCESS + assert call(url).body == SUCCESS + with edge(CacheEdge(store, SECRET), provider) as other: + assert call(other).body == SUCCESS + assert len(provider.hits) == 1 + + +@pytest.mark.parametrize("body", [BODY + b" ", BODY.replace(b"hello", b"Hello"), BODY.replace(b"test", b"test2")]) +def test_any_body_change_calls_live(store: RedisResponseStore, provider: Provider, body: bytes) -> None: + with edge(CacheEdge(store, SECRET), provider) as url: + call(url) + call(url, body) + call(url, body) + assert len(provider.hits) == 2 + + +@pytest.mark.parametrize("name,value", [("authorization", "Bearer another-account"), ("x-request-id", "one"), ("anthropic-version", "new")]) +def test_changed_header_cannot_reuse(store: RedisResponseStore, provider: Provider, name: str, value: str) -> None: + with edge(CacheEdge(store, SECRET), provider) as url: + call(url) + call(url, headers=HEADERS | {name: value}) + call(url + "?x=1") + assert len(provider.hits) == 3 + + +@pytest.mark.parametrize("status,response", [(429, b'{"error":"rate limited"}'), (500, b'failed'), (200, b'{"error":"bad"}'), (200, b'not json')]) +def test_failed_provider_responses_never_enter_cache(store: RedisResponseStore, provider: Provider, status: int, response: bytes) -> None: + provider.status = status + provider.response = response + with edge(CacheEdge(store, SECRET), provider) as url: + assert call(url).status_code == status + assert call(url).body == response + assert len(provider.hits) == 2 + + +def test_expiry_does_not_slide(store: RedisResponseStore, provider: Provider) -> None: + short: Final = replace(store, lifetime_ms=250) + with edge(CacheEdge(short, SECRET), provider) as url: + call(url) + call(url) + time.sleep(0.3) + call(url) + call(url) + assert len(provider.hits) == 2 + + +def test_concurrent_requests_publish_atomically(store: RedisResponseStore, provider: Provider) -> None: + provider.delay = 0.15 + with edge(CacheEdge(store, SECRET), provider) as url: + with ThreadPoolExecutor(max_workers=5) as executor: + replies: Final = tuple(executor.map(lambda _: call(url).body, range(5))) + assert replies == (SUCCESS,) * 5 + assert len(provider.hits) == 1 + + +@pytest.mark.parametrize("truncated", [False, True]) +def test_stream_completion_controls_publication(store: RedisResponseStore, provider: Provider, truncated: bool) -> None: + provider.stream = True + provider.truncated = truncated + provider.response = b'data: {"choices":[{"index":0,"delta":{"content":"hello"},"finish_reason":"stop"}]}\n\ndata: [DONE]\n\n' + with edge(CacheEdge(store, SECRET), provider) as url: + for _ in range(2): + result: Final = forward("POST", url, headers=HEADERS, body=BODY, timeout=5) + if truncated: + assert isinstance(result, NetworkError) + else: + assert isinstance(result, RawResponse) and result.body == provider.response + assert len(provider.hits) == (2 if truncated else 1) + + +def test_store_outage_preserves_provider_success(provider: Provider) -> None: + with socket.socket() as probe: + probe.bind(("127.0.0.1", 0)) + port: Final = probe.getsockname()[1] + unavailable: Final = redis_store(f"redis://127.0.0.1:{port}/0", "unavailable") + with edge(CacheEdge(unavailable, SECRET), provider) as url: + assert call(url).body == SUCCESS + assert call(url).body == SUCCESS + assert len(provider.hits) == 2 + + +def test_old_lease_cannot_overwrite_new_owner(store: RedisResponseStore) -> None: + short: Final = replace(store, lease_ms=50) + old: Final = short.lookup("key") + assert isinstance(old, CaptureLease) + time.sleep(0.08) + current: Final = short.lookup("key") + assert isinstance(current, CaptureLease) + assert not short.publish("key", old, b"old") + assert short.publish("key", current, b"new") + hit: Final = short.lookup("key") + assert isinstance(hit, CacheHit) and hit.payload == b"new" + + +def test_identity_preserves_values_and_never_contains_credentials() -> None: + variants: Final = (b'{}', b'{"a":null}', b'{"a":false}', b'{"a":0}', b'{"a":0.0}', b'{"a":"0"}', b' { }', None, b'') + keys: Final = tuple(exact_key(SECRET, "POST", "https://example.invalid/v1/chat/completions", HEADERS, body) for body in variants) + assert len(set(keys)) == len(variants) + assert all(len(key) == 64 and "synthetic-account" not in key for key in keys) + + +@pytest.mark.parametrize("payload", [b"corrupt response", '{"response":"{}","signature":"é"}'.encode()]) +def test_corrupt_entry_is_replaced_by_same_successful_request(store: RedisResponseStore, provider: Provider, payload: bytes) -> None: + upstream: Final = f"http://127.0.0.1:{provider.server_port}/v1/chat/completions" + prepared: Final = prepare_forward("POST", upstream, HEADERS, BODY) + assert isinstance(prepared, PreparedForward) + key: Final = exact_key(SECRET, "POST", upstream, prepared.headers, BODY) + lease: Final = store.lookup(key) + assert isinstance(lease, CaptureLease) + assert store.publish(key, lease, payload) + cache: Final = CacheEdge(store, SECRET) + for _ in range(2): + head = cache.forward("POST", upstream, HEADERS, BODY, 5) + assert isinstance(head, StreamHead) + assert b"".join(step.data for step in head.steps if isinstance(step, StreamChunk)) == SUCCESS + assert len(provider.hits) == 1 + assert dict(cache.counters.counts) == { + "corrupt": 1, "misses": 1, "upstream_attempts": 1, "writes": 1, "hits": 1, + } + + +@pytest.mark.parametrize("payload", [ + b'data: {}\n\ndata: [DONE]\n\n', + b'data: {"choices":[{"index":0,"delta":{}}]}\n\ndata: [DONE]\n\n', + b'data: {"choices":[{"index":0,"delta":{},"finish_reason":"stop"}]}\n\ndata: [DONE]', + b'data: {"error":{"message":"failed"}}\n\ndata: [DONE]\n\n', +]) +def test_malformed_success_stream_is_never_cached(store: RedisResponseStore, provider: Provider, payload: bytes) -> None: + provider.stream = True + provider.response = payload + with edge(CacheEdge(store, SECRET), provider) as url: + assert call(url).body == payload + assert call(url).body == payload + assert len(provider.hits) == 2 + + +def test_anthropic_stream_requires_start_finish_and_stop() -> None: + start: Final = b'data: {"type":"message_start","message":{}}\n\n' + finish: Final = b'data: {"type":"message_delta","delta":{"stop_reason":"end_turn"}}\n\n' + stop: Final = b'data: {"type":"message_stop"}\n\n' + url: Final = "https://example.invalid/v1/messages" + headers: Final = {"content-type": "text/event-stream"} + assert successful_response(url, 200, headers, start + finish + stop) + assert not successful_response(url, 200, headers, start + stop) + assert not successful_response(url, 200, headers, finish + stop) + assert not successful_response(url, 200, headers, start + finish) + + +@pytest.mark.parametrize("provider,suffix", [("openai", "/v1"), ("anthropic", "")]) +def test_normal_registration_routes_supported_providers(provider: str, suffix: str) -> None: + params: Final = LiteLLMParamsBody(model=f"{provider}/test", api_key="os.environ/SYNTHETIC_KEY", timeout=12) + routed: Final = route_cache_model(params, lambda mount: f"http://edge.invalid/{mount}", enabled=True) + assert routed.api_base == f"http://edge.invalid/{provider}{suffix}" + assert routed.model_dump(exclude={"api_base"}) == params.model_dump(exclude={"api_base"}) + assert params.api_base is None + + +@pytest.mark.parametrize("params", [ + LiteLLMParamsBody(model="bedrock/test"), + LiteLLMParamsBody(model="azure/test"), + LiteLLMParamsBody(model="openai/test", api_base="https://custom.invalid/v1"), + LiteLLMParamsBody(model="openai/test", api_base=""), + LiteLLMParamsBody(model="openai/test", litellm_credential_name="named-credential"), + LiteLLMParamsBody(model="openai/test", mock_response="synthetic"), +]) +def test_registration_preserves_unsupported_or_explicit_routes(params: LiteLLMParamsBody) -> None: + def unexpected_edge(mount: str) -> str: + pytest.fail(f"should not start edge for {mount}") + assert route_cache_model(params, unexpected_edge, enabled=True) is params + + +def test_rollback_and_live_only_policy_keep_direct_provider_route() -> None: + params: Final = LiteLLMParamsBody(model="openai/test") + assert route_cache_model(params, lambda _: "http://edge.invalid", enabled=False) is params + assert route_cache_model(params, lambda _: "http://edge.invalid", enabled=True, mode="realtime") is params + token: Final = LIVE_PROVIDER_REQUIRED.set(True) + try: + assert route_cache_model(params, lambda _: "http://edge.invalid", enabled=True) is params + finally: + LIVE_PROVIDER_REQUIRED.reset(token) + assert route_cache_model(params, lambda _: "http://edge.invalid", enabled=True).api_base == "http://edge.invalid/v1" + + +@dataclass(frozen=True) +class PublishOutage: + client: RedisCommands + + def eval(self, script: str, numkeys: int, *args: str | bytes | int) -> object: + if script == PUBLISH: + raise RedisConnectionError("synthetic publication outage") + return self.client.eval(script, numkeys, *args) + + +def test_write_outage_preserves_success_without_hidden_retry(store: RedisResponseStore, provider: Provider) -> None: + unavailable: Final = replace(store, client=PublishOutage(store.client)) + cache: Final = CacheEdge(unavailable, SECRET) + with edge(cache, provider) as url: + assert call(url).body == SUCCESS + assert call(url).body == SUCCESS + assert len(provider.hits) == 2 + assert dict(cache.counters.counts)["write_failures"] == 2 + with edge(CacheEdge(store, SECRET), provider) as url: + assert call(url).body == SUCCESS + assert call(url).body == SUCCESS + assert len(provider.hits) == 3 + + +def test_connection_failure_releases_capture_lease(store: RedisResponseStore) -> None: + with socket.socket() as unavailable: + unavailable.bind(("127.0.0.1", 0)) + url: Final = f"http://127.0.0.1:{unavailable.getsockname()[1]}/v1/chat/completions" + cache: Final = CacheEdge(store, SECRET) + assert isinstance(cache.forward("POST", url, HEADERS, BODY, 0.2), NetworkError) + prepared: Final = prepare_forward("POST", url, HEADERS, BODY) + assert isinstance(prepared, PreparedForward) + key: Final = exact_key(SECRET, "POST", url, prepared.headers, BODY) + slot: Final = store.lookup(key) + assert isinstance(slot, CaptureLease) + assert store.release(key, slot) + assert dict(cache.counters.counts)["rejected"] == 1 + + +def test_close_before_first_chunk_releases_lease(store: RedisResponseStore, provider: Provider) -> None: + url: Final = f"http://127.0.0.1:{provider.server_port}/v1/chat/completions" + cache: Final = CacheEdge(store, SECRET) + head: Final = cache.forward("POST", url, HEADERS, BODY, 5) + assert isinstance(head, StreamHead) + head.steps.close() + prepared: Final = prepare_forward("POST", url, HEADERS, BODY) + assert isinstance(prepared, PreparedForward) + key: Final = exact_key(SECRET, "POST", url, prepared.headers, BODY) + slot: Final = store.lookup(key) + assert isinstance(slot, CaptureLease) + assert store.release(key, slot) + + +def test_effective_account_change_cannot_reuse_cache( + store: RedisResponseStore, provider: Provider, monkeypatch: pytest.MonkeyPatch, tmp_path, +) -> None: + url: Final = f"http://127.0.0.1:{provider.server_port}/v1/chat/completions" + cache: Final = CacheEdge(store, SECRET) + for account in ("account-a", "account-b", "account-b"): + netrc = tmp_path / account + netrc.write_text(f"machine 127.0.0.1 login {account} password synthetic\n") + monkeypatch.setenv("NETRC", str(netrc)) + head = cache.forward("POST", url, HEADERS, BODY, 5) + assert isinstance(head, StreamHead) + assert b"".join(step.data for step in head.steps if isinstance(step, StreamChunk)) == SUCCESS + assert len(provider.hits) == 2 + assert dict(cache.counters.counts)["hits"] == 1 + + +def test_enabled_environment_reuses_store_across_fresh_backends( + redis_url: str, provider: Provider, monkeypatch: pytest.MonkeyPatch, +) -> None: + monkeypatch.setenv("E2E_PROVIDER_CACHE", "1") + monkeypatch.setenv("E2E_PROVIDER_CACHE_REDIS_URL", redis_url) + monkeypatch.setenv("E2E_PROVIDER_CACHE_HMAC_KEY", SECRET.decode()) + monkeypatch.setenv("E2E_PROVIDER_CACHE_NAMESPACE", "environment-" + uuid.uuid4().hex) + configured_cache.cache_clear() + try: + for _ in range(2): + backend = configured_cache_backend() + assert isinstance(backend, CacheEdge) + with edge(backend, provider) as url: + assert call(url).body == SUCCESS + configured_cache.cache_clear() + assert len(provider.hits) == 1 + monkeypatch.setenv("E2E_PROVIDER_CACHE", "0") + assert configured_cache_backend() is None + finally: + configured_cache.cache_clear() diff --git a/tests/e2e/PROVIDER_CACHE.md b/tests/e2e/PROVIDER_CACHE.md new file mode 100644 index 00000000000..280132d0940 --- /dev/null +++ b/tests/e2e/PROVIDER_CACHE.md @@ -0,0 +1,27 @@ +# Shared provider-response cache + +`E2E_PROVIDER_CACHE=1` enables automatic response reuse in the live E2E mode. Standard OpenAI and Anthropic model registrations use the provider edge. Existing custom API bases, named credentials, mocked models and realtime WebSocket deployments keep their existing routing. Other provider protocols remain live + +The edge caches complete successful POST responses for `/v1/chat/completions` and `/v1/messages`, including streams. Unsupported endpoints pass through. It matches the method, original URL, effective outbound headers (including authentication and HTTP-library defaults), body presence and exact body bytes using a full keyed digest. It sends the same prepared request used for matching. No prompts, random markers, JSON values or credentials are normalized away + +An eligible miss calls the provider. A complete successful response is stored immediately even if a later test assertion fails. Provider errors, malformed responses, truncated streams and cancelled captures are not stored. Cache reads, writes and lease failures fall through to normal provider behavior; they introduce no provider retry. An already-started response cannot be restarted after a delivery failure + +Recordings are shared across workers and builds through dedicated Redis, separate from the candidate's own cache. They expire 86,400 seconds after capture starts, based on Redis time. Reads never extend expiry. There is no scheduled recapture: the next miss calls the provider again. Bounded coordination reduces duplicate concurrent calls, but slow or failed captures may lead to extra live calls after the wait expires + +## Configuration + +The trusted runner receives: + +- `E2E_PROVIDER_CACHE`: `1` to enable, `0` to use the normal live path +- `E2E_PROVIDER_CACHE_REDIS_URL`: authenticated dedicated Redis URL +- `E2E_PROVIDER_CACHE_HMAC_KEY`: dedicated secret containing at least 32 bytes +- `E2E_PROVIDER_CACHE_NAMESPACE`: shared environment namespace, independent of build and candidate revision +- `E2E_PROVIDER_CACHE_METRICS_DIR`: optional per-process counter artifact directory + +Do not give cache credentials to candidate deployments. Counter artifacts contain no recorded payloads or credentials. Hits count shared-cache responses; upstream attempts count actual forwards from the edge. Existing application-cache observations still count requests arriving at the edge, including shared-cache hits + +Tests that require real provider timing, limits or state use `@pytest.mark.provider_live`. The marker keeps newly registered models on live routes without weakening their assertions. Ordinary assertion failures still fail E2E. The shared cache does not modify provider response IDs or make the proxy aware of replay + +## Qualification + +`tests/code_coverage_tests/test_provider_cache.py` exercises local HTTP providers and disposable real Redis. CI runs these checks with the existing provider-edge and replay harness tests. These component checks do not establish Buildkite deployment, full-suite cross-build reuse or a genuine 24-hour expiry observation; those require separate runtime evidence diff --git a/tests/e2e/conftest.py b/tests/e2e/conftest.py index b1a75d5f862..829c84910a9 100644 --- a/tests/e2e/conftest.py +++ b/tests/e2e/conftest.py @@ -22,7 +22,6 @@ from typing import Final import pytest import requests - from e2e_config import ( CONTROL_PLANE_BASE_URL, FIXTURE_DIR, @@ -41,6 +40,7 @@ from idp import Identity, Keycloak, keycloak_from_env from junit_properties import attach_result_properties from lifecycle import ProxyClientProvider, ResourceManager from models import TeamNewBody, UserNewBody, UserNewResponse +from provider_cache_routing import LIVE_PROVIDER_REQUIRED from provider_edge import replay_leftover_error from proxy_client import ProxyClient, build_proxy_client @@ -85,6 +85,7 @@ def jwt_identity(idp: Keycloak, resources: ResourceManager, proxy: ProxyClient) def pytest_configure(config: pytest.Config) -> None: + config.addinivalue_line("markers", "provider_live: requires actual provider timing, limits or state; bypass shared cache") config.addinivalue_line( "markers", "e2e: live test that requires a running proxy and real provider keys", @@ -192,11 +193,13 @@ def _proxy_fail_reason() -> str | None: return None +@pytest.hookimpl(tryfirst=True) def pytest_runtest_setup(item: pytest.Item) -> None: """Hard-fail `e2e`-marked tests unless a proxy answers its liveness probe. Unmarked tests (unit coverage of the harness) don't touch the proxy, so they run even when none is up. Never skip for a missing proxy. Replay mode needs the proxy too: only provider-bound traffic replays from the bundle.""" + LIVE_PROVIDER_REQUIRED.set(item.get_closest_marker("provider_live") is not None) if item.get_closest_marker("e2e") is None: return reason = _proxy_fail_reason() @@ -235,6 +238,7 @@ def pytest_runtest_teardown(item: pytest.Item) -> Generator[None, None, None]: yield so fixture finalizers replay their recorded calls first. Failed tests are left alone - their own failure already explains any unconsumed tail.""" result = yield + LIVE_PROVIDER_REQUIRED.set(False) if not item.stash.get(_CALL_PASSED, False): return result reason = replay_leftover_error( diff --git a/tests/e2e/e2e_http.py b/tests/e2e/e2e_http.py index 1992f419823..4184b6cbefc 100644 --- a/tests/e2e/e2e_http.py +++ b/tests/e2e/e2e_http.py @@ -853,6 +853,7 @@ def _stream_steps(resp: requests.Response) -> Generator[StreamStep, None, None]: the chunks already delivered are exactly what makes a mid-stream failure different from a request that never streamed at all.""" try: + yield StreamChunk(b"") for piece in cast("Iterator[bytes]", resp.iter_content(chunk_size=None)): if piece: yield StreamChunk(data=piece) @@ -862,6 +863,44 @@ def _stream_steps(resp: requests.Response) -> Generator[StreamStep, None, None]: resp.close() +def primed_steps(steps: Generator[StreamStep, None, None]) -> Generator[StreamStep, None, None]: + first: Final = next(steps) + assert isinstance(first, StreamChunk) and first.data == b"" + return steps + + +@dataclass(frozen=True, slots=True, repr=False) +class PreparedForward: + request: requests.PreparedRequest + url: str + headers: dict[str, str] + + +def prepare_forward( + method: str, url: str, headers: dict[str, str], body: bytes | None, +) -> PreparedForward | NetworkError: + try: + with requests.Session() as session: + request: Final = session.prepare_request(requests.Request(method, url, headers=headers, data=body)) + except requests.RequestException as exc: + return NetworkError(message=str(exc)) + assert request.url is not None + return PreparedForward(request, request.url, dict(request.headers)) + + +def forward_prepared_stream(prepared: PreparedForward, timeout: float) -> StreamHead | NetworkError: + try: + with requests.Session() as session: + settings: Final = session.merge_environment_settings(prepared.url, {}, True, None, None) + resp: Final = session.send(prepared.request, timeout=timeout, allow_redirects=False, **settings) + except requests.RequestException as exc: + return NetworkError(message=str(exc)) + return StreamHead( + resp.status_code, {name.lower(): value for name, value in resp.headers.items()}, + primed_steps(_stream_steps(resp)), + ) + + def open_stream(url: URL, *, headers: BaseModel, json: BaseModel, timeout: float = 60.0) -> StreamHead | NetworkError: """POST a streaming request and return the moment its response head arrives, leaving the body unread behind ``StreamHead.steps``. For a test that must keep @@ -907,5 +946,5 @@ def forward_stream( return StreamHead( status_code=resp.status_code, headers={name.lower(): value for name, value in resp.headers.items()}, - steps=_stream_steps(resp), + steps=primed_steps(_stream_steps(resp)), ) diff --git a/tests/e2e/llm_translation/test_messages_e2e.py b/tests/e2e/llm_translation/test_messages_e2e.py index ca58c30d40c..44c416a3e78 100644 --- a/tests/e2e/llm_translation/test_messages_e2e.py +++ b/tests/e2e/llm_translation/test_messages_e2e.py @@ -24,10 +24,10 @@ from models import ( AnthropicAssistantTurn, AnthropicContentBlock, AnthropicCustomTool, + AnthropicMessagesBody, AnthropicToolChoice, AnthropicToolResultBlock, AnthropicToolResultTurn, - AnthropicMessagesBody, ChatMessage, JsonSchemaProperty, LiteLLMParamsBody, @@ -165,6 +165,7 @@ class TestAnthropicMessages: ) @pytest.mark.covers("llm.messages.anthropic.basic.stream.works") + @pytest.mark.provider_live def test_messages_streams_completion( self, endpoints_client: EndpointsClient, resources: ResourceManager ) -> None: diff --git a/tests/e2e/llm_translation/test_together_ai_e2e.py b/tests/e2e/llm_translation/test_together_ai_e2e.py index 31e74c22e17..8dd7e7c1a31 100644 --- a/tests/e2e/llm_translation/test_together_ai_e2e.py +++ b/tests/e2e/llm_translation/test_together_ai_e2e.py @@ -744,6 +744,7 @@ class TestTogetherMessages: assert "22" in text, f"the model never saw the tool result: {response.content}" @pytest.mark.covers("llm.messages.together_ai.basic.stream.works") + @pytest.mark.provider_live def test_streams_text_deltas( self, client: PassthroughClient, resources: ResourceManager, reasoning_tool_backend: str ) -> None: diff --git a/tests/e2e/provider_cache.py b/tests/e2e/provider_cache.py new file mode 100644 index 00000000000..50b336f5263 --- /dev/null +++ b/tests/e2e/provider_cache.py @@ -0,0 +1,297 @@ +from __future__ import annotations + +import base64 +import hashlib +import hmac +import io +import threading +import time +from collections.abc import Callable, Generator, Mapping +from contextlib import closing +from dataclasses import dataclass, field +from typing import Final, Literal, Protocol +from urllib.parse import urlsplit + +from e2e_http import ( + NetworkError, + StreamChunk, + StreamHead, + StreamStep, + StreamTruncation, + forward_prepared_stream, + forward_stream, + prepare_forward, + primed_steps, +) +from pydantic import BaseModel, ConfigDict, JsonValue, TypeAdapter, ValidationError + +LIFETIME_SECONDS: Final = 86_400 +MAX_REQUEST_BYTES: Final = 256 * 1024 +MAX_RESPONSE_BYTES: Final = 8 * 1024 * 1024 +JSON_VALUE: Final[TypeAdapter[JsonValue]] = TypeAdapter(JsonValue) + + +@dataclass(frozen=True, slots=True) +class CacheHit: + payload: bytes + valid_until: float + + +@dataclass(frozen=True, slots=True) +class CaptureLease: + token: str + captured_at_ms: int + expires_at_ms: int + + +@dataclass(frozen=True, slots=True) +class CacheBusy: + pass + + +@dataclass(frozen=True, slots=True) +class CacheUnavailable: + pass + + +type CacheLookup = CacheHit | CaptureLease | CacheBusy | CacheUnavailable + + +class ResponseStore(Protocol): + def lookup(self, key: str) -> CacheLookup: ... + + def publish(self, key: str, lease: CaptureLease, payload: bytes) -> bool: ... + + def release(self, key: str, lease: CaptureLease) -> bool: ... + + def discard(self, key: str, payload: bytes) -> bool: ... + + +class CachedResponse(BaseModel): + model_config = ConfigDict(frozen=True, extra="forbid", strict=True) + format_version: Literal[1] = 1 + request_key: str + status_code: int + headers: dict[str, str] + chunks: tuple[str, ...] + + +class SignedResponse(BaseModel): + model_config = ConfigDict(frozen=True, extra="forbid", strict=True) + response: str + signature: str + + +def exact_key(secret: bytes, method: str, url: str, headers: Mapping[str, str], body: bytes | None) -> str: + fields: Final = ( + b"provider-cache-exact-v1", method.encode(), url.encode(), + *(part.encode() for pair in sorted(headers.items()) for part in pair), + b"no-body" if body is None else b"body", b"" if body is None else body, + ) + encoded: Final = b"".join(len(part).to_bytes(8, "big") + part for part in fields) + return hmac.new(secret, encoded, hashlib.sha256).hexdigest() + + +def cacheable_endpoint(method: str, url: str, body: bytes | None) -> bool: + return ( + method == "POST" + and urlsplit(url).path in {"/v1/chat/completions", "/v1/messages"} + and body is not None + and len(body) <= MAX_REQUEST_BYTES + ) + + +def successful_response(url: str, status: int, headers: Mapping[str, str], body: bytes) -> bool: + if not 200 <= status < 300 or len(body) > MAX_RESPONSE_BYTES: + return False + if any(name.lower() == "set-cookie" for name in headers): + return False + streaming: Final = "text/event-stream" in headers.get("content-type", "").lower() + if streaming: + try: + text: Final = body.decode("utf-8").replace("\r\n", "\n") + if not text.endswith("\n\n"): + return False + events: Final = tuple( + "\n".join(line[5:].removeprefix(" ") for line in event.split("\n") if line.startswith("data:")) + for event in text.split("\n\n") if any(line.startswith("data:") for line in event.split("\n")) + ) + values: Final = tuple(JSON_VALUE.validate_json(event) for event in events if event != "[DONE]") + except (UnicodeDecodeError, ValidationError): + return False + if not values or any(not isinstance(value, dict) or "error" in value or value.get("type") == "error" for value in values): + return False + if urlsplit(url).path == "/v1/chat/completions": + return events[-1] == "[DONE]" and "[DONE]" not in events[:-1] and complete_chat_stream(values) + return ( + "[DONE]" not in events + and isinstance(values[0], dict) and values[0].get("type") == "message_start" + and isinstance(values[-1], dict) and values[-1].get("type") == "message_stop" + and any( + isinstance(value, dict) and value.get("type") == "message_delta" + and isinstance(delta := value.get("delta"), dict) and isinstance(delta.get("stop_reason"), str) + for value in values + ) + ) + try: + value: Final = JSON_VALUE.validate_json(body) + except ValidationError: + return False + if not isinstance(value, dict) or "error" in value: + return False + if urlsplit(url).path == "/v1/messages": + return value.get("type") == "message" and isinstance(value.get("content"), list) and isinstance(value.get("stop_reason"), str) + choices: Final = value.get("choices") + return isinstance(choices, list) and bool(choices) and all( + isinstance(choice, dict) and isinstance(choice.get("message"), dict) and isinstance(choice.get("finish_reason"), str) + for choice in choices + ) + + +def complete_chat_stream(values: tuple[JsonValue, ...]) -> bool: + if any(not isinstance(value, dict) or not isinstance(value.get("choices"), list) for value in values): + return False + choices: Final = tuple( + choice for value in values if isinstance(value, dict) + if isinstance(items := value.get("choices"), list) for choice in items + ) + if not choices or any( + not isinstance(choice, dict) or type(choice.get("index")) is not int + or not isinstance(choice.get("delta"), dict) + for choice in choices + ): + return False + indices: Final = frozenset(choice["index"] for choice in choices if isinstance(choice, dict)) + return all( + isinstance(tuple(choice for choice in choices if isinstance(choice, dict) and choice["index"] == index)[-1].get("finish_reason"), str) + for index in indices + ) + + +def encode_response(secret: bytes, response: CachedResponse) -> bytes: + raw: Final = response.model_dump_json() + return SignedResponse(response=raw, signature=hmac.new(secret, raw.encode(), hashlib.sha256).hexdigest()).model_dump_json().encode() + + +def decode_response(secret: bytes, key: str, payload: bytes, url: str) -> CachedResponse | None: + if len(payload) > 2 * MAX_RESPONSE_BYTES: + return None + try: + signed: Final = SignedResponse.model_validate_json(payload) + if not hmac.compare_digest(signed.signature.encode(), hmac.new(secret, signed.response.encode(), hashlib.sha256).hexdigest().encode()): + return None + response: Final = CachedResponse.model_validate_json(signed.response) + chunks: Final = tuple(base64.b64decode(chunk, validate=True) for chunk in response.chunks) + except (ValidationError, ValueError): + return None + if response.request_key != key or not successful_response(url, response.status_code, response.headers, b"".join(chunks)): + return None + return response + + +@dataclass(slots=True) +class CacheCounters: + counts: tuple[tuple[str, int], ...] = () + lock: threading.Lock = field(default_factory=threading.Lock) + + def increment(self, name: str) -> None: + with self.lock: + current: Final = dict(self.counts) + self.counts = tuple((current | {name: current.get(name, 0) + 1}).items()) + + +@dataclass(slots=True) +class ResponseCapture: + buffer: io.BytesIO = field(default_factory=io.BytesIO) + size: int = 0 + eligible: bool = True + + def observe(self, step: StreamStep) -> None: + if not self.eligible: + return + if isinstance(step, StreamTruncation) or self.size + len(step.data) + 8 > MAX_RESPONSE_BYTES: + self.eligible = False + self.buffer.close() + return + self.buffer.write(len(step.data).to_bytes(8, "big")) + self.buffer.write(step.data) + self.size += len(step.data) + 8 + + def chunks(self) -> tuple[bytes, ...]: + self.buffer.seek(0) + return tuple(self.buffer.read(int.from_bytes(size, "big")) for size in iter(lambda: self.buffer.read(8), b"")) + + +def response_steps(response: CachedResponse) -> Generator[StreamStep, None, None]: + for chunk in response.chunks: + yield StreamChunk(base64.b64decode(chunk, validate=True)) + + +@dataclass(frozen=True, slots=True) +class CacheEdge: + store: ResponseStore + secret: bytes = field(repr=False) + counters: CacheCounters = field(default_factory=CacheCounters) + wait_seconds: float = 2.0 + clock: Callable[[], float] = time.monotonic + sleep: Callable[[float], None] = time.sleep + + def lookup(self, key: str) -> CacheLookup: + deadline: Final = self.clock() + self.wait_seconds + while isinstance(result := self.store.lookup(key), CacheBusy) and self.clock() < deadline: + self.sleep(min(0.05, max(0, deadline - self.clock()))) + return result + + def forward(self, method: str, url: str, headers: dict[str, str], body: bytes | None, timeout: float) -> StreamHead | NetworkError: + if not cacheable_endpoint(method, url, body): + self.counters.increment("bypass") + self.counters.increment("upstream_attempts") + return forward_stream(method, url, headers=headers, body=body, timeout=timeout) + prepared: Final = prepare_forward(method, url, headers, body) + if isinstance(prepared, NetworkError): + self.counters.increment("rejected") + return prepared + key: Final = exact_key(self.secret, method, url, prepared.headers, body) + found: Final = self.lookup(key) + if isinstance(found, CacheHit): + response: Final = decode_response(self.secret, key, found.payload, url) + if response is not None and self.clock() < found.valid_until: + self.counters.increment("hits") + return StreamHead(response.status_code, response.headers, response_steps(response)) + self.counters.increment("corrupt" if response is None else "expired") + self.store.discard(key, found.payload) + capture_slot: Final = self.lookup(key) if isinstance(found, CacheHit) else found + self.counters.increment("misses") + if isinstance(capture_slot, CacheUnavailable): + self.counters.increment("cache_errors") + self.counters.increment("upstream_attempts") + head: Final = forward_prepared_stream(prepared, timeout) + if not isinstance(capture_slot, CaptureLease): + return head + if isinstance(head, NetworkError): + self.store.release(key, capture_slot) + self.counters.increment("rejected") + return head + return StreamHead(head.status_code, head.headers, primed_steps(self.capture(key, capture_slot, url, head))) + + def capture(self, key: str, lease: CaptureLease, url: str, head: StreamHead) -> Generator[StreamStep, None, None]: + capture: Final = ResponseCapture() + try: + with closing(head.steps): + yield StreamChunk(b"") + for step in head.steps: + yield step + capture.observe(step) + chunks: Final = capture.chunks() if capture.eligible else () + if not capture.eligible or not successful_response(url, head.status_code, head.headers, b"".join(chunks)): + self.counters.increment("rejected") + return + response: Final = CachedResponse( + request_key=key, status_code=head.status_code, headers=head.headers, + chunks=tuple(base64.b64encode(chunk).decode("ascii") for chunk in chunks), + ) + published: Final = self.store.publish(key, lease, encode_response(self.secret, response)) + self.counters.increment("writes" if published else "write_failures") + finally: + self.store.release(key, lease) + capture.buffer.close() diff --git a/tests/e2e/provider_cache_redis.py b/tests/e2e/provider_cache_redis.py new file mode 100644 index 00000000000..be4e31b2c49 --- /dev/null +++ b/tests/e2e/provider_cache_redis.py @@ -0,0 +1,154 @@ +from __future__ import annotations + +import atexit +import functools +import json +import logging +import os +import re +import time +import uuid +from dataclasses import dataclass +from pathlib import Path +from typing import Final, Protocol, cast + +from provider_cache import LIFETIME_SECONDS, CacheBusy, CacheEdge, CacheHit, CacheLookup, CacheUnavailable, CaptureLease +from pydantic import TypeAdapter, ValidationError +from redis import Redis +from redis.exceptions import RedisError + +REDIS_ARRAY: Final[TypeAdapter[list[bytes]]] = TypeAdapter(list[bytes]) + +LOOKUP: Final = """ +local clock = redis.call('TIME') +local now = clock[1] * 1000 + math.floor(clock[2] / 1000) +local row = redis.call('HMGET', KEYS[1], 'captured', 'expires', 'payload') +if row[3] then + local captured = tonumber(row[1]) + local expires = tonumber(row[2]) + if captured and expires and captured <= now and expires > now + and expires - captured == tonumber(ARGV[2]) then + return {'hit', row[3], tostring(expires - now)} + end + redis.call('DEL', KEYS[1]) +end +if redis.call('SET', KEYS[2], ARGV[1], 'NX', 'PX', ARGV[3]) then + return {'lease', tostring(now), tostring(now + tonumber(ARGV[2]))} +end +return {'busy'} +""" + +PUBLISH: Final = """ +if redis.call('GET', KEYS[2]) ~= ARGV[1] then return 0 end +local clock = redis.call('TIME') +local now = clock[1] * 1000 + math.floor(clock[2] / 1000) +local captured = tonumber(ARGV[2]) +local expires = tonumber(ARGV[3]) +if captured > now or expires <= now or expires - captured ~= tonumber(ARGV[5]) then return 0 end +if redis.call('EXISTS', KEYS[1]) == 1 then return 0 end +redis.call('HSET', KEYS[1], 'captured', ARGV[2], 'expires', ARGV[3], 'payload', ARGV[4]) +redis.call('PEXPIREAT', KEYS[1], expires) +redis.call('DEL', KEYS[2]) +return 1 +""" + +RELEASE: Final = """ +if redis.call('GET', KEYS[1]) ~= ARGV[1] then return 0 end +return redis.call('DEL', KEYS[1]) +""" + +DISCARD: Final = """ +if redis.call('HGET', KEYS[1], 'payload') ~= ARGV[1] then return 0 end +return redis.call('DEL', KEYS[1]) +""" + + +class RedisCommands(Protocol): + def eval(self, script: str, numkeys: int, *args: str | bytes | int) -> object: ... + + +@dataclass(frozen=True, slots=True) +class RedisResponseStore: + client: RedisCommands + namespace: str + lifetime_ms: int = LIFETIME_SECONDS * 1000 + lease_ms: int = 120_000 + + def keys(self, key: str) -> tuple[str, str]: + prefix: Final = f"e2e-provider-cache:v1:{self.namespace}:{{{key}}}" + return prefix + ":response", prefix + ":lease" + + def lookup(self, key: str) -> CacheLookup: + token: Final = uuid.uuid4().hex + started: Final = time.monotonic() + try: + result: Final = self.client.eval(LOOKUP, 2, *self.keys(key), token, self.lifetime_ms, self.lease_ms) + except (RedisError, OSError): + return CacheUnavailable() + try: + parts: Final = tuple(REDIS_ARRAY.validate_python(result, strict=True)) + except ValidationError: + return CacheUnavailable() + if len(parts) == 3 and parts[0] == b"hit" and parts[2].isdigit(): + return CacheHit(parts[1], started + int(parts[2]) / 1000) + if len(parts) == 3 and parts[0] == b"lease" and parts[1].isdigit() and parts[2].isdigit(): + return CaptureLease(token, int(parts[1]), int(parts[2])) + if parts == (b"busy",): + return CacheBusy() + return CacheUnavailable() + + def publish(self, key: str, lease: CaptureLease, payload: bytes) -> bool: + try: + result: Final = self.client.eval( + PUBLISH, 2, *self.keys(key), lease.token, lease.captured_at_ms, lease.expires_at_ms, payload, self.lifetime_ms, + ) + except (RedisError, OSError): + return False + return result == 1 + + def release(self, key: str, lease: CaptureLease) -> bool: + try: + result: Final = self.client.eval(RELEASE, 1, self.keys(key)[1], lease.token) + except (RedisError, OSError): + return False + return result == 1 + + def discard(self, key: str, payload: bytes) -> bool: + try: + result: Final = self.client.eval(DISCARD, 1, self.keys(key)[0], payload) + except (RedisError, OSError): + return False + return result == 1 + + +def redis_store(url: str, namespace: str) -> RedisResponseStore: + client: Final = Redis.from_url(url, socket_timeout=0.25, socket_connect_timeout=0.25, decode_responses=False) + return RedisResponseStore(cast(RedisCommands, client), namespace) + + +def write_metrics(cache: CacheEdge) -> None: + report: Final = json.dumps({"provider_cache": dict(cache.counters.counts)}) + directory: Final = os.environ.get("E2E_PROVIDER_CACHE_METRICS_DIR") + if directory: + try: + root: Final = Path(directory) + root.mkdir(parents=True, exist_ok=True) + (root / f"{os.getpid()}.json").write_text(report + "\n") + except OSError: + logging.getLogger(__name__).warning("provider cache metrics artifact unavailable") + logging.getLogger(__name__).info("%s", report) + + +@functools.lru_cache(maxsize=1) +def configured_cache() -> CacheEdge | None: + if os.environ.get("E2E_PROVIDER_CACHE", "0") == "0": + return None + if os.environ.get("E2E_PROVIDER_CACHE") != "1": + raise ValueError("E2E_PROVIDER_CACHE must be 0 or 1") + secret: Final = os.environ.get("E2E_PROVIDER_CACHE_HMAC_KEY", "").encode() + namespace: Final = os.environ.get("E2E_PROVIDER_CACHE_NAMESPACE", "") + if len(secret) < 32 or re.fullmatch(r"[a-zA-Z0-9_-]{1,64}", namespace) is None: + raise ValueError("provider cache requires a dedicated key and namespace") + cache: Final = CacheEdge(redis_store(os.environ["E2E_PROVIDER_CACHE_REDIS_URL"], namespace), secret) + atexit.register(write_metrics, cache) + return cache diff --git a/tests/e2e/provider_cache_routing.py b/tests/e2e/provider_cache_routing.py new file mode 100644 index 00000000000..24599b5a313 --- /dev/null +++ b/tests/e2e/provider_cache_routing.py @@ -0,0 +1,23 @@ +from __future__ import annotations + +from collections.abc import Callable +from contextvars import ContextVar +from typing import Final + +from models import LiteLLMParamsBody, ModelMode + +LIVE_PROVIDER_REQUIRED: Final[ContextVar[bool]] = ContextVar("live_provider_required", default=False) + + +def route_cache_model( + params: LiteLLMParamsBody, base_for: Callable[[str], str | None], *, enabled: bool, mode: ModelMode | None = None, +) -> LiteLLMParamsBody: + if not enabled or mode == "realtime" or LIVE_PROVIDER_REQUIRED.get() or params.api_base is not None or params.mock_response is not None: + return params + provider: Final = params.model.partition("/")[0] + if provider not in {"openai", "anthropic"} or params.litellm_credential_name is not None: + return params + base: Final = base_for(provider) + if base is None: + return params + return params.model_copy(update={"api_base": f"{base}/v1" if provider == "openai" else base}) diff --git a/tests/e2e/provider_edge.py b/tests/e2e/provider_edge.py index de36895ebb6..49d574ee957 100644 --- a/tests/e2e/provider_edge.py +++ b/tests/e2e/provider_edge.py @@ -42,6 +42,7 @@ import base64 import difflib import functools import hashlib +import os import re import threading from collections import deque @@ -93,6 +94,8 @@ from fixture_mode import ( parse_fixture_mode, ) from fixture_profile import IneligibleRequest, MatchProfile, match_profile, strict_identity +from provider_cache import CacheEdge +from provider_cache_routing import LIVE_PROVIDER_REQUIRED from pydantic import JsonValue, TypeAdapter EDGE_MOUNTS: Final[Mapping[str, str]] = MappingProxyType( @@ -506,7 +509,7 @@ class LiveEdge: pass -type EdgeBackend = RecordEdge | ReplayEdge | LiveEdge +type EdgeBackend = RecordEdge | ReplayEdge | LiveEdge | CacheEdge @dataclass(slots=True) @@ -750,12 +753,16 @@ def _handle_record( def _handle_live( - method: str, url: str, headers: Mapping[str, str], body: bytes | None, timeout: float + method: str, url: str, headers: Mapping[str, str], body: bytes | None, timeout: float, + cache: CacheEdge | None = None, ) -> EdgeOutcome: forwarded: Final = { name: value for name, value in headers.items() if name.lower() not in _REQUEST_DROPPED_HEADERS } - head: Final = forward_stream(method, url, headers=forwarded, body=body, timeout=timeout) + head: Final = ( + forward_stream(method, url, headers=forwarded, body=body, timeout=timeout) + if cache is None else cache.forward(method, url, forwarded, body, timeout) + ) match head: case NetworkError(message=message): return _recorded_outcome(_network_error_response(message)) @@ -821,6 +828,10 @@ def handle_edge_request( else edge_request(method, split.path, split.query, body, _header_value(headers, "content-type")) ) match backend: + case CacheEdge(): + return _handle_live( + method, _upstream_url(upstream_base, upstream_path, split.query), headers, body, timeout, backend, + ) case LiveEdge(): return _handle_live( method, _upstream_url(upstream_base, upstream_path, split.query), headers, body, timeout @@ -871,11 +882,17 @@ class _EdgeHandler(BaseHTTPRequestHandler): or isinstance(edge_server.backend, ReplayEdge) and edge_server.backend.source.bundle.manifest.match_profile == "stateless_v1" ) - if strict and len({name.lower() for name in self.headers.keys()}) != len(self.headers): + if strict and len({name.lower() for name in self.headers}) != len(self.headers): self._write_reply(_text_reply(REPLAY_MISS_STATUS, "stateless_v1 eligibility error: duplicate headers")) return + duplicate_headers: Final = len({name.lower() for name in self.headers}) != len(self.headers) + selected_backend: Final = ( + LiveEdge() if isinstance(edge_server.backend, CacheEdge) and duplicate_headers else edge_server.backend + ) + if isinstance(edge_server.backend, CacheEdge) and duplicate_headers: + edge_server.backend.counters.increment("duplicate_header_bypass") outcome: Final = handle_edge_request( - edge_server.backend, + selected_backend, edge_server.mounts, self.command, self.path, @@ -908,12 +925,12 @@ class _EdgeHandler(BaseHTTPRequestHandler): shuts down write-side first: the proxy sees a graceful close mid-message, which is the incomplete chunked read a provider hanging up produces, and not the reset that could discard the chunks already in flight.""" - self.send_response(stream.status_code) - for name, value in stream.headers.items(): - self.send_header(name, value) - self.send_header("transfer-encoding", "chunked") - self.end_headers() with closing(stream.steps) as steps: + self.send_response(stream.status_code) + for name, value in stream.headers.items(): + self.send_header(name, value) + self.send_header("transfer-encoding", "chunked") + self.end_headers() for step in steps: match step: case StreamChunk(data=data): @@ -923,7 +940,7 @@ class _EdgeHandler(BaseHTTPRequestHandler): return case _: assert_never(step) - self.wfile.write(b"0\r\n\r\n") + self.wfile.write(b"0\r\n\r\n") def log_message(self, format: str, *args: object) -> None: """Silence the per-request stderr line BaseHTTPRequestHandler emits.""" @@ -1056,6 +1073,8 @@ def provider_edge_api_base( case InvalidFixtureMode(value=value): raise ValueError(f"E2E_FIXTURE_MODE={value!r} is not one of {', '.join(FIXTURE_MODES)}") case "live": + if configured_cache_backend() is not None: + return _shared_cache_edge(bind_host, advertise_host, forward_timeout).api_base(mount) return None case "record" | "replay": if mount not in EDGE_MOUNTS: @@ -1073,7 +1092,7 @@ def _observed_backend(mode_raw: str, bundle_dir: Path) -> EdgeBackend: case InvalidFixtureMode(value=value): raise ValueError(f"E2E_FIXTURE_MODE={value!r} is not one of {', '.join(FIXTURE_MODES)}") case "live": - return LiveEdge() + return configured_cache_backend() or LiveEdge() case "record": return RecordEdge(_shared_recorder(bundle_dir, match_profile()), threading.Lock()) case "replay": @@ -1082,6 +1101,24 @@ def _observed_backend(mode_raw: str, bundle_dir: Path) -> EdgeBackend: assert_never(mode) +def configured_cache_backend() -> CacheEdge | None: + if LIVE_PROVIDER_REQUIRED.get() or os.environ.get("E2E_PROVIDER_CACHE", "0") == "0": + return None + from provider_cache_redis import configured_cache + + return configured_cache() + + +@functools.lru_cache(maxsize=8) +def _shared_cache_edge(bind_host: str, advertise_host: str, forward_timeout: float) -> ProviderEdge: + backend: Final = configured_cache_backend() + assert backend is not None + return start_provider_edge( + backend, mounts=EDGE_MOUNTS, bind_host=bind_host, + advertise_host=advertise_host, forward_timeout=forward_timeout, + ).edge + + @contextmanager def observed_provider_edge( observation: ProviderRequestObservation, diff --git a/tests/e2e/proxy_client.py b/tests/e2e/proxy_client.py index 3f7fba5ffec..f8ed8843461 100644 --- a/tests/e2e/proxy_client.py +++ b/tests/e2e/proxy_client.py @@ -8,6 +8,7 @@ ProxyClient's key/customer methods for cleanup. Read-backs are eventually consis from __future__ import annotations +import os import time import warnings from collections.abc import Callable, Mapping @@ -26,6 +27,7 @@ from e2e_config import ( PROXY_REPLICA_URLS, REQUEST_TIMEOUT, SLOW_PROVIDER_TIMEOUT_SECONDS, + provider_edge_base, settle_propagation, ) from e2e_http import ( @@ -93,6 +95,7 @@ from models import ( UserDeleteBody, UserDeleteResponse, ) +from provider_cache_routing import route_cache_model from pydantic import BaseModel from transport import HttpTransport, SplitTransport, Transport, is_control_plane_path @@ -645,7 +648,10 @@ class ProxyClient: self.transport.post( "/model/new", headers=self.management_headers(), - json=body, + json=body.model_copy(update={"litellm_params": route_cache_model( + body.litellm_params, provider_edge_base, + enabled=os.environ.get("E2E_PROVIDER_CACHE", "0") == "1", mode=body.model_info.mode, + )}), response_type=ModelNewResponse, ) ).model_id From 44a6d1988958d3614f602d49746e83ae603ccb3b Mon Sep 17 00:00:00 2001 From: kerry Date: Wed, 16 Sep 2026 01:14:37 +0000 Subject: [PATCH 158/225] test(gemini): drop review narration from the wrapper test docstring Co-Authored-By: Devin AI <158243242+devin-ai-integration[bot]@users.noreply.github.com> --- .../gemini/test_vertex_and_google_ai_studio_gemini.py | 5 ----- 1 file changed, 5 deletions(-) diff --git a/tests/test_litellm/llms/vertex_ai/gemini/test_vertex_and_google_ai_studio_gemini.py b/tests/test_litellm/llms/vertex_ai/gemini/test_vertex_and_google_ai_studio_gemini.py index 3fb00a7d962..cd0ed0057c0 100644 --- a/tests/test_litellm/llms/vertex_ai/gemini/test_vertex_and_google_ai_studio_gemini.py +++ b/tests/test_litellm/llms/vertex_ai/gemini/test_vertex_and_google_ai_studio_gemini.py @@ -5899,11 +5899,6 @@ def test_streaming_chunk_carries_model_version(): def test_served_model_version_reaches_assembled_stream_through_custom_stream_wrapper(): - """Greptile claimed CustomStreamWrapper drops the served modelVersion before - pricing. It does not: chunk_creator stashes the parser chunk's model into each - yielded chunk's _hidden_params["provider_response_model"], and - stream_chunk_builder carries it onto the assembled response. The wrapper's - trailing bookkeeping chunk is the one emission that legitimately omits it.""" from litellm.litellm_core_utils.streaming_handler import CustomStreamWrapper from litellm.llms.vertex_ai.gemini.vertex_and_google_ai_studio_gemini import ( ModelResponseIterator, From f2145550680b85ed7f9b67826467125077cc8e8c Mon Sep 17 00:00:00 2001 From: Yuneng Jiang Date: Tue, 15 Sep 2026 18:21:43 -0700 Subject: [PATCH 159/225] fix(e2e): expect models filters to persist after reload --- .../tests/internal-user/modelsByTeam.spec.ts | 21 ++++++++++++++----- 1 file changed, 16 insertions(+), 5 deletions(-) diff --git a/tests/e2e/ui/tests/internal-user/modelsByTeam.spec.ts b/tests/e2e/ui/tests/internal-user/modelsByTeam.spec.ts index 5e2c80b5845..736c352e3ee 100644 --- a/tests/e2e/ui/tests/internal-user/modelsByTeam.spec.ts +++ b/tests/e2e/ui/tests/internal-user/modelsByTeam.spec.ts @@ -7,6 +7,7 @@ import { import { E2E_TEAM_CRUD_ALIAS, E2E_TEAM_ORG_ALIAS, + E2E_TEAM_ORG_ID, INTERNAL_USER_STORAGE_PATH, } from "../../constants"; import { Page } from "../../fixtures/pages"; @@ -179,18 +180,28 @@ test.describe("Models and Endpoints for an internal user", () => { `switching to ${ALL_MODELS_VIEW} leaves the table populated rather than blanking it`, ).toHaveCount(1, { timeout: 15_000 }); + await expect(page).toHaveURL((url) => + url.searchParams.get("filter_team") === E2E_TEAM_ORG_ID && + url.searchParams.get("view_mode") === "all", + ); await page.reload(); await expect( teamSelector(page), - "the team selection is not persisted across a reload, so the table returns to the personal view", - ).toContainText(PERSONAL_TEAM, { timeout: 15_000 }); + "the selected team is restored from the URL after a reload", + ).toContainText(E2E_TEAM_ORG_ALIAS, { timeout: 15_000 }); await expect( viewSelector(page), - "the view selection is not persisted across a reload either", - ).toContainText(CURRENT_TEAM_VIEW, { timeout: 15_000 }); + "the selected view is restored from the URL after a reload", + ).toContainText(ALL_MODELS_VIEW, { timeout: 15_000 }); + await expect(modelRow(page, CHAT_MODEL_A)).toHaveCount(1, { timeout: 15_000 }); + await expect(page.getByTestId("pagination-range")).toHaveText("Showing 1-1 of 1"); + await expect(modelRow(page, CHAT_MODEL_B)).toHaveCount(0); + await expect(modelRow(page, ungrantedModelName)).toHaveCount(0); + + await chooseOption(page, teamSelector(page), PERSONAL_TEAM); await expect( modelRow(page, ungrantedModelName), - "the personal view still renders models after a reload rather than coming back empty", + "switching back to the personal team restores models outside the selected team", ).toHaveCount(1, { timeout: 30_000 }); }); }); From 5a9fe56aff9ac8e3136f7faeb40891718262e3a3 Mon Sep 17 00:00:00 2001 From: kerry Date: Wed, 16 Sep 2026 01:24:05 +0000 Subject: [PATCH 160/225] fix(responses): count custom-tool and MCP argument deltas in the streamed usage fallback Co-Authored-By: Devin AI <158243242+devin-ai-integration[bot]@users.noreply.github.com> --- litellm/responses/streaming_iterator.py | 11 +++++++- .../responses/test_streaming_iterator.py | 28 +++++++++++++++++++ 2 files changed, 38 insertions(+), 1 deletion(-) diff --git a/litellm/responses/streaming_iterator.py b/litellm/responses/streaming_iterator.py index 6cf36e3e660..df3c26c1a4f 100644 --- a/litellm/responses/streaming_iterator.py +++ b/litellm/responses/streaming_iterator.py @@ -356,7 +356,7 @@ class BaseResponsesAPIStreamingIterator: _delta: Final = getattr(openai_responses_api_chunk, "delta", None) if isinstance(_delta, str): self._generated_content += _delta - elif _event_type == ResponsesAPIStreamEvents.FUNCTION_CALL_ARGUMENTS_DELTA: + elif _event_type in _TOOL_ARGUMENTS_DELTA_EVENTS: _args_delta: Final = getattr(openai_responses_api_chunk, "delta", None) if isinstance(_args_delta, str): self._generated_tool_arguments += _args_delta @@ -1358,6 +1358,15 @@ def _usage_as_model(usage: object) -> ResponseAPIUsage | None: return None +_TOOL_ARGUMENTS_DELTA_EVENTS: Final = frozenset( + { + ResponsesAPIStreamEvents.FUNCTION_CALL_ARGUMENTS_DELTA, + ResponsesAPIStreamEvents.CUSTOM_TOOL_CALL_INPUT_DELTA, + ResponsesAPIStreamEvents.MCP_CALL_ARGUMENTS_DELTA, + } +) + + def _estimate_usage_from_text( model: str, request_input: object, diff --git a/tests/test_litellm/responses/test_streaming_iterator.py b/tests/test_litellm/responses/test_streaming_iterator.py index 234e63ca3be..7bd03863717 100644 --- a/tests/test_litellm/responses/test_streaming_iterator.py +++ b/tests/test_litellm/responses/test_streaming_iterator.py @@ -816,3 +816,31 @@ async def test_completed_event_survives_a_failing_usage_estimate(): assert yielded assert iterator.completed_response.response.usage is None + + +@pytest.mark.asyncio +@pytest.mark.parametrize( + "tool_delta_event_type", + ["response.custom_tool_call_input.delta", "response.mcp_call_arguments.delta"], +) +async def test_completed_event_without_usage_counts_tool_input_deltas(tool_delta_event_type): + """Custom-tool and MCP argument deltas feed the streamed usage fallback the + same way function_call_arguments deltas do.""" + response = _responses_api_response_without_usage() + iterator = _make_iterator( + sse_events=[ + _sse_event({"type": tool_delta_event_type, "delta": '{"query": "weather in sf"}'}), + _sse_event({"type": "response.completed", "response": {}}), + ], + logging_obj=_logging_obj_stub(), + config=_mock_config_with_completed_response(response), + request_data={"input": "what is the weather in san francisco"}, + ) + + async for _ in iterator: + pass + + usage = iterator.completed_response.response.usage + assert usage is not None + assert usage.output_tokens > 0 + assert usage.total_tokens == usage.input_tokens + usage.output_tokens From a6d2332f7a19822ada77c4fd2cc13b853d1f9cad Mon Sep 17 00:00:00 2001 From: kerry Date: Wed, 16 Sep 2026 01:24:35 +0000 Subject: [PATCH 161/225] test(responses): drive the usage-estimate failure path without patching litellm Co-Authored-By: Devin AI <158243242+devin-ai-integration[bot]@users.noreply.github.com> --- .../responses/test_streaming_iterator.py | 19 ++++++++++++------- 1 file changed, 12 insertions(+), 7 deletions(-) diff --git a/tests/test_litellm/responses/test_streaming_iterator.py b/tests/test_litellm/responses/test_streaming_iterator.py index 7bd03863717..d3849e6ffce 100644 --- a/tests/test_litellm/responses/test_streaming_iterator.py +++ b/tests/test_litellm/responses/test_streaming_iterator.py @@ -17,6 +17,7 @@ from litellm.llms.base_llm.responses.transformation import BaseResponsesAPIConfi from litellm.responses.streaming_iterator import ( ResponsesAPIStreamingIterator, SyncResponsesAPIStreamingIterator, + _estimate_usage_from_text, ) from litellm.types.llms.openai import ( ResponseAPIUsage, @@ -796,8 +797,13 @@ async def test_completed_event_without_usage_counts_multimodal_input_as_messages @pytest.mark.asyncio async def test_completed_event_survives_a_failing_usage_estimate(): - """A raising token_counter must not break a stream that previously completed: - the estimate is best-effort and falls back to usage None.""" + """A malformed request input that makes the message transformer raise must not + break a stream that previously completed: the estimate is best-effort and + falls back to usage None.""" + malformed_input: Final = [{"type": "message", "role": "user", "content": 42}] + with pytest.raises(ValueError): + _estimate_usage_from_text("gpt-4o-mini", malformed_input, {"input": malformed_input}, "hello world") + response = _responses_api_response_without_usage() iterator = _make_iterator( sse_events=[ @@ -806,13 +812,12 @@ async def test_completed_event_survives_a_failing_usage_estimate(): ], logging_obj=_logging_obj_stub(), config=_mock_config_with_completed_response(response), - request_data={"input": "count these input tokens please"}, + request_data={"input": malformed_input}, ) - with patch.object(litellm, "token_counter", side_effect=RuntimeError("tokenizer exploded")): - yielded: list = [] - async for chunk in iterator: - yielded.append(chunk) + yielded: list = [] + async for chunk in iterator: + yielded.append(chunk) assert yielded assert iterator.completed_response.response.usage is None From 45d5e6b8336fa4383e0615d2c1020ebeca34aab6 Mon Sep 17 00:00:00 2001 From: Yuneng Jiang Date: Tue, 15 Sep 2026 18:26:44 -0700 Subject: [PATCH 162/225] fix(e2e): start cache CI service and count bypass calls --- .github/workflows/test-provider-cache.yml | 8 +++---- .../test_provider_cache.py | 23 +++++++++++++++++++ tests/e2e/provider_edge.py | 1 + 3 files changed, 28 insertions(+), 4 deletions(-) diff --git a/.github/workflows/test-provider-cache.yml b/.github/workflows/test-provider-cache.yml index 29b9d08a267..0507f90cf89 100644 --- a/.github/workflows/test-provider-cache.yml +++ b/.github/workflows/test-provider-cache.yml @@ -24,7 +24,7 @@ jobs: ports: - 6379:6379 options: >- - --health-cmd 'redis-cli ping' + --health-cmd "redis-cli ping" --health-interval 5s --health-timeout 3s --health-retries 10 @@ -35,10 +35,10 @@ jobs: E2E_PROVIDER_CACHE: '0' E2E_FIXTURE_MODE: live steps: - - uses: actions/checkout@08eba0b27e820071cde6df949e0beb9ba4906955 + - uses: actions/checkout@08eba0b27e820071cde6df949e0beb9ba4906955 # v4.3.0 with: persist-credentials: false - - uses: actions/setup-python@a26af69be951a213d495a4c3e4e4022e16d87065 + - uses: actions/setup-python@a26af69be951a213d495a4c3e4e4022e16d87065 # v5.6.0 with: python-version: '3.13' - uses: ./.github/actions/setup-uv-with-retries @@ -56,7 +56,7 @@ jobs: -q --junitxml=provider-cache-results.xml - name: Save test results if: always() - uses: actions/upload-artifact@4cec3d8aa04e39d1a68397de0c4cd6fb9dce8ec1 + uses: actions/upload-artifact@4cec3d8aa04e39d1a68397de0c4cd6fb9dce8ec1 # v4.6.1 with: name: provider-cache-results path: provider-cache-results.xml diff --git a/tests/code_coverage_tests/test_provider_cache.py b/tests/code_coverage_tests/test_provider_cache.py index 7edca8151af..d0e8c5296dc 100644 --- a/tests/code_coverage_tests/test_provider_cache.py +++ b/tests/code_coverage_tests/test_provider_cache.py @@ -11,8 +11,10 @@ from collections.abc import Generator from concurrent.futures import ThreadPoolExecutor from contextlib import contextmanager from dataclasses import dataclass, replace +from http.client import HTTPConnection from http.server import BaseHTTPRequestHandler, ThreadingHTTPServer from typing import Final +from urllib.parse import urlsplit import pytest from e2e_http import NetworkError, PreparedForward, RawResponse, StreamChunk, StreamHead, forward, prepare_forward @@ -407,3 +409,24 @@ def test_enabled_environment_reuses_store_across_fresh_backends( assert configured_cache_backend() is None finally: configured_cache.cache_clear() + + +def test_duplicate_headers_bypass_cache_and_count_live_calls(store: RedisResponseStore, provider: Provider) -> None: + cache: Final = CacheEdge(store, SECRET) + with edge(cache, provider) as url: + parsed: Final = urlsplit(url) + for _ in range(2): + connection = HTTPConnection(str(parsed.hostname), parsed.port, timeout=5) + try: + connection.putrequest("POST", parsed.path) + connection.putheader("content-length", str(len(BODY))) + connection.putheader("content-type", "application/json") + connection.putheader("x-duplicate", "first") + connection.putheader("x-duplicate", "second") + connection.endheaders(BODY) + assert connection.getresponse().read() == SUCCESS + finally: + connection.close() + assert len(provider.hits) == 2 + assert dict(cache.counters.counts)["duplicate_header_bypass"] == 2 + assert dict(cache.counters.counts)["upstream_attempts"] == 2 diff --git a/tests/e2e/provider_edge.py b/tests/e2e/provider_edge.py index 49d574ee957..053a2b5c0d3 100644 --- a/tests/e2e/provider_edge.py +++ b/tests/e2e/provider_edge.py @@ -891,6 +891,7 @@ class _EdgeHandler(BaseHTTPRequestHandler): ) if isinstance(edge_server.backend, CacheEdge) and duplicate_headers: edge_server.backend.counters.increment("duplicate_header_bypass") + edge_server.backend.counters.increment("upstream_attempts") outcome: Final = handle_edge_request( selected_backend, edge_server.mounts, From 10aef224e7acc6a5f47c77d63c7a98f81728f377 Mon Sep 17 00:00:00 2001 From: kerry Date: Wed, 16 Sep 2026 01:34:28 +0000 Subject: [PATCH 163/225] style(anthropic): apply repository conventions to the missing-usage change Co-Authored-By: Devin AI <158243242+devin-ai-integration[bot]@users.noreply.github.com> --- litellm/types/llms/anthropic.py | 2 +- .../llms/anthropic/chat/test_anthropic_chat_handler.py | 5 ++--- 2 files changed, 3 insertions(+), 4 deletions(-) diff --git a/litellm/types/llms/anthropic.py b/litellm/types/llms/anthropic.py index ecb8dbb2502..bcdee86360e 100644 --- a/litellm/types/llms/anthropic.py +++ b/litellm/types/llms/anthropic.py @@ -586,7 +586,7 @@ class MessageBlockDelta(TypedDict): type: Literal["message_delta"] delta: MessageDelta - usage: NotRequired[UsageDelta] + usage: NotRequired[ReadOnly[UsageDelta]] context_management: NotRequired[ContextManagementResponse] diff --git a/tests/test_litellm/llms/anthropic/chat/test_anthropic_chat_handler.py b/tests/test_litellm/llms/anthropic/chat/test_anthropic_chat_handler.py index ac05cdcf251..32c98a83948 100644 --- a/tests/test_litellm/llms/anthropic/chat/test_anthropic_chat_handler.py +++ b/tests/test_litellm/llms/anthropic/chat/test_anthropic_chat_handler.py @@ -580,10 +580,9 @@ def test_text_only_streaming_has_index_zero(): def test_message_delta_without_usage_returns_chunk_with_no_usage(): - """A message_delta event may carry no usage field; it must not raise.""" - iterator = ModelResponseIterator(None, sync_stream=True) + iterator: Final = ModelResponseIterator(None, sync_stream=True) - model_response = iterator.chunk_parser( + model_response: Final = iterator.chunk_parser( { "type": "message_delta", "delta": {"stop_reason": "end_turn", "stop_sequence": None}, From 8d625d0400befd2043806d71caec103c68489be0 Mon Sep 17 00:00:00 2001 From: Devin AI <158243242+devin-ai-integration[bot]@users.noreply.github.com> Date: Tue, 25 Aug 2026 19:43:01 +0000 Subject: [PATCH 164/225] fix(xai): keep 'instructions' on the xAI Responses API so system messages survive web_search bridging Co-Authored-By: Devin AI <158243242+devin-ai-integration[bot]@users.noreply.github.com> --- litellm/llms/xai/responses/transformation.py | 29 ++-------- .../test_xai_responses_transformation.py | 12 ++-- .../xai/xai_responses/test_transformation.py | 12 ++-- .../test_xai_responses_auto_routing.py | 56 ++++++++++++++++++- 4 files changed, 71 insertions(+), 38 deletions(-) diff --git a/litellm/llms/xai/responses/transformation.py b/litellm/llms/xai/responses/transformation.py index 1f977a66186..2f638da49c8 100644 --- a/litellm/llms/xai/responses/transformation.py +++ b/litellm/llms/xai/responses/transformation.py @@ -49,7 +49,6 @@ class XAIResponsesAPIConfig(OpenAIResponsesAPIConfig): Inherits from OpenAIResponsesAPIConfig since XAI's Responses API is largely compatible with OpenAI's, with a few differences: - - Does not support the 'instructions' parameter - Requires code_interpreter tools to have 'container' field removed - Recommends store=false when sending images @@ -60,20 +59,6 @@ class XAIResponsesAPIConfig(OpenAIResponsesAPIConfig): def custom_llm_provider(self) -> LlmProviders: return LlmProviders.XAI - def get_supported_openai_params(self, model: str) -> list: - """ - Get supported parameters for XAI Responses API. - - XAI supports most OpenAI Responses API params except 'instructions'. - """ - supported_params: Final = super().get_supported_openai_params(model) - - # Remove 'instructions' as it's not supported by XAI - if "instructions" in supported_params: - supported_params.remove("instructions") - - return supported_params - def _transform_web_search_tool(self, tool: Mapping[str, object]) -> Mapping[str, object]: """ Transform web_search tool to XAI format. @@ -158,19 +143,13 @@ class XAIResponsesAPIConfig(OpenAIResponsesAPIConfig): Map parameters for XAI Responses API. Handles XAI-specific transformations: - 1. Drops 'instructions' parameter (not supported) - 2. Transforms code_interpreter tools to remove 'container' field - 3. Transforms web_search tools to XAI format (removes search_context_size, adds filters) - 4. Transforms x_search tools to XAI format - 5. Sets store=false when images are detected (recommended by XAI) + 1. Transforms code_interpreter tools to remove 'container' field + 2. Transforms web_search tools to XAI format (removes search_context_size, adds filters) + 3. Transforms x_search tools to XAI format + 4. Sets store=false when images are detected (recommended by XAI) """ params: Final = dict(response_api_optional_params) - # Drop instructions parameter (not supported by XAI) - if "instructions" in params: - verbose_logger.debug("XAI Responses API does not support 'instructions' parameter. Dropping it.") - params.pop("instructions") - if "metadata" in params: verbose_logger.debug("XAI Responses API does not support 'metadata' parameter. Dropping it.") params.pop("metadata") diff --git a/tests/test_litellm/llms/xai/responses/test_xai_responses_transformation.py b/tests/test_litellm/llms/xai/responses/test_xai_responses_transformation.py index e3feb7d5342..34ad4b9075d 100644 --- a/tests/test_litellm/llms/xai/responses/test_xai_responses_transformation.py +++ b/tests/test_litellm/llms/xai/responses/test_xai_responses_transformation.py @@ -51,23 +51,23 @@ class TestXAIResponsesAPITransformation: assert result["tools"][0]["type"] == "code_interpreter" assert "container" not in result["tools"][0], "Container field should be removed" - def test_instructions_parameter_dropped(self): - """Test that instructions parameter is dropped for XAI""" + def test_instructions_parameter_forwarded(self): + """xAI supports 'instructions' on /v1/responses, so it must survive param mapping""" config = XAIResponsesAPIConfig() params = ResponsesAPIOptionalRequestParams(instructions="You are a helpful assistant.", temperature=0.7) result = config.map_openai_params(response_api_optional_params=params, model="grok-4-fast", drop_params=False) - assert "instructions" not in result, "Instructions should be dropped" + assert result.get("instructions") == "You are a helpful assistant." assert result.get("temperature") == 0.7, "Other params should be preserved" - def test_supported_params_excludes_instructions(self): - """Test that get_supported_openai_params excludes instructions""" + def test_supported_params_includes_instructions(self): + """A system message bridged to 'instructions' must not be rejected for xAI""" config = XAIResponsesAPIConfig() supported = config.get_supported_openai_params("grok-4-fast") - assert "instructions" not in supported, "instructions should not be supported" + assert "instructions" in supported, "instructions should be supported" assert "tools" in supported, "tools should be supported" assert "temperature" in supported, "temperature should be supported" assert "model" in supported, "model should be supported" diff --git a/tests/test_litellm/llms/xai/xai_responses/test_transformation.py b/tests/test_litellm/llms/xai/xai_responses/test_transformation.py index c783918ca06..3ea3fe631bd 100644 --- a/tests/test_litellm/llms/xai/xai_responses/test_transformation.py +++ b/tests/test_litellm/llms/xai/xai_responses/test_transformation.py @@ -53,8 +53,8 @@ class TestXAIResponsesAPITransformation: "container" not in result["tools"][0] ), "Container field should be removed" - def test_instructions_parameter_dropped(self): - """Test that instructions parameter is dropped for XAI""" + def test_instructions_parameter_forwarded(self): + """xAI supports 'instructions' on /v1/responses, so it must survive param mapping""" config = XAIResponsesAPIConfig() params = ResponsesAPIOptionalRequestParams( @@ -65,15 +65,15 @@ class TestXAIResponsesAPITransformation: response_api_optional_params=params, model="grok-4-fast", drop_params=False ) - assert "instructions" not in result, "Instructions should be dropped" + assert result.get("instructions") == "You are a helpful assistant." assert result.get("temperature") == 0.7, "Other params should be preserved" - def test_supported_params_excludes_instructions(self): - """Test that get_supported_openai_params excludes instructions""" + def test_supported_params_includes_instructions(self): + """A system message bridged to 'instructions' must not be rejected for xAI""" config = XAIResponsesAPIConfig() supported = config.get_supported_openai_params("grok-4-fast") - assert "instructions" not in supported, "instructions should not be supported" + assert "instructions" in supported, "instructions should be supported" assert "tools" in supported, "tools should be supported" assert "temperature" in supported, "temperature should be supported" assert "model" in supported, "model should be supported" diff --git a/tests/test_litellm/test_xai_responses_auto_routing.py b/tests/test_litellm/test_xai_responses_auto_routing.py index fbf2453d7fb..d405ea1e6c6 100644 --- a/tests/test_litellm/test_xai_responses_auto_routing.py +++ b/tests/test_litellm/test_xai_responses_auto_routing.py @@ -2,14 +2,30 @@ Test automatic routing to xAI Responses API when tools are present """ +import json +from collections.abc import Mapping +from typing import Final from unittest.mock import MagicMock, patch - +import httpx import pytest import litellm +from litellm.llms.custom_httpx.http_handler import HTTPHandler from litellm.main import responses_api_bridge_check +class _RecordingResponsesHandler: + """MockTransport handler that serves a canned /responses reply and keeps the body xAI would have received""" + + def __init__(self, reply: Mapping[str, object]) -> None: + self.reply: Final = reply + self.request_body: Mapping[str, object] | None = None + + def __call__(self, request: httpx.Request) -> httpx.Response: + self.request_body = json.loads(request.content) + return httpx.Response(200, json=dict(self.reply), request=request) + + class TestXAIResponsesAutoRouting: """Test that xAI requests with tools automatically route to Responses API""" @@ -254,6 +270,44 @@ class TestXAIResponsesAutoRouting: # Note: This test may need adjustment based on actual mock_response behavior # The key is that the responses_api_bridge_check logic routes correctly + def test_system_message_survives_web_search_bridge(self): + """A system message becomes 'instructions' on the bridged /responses call, and xAI accepts it""" + handler: Final = _RecordingResponsesHandler( + reply={ + "id": "resp_test", + "object": "response", + "created_at": 0, + "status": "completed", + "model": "grok-4.6", + "output": [ + { + "type": "message", + "id": "msg_test", + "status": "completed", + "role": "assistant", + "content": [{"type": "output_text", "text": "1.0.0", "annotations": []}], + } + ], + "usage": {"input_tokens": 1, "output_tokens": 1, "total_tokens": 2}, + } + ) + + response: Final = litellm.completion( + model="xai/grok-4.6", + messages=[ + {"role": "system", "content": "Answer briefly."}, + {"role": "user", "content": "newest litellm version?"}, + ], + web_search_options={"search_context_size": "medium"}, + api_key="fake-key", + client=HTTPHandler(client=httpx.Client(transport=httpx.MockTransport(handler))), + ) + + assert response.choices[0].message.content == "1.0.0" + assert handler.request_body is not None + assert handler.request_body["instructions"] == "Answer briefly." + assert handler.request_body["tools"] == [{"type": "web_search"}] + if __name__ == "__main__": pytest.main([__file__, "-v"]) From 8352045f32840642f2dd89d9e39a5bd41c6d6bd2 Mon Sep 17 00:00:00 2001 From: kerry Date: Wed, 16 Sep 2026 01:39:24 +0000 Subject: [PATCH 165/225] style(fireworks-ai): apply repository conventions to the cost component change Co-Authored-By: Devin AI <158243242+devin-ai-integration[bot]@users.noreply.github.com> --- litellm/llms/fireworks_ai/cost_calculator.py | 31 +++++----- .../test_fireworks_ai_cost_calculator.py | 62 ++++++++++++------- 2 files changed, 57 insertions(+), 36 deletions(-) diff --git a/litellm/llms/fireworks_ai/cost_calculator.py b/litellm/llms/fireworks_ai/cost_calculator.py index f323d78e22d..a92e5208471 100644 --- a/litellm/llms/fireworks_ai/cost_calculator.py +++ b/litellm/llms/fireworks_ai/cost_calculator.py @@ -68,24 +68,25 @@ def _resolve_model_info(model: str) -> ModelInfo: def _with_cache_read_fallback(model_info: ModelInfo) -> ModelInfo: - """Most fireworks_ai price-map entries publish no cache-read rate, and the provider bills - cached reads at the input rate. generic_cost_per_token prices a missing rate at $0, so the - fallback is written into a copy of the entry (the shared model-cost dict must not be - mutated), including inside off_peak_pricing so cached reads track the off-peak input rate - the way the previous calculator did.""" - if model_info.get("cache_read_input_token_cost") is not None: - return model_info + """Most fireworks_ai price-map entries publish no cache-read rate though the provider bills + cached reads at the input rate; the shared map is never mutated, so a copy carries the fallback.""" input_rate: Final = model_info.get("input_cost_per_token") - if input_rate is None: + if model_info.get("cache_read_input_token_cost") is not None or input_rate is None: return model_info - effective: Final[dict[str, object]] = dict(model_info) - effective["cache_read_input_token_cost"] = input_rate off_peak: Final = model_info.get("off_peak_pricing") - if off_peak is not None and "cache_read_input_token_cost" not in off_peak: - off_peak_copy: Final[dict[str, object]] = dict(off_peak) - off_peak_copy["cache_read_input_token_cost"] = off_peak_copy.get("input_cost_per_token", input_rate) - effective["off_peak_pricing"] = off_peak_copy - return cast(ModelInfo, effective) + if off_peak is None or "cache_read_input_token_cost" in off_peak: + return cast(ModelInfo, {**model_info, "cache_read_input_token_cost": input_rate}) + return cast( + ModelInfo, + { + **model_info, + "cache_read_input_token_cost": input_rate, + "off_peak_pricing": { + **off_peak, + "cache_read_input_token_cost": off_peak.get("input_cost_per_token", input_rate), + }, + }, + ) def cost_per_token(model: str, usage: Usage, current_time: datetime | None = None) -> tuple[float, float]: diff --git a/tests/test_litellm/llms/fireworks_ai/test_fireworks_ai_cost_calculator.py b/tests/test_litellm/llms/fireworks_ai/test_fireworks_ai_cost_calculator.py index 0ad5eddcc7a..f461f17b627 100644 --- a/tests/test_litellm/llms/fireworks_ai/test_fireworks_ai_cost_calculator.py +++ b/tests/test_litellm/llms/fireworks_ai/test_fireworks_ai_cost_calculator.py @@ -1,5 +1,6 @@ import math from datetime import datetime, timezone +from typing import Final import pytest @@ -51,13 +52,16 @@ STANDARD_CACHE_READ_COST = 1.5e-08 def _register_off_peak_model( off_peak_pricing: OffPeakPricing, cache_read_cost: float | None = STANDARD_CACHE_READ_COST ) -> None: - litellm.model_cost[f"fireworks_ai/{OFF_PEAK_MODEL}"] = { - "litellm_provider": "fireworks_ai", - "mode": "chat", - "input_cost_per_token": STANDARD_INPUT_COST, - "output_cost_per_token": STANDARD_OUTPUT_COST, - "off_peak_pricing": off_peak_pricing, - **({} if cache_read_cost is None else {"cache_read_input_token_cost": cache_read_cost}), + litellm.model_cost = { # test-quality-ok: the save/restore conftest returns litellm.model_cost to the original object after each test, so replacing the map for this entry leaks nothing + **litellm.model_cost, + f"fireworks_ai/{OFF_PEAK_MODEL}": { + "litellm_provider": "fireworks_ai", + "mode": "chat", + "input_cost_per_token": STANDARD_INPUT_COST, + "output_cost_per_token": STANDARD_OUTPUT_COST, + "off_peak_pricing": off_peak_pricing, + **({} if cache_read_cost is None else {"cache_read_input_token_cost": cache_read_cost}), + }, } @@ -145,20 +149,19 @@ COMPONENT_AUDIO_OUT_COST = 6e-06 def test_cache_write_reasoning_and_audio_tokens_are_billed_at_their_component_rates(): - """Regression (LIT-7837): the hand-rolled fireworks_ai calculator billed every - cache-creation, reasoning and audio token at $0. The shared calculator treats the - prompt detail counts as subsets of prompt_tokens and the completion detail counts as - subsets of completion_tokens, billing each remainder at the text rate.""" - litellm.model_cost[f"fireworks_ai/{COMPONENT_MODEL}"] = { - "litellm_provider": "fireworks_ai", - "mode": "chat", - "input_cost_per_token": COMPONENT_INPUT_COST, - "output_cost_per_token": COMPONENT_OUTPUT_COST, - "cache_read_input_token_cost": COMPONENT_CACHE_READ_COST, - "cache_creation_input_token_cost": COMPONENT_CACHE_CREATION_COST, - "output_cost_per_reasoning_token": COMPONENT_REASONING_COST, - "input_cost_per_audio_token": COMPONENT_AUDIO_IN_COST, - "output_cost_per_audio_token": COMPONENT_AUDIO_OUT_COST, + litellm.model_cost = { # test-quality-ok: the save/restore conftest returns litellm.model_cost to the original object after each test, so replacing the map for this entry leaks nothing + **litellm.model_cost, + f"fireworks_ai/{COMPONENT_MODEL}": { + "litellm_provider": "fireworks_ai", + "mode": "chat", + "input_cost_per_token": COMPONENT_INPUT_COST, + "output_cost_per_token": COMPONENT_OUTPUT_COST, + "cache_read_input_token_cost": COMPONENT_CACHE_READ_COST, + "cache_creation_input_token_cost": COMPONENT_CACHE_CREATION_COST, + "output_cost_per_reasoning_token": COMPONENT_REASONING_COST, + "input_cost_per_audio_token": COMPONENT_AUDIO_IN_COST, + "output_cost_per_audio_token": COMPONENT_AUDIO_OUT_COST, + }, } usage = Usage( prompt_tokens=1000, @@ -188,3 +191,20 @@ def test_cache_write_reasoning_and_audio_tokens_are_billed_at_their_component_ra ) assert prompt_cost == pytest.approx(expected_prompt_cost) assert completion_cost == pytest.approx(expected_completion_cost) + + +def test_an_entry_without_an_input_rate_gets_no_cache_read_fallback(): + litellm.model_cost = { # test-quality-ok: the save/restore conftest returns litellm.model_cost to the original object after each test, so replacing the map for this entry leaks nothing + **litellm.model_cost, # pyright: ignore[reportUnknownMemberType] # the SDK types model_cost as dict[Unknown, Unknown] + "fireworks_ai/accounts/fireworks/models/no-input-rate-test": { + "litellm_provider": "fireworks_ai", + "mode": "chat", + "output_cost_per_token": 2e-06, + }, + } + usage: Final = _usage(prompt_tokens=1000, cached_tokens=300, completion_tokens=200) + + prompt_cost, completion_cost = cost_per_token(model="accounts/fireworks/models/no-input-rate-test", usage=usage) + + assert prompt_cost == 0 + assert completion_cost == 200 * 2e-06 From c4c96180e301179259236953ab0a62e3008fcd95 Mon Sep 17 00:00:00 2001 From: kerry Date: Wed, 16 Sep 2026 01:42:36 +0000 Subject: [PATCH 166/225] fix(gemini): strip version suffix from modelVersion and keep it on blocked streams Co-Authored-By: Devin AI <158243242+devin-ai-integration[bot]@users.noreply.github.com> --- .../vertex_and_google_ai_studio_gemini.py | 18 +++++++--- ...test_vertex_and_google_ai_studio_gemini.py | 33 +++++++++++++++++++ 2 files changed, 46 insertions(+), 5 deletions(-) diff --git a/litellm/llms/vertex_ai/gemini/vertex_and_google_ai_studio_gemini.py b/litellm/llms/vertex_ai/gemini/vertex_and_google_ai_studio_gemini.py index eb03b17435c..b4712fd376b 100644 --- a/litellm/llms/vertex_ai/gemini/vertex_and_google_ai_studio_gemini.py +++ b/litellm/llms/vertex_ai/gemini/vertex_and_google_ai_studio_gemini.py @@ -124,6 +124,12 @@ def _unsupported_reasoning_effort(reasoning_effort: str) -> UnsupportedParamsErr ) +def _served_model_name(model_version: object) -> str | None: + if not isinstance(model_version, str) or not model_version: + return None + return model_version.split("@", 1)[0] + + class VertexAIBaseConfig: def get_mapped_special_auth_params(self) -> dict: """ @@ -1951,6 +1957,7 @@ class VertexGeminiConfig(VertexAIBaseConfig, BaseConfig): def _check_prompt_level_content_filter( processed_chunk: GenerateContentResponseBody, response_id: str | None, + model: str | None = None, ) -> Optional["ModelResponseStream"]: """ Check if prompt is blocked due to content filtering at the prompt level. @@ -1990,7 +1997,7 @@ class VertexGeminiConfig(VertexAIBaseConfig, BaseConfig): enhancements=None, ) - model_response: Final = ModelResponseStream(choices=[choice], id=response_id) + model_response: Final = ModelResponseStream(choices=[choice], id=response_id, model=model) return model_response return None @@ -2434,8 +2441,8 @@ class VertexGeminiConfig(VertexAIBaseConfig, BaseConfig): completion_response = GenerateContentResponseBody(**completion_response) ## GET MODEL ## - model_version: Final = completion_response.get("modelVersion") - model_response.model = model_version if isinstance(model_version, str) else model + served: Final = _served_model_name(completion_response.get("modelVersion")) + model_response.model = served if served is not None else model ## CHECK IF RESPONSE FLAGGED if "promptFeedback" in completion_response and "blockReason" in completion_response["promptFeedback"]: @@ -3265,17 +3272,18 @@ class ModelResponseIterator: processed_chunk: Final = GenerateContentResponseBody(**chunk) response_id: Final = processed_chunk.get("responseId") - chunk_model_version: Final = processed_chunk.get("modelVersion") + served: Final = _served_model_name(processed_chunk.get("modelVersion")) model_response = ModelResponseStream( choices=[], id=response_id, - model=chunk_model_version if isinstance(chunk_model_version, str) else None, + model=served, ) # Check if prompt is blocked due to content filtering blocked_response: Final = VertexGeminiConfig._check_prompt_level_content_filter( processed_chunk=processed_chunk, response_id=response_id, + model=served, ) if blocked_response is not None: model_response = blocked_response diff --git a/tests/test_litellm/llms/vertex_ai/gemini/test_vertex_and_google_ai_studio_gemini.py b/tests/test_litellm/llms/vertex_ai/gemini/test_vertex_and_google_ai_studio_gemini.py index cd0ed0057c0..001105fc53d 100644 --- a/tests/test_litellm/llms/vertex_ai/gemini/test_vertex_and_google_ai_studio_gemini.py +++ b/tests/test_litellm/llms/vertex_ai/gemini/test_vertex_and_google_ai_studio_gemini.py @@ -5926,3 +5926,36 @@ def test_served_model_version_reaches_assembled_stream_through_custom_stream_wra assert chunk._hidden_params["provider_response_model"] == served_model assembled: Final = litellm.stream_chunk_builder(chunks=list(chunks), messages=[{"role": "user", "content": "hi"}]) assert assembled._hidden_params["provider_response_model"] == served_model + + +def test_generate_content_transform_strips_version_suffix_from_model_version(): + import httpx + + body: Final = {**_generate_content_body(), "modelVersion": "gemini-3.8-flash-001@default"} + response: Final = VertexGeminiConfig()._transform_google_generate_content_to_openai_model_response( + completion_response=body, + model_response=ModelResponse(), + model="gemini-3.8-flash", + logging_obj=MagicMock(), + raw_response=httpx.Response(200, headers={}), + ) + + assert response.model == "gemini-3.8-flash-001" + + +def test_prompt_blocked_chunk_keeps_served_model_version(): + from litellm.llms.vertex_ai.gemini.vertex_and_google_ai_studio_gemini import ( + ModelResponseIterator, + ) + + chunk: Final = { + "promptFeedback": {"blockReason": "SAFETY", "blockReasonMessage": "prompt was blocked"}, + "modelVersion": "gemini-3.8-flash-001", + "responseId": "resp-1", + } + iterator: Final = ModelResponseIterator(streaming_response=[], sync_stream=True, logging_obj=MagicMock()) + + streaming_chunk: Final = iterator.chunk_parser(chunk) + + assert streaming_chunk.model == "gemini-3.8-flash-001" + assert streaming_chunk.choices[0].finish_reason == "content_filter" From 091a38cce6b569439e116e8be5f493f06a0974de Mon Sep 17 00:00:00 2001 From: kerry Date: Wed, 16 Sep 2026 01:43:08 +0000 Subject: [PATCH 167/225] test(anthropic): import Final for the annotated locals Co-Authored-By: Devin AI <158243242+devin-ai-integration[bot]@users.noreply.github.com> --- .../llms/anthropic/chat/test_anthropic_chat_handler.py | 1 + 1 file changed, 1 insertion(+) diff --git a/tests/test_litellm/llms/anthropic/chat/test_anthropic_chat_handler.py b/tests/test_litellm/llms/anthropic/chat/test_anthropic_chat_handler.py index 32c98a83948..c3400dc40c3 100644 --- a/tests/test_litellm/llms/anthropic/chat/test_anthropic_chat_handler.py +++ b/tests/test_litellm/llms/anthropic/chat/test_anthropic_chat_handler.py @@ -1,6 +1,7 @@ import json import threading from http.server import BaseHTTPRequestHandler, ThreadingHTTPServer +from typing import Final from unittest.mock import AsyncMock, MagicMock, patch import httpx From 91c7f758640b5d60c3fcfd9e51bcd1770a74fd25 Mon Sep 17 00:00:00 2001 From: kerry Date: Wed, 16 Sep 2026 01:44:30 +0000 Subject: [PATCH 168/225] docs(fireworks-ai): describe the cache-read fallback without asserting provider billing Co-Authored-By: Devin AI <158243242+devin-ai-integration[bot]@users.noreply.github.com> --- litellm/llms/fireworks_ai/cost_calculator.py | 4 ++-- 1 file changed, 2 insertions(+), 2 deletions(-) diff --git a/litellm/llms/fireworks_ai/cost_calculator.py b/litellm/llms/fireworks_ai/cost_calculator.py index a92e5208471..1795a700d25 100644 --- a/litellm/llms/fireworks_ai/cost_calculator.py +++ b/litellm/llms/fireworks_ai/cost_calculator.py @@ -68,8 +68,8 @@ def _resolve_model_info(model: str) -> ModelInfo: def _with_cache_read_fallback(model_info: ModelInfo) -> ModelInfo: - """Most fireworks_ai price-map entries publish no cache-read rate though the provider bills - cached reads at the input rate; the shared map is never mutated, so a copy carries the fallback.""" + """Entries without a cache-read rate keep the previous calculator's input-rate fallback for cached + reads (LIT-7845 tracks the documented discount); the shared map is never mutated, so a copy carries it.""" input_rate: Final = model_info.get("input_cost_per_token") if model_info.get("cache_read_input_token_cost") is not None or input_rate is None: return model_info From 15f2e25e8af3015b408ceb4a2f057a93bf1bed85 Mon Sep 17 00:00:00 2001 From: yassin Date: Wed, 16 Sep 2026 01:44:36 +0000 Subject: [PATCH 169/225] refactor(proxy): replace configurable model access denied message with a fixed clean client message Drop the model_access_denied_message setting, its {model} template, the DB override entry and the Admin UI field. Model access denials now always return the fixed client message while the allowlist diagnostic is logged at the final HTTP, realtime and MCP boundaries Co-Authored-By: Devin AI <158243242+devin-ai-integration[bot]@users.noreply.github.com> --- litellm/__init__.py | 1 - litellm/constants.py | 3 - litellm/proxy/auth/auth_checks.py | 8 +- litellm/proxy/auth/handle_jwt.py | 10 +- litellm/proxy/auth/model_access_denied.py | 13 +- litellm/proxy/proxy_server.py | 39 +----- tests/otel_tests/test_e2e_model_access.py | 8 +- tests/proxy_unit_tests/test_auth_checks.py | 4 +- .../test_mcp_sampling_model_access.py | 4 +- .../proxy/auth/test_auth_checks.py | 68 +++++----- .../proxy/auth/test_auth_exception_handler.py | 56 ++------- .../proxy/auth/test_auth_utils.py | 2 +- .../proxy/auth/test_handle_jwt.py | 34 ++--- .../test_realtime_webrtc_endpoints.py | 14 +-- tests/test_litellm/proxy/test_proxy_server.py | 119 +----------------- tests/test_litellm/test_router.py | 6 +- tests/test_openai_endpoints.py | 2 +- .../general_settings.integration.test.tsx | 58 +-------- .../_components/general_settings.tsx | 42 +++---- 19 files changed, 114 insertions(+), 377 deletions(-) diff --git a/litellm/__init__.py b/litellm/__init__.py index 73714cd0c9c..3668e6efb0c 100644 --- a/litellm/__init__.py +++ b/litellm/__init__.py @@ -219,7 +219,6 @@ redact_user_api_key_info: Optional[bool] = False # major release; opt in early with `litellm.expose_router_debug_in_errors # = False`. expose_router_debug_in_errors: bool = True -model_access_denied_message: str | None = None filter_invalid_headers: Optional[bool] = False add_user_information_to_llm_headers: Optional[bool] = ( None # adds user_id, team_id, token hash (params from StandardLoggingMetadata) to request headers diff --git a/litellm/constants.py b/litellm/constants.py index 7d546fdccdf..745a4d9294e 100644 --- a/litellm/constants.py +++ b/litellm/constants.py @@ -98,7 +98,6 @@ BASE64_TRUNCATION_OFFLOAD_THRESHOLD_CHARS: Final = 256 * 1024 REDACTED_BY_LITELLM: Final = "redacted-by-litellm" # in-memory stand-in handed to provider converters for redacted arguments; never stored REDACTED_TOOL_CALL_ARGUMENTS_PLACEHOLDER: Final = "{}" -MODEL_ACCESS_DENIED_MESSAGE_MODEL_PLACEHOLDER: Final = "{model}" MAX_STRING_LENGTH_STDOUT_LOG: Final = get_env_int("MAX_STRING_LENGTH_STDOUT_LOG", 4096) @@ -1801,8 +1800,6 @@ LITELLM_SETTINGS_SAFE_DB_OVERRIDES: Final = [ "max_ui_session_budget", "budget_rollover", "mcp_tool_search", - "model_access_denied_message", - "expose_router_debug_in_errors", ] SPECIAL_LITELLM_AUTH_TOKEN: Final = ["ui-token"] DEFAULT_MANAGEMENT_OBJECT_IN_MEMORY_CACHE_TTL = int(os.getenv("DEFAULT_MANAGEMENT_OBJECT_IN_MEMORY_CACHE_TTL", 60)) diff --git a/litellm/proxy/auth/auth_checks.py b/litellm/proxy/auth/auth_checks.py index 6b67fc0cb28..ba68dc8a17f 100644 --- a/litellm/proxy/auth/auth_checks.py +++ b/litellm/proxy/auth/auth_checks.py @@ -72,7 +72,7 @@ from litellm.proxy.auth.budget_throttle import ( budget_throttle_percentage, should_throttle_budget_exceeded, ) -from litellm.proxy.auth.model_access_denied import client_facing_model_access_denied_message +from litellm.proxy.auth.model_access_denied import model_access_denied_client_message from litellm.proxy.auth.route_checks import RouteChecks from litellm.proxy.common_utils.auth_cache_invalidation_pubsub import publish_auth_cache_invalidation from litellm.proxy.common_utils.cache_pydantic_utils import CacheCodec @@ -4177,7 +4177,7 @@ def _can_object_call_model( f"Tried to access {model}" ) raise ModelAccessDeniedProxyException( - message=client_facing_model_access_denied_message(internal_message=internal_message, model=model), + message=model_access_denied_client_message(model=model), internal_message=internal_message, type=ProxyErrorTypes.get_model_access_error_type_for_object(object_type=object_type), param="model", @@ -4808,7 +4808,7 @@ async def can_user_call_model( f"Tried to access {model}" ) raise ModelAccessDeniedProxyException( - message=client_facing_model_access_denied_message(internal_message=internal_message, model=model), + message=model_access_denied_client_message(model=model), internal_message=internal_message, type=ProxyErrorTypes.key_model_access_denied, param="model", @@ -5415,7 +5415,7 @@ async def _check_team_member_model_access( f"Model={model}. Allowed member models = {member_allowed_models}" ) raise ModelAccessDeniedProxyException( - message=client_facing_model_access_denied_message(internal_message=internal_message, model=model), + message=model_access_denied_client_message(model=model), internal_message=internal_message, type=ProxyErrorTypes.team_model_access_denied, param="model", diff --git a/litellm/proxy/auth/handle_jwt.py b/litellm/proxy/auth/handle_jwt.py index ea6d52b28f0..0389f69cfeb 100644 --- a/litellm/proxy/auth/handle_jwt.py +++ b/litellm/proxy/auth/handle_jwt.py @@ -54,7 +54,7 @@ from litellm.proxy._types import ( from litellm.proxy.auth.auth_checks import can_team_access_model from litellm.proxy.auth.model_access_denied import ( ModelAccessDeniedHTTPException, - client_facing_model_access_denied_message, + model_access_denied_client_message, ) from litellm.proxy.auth.resolvers.grants import GrantResolver, UserLookup, canonical_user_id from litellm.proxy.auth.route_checks import RouteChecks @@ -1347,7 +1347,7 @@ class JWTAuthManager: raise ModelAccessDeniedHTTPException( internal_message=internal_message, status_code=403, - detail=client_facing_model_access_denied_message(internal_message=internal_message, model=model), + detail=model_access_denied_client_message(model=model), ) return True @@ -1380,11 +1380,7 @@ class JWTAuthManager: raise ModelAccessDeniedHTTPException( internal_message=internal_message, status_code=403, - detail={ - "error": client_facing_model_access_denied_message( - internal_message=internal_message, model=requested_model - ) - }, + detail={"error": model_access_denied_client_message(model=requested_model)}, ) return diff --git a/litellm/proxy/auth/model_access_denied.py b/litellm/proxy/auth/model_access_denied.py index 8164e06c42a..ffb73b343cd 100644 --- a/litellm/proxy/auth/model_access_denied.py +++ b/litellm/proxy/auth/model_access_denied.py @@ -2,15 +2,14 @@ from typing import Final from fastapi import HTTPException -import litellm -from litellm.constants import MODEL_ACCESS_DENIED_MESSAGE_MODEL_PLACEHOLDER +MODEL_ACCESS_DENIED_CLIENT_MESSAGE: Final = ( + "The requested model '{model}' is not available for this API key, or the model name is invalid. " + "Check the models available to you and try again." +) -def client_facing_model_access_denied_message(internal_message: str, model: str | list[str]) -> str: - template: Final = litellm.model_access_denied_message - if not template: - return internal_message - return template.replace(MODEL_ACCESS_DENIED_MESSAGE_MODEL_PLACEHOLDER, str(model)) +def model_access_denied_client_message(model: str | list[str]) -> str: + return MODEL_ACCESS_DENIED_CLIENT_MESSAGE.format(model=model) class ModelAccessDeniedHTTPException(HTTPException): diff --git a/litellm/proxy/proxy_server.py b/litellm/proxy/proxy_server.py index 12d62f6620d..23bb8b6225b 100644 --- a/litellm/proxy/proxy_server.py +++ b/litellm/proxy/proxy_server.py @@ -1682,7 +1682,7 @@ async def openai_exception_handler(request: Request, exc: ProxyException): def _log_model_access_denial(exc: ProxyException) -> None: - if not litellm.model_access_denied_message or not isinstance(exc, ModelAccessDeniedProxyException): + if not isinstance(exc, ModelAccessDeniedProxyException): return verbose_proxy_logger.warning(exc.sanitized_internal_message()) @@ -17455,13 +17455,11 @@ GeneralSettingsUILiteLLMValue = float | bool | str | None class GeneralSettingsUILiteLLMFieldSpec(TypedDict): - type: ReadOnly[Literal["Float", "Dollar", "Boolean", "Select", "String"]] - description: ReadOnly[str] - options: ReadOnly[NotRequired[tuple[str, ...]]] - tab: ReadOnly[NotRequired[str]] # Admin UI sub-tab this field renders under; None groups it with the rest - default: ReadOnly[ - NotRequired[float | bool] - ] # reset/clear restores this instead of None; fields whose None means fail-open set it + type: Literal["Float", "Dollar", "Boolean", "Select"] + description: str + options: NotRequired[tuple[str, ...]] + tab: NotRequired[str] # Admin UI sub-tab this field renders under; None groups it with the rest + default: NotRequired[float] # reset/clear restores this instead of None; fields whose None means fail-open set it _GENERAL_SETTINGS_UI_LITELLM_FIELDS: Final[dict[str, GeneralSettingsUILiteLLMFieldSpec]] = { @@ -17513,24 +17511,6 @@ _GENERAL_SETTINGS_UI_LITELLM_FIELDS: Final[dict[str, GeneralSettingsUILiteLLMFie "with this budget. Clearing restores the $1 default." ), }, - "model_access_denied_message": { - "type": "String", - "description": ( - "Client-facing error message returned when a key, team, user, org or project is not allowed " - "to call the requested model. {model} is replaced with the requested model name. The full " - "denial reason (allowed models and access groups) is still written to the proxy logs. " - "Leave empty to return the detailed message to clients." - ), - }, - "expose_router_debug_in_errors": { - "type": "Boolean", - "default": True, - "description": ( - "Append router debug details (model group, configured fallbacks, fallback errors, cooldown " - "info) to error messages returned to clients. Turn off to keep those details in the proxy " - "logs only." - ), - }, } @@ -17578,13 +17558,6 @@ def _validate_general_settings_ui_litellm_value(field_name: str, value: object) detail={"error": f"{field_name} must be a positive dollar amount or empty"}, ) return float(value) - case "String": - if not isinstance(value, str): - raise HTTPException( - status_code=400, - detail={"error": f"{field_name} must be a string or empty"}, - ) - return value case _: assert_never(field_type) diff --git a/tests/otel_tests/test_e2e_model_access.py b/tests/otel_tests/test_e2e_model_access.py index e5e93c0b179..6017a820299 100644 --- a/tests/otel_tests/test_e2e_model_access.py +++ b/tests/otel_tests/test_e2e_model_access.py @@ -101,7 +101,7 @@ async def test_model_access_patterns(key_models, test_model, expect_success): assert _error_body["type"] == "key_model_access_denied" assert _error_body["param"] == "model" assert _error_body["code"] == "403" - assert "key not allowed to access model" in _error_body["message"] + assert "is not available for this API key" in _error_body["message"] @pytest.mark.asyncio @@ -299,7 +299,5 @@ def _validate_model_access_exception( assert _error_body["type"] == expected_type assert _error_body["param"] == "model" assert _error_body["code"] == "403" - if expected_type == "key_model_access_denied": - assert "key not allowed to access model" in _error_body["message"] - elif expected_type == "team_model_access_denied": - assert "eam not allowed to access model" in _error_body["message"] + assert "is not available for this API key" in _error_body["message"] + assert "not allowed to access model" not in _error_body["message"] diff --git a/tests/proxy_unit_tests/test_auth_checks.py b/tests/proxy_unit_tests/test_auth_checks.py index d436c99cd20..2538556d3b5 100644 --- a/tests/proxy_unit_tests/test_auth_checks.py +++ b/tests/proxy_unit_tests/test_auth_checks.py @@ -163,7 +163,7 @@ async def test_can_key_call_model(model, expect_to_work): if expect_to_work: await can_key_call_model(**args) else: - with pytest.raises(Exception, match='key not allowed to access model\\. This key can only access') as e: + with pytest.raises(Exception, match='is not available for this API key') as e: await can_key_call_model(**args) print(e) @@ -943,7 +943,7 @@ async def test_can_key_call_model_with_aliases(model, alias_map, expect_to_work) llm_router=router, ) else: - with pytest.raises(Exception, match='key not allowed to access model\\. This key can only access') as e: + with pytest.raises(Exception, match='is not available for this API key') as e: await can_key_call_model( model=model, llm_model_list=llm_model_list, diff --git a/tests/test_litellm/proxy/_experimental/mcp_server/test_mcp_sampling_model_access.py b/tests/test_litellm/proxy/_experimental/mcp_server/test_mcp_sampling_model_access.py index d98db5518c3..7eebf1eb436 100644 --- a/tests/test_litellm/proxy/_experimental/mcp_server/test_mcp_sampling_model_access.py +++ b/tests/test_litellm/proxy/_experimental/mcp_server/test_mcp_sampling_model_access.py @@ -138,11 +138,9 @@ class TestCheckModelAccess: assert "claude-3-opus-20240229" in result.message @pytest.mark.asyncio - async def test_should_log_internal_denial_reason_when_client_message_is_configured(self, monkeypatch, caplog): - import litellm + async def test_should_log_internal_denial_reason_and_hide_allowlist_from_client(self, caplog): from litellm.proxy._types import UserAPIKeyAuth - monkeypatch.setattr(litellm, "model_access_denied_message", "The model `{model}` is unavailable for this API key.") auth = UserAPIKeyAuth(api_key="sk-test-key", models=["gpt-3.5-turbo"]) with caplog.at_level("WARNING", logger="LiteLLM"): diff --git a/tests/test_litellm/proxy/auth/test_auth_checks.py b/tests/test_litellm/proxy/auth/test_auth_checks.py index 0677fb8af29..26ae28a57d2 100644 --- a/tests/test_litellm/proxy/auth/test_auth_checks.py +++ b/tests/test_litellm/proxy/auth/test_auth_checks.py @@ -531,12 +531,14 @@ async def test_can_team_access_model_error_lists_direct_and_access_group_models( assert await can_team_access_model("direct-model", team_object, None) is True assert await can_team_access_model("group-model", team_object, None) is True - with pytest.raises(ProxyException) as exc_info: + with pytest.raises(ModelAccessDeniedProxyException) as exc_info: await can_team_access_model("blocked-model", team_object, None) assert exc_info.value.type == ProxyErrorTypes.team_model_access_denied - assert "direct-model" in exc_info.value.message - assert "group-model" in exc_info.value.message + assert "direct-model" in exc_info.value.internal_message + assert "group-model" in exc_info.value.internal_message + assert "direct-model" not in exc_info.value.message + assert "group-model" not in exc_info.value.message @pytest.mark.asyncio @@ -1676,16 +1678,17 @@ def test_can_object_call_model_no_access_to_alias_or_underlying(): # Should raise ProxyException with appropriate error type assert exc_info.value.type == ProxyErrorTypes.key_model_access_denied - assert "key not allowed to access model" in str(exc_info.value.message) + assert "is not available for this API key" in str(exc_info.value.message) assert "my-fake-gpt" in str(exc_info.value.message) -_DENIED_MESSAGE_TEMPLATE: Final = "The model `{model}` is unavailable for this API key or does not exist." +_DENIED_MESSAGE_TEMPLATE: Final = ( + "The requested model '{model}' is not available for this API key, or the model name is invalid. " + "Check the models available to you and try again." +) -def test_can_object_call_model_denial_uses_configured_message_and_keeps_detail_on_exception(monkeypatch, caplog): - monkeypatch.setattr(litellm, "model_access_denied_message", _DENIED_MESSAGE_TEMPLATE) - +def test_can_object_call_model_denial_hides_allowlist_and_keeps_detail_on_exception(caplog): with caplog.at_level("DEBUG", logger="LiteLLM Proxy"): with pytest.raises(ModelAccessDeniedProxyException) as exc_info: _can_object_call_model( @@ -1695,9 +1698,8 @@ def test_can_object_call_model_denial_uses_configured_message_and_keeps_detail_o object_type="key", ) - assert ( - exc_info.value.message == "The model `anthropic-sonnet-4-5` is unavailable for this API key or does not exist." - ) + assert exc_info.value.message == _DENIED_MESSAGE_TEMPLATE.format(model="anthropic-sonnet-4-5") + assert "internal-models" not in exc_info.value.message assert exc_info.value.type == ProxyErrorTypes.key_model_access_denied assert exc_info.value.param == "model" assert int(exc_info.value.code) == status.HTTP_403_FORBIDDEN @@ -1709,10 +1711,9 @@ def test_can_object_call_model_denial_uses_configured_message_and_keeps_detail_o @pytest.mark.asyncio -async def test_access_group_fallback_grant_does_not_log_a_denial(monkeypatch, caplog): +async def test_access_group_fallback_grant_does_not_log_a_denial(caplog): from litellm.proxy.auth.auth_checks import can_team_access_model - monkeypatch.setattr(litellm, "model_access_denied_message", _DENIED_MESSAGE_TEMPLATE) team_object = LiteLLM_TeamTable(team_id="team-123", models=["direct-model"], access_group_ids=["ag-1"]) with ( @@ -1727,46 +1728,49 @@ async def test_access_group_fallback_grant_does_not_log_a_denial(monkeypatch, ca assert "not allowed to access model" not in caplog.text -@pytest.mark.parametrize("unset_value", [None, ""]) -def test_can_object_call_model_denial_unchanged_when_message_not_configured(monkeypatch, unset_value): - monkeypatch.setattr(litellm, "model_access_denied_message", unset_value) - - with pytest.raises(ProxyException) as exc_info: +@pytest.mark.parametrize( + "object_type, expected_type", + [ + ("team", ProxyErrorTypes.team_model_access_denied), + ("user", ProxyErrorTypes.user_model_access_denied), + ("org", ProxyErrorTypes.org_model_access_denied), + ], +) +def test_can_object_call_model_denial_same_client_message_for_every_object_type(object_type, expected_type): + with pytest.raises(ModelAccessDeniedProxyException) as exc_info: _can_object_call_model( model="anthropic-sonnet-4-5", llm_router=None, models=["internal-models"], - object_type="team", + object_type=object_type, ) - assert exc_info.value.message == ( - "team not allowed to access model. This team can only access models=['internal-models']. " - "Tried to access anthropic-sonnet-4-5" - ) + assert exc_info.value.message == _DENIED_MESSAGE_TEMPLATE.format(model="anthropic-sonnet-4-5") + assert exc_info.value.type == expected_type + assert f"{object_type} not allowed to access model" in exc_info.value.internal_message @pytest.mark.asyncio -async def test_can_user_call_model_no_default_models_uses_configured_message(monkeypatch): +async def test_can_user_call_model_no_default_models_hides_policy_detail(): from litellm.proxy._types import SpecialModelNames from litellm.proxy.auth.auth_checks import can_user_call_model - monkeypatch.setattr(litellm, "model_access_denied_message", _DENIED_MESSAGE_TEMPLATE) user_object = LiteLLM_UserTable(user_id="test-user", models=[SpecialModelNames.no_default_models.value]) - with pytest.raises(ProxyException) as exc_info: + with pytest.raises(ModelAccessDeniedProxyException) as exc_info: await can_user_call_model(model="restricted-model", llm_router=None, user_object=user_object) - assert exc_info.value.message == "The model `restricted-model` is unavailable for this API key or does not exist." + assert exc_info.value.message == _DENIED_MESSAGE_TEMPLATE.format(model="restricted-model") + assert "only team models allowed" in exc_info.value.internal_message assert int(exc_info.value.code) == status.HTTP_403_FORBIDDEN @pytest.mark.asyncio -async def test_check_team_member_model_access_denied_uses_configured_message(monkeypatch): +async def test_check_team_member_model_access_denied_hides_member_allowlist(): from litellm.proxy._types import LiteLLM_TeamMembership from litellm.proxy.auth.auth_checks import _check_team_member_model_access from litellm.proxy.common_utils.user_api_key_cache import team_membership_reservation_cache_key - monkeypatch.setattr(litellm, "model_access_denied_message", _DENIED_MESSAGE_TEMPLATE) membership = LiteLLM_TeamMembership( user_id="alice", team_id="team-a", @@ -1779,7 +1783,7 @@ async def test_check_team_member_model_access_denied_uses_configured_message(mon model_type=LiteLLM_TeamMembership, ) - with pytest.raises(ProxyException) as exc_info: + with pytest.raises(ModelAccessDeniedProxyException) as exc_info: await _check_team_member_model_access( model="mock-vision", team_object=LiteLLM_TeamTable(team_id="team-a"), @@ -1790,7 +1794,9 @@ async def test_check_team_member_model_access_denied_uses_configured_message(mon proxy_logging_obj=MagicMock(), ) - assert exc_info.value.message == "The model `mock-vision` is unavailable for this API key or does not exist." + assert exc_info.value.message == _DENIED_MESSAGE_TEMPLATE.format(model="mock-vision") + assert "fast-models" not in exc_info.value.message + assert "Allowed member models = ['fast-models']" in exc_info.value.internal_message assert exc_info.value.type == ProxyErrorTypes.team_model_access_denied diff --git a/tests/test_litellm/proxy/auth/test_auth_exception_handler.py b/tests/test_litellm/proxy/auth/test_auth_exception_handler.py index d5ef71cd1d1..125b8862dfc 100644 --- a/tests/test_litellm/proxy/auth/test_auth_exception_handler.py +++ b/tests/test_litellm/proxy/auth/test_auth_exception_handler.py @@ -26,7 +26,6 @@ from prisma.errors import ( ) -import litellm from litellm._logging import verbose_proxy_logger from litellm.constants import INVALID_VIRTUAL_KEY_ERROR_MARKER from litellm.exceptions import BudgetExceededError @@ -991,12 +990,15 @@ async def test_handle_authentication_error_traceback_only_for_unexpected_errors( assert records[0].name == expected_logger_name -_DENIED_MESSAGE_TEMPLATE = "The model `{model}` is unavailable for this API key or does not exist." +_DENIED_CLIENT_MESSAGE = ( + "The requested model 'gpt-5.6' is not available for this API key, or the model name is invalid. " + "Check the models available to you and try again." +) def _denied_proxy_exception() -> ModelAccessDeniedProxyException: return ModelAccessDeniedProxyException( - message="The model `gpt-5.6\r\nWARNING forged log line` is unavailable for this API key or does not exist.", + message=_DENIED_CLIENT_MESSAGE, internal_message="key not allowed to access model. This key can only access models=['internal-models']. " "Tried to access gpt-5.6\r\nWARNING forged log line", type=ProxyErrorTypes.key_model_access_denied, @@ -1010,7 +1012,7 @@ def _denied_jwt_exception() -> ModelAccessDeniedHTTPException: internal_message="Role=engineer not allowed to call model=gpt-5.6\r\nWARNING forged log line. " "Allowed models=['internal-models']", status_code=status.HTTP_403_FORBIDDEN, - detail="The model `gpt-5.6` is unavailable for this API key or does not exist.", + detail=_DENIED_CLIENT_MESSAGE, ) @@ -1022,10 +1024,7 @@ def _denied_jwt_exception() -> ModelAccessDeniedHTTPException: pytest.param(_denied_jwt_exception, id="jwt_http_exception"), ], ) -async def test_handle_authentication_error_keeps_internal_message_on_model_access_denial( - monkeypatch, make_denial, caplog -): - monkeypatch.setattr(litellm, "model_access_denied_message", _DENIED_MESSAGE_TEMPLATE) +async def test_handle_authentication_error_keeps_internal_message_on_model_access_denial(make_denial, caplog): handler = UserAPIKeyAuthExceptionHandler() denial = make_denial() @@ -1054,7 +1053,7 @@ async def test_handle_authentication_error_keeps_internal_message_on_model_acces def test_as_proxy_exception_keeps_jwt_scope_denial_message_shape(): - detail = {"error": "The model `gpt-5.6` is unavailable for this API key or does not exist."} + detail = {"error": _DENIED_CLIENT_MESSAGE} denial = ModelAccessDeniedHTTPException( internal_message="model=gpt-5.6 not allowed. Allowed_models=['internal-models']", status_code=status.HTTP_403_FORBIDDEN, @@ -1066,42 +1065,3 @@ def test_as_proxy_exception_keeps_jwt_scope_denial_message_shape(): assert converted.to_dict() == plain.to_dict() assert converted.internal_message == denial.internal_message - - -@pytest.mark.asyncio -@pytest.mark.parametrize("unset_value", [None, ""]) -async def test_handle_authentication_error_no_extra_denial_log_when_message_not_configured( - monkeypatch, unset_value, caplog -): - monkeypatch.setattr(litellm, "model_access_denied_message", unset_value) - handler = UserAPIKeyAuthExceptionHandler() - denial = ModelAccessDeniedProxyException( - message="key not allowed to access model. This key can only access models=['internal-models']. " - "Tried to access gpt-5.6", - internal_message="key not allowed to access model. This key can only access models=['internal-models']. " - "Tried to access gpt-5.6", - type=ProxyErrorTypes.key_model_access_denied, - param="model", - code=status.HTTP_403_FORBIDDEN, - ) - - with ( - patch( # test-quality-ok: handler reads proxy_server globals at call time - "litellm.proxy.proxy_server.proxy_logging_obj.post_call_failure_hook", - new_callable=AsyncMock, - return_value=None, - ), - patch( # test-quality-ok: handler reads proxy_server globals at call time - "litellm.proxy.auth.auth_exception_handler.seed_request_identity", - ), - patch( # test-quality-ok: handler reads proxy_server globals at call time - "litellm.proxy.proxy_server.general_settings", - {"allow_requests_on_db_unavailable": False}, - ), - caplog.at_level("WARNING", logger="LiteLLM Proxy"), - pytest.raises(ProxyException) as exc_info, - ): - await handler._handle_authentication_error(denial, MagicMock(), {}, "/v1/chat/completions", None, "sk-bad-key") - - assert "internal-models" in str(exc_info.value.message) - assert [r for r in caplog.records if r.levelname == "WARNING" and "internal-models" in r.getMessage()] == [] diff --git a/tests/test_litellm/proxy/auth/test_auth_utils.py b/tests/test_litellm/proxy/auth/test_auth_utils.py index bd6a14cad21..db16da7237c 100644 --- a/tests/test_litellm/proxy/auth/test_auth_utils.py +++ b/tests/test_litellm/proxy/auth/test_auth_utils.py @@ -1053,7 +1053,7 @@ async def test_managed_batch_routes_pass_team_model_access_check(route, request_ is True ) - with pytest.raises(Exception, match="team not allowed to access model"): + with pytest.raises(Exception, match="is not available for this API key"): await can_team_access_model( model=model, team_object=LiteLLM_TeamTable(team_id="team-other", models=["some-other-model"]), diff --git a/tests/test_litellm/proxy/auth/test_handle_jwt.py b/tests/test_litellm/proxy/auth/test_handle_jwt.py index 9fab1e1785a..a8385eadc59 100644 --- a/tests/test_litellm/proxy/auth/test_handle_jwt.py +++ b/tests/test_litellm/proxy/auth/test_handle_jwt.py @@ -6972,19 +6972,13 @@ async def test_auth_builder_denies_jwt_naming_unregistered_agent_before_admin_ch assert exc_info.value.status_code == 403 -_JWT_DENIED_MESSAGE_TEMPLATE = "The model `{model}` is unavailable for this identity." - - -@pytest.mark.parametrize( - "configured_message, expected_detail", - [ - (None, "Role=internal_user not allowed to call model=gpt-5.6. Allowed models=['gpt-5.6-mini']"), - ("", "Role=internal_user not allowed to call model=gpt-5.6. Allowed models=['gpt-5.6-mini']"), - (_JWT_DENIED_MESSAGE_TEMPLATE, "The model `gpt-5.6` is unavailable for this identity."), - ], +_JWT_DENIED_CLIENT_MESSAGE = ( + "The requested model 'gpt-5.6' is not available for this API key, or the model name is invalid. " + "Check the models available to you and try again." ) -def test_can_rbac_role_call_model_denial_honors_configured_message(monkeypatch, configured_message, expected_detail): - monkeypatch.setattr(litellm, "model_access_denied_message", configured_message) + + +def test_can_rbac_role_call_model_denial_hides_role_allowlist_from_client(): general_settings = { "role_permissions": [ RoleBasedPermissions(role=LitellmUserRoles.INTERNAL_USER, models=["gpt-5.6-mini"]), @@ -6999,23 +6993,13 @@ def test_can_rbac_role_call_model_denial_honors_configured_message(monkeypatch, ) assert exc_info.value.status_code == 403 - assert exc_info.value.detail == expected_detail + assert exc_info.value.detail == _JWT_DENIED_CLIENT_MESSAGE assert exc_info.value.internal_message == ( "Role=internal_user not allowed to call model=gpt-5.6. Allowed models=['gpt-5.6-mini']" ) -@pytest.mark.parametrize( - "configured_message, expected_error", - [ - (None, "model=gpt-5.6 not allowed. Allowed_models=['gpt-5.6-mini']"), - ("", "model=gpt-5.6 not allowed. Allowed_models=['gpt-5.6-mini']"), - (_JWT_DENIED_MESSAGE_TEMPLATE, "The model `gpt-5.6` is unavailable for this identity."), - ], -) -def test_check_scope_based_access_denial_honors_configured_message(monkeypatch, configured_message, expected_error): - monkeypatch.setattr(litellm, "model_access_denied_message", configured_message) - +def test_check_scope_based_access_denial_hides_scope_allowlist_from_client(): with pytest.raises(ModelAccessDeniedHTTPException) as exc_info: JWTAuthManager.check_scope_based_access( scope_mappings=[ScopeMapping(scope="litellm.api.consumer", models=["gpt-5.6-mini"])], @@ -7025,5 +7009,5 @@ def test_check_scope_based_access_denial_honors_configured_message(monkeypatch, ) assert exc_info.value.status_code == 403 - assert exc_info.value.detail == {"error": expected_error} + assert exc_info.value.detail == {"error": _JWT_DENIED_CLIENT_MESSAGE} assert exc_info.value.internal_message == "model=gpt-5.6 not allowed. Allowed_models=['gpt-5.6-mini']" diff --git a/tests/test_litellm/proxy/realtime_endpoints/test_realtime_webrtc_endpoints.py b/tests/test_litellm/proxy/realtime_endpoints/test_realtime_webrtc_endpoints.py index 82f2ef097aa..f5c97142dde 100644 --- a/tests/test_litellm/proxy/realtime_endpoints/test_realtime_webrtc_endpoints.py +++ b/tests/test_litellm/proxy/realtime_endpoints/test_realtime_webrtc_endpoints.py @@ -287,7 +287,7 @@ async def test_client_secrets_transcription_rejects_disallowed_nested_model( ) assert response.status_code == 403 - assert "Tried to access gpt-realtime-whisper" in response.text + assert "The requested model 'gpt-realtime-whisper' is not available for this API key" in response.text mock_route_request.assert_not_called() finally: proxy_app.dependency_overrides.pop(user_api_key_auth, None) @@ -611,7 +611,7 @@ async def test_transcription_sessions_rejects_disallowed_resolved_model( ) assert response.status_code == 403 - assert "Tried to access gpt-realtime-whisper" in response.text + assert "The requested model 'gpt-realtime-whisper' is not available for this API key" in response.text mock_route_request.assert_not_called() finally: proxy_app.dependency_overrides.pop(user_api_key_auth, None) @@ -658,7 +658,7 @@ async def test_transcription_sessions_rejects_disallowed_team_model_scope( assert response.status_code == 403 assert "team" in response.text.lower() - assert "Tried to access gpt-realtime-whisper" in response.text + assert "The requested model 'gpt-realtime-whisper' is not available for this API key" in response.text mock_route_request.assert_not_called() finally: proxy_app.dependency_overrides.pop(user_api_key_auth, None) @@ -703,7 +703,7 @@ async def test_transcription_sessions_rejects_disallowed_project_model_scope( assert response.status_code == 403 assert "project" in response.text.lower() - assert "Tried to access gpt-realtime-whisper" in response.text + assert "The requested model 'gpt-realtime-whisper' is not available for this API key" in response.text mock_route_request.assert_not_called() finally: proxy_app.dependency_overrides.pop(user_api_key_auth, None) @@ -757,7 +757,7 @@ async def test_transcription_sessions_rejects_disallowed_team_member_model_scope ) assert response.status_code == 403 - assert "Team member not allowed to access model" in response.text + assert "is not available for this API key" in response.text mock_route_request.assert_not_called() finally: proxy_app.dependency_overrides.pop(user_api_key_auth, None) @@ -783,7 +783,7 @@ async def test_realtime_transcription_websocket_default_model_checks_key_scope() websocket.close.assert_awaited_once() _, close_kwargs = websocket.close.call_args assert close_kwargs["code"] == 1008 - assert "not allowed to access model" in close_kwargs["reason"] + assert "is not available for this API key" in close_kwargs["reason"] @pytest.mark.asyncio @@ -825,7 +825,7 @@ async def test_realtime_transcription_websocket_default_model_checks_team_scope( websocket.close.assert_awaited_once() _, close_kwargs = websocket.close.call_args assert close_kwargs["code"] == 1008 - assert "not allowed to access model" in close_kwargs["reason"] + assert "is not available for this API key" in close_kwargs["reason"] @pytest.mark.asyncio diff --git a/tests/test_litellm/proxy/test_proxy_server.py b/tests/test_litellm/proxy/test_proxy_server.py index ee85c9ba6a5..d1928b9cd52 100644 --- a/tests/test_litellm/proxy/test_proxy_server.py +++ b/tests/test_litellm/proxy/test_proxy_server.py @@ -10955,60 +10955,10 @@ def test_validate_max_ui_session_budget_empty_restores_default(empty_value): assert _validate_general_settings_ui_litellm_value("max_ui_session_budget", empty_value) == 1.0 -@pytest.mark.asyncio -async def test_update_config_field_model_access_denied_message_sets_live_value(monkeypatch): - from unittest.mock import AsyncMock, MagicMock - - import litellm.proxy.proxy_server as ps - from litellm.proxy._types import ConfigFieldUpdate, LitellmUserRoles, UserAPIKeyAuth - from litellm.proxy.proxy_server import update_config_general_settings - - save_config = AsyncMock() - monkeypatch.setattr(ps.proxy_config, "get_config", AsyncMock(return_value={"litellm_settings": {}})) - monkeypatch.setattr(ps.proxy_config, "save_config", save_config) - monkeypatch.setattr(ps, "prisma_client", MagicMock()) - monkeypatch.setattr(litellm, "store_audit_logs", False) - monkeypatch.setattr(litellm, "model_access_denied_message", None) - - admin = UserAPIKeyAuth(api_key="k", user_id="a", user_role=LitellmUserRoles.PROXY_ADMIN) - await update_config_general_settings( - data=ConfigFieldUpdate( - field_name="model_access_denied_message", - field_value="Model `{model}` is unavailable for this key.", - config_type="general_settings", - ), - user_api_key_dict=admin, - ) - - assert litellm.model_access_denied_message == "Model `{model}` is unavailable for this key." - save_config.assert_awaited_once() - saved_config = save_config.await_args.kwargs["new_config"] - assert saved_config["litellm_settings"]["model_access_denied_message"] == ( - "Model `{model}` is unavailable for this key." - ) - - -@pytest.mark.parametrize("bad_value", [True, 3, 1.5, ["x"], {"a": "b"}]) -def test_validate_model_access_denied_message_rejects_non_strings(bad_value): - from fastapi import HTTPException - - from litellm.proxy.proxy_server import _validate_general_settings_ui_litellm_value - - with pytest.raises(HTTPException) as exc_info: - _validate_general_settings_ui_litellm_value("model_access_denied_message", bad_value) - assert exc_info.value.status_code == 400 - - -@pytest.mark.parametrize("empty_value", [None, ""]) -def test_validate_model_access_denied_message_empty_restores_detailed_default(empty_value): - from litellm.proxy.proxy_server import _validate_general_settings_ui_litellm_value - - assert _validate_general_settings_ui_litellm_value("model_access_denied_message", empty_value) is None - - def _model_access_denied_proxy_exception(): return ModelAccessDeniedProxyException( - message="The model `gpt-5.6\r\nWARNING forged log line` is unavailable for this API key or does not exist.", + message="The requested model 'gpt-5.6\r\nWARNING forged log line' is not available for this API key, " + "or the model name is invalid. Check the models available to you and try again.", internal_message="key not allowed to access model. This key can only access models=['internal-models']. " "Tried to access gpt-5.6\r\nWARNING forged log line", type=ProxyErrorTypes.key_model_access_denied, @@ -11022,11 +10972,7 @@ def _http_request_scope(): @pytest.mark.asyncio -async def test_openai_exception_handler_logs_sanitized_model_access_denial(monkeypatch, caplog): - monkeypatch.setattr( - litellm, "model_access_denied_message", "The model `{model}` is unavailable for this API key or does not exist." - ) - +async def test_openai_exception_handler_logs_sanitized_model_access_denial(caplog): with caplog.at_level("WARNING", logger="LiteLLM Proxy"): response = await openai_exception_handler(_http_request_scope(), _model_access_denied_proxy_exception()) @@ -11042,22 +10988,7 @@ async def test_openai_exception_handler_logs_sanitized_model_access_denial(monke @pytest.mark.asyncio -@pytest.mark.parametrize("unset_value", [None, ""]) -async def test_openai_exception_handler_no_denial_log_when_message_not_configured(monkeypatch, unset_value, caplog): - monkeypatch.setattr(litellm, "model_access_denied_message", unset_value) - - with caplog.at_level("WARNING", logger="LiteLLM Proxy"): - response = await openai_exception_handler(_http_request_scope(), _model_access_denied_proxy_exception()) - - assert response.status_code == 403 - assert [r for r in caplog.records if r.levelname == "WARNING" and "internal-models" in r.getMessage()] == [] - - -@pytest.mark.asyncio -async def test_openai_exception_handler_no_denial_log_for_plain_proxy_exception(monkeypatch, caplog): - monkeypatch.setattr( - litellm, "model_access_denied_message", "The model `{model}` is unavailable for this API key or does not exist." - ) +async def test_openai_exception_handler_no_denial_log_for_plain_proxy_exception(caplog): denial = ProxyException( message="Authentication Error, Invalid proxy server token passed", type=ProxyErrorTypes.auth_error, @@ -11073,10 +11004,7 @@ async def test_openai_exception_handler_no_denial_log_for_plain_proxy_exception( @pytest.mark.asyncio -async def test_realtime_model_access_denial_logs_sanitized_internal_message(monkeypatch, caplog): - monkeypatch.setattr( - litellm, "model_access_denied_message", "The model `{model}` is unavailable for this API key or does not exist." - ) +async def test_realtime_model_access_denial_logs_sanitized_internal_message(caplog): reservation = {"reserved_cost": 0.0, "input_cost": 0.0, "finalized": False, "entries": []} with caplog.at_level("WARNING", logger="LiteLLM Proxy"): @@ -11095,43 +11023,6 @@ async def test_realtime_model_access_denial_logs_sanitized_internal_message(monk assert "gpt-5.6WARNING forged log line" in denial_records[0].getMessage() -@pytest.mark.parametrize("empty_value", [None, ""]) -def test_validate_expose_router_debug_in_errors_empty_restores_true_default(empty_value): - from litellm.proxy.proxy_server import _validate_general_settings_ui_litellm_value - - assert _validate_general_settings_ui_litellm_value("expose_router_debug_in_errors", empty_value) is True - assert _validate_general_settings_ui_litellm_value("expose_router_debug_in_errors", False) is False - - -@pytest.mark.parametrize( - "field_name, booted_value, db_value, read_setting", - [ - ( - "model_access_denied_message", - None, - "Model `{model}` is unavailable for this key.", - lambda: litellm.model_access_denied_message, - ), - ("expose_router_debug_in_errors", True, False, lambda: litellm.expose_router_debug_in_errors), - ], -) -def test_model_access_denied_settings_propagate_on_config_reload( - monkeypatch, field_name, booted_value, db_value, read_setting -): - import litellm.proxy.proxy_server as ps - - monkeypatch.setattr(litellm, field_name, booted_value) - assert read_setting() == booted_value - - ps.ProxyConfig()._update_config_fields( - current_config={"litellm_settings": {}}, - param_name="litellm_settings", - db_param_value={field_name: db_value}, - ) - - assert read_setting() == db_value - - def test_general_settings_ui_defaults_unchanged_for_existing_fields(): """The spec-default mechanism added for max_ui_session_budget must not change what clearing the pre-existing fields restores (None for Float/Select, False for Boolean).""" diff --git a/tests/test_litellm/test_router.py b/tests/test_litellm/test_router.py index fb42ab6c893..1e6636ec3d6 100644 --- a/tests/test_litellm/test_router.py +++ b/tests/test_litellm/test_router.py @@ -16424,7 +16424,7 @@ class TestMemberAutoRouterInference: project_id="router-project", team_id="router-team", models=["restricted-model"], ), model_type=LiteLLM_ProjectTableCachedObj, ) - with pytest.raises(ProxyException, match="not allowed to access model"): + with pytest.raises(ProxyException, match="is not available for this API key"): await self._route(self._router(), self._request(actor=self.actor.model_copy(update={ "models": ["member-router"] if ceiling == "key" else self.actor.models, "project_id": "router-project" if ceiling == "project" else None, @@ -16453,7 +16453,7 @@ class TestMemberAutoRouterInference: assert self.database.db.litellm_accessgrouptable.find_unique.await_count == 1 self.database.db.litellm_accessgrouptable.find_unique.return_value = group.model_copy(update={"access_model_names": []}) await evict_and_broadcast(cache_keys=("access_group_id:router-group",), user_api_key_cache=self.cache) - with pytest.raises(ProxyException, match="not allowed to access model"): + with pytest.raises(ProxyException, match="is not available for this API key"): await self._route(router, request) assert self.database.db.litellm_accessgrouptable.find_unique.await_count == 2 @@ -16471,7 +16471,7 @@ class TestMemberAutoRouterInference: key="team_id:router-team", model_type=LiteLLM_TeamTable, value=self.team.model_copy(update={"models": ["member-router"]}), ) - with pytest.raises(ProxyException, match="not allowed to access model"): + with pytest.raises(ProxyException, match="is not available for this API key"): await self._route(router, self._request()) self.database.db.litellm_teamtable.find_unique.reset_mock() admin: Final = self._request(tag="admin") diff --git a/tests/test_openai_endpoints.py b/tests/test_openai_endpoints.py index ab43d1acb00..e8a7732e4cb 100644 --- a/tests/test_openai_endpoints.py +++ b/tests/test_openai_endpoints.py @@ -307,7 +307,7 @@ async def test_chat_completion(): model="gpt-4", messages=[{"role": "user", "content": "Hello!"}], ) - assert "key not allowed to access model." in str(e) + assert "is not available for this API key" in str(e) @pytest.mark.asyncio diff --git a/ui/litellm-dashboard/src/app/(dashboard)/router-settings/_components/general_settings.integration.test.tsx b/ui/litellm-dashboard/src/app/(dashboard)/router-settings/_components/general_settings.integration.test.tsx index 2dc6ecf09e5..b4df567e250 100644 --- a/ui/litellm-dashboard/src/app/(dashboard)/router-settings/_components/general_settings.integration.test.tsx +++ b/ui/litellm-dashboard/src/app/(dashboard)/router-settings/_components/general_settings.integration.test.tsx @@ -1,4 +1,4 @@ -import { fireEvent, renderWithProviders, screen, within } from "../../../../../tests/test-utils"; +import { renderWithProviders, screen, within } from "../../../../../tests/test-utils"; import userEvent from "@testing-library/user-event"; import { beforeEach, describe, expect, it, vi } from "vitest"; import GeneralSettings from "./general_settings"; @@ -62,14 +62,6 @@ const SETTINGS_FIXTURE = [ stored_in_db: true, field_default_value: 1.0, }, - { - field_name: "model_access_denied_message", - field_type: "String", - field_value: null, - field_description: "client-facing denial message", - stored_in_db: null, - field_default_value: null, - }, ]; const settingsRow = async (fieldName: string) => { @@ -116,54 +108,6 @@ describe("GeneralSettings General tab", () => { expect(deleteConfigFieldSetting).toHaveBeenCalledWith("token", "max_ui_session_budget"); expect(numericValueIn(row)).toBe(1); }); - - it("saves a typed model_access_denied_message and resets it when cleared", async () => { - const user = userEvent.setup(); - renderWithProviders(); - - await user.click(screen.getByText("General")); - const row = await settingsRow("model_access_denied_message"); - const input = within(row).getByRole("textbox") as HTMLInputElement; - expect(input.value).toBe(""); - - fireEvent.change(input, { target: { value: "Model `{model}` is unavailable for this key." } }); - await user.click(within(row).getByRole("button", { name: /update/i })); - expect(updateConfigFieldSetting).toHaveBeenCalledWith( - "token", - "model_access_denied_message", - "Model `{model}` is unavailable for this key.", - ); - - fireEvent.change(input, { target: { value: "" } }); - await user.click(within(row).getByRole("button", { name: /update/i })); - expect(deleteConfigFieldSetting).toHaveBeenCalledWith("token", "model_access_denied_message"); - expect(vi.mocked(updateConfigFieldSetting).mock.calls).toHaveLength(1); - }); - - it("keeps the stored value visible when the reset request fails", async () => { - vi.mocked(getGeneralSettingsCall).mockResolvedValue( - SETTINGS_FIXTURE.map((s) => - s.field_name === "model_access_denied_message" - ? { ...s, field_value: "Model `{model}` is unavailable.", stored_in_db: true } - : { ...s }, - ), - ); - vi.mocked(deleteConfigFieldSetting).mockRejectedValueOnce(new Error("proxy unreachable")); - const user = userEvent.setup(); - renderWithProviders(); - - await user.click(screen.getByText("General")); - const row = await settingsRow("model_access_denied_message"); - const input = within(row).getByRole("textbox") as HTMLInputElement; - expect(within(row).getByText("In DB")).toBeInTheDocument(); - - fireEvent.change(input, { target: { value: "" } }); - await user.click(within(row).getByRole("button", { name: /update/i })); - - expect(deleteConfigFieldSetting).toHaveBeenCalledWith("token", "model_access_denied_message"); - expect(within(row).getByText("In DB")).toBeInTheDocument(); - expect(within(row).queryByText("Not Set")).not.toBeInTheDocument(); - }); }); describe("GeneralSettings Prompt Caching tab", () => { diff --git a/ui/litellm-dashboard/src/app/(dashboard)/router-settings/_components/general_settings.tsx b/ui/litellm-dashboard/src/app/(dashboard)/router-settings/_components/general_settings.tsx index ca2d80b856a..9a718cbe9b8 100644 --- a/ui/litellm-dashboard/src/app/(dashboard)/router-settings/_components/general_settings.tsx +++ b/ui/litellm-dashboard/src/app/(dashboard)/router-settings/_components/general_settings.tsx @@ -42,8 +42,6 @@ export interface generalSettingsItem { const NUMERIC_INPUT_WIDTH = "w-36"; const toNumericValue = (raw: string): number | null => (raw === "" ? null : Number(raw)); -const toStringValue = (raw: string): string | null => (raw === "" ? null : raw); -const RESETS_WHEN_CLEARED: ReadonlySet = new Set(["Select", "String"]); const SettingValueEditor: React.FC<{ setting: generalSettingsItem; @@ -112,16 +110,6 @@ const SettingValueEditor: React.FC<{ ); } - if (setting.field_type === "String") { - return ( - onChange(setting.field_name, toStringValue(event.target.value))} - /> - ); - } return null; }; @@ -231,7 +219,7 @@ const GeneralSettings: React.FC = ({ accessToken, user setGeneralSettings(updatedSettings); }; - const handleUpdateField = async (fieldName: string) => { + const handleUpdateField = (fieldName: string) => { if (!accessToken) { return; } @@ -240,33 +228,37 @@ const GeneralSettings: React.FC = ({ accessToken, user const fieldValue = setting?.field_value; if (fieldValue == null) { - if (setting && RESETS_WHEN_CLEARED.has(setting.field_type)) await handleResetField(fieldName); + if (setting?.field_type === "Select") handleResetField(fieldName); return; } try { - await updateConfigFieldSetting(accessToken, fieldName, fieldValue); - setGeneralSettings((current) => - current.map((setting) => (setting.field_name === fieldName ? { ...setting, stored_in_db: true } : setting)), + updateConfigFieldSetting(accessToken, fieldName, fieldValue); + // update value in state + + const updatedSettings = generalSettings.map((setting) => + setting.field_name === fieldName ? { ...setting, stored_in_db: true } : setting, ); + setGeneralSettings(updatedSettings); } catch (error) { // do something } }; - const handleResetField = async (fieldName: string) => { + const handleResetField = (fieldName: string) => { if (!accessToken) { return; } try { - await deleteConfigFieldSetting(accessToken, fieldName); - setGeneralSettings((current) => - current.map((setting) => - setting.field_name === fieldName - ? { ...setting, stored_in_db: null, field_value: setting.field_default_value ?? null } - : setting, - ), + deleteConfigFieldSetting(accessToken, fieldName); + // update value in state + + const updatedSettings = generalSettings.map((setting) => + setting.field_name === fieldName + ? { ...setting, stored_in_db: null, field_value: setting.field_default_value ?? null } + : setting, ); + setGeneralSettings(updatedSettings); } catch (error) { // do something } From aa15e9f23f2d5a1fe46ed782d1605c3840a28d04 Mon Sep 17 00:00:00 2001 From: Yuneng Jiang Date: Tue, 15 Sep 2026 18:45:43 -0700 Subject: [PATCH 170/225] test(e2e): reject expired cache entries before physical eviction --- .../test_provider_cache.py | 23 +++++++++++++++++++ 1 file changed, 23 insertions(+) diff --git a/tests/code_coverage_tests/test_provider_cache.py b/tests/code_coverage_tests/test_provider_cache.py index d0e8c5296dc..9a4cba7f322 100644 --- a/tests/code_coverage_tests/test_provider_cache.py +++ b/tests/code_coverage_tests/test_provider_cache.py @@ -192,6 +192,29 @@ def test_concurrent_requests_publish_atomically(store: RedisResponseStore, provi assert len(provider.hits) == 1 +@pytest.mark.parametrize("age_past_expiry_ms", [0, 1]) +def test_expired_response_is_rejected_without_physical_eviction( + store: RedisResponseStore, age_past_expiry_ms: int, +) -> None: + response_key: Final = store.keys("expired")[0] + retained: Final = store.client.eval( + """ +local clock = redis.call('TIME') +local expires = clock[1] * 1000 + math.floor(clock[2] / 1000) - tonumber(ARGV[1]) +redis.call('HSET', KEYS[1], 'captured', expires - 86400000, 'expires', expires, 'payload', 'old-response') +return redis.call('PTTL', KEYS[1]) +""", + 1, response_key, age_past_expiry_ms, + ) + assert retained == -1 + replacement: Final = store.lookup("expired") + assert isinstance(replacement, CaptureLease) + assert replacement.expires_at_ms - replacement.captured_at_ms == 86_400_000 + assert store.publish("expired", replacement, b"fresh-response") + hit: Final = store.lookup("expired") + assert isinstance(hit, CacheHit) and hit.payload == b"fresh-response" + + @pytest.mark.parametrize("truncated", [False, True]) def test_stream_completion_controls_publication(store: RedisResponseStore, provider: Provider, truncated: bool) -> None: provider.stream = True From c5a388a94cfec0fd06793ddec7012243c0f270bc Mon Sep 17 00:00:00 2001 From: "berriai-litellm-provider-info-sync[bot]" <328147090+berriai-litellm-provider-info-sync[bot]@users.noreply.github.com> Date: Wed, 16 Sep 2026 01:46:17 +0000 Subject: [PATCH 171/225] chore(prices): sync Google Gemini prices: 1 model [enrichment failed: Google Gemini, sync failed: AWS Bedrock, 6 held] gemini/gemini-3.1-pro-preview-customtools: input_cost_per_token_flex, output_cost_per_token_flex, cache_read_input_token_cost_flex --- litellm/model_prices_and_context_window_backup.json | 7 +++++-- model_prices_and_context_window.json | 7 +++++-- 2 files changed, 10 insertions(+), 4 deletions(-) diff --git a/litellm/model_prices_and_context_window_backup.json b/litellm/model_prices_and_context_window_backup.json index 5fb4aad96d2..f6589351cd4 100644 --- a/litellm/model_prices_and_context_window_backup.json +++ b/litellm/model_prices_and_context_window_backup.json @@ -27738,7 +27738,7 @@ "output_cost_per_token_above_200k_tokens": 1.8e-05, "output_cost_per_token_batches": 6e-06, "rpm": 2000, - "source": "https://ai.google.dev/gemini-api/docs/models#gemini-3.1-pro-preview", + "source": "https://ai.google.dev/gemini-api/docs/pricing", "supported_endpoints": [ "/v1/chat/completions", "/v1/completions", @@ -27773,13 +27773,16 @@ "output_cost_per_token_above_200k_tokens_priority": 3.24e-05, "cache_read_input_token_cost_priority": 3.6e-07, "cache_read_input_token_cost_above_200k_tokens_priority": 7.2e-07, + "cache_read_input_token_cost_flex": 2e-07, "search_context_cost_per_query": { "search_context_size_low": 0.014, "search_context_size_medium": 0.014, "search_context_size_high": 0.014 }, "web_search_billing_unit": "per_query", - "google_maps_grounding_cost_per_query": 0.014 + "google_maps_grounding_cost_per_query": 0.014, + "input_cost_per_token_flex": 1e-06, + "output_cost_per_token_flex": 6e-06 }, "gemini-3-flash-preview": { "cache_read_input_audio_token_cost": 1e-07, diff --git a/model_prices_and_context_window.json b/model_prices_and_context_window.json index 5fb4aad96d2..f6589351cd4 100644 --- a/model_prices_and_context_window.json +++ b/model_prices_and_context_window.json @@ -27738,7 +27738,7 @@ "output_cost_per_token_above_200k_tokens": 1.8e-05, "output_cost_per_token_batches": 6e-06, "rpm": 2000, - "source": "https://ai.google.dev/gemini-api/docs/models#gemini-3.1-pro-preview", + "source": "https://ai.google.dev/gemini-api/docs/pricing", "supported_endpoints": [ "/v1/chat/completions", "/v1/completions", @@ -27773,13 +27773,16 @@ "output_cost_per_token_above_200k_tokens_priority": 3.24e-05, "cache_read_input_token_cost_priority": 3.6e-07, "cache_read_input_token_cost_above_200k_tokens_priority": 7.2e-07, + "cache_read_input_token_cost_flex": 2e-07, "search_context_cost_per_query": { "search_context_size_low": 0.014, "search_context_size_medium": 0.014, "search_context_size_high": 0.014 }, "web_search_billing_unit": "per_query", - "google_maps_grounding_cost_per_query": 0.014 + "google_maps_grounding_cost_per_query": 0.014, + "input_cost_per_token_flex": 1e-06, + "output_cost_per_token_flex": 6e-06 }, "gemini-3-flash-preview": { "cache_read_input_audio_token_cost": 1e-07, From 7ed20406d7c2f7df3ebfce179769dd6ce26a5383 Mon Sep 17 00:00:00 2001 From: kerry Date: Wed, 16 Sep 2026 01:47:45 +0000 Subject: [PATCH 172/225] test(responses): narrow the ValueError assertion to satisfy PT011 Co-Authored-By: Devin AI <158243242+devin-ai-integration[bot]@users.noreply.github.com> --- tests/test_litellm/responses/test_streaming_iterator.py | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/tests/test_litellm/responses/test_streaming_iterator.py b/tests/test_litellm/responses/test_streaming_iterator.py index d3849e6ffce..8a76b0bedff 100644 --- a/tests/test_litellm/responses/test_streaming_iterator.py +++ b/tests/test_litellm/responses/test_streaming_iterator.py @@ -801,7 +801,7 @@ async def test_completed_event_survives_a_failing_usage_estimate(): break a stream that previously completed: the estimate is best-effort and falls back to usage None.""" malformed_input: Final = [{"type": "message", "role": "user", "content": 42}] - with pytest.raises(ValueError): + with pytest.raises(ValueError, match="Invalid content type"): _estimate_usage_from_text("gpt-4o-mini", malformed_input, {"input": malformed_input}, "hello world") response = _responses_api_response_without_usage() From b085a3c1517f5db7bacedb74cc552376544d83b6 Mon Sep 17 00:00:00 2001 From: yassin Date: Wed, 16 Sep 2026 01:51:29 +0000 Subject: [PATCH 173/225] fix(mcp): return fixed client message on sampling model access denial Co-Authored-By: Devin AI <158243242+devin-ai-integration[bot]@users.noreply.github.com> --- .../_experimental/mcp_server/sampling_handler.py | 15 ++++++++------- .../mcp_server/test_mcp_sampling_model_access.py | 4 ++-- 2 files changed, 10 insertions(+), 9 deletions(-) diff --git a/litellm/proxy/_experimental/mcp_server/sampling_handler.py b/litellm/proxy/_experimental/mcp_server/sampling_handler.py index 6072e747bfe..fec2a1f9ee6 100644 --- a/litellm/proxy/_experimental/mcp_server/sampling_handler.py +++ b/litellm/proxy/_experimental/mcp_server/sampling_handler.py @@ -885,13 +885,14 @@ async def _check_model_access(model: str, user_api_key_auth: "UserAPIKeyAuth | N ) return None except Exception as access_err: - verbose_logger.warning( - "MCP sampling: model access denied for model=%s: %s", - model, - access_err.sanitized_internal_message() - if isinstance(access_err, ModelAccessDeniedProxyException) - else access_err, - ) + if isinstance(access_err, ModelAccessDeniedProxyException): + verbose_logger.warning( + "MCP sampling: model access denied for model=%s: %s", + model, + access_err.sanitized_internal_message(), + ) + return ErrorData(code=-1, message=access_err.message) + verbose_logger.warning("MCP sampling: model access denied for model=%s: %s", model, access_err) return ErrorData( code=-1, message=(f"Model access denied: the API key is not authorized to use model '{model}'. {access_err}"), diff --git a/tests/test_litellm/proxy/_experimental/mcp_server/test_mcp_sampling_model_access.py b/tests/test_litellm/proxy/_experimental/mcp_server/test_mcp_sampling_model_access.py index 7eebf1eb436..7c5320ed4f4 100644 --- a/tests/test_litellm/proxy/_experimental/mcp_server/test_mcp_sampling_model_access.py +++ b/tests/test_litellm/proxy/_experimental/mcp_server/test_mcp_sampling_model_access.py @@ -140,6 +140,7 @@ class TestCheckModelAccess: @pytest.mark.asyncio async def test_should_log_internal_denial_reason_and_hide_allowlist_from_client(self, caplog): from litellm.proxy._types import UserAPIKeyAuth + from litellm.proxy.auth.model_access_denied import model_access_denied_client_message auth = UserAPIKeyAuth(api_key="sk-test-key", models=["gpt-3.5-turbo"]) @@ -147,8 +148,7 @@ class TestCheckModelAccess: result = await _check_model_access("gpt-4o\r\nforged", user_api_key_auth=auth) assert result is not None - assert "gpt-4o\r\nforged" in result.message - assert "gpt-3.5-turbo" not in result.message + assert result.message == model_access_denied_client_message(model="gpt-4o\r\nforged") denial_records = [r for r in caplog.records if "gpt-3.5-turbo" in r.getMessage()] assert len(denial_records) == 1 assert "Tried to access gpt-4oforged" in denial_records[0].getMessage() From 97211bc356d47d39214311e7cea432f858d28445 Mon Sep 17 00:00:00 2001 From: Joshua Valluru <326636767+joshua-berri@users.noreply.github.com> Date: Tue, 15 Sep 2026 19:00:47 -0700 Subject: [PATCH 174/225] fix(mcp): authorize per-user OAuth credential writes --- .../mcp_server/bridge_token_flow.py | 64 +++++-- .../mcp_server/discoverable_endpoints.py | 24 ++- .../mcp_server/ui_session_utils.py | 13 ++ litellm/proxy/auth/handle_jwt.py | 172 +++++++++++++----- litellm/proxy/auth/user_api_key_auth.py | 37 +--- .../mcp_management_endpoints.py | 10 +- .../mcp_server/test_discoverable_endpoints.py | 157 +++++++++++++++- .../proxy/auth/test_handle_jwt.py | 8 +- 8 files changed, 369 insertions(+), 116 deletions(-) diff --git a/litellm/proxy/_experimental/mcp_server/bridge_token_flow.py b/litellm/proxy/_experimental/mcp_server/bridge_token_flow.py index b693cc046c8..962e39d7dd6 100644 --- a/litellm/proxy/_experimental/mcp_server/bridge_token_flow.py +++ b/litellm/proxy/_experimental/mcp_server/bridge_token_flow.py @@ -304,24 +304,55 @@ async def _revalidate_active_subject(identity: "EnvelopeIdentity") -> "_KeyResol assert_never(identity.subject_type) -async def _extract_user_id_from_request(request: Request) -> str | None: - """The litellm ``user_id`` for the token request, so a per-user token is stored under the same - identity the egress later reads it by. Storage is best-effort, so every non-resolved outcome - (including a transient DB outage) collapses to ``None`` here and the caller simply skips the store; - the bridge mint, which must status those outcomes differently, consumes - :func:`_resolve_active_litellm_key` directly.""" +async def _extract_user_id_from_request(request: Request, server_id: str | None = None) -> str | None: + """Resolve identity for binding, or authorize the credential-write action for a target server.""" + from litellm.proxy._experimental.mcp_server.mcp_server_manager import ( # noqa: PLC0415 # registry imports auth helpers + global_mcp_server_manager, + ) + from litellm.proxy._experimental.mcp_server.ui_session_utils import ( + can_access_mcp_server, # noqa: PLC0415 # proxy import cycle + ) + from litellm.proxy._types import UserAPIKeyAuth # noqa: PLC0415 # proxy import cycle from litellm.proxy.auth.handle_jwt import JWTHandler # noqa: PLC0415 # proxy import cycle + from litellm.proxy.auth.route_checks import RouteChecks # noqa: PLC0415 # proxy import cycle + from litellm.proxy.auth.user_api_key_auth import ( # noqa: PLC0415 # proxy import cycle + _run_centralized_common_checks, # pyright: ignore[reportPrivateUsage] # reuse admission policy for the credential-write action + ) token: Final = _litellm_key_from_request(request) - if token is not None and JWTHandler.is_jwt(token): - return await _extract_jwt_user_id(request, token) - resolved: Final = await _resolve_active_litellm_key(request) - if not isinstance(resolved, _ResolvedKey): + # The OAuth relay is public; the optional server-side write is the same protected action + # as the direct credential endpoint. Authorize that action without rewriting the Request. + write_route: Final = f"/v1/mcp/server/{server_id}/oauth-user-credential" if server_id is not None else None + resolved: Final = ( + await _resolve_jwt_auth(request, token, write_route) + if token is not None and JWTHandler.is_jwt(token) + else await _resolve_active_litellm_key(request) + ) + auth: Final = resolved.key if isinstance(resolved, _ResolvedKey) else resolved + if not isinstance(auth, UserAPIKeyAuth) or not _active_key_user_id(auth): return None - return _active_key_user_id(resolved.key) + if write_route is not None and server_id is not None: + try: + RouteChecks.is_virtual_key_allowed_to_call_route(route=write_route, valid_token=auth, request=request) + await _run_centralized_common_checks( + user_api_key_auth_obj=auth, + request=request, + request_data={}, + route=write_route, + ) + if not await can_access_mcp_server(auth, server_id, global_mcp_server_manager.get_allowed_mcp_servers): + return None + except Exception as exc: # noqa: BLE001 # authorization failure must never write credentials + verbose_logger.debug("OAuth credential write not authorized (%s)", type(exc).__name__) + return None + return auth.user_id -async def _extract_jwt_user_id(request: Request, token: str) -> str | None: +async def _resolve_jwt_auth( + request: Request, + token: str, + write_route: str | None, +) -> "UserAPIKeyAuth | None": from litellm.proxy._types import UserAPIKeyAuth # noqa: PLC0415 # proxy import cycle from litellm.proxy.auth.handle_jwt import JWTAuthManager # noqa: PLC0415 # proxy import cycle from litellm.proxy.auth.user_api_key_auth import ( # noqa: PLC0415 # proxy import cycle @@ -353,7 +384,7 @@ async def _extract_jwt_user_id(request: Request, token: str) -> str | None: proxy_logging_obj=proxy_logging_obj, ) if isinstance(mapped, UserAPIKeyAuth): - return None if await _key_owner_scim_deactivated(mapped) else _active_key_user_id(mapped) + return None if await _key_owner_scim_deactivated(mapped) or not _active_key_user_id(mapped) else mapped if mapped is not None: return None identity: Final = await JWTAuthManager.auth_builder( @@ -361,19 +392,20 @@ async def _extract_jwt_user_id(request: Request, token: str) -> str | None: jwt_handler=jwt_handler, request_data={}, general_settings=general_settings, - route=request.url.path, + route=write_route or request.url.path, prisma_client=prisma_client, user_api_key_cache=user_api_key_cache, parent_otel_span=None, proxy_logging_obj=proxy_logging_obj, request_headers=dict(request.headers), request_method=request.method, - identity_only=True, + identity_only=write_route is None, + allow_provisioning=False, ) resolved_user: Final = identity["user_object"] if resolved_user is not None and isinstance(_active_user_record(resolved_user), str): return None - return identity["user_id"] + return JWTAuthManager.user_api_key_auth_from_result(identity) except Exception as exc: # noqa: BLE001 # public OAuth exchange stays available; unvalidated identities never write credentials verbose_logger.debug("OAuth JWT identity could not be validated (%s)", type(exc).__name__) return None diff --git a/litellm/proxy/_experimental/mcp_server/discoverable_endpoints.py b/litellm/proxy/_experimental/mcp_server/discoverable_endpoints.py index 94b6348b0f0..56968745ea9 100644 --- a/litellm/proxy/_experimental/mcp_server/discoverable_endpoints.py +++ b/litellm/proxy/_experimental/mcp_server/discoverable_endpoints.py @@ -1218,12 +1218,26 @@ async def exchange_token_with_server( user_id: Final = resolved_user_id if user_id: try: - await _store_per_user_token_server_side( - server=resolved_server, - user_id=user_id, - token_response=token_response, - identity_binding_proof=binding_proof, + # Identity binding above must retain the verified caller even when a write is + # denied. Authorize persistence separately, immediately before its side effect. + can_store: Final = ( + await _user_can_reach_mcp_server(user_id, resolved_server.server_id) + if bridge_identity is not None + else await _extract_user_id_from_request(request, resolved_server.server_id) == user_id ) + if can_store: + await _store_per_user_token_server_side( + server=resolved_server, + user_id=user_id, + token_response=token_response, + identity_binding_proof=binding_proof, + ) + else: + verbose_logger.warning( + "OAuth credential storage not authorized for user=%s server=%s", + user_id, + resolved_server.server_id, + ) except Exception as exc: verbose_logger.warning( "exchange_token_with_server: server-side storage failed for user=%s server=%s: %s", diff --git a/litellm/proxy/_experimental/mcp_server/ui_session_utils.py b/litellm/proxy/_experimental/mcp_server/ui_session_utils.py index 188bfce1484..107a4818de1 100644 --- a/litellm/proxy/_experimental/mcp_server/ui_session_utils.py +++ b/litellm/proxy/_experimental/mcp_server/ui_session_utils.py @@ -2,6 +2,7 @@ from __future__ import annotations +from collections.abc import Awaitable, Callable from typing import Final from fastapi import HTTPException @@ -137,3 +138,15 @@ async def build_effective_auth_contexts( if admitted_context is None: return team_contexts return [*team_contexts, admitted_context] + + +async def can_access_mcp_server( + user_api_key_auth: UserAPIKeyAuth, + server_id: str, + allowed_servers: Callable[[UserAPIKeyAuth], Awaitable[list[str]]], +) -> bool: + """Resolve server access through the same credential contexts as MCP management.""" + for context in await build_effective_auth_contexts(user_api_key_auth): + if server_id in await allowed_servers(context): + return True + return False diff --git a/litellm/proxy/auth/handle_jwt.py b/litellm/proxy/auth/handle_jwt.py index 2d8cfb614ce..f2bdbdd9341 100644 --- a/litellm/proxy/auth/handle_jwt.py +++ b/litellm/proxy/auth/handle_jwt.py @@ -9,6 +9,7 @@ JWT token must have 'litellm_proxy_admin' in scope. from __future__ import annotations import asyncio +import copy import fnmatch import hashlib import os @@ -54,7 +55,7 @@ from litellm.proxy._types import ( from litellm.proxy.auth.auth_checks import can_team_access_model from litellm.proxy.auth.resolvers.grants import GrantResolver, UserLookup, canonical_user_id from litellm.proxy.auth.route_checks import RouteChecks -from litellm.proxy.auth.team_grants import team_model_aliases +from litellm.proxy.auth.team_grants import team_grants, team_model_aliases from litellm.proxy.common_utils.user_api_key_cache import ( UserApiKeyCache, get_management_object_ttl, @@ -2268,36 +2269,49 @@ class JWTAuthManager: request_headers: dict | None = None, request_method: str | None = None, identity_only: bool = False, + allow_provisioning: bool = True, ) -> JWTAuthBuilderResult: """Build JWT authentication and authorization context. - Public OAuth endpoints use identity_only to resolve an existing credential owner - without authorizing the OAuth route or provisioning users/teams. The returned - identity does not grant permission to execute an MCP or model request. + identity_only resolves the caller for OAuth identity binding and grants no permission. + Credential writes use full authorization with allow_provisioning=False: resolve the + existing policy context without creating users/teams or synchronizing membership. + A private handler configuration keeps that restriction out of concurrent normal requests. """ + handler: Final = jwt_handler if allow_provisioning else copy.copy(jwt_handler) + if not allow_provisioning: + handler.update_environment( + prisma_client=jwt_handler.prisma_client, + user_api_key_cache=jwt_handler.user_api_key_cache, + litellm_jwtauth=jwt_handler.litellm_jwtauth.model_copy( + update={"user_id_upsert": False, "team_id_upsert": False, "sync_user_role_and_teams": False} + ), + leeway=jwt_handler.leeway, + ) + # Check if OIDC UserInfo endpoint is enabled, but fall back to standard # JWT auth if the token itself is a well-formed JWT (3-part structure). - if jwt_handler.litellm_jwtauth.oidc_userinfo_enabled and not jwt_handler.is_jwt(token=api_key): + if handler.litellm_jwtauth.oidc_userinfo_enabled and not handler.is_jwt(token=api_key): verbose_proxy_logger.debug("OIDC UserInfo is enabled. Fetching user info from UserInfo endpoint.") # Use the access token to fetch user info from OIDC UserInfo endpoint - jwt_valid_token: dict = await jwt_handler.get_oidc_userinfo(token=api_key) + jwt_valid_token: dict = await handler.get_oidc_userinfo(token=api_key) else: # Default behavior: decode and validate the JWT token - jwt_valid_token = await jwt_handler.auth_jwt(token=api_key) + jwt_valid_token = await handler.auth_jwt(token=api_key) # Check custom validate - if jwt_handler.litellm_jwtauth.custom_validate: - if not jwt_handler.litellm_jwtauth.custom_validate(jwt_valid_token): + if handler.litellm_jwtauth.custom_validate: + if not handler.litellm_jwtauth.custom_validate(jwt_valid_token): raise HTTPException( status_code=403, detail="Invalid JWT token", ) # Check RBAC - rbac_role: Final = jwt_handler.get_rbac_role(token=jwt_valid_token) + rbac_role: Final = handler.get_rbac_role(token=jwt_valid_token) if not identity_only: await JWTAuthManager.check_rbac_role( - jwt_handler, + handler, jwt_valid_token, general_settings, request_data, @@ -2306,30 +2320,30 @@ class JWTAuthManager: ) # Check Scope Based Access - scopes: Final = jwt_handler.get_scopes(token=jwt_valid_token) + scopes: Final = handler.get_scopes(token=jwt_valid_token) if ( not identity_only - and jwt_handler.litellm_jwtauth.enforce_scope_based_access - and jwt_handler.litellm_jwtauth.scope_mappings + and handler.litellm_jwtauth.enforce_scope_based_access + and handler.litellm_jwtauth.scope_mappings ): JWTAuthManager.check_scope_based_access( - scope_mappings=jwt_handler.litellm_jwtauth.scope_mappings, + scope_mappings=handler.litellm_jwtauth.scope_mappings, scopes=scopes, request_data=request_data, general_settings=general_settings, ) - object_id = jwt_handler.get_object_id(token=jwt_valid_token, default_value=None) + object_id = handler.get_object_id(token=jwt_valid_token, default_value=None) # Get basic user info - user_id, user_email, valid_user_email = await JWTAuthManager.get_user_info(jwt_handler, jwt_valid_token) + user_id, user_email, valid_user_email = await JWTAuthManager.get_user_info(handler, jwt_valid_token) # Get IDs - org_id: Final = jwt_handler.get_org_id(token=jwt_valid_token, default_value=None) - end_user_id: Final = jwt_handler.get_end_user_id(token=jwt_valid_token, default_value=None) + org_id: Final = handler.get_org_id(token=jwt_valid_token, default_value=None) + end_user_id: Final = handler.get_end_user_id(token=jwt_valid_token, default_value=None) team_id: str | None = None team_object: LiteLLM_TeamTable | None = None - object_id = jwt_handler.get_object_id(token=jwt_valid_token, default_value=None) + object_id = handler.get_object_id(token=jwt_valid_token, default_value=None) if rbac_role and object_id: if rbac_role == LitellmUserRoles.TEAM: @@ -2338,12 +2352,12 @@ class JWTAuthManager: user_id = object_id agent_id: Final = JWTAuthManager.resolve_agent_id( - jwt_handler=jwt_handler, + jwt_handler=handler, jwt_valid_token=jwt_valid_token, - agent_registry=jwt_handler.agent_lookup, + agent_registry=handler.agent_lookup, ) - if identity_only: + if identity_only or (not allow_provisioning and handler.is_admin(scopes=scopes)): try: identity_user, _, _, _, identity_user_id = await JWTAuthManager.get_objects( user_id=user_id, @@ -2352,7 +2366,7 @@ class JWTAuthManager: end_user_id=None, team_id=None, valid_user_email=valid_user_email, - jwt_handler=jwt_handler, + jwt_handler=handler, prisma_client=prisma_client, user_api_key_cache=user_api_key_cache, parent_otel_span=parent_otel_span, @@ -2361,13 +2375,37 @@ class JWTAuthManager: user_id_upsert=False, ) except UserNotFoundError: - if not jwt_handler.is_admin(scopes=scopes): + if not handler.is_admin(scopes=scopes): raise identity_user, identity_user_id = None, user_id + if not identity_only: + admin: Final = await JWTAuthManager.check_admin_access( + handler, + scopes, + route, + user_id, + org_id, + api_key, + jwt_valid_token, + user_email=user_email, + agent_id=agent_id, + ) + if admin is not None: + await JWTAuthManager._attach_team_from_header_for_admin( + admin_result=admin, + route=route, + request_headers=request_headers, + jwt_handler=handler, + prisma_client=prisma_client, + user_api_key_cache=user_api_key_cache, + parent_otel_span=parent_otel_span, + proxy_logging_obj=proxy_logging_obj, + ) + return {**admin, "user_object": identity_user} return JWTAuthBuilderResult( is_proxy_admin=False, # Admin admission uses the claim ID; other callers use the canonical DB ID. - user_id=user_id if jwt_handler.is_admin(scopes=scopes) else identity_user_id, + user_id=user_id if handler.is_admin(scopes=scopes) else identity_user_id, user_email=identity_user.user_email if identity_user is not None else user_email, user_object=identity_user, team_id=None, @@ -2384,7 +2422,7 @@ class JWTAuthManager: # Check admin access admin_result: Final = await JWTAuthManager.check_admin_access( - jwt_handler, + handler, scopes, route, user_id, @@ -2399,7 +2437,7 @@ class JWTAuthManager: admin_result=admin_result, route=route, request_headers=request_headers, - jwt_handler=jwt_handler, + jwt_handler=handler, prisma_client=prisma_client, user_api_key_cache=user_api_key_cache, parent_otel_span=parent_otel_span, @@ -2409,8 +2447,8 @@ class JWTAuthManager: # Get team with model access ## Check if team_id is specified via x-litellm-team-id header - all_team_ids: Final = JWTAuthManager.get_all_team_ids(jwt_handler, jwt_valid_token) - specific_team_id: Final = jwt_handler.get_team_id(token=jwt_valid_token, default_value=None) + all_team_ids: Final = JWTAuthManager.get_all_team_ids(handler, jwt_valid_token) + specific_team_id: Final = handler.get_team_id(token=jwt_valid_token, default_value=None) # The DB fallback only applies when the token carries no team identity at # all. `get_all_jwt_team_ids` ignores `team_id_default` so a configured @@ -2420,9 +2458,9 @@ class JWTAuthManager: # the RBAC team-role path (which already set `team_id`); otherwise a # provisional x-litellm-team-id header could override an RBAC-asserted team. db_team_fallback: Final = ( - jwt_handler.litellm_jwtauth.fallback_to_db_teams - and not jwt_handler.get_all_jwt_team_ids(token=jwt_valid_token) - and not jwt_handler.get_team_alias(token=jwt_valid_token, default_value=None) + handler.litellm_jwtauth.fallback_to_db_teams + and not handler.get_all_jwt_team_ids(token=jwt_valid_token) + and not handler.get_team_alias(token=jwt_valid_token, default_value=None) and team_id is None ) if specific_team_id and not db_team_fallback: @@ -2447,7 +2485,7 @@ class JWTAuthManager: user_api_key_cache=user_api_key_cache, parent_otel_span=parent_otel_span, proxy_logging_obj=proxy_logging_obj, - team_id_upsert=(jwt_handler.litellm_jwtauth.team_id_upsert and not db_team_fallback), + team_id_upsert=(handler.litellm_jwtauth.team_id_upsert and not db_team_fallback), ) except HTTPException: if not db_team_fallback: @@ -2459,7 +2497,7 @@ class JWTAuthManager: team_id, team_object, ) = await JWTAuthManager.find_and_validate_specific_team_id( - jwt_handler, + handler, jwt_valid_token, prisma_client, user_api_key_cache, @@ -2474,7 +2512,7 @@ class JWTAuthManager: requested_model=request_data.get("model"), route=route, request_method=request_method, - jwt_handler=jwt_handler, + jwt_handler=handler, prisma_client=prisma_client, user_api_key_cache=user_api_key_cache, parent_otel_span=parent_otel_span, @@ -2498,7 +2536,7 @@ class JWTAuthManager: user_api_key_cache=user_api_key_cache, parent_otel_span=parent_otel_span, proxy_logging_obj=proxy_logging_obj, - team_id_upsert=jwt_handler.litellm_jwtauth.team_id_upsert, + team_id_upsert=handler.litellm_jwtauth.team_id_upsert, ) if team_id and not JWTAuthManager._team_has_passthrough_route_access( @@ -2509,7 +2547,7 @@ class JWTAuthManager: JWTAuthManager._raise_team_passthrough_route_denial(route=route) # Extract alias fields for resolution (if configured) - org_alias: Final = jwt_handler.get_org_alias(token=jwt_valid_token, default_value=None) + org_alias: Final = handler.get_org_alias(token=jwt_valid_token, default_value=None) # get_objects returns effective_user_id for downstream spend attribution (GH #26789). ( @@ -2525,7 +2563,7 @@ class JWTAuthManager: end_user_id=end_user_id, team_id=team_id, valid_user_email=valid_user_email, - jwt_handler=jwt_handler, + jwt_handler=handler, prisma_client=prisma_client, user_api_key_cache=user_api_key_cache, parent_otel_span=parent_otel_span, @@ -2538,7 +2576,7 @@ class JWTAuthManager: resolved_org_id: Final = org_object.organization_id if org_object else org_id await JWTAuthManager.sync_user_role_and_teams( - jwt_handler=jwt_handler, + jwt_handler=handler, jwt_valid_token=jwt_valid_token, user_object=user_object, prisma_client=prisma_client, @@ -2556,9 +2594,9 @@ class JWTAuthManager: user_id=user_id, requested_model=request_data.get("model"), route=route, - jwt_handler=jwt_handler, - enforce_team_based_model_access=jwt_handler.litellm_jwtauth.enforce_team_based_model_access, - team_id_upsert=jwt_handler.litellm_jwtauth.team_id_upsert, + jwt_handler=handler, + enforce_team_based_model_access=handler.litellm_jwtauth.enforce_team_based_model_access, + team_id_upsert=handler.litellm_jwtauth.team_id_upsert, prisma_client=prisma_client, user_api_key_cache=user_api_key_cache, parent_otel_span=parent_otel_span, @@ -2586,7 +2624,7 @@ class JWTAuthManager: user_api_key_cache=user_api_key_cache, parent_otel_span=parent_otel_span, proxy_logging_obj=proxy_logging_obj, - team_id_upsert=jwt_handler.litellm_jwtauth.team_id_upsert, + team_id_upsert=handler.litellm_jwtauth.team_id_upsert, ) elif db_team_fallback and team_id == header_team_id: JWTAuthManager._validate_header_team_in_db_membership( @@ -2596,7 +2634,7 @@ class JWTAuthManager: if not JWTAuthManager._is_team_route_allowed( route=route, request_method=request_method, - jwt_handler=jwt_handler, + jwt_handler=handler, ): raise HTTPException( status_code=403, @@ -2606,10 +2644,11 @@ class JWTAuthManager: ) ## MAP USER TO TEAMS - await JWTAuthManager.map_user_to_teams( - user_object=user_object, - team_object=team_object, - ) + if allow_provisioning: + await JWTAuthManager.map_user_to_teams( + user_object=user_object, + team_object=team_object, + ) # Validate that a valid rbac id is returned for spend tracking JWTAuthManager.validate_object_id( @@ -2638,3 +2677,38 @@ class JWTAuthManager: jwt_claims=jwt_valid_token, agent_id=agent_id, ) + + @staticmethod + def user_api_key_auth_from_result( + result: JWTAuthBuilderResult, + parent_otel_span: Span | None = None, + ) -> UserAPIKeyAuth: + """Keep JWT identity and permission attribution identical across consumers.""" + user: Final = result["user_object"] + admin: Final = result["is_proxy_admin"] + return UserAPIKeyAuth( + api_key=None, + user_role=( + LitellmUserRoles.PROXY_ADMIN + if admin + else LitellmUserRoles(user.user_role) + if user is not None and user.user_role is not None + else LitellmUserRoles.INTERNAL_USER + ), + user_id=result["user_id"], + user_email=result["user_email"], + team_id=result["team_id"], + org_id=result["org_id"], + end_user_id=result["end_user_id"], + parent_otel_span=parent_otel_span, + jwt_claims=result["jwt_claims"], + agent_id=result.get("agent_id"), + user_tpm_limit=user.tpm_limit if user is not None and not admin else None, + user_rpm_limit=user.rpm_limit if user is not None and not admin else None, + user_model_max_budget=user.model_max_budget if user is not None and not admin else None, + **team_grants( + team_object=result["team_object"], + team_membership=result.get("team_membership"), + user_id=result["user_id"], + ), + ) diff --git a/litellm/proxy/auth/user_api_key_auth.py b/litellm/proxy/auth/user_api_key_auth.py index c5297ac83dc..ba267114bac 100644 --- a/litellm/proxy/auth/user_api_key_auth.py +++ b/litellm/proxy/auth/user_api_key_auth.py @@ -1669,13 +1669,11 @@ async def _user_api_key_auth_builder( is_proxy_admin: Final = result["is_proxy_admin"] team_id: Final = result["team_id"] - team_object: Final = result["team_object"] user_id: Final = result["user_id"] user_email: Final = result["user_email"] user_object: Final = result["user_object"] end_user_id = result["end_user_id"] org_id: Final = result["org_id"] - team_membership: Final[LiteLLM_TeamMembership | None] = result.get("team_membership", None) jwt_claims = result.get("jwt_claims", None) agent_id: Final[str | None] = result.get("agent_id") @@ -1693,40 +1691,9 @@ async def _user_api_key_auth_builder( value=_JWT_PROXY_ADMIN_SENTINEL, ttl=jwt_handler.litellm_jwtauth.virtual_key_mapping_cache_ttl, ) - return UserAPIKeyAuth( - api_key=None, - user_role=LitellmUserRoles.PROXY_ADMIN, - user_id=user_id, - user_email=user_email, - team_id=team_id, - org_id=org_id, - end_user_id=end_user_id, - parent_otel_span=parent_otel_span, - jwt_claims=jwt_claims, - agent_id=agent_id, - **team_grants(team_object=team_object, team_membership=team_membership, user_id=user_id), - ) + return JWTAuthManager.user_api_key_auth_from_result(result, parent_otel_span) - valid_token = UserAPIKeyAuth( - api_key=None, - team_id=team_id, - user_role=( - LitellmUserRoles(user_object.user_role) - if user_object is not None and user_object.user_role is not None - else LitellmUserRoles.INTERNAL_USER - ), - user_id=user_id, - user_email=user_email, - org_id=org_id, - parent_otel_span=parent_otel_span, - end_user_id=end_user_id, - user_tpm_limit=(user_object.tpm_limit if user_object is not None else None), - user_rpm_limit=(user_object.rpm_limit if user_object is not None else None), - user_model_max_budget=(user_object.model_max_budget if user_object is not None else None), - jwt_claims=jwt_claims, - agent_id=agent_id, - **team_grants(team_object=team_object, team_membership=team_membership, user_id=user_id), - ) + valid_token = JWTAuthManager.user_api_key_auth_from_result(result, parent_otel_span) # AUTO_REGISTER deferred from _resolve_jwt_to_virtual_key. # JWT policy (RBAC, scope, custom_validate, email-domain) diff --git a/litellm/proxy/management_endpoints/mcp_management_endpoints.py b/litellm/proxy/management_endpoints/mcp_management_endpoints.py index 4c97bbaf5de..918a55bb9ce 100644 --- a/litellm/proxy/management_endpoints/mcp_management_endpoints.py +++ b/litellm/proxy/management_endpoints/mcp_management_endpoints.py @@ -170,6 +170,7 @@ if MCP_AVAILABLE: from litellm.proxy._experimental.mcp_server.ui_session_utils import ( admitted_user_context, build_effective_auth_contexts, + can_access_mcp_server, is_ui_session_credential, ) from litellm.proxy._types import ( @@ -2483,10 +2484,11 @@ if MCP_AVAILABLE: ) return server - allowed_server_ids: Final[set[str]] = set() - for auth_context in await build_effective_auth_contexts(user_api_key_dict): - allowed_server_ids.update(await global_mcp_server_manager.get_allowed_mcp_servers(auth_context)) - if server is None or server.server_id not in allowed_server_ids: + if server is None or not await can_access_mcp_server( + user_api_key_dict, + server.server_id, + global_mcp_server_manager.get_allowed_mcp_servers, + ): raise HTTPException( status_code=status.HTTP_403_FORBIDDEN, detail={ diff --git a/tests/test_litellm/proxy/_experimental/mcp_server/test_discoverable_endpoints.py b/tests/test_litellm/proxy/_experimental/mcp_server/test_discoverable_endpoints.py index f6dc7932e2f..8c179eea4cb 100644 --- a/tests/test_litellm/proxy/_experimental/mcp_server/test_discoverable_endpoints.py +++ b/tests/test_litellm/proxy/_experimental/mcp_server/test_discoverable_endpoints.py @@ -11422,6 +11422,7 @@ def _oauth_identity_jwt( issuer: str = "https://idp.example.test", owner: str | None = "jwt-owner", scope: str = "", + claims: dict[str, object] | None = None, ) -> str: import jwt @@ -11434,6 +11435,7 @@ def _oauth_identity_jwt( "aud": audience, "exp": int(time.time()) + expires_in, "scope": scope, + **(claims or {}), }, signing_key, algorithm="RS256", @@ -11443,12 +11445,14 @@ def _oauth_identity_jwt( @pytest.mark.asyncio @pytest.mark.parametrize("header", ["Authorization", "x-litellm-api-key"]) @pytest.mark.parametrize("policy_allowed", [False, True]) +@pytest.mark.parametrize("server_allowed", [False, True]) @pytest.mark.parametrize("admin", [False, True]) @pytest.mark.parametrize("owner_state", ["active", "missing", "inactive", "database_error"]) async def test_oauth_exchange_stores_token_for_validated_jwt_user( jwt_oauth_identity: tuple["JWTHandler", "RSAPrivateKey"], header: str, policy_allowed: bool, + server_allowed: bool, admin: bool, owner_state: str, monkeypatch: pytest.MonkeyPatch, @@ -11461,6 +11465,12 @@ async def test_oauth_exchange_stores_token_for_validated_jwt_user( handler, signing_key = jwt_oauth_identity handler.litellm_jwtauth.custom_validate = lambda claims: policy_allowed + from litellm.proxy._experimental.mcp_server import mcp_server_manager + + manager: Final = MagicMock() + manager.get_allowed_mcp_servers = AsyncMock(return_value=["jwt-oauth-server"] if server_allowed else []) + manager.invalidate_user_oauth_token_cache = AsyncMock() + monkeypatch.setattr(mcp_server_manager, "global_mcp_server_manager", manager) bearer: Final = _oauth_identity_jwt(signing_key, scope="litellm_proxy_admin" if admin else "") request: Final = _token_request({header: f"Bearer {bearer}"}, path="/jwt-oauth-server/token") server: Final = MCPServer( @@ -11525,7 +11535,12 @@ async def test_oauth_exchange_stores_token_for_validated_jwt_user( assert response.status_code == 200 assert json.loads(response.body)["access_token"] == "upstream-token" users.create.assert_not_awaited() - if not policy_allowed or owner_state in ("inactive", "database_error") or (owner_state == "missing" and not admin): + if ( + not server_allowed + or not policy_allowed + or owner_state in ("inactive", "database_error") + or (owner_state == "missing" and not admin) + ): table.upsert.assert_not_awaited() return table.upsert.assert_awaited_once() @@ -11755,9 +11770,7 @@ async def test_oauth_jwt_resolves_canonical_owner_without_cached_identity( table.find_first = AsyncMock(return_value=owner) table.update = AsyncMock(return_value=owner) monkeypatch.setattr(proxy_server, "prisma_client", database) - bearer: Final = _oauth_identity_jwt( - signing_key, owner=external_id, scope="litellm_proxy_admin" if admin else "" - ) + bearer: Final = _oauth_identity_jwt(signing_key, owner=external_id, scope="litellm_proxy_admin" if admin else "") request: Final = _token_request({"Authorization": f"Bearer {bearer}"}) stored_owner: Final = await _extract_user_id_from_request(request) assert stored_owner == (None if inactive else external_id if admin else "canonical-oauth-owner") @@ -11823,3 +11836,139 @@ async def test_oauth_refresh_revalidates_the_same_active_user_rule( monkeypatch.setattr(proxy_server, "prisma_client", None) expected: Final = None if state == "active" else "no_active_key" if state == "inactive" else "unresolvable" assert await _reload_active_user_by_id("jwt-owner") == expected + + +@pytest.mark.asyncio +@pytest.mark.parametrize("mapped", [False, True]) +@pytest.mark.parametrize("state", ["allowed", "route_denied", "server_denied", "blocked", "expired", "lookup_error", "cancelled"]) +async def test_oauth_credential_write_keeps_virtual_key_permissions( + jwt_oauth_identity: tuple["JWTHandler", "RSAPrivateKey"], + monkeypatch: pytest.MonkeyPatch, + mapped: bool, + state: str, +) -> None: + import asyncio + + from litellm.proxy._experimental.mcp_server import mcp_server_manager + from litellm.proxy._experimental.mcp_server.bridge_token_flow import _extract_user_id_from_request + from litellm.proxy._types import UserAPIKeyAuth, hash_token + from litellm.proxy.auth.auth_checks import jwt_key_mapping_cache_key + + handler, signing_key = jwt_oauth_identity + key: Final = "sk-oauth-permission-test" + hashed: Final = hash_token(key) + credential: Final = UserAPIKeyAuth( + token=hashed, + user_id="jwt-owner", + blocked=state == "blocked", + expires=datetime.now(timezone.utc) - timedelta(seconds=60) if state == "expired" else None, + allowed_routes=["openai_routes"] if state == "route_denied" else ["mcp_routes"], + agent_id="agent-scope", + org_id="org-scope", + end_user_id="end-user-scope", + ) + handler.user_api_key_cache.set_cache(hashed, credential) + if mapped: + handler.litellm_jwtauth.virtual_key_claim_field = "sub" + handler.user_api_key_cache.set_cache(jwt_key_mapping_cache_key("sub", "not-the-configured-user-id"), hashed) + manager: Final = MagicMock() + manager.get_allowed_mcp_servers = AsyncMock( + return_value=[] if state == "server_denied" else ["server-a"], + side_effect=(asyncio.CancelledError() if state == "cancelled" else RuntimeError("permission lookup unavailable") if state == "lookup_error" else None), + ) + monkeypatch.setattr(mcp_server_manager, "global_mcp_server_manager", manager) + bearer: Final = _oauth_identity_jwt(signing_key) if mapped else key + request: Final = _token_request({"Authorization": f"Bearer {bearer}"}, path="/server-a/token") + if state == "cancelled": + with pytest.raises(asyncio.CancelledError): + await _extract_user_id_from_request(request, "server-a") + manager.get_allowed_mcp_servers.assert_awaited_once() + return + assert await _extract_user_id_from_request(request, "server-a") == ("jwt-owner" if state == "allowed" else None) + if state in ("allowed", "server_denied", "lookup_error"): + manager.get_allowed_mcp_servers.assert_awaited_once() + writer: Final = manager.get_allowed_mcp_servers.call_args.args[0] + assert (writer.user_id, writer.token, writer.org_id, writer.agent_id, writer.end_user_id) == ( + "jwt-owner", + hashed, + "org-scope", + "agent-scope", + "end-user-scope", + ) + + +@pytest.mark.asyncio +@pytest.mark.parametrize("server_id", ["team-a-server", "team-b-server"]) +async def test_oauth_writer_preserves_claimed_team_instead_of_expanding_user_roster( + jwt_oauth_identity: tuple["JWTHandler", "RSAPrivateKey"], + monkeypatch: pytest.MonkeyPatch, + server_id: str, +) -> None: + from litellm.models.user import LiteLLM_UserTable + from litellm.proxy import proxy_server + from litellm.proxy._experimental.mcp_server import mcp_server_manager + from litellm.proxy._experimental.mcp_server.bridge_token_flow import _extract_user_id_from_request + from litellm.proxy._types import LiteLLM_TeamTable, Member + + handler, signing_key = jwt_oauth_identity + handler.litellm_jwtauth.team_id_jwt_field = "team" + handler.litellm_jwtauth.team_id_upsert = True + handler.litellm_jwtauth.user_id_upsert = True + handler.litellm_jwtauth.sync_user_role_and_teams = True + handler.user_api_key_cache.set_cache("jwt-owner", LiteLLM_UserTable(user_id="jwt-owner", teams=["a", "b"])) + handler.user_api_key_cache.set_cache( + "team_id:a", + LiteLLM_TeamTable(team_id="a", models=[], members_with_roles=[Member(user_id="jwt-owner", role="user")]), + ) + manager: Final = MagicMock() + manager.get_allowed_mcp_servers = AsyncMock(return_value=["team-a-server"]) + monkeypatch.setattr(mcp_server_manager, "global_mcp_server_manager", manager) + bearer: Final = _oauth_identity_jwt(signing_key, claims={"team": "a"}) + request: Final = _token_request({"Authorization": f"Bearer {bearer}"}, path=f"/{server_id}/token") + assert await _extract_user_id_from_request(request, server_id) == ( + "jwt-owner" if server_id == "team-a-server" else None + ) + manager.get_allowed_mcp_servers.assert_awaited_once() + writer: Final = manager.get_allowed_mcp_servers.call_args.args[0] + assert writer.team_id == "a" + assert not writer.mcp_admitted_user_subject + proxy_server.prisma_client.db.litellm_teamtable.create.assert_not_called() + proxy_server.prisma_client.db.litellm_usertable.create.assert_not_called() + proxy_server.prisma_client.db.litellm_usertable.update.assert_not_called() + assert handler.litellm_jwtauth.user_id_upsert and handler.litellm_jwtauth.team_id_upsert + assert handler.litellm_jwtauth.sync_user_role_and_teams + + +@pytest.mark.asyncio +async def test_oauth_write_denial_does_not_erase_identity_binding( + jwt_oauth_identity: tuple["JWTHandler", "RSAPrivateKey"], monkeypatch: pytest.MonkeyPatch, +) -> None: + from litellm.proxy._experimental.mcp_server import discoverable_endpoints, mcp_server_manager + from litellm.proxy._types import MCPTransport + from litellm.types.mcp_server.mcp_server_manager import MCPOAuthIdentityBinding, MCPServer + + _, signing_key = jwt_oauth_identity + monkeypatch.setenv("LITELLM_SALT_KEY", "oauth-identity-binding-test-salt") + manager: Final = MagicMock() + manager.get_allowed_mcp_servers = AsyncMock(return_value=[]) + monkeypatch.setattr(mcp_server_manager, "global_mcp_server_manager", manager) + server: Final = MCPServer( + server_id="bound-server", name="bound-server", transport=MCPTransport.http, + auth_type=MCPAuth.oauth2, oauth2_flow="authorization_code", client_id="client", + token_url="https://upstream.example.test/token", + oauth_identity_binding=MCPOAuthIdentityBinding( + mode="enforce", issuer="https://upstream.example.test", audiences=["client"], + ), + ) + request: Final = _token_request({"Authorization": f"Bearer {_oauth_identity_jwt(signing_key)}"}) + code: Final = discoverable_endpoints.seal_bridge_authorization_code( + "upstream-code", "another-owner", server.server_id, "bound-nonce", + ) + with pytest.raises(HTTPException) as denied: + await discoverable_endpoints.exchange_token_with_server( + request=request, mcp_server=server, grant_type="authorization_code", code=code, + redirect_uri="http://localhost/callback", client_id="client", client_secret=None, code_verifier="verifier", + ) + assert denied.value.status_code == 403 + assert denied.value.detail == {"error": "oauth_principal_mismatch"} + manager.get_allowed_mcp_servers.assert_not_awaited() diff --git a/tests/test_litellm/proxy/auth/test_handle_jwt.py b/tests/test_litellm/proxy/auth/test_handle_jwt.py index 4d86e395358..babbf88dc29 100644 --- a/tests/test_litellm/proxy/auth/test_handle_jwt.py +++ b/tests/test_litellm/proxy/auth/test_handle_jwt.py @@ -6792,10 +6792,11 @@ async def test_sync_user_role_and_teams_singular_claim_only_recognized_under_fla @pytest.mark.asyncio @pytest.mark.parametrize("identity_only", [False, True]) +@pytest.mark.parametrize("allow_provisioning", [False, True]) @pytest.mark.parametrize("existing_user", [False, True]) @pytest.mark.parametrize("model_allowed", [False, True]) async def test_auth_builder_identity_lookup_does_not_provision_users( - monkeypatch: pytest.MonkeyPatch, identity_only: bool, existing_user: bool, model_allowed: bool + monkeypatch: pytest.MonkeyPatch, identity_only: bool, allow_provisioning: bool, existing_user: bool, model_allowed: bool ) -> None: from litellm.proxy._types import ScopeMapping from litellm.proxy.auth.auth_checks import UserNotFoundError @@ -6841,6 +6842,7 @@ async def test_auth_builder_identity_lookup_does_not_provision_users( parent_otel_span=None, proxy_logging_obj=MagicMock(), identity_only=identity_only, + allow_provisioning=allow_provisioning, ) if not identity_only and not model_allowed: with pytest.raises(HTTPException) as denial: @@ -6848,7 +6850,7 @@ async def test_auth_builder_identity_lookup_does_not_provision_users( assert denial.value.status_code == 403 users.create.assert_not_awaited() return - if identity_only and not existing_user: + if (identity_only or not allow_provisioning) and not existing_user: with pytest.raises(UserNotFoundError): await pending else: @@ -6856,7 +6858,7 @@ async def test_auth_builder_identity_lookup_does_not_provision_users( assert result["user_id"] == user_id assert result["user_object"] is not None assert result["user_object"].user_id == user_id - assert users.create.await_count == (0 if identity_only or existing_user else 1) + assert users.create.await_count == (0 if identity_only or not allow_provisioning or existing_user else 1) def _entra_agent_registry() -> AgentRegistry: From a6228fab66f289b9e8014c4fc75646eae821d1aa Mon Sep 17 00:00:00 2001 From: kerry Date: Wed, 16 Sep 2026 02:03:21 +0000 Subject: [PATCH 175/225] fix(anthropic): write usage into copies of the read-only message_delta chunk Co-Authored-By: Devin AI <158243242+devin-ai-integration[bot]@users.noreply.github.com> --- .../adapters/streaming_iterator.py | 6 ++---- 1 file changed, 2 insertions(+), 4 deletions(-) diff --git a/litellm/llms/anthropic/experimental_pass_through/adapters/streaming_iterator.py b/litellm/llms/anthropic/experimental_pass_through/adapters/streaming_iterator.py index e7179aad25b..4486eb0985a 100644 --- a/litellm/llms/anthropic/experimental_pass_through/adapters/streaming_iterator.py +++ b/litellm/llms/anthropic/experimental_pass_through/adapters/streaming_iterator.py @@ -376,10 +376,9 @@ class AnthropicStreamWrapper(AdapterCompletionStreamWrapper): usage_dict: UsageDelta = LiteLLMAnthropicMessagesAdapter._translate_openai_usage_to_anthropic_usage_delta( chunk.usage ) - merged_chunk["usage"] = usage_dict if self.applied_edits and "context_management" not in merged_chunk: merged_chunk["context_management"] = ContextManagementResponse(applied_edits=list(self.applied_edits)) - return self._augment_message_delta_usage(merged_chunk) + return self._augment_message_delta_usage({**merged_chunk, "usage": usage_dict}) def _handle_choiceless_chunk(self, chunk: "ModelResponseStream") -> bool: """Consume an OpenAI-compatible chunk that carries no ``choices``. @@ -448,8 +447,7 @@ class AnthropicStreamWrapper(AdapterCompletionStreamWrapper): } iterations.append(message_iteration) augmented_usage["iterations"] = iterations - augmented["usage"] = augmented_usage - return augmented + return {**augmented, "usage": augmented_usage} def _next_compaction_event(self) -> dict[str, object] | None: """Return the next compaction content-block SSE event, or ``None``. From 7680d3de863648b500fc605d0eaa0c894d0a6bf4 Mon Sep 17 00:00:00 2001 From: kerry Date: Wed, 16 Sep 2026 02:05:44 +0000 Subject: [PATCH 176/225] fix(responses): tolerate dict terminal responses when estimating usage Co-Authored-By: Devin AI <158243242+devin-ai-integration[bot]@users.noreply.github.com> --- litellm/responses/streaming_iterator.py | 35 ++++++++-------- .../responses/test_streaming_iterator.py | 42 +++++++++++++++++++ 2 files changed, 60 insertions(+), 17 deletions(-) diff --git a/litellm/responses/streaming_iterator.py b/litellm/responses/streaming_iterator.py index df3c26c1a4f..10ddf07ac7e 100644 --- a/litellm/responses/streaming_iterator.py +++ b/litellm/responses/streaming_iterator.py @@ -428,24 +428,25 @@ class BaseResponsesAPIStreamingIterator: openai_types.ResponsesAPIStreamEvents.RESPONSE_FAILED, ): self.completed_response = openai_responses_api_chunk - _response_obj: Final[ResponsesAPIResponse | None] = getattr( - openai_responses_api_chunk, "response", None - ) - if ( - _chunk_type - in ( - openai_types.ResponsesAPIStreamEvents.RESPONSE_COMPLETED, - openai_types.ResponsesAPIStreamEvents.RESPONSE_INCOMPLETE, - ) - and _response_obj is not None - and _response_obj.usage is None + _response_obj: Final[object] = getattr(openai_responses_api_chunk, "response", None) + if _chunk_type in ( + openai_types.ResponsesAPIStreamEvents.RESPONSE_COMPLETED, + openai_types.ResponsesAPIStreamEvents.RESPONSE_INCOMPLETE, ): - _response_obj.usage = _estimate_usage_safely( - self.model or "", - self.request_data.get("input"), - self.request_data, - self._generated_content + self._generated_tool_arguments, - ) + if isinstance(_response_obj, ResponsesAPIResponse) and _response_obj.usage is None: + _response_obj.usage = _estimate_usage_safely( + self.model or "", + self.request_data.get("input"), + self.request_data, + self._generated_content + self._generated_tool_arguments, + ) + elif isinstance(_response_obj, dict) and _response_obj.get("usage") is None: # pyright: ignore[reportUnknownMemberType] # the model_constructed terminal event leaves response as an untyped dict + _response_obj["usage"] = _estimate_usage_safely( + self.model or "", + self.request_data.get("input"), + self.request_data, + self._generated_content + self._generated_tool_arguments, + ) _stamp_responses_usage_cost(getattr(openai_responses_api_chunk, "response", None), self.logging_obj) if _chunk_type == openai_types.ResponsesAPIStreamEvents.RESPONSE_FAILED: diff --git a/tests/test_litellm/responses/test_streaming_iterator.py b/tests/test_litellm/responses/test_streaming_iterator.py index 8a76b0bedff..8cf9556761e 100644 --- a/tests/test_litellm/responses/test_streaming_iterator.py +++ b/tests/test_litellm/responses/test_streaming_iterator.py @@ -849,3 +849,45 @@ async def test_completed_event_without_usage_counts_tool_input_deltas(tool_delta assert usage is not None assert usage.output_tokens > 0 assert usage.total_tokens == usage.input_tokens + usage.output_tokens + + +@pytest.mark.asyncio +async def test_completed_event_with_a_dict_response_still_gets_the_usage_estimate(): + """transform_streaming_response can model_construct a terminal event whose + response stays a plain dict; the estimate must fill it without raising.""" + dict_response: Final = { + "id": "resp_dict", + "model": "gpt-4o-mini", + "object": "response", + "output": [], + "usage": None, + } + + def _transform(model, parsed_chunk, logging_obj): + if parsed_chunk.get("type") == "response.completed": + return ResponseCompletedEvent.model_construct(type="response.completed", response=dict_response) + stub: Final = Mock() + stub.type = parsed_chunk.get("type") + if "delta" in parsed_chunk: + stub.delta = parsed_chunk.get("delta") + return stub + + config: Final = Mock(spec=BaseResponsesAPIConfig) + config.transform_streaming_response.side_effect = _transform + iterator: Final = _make_iterator( + sse_events=[ + _sse_event({"type": "response.output_text.delta", "delta": "hello world"}), + _sse_event({"type": "response.completed", "response": {}}), + ], + logging_obj=_logging_obj_stub(), + config=config, + request_data={"input": "count these input tokens please"}, + ) + + async for _ in iterator: + pass + + usage: Final = iterator.completed_response.response["usage"] + assert usage is not None + assert usage.input_tokens > 0 + assert usage.output_tokens > 0 From 133f1e8ef56a2870e175128f68eb434c5061d8d4 Mon Sep 17 00:00:00 2001 From: kerry Date: Wed, 16 Sep 2026 02:06:42 +0000 Subject: [PATCH 177/225] test(fireworks-ai): drop the explanatory comment on the cache-read constant Co-Authored-By: Devin AI <158243242+devin-ai-integration[bot]@users.noreply.github.com> --- .../llms/fireworks_ai/test_fireworks_ai_cost_calculator.py | 2 -- 1 file changed, 2 deletions(-) diff --git a/tests/test_litellm/llms/fireworks_ai/test_fireworks_ai_cost_calculator.py b/tests/test_litellm/llms/fireworks_ai/test_fireworks_ai_cost_calculator.py index f461f17b627..1bee310d9d3 100644 --- a/tests/test_litellm/llms/fireworks_ai/test_fireworks_ai_cost_calculator.py +++ b/tests/test_litellm/llms/fireworks_ai/test_fireworks_ai_cost_calculator.py @@ -15,8 +15,6 @@ from litellm.types.utils import ( MODEL = "accounts/fireworks/models/glm-5p2" INPUT_COST = 1.4e-06 -# Read the cached rate from the price map so this test tracks the shipped value -# (glm-5p2 is $0.14/1M) instead of hardcoding a number that breaks when it changes. CACHE_READ_COST = litellm.get_model_info(model=MODEL, custom_llm_provider="fireworks_ai")["cache_read_input_token_cost"] OUTPUT_COST = 4.4e-06 From 8cce2b196a0388873d34b3bf42f3f772105df3c4 Mon Sep 17 00:00:00 2001 From: aniket-kardile Date: Tue, 15 Sep 2026 19:14:49 -0700 Subject: [PATCH 178/225] feat(guardrails): singulr v2 API contract with logging_only, pre_mcp_call and post_mcp_call Squash of BerriAI/litellm#37464 (head da298ca7) by @aniket-kardile, adopted onto main: v2 gateway payload contract with request, response, mcp_request and mcp_response scopes, typed payload models, proxy user, org and team metadata forwarded to Singulr, and the logging_only, pre_mcp_call and post_mcp_call modes. --- .../guardrail_hooks/singulr/singulr.py | 472 ++++++- .../guardrails/guardrail_hooks/singulr.py | 55 +- .../guardrail_hooks/test_singulr.py | 1127 +++++++++++++++-- 3 files changed, 1447 insertions(+), 207 deletions(-) diff --git a/litellm/proxy/guardrails/guardrail_hooks/singulr/singulr.py b/litellm/proxy/guardrails/guardrail_hooks/singulr/singulr.py index 5109f09d9c2..e0dcdf02069 100644 --- a/litellm/proxy/guardrails/guardrail_hooks/singulr/singulr.py +++ b/litellm/proxy/guardrails/guardrail_hooks/singulr/singulr.py @@ -1,4 +1,9 @@ +import asyncio +import json import os +from collections.abc import Mapping, Sequence +from datetime import datetime, timezone +from types import MappingProxyType from typing import Any, Final from urllib.parse import urlparse @@ -19,20 +24,30 @@ from litellm.llms.custom_httpx.http_handler import ( get_async_httpx_client, httpxSpecialProvider, ) +from litellm.proxy._types import UserAPIKeyAuth from litellm.types.guardrails import GuardrailEventHooks from litellm.types.proxy.guardrails.guardrail_hooks.base import ( GuardrailConfigModel, ) from litellm.types.proxy.guardrails.guardrail_hooks.singulr import ( + AssistantMessage, SingulrGuardrailPayload, - SingulrGuardrailRequest, SingulrGuardrailResponse, + SingulrMcpGuardrailPayload, + ToolCall, + ToolCallFunction, +) +from litellm.types.utils import ( + GenericGuardrailAPIInputs, + GuardrailStatus, + StandardLoggingGuardrailInformation, ) -from litellm.types.utils import GenericGuardrailAPIInputs _DEFAULT_API_BASE: Final = "http://localhost:8003" -_GUARD_ENDPOINT: Final = "/api/v1/ai-gateway/litellm" +_GUARD_ENDPOINT: Final = "/api/v1/ai-gateway/litellm-v2" _DEFAULT_TIMEOUT: Final = 30.0 +_EMPTY_MAPPING: Final[Mapping[str, Any]] = MappingProxyType({}) +_MCP_MODEL_PREFIX: Final = "MCP:" class _CustomGuardrailOptions(TypedDict, total=False, extra_items=object): @@ -51,8 +66,8 @@ class SingulrGuardrail(CustomGuardrail): **kwargs: Unpack[_CustomGuardrailOptions], ) -> None: self.singulr_api_key = singulr_api_key or os.environ.get("SINGULR_API_KEY") - self.singulr_api_base = (singulr_api_base or os.environ.get("SINGULR_API_BASE") or _DEFAULT_API_BASE).rstrip( - "/" + self.singulr_api_base = ( + (singulr_api_base or os.environ.get("SINGULR_API_BASE") or _DEFAULT_API_BASE).strip().rstrip("/") ) parsed: Final = urlparse(self.singulr_api_base) if parsed.scheme == "http" and parsed.hostname not in ( @@ -85,6 +100,9 @@ class SingulrGuardrail(CustomGuardrail): kwargs["supported_event_hooks"] = [ GuardrailEventHooks.pre_call, GuardrailEventHooks.post_call, + GuardrailEventHooks.logging_only, + GuardrailEventHooks.pre_mcp_call, + GuardrailEventHooks.post_mcp_call, ] super().__init__(**kwargs) @@ -97,52 +115,77 @@ class SingulrGuardrail(CustomGuardrail): return SingulrGuardrailConfigModel - def _build_payload( - self, - request_data: dict[str, Any], - inputs: GenericGuardrailAPIInputs, - input_type: str, - ) -> dict[str, object]: - if not request_data: - texts: Final = inputs.get("texts", []) + @staticmethod + def _metadata_containers(request_data: Mapping[str, Any]) -> tuple[Mapping[str, Any], ...]: + """Candidate metadata dicts to check, in priority order. - payload = SingulrGuardrailPayload( - input_type=input_type, - is_playground_request=True, - playground_text=texts[0] if texts else None, + Most call paths put metadata at the top level of ``request_data`` + (``litellm_metadata`` or ``metadata``). ``post_mcp_call`` instead hands + us ``litellm_logging_obj.model_call_details``, which nests it under + ``litellm_params`` instead, so that's checked as a fallback. + """ + litellm_params: Final = request_data.get("litellm_params") or _EMPTY_MAPPING + return tuple( + container + for container in ( + request_data.get("litellm_metadata"), + request_data.get("metadata"), + litellm_params.get("litellm_metadata") if litellm_params else None, + litellm_params.get("metadata") if litellm_params else None, ) - else: - response: Final = request_data.get("response") - singulr_req_object: Final = SingulrGuardrailRequest( - model=request_data.get("model"), - messages=request_data.get("messages"), - tools=request_data.get("tools"), - model_response=response.model_dump(mode="json") if input_type == "response" and response else None, - litellm_metadata=request_data.get("litellm_metadata"), - ) - payload = SingulrGuardrailPayload( - litellm_call_id=request_data.get("litellm_call_id"), - request_data=singulr_req_object, - input_type=input_type, - ) - - return payload.model_dump(mode="json") - - def _build_headers(self) -> dict[str, str]: - return dict( - (header, value) - for header, value in ( - ("Content-Type", "application/json"), - ("X-Singulr-Gateway-Token", self.singulr_api_key), - ( - "X-Singulr-Enforcement-Entity-Id", - self.singulr_application_id or "", - ), - ("X-Singulr-Guardrail-Id", self.singulr_guardrail_id or ""), - ) - if value + if container ) + @classmethod + def _resolve_metadata_value(cls, request_data: Mapping[str, Any], key: str) -> str | None: + for container in cls._metadata_containers(request_data=request_data): + value = container.get(key) + if value: + return value + return None + + @classmethod + def _resolve_user_role_from_request_data(cls, request_data: Mapping[str, Any]) -> str | None: + for container in cls._metadata_containers(request_data=request_data): + auth = container.get("user_api_key_auth") + if isinstance(auth, UserAPIKeyAuth) and auth.user_role: + return auth.user_role.value + return None + + @classmethod + def _build_metadata(cls, request_data: Mapping[str, Any]) -> Mapping[str, Any] | None: + fields: Final = ( + "user_api_key_alias", + "user_api_key_user_id", + "user_api_key_user_email", + "user_api_key_org_id", + "user_api_key_org_alias", + "user_api_key_team_id", + "user_api_key_team_alias", + ) + resolved: Final = ( + *((field, cls._resolve_metadata_value(request_data=request_data, key=field)) for field in fields), + ("user_api_key_user_role", cls._resolve_user_role_from_request_data(request_data=request_data)), + ) + if not any(value for _, value in resolved): + return None + return {key: value for key, value in resolved if value} # mutable-ok: short-lived JSON payload dict + + @staticmethod + def _build_user_message(text: str) -> Mapping[str, Any]: + return {"role": "user", "content": text} # mutable-ok: short-lived JSON payload dict + + def _build_headers(self) -> Mapping[str, str]: + all_headers: Final = MappingProxyType( + { + "Content-Type": "application/json", + "X-Singulr-Gateway-Token": self.singulr_api_key, + "X-Singulr-Enforcement-Entity-Id": self.singulr_application_id, + "X-Singulr-Guardrail-Id": self.singulr_guardrail_id, + } + ) + return MappingProxyType({header: value for header, value in all_headers.items() if value}) + async def _call_api(self, payload: dict[str, object]) -> SingulrGuardrailResponse | None: endpoint: Final = f"{self.singulr_api_base}{_GUARD_ENDPOINT}" verbose_proxy_logger.debug("Singulr: %s", endpoint) @@ -168,7 +211,7 @@ class SingulrGuardrail(CustomGuardrail): if self.block_on_error: raise GuardrailRaisedException( guardrail_name=self.guardrail_name, - message=(f"Singulr API returned HTTP {exc.response.status_code}: {exc.response.text}"), + message=f"Singulr API returned HTTP {exc.response.status_code}: {exc.response.text}", ) from exc return None @@ -190,33 +233,328 @@ class SingulrGuardrail(CustomGuardrail): ) from exc return None - @log_guardrail_information - async def apply_guardrail( + async def _apply_guardrail_on_request( self, inputs: GenericGuardrailAPIInputs, - request_data: dict, - input_type: str, - logging_obj: "LiteLLMLoggingObj | None" = None, + texts: Sequence[str], + structured_messages: Sequence[Any], + request_data: Mapping[str, Any], ) -> GenericGuardrailAPIInputs: - payload: Final = self._build_payload(request_data, inputs, input_type) - if not payload: - return inputs - - result: Final = await self._call_api(payload) - if result is None: - return inputs - - verbose_proxy_logger.debug( - "Singulr: should_block=%s blocking_due_to=%s", - result.should_block, - result.blocking_due_to, + messages: Final = ( + tuple(structured_messages) + if structured_messages + else tuple(self._build_user_message(text) for text in texts) ) - if result.should_block: + images: Final = inputs.get("images") + tools: Final = inputs.get("tools") + + if not messages and not images and not tools: + verbose_proxy_logger.debug("Singulr: No messages, images, or tools to check after filtering") + return inputs + + metadata: Final = self._build_metadata(request_data=request_data) + + singulr_req_obj = SingulrGuardrailPayload( + correlation_id=request_data.get("litellm_call_id"), + model_name=inputs.get("model"), + guardrail_scope="request", + messages=messages, + images=images, + tools=tools, + metadata=metadata, + ) + payload = singulr_req_obj.model_dump(mode="json") + guardrail_resp = await self._call_api(payload) + + if guardrail_resp is None: + return inputs + + if guardrail_resp.should_block: raise GuardrailRaisedException( guardrail_name=self.guardrail_name, - message=f"Blocked by Singulr: {result.blocking_due_to or 'unknown'}", + status_code=400, + message=f"Blocked by Singulr, Blocking due to {guardrail_resp.blocking_due_to or 'unknown'}", + blocked_content=True, + ) + return inputs + + async def _apply_guardrail_on_mcp_request(self, request_data: Mapping[str, Any]) -> None: + metadata: Final = self._build_metadata(request_data=request_data) + + singulr_mcp_obj = SingulrMcpGuardrailPayload( + guardrail_scope="mcp_request", + tool_name=request_data.get("mcp_tool_name"), + tool_arguments=request_data.get("mcp_arguments"), + mcp_server_name=request_data.get("mcp_server_name"), + metadata=metadata, + ) + payload = singulr_mcp_obj.model_dump(mode="json") + guardrail_resp = await self._call_api(payload) + + if guardrail_resp is None: + return + + if guardrail_resp.should_block: + raise GuardrailRaisedException( + guardrail_name=self.guardrail_name, + status_code=400, + message=f"Blocked by Singulr, Blocking due to {guardrail_resp.blocking_due_to or 'unknown'}", + blocked_content=True, + ) + + async def _apply_guardrail_on_mcp_response( + self, inputs: GenericGuardrailAPIInputs, texts: Sequence[str], request_data: Mapping[str, Any] + ) -> GenericGuardrailAPIInputs: + if not texts: + return inputs + + metadata: Final = self._build_metadata(request_data=request_data) + + singulr_mcp_obj = SingulrMcpGuardrailPayload( + model_name=request_data.get("model"), + guardrail_scope="mcp_response", + tool_result=texts, + metadata=metadata, + ) + payload = singulr_mcp_obj.model_dump(mode="json") + guardrail_resp = await self._call_api(payload) + + if guardrail_resp is None: + return inputs + + if guardrail_resp.should_block: + raise GuardrailRaisedException( + guardrail_name=self.guardrail_name, + status_code=400, + message=f"Blocked by Singulr, Blocking due to {guardrail_resp.blocking_due_to or 'unknown'}", blocked_content=True, ) return inputs + + @staticmethod + def _build_tool_call(tool_call: Mapping[str, Any]) -> "ToolCall | None": + tool_call_id: Final = tool_call.get("id") + fun: Final = tool_call.get("function") + if not tool_call_id or not fun: + return None + func_name: Final = fun.get("name") + args: Final = fun.get("arguments") + if not func_name or args is None: + return None + call_type: Final = tool_call.get("type") + return ToolCall( + id=tool_call_id, + type=call_type if isinstance(call_type, str) and call_type else "function", + function=ToolCallFunction( + name=func_name, + arguments=args if isinstance(args, str) else json.dumps(args, default=str), + ), + ) + + async def _apply_guardrail_on_response( + self, inputs: GenericGuardrailAPIInputs, texts: Sequence[str], request_data: Mapping[str, Any] + ) -> GenericGuardrailAPIInputs: + combined_texts: Final = "\n".join(texts) if texts else None + + tool_calls: Final = inputs.get("tool_calls", ()) + tool_calls_res: Final = tuple( + tool_call_res + for tool_call_res in (self._build_tool_call(tool_call) for tool_call in tool_calls) + if tool_call_res is not None + ) + + assistant_message: Final = AssistantMessage( + role="assistant", + content=combined_texts, + tool_calls=tool_calls_res, + ) + + metadata: Final = self._build_metadata(request_data=request_data) + + singulr_resp_obj = SingulrGuardrailPayload( + correlation_id=request_data.get("litellm_call_id"), + guardrail_scope="response", + model_name=request_data.get("model"), + messages=request_data.get("messages"), + images=inputs.get("images"), + response=assistant_message, + metadata=metadata, + ) + + payload = singulr_resp_obj.model_dump(mode="json") + guardrail_resp = await self._call_api(payload) + + if guardrail_resp is None: + return inputs + + if guardrail_resp.should_block: + raise GuardrailRaisedException( + guardrail_name=self.guardrail_name, + status_code=400, + message=f"Blocked by Singulr, Blocking due to {guardrail_resp.blocking_due_to or 'unknown'}", + blocked_content=True, + ) + return inputs + + def _logging_only_response_payload( + self, + kwargs: Mapping[str, Any], + result: Any, # noqa: ANN401 # result can be any callback shape + ) -> Mapping[str, Any]: + metadata: Final = self._build_metadata(request_data=kwargs) + try: + return SingulrGuardrailPayload( + correlation_id=kwargs.get("litellm_call_id"), + model_name=kwargs.get("model"), + guardrail_scope="response", + response=result, + metadata=metadata, + ).model_dump(mode="json") + except Exception as exc: # noqa: BLE001 # result can be any callback shape; fall back to a stringified report + verbose_proxy_logger.debug("Singulr: could not JSON-serialize response, falling back: %s", exc) + return { # mutable-ok: short-lived JSON payload dict + "correlation_id": kwargs.get("litellm_call_id"), + "guardrail_scope": "response", + "response": str(result), + "metadata": metadata, + } + + async def _report_logging_only( + self, + kwargs: Mapping[str, Any], + result: Any, # noqa: ANN401 # result can be any callback shape + ) -> tuple[SingulrGuardrailResponse | None, ...]: + messages: Final = kwargs.get("messages") or () + request_verdict: Final = ( + await self._call_api( + SingulrGuardrailPayload( + correlation_id=kwargs.get("litellm_call_id"), + model_name=kwargs.get("model"), + guardrail_scope="request", + messages=messages, + metadata=self._build_metadata(request_data=kwargs), + ).model_dump(mode="json") + ) + if messages + else None + ) + response_verdict: Final = ( + await self._call_api(self._logging_only_response_payload(kwargs=kwargs, result=result)) if result else None + ) + return (request_verdict, response_verdict) + + async def _logging_only_guardrail_status( + self, + kwargs: Mapping[str, Any], + result: Any, # noqa: ANN401 # result can be any callback shape + ) -> GuardrailStatus | None: + """``None`` means no verdict was reached, so nothing should be logged.""" + try: + verdicts: Final = await self._report_logging_only(kwargs=kwargs, result=result) + except GuardrailRaisedException: + return "guardrail_intervened" + except Exception as exc: # noqa: BLE001 # logging_only must never break the request + verbose_proxy_logger.debug("Singulr: logging_only hook swallowed exception: %s", exc) + return None + if any(verdict is not None and verdict.should_block for verdict in verdicts): + return "guardrail_intervened" + return "success" + + @staticmethod + def _is_mcp_call(kwargs: Mapping[str, Any]) -> bool: + model: Final = kwargs.get("model") + return isinstance(model, str) and model.startswith(_MCP_MODEL_PREFIX) + + async def async_logging_hook( + self, + kwargs: dict, # mutable-ok: matches CustomLogger override; mutated via setdefault + result: Any, # noqa: ANN401 # required by CustomLogger.async_logging_hook override signature + call_type: str, + ) -> tuple[dict, Any]: + if self._is_mcp_call(kwargs): + verbose_proxy_logger.debug("Singulr: skipping logging_only report for MCP call %s", kwargs.get("model")) + return kwargs, result + + start_time: Final = datetime.now(timezone.utc) + guardrail_status: Final = await self._logging_only_guardrail_status(kwargs=kwargs, result=result) + if guardrail_status is None: + return kwargs, result + + end_time: Final = datetime.now(timezone.utc) + slg: Final = StandardLoggingGuardrailInformation( + guardrail_name=self.guardrail_name or "singulr", + guardrail_mode=GuardrailEventHooks.logging_only, + guardrail_status=guardrail_status, + start_time=start_time.timestamp(), + end_time=end_time.timestamp(), + duration=(end_time - start_time).total_seconds(), + masked_entity_count=None, + ) + standard_logging_object: Final = kwargs.setdefault( + "standard_logging_object", + {}, # mutable-ok: shared, mutated accumulator + ) + existing = standard_logging_object.get("guardrail_information") + if isinstance(existing, list): + existing.append(slg) + else: + standard_logging_object["guardrail_information"] = [slg] # mutable-ok: shared accumulator + + return kwargs, result + + def logging_hook( + self, + kwargs: dict, # mutable-ok: required by CustomLogger.logging_hook override signature + result: Any, # noqa: ANN401 # required by CustomLogger.logging_hook override signature + call_type: str, + ) -> tuple[dict, Any]: + try: + try: + loop = asyncio.get_event_loop() + except RuntimeError: + loop = asyncio.new_event_loop() + asyncio.set_event_loop(loop) + if loop.is_running(): + verbose_proxy_logger.debug( + "Singulr: sync logging_hook called from a running loop; skipping logging_only report" + ) + return kwargs, result + loop.run_until_complete(self.async_logging_hook(kwargs=kwargs, result=result, call_type=call_type)) + except Exception as exc: # noqa: BLE001 # logging_only must never break the request + verbose_proxy_logger.debug("Singulr: sync logging_hook swallowed exception: %s", exc) + return kwargs, result + + @log_guardrail_information + async def apply_guardrail( + self, + inputs: GenericGuardrailAPIInputs, + request_data: dict, # mutable-ok: required by CustomGuardrail.apply_guardrail override signature + input_type: str, + logging_obj: "LiteLLMLoggingObj | None" = None, + ) -> GenericGuardrailAPIInputs: + texts: Final = inputs.get("texts", ()) + structured_messages: Final = inputs.get("structured_messages", ()) + + verbose_proxy_logger.debug( + "Singulr Guardrail: apply_guardrail called with input_type=%s, texts=%d, structured_messages=%d", + input_type, + len(texts), + len(structured_messages), + ) + + if input_type == "request": + if request_data.get("mcp_tool_name"): + await self._apply_guardrail_on_mcp_request(request_data=request_data) + return inputs + return await self._apply_guardrail_on_request( + inputs=inputs, texts=texts, structured_messages=structured_messages, request_data=request_data + ) + elif input_type == "response": + if request_data.get("call_type") == "call_mcp_tool": + return await self._apply_guardrail_on_mcp_response( + inputs=inputs, texts=texts, request_data=request_data + ) + return await self._apply_guardrail_on_response(inputs=inputs, texts=texts, request_data=request_data) + return inputs diff --git a/litellm/types/proxy/guardrails/guardrail_hooks/singulr.py b/litellm/types/proxy/guardrails/guardrail_hooks/singulr.py index d0d19d191c1..fd349b44e0c 100644 --- a/litellm/types/proxy/guardrails/guardrail_hooks/singulr.py +++ b/litellm/types/proxy/guardrails/guardrail_hooks/singulr.py @@ -1,30 +1,59 @@ -from typing import Any +from collections.abc import Mapping, Sequence +from typing import Any, Literal from pydantic import BaseModel, Field from .base import GuardrailConfigModel -class SingulrGuardrailRequest(BaseModel): - model: str | None = None - messages: list[dict[str, Any]] | None = None - tools: list[dict[str, Any]] | None = None - model_response: dict[str, Any] | None = None - litellm_metadata: dict[str, Any] | None = None +class ContentBlock(BaseModel): + type: str | None = None + text: str | None = None + + +class ToolCallFunction(BaseModel): + name: str + arguments: str + + +class ToolCall(BaseModel): + id: str + type: str = "function" + function: ToolCallFunction + + +class AssistantMessage(BaseModel): + role: Literal["assistant"] = "assistant" + content: str | Sequence[ContentBlock] | None = None + tool_calls: Sequence[ToolCall] | None = None class SingulrGuardrailPayload(BaseModel): - litellm_call_id: str | None = None - request_data: SingulrGuardrailRequest | None = None - input_type: str - is_playground_request: bool | None = None - playground_text: str | None = None + correlation_id: str | None = None + model_name: str | None = None + model_provider_name: str | None = None + guardrail_scope: str | None = None + messages: Sequence[Any] | None = None + images: Sequence[str] | None = None + tools: Sequence[Any] | None = None # pyright: ignore[reportExplicitAny] # forwards caller-supplied OpenAI tool defs verbatim + response: Any = None # pyright: ignore[reportExplicitAny] # logging_only reports raw litellm callback results (ModelResponse, EmbeddingResponse, etc.) + metadata: Mapping[str, Any] | None = None + + +class SingulrMcpGuardrailPayload(BaseModel): + model_name: str | None = None + guardrail_scope: str | None = None + tool_name: str | None = None + tool_arguments: Mapping[str, Any] | None = None + mcp_server_name: str | None = None + tool_result: Sequence[str] | None = None + metadata: Mapping[str, Any] | None = None class SingulrGuardrailResponse(BaseModel): """Response returned by the Singulr guardrail API.""" - should_block: bool = False + should_block: bool | None = None blocking_due_to: str | None = None diff --git a/tests/test_litellm/proxy/guardrails/guardrail_hooks/test_singulr.py b/tests/test_litellm/proxy/guardrails/guardrail_hooks/test_singulr.py index 14d8e90e027..7a228a7c3fb 100644 --- a/tests/test_litellm/proxy/guardrails/guardrail_hooks/test_singulr.py +++ b/tests/test_litellm/proxy/guardrails/guardrail_hooks/test_singulr.py @@ -1,14 +1,17 @@ +import json from unittest.mock import MagicMock, patch import httpx import pytest from litellm.exceptions import GuardrailRaisedException +from litellm.proxy._types import LitellmUserRoles, UserAPIKeyAuth from litellm.proxy.guardrails.guardrail_hooks.singulr.singulr import SingulrGuardrail from litellm.types.guardrails import GuardrailEventHooks from litellm.types.proxy.guardrails.guardrail_hooks.singulr import ( SingulrGuardrailConfigModel, ) +from litellm.types.utils import ModelResponse # --------------------------------------------------------------------------- @@ -55,6 +58,31 @@ class TestSingulrConfiguration: assert guardrail.singulr_guardrail_id == "id123" assert guardrail.singulr_application_id == "entity123" + def test_api_base_strips_surrounding_whitespace(self): + """Regression: a UI-saved api_base with a trailing space + (e.g. "https://custom.api.local ") broke urlparse's port parsing and + made every guardrail call fail with a connection error, even though + the configured host was reachable.""" + guardrail = SingulrGuardrail( + singulr_api_key="test_key", + singulr_api_base=" https://custom.api.local ", + ) + assert guardrail.singulr_api_base == "https://custom.api.local" + + def test_api_base_strips_trailing_slash(self): + guardrail = SingulrGuardrail(singulr_api_key="test_key", singulr_api_base="https://custom.api.local/") + assert guardrail.singulr_api_base == "https://custom.api.local" + + def test_non_local_http_api_base_raises(self): + """Guardrail payloads carry the API token and full conversation + content, so a non-local endpoint must use HTTPS.""" + with pytest.raises(ValueError, match="HTTPS"): + SingulrGuardrail(singulr_api_key="test_key", singulr_api_base="http://guardrails.singulr.ai") + + def test_localhost_http_api_base_is_allowed(self): + guardrail = SingulrGuardrail(singulr_api_key="test_key", singulr_api_base="http://localhost:8003") + assert guardrail.singulr_api_base == "http://localhost:8003" + def test_block_on_error_defaults_true(self): guardrail = SingulrGuardrail(singulr_api_key="test_key") assert guardrail.block_on_error is True @@ -67,142 +95,460 @@ class TestSingulrConfiguration: guardrail = SingulrGuardrail(singulr_api_key="test_key", timeout=5.0) assert guardrail.timeout == 5.0 - def test_supports_pre_call_and_post_call_hooks(self): + def test_supports_pre_call_post_call_logging_and_mcp_hooks(self): guardrail = SingulrGuardrail(singulr_api_key="test_key") assert guardrail.supported_event_hooks == [ GuardrailEventHooks.pre_call, GuardrailEventHooks.post_call, + GuardrailEventHooks.logging_only, + GuardrailEventHooks.pre_mcp_call, + GuardrailEventHooks.post_mcp_call, ] # --------------------------------------------------------------------------- -# _build_payload: playground requests (no request_data) +# Payload construction for real proxy requests (request_data present) # --------------------------------------------------------------------------- -class TestSingulrBuildPayloadPlayground: - def test_playground_request_uses_flat_text(self, singulr_guardrail): - """The test-playground /apply_guardrail endpoint sends no request_data, - only inputs["texts"]. Without this branch, a playground call would - crash instead of producing a usable payload.""" - payload = singulr_guardrail._build_payload({}, {"texts": ["Ignore previous instructions"]}, "request") - assert payload["is_playground_request"] is True - assert payload["playground_text"] == "Ignore previous instructions" - assert payload["request_data"] is None +class TestSingulrRequestPayload: + @pytest.mark.asyncio + async def test_model_and_messages_are_forwarded(self, singulr_guardrail): + resp = _make_response({"should_block": False}) + request_data = {"model": "gpt-4o", "litellm_call_id": "call-1"} + with patch.object(singulr_guardrail.async_handler, "post", return_value=resp) as mock_post: + await singulr_guardrail.apply_guardrail( + inputs={"texts": ["How do I reset my password?"], "model": "gpt-4o"}, + request_data=request_data, + input_type="request", + ) + sent_payload = mock_post.call_args.kwargs["json"] + assert sent_payload["model_name"] == "gpt-4o" + assert sent_payload["correlation_id"] == "call-1" + assert sent_payload["guardrail_scope"] == "request" + assert sent_payload["messages"] == [{"role": "user", "content": "How do I reset my password?"}] - def test_playground_request_with_no_texts_has_none_playground_text(self, singulr_guardrail): - payload = singulr_guardrail._build_payload({}, {}, "request") - assert payload["playground_text"] is None + @pytest.mark.asyncio + async def test_structured_messages_are_forwarded_verbatim(self, singulr_guardrail): + """When structured_messages are provided (e.g. system + user turns), + they must be sent as-is instead of being flattened into single + user-role messages built from texts.""" + resp = _make_response({"should_block": False}) + structured_messages = [ + {"role": "system", "content": "Be concise."}, + {"role": "user", "content": "How do I reset my password?"}, + ] + with patch.object(singulr_guardrail.async_handler, "post", return_value=resp) as mock_post: + await singulr_guardrail.apply_guardrail( + inputs={"texts": ["How do I reset my password?"], "structured_messages": structured_messages}, + request_data={}, + input_type="request", + ) + sent_payload = mock_post.call_args.kwargs["json"] + assert sent_payload["messages"] == structured_messages - def test_playground_input_type_is_included(self, singulr_guardrail): - payload = singulr_guardrail._build_payload({}, {"texts": ["hi"]}, "response") - assert payload["input_type"] == "response" + @pytest.mark.asyncio + async def test_images_are_forwarded(self, singulr_guardrail): + resp = _make_response({"should_block": False}) + with patch.object(singulr_guardrail.async_handler, "post", return_value=resp) as mock_post: + await singulr_guardrail.apply_guardrail( + inputs={"texts": [], "images": ["data:image/png;base64,abc123"]}, + request_data={}, + input_type="request", + ) + sent_payload = mock_post.call_args.kwargs["json"] + assert sent_payload["images"] == ["data:image/png;base64,abc123"] + @pytest.mark.asyncio + async def test_no_messages_or_images_skips_the_api_call(self, singulr_guardrail): + with patch.object(singulr_guardrail.async_handler, "post") as mock_post: + result = await singulr_guardrail.apply_guardrail( + inputs={"texts": []}, + request_data={}, + input_type="request", + ) + mock_post.assert_not_called() + assert result == {"texts": []} -# --------------------------------------------------------------------------- -# _build_payload: real proxy requests (request_data present) -# --------------------------------------------------------------------------- + @pytest.mark.asyncio + @pytest.mark.parametrize( + "extra_inputs", + [ + {"tools": [{"type": "function", "function": {"name": "delete_file", "description": "", "parameters": {}}}]}, + {"images": ["data:image/png;base64,abc123"]}, + ], + ids=["tools_alone", "images_alone"], + ) + async def test_tools_or_images_alone_still_trigger_the_api_call(self, singulr_guardrail, extra_inputs): + """Regression: a request with only tool definitions or only images and + no text must still be checked, not skipped for lack of a message.""" + resp = _make_response({"should_block": False}) + with patch.object(singulr_guardrail.async_handler, "post", return_value=resp) as mock_post: + await singulr_guardrail.apply_guardrail( + inputs={"texts": [], **extra_inputs}, + request_data={}, + input_type="request", + ) + sent_payload = mock_post.call_args.kwargs["json"] + for key, value in extra_inputs.items(): + assert sent_payload[key] == value + @pytest.mark.asyncio + async def test_tools_are_forwarded(self, singulr_guardrail): + """Regression: tool/function definitions are client-controlled and can + carry prompt-injection content, so they must reach Singulr for + inspection instead of only messages and images.""" + resp = _make_response({"should_block": False}) + tools = [ + { + "type": "function", + "function": {"name": "search_docs", "description": "Search internal docs", "parameters": {}}, + } + ] + with patch.object(singulr_guardrail.async_handler, "post", return_value=resp) as mock_post: + await singulr_guardrail.apply_guardrail( + inputs={"texts": ["How do I reset my password?"], "tools": tools}, + request_data={}, + input_type="request", + ) + sent_payload = mock_post.call_args.kwargs["json"] + assert sent_payload["tools"] == tools -class TestSingulrBuildPayloadRequestData: - def test_model_messages_and_tools_are_forwarded(self, singulr_guardrail): + @pytest.mark.asyncio + async def test_responses_api_mcp_tools_are_forwarded(self, singulr_guardrail): + """Regression: Responses API tools (e.g. {"type": "mcp", "server_label": ...}) + have no "function" key, unlike Chat Completions tools. SingulrGuardrailPayload + rejected them with a pydantic ValidationError, turning every Responses API + request carrying an MCP tool into a 500.""" + resp = _make_response({"should_block": False}) + tools = [ + { + "type": "mcp", + "server_label": "docs-server", + "server_url": "https://mcp.example.com", + "allowed_tools": ["search_docs"], + } + ] + with patch.object(singulr_guardrail.async_handler, "post", return_value=resp) as mock_post: + await singulr_guardrail.apply_guardrail( + inputs={"texts": ["How do I reset my password?"], "tools": tools}, + request_data={}, + input_type="request", + ) + sent_payload = mock_post.call_args.kwargs["json"] + assert sent_payload["tools"] == tools + + @pytest.mark.asyncio + async def test_user_api_key_alias_is_forwarded_in_metadata(self, singulr_guardrail): + """Regression: the alias must be sent as {"user_api_key_alias": }, + not as a dict whose key is the alias value itself.""" + resp = _make_response({"should_block": False}) + request_data = {"litellm_metadata": {"user_api_key_alias": "my-key-alias"}} + with patch.object(singulr_guardrail.async_handler, "post", return_value=resp) as mock_post: + await singulr_guardrail.apply_guardrail( + inputs={"texts": ["hi"]}, + request_data=request_data, + input_type="request", + ) + sent_payload = mock_post.call_args.kwargs["json"] + assert sent_payload["metadata"] == {"user_api_key_alias": "my-key-alias"} + + @pytest.mark.asyncio + async def test_falls_back_to_regular_metadata_for_key_alias(self, singulr_guardrail): + resp = _make_response({"should_block": False}) + request_data = {"metadata": {"user_api_key_alias": "fallback-alias"}} + with patch.object(singulr_guardrail.async_handler, "post", return_value=resp) as mock_post: + await singulr_guardrail.apply_guardrail( + inputs={"texts": ["hi"]}, + request_data=request_data, + input_type="request", + ) + sent_payload = mock_post.call_args.kwargs["json"] + assert sent_payload["metadata"] == {"user_api_key_alias": "fallback-alias"} + + @pytest.mark.asyncio + async def test_user_api_key_user_id_is_forwarded_in_metadata(self, singulr_guardrail): + resp = _make_response({"should_block": False}) + request_data = {"litellm_metadata": {"user_api_key_user_id": "my-user-id"}} + with patch.object(singulr_guardrail.async_handler, "post", return_value=resp) as mock_post: + await singulr_guardrail.apply_guardrail( + inputs={"texts": ["hi"]}, + request_data=request_data, + input_type="request", + ) + sent_payload = mock_post.call_args.kwargs["json"] + assert sent_payload["metadata"] == {"user_api_key_user_id": "my-user-id"} + + @pytest.mark.asyncio + async def test_falls_back_to_regular_metadata_for_user_id(self, singulr_guardrail): + resp = _make_response({"should_block": False}) + request_data = {"metadata": {"user_api_key_user_id": "fallback-user-id"}} + with patch.object(singulr_guardrail.async_handler, "post", return_value=resp) as mock_post: + await singulr_guardrail.apply_guardrail( + inputs={"texts": ["hi"]}, + request_data=request_data, + input_type="request", + ) + sent_payload = mock_post.call_args.kwargs["json"] + assert sent_payload["metadata"] == {"user_api_key_user_id": "fallback-user-id"} + + @pytest.mark.asyncio + async def test_user_api_key_user_email_is_forwarded_in_metadata(self, singulr_guardrail): + resp = _make_response({"should_block": False}) + request_data = {"litellm_metadata": {"user_api_key_user_email": "user@example.com"}} + with patch.object(singulr_guardrail.async_handler, "post", return_value=resp) as mock_post: + await singulr_guardrail.apply_guardrail( + inputs={"texts": ["hi"]}, + request_data=request_data, + input_type="request", + ) + sent_payload = mock_post.call_args.kwargs["json"] + assert sent_payload["metadata"] == {"user_api_key_user_email": "user@example.com"} + + @pytest.mark.asyncio + async def test_user_api_key_organization_alias_is_forwarded_in_metadata(self, singulr_guardrail): + resp = _make_response({"should_block": False}) + request_data = {"litellm_metadata": {"user_api_key_org_alias": "Acme Org"}} + with patch.object(singulr_guardrail.async_handler, "post", return_value=resp) as mock_post: + await singulr_guardrail.apply_guardrail( + inputs={"texts": ["hi"]}, + request_data=request_data, + input_type="request", + ) + sent_payload = mock_post.call_args.kwargs["json"] + assert sent_payload["metadata"] == {"user_api_key_org_alias": "Acme Org"} + + @pytest.mark.asyncio + async def test_user_api_key_team_alias_is_forwarded_in_metadata(self, singulr_guardrail): + resp = _make_response({"should_block": False}) + request_data = {"litellm_metadata": {"user_api_key_team_alias": "AI Content Security Team"}} + with patch.object(singulr_guardrail.async_handler, "post", return_value=resp) as mock_post: + await singulr_guardrail.apply_guardrail( + inputs={"texts": ["hi"]}, + request_data=request_data, + input_type="request", + ) + sent_payload = mock_post.call_args.kwargs["json"] + assert sent_payload["metadata"] == {"user_api_key_team_alias": "AI Content Security Team"} + + @pytest.mark.asyncio + async def test_user_api_key_org_id_is_forwarded_in_metadata(self, singulr_guardrail): + resp = _make_response({"should_block": False}) + request_data = {"litellm_metadata": {"user_api_key_org_id": "org-123"}} + with patch.object(singulr_guardrail.async_handler, "post", return_value=resp) as mock_post: + await singulr_guardrail.apply_guardrail( + inputs={"texts": ["hi"]}, + request_data=request_data, + input_type="request", + ) + sent_payload = mock_post.call_args.kwargs["json"] + assert sent_payload["metadata"] == {"user_api_key_org_id": "org-123"} + + @pytest.mark.asyncio + async def test_user_api_key_team_id_is_forwarded_in_metadata(self, singulr_guardrail): + resp = _make_response({"should_block": False}) + request_data = {"litellm_metadata": {"user_api_key_team_id": "team-456"}} + with patch.object(singulr_guardrail.async_handler, "post", return_value=resp) as mock_post: + await singulr_guardrail.apply_guardrail( + inputs={"texts": ["hi"]}, + request_data=request_data, + input_type="request", + ) + sent_payload = mock_post.call_args.kwargs["json"] + assert sent_payload["metadata"] == {"user_api_key_team_id": "team-456"} + + @pytest.mark.asyncio + async def test_user_api_key_user_role_is_forwarded_in_metadata(self, singulr_guardrail): + resp = _make_response({"should_block": False}) + auth = UserAPIKeyAuth(user_role=LitellmUserRoles.INTERNAL_USER_VIEW_ONLY) + request_data = {"litellm_metadata": {"user_api_key_auth": auth}} + with patch.object(singulr_guardrail.async_handler, "post", return_value=resp) as mock_post: + await singulr_guardrail.apply_guardrail( + inputs={"texts": ["hi"]}, + request_data=request_data, + input_type="request", + ) + sent_payload = mock_post.call_args.kwargs["json"] + assert sent_payload["metadata"] == {"user_api_key_user_role": LitellmUserRoles.INTERNAL_USER_VIEW_ONLY.value} + + @pytest.mark.asyncio + async def test_no_user_role_available_omits_role_from_metadata(self, singulr_guardrail): + resp = _make_response({"should_block": False}) + request_data = {"litellm_metadata": {"user_api_key_alias": "my-key-alias"}} + with patch.object(singulr_guardrail.async_handler, "post", return_value=resp) as mock_post: + await singulr_guardrail.apply_guardrail( + inputs={"texts": ["hi"]}, + request_data=request_data, + input_type="request", + ) + sent_payload = mock_post.call_args.kwargs["json"] + assert "user_api_key_user_role" not in sent_payload["metadata"] + + @pytest.mark.asyncio + async def test_all_user_metadata_fields_forwarded_together(self, singulr_guardrail): + resp = _make_response({"should_block": False}) + auth = UserAPIKeyAuth(user_role=LitellmUserRoles.INTERNAL_USER_VIEW_ONLY) request_data = { - "model": "gpt-4o", - "messages": [{"role": "user", "content": "How do I reset my password?"}], - "tools": [{"type": "function", "function": {"name": "get_weather"}}], + "litellm_metadata": { + "user_api_key_alias": "my-key-alias", + "user_api_key_user_id": "my-user-id", + "user_api_key_user_email": "user@example.com", + "user_api_key_org_id": "org-123", + "user_api_key_org_alias": "Acme Org", + "user_api_key_team_id": "team-456", + "user_api_key_team_alias": "AI Content Security Team", + "user_api_key_auth": auth, + } + } + with patch.object(singulr_guardrail.async_handler, "post", return_value=resp) as mock_post: + await singulr_guardrail.apply_guardrail( + inputs={"texts": ["hi"]}, + request_data=request_data, + input_type="request", + ) + sent_payload = mock_post.call_args.kwargs["json"] + assert sent_payload["metadata"] == { + "user_api_key_alias": "my-key-alias", + "user_api_key_user_id": "my-user-id", + "user_api_key_user_email": "user@example.com", + "user_api_key_org_id": "org-123", + "user_api_key_org_alias": "Acme Org", + "user_api_key_team_id": "team-456", + "user_api_key_team_alias": "AI Content Security Team", + "user_api_key_user_role": LitellmUserRoles.INTERNAL_USER_VIEW_ONLY.value, } - payload = singulr_guardrail._build_payload(request_data, {"texts": []}, "request") - assert payload["request_data"]["model"] == "gpt-4o" - assert payload["request_data"]["messages"] == request_data["messages"] - assert payload["request_data"]["tools"] == request_data["tools"] - assert payload["is_playground_request"] is None - def test_model_response_absent_on_request_side(self, singulr_guardrail): - """The response hasn't happened yet at request time, so model_response - must not be forwarded even if request_data carries a stale response - object from a previous call.""" - from litellm.types.utils import ModelResponse + @pytest.mark.asyncio + async def test_no_key_alias_available_sends_no_metadata(self, singulr_guardrail): + """Regression: with no alias found, metadata must be omitted (None), + not a {None: None} dict that fails payload validation.""" + resp = _make_response({"should_block": False}) + with patch.object(singulr_guardrail.async_handler, "post", return_value=resp) as mock_post: + await singulr_guardrail.apply_guardrail( + inputs={"texts": ["hi"]}, + request_data={}, + input_type="request", + ) + sent_payload = mock_post.call_args.kwargs["json"] + assert sent_payload["metadata"] is None - request_data = {"model": "gpt-4o", "response": ModelResponse()} - payload = singulr_guardrail._build_payload(request_data, {"texts": []}, "request") - assert payload["request_data"]["model_response"] is None - def test_model_response_is_forwarded_and_json_serializable(self, singulr_guardrail): - """Regression: request_data["response"] is a ModelResponse (pydantic) - object containing nested non-JSON-safe values (e.g. a `created` - unix timestamp is fine, but nested pydantic submodels are not plain - dicts). Without mode="json" on both the inner and outer dumps, this - payload cannot be sent via httpx's json= kwarg.""" - import json as _json +# --------------------------------------------------------------------------- +# Payload construction for responses +# --------------------------------------------------------------------------- - from litellm.types.utils import Choices, Message, ModelResponse, Usage - response = ModelResponse( - choices=[Choices(message=Message(role="assistant", content="Go to settings."))], - usage=Usage(prompt_tokens=10, completion_tokens=5, total_tokens=15), - ) - request_data = {"model": "gpt-4o", "response": response} - payload = singulr_guardrail._build_payload(request_data, {"texts": ["Go to settings."]}, "response") - - # Must not raise - this is what httpx's json= kwarg effectively does. - serialized = _json.dumps(payload) - assert "Go to settings." in serialized - assert payload["request_data"]["model_response"]["choices"][0]["message"]["content"] == "Go to settings." - - def test_model_requested_tool_calls_are_forwarded_in_model_response(self, singulr_guardrail): - """Tool calls the model requests arrive inside response.choices[].message.tool_calls. - They must survive the dump so Singulr can inspect what tools the - model is trying to invoke.""" - from litellm.types.utils import Choices, Message, ModelResponse - - response = ModelResponse( - choices=[ - Choices( - message=Message( - role="assistant", - content=None, - tool_calls=[ - { - "id": "call_1", - "type": "function", - "function": {"name": "get_current_time", "arguments": "{}"}, - } - ], - ) - ) +class TestSingulrResponsePayload: + @pytest.mark.asyncio + async def test_assistant_text_and_tool_calls_are_forwarded(self, singulr_guardrail): + resp = _make_response({"should_block": False}) + inputs = { + "texts": ["Go to settings."], + "tool_calls": [ + { + "id": "call_1", + "type": "function", + "function": {"name": "get_current_time", "arguments": "{}"}, + } ], - ) - request_data = {"model": "gpt-4o", "response": response} - payload = singulr_guardrail._build_payload(request_data, {"texts": []}, "response") - - tool_calls = payload["request_data"]["model_response"]["choices"][0]["message"]["tool_calls"] - assert tool_calls[0]["function"]["name"] == "get_current_time" - - def test_litellm_metadata_is_forwarded(self, singulr_guardrail): - request_data = {"model": "gpt-4o", "litellm_metadata": {"user_api_key_hash": "abc123"}} - payload = singulr_guardrail._build_payload(request_data, {"texts": []}, "request") - assert payload["request_data"]["litellm_metadata"] == {"user_api_key_hash": "abc123"} - - def test_internal_logging_object_is_not_forwarded(self, singulr_guardrail): - """Regression: request_data can carry internal proxy objects (e.g. the - Logging instance) that aren't JSON-serializable at all. _build_payload - must only pull known request/response fields out of request_data, - not dump it wholesale, or this crashes on every real proxy call.""" - import json as _json - - class _NotSerializable: - pass - - request_data = { - "model": "gpt-4o", - "messages": [{"role": "user", "content": "hi"}], - "litellm_logging_obj": _NotSerializable(), } - payload = singulr_guardrail._build_payload(request_data, {"texts": ["hi"]}, "request") + with patch.object(singulr_guardrail.async_handler, "post", return_value=resp) as mock_post: + await singulr_guardrail.apply_guardrail( + inputs=inputs, + request_data={}, + input_type="response", + ) + sent_payload = mock_post.call_args.kwargs["json"] + assert sent_payload["guardrail_scope"] == "response" + assert sent_payload["response"]["content"] == "Go to settings." + assert sent_payload["response"]["tool_calls"][0]["function"]["name"] == "get_current_time" - # Must not raise. - _json.dumps(payload) - assert "litellm_logging_obj" not in payload["request_data"] + @pytest.mark.asyncio + async def test_response_images_are_forwarded(self, singulr_guardrail): + resp = _make_response({"should_block": False}) + inputs = {"texts": ["ok"], "images": ["data:image/png;base64,xyz"]} + with patch.object(singulr_guardrail.async_handler, "post", return_value=resp) as mock_post: + await singulr_guardrail.apply_guardrail( + inputs=inputs, + request_data={}, + input_type="response", + ) + sent_payload = mock_post.call_args.kwargs["json"] + assert sent_payload["images"] == ["data:image/png;base64,xyz"] + + @pytest.mark.asyncio + async def test_incomplete_tool_calls_are_dropped(self, singulr_guardrail): + resp = _make_response({"should_block": False}) + inputs = { + "texts": [], + "tool_calls": [ + {"id": None, "type": "function", "function": {"name": "f", "arguments": "{}"}}, + {"id": "call_2", "type": "function", "function": None}, + {"id": "call_3", "type": "function", "function": {"name": None, "arguments": "{}"}}, + {"id": "call_4", "type": "function", "function": {"name": "f", "arguments": None}}, + ], + } + with patch.object(singulr_guardrail.async_handler, "post", return_value=resp) as mock_post: + await singulr_guardrail.apply_guardrail( + inputs=inputs, + request_data={}, + input_type="response", + ) + sent_payload = mock_post.call_args.kwargs["json"] + assert sent_payload["response"]["tool_calls"] == [] + + @pytest.mark.asyncio + @pytest.mark.parametrize( + "raw_type, expected_type", + [(None, "function"), ("custom", "custom")], + ids=["type_missing", "type_not_function"], + ) + async def test_tool_call_type_other_than_function_is_still_scanned( + self, singulr_guardrail, raw_type, expected_type + ): + """Regression: a tool call whose type is absent or isn't "function" used + to raise a pydantic ValidationError while building the payload, which + escaped apply_guardrail as a 500 instead of reaching the scan at all.""" + resp = _make_response({"should_block": False}) + tool_call = {"id": "call_1", "function": {"name": "get_current_time", "arguments": "{}"}} + inputs = { + "texts": [], + "tool_calls": [tool_call if raw_type is None else {**tool_call, "type": raw_type}], + } + with patch.object(singulr_guardrail.async_handler, "post", return_value=resp) as mock_post: + await singulr_guardrail.apply_guardrail(inputs=inputs, request_data={}, input_type="response") + sent_tool_calls = mock_post.call_args.kwargs["json"]["response"]["tool_calls"] + assert [call["type"] for call in sent_tool_calls] == [expected_type] + assert sent_tool_calls[0]["function"]["name"] == "get_current_time" + + @pytest.mark.asyncio + async def test_non_string_tool_call_arguments_are_serialized(self, singulr_guardrail): + """Some providers hand back already-parsed arguments; they must be + scanned as JSON text rather than crashing the payload build.""" + resp = _make_response({"should_block": False}) + inputs = { + "texts": [], + "tool_calls": [ + {"id": "call_1", "type": "function", "function": {"name": "rm", "arguments": {"path": "/etc/passwd"}}} + ], + } + with patch.object(singulr_guardrail.async_handler, "post", return_value=resp) as mock_post: + await singulr_guardrail.apply_guardrail(inputs=inputs, request_data={}, input_type="response") + sent_tool_calls = mock_post.call_args.kwargs["json"]["response"]["tool_calls"] + assert json.loads(sent_tool_calls[0]["function"]["arguments"]) == {"path": "/etc/passwd"} + + @pytest.mark.asyncio + async def test_block_verdict_still_raises_for_a_non_function_tool_call(self, singulr_guardrail): + """The point of scanning these calls: the verdict must still be enforced.""" + resp = _make_response({"should_block": True, "blocking_due_to": "dangerous_tool"}) + inputs = { + "texts": [], + "tool_calls": [{"id": "call_1", "function": {"name": "rm", "arguments": "{}"}}], + } + with patch.object(singulr_guardrail.async_handler, "post", return_value=resp): + with pytest.raises(GuardrailRaisedException) as exc_info: + await singulr_guardrail.apply_guardrail(inputs=inputs, request_data={}, input_type="response") + assert "dangerous_tool" in str(exc_info.value) # --------------------------------------------------------------------------- @@ -212,8 +558,15 @@ class TestSingulrBuildPayloadRequestData: class TestSingulrAllowAction: @pytest.mark.asyncio - async def test_allow_returns_inputs_unchanged(self, singulr_guardrail): - resp = _make_response({"should_block": False}) + @pytest.mark.parametrize( + "guard_response", + [{"should_block": False}, {}], + ids=["should_block_false", "should_block_omitted"], + ) + async def test_should_block_falsy_returns_inputs_unchanged_on_request(self, singulr_guardrail, guard_response): + """should_block is optional on the wire; a response that omits it + entirely must be treated as allow, not block.""" + resp = _make_response(guard_response) inputs = {"texts": ["How do I reset my password?"]} with patch.object(singulr_guardrail.async_handler, "post", return_value=resp): result = await singulr_guardrail.apply_guardrail( @@ -223,18 +576,40 @@ class TestSingulrAllowAction: ) assert result is inputs + @pytest.mark.asyncio + async def test_should_block_false_returns_inputs_unchanged_on_response(self, singulr_guardrail): + resp = _make_response({"should_block": False}) + inputs = {"texts": ["Here is your answer."]} + with patch.object(singulr_guardrail.async_handler, "post", return_value=resp): + result = await singulr_guardrail.apply_guardrail( + inputs=inputs, + request_data={}, + input_type="response", + ) + assert result is inputs + + @pytest.mark.asyncio + async def test_response_returns_inputs_unchanged_when_api_unreachable_and_block_on_error_false(self): + guardrail = SingulrGuardrail( + singulr_api_base="https://api.test.singulr.ai", + singulr_api_key="test_token_1234", + guardrail_name="test-singulr", + block_on_error=False, + ) + inputs = {"texts": ["Here is your answer."]} + with patch.object(guardrail.async_handler, "post", side_effect=httpx.TransportError("unreachable")): + result = await guardrail.apply_guardrail( + inputs=inputs, + request_data={}, + input_type="response", + ) + assert result is inputs + class TestSingulrBlockAction: @pytest.mark.asyncio - async def test_block_raises_guardrail_exception(self, singulr_guardrail): - """Regression: a should_block=True response must stop the request - instead of silently letting it through.""" - resp = _make_response( - { - "should_block": True, - "blocking_due_to": "PII Information detected", - } - ) + async def test_should_block_true_raises_on_request(self, singulr_guardrail): + resp = _make_response({"should_block": True, "blocking_due_to": "PII Information detected"}) with patch.object(singulr_guardrail.async_handler, "post", return_value=resp): with pytest.raises(GuardrailRaisedException) as exc_info: await singulr_guardrail.apply_guardrail( @@ -243,6 +618,25 @@ class TestSingulrBlockAction: input_type="request", ) assert "PII Information detected" in str(exc_info.value) + assert exc_info.value.blocked_content is True + + @pytest.mark.asyncio + async def test_should_block_true_raises_on_response(self, singulr_guardrail): + """Regression: apply_guardrail's response path compared + should_block (a bool) against the string "block", which is always + False, so a should_block=True response never blocked the assistant's + reply. It must raise on any truthy should_block, matching the + request path.""" + resp = _make_response({"should_block": True, "blocking_due_to": "Toxic content detected"}) + with patch.object(singulr_guardrail.async_handler, "post", return_value=resp): + with pytest.raises(GuardrailRaisedException) as exc_info: + await singulr_guardrail.apply_guardrail( + inputs={"texts": ["Here is something toxic."]}, + request_data={}, + input_type="response", + ) + assert "Toxic content detected" in str(exc_info.value) + assert exc_info.value.blocked_content is True @pytest.mark.asyncio async def test_block_without_reason_uses_unknown_placeholder(self, singulr_guardrail): @@ -256,6 +650,478 @@ class TestSingulrBlockAction: ) +# --------------------------------------------------------------------------- +# MCP tool call guardrail (pre_mcp_call / post_mcp_call) +# --------------------------------------------------------------------------- + + +class TestSingulrMcpRequest: + @pytest.mark.asyncio + async def test_mcp_tool_name_routes_to_mcp_request_payload(self, singulr_guardrail): + resp = _make_response({"should_block": False}) + request_data = { + "mcp_tool_name": "search_docs", + "mcp_arguments": {"query": "reset password"}, + "mcp_server_name": "docs-server", + } + with patch.object(singulr_guardrail.async_handler, "post", return_value=resp) as mock_post: + result = await singulr_guardrail.apply_guardrail( + inputs={"texts": []}, + request_data=request_data, + input_type="request", + ) + sent_payload = mock_post.call_args.kwargs["json"] + assert sent_payload["guardrail_scope"] == "mcp_request" + assert sent_payload["tool_name"] == "search_docs" + assert sent_payload["tool_arguments"] == {"query": "reset password"} + assert sent_payload["mcp_server_name"] == "docs-server" + assert result == {"texts": []} + + @pytest.mark.asyncio + async def test_mcp_request_should_block_true_raises(self, singulr_guardrail): + resp = _make_response({"should_block": True, "blocking_due_to": "Disallowed tool"}) + request_data = {"mcp_tool_name": "delete_file", "mcp_arguments": {"path": "/etc/passwd"}} + with patch.object(singulr_guardrail.async_handler, "post", return_value=resp): + with pytest.raises(GuardrailRaisedException, match="Disallowed tool") as exc_info: + await singulr_guardrail.apply_guardrail( + inputs={"texts": []}, + request_data=request_data, + input_type="request", + ) + assert exc_info.value.blocked_content is True + + @pytest.mark.asyncio + async def test_mcp_request_is_a_noop_when_api_unreachable_and_block_on_error_false(self): + guardrail = SingulrGuardrail( + singulr_api_base="https://api.test.singulr.ai", + singulr_api_key="test_token_1234", + guardrail_name="test-singulr", + block_on_error=False, + ) + request_data = {"mcp_tool_name": "search_docs", "mcp_arguments": {"query": "reset password"}} + with patch.object(guardrail.async_handler, "post", side_effect=httpx.TransportError("unreachable")): + result = await guardrail.apply_guardrail( + inputs={"texts": []}, + request_data=request_data, + input_type="request", + ) + assert result == {"texts": []} + + +class TestSingulrMcpResponse: + @pytest.mark.asyncio + async def test_call_mcp_tool_response_routes_to_mcp_response_payload(self, singulr_guardrail): + resp = _make_response({"should_block": False}) + request_data = { + "call_type": "call_mcp_tool", + "mcp_tool_name": "search_docs", + "mcp_server_name": "docs-server", + "model": "MCP: docs-server", + } + with patch.object(singulr_guardrail.async_handler, "post", return_value=resp) as mock_post: + await singulr_guardrail.apply_guardrail( + inputs={"texts": ["Result: password reset link sent."]}, + request_data=request_data, + input_type="response", + ) + sent_payload = mock_post.call_args.kwargs["json"] + assert sent_payload["guardrail_scope"] == "mcp_response" + assert sent_payload["model_name"] == "MCP: docs-server" + assert sent_payload["tool_result"] == ["Result: password reset link sent."] + + @pytest.mark.asyncio + async def test_mcp_response_with_no_texts_skips_the_api_call(self, singulr_guardrail): + request_data = {"call_type": "call_mcp_tool", "mcp_tool_name": "search_docs"} + with patch.object(singulr_guardrail.async_handler, "post") as mock_post: + result = await singulr_guardrail.apply_guardrail( + inputs={"texts": []}, + request_data=request_data, + input_type="response", + ) + mock_post.assert_not_called() + assert result == {"texts": []} + + @pytest.mark.asyncio + async def test_mcp_response_should_block_true_raises(self, singulr_guardrail): + resp = _make_response({"should_block": True, "blocking_due_to": "Sensitive tool output"}) + request_data = {"call_type": "call_mcp_tool", "mcp_tool_name": "search_docs"} + with patch.object(singulr_guardrail.async_handler, "post", return_value=resp): + with pytest.raises(GuardrailRaisedException, match="Sensitive tool output") as exc_info: + await singulr_guardrail.apply_guardrail( + inputs={"texts": ["leaked secret"]}, + request_data=request_data, + input_type="response", + ) + assert exc_info.value.blocked_content is True + + @pytest.mark.asyncio + async def test_mcp_response_resolves_metadata_from_nested_litellm_params(self, singulr_guardrail): + """post_mcp_call hands apply_guardrail litellm_logging_obj.model_call_details, + which nests metadata under litellm_params instead of at the top level.""" + resp = _make_response({"should_block": False}) + auth = UserAPIKeyAuth(user_role=LitellmUserRoles.INTERNAL_USER_VIEW_ONLY) + request_data = { + "call_type": "call_mcp_tool", + "mcp_tool_name": "search_docs", + "litellm_params": { + "metadata": { + "user_api_key_alias": "my-key-alias", + "user_api_key_user_id": "my-user-id", + "user_api_key_user_email": "user@example.com", + "user_api_key_org_id": "org-123", + "user_api_key_org_alias": "Acme Org", + "user_api_key_team_id": "team-456", + "user_api_key_team_alias": "AI Content Security Team", + "user_api_key_auth": auth, + } + }, + } + with patch.object(singulr_guardrail.async_handler, "post", return_value=resp) as mock_post: + await singulr_guardrail.apply_guardrail( + inputs={"texts": ["Result: password reset link sent."]}, + request_data=request_data, + input_type="response", + ) + sent_payload = mock_post.call_args.kwargs["json"] + assert sent_payload["metadata"] == { + "user_api_key_alias": "my-key-alias", + "user_api_key_user_id": "my-user-id", + "user_api_key_user_email": "user@example.com", + "user_api_key_org_id": "org-123", + "user_api_key_org_alias": "Acme Org", + "user_api_key_team_id": "team-456", + "user_api_key_team_alias": "AI Content Security Team", + "user_api_key_user_role": LitellmUserRoles.INTERNAL_USER_VIEW_ONLY.value, + } + + @pytest.mark.asyncio + async def test_mcp_response_prefers_top_level_metadata_over_nested_litellm_params(self, singulr_guardrail): + resp = _make_response({"should_block": False}) + request_data = { + "call_type": "call_mcp_tool", + "mcp_tool_name": "search_docs", + "litellm_metadata": {"user_api_key_alias": "top-level-alias"}, + "litellm_params": {"metadata": {"user_api_key_alias": "nested-alias"}}, + } + with patch.object(singulr_guardrail.async_handler, "post", return_value=resp) as mock_post: + await singulr_guardrail.apply_guardrail( + inputs={"texts": ["hi"]}, + request_data=request_data, + input_type="response", + ) + sent_payload = mock_post.call_args.kwargs["json"] + assert sent_payload["metadata"] == {"user_api_key_alias": "top-level-alias"} + + @pytest.mark.asyncio + async def test_mcp_response_returns_inputs_unchanged_when_api_unreachable_and_block_on_error_false(self): + guardrail = SingulrGuardrail( + singulr_api_base="https://api.test.singulr.ai", + singulr_api_key="test_token_1234", + guardrail_name="test-singulr", + block_on_error=False, + ) + request_data = {"call_type": "call_mcp_tool", "mcp_tool_name": "search_docs"} + inputs = {"texts": ["leaked secret"]} + with patch.object(guardrail.async_handler, "post", side_effect=httpx.TransportError("unreachable")): + result = await guardrail.apply_guardrail( + inputs=inputs, + request_data=request_data, + input_type="response", + ) + assert result is inputs + + +# --------------------------------------------------------------------------- +# apply_guardrail dispatch (request vs response vs unknown input_type) +# --------------------------------------------------------------------------- + + +class TestSingulrApplyGuardrailDispatch: + @pytest.mark.asyncio + async def test_unknown_input_type_returns_inputs_unchanged(self, singulr_guardrail): + with patch.object(singulr_guardrail.async_handler, "post") as mock_post: + inputs = {"texts": ["hi"]} + result = await singulr_guardrail.apply_guardrail( + inputs=inputs, + request_data={}, + input_type="unsupported", + ) + mock_post.assert_not_called() + assert result is inputs + + +# --------------------------------------------------------------------------- +# logging_only hook +# --------------------------------------------------------------------------- + + +class TestSingulrLoggingHook: + @pytest.mark.asyncio + async def test_forwards_request_messages_and_response_text(self, singulr_guardrail): + resp = _make_response({"should_block": False}) + kwargs = {"messages": [{"role": "user", "content": "hi"}], "model": "gpt-4o", "litellm_call_id": "call-1"} + result = {"choices": [{"finish_reason": "stop", "message": {"content": "hello there"}}]} + with patch.object(singulr_guardrail.async_handler, "post", return_value=resp) as mock_post: + await singulr_guardrail.async_logging_hook(kwargs=kwargs, result=result, call_type="acompletion") + + request_payload = mock_post.call_args_list[0].kwargs["json"] + response_payload = mock_post.call_args_list[1].kwargs["json"] + assert request_payload["guardrail_scope"] == "request" + assert request_payload["messages"] == kwargs["messages"] + assert response_payload["guardrail_scope"] == "response" + assert response_payload["response"] == result + + @pytest.mark.asyncio + async def test_forwards_user_metadata_in_both_request_and_response_payloads(self, singulr_guardrail): + resp = _make_response({"should_block": False}) + kwargs = { + "messages": [{"role": "user", "content": "hi"}], + "model": "gpt-4o", + "litellm_call_id": "call-1", + "litellm_metadata": {"user_api_key_alias": "my-key-alias", "user_api_key_org_id": "org-123"}, + } + result = {"choices": [{"finish_reason": "stop", "message": {"content": "hello there"}}]} + with patch.object(singulr_guardrail.async_handler, "post", return_value=resp) as mock_post: + await singulr_guardrail.async_logging_hook(kwargs=kwargs, result=result, call_type="acompletion") + + request_payload = mock_post.call_args_list[0].kwargs["json"] + response_payload = mock_post.call_args_list[1].kwargs["json"] + expected_metadata = {"user_api_key_alias": "my-key-alias", "user_api_key_org_id": "org-123"} + assert request_payload["metadata"] == expected_metadata + assert response_payload["metadata"] == expected_metadata + + @pytest.mark.asyncio + async def test_forwards_a_real_model_response_without_swallowing_it(self, singulr_guardrail): + """Regression: a normal completion callback passes a ModelResponse, not a + dict. The response payload must carry its actual serialized content instead + of silently dropping it because ModelResponse isn't a Mapping.""" + resp = _make_response({"should_block": False}) + result = ModelResponse( + choices=[{"index": 0, "finish_reason": "stop", "message": {"role": "assistant", "content": "hello there"}}] + ) + with patch.object(singulr_guardrail.async_handler, "post", return_value=resp) as mock_post: + await singulr_guardrail.async_logging_hook(kwargs={}, result=result, call_type="acompletion") + + response_payload = mock_post.call_args.kwargs["json"] + assert response_payload["guardrail_scope"] == "response" + assert response_payload["response"]["choices"][0]["message"]["content"] == "hello there" + + @pytest.mark.asyncio + async def test_non_serializable_result_falls_back_to_string_report(self, singulr_guardrail): + """A result that pydantic can't serialize to JSON must still get reported, + as a stringified fallback, instead of raising out of the logging_only hook.""" + resp = _make_response({"should_block": False}) + + class Unserializable: + def __repr__(self) -> str: + return "" + + kwargs = {"litellm_metadata": {"user_api_key_alias": "my-key-alias"}} + with patch.object(singulr_guardrail.async_handler, "post", return_value=resp) as mock_post: + await singulr_guardrail.async_logging_hook(kwargs=kwargs, result=Unserializable(), call_type="acompletion") + + response_payload = mock_post.call_args.kwargs["json"] + assert response_payload["response"] == "" + assert response_payload["metadata"] == {"user_api_key_alias": "my-key-alias"} + + @pytest.mark.asyncio + async def test_no_messages_and_no_result_skips_both_api_calls(self, singulr_guardrail): + with patch.object(singulr_guardrail.async_handler, "post") as mock_post: + returned_kwargs, returned_result = await singulr_guardrail.async_logging_hook( + kwargs={}, result=None, call_type="acompletion" + ) + mock_post.assert_not_called() + assert returned_result is None + guardrail_information = returned_kwargs["standard_logging_object"]["guardrail_information"] + assert guardrail_information[0]["guardrail_status"] == "success" + + @pytest.mark.asyncio + async def test_mcp_tool_call_is_not_reported(self, singulr_guardrail): + """MCP traffic is already covered by the pre/post_mcp_call hooks, which send + the richer mcp_request/mcp_response payloads. The logging_only hook sees the + same call again with model="MCP: " and must skip it so Singulr + doesn't get a duplicate, lower-fidelity report of every tool call.""" + kwargs = {"model": "MCP: get_weather", "messages": [{"role": "user", "content": "hi"}]} + with patch.object(singulr_guardrail.async_handler, "post") as mock_post: + updated_kwargs, result = await singulr_guardrail.async_logging_hook( + kwargs=kwargs, result={"choices": []}, call_type="acompletion" + ) + mock_post.assert_not_called() + assert "standard_logging_object" not in updated_kwargs + assert result == {"choices": []} + + @pytest.mark.asyncio + async def test_mcp_list_tools_call_is_not_reported(self, singulr_guardrail): + kwargs = {"model": "MCP: list_tools", "messages": [{"role": "user", "content": "hi"}]} + with patch.object(singulr_guardrail.async_handler, "post") as mock_post: + await singulr_guardrail.async_logging_hook(kwargs=kwargs, result=None, call_type="acompletion") + mock_post.assert_not_called() + + @pytest.mark.asyncio + async def test_non_mcp_model_is_still_reported(self, singulr_guardrail): + """Guard against the skip being too broad: a normal LLM call whose model + merely mentions MCP later in the name must still be reported.""" + resp = _make_response({"should_block": False}) + kwargs = {"model": "gpt-4o-mcp", "messages": [{"role": "user", "content": "hi"}]} + with patch.object(singulr_guardrail.async_handler, "post", return_value=resp) as mock_post: + updated_kwargs, _ = await singulr_guardrail.async_logging_hook( + kwargs=kwargs, result=None, call_type="acompletion" + ) + mock_post.assert_called_once() + assert updated_kwargs["standard_logging_object"]["guardrail_information"][0]["guardrail_status"] == "success" + + @pytest.mark.asyncio + async def test_records_standard_logging_guardrail_information(self, singulr_guardrail): + resp = _make_response({"should_block": False}) + kwargs = {"messages": [{"role": "user", "content": "hi"}]} + with patch.object(singulr_guardrail.async_handler, "post", return_value=resp): + updated_kwargs, _ = await singulr_guardrail.async_logging_hook( + kwargs=kwargs, result=None, call_type="acompletion" + ) + guardrail_information = updated_kwargs["standard_logging_object"]["guardrail_information"] + assert len(guardrail_information) == 1 + assert guardrail_information[0]["guardrail_name"] == "test-singulr" + assert guardrail_information[0]["guardrail_status"] == "success" + + @pytest.mark.asyncio + async def test_request_block_verdict_marks_guardrail_status_intervened(self, singulr_guardrail): + """Regression: a successful HTTP call whose body says should_block is a + real intervention. logging_only can't fail the request, so the verdict + only ever surfaces through guardrail_status, and it used to be recorded + as a plain success.""" + resp = _make_response({"should_block": True, "blocking_due_to": "pii"}) + kwargs = {"messages": [{"role": "user", "content": "my ssn is 123-45-6789"}]} + with patch.object(singulr_guardrail.async_handler, "post", return_value=resp): + updated_kwargs, result = await singulr_guardrail.async_logging_hook( + kwargs=kwargs, result=None, call_type="acompletion" + ) + assert result is None + guardrail_information = updated_kwargs["standard_logging_object"]["guardrail_information"] + assert guardrail_information[0]["guardrail_status"] == "guardrail_intervened" + + @pytest.mark.asyncio + async def test_response_block_verdict_marks_guardrail_status_intervened(self, singulr_guardrail): + """Only the response leg blocks here, so a request verdict of False must + not mask it.""" + responses = [_make_response({"should_block": False}), _make_response({"should_block": True})] + kwargs = {"messages": [{"role": "user", "content": "hi"}]} + with patch.object(singulr_guardrail.async_handler, "post", side_effect=responses): + updated_kwargs, _ = await singulr_guardrail.async_logging_hook( + kwargs=kwargs, result={"choices": []}, call_type="acompletion" + ) + guardrail_information = updated_kwargs["standard_logging_object"]["guardrail_information"] + assert guardrail_information[0]["guardrail_status"] == "guardrail_intervened" + + @pytest.mark.asyncio + async def test_block_verdict_still_reports_both_legs_and_returns_result(self, singulr_guardrail): + """A block verdict on the request leg is logging-only: it must not + short-circuit the response report or alter what the hook returns.""" + resp = _make_response({"should_block": True}) + kwargs = {"messages": [{"role": "user", "content": "hi"}]} + result = {"choices": [{"finish_reason": "stop", "message": {"content": "hello"}}]} + with patch.object(singulr_guardrail.async_handler, "post", return_value=resp) as mock_post: + returned_kwargs, returned_result = await singulr_guardrail.async_logging_hook( + kwargs=kwargs, result=result, call_type="acompletion" + ) + assert [call.kwargs["json"]["guardrail_scope"] for call in mock_post.call_args_list] == ["request", "response"] + assert returned_result is result + assert returned_kwargs is kwargs + + @pytest.mark.asyncio + async def test_api_error_marks_guardrail_status_intervened(self, singulr_guardrail): + """With block_on_error=True (the default), a transport failure while + reporting to Singulr raises internally; async_logging_hook must catch + it, mark the status accordingly, and still return (kwargs, result) + instead of propagating -- logging_only must never block the call.""" + kwargs = {"messages": [{"role": "user", "content": "hi"}]} + with patch.object( + singulr_guardrail.async_handler, + "post", + side_effect=httpx.TransportError("connection refused"), + ): + updated_kwargs, result = await singulr_guardrail.async_logging_hook( + kwargs=kwargs, result=None, call_type="acompletion" + ) + assert result is None + guardrail_information = updated_kwargs["standard_logging_object"]["guardrail_information"] + assert guardrail_information[0]["guardrail_status"] == "guardrail_intervened" + + @pytest.mark.asyncio + async def test_unexpected_exception_is_swallowed_without_recording_guardrail_information(self, singulr_guardrail): + """A non-guardrail exception (e.g. a bug in a downstream integration) + must not propagate out of the logging_only hook, and must not record + standard_logging_guardrail_information since no verdict was reached.""" + kwargs = {"messages": [{"role": "user", "content": "hi"}]} + with patch.object(singulr_guardrail.async_handler, "post", side_effect=RuntimeError("boom")): + updated_kwargs, result = await singulr_guardrail.async_logging_hook( + kwargs=kwargs, result=None, call_type="acompletion" + ) + assert result is None + assert "standard_logging_object" not in updated_kwargs + + @pytest.mark.asyncio + async def test_appends_to_existing_guardrail_information_list(self, singulr_guardrail): + resp = _make_response({"should_block": False}) + existing_entry = {"guardrail_name": "other-guardrail"} + kwargs = { + "messages": [{"role": "user", "content": "hi"}], + "standard_logging_object": {"guardrail_information": [existing_entry]}, + } + with patch.object(singulr_guardrail.async_handler, "post", return_value=resp): + updated_kwargs, _ = await singulr_guardrail.async_logging_hook( + kwargs=kwargs, result=None, call_type="acompletion" + ) + guardrail_information = updated_kwargs["standard_logging_object"]["guardrail_information"] + assert guardrail_information[0] is existing_entry + assert guardrail_information[1]["guardrail_name"] == "test-singulr" + + def test_sync_logging_hook_returns_kwargs_and_result_unchanged_when_loop_running(self, singulr_guardrail): + """logging_hook is the sync entrypoint used outside an event loop; + inside a running loop it must no-op rather than deadlock or raise.""" + import asyncio + + async def _drive(): + kwargs = {"messages": [{"role": "user", "content": "hi"}]} + return singulr_guardrail.logging_hook(kwargs=kwargs, result=None, call_type="acompletion") + + returned_kwargs, returned_result = asyncio.run(_drive()) + assert returned_result is None + assert returned_kwargs == {"messages": [{"role": "user", "content": "hi"}]} + + def test_sync_logging_hook_creates_a_new_event_loop_when_none_is_set(self, singulr_guardrail): + """A thread with no current event loop must get a fresh one instead + of raising RuntimeError out of the sync entrypoint.""" + from concurrent.futures import ThreadPoolExecutor + + resp = _make_response({"should_block": False}) + + def _run(): + kwargs = {"messages": [{"role": "user", "content": "hi"}]} + with patch.object(singulr_guardrail.async_handler, "post", return_value=resp): + return singulr_guardrail.logging_hook(kwargs=kwargs, result=None, call_type="acompletion") + + with ThreadPoolExecutor(max_workers=1) as pool: + returned_kwargs, returned_result = pool.submit(_run).result() + assert returned_result is None + guardrail_information = returned_kwargs["standard_logging_object"]["guardrail_information"] + assert guardrail_information[0]["guardrail_status"] == "success" + + def test_sync_logging_hook_swallows_unexpected_exception(self, singulr_guardrail): + """A bug surfacing from async_logging_hook itself, not just the + Singulr API call, must not propagate out of the sync entrypoint.""" + from concurrent.futures import ThreadPoolExecutor + + def _run(): + kwargs = {"messages": [{"role": "user", "content": "hi"}]} + with patch.object(singulr_guardrail, "async_logging_hook", side_effect=RuntimeError("boom")): + return singulr_guardrail.logging_hook(kwargs=kwargs, result=None, call_type="acompletion") + + with ThreadPoolExecutor(max_workers=1) as pool: + returned_kwargs, returned_result = pool.submit(_run).result() + assert returned_result is None + assert returned_kwargs == {"messages": [{"role": "user", "content": "hi"}]} + + # --------------------------------------------------------------------------- # HTTP call wiring (endpoint, timeout, headers) # --------------------------------------------------------------------------- @@ -263,7 +1129,7 @@ class TestSingulrBlockAction: class TestSingulrRequestWiring: @pytest.mark.asyncio - async def test_sends_configured_timeout(self): + async def test_sends_configured_timeout_and_calls_the_guard_endpoint(self): """litellm_params.timeout must reach the httpx call so operators can tighten or loosen the latency budget instead of being stuck with a hardcoded 30s regardless of configuration.""" @@ -279,7 +1145,9 @@ class TestSingulrRequestWiring: request_data={}, input_type="request", ) - assert mock_post.call_args.kwargs["timeout"] == 5.0 + call_kwargs = mock_post.call_args.kwargs + assert call_kwargs["timeout"] == 5.0 + assert call_kwargs["url"] == "https://api.test.singulr.ai/api/v1/ai-gateway/litellm-v2" class TestSingulrBuildHeaders: @@ -350,17 +1218,19 @@ class TestSingulrInvalidResponse: @pytest.mark.asyncio async def test_response_missing_expected_fields_block_on_error_true_raises(self): """Regression: a response body that fails SingulrGuardrailResponse - validation (e.g. should_block is a string, not a bool) must raise - GuardrailRaisedException instead of letting pydantic.ValidationError - propagate unhandled.""" + validation must raise GuardrailRaisedException instead of letting + pydantic.ValidationError propagate unhandled.""" guardrail = SingulrGuardrail( singulr_api_base="https://api.test.singulr.ai", singulr_api_key="test_token_1234", guardrail_name="test-singulr", block_on_error=True, ) - resp = _make_response({"should_block": "not-a-bool"}) - with patch.object(guardrail.async_handler, "post", return_value=resp): + mock_resp = MagicMock() + mock_resp.raise_for_status = MagicMock() + mock_resp.json.side_effect = ValueError("not valid json") + + with patch.object(guardrail.async_handler, "post", return_value=mock_resp): with pytest.raises(GuardrailRaisedException): await guardrail.apply_guardrail( inputs={"texts": ["test"]}, @@ -481,6 +1351,9 @@ class TestSingulrConfigModel: def test_ui_friendly_name(self): assert SingulrGuardrailConfigModel.ui_friendly_name() == "Singulr" + def test_get_config_model_returns_singulr_config_model(self): + assert SingulrGuardrail.get_config_model() is SingulrGuardrailConfigModel + # --------------------------------------------------------------------------- # Initializer and registry From 146085669c1f3a70e413aeb4cffb1d522706e47b Mon Sep 17 00:00:00 2001 From: Yucheng He Date: Tue, 15 Sep 2026 19:14:49 -0700 Subject: [PATCH 179/225] fix(guardrails): run Singulr logging_only through the base hook, key MCP scans off the proxy call type and type the payloads Removes the Singulr async_logging_hook and logging_hook overrides so logging_only runs through CustomGuardrail.async_logging_hook: the response scope reaches Singulr as an assistant message instead of a raw ModelResponse dump, a vendor timeout is recorded as guardrail_failed_to_respond, a request-scope block ends the scan, and the sync success callback thread makes no Singulr call. Decides MCP versus LLM by the proxy logging object's call_type (then the call_type or server-only markers in request_data), never by name, arguments or mcp_tool_name keys a client can put in a chat body. REST /mcp-rest/tools/call pre-scans reach Singulr as mcp_request and a non-mapping arguments value is forwarded as tool_arguments instead of raising. should_block is a strict bool defaulting to false so a null verdict is an invalid response that block_on_error decides; payload fields drop Any for Sequence, Mapping and AssistantMessage types; metadata carries only the keys present; docstrings and section comments removed per the repo comment policy. --- .../guardrail_hooks/singulr/singulr.py | 171 +----- .../guardrails/guardrail_hooks/singulr.py | 16 +- .../guardrail_hooks/test_singulr.py | 577 +++++++----------- 3 files changed, 256 insertions(+), 508 deletions(-) diff --git a/litellm/proxy/guardrails/guardrail_hooks/singulr/singulr.py b/litellm/proxy/guardrails/guardrail_hooks/singulr/singulr.py index e0dcdf02069..a91812bb474 100644 --- a/litellm/proxy/guardrails/guardrail_hooks/singulr/singulr.py +++ b/litellm/proxy/guardrails/guardrail_hooks/singulr/singulr.py @@ -1,8 +1,6 @@ -import asyncio import json import os from collections.abc import Mapping, Sequence -from datetime import datetime, timezone from types import MappingProxyType from typing import Any, Final from urllib.parse import urlparse @@ -37,11 +35,7 @@ from litellm.types.proxy.guardrails.guardrail_hooks.singulr import ( ToolCall, ToolCallFunction, ) -from litellm.types.utils import ( - GenericGuardrailAPIInputs, - GuardrailStatus, - StandardLoggingGuardrailInformation, -) +from litellm.types.utils import CallTypes, GenericGuardrailAPIInputs _DEFAULT_API_BASE: Final = "http://localhost:8003" _GUARD_ENDPOINT: Final = "/api/v1/ai-gateway/litellm-v2" @@ -117,13 +111,6 @@ class SingulrGuardrail(CustomGuardrail): @staticmethod def _metadata_containers(request_data: Mapping[str, Any]) -> tuple[Mapping[str, Any], ...]: - """Candidate metadata dicts to check, in priority order. - - Most call paths put metadata at the top level of ``request_data`` - (``litellm_metadata`` or ``metadata``). ``post_mcp_call`` instead hands - us ``litellm_logging_obj.model_call_details``, which nests it under - ``litellm_params`` instead, so that's checked as a fallback. - """ litellm_params: Final = request_data.get("litellm_params") or _EMPTY_MAPPING return tuple( container @@ -153,7 +140,7 @@ class SingulrGuardrail(CustomGuardrail): return None @classmethod - def _build_metadata(cls, request_data: Mapping[str, Any]) -> Mapping[str, Any] | None: + def _build_metadata(cls, request_data: Mapping[str, Any]) -> Mapping[str, str] | None: fields: Final = ( "user_api_key_alias", "user_api_key_user_id", @@ -279,13 +266,30 @@ class SingulrGuardrail(CustomGuardrail): ) return inputs + @staticmethod + def _mcp_tool_name(request_data: Mapping[str, Any]) -> str | None: + return request_data.get("mcp_tool_name") or request_data.get("name") + + @staticmethod + def _mcp_arguments(request_data: Mapping[str, Any]) -> object: + arguments: Final = request_data.get("mcp_arguments") + return arguments if arguments is not None else request_data.get("arguments") + + @staticmethod + def _is_mcp_call(request_data: Mapping[str, Any], logging_obj: LiteLLMLoggingObj | None) -> bool: + call_type: Final = logging_obj.call_type if logging_obj is not None else request_data.get("call_type") + if call_type is not None: + return call_type == CallTypes.call_mcp_tool.value + model: Final = request_data.get("model") + return "mcp_tool_name" in request_data or (isinstance(model, str) and model.startswith(_MCP_MODEL_PREFIX)) + async def _apply_guardrail_on_mcp_request(self, request_data: Mapping[str, Any]) -> None: metadata: Final = self._build_metadata(request_data=request_data) singulr_mcp_obj = SingulrMcpGuardrailPayload( guardrail_scope="mcp_request", - tool_name=request_data.get("mcp_tool_name"), - tool_arguments=request_data.get("mcp_arguments"), + tool_name=self._mcp_tool_name(request_data), + tool_arguments=self._mcp_arguments(request_data), mcp_server_name=request_data.get("mcp_server_name"), metadata=metadata, ) @@ -398,134 +402,6 @@ class SingulrGuardrail(CustomGuardrail): ) return inputs - def _logging_only_response_payload( - self, - kwargs: Mapping[str, Any], - result: Any, # noqa: ANN401 # result can be any callback shape - ) -> Mapping[str, Any]: - metadata: Final = self._build_metadata(request_data=kwargs) - try: - return SingulrGuardrailPayload( - correlation_id=kwargs.get("litellm_call_id"), - model_name=kwargs.get("model"), - guardrail_scope="response", - response=result, - metadata=metadata, - ).model_dump(mode="json") - except Exception as exc: # noqa: BLE001 # result can be any callback shape; fall back to a stringified report - verbose_proxy_logger.debug("Singulr: could not JSON-serialize response, falling back: %s", exc) - return { # mutable-ok: short-lived JSON payload dict - "correlation_id": kwargs.get("litellm_call_id"), - "guardrail_scope": "response", - "response": str(result), - "metadata": metadata, - } - - async def _report_logging_only( - self, - kwargs: Mapping[str, Any], - result: Any, # noqa: ANN401 # result can be any callback shape - ) -> tuple[SingulrGuardrailResponse | None, ...]: - messages: Final = kwargs.get("messages") or () - request_verdict: Final = ( - await self._call_api( - SingulrGuardrailPayload( - correlation_id=kwargs.get("litellm_call_id"), - model_name=kwargs.get("model"), - guardrail_scope="request", - messages=messages, - metadata=self._build_metadata(request_data=kwargs), - ).model_dump(mode="json") - ) - if messages - else None - ) - response_verdict: Final = ( - await self._call_api(self._logging_only_response_payload(kwargs=kwargs, result=result)) if result else None - ) - return (request_verdict, response_verdict) - - async def _logging_only_guardrail_status( - self, - kwargs: Mapping[str, Any], - result: Any, # noqa: ANN401 # result can be any callback shape - ) -> GuardrailStatus | None: - """``None`` means no verdict was reached, so nothing should be logged.""" - try: - verdicts: Final = await self._report_logging_only(kwargs=kwargs, result=result) - except GuardrailRaisedException: - return "guardrail_intervened" - except Exception as exc: # noqa: BLE001 # logging_only must never break the request - verbose_proxy_logger.debug("Singulr: logging_only hook swallowed exception: %s", exc) - return None - if any(verdict is not None and verdict.should_block for verdict in verdicts): - return "guardrail_intervened" - return "success" - - @staticmethod - def _is_mcp_call(kwargs: Mapping[str, Any]) -> bool: - model: Final = kwargs.get("model") - return isinstance(model, str) and model.startswith(_MCP_MODEL_PREFIX) - - async def async_logging_hook( - self, - kwargs: dict, # mutable-ok: matches CustomLogger override; mutated via setdefault - result: Any, # noqa: ANN401 # required by CustomLogger.async_logging_hook override signature - call_type: str, - ) -> tuple[dict, Any]: - if self._is_mcp_call(kwargs): - verbose_proxy_logger.debug("Singulr: skipping logging_only report for MCP call %s", kwargs.get("model")) - return kwargs, result - - start_time: Final = datetime.now(timezone.utc) - guardrail_status: Final = await self._logging_only_guardrail_status(kwargs=kwargs, result=result) - if guardrail_status is None: - return kwargs, result - - end_time: Final = datetime.now(timezone.utc) - slg: Final = StandardLoggingGuardrailInformation( - guardrail_name=self.guardrail_name or "singulr", - guardrail_mode=GuardrailEventHooks.logging_only, - guardrail_status=guardrail_status, - start_time=start_time.timestamp(), - end_time=end_time.timestamp(), - duration=(end_time - start_time).total_seconds(), - masked_entity_count=None, - ) - standard_logging_object: Final = kwargs.setdefault( - "standard_logging_object", - {}, # mutable-ok: shared, mutated accumulator - ) - existing = standard_logging_object.get("guardrail_information") - if isinstance(existing, list): - existing.append(slg) - else: - standard_logging_object["guardrail_information"] = [slg] # mutable-ok: shared accumulator - - return kwargs, result - - def logging_hook( - self, - kwargs: dict, # mutable-ok: required by CustomLogger.logging_hook override signature - result: Any, # noqa: ANN401 # required by CustomLogger.logging_hook override signature - call_type: str, - ) -> tuple[dict, Any]: - try: - try: - loop = asyncio.get_event_loop() - except RuntimeError: - loop = asyncio.new_event_loop() - asyncio.set_event_loop(loop) - if loop.is_running(): - verbose_proxy_logger.debug( - "Singulr: sync logging_hook called from a running loop; skipping logging_only report" - ) - return kwargs, result - loop.run_until_complete(self.async_logging_hook(kwargs=kwargs, result=result, call_type=call_type)) - except Exception as exc: # noqa: BLE001 # logging_only must never break the request - verbose_proxy_logger.debug("Singulr: sync logging_hook swallowed exception: %s", exc) - return kwargs, result - @log_guardrail_information async def apply_guardrail( self, @@ -544,15 +420,16 @@ class SingulrGuardrail(CustomGuardrail): len(structured_messages), ) + is_mcp_call: Final = self._is_mcp_call(request_data, logging_obj) if input_type == "request": - if request_data.get("mcp_tool_name"): + if is_mcp_call: await self._apply_guardrail_on_mcp_request(request_data=request_data) return inputs return await self._apply_guardrail_on_request( inputs=inputs, texts=texts, structured_messages=structured_messages, request_data=request_data ) elif input_type == "response": - if request_data.get("call_type") == "call_mcp_tool": + if is_mcp_call: return await self._apply_guardrail_on_mcp_response( inputs=inputs, texts=texts, request_data=request_data ) diff --git a/litellm/types/proxy/guardrails/guardrail_hooks/singulr.py b/litellm/types/proxy/guardrails/guardrail_hooks/singulr.py index fd349b44e0c..ea1e6238181 100644 --- a/litellm/types/proxy/guardrails/guardrail_hooks/singulr.py +++ b/litellm/types/proxy/guardrails/guardrail_hooks/singulr.py @@ -1,5 +1,5 @@ from collections.abc import Mapping, Sequence -from typing import Any, Literal +from typing import Literal from pydantic import BaseModel, Field @@ -33,27 +33,27 @@ class SingulrGuardrailPayload(BaseModel): model_name: str | None = None model_provider_name: str | None = None guardrail_scope: str | None = None - messages: Sequence[Any] | None = None + messages: Sequence[Mapping[str, object]] | None = None images: Sequence[str] | None = None - tools: Sequence[Any] | None = None # pyright: ignore[reportExplicitAny] # forwards caller-supplied OpenAI tool defs verbatim - response: Any = None # pyright: ignore[reportExplicitAny] # logging_only reports raw litellm callback results (ModelResponse, EmbeddingResponse, etc.) - metadata: Mapping[str, Any] | None = None + tools: Sequence[Mapping[str, object]] | None = None + response: AssistantMessage | None = None + metadata: Mapping[str, str] | None = None class SingulrMcpGuardrailPayload(BaseModel): model_name: str | None = None guardrail_scope: str | None = None tool_name: str | None = None - tool_arguments: Mapping[str, Any] | None = None + tool_arguments: object = None mcp_server_name: str | None = None tool_result: Sequence[str] | None = None - metadata: Mapping[str, Any] | None = None + metadata: Mapping[str, str] | None = None class SingulrGuardrailResponse(BaseModel): """Response returned by the Singulr guardrail API.""" - should_block: bool | None = None + should_block: bool = False blocking_due_to: str | None = None diff --git a/tests/test_litellm/proxy/guardrails/guardrail_hooks/test_singulr.py b/tests/test_litellm/proxy/guardrails/guardrail_hooks/test_singulr.py index 7a228a7c3fb..b775d399b86 100644 --- a/tests/test_litellm/proxy/guardrails/guardrail_hooks/test_singulr.py +++ b/tests/test_litellm/proxy/guardrails/guardrail_hooks/test_singulr.py @@ -4,6 +4,7 @@ from unittest.mock import MagicMock, patch import httpx import pytest +import litellm from litellm.exceptions import GuardrailRaisedException from litellm.proxy._types import LitellmUserRoles, UserAPIKeyAuth from litellm.proxy.guardrails.guardrail_hooks.singulr.singulr import SingulrGuardrail @@ -14,12 +15,8 @@ from litellm.types.proxy.guardrails.guardrail_hooks.singulr import ( from litellm.types.utils import ModelResponse -# --------------------------------------------------------------------------- -# Fixtures -# --------------------------------------------------------------------------- @pytest.fixture def singulr_guardrail(): - """Create a SingulrGuardrail instance with test credentials.""" return SingulrGuardrail( singulr_api_base="https://api.test.singulr.ai", singulr_api_key="test_token_1234", @@ -31,8 +28,26 @@ def singulr_guardrail(): ) +@pytest.fixture +def logging_only_guardrail(): + return SingulrGuardrail( + singulr_api_base="https://api.test.singulr.ai", + singulr_api_key="test_token_1234", + singulr_guardrail_id="test_guardrail_id", + singulr_application_id="test_enforcement_entity", + guardrail_name="test-singulr", + event_hook="logging_only", + default_on=True, + ) + + +def _logging_obj(call_type: str) -> MagicMock: + logging_obj = MagicMock() + logging_obj.call_type = call_type + return logging_obj + + def _make_response(body: dict) -> MagicMock: - """Build a mock httpx response with the given JSON body.""" mock = MagicMock() mock.json.return_value = body mock.raise_for_status = MagicMock() @@ -40,11 +55,6 @@ def _make_response(body: dict) -> MagicMock: return mock -# --------------------------------------------------------------------------- -# Configuration -# --------------------------------------------------------------------------- - - class TestSingulrConfiguration: def test_init_with_explicit_credentials(self): guardrail = SingulrGuardrail( @@ -59,10 +69,6 @@ class TestSingulrConfiguration: assert guardrail.singulr_application_id == "entity123" def test_api_base_strips_surrounding_whitespace(self): - """Regression: a UI-saved api_base with a trailing space - (e.g. "https://custom.api.local ") broke urlparse's port parsing and - made every guardrail call fail with a connection error, even though - the configured host was reachable.""" guardrail = SingulrGuardrail( singulr_api_key="test_key", singulr_api_base=" https://custom.api.local ", @@ -74,8 +80,6 @@ class TestSingulrConfiguration: assert guardrail.singulr_api_base == "https://custom.api.local" def test_non_local_http_api_base_raises(self): - """Guardrail payloads carry the API token and full conversation - content, so a non-local endpoint must use HTTPS.""" with pytest.raises(ValueError, match="HTTPS"): SingulrGuardrail(singulr_api_key="test_key", singulr_api_base="http://guardrails.singulr.ai") @@ -106,11 +110,6 @@ class TestSingulrConfiguration: ] -# --------------------------------------------------------------------------- -# Payload construction for real proxy requests (request_data present) -# --------------------------------------------------------------------------- - - class TestSingulrRequestPayload: @pytest.mark.asyncio async def test_model_and_messages_are_forwarded(self, singulr_guardrail): @@ -130,9 +129,6 @@ class TestSingulrRequestPayload: @pytest.mark.asyncio async def test_structured_messages_are_forwarded_verbatim(self, singulr_guardrail): - """When structured_messages are provided (e.g. system + user turns), - they must be sent as-is instead of being flattened into single - user-role messages built from texts.""" resp = _make_response({"should_block": False}) structured_messages = [ {"role": "system", "content": "Be concise."}, @@ -180,8 +176,6 @@ class TestSingulrRequestPayload: ids=["tools_alone", "images_alone"], ) async def test_tools_or_images_alone_still_trigger_the_api_call(self, singulr_guardrail, extra_inputs): - """Regression: a request with only tool definitions or only images and - no text must still be checked, not skipped for lack of a message.""" resp = _make_response({"should_block": False}) with patch.object(singulr_guardrail.async_handler, "post", return_value=resp) as mock_post: await singulr_guardrail.apply_guardrail( @@ -195,9 +189,6 @@ class TestSingulrRequestPayload: @pytest.mark.asyncio async def test_tools_are_forwarded(self, singulr_guardrail): - """Regression: tool/function definitions are client-controlled and can - carry prompt-injection content, so they must reach Singulr for - inspection instead of only messages and images.""" resp = _make_response({"should_block": False}) tools = [ { @@ -216,10 +207,6 @@ class TestSingulrRequestPayload: @pytest.mark.asyncio async def test_responses_api_mcp_tools_are_forwarded(self, singulr_guardrail): - """Regression: Responses API tools (e.g. {"type": "mcp", "server_label": ...}) - have no "function" key, unlike Chat Completions tools. SingulrGuardrailPayload - rejected them with a pydantic ValidationError, turning every Responses API - request carrying an MCP tool into a 500.""" resp = _make_response({"should_block": False}) tools = [ { @@ -240,8 +227,6 @@ class TestSingulrRequestPayload: @pytest.mark.asyncio async def test_user_api_key_alias_is_forwarded_in_metadata(self, singulr_guardrail): - """Regression: the alias must be sent as {"user_api_key_alias": }, - not as a dict whose key is the alias value itself.""" resp = _make_response({"should_block": False}) request_data = {"litellm_metadata": {"user_api_key_alias": "my-key-alias"}} with patch.object(singulr_guardrail.async_handler, "post", return_value=resp) as mock_post: @@ -420,8 +405,6 @@ class TestSingulrRequestPayload: @pytest.mark.asyncio async def test_no_key_alias_available_sends_no_metadata(self, singulr_guardrail): - """Regression: with no alias found, metadata must be omitted (None), - not a {None: None} dict that fails payload validation.""" resp = _make_response({"should_block": False}) with patch.object(singulr_guardrail.async_handler, "post", return_value=resp) as mock_post: await singulr_guardrail.apply_guardrail( @@ -433,11 +416,6 @@ class TestSingulrRequestPayload: assert sent_payload["metadata"] is None -# --------------------------------------------------------------------------- -# Payload construction for responses -# --------------------------------------------------------------------------- - - class TestSingulrResponsePayload: @pytest.mark.asyncio async def test_assistant_text_and_tool_calls_are_forwarded(self, singulr_guardrail): @@ -506,9 +484,6 @@ class TestSingulrResponsePayload: async def test_tool_call_type_other_than_function_is_still_scanned( self, singulr_guardrail, raw_type, expected_type ): - """Regression: a tool call whose type is absent or isn't "function" used - to raise a pydantic ValidationError while building the payload, which - escaped apply_guardrail as a 500 instead of reaching the scan at all.""" resp = _make_response({"should_block": False}) tool_call = {"id": "call_1", "function": {"name": "get_current_time", "arguments": "{}"}} inputs = { @@ -523,8 +498,6 @@ class TestSingulrResponsePayload: @pytest.mark.asyncio async def test_non_string_tool_call_arguments_are_serialized(self, singulr_guardrail): - """Some providers hand back already-parsed arguments; they must be - scanned as JSON text rather than crashing the payload build.""" resp = _make_response({"should_block": False}) inputs = { "texts": [], @@ -539,7 +512,6 @@ class TestSingulrResponsePayload: @pytest.mark.asyncio async def test_block_verdict_still_raises_for_a_non_function_tool_call(self, singulr_guardrail): - """The point of scanning these calls: the verdict must still be enforced.""" resp = _make_response({"should_block": True, "blocking_due_to": "dangerous_tool"}) inputs = { "texts": [], @@ -551,11 +523,6 @@ class TestSingulrResponsePayload: assert "dangerous_tool" in str(exc_info.value) -# --------------------------------------------------------------------------- -# Allow / block decisions -# --------------------------------------------------------------------------- - - class TestSingulrAllowAction: @pytest.mark.asyncio @pytest.mark.parametrize( @@ -564,8 +531,6 @@ class TestSingulrAllowAction: ids=["should_block_false", "should_block_omitted"], ) async def test_should_block_falsy_returns_inputs_unchanged_on_request(self, singulr_guardrail, guard_response): - """should_block is optional on the wire; a response that omits it - entirely must be treated as allow, not block.""" resp = _make_response(guard_response) inputs = {"texts": ["How do I reset my password?"]} with patch.object(singulr_guardrail.async_handler, "post", return_value=resp): @@ -605,6 +570,34 @@ class TestSingulrAllowAction: ) assert result is inputs + @pytest.mark.asyncio + async def test_explicit_null_verdict_fails_closed_by_default(self, singulr_guardrail): + resp = _make_response({"should_block": None}) + with patch.object(singulr_guardrail.async_handler, "post", return_value=resp): + with pytest.raises(GuardrailRaisedException, match="invalid response"): + await singulr_guardrail.apply_guardrail( + inputs={"texts": ["hi"]}, + request_data={"model": "gpt-4o"}, + input_type="request", + ) + + @pytest.mark.asyncio + async def test_explicit_null_verdict_fails_open_when_block_on_error_false(self): + guardrail = SingulrGuardrail( + singulr_api_base="https://api.test.singulr.ai", + singulr_api_key="test_token_1234", + guardrail_name="test-singulr", + block_on_error=False, + ) + resp = _make_response({"should_block": None}) + inputs = {"texts": ["hi"]} + with patch.object(guardrail.async_handler, "post", return_value=resp): + assert await guardrail._call_api({"guardrail_scope": "request"}) is None + result = await guardrail.apply_guardrail( + inputs=inputs, request_data={"model": "gpt-4o"}, input_type="request" + ) + assert result is inputs + class TestSingulrBlockAction: @pytest.mark.asyncio @@ -622,11 +615,6 @@ class TestSingulrBlockAction: @pytest.mark.asyncio async def test_should_block_true_raises_on_response(self, singulr_guardrail): - """Regression: apply_guardrail's response path compared - should_block (a bool) against the string "block", which is always - False, so a should_block=True response never blocked the assistant's - reply. It must raise on any truthy should_block, matching the - request path.""" resp = _make_response({"should_block": True, "blocking_due_to": "Toxic content detected"}) with patch.object(singulr_guardrail.async_handler, "post", return_value=resp): with pytest.raises(GuardrailRaisedException) as exc_info: @@ -650,11 +638,6 @@ class TestSingulrBlockAction: ) -# --------------------------------------------------------------------------- -# MCP tool call guardrail (pre_mcp_call / post_mcp_call) -# --------------------------------------------------------------------------- - - class TestSingulrMcpRequest: @pytest.mark.asyncio async def test_mcp_tool_name_routes_to_mcp_request_payload(self, singulr_guardrail): @@ -707,6 +690,97 @@ class TestSingulrMcpRequest: ) assert result == {"texts": []} + @pytest.mark.asyncio + async def test_mcp_rest_body_shape_routes_to_mcp_request_payload(self, singulr_guardrail): + resp = _make_response({"should_block": False}) + request_data = {"name": "echo", "arguments": {"text": "my ssn is 123-45-6789"}, "server_id": "srv-1"} + with patch.object(singulr_guardrail.async_handler, "post", return_value=resp) as mock_post: + await singulr_guardrail.apply_guardrail( + inputs={"texts": ["my ssn is 123-45-6789"], "tools": [{"type": "function"}]}, + request_data=request_data, + input_type="request", + logging_obj=_logging_obj("call_mcp_tool"), + ) + sent_payload = mock_post.call_args.kwargs["json"] + assert sent_payload["guardrail_scope"] == "mcp_request" + assert sent_payload["tool_name"] == "echo" + assert sent_payload["tool_arguments"] == {"text": "my ssn is 123-45-6789"} + assert "messages" not in sent_payload + + @pytest.mark.asyncio + async def test_mcp_rest_body_without_arguments_still_routes_to_mcp_request(self, singulr_guardrail): + resp = _make_response({"should_block": False}) + with patch.object(singulr_guardrail.async_handler, "post", return_value=resp) as mock_post: + await singulr_guardrail.apply_guardrail( + inputs={"texts": [], "tools": [{"type": "function"}]}, + request_data={"name": "echo", "server_id": "srv-1"}, + input_type="request", + logging_obj=_logging_obj("call_mcp_tool"), + ) + sent_payload = mock_post.call_args.kwargs["json"] + assert sent_payload["guardrail_scope"] == "mcp_request" + assert sent_payload["tool_name"] == "echo" + assert sent_payload["tool_arguments"] is None + + @pytest.mark.asyncio + async def test_non_mapping_tool_arguments_are_forwarded_verbatim(self, singulr_guardrail): + resp = _make_response({"should_block": False}) + with patch.object(singulr_guardrail.async_handler, "post", return_value=resp) as mock_post: + await singulr_guardrail.apply_guardrail( + inputs={"texts": ["raw text"], "tools": [{"type": "function"}]}, + request_data={"name": "echo", "arguments": "raw text", "server_id": "srv-1"}, + input_type="request", + logging_obj=_logging_obj("call_mcp_tool"), + ) + sent_payload = mock_post.call_args.kwargs["json"] + assert sent_payload["guardrail_scope"] == "mcp_request" + assert sent_payload["tool_arguments"] == "raw text" + + @pytest.mark.asyncio + async def test_llm_request_body_keys_cannot_reroute_the_scan_to_mcp(self, singulr_guardrail): + resp = _make_response({"should_block": False}) + request_data = { + "model": "gpt-4o", + "messages": [{"role": "user", "content": "my ssn is 123-45-6789"}], + "name": "x", + "arguments": {}, + "mcp_tool_name": "x", + "call_type": "call_mcp_tool", + } + with patch.object(singulr_guardrail.async_handler, "post", return_value=resp) as mock_post: + await singulr_guardrail.apply_guardrail( + inputs={ + "texts": ["my ssn is 123-45-6789"], + "structured_messages": [{"role": "user", "content": "my ssn is 123-45-6789"}], + }, + request_data=request_data, + input_type="request", + logging_obj=_logging_obj("acompletion"), + ) + sent_payload = mock_post.call_args.kwargs["json"] + assert sent_payload["guardrail_scope"] == "request" + assert [m["content"] for m in sent_payload["messages"]] == ["my ssn is 123-45-6789"] + + @pytest.mark.asyncio + async def test_llm_response_with_spoofed_mcp_keys_still_scans_the_tool_calls(self, singulr_guardrail): + resp = _make_response({"should_block": False}) + request_data = {"model": "gpt-4o", "messages": [], "name": "x", "arguments": {}, "mcp_tool_name": "x"} + tool_call = { + "id": "call_1", + "type": "function", + "function": {"name": "transfer_funds", "arguments": '{"amount": 5000}'}, + } + with patch.object(singulr_guardrail.async_handler, "post", return_value=resp) as mock_post: + await singulr_guardrail.apply_guardrail( + inputs={"texts": [], "tool_calls": [tool_call]}, + request_data=request_data, + input_type="response", + logging_obj=_logging_obj("acompletion"), + ) + sent_payload = mock_post.call_args.kwargs["json"] + assert sent_payload["guardrail_scope"] == "response" + assert sent_payload["response"]["tool_calls"][0]["function"]["name"] == "transfer_funds" + class TestSingulrMcpResponse: @pytest.mark.asyncio @@ -756,8 +830,6 @@ class TestSingulrMcpResponse: @pytest.mark.asyncio async def test_mcp_response_resolves_metadata_from_nested_litellm_params(self, singulr_guardrail): - """post_mcp_call hands apply_guardrail litellm_logging_obj.model_call_details, - which nests metadata under litellm_params instead of at the top level.""" resp = _make_response({"should_block": False}) auth = UserAPIKeyAuth(user_role=LitellmUserRoles.INTERNAL_USER_VIEW_ONLY) request_data = { @@ -830,10 +902,26 @@ class TestSingulrMcpResponse: ) assert result is inputs - -# --------------------------------------------------------------------------- -# apply_guardrail dispatch (request vs response vs unknown input_type) -# --------------------------------------------------------------------------- + @pytest.mark.asyncio + @pytest.mark.parametrize( + ("request_data", "logging_obj"), + [ + ({"call_type": "call_mcp_tool", "model": "MCP: echo"}, None), + ({"model": "MCP: echo"}, None), + ({"name": "echo", "arguments": {"text": "hi"}}, _logging_obj("call_mcp_tool")), + ], + ids=["post_mcp_call_model_call_details", "logging_only_scratch_request", "rest_pre_call_logger"], + ) + async def test_mcp_response_is_detected_from_each_producer(self, singulr_guardrail, request_data, logging_obj): + resp = _make_response({"should_block": False}) + with patch.object(singulr_guardrail.async_handler, "post", return_value=resp) as mock_post: + await singulr_guardrail.apply_guardrail( + inputs={"texts": ["tool output"]}, + request_data=request_data, + input_type="response", + logging_obj=logging_obj, + ) + assert mock_post.call_args.kwargs["json"]["guardrail_scope"] == "mcp_response" class TestSingulrApplyGuardrailDispatch: @@ -850,289 +938,109 @@ class TestSingulrApplyGuardrailDispatch: assert result is inputs -# --------------------------------------------------------------------------- -# logging_only hook -# --------------------------------------------------------------------------- - - class TestSingulrLoggingHook: - @pytest.mark.asyncio - async def test_forwards_request_messages_and_response_text(self, singulr_guardrail): - resp = _make_response({"should_block": False}) - kwargs = {"messages": [{"role": "user", "content": "hi"}], "model": "gpt-4o", "litellm_call_id": "call-1"} - result = {"choices": [{"finish_reason": "stop", "message": {"content": "hello there"}}]} - with patch.object(singulr_guardrail.async_handler, "post", return_value=resp) as mock_post: - await singulr_guardrail.async_logging_hook(kwargs=kwargs, result=result, call_type="acompletion") - - request_payload = mock_post.call_args_list[0].kwargs["json"] - response_payload = mock_post.call_args_list[1].kwargs["json"] - assert request_payload["guardrail_scope"] == "request" - assert request_payload["messages"] == kwargs["messages"] - assert response_payload["guardrail_scope"] == "response" - assert response_payload["response"] == result - - @pytest.mark.asyncio - async def test_forwards_user_metadata_in_both_request_and_response_payloads(self, singulr_guardrail): - resp = _make_response({"should_block": False}) + @staticmethod + def _logged_call(**overrides): kwargs = { - "messages": [{"role": "user", "content": "hi"}], "model": "gpt-4o", + "messages": [{"role": "user", "content": "hi"}], "litellm_call_id": "call-1", - "litellm_metadata": {"user_api_key_alias": "my-key-alias", "user_api_key_org_id": "org-123"}, + "litellm_params": {"metadata": {"user_api_key_alias": "my-key-alias", "user_api_key_org_id": "org-123"}}, + "standard_logging_object": {"guardrail_information": []}, } - result = {"choices": [{"finish_reason": "stop", "message": {"content": "hello there"}}]} - with patch.object(singulr_guardrail.async_handler, "post", return_value=resp) as mock_post: - await singulr_guardrail.async_logging_hook(kwargs=kwargs, result=result, call_type="acompletion") - - request_payload = mock_post.call_args_list[0].kwargs["json"] - response_payload = mock_post.call_args_list[1].kwargs["json"] - expected_metadata = {"user_api_key_alias": "my-key-alias", "user_api_key_org_id": "org-123"} - assert request_payload["metadata"] == expected_metadata - assert response_payload["metadata"] == expected_metadata + return {**kwargs, **overrides} @pytest.mark.asyncio - async def test_forwards_a_real_model_response_without_swallowing_it(self, singulr_guardrail): - """Regression: a normal completion callback passes a ModelResponse, not a - dict. The response payload must carry its actual serialized content instead - of silently dropping it because ModelResponse isn't a Mapping.""" + async def test_scans_request_then_response_as_an_assistant_message(self, logging_only_guardrail): resp = _make_response({"should_block": False}) result = ModelResponse( choices=[{"index": 0, "finish_reason": "stop", "message": {"role": "assistant", "content": "hello there"}}] ) - with patch.object(singulr_guardrail.async_handler, "post", return_value=resp) as mock_post: - await singulr_guardrail.async_logging_hook(kwargs={}, result=result, call_type="acompletion") - - response_payload = mock_post.call_args.kwargs["json"] - assert response_payload["guardrail_scope"] == "response" - assert response_payload["response"]["choices"][0]["message"]["content"] == "hello there" - - @pytest.mark.asyncio - async def test_non_serializable_result_falls_back_to_string_report(self, singulr_guardrail): - """A result that pydantic can't serialize to JSON must still get reported, - as a stringified fallback, instead of raising out of the logging_only hook.""" - resp = _make_response({"should_block": False}) - - class Unserializable: - def __repr__(self) -> str: - return "" - - kwargs = {"litellm_metadata": {"user_api_key_alias": "my-key-alias"}} - with patch.object(singulr_guardrail.async_handler, "post", return_value=resp) as mock_post: - await singulr_guardrail.async_logging_hook(kwargs=kwargs, result=Unserializable(), call_type="acompletion") - - response_payload = mock_post.call_args.kwargs["json"] - assert response_payload["response"] == "" - assert response_payload["metadata"] == {"user_api_key_alias": "my-key-alias"} - - @pytest.mark.asyncio - async def test_no_messages_and_no_result_skips_both_api_calls(self, singulr_guardrail): - with patch.object(singulr_guardrail.async_handler, "post") as mock_post: - returned_kwargs, returned_result = await singulr_guardrail.async_logging_hook( - kwargs={}, result=None, call_type="acompletion" + with patch.object(logging_only_guardrail.async_handler, "post", return_value=resp) as mock_post: + updated_kwargs, returned = await logging_only_guardrail.async_logging_hook( + kwargs=self._logged_call(), result=result, call_type="acompletion" ) - mock_post.assert_not_called() - assert returned_result is None - guardrail_information = returned_kwargs["standard_logging_object"]["guardrail_information"] - assert guardrail_information[0]["guardrail_status"] == "success" + + assert returned is result + scopes = [call.kwargs["json"]["guardrail_scope"] for call in mock_post.call_args_list] + assert scopes == ["request", "response"] + request_payload = mock_post.call_args_list[0].kwargs["json"] + response_payload = mock_post.call_args_list[1].kwargs["json"] + assert request_payload["messages"] == [{"role": "user", "content": "hi"}] + assert request_payload["correlation_id"] == "call-1" + assert response_payload["response"] == {"role": "assistant", "content": "hello there", "tool_calls": []} + expected_metadata = {"user_api_key_alias": "my-key-alias", "user_api_key_org_id": "org-123"} + assert request_payload["metadata"] == expected_metadata + assert response_payload["metadata"] == expected_metadata + statuses = [ + entry["guardrail_status"] for entry in updated_kwargs["standard_logging_object"]["guardrail_information"] + ] + assert statuses == ["success", "success"] @pytest.mark.asyncio - async def test_mcp_tool_call_is_not_reported(self, singulr_guardrail): - """MCP traffic is already covered by the pre/post_mcp_call hooks, which send - the richer mcp_request/mcp_response payloads. The logging_only hook sees the - same call again with model="MCP: " and must skip it so Singulr - doesn't get a duplicate, lower-fidelity report of every tool call.""" - kwargs = {"model": "MCP: get_weather", "messages": [{"role": "user", "content": "hi"}]} - with patch.object(singulr_guardrail.async_handler, "post") as mock_post: - updated_kwargs, result = await singulr_guardrail.async_logging_hook( - kwargs=kwargs, result={"choices": []}, call_type="acompletion" - ) - mock_post.assert_not_called() - assert "standard_logging_object" not in updated_kwargs - assert result == {"choices": []} - - @pytest.mark.asyncio - async def test_mcp_list_tools_call_is_not_reported(self, singulr_guardrail): - kwargs = {"model": "MCP: list_tools", "messages": [{"role": "user", "content": "hi"}]} - with patch.object(singulr_guardrail.async_handler, "post") as mock_post: - await singulr_guardrail.async_logging_hook(kwargs=kwargs, result=None, call_type="acompletion") - mock_post.assert_not_called() - - @pytest.mark.asyncio - async def test_non_mcp_model_is_still_reported(self, singulr_guardrail): - """Guard against the skip being too broad: a normal LLM call whose model - merely mentions MCP later in the name must still be reported.""" - resp = _make_response({"should_block": False}) - kwargs = {"model": "gpt-4o-mcp", "messages": [{"role": "user", "content": "hi"}]} - with patch.object(singulr_guardrail.async_handler, "post", return_value=resp) as mock_post: - updated_kwargs, _ = await singulr_guardrail.async_logging_hook( - kwargs=kwargs, result=None, call_type="acompletion" - ) - mock_post.assert_called_once() - assert updated_kwargs["standard_logging_object"]["guardrail_information"][0]["guardrail_status"] == "success" - - @pytest.mark.asyncio - async def test_records_standard_logging_guardrail_information(self, singulr_guardrail): - resp = _make_response({"should_block": False}) - kwargs = {"messages": [{"role": "user", "content": "hi"}]} - with patch.object(singulr_guardrail.async_handler, "post", return_value=resp): - updated_kwargs, _ = await singulr_guardrail.async_logging_hook( - kwargs=kwargs, result=None, call_type="acompletion" - ) - guardrail_information = updated_kwargs["standard_logging_object"]["guardrail_information"] - assert len(guardrail_information) == 1 - assert guardrail_information[0]["guardrail_name"] == "test-singulr" - assert guardrail_information[0]["guardrail_status"] == "success" - - @pytest.mark.asyncio - async def test_request_block_verdict_marks_guardrail_status_intervened(self, singulr_guardrail): - """Regression: a successful HTTP call whose body says should_block is a - real intervention. logging_only can't fail the request, so the verdict - only ever surfaces through guardrail_status, and it used to be recorded - as a plain success.""" + async def test_block_verdict_is_recorded_as_intervened_without_failing_the_call(self, logging_only_guardrail): resp = _make_response({"should_block": True, "blocking_due_to": "pii"}) - kwargs = {"messages": [{"role": "user", "content": "my ssn is 123-45-6789"}]} - with patch.object(singulr_guardrail.async_handler, "post", return_value=resp): - updated_kwargs, result = await singulr_guardrail.async_logging_hook( - kwargs=kwargs, result=None, call_type="acompletion" + with patch.object(logging_only_guardrail.async_handler, "post", return_value=resp): + updated_kwargs, returned = await logging_only_guardrail.async_logging_hook( + kwargs=self._logged_call(messages=[{"role": "user", "content": "my ssn is 123-45-6789"}]), + result=None, + call_type="acompletion", ) - assert result is None - guardrail_information = updated_kwargs["standard_logging_object"]["guardrail_information"] - assert guardrail_information[0]["guardrail_status"] == "guardrail_intervened" + assert returned is None + entries = updated_kwargs["standard_logging_object"]["guardrail_information"] + assert entries[0]["guardrail_status"] == "guardrail_intervened" + assert entries[0]["guardrail_mode"] == "logging_only" + assert "Blocking due to pii" in str(entries[0]["guardrail_response"]) @pytest.mark.asyncio - async def test_response_block_verdict_marks_guardrail_status_intervened(self, singulr_guardrail): - """Only the response leg blocks here, so a request verdict of False must - not mask it.""" - responses = [_make_response({"should_block": False}), _make_response({"should_block": True})] - kwargs = {"messages": [{"role": "user", "content": "hi"}]} - with patch.object(singulr_guardrail.async_handler, "post", side_effect=responses): - updated_kwargs, _ = await singulr_guardrail.async_logging_hook( - kwargs=kwargs, result={"choices": []}, call_type="acompletion" + async def test_vendor_timeout_is_recorded_as_failed_to_respond(self, logging_only_guardrail): + timeout = litellm.Timeout("Singulr timed out", model="gpt-4o", llm_provider="singulr") + with patch.object(logging_only_guardrail.async_handler, "post", side_effect=timeout): + updated_kwargs, returned = await logging_only_guardrail.async_logging_hook( + kwargs=self._logged_call(), result=None, call_type="acompletion" ) - guardrail_information = updated_kwargs["standard_logging_object"]["guardrail_information"] - assert guardrail_information[0]["guardrail_status"] == "guardrail_intervened" + assert returned is None + entries = updated_kwargs["standard_logging_object"]["guardrail_information"] + assert [entry["guardrail_status"] for entry in entries] == ["guardrail_failed_to_respond"] + assert "timed out" in str(entries[0]["guardrail_response"]) @pytest.mark.asyncio - async def test_block_verdict_still_reports_both_legs_and_returns_result(self, singulr_guardrail): - """A block verdict on the request leg is logging-only: it must not - short-circuit the response report or alter what the hook returns.""" - resp = _make_response({"should_block": True}) - kwargs = {"messages": [{"role": "user", "content": "hi"}]} - result = {"choices": [{"finish_reason": "stop", "message": {"content": "hello"}}]} - with patch.object(singulr_guardrail.async_handler, "post", return_value=resp) as mock_post: - returned_kwargs, returned_result = await singulr_guardrail.async_logging_hook( - kwargs=kwargs, result=result, call_type="acompletion" - ) - assert [call.kwargs["json"]["guardrail_scope"] for call in mock_post.call_args_list] == ["request", "response"] - assert returned_result is result - assert returned_kwargs is kwargs + async def test_mcp_tool_result_is_scanned_as_mcp_response(self, logging_only_guardrail): + from mcp.types import CallToolResult, TextContent - @pytest.mark.asyncio - async def test_api_error_marks_guardrail_status_intervened(self, singulr_guardrail): - """With block_on_error=True (the default), a transport failure while - reporting to Singulr raises internally; async_logging_hook must catch - it, mark the status accordingly, and still return (kwargs, result) - instead of propagating -- logging_only must never block the call.""" - kwargs = {"messages": [{"role": "user", "content": "hi"}]} - with patch.object( - singulr_guardrail.async_handler, - "post", - side_effect=httpx.TransportError("connection refused"), - ): - updated_kwargs, result = await singulr_guardrail.async_logging_hook( - kwargs=kwargs, result=None, call_type="acompletion" - ) - assert result is None - guardrail_information = updated_kwargs["standard_logging_object"]["guardrail_information"] - assert guardrail_information[0]["guardrail_status"] == "guardrail_intervened" - - @pytest.mark.asyncio - async def test_unexpected_exception_is_swallowed_without_recording_guardrail_information(self, singulr_guardrail): - """A non-guardrail exception (e.g. a bug in a downstream integration) - must not propagate out of the logging_only hook, and must not record - standard_logging_guardrail_information since no verdict was reached.""" - kwargs = {"messages": [{"role": "user", "content": "hi"}]} - with patch.object(singulr_guardrail.async_handler, "post", side_effect=RuntimeError("boom")): - updated_kwargs, result = await singulr_guardrail.async_logging_hook( - kwargs=kwargs, result=None, call_type="acompletion" - ) - assert result is None - assert "standard_logging_object" not in updated_kwargs - - @pytest.mark.asyncio - async def test_appends_to_existing_guardrail_information_list(self, singulr_guardrail): resp = _make_response({"should_block": False}) - existing_entry = {"guardrail_name": "other-guardrail"} - kwargs = { - "messages": [{"role": "user", "content": "hi"}], - "standard_logging_object": {"guardrail_information": [existing_entry]}, - } - with patch.object(singulr_guardrail.async_handler, "post", return_value=resp): - updated_kwargs, _ = await singulr_guardrail.async_logging_hook( - kwargs=kwargs, result=None, call_type="acompletion" + result = CallToolResult(content=[TextContent(type="text", text="ssn 123-45-6789")]) + with patch.object(logging_only_guardrail.async_handler, "post", return_value=resp) as mock_post: + await logging_only_guardrail.async_logging_hook( + kwargs=self._logged_call(model="MCP: get_customer_record", messages=None), + result=result, + call_type="call_mcp_tool", ) - guardrail_information = updated_kwargs["standard_logging_object"]["guardrail_information"] - assert guardrail_information[0] is existing_entry - assert guardrail_information[1]["guardrail_name"] == "test-singulr" + payloads = [call.kwargs["json"] for call in mock_post.call_args_list] + assert [payload["guardrail_scope"] for payload in payloads] == ["mcp_response"] + assert payloads[0]["tool_result"] == ["ssn 123-45-6789"] + assert payloads[0]["model_name"] == "MCP: get_customer_record" - def test_sync_logging_hook_returns_kwargs_and_result_unchanged_when_loop_running(self, singulr_guardrail): - """logging_hook is the sync entrypoint used outside an event loop; - inside a running loop it must no-op rather than deadlock or raise.""" - import asyncio - - async def _drive(): - kwargs = {"messages": [{"role": "user", "content": "hi"}]} - return singulr_guardrail.logging_hook(kwargs=kwargs, result=None, call_type="acompletion") - - returned_kwargs, returned_result = asyncio.run(_drive()) - assert returned_result is None - assert returned_kwargs == {"messages": [{"role": "user", "content": "hi"}]} - - def test_sync_logging_hook_creates_a_new_event_loop_when_none_is_set(self, singulr_guardrail): - """A thread with no current event loop must get a fresh one instead - of raising RuntimeError out of the sync entrypoint.""" + def test_sync_logging_hook_never_calls_singulr(self, logging_only_guardrail): from concurrent.futures import ThreadPoolExecutor - resp = _make_response({"should_block": False}) + kwargs = {"messages": [{"role": "user", "content": "hi"}], "standard_logging_object": {}} def _run(): - kwargs = {"messages": [{"role": "user", "content": "hi"}]} - with patch.object(singulr_guardrail.async_handler, "post", return_value=resp): - return singulr_guardrail.logging_hook(kwargs=kwargs, result=None, call_type="acompletion") + with patch.object(logging_only_guardrail.async_handler, "post") as mock_post: + returned = logging_only_guardrail.logging_hook(kwargs=kwargs, result=None, call_type="acompletion") + mock_post.assert_not_called() + return returned with ThreadPoolExecutor(max_workers=1) as pool: returned_kwargs, returned_result = pool.submit(_run).result() assert returned_result is None - guardrail_information = returned_kwargs["standard_logging_object"]["guardrail_information"] - assert guardrail_information[0]["guardrail_status"] == "success" - - def test_sync_logging_hook_swallows_unexpected_exception(self, singulr_guardrail): - """A bug surfacing from async_logging_hook itself, not just the - Singulr API call, must not propagate out of the sync entrypoint.""" - from concurrent.futures import ThreadPoolExecutor - - def _run(): - kwargs = {"messages": [{"role": "user", "content": "hi"}]} - with patch.object(singulr_guardrail, "async_logging_hook", side_effect=RuntimeError("boom")): - return singulr_guardrail.logging_hook(kwargs=kwargs, result=None, call_type="acompletion") - - with ThreadPoolExecutor(max_workers=1) as pool: - returned_kwargs, returned_result = pool.submit(_run).result() - assert returned_result is None - assert returned_kwargs == {"messages": [{"role": "user", "content": "hi"}]} - - -# --------------------------------------------------------------------------- -# HTTP call wiring (endpoint, timeout, headers) -# --------------------------------------------------------------------------- + assert returned_kwargs == {"messages": [{"role": "user", "content": "hi"}], "standard_logging_object": {}} class TestSingulrRequestWiring: @pytest.mark.asyncio async def test_sends_configured_timeout_and_calls_the_guard_endpoint(self): - """litellm_params.timeout must reach the httpx call so operators can - tighten or loosen the latency budget instead of being stuck with a - hardcoded 30s regardless of configuration.""" guardrail = SingulrGuardrail( singulr_api_key="test_key", singulr_api_base="https://api.test.singulr.ai", @@ -1168,11 +1076,6 @@ class TestSingulrBuildHeaders: assert "X-Singulr-Guardrail-Id" not in headers -# --------------------------------------------------------------------------- -# Non-JSON / malformed response handling -# --------------------------------------------------------------------------- - - class TestSingulrInvalidResponse: @pytest.mark.asyncio async def test_non_json_response_block_on_error_false_returns_inputs(self): @@ -1217,9 +1120,6 @@ class TestSingulrInvalidResponse: @pytest.mark.asyncio async def test_response_missing_expected_fields_block_on_error_true_raises(self): - """Regression: a response body that fails SingulrGuardrailResponse - validation must raise GuardrailRaisedException instead of letting - pydantic.ValidationError propagate unhandled.""" guardrail = SingulrGuardrail( singulr_api_base="https://api.test.singulr.ai", singulr_api_key="test_token_1234", @@ -1239,11 +1139,6 @@ class TestSingulrInvalidResponse: ) -# --------------------------------------------------------------------------- -# Transport error handling -# --------------------------------------------------------------------------- - - class TestSingulrTransportError: @pytest.mark.asyncio async def test_remote_protocol_error_block_on_error_false_returns_inputs(self): @@ -1287,11 +1182,6 @@ class TestSingulrTransportError: ) -# --------------------------------------------------------------------------- -# HTTP status error handling -# --------------------------------------------------------------------------- - - class TestSingulrHttpStatusError: @pytest.mark.asyncio async def test_http_error_message_names_status_code_not_unreachable(self): @@ -1342,11 +1232,6 @@ class TestSingulrHttpStatusError: assert result is inputs -# --------------------------------------------------------------------------- -# Config model -# --------------------------------------------------------------------------- - - class TestSingulrConfigModel: def test_ui_friendly_name(self): assert SingulrGuardrailConfigModel.ui_friendly_name() == "Singulr" @@ -1355,11 +1240,6 @@ class TestSingulrConfigModel: assert SingulrGuardrail.get_config_model() is SingulrGuardrailConfigModel -# --------------------------------------------------------------------------- -# Initializer and registry -# --------------------------------------------------------------------------- - - class TestSingulrInitializer: def test_guardrail_initializer_registry_has_entry(self): from litellm.proxy.guardrails.guardrail_hooks.singulr import ( @@ -1369,11 +1249,6 @@ class TestSingulrInitializer: assert callable(initialize_guardrail) def test_initialize_guardrail_reads_singulr_prefixed_fields(self): - """Regression: the UI config form (and YAML config) populate the - singulr_-prefixed fields declared on SingulrGuardrailConfigModel, not - the generic api_base/api_key fields. initialize_guardrail must read - those, or a UI-configured singulr_api_base is silently ignored and - the guardrail falls back to the localhost default.""" from litellm.proxy.guardrails.guardrail_hooks.singulr import ( initialize_guardrail, ) @@ -1398,10 +1273,6 @@ class TestSingulrInitializer: assert cb.singulr_guardrail_id == "configured_guardrail_id" def test_initialize_guardrail_wires_timeout(self): - """BaseLitellmParams.timeout exists so operators can override the - per-request latency budget. initialize_guardrail must forward it to - SingulrGuardrail instead of leaving every deployment stuck on the - hardcoded default regardless of configuration.""" from litellm.proxy.guardrails.guardrail_hooks.singulr import ( initialize_guardrail, ) From c7e4160ee6ce40493e5e3d896a30ac1668a55c70 Mon Sep 17 00:00:00 2001 From: Joshua Valluru <326636767+joshua-berri@users.noreply.github.com> Date: Tue, 15 Sep 2026 19:15:34 -0700 Subject: [PATCH 180/225] fix(mcp): enforce OAuth write policy across signed callbacks --- .../mcp_server/bridge_token_flow.py | 54 ++++---- .../mcp_server/discoverable_endpoints.py | 58 +++++---- .../test_user_api_key_auth.py | 1 + .../mcp_server/test_discoverable_endpoints.py | 118 +++++++++++++++++- 4 files changed, 181 insertions(+), 50 deletions(-) diff --git a/litellm/proxy/_experimental/mcp_server/bridge_token_flow.py b/litellm/proxy/_experimental/mcp_server/bridge_token_flow.py index 962e39d7dd6..5fe0929773c 100644 --- a/litellm/proxy/_experimental/mcp_server/bridge_token_flow.py +++ b/litellm/proxy/_experimental/mcp_server/bridge_token_flow.py @@ -306,18 +306,8 @@ async def _revalidate_active_subject(identity: "EnvelopeIdentity") -> "_KeyResol async def _extract_user_id_from_request(request: Request, server_id: str | None = None) -> str | None: """Resolve identity for binding, or authorize the credential-write action for a target server.""" - from litellm.proxy._experimental.mcp_server.mcp_server_manager import ( # noqa: PLC0415 # registry imports auth helpers - global_mcp_server_manager, - ) - from litellm.proxy._experimental.mcp_server.ui_session_utils import ( - can_access_mcp_server, # noqa: PLC0415 # proxy import cycle - ) from litellm.proxy._types import UserAPIKeyAuth # noqa: PLC0415 # proxy import cycle from litellm.proxy.auth.handle_jwt import JWTHandler # noqa: PLC0415 # proxy import cycle - from litellm.proxy.auth.route_checks import RouteChecks # noqa: PLC0415 # proxy import cycle - from litellm.proxy.auth.user_api_key_auth import ( # noqa: PLC0415 # proxy import cycle - _run_centralized_common_checks, # pyright: ignore[reportPrivateUsage] # reuse admission policy for the credential-write action - ) token: Final = _litellm_key_from_request(request) # The OAuth relay is public; the optional server-side write is the same protected action @@ -331,23 +321,39 @@ async def _extract_user_id_from_request(request: Request, server_id: str | None auth: Final = resolved.key if isinstance(resolved, _ResolvedKey) else resolved if not isinstance(auth, UserAPIKeyAuth) or not _active_key_user_id(auth): return None - if write_route is not None and server_id is not None: - try: - RouteChecks.is_virtual_key_allowed_to_call_route(route=write_route, valid_token=auth, request=request) - await _run_centralized_common_checks( - user_api_key_auth_obj=auth, - request=request, - request_data={}, - route=write_route, - ) - if not await can_access_mcp_server(auth, server_id, global_mcp_server_manager.get_allowed_mcp_servers): - return None - except Exception as exc: # noqa: BLE001 # authorization failure must never write credentials - verbose_logger.debug("OAuth credential write not authorized (%s)", type(exc).__name__) - return None + if server_id is not None and not await can_store_oauth_credential(request, auth, server_id): + return None return auth.user_id +async def can_store_oauth_credential(request: Request, auth: "UserAPIKeyAuth", server_id: str) -> bool: + """Apply the same write policy to request credentials and verified signed-callback users.""" + from litellm.proxy._experimental.mcp_server.mcp_server_manager import ( # noqa: PLC0415 # registry imports auth helpers + global_mcp_server_manager, + ) + from litellm.proxy._experimental.mcp_server.ui_session_utils import ( + can_access_mcp_server, # noqa: PLC0415 # proxy import cycle + ) + from litellm.proxy.auth.route_checks import RouteChecks # noqa: PLC0415 # proxy import cycle + from litellm.proxy.auth.user_api_key_auth import ( # noqa: PLC0415 # proxy import cycle + _run_centralized_common_checks, # pyright: ignore[reportPrivateUsage] # reuse admission policy for the credential-write action + ) + + write_route: Final = f"/v1/mcp/server/{server_id}/oauth-user-credential" + try: + RouteChecks.is_virtual_key_allowed_to_call_route(route=write_route, valid_token=auth, request=request) + await _run_centralized_common_checks( + user_api_key_auth_obj=auth, + request=request, + request_data={}, + route=write_route, + ) + return await can_access_mcp_server(auth, server_id, global_mcp_server_manager.get_allowed_mcp_servers) + except Exception as exc: # noqa: BLE001 # authorization failure must never write credentials + verbose_logger.debug("OAuth credential write not authorized (%s)", type(exc).__name__) + return False + + async def _resolve_jwt_auth( request: Request, token: str, diff --git a/litellm/proxy/_experimental/mcp_server/discoverable_endpoints.py b/litellm/proxy/_experimental/mcp_server/discoverable_endpoints.py index 56968745ea9..9ba67f966a2 100644 --- a/litellm/proxy/_experimental/mcp_server/discoverable_endpoints.py +++ b/litellm/proxy/_experimental/mcp_server/discoverable_endpoints.py @@ -29,9 +29,11 @@ from litellm.proxy._experimental.mcp_server.bridge_token_flow import ( _BridgeRefreshReady, _extract_user_id_from_request, _finish_bridge_mint, + _litellm_key_from_request, # pyright: ignore[reportPrivateUsage] # shared credential precedence for authorization issuance _prepare_bridge_mint, _prepare_bridge_refresh, _reload_active_user_by_id, + can_store_oauth_credential, ) from litellm.proxy._experimental.mcp_server.faults import ( CallerRejected, @@ -836,16 +838,29 @@ async def _user_can_reach_mcp_server(user_id: str, server_id: str) -> bool: return server_id in await global_mcp_server_manager.get_allowed_mcp_servers(admitted) -async def _bridge_authorize_access_denial( - litellm_user_id: str, +async def _resolve_oauth_authorization_user( + request: Request, mcp_server: MCPServer, redirect_uri: str, state: str, -) -> RedirectResponse | None: - """The denial redirect for a signed-in user who cannot reach the target server, or None to proceed.""" - if await _user_can_reach_mcp_server(litellm_user_id, mcp_server.server_id): - return None - return _bridge_access_denied_redirect(redirect_uri, state, mcp_server) + enforce_binding: bool, +) -> str | RedirectResponse: + """Resolve the authorization subject without replacing denied credentials with cookie grants.""" + from litellm.proxy._experimental.mcp_server.byok_oauth_endpoints import ( # noqa: PLC0415 # proxy import cycle + _user_id_from_session_cookie, + ) + + request_user_id: Final = ( + await _extract_user_id_from_request(request, mcp_server.server_id) if enforce_binding else None + ) + if enforce_binding and request_user_id is None and _litellm_key_from_request(request): + return _bridge_access_denied_redirect(redirect_uri, state, mcp_server) + user_id: Final = request_user_id or _user_id_from_session_cookie(request) + if user_id is None: + return _redirect_to_litellm_login(request) + if not await _user_can_reach_mcp_server(user_id, mcp_server.server_id): + return _bridge_access_denied_redirect(redirect_uri, state, mcp_server) + return user_id async def authorize_with_server( @@ -911,23 +926,12 @@ async def authorize_with_server( # Seal the authenticated caller into state so the token exchange cannot select another credential owner. litellm_user_id: str | None = None if enforce_binding or (resolved_server.is_dcr_bridge and resolved_server.is_oauth_delegate): - from litellm.proxy._experimental.mcp_server.byok_oauth_endpoints import ( # noqa: PLC0415 # inline import avoids a module-load circular import - _user_id_from_session_cookie, + subject: Final = await _resolve_oauth_authorization_user( + request, resolved_server, redirect_uri, state, enforce_binding ) - - litellm_user_id = ( - await _extract_user_id_from_request(request) if enforce_binding else None - ) or _user_id_from_session_cookie(request) - if litellm_user_id is None: - return _redirect_to_litellm_login(request) - denial: Final = await _bridge_authorize_access_denial( - litellm_user_id=litellm_user_id, - mcp_server=resolved_server, - redirect_uri=redirect_uri, - state=state, - ) - if denial is not None: - return denial + if isinstance(subject, RedirectResponse): + return subject + litellm_user_id = subject oauth_nonce: Final = secrets.token_urlsafe(32) if enforce_binding else None encoded_state: Final = encode_state_with_base_url( @@ -1220,8 +1224,14 @@ async def exchange_token_with_server( try: # Identity binding above must retain the verified caller even when a write is # denied. Authorize persistence separately, immediately before its side effect. + from litellm.proxy._experimental.mcp_server.auth.user_api_key_auth_mcp import MCPRequestHandler + + # A sealed code delegates a verified user for this authorized server. Raw + # request credentials retain their own JWT/key restrictions during resolution. can_store: Final = ( - await _user_can_reach_mcp_server(user_id, resolved_server.server_id) + await can_store_oauth_credential( + request, await MCPRequestHandler.reload_admitted_user(user_id), resolved_server.server_id + ) if bridge_identity is not None else await _extract_user_id_from_request(request, resolved_server.server_id) == user_id ) diff --git a/tests/proxy_unit_tests/test_user_api_key_auth.py b/tests/proxy_unit_tests/test_user_api_key_auth.py index 0cdf3500d50..a8fce58c60b 100644 --- a/tests/proxy_unit_tests/test_user_api_key_auth.py +++ b/tests/proxy_unit_tests/test_user_api_key_auth.py @@ -1069,6 +1069,7 @@ async def test_jwt_non_admin_team_route_access(monkeypatch): mock_jwt_response = { "is_proxy_admin": False, + "jwt_claims": {}, "team_id": None, "team_object": None, "user_id": None, diff --git a/tests/test_litellm/proxy/_experimental/mcp_server/test_discoverable_endpoints.py b/tests/test_litellm/proxy/_experimental/mcp_server/test_discoverable_endpoints.py index 8c179eea4cb..1739ac6d743 100644 --- a/tests/test_litellm/proxy/_experimental/mcp_server/test_discoverable_endpoints.py +++ b/tests/test_litellm/proxy/_experimental/mcp_server/test_discoverable_endpoints.py @@ -11171,8 +11171,8 @@ async def test_identity_bound_authorization_carries_nonce_and_caller_through_cal "litellm.proxy._experimental.mcp_server.discoverable_endpoints._extract_user_id_from_request", new=AsyncMock(return_value="alice")), patch( # test-quality-ok: isolate user access lookup while testing nonce and caller preservation - "litellm.proxy._experimental.mcp_server.discoverable_endpoints._bridge_authorize_access_denial", - new=AsyncMock(return_value=None)), + "litellm.proxy._experimental.mcp_server.discoverable_endpoints._user_can_reach_mcp_server", + new=AsyncMock(return_value=True)), ): authorized = await authorize_with_server( request, server, "client", "http://127.0.0.1:6274/callback", state="client-state", @@ -11972,3 +11972,117 @@ async def test_oauth_write_denial_does_not_erase_identity_binding( assert denied.value.status_code == 403 assert denied.value.detail == {"error": "oauth_principal_mismatch"} manager.get_allowed_mcp_servers.assert_not_awaited() + + +@pytest.mark.asyncio +@pytest.mark.parametrize("admin_only", [False, True]) +async def test_signed_oauth_callback_honors_credential_write_policy( + jwt_oauth_identity: tuple["JWTHandler", "RSAPrivateKey"], + monkeypatch: pytest.MonkeyPatch, + admin_only: bool, +) -> None: + import httpx + import litellm + + from litellm.caching.llm_caching_handler import LLMClientCache + from litellm.llms.custom_httpx.http_handler import AsyncHTTPHandler + from litellm.proxy import proxy_server + from litellm.proxy._experimental.mcp_server import discoverable_endpoints, mcp_server_manager + from litellm.proxy._types import MCPTransport + from litellm.types.llms.custom_http import httpxSpecialProvider + from litellm.types.mcp_server.mcp_server_manager import MCPServer + + server: Final = MCPServer( + server_id="signed-server", name="signed-server", transport=MCPTransport.http, + auth_type=MCPAuth.oauth2, oauth2_flow="authorization_code", client_id="client", + token_url="https://upstream.example.test/token", + ) + monkeypatch.setattr(proxy_server, "general_settings", { + "enable_jwt_auth": True, + "admin_only_routes": [f"/v1/mcp/server/{server.server_id}/oauth-user-credential"] if admin_only else [], + }) + monkeypatch.setenv("LITELLM_SALT_KEY", "signed-oauth-test-salt") + manager: Final = MagicMock() + manager.get_allowed_mcp_servers = AsyncMock(return_value=[server.server_id]) + manager.invalidate_user_oauth_token_cache = AsyncMock() + monkeypatch.setattr(mcp_server_manager, "global_mcp_server_manager", manager) + table: Final = proxy_server.prisma_client.db.litellm_mcpusercredentials + table.find_unique = AsyncMock(return_value=None) + table.upsert = AsyncMock() + clients: Final = LLMClientCache() + monkeypatch.setattr(litellm, "in_memory_llm_clients_cache", clients) + + def upstream_response(outbound: httpx.Request) -> httpx.Response: + assert outbound.url == server.token_url + assert b"code=upstream-code" in outbound.content + return httpx.Response(200, json={"access_token": "upstream-token", "token_type": "Bearer"}) + + async with httpx.AsyncClient(transport=httpx.MockTransport(upstream_response)) as transport: + upstream: Final = AsyncHTTPHandler() + await upstream.client.aclose() + upstream.client = transport + clients.set_cache("async_httpx_client" + httpxSpecialProvider.Oauth2Check, upstream) + response: Final = await discoverable_endpoints.exchange_token_with_server( + request=_token_request({}, path="/signed-server/token"), mcp_server=server, + grant_type="authorization_code", + code=discoverable_endpoints.seal_bridge_authorization_code("upstream-code", "jwt-owner", server.server_id), + redirect_uri="http://localhost/callback", client_id="client", client_secret=None, code_verifier=None, + ) + assert response.status_code == 200 + assert json.loads(response.body)["access_token"] == "upstream-token" + if admin_only: + table.upsert.assert_not_awaited() + else: + table.upsert.assert_awaited_once() + assert table.upsert.call_args.kwargs["where"]["user_id_server_id"] == { + "user_id": "jwt-owner", "server_id": server.server_id, + } + + +@pytest.mark.asyncio +@pytest.mark.parametrize("allowed", [False, True]) +async def test_identity_bound_authorize_preserves_presented_jwt_permissions( + jwt_oauth_identity: tuple["JWTHandler", "RSAPrivateKey"], + monkeypatch: pytest.MonkeyPatch, + allowed: bool, +) -> None: + from urllib.parse import parse_qs, urlparse + + from litellm.proxy._experimental.mcp_server import byok_oauth_endpoints, discoverable_endpoints, mcp_server_manager + from litellm.proxy._types import MCPTransport + from litellm.types.mcp_server.mcp_server_manager import MCPOAuthIdentityBinding, MCPServer + + _, signing_key = jwt_oauth_identity + monkeypatch.setenv("LITELLM_SALT_KEY", "authorize-policy-test-salt") + server: Final = MCPServer( + server_id="bound-server", name="bound-server", transport=MCPTransport.http, + auth_type=MCPAuth.oauth2, oauth2_flow="authorization_code", client_id="client", + authorization_url="https://upstream.example.test/authorize", token_url="https://upstream.example.test/token", + oauth_identity_binding=MCPOAuthIdentityBinding( + mode="enforce", issuer="https://upstream.example.test", audiences=["client"], + ), + ) + manager: Final = MagicMock() + # The full user roster permits the server; the presented JWT may have narrower access. + manager.get_allowed_mcp_servers = AsyncMock( + side_effect=lambda auth: [server.server_id] if allowed or auth.mcp_admitted_user_subject else [], + ) + monkeypatch.setattr(mcp_server_manager, "global_mcp_server_manager", manager) + monkeypatch.setattr( # test-quality-ok: session-cookie decoder is the separate authentication boundary; a valid cookie must not override a denied explicit credential + byok_oauth_endpoints, "_user_id_from_session_cookie", lambda request: "jwt-owner", + ) + response: Final = await discoverable_endpoints.authorize_with_server( + request=_token_request({"Authorization": f"Bearer {_oauth_identity_jwt(signing_key)}"}), + mcp_server=server, client_id="client", redirect_uri="http://127.0.0.1:6274/callback", + state="client-state", code_challenge="pkce-challenge", code_challenge_method="S256", + ) + redirect: Final = urlparse(response.headers["location"]) + query: Final = parse_qs(redirect.query) + if allowed: + assert redirect.hostname == "upstream.example.test" + assert query["nonce"] and response.headers.get("set-cookie") + else: + assert redirect.hostname == "127.0.0.1" + assert query["error"] == ["access_denied"] + assert query["state"] == ["client-state"] + assert "set-cookie" not in response.headers From d94b9d117d70e42866948c2986a9ea0c3a4533e6 Mon Sep 17 00:00:00 2001 From: Yuneng Jiang Date: Tue, 15 Sep 2026 19:16:45 -0700 Subject: [PATCH 181/225] docs(e2e): clarify recorded IDs and quota header semantics --- tests/e2e/PROVIDER_CACHE.md | 6 ++++++ 1 file changed, 6 insertions(+) diff --git a/tests/e2e/PROVIDER_CACHE.md b/tests/e2e/PROVIDER_CACHE.md index 280132d0940..efd9eb66daa 100644 --- a/tests/e2e/PROVIDER_CACHE.md +++ b/tests/e2e/PROVIDER_CACHE.md @@ -22,6 +22,12 @@ Do not give cache credentials to candidate deployments. Counter artifacts contai Tests that require real provider timing, limits or state use `@pytest.mark.provider_live`. The marker keeps newly registered models on live routes without weakening their assertions. Ordinary assertion failures still fail E2E. The shared cache does not modify provider response IDs or make the proxy aware of replay +## Recorded response semantics + +Replay preserves the original response ID, usage and end-to-end headers. The proxy can therefore deduplicate repeated provider IDs when storing spend-log rows, just as it does when a live upstream returns the same ID twice. One spend-log row per invocation is not guaranteed for identical recorded responses. Existing spend reconciliation requests use distinct prompt markers and retain their distinct-ID and row-count assertions; accounting tests are not automatically excluded from caching + +Provider remaining-quota headers describe the captured response. Metrics derived from them are historical on a cache hit, not a measurement of current provider capacity. Gateway-generated API-key quota headers are a separate contract. A test of fresh provider quota or timing must use the live-provider policy; replay can still exercise how the proxy processes the recorded headers + ## Qualification `tests/code_coverage_tests/test_provider_cache.py` exercises local HTTP providers and disposable real Redis. CI runs these checks with the existing provider-edge and replay harness tests. These component checks do not establish Buildkite deployment, full-suite cross-build reuse or a genuine 24-hour expiry observation; those require separate runtime evidence From 064d5d61dad263bad11a77668d1de75176767b69 Mon Sep 17 00:00:00 2001 From: Yuneng Jiang Date: Tue, 15 Sep 2026 18:51:55 -0700 Subject: [PATCH 182/225] ci(image-scan): ignore zlib CVE-2026-85091 until Wolfi ships the fix Wolfi's security database names zlib 1.3.3-r0 as the fix for CVE-2026-85091, but the newest zlib published to the Wolfi apk repo is 1.3.2-r7. Every wolfi-base digest, including the current latest, still reports the CVE, so no base image bump or apk upgrade can clear it and image-scan fails on every PR touching a Dockerfile or the lockfile, and on the nightly schedule. Ignore that CVE and its GHSA alias for the zlib apk package only, so a fixable High in anything else still fails the job. --- .github/workflows/image-scan.yml | 2 ++ .grype.yaml | 13 +++++++++++++ 2 files changed, 15 insertions(+) create mode 100644 .grype.yaml diff --git a/.github/workflows/image-scan.yml b/.github/workflows/image-scan.yml index 206bb809e0c..c27d49ed610 100644 --- a/.github/workflows/image-scan.yml +++ b/.github/workflows/image-scan.yml @@ -26,6 +26,7 @@ on: - ui/Dockerfile - ui/nginx.conf - .github/workflows/image-scan.yml + - .grype.yaml schedule: - cron: "41 6 * * *" workflow_dispatch: @@ -93,6 +94,7 @@ jobs: GRYPE_MATCH_PYTHON_USING_CPES: "true" run: | "$RUNNER_TEMP/grype" litellm-image-scan:${{ github.sha }} \ + --config .grype.yaml \ --only-fixed \ --fail-on high \ --output table diff --git a/.grype.yaml b/.grype.yaml new file mode 100644 index 00000000000..c5e49851dc9 --- /dev/null +++ b/.grype.yaml @@ -0,0 +1,13 @@ +# Wolfi's security database names zlib 1.3.3-r0 as the fix for CVE-2026-85091, +# but the newest zlib published to the Wolfi apk repo is 1.3.2-r7, so every +# wolfi-base digest reports it and no `apk upgrade` can clear it. +# Drop this once Wolfi ships zlib >= 1.3.3-r0; expected by 2026-10-15. +ignore: + - vulnerability: CVE-2026-85091 + package: + name: zlib + type: apk + - vulnerability: GHSA-g5fp-32jq-cfw2 + package: + name: zlib + type: apk From 3dda798c8a24f255219a2808eccbd2f66434f681 Mon Sep 17 00:00:00 2001 From: Yuneng Jiang Date: Tue, 15 Sep 2026 19:28:50 -0700 Subject: [PATCH 183/225] ci(e2e): consolidate cache contracts in filtered CircleCI job --- .circleci/config.yml | 17 ++++- .circleci/scripts/classify_changes.sh | 11 +++- .circleci/scripts/path_filter.sh | 4 +- .github/workflows/test-provider-cache.yml | 63 ------------------- .../test_litellm/test_circleci_path_filter.py | 14 +++++ 5 files changed, 40 insertions(+), 69 deletions(-) delete mode 100644 .github/workflows/test-provider-cache.yml diff --git a/.circleci/config.yml b/.circleci/config.yml index bb4ad0f4019..e2102a9ae91 100644 --- a/.circleci/config.yml +++ b/.circleci/config.yml @@ -9,7 +9,7 @@ commands: parameters: category: type: enum - enum: ["backend", "client"] + enum: ["backend", "client", "provider-harness"] default: "backend" steps: - run: @@ -2918,19 +2918,30 @@ jobs: provider_replay_harness: docker: - *python312_image + - image: redis@sha256:e2debfb7956fa12c7ddc79d7e645c8cf26b30c99a6e9161ea9bf4171e1668a5f working_directory: ~/project resource_class: medium + environment: + E2E_CACHE_TEST_REDIS_URL: redis://127.0.0.1:6379/0 + E2E_PROVIDER_CACHE: "0" + E2E_FIXTURE_MODE: live steps: + - checkout + - skip_if_unrelated_changes: + category: provider-harness - setup_litellm_test_deps + - wait_for_service: + url: tcp://localhost:6379 - run: - name: Test provider replay harness + name: Test provider capture and replay harness command: | mkdir -p test-results/provider-replay-harness uv run --no-sync pytest -q --noconftest -o addopts= -o pythonpath=tests/e2e -p no:rerunfailures \ --junitxml=test-results/provider-replay-harness/junit.xml \ tests/e2e/test_provider_edge.py tests/e2e/test_fixture_bundle.py \ tests/e2e/test_fixture_canonical.py tests/e2e/test_fixture_mode.py \ - tests/code_coverage_tests/test_provider_replay_harness.py + tests/code_coverage_tests/test_provider_replay_harness.py \ + tests/code_coverage_tests/test_provider_cache.py - store_test_results: path: test-results/provider-replay-harness diff --git a/.circleci/scripts/classify_changes.sh b/.circleci/scripts/classify_changes.sh index 7aa0c3544ee..9dc7b76b23f 100755 --- a/.circleci/scripts/classify_changes.sh +++ b/.circleci/scripts/classify_changes.sh @@ -1,13 +1,19 @@ #!/usr/bin/env bash set -uo pipefail -category="${1:?usage: classify_changes.sh }" +category="${1:?usage: classify_changes.sh }" has_client=false has_backend=false has_ci=false +has_provider_harness=false while IFS= read -r file || [ -n "$file" ]; do [ -n "$file" ] || continue + case "$file" in + tests/e2e/*/*.py) : ;; + tests/e2e/*.py | tests/code_coverage_tests/test_provider_cache.py | tests/code_coverage_tests/test_provider_replay_harness.py | tests/test_litellm/test_circleci_path_filter.py | .circleci/* | pyproject.toml | uv.lock) + has_provider_harness=true ;; + esac case "$file" in ui/* | tests/e2e/ui/*) has_client=true ;; docs/* | *.md | *.mdx) : ;; @@ -17,6 +23,9 @@ while IFS= read -r file || [ -n "$file" ]; do done case "$category" in + provider-harness) + [ "$has_provider_harness" = true ] && echo run || echo skip + ;; backend) [ "$has_backend" = true ] && echo run || echo skip ;; diff --git a/.circleci/scripts/path_filter.sh b/.circleci/scripts/path_filter.sh index dcf64a24399..cdadde732bd 100755 --- a/.circleci/scripts/path_filter.sh +++ b/.circleci/scripts/path_filter.sh @@ -1,7 +1,7 @@ #!/usr/bin/env bash set -uo pipefail -category="${1:?usage: path_filter.sh }" +category="${1:?usage: path_filter.sh }" here="$(cd "$(dirname "${BASH_SOURCE[0]}")" && pwd)" run_full() { @@ -36,5 +36,5 @@ if [ "$decision" = run ]; then run_full "$category-relevant changes detected" fi -echo "path-filter[$category]: only unrelated (docs/client) changes detected; halting job as successful" +echo "path-filter[$category]: only unrelated changes detected; halting job as successful" circleci-agent step halt diff --git a/.github/workflows/test-provider-cache.yml b/.github/workflows/test-provider-cache.yml deleted file mode 100644 index 0507f90cf89..00000000000 --- a/.github/workflows/test-provider-cache.yml +++ /dev/null @@ -1,63 +0,0 @@ -name: Provider cache contracts - -on: - pull_request: - paths: - - 'tests/e2e/**' - - 'tests/code_coverage_tests/test_provider_cache.py' - - 'tests/code_coverage_tests/test_provider_replay_harness.py' - - '.github/workflows/test-provider-cache.yml' - - 'pyproject.toml' - - 'uv.lock' - workflow_dispatch: - -permissions: - contents: read - -jobs: - provider-cache: - runs-on: ubuntu-latest - timeout-minutes: 20 - services: - redis: - image: redis@sha256:e2debfb7956fa12c7ddc79d7e645c8cf26b30c99a6e9161ea9bf4171e1668a5f - ports: - - 6379:6379 - options: >- - --health-cmd "redis-cli ping" - --health-interval 5s - --health-timeout 3s - --health-retries 10 - env: - PYTHONDONTWRITEBYTECODE: '1' - PYTHONPATH: tests/e2e - E2E_CACHE_TEST_REDIS_URL: redis://127.0.0.1:6379/0 - E2E_PROVIDER_CACHE: '0' - E2E_FIXTURE_MODE: live - steps: - - uses: actions/checkout@08eba0b27e820071cde6df949e0beb9ba4906955 # v4.3.0 - with: - persist-credentials: false - - uses: actions/setup-python@a26af69be951a213d495a4c3e4e4022e16d87065 # v5.6.0 - with: - python-version: '3.13' - - uses: ./.github/actions/setup-uv-with-retries - with: - version: '0.10.9' - - uses: ./.github/actions/cache-cargo-build - - name: Install locked dependencies - run: .github/scripts/uv_sync_with_retries.sh --frozen --group proxy-dev --group e2e-dev - - name: Verify cache and existing edge contracts - run: >- - uv run --no-sync pytest -c /dev/null -p no:cacheprovider - tests/code_coverage_tests/test_provider_cache.py - tests/code_coverage_tests/test_provider_replay_harness.py - tests/e2e/test_provider_edge.py - -q --junitxml=provider-cache-results.xml - - name: Save test results - if: always() - uses: actions/upload-artifact@4cec3d8aa04e39d1a68397de0c4cd6fb9dce8ec1 # v4.6.1 - with: - name: provider-cache-results - path: provider-cache-results.xml - if-no-files-found: warn diff --git a/tests/test_litellm/test_circleci_path_filter.py b/tests/test_litellm/test_circleci_path_filter.py index b427a1a3bd8..af0e932400f 100644 --- a/tests/test_litellm/test_circleci_path_filter.py +++ b/tests/test_litellm/test_circleci_path_filter.py @@ -49,6 +49,20 @@ CI = [".github/workflows/test-litellm-ui-unit.yml"] @pytest.mark.parametrize( "category,changed,expected", [ + ("provider-harness", ["tests/e2e/provider_cache.py"], "run"), + ("provider-harness", ["tests/e2e/conftest.py"], "run"), + ("provider-harness", ["tests/e2e/e2e_http.py"], "run"), + ("provider-harness", ["tests/code_coverage_tests/test_provider_cache.py"], "run"), + ("provider-harness", ["tests/code_coverage_tests/test_provider_replay_harness.py"], "run"), + ("provider-harness", [".circleci/config.yml"], "run"), + ("provider-harness", [".circleci/scripts/classify_changes.sh"], "run"), + ("provider-harness", ["pyproject.toml"], "run"), + ("provider-harness", ["uv.lock"], "run"), + ("provider-harness", ["tests/e2e/PROVIDER_CACHE.md"], "skip"), + ("provider-harness", ["tests/e2e/ui/test_example.py"], "skip"), + ("provider-harness", ["tests/e2e/quota_management/test_quota.py"], "skip"), + ("provider-harness", ["litellm/main.py"], "skip"), + ("provider-harness", ["ui/litellm-dashboard/src/App.tsx"], "skip"), # docs-only: skip everything ("backend", DOCS, "skip"), ("client", DOCS, "skip"), From 39b78128103b1c0a2f703e275ddef60e1c1a98e1 Mon Sep 17 00:00:00 2001 From: Tin Chi Lo Date: Tue, 15 Sep 2026 18:28:21 -0700 Subject: [PATCH 184/225] feat(ui): configure capability and Fuse v2 classifiers --- .../AutoRouters/AutoRoutersPanel.tsx | 4 +- .../components/AutoRouters/autoRouterRows.ts | 2 + ...oRouterClassifierTabs.integration.test.tsx | 75 +++ .../add_model/AutoRouterClassifierTabs.tsx | 58 ++ .../add_model/ClassificationMethodConfig.tsx | 55 +- .../add_model/ComplexityRouterConfig.tsx | 636 +++++++++--------- ...plexityRouterFastMode.integration.test.tsx | 186 ++++- .../add_model/DefaultModelField.tsx | 56 ++ ...ecastClassifierConfig.integration.test.tsx | 224 ++++++ .../add_model/ForecastClassifierConfig.tsx | 433 ++++++++++++ .../add_model/PlanModeOverrideControls.tsx | 44 ++ .../components/add_model/RoutingOptions.tsx | 23 + .../add_model/TierModelEffortRows.tsx | 10 +- .../components/add_model/TierRestrictions.tsx | 12 +- .../add_model/add_auto_router_tab.test.tsx | 108 +++ .../add_model/add_auto_router_tab.tsx | 451 +++++++------ .../build_complexity_router_config.ts | 132 +++- .../classifier_type_transition.test.ts | 80 +++ .../add_model/classifier_type_transition.ts | 50 ++ .../forecast_classifier_config.test.ts | 284 ++++++++ .../add_model/forecast_classifier_config.ts | 188 ++++++ .../src/components/add_model/tier_rows.ts | 11 +- .../add_model/tier_set_actions.test.ts | 29 + .../components/add_model/tier_set_actions.ts | 30 +- ...d_updated_complexity_router_config.test.ts | 13 +- .../edit_auto_router_modal.tsx | 141 ++-- 26 files changed, 2593 insertions(+), 742 deletions(-) create mode 100644 ui/litellm-dashboard/src/components/add_model/AutoRouterClassifierTabs.integration.test.tsx create mode 100644 ui/litellm-dashboard/src/components/add_model/AutoRouterClassifierTabs.tsx create mode 100644 ui/litellm-dashboard/src/components/add_model/DefaultModelField.tsx create mode 100644 ui/litellm-dashboard/src/components/add_model/ForecastClassifierConfig.integration.test.tsx create mode 100644 ui/litellm-dashboard/src/components/add_model/ForecastClassifierConfig.tsx create mode 100644 ui/litellm-dashboard/src/components/add_model/PlanModeOverrideControls.tsx create mode 100644 ui/litellm-dashboard/src/components/add_model/RoutingOptions.tsx create mode 100644 ui/litellm-dashboard/src/components/add_model/classifier_type_transition.test.ts create mode 100644 ui/litellm-dashboard/src/components/add_model/classifier_type_transition.ts create mode 100644 ui/litellm-dashboard/src/components/add_model/forecast_classifier_config.test.ts create mode 100644 ui/litellm-dashboard/src/components/add_model/forecast_classifier_config.ts diff --git a/ui/litellm-dashboard/src/app/(dashboard)/models-and-endpoints/components/AutoRouters/AutoRoutersPanel.tsx b/ui/litellm-dashboard/src/app/(dashboard)/models-and-endpoints/components/AutoRouters/AutoRoutersPanel.tsx index f11b74c939d..1625e0cbfb8 100644 --- a/ui/litellm-dashboard/src/app/(dashboard)/models-and-endpoints/components/AutoRouters/AutoRoutersPanel.tsx +++ b/ui/litellm-dashboard/src/app/(dashboard)/models-and-endpoints/components/AutoRouters/AutoRoutersPanel.tsx @@ -104,8 +104,8 @@ export function AutoRoutersPanel({ Add Auto Router - Routes each request to a model by classifying its complexity. Called like any other model, so clients keep - using a single model name. + Choose a classifier to route each request to a model. Called like any other model, so clients keep using a + single model name. Array.from(new Set(models)); const COMPLEXITY_TYPE_LABELS: Record = { llm: "LLM Classifier", + capability: "Capability", + llm_v2: "Fuse v2", heuristic_first: "Heuristic first", hybrid: "Hybrid", custom: "Custom classifier", diff --git a/ui/litellm-dashboard/src/components/add_model/AutoRouterClassifierTabs.integration.test.tsx b/ui/litellm-dashboard/src/components/add_model/AutoRouterClassifierTabs.integration.test.tsx new file mode 100644 index 00000000000..ac6851349ea --- /dev/null +++ b/ui/litellm-dashboard/src/components/add_model/AutoRouterClassifierTabs.integration.test.tsx @@ -0,0 +1,75 @@ +import React, { useState } from "react"; +import { describe, expect, it, vi } from "vitest"; +import { fireEvent, renderWithProviders, screen } from "../../../tests/test-utils"; +import AutoRouterClassifierTabs from "./AutoRouterClassifierTabs"; +import type { ComplexityRouterConfigValue } from "./ComplexityRouterConfig"; + +const initial: ComplexityRouterConfigValue = { + classifier_type: "llm", + tiers: { SIMPLE: ["efficient"], MEDIUM: [], COMPLEX: [], REASONING: ["capable"] }, +}; + +function Form({ initialValue = initial }: { initialValue?: ComplexityRouterConfigValue }) { + const [value, setValue] = useState(initialValue); + return ( + + {value.classifier_type} + + ); +} + +describe("AutoRouterClassifierTabs", () => { + it.each(["heuristic", "heuristic_v2", "llm", "heuristic_first", "hybrid"] as const)( + "groups %s under Complexity without resetting its configuration", + (classifier_type) => { + const onChange = vi.fn(); + renderWithProviders( + + Existing classifier settings + , + ); + expect(screen.getByRole("tab", { name: "Complexity" })).toHaveAttribute("aria-selected", "true"); + expect(screen.getByRole("tabpanel", { name: "Complexity" })).toHaveTextContent("Existing classifier settings"); + fireEvent.click(screen.getByRole("tab", { name: "Complexity" })); + expect(onChange).not.toHaveBeenCalled(); + }, + ); + + it.each([ + ["capability", "Capability"], + ["llm_v2", "Fuse v2"], + ] as const)("opens saved %s settings and switches back to local Complexity", (classifier_type, label) => { + renderWithProviders(
); + expect(screen.getByRole("tab", { name: label })).toHaveAttribute("aria-selected", "true"); + fireEvent.click(screen.getByRole("tab", { name: "Complexity" })); + expect(screen.getByRole("tab", { name: "Complexity" })).toHaveAttribute("aria-selected", "true"); + expect(screen.getByRole("status", { name: "Classifier type" })).toHaveTextContent("heuristic"); + }); + + it("keeps custom tiers editable under Complexity and explains why forecast tabs are disabled", () => { + const onChange = vi.fn(); + renderWithProviders( + + Custom tiers + , + ); + expect(screen.getByRole("tabpanel", { name: "Complexity" })).toHaveTextContent("Custom tiers"); + for (const name of ["Capability", "Fuse v2"]) { + const tab = screen.getByRole("tab", { name }); + expect(tab).toHaveAttribute("aria-disabled", "true"); + expect(tab).toHaveAccessibleDescription("Restore standard tiers to use Capability or Fuse v2."); + fireEvent.click(tab); + } + expect(onChange).not.toHaveBeenCalled(); + expect(screen.getByText("Restore standard tiers to use Capability or Fuse v2.")).toBeVisible(); + }); +}); diff --git a/ui/litellm-dashboard/src/components/add_model/AutoRouterClassifierTabs.tsx b/ui/litellm-dashboard/src/components/add_model/AutoRouterClassifierTabs.tsx new file mode 100644 index 00000000000..98c0d4aab2f --- /dev/null +++ b/ui/litellm-dashboard/src/components/add_model/AutoRouterClassifierTabs.tsx @@ -0,0 +1,58 @@ +import React, { useId } from "react"; +import { Tabs, TabsContent, TabsList, TabsTrigger } from "@/components/ui/tabs"; +import { effectiveClassifierType, type ComplexityRouterConfigValue } from "./ComplexityRouterConfig"; +import { transitionClassifierType } from "./classifier_type_transition"; +import { isForecastClassifier } from "./forecast_classifier_config"; + +interface AutoRouterClassifierTabsProps { + value: ComplexityRouterConfigValue; + onChange: (value: ComplexityRouterConfigValue) => void; + children: React.ReactNode; +} + +const AutoRouterClassifierTabs: React.FC = ({ value, onChange, children }) => { + const restrictionId = useId(); + const classifierType = effectiveClassifierType(value); + const selected = isForecastClassifier(classifierType) ? classifierType : "complexity"; + const hasCustomTiers = Boolean(value.custom_tier_set); + + const handleChange = (tab: unknown) => { + if (tab === selected) return; + if (tab === "complexity") { + onChange(transitionClassifierType(value, isForecastClassifier(classifierType) ? "heuristic" : classifierType)); + } else if (!hasCustomTiers && (tab === "capability" || tab === "llm_v2")) { + onChange(transitionClassifierType(value, tab)); + } + }; + + return ( + +

Classifier type

+ + Complexity + + Capability + + + Fuse v2 + + + {hasCustomTiers && ( +

+ Restore standard tiers to use Capability or Fuse v2. +

+ )} + {children} + + ); +}; + +export default AutoRouterClassifierTabs; diff --git a/ui/litellm-dashboard/src/components/add_model/ClassificationMethodConfig.tsx b/ui/litellm-dashboard/src/components/add_model/ClassificationMethodConfig.tsx index a6f2e65793a..64b08fc9ed1 100644 --- a/ui/litellm-dashboard/src/components/add_model/ClassificationMethodConfig.tsx +++ b/ui/litellm-dashboard/src/components/add_model/ClassificationMethodConfig.tsx @@ -1,3 +1,4 @@ +import { transitionClassifierType } from "./classifier_type_transition"; import { Info } from "lucide-react"; import { SimpleTooltip } from "@/components/ui/tooltip"; import { MultiSelect } from "@/components/shared/MultiSelect"; @@ -17,7 +18,6 @@ import ClassifierReasoningEffortSelect from "./ClassifierReasoningEffortSelect"; import ClassifierCircuitBreakerConfig from "./ClassifierCircuitBreakerConfig"; import ClassifierVisionConfig from "./ClassifierVisionConfig"; import type { ReasoningEffort } from "./complexity_router_tiers"; -import { nonReasoningTierFields } from "./nonReasoningTierFields"; import { useComplexityScorerDefaults } from "@/app/(dashboard)/hooks/autoRouter/useComplexityScorerDefaults"; import { ClassificationFrequency, @@ -33,12 +33,10 @@ import { DEFAULT_CLASSIFIER_FALLBACK, DEFAULT_CLASSIFIER_TIMEOUT_MS, DEFAULT_CLASSIFICATION_RUBRIC, - NEW_CLASSIFIER_CLASSIFICATION_RUBRIC, ClassificationRubric, effectiveTierLabel, heuristicScoringRole, usesLlmClassifier, - DEFAULT_HEURISTIC_FIRST_MAX_TIER, DEFAULT_HYBRID_BOUNDARY_MARGIN, HEURISTIC_FIRST_MAX_TIER_KEYS, effectiveClassifierType, @@ -263,35 +261,7 @@ const ClassificationMethodConfig: React.FC = ({ const explicitlySupportedClassifierEfforts = effortOptionsByModel[classifierModel]; const handleClassifierTypeChange = (classifierType: ClassifierType) => { - const nextValue: ComplexityRouterConfigValue = { - ...value, - classifier_type: classifierType, - classifier_llm_config: usesLlmClassifier(classifierType) - ? value.classifier_llm_config ?? { - model: "", - timeout_ms: DEFAULT_CLASSIFIER_TIMEOUT_MS, - classification_rubric: NEW_CLASSIFIER_CLASSIFICATION_RUBRIC, - } - : undefined, - classifier_context_window_size: usesLlmClassifier(classifierType) - ? value.classifier_context_window_size ?? DEFAULT_CLASSIFIER_CONTEXT_WINDOW_SIZE - : undefined, - classifier_context_budget_chars: usesLlmClassifier(classifierType) - ? value.classifier_context_budget_chars ?? DEFAULT_CLASSIFIER_CONTEXT_BUDGET_CHARS - : undefined, - classifier_context_include_assistant_turns: usesLlmClassifier(classifierType) - ? value.classifier_context_include_assistant_turns - : undefined, - classifier_fallback: usesLlmClassifier(classifierType) ? value.classifier_fallback : undefined, - heuristic_first_max_tier: - classifierType === "heuristic_first" - ? value.heuristic_first_max_tier ?? DEFAULT_HEURISTIC_FIRST_MAX_TIER - : undefined, - hybrid_boundary_margin: - classifierType === "hybrid" ? value.hybrid_boundary_margin ?? DEFAULT_HYBRID_BOUNDARY_MARGIN : undefined, - ...nonReasoningTierFields(classifierType, value), - }; - onChange(nextValue); + onChange(transitionClassifierType(value, classifierType)); }; const handleHeuristicFirstMaxTierChange = (tier: string) => { @@ -433,27 +403,6 @@ const ClassificationMethodConfig: React.FC = ({ }); }; - if (classifierType === "capability") { - return ( -

- This router uses capability forecasting. Configure its classifier, threshold, and calibration through YAML or - the API. Saving preserves those settings -

- ); - } - - if (classifierType === "llm_v2") { - return ( -
- LLM V2 classifier (experimental) -

- Combines task demands and model capability in one forecast. Its solver profiles and quality allowance are - configured through the API. Saving this router preserves those settings -

-
- ); - } - return ( <> diff --git a/ui/litellm-dashboard/src/components/add_model/ComplexityRouterConfig.tsx b/ui/litellm-dashboard/src/components/add_model/ComplexityRouterConfig.tsx index c6b9a69e76a..4ccabff18b9 100644 --- a/ui/litellm-dashboard/src/components/add_model/ComplexityRouterConfig.tsx +++ b/ui/litellm-dashboard/src/components/add_model/ComplexityRouterConfig.tsx @@ -1,8 +1,11 @@ +import RoutingOptions from "./RoutingOptions"; +import PlanModeOverrideControls from "./PlanModeOverrideControls"; +import ForecastClassifierConfig, { ForecastSolverModels } from "./ForecastClassifierConfig"; +import { isForecastClassifier, type CapabilitySettings, type FuseSettings } from "./forecast_classifier_config"; import { SimpleTooltip } from "@/components/ui/tooltip"; import { MultiSelect } from "@/components/shared/MultiSelect"; -import { SearchSelect } from "@/components/shared/SearchSelect"; +import DefaultModelField from "./DefaultModelField"; import { ChevronRight, Info, Plus, Trash2, X } from "lucide-react"; -import { Switch } from "@/components/ui/switch"; import { AffinityControls } from "./AffinityControls"; import NonReasoningTierToggle from "./NonReasoningTierToggle"; @@ -204,11 +207,6 @@ const rowOrigin = (row: TierRow, editing: boolean): string => { return isBuiltInTierName(row.name) ? "built-in" : "custom"; }; -const defaultModelPlaceholderFor = (derivedDefaultModel: string | undefined, isCustomSet: boolean): string => { - if (derivedDefaultModel) return `Derived from tiers: ${derivedDefaultModel}`; - return isCustomSet ? "Add a model to your fallback tier" : "Add a model to the Simple or Medium tier"; -}; - const builtInTierInfo = (rowId: string): { label: string; description: string; examples: string } | undefined => { const builtIn = ALL_BUILT_IN_TIERS.find((tier) => tier === rowId); return builtIn ? TIER_DESCRIPTIONS[builtIn] : undefined; @@ -376,6 +374,8 @@ export interface ComplexityRouterConfigValue { /** An explicit pin. Unset means the default tracks the tiers - see resolveComplexityDefaultModel. */ default_model?: string; classifier_type: ClassifierType; + capability_classifier_config?: CapabilitySettings; + llm_v2_config?: FuseSettings; classifier_llm_config?: ClassifierLLMConfig; classifier_context_window_size?: number; classifier_context_budget_chars?: number; @@ -535,44 +535,6 @@ export const DEFAULT_HYBRID_BOUNDARY_MARGIN = 0.03; */ export const HEURISTIC_FIRST_MAX_TIER_KEYS = TIER_ORDER.slice(0, -1); -const PlanModeOverrideControls: React.FC<{ - value: ComplexityRouterConfigValue; - onChange: (value: ComplexityRouterConfigValue) => void; - planModeTierOptions: { value: string; label: string }[]; -}> = ({ value, onChange, planModeTierOptions }) => ( - <> -
- - onChange({ - ...value, - plan_mode_min_tier: enabled ? planModeTierOptions.at(-1)?.value : undefined, - }) - } - aria-label="Route plan-mode requests to a minimum tier" - /> - Route plan-mode requests to a minimum tier -
- - Requests from coding agents in plan mode (Claude Code, GitHub Copilot) route to at least this tier. The classifier - still wins when it picks higher, and the override only lasts while plan mode is active. - {planModeTierOptions.length === 0 && " Add models to a tier to enable this."} - - {value.plan_mode_min_tier !== undefined && ( -
- onChange({ ...value, plan_mode_min_tier: tier })} - /> -
- )} - -); - const ComplexityRouterConfig: React.FC = ({ modelInfo, value, @@ -596,6 +558,7 @@ const ComplexityRouterConfig: React.FC = ({ onAutoRouterCompressionChange, showValidationErrors = false, }) => { + const forecast = isForecastClassifier(value.classifier_type); const customTierSet = value.custom_tier_set; const tierRows = activeTierRows(value); const tierRowsError = customTierSet ? getCustomTierRowsError(customTierSet) : null; @@ -605,8 +568,6 @@ const ComplexityRouterConfig: React.FC = ({ value: row.id, label: tierRowLabel(row, value.tier_labels), })); - const derivedDefaultModel = resolveComplexityDefaultModel(value); - const defaultModelPlaceholder = defaultModelPlaceholderFor(derivedDefaultModel, Boolean(customTierSet)); const defaultModel = resolveComplexityDefaultModel(value, value.default_model); const dispatch = (action: TierSetAction) => { @@ -641,298 +602,321 @@ const ComplexityRouterConfig: React.FC = ({ tier_model_params: setTierModelParam(value.tier_model_params, tier, model, change), }); - // Clearing the select drops the key entirely rather than storing "", so an emptied pin reads as - // "track the tiers" everywhere downstream instead of as a blank model name. - const handleDefaultModelChange = (model: string | null | undefined) => { - onChange({ ...value, default_model: model || undefined }); - }; - - const handleTierLabelChange = (tier: keyof ComplexityTiers, label: string) => { - onChange({ - ...value, - tier_labels: { ...value.tier_labels, [tier]: label }, - }); - }; + const handleTierLabelChange = (tier: keyof ComplexityTiers, label: string) => + onChange({ ...value, tier_labels: { ...value.tier_labels, [tier]: label } }); return (
-

Complexity Tier Configuration

- - - +

+ {forecast ? "Solver models" : "Complexity Tier Configuration"} +

+ {!forecast && ( + + + + )}
- - - - - {!customTierSet && ( - - )} - - {tierRows.map((row, index) => { - const tierInfo = builtInTierInfo(row.id); - const label = tierRowLabel(row, value.tier_labels); - const tierMissing = showValidationErrors && row.models.length === 0; - const needsDefinition = Boolean(customTierSet) && !row.definition.trim() && !isBuiltInTierName(row.name); - const definitionMissing = showValidationErrors && needsDefinition; - const showsDisplayName = !customTierSet && !editingTiers; - return ( -
- {index > 0 && } -
- removeTierRow(row.id)} - /> - {tierInfo && !customTierSet && ( - Examples: {tierInfo.examples} - )} - {editingTiers && ( - updateTierRow(row.id, patch)} - /> - )} - {showsDisplayName && tierInfo && ( - - handleTierLabelChange(row.id as keyof ComplexityTiers, event.target.value)} - placeholder={`Display name (default: ${tierInfo.label})`} - aria-label={`Display name for the ${tierInfo.label} tier`} - /> - {value.tier_labels?.[row.id as keyof ComplexityTiers] && ( - - handleTierLabelChange(row.id as keyof ComplexityTiers, "")} - > - - - - )} - - )} - setRowModels(row, models)} - placeholder={`Select model(s) for ${label.toLowerCase()} queries`} - emptyText="No models found" - className={tierMissing ? "w-full border-destructive" : "w-full"} - /> - - handleTierModelParamChange(row.id, model, ["reasoning_effort", effort]) - } - onFastModeChange={(model, enabled) => - handleTierModelParamChange(row.id, model, ["speed", enabled ? "fast" : undefined]) - } - /> - {row.models.length > 1 && ( - - Multiple models selected: the router randomly picks among them per request (or Thompson-samples - within the pool when adaptive routing is on). - - )} - {tierMissing && The {label} tier is required} -
-
- ); - })} - - + + + + ) : ( + <> + - {customTierSet && ( - onChange(setFallbackTier(value, fallbackTierId))} - /> - )} + + + {!customTierSet && ( + + )} - + {tierRows.map((row, index) => { + const tierInfo = builtInTierInfo(row.id); + const label = tierRowLabel(row, value.tier_labels); + const tierMissing = showValidationErrors && row.models.length === 0; + const needsDefinition = + Boolean(customTierSet) && !row.definition.trim() && !isBuiltInTierName(row.name); + const definitionMissing = showValidationErrors && needsDefinition; + const showsDisplayName = !customTierSet && !editingTiers; + return ( +
+ {index > 0 && } +
+ removeTierRow(row.id)} + /> + {tierInfo && !customTierSet && ( + Examples: {tierInfo.examples} + )} + {editingTiers && ( + updateTierRow(row.id, patch)} + /> + )} + {showsDisplayName && tierInfo && ( + + + handleTierLabelChange(row.id as keyof ComplexityTiers, event.target.value) + } + placeholder={`Display name (default: ${tierInfo.label})`} + aria-label={`Display name for the ${tierInfo.label} tier`} + /> + {value.tier_labels?.[row.id as keyof ComplexityTiers] && ( + + handleTierLabelChange(row.id as keyof ComplexityTiers, "")} + > + + + + )} + + )} + setRowModels(row, models)} + placeholder={`Select model(s) for ${label.toLowerCase()} queries`} + emptyText="No models found" + className={tierMissing ? "w-full border-destructive" : "w-full"} + /> + + handleTierModelParamChange(row.id, model, ["reasoning_effort", effort]) + } + onFastModeChange={(model, enabled) => + handleTierModelParamChange(row.id, model, ["speed", enabled ? "fast" : undefined]) + } + /> + {row.models.length > 1 && ( + + Multiple models selected: the router randomly picks among them per request (or + Thompson-samples within the pool when adaptive routing is on). + + )} + {tierMissing && The {label} tier is required} +
+
+ ); + })} -
-
- Default Model - - - -
- - - Used when the tier the request lands in has no model, and when the classifier fails with "Route to - the default model" selected. - -
- - + + {customTierSet && ( + onChange(setFallbackTier(value, fallbackTierId))} + /> + )} + + + + )} + {!forecast && } -
- {[ - { - key: "classifier", - label: Advanced: Classification Method, - children: ( - - ), - }, - { - key: "adaptive", - label: Advanced: Adaptive Routing, - children: ( - - - - ), - }, - { - key: "affinity", - label: Advanced: Affinity, - children: , - }, - { - key: "modality", - label: Advanced: Modality Routing, - children: , - }, - { - key: "plan-mode", - label: Advanced: Plan-Mode Override, - children: ( - - ), - }, - { - key: "context-window", - label: Advanced: Context Window Escalation, - children: , - }, - { - key: "stall-escalation", - label: Advanced: Stalled Task Escalation, - children: ( - - - - ), - }, - { - key: "response", - label: Advanced: Response Format, - children: , - }, - ...(onEscalationKeywordsChange - ? [ - { - key: "escalation", - label: Advanced: Escalation Keywords, - children: ( - - - - ), - }, - ] - : []), - ...(onAutoRouterCompressionChange - ? [ - { - key: "compression", - label: Advanced: Compression, - children: ( - - ), - }, - ] - : []), - ...(onKeywordTierRulesChange || onSemanticMatchingEnabledChange - ? [ - { - key: "keyword-semantic", - label: Advanced: Keyword/Semantic Matching, - children: ( - <> - {onKeywordTierRulesChange && ( - - )} - {onKeywordTierRulesChange && onSemanticMatchingEnabledChange && } - {onSemanticMatchingEnabledChange && ( - - )} - - ), - }, - ] - : []), - ].map(({ key, label, children }) => ( - - - - {label} - - {children} - - ))} -
+ + {forecast && ( + <> + + + + )} +
+ {[ + ...(!forecast + ? [ + { + key: "classifier", + label: Advanced: Classification Method, + children: ( + + ), + }, + ] + : []), + ...(value.classifier_type !== "llm_v2" + ? [ + { + key: "adaptive", + label: Advanced: Adaptive Routing, + children: ( + + + + ), + }, + ] + : []), + { + key: "affinity", + label: Advanced: Affinity, + children: , + }, + { + key: "modality", + label: Advanced: Modality Routing, + children: , + }, + { + key: "plan-mode", + label: Advanced: Plan-Mode Override, + children: ( + + ), + }, + { + key: "context-window", + label: Advanced: Context Window Escalation, + children: , + }, + { + key: "stall-escalation", + label: Advanced: Stalled Task Escalation, + children: ( + + + + ), + }, + { + key: "response", + label: Advanced: Response Format, + children: , + }, + ...(onEscalationKeywordsChange + ? [ + { + key: "escalation", + label: Advanced: Escalation Keywords, + children: ( + + + + ), + }, + ] + : []), + ...(onAutoRouterCompressionChange + ? [ + { + key: "compression", + label: Advanced: Compression, + children: ( + + ), + }, + ] + : []), + ...(onKeywordTierRulesChange || onSemanticMatchingEnabledChange + ? [ + { + key: "keyword-semantic", + label: ( + Advanced: Keyword/Semantic Matching + ), + children: ( + <> + {onKeywordTierRulesChange && ( + + )} + {onKeywordTierRulesChange && onSemanticMatchingEnabledChange && } + {onSemanticMatchingEnabledChange && ( + + )} + + ), + }, + ] + : []), + ].map(({ key, label, children }) => ( + + + + {label} + + {children} + + ))} +
+
); }; diff --git a/ui/litellm-dashboard/src/components/add_model/ComplexityRouterFastMode.integration.test.tsx b/ui/litellm-dashboard/src/components/add_model/ComplexityRouterFastMode.integration.test.tsx index 34d14091bde..810289da79f 100644 --- a/ui/litellm-dashboard/src/components/add_model/ComplexityRouterFastMode.integration.test.tsx +++ b/ui/litellm-dashboard/src/components/add_model/ComplexityRouterFastMode.integration.test.tsx @@ -1,11 +1,12 @@ import userEvent from "@testing-library/user-event"; import React from "react"; import { describe, expect, it, vi } from "vitest"; -import { renderWithProviders, screen } from "../../../tests/test-utils"; +import { renderWithProviders, screen, within } from "../../../tests/test-utils"; import { buildUpdatedComplexityRouterConfig, hydrateComplexityRouterConfig, } from "../edit_auto_router/edit_auto_router_modal"; +import type { KeywordTierRule } from "./KeywordTierRules"; import type { ModelGroup } from "../llm_calls/fetch_models"; import ComplexityRouterConfig, { type ComplexityRouterConfigValue } from "./ComplexityRouterConfig"; @@ -50,8 +51,8 @@ it.each([false, true])("edits and round-trips independent model settings with cu const view = renderWithProviders(editor(initial)); const fast = () => screen.getByRole("switch", { name: `Fast mode for primary in the ${label} tier` }); - expect(screen.getAllByRole("switch", { name: /^Fast mode for/ })).toHaveLength(3); - expect(screen.queryByRole("switch", { name: /^Fast mode for (blocked|missing)/ })).not.toBeInTheDocument(); + expect(screen.getAllByRole("switch", { name: /^Fast mode for/ })).toHaveLength(4); + expect(screen.queryByRole("switch", { name: /^Fast mode for missing/ })).not.toBeInTheDocument(); expect(screen.queryByRole("combobox", { name: /^Reasoning effort for secondary/ })).not.toBeInTheDocument(); expect(screen.getByRole("switch", { name: `Fast mode for secondary in the ${label} tier` })).toBeChecked(); expect(fast()).not.toBeChecked(); @@ -125,19 +126,170 @@ it.each([false, true])("edits and round-trips independent model settings with cu ); }); -describe("Fast mode metadata", () => { - it("offers nothing before model capabilities load and leaves stored speed untouched", () => { - const value: ComplexityRouterConfigValue = { - tiers: { SIMPLE: ["primary"], MEDIUM: [], COMPLEX: [], REASONING: [] }, - classifier_type: "heuristic", - tier_model_params: { SIMPLE: { primary: { speed: "fast" } } }, - }; - const onChange = vi.fn(); - renderWithProviders(); - expect(screen.queryByRole("switch", { name: /^Fast mode for/ })).not.toBeInTheDocument(); - expect(onChange).not.toHaveBeenCalled(); - expect(buildUpdatedComplexityRouterConfig({}, value).tier_model_configs).toEqual({ - SIMPLE: [{ model_name: "primary", litellm_params: { speed: "fast" } }], - }); +it.each(["capability", "llm_v2"] as const)("preserves Fast mode controls for %s solvers", async (classifierType) => { + const user = userEvent.setup(); + const initial: ComplexityRouterConfigValue = { + classifier_type: classifierType, + classifier_llm_config: { model: "primary", timeout_ms: 3000 }, + tiers: { SIMPLE: ["primary"], MEDIUM: [], COMPLEX: [], REASONING: ["blocked"] }, + capability_classifier_config: { efficient_tier: "SIMPLE", capable_tier: "REASONING", base_threshold: 0.7 }, + llm_v2_config: { + efficient_profile: "Small solver", + capable_profile: "Large solver", + harness: "One attempt", + max_quality_gap: 0.05, + }, + tier_model_params: { SIMPLE: { primary: { reasoning_effort: "high", max_tokens: 1024 } } }, + }; + const onChange = vi.fn<(value: ComplexityRouterConfigValue) => void>(); + const editor = (value: ComplexityRouterConfigValue) => ( + + ); + const view = renderWithProviders(editor(initial)); + const fast = () => screen.getByRole("switch", { name: "Fast mode for primary in the Efficient solver tier" }); + expect(screen.getAllByRole("switch", { name: /^Fast mode for/ })).toHaveLength(1); + expect(fast()).not.toBeChecked(); + await user.click(fast()); + const enabled = onChange.mock.lastCall![0]; + expect(enabled.tier_model_params?.SIMPLE.primary).toEqual({ + reasoning_effort: "high", + max_tokens: 1024, + speed: "fast", + }); + const saved = buildUpdatedComplexityRouterConfig({}, enabled); + expect(saved.tier_model_configs).toEqual({ + SIMPLE: [{ model_name: "primary", litellm_params: { reasoning_effort: "high", max_tokens: 1024, speed: "fast" } }], + }); + view.rerender(editor(hydrateComplexityRouterConfig(saved, undefined))); + expect(fast()).toBeChecked(); + await user.click(fast()); + expect(onChange.mock.lastCall![0].tier_model_params?.SIMPLE.primary).toEqual({ + reasoning_effort: "high", + max_tokens: 1024, }); }); + +describe("Fast mode metadata", () => { + it.each(["heuristic", "capability", "llm_v2"] as const)( + "can clear stored Fast mode without current capability metadata for %s", + async (classifier_type) => { + const user = userEvent.setup(); + const value: ComplexityRouterConfigValue = { + tiers: { SIMPLE: ["primary"], MEDIUM: [], COMPLEX: [], REASONING: ["secondary"] }, + classifier_type, + tier_model_params: { SIMPLE: { primary: { speed: "fast", max_tokens: 512 } } }, + }; + const onChange = vi.fn<(value: ComplexityRouterConfigValue) => void>(); + const editor = (current: ComplexityRouterConfigValue, info: ModelGroup[]) => ( + + ); + const view = renderWithProviders(editor(value, [])); + const fast = () => screen.getByRole("switch", { name: /^Fast mode for primary/ }); + expect(fast()).toBeChecked(); + expect(onChange).not.toHaveBeenCalled(); + view.rerender(editor(value, [{ model_group: "primary", supports_fast_mode: false }])); + expect(fast()).toBeChecked(); + await user.click(fast()); + const cleared = onChange.mock.lastCall![0]; + expect(cleared.tier_model_params?.SIMPLE.primary).toEqual({ max_tokens: 512 }); + const saved = buildUpdatedComplexityRouterConfig({}, cleared); + expect(saved.tier_model_configs).toEqual({ + SIMPLE: [{ model_name: "primary", litellm_params: { max_tokens: 512 } }], + }); + view.rerender(editor(hydrateComplexityRouterConfig(saved, undefined), [])); + expect(screen.queryByRole("switch", { name: /^Fast mode for primary/ })).not.toBeInTheDocument(); + view.rerender(editor(cleared, modelInfo)); + expect(fast()).not.toBeChecked(); + }, + ); +}); + +it.each(["MEDIUM", "REASONING"])("clears a legacy Capability pool while reconciling plan floor %s", async (floor) => { + const user = userEvent.setup(); + const stored = { + classifier_type: "capability" as const, + plan_mode_min_tier: floor, + tiers: { SIMPLE: ["primary"], MEDIUM: ["secondary"], COMPLEX: [], REASONING: ["blocked"] }, + tier_model_configs: { MEDIUM: [{ model_name: "secondary", litellm_params: { speed: "fast" } }] }, + }; + const value = hydrateComplexityRouterConfig(stored, undefined); + const onChange = vi.fn<(value: ComplexityRouterConfigValue) => void>(); + renderWithProviders(); + await user.click(screen.getByRole("button", { name: "Advanced routing options" })); + expect(screen.getByRole("switch", { name: "Fast mode for secondary in the Medium routing pool tier" })).toBeChecked(); + expect(onChange).not.toHaveBeenCalled(); + await user.click(screen.getByRole("combobox", { name: "Select medium routing pool models" })); + await user.click(await screen.findByRole("option", { name: "secondary" })); + await user.keyboard("{Escape}"); + const cleared = onChange.mock.lastCall![0]; + expect(cleared.tiers.MEDIUM).toEqual([]); + expect(cleared.plan_mode_min_tier).toBe(floor === "MEDIUM" ? undefined : floor); + expect(cleared.tier_model_params).toBeUndefined(); + expect(buildUpdatedComplexityRouterConfig(stored, cleared).tiers).toEqual({ + SIMPLE: ["primary"], + REASONING: ["blocked"], + }); +}); + +it.each(["capability", "llm_v2"] as const)( + "shows and clears a persisted default model in %s", + async (classifier_type) => { + const user = userEvent.setup(); + const stored = { + classifier_type, + default_model: "legacy-default", + tiers: { SIMPLE: ["primary"], REASONING: ["secondary"] }, + }; + const onChange = vi.fn<(value: ComplexityRouterConfigValue) => void>(); + const editor = (value: ComplexityRouterConfigValue) => ( + + ); + const view = renderWithProviders(editor(hydrateComplexityRouterConfig(stored, undefined))); + await user.click(screen.getByRole("button", { name: "Advanced routing options" })); + const select = () => screen.getByRole("combobox", { name: "Default model" }); + expect(select()).toHaveValue("legacy-default"); + expect(onChange).not.toHaveBeenCalled(); + await user.click(select()); + await user.click(await screen.findByRole("option", { name: "blocked" })); + const changed = onChange.mock.lastCall![0]; + expect(buildUpdatedComplexityRouterConfig(stored, changed).default_model).toBe("blocked"); + view.rerender(editor(changed)); + await user.click( + within(screen.getByRole("group", { name: "Default model configuration" })).getByRole("button", { name: "Clear" }), + ); + const cleared = onChange.mock.lastCall![0]; + expect(cleared.default_model).toBeUndefined(); + const saved = buildUpdatedComplexityRouterConfig(stored, cleared); + expect(saved).not.toHaveProperty("default_model"); + view.rerender(editor(hydrateComplexityRouterConfig(saved, undefined))); + expect(select()).toHaveValue(""); + expect(select()).toHaveAttribute("placeholder", expect.stringContaining("primary")); + }, +); + +it.each(["capability", "llm_v2"] as const)("offers only populated keyword targets for %s", async (classifier_type) => { + const user = userEvent.setup(); + const value: ComplexityRouterConfigValue = { + classifier_type, + tiers: { SIMPLE: ["primary"], MEDIUM: [], COMPLEX: [], REASONING: ["secondary"] }, + }; + const onRulesChange = vi.fn<(rules: KeywordTierRule[]) => void>(); + const editor = (rules: KeywordTierRule[]) => ( + + ); + const view = renderWithProviders(editor([])); + await user.click(screen.getByRole("button", { name: "Advanced routing options" })); + await user.click(screen.getByText("Advanced: Keyword/Semantic Matching")); + await user.click(screen.getByRole("button", { name: "Add keyword rule" })); + const rules = onRulesChange.mock.lastCall![0]; + expect(rules[0].tier).toBe("SIMPLE"); + view.rerender(editor(rules)); + await user.click(screen.getByRole("combobox", { name: "Route keyword rule 1 to tier" })); + expect((await screen.findAllByRole("option")).map((option) => option.textContent)).toEqual(["Simple", "Reasoning"]); +}); diff --git a/ui/litellm-dashboard/src/components/add_model/DefaultModelField.tsx b/ui/litellm-dashboard/src/components/add_model/DefaultModelField.tsx new file mode 100644 index 00000000000..a3ab85f7c13 --- /dev/null +++ b/ui/litellm-dashboard/src/components/add_model/DefaultModelField.tsx @@ -0,0 +1,56 @@ +import React from "react"; +import { Info } from "lucide-react"; +import { SearchSelect } from "@/components/shared/SearchSelect"; +import { SimpleTooltip } from "@/components/ui/tooltip"; +import type { ComplexityRouterConfigValue } from "./ComplexityRouterConfig"; +import { isForecastClassifier } from "./forecast_classifier_config"; +import { resolveComplexityDefaultModel } from "./tier_rows"; + +interface DefaultModelFieldProps { + value: ComplexityRouterConfigValue; + onChange: (value: ComplexityRouterConfigValue) => void; + modelOptions: { value: string; label: string }[]; +} + +const defaultModelPlaceholderFor = (derivedDefaultModel: string | undefined, isCustomSet: boolean): string => { + if (derivedDefaultModel) return `Derived from tiers: ${derivedDefaultModel}`; + return isCustomSet ? "Add a model to your fallback tier" : "Add a model to the Simple or Medium tier"; +}; + +const DefaultModelField = ({ value, onChange, modelOptions }: DefaultModelFieldProps) => { + const defaultModelPlaceholder = defaultModelPlaceholderFor( + resolveComplexityDefaultModel(value), + Boolean(value.custom_tier_set), + ); + // Clearing the select drops the key entirely rather than storing "", so an emptied pin reads as + // "track the tiers" everywhere downstream instead of as a blank model name. + const handleDefaultModelChange = (model: string | null | undefined) => { + onChange({ ...value, default_model: model || undefined }); + }; + + return ( +
+
+ Default Model + + + +
+ + + {isForecastClassifier(value.classifier_type) + ? "Used when routing cannot find a suitable model. Classifier failures route to the capable solver." + : 'Used when the tier the request lands in has no model, and when the classifier fails with "Route to the default model" selected.'} + +
+ ); +}; + +export default DefaultModelField; diff --git a/ui/litellm-dashboard/src/components/add_model/ForecastClassifierConfig.integration.test.tsx b/ui/litellm-dashboard/src/components/add_model/ForecastClassifierConfig.integration.test.tsx new file mode 100644 index 00000000000..c249a63899a --- /dev/null +++ b/ui/litellm-dashboard/src/components/add_model/ForecastClassifierConfig.integration.test.tsx @@ -0,0 +1,224 @@ +import React, { useState } from "react"; +import { describe, expect, it, vi } from "vitest"; +import userEvent from "@testing-library/user-event"; +import { fireEvent, renderWithProviders, screen } from "../../../tests/test-utils"; +import ClassificationMethodConfig from "./ClassificationMethodConfig"; +import AutoRouterClassifierTabs from "./AutoRouterClassifierTabs"; +import ForecastClassifierConfig from "./ForecastClassifierConfig"; +import type { ComplexityRouterConfigValue } from "./ComplexityRouterConfig"; +import { getForecastConfigError, isForecastClassifier } from "./forecast_classifier_config"; +import { buildUpdatedComplexityRouterConfig } from "../edit_auto_router/edit_auto_router_modal"; + +vi.mock("@/components/networking", async (importOriginal) => ({ + ...(await importOriginal()), + getComplexityScorerDefaults: vi.fn(async () => ({ + tier_boundaries: {}, + token_thresholds: {}, + dimension_weights: {}, + })), +})); + +const initial: ComplexityRouterConfigValue = { + classifier_type: "capability", + classifier_llm_config: { model: "judge", timeout_ms: 20000 }, + tiers: { SIMPLE: ["efficient"], MEDIUM: [], COMPLEX: [], REASONING: ["capable"] }, + capability_classifier_config: { efficient_tier: "SIMPLE", capable_tier: "REASONING", base_threshold: 0.7 }, +}; +const fuseInitial: ComplexityRouterConfigValue = { + ...initial, + classifier_type: "llm_v2", + capability_classifier_config: undefined, + adaptive: false, + llm_v2_config: { + efficient_profile: "Small solver", + capable_profile: "Larger solver", + harness: "One attempt", + max_quality_gap: 0.05, + }, +}; +const options = ["judge", "efficient", "capable"].map((model) => ({ value: model, label: model })); + +function Form({ initialValue = initial }: { initialValue?: ComplexityRouterConfigValue }) { + const [value, setValue] = useState(initialValue); + const [saved, setSaved] = useState(""); + return ( + <> + + {isForecastClassifier(value.classifier_type) ? ( + + ) : ( + + )} + + + {saved} + + ); +} + +describe("forecast classifier form", () => { + it("switches a populated standard router to Capability without saving hidden pools or their overrides", () => { + renderWithProviders( + , + ); + fireEvent.click(screen.getByRole("tab", { name: "Capability" })); + fireEvent.change(screen.getByLabelText("Solve probability threshold"), { target: { value: "0.7" } }); + expect(screen.getByRole("button", { name: "Save configuration" })).toBeEnabled(); + fireEvent.click(screen.getByRole("button", { name: "Save configuration" })); + const output = screen.getByRole("status", { name: "Saved configuration" }); + expect(output).toHaveTextContent('"classifier_type":"capability"'); + expect(output).toHaveTextContent('"SIMPLE":["efficient","second-efficient"]'); + expect(output).toHaveTextContent('"REASONING":["capable"]'); + expect(output).toHaveTextContent('"reasoning_effort":"low","speed":"fast","max_tokens":1024'); + expect(output).toHaveTextContent('"reasoning_effort":"high"'); + expect(output).toHaveTextContent('"adaptive":true'); + expect(output).not.toHaveTextContent("leftover-medium"); + expect(output).not.toHaveTextContent("leftover-complex"); + expect(output).not.toHaveTextContent('"plan_mode_min_tier"'); + }); + + it.each(["capability", "llm_v2"] as const)( + "carries non-default solver assignments when switching away from %s", + (source) => { + const pair = { efficient_tier: "MEDIUM", capable_tier: "COMPLEX" }; + const previous: ComplexityRouterConfigValue = { + ...(source === "capability" ? initial : fuseInitial), + tiers: { SIMPLE: [], MEDIUM: ["efficient"], COMPLEX: ["capable"], REASONING: [] }, + capability_classifier_config: + source === "capability" ? { ...initial.capability_classifier_config!, ...pair } : undefined, + llm_v2_config: source === "llm_v2" ? { ...fuseInitial.llm_v2_config!, ...pair } : undefined, + plan_mode_min_tier: "COMPLEX", + tier_model_params: { MEDIUM: { efficient: { max_tokens: 128 } }, COMPLEX: { capable: { speed: "fast" } } }, + }; + renderWithProviders(); + fireEvent.click(screen.getByRole("tab", { name: source === "capability" ? "Fuse v2" : "Capability" })); + if (source === "capability") { + fireEvent.change(screen.getByLabelText("Efficient solver profile"), { target: { value: "Small solver" } }); + fireEvent.change(screen.getByLabelText("Capable solver profile"), { target: { value: "Large solver" } }); + fireEvent.change(screen.getByLabelText("Harness and budget"), { target: { value: "One attempt" } }); + fireEvent.change(screen.getByLabelText("Maximum quality gap"), { target: { value: "0.05" } }); + } else { + fireEvent.change(screen.getByLabelText("Solve probability threshold"), { target: { value: "0.7" } }); + } + expect(screen.getByRole("button", { name: "Save configuration" })).toBeEnabled(); + fireEvent.click(screen.getByRole("button", { name: "Save configuration" })); + const output = screen.getByRole("status", { name: "Saved configuration" }); + expect(output).toHaveTextContent('"efficient_tier":"MEDIUM","capable_tier":"COMPLEX"'); + expect(output).toHaveTextContent('"tiers":{"MEDIUM":["efficient"],"COMPLEX":["capable"]}'); + expect(output).toHaveTextContent('"plan_mode_min_tier":"COMPLEX"'); + expect(output).toHaveTextContent('"max_tokens":128'); + expect(output).toHaveTextContent('"speed":"fast"'); + }, + ); + + it("keeps decimal and negative numbers when entered one character at a time", async () => { + const user = userEvent.setup(); + renderWithProviders(); + const threshold = screen.getByLabelText("Solve probability threshold"); + await user.clear(threshold); + await user.type(threshold, "0.65"); + expect(threshold).toHaveValue(0.65); + await user.click(screen.getByRole("button", { name: "Classifier options" })); + await user.click(screen.getByRole("switch", { name: "Use fitted calibration" })); + await user.type(screen.getByLabelText("Efficient intercept"), "-0.3"); + expect(screen.getByLabelText("Efficient intercept")).toHaveValue(-0.3); + }); + + it.each([ + ["capability", "LLM Classifier"], + ["capability", "Heuristic first"], + ["capability", "Hybrid"], + ["llm_v2", "LLM Classifier"], + ["llm_v2", "Heuristic first"], + ["llm_v2", "Hybrid"], + ] as const)("restores the current rubric when switching %s through Complexity to %s", async (source, target) => { + const user = userEvent.setup(); + renderWithProviders(); + fireEvent.click(screen.getByRole("tab", { name: "Complexity" })); + fireEvent.click(screen.getByRole("radio", { name: new RegExp(`^${target}`) })); + await user.click(screen.getByRole("combobox", { name: "Classifier Model" })); + await user.click(screen.getByRole("option", { name: "judge", exact: true })); + fireEvent.click(screen.getByRole("button", { name: "Save configuration" })); + const output = screen.getByRole("status", { name: "Saved configuration" }); + expect(output).toHaveTextContent('"classification_rubric":"agentic"'); + expect(output).toHaveTextContent('"model":"judge"'); + expect(output).toHaveTextContent('"timeout_ms":3000'); + expect(output).not.toHaveTextContent('"capability_classifier_config"'); + expect(output).not.toHaveTextContent('"llm_v2_config"'); + }); + + it("saves capability threshold edits together with fitted calibration", () => { + renderWithProviders(); + fireEvent.change(screen.getByLabelText("Solve probability threshold"), { target: { value: "0.6" } }); + fireEvent.click(screen.getByRole("button", { name: "Classifier options" })); + fireEvent.click(screen.getByRole("switch", { name: "Use fitted calibration" })); + expect(screen.getByRole("button", { name: "Save configuration" })).toBeDisabled(); + fireEvent.change(screen.getByLabelText("Calibration version"), { target: { value: "eval-a" } }); + fireEvent.change(screen.getByLabelText("Efficient slope"), { target: { value: "1.2" } }); + fireEvent.change(screen.getByLabelText("Efficient intercept"), { target: { value: "-0.3" } }); + fireEvent.click(screen.getByRole("button", { name: "Save configuration" })); + const output = screen.getByRole("status", { name: "Saved configuration" }); + expect(output).toHaveTextContent('"base_threshold":0.6'); + expect(output).toHaveTextContent('"calibration":{"version":"eval-a","slope":1.2,"intercept":-0.3}'); + fireEvent.change(screen.getByLabelText("Solve probability threshold"), { target: { value: "" } }); + expect(screen.getByRole("button", { name: "Save configuration" })).toBeDisabled(); + }); + + it("switches to Fuse, requires solver context, and saves the filled fields", () => { + renderWithProviders(); + fireEvent.click(screen.getByRole("tab", { name: "Fuse v2" })); + expect(screen.queryByLabelText("Solve probability threshold")).not.toBeInTheDocument(); + expect(screen.getByRole("button", { name: "Save configuration" })).toBeDisabled(); + fireEvent.change(screen.getByLabelText("Efficient solver profile"), { + target: { value: "Short reasoning budget" }, + }); + fireEvent.change(screen.getByLabelText("Capable solver profile"), { target: { value: "Larger reasoning budget" } }); + fireEvent.change(screen.getByLabelText("Harness and budget"), { + target: { value: "Shell and test runner, one attempt" }, + }); + fireEvent.change(screen.getByLabelText("Maximum quality gap"), { target: { value: "0.05" } }); + fireEvent.click(screen.getByRole("button", { name: "Save configuration" })); + const output = screen.getByRole("status", { name: "Saved configuration" }); + expect(output).toHaveTextContent('"classifier_type":"llm_v2"'); + expect(output).toHaveTextContent('"efficient_profile":"Short reasoning budget"'); + expect(output).toHaveTextContent('"capable_profile":"Larger reasoning budget"'); + expect(output).toHaveTextContent('"harness":"Shell and test runner, one attempt"'); + expect(output).toHaveTextContent('"max_quality_gap":0.05'); + expect(output).toHaveTextContent('"adaptive":false'); + expect(output).not.toHaveTextContent('"capability_classifier_config"'); + }); +}); diff --git a/ui/litellm-dashboard/src/components/add_model/ForecastClassifierConfig.tsx b/ui/litellm-dashboard/src/components/add_model/ForecastClassifierConfig.tsx new file mode 100644 index 00000000000..b901a509435 --- /dev/null +++ b/ui/litellm-dashboard/src/components/add_model/ForecastClassifierConfig.tsx @@ -0,0 +1,433 @@ +import React from "react"; +import { Collapsible, CollapsibleContent, CollapsibleTrigger } from "@/components/ui/collapsible"; +import { ChevronRight } from "lucide-react"; +import { Input } from "@/components/ui/input"; +import { Label } from "@/components/ui/label"; +import { Textarea } from "@/components/ui/textarea"; +import { Switch } from "@/components/ui/switch"; +import { SearchSelect } from "@/components/shared/SearchSelect"; +import { MultiSelect } from "@/components/shared/MultiSelect"; +import { + type ComplexityRouterConfigValue, + type ClassificationFrequency, + classificationFrequency, + withClassificationFrequency, + DEFAULT_CLASSIFIER_TIMEOUT_MS, +} from "./ComplexityRouterConfig"; +import { + forecastTierNames, + forecastModels, + getForecastConfigError, + newCapabilitySettings, + newFuseSettings, + type CapabilitySettings, + type FuseSettings, +} from "./forecast_classifier_config"; +import ClassifierReasoningEffortSelect from "./ClassifierReasoningEffortSelect"; +import ClassifierCircuitBreakerConfig from "./ClassifierCircuitBreakerConfig"; +import ClassifierVisionConfig from "./ClassifierVisionConfig"; +import TierModelEffortRows from "./TierModelEffortRows"; +import { activeTierRows } from "./tier_rows"; +import { setTierModels } from "./tier_set_actions"; +import { tierRowLabel, setTierModelParam, setTierModelReasoningEffort } from "./complexity_router_tiers"; + +interface Props { + value: ComplexityRouterConfigValue; + onChange: (value: ComplexityRouterConfigValue) => void; + modelOptions: { value: string; label: string }[]; + effortOptionsByModel: Record; +} + +const NumberField = ({ + label, + value, + onChange, + min, + max, + step = "any", + help, +}: { + label: string; + value: number; + onChange: (value: number) => void; + min?: number; + max?: number; + step?: number | "any"; + help?: string; +}) => { + const id = React.useId(); + return ( +
+ + onChange(event.target.value === "" ? Number.NaN : Number(event.target.value))} + /> + {help &&

{help}

} +
+ ); +}; + +export const ForecastSolverModels = ({ + value, + onChange, + modelOptions, + effortOptionsByModel, + fastModeByModel, + additionalPoolsOnly = false, +}: Props & { fastModeByModel: Record; additionalPoolsOnly?: boolean }) => { + const id = React.useId(); + const names = forecastTierNames(value); + const additionalRows = + value.classifier_type === "capability" + ? activeTierRows(value) + .filter((row) => !names.includes(row.id) && row.models.length > 0) + .map((row) => ({ tier: row.id, label: `${tierRowLabel(row, value.tier_labels)} routing pool` })) + : []; + const rows = additionalPoolsOnly + ? additionalRows + : names.map((tier, index) => ({ tier, label: index === 0 ? "Efficient solver" : "Capable solver" })); + if (rows.length === 0) return null; + return ( +
+ {rows.map(({ tier, label }) => { + const models = forecastModels(value.tiers, tier); + const setModels = (next: string[]) => onChange(setTierModels(value, tier, next)); + return ( +
+ + {value.classifier_type === "llm_v2" ? ( + setModels(model ? [model] : [])} + /> + ) : ( + + )} + [model, efforts ?? []]), + )} + paramsByModel={value.tier_model_params?.[tier] ?? {}} + fastModeByModel={fastModeByModel} + onFastModeChange={(model, enabled) => + onChange({ + ...value, + tier_model_params: setTierModelParam(value.tier_model_params, tier, model, [ + "speed", + enabled ? "fast" : undefined, + ]), + }) + } + onEffortChange={(model, effort) => + onChange({ + ...value, + tier_model_params: setTierModelReasoningEffort(value.tier_model_params, tier, model, effort), + }) + } + /> +
+ ); + })} + {!additionalPoolsOnly && ( +

+ Invalid forecasts and classifier failures route to the capable solver +

+ )} +
+ ); +}; + +const CalibrationFields = ({ + label, + value, + onChange, + bounded = false, +}: { + label: string; + bounded?: boolean; + value: { slope: number; intercept: number }; + onChange: (value: { slope: number; intercept: number }) => void; +}) => ( +
+ onChange({ ...value, slope })} + /> + onChange({ ...value, intercept })} + /> +
+); + +const emptyCoefficients = () => ({ slope: Number.NaN, intercept: Number.NaN }); + +const ForecastClassifierConfig = ({ value, onChange, modelOptions, effortOptionsByModel }: Props) => { + const id = React.useId(); + const isCapability = value.classifier_type === "capability"; + const capability = value.capability_classifier_config ?? newCapabilitySettings(); + const fuse = value.llm_v2_config ?? newFuseSettings(); + const config = isCapability ? capability : fuse; + const llm = value.classifier_llm_config ?? { model: "", timeout_ms: DEFAULT_CLASSIFIER_TIMEOUT_MS }; + const updateCapability = (next: CapabilitySettings) => onChange({ ...value, capability_classifier_config: next }); + const updateFuse = (next: FuseSettings) => onChange({ ...value, llm_v2_config: next }); + const updateTransport = (patch: { max_output_tokens?: number; response_format?: "json_schema" | "json_object" }) => + isCapability ? updateCapability({ ...capability, ...patch }) : updateFuse({ ...fuse, ...patch }); + const setCalibrationVersion = (version: string) => { + if (isCapability && capability.calibration) + updateCapability({ ...capability, calibration: { ...capability.calibration, version } }); + if (!isCapability && fuse.calibration) updateFuse({ ...fuse, calibration: { ...fuse.calibration, version } }); + }; + const error = getForecastConfigError(value); + return ( +
+

+ {isCapability + ? "Forecasts whether the efficient solver can complete the task using the bundled capability card" + : "Forecasts success for both solvers and selects efficient when the estimated quality gap is within your allowance"} +

+
+ + { + if (model === llm.model) return; + onChange({ ...value, classifier_llm_config: { ...llm, model: model ?? "", reasoning_effort: undefined } }); + }} + /> +
+ {isCapability ? ( + <> + updateCapability({ ...capability, base_threshold })} + /> + + ) : ( + <> + {(["efficient_profile", "capable_profile", "harness"] as const).map((field) => { + const label = { + efficient_profile: "Efficient solver profile", + capable_profile: "Capable solver profile", + harness: "Harness and budget", + }[field]; + return ( +
+ +