langgraph-prebuilt was previously pulled in as a transitive of langgraph
so PyPI license metadata was reported as unknown. Now that it is
explicitly pinned (==1.0.8) to avoid the broken 1.0.9 release, the
license checker flags it. It is published under MIT by the same
langchain-ai/langgraph repository as langgraph itself.
langgraph-prebuilt 1.0.9 imports ExecutionInfo and ServerInfo from
langgraph.runtime, but those symbols are not exported until
langgraph 1.1.0. Our pin of langgraph==1.0.10 allows
langgraph-prebuilt<1.1.0,>=1.0.8, and uv resolves to 1.0.9 (the
latest in range), which breaks at import time in every test that
touches langgraph.prebuilt (e.g. tests/pass_through_tests/test_mcp_routes.py):
ImportError: cannot import name 'ExecutionInfo' from 'langgraph.runtime'
Pinning langgraph-prebuilt to 1.0.8 pairs correctly with
langgraph==1.0.10 and restores the import path.
Bedrock rejects clear_thinking_20251015 unless thinking is enabled or adaptive.
Inject minimal extended thinking and interleaved-thinking beta when Claude Code
sends context_management without thinking. Adds unit tests.
Made-with: Cursor
Unit Tests: Proxy DB Operations / proxy-db (auth-checks, tests/proxy_unit_tests/test_auth_checks.py tests/proxy_unit_tests/test_user_api_key_auth.py, 20, 8) (push) Has been cancelled
Unit Tests: Proxy DB Operations / proxy-db (remaining, tests/proxy_unit_tests --ignore=tests/proxy_unit_tests/test_key_generate_prisma.py --ignore=tests/proxy_unit_tests/test_auth_checks.py --ignore=tests/proxy_unit_tests/test_user_api_key_auth.py, 30, 8) (push) Has been cancelled
* feat(proxy): add NO_OPENAPI env var to disable /openapi.json endpoint (#25696)
* feat(proxy): add NO_OPENAPI env var to disable /openapi.json endpoint - Fixes#25538
* test(proxy): add tests for _get_openapi_url
---------
Co-authored-by: Progressive-engg <lov.kumari55@gmail.com>
* feat(prometheus): add api_provider label to spend metric (#25693)
* feat(prometheus): add api_provider label to spend metric
Add `api_provider` to `litellm_spend_metric` labels so users can
build Grafana dashboards that break down spend by cloud provider
(e.g. bedrock, anthropic, openai, azure, vertex_ai).
The `api_provider` label already exists in UserAPIKeyLabelValues and
is populated from `standard_logging_payload["custom_llm_provider"]`,
but was not included in the spend metric's label list.
* add api_provider to requests metric + add test
Address review feedback:
- Add api_provider to litellm_requests_metric too (same call-site as
spend metric, keeps label sets in sync)
- Add test_api_provider_in_spend_and_requests_metrics following the
existing pattern in test_prometheus_labels.py
* fix: ensure `litellm_metadata` is attached to `pre_call` guardrail to align with `post_call` guardrail (#25641)
* fix: ensure `litellm_metadata` is attached to pre_call to align with post_call
* refactor: remove unused BaseTranslation._ensure_litellm_metadata
* refactor: module level imports for ensure_litellm_metadata and CodeQL
* fix: update based off of Codex comment
* revert: undo usage of `_guardrail_litellm_metadata`
* feat: add pricing entry for openrouter/google/gemini-3.1-flash-lite-preview (#25610)
* fix(bedrock): skip synthetic tool injection for json_object with no schema (#25740)
When response_format={"type": "json_object"} is sent without a JSON
schema, _create_json_tool_call_for_response_format builds a tool with an
empty schema (properties: {}). The model follows the empty schema and
returns {} instead of the actual JSON the caller asked for.
This patch:
- Skips synthetic json_tool_call injection when no schema is provided.
The model already returns JSON when the prompt asks for it.
- Fixes finish_reason: after _filter_json_mode_tools strips all
synthetic tool calls, finish_reason stays "tool_calls" instead of
"stop". Callers (like the OpenAI SDK) misinterpret this as a pending
tool invocation.
json_schema requests with an explicit schema are unchanged.
Co-authored-by: Claude <noreply@anthropic.com>
* fix(utils): allowed_openai_params must not forward unset params as None
`_apply_openai_param_overrides` iterated `allowed_openai_params` and
unconditionally wrote `optional_params[param] = non_default_params.pop(param, None)`
for each entry. If the caller listed a param name but did not actually
send that param in the request, the pop returned `None` and `None` was
still written to `optional_params`. The openai SDK then rejected it as
a top-level kwarg:
AsyncCompletions.create() got an unexpected keyword argument 'enable_thinking'
Reproducer (from #25697):
allowed_openai_params = ["chat_template_kwargs", "enable_thinking"]
body = {"chat_template_kwargs": {"enable_thinking": False}}
Here `enable_thinking` is only present nested inside
`chat_template_kwargs`, so the helper should forward
`chat_template_kwargs` and leave `enable_thinking` alone. Instead it
wrote `optional_params["enable_thinking"] = None`.
Fix: only forward a param if it was actually present in
`non_default_params`. Behavior is unchanged for the happy path (param
sent → still forwarded), and the explicit `None` leakage is gone.
Adds a regression test exercising the helper in isolation so the test
does not depend on any provider-specific `map_openai_params` plumbing.
Fixes#25697
---------
Co-authored-by: lovek629 <59618812+lovek629@users.noreply.github.com>
Co-authored-by: Progressive-engg <lov.kumari55@gmail.com>
Co-authored-by: Ori Kotek <ori.k@codium.ai>
Co-authored-by: Alexander Grattan <51346343+agrattan0820@users.noreply.github.com>
Co-authored-by: Mohana Siddhartha Chivukula <103447836+iamsiddhu3007@users.noreply.github.com>
Co-authored-by: Amiram Mizne <amiramm@users.noreply.github.com>
Co-authored-by: Claude <noreply@anthropic.com>
Noma v1 resolved application_id from user_api_key_alias when no explicit
value was set (PR #16832). Noma v2 (PR #21400) was rewritten from scratch
and this fallback was not ported, causing all requests from shared LiteLLM
instances to appear as a single generic "litellm" application in the Noma
dashboard — breaking per-user traceability.
Fix: after checking dynamic_params and self.application_id, fall back to
user_api_key_alias from litellm_metadata or metadata. This matches the
pattern used by PromptSecurityGuardrail._resolve_key_alias_from_request_data()
and restores the v1 behavior where each API key gets its own application
entry in the Noma dashboard.
Fixes#25794
Co-authored-by: Brendan Smith-Elion <brendan.smith-elion@arcadia.io>
Co-authored-by: Claude Sonnet 4.6 <noreply@anthropic.com>
* fix(ollama): propagate done_reason='length' as finish_reason for max_tokens truncation
Ollama returns done_reason='length' when a response is cut off by num_predict
(the max_tokens limit). Previously, non-streaming responses hardcoded
finish_reason='stop', and streaming used chunk.get('done_reason', 'stop')
which also defaulted to 'stop' when done_reason was absent.
This meant callers (e.g. the Anthropic pass-through adapter, which maps
OpenAI 'length' -> Anthropic 'max_tokens') could never detect truncation,
making stop_reason always appear as 'end_turn' even for cut-off responses.
Fix: read done_reason from the response JSON in the non-streaming path and
use `chunk.get('done_reason') or 'stop'` in the streaming path, so Ollama's
actual done_reason passes through to the caller unchanged.
Co-Authored-By: Claude Sonnet 4.6 <noreply@anthropic.com>
* Update test_ollama_chat_transformation.py
* Update litellm/llms/ollama/chat/transformation.py
Co-authored-by: greptile-apps[bot] <165735046+greptile-apps[bot]@users.noreply.github.com>
---------
Co-authored-by: Claude Sonnet 4.6 <noreply@anthropic.com>
Co-authored-by: greptile-apps[bot] <165735046+greptile-apps[bot]@users.noreply.github.com>
The Vertex AI count-tokens endpoint rejects model names that include
version suffixes (@default, @20251001, etc.) with:
"claude-sonnet-4-6@default is not supported for token counting"
The same model without the suffix ("claude-sonnet-4-6") works correctly.
Strip @suffix from both the model parameter and request_data["model"]
in handle_count_tokens_request before sending to the API.
Co-authored-by: Claude Opus 4.6 (1M context) <noreply@anthropic.com>
Bedrock /v1/messages streams can report cache tokens only on message_start while message_delta carries only uncached input tokens. Merge cache fields onto the final delta usage and clamp negative text-token remainders in cost calc to keep usage/cost consistent.
Made-with: Cursor
- Release v1.82.3: note removal of injected default when reasoning_effort omitted
- Blog gemini_3: correct defaults and reasoning_effort mapping guidance
- Provider gemini.md: align tip and mapping table with implementation
Made-with: Cursor
MaskedHTTPStatusError constructs a new httpx.Response from the original
error. Two bugs surfaced under real HTTP error responses:
1. The new Response was created without request=, so response.request
raised RuntimeError("The .request property has not been set.") for
any downstream caller (e.g. exception_mapping_utils) that inspected it.
2. The decoded response bytes were passed together with the original
Content-Encoding header. On construction httpx tried to decompress
the already-decoded bytes and raised httpx.DecodingError
("Error -3 while decompressing data: incorrect header check").
Set response.request to the masked Request and strip Content-Encoding
(and the now-stale Content-Length) before rebuilding the Response.
URL/message masking is unchanged; the new request carries the already
masked URL.
Also update test_logging_key_masking_gemini: the security commit
25f93bed91 moved Gemini API keys from ?key=... URL params to the
x-goog-api-key header, so api_base no longer contains the key.
The projected-spend alert in _update_key_cache read from
existing_spend_obj.litellm_budget_table["soft_budget"], but the nested
dict is never populated for virtual keys (the combined_view SQL maps
budget fields to flat top-level attributes instead). This made the
check dead code — it silently short-circuited on every request, and
when unblocked, crashed update_cache with a Pydantic ValidationError
because _get_projected_spend_over_limit returns a date object but
CallInfo.projected_exceeded_date expects str.
Fixes: read from the flat existing_spend_obj.soft_budget field that IS
populated, and stringify projected_exceeded_date.
Also marks team soft budget email alerts as enterprise in docs.
Closes#20324
RestrictedPython (ZPL-2.1, a BSD-style permissive license) was added as
a dependency for the custom_code guardrail sandbox, but the license
checker didn't recognize it. Add to authorized packages list.
- vertex_ai_context_caching.py: add explicit Optional[str] annotation on
auth_header so later branches that assign vertex_auth_header (Optional[str])
type-check against the first branch's dict assignment (which already has
type: ignore[assignment]).
- path_utils.py: remove unused pathlib.Path import (F401).
- emulated_handler.py: extract _extract_tool_call_fields,
_resolve_queries_from_args, _execute_file_search_tool_calls, and
_build_follow_up_input helpers to drop aresponses_with_emulated_file_search
below ruff's PLR0915 statement limit. Behavior unchanged.
Add null byte rejection to safe_join and safe_filename. Normalize
backslash separators in safe_filename for cross-platform safety.
Include resolved path in ValueError for debugging. Move imports
to module level per project conventions.
Add safe_join() and safe_filename() in proxy/common_utils/path_utils.py
for constructing filesystem paths from user-controlled inputs. Apply to
guardrail category YAML endpoint and dotprompt file converter.
- factory.py: fix _sort_bedrock_assistant_content_blocks to treat
cachePoint blocks with the same sort key as toolUse so Python's
stable sort keeps each cachePoint paired with its preceding toolUse
block (PR #24368)
- responses/transformation.py: remove cyclic import of OpenAIGPT5Config
inside map_openai_params; add _is_gpt_5_model and
_supports_reasoning_effort_none static methods that replicate the
same logic without the import cycle. _is_gpt_5_model now also
excludes pass-through models from other providers (e.g.
perplexity/openai/gpt-5.2) that contain 'gpt-5' in their name but
should not be subject to OpenAI GPT-5 temperature restrictions
(PR #24371)
- streaming_iterator.py: adopted main's more defensive version of the
tool-arg queueing check (.get() instead of [], isinstance guard) —
same logic, same behavior, lower crash surface
- model_prices_and_context_window.json + backup: combined staging's
search_context_cost_per_query fields (PR #24372) with main's new
supports_service_tier field — both are independent additions to the
same Gemini model entries
- test_streaming_handler.py: kept Azure streaming regression test
(PR #24354) and added main's two new Gemini legacy vertex
finish_reason normalization tests
- test_gemini_batch_embeddings.py: kept staging's unsupported-params
filtering tests (PR #24370) and added main's index/order test
Resolved conflicts:
- streaming_handler.py: combined role check (PR #24354, Azure streaming)
with reasoning_items check (new in main) — both are independent OR
conditions in is_chunk_non_empty()
- CI/CD: accepted main's versions throughout
- Redis tests migrated to CircleCI (PR #25354): removed enable-redis
from GH Actions workflows
- E2E UI tests restructured (PR #25365): simplified CircleCI job
- Coverage via Codecov added to all GH Actions unit test workflows
- Deleted test-litellm-matrix.yml and test-proxy-e2e-azure-batches.yml
(removed in main)
* [Test] Add Azure async chat completion timeout test. WIP
* Capture TTFT for /v1/messages streaming responses
The pass-through streaming path for /v1/messages (Anthropic, Bedrock,
Vertex AI, Azure AI, Minimax) logged completion_start_time only after
the entire stream finished. async_success_handler then fell back to
end_time, making TTFT equal to total duration or null in the UI and
Prometheus.
Record the timestamp of the first chunk in async_sse_wrapper and
propagate it to model_call_details before the logging handler runs,
so gen_ai.response.time_to_first_token reflects the real first-chunk
latency.
Fixes#25598
* [Refactor] Implement timeout resolution logic in completion function
add fetch ``request_timeout`` from litellm_settings
* remove stale test case
* remove extra print statement
* default request timeout value in constants to 600s to match timeout defaults handled in the proxy
* fix request timeout if using default value from constants.py
* update code structure, test cases
* only override if the global timeout sets timeout to 6000s
* update code structure, move hard coded values to const and make the reslve function readable by moving fallback logic to a seperate function
* modify default timeout values, replacing hard coded ones with default values defined
---------
Co-authored-by: harish876 <harishgokul01@gmail.com>
Co-authored-by: Joaquin Hui Gomez <joaquinhuigomez@users.noreply.github.com>
