Merge pull request #45011 from BerriAI/litellm_pgb_bump_1103

chore(docker): bump pgbouncer to 1.26.0

Dockerfile

@@ -1,16 +1,16 @@
 # syntax=docker/dockerfile:1.7
 
 # Base image for building
-ARG LITELLM_BUILD_IMAGE=cgr.dev/chainguard/wolfi-base@sha256:e624c5d5e42382ce7165ddafcbbf8e6769a24cbd02ea6114b880b05ae5ba2a8d
+ARG LITELLM_BUILD_IMAGE=cgr.dev/chainguard/wolfi-base@sha256:1d95114038f76513a9ace6fca107d5582b08c65981f81f61cb56bf7fd2ef216d
 
 # Runtime image
-ARG LITELLM_RUNTIME_IMAGE=cgr.dev/chainguard/wolfi-base@sha256:e624c5d5e42382ce7165ddafcbbf8e6769a24cbd02ea6114b880b05ae5ba2a8d
+ARG LITELLM_RUNTIME_IMAGE=cgr.dev/chainguard/wolfi-base@sha256:1d95114038f76513a9ace6fca107d5582b08c65981f81f61cb56bf7fd2ef216d
 ARG UV_IMAGE=ghcr.io/astral-sh/uv:0.11.7@sha256:240fb85ab0f263ef12f492d8476aa3a2e4e1e333f7d67fbdd923d00a506a516a
 # Pinned by digest like the other base images; bump explicitly on Node upgrades.
 ARG UI_BUILD_IMAGE=node:24.19-alpine3.24@sha256:d32cdf619f63fe0471182d08996dd516c6275bb5fd31ae06e55a570bd9e1ad43
 # Checksum from https://www.pgbouncer.org/downloads/ (the Wolfi repo only carries 1.24.x)
-ARG PGBOUNCER_VERSION=1.25.2
+ARG PGBOUNCER_VERSION=1.26.0
-ARG PGBOUNCER_SHA256=924ad35113fd0a71c8e2dbe85b5d03445532e2b7b37a9f8a48983beea238b332
+ARG PGBOUNCER_SHA256=afd25dd61ee6775d37b40629b87ce08736b3e6955f3057bb212e410fbf21c71d
 
 FROM $UV_IMAGE AS uvbin
 

backend/Dockerfile

@@ -1,5 +1,5 @@
-ARG LITELLM_BUILD_IMAGE=cgr.dev/chainguard/wolfi-base@sha256:e624c5d5e42382ce7165ddafcbbf8e6769a24cbd02ea6114b880b05ae5ba2a8d
+ARG LITELLM_BUILD_IMAGE=cgr.dev/chainguard/wolfi-base@sha256:1d95114038f76513a9ace6fca107d5582b08c65981f81f61cb56bf7fd2ef216d
-ARG LITELLM_RUNTIME_IMAGE=cgr.dev/chainguard/wolfi-base@sha256:e624c5d5e42382ce7165ddafcbbf8e6769a24cbd02ea6114b880b05ae5ba2a8d
+ARG LITELLM_RUNTIME_IMAGE=cgr.dev/chainguard/wolfi-base@sha256:1d95114038f76513a9ace6fca107d5582b08c65981f81f61cb56bf7fd2ef216d
 ARG UV_IMAGE=ghcr.io/astral-sh/uv:0.11.7@sha256:240fb85ab0f263ef12f492d8476aa3a2e4e1e333f7d67fbdd923d00a506a516a
 
 FROM $UV_IMAGE AS uvbin

docker/Dockerfile.database

@@ -1,16 +1,16 @@
 # syntax=docker/dockerfile:1.7
 
 # Base image for building
-ARG LITELLM_BUILD_IMAGE=cgr.dev/chainguard/wolfi-base@sha256:e624c5d5e42382ce7165ddafcbbf8e6769a24cbd02ea6114b880b05ae5ba2a8d
+ARG LITELLM_BUILD_IMAGE=cgr.dev/chainguard/wolfi-base@sha256:1d95114038f76513a9ace6fca107d5582b08c65981f81f61cb56bf7fd2ef216d
 
 # Runtime image
-ARG LITELLM_RUNTIME_IMAGE=cgr.dev/chainguard/wolfi-base@sha256:e624c5d5e42382ce7165ddafcbbf8e6769a24cbd02ea6114b880b05ae5ba2a8d
+ARG LITELLM_RUNTIME_IMAGE=cgr.dev/chainguard/wolfi-base@sha256:1d95114038f76513a9ace6fca107d5582b08c65981f81f61cb56bf7fd2ef216d
 ARG UV_IMAGE=ghcr.io/astral-sh/uv:0.11.7@sha256:240fb85ab0f263ef12f492d8476aa3a2e4e1e333f7d67fbdd923d00a506a516a
 # Pinned by digest like the other base images; bump explicitly on Node upgrades.
 ARG UI_BUILD_IMAGE=node:24.19-alpine3.24@sha256:d32cdf619f63fe0471182d08996dd516c6275bb5fd31ae06e55a570bd9e1ad43
 # Checksum from https://www.pgbouncer.org/downloads/ (the Wolfi repo only carries 1.24.x)
-ARG PGBOUNCER_VERSION=1.25.2
+ARG PGBOUNCER_VERSION=1.26.0
-ARG PGBOUNCER_SHA256=924ad35113fd0a71c8e2dbe85b5d03445532e2b7b37a9f8a48983beea238b332
+ARG PGBOUNCER_SHA256=afd25dd61ee6775d37b40629b87ce08736b3e6955f3057bb212e410fbf21c71d
 
 FROM $UV_IMAGE AS uvbin
 

docker/Dockerfile.non_root

@@ -1,15 +1,15 @@
 # syntax=docker/dockerfile:1.7
 
 # Base images
-ARG LITELLM_BUILD_IMAGE=cgr.dev/chainguard/wolfi-base@sha256:e624c5d5e42382ce7165ddafcbbf8e6769a24cbd02ea6114b880b05ae5ba2a8d
+ARG LITELLM_BUILD_IMAGE=cgr.dev/chainguard/wolfi-base@sha256:1d95114038f76513a9ace6fca107d5582b08c65981f81f61cb56bf7fd2ef216d
-ARG LITELLM_RUNTIME_IMAGE=cgr.dev/chainguard/wolfi-base@sha256:e624c5d5e42382ce7165ddafcbbf8e6769a24cbd02ea6114b880b05ae5ba2a8d
+ARG LITELLM_RUNTIME_IMAGE=cgr.dev/chainguard/wolfi-base@sha256:1d95114038f76513a9ace6fca107d5582b08c65981f81f61cb56bf7fd2ef216d
 ARG PROXY_EXTRAS_SOURCE=published
 ARG UV_IMAGE=ghcr.io/astral-sh/uv:0.11.7@sha256:240fb85ab0f263ef12f492d8476aa3a2e4e1e333f7d67fbdd923d00a506a516a
 # Pinned by digest like the other base images; bump explicitly on Node upgrades.
 ARG UI_BUILD_IMAGE=node:24.19-alpine3.24@sha256:d32cdf619f63fe0471182d08996dd516c6275bb5fd31ae06e55a570bd9e1ad43
 # Checksum from https://www.pgbouncer.org/downloads/ (the Wolfi repo only carries 1.24.x)
-ARG PGBOUNCER_VERSION=1.25.2
+ARG PGBOUNCER_VERSION=1.26.0
-ARG PGBOUNCER_SHA256=924ad35113fd0a71c8e2dbe85b5d03445532e2b7b37a9f8a48983beea238b332
+ARG PGBOUNCER_SHA256=afd25dd61ee6775d37b40629b87ce08736b3e6955f3057bb212e410fbf21c71d
 
 FROM $UV_IMAGE AS uvbin
 

enterprise/litellm_enterprise/proxy/common_utils/check_batch_cost.py

@@ -10,6 +10,7 @@ from typing import TYPE_CHECKING, Final, List, Literal, Optional, Tuple, cast
 from litellm._logging import verbose_proxy_logger
 from litellm._uuid import uuid
 from litellm.constants import (
+    CLI_SESSION_KEY_PREFIX,
     MANAGED_OBJECT_STALENESS_CUTOFF_DAYS,
     MAX_OBJECTS_PER_POLL_CYCLE,
 )
@@ -112,10 +113,12 @@ class CheckBatchCost:
             verbose_proxy_logger.error(f"CheckBatchCost: could not look up user {user_id} for batch {batch_id}: {e}")
             return {}
 
-    async def _get_key_alias(self, batch_id: str, api_key: str | None) -> str | None:
+    async def _get_key_alias(self, batch_id: str, api_key: str | None, created_by: str | None) -> str | None:
         """Resolve the creating virtual key's alias from its hashed token."""
         if not api_key:
             return None
+        if created_by and api_key == f"{CLI_SESSION_KEY_PREFIX}-{created_by}":
+            return api_key
         try:
             key_row: prisma_models.LiteLLM_VerificationToken | None = (
                 await self.prisma_client.db.litellm_verificationtoken.find_unique(
@@ -204,7 +207,7 @@ class CheckBatchCost:
             **(await self._get_user_info(batch_id, job.created_by)),
         }
 
-        key_alias = await self._get_key_alias(batch_id, api_key)
+        key_alias = await self._get_key_alias(batch_id, api_key, job.created_by)
         if key_alias is not None:
             metadata["user_api_key_alias"] = key_alias
         team_alias = await self._get_team_alias(team_id)

enterprise/litellm_enterprise/proxy/hooks/managed_files.py

@@ -50,6 +50,7 @@ from litellm.proxy._types import (
     ProxyException,
     UserAPIKeyAuth,
 )
+from litellm.proxy.litellm_pre_call_utils import LiteLLMProxyRequestSetup
 from litellm.proxy.openai_files_endpoints.common_utils import (
     BATCH_CREATE_HIDDEN_PARAM,
     FILE_LIST_CONTINUATION_CHUNK_SIZE,
@@ -359,7 +360,7 @@ class _PROXY_LiteLLMManagedFiles(CustomLogger, BaseFileEndpoints):
 
         from prisma import Json
 
-        api_key = user_api_key_dict.api_key or None
+        api_key = LiteLLMProxyRequestSetup.get_logged_api_key(user_api_key_dict) or None
         attribution_columns = (
             {
                 **({"api_key": api_key} if api_key is not None else {}),

enterprise/pyproject.toml

@@ -1,6 +1,6 @@
 [project]
 name = "litellm-enterprise"
-version = "0.1.69"
+version = "0.1.69.post1"
 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 = "0.1.69"
+version = "0.1.69.post1"
 version_files = [
     "pyproject.toml:^version",
     "../pyproject.toml:litellm-enterprise==",

gateway/Dockerfile

@@ -1,9 +1,9 @@
-ARG LITELLM_BUILD_IMAGE=cgr.dev/chainguard/wolfi-base@sha256:e624c5d5e42382ce7165ddafcbbf8e6769a24cbd02ea6114b880b05ae5ba2a8d
+ARG LITELLM_BUILD_IMAGE=cgr.dev/chainguard/wolfi-base@sha256:1d95114038f76513a9ace6fca107d5582b08c65981f81f61cb56bf7fd2ef216d
-ARG LITELLM_RUNTIME_IMAGE=cgr.dev/chainguard/wolfi-base@sha256:e624c5d5e42382ce7165ddafcbbf8e6769a24cbd02ea6114b880b05ae5ba2a8d
+ARG LITELLM_RUNTIME_IMAGE=cgr.dev/chainguard/wolfi-base@sha256:1d95114038f76513a9ace6fca107d5582b08c65981f81f61cb56bf7fd2ef216d
 ARG UV_IMAGE=ghcr.io/astral-sh/uv:0.11.7@sha256:240fb85ab0f263ef12f492d8476aa3a2e4e1e333f7d67fbdd923d00a506a516a
 # Checksum from https://www.pgbouncer.org/downloads/ (the Wolfi repo only carries 1.24.x)
-ARG PGBOUNCER_VERSION=1.25.2
+ARG PGBOUNCER_VERSION=1.26.0
-ARG PGBOUNCER_SHA256=924ad35113fd0a71c8e2dbe85b5d03445532e2b7b37a9f8a48983beea238b332
+ARG PGBOUNCER_SHA256=afd25dd61ee6775d37b40629b87ce08736b3e6955f3057bb212e410fbf21c71d
 
 FROM $UV_IMAGE AS uvbin
 

litellm-proxy-extras/litellm_proxy_extras/migrations/20260823000000_add_spend_logs_api_key_starttime_index/migration.sql

@@ -1,2 +1 @@
--- CreateIndex
+SELECT 1;
-CREATE INDEX IF NOT EXISTS "LiteLLM_SpendLogs_api_key_startTime_idx" ON "LiteLLM_SpendLogs"("api_key", "startTime");

litellm-proxy-extras/litellm_proxy_extras/migrations/20260831120001_spend_logs_litellm_call_id_index/migration.sql

@@ -1,12 +1 @@
--- CreateIndex (CONCURRENTLY)
+SELECT 1;
---
--- Disclaimer:
--- - CREATE INDEX CONCURRENTLY cannot run inside a transaction. This migration must stay a
---   single statement so Prisma Migrate on PostgreSQL can apply it outside a transaction.
--- - Builds are slower and use more I/O than a blocking CREATE INDEX; if the build is
---   interrupted, Postgres may leave an INVALID index that must be dropped and recreated.
--- - Do not edit this file after it has been applied to any database: Prisma checksums
---   migrations; add a new migration instead.
--- - Requires PostgreSQL that supports CONCURRENTLY with IF NOT EXISTS (use a new migration
---   without IF NOT EXISTS if you must support older versions).
-CREATE INDEX CONCURRENTLY IF NOT EXISTS "LiteLLM_SpendLogs_litellm_call_id_idx" ON "LiteLLM_SpendLogs"("litellm_call_id");

litellm-proxy-extras/litellm_proxy_extras/utils.py

@@ -115,6 +115,17 @@ _SPEND_LOGS_PK_CLAUSE_RE = re.compile(
     re.IGNORECASE,
 )
 
+HAND_BUILT_SPEND_LOGS_INDEXES: Final = frozenset(
+    {
+        "LiteLLM_SpendLogs_api_key_startTime_idx",
+        "LiteLLM_SpendLogs_litellm_call_id_idx",
+    }
+)
+_CREATE_INDEX_RE: Final = re.compile(
+    r'^\s*CREATE\s+(?:UNIQUE\s+)?INDEX\s+(?:CONCURRENTLY\s+)?(?:IF\s+NOT\s+EXISTS\s+)?"(?P<index>[^"]+)"\s+ON\b',
+    re.IGNORECASE,
+)
+
 PARTITIONED_SPEND_LOGS_PUSH_ERROR = (
     "LiteLLM_SpendLogs is a partitioned table (see db_scripts/partition_spend_logs.sql), "
     "so its primary key must include the partition key (\"startTime\"). `prisma db push` "
@@ -165,6 +176,21 @@ def filter_partitioned_spend_logs_diff(diff_sql: str) -> str:
     return "".join(f"{statement};\n\n" for statement in kept)
 
 
+def filter_hand_built_spend_logs_index_diff(diff_sql: str) -> str:
+    """The `prisma migrate diff` script without the statements that create the
+    LiteLLM_SpendLogs indexes schema.prisma declares but the migrations deliberately
+    leave for operators to build online."""
+    kept: Final = tuple(
+        statement for statement in diff_sql.split(";") if not _creates_hand_built_spend_logs_index(statement)
+    )
+    return ";".join(kept) if any(part.strip() for part in kept) else ""
+
+
+def _creates_hand_built_spend_logs_index(statement: str) -> bool:
+    match: Final = _CREATE_INDEX_RE.match(_without_sql_comments(statement))
+    return match is not None and match["index"] in HAND_BUILT_SPEND_LOGS_INDEXES
+
+
 def _migration_timestamp(name: str) -> int:
     """Extract the leading `YYYYMMDDHHMMSS` timestamp from a migration name.
 
@@ -433,6 +459,20 @@ class ProxyExtrasDBManager:
                 return True
         return False
 
+    @staticmethod
+    def _filter_drift_script(diff_sql: str) -> str:
+        """The drift script without the hand-built SpendLogs indexes and, when
+        LiteLLM_SpendLogs is partitioned, without its primary-key rewrite and
+        partitioning artifacts."""
+        without_indexes: Final = filter_hand_built_spend_logs_index_diff(diff_sql)
+        if not ProxyExtrasDBManager.spend_logs_is_partitioned():
+            return without_indexes
+        logger.info(
+            "LiteLLM_SpendLogs is partitioned; removed its primary-key "
+            "rewrite and partitioning artifacts from the drift script"
+        )
+        return filter_partitioned_spend_logs_diff(without_indexes)
+
     @staticmethod
     def _resolve_all_migrations(
         migrations_dir: str, schema_path: str, mark_all_applied: bool = True
@@ -513,21 +553,14 @@ class ProxyExtrasDBManager:
             return
         logger.info(f"Migration diff created at {diff_sql_path}")
 
-        if ProxyExtrasDBManager.spend_logs_is_partitioned():
+        filtered_sql: Final = ProxyExtrasDBManager._filter_drift_script(diff_sql_path.read_text())
-            filtered_sql = filter_partitioned_spend_logs_diff(
+        diff_sql_path.write_text(filtered_sql)
-                diff_sql_path.read_text()
+        if not filtered_sql.strip():
-            )
+            logger.info("Drift script is empty after filtering; nothing to apply")
-            diff_sql_path.write_text(filtered_sql)
+            if not mark_all_applied:
-            logger.info(
-                "LiteLLM_SpendLogs is partitioned; removed its primary-key "
-                "rewrite and partitioning artifacts from the drift script"
-            )
-            if not filtered_sql.strip():
-                logger.info("Drift script is empty after filtering; nothing to apply")
-                if not mark_all_applied:
-                    return
-                ProxyExtrasDBManager._mark_migrations_applied(migrations_dir)
                 return
+            ProxyExtrasDBManager._mark_migrations_applied(migrations_dir)
+            return
 
         # 2. Run prisma db execute to apply the migration
         applied_ok = False

litellm-proxy-extras/pyproject.toml

@@ -1,6 +1,6 @@
 [project]
 name = "litellm-proxy-extras"
-version = "0.4.100"
+version = "0.4.100.post1"
 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 = "0.4.100"
+version = "0.4.100.post1"
 version_files = [
     "pyproject.toml:^version",
     "../pyproject.toml:litellm-proxy-extras==",

litellm/anthropic_beta_headers_config.json

@@ -11,6 +11,7 @@
     "computer-use-2025-11-24": "computer-use-2025-11-24",
     "context-1m-2025-08-07": "context-1m-2025-08-07",
     "context-management-2025-06-27": "context-management-2025-06-27",
+    "dangerous-tool-use-2026-09-03": "dangerous-tool-use-2026-09-03",
     "effort-2025-11-24": "effort-2025-11-24",
     "fast-mode-2026-02-01": "fast-mode-2026-02-01",
     "files-api-2025-04-14": "files-api-2025-04-14",
@@ -44,6 +45,7 @@
     "computer-use-2025-11-24": "computer-use-2025-11-24",
     "context-1m-2025-08-07": "context-1m-2025-08-07",
     "context-management-2025-06-27": "context-management-2025-06-27",
+    "dangerous-tool-use-2026-09-03": "dangerous-tool-use-2026-09-03",
     "effort-2025-11-24": "effort-2025-11-24",
     "fast-mode-2026-02-01": null,
     "files-api-2025-04-14": "files-api-2025-04-14",
@@ -76,6 +78,7 @@
     "computer-use-2025-11-24": "computer-use-2025-11-24",
     "context-1m-2025-08-07": "context-1m-2025-08-07",
     "context-management-2025-06-27": null,
+    "dangerous-tool-use-2026-09-03": null,
     "effort-2025-11-24": "effort-2025-11-24",
     "fast-mode-2026-02-01": null,
     "files-api-2025-04-14": null,
@@ -109,6 +112,7 @@
     "computer-use-2025-11-24": "computer-use-2025-11-24",
     "context-1m-2025-08-07": "context-1m-2025-08-07",
     "context-management-2025-06-27": "context-management-2025-06-27",
+    "dangerous-tool-use-2026-09-03": "dangerous-tool-use-2026-09-03",
     "effort-2025-11-24": "effort-2025-11-24",
     "fast-mode-2026-02-01": null,
     "files-api-2025-04-14": null,
@@ -142,6 +146,7 @@
     "computer-use-2025-11-24": "computer-use-2025-11-24",
     "context-1m-2025-08-07": "context-1m-2025-08-07",
     "context-management-2025-06-27": "context-management-2025-06-27",
+    "dangerous-tool-use-2026-09-03": "dangerous-tool-use-2026-09-03",
     "effort-2025-11-24": null,
     "fast-mode-2026-02-01": null,
     "files-api-2025-04-14": null,
@@ -175,6 +180,7 @@
     "computer-use-2025-11-24": "computer-use-2025-11-24",
     "context-1m-2025-08-07": "context-1m-2025-08-07",
     "context-management-2025-06-27": "context-management-2025-06-27",
+    "dangerous-tool-use-2026-09-03": null,
     "effort-2025-11-24": "effort-2025-11-24",
     "fast-mode-2026-02-01": "fast-mode-2026-02-01",
     "files-api-2025-04-14": "files-api-2025-04-14",

litellm/completion_extras/litellm_responses_transformation/transformation.py

@@ -188,7 +188,7 @@ def _reasoning_items_from_output_items(output_items: Sequence[object]) -> tuple[
 
 
 def _as_chat_reasoning_items(
-    reasoning_items: Sequence[_BuiltReasoningItem],
+    reasoning_items: Sequence[_BuiltReasoningItem | ChatCompletionReasoningItem],
 ) -> list[ChatCompletionReasoningItem] | None:
     if not reasoning_items:
         return None
@@ -782,7 +782,32 @@ class LiteLLMResponsesTransformationHandler(CompletionTransformationBridge):
             else:
                 pass  # don't fail request if item in list is not supported
 
-        # If we accumulated tool calls, create a single choice with all of them
+        if accumulated_tool_calls and choices:
+            last_choice: Final = choices[-1]
+            last_reasoning_content: Final = getattr(last_choice.message, "reasoning_content", None)
+            last_reasoning_items: Final = getattr(last_choice.message, "reasoning_items", None)
+            merged_reasoning_content: Final = (
+                " ".join(value for value in (last_reasoning_content, reasoning_content) if value) or None
+            )
+            merged_reasoning_items: Final = _as_chat_reasoning_items(
+                (
+                    *(last_reasoning_items or ()),
+                    *(() if pending_reasoning_item is None else (pending_reasoning_item,)),
+                )
+            )
+            merged_message: Final = Message(
+                role=last_choice.message.role,
+                content=last_choice.message.content,
+                annotations=getattr(last_choice.message, "annotations", None),
+                tool_calls=accumulated_tool_calls,
+                reasoning_content=merged_reasoning_content,
+                reasoning_items=merged_reasoning_items,
+            )
+            return [
+                *choices[:-1],
+                Choices(message=merged_message, finish_reason="tool_calls", index=last_choice.index),
+            ]
+
         if accumulated_tool_calls:
             msg = Message(
                 content=None,

litellm/constants.py

@@ -1861,6 +1861,7 @@ SPEND_LOG_KEY_METADATA_CACHE_TTL: Final = 600
 SPEND_LOG_KEY_METADATA_MISS_CACHE_TTL: Final = 30
 SPEND_LOG_KEY_METADATA_CACHE_MAX_ITEMS: Final = 10000
 SPEND_LOG_KEY_METADATA_QUERY_TIMEOUT_MS: Final = 5000
+SPEND_LOG_KEY_METADATA_ROWS_PER_PROBE: Final = 100
 # Short TTL for negative MCP access-group existence lookups. Keeps unauthenticated
 # callers from forcing a DB query per request for unknown names, while bounding
 # staleness so a transient DB error (which surfaces as an empty list) cannot
@@ -2084,16 +2085,12 @@ MCP_SPEND_LOG_MODEL_PREFIX: Final[str] = "MCP: "
 PTU_SENTINEL_API_KEY: Final[str] = "__ptu_flat_cost__"
 PTU_ROLLUP_JOB_ID: Final[str] = "ptu_flat_cost_rollup_job"
 PTU_ROLLUP_LOCK_TTL_SECONDS: Final[int] = 900
-USAGE_TOP_API_KEYS_LIMIT: Final[int] = int(os.getenv("USAGE_TOP_API_KEYS_LIMIT", "100"))
 # Furthest back the catch-up pass looks for unpriced PTU days when a deployment
 # declares no ptu_effective_from, bounding the scan for an open-ended window.
 PTU_ROLLUP_MAX_BACKFILL_DAYS: Final[int] = 90
 # Deployments named in the lapsed-window alert before it is truncated, so a fleet-wide
 # expiry cannot produce an alert too large for the channel delivering it.
 PTU_LAPSED_ALERT_LIMIT: Final[int] = 10
-DAILY_GLOBAL_SPEND_RECONCILE_JOB_ID: Final[str] = "daily_global_spend_reconcile_job"
-DAILY_GLOBAL_SPEND_RECONCILE_LOCK_TTL_SECONDS: Final[int] = 3600
-DAILY_GLOBAL_SPEND_RECONCILED_THROUGH_PARAM: Final[str] = "daily_global_spend_reconciled_through"
 # Slack allowed when deciding a sentinel row is stale. The row's updated_at and the
 # run's cutoff are stamped by different hosts, so clock skew between them must not let
 # one run delete a charge another just wrote. A stale row is hours old and a concurrent

litellm/integrations/SlackAlerting/slack_alerting.py

@@ -114,6 +114,7 @@ class SlackAlerting(CustomBatchLogger):
         self.default_webhook_url = default_webhook_url
         self.flush_lock = asyncio.Lock()
         self.periodic_started = False
+        self._periodic_flush_task: asyncio.Task[None] | None = None
         self.hanging_request_check = AlertingHangingRequestCheck(
             slack_alerting_object=self,
         )
@@ -125,6 +126,12 @@ class SlackAlerting(CustomBatchLogger):
         self.digest_lock = asyncio.Lock()
         super().__init__(**kwargs, flush_lock=self.flush_lock)
 
+    def _ensure_periodic_flush_task(self) -> None:
+        if self.periodic_started and (self._periodic_flush_task is None or not self._periodic_flush_task.done()):
+            return
+        self._periodic_flush_task = asyncio.create_task(self.periodic_flush())
+        self.periodic_started = True
+
     def update_values(
         self,
         alerting: list | None = None,
@@ -137,17 +144,14 @@ class SlackAlerting(CustomBatchLogger):
     ):
         if alerting is not None:
             self.alerting = alerting
-            asyncio.create_task(self.periodic_flush())
+            self._ensure_periodic_flush_task()
-            self.periodic_started = True
         if alerting_threshold is not None:
             self.alerting_threshold = alerting_threshold
         if alert_types is not None:
             self.alert_types = alert_types
         if alerting_args is not None:
             self.alerting_args = SlackAlertingArgs(**alerting_args)
-            if not self.periodic_started:
+            self._ensure_periodic_flush_task()
-                asyncio.create_task(self.periodic_flush())
-                self.periodic_started = True
         if alert_type_config is not None:
             for key, val in alert_type_config.items():
                 self.alert_type_config[key] = AlertTypeConfig(**val) if isinstance(val, dict) else val
@@ -1442,9 +1446,8 @@ Model Info:
             return
 
         # Start periodic flush if not already started
-        if not self.periodic_started and self.alerting is not None and len(self.alerting) > 0:
+        if self.alerting is not None and len(self.alerting) > 0:
-            asyncio.create_task(self.periodic_flush())
+            self._ensure_periodic_flush_task()
-            self.periodic_started = True
 
         if "webhook" in self.alerting and alert_type == "budget_alerts" and user_info is not None:
             await self.send_webhook_alert(webhook_event=user_info)

litellm/integrations/anthropic_cache_control_hook.py

@@ -36,6 +36,7 @@ from litellm.types.integrations.anthropic_cache_control_hook import (
     CacheControlMessageInjectionPoint,
 )
 from litellm.types.llms.anthropic import (
+    ANTHROPIC_TOOL_SEARCH_TOOL_TYPES,
     AllAnthropicToolsValues,
     AnthropicSystemMessageContent,
 )
@@ -124,6 +125,16 @@ def _carries_cache_breakpoint(block: object) -> bool:
     return isinstance(block, dict) and any(block.get(key) is not None for key in CACHE_BREAKPOINT_KEYS)
 
 
+def _tool_carries_cache_breakpoint(tool: object) -> bool:
+    return _carries_cache_breakpoint(tool) or (
+        isinstance(tool, dict) and _carries_cache_breakpoint(tool.get("function"))
+    )
+
+
+def _chat_transform_drops_tool_cache_control(tool: object) -> bool:
+    return isinstance(tool, dict) and tool.get("type") in ANTHROPIC_TOOL_SEARCH_TOOL_TYPES
+
+
 def _accepts_prompt_cache_breakpoint(block: object) -> bool:
     return isinstance(block, dict) and block.get("type") in OPENAI_PROMPT_CACHE_BREAKPOINT_BLOCK_TYPES
 
@@ -134,6 +145,8 @@ def _accepts_prompt_cache_breakpoint(block: object) -> bool:
 # rather than spending them on a list that is still missing some of their targets.
 CARRY_UNMATCHED_MESSAGE_POINTS: Final = "_litellm_carry_unmatched_cache_control_points"
 
+EXTERNAL_BREAKPOINTS_STAMP: Final = "_litellm_external_breakpoints"
+
 
 class AnthropicCacheControlHook(CustomPromptManagement):
     @staticmethod
@@ -199,19 +212,13 @@ class AnthropicCacheControlHook(CustomPromptManagement):
         # Create a deep copy of messages to avoid modifying the original list
         processed_messages = copy.deepcopy(messages)
 
-        # Separate message-level and non-message-level injection points
+        message_points: Final = tuple(
-        message_points: Final[list[CacheControlMessageInjectionPoint]] = []
+            cast(CacheControlMessageInjectionPoint, point)
-        remaining_points: Final[list[CacheControlInjectionPoint]] = []
+            for point in injection_points
-        for point in injection_points:
+            if point.get("location") == "message"
-            if point.get("location") == "message":
+        )
-                message_points.append(cast(CacheControlMessageInjectionPoint, point))
+        remaining_points: Final = tuple(point for point in injection_points if point.get("location") != "message")
-            else:
-                remaining_points.append(point)
 
-        # Non-message points (currently Bedrock tool_config) are handled in the
-        # provider transform, where each tool_config point appends at most one
-        # cachePoint to the tools. That block also counts toward Anthropic's
-        # limit, so reserve a slot for it here to leave room.
         stamped_dialect: Final = injection_points[0].get("_litellm_openai_dialect")
         openai_dialect: Final = (
             stamped_dialect
@@ -236,8 +243,10 @@ class AnthropicCacheControlHook(CustomPromptManagement):
             if carry_unmatched
             else tuple(message_points)
         )
-        reserved_blocks: Final = (
+        stamped_external: Final = injection_points[0].get(EXTERNAL_BREAKPOINTS_STAMP)
-            1 if not openai_dialect and any(p.get("location") == "tool_config" for p in remaining_points) else 0
+        external_breakpoints: Final = stamped_external if isinstance(stamped_external, int) else 0
+        reserved_blocks: Final = AnthropicCacheControlHook._blocks_reserved_outside_messages(
+            remaining_points, external_breakpoints, openai_dialect
         )
         breakpoints_before: Final = AnthropicCacheControlHook.count_request_cache_breakpoints(processed_messages)
         processed_messages = self._apply_message_injections(
@@ -254,14 +263,19 @@ class AnthropicCacheControlHook(CustomPromptManagement):
 
         # Points this pass did not place: non-message ones for the provider transform, and
         # the deferred role-targeted ones. Deferring is what reaches the Responses API's
-        # `instructions`, which is only a system message once the bridge builds one. The
+        # `instructions`, which is only a system message once the bridge builds one. A later
-        # judged stamp is what makes it safe: the next pass must not re-judge points
+        # pass re-applies them safely: a target that already carries a mark is skipped and
-        # against messages this pass already marked (see `_should_stand_down`).
+        # the census counts every mark on the wire, litellm's own included.
-        carried_points: Final[Sequence[CacheControlInjectionPoint]] = (*remaining_points, *carried_message_points)
+        carried_points: Final[Sequence[CacheControlInjectionPoint]] = (
+            *AnthropicCacheControlHook._points_with_a_slot_left(
+                remaining_points,
+                AnthropicCacheControlHook.count_request_cache_breakpoints(processed_messages) + external_breakpoints,
+                openai_dialect,
+            ),
+            *carried_message_points,
+        )
         if carried_points:
-            non_default_params["cache_control_injection_points"] = AnthropicCacheControlHook._stamped_as_judged(
+            non_default_params["cache_control_injection_points"] = list(carried_points)
-                carried_points
-            )
 
         return model, processed_messages, non_default_params
 
@@ -296,6 +310,72 @@ class AnthropicCacheControlHook(CustomPromptManagement):
         )
         return system_blocks + sum(AnthropicCacheControlHook._count_cache_control_blocks(msg) for msg in messages)
 
+    @staticmethod
+    def count_external_cache_breakpoints(
+        tools: Iterable[object] | None, cache_control: object = None, request_kwargs: object = None
+    ) -> int:
+        """Client breakpoints outside messages and system that the provider cap still counts.
+
+        A tool carries its mark at the top level (Anthropic shape) or under ``function``
+        (OpenAI shape). A top-level ``cache_control`` is Anthropic's automatic caching,
+        which places one breakpoint of its own on top of the explicit ones. The
+        ``extra_body`` envelope of ``request_kwargs`` is merged over the request on the
+        wire, so a ``tools`` or ``cache_control`` it carries replaces the direct value
+        and is counted in its place. Callers pass only the tools whose mark reaches the
+        provider on their path.
+        """
+        extra_body: Final = (
+            _validated_object_mapping(AnthropicCacheControlHook._request_value(request_kwargs, "extra_body")) or {}
+        )
+        wire_cache_control: Final = extra_body.get("cache_control", cache_control)
+        wire_tools: Final = _validated_object_list(extra_body["tools"]) if "tools" in extra_body else tools
+        tool_blocks: Final = sum(1 for tool in wire_tools or () if _tool_carries_cache_breakpoint(tool))
+        envelope_blocks: Final = AnthropicCacheControlHook.count_request_cache_breakpoints(
+            _validated_object_list(extra_body.get("messages")) or (), extra_body.get("system")
+        )
+        return int(wire_cache_control is not None) + tool_blocks + envelope_blocks
+
+    @staticmethod
+    def count_external_cache_breakpoints_on_messages_route(
+        tools: Iterable[object] | None, cache_control: object, request_kwargs: object
+    ) -> int:
+        """The /v1/messages census before the route splits.
+
+        The native messages transforms drop the ``extra_body`` envelope while the
+        chat bridge merges it, so the cap reserves for whichever census is larger
+        rather than letting an envelope that unmarks a direct tool free a slot the
+        provider still counts.
+        """
+        return max(
+            AnthropicCacheControlHook.count_external_cache_breakpoints(tools, cache_control),
+            AnthropicCacheControlHook.count_external_cache_breakpoints(tools, cache_control, request_kwargs),
+        )
+
+    @staticmethod
+    def _blocks_reserved_outside_messages(
+        remaining_points: Sequence[CacheControlInjectionPoint], external_breakpoints: int, openai_dialect: bool
+    ) -> int:
+        """Slots of the provider cap that the message census cannot see.
+
+        The client's breakpoints on tools and its automatic top-level ``cache_control``
+        are already on the wire, and a ``tool_config`` point becomes one more cachePoint
+        in the Bedrock converse transform. OpenAI's cap counts only its own block markers.
+        """
+        if openai_dialect:
+            return 0
+        tool_config_blocks: Final = 1 if any(p.get("location") == "tool_config" for p in remaining_points) else 0
+        return external_breakpoints + tool_config_blocks
+
+    @staticmethod
+    def _points_with_a_slot_left(
+        remaining_points: Sequence[CacheControlInjectionPoint], breakpoints_on_wire: int, openai_dialect: bool
+    ) -> tuple[CacheControlInjectionPoint, ...]:
+        """A ``tool_config`` point becomes a cachePoint the Bedrock converse transform never
+        counts against the cap, so it is forwarded only while the wire still has a slot."""
+        if openai_dialect or breakpoints_on_wire < MAX_CACHE_CONTROL_BLOCKS:
+            return tuple(remaining_points)
+        return tuple(point for point in remaining_points if point.get("location") != "tool_config")
+
     @staticmethod
     def _apply_message_injections(
         points: Sequence[CacheControlMessageInjectionPoint],
@@ -476,11 +556,16 @@ class AnthropicCacheControlHook(CustomPromptManagement):
     def apply_to_anthropic_messages_request(
         messages: list[dict],
         system: str | list | None,
-        injection_points: list[CacheControlInjectionPoint],
+        injection_points: Sequence[CacheControlInjectionPoint],
         openai_dialect: bool = False,
+        external_breakpoints: int = 0,
     ) -> tuple[list[dict], str | list | None, list[CacheControlInjectionPoint]]:
         """Apply cache control injection for the Anthropic-native v1/messages endpoint.
 
+        ``external_breakpoints`` is the client's breakpoint count outside ``messages`` and
+        ``system`` (see ``count_external_cache_breakpoints``); it shrinks the budget so
+        the request never exceeds the provider cap.
+
         Returns (messages, system, remaining_non_message_points).
         """
         if not injection_points:
@@ -489,22 +574,17 @@ class AnthropicCacheControlHook(CustomPromptManagement):
         processed_messages: list[dict] = copy.deepcopy(messages)
         processed_system = copy.deepcopy(system) if system is not None else None
 
-        message_points: Final[list[CacheControlMessageInjectionPoint]] = []
+        role_points: Final = tuple(
-        system_points: Final[list[CacheControlMessageInjectionPoint]] = []
+            cast(CacheControlMessageInjectionPoint, point)
-        remaining_points: Final[list[CacheControlInjectionPoint]] = []
+            for point in injection_points
+            if point.get("location") == "message"
+        )
+        system_points: Final = tuple(point for point in role_points if point.get("role") == "system")
+        message_points: Final = tuple(point for point in role_points if point.get("role") != "system")
+        remaining_points: Final = tuple(point for point in injection_points if point.get("location") != "message")
 
-        for point in injection_points:
+        reserved_blocks: Final = AnthropicCacheControlHook._blocks_reserved_outside_messages(
-            if point.get("location") == "message":
+            remaining_points, external_breakpoints, openai_dialect
-                msg_point = cast(CacheControlMessageInjectionPoint, point)
-                if msg_point.get("role") == "system":
-                    system_points.append(msg_point)
-                else:
-                    message_points.append(msg_point)
-            else:
-                remaining_points.append(point)
-
-        reserved_blocks: Final = (
-            1 if not openai_dialect and any(p.get("location") == "tool_config" for p in remaining_points) else 0
         )
         max_blocks: Final = MAX_CACHE_CONTROL_BLOCKS - reserved_blocks
 
@@ -541,8 +621,14 @@ class AnthropicCacheControlHook(CustomPromptManagement):
             max_blocks=max_blocks - system_blocks,
             openai_dialect=openai_dialect,
         )
+        forwarded_points: Final = AnthropicCacheControlHook._points_with_a_slot_left(
+            remaining_points,
+            AnthropicCacheControlHook.count_request_cache_breakpoints(processed_messages, processed_system)
+            + external_breakpoints,
+            openai_dialect,
+        )
 
-        return processed_messages, processed_system, remaining_points
+        return processed_messages, processed_system, list(forwarded_points)
 
     @staticmethod
     def _default_control() -> ChatCompletionCachedContent:
@@ -559,31 +645,26 @@ class AnthropicCacheControlHook(CustomPromptManagement):
         return ChatCompletionCachedContent(type="ephemeral")
 
     @staticmethod
-    def _stamped_as_judged(points: Sequence[CacheControlInjectionPoint]) -> Sequence[Mapping[str, object]]:
+    def _stamped_for_prompt_hook(
-        """Mark written-back points as having passed the client cache_control judgment.
-
-        Builds copies because config-owned point dicts are shared across
-        requests; mutating them would leak the stamp into future requests.
-        """
-        return AnthropicCacheControlHook._stamped(points, "_litellm_judged", True)
-
-    @staticmethod
-    def _judged_configured_points(
         points: Sequence[CacheControlInjectionPoint],
-        messages: list[AllMessageValues],
+        external_breakpoints: int,
-        tools: list[object] | None,
-        cache_control: object,
         model: str,
         custom_llm_provider: str | None,
         api_base: object,
         prompt_cache_options: object,
-        request_kwargs: object,
+    ) -> Sequence[Mapping[str, object]]:
-    ) -> Sequence[Mapping[str, object]] | None:
+        """Carry onto the points what the prompt-management hook never receives.
-        if AnthropicCacheControlHook._should_stand_down(points, messages, None, tools, cache_control, request_kwargs):
+
-            return None
+        The hook sees neither the tools nor the request kwargs, so the target dialect
-        return AnthropicCacheControlHook._stamped_with_dialect(
+        and the client's breakpoint count outside the message list ride on the points.
+        Builds copies because config-owned point dicts are shared across requests.
+        """
+        with_dialect: Final = AnthropicCacheControlHook._stamped_with_dialect(
             points, model, custom_llm_provider, api_base, prompt_cache_options
         )
+        if external_breakpoints == 0:
+            return with_dialect
+        return AnthropicCacheControlHook._stamped(with_dialect, EXTERNAL_BREAKPOINTS_STAMP, external_breakpoints)
 
     @staticmethod
     def _stamped_with_dialect(
@@ -604,35 +685,9 @@ class AnthropicCacheControlHook(CustomPromptManagement):
         )
 
     @staticmethod
-    def _stamped(
+    def _stamped(points: Sequence[Mapping[str, object]], key: str, value: object) -> Sequence[Mapping[str, object]]:
-        points: Sequence[CacheControlInjectionPoint], key: str, value: object
-    ) -> Sequence[Mapping[str, object]]:
         return [{**point, key: value} for point in points]
 
-    @staticmethod
-    def _should_stand_down(
-        points: Sequence[CacheControlInjectionPoint],
-        messages: list[AllMessageValues],
-        system: str | list | None,
-        tools: list | None,
-        cache_control: object = None,
-        request_kwargs: object = None,
-    ) -> bool:
-        """Whether configured injection points must yield to client-set cache_control.
-
-        Points that a prior pass over this request already judged and wrote
-        back carry the internal judged stamp; any re-entry (acompletion
-        re-entering completion, the async-to-sync /v1/messages dispatch,
-        interceptor sub-calls reusing the request kwargs) must not re-judge
-        them, because by then the messages carry litellm's own injected marks
-        and the judgment would misread those as client breakpoints.
-        """
-        if all(point.get("_litellm_judged") for point in points):
-            return False
-        return AnthropicCacheControlHook._request_has_cache_control(
-            messages, system, tools, cache_control, request_kwargs
-        )
-
     @staticmethod
     def _request_has_cache_control(
         messages: list[AllMessageValues],
@@ -640,28 +695,24 @@ class AnthropicCacheControlHook(CustomPromptManagement):
         tools: list | None = None,
         cache_control: object = None,
         request_kwargs: object = None,
+        on_messages_route: bool = False,
     ) -> bool:
-        """Client breakpoints own caching in both the request and its extra_body envelope."""
+        """Return True if the request already carries any client-supplied cache_control.
-        bodies: Final = (
+
-            {"messages": messages, "system": system, "tools": tools, "cache_control": cache_control},
+        Only the automatic defaults stand down on it: a client that marks its own
-            _validated_object_mapping(AnthropicCacheControlHook._request_value(request_kwargs, "extra_body")) or {},
+        breakpoints (Claude Code does) has a caching strategy the defaults would
-        )
+        clash with, whether the marks sit in the request or in its ``extra_body``
-        return any(
+        envelope. Configured injection points are an explicit instruction and are
-            body.get("cache_control") is not None
+        applied alongside the client's marks, bounded by the provider cap.
-            or AnthropicCacheControlHook.count_request_cache_breakpoints(
+        """
-                _validated_object_list(body.get("messages")) or (), body.get("system")
+        external_breakpoints: Final = (
+            AnthropicCacheControlHook.count_external_cache_breakpoints_on_messages_route(
+                tools, cache_control, request_kwargs
             )
-            > 0
+            if on_messages_route
-            or any(
+            else AnthropicCacheControlHook.count_external_cache_breakpoints(tools, cache_control, request_kwargs)
-                AnthropicCacheControlHook._request_value(tool, "cache_control") is not None
-                or AnthropicCacheControlHook._request_value(
-                    AnthropicCacheControlHook._request_value(tool, "function"), "cache_control"
-                )
-                is not None
-                for tool in (_validated_object_list(body.get("tools")) or ())
-            )
-            for body in bodies
         )
+        return AnthropicCacheControlHook.count_request_cache_breakpoints(messages, system) + external_breakpoints > 0
 
     @staticmethod
     def get_default_injection_points(
@@ -673,6 +724,7 @@ class AnthropicCacheControlHook(CustomPromptManagement):
         enable_prompt_caching: bool | None = None,
         cache_control: object = None,
         request_kwargs: object = None,
+        on_messages_route: bool = False,
     ) -> list[CacheControlInjectionPoint]:
         """Default breakpoints when ``litellm.enable_anthropic_prompt_caching`` is on.
 
@@ -693,7 +745,9 @@ class AnthropicCacheControlHook(CustomPromptManagement):
         if not supports_anthropic_cache_control(model, custom_llm_provider):
             return []
 
-        if AnthropicCacheControlHook._request_has_cache_control(messages, system, tools, cache_control, request_kwargs):
+        if AnthropicCacheControlHook._request_has_cache_control(
+            messages, system, tools, cache_control, request_kwargs, on_messages_route
+        ):
             return []
 
         if is_claude_code_one_shot_subagent_request(
@@ -769,34 +823,30 @@ class AnthropicCacheControlHook(CustomPromptManagement):
     ) -> None:
         """For /chat/completions: resolve the injection points the request should carry.
 
-        Configured injection points win over the automatic defaults, but stand
+        Configured injection points win over the automatic defaults and are applied
-        down entirely when the client already marked its own cache_control
+        even when the client marked its own cache_control elsewhere in the request;
-        breakpoints (messages or tools): injecting alongside them clashes with
+        the provider's four-block cap bounds them, counting the client's marks on
-        the client's caching strategy and can exceed the provider's four-block
+        messages, tools and the top-level ``cache_control``. Only the defaults stand
-        limit. The judgment happens once per request; points a prior pass
+        down on client marks. Seeding the param lets the existing prompt-management
-        wrote back carry the judged stamp and are never re-judged (see
+        gate and the AnthropicCacheControlHook run unchanged.
-        ``_should_stand_down``). Seeding the param lets the existing
-        prompt-management gate and the AnthropicCacheControlHook run
-        unchanged.
         """
         import litellm
 
-        if non_default_params.get("cache_control_injection_points"):
+        configured: Final = non_default_params.get("cache_control_injection_points")
-            judged: Final = AnthropicCacheControlHook._judged_configured_points(
+        if configured:
-                non_default_params["cache_control_injection_points"],
+            tools_keeping_marks: Final = tuple(
-                messages,
+                tool for tool in tools or () if not _chat_transform_drops_tool_cache_control(tool)
-                tools,
+            )
-                non_default_params.get("cache_control"),
+            non_default_params["cache_control_injection_points"] = AnthropicCacheControlHook._stamped_for_prompt_hook(
+                configured,
+                AnthropicCacheControlHook.count_external_cache_breakpoints(
+                    tools_keeping_marks, non_default_params.get("cache_control"), non_default_params
+                ),
                 model,
                 custom_llm_provider,
                 api_base,
                 non_default_params.get("prompt_cache_options"),
-                non_default_params,
             )
-            if judged is None:
-                non_default_params.pop("cache_control_injection_points")
-            else:
-                non_default_params["cache_control_injection_points"] = judged
             return
         points: Final = AnthropicCacheControlHook.get_default_injection_points(
             messages=messages,
@@ -897,15 +947,14 @@ class AnthropicCacheControlHook(CustomPromptManagement):
     ) -> tuple[list[dict], str | list | None]:
         """Extract cache_control_injection_points from kwargs and apply if present.
 
-        Configured points stand down entirely when the client already marked
+        Configured points are applied even when the client marked its own
-        its own cache_control breakpoints anywhere in the request. The
+        cache_control elsewhere in the request, bounded by the provider cap,
-        judgment happens once per request; points a prior pass wrote back
+        which counts the client's marks on messages, system, tools and the
-        carry the judged stamp and are never re-judged (see
+        top-level ``cache_control``. When none are configured but
-        ``_should_stand_down``). When none are configured but
         ``litellm.enable_anthropic_prompt_caching`` or the per-request
         ``enable_prompt_caching`` kwarg (stamped from key metadata) is on,
-        synthesize default breakpoints for the native /v1/messages path. Pops
+        synthesize default breakpoints for the native /v1/messages path; those
-        both keys from kwargs;
+        defaults alone stand down on client marks. Pops both keys from kwargs;
         if remaining (non-message) points exist they are written back so
         downstream transforms can handle them.
         """
@@ -917,13 +966,8 @@ class AnthropicCacheControlHook(CustomPromptManagement):
         configured: Final = cast(  # cast-ok: kwargs is untyped; this key only holds the documented injection-point list
             list[CacheControlInjectionPoint] | None, kwargs.pop("cache_control_injection_points", None)
         )
-        if configured and AnthropicCacheControlHook._should_stand_down(
+        injection_points: Final[Sequence[CacheControlInjectionPoint]] = configured or (
-            configured, typed_messages, system, tools, cache_control, kwargs
+            AnthropicCacheControlHook.get_default_injection_points(
-        ):
-            return messages, system
-        injection_points: list[CacheControlInjectionPoint] = configured or []
-        if not injection_points and model is not None:
-            injection_points = AnthropicCacheControlHook.get_default_injection_points(
                 messages=typed_messages,
                 system=system,
                 tools=tools,
@@ -932,7 +976,11 @@ class AnthropicCacheControlHook(CustomPromptManagement):
                 enable_prompt_caching=enable_prompt_caching,
                 cache_control=cache_control,
                 request_kwargs=kwargs,
+                on_messages_route=True,
             )
+            if model is not None
+            else ()
+        )
         if not injection_points:
             return messages, system
 
@@ -945,6 +993,9 @@ class AnthropicCacheControlHook(CustomPromptManagement):
             system=system,
             injection_points=injection_points,
             openai_dialect=openai_dialect,
+            external_breakpoints=AnthropicCacheControlHook.count_external_cache_breakpoints_on_messages_route(
+                tools, cache_control, kwargs
+            ),
         )
         breakpoints_added: Final = (
             AnthropicCacheControlHook.count_request_cache_breakpoints(messages, system) - breakpoints_before
@@ -953,7 +1004,7 @@ class AnthropicCacheControlHook(CustomPromptManagement):
         if openai_dialect and breakpoints_added > 0:
             kwargs.setdefault("prompt_cache_options", PromptCacheOptions(mode="explicit"))
         if remaining:
-            kwargs["cache_control_injection_points"] = AnthropicCacheControlHook._stamped_as_judged(remaining)
+            kwargs["cache_control_injection_points"] = remaining
         return messages, system
 
     @property

litellm/integrations/otel/logger.py

@@ -561,6 +561,7 @@ class OpenTelemetryV2(CustomLogger):
             time_to_first_chunk_seconds=call.time_to_first_chunk_seconds,
             request_route=request_root_http_route(),
             trace=call.trace,
+            session_id=call.session_id,
         )
         end_time_ns: Final = to_ns(end_time)
         if carrier is not None and carrier.span is not None:

litellm/integrations/otel/mappers/genai.py

@@ -43,6 +43,7 @@ class GenAIMapper:
         GenAI.OPERATION_NAME: lambda d: d.operation.value,
         GenAI.PROVIDER_NAME: lambda d: d.provider or None,
         GenAI.OUTPUT_TYPE: lambda d: d.output_type.value if d.output_type else None,
+        GenAI.CONVERSATION_ID: lambda d: d.session_id,
         GenAI.REQUEST_MODEL: lambda d: d.request_model or None,
         GenAI.REQUEST_TEMPERATURE: lambda d: d.request_params.temperature,
         GenAI.REQUEST_TOP_P: lambda d: d.request_params.top_p,

litellm/integrations/otel/model/metadata.py

@@ -41,7 +41,7 @@ from dataclasses import dataclass, field
 from types import MappingProxyType
 from typing import TYPE_CHECKING, Any, Final, cast
 
-from litellm.constants import LITELLM_LOGGING_NO_UPSTREAM_LLM_CALL
+from litellm.constants import LITELLM_LOGGING_NO_UPSTREAM_LLM_CALL, SESSION_ID_GENERATED_METADATA_KEY
 from litellm.integrations.otel.model.semconv import resolve_operation
 from litellm.integrations.otel.model.trace_controls import TraceControls, caller_trace_controls
 from litellm.integrations.otel.model.utils import as_str, as_str_mapping, to_seconds
@@ -226,6 +226,7 @@ class LLMCallEvent:
     provisional_span_name: str
     time_to_first_chunk_seconds: float | None
     trace: TraceControls
+    session_id: str | None
 
     @classmethod
     def from_dict(cls, kwargs: Mapping[str, Any]) -> LLMCallEvent:
@@ -233,6 +234,7 @@ class LLMCallEvent:
         payload: Final = cast("StandardLoggingPayload", raw_payload) if raw_payload else None
         operation: Final = resolve_operation(as_str(kwargs.get("call_type")))
         model: Final = as_str(kwargs.get("model")) or ""
+        trace: Final = caller_trace_controls(kwargs)
         return cls(
             call_id=_call_id(payload, kwargs),
             payload=payload,
@@ -242,10 +244,40 @@ class LLMCallEvent:
             upstream_started=kwargs.get("api_call_start_time") is not None,
             provisional_span_name=f"{operation.value} {model}".strip(),
             time_to_first_chunk_seconds=time_to_first_chunk_seconds(kwargs),
-            trace=caller_trace_controls(kwargs),
+            trace=trace,
+            session_id=caller_session_id(kwargs, trace),
         )
 
 
+def caller_session_id(kwargs: Mapping[str, object], trace: TraceControls) -> str | None:
+    """The conversation id the caller sent (``litellm_session_id``, else the
+    ``session_id`` trace control); ``None`` when the request carried none.
+
+    ``get_litellm_params`` back-fills ``litellm_session_id`` from ``metadata.trace_id``
+    (which the proxy stamps with the OTel trace id) and ``missing_session_id: generate``
+    mints one into the body; neither is a caller conversation, so both are ignored,
+    while a ``langfuse_session_id`` header still counts under the generate policy.
+    ``StandardLoggingPayload.session_id`` is never read: the payload drops the
+    generated marker, so a replayed minted id would pass for a caller's."""
+    params: Final[Mapping[str, object]] = as_str_mapping(kwargs.get("litellm_params")) or MappingProxyType({})
+    bodies: Final = tuple(
+        metadata
+        for key in ("metadata", "litellm_metadata")
+        if (metadata := as_str_mapping(params.get(key))) is not None
+    )
+    from_body: Final = tuple(session for body in bodies if (session := as_str(body.get("session_id"))))
+    minted: Final = frozenset(
+        session
+        for body in bodies
+        if body.get(SESSION_ID_GENERATED_METADATA_KEY) and (session := as_str(body.get("session_id")))
+    )
+    if minted:
+        return next((session for session in (trace.session_id, *from_body) if session and session not in minted), None)
+    explicit: Final = as_str(params.get("litellm_session_id"))
+    echoes_trace_id: Final = explicit is not None and any(as_str(body.get("trace_id")) == explicit for body in bodies)
+    return (None if echoes_trace_id else explicit) or trace.session_id or None
+
+
 def time_to_first_chunk_seconds(kwargs: Mapping[str, Any]) -> float | None:
     """Seconds from the upstream request being issued (``api_call_start_time``)
     to the first streamed chunk (``completion_start_time``); ``None`` for

litellm/integrations/otel/model/payloads.py

@@ -407,6 +407,7 @@ class LLMCallSpanData:
     call_type: str | None = None
     request_route: str | None = None
     trace: TraceControls = field(default_factory=TraceControls)
+    session_id: str | None = None
     embedding_output: EmbeddingOutput | None = None
 
     @classmethod
@@ -417,6 +418,7 @@ class LLMCallSpanData:
         time_to_first_chunk_seconds: float | None = None,
         request_route: str | None = None,
         trace: TraceControls | None = None,
+        session_id: str | None = None,
     ) -> LLMCallSpanData:
         params: Final = cast(Mapping[str, object], payload.get("model_parameters") or {})
         # The single parse of the request's metadata — the request-vs-provider
@@ -463,6 +465,7 @@ class LLMCallSpanData:
             call_type=call_type or None,
             request_route=request_route or context.identity.request_route,
             trace=trace or TraceControls(),
+            session_id=session_id or None,
             embedding_output=embedding_output if capture_content else None,
         )
 

litellm/integrations/prometheus.py

@@ -2553,6 +2553,7 @@ class PrometheusLogger(CustomLogger):
         from litellm.litellm_core_utils.litellm_logging import (
             StandardLoggingPayloadSetup,
         )
+        from litellm.proxy.litellm_pre_call_utils import LiteLLMProxyRequestSetup
 
         status_code: Final = self._extract_status_code(exception=original_exception)
 
@@ -2571,7 +2572,9 @@ class PrometheusLogger(CustomLogger):
                 end_user=user_api_key_dict.end_user_id,
                 user=user_api_key_dict.user_id,
                 user_email=user_api_key_dict.user_email,
-                hashed_api_key=None if status_code == 401 else user_api_key_dict.api_key,
+                hashed_api_key=None
+                if status_code == 401
+                else LiteLLMProxyRequestSetup.get_logged_api_key(user_api_key_dict),
                 api_key_alias=user_api_key_dict.key_alias,
                 team=user_api_key_dict.team_id,
                 team_alias=user_api_key_dict.team_alias,

litellm/integrations/websearch_interception/handler.py

@@ -1638,7 +1638,7 @@ class WebSearchInterceptionLogger(CustomLogger):
         return {  # mutable-ok: litellm's metadata channel is a plain dict its logging path reads and enriches
             **user_api_key_metadata,
             "model_group": search_tool_name,
-            "user_api_key": user_api_key_auth.api_key,
+            "user_api_key": LiteLLMProxyRequestSetup.get_logged_api_key(user_api_key_auth),
             "user_api_key_auth": user_api_key_auth,
         }
 

litellm/litellm_core_utils/litellm_logging.py

@@ -610,6 +610,9 @@ class Logging(LiteLLMLoggingBaseClass):
         self.truncated_messages_for_logging: str | list | dict | None = None  # mutable-ok: logged messages shape
         ## TIME TO FIRST TOKEN LOGGING ##
         self.completion_start_time: datetime.datetime | None = None
+        # The model the proxy shows the client on streamed chunks. The logged streamed response carries it
+        # once that response is priced, the same way a non-streamed response is logged
+        self.client_facing_stream_model: str | None = None
         self._llm_caching_handler: LLMCachingHandler | None = None
 
         # INITIAL LITELLM_PARAMS
@@ -2279,6 +2282,15 @@ class Logging(LiteLLMLoggingBaseClass):
             setattr(result, "usage", transformed_usage)
         return result
 
+    def _with_client_facing_stream_model(
+        self,
+        response: ModelResponse | TextCompletionResponse | ResponsesAPIResponse | InteractionsAPIResponse,
+    ) -> ModelResponse | TextCompletionResponse | ResponsesAPIResponse | InteractionsAPIResponse:
+        model: Final = self.client_facing_stream_model
+        if model is None or getattr(response, "model", None) in (None, model):
+            return response
+        return response.model_copy(update={"model": model})
+
     def _success_handler_helper_fn(
         self,
         result=None,
@@ -2592,9 +2604,11 @@ class Logging(LiteLLMLoggingBaseClass):
                     result=complete_streaming_response
                 )
                 self._merge_hidden_params_from_response_into_metadata(complete_streaming_response)
+                logged_streaming_response: Final = self._with_client_facing_stream_model(complete_streaming_response)
+                self.model_call_details["complete_streaming_response"] = logged_streaming_response
                 ## STANDARDIZED LOGGING PAYLOAD
                 self.model_call_details["standard_logging_object"] = self._build_standard_logging_payload(
-                    complete_streaming_response, start_time, end_time
+                    logged_streaming_response, start_time, end_time
                 )
                 standard_logging_payload: Final[StandardLoggingPayload | None] = self.model_call_details.get(
                     "standard_logging_object"
@@ -3130,10 +3144,13 @@ class Logging(LiteLLMLoggingBaseClass):
 
             await self._prepare_baseline_cache_estimate(complete_streaming_response)
 
+            logged_streaming_response: Final = self._with_client_facing_stream_model(complete_streaming_response)
+            self.model_call_details["async_complete_streaming_response"] = logged_streaming_response
+
             ## STANDARDIZED LOGGING PAYLOAD
             try:
                 self.model_call_details["standard_logging_object"] = self._build_standard_logging_payload(
-                    complete_streaming_response, start_time, end_time
+                    logged_streaming_response, start_time, end_time
                 )
             except Exception:  # noqa: BLE001  # payload build must never block later callbacks (slot release)
                 verbose_logger.exception(

litellm/litellm_core_utils/streaming_handler.py

@@ -1504,8 +1504,11 @@ class CustomStreamWrapper:
 
                 self.tool_call = True
 
-            if hasattr(chunk, "usage") and chunk.usage is not None:
+            chunk_usage: Final = getattr(chunk, "usage", None)
-                model_response.usage = chunk.usage
+            if isinstance(chunk_usage, Usage):
+                model_response.usage = chunk_usage
+            elif isinstance(chunk_usage, BaseModel):
+                model_response.usage = Usage(**chunk_usage.model_dump())
 
             ## RETURN ARG
             result: Final = self.return_processed_chunk_logic(

litellm/litellm_core_utils/token_counter.py

@@ -46,6 +46,8 @@ from litellm.types.llms.openai import (
     AllMessageValues,
     ChatCompletionDocumentObject,
     ChatCompletionNamedToolChoiceParam,
+    ChatCompletionRedactedThinkingBlock,
+    ChatCompletionThinkingBlock,
     ChatCompletionToolParam,
     OpenAIMessageContentListBlock,
 )
@@ -854,6 +856,8 @@ def _count_content_list(
     content_list: str
     | Iterable[
         OpenAIMessageContentListBlock
+        | ChatCompletionThinkingBlock
+        | ChatCompletionRedactedThinkingBlock
         | AnthropicMessagesTextParam
         | AnthropicMessagesImageParam
         | AnthropicMessagesDocumentParam
@@ -898,9 +902,9 @@ def _count_content_list(
                     use_default_image_token_count,
                     default_token_count,
                 )
-            elif c["type"] == "thinking":
+            elif c["type"] in ("thinking", "redacted_thinking"):
                 # Claude extended thinking content block
-                # Count the thinking text and skip signature (opaque signature blob)
+                # Count the thinking text and skip the opaque blobs (signature, redacted data)
                 thinking_text = str(c.get("thinking", ""))
                 if thinking_text:
                     num_tokens += count_function(thinking_text)
@@ -920,7 +924,8 @@ def _count_content_list(
                 raise ValueError(
                     f"Invalid content item type: {content_type}. "
                     f"Expected str or dict with 'type' field "
-                    f"(text, image_url, image, document, file, tool_use, tool_result, thinking, tool_reference)."
+                    f"(text, image_url, image, document, file, tool_use, tool_result, thinking, redacted_thinking, "
+                    f"tool_reference)."
                 )
         return num_tokens
     except Exception as e:

litellm/llms/anthropic/experimental_pass_through/adapters/handler.py

@@ -35,7 +35,7 @@ if TYPE_CHECKING:
     from litellm.router import Router
 
 # Anthropic-only keys already mapped by the translator; strip on extra_kwargs re-merge.
-ANTHROPIC_ONLY_REQUEST_KEYS: Final[frozenset[str]] = frozenset({"output_config"})
+ANTHROPIC_ONLY_REQUEST_KEYS: Final[frozenset[str]] = frozenset({"output_config", "safeguards"})
 
 _AnthropicMessages: TypeAlias = "list[dict[str, object]]"
 _AnthropicSystem: TypeAlias = "str | list[dict[str, object]] | None"

litellm/llms/anthropic/experimental_pass_through/messages/transformation.py

@@ -79,10 +79,14 @@ class AnthropicMessagesConfig(BaseAnthropicMessagesConfig):
             "speed",
             "output_config",
             "reasoning_effort",
+            "safeguards",
             # TODO: Add Anthropic `metadata` support
             # "metadata",
         ]
 
+    def should_filter_anthropic_beta_headers(self) -> bool:
+        return self._resolved_provider != "anthropic"
+
     def _remove_scope_from_cache_control(self, anthropic_messages_request: dict) -> None:
         """
         Remove `scope` field from cache_control blocks.

litellm/llms/azure_ai/anthropic/messages_transformation.py

@@ -2,14 +2,18 @@
 Azure Anthropic messages transformation config - extends AnthropicMessagesConfig with Azure authentication
 """
 
+from collections.abc import Mapping
 from typing import Any, Final
 
 from litellm.llms.anthropic.experimental_pass_through.messages.transformation import (
     AnthropicMessagesConfig,
 )
 from litellm.llms.azure.common_utils import BaseAzureLLM
+from litellm.types.llms.anthropic import ANTHROPIC_BETA_HEADER_VALUES
 from litellm.types.router import GenericLiteLLMParams
 
+DANGEROUS_TOOL_USE_BETA: Final = ANTHROPIC_BETA_HEADER_VALUES.DANGEROUS_TOOL_USE_2026_09_03.value
+
 
 class AzureAnthropicMessagesConfig(AnthropicMessagesConfig):
     """
@@ -71,7 +75,7 @@ class AzureAnthropicMessagesConfig(AnthropicMessagesConfig):
             messages=messages,
         )
 
-        return headers, api_base
+        return _with_dangerous_tool_use_beta_for_safeguards(headers, optional_params), api_base
 
     def get_complete_url(
         self,
@@ -164,3 +168,14 @@ class AzureAnthropicMessagesConfig(AnthropicMessagesConfig):
         self._normalize_system_role_messages(anthropic_messages_request, model=model)
         self._remove_scope_from_cache_control(anthropic_messages_request)
         return anthropic_messages_request
+
+
+def _with_dangerous_tool_use_beta_for_safeguards(
+    headers: Mapping[str, str], optional_params: Mapping[str, object]
+) -> dict[str, str]:
+    if optional_params.get("safeguards") is None:
+        return dict(headers)
+    existing: Final = ",".join(value for key, value in headers.items() if key.lower() == "anthropic-beta")
+    betas: Final = {piece.strip() for piece in existing.split(",") if piece.strip()}
+    others: Final = {key: value for key, value in headers.items() if key.lower() != "anthropic-beta"}
+    return {**others, "anthropic-beta": ",".join(sorted(betas | {DANGEROUS_TOOL_USE_BETA}))}

litellm/llms/bedrock/messages/invoke_transformations/anthropic_claude3_transformation.py

@@ -530,6 +530,9 @@ class AmazonAnthropicClaudeMessagesConfig(
         if anthropic_model_info.is_eager_input_streaming_used(tools):
             beta_set.add(ANTHROPIC_FINE_GRAINED_TOOL_STREAMING_BETA_HEADER)
 
+        if anthropic_messages_optional_request_params.get("safeguards") is not None:
+            beta_set.add(ANTHROPIC_BETA_HEADER_VALUES.DANGEROUS_TOOL_USE_2026_09_03.value)
+
         self._filter_context_management_for_bedrock_invoke(
             anthropic_messages_request=anthropic_messages_request,
             beta_set=beta_set,
@@ -759,9 +762,7 @@ class AmazonAnthropicClaudeMessagesConfig(
         aws_decoder: Final = AmazonAnthropicClaudeMessagesStreamDecoder(
             model=model,
         )
-        completion_stream: Final = aws_decoder.aiter_bytes(
+        completion_stream: Final = aws_decoder.aiter_bytes(httpx_response.aiter_bytes())
-            httpx_response.aiter_bytes(chunk_size=aws_decoder.DEFAULT_CHUNK_SIZE)
-        )
         # Convert decoded Bedrock events to Server-Sent Events expected by Anthropic clients.
         return self.bedrock_sse_wrapper(
             completion_stream=completion_stream,
@@ -908,16 +909,6 @@ class AmazonAnthropicClaudeMessagesConfig(
 
 
 class AmazonAnthropicClaudeMessagesStreamDecoder(AWSEventStreamDecoder):
-    def __init__(
-        self,
-        model: str,
-    ) -> None:
-        """
-        Iterator to return Bedrock invoke response in anthropic /messages format
-        """
-        super().__init__(model=model)
-        self.DEFAULT_CHUNK_SIZE = 1024
-
     def _chunk_parser(self, chunk_data: dict) -> GChunk | ModelResponseStream | dict:
         """
         Parse the chunk data into anthropic /messages format

litellm/llms/vertex_ai/vertex_ai_partner_models/anthropic/experimental_pass_through/transformation.py

@@ -108,6 +108,9 @@ class VertexAIPartnerModelsAnthropicMessagesConfig(AnthropicMessagesConfig, Vert
         if anthropic_model_info.is_tool_search_used(tools):
             beta_values.add(get_tool_search_beta_header("vertex_ai"))
 
+        if optional_params.get("safeguards") is not None:
+            beta_values.add(ANTHROPIC_BETA_HEADER_VALUES.DANGEROUS_TOOL_USE_2026_09_03.value)
+
         if beta_values:
             headers["anthropic-beta"] = ",".join(beta_values)
 

litellm/proxy/_experimental/out/__next._tree.txt

@@ -1,4 +1,4 @@
 :HL["/litellm-asset-prefix/_next/static/chunks/1kid9zr1--h6y.css","style"]
 :HL["/litellm-asset-prefix/_next/static/chunks/3146e697tym4_.css","style"]
 :HL["/litellm-asset-prefix/_next/static/media/83afe278b6a6bb3c-s.p.2bn3s6zvc0dyp.woff2","font",{"crossOrigin":"","type":"font/woff2"}]
-0:{"tree":{"name":"","param":null,"prefetchHints":4176,"slots":{"children":{"name":"(dashboard)","param":null,"prefetchHints":4192,"slots":{"children":{"name":"__PAGE__","param":null,"prefetchHints":4256,"slots":null}}}}},"staleTime":300,"buildId":"kXnLzJ6ylsRPmgSkCkCKM"}
+0:{"tree":{"name":"","param":null,"prefetchHints":4176,"slots":{"children":{"name":"(dashboard)","param":null,"prefetchHints":4192,"slots":{"children":{"name":"__PAGE__","param":null,"prefetchHints":4256,"slots":null}}}}},"staleTime":300,"buildId":"RvBoD6No6gfcqg-cP5_LO"}

litellm/proxy/_experimental/out/_not-found/__next._not-found.__PAGE__.txt

@@ -16,7 +16,7 @@ f:I[363178,["/litellm-asset-prefix/_next/static/chunks/1kpvojzxb_2ce.js","/litel
 6:X
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