Merge branch 'litellm_internal_staging' of https://github.com/BerriAI/litellm into litellm_internal_copy_38013

# Conflicts:
#	litellm/types/proxy/public_endpoints/public_endpoints.py
#	tests/test_litellm/proxy/public_endpoints/test_public_endpoints.py

.github/workflows/test-code-quality.yml

@@ -128,6 +128,9 @@ jobs:
       - name: check_fastuuid_usage
         run: uv run --no-sync python ./tests/code_coverage_tests/check_fastuuid_usage.py
 
+      - name: check_py310_typing_imports
+        run: uv run --no-sync python ./tests/code_coverage_tests/check_py310_typing_imports.py
+
       - name: check_e2e_no_raw_requests
         run: uv run --no-sync python ./tests/code_coverage_tests/check_e2e_no_raw_requests.py
 
@@ -145,3 +148,33 @@ jobs:
 
       - name: documentation_test_api_docs
         run: uv run --no-sync python ./tests/documentation_tests/test_api_docs.py
+
+  python-310-import-smoke:
+    runs-on: ubuntu-latest
+    timeout-minutes: 15
+
+    steps:
+      - uses: actions/checkout@08eba0b27e820071cde6df949e0beb9ba4906955 # v4.3.0
+        with:
+          persist-credentials: false
+
+      - name: Set up Python
+        uses: actions/setup-python@a26af69be951a213d495a4c3e4e4022e16d87065 # v5.6.0
+        with:
+          python-version: "3.10"
+
+      - name: Set up uv
+        uses: ./.github/actions/setup-uv-with-retries
+        with:
+          version: "0.10.9"
+
+      - name: Install dependencies
+        run: uv sync --frozen --extra proxy --python 3.10
+
+      - run: uv run --no-sync python --version
+
+      - name: Import litellm
+        run: uv run --no-sync python -c "import litellm"
+
+      - name: Check litellm CLI
+        run: uv run --no-sync litellm --version

basedpyright-code-budget.json

@@ -24,7 +24,7 @@
     "limit": 19
   },
   "reportExplicitAny": {
-    "limit": 4125
+    "limit": 4124
   },
   "reportFunctionMemberAccess": {
     "limit": 7
@@ -57,7 +57,7 @@
     "limit": 5601
   },
   "reportMissingTypeArgument": {
-    "limit": 15306
+    "limit": 15290
   },
   "reportMissingTypeStubs": {
     "limit": 40
@@ -99,31 +99,31 @@
     "limit": 0
   },
   "reportUnknownArgumentType": {
-    "limit": 44364
+    "limit": 44362
   },
   "reportUnknownLambdaType": {
     "limit": 109
   },
   "reportUnknownMemberType": {
-    "limit": 38350
+    "limit": 38332
   },
   "reportUnknownParameterType": {
     "limit": 19625
   },
   "reportUnknownVariableType": {
-    "limit": 29877
+    "limit": 29861
   },
   "reportUnnecessaryCast": {
     "limit": 111
   },
   "reportUnnecessaryComparison": {
-    "limit": 692
+    "limit": 687
   },
   "reportUnnecessaryContains": {
-    "limit": 5
+    "limit": 4
   },
   "reportUnnecessaryIsInstance": {
-    "limit": 826
+    "limit": 823
   },
   "reportUntypedBaseClass": {
     "limit": 0

litellm-proxy-extras/litellm_proxy_extras/utils.py

@@ -43,6 +43,8 @@ def _get_prisma_env() -> dict:
 
 _MIGRATION_TS_RE = re.compile(r"^(\d{14})_")
 
+_MIGRATION_DEADLOCK_MARKER = "deadlock detected"
+
 _SPEND_LOGS_ALTER_RE = re.compile(r'^ALTER\s+TABLE\s+"LiteLLM_SpendLogs"\s', re.IGNORECASE)
 _SPEND_LOGS_ARTIFACT_DROP_RE = re.compile(
     r'^DROP\s+TABLE\s+"LiteLLM_SpendLogs_[^"]*"', re.IGNORECASE
@@ -265,6 +267,50 @@ class ProxyExtrasDBManager:
             env=prisma_env,
         )
 
+    @staticmethod
+    def _roll_back_migration_best_effort(migration_name: str) -> None:
+        """Mark a migration rolled back, tolerating a concurrent resolver
+        having already done it."""
+        try:
+            ProxyExtrasDBManager._roll_back_migration(migration_name)
+        except (subprocess.CalledProcessError, subprocess.TimeoutExpired):
+            pass
+
+    @staticmethod
+    def _failed_migration_logs(migration_name: str) -> Optional[str]:
+        """Return failed migration logs, or None if the ledger is unavailable."""
+        database_url = os.getenv("DATABASE_URL")
+        if not database_url:
+            return None
+
+        try:
+            import psycopg
+        except ImportError:
+            return None
+
+        cleaned_url = ProxyExtrasDBManager._strip_prisma_query_params(database_url)
+        ledger_table = psycopg.sql.SQL("{}.{}").format(
+            psycopg.sql.Identifier(
+                ProxyExtrasDBManager._prisma_schema_param(database_url) or "public"
+            ),
+            psycopg.sql.Identifier("_prisma_migrations"),
+        )
+        try:
+            with psycopg.connect(
+                cleaned_url, connect_timeout=10, autocommit=True
+            ) as conn:
+                row = conn.execute(
+                    psycopg.sql.SQL(
+                        "SELECT logs FROM {} "
+                        "WHERE migration_name = %s AND finished_at IS NULL "
+                        "AND rolled_back_at IS NULL"
+                    ).format(ledger_table),
+                    (migration_name,),
+                ).fetchone()
+        except (psycopg.OperationalError, psycopg.DatabaseError):
+            return None
+        return (row[0] or "") if row else ""
+
     @staticmethod
     def _resolve_specific_migration(migration_name: str):
         """Mark a specific migration as applied"""
@@ -661,7 +707,8 @@ class ProxyExtrasDBManager:
         v2 migration resolver (opt-in via --use_v2_migration_resolver).
 
         Runs `prisma migrate deploy` and handles standard recovery paths
-        (P3005 baseline, P3009/P3018 idempotent errors). Critically, it does
+        (P3005 baseline, P3009/P3018 idempotent errors, deadlocks against a
+        concurrent migrate deploy). Critically, it does
         NOT call `_resolve_all_migrations` — the diff-and-force recovery that
         caused schema thrashing when two LiteLLM versions contended for the
         same DB during rolling deploys.
@@ -772,6 +819,20 @@ class ProxyExtrasDBManager:
                                     f"Detail: {resolve_err}"
                                 ) from resolve_err
                             continue
+                        if migration_match:
+                            migration_name = migration_match.group(1)
+                            ledger_logs = ProxyExtrasDBManager._failed_migration_logs(migration_name)
+                            if ledger_logs is not None and (
+                                ledger_logs == "" or _MIGRATION_DEADLOCK_MARKER in ledger_logs
+                            ):
+                                logger.info(
+                                    "Migration %s failed in a concurrent migrate deploy "
+                                    "deadlock race, rolling its ledger row back and retrying",
+                                    migration_name,
+                                )
+                                ProxyExtrasDBManager._roll_back_migration_best_effort(migration_name)
+                                time.sleep(random.randrange(5, 15))
+                                continue
                         raise RuntimeError(
                             "Database migration failed and cannot be auto-recovered. "
                             f"Manual intervention required.\n\nPrisma error:\n{stderr}"
@@ -817,11 +878,42 @@ class ProxyExtrasDBManager:
                                 ) from resolve_err
                             continue
 
+                        if migration_match and _MIGRATION_DEADLOCK_MARKER in stderr:
+                            logger.info(
+                                "Migration %s deadlocked against a concurrent "
+                                "migrate deploy, rolling its ledger row back "
+                                "and retrying",
+                                migration_match.group(1),
+                            )
+                            ProxyExtrasDBManager._roll_back_migration_best_effort(
+                                migration_match.group(1)
+                            )
+                            time.sleep(random.randrange(5, 15))
+                            continue
+
                         raise RuntimeError(
                             "Database migration failed and cannot be auto-recovered. "
                             f"Manual intervention required.\n\nPrisma error:\n{stderr}"
                         ) from e
 
+                    if _MIGRATION_DEADLOCK_MARKER in stderr:
+                        logger.info(
+                            "prisma migrate deploy attempt %s deadlocked against "
+                            "a concurrent migrate deploy, retrying",
+                            attempt + 1,
+                        )
+                        time.sleep(random.randrange(5, 15))
+                        continue
+
+                    if "P1002" in stderr and "advisory lock" in stderr:
+                        logger.info(
+                            "prisma migrate deploy attempt %s timed out waiting for "
+                            "the advisory lock a concurrent migrate deploy holds, retrying",
+                            attempt + 1,
+                        )
+                        time.sleep(random.randrange(5, 15))
+                        continue
+
                     raise RuntimeError(
                         "Database migration failed and cannot be auto-recovered. "
                         f"Manual intervention required.\n\nPrisma error:\n{stderr}"
@@ -829,9 +921,9 @@ class ProxyExtrasDBManager:
 
             raise RuntimeError(
                 "Database migration failed after 4 attempts (retry loop "
-                "exhausted by timeouts or repeated idempotent-recovery "
-                "continues). Check database connectivity, load, and "
-                "_prisma_migrations ledger state, and raise "
+                "exhausted by timeouts, deadlock retries, or repeated "
+                "idempotent-recovery continues). Check database connectivity, "
+                "load, and _prisma_migrations ledger state, and raise "
                 f"{PRISMA_MIGRATE_DEPLOY_TIMEOUT_ENV_VAR} if the attempts timed out."
             )
         finally:

litellm-proxy-extras/tests/test_setup_database_fail_fast.py

@@ -240,3 +240,223 @@ def test_v2_does_not_call_resolve_all_migrations(monkeypatch, tmp_path):
     ok = ProxyExtrasDBManager.setup_database(use_migrate=True, use_v2_resolver=True)
     assert ok is True
     assert resolve_called["n"] == 0, "v2 must not invoke the diff-and-force recovery"
+
+
+_DEADLOCK_P3018_STDERR = (
+    "Error: P3018\n"
+    "Migration name: 20260415120000_health_check_latest_per_model_index\n"
+    "Database error code: 40P01\n"
+    "deadlock detected"
+)
+
+
+def _stub_v2_env(monkeypatch, tmp_path):
+    monkeypatch.setenv("DATABASE_URL", "postgresql://u:p@localhost:9/x")
+    monkeypatch.setattr(
+        ProxyExtrasDBManager, "_warn_if_db_ahead_of_head", lambda _: None
+    )
+    monkeypatch.setattr(ProxyExtrasDBManager, "_get_prisma_dir", lambda: str(tmp_path))
+    (tmp_path / "schema.prisma").write_text("// stub")
+    monkeypatch.setattr("time.sleep", lambda _: None)
+
+
+def _succeed_after(failures: int, stderr: str):
+    calls = {"n": 0}
+
+    class _OkResult:
+        stdout = "Applied migration.\n"
+        stderr = ""
+
+    def _run(*args, **kwargs):
+        if "deploy" not in args[0]:
+            return _OkResult()
+        calls["n"] += 1
+        if calls["n"] <= failures:
+            raise subprocess.CalledProcessError(
+                returncode=1, cmd=args[0], stderr=stderr, output=""
+            )
+        return _OkResult()
+
+    return _run
+
+
+def test_v2_p3018_deadlock_rolls_back_and_retries(monkeypatch, tmp_path):
+    """v2: losing the migrate deploy deadlock race against a concurrent
+    instance rolls the ledger row back and retries instead of dying."""
+    _stub_v2_env(monkeypatch, tmp_path)
+
+    rolled_back = []
+    monkeypatch.setattr(
+        ProxyExtrasDBManager,
+        "_roll_back_migration",
+        lambda name: rolled_back.append(name),
+    )
+    monkeypatch.setattr(
+        ProxyExtrasDBManager,
+        "_resolve_specific_migration",
+        lambda name: pytest.fail("a deadlocked migration must never be marked applied"),
+    )
+    monkeypatch.setattr("subprocess.run", _succeed_after(1, _DEADLOCK_P3018_STDERR))
+
+    ok = ProxyExtrasDBManager.setup_database(use_migrate=True, use_v2_resolver=True)
+    assert ok is True
+    assert rolled_back == ["20260415120000_health_check_latest_per_model_index"]
+
+
+def test_v2_p3018_persistent_deadlock_exhausts_attempts(monkeypatch, tmp_path):
+    """v2: a deadlock on every attempt still fails after the retry budget."""
+    _stub_v2_env(monkeypatch, tmp_path)
+    monkeypatch.setattr(ProxyExtrasDBManager, "_roll_back_migration", lambda name: None)
+
+    with patch(
+        "subprocess.run",
+        side_effect=_fake_migrate_deploy_failure(1, _DEADLOCK_P3018_STDERR),
+    ):
+        with pytest.raises(RuntimeError, match="after 4 attempts"):
+            ProxyExtrasDBManager.setup_database(use_migrate=True, use_v2_resolver=True)
+
+
+def test_v2_p3009_deadlocked_ledger_row_rolls_back_and_retries(monkeypatch, tmp_path):
+    """v2: the surviving instance sees the victim's failed ledger row as P3009.
+    When that row's logs show a deadlock, roll it back and retry."""
+    _stub_v2_env(monkeypatch, tmp_path)
+
+    stderr = (
+        "Error: P3009\n"
+        "migrate found failed migrations in the target database\n"
+        "The `20260415120000_health_check_latest_per_model_index` migration "
+        "started at 2026-09-01 18:46:13 UTC failed"
+    )
+    monkeypatch.setattr(
+        ProxyExtrasDBManager,
+        "_failed_migration_logs",
+        lambda name: "ERROR: deadlock detected\nDETAIL: Process 72 waits for ShareLock",
+    )
+    rolled_back = []
+    monkeypatch.setattr(
+        ProxyExtrasDBManager,
+        "_roll_back_migration",
+        lambda name: rolled_back.append(name),
+    )
+    monkeypatch.setattr(
+        ProxyExtrasDBManager,
+        "_resolve_specific_migration",
+        lambda name: pytest.fail("a deadlocked migration must never be marked applied"),
+    )
+    monkeypatch.setattr("subprocess.run", _succeed_after(1, stderr))
+
+    ok = ProxyExtrasDBManager.setup_database(use_migrate=True, use_v2_resolver=True)
+    assert ok is True
+    assert rolled_back == ["20260415120000_health_check_latest_per_model_index"]
+
+
+def test_v2_p3009_empty_ledger_logs_rolls_back_and_retries(monkeypatch, tmp_path):
+    """v2: empty failed ledger logs mean a concurrent deploy moved it on."""
+    _stub_v2_env(monkeypatch, tmp_path)
+
+    stderr = (
+        "Error: P3009\n"
+        "migrate found failed migrations in the target database\n"
+        "The `20260415120000_health_check_latest_per_model_index` migration "
+        "started at 2026-09-01 18:46:13 UTC failed"
+    )
+    monkeypatch.setattr(ProxyExtrasDBManager, "_failed_migration_logs", lambda name: "")
+    rolled_back = []
+    monkeypatch.setattr(
+        ProxyExtrasDBManager,
+        "_roll_back_migration",
+        lambda name: rolled_back.append(name),
+    )
+    monkeypatch.setattr(
+        ProxyExtrasDBManager,
+        "_resolve_specific_migration",
+        lambda name: pytest.fail("a deadlocked migration must never be marked applied"),
+    )
+    monkeypatch.setattr("subprocess.run", _succeed_after(1, stderr))
+
+    ok = ProxyExtrasDBManager.setup_database(use_migrate=True, use_v2_resolver=True)
+    assert ok is True
+    assert rolled_back == ["20260415120000_health_check_latest_per_model_index"]
+
+
+def test_v2_p3009_unreadable_ledger_still_raises(monkeypatch, tmp_path):
+    """v2: an unreadable ledger cannot establish that P3009 was a deadlock."""
+    _stub_v2_env(monkeypatch, tmp_path)
+
+    stderr = (
+        "Error: P3009\n"
+        "migrate found failed migrations in the target database\n"
+        "The `20260415120000_health_check_latest_per_model_index` migration "
+        "started at 2026-09-01 18:46:13 UTC failed"
+    )
+    monkeypatch.setattr(ProxyExtrasDBManager, "_failed_migration_logs", lambda name: None)
+    monkeypatch.setattr(
+        ProxyExtrasDBManager,
+        "_roll_back_migration",
+        lambda name: pytest.fail("an unreadable ledger must not trigger a retry"),
+    )
+    monkeypatch.setattr("subprocess.run", _succeed_after(1, stderr))
+
+    with pytest.raises(RuntimeError, match="cannot be auto-recovered"):
+        ProxyExtrasDBManager.setup_database(use_migrate=True, use_v2_resolver=True)
+
+
+def test_v2_p3009_non_deadlock_ledger_row_still_raises(monkeypatch, tmp_path):
+    """v2: a failed ledger row whose logs show a real SQL error stays fatal."""
+    _stub_v2_env(monkeypatch, tmp_path)
+
+    stderr = (
+        "Error: P3009\n"
+        "migrate found failed migrations in the target database\n"
+        "The `20260101000000_genuinely_broken` migration started at "
+        "2026-09-01 18:46:13 UTC failed"
+    )
+    monkeypatch.setattr(
+        ProxyExtrasDBManager,
+        "_failed_migration_logs",
+        lambda name: 'ERROR: syntax error at or near "BRKN"',
+    )
+
+    with patch("subprocess.run", side_effect=_fake_migrate_deploy_failure(1, stderr)):
+        with pytest.raises(RuntimeError, match="cannot be auto-recovered"):
+            ProxyExtrasDBManager.setup_database(use_migrate=True, use_v2_resolver=True)
+
+
+def test_v2_bare_deadlock_stderr_retries(monkeypatch, tmp_path):
+    """v2: a deadlock reported without a Prisma error code (the advisory-lock
+    waiter as victim) is retried, not fatal."""
+    _stub_v2_env(monkeypatch, tmp_path)
+    monkeypatch.setattr(
+        "subprocess.run", _succeed_after(1, "Database error: deadlock detected")
+    )
+
+    ok = ProxyExtrasDBManager.setup_database(use_migrate=True, use_v2_resolver=True)
+    assert ok is True
+
+
+_P1002_ADVISORY_LOCK_STDERR = (
+    "Error: P1002\n\n"
+    "The database server at `127.0.0.1`:`45743` was reached but timed out.\n\n"
+    "Context: Timed out trying to acquire a postgres advisory lock "
+    "(SELECT pg_advisory_lock(72707369)). Elapsed: 10000ms."
+)
+
+
+def test_v2_advisory_lock_timeout_retries(monkeypatch, tmp_path):
+    """v2: the advisory-lock waiter that times out while a peer's retry holds
+    the lock retries instead of dying."""
+    _stub_v2_env(monkeypatch, tmp_path)
+    monkeypatch.setattr("subprocess.run", _succeed_after(2, _P1002_ADVISORY_LOCK_STDERR))
+
+    ok = ProxyExtrasDBManager.setup_database(use_migrate=True, use_v2_resolver=True)
+    assert ok is True
+
+
+def test_v2_p1002_without_advisory_lock_context_still_raises(monkeypatch, tmp_path):
+    """v2: a plain P1002 (database unreachable) stays fatal."""
+    _stub_v2_env(monkeypatch, tmp_path)
+    stderr = "Error: P1002\n\nThe database server at `db`:`5432` was reached but timed out."
+    monkeypatch.setattr("subprocess.run", _succeed_after(1, stderr))
+
+    with pytest.raises(RuntimeError, match="cannot be auto-recovered"):
+        ProxyExtrasDBManager.setup_database(use_migrate=True, use_v2_resolver=True)

litellm/__init__.py

@@ -424,6 +424,10 @@ anthropic_beta_headers_url: str = os.getenv(
     "LITELLM_ANTHROPIC_BETA_HEADERS_URL",
     "https://raw.githubusercontent.com/BerriAI/litellm/main/litellm/anthropic_beta_headers_config.json",
 )
+autorouter_presets_url: str = os.getenv(
+    "LITELLM_AUTOROUTER_PRESETS_URL",
+    "https://raw.githubusercontent.com/BerriAI/litellm/main/litellm/proxy/public_endpoints/autorouter_presets.json",
+)
 suppress_debug_info: bool = False
 dynamodb_table_name: Optional[str] = None
 s3_callback_params: Optional[Dict] = None

litellm/_logging.py

@@ -17,7 +17,11 @@ from litellm.constants import (
 from litellm.litellm_core_utils.env_utils import get_env_int
 from litellm.litellm_core_utils.safe_json_dumps import safe_dumps
 from litellm.litellm_core_utils.safe_json_loads import safe_json_loads
-from litellm.litellm_core_utils.secret_redaction import redact_string, redact_structured_value
+from litellm.litellm_core_utils.secret_redaction import (
+    redact_internal_details,
+    redact_string,
+    redact_structured_value,
+)
 
 set_verbose = False
 
@@ -89,6 +93,14 @@ def redact_secrets(value: str) -> str:
     return _redact_string(value)
 
 
+def redact_internal_details_from_client_message(value: str) -> str:
+    """Public API: redact_secrets() plus filesystem paths, internal hostnames, and an
+    embedded traceback, for a string about to leave the process in an HTTP response."""
+    if not _ENABLE_SECRET_REDACTION:
+        return value
+    return redact_internal_details(value)
+
+
 def _substituted_color_message(record: logging.LogRecord) -> str | None:
     """Render a record's ``color_message`` against its args, or None if absent.
 

litellm/constants.py

@@ -1449,6 +1449,7 @@ RETURN_RAW_MODEL_NAME_METADATA_KEY: Final = "_complexity_router_return_raw_model
 SESSION_DEPLOYMENT_AFFINITY_TTL_METADATA_KEY: Final = "_session_deployment_affinity_ttl"
 CONSUMED_REQUEST_TAGS_METADATA_KEY: Final = "_consumed_request_tags"
 INTERNAL_CALL_ORIGIN_METADATA_KEY: Final = "internal_call_origin"
+SESSION_ID_GENERATED_METADATA_KEY: Final = "litellm_session_id_generated"
 LITELLM_TRUNCATED_PAYLOAD_FIELD: Final = "litellm_truncated"
 LITELLM_TRUNCATION_DB_SAFEGUARD_NOTE: Final = (
     "Truncation is a DB storage safeguard. "

litellm/integrations/SlackAlerting/slack_alerting.py

@@ -1955,11 +1955,10 @@ Model Info:
         if not thresholds_enabled and not anomalies_enabled:
             return
 
-        if prisma_client is None:
-            from litellm.proxy.proxy_server import prisma_client as global_prisma_client
+        from litellm.proxy.proxy_server import prisma_client as global_prisma_client
 
-            prisma_client = global_prisma_client  # rebind-ok: fall back to the proxy's global client
-        if prisma_client is None:
+        client: Final = prisma_client if prisma_client is not None else global_prisma_client
+        if client is None:
             return
 
         from litellm.integrations.SlackAlerting.user_spend_alerts import (
@@ -1970,7 +1969,7 @@ Model Info:
         try:
             today: Final = datetime.datetime.now(datetime.timezone.utc).date()
             rows: Final = await fetch_user_spend_rows(
-                prisma_client=prisma_client,
+                prisma_client=client,
                 today=today,
                 baseline_days=self.alerting_args.spend_anomaly_baseline_days,
             )

litellm/integrations/vector_store_integrations/vector_store_pre_call_hook.py

@@ -5,6 +5,7 @@ This hook is called before making an LLM request when a vector store is configur
 It searches the vector store for relevant context and appends it to the messages.
 """
 
+from collections.abc import Awaitable, Callable
 from typing import TYPE_CHECKING, Any, Final, cast
 
 import litellm
@@ -80,10 +81,17 @@ class VectorStorePreCallHook(CustomLogger):
 
             # Get prisma_client for database fallback
             prisma_client = None
+            llm_router = None
             try:
-                from litellm.proxy.proxy_server import prisma_client as _prisma_client
+                from litellm.proxy.proxy_server import (
+                    llm_router as _llm_router,
+                )
+                from litellm.proxy.proxy_server import (
+                    prisma_client as _prisma_client,
+                )
 
                 prisma_client = _prisma_client
+                llm_router = _llm_router
             except ImportError:
                 pass
 
@@ -114,12 +122,26 @@ class VectorStorePreCallHook(CustomLogger):
                 vector_store_id = vector_store_to_run.get("vector_store_id", "")
                 custom_llm_provider = vector_store_to_run.get("custom_llm_provider")
                 litellm_params_for_vector_store = vector_store_to_run.get("litellm_params", {}) or {}
-                # Call litellm.vector_stores.search() with the required parameters
-                search_response = await litellm.vector_stores.asearch(
+                request_litellm_params = litellm_logging_obj.model_call_details.get("litellm_params", {})
+                request_metadata = (
+                    request_litellm_params.get("metadata", {}) if isinstance(request_litellm_params, dict) else {}
+                )
+                if llm_router is not None:
+                    search_function = cast(  # cast-ok: normalize router search callable
+                        Callable[..., Awaitable[VectorStoreSearchResponse]],
+                        llm_router.avector_store_search,
+                    )
+                else:
+                    search_function = cast(  # cast-ok: normalize SDK search callable
+                        Callable[..., Awaitable[VectorStoreSearchResponse]],
+                        litellm.vector_stores.asearch,
+                    )
+                search_response = await search_function(
                     **{
                         "vector_store_id": vector_store_id,
                         "query": query,
                         "custom_llm_provider": custom_llm_provider,
+                        "metadata": request_metadata,
                         **litellm_params_for_vector_store,
                     },
                 )

litellm/integrations/websearch_interception/handler.py

@@ -10,9 +10,9 @@ import asyncio
 import math
 import uuid
 from collections.abc import AsyncIterator, Mapping, Sequence
-from typing import TYPE_CHECKING, Any, Final, Literal, Never, TypedDict, TypeVar, cast
+from typing import TYPE_CHECKING, Any, Final, Literal, TypedDict, TypeVar, cast
 
-from typing_extensions import ReadOnly
+from typing_extensions import Never, ReadOnly
 
 import litellm
 from litellm._logging import verbose_logger
@@ -419,7 +419,6 @@ class WebSearchInterceptionLogger(CustomLogger):
         if call_type in (CallTypes.responses, CallTypes.aresponses):
             return self._convert_responses_tools(kwargs=kwargs, tools=tools)
 
-        # Check if any tool is a web search tool (native or already LiteLLM standard)
         has_websearch: Final = any(is_web_search_tool(t) for t in tools)
 
         if not has_websearch:

litellm/litellm_core_utils/secret_redaction.py

@@ -92,6 +92,27 @@ def redact_string(value: str) -> str:
     return _SECRET_RE.sub(_REDACTED, value)
 
 
+_UNIX_SYSTEM_PATH: Final = r"/(?:etc|var|opt|usr|home|root|private|Users|tmp|mnt|srv)/[^\s'\"\)\]}>,]+"
+_WINDOWS_DRIVE_PATH: Final = r"[A-Za-z]:\\[^\s'\"\)\]}>,]+"
+_PRIVATE_OR_LOOPBACK_IPV4: Final = (
+    r"\b(?:10(?:\.\d{1,3}){3}|172\.(?:1[6-9]|2\d|3[01])(?:\.\d{1,3}){2}|192\.168(?:\.\d{1,3}){2}|127(?:\.\d{1,3}){3})\b"
+)
+_INTERNAL_SUFFIX_HOSTNAME: Final = r"\b[A-Za-z0-9-]+(?:\.[A-Za-z0-9-]+)*\.(?:internal|local|corp|lan|intra|private)\b"
+_INTERNAL_DETAIL_RE: Final = re.compile(
+    "|".join((_UNIX_SYSTEM_PATH, _WINDOWS_DRIVE_PATH, _PRIVATE_OR_LOOPBACK_IPV4, _INTERNAL_SUFFIX_HOSTNAME)),
+    re.IGNORECASE,
+)
+_TRACEBACK_MARKER: Final = "Traceback (most recent call last):"
+
+
+def redact_internal_details(value: str) -> str:
+    """Drop an embedded traceback and scrub filesystem paths and internal hostnames,
+    on top of redact_string(). For client-facing messages only: server logs keep this detail."""
+    marker_index: Final = value.find(_TRACEBACK_MARKER)
+    without_traceback: Final = value[:marker_index].rstrip() if marker_index != -1 else value
+    return _INTERNAL_DETAIL_RE.sub(_REDACTED, redact_string(without_traceback))
+
+
 def redact_structured_value(key: str | None, value: str) -> str:
     """Scrub *value* as it appeared under *key* inside a structured record.
 

litellm/llms/a2a/chat/guardrail_translation/handler.py

@@ -17,7 +17,10 @@ from typing import TYPE_CHECKING, Any, Final, Optional
 from typing_extensions import ReadOnly, TypedDict
 
 from litellm._logging import verbose_proxy_logger
-from litellm.llms.base_llm.guardrail_translation.base_translation import BaseTranslation
+from litellm.llms.base_llm.guardrail_translation.base_translation import (
+    BaseTranslation,
+    StreamingScanKey,
+)
 from litellm.types.utils import GenericGuardrailAPIInputs
 
 if TYPE_CHECKING:
@@ -313,9 +316,14 @@ class A2AGuardrailHandler(BaseTranslation):
 
         return responses_so_far
 
+    def get_streaming_scan_key(self, responses_so_far: Sequence[object]) -> StreamingScanKey | None:
+        _, valid_parsed = self._parse_streaming_responses(responses_so_far)
+        combined_text, _ = self._collect_text_from_parsed_chunks(valid_parsed)
+        return StreamingScanKey(texts=(combined_text,))
+
     def _parse_streaming_responses(
         self,
-        responses_so_far: list[object],
+        responses_so_far: Sequence[object],
     ) -> tuple[list[dict[str, object] | None], list[tuple[int, dict[str, object]]]]:
         """Parse JSON-RPC items, returning aligned parsed list and valid entries."""
         parsed: Final[list[dict[str, object] | None]] = [None] * len(responses_so_far)

litellm/llms/anthropic/chat/guardrail_translation/handler.py

@@ -26,7 +26,10 @@ from litellm.llms.anthropic.experimental_pass_through.adapters.transformation im
     LiteLLMAnthropicMessagesAdapter,
     is_provider_native_tool_dict,
 )
-from litellm.llms.base_llm.guardrail_translation.base_translation import BaseTranslation
+from litellm.llms.base_llm.guardrail_translation.base_translation import (
+    BaseTranslation,
+    StreamingScanKey,
+)
 from litellm.llms.base_llm.guardrail_translation.utils import (
     anthropic_tool_name,
     anthropic_tool_names,
@@ -36,6 +39,7 @@ from litellm.llms.base_llm.guardrail_translation.utils import (
     merge_guardrailed_scoped_messages,
     merge_returned_tools_into_request_tools,
     scoped_structured_message_indices,
+    stream_item_fingerprint,
 )
 from litellm.proxy.pass_through_endpoints.llm_provider_handlers.anthropic_passthrough_logging_handler import (
     AnthropicPassthroughLoggingHandler,
@@ -1176,6 +1180,25 @@ class AnthropicMessagesHandler(BaseTranslation):
             inputs["model"] = response_model
         return inputs
 
+    def get_streaming_scan_key(self, responses_so_far: Sequence[object]) -> StreamingScanKey | None:
+        stream_ended: Final = self._check_streaming_has_ended(responses_so_far)
+        return StreamingScanKey(
+            texts=(self.get_streaming_string_so_far(responses_so_far),),
+            tool_calls=self._streamed_tool_use_fingerprints(responses_so_far) if stream_ended else (),
+            stream_ended=stream_ended,
+        )
+
+    @classmethod
+    def _streamed_tool_use_fingerprints(cls, responses_so_far: Sequence[object]) -> tuple[str, ...]:
+        return tuple(
+            stream_item_fingerprint(block)
+            for item in responses_so_far
+            for event in cls._iter_sse_events(item)
+            if event.get("type") == "content_block_start"
+            and isinstance(block := event.get("content_block"), Mapping)
+            and block.get("type") == "tool_use"
+        )
+
     def get_streaming_string_so_far(self, responses_so_far: Sequence[object]) -> str:
         """
         Parse streaming responses and extract accumulated text content.

litellm/llms/anthropic/common_utils.py

@@ -1641,6 +1641,97 @@ def flatten_unencrypted_web_search_results_in_anthropic_messages(  # mutable-ok:
     return [_flatten_web_search_results_in_message(m) for m in messages]  # mutable-ok: JSON wire format
 
 
+def _normalized_cache_control(cache_control: object) -> dict[str, str] | None:  # mutable-ok: JSON wire format
+    if not isinstance(cache_control, Mapping):
+        return None
+    cache_type: Final = cache_control.get("type")
+    return {"type": cache_type if isinstance(cache_type, str) else "ephemeral"}  # mutable-ok: JSON wire format
+
+
+def _with_portable_cache_control(block: Mapping[str, object]) -> dict[str, object]:  # mutable-ok: JSON wire format
+    if "cache_control" not in block:
+        return dict(block)  # mutable-ok: JSON wire format
+    normalized: Final = _normalized_cache_control(block["cache_control"])
+    rest: Final = {key: value for key, value in block.items() if key != "cache_control"}  # mutable-ok: JSON wire format
+    return rest if normalized is None else {**rest, "cache_control": normalized}  # mutable-ok: JSON wire format
+
+
+def _with_portable_cache_control_in_blocks(blocks: object) -> object:
+    if isinstance(blocks, str) or not isinstance(blocks, Sequence):
+        return blocks
+    return [  # mutable-ok: JSON wire format
+        _with_portable_cache_control(block) if isinstance(block, Mapping) else block for block in blocks
+    ]
+
+
+def _with_portable_cache_control_in_content_block(block: object) -> object:
+    if not isinstance(block, Mapping):
+        return block
+    portable: Final = _with_portable_cache_control(block)
+    if portable.get("type") != "tool_result" or "content" not in portable:
+        return portable
+    return {  # mutable-ok: JSON wire format
+        **portable,
+        "content": _with_portable_cache_control_in_blocks(portable["content"]),
+    }
+
+
+def _with_portable_cache_control_in_message(message: object) -> object:
+    if not isinstance(message, Mapping) or "content" not in message:
+        return message
+    content: Final = message["content"]
+    if isinstance(content, str) or not isinstance(content, Sequence):
+        return message
+    return {  # mutable-ok: JSON wire format
+        **message,
+        "content": [  # mutable-ok: JSON wire format
+            _with_portable_cache_control_in_content_block(block) for block in content
+        ],
+    }
+
+
+def _with_portable_cache_control_in_messages(messages: object) -> object:
+    if isinstance(messages, str) or not isinstance(messages, Sequence):
+        return messages
+    return [  # mutable-ok: JSON wire format
+        _with_portable_cache_control_in_message(message) for message in messages
+    ]
+
+
+def _with_portable_cache_control_in_scoped_value(key: str, value: object) -> object:
+    match key:
+        case "system" | "tools":
+            return _with_portable_cache_control_in_blocks(value)
+        case "messages":
+            return _with_portable_cache_control_in_messages(value)
+        case _:
+            return value
+
+
+def normalize_cache_control_in_anthropic_payload(
+    payload: Mapping[str, object],
+) -> dict[str, object]:  # mutable-ok: JSON wire format
+    """
+    Return a copy of an Anthropic /v1/messages payload with every
+    ``cache_control`` entry reduced to ``{"type": <its type, or "ephemeral">}``
+    at the places the Messages API defines it: the request itself, system
+    blocks, tools, message content blocks, and ``tool_result`` content blocks.
+    Application data such as ``tool_use.input`` and tool ``input_schema`` is
+    never touched, even when it happens to contain a ``cache_control`` key.
+
+    Anthropic itself accepts prompt-caching extensions such as ``ttl``, but
+    strict non-Anthropic implementations of the Messages API validate the field
+    literally and reject the whole request (``cache_control.ttl: 1h is not
+    supported``, ``cache_control.type is required``), which 400s clients like
+    Claude Code that send cache hints. Non-dict ``cache_control`` values are
+    dropped entirely. The caller's payload is never mutated.
+    """
+    portable: Final = _with_portable_cache_control(payload)
+    return {  # mutable-ok: JSON wire format
+        key: _with_portable_cache_control_in_scoped_value(key, value) for key, value in portable.items()
+    }
+
+
 def process_anthropic_headers(headers: httpx.Headers | dict) -> dict:
     openai_headers: Final = {}
     if "anthropic-ratelimit-requests-limit" in headers:

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

@@ -99,6 +99,10 @@ def _deployment_passes_through_anthropic_messages(model_info: object) -> bool:
     return isinstance(supported_endpoints, (list, tuple)) and "/v1/messages" in supported_endpoints
 
 
+def _deployment_supports_cache_control_ttl(model_info: object) -> bool:
+    return isinstance(model_info, dict) and model_info.get("cache_control_ttl") is True
+
+
 ####### ENVIRONMENT VARIABLES ###################
 # Initialize any necessary instances or variables here
 base_llm_http_handler = BaseLLMHTTPHandler()
@@ -568,7 +572,9 @@ def anthropic_messages_handler(
             OpenAILikeAnthropicMessagesConfig,
         )
 
-        anthropic_messages_provider_config = OpenAILikeAnthropicMessagesConfig()
+        anthropic_messages_provider_config = OpenAILikeAnthropicMessagesConfig(
+            cache_control_ttl=_deployment_supports_cache_control_ttl(kwargs.get("model_info")),
+        )
     if anthropic_messages_provider_config is None:
         # Route to Responses API for OpenAI / Azure, chat/completions for everything else.
         if _should_route_to_responses_api(custom_llm_provider, original_model, model):

litellm/llms/azure_ai/vector_stores/transformation.py

@@ -1,10 +1,15 @@
+from __future__ import annotations
+
+from collections.abc import Mapping, Sequence
 from typing import TYPE_CHECKING, Any, Final
 
 import httpx
 
-import litellm
 from litellm.llms.azure.common_utils import BaseAzureLLM
-from litellm.llms.base_llm.vector_store.transformation import BaseVectorStoreConfig
+from litellm.llms.base_llm.vector_store.transformation import (
+    BaseQueryEmbeddingVectorStoreConfig,
+    VectorStoreEmbeddingExecutor,
+)
 from litellm.types.router import GenericLiteLLMParams
 from litellm.types.vector_stores import (
     BaseVectorStoreAuthCredentials,
@@ -26,7 +31,7 @@ else:
     LiteLLMLoggingObj = Any
 
 
-class AzureAIVectorStoreConfig(BaseVectorStoreConfig, BaseAzureLLM):
+class AzureAIVectorStoreConfig(BaseQueryEmbeddingVectorStoreConfig, BaseAzureLLM):
     """
     Configuration for Azure AI Search Vector Store
 
@@ -110,83 +115,73 @@ class AzureAIVectorStoreConfig(BaseVectorStoreConfig, BaseAzureLLM):
     def transform_search_vector_store_request(
         self,
         vector_store_id: str,
-        query: str | list[str],
+        query: str | Sequence[str],
         vector_store_search_optional_params: VectorStoreSearchOptionalRequestParams,
         api_base: str,
         litellm_logging_obj: LiteLLMLoggingObj,
-        litellm_params: dict,
-        extra_body: dict[str, Any] | None = None,
-        router: "Router | None" = None,
-    ) -> tuple[str, dict[str, Any]]:
-        """
-        Transform search request for Azure AI Search API
+        litellm_params: Mapping[str, object],
+        extra_body: Mapping[str, object] | None = None,
+        router: Router | None = None,
+        embedding_executor: VectorStoreEmbeddingExecutor | None = None,
+    ) -> tuple[str, dict[str, object]]:
+        query_text: Final = self.query_text(query)
+        query_vector: Final = self.embed_query(query_text, litellm_params, embedding_executor, router)
+        return self._search_request(
+            vector_store_id,
+            query_text,
+            query_vector,
+            vector_store_search_optional_params,
+            api_base,
+            litellm_logging_obj,
+            litellm_params,
+        )
 
-        Generates embeddings using litellm.embeddings and constructs Azure AI Search request
-        """
-        # Convert query to string if it's a list
-        if isinstance(query, list):
-            query = " ".join(query)
+    async def atransform_search_vector_store_request(
+        self,
+        vector_store_id: str,
+        query: str | Sequence[str],
+        vector_store_search_optional_params: VectorStoreSearchOptionalRequestParams,
+        api_base: str,
+        litellm_logging_obj: LiteLLMLoggingObj,
+        litellm_params: Mapping[str, object],
+        extra_body: Mapping[str, object] | None = None,
+        router: Router | None = None,
+        embedding_executor: VectorStoreEmbeddingExecutor | None = None,
+    ) -> tuple[str, dict[str, object]]:
+        query_text: Final = self.query_text(query)
+        query_vector: Final = await self.aembed_query(query_text, litellm_params, embedding_executor, router)
+        return self._search_request(
+            vector_store_id,
+            query_text,
+            query_vector,
+            vector_store_search_optional_params,
+            api_base,
+            litellm_logging_obj,
+            litellm_params,
+        )
 
-        # Get embedding model from litellm_params (required)
-        embedding_model: Final = litellm_params.get("litellm_embedding_model")
-        if not embedding_model:
-            raise ValueError(
-                "embedding_model is required in litellm_params for Azure AI Search. "
-                "Example: litellm_params['embedding_model'] = 'azure/text-embedding-3-large'"
-            )
-
-        embedding_config: Final = litellm_params.get("litellm_embedding_config", {})
-        if not embedding_config:
-            raise ValueError(
-                "embedding_config is required in litellm_params for Azure AI Search. "
-                "Example: litellm_params['embedding_config'] = {'api_base': 'https://krris-mh44uf7y-eastus2.cognitiveservices.azure.com/', 'api_key': 'os.environ/AZURE_API_KEY', 'api_version': '2025-09-01'}"
-            )
-
-        # Get vector field name (defaults to contentVector)
+    @staticmethod
+    def _search_request(
+        vector_store_id: str,
+        query_text: str,
+        query_vector: Sequence[float],
+        vector_store_search_optional_params: VectorStoreSearchOptionalRequestParams,
+        api_base: str,
+        litellm_logging_obj: LiteLLMLoggingObj,
+        litellm_params: Mapping[str, object],
+    ) -> tuple[str, dict[str, object]]:
         vector_field: Final = litellm_params.get("azure_search_vector_field", "contentVector")
-
-        # Get top_k (number of results to return)
         top_k: Final = vector_store_search_optional_params.get("top_k", 10)
-
-        # Generate embedding for the query using litellm.embeddings
-        try:
-            embedding_response: Final = litellm.embedding(
-                model=embedding_model,
-                input=[query],
-                **embedding_config,
-            )
-            query_vector: Final = embedding_response.data[0]["embedding"]
-        except Exception as e:
-            raise Exception(f"Failed to generate embedding for query: {e}")
-
-        # Azure AI Search endpoint for search
-        index_name: Final = vector_store_id  # vector_store_id is the index name
-        url: Final = f"{api_base}/indexes/{index_name}/docs/search?api-version=2024-07-01"
-
-        # Build the request body for Azure AI Search with vector search
-        request_body: Final = {
-            "search": "*",  # Get all documents (filtered by vector similarity)
-            "vectorQueries": [
-                {
-                    "vector": query_vector,
-                    "fields": vector_field,
-                    "kind": "vector",
-                    "k": top_k,  # Number of nearest neighbors to return
-                }
-            ],
-            "select": "id,content",  # Fields to return (customize based on schema)
+        litellm_logging_obj.model_call_details["input"] = query_text
+        litellm_logging_obj.model_call_details["embedding_model"] = litellm_params.get("litellm_embedding_model")
+        litellm_logging_obj.model_call_details["top_k"] = top_k
+        return f"{api_base}/indexes/{vector_store_id}/docs/search?api-version=2024-07-01", {
+            "search": "*",
+            "vectorQueries": [{"vector": query_vector, "fields": vector_field, "kind": "vector", "k": top_k}],
+            "select": "id,content",
             "top": top_k,
         }
 
-        #########################################################
-        # Update logging object with details of the request
-        #########################################################
-        litellm_logging_obj.model_call_details["input"] = query
-        litellm_logging_obj.model_call_details["embedding_model"] = embedding_model
-        litellm_logging_obj.model_call_details["top_k"] = top_k
-
-        return url, request_body
-
     def transform_search_vector_store_response(
         self, response: httpx.Response, litellm_logging_obj: LiteLLMLoggingObj
     ) -> VectorStoreSearchResponse:

litellm/llms/base_llm/auth/client_credentials.py

@@ -26,6 +26,7 @@ from litellm.llms.base_llm.auth.token_exchange import (
     MAX_RESPONSE_BYTES,
     endpoint_url_for_error_message,
     redact_oauth_error_body,
+    require_posted_response,
     validate_token_endpoint_url,
 )
 from litellm.llms.base_llm.auth.types import InsecureTokenUrl, SyncTokenPoster
@@ -85,9 +86,7 @@ class _HttpxSyncKeycloakPoster:
             )
         except httpx.HTTPStatusError as e:
             return e.response
-        if response is None:
-            raise httpx.TransportError("keycloak token endpoint returned no response")
-        return response
+        return require_posted_response(response, "keycloak token endpoint")
 
 
 _DEFAULT_POSTER: Final[SyncTokenPoster] = _HttpxSyncKeycloakPoster()

litellm/llms/base_llm/auth/token_exchange.py

@@ -381,6 +381,14 @@ def _new_exchange_handler() -> "HTTPHandler":
     return handler
 
 
+def require_posted_response(response: httpx.Response | None, endpoint_label: str) -> httpx.Response:
+    """The legacy ``HTTPHandler`` carries no return annotation, so a patched or stubbed client can
+    hand a poster ``None`` back; a transport error beats dereferencing it."""
+    if response is None:
+        raise httpx.TransportError(f"{endpoint_label} returned no response")
+    return response
+
+
 class _HttpxSyncTokenPoster:
     """Default poster: a dedicated HTTPHandler (no logging_obj, so litellm's
     pre/post-call body logging never sees the exchange POST); returns the
@@ -407,9 +415,7 @@ class _HttpxSyncTokenPoster:
             )
         except httpx.HTTPStatusError as e:
             return e.response
-        if response is None:
-            raise httpx.TransportError("token endpoint returned no response")
-        return response
+        return require_posted_response(response, "token endpoint")
 
 
 class _ServiceLoggingHooks(Protocol):

litellm/llms/base_llm/guardrail_translation/base_translation.py

@@ -35,6 +35,22 @@ class StreamTransformSink:
     holdback_per_choice: dict[int, int] = field(default_factory=dict)
 
 
+@dataclass(frozen=True, slots=True)
+class StreamingScanKey:
+    """What a streaming guardrail round would hand to ``apply_guardrail``. Two keys
+    compare equal when the round would scan the same content again; ``stream_ended``
+    stays out of the comparison and only says whether the handler is on its
+    end-of-stream path, where an empty payload is still scanned today."""
+
+    texts: tuple[str, ...]
+    tool_calls: tuple[str, ...] = ()
+    stream_ended: bool = field(default=False, compare=False)
+
+    @property
+    def has_nothing_to_scan(self) -> bool:
+        return not self.stream_ended and not any(self.texts) and not self.tool_calls
+
+
 class BaseTranslation(ABC):
     @staticmethod
     def transform_user_api_key_dict_to_metadata(
@@ -151,6 +167,9 @@ class BaseTranslation(ABC):
         """
         return responses_so_far
 
+    def get_streaming_scan_key(self, responses_so_far: Sequence[object]) -> StreamingScanKey | None:
+        return None
+
     def build_block_sse_chunks(
         self,
         exc: "ModifyResponseException",

litellm/llms/base_llm/guardrail_translation/utils.py

@@ -4,6 +4,8 @@ import json
 from collections.abc import Callable, Iterator, Sequence
 from typing import Any, Final, TypeVar
 
+from pydantic import BaseModel
+
 from litellm.types.llms.anthropic_messages.anthropic_response import AnthropicUsage
 from litellm.types.llms.openai import AllMessageValues, ResponseAPIUsage
 
@@ -130,6 +132,16 @@ def stream_item_field(item: object, field: str) -> object | None:
     return getattr(item, field, None)
 
 
+def stream_item_fingerprint(item: object) -> str:
+    plain: Final = item.model_dump() if isinstance(item, BaseModel) else item
+    return json.dumps(plain, sort_keys=True, default=str)
+
+
+def stream_item_items(item: object, field: str) -> tuple[object, ...]:
+    value: Final = stream_item_field(item, field)
+    return tuple(value) if isinstance(value, (list, tuple)) else ()
+
+
 def blocked_chat_stream_usage(original_response: object) -> tuple[int, int]:
     """
     ``(prompt_tokens, completion_tokens)`` for a synthetic guardrail-blocked

litellm/llms/base_llm/vector_store/transformation.py

@@ -1,10 +1,16 @@
+from __future__ import annotations
+
 from abc import abstractmethod
 from collections.abc import Mapping, Sequence
-from typing import TYPE_CHECKING, Any, NoReturn
+from dataclasses import dataclass
+from types import MappingProxyType
+from typing import TYPE_CHECKING, Any, Final, NoReturn, Protocol, runtime_checkable
 
 import httpx
+from pydantic import TypeAdapter
 
 from litellm.types.router import GenericLiteLLMParams
+from litellm.types.utils import EmbeddingResponse
 from litellm.types.vector_stores import (
     VECTOR_STORE_OPENAI_PARAMS,
     BaseVectorStoreAuthCredentials,
@@ -28,6 +34,95 @@ else:
     BaseLLMException = Any
 
 
+@runtime_checkable
+class VectorStoreEmbeddingExecutor(Protocol):
+    def embed(self, model: str, query: str, configuration: Mapping[str, object]) -> EmbeddingResponse: ...
+
+    async def aembed(self, model: str, query: str, configuration: Mapping[str, object]) -> EmbeddingResponse: ...
+
+
+@dataclass(frozen=True, slots=True)
+class LiteLLMVectorStoreEmbeddingExecutor:
+    def embed(self, model: str, query: str, configuration: Mapping[str, object]) -> EmbeddingResponse:
+        import litellm
+
+        return litellm.embedding(  # pyright: ignore[reportCallIssue, reportUnknownMemberType, reportUnknownVariableType]  # provider kwargs are intentionally dynamic
+            model=model,
+            input=[query],  # mutable-ok: LiteLLM embedding requires a mutable input list
+            **dict(configuration),  # pyright: ignore[reportArgumentType]  # provider-specific embedding config is validated downstream  # mutable-ok: kwargs require a concrete dict
+        )
+
+    async def aembed(self, model: str, query: str, configuration: Mapping[str, object]) -> EmbeddingResponse:
+        import litellm
+
+        return await litellm.aembedding(  # pyright: ignore[reportUnknownMemberType]  # provider kwargs are intentionally dynamic
+            model=model,
+            input=[query],  # mutable-ok: LiteLLM embedding requires a mutable input list
+            **dict(configuration),  # pyright: ignore[reportArgumentType]  # provider-specific embedding config is validated downstream  # mutable-ok: kwargs require a concrete dict
+        )
+
+
+_REQUEST_METADATA: Final = TypeAdapter(dict[str, object])
+
+
+def vector_store_request_metadata(kwargs: Mapping[str, object]) -> Mapping[str, object]:
+    litellm_metadata: Final = kwargs.get("litellm_metadata")
+    if isinstance(litellm_metadata, dict):
+        return _REQUEST_METADATA.validate_python(litellm_metadata)
+    metadata: Final = kwargs.get("metadata")
+    if isinstance(metadata, dict):
+        return _REQUEST_METADATA.validate_python(metadata)
+    return MappingProxyType({})
+
+
+@dataclass(frozen=True, slots=True)
+class RouterVectorStoreEmbeddingExecutor:
+    router: Router
+    metadata: Mapping[str, object]
+
+    def _embedding_kwargs(self, configuration: Mapping[str, object]) -> Mapping[str, object]:
+        configured_metadata: Final = configuration.get("metadata")
+        metadata: Final = {
+            **(configured_metadata if isinstance(configured_metadata, Mapping) else {}),
+            **self.metadata,
+        }
+        return {
+            **{key: value for key, value in configuration.items() if key not in ("input", "metadata", "model")},
+            "metadata": metadata,
+        }
+
+    def _router_serves(self, model: str) -> bool:
+        team_id: Final = self.metadata.get("user_api_key_team_id")
+        resolved: Final = self.router.resolved_litellm_models(model, team_id if isinstance(team_id, str) else None)
+        deployment_models: Final = (
+            deployment.get("litellm_params", {}).get("model") for deployment in self.router.get_model_list() or ()
+        )
+        return bool(resolved) or model in deployment_models
+
+    def _embeds_through_sdk(self, model: str, configuration: Mapping[str, object]) -> bool:
+        return bool(configuration) and not self._router_serves(model)
+
+    def embed(self, model: str, query: str, configuration: Mapping[str, object]) -> EmbeddingResponse:
+        embedding_kwargs: Final = self._embedding_kwargs(configuration)
+        if self._embeds_through_sdk(model, configuration):
+            return LiteLLMVectorStoreEmbeddingExecutor().embed(model, query, embedding_kwargs)
+        return self.router.embedding(  # pyright: ignore[reportUnknownMemberType]  # Router embedding input retains a legacy untyped list
+            model=model,
+            input=[query],  # mutable-ok: Router embedding requires a mutable input list
+            **embedding_kwargs,  # pyright: ignore[reportArgumentType]  # provider kwargs are intentionally dynamic
+        )
+
+    async def aembed(self, model: str, query: str, configuration: Mapping[str, object]) -> EmbeddingResponse:
+        embedding_kwargs: Final = self._embedding_kwargs(configuration)
+        if self._embeds_through_sdk(model, configuration):
+            return await LiteLLMVectorStoreEmbeddingExecutor().aembed(model, query, embedding_kwargs)
+        return await self.router.aembedding(  # pyright: ignore[reportUnknownMemberType]  # Router embedding input retains a legacy untyped list
+            model=model,
+            input=[query],  # mutable-ok: Router embedding requires a mutable input list
+            **embedding_kwargs,  # pyright: ignore[reportArgumentType]  # provider kwargs are intentionally dynamic
+        )
+
+
 class BaseVectorStoreConfig:
     def get_supported_openai_params(self, model: str) -> list[VECTOR_STORE_OPENAI_PARAMS]:
         return []
@@ -58,7 +153,7 @@ class BaseVectorStoreConfig:
         litellm_logging_obj: LiteLLMLoggingObj,
         litellm_params: dict,
         extra_body: dict[str, Any] | None = None,
-        router: "Router | None" = None,
+        router: Router | None = None,
     ) -> tuple[str, dict]:
         pass
 
@@ -71,7 +166,7 @@ class BaseVectorStoreConfig:
         litellm_logging_obj: LiteLLMLoggingObj,
         litellm_params: dict,
         extra_body: dict[str, Any] | None = None,
-        router: "Router | None" = None,
+        router: Router | None = None,
     ) -> tuple[str, dict]:
         """
         Optional async version of transform_search_vector_store_request.
@@ -161,6 +256,116 @@ class BaseVectorStoreConfig:
         return 0.0, 0.0
 
 
+_EMPTY_EMBEDDING_CONFIGURATION: Final[Mapping[str, object]] = MappingProxyType({})
+_QUERY_VECTOR: Final = TypeAdapter(list[float])
+
+
+class BaseQueryEmbeddingVectorStoreConfig(BaseVectorStoreConfig):
+    @abstractmethod
+    def transform_search_vector_store_request(
+        self,
+        vector_store_id: str,
+        query: str | Sequence[str],
+        vector_store_search_optional_params: VectorStoreSearchOptionalRequestParams,
+        api_base: str,
+        litellm_logging_obj: LiteLLMLoggingObj,
+        litellm_params: Mapping[str, object],
+        extra_body: Mapping[str, object] | None = None,
+        router: Router | None = None,
+        embedding_executor: VectorStoreEmbeddingExecutor | None = None,
+    ) -> tuple[str, dict[str, object]]:
+        pass
+
+    async def atransform_search_vector_store_request(
+        self,
+        vector_store_id: str,
+        query: str | Sequence[str],
+        vector_store_search_optional_params: VectorStoreSearchOptionalRequestParams,
+        api_base: str,
+        litellm_logging_obj: LiteLLMLoggingObj,
+        litellm_params: Mapping[str, object],
+        extra_body: Mapping[str, object] | None = None,
+        router: Router | None = None,
+        embedding_executor: VectorStoreEmbeddingExecutor | None = None,
+    ) -> tuple[str, dict[str, object]]:
+        return self.transform_search_vector_store_request(
+            vector_store_id=vector_store_id,
+            query=query,
+            vector_store_search_optional_params=vector_store_search_optional_params,
+            api_base=api_base,
+            litellm_logging_obj=litellm_logging_obj,
+            litellm_params=litellm_params,
+            extra_body=extra_body,
+            router=router,
+            embedding_executor=embedding_executor,
+        )
+
+    @staticmethod
+    def query_text(query: str | Sequence[str]) -> str:
+        return query if isinstance(query, str) else " ".join(query)
+
+    @staticmethod
+    def query_embedding_model(litellm_params: Mapping[str, object]) -> str:
+        embedding_model: Final = litellm_params.get("litellm_embedding_model")
+        if isinstance(embedding_model, str) and embedding_model:
+            return embedding_model
+        raise ValueError(
+            "litellm_embedding_model is required in litellm_params for this vector store. "
+            "Example: litellm_params['litellm_embedding_model'] = 'openai/text-embedding-3-small'"
+        )
+
+    @staticmethod
+    def query_embedding_configuration(litellm_params: Mapping[str, object]) -> Mapping[str, object]:
+        configuration: Final = litellm_params.get("litellm_embedding_config")
+        if isinstance(configuration, Mapping):
+            return {str(key): value for key, value in configuration.items()}  # pyright: ignore[reportUnknownVariableType, reportUnknownArgumentType]  # litellm_params is an untyped dict, keys are re-validated as str here
+        return _EMPTY_EMBEDDING_CONFIGURATION
+
+    @staticmethod
+    def query_embedding_executor(
+        embedding_executor: VectorStoreEmbeddingExecutor | None,
+        router: Router | None,
+        request_metadata: Mapping[str, object] = MappingProxyType({}),
+    ) -> VectorStoreEmbeddingExecutor:
+        if embedding_executor is not None:
+            return embedding_executor
+        if router is not None:
+            return RouterVectorStoreEmbeddingExecutor(router=router, metadata=request_metadata)
+        return LiteLLMVectorStoreEmbeddingExecutor()
+
+    def embed_query(
+        self,
+        query_text: str,
+        litellm_params: Mapping[str, object],
+        embedding_executor: VectorStoreEmbeddingExecutor | None,
+        router: Router | None = None,
+    ) -> Sequence[float]:
+        model: Final = self.query_embedding_model(litellm_params)
+        configuration: Final = self.query_embedding_configuration(litellm_params)
+        executor: Final = self.query_embedding_executor(embedding_executor, router)
+        try:
+            response: Final = executor.embed(model, query_text, configuration)
+        except Exception as e:
+            raise Exception(f"Failed to generate embedding for query: {e}")
+        return _QUERY_VECTOR.validate_python(response.data[0]["embedding"])  # pyright: ignore[reportUnknownMemberType]  # EmbeddingResponse.data is an untyped list, the vector is validated here
+
+    async def aembed_query(
+        self,
+        query_text: str,
+        litellm_params: Mapping[str, object],
+        embedding_executor: VectorStoreEmbeddingExecutor | None,
+        router: Router | None = None,
+    ) -> Sequence[float]:
+        model: Final = self.query_embedding_model(litellm_params)
+        configuration: Final = self.query_embedding_configuration(litellm_params)
+        executor: Final = self.query_embedding_executor(embedding_executor, router)
+        try:
+            response: Final = await executor.aembed(model, query_text, configuration)
+        except Exception as e:
+            raise Exception(f"Failed to generate embedding for query: {e}")
+        return _QUERY_VECTOR.validate_python(response.data[0]["embedding"])  # pyright: ignore[reportUnknownMemberType]  # EmbeddingResponse.data is an untyped list, the vector is validated here
+
+
 class BaseDirectVectorStoreConfig(BaseVectorStoreConfig):
     """
     Base config for vector store providers whose datastore has no HTTP API
@@ -176,6 +381,7 @@ class BaseDirectVectorStoreConfig(BaseVectorStoreConfig):
         vector_store_search_optional_params: VectorStoreSearchOptionalRequestParams,
         litellm_logging_obj: LiteLLMLoggingObj,
         litellm_params: Mapping[str, object],
+        embedding_executor: VectorStoreEmbeddingExecutor | None = None,
         timeout: float | httpx.Timeout | None = None,
     ) -> VectorStoreSearchResponse:
         pass
@@ -188,6 +394,7 @@ class BaseDirectVectorStoreConfig(BaseVectorStoreConfig):
         vector_store_search_optional_params: VectorStoreSearchOptionalRequestParams,
         litellm_logging_obj: LiteLLMLoggingObj,
         litellm_params: Mapping[str, object],
+        embedding_executor: VectorStoreEmbeddingExecutor | None = None,
         timeout: float | httpx.Timeout | None = None,
     ) -> VectorStoreSearchResponse:
         pass
@@ -201,7 +408,7 @@ class BaseDirectVectorStoreConfig(BaseVectorStoreConfig):
         litellm_logging_obj: LiteLLMLoggingObj,
         litellm_params: Mapping[str, object],
         extra_body: Mapping[str, object] | None = None,
-        router: "Router | None" = None,
+        router: Router | None = None,
     ) -> NoReturn:
         raise NotImplementedError("Direct vector store providers execute the search themselves; no HTTP request shape")
 

litellm/llms/bedrock/chat/converse_transformation.py

@@ -1337,6 +1337,7 @@ class AmazonConverseConfig(BaseConfig):
             )
 
         additional_request_params.pop("parallel_tool_calls", None)
+        additional_request_params.pop("client_metadata", None)
 
         # Only set the topK value in for models that support it
         additional_request_params.update(self._handle_top_k_value(model, inference_params, drop_params))

litellm/llms/bedrock/common_utils.py

@@ -748,6 +748,15 @@ def strip_bedrock_throughput_suffix(model: str) -> str:
 
 
 MANTLE_MESSAGES_PATH: Final = "/anthropic/v1/messages"
+_MANTLE_OPENAI_BASE_SUFFIXES: Final = ("/openai/v1", "/v1")
+
+
+def _mantle_api_base_from_env() -> str | None:
+    env_base: Final = get_secret_str("BEDROCK_MANTLE_API_BASE")
+    if env_base is None:
+        return None
+    base: Final = env_base.rstrip("/")
+    return next((base[: -len(suffix)] for suffix in _MANTLE_OPENAI_BASE_SUFFIXES if base.endswith(suffix)), base)
 
 
 def build_mantle_messages_url(
@@ -758,12 +767,15 @@ def build_mantle_messages_url(
     """Build the bedrock-mantle Anthropic /messages URL.
 
     Honors an explicit endpoint override (``api_base``, then
-    ``aws_bedrock_runtime_endpoint``) so private VPC / VPCE / GovCloud Mantle
-    endpoints are reachable; otherwise falls back to the public regional host.
+    ``aws_bedrock_runtime_endpoint``, then ``BEDROCK_MANTLE_API_BASE``) so
+    private VPC / VPCE / GovCloud Mantle endpoints are reachable; otherwise
+    falls back to the public regional host.
     The mantle messages path is appended unless the override already carries it,
-    so callers can pass either the host or the full messages URL.
+    so callers can pass either the host or the full messages URL. The env var is
+    shared with the OpenAI-surface ``bedrock_mantle/*`` routes, which need it to
+    carry their ``/v1`` or ``/openai/v1`` base, so that suffix is dropped first.
     """
-    override: Final = api_base or aws_bedrock_runtime_endpoint
+    override: Final = api_base or aws_bedrock_runtime_endpoint or _mantle_api_base_from_env()
     if override:
         base: Final = override.rstrip("/")
         if base.endswith(MANTLE_MESSAGES_PATH):

litellm/llms/custom_httpx/http_handler.py

@@ -9,13 +9,15 @@ import threading
 import time
 from collections.abc import AsyncIterable, Callable, Iterable, Mapping
 from http.cookiejar import CookieJar, DefaultCookiePolicy
-from typing import TYPE_CHECKING, Any, ClassVar, Final, NoReturn, Optional, TypeAlias, TypedDict
+from types import MappingProxyType
+from typing import TYPE_CHECKING, Any, ClassVar, Final, NoReturn, Optional, TypeAlias, TypedDict, TypeVar
 
 import certifi
 import httpx
 from aiohttp import ClientSession, DummyCookieJar, TCPConnector
 from httpx import USE_CLIENT_DEFAULT, AsyncHTTPTransport, HTTPTransport
-from httpx._types import RequestFiles
+from httpx._types import CertTypes, RequestFiles
+from httpx._utils import get_environment_proxies
 
 import litellm
 from litellm._logging import verbose_logger
@@ -66,6 +68,22 @@ _AddrInfo: TypeAlias = tuple[int | socket.AddressFamily, int | socket.SocketKind
 
 _RequestContent: TypeAlias = str | bytes | Iterable[bytes] | AsyncIterable[bytes]
 
+_IPV4_LOCAL_ADDRESS: Final = "0.0.0.0"
+
+_HttpxTransportT = TypeVar("_HttpxTransportT", HTTPTransport, AsyncHTTPTransport)
+
+
+def _environment_proxy_mounts(
+    build_proxy_transport: Callable[[str], _HttpxTransportT],
+) -> Mapping[str, _HttpxTransportT | None]:
+    """httpx skips its own HTTP(S)_PROXY / NO_PROXY mounts whenever an explicit `transport=` is passed."""
+    return MappingProxyType(
+        {
+            pattern: None if proxy_url is None else build_proxy_transport(proxy_url)
+            for pattern, proxy_url in get_environment_proxies().items()
+        }
+    )
+
 
 class _TCPConnectorKwargs(TypedDict, total=False):
     local_addr: tuple[str, int] | None
@@ -607,6 +625,7 @@ class AsyncHTTPHandler:
 
         return httpx.AsyncClient(
             transport=transport,
+            mounts=AsyncHTTPHandler._create_httpx_proxy_mounts(transport, verify=ssl_config, cert=cert),
             event_hooks=event_hooks,
             timeout=timeout,
             verify=ssl_config,
@@ -1191,10 +1210,22 @@ class AsyncHTTPHandler:
         - [Default] If force_ipv4 is False, it will return None
         """
         if litellm.force_ipv4:
-            return AsyncHTTPTransport(local_address="0.0.0.0")
+            return AsyncHTTPTransport(local_address=_IPV4_LOCAL_ADDRESS)
         else:
             return None
 
+    @staticmethod
+    def _create_httpx_proxy_mounts(
+        transport: LiteLLMAiohttpTransport | AsyncHTTPTransport | None,
+        verify: VerifyTypes,
+        cert: CertTypes | None,
+    ) -> Mapping[str, AsyncHTTPTransport | None] | None:
+        if not isinstance(transport, AsyncHTTPTransport):
+            return None
+        return _environment_proxy_mounts(
+            lambda proxy_url: AsyncHTTPTransport(proxy=proxy_url, verify=verify, cert=cert)
+        )
+
 
 class HTTPHandler:
     def __init__(
@@ -1227,6 +1258,7 @@ class HTTPHandler:
         # Create a client with a connection pool
         return httpx.Client(
             transport=self._create_sync_transport(),
+            mounts=self._create_sync_proxy_mounts(verify=ssl_config, cert=cert),
             timeout=self.timeout if self.timeout is not None else _DEFAULT_TIMEOUT,
             verify=ssl_config,
             cert=cert,
@@ -1507,10 +1539,19 @@ 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="0.0.0.0")
+            return HTTPTransport(local_address=_IPV4_LOCAL_ADDRESS)
         else:
             return getattr(litellm, "sync_transport", None)
 
+    @staticmethod
+    def _create_sync_proxy_mounts(
+        verify: VerifyTypes,
+        cert: CertTypes | None,
+    ) -> 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))
+
 
 def get_async_httpx_client(
     llm_provider: LlmProviders | httpxSpecialProvider,

litellm/llms/custom_httpx/llm_http_handler.py

@@ -42,6 +42,7 @@ from litellm.litellm_core_utils.audio_utils.subtitle_utils import (
     SUBTITLE_RESPONSE_FORMATS,
     synthesize_subtitle_document,
 )
+from litellm.litellm_core_utils.get_litellm_params import AWS_CREDENTIAL_KWARGS_KEYS
 from litellm.litellm_core_utils.llm_request_utils import serialize_multipart_form_fields
 from litellm.litellm_core_utils.realtime_errors import realtime_error_event, websocket_close_reason
 from litellm.litellm_core_utils.realtime_streaming import RealTimeStreaming
@@ -82,7 +83,9 @@ from litellm.llms.base_llm.skills.transformation import BaseSkillsAPIConfig
 from litellm.llms.base_llm.text_to_speech.transformation import BaseTextToSpeechConfig
 from litellm.llms.base_llm.vector_store.transformation import (
     BaseDirectVectorStoreConfig,
+    BaseQueryEmbeddingVectorStoreConfig,
     BaseVectorStoreConfig,
+    VectorStoreEmbeddingExecutor,
 )
 from litellm.llms.base_llm.vector_store_files.transformation import (
     BaseVectorStoreFilesConfig,
@@ -337,6 +340,16 @@ def _has_pre_call_deployment_hook(logging_obj: LiteLLMLoggingObj) -> bool:
     return False
 
 
+def _aws_signing_overrides(optional_params: Mapping[str, Any], litellm_params: Mapping[str, Any]) -> Mapping[str, Any]:
+    return MappingProxyType(
+        {
+            key: litellm_params[key]
+            for key in AWS_CREDENTIAL_KWARGS_KEYS
+            if optional_params.get(key) is None and litellm_params.get(key) is not None
+        }
+    )
+
+
 def _collect_ws_project_quota_callbacks() -> tuple[ProjectQuotaCallback, ...]:
     """Duck-type discover proxy hooks exposing per-frame project ITPM/OTPM
     enforcement, so the Responses WebSocket loop can charge every
@@ -601,7 +614,10 @@ class BaseLLMHTTPHandler:
 
         headers, signed_json_body = provider_config.sign_request(
             headers=headers,
-            optional_params=optional_params,
+            optional_params={
+                **optional_params,
+                **_aws_signing_overrides(optional_params, litellm_params),
+            },
             request_data=data,
             api_base=api_base,
             api_key=api_key,
@@ -3755,8 +3771,6 @@ class BaseLLMHTTPHandler:
             litellm_params=litellm_params,
             data=create_file_data,
         )
-        if complete_api_base is None:
-            raise ValueError("api_base is required for create_file")
         return await self.async_create_file(
             transformed_request=provider_config.transform_create_file_request(
                 model="",
@@ -4232,8 +4246,6 @@ class BaseLLMHTTPHandler:
             litellm_params=litellm_params,
             data=create_batch_data,
         )
-        if complete_api_base is None:
-            raise ValueError("api_base is required for create_batch")
         return await self.async_create_batch(
             transformed_request=provider_config.transform_create_batch_request(
                 model=model,
@@ -9854,6 +9866,7 @@ class BaseLLMHTTPHandler:
         custom_llm_provider: str,
         litellm_params: GenericLiteLLMParams,
         logging_obj: LiteLLMLoggingObj,
+        embedding_executor: VectorStoreEmbeddingExecutor | None = None,
         extra_headers: dict[str, object] | None = None,
         extra_body: dict[str, object] | None = None,
         timeout: float | httpx.Timeout | None = None,
@@ -9874,6 +9887,7 @@ class BaseLLMHTTPHandler:
                 vector_store_search_optional_params=vector_store_search_optional_params,
                 litellm_logging_obj=logging_obj,
                 litellm_params=dict(litellm_params),  # mutable-ok: snapshot GenericLiteLLMParams into the Mapping shape
+                embedding_executor=embedding_executor,
                 timeout=timeout,
             )
 
@@ -9897,8 +9911,7 @@ class BaseLLMHTTPHandler:
             litellm_params=dict(litellm_params),
         )
 
-        # Check if provider has async transform method
-        if hasattr(vector_store_provider_config, "atransform_search_vector_store_request"):
+        if isinstance(vector_store_provider_config, BaseQueryEmbeddingVectorStoreConfig):
             (
                 url,
                 request_body,
@@ -9911,12 +9924,13 @@ class BaseLLMHTTPHandler:
                 litellm_params=dict(litellm_params),
                 extra_body=extra_body,
                 router=router,
+                embedding_executor=embedding_executor,
             )
         else:
             (
                 url,
                 request_body,
-            ) = vector_store_provider_config.transform_search_vector_store_request(
+            ) = await vector_store_provider_config.atransform_search_vector_store_request(
                 vector_store_id=vector_store_id,
                 query=query,
                 vector_store_search_optional_params=vector_store_search_optional_params,
@@ -9971,6 +9985,7 @@ class BaseLLMHTTPHandler:
         custom_llm_provider: str,
         litellm_params: GenericLiteLLMParams,
         logging_obj: LiteLLMLoggingObj,
+        embedding_executor: VectorStoreEmbeddingExecutor | None = None,
         extra_headers: dict[str, object] | None = None,
         extra_body: dict[str, object] | None = None,
         timeout: float | httpx.Timeout | None = None,
@@ -9987,6 +10002,7 @@ class BaseLLMHTTPHandler:
                 litellm_params=litellm_params,
                 logging_obj=logging_obj,
                 custom_llm_provider=custom_llm_provider,
+                embedding_executor=embedding_executor,
                 extra_headers=extra_headers,
                 extra_body=extra_body,
                 timeout=timeout,
@@ -10007,6 +10023,7 @@ class BaseLLMHTTPHandler:
                 vector_store_search_optional_params=vector_store_search_optional_params,
                 litellm_logging_obj=logging_obj,
                 litellm_params=dict(litellm_params),  # mutable-ok: snapshot GenericLiteLLMParams into the Mapping shape
+                embedding_executor=embedding_executor,
                 timeout=timeout,
             )
 
@@ -10027,19 +10044,35 @@ class BaseLLMHTTPHandler:
             litellm_params=dict(litellm_params),
         )
 
-        (
-            url,
-            request_body,
-        ) = vector_store_provider_config.transform_search_vector_store_request(
-            vector_store_id=vector_store_id,
-            query=query,
-            vector_store_search_optional_params=vector_store_search_optional_params,
-            api_base=api_base,
-            litellm_logging_obj=logging_obj,
-            litellm_params=dict(litellm_params),
-            extra_body=extra_body,
-            router=router,
-        )
+        if isinstance(vector_store_provider_config, BaseQueryEmbeddingVectorStoreConfig):
+            (
+                url,
+                request_body,
+            ) = vector_store_provider_config.transform_search_vector_store_request(
+                vector_store_id=vector_store_id,
+                query=query,
+                vector_store_search_optional_params=vector_store_search_optional_params,
+                api_base=api_base,
+                litellm_logging_obj=logging_obj,
+                litellm_params=dict(litellm_params),
+                extra_body=extra_body,
+                router=router,
+                embedding_executor=embedding_executor,
+            )
+        else:
+            (
+                url,
+                request_body,
+            ) = vector_store_provider_config.transform_search_vector_store_request(
+                vector_store_id=vector_store_id,
+                query=query,
+                vector_store_search_optional_params=vector_store_search_optional_params,
+                api_base=api_base,
+                litellm_logging_obj=logging_obj,
+                litellm_params=dict(litellm_params),
+                extra_body=extra_body,
+                router=router,
+            )
 
         all_optional_params: Final[dict[str, object]] = dict(litellm_params)
         all_optional_params.update(vector_store_search_optional_params or {})

litellm/llms/fireworks_ai/common_utils.py

@@ -2,6 +2,7 @@ from typing import Final
 
 from httpx import Headers
 
+from litellm.constants import SESSION_ID_GENERATED_METADATA_KEY
 from litellm.secret_managers.main import get_secret_str
 from litellm.types.llms.openai import AllMessageValues
 
@@ -16,16 +17,18 @@ def get_fireworks_session_id(litellm_params: dict) -> str | None:
     """
     Session id to send as `x-session-affinity`, or None when the caller gave none.
 
-    Deliberately does not fall back to `litellm_trace_id`: that is generated per
-    request (`str(uuid.uuid4())` when absent), so using it pins every request to a
-    different Fireworks node and prompt caching never hits.
+    Deliberately does not fall back to `litellm_trace_id`, and ignores session ids the
+    proxy generated for a request that had none: both are per request, so using them
+    pins every request to a different Fireworks node and prompt caching never hits.
     """
     params: Final = litellm_params
+    metadata: Final = params.get("metadata")
+    if isinstance(metadata, dict) and metadata.get(SESSION_ID_GENERATED_METADATA_KEY):
+        return None
     for key in ("litellm_session_id", "session_id"):
         value = params.get(key)
         if value:
             return str(value)
-    metadata: Final = params.get("metadata")
     if isinstance(metadata, dict):
         value = metadata.get("session_id")
         if value:

litellm/llms/gigachat/authenticator.py

@@ -8,6 +8,7 @@ Based on official GigaChat SDK authentication flow.
 import time
 import uuid
 from collections.abc import Mapping
+from types import MappingProxyType
 from typing import Final
 
 import httpx
@@ -32,8 +33,8 @@ GIGACHAT_SCOPE: Final = "GIGACHAT_API_PERS"
 # Token expiry buffer in milliseconds (refresh token 60s before expiry)
 TOKEN_EXPIRY_BUFFER_MS: Final = 60000
 
-# Cache for access tokens
 _token_cache: Final = InMemoryCache()
+_NO_LITELLM_PARAMS: Final[Mapping[str, object]] = MappingProxyType({})
 
 
 class GigaChatAuthError(BaseLLMException):
@@ -80,10 +81,9 @@ def get_access_token(
     Raises:
         GigaChatAuthError: If authentication fails
     """
-    if not litellm_params:
-        litellm_params = {}  # mutable-ok: empty dict default; rebind-ok: provide default
+    params: Final = litellm_params or _NO_LITELLM_PARAMS
 
-    access_token: Final = litellm_params.get("gigachat_access_token") or get_secret_str("GIGACHAT_ACCESS_TOKEN")
+    access_token: Final = params.get("gigachat_access_token") or get_secret_str("GIGACHAT_ACCESS_TOKEN")
     if access_token:
         return access_token
 
@@ -94,24 +94,20 @@ def get_access_token(
             message="GigaChat credentials not provided. Set GIGACHAT_CREDENTIALS or GIGACHAT_API_KEY environment variable.",
         )
 
-    effective_scope: Final = scope or litellm_params.get("gigachat_scope") or _get_scope()
-    effective_auth_url: Final = auth_url or litellm_params.get("gigachat_auth_url") or _get_auth_url()
+    effective_scope: Final = scope or params.get("gigachat_scope") or _get_scope()
+    effective_auth_url: Final = auth_url or params.get("gigachat_auth_url") or _get_auth_url()
 
-    # Check cache
     cache_key: Final = f"gigachat_token:{effective_credentials[:16]}"
     cached: Final = _token_cache.get_cache(cache_key)
     if cached:
         _token, _expires_at = cached
-        # Check if token is still valid (with buffer)
         if time.time() * 1000 < _expires_at - TOKEN_EXPIRY_BUFFER_MS:
             verbose_logger.debug("Using cached GigaChat access token")
             return _token
 
-    # Request new token
     new_token, new_expires_at = _request_token_sync(effective_credentials, effective_scope, effective_auth_url)  # pyright: ignore[reportArgumentType]  # credential keys may be broader than str
 
     if new_expires_at:
-        # Cache token
         ttl_seconds: Final = max(0, (new_expires_at - TOKEN_EXPIRY_BUFFER_MS - time.time() * 1000) / 1000)
         if ttl_seconds > 0:
             _token_cache.set_cache(cache_key, (new_token, new_expires_at), ttl=ttl_seconds)
@@ -126,10 +122,9 @@ async def get_access_token_async(
     litellm_params: Mapping[str, object] | None = None,
 ) -> str:
     """Async version of get_access_token."""
-    if not litellm_params:
-        litellm_params = {}  # mutable-ok: empty dict default; rebind-ok: provide default
+    params: Final = litellm_params or _NO_LITELLM_PARAMS
 
-    access_token: Final = litellm_params.get("gigachat_access_token") or get_secret_str("GIGACHAT_ACCESS_TOKEN")
+    access_token: Final = params.get("gigachat_access_token") or get_secret_str("GIGACHAT_ACCESS_TOKEN")
     if access_token:
         return access_token
 
@@ -140,10 +135,9 @@ async def get_access_token_async(
             message="GigaChat credentials not provided. Set GIGACHAT_CREDENTIALS or GIGACHAT_API_KEY environment variable.",
         )
 
-    effective_scope: Final = scope or litellm_params.get("gigachat_scope") or _get_scope()
-    effective_auth_url: Final = auth_url or litellm_params.get("gigachat_auth_url") or _get_auth_url()
+    effective_scope: Final = scope or params.get("gigachat_scope") or _get_scope()
+    effective_auth_url: Final = auth_url or params.get("gigachat_auth_url") or _get_auth_url()
 
-    # Check cache
     cache_key: Final = f"gigachat_token:{effective_credentials[:16]}"
     cached: Final = _token_cache.get_cache(cache_key)
     if cached:
@@ -152,11 +146,9 @@ async def get_access_token_async(
             verbose_logger.debug("Using cached GigaChat access token")
             return _token
 
-    # Request new token
     new_token, new_expires_at = await _request_token_async(effective_credentials, effective_scope, effective_auth_url)  # pyright: ignore[reportArgumentType]  # credential keys may be broader than str
 
     if new_expires_at:
-        # Cache token
         ttl_seconds: Final = max(0, (new_expires_at - TOKEN_EXPIRY_BUFFER_MS - time.time() * 1000) / 1000)
         if ttl_seconds > 0:
             _token_cache.set_cache(cache_key, (new_token, new_expires_at), ttl=ttl_seconds)

litellm/llms/gigachat/chat/streaming.py

@@ -52,7 +52,6 @@ class GigaChatModelResponseIterator:
         tool_use: ChatCompletionToolCallChunk | None = None  # rebind-ok: conditionally assigned on function_call
         finish_reason: str | None = chunk_finish_reason
 
-        # Handle function_call in stream
         raw_function_call: Final = delta.get("function_call")
         if chunk_finish_reason == "function_call" and isinstance(raw_function_call, Mapping) and raw_function_call:
             func_call: Final[Mapping[str, object]] = raw_function_call

litellm/llms/gigachat/chat/transformation.py

@@ -10,6 +10,7 @@ import json
 import time
 import uuid
 from collections.abc import AsyncIterator, Iterator, Mapping, Sequence
+from types import MappingProxyType
 from typing import TYPE_CHECKING, Any, Final
 
 import httpx
@@ -34,6 +35,9 @@ else:
     LiteLLMLoggingObj = Any
 
 
+_EMPTY_FUNCTION: Final[Mapping[str, object]] = MappingProxyType({})
+
+
 def is_valid_json(value: str) -> bool:
     """Checks whether the value passed is a valid serialized JSON string"""
     try:
@@ -111,11 +115,9 @@ class GigaChatConfig(BaseConfig):
         """
         Set up headers with OAuth token.
         """
-        # Get access token
         credentials: Final = api_key or get_secret_str("GIGACHAT_CREDENTIALS") or get_secret_str("GIGACHAT_API_KEY")
         access_token: Final = get_access_token(credentials=credentials, litellm_params=litellm_params)
 
-        # Store credentials for image uploads
         self._current_credentials = credentials
         self._current_api_base = api_base
 
@@ -208,18 +210,18 @@ class GigaChatConfig(BaseConfig):
 
     def _convert_tools_to_functions(self, tools: Sequence) -> Sequence[dict]:
         """Convert OpenAI tools format to GigaChat functions format."""
-        functions: Final[list[dict]] = []  # mutable-ok: accumulator for building functions list
-        for tool in tools:
-            if isinstance(tool, dict) and tool.get("type") == "function":
-                func = tool.get("function", {})
-                functions.append(
-                    {
-                        "name": func.get("name", ""),
-                        "description": func.get("description", ""),
-                        "parameters": func.get("parameters", {}),
-                    }
-                )
-        return functions
+        return [
+            {
+                "name": function.get("name", ""),
+                "description": function.get("description", ""),
+                "parameters": function.get("parameters", {}),
+            }
+            for function in (
+                tool.get("function", _EMPTY_FUNCTION)
+                for tool in tools
+                if isinstance(tool, dict) and tool.get("type") == "function"
+            )
+        ]
 
     def _map_tool_choice(self, tool_choice: str | Mapping[str, object]) -> str | Mapping[str, object] | None:
         """
@@ -299,7 +301,6 @@ class GigaChatConfig(BaseConfig):
                 if part.get("type") == "text":
                     texts.append(part.get("text", ""))
                 elif part.get("type") == "image_url":
-                    # Extract image URL and upload to GigaChat
                     image_url: object = part.get("image_url", {})
                     upload_url: str
                     if isinstance(image_url, str):
@@ -322,16 +323,13 @@ class GigaChatConfig(BaseConfig):
         headers: Mapping[str, object],
     ) -> dict:  # mutable-ok: request payload sent to httpx
         """Transform OpenAI request to GigaChat format."""
-        # Transform messages
         giga_messages: Final = self._transform_messages(messages)
 
-        # Build request
         request_data: Final[dict[str, object]] = {
             "model": model.replace("gigachat/", ""),
             "messages": giga_messages,
         }
 
-        # Add optional params
         for key in [
             "temperature",
             "top_p",
@@ -343,7 +341,6 @@ class GigaChatConfig(BaseConfig):
             if key in optional_params:
                 request_data[key] = optional_params[key]
 
-        # Add functions if present
         if "functions" in optional_params:
             request_data["functions"] = optional_params["functions"]
         if "function_call" in optional_params:
@@ -358,10 +355,8 @@ class GigaChatConfig(BaseConfig):
         for i, msg in enumerate(messages):
             message = dict(msg)
 
-            # Remove unsupported fields
             message.pop("name", None)
 
-            # Transform roles
             role = message.get("role", "user")
             if role == "developer":
                 message["role"] = "system"
@@ -374,18 +369,15 @@ class GigaChatConfig(BaseConfig):
                 if not isinstance(content, str) or not is_valid_json(content):
                     message["content"] = json.dumps(content, ensure_ascii=False)
 
-            # Handle None content
             if message.get("content") is None:
                 message["content"] = ""
 
-            # Handle list content (multimodal) - extract text and images
             content = message.get("content")
             if isinstance(content, list):
                 message["content"], attachments = self._transform_list_content(content)
                 if attachments:
                     message["attachments"] = attachments
 
-            # Transform tool_calls to function_call
             tool_calls = message.get("tool_calls")
             if tool_calls and isinstance(tool_calls, list) and len(tool_calls) > 0:
                 tool_call = tool_calls[0]
@@ -436,13 +428,11 @@ class GigaChatConfig(BaseConfig):
             message_data = choice.get("message", {})
             finish_reason = choice.get("finish_reason", "stop")
 
-            # Transform function_call to tool_calls or content
             if finish_reason == "function_call" and message_data.get("function_call"):
                 func_call = message_data["function_call"]
                 args = func_call.get("arguments", {})
 
                 if is_structured_output:
-                    # Convert to content for structured output
                     if isinstance(args, dict):
                         content = json.dumps(args, ensure_ascii=False)
                     else:
@@ -452,7 +442,6 @@ class GigaChatConfig(BaseConfig):
                     message_data.pop("functions_state_id", None)
                     finish_reason = "stop"
                 else:
-                    # Convert to tool_calls format
                     if isinstance(args, dict):
                         args = json.dumps(args, ensure_ascii=False)
                     message_data["tool_calls"] = [
@@ -468,7 +457,6 @@ class GigaChatConfig(BaseConfig):
                     message_data.pop("function_call", None)
                     finish_reason = "tool_calls"
 
-            # Clean up GigaChat-specific fields
             message_data.pop("functions_state_id", None)
 
             choices.append(

litellm/llms/gigachat/embedding/transformation.py

@@ -112,18 +112,10 @@ class GigaChatEmbeddingConfig(BaseEmbeddingConfig):
             "input": ["text1", "text2", ...]
         }
         """
-        # Normalize input to list
-        if isinstance(input, str):
-            input_list: list = [input]  # rebind-ok: locally scoped conversion
-        else:
-            input_list = input
-
-        # Remove gigachat/ prefix from model if present
-        model = model.removeprefix("gigachat/")  # rebind-ok: parameter reassignment for normalization
-
+        normalized_input: Final = [input] if isinstance(input, str) else input  # mutable-ok: preserve list API
         return {
-            "model": model,
-            "input": input_list,
+            "model": model.removeprefix("gigachat/"),
+            "input": normalized_input,
         }
 
     def transform_embedding_response(

litellm/llms/gigachat/passthrough/transformation.py

@@ -60,7 +60,6 @@ class GigaChatPassthroughConfig(BasePassthroughConfig):
         """
         Set up headers with OAuth token.
         """
-        # Get access token
         access_token: Final = get_access_token(credentials=api_key, litellm_params=litellm_params)
 
         headers["Authorization"] = f"Bearer {access_token}"  # rebind-ok: mutating for OAuth setup
@@ -82,7 +81,6 @@ class GigaChatPassthroughConfig(BasePassthroughConfig):
         from litellm.types.utils import LlmProviders, ModelResponse
         from litellm.utils import ProviderConfigManager
 
-        # cost tracking only for completions and embeddings
         if "completions" in endpoint:
             provider_chat_config: Final = ProviderConfigManager.get_provider_chat_config(
                 provider=LlmProviders(custom_llm_provider),

litellm/llms/gigachat/utils.py

@@ -4,7 +4,6 @@ from typing import Final
 from litellm.secret_managers.main import get_secret_str
 from litellm.types.utils import PromptTokensDetailsWrapper, Usage
 
-# GigaChat API endpoint
 GIGACHAT_BASE_URL: Final = "https://gigachat.devices.sberbank.ru/api/v1"
 
 

litellm/llms/hosted_vllm/rerank/transformation.py

@@ -3,16 +3,20 @@ Transformation logic for Hosted VLLM rerank
 """
 
 from collections.abc import Mapping
+from types import MappingProxyType
 from typing import Any, Final
 
 import httpx
+from pydantic import ValidationError
 
 from litellm._uuid import uuid
+from litellm.exceptions import UnsupportedParamsError
 from litellm.litellm_core_utils.litellm_logging import Logging as LiteLLMLoggingObj
 from litellm.llms.base_llm.chat.transformation import BaseLLMException
 from litellm.llms.base_llm.rerank.transformation import BaseRerankConfig
 from litellm.secret_managers.main import get_secret_str
 from litellm.types.rerank import (
+    HostedVLLMRerankTruncationParams,
     OptionalRerankParams,
     RerankBilledUnits,
     RerankRequest,
@@ -34,6 +38,13 @@ class HostedVLLMRerankError(BaseLLMException):
         super().__init__(status_code=status_code, message=message, headers=headers)
 
 
+def validated_truncation_params(non_default_params: Mapping[str, object] | None) -> HostedVLLMRerankTruncationParams:
+    try:
+        return HostedVLLMRerankTruncationParams.model_validate(non_default_params or MappingProxyType({}))
+    except ValidationError as error:
+        raise UnsupportedParamsError(status_code=400, message=f"hosted_vllm rerank: {error}") from error
+
+
 class HostedVLLMRerankConfig(BaseRerankConfig):
     def __init__(self) -> None:
         pass
@@ -62,7 +73,11 @@ class HostedVLLMRerankConfig(BaseRerankConfig):
             "top_n",
             "rank_fields",
             "return_documents",
+            "max_tokens_per_doc",
             "instruction",
+            "truncate_prompt_tokens",
+            "truncation_side",
+            "max_tokens_per_query",
         ]
 
     def map_cohere_rerank_params(
@@ -100,7 +115,15 @@ class HostedVLLMRerankConfig(BaseRerankConfig):
         if instruction is not None:
             mapped_params["instruction"] = instruction
 
-        return dict(mapped_params)
+        truncation: Final = validated_truncation_params(non_default_params)
+        forwarded: Final[OptionalRerankParams] = {
+            **mapped_params,
+            "max_tokens_per_doc": max_tokens_per_doc,
+            "truncate_prompt_tokens": truncation.truncate_prompt_tokens,
+            "truncation_side": truncation.truncation_side,
+            "max_tokens_per_query": truncation.max_tokens_per_query,
+        }
+        return dict(forwarded)
 
     def validate_environment(
         self,
@@ -138,6 +161,7 @@ class HostedVLLMRerankConfig(BaseRerankConfig):
         if "documents" not in optional_rerank_params:
             raise ValueError("documents is required for Hosted VLLM rerank")
 
+        truncation: Final = HostedVLLMRerankTruncationParams.model_validate(optional_rerank_params)
         rerank_request: Final = RerankRequest(
             model=model,
             query=optional_rerank_params["query"],
@@ -146,6 +170,10 @@ class HostedVLLMRerankConfig(BaseRerankConfig):
             rank_fields=optional_rerank_params.get("rank_fields", None),
             return_documents=optional_rerank_params.get("return_documents", None),
             instruction=optional_rerank_params.get("instruction", None),
+            max_tokens_per_doc=truncation.max_tokens_per_doc,
+            truncate_prompt_tokens=truncation.truncate_prompt_tokens,
+            truncation_side=truncation.truncation_side,
+            max_tokens_per_query=truncation.max_tokens_per_query,
         )
         return rerank_request.model_dump(exclude_none=True)
 

litellm/llms/milvus/vector_stores/transformation.py

@@ -1,9 +1,14 @@
+from __future__ import annotations
+
+from collections.abc import Mapping, Sequence
 from typing import TYPE_CHECKING, Any, Final
 
 import httpx
 
-import litellm
-from litellm.llms.base_llm.vector_store.transformation import BaseVectorStoreConfig
+from litellm.llms.base_llm.vector_store.transformation import (
+    BaseQueryEmbeddingVectorStoreConfig,
+    VectorStoreEmbeddingExecutor,
+)
 from litellm.secret_managers.main import get_secret_str
 from litellm.types.router import GenericLiteLLMParams
 from litellm.types.vector_stores import (
@@ -37,7 +42,7 @@ MILVUS_OPTIONAL_PARAMS: Final = {
 }
 
 
-class MilvusVectorStoreConfig(BaseVectorStoreConfig):
+class MilvusVectorStoreConfig(BaseQueryEmbeddingVectorStoreConfig):
     """
     Configuration for Milvus Vector Store
 
@@ -118,78 +123,79 @@ class MilvusVectorStoreConfig(BaseVectorStoreConfig):
     def transform_search_vector_store_request(
         self,
         vector_store_id: str,
-        query: str | list[str],
+        query: str | Sequence[str],
         vector_store_search_optional_params: VectorStoreSearchOptionalRequestParams,
         api_base: str,
         litellm_logging_obj: LiteLLMLoggingObj,
-        litellm_params: dict,
-        extra_body: dict[str, Any] | None = None,
-        router: "Router | None" = None,
-    ) -> tuple[str, dict[str, Any]]:
-        """
-        Transform search request for Azure AI Search API
+        litellm_params: Mapping[str, object],
+        extra_body: Mapping[str, object] | None = None,
+        router: Router | None = None,
+        embedding_executor: VectorStoreEmbeddingExecutor | None = None,
+    ) -> tuple[str, dict[str, object]]:
+        query_text: Final = self.query_text(query)
+        query_vector: Final = self.embed_query(query_text, litellm_params, embedding_executor, router)
+        return self._search_request(
+            vector_store_id,
+            query_text,
+            query_vector,
+            vector_store_search_optional_params,
+            api_base,
+            litellm_logging_obj,
+            litellm_params,
+        )
 
-        Generates embeddings using litellm.embeddings and constructs Azure AI Search request
-        """
-        # Convert query to string if it's a list
-        if isinstance(query, list):
-            query = " ".join(query)
+    async def atransform_search_vector_store_request(
+        self,
+        vector_store_id: str,
+        query: str | Sequence[str],
+        vector_store_search_optional_params: VectorStoreSearchOptionalRequestParams,
+        api_base: str,
+        litellm_logging_obj: LiteLLMLoggingObj,
+        litellm_params: Mapping[str, object],
+        extra_body: Mapping[str, object] | None = None,
+        router: Router | None = None,
+        embedding_executor: VectorStoreEmbeddingExecutor | None = None,
+    ) -> tuple[str, dict[str, object]]:
+        query_text: Final = self.query_text(query)
+        query_vector: Final = await self.aembed_query(query_text, litellm_params, embedding_executor, router)
+        return self._search_request(
+            vector_store_id,
+            query_text,
+            query_vector,
+            vector_store_search_optional_params,
+            api_base,
+            litellm_logging_obj,
+            litellm_params,
+        )
 
-        # Get embedding model from litellm_params (required)
-        embedding_model: Final = litellm_params.get("litellm_embedding_model")
-        if not embedding_model:
-            raise ValueError(
-                "embedding_model is required in litellm_params for Milvus. You can call any litellm embedding model."
-                "Example: litellm_params['embedding_model'] = 'azure/text-embedding-3-large'"
+    @staticmethod
+    def _search_request(
+        vector_store_id: str,
+        query_text: str,
+        query_vector: Sequence[float],
+        vector_store_search_optional_params: VectorStoreSearchOptionalRequestParams,
+        api_base: str,
+        litellm_logging_obj: LiteLLMLoggingObj,
+        litellm_params: Mapping[str, object],
+    ) -> tuple[str, dict[str, object]]:
+        scope: Final = {
+            key: value
+            for key, value in (
+                ("dbName", litellm_params.get("milvus_db_name")),
+                ("partitionNames", litellm_params.get("milvus_partition_names")),
             )
-
-        embedding_config: Final = litellm_params.get("litellm_embedding_config", {})
-        if not embedding_config:
-            raise ValueError(
-                "embedding_config is required in litellm_params for Milvus. You can call any litellm embedding model."
-                "Example: litellm_params['embedding_config'] = {'api_base': 'https://krris-mh44uf7y-eastus2.cognitiveservices.azure.com/', 'api_key': 'os.environ/AZURE_API_KEY', 'api_version': '2025-09-01'}"
-            )
-
-        # Get top_k (number of results to return)
-        # Generate embedding for the query using litellm.embeddings
-        try:
-            embedding_response: Final = litellm.embedding(
-                model=embedding_model,
-                input=[query],
-                **embedding_config,
-            )
-            query_vector: Final = embedding_response.data[0]["embedding"]
-        except Exception as e:
-            raise Exception(f"Failed to generate embedding for query: {e}")
-
-        # Azure AI Search endpoint for search
-        index_name: Final = vector_store_id  # vector_store_id is the index name
-        url: Final = f"{api_base}/v2/vectordb/entities/search"
-
-        # Build the request body for Azure AI Search with vector search
-        request_body: Final[dict[str, Any]] = {
-            "collectionName": index_name,
+            if value
+        }
+        litellm_logging_obj.model_call_details["input"] = query_text
+        litellm_logging_obj.model_call_details["embedding_model"] = litellm_params.get("litellm_embedding_model")
+        return f"{api_base}/v2/vectordb/entities/search", {
+            "collectionName": vector_store_id,
             "data": [query_vector],
             "annsField": "book_intro_vector",
             **vector_store_search_optional_params,
+            **scope,
         }
 
-        db_name: Final = litellm_params.get("milvus_db_name")
-        if db_name:
-            request_body["dbName"] = db_name
-
-        partition_names: Final = litellm_params.get("milvus_partition_names")
-        if partition_names:
-            request_body["partitionNames"] = partition_names
-
-        #########################################################
-        # Update logging object with details of the request
-        #########################################################
-        litellm_logging_obj.model_call_details["input"] = query
-        litellm_logging_obj.model_call_details["embedding_model"] = embedding_model
-
-        return url, request_body
-
     def transform_search_vector_store_response(
         self, response: httpx.Response, litellm_logging_obj: LiteLLMLoggingObj
     ) -> VectorStoreSearchResponse:

litellm/llms/ollama/chat/transformation.py

@@ -423,6 +423,7 @@ class OllamaChatConfig(BaseConfig):
 class OllamaChatCompletionResponseIterator(BaseModelResponseIterator):
     started_reasoning_content: bool = False
     finished_reasoning_content: bool = False
+    seen_tool_calls: bool = False
 
     def _is_function_call_complete(self, function_args: str | dict) -> bool:
         if isinstance(function_args, dict):
@@ -468,6 +469,7 @@ class OllamaChatCompletionResponseIterator(BaseModelResponseIterator):
             # process tool calls - if complete function arg - add id to tool call
             tool_calls: Final = chunk["message"].get("tool_calls")
             if tool_calls is not None:
+                self.seen_tool_calls = True
                 for tool_call in tool_calls:
                     function_args = tool_call.get("function").get("arguments")
                     if function_args is not None and len(function_args) > 0:
@@ -508,9 +510,10 @@ class OllamaChatCompletionResponseIterator(BaseModelResponseIterator):
 
             if chunk["done"] is True:
                 finish_reason = chunk.get("done_reason") or "stop"
-                # Override finish_reason when tool_calls are present
+                # Override finish_reason when tool_calls appeared in any chunk
                 # Fixes: https://github.com/BerriAI/litellm/issues/18922
-                if tool_calls is not None:
+                # Fixes: https://github.com/BerriAI/litellm/issues/34692
+                if self.seen_tool_calls:
                     finish_reason = "tool_calls"
                 choices = [
                     StreamingChoices(

litellm/llms/openai/chat/guardrail_translation/handler.py

@@ -26,6 +26,7 @@ import litellm
 from litellm._logging import verbose_proxy_logger
 from litellm.llms.base_llm.guardrail_translation.base_translation import (
     BaseTranslation,
+    StreamingScanKey,
     StreamTransformSink,
 )
 from litellm.llms.base_llm.guardrail_translation.utils import (
@@ -39,6 +40,8 @@ from litellm.llms.base_llm.guardrail_translation.utils import (
     role_out_of_guardrail_scope,
     scoped_structured_message_indices,
     stream_item_field,
+    stream_item_fingerprint,
+    stream_item_items,
 )
 from litellm.main import stream_chunk_builder
 from litellm.types.llms.openai import AllMessageValues, ChatCompletionToolParam
@@ -503,12 +506,7 @@ class OpenAIChatCompletionsHandler(BaseTranslation):
         """Block-only streaming path: run the guardrail so an in-flight BLOCK can
         terminate the stream. Text rewrites are not propagated to the client here
         (see ``_process_streaming_transform`` for the incremental_diff path)."""
-        # check if the stream has ended
-        has_stream_ended = False
-        for chunk in responses_so_far:
-            if chunk.choices and chunk.choices[0].finish_reason is not None:
-                has_stream_ended = True
-                break
+        has_stream_ended: Final = self._first_choice_has_finished(responses_so_far)
 
         if has_stream_ended:
             # convert to model response
@@ -706,8 +704,33 @@ class OpenAIChatCompletionsHandler(BaseTranslation):
             indices[i]: coerce_stream_holdback_value(holdback[i]) for i in range(len(indices)) if i < len(holdback)
         }
 
+    def get_streaming_scan_key(self, responses_so_far: Sequence[object]) -> StreamingScanKey | None:
+        chunks: Final = tuple(chunk for chunk in responses_so_far if isinstance(chunk, ModelResponseStream))
+        stream_ended: Final = self._first_choice_has_finished(responses_so_far)
+        return StreamingScanKey(
+            texts=tuple(self._combine_streaming_texts(chunks).values()),
+            tool_calls=self._streamed_tool_call_fingerprints(responses_so_far) if stream_ended else (),
+            stream_ended=stream_ended,
+        )
+
+    @staticmethod
+    def _streamed_tool_call_fingerprints(responses_so_far: Sequence[object]) -> tuple[str, ...]:
+        return tuple(
+            stream_item_fingerprint(tool_call)
+            for chunk in responses_so_far
+            for choice in _stream_chunk_choices(chunk)
+            for tool_call in stream_item_items(stream_item_field(choice, "delta"), "tool_calls")
+        )
+
+    @staticmethod
+    def _first_choice_has_finished(responses_so_far: Sequence[object]) -> bool:
+        first_choices: Final = tuple(
+            choices[0] for choices in (_stream_chunk_choices(chunk) for chunk in responses_so_far) if choices
+        )
+        return any(stream_item_field(choice, "finish_reason") is not None for choice in first_choices)
+
     def _combine_streaming_texts(
-        self, responses_so_far: list["ModelResponseStream"]
+        self, responses_so_far: Sequence["ModelResponseStream"]
     ) -> dict[tuple[int, int | None], str]:
         """
         Combine all streaming chunks into complete text per choice.

litellm/llms/openai/common_utils.py

@@ -305,14 +305,16 @@ class BaseOpenAILLM:
 
         # Get unified SSL configuration
         ssl_config: Final = get_ssl_configuration()
+        transport: Final = AsyncHTTPHandler._create_async_transport(
+            ssl_context=(ssl_config if isinstance(ssl_config, ssl.SSLContext) else None),
+            ssl_verify=ssl_config if isinstance(ssl_config, bool) else None,
+            shared_session=shared_session,
+        )
 
         return httpx.AsyncClient(
             verify=ssl_config,
-            transport=AsyncHTTPHandler._create_async_transport(
-                ssl_context=(ssl_config if isinstance(ssl_config, ssl.SSLContext) else None),
-                ssl_verify=ssl_config if isinstance(ssl_config, bool) else None,
-                shared_session=shared_session,
-            ),
+            transport=transport,
+            mounts=AsyncHTTPHandler._create_httpx_proxy_mounts(transport, verify=ssl_config, cert=None),
             follow_redirects=True,
         )
 

litellm/llms/openai/responses/guardrail_translation/handler.py

@@ -28,6 +28,7 @@ Output: response.output is List[GenericResponseOutputItem] where each has:
     - text: str
 """
 
+import copy
 import time
 import uuid
 from collections.abc import Mapping, Sequence
@@ -36,7 +37,6 @@ from types import MappingProxyType
 from typing import TYPE_CHECKING, Any, Final, Union, cast
 
 from openai.types.responses.response_function_tool_call import ResponseFunctionToolCall
-from openai.types.responses.tool_param import FunctionToolParam
 from pydantic import BaseModel, TypeAdapter
 from typing_extensions import ReadOnly, TypedDict
 
@@ -44,11 +44,17 @@ from litellm._logging import verbose_proxy_logger
 from litellm.completion_extras.litellm_responses_transformation.transformation import (
     OpenAiResponsesToChatCompletionStreamIterator,
 )
-from litellm.llms.base_llm.guardrail_translation.base_translation import BaseTranslation
+from litellm.llms.base_llm.guardrail_translation.base_translation import (
+    BaseTranslation,
+    StreamingScanKey,
+)
 from litellm.llms.base_llm.guardrail_translation.utils import (
     blocked_responses_stream_usage,
     stream_item_field,
+    stream_item_fingerprint,
+    stream_item_items,
 )
+from litellm.llms.openai.responses.guardrail_translation.tool_merge import merge_guardrailed_tools
 from litellm.responses.litellm_completion_transformation.transformation import (
     LiteLLMCompletionResponsesConfig,
 )
@@ -62,7 +68,6 @@ from litellm.types.llms.openai import (
     ContentPartDonePartOutputText,
     ErrorEvent,
     ErrorEventError,
-    OpenAIMcpServerTool,
     OutputItemAddedEvent,
     OutputItemDoneEvent,
     OutputTextDeltaEvent,
@@ -157,23 +162,31 @@ class OpenAIResponsesHandler(BaseTranslation):
         Handles both string input and list of message objects.
         """
         input_data: Final[str | ResponseInputParam | None] = data.get("input")
-        tools_to_check: Final[list[ChatCompletionToolParam]] = []
         if input_data is None:
             return data
 
         structured_messages: Final = self.get_structured_messages(data)
+        raw_tools: Final = data.get("tools")
+        original_tools: Final[tuple[Mapping[str, object], ...]] = (
+            tuple(raw_tools) if isinstance(raw_tools, list) else ()
+        )
+        flattened_tool_groups: Final = tuple(
+            form.chat_tools for form in LiteLLMCompletionResponsesConfig.responses_tools_to_chat_forms(original_tools)
+        )
+        flattened_tools: Final = tuple(
+            cast(ChatCompletionToolParam, tool)  # cast-ok: mcp tools ride along in the guardrail's tool list
+            for group in flattened_tool_groups
+            for tool in group
+        )
+        tools_to_check: Final[list[ChatCompletionToolParam]] = list(  # mutable-ok: guardrail inputs want a list
+            copy.deepcopy(flattened_tools)
+        )
 
         # Handle simple string input
         if isinstance(input_data, str):
             inputs = GenericGuardrailAPIInputs(texts=[input_data])
-            original_tools: list[dict[str, object]] = []
-
-            # Extract and transform tools if present
-            if "tools" in data and data["tools"]:
-                original_tools = list(data["tools"])
-                self._extract_and_transform_tools(data["tools"], tools_to_check)
-                if tools_to_check:
-                    inputs["tools"] = tools_to_check
+            if tools_to_check:
+                inputs["tools"] = tools_to_check
             if structured_messages:
                 inputs["structured_messages"] = structured_messages
             # Include model information if available
@@ -189,7 +202,9 @@ class OpenAIResponsesHandler(BaseTranslation):
             )
             guardrailed_texts = guardrailed_inputs.get("texts", [])
             data["input"] = guardrailed_texts[0] if guardrailed_texts else input_data
-            self._apply_guardrailed_tools_to_data(data, original_tools, guardrailed_inputs.get("tools"))
+            self._apply_guardrailed_tools_to_data(
+                data, original_tools, flattened_tool_groups, guardrailed_inputs.get("tools")
+            )
             verbose_proxy_logger.debug("OpenAI Responses API: Processed string input")
             return data
 
@@ -200,7 +215,6 @@ class OpenAIResponsesHandler(BaseTranslation):
         texts_to_check: Final[list[str]] = []
         images_to_check: Final[list[str]] = []
         task_mappings: Final[list[tuple[int, int | None]]] = []
-        original_tools_list: Final[list[dict[str, object]]] = list(data.get("tools") or [])
 
         # Step 1: Extract all text content, images, and tools
         for msg_idx, message in enumerate(input_data):
@@ -212,10 +226,6 @@ class OpenAIResponsesHandler(BaseTranslation):
                 task_mappings=task_mappings,
             )
 
-        # Extract and transform tools if present
-        if "tools" in data and data["tools"]:
-            self._extract_and_transform_tools(data["tools"], tools_to_check)
-
         # Step 2: Apply guardrail to all texts in batch
         if texts_to_check:
             inputs = GenericGuardrailAPIInputs(texts=texts_to_check)
@@ -238,9 +248,7 @@ class OpenAIResponsesHandler(BaseTranslation):
 
             guardrailed_texts = guardrailed_inputs.get("texts", [])
             self._apply_guardrailed_tools_to_data(
-                data,
-                original_tools_list,
-                guardrailed_inputs.get("tools"),
+                data, original_tools, flattened_tool_groups, guardrailed_inputs.get("tools")
             )
 
             # Step 3: Map guardrail responses back to original input structure
@@ -267,73 +275,18 @@ class OpenAIResponsesHandler(BaseTranslation):
                 names.append(str(tool["server_label"]))
         return names
 
-    def _extract_and_transform_tools(
-        self,
-        tools: list[FunctionToolParam | OpenAIMcpServerTool],
-        tools_to_check: list[ChatCompletionToolParam],
-    ) -> None:
-        """
-        Extract and transform tools from Responses API format to Chat Completion format.
-
-        Uses the LiteLLM transformation function to convert Responses API tools
-        to Chat Completion tools that can be passed to guardrails.
-        """
-        if tools is not None and isinstance(tools, list):
-            # Transform Responses API tools to Chat Completion tools
-            (
-                transformed_tools,
-                _,
-            ) = LiteLLMCompletionResponsesConfig.transform_responses_api_tools_to_chat_completion_tools(tools)
-            tools_to_check.extend(cast(list[ChatCompletionToolParam], transformed_tools))
-
-    def _remap_tools_to_responses_api_format(self, guardrailed_tools: list[Any]) -> list[dict[str, object]]:
-        """
-        Remap guardrail-returned tools (Chat Completion format) back to
-        Responses API request tool format.
-        """
-        return LiteLLMCompletionResponsesConfig.transform_chat_completion_tool_params_to_responses_api_tools(
-            guardrailed_tools
-        )
-
-    def _merge_tools_after_guardrail(
-        self,
-        original_tools: list[dict[str, object]],
-        remapped: list[dict[str, object]],
-    ) -> list[dict[str, object]]:
-        """
-        Merge remapped guardrailed tools with original tools that were not sent
-        to the guardrail (e.g. web_search, web_search_preview), preserving order.
-        Tools a guardrail appended (``remapped`` longer than ``original_tools``)
-        have no original slot and are kept so an injected tool is not dropped.
-        """
-        if not original_tools:
-            return remapped
-        result: Final[list[dict[str, object]]] = []
-        j = 0
-        for tool in original_tools:
-            if isinstance(tool, dict) and tool.get("type") in (
-                "web_search",
-                "web_search_preview",
-            ):
-                result.append(tool)
-            else:
-                if j < len(remapped):
-                    result.append(remapped[j])
-                    j += 1
-        # Keep guardrail-appended tools that matched no original slot above.
-        result.extend(remapped[j:])
-        return result
-
     def _apply_guardrailed_tools_to_data(
         self,
         data: dict,
-        original_tools: list[dict[str, object]],
-        guardrailed_tools: list[ChatCompletionToolParam] | None,
+        original_tools: Sequence[Mapping[str, object]],
+        flattened_tool_groups: Sequence[Sequence[Mapping[str, object]]],
+        guardrailed_tools: Sequence[ChatCompletionToolParam] | None,
     ) -> None:
-        """Remap guardrailed tools to Responses API format and merge with original, then set data['tools']."""
-        if guardrailed_tools is not None:
-            remapped: Final = self._remap_tools_to_responses_api_format(guardrailed_tools)
-            data["tools"] = self._merge_tools_after_guardrail(original_tools, remapped)
+        if guardrailed_tools is None:
+            return
+        data["tools"] = list(  # mutable-ok: downstream wants a list  # rebind-ok: in-place request rewrite
+            merge_guardrailed_tools(original_tools, flattened_tool_groups, guardrailed_tools)
+        )
 
     def _extract_input_text_and_images(
         self,
@@ -645,18 +598,55 @@ class OpenAIResponsesHandler(BaseTranslation):
             )
         return responses_so_far
 
-    def _check_streaming_has_ended(self, responses_so_far: Sequence[ResponsesStreamChunk]) -> bool:
+    def _check_streaming_has_ended(self, responses_so_far: Sequence[object]) -> bool:
         """
         Check if the streaming has ended.
         """
         if not responses_so_far:
             return False
-        terminal_types: Final = {
-            ResponsesAPIStreamEvents.RESPONSE_COMPLETED.value,
-            ResponsesAPIStreamEvents.RESPONSE_FAILED.value,
-            ResponsesAPIStreamEvents.RESPONSE_INCOMPLETE.value,
-        }
-        return responses_so_far[-1].get("type") in terminal_types
+        terminal_types: Final = frozenset(
+            (
+                ResponsesAPIStreamEvents.RESPONSE_COMPLETED.value,
+                ResponsesAPIStreamEvents.RESPONSE_FAILED.value,
+                ResponsesAPIStreamEvents.RESPONSE_INCOMPLETE.value,
+            )
+        )
+        return stream_item_field(responses_so_far[-1], "type") in terminal_types
+
+    def get_streaming_scan_key(self, responses_so_far: Sequence[object]) -> StreamingScanKey | None:
+        if not responses_so_far or not hasattr(responses_so_far[-1], "get"):
+            return None
+        last_event: Final = responses_so_far[-1]
+        last_event_type: Final = stream_item_field(last_event, "type")
+        if last_event_type == ResponsesAPIStreamEvents.OUTPUT_ITEM_DONE.value:
+            return None
+        if last_event_type == ResponsesAPIStreamEvents.RESPONSE_COMPLETED.value:
+            return self._completed_response_scan_key(stream_item_field(last_event, "response"))
+        return StreamingScanKey(
+            texts=(self.get_streaming_string_so_far(responses_so_far),),
+            stream_ended=self._check_streaming_has_ended(responses_so_far),
+        )
+
+    @staticmethod
+    def _completed_response_scan_key(response: object) -> StreamingScanKey:
+        output_items: Final = stream_item_items(response, "output")
+        message_items: Final = tuple(
+            item for item in output_items if stream_item_field(item, "type") != "function_call"
+        )
+        return StreamingScanKey(
+            texts=tuple(
+                text
+                for item in message_items
+                for part in stream_item_items(item, "content")
+                if isinstance(text := stream_item_field(part, "text"), str) and text
+            ),
+            tool_calls=tuple(
+                stream_item_fingerprint(item)
+                for item in output_items
+                if stream_item_field(item, "type") == "function_call"
+            ),
+            stream_ended=True,
+        )
 
     def build_stream_error_items(
         self,
@@ -681,7 +671,7 @@ class OpenAIResponsesHandler(BaseTranslation):
             ),
         )
 
-    def get_streaming_string_so_far(self, responses_so_far: Sequence[ResponsesStreamChunk]) -> str:
+    def get_streaming_string_so_far(self, responses_so_far: Sequence[object]) -> str:
         """
         Get the string so far from the responses so far.
 
@@ -693,12 +683,16 @@ class OpenAIResponsesHandler(BaseTranslation):
         """
         keyed_events: Final = tuple(
             (
-                (event.get("item_id"), event.get("output_index"), event.get("content_index")),
-                event.get("text"),
-                event.get("delta"),
+                (
+                    stream_item_field(event, "item_id"),
+                    stream_item_field(event, "output_index"),
+                    stream_item_field(event, "content_index"),
+                ),
+                stream_item_field(event, "text"),
+                stream_item_field(event, "delta"),
             )
             for event in responses_so_far
-            if isinstance(event.get("text"), str) or isinstance(event.get("delta"), str)
+            if isinstance(stream_item_field(event, "text"), str) or isinstance(stream_item_field(event, "delta"), str)
         )
 
         def part_text(part_key: tuple[object, object, object]) -> str:

litellm/llms/openai/responses/guardrail_translation/tool_merge.py

@@ -0,0 +1,182 @@
+from collections.abc import Iterable, Mapping, Sequence
+from itertools import accumulate, chain, groupby
+from types import MappingProxyType
+from typing import Final, TypeAlias
+
+from pydantic import BaseModel, TypeAdapter, ValidationError
+
+from litellm._logging import verbose_logger
+from litellm.responses.litellm_completion_transformation.transformation import (
+    NAMESPACE_DESCRIPTION_SEPARATOR,
+    LiteLLMCompletionResponsesConfig,
+)
+
+Tool: TypeAlias = Mapping[str, object]
+IndexedKey: TypeAlias = tuple[str, int]
+
+_TOOL_ADAPTER: Final = TypeAdapter(dict[str, object])
+_CHAT_TOOL_TOP_LEVEL_KEYS: Final = frozenset({"type", "function"})
+
+
+def _as_tool(value: object) -> Tool | None:
+    candidate: Final = value.model_dump(exclude_unset=True) if isinstance(value, BaseModel) else value
+    try:
+        return _TOOL_ADAPTER.validate_python(candidate)
+    except ValidationError:
+        return None
+
+
+def _validated_tools(values: Iterable[object]) -> tuple[Tool, ...]:
+    validated: Final = tuple(map(_as_tool, values))
+    dropped: Final = sum(tool is None for tool in validated)
+    if dropped:
+        verbose_logger.warning("Dropping %d guardrail-returned tools that are not objects", dropped)
+    return tuple(tool for tool in validated if tool is not None)
+
+
+def _is_function(tool: Tool) -> bool:
+    return tool.get("type") == "function"
+
+
+def _chat_tool_key(tool: Tool) -> str:
+    tool_type: Final = str(tool.get("type") or "")
+    function: Final = _as_tool(tool.get("function"))
+    if function is not None:
+        return f"{tool_type}:{function.get('name') or ''}"
+    return f"{tool_type}:{tool.get('server_label') or tool.get('name') or ''}"
+
+
+def _indexed_keys(tools: Sequence[Tool]) -> tuple[IndexedKey, ...]:
+    keys: Final = tuple(_chat_tool_key(tool) for tool in tools)
+    positions_by_key: Final = groupby(sorted(range(len(keys)), key=keys.__getitem__), key=keys.__getitem__)
+    ordinal_by_position: Final = MappingProxyType(
+        {position: ordinal for _, positions in positions_by_key for ordinal, position in enumerate(positions)}
+    )
+    return tuple((key, ordinal_by_position[position]) for position, key in enumerate(keys))
+
+
+def _namespace_members(namespace: Tool) -> tuple[Tool, ...]:
+    members: Final = namespace.get("tools")
+    if not isinstance(members, Sequence) or isinstance(members, (str, bytes)):
+        return ()
+    return tuple(member for member in map(_as_tool, members) if member is not None)
+
+
+def _function_fields(tool: Tool) -> Tool:
+    function: Final = _as_tool(tool.get("function"))
+    return function if function is not None else MappingProxyType({})
+
+
+def _without_namespace_prefix(key: str, value: object, prefix: str) -> object:
+    if key != "description" or not isinstance(value, str) or not value.startswith(prefix):
+        return value
+    return value[len(prefix) :]
+
+
+def _rebuilt_member(member: Tool, flattened: Tool, guardrailed: Tool, namespace_description: str) -> Tool:
+    flattened_function: Final = _function_fields(flattened)
+    prefix: Final = f"{namespace_description}{NAMESPACE_DESCRIPTION_SEPARATOR}" if namespace_description else ""
+    changed_function: Final = MappingProxyType(
+        {
+            key: _without_namespace_prefix(key, value, prefix)
+            for key, value in _function_fields(guardrailed).items()
+            if flattened_function.get(key) != value
+        }
+    )
+    changed_extras: Final = MappingProxyType(
+        {
+            key: value
+            for key, value in guardrailed.items()
+            if key not in _CHAT_TOOL_TOP_LEVEL_KEYS and flattened.get(key) != value
+        }
+    )
+    return {**member, **changed_extras, **changed_function}  # mutable-ok: json.dumps rejects MappingProxyType
+
+
+def _rebuilt_function_members(
+    function_members: Sequence[Tool],
+    flattened_group: Sequence[Tool],
+    group_keys: Sequence[IndexedKey],
+    guardrailed_by_key: Mapping[IndexedKey, Tool],
+    namespace_description: str,
+) -> tuple[Tool | None, ...]:
+    return tuple(
+        None
+        if key not in guardrailed_by_key
+        else member
+        if guardrailed_by_key[key] == flattened
+        else _rebuilt_member(member, flattened, guardrailed_by_key[key], namespace_description)
+        for member, flattened, key in zip(function_members, flattened_group, group_keys)
+    )
+
+
+def _rebuilt_namespace(
+    original: Tool,
+    members: Sequence[Tool],
+    flattened_group: Sequence[Tool],
+    group_keys: Sequence[IndexedKey],
+    guardrailed_by_key: Mapping[IndexedKey, Tool],
+) -> tuple[Tool, ...]:
+    namespace_description: Final = str(original.get("description") or "")
+    rebuilt_functions: Final = iter(
+        _rebuilt_function_members(
+            tuple(member for member in members if _is_function(member)),
+            flattened_group,
+            group_keys,
+            guardrailed_by_key,
+            namespace_description,
+        )
+    )
+    rebuilt_members: Final = tuple(
+        rebuilt
+        for rebuilt in (next(rebuilt_functions) if _is_function(member) else member for member in members)
+        if rebuilt is not None
+    )
+    if not rebuilt_members:
+        return ()
+    return ({**original, "tools": list(rebuilt_members)},)  # mutable-ok: json.dumps needs a plain dict and list
+
+
+def _merged_original(
+    original: Tool,
+    flattened_group: Sequence[Tool],
+    group_keys: Sequence[IndexedKey],
+    guardrailed_by_key: Mapping[IndexedKey, Tool],
+) -> tuple[Tool, ...]:
+    if not group_keys:
+        return (original,)
+    guardrailed_group: Final = tuple(guardrailed_by_key[key] for key in group_keys if key in guardrailed_by_key)
+    if guardrailed_group == tuple(flattened_group):
+        return (original,)
+    members: Final = _namespace_members(original) if original.get("type") == "namespace" else ()
+    if members and sum(map(_is_function, members)) == len(flattened_group):
+        return _rebuilt_namespace(original, members, flattened_group, group_keys, guardrailed_by_key)
+    if not guardrailed_group:
+        return ()
+    return tuple(
+        LiteLLMCompletionResponsesConfig.transform_chat_completion_tool_params_to_responses_api_tools(guardrailed_group)
+    )
+
+
+def merge_guardrailed_tools(
+    original_tools: Sequence[Tool],
+    flattened_groups: Sequence[Sequence[Tool]],
+    guardrailed_tools: Iterable[object],
+) -> tuple[Tool, ...]:
+    guardrailed: Final = _validated_tools(guardrailed_tools)
+    flattened_keys: Final = _indexed_keys(tuple(chain.from_iterable(flattened_groups)))
+    guardrailed_keys: Final = _indexed_keys(guardrailed)
+    guardrailed_by_key: Final = MappingProxyType(dict(zip(guardrailed_keys, guardrailed)))
+    group_ends: Final = tuple(accumulate(len(group) for group in flattened_groups))
+    group_key_slices: Final = tuple(
+        flattened_keys[end - len(group) : end] for group, end in zip(flattened_groups, group_ends)
+    )
+    merged_originals: Final = chain.from_iterable(
+        _merged_original(original, group, group_keys, guardrailed_by_key)
+        for original, group, group_keys in zip(original_tools, flattened_groups, group_key_slices)
+    )
+    owned_keys: Final = frozenset(flattened_keys)
+    appended: Final = LiteLLMCompletionResponsesConfig.transform_chat_completion_tool_params_to_responses_api_tools(
+        tuple(tool for key, tool in zip(guardrailed_keys, guardrailed) if key not in owned_keys)
+    )
+    return tuple(chain(merged_originals, appended))

litellm/llms/openai_like/README.md

@@ -54,7 +54,10 @@ That's it! The provider will be automatically loaded and available.
     "constraints": {
       "temperature_max": 1.0,
       "temperature_min": 0.0,
-      "temperature_min_with_n_gt_1": 0.3
+      "temperature_min_with_n_gt_1": 0.3,
+      // /v1/messages providers only: keep Anthropic cache_control extensions
+      // such as ttl instead of stripping them down to {"type": ...}
+      "cache_control_ttl": true
     },
     
     // Optional: Special handling flags

litellm/llms/openai_like/messages/transformation.py

@@ -1,11 +1,13 @@
 from typing import Any, Final
 
 import litellm
+from litellm.llms.anthropic.common_utils import normalize_cache_control_in_anthropic_payload
 from litellm.llms.anthropic.experimental_pass_through.messages.transformation import (
     AnthropicMessagesConfig,
 )
 from litellm.llms.openai_like.json_loader import SimpleProviderConfig
 from litellm.secret_managers.main import get_secret_str
+from litellm.types.router import GenericLiteLLMParams
 
 DEFAULT_ANTHROPIC_API_VERSION: Final = "2023-06-01"
 
@@ -19,10 +21,17 @@ class OpenAILikeAnthropicMessagesConfig(AnthropicMessagesConfig):
     ``"/v1/messages"``. The inbound Anthropic payload (system, cache_control,
     thinking, tools, ...) is forwarded essentially unchanged to
     ``{api_base}/v1/messages``, so Anthropic-only features that the
-    Anthropic->OpenAI translation would otherwise drop are preserved. Response
-    parsing and streaming are inherited from the native Anthropic config.
+    Anthropic->OpenAI translation would otherwise drop are preserved. The one
+    exception is ``cache_control``, whose Anthropic-only extensions (``ttl``)
+    are stripped unless the deployment opts in with
+    ``model_info.cache_control_ttl: true``. Response parsing and streaming are
+    inherited from the native Anthropic config.
     """
 
+    def __init__(self, cache_control_ttl: bool = False) -> None:
+        super().__init__()
+        self._cache_control_ttl: Final = cache_control_ttl
+
     def validate_anthropic_messages_environment(
         self,
         headers: dict[str, str],
@@ -53,6 +62,35 @@ class OpenAILikeAnthropicMessagesConfig(AnthropicMessagesConfig):
     def should_filter_anthropic_beta_headers(self) -> bool:
         return False
 
+    def supports_cache_control_ttl(self) -> bool:
+        return self._cache_control_ttl
+
+    def transform_anthropic_messages_request(
+        self,
+        model: str,
+        messages: list[dict],  # mutable-ok: matches dict-typed base signature
+        anthropic_messages_optional_request_params: dict,  # mutable-ok: matches dict-typed base signature
+        litellm_params: GenericLiteLLMParams,
+        headers: dict,  # mutable-ok: matches dict-typed base signature
+    ) -> dict:  # mutable-ok: matches dict-typed base signature
+        """
+        Anthropic ignores prompt-caching hints it cannot honor, but strict
+        non-Anthropic implementations of the Messages API 400 the whole request
+        on Anthropic-only ``cache_control`` extensions (``cache_control.ttl: 1h
+        is not supported``), so unless the provider declares ttl support the
+        hints are reduced to their portable ``{"type": ...}`` core.
+        """
+        request: Final = super().transform_anthropic_messages_request(
+            model=model,
+            messages=messages,
+            anthropic_messages_optional_request_params=anthropic_messages_optional_request_params,
+            litellm_params=litellm_params,
+            headers=headers,
+        )
+        if self.supports_cache_control_ttl():
+            return request
+        return normalize_cache_control_in_anthropic_payload(request)
+
     def get_complete_url(
         self,
         api_base: str | None,
@@ -81,7 +119,7 @@ class JSONProviderAnthropicMessagesConfig(OpenAILikeAnthropicMessagesConfig):
     """
 
     def __init__(self, provider: SimpleProviderConfig):
-        super().__init__()
+        super().__init__(cache_control_ttl=bool(provider.constraints.get("cache_control_ttl")))
         self._provider = provider
 
     @property

litellm/llms/valkey/vector_stores/transformation.py

@@ -15,7 +15,10 @@ import httpx
 from pydantic import BaseModel, ConfigDict
 
 import litellm
-from litellm.llms.base_llm.vector_store.transformation import BaseDirectVectorStoreConfig
+from litellm.llms.base_llm.vector_store.transformation import (
+    BaseDirectVectorStoreConfig,
+    VectorStoreEmbeddingExecutor,
+)
 from litellm.llms.valkey.common_utils import build_valkey_url, pack_vector
 from litellm.types.utils import EmbeddingResponse
 from litellm.types.vector_stores import (
@@ -213,6 +216,7 @@ class ValkeyVectorStoreConfig(BaseDirectVectorStoreConfig):
         vector_store_search_optional_params: VectorStoreSearchOptionalRequestParams,
         litellm_logging_obj: "LiteLLMLoggingObj",
         litellm_params: Mapping[str, object],
+        embedding_executor: VectorStoreEmbeddingExecutor | None = None,
         timeout: float | httpx.Timeout | None = None,
     ) -> VectorStoreSearchResponse:
         params: Final = _ValkeySearchParams.model_validate(litellm_params)
@@ -222,10 +226,18 @@ class ValkeyVectorStoreConfig(BaseDirectVectorStoreConfig):
             embedding_field=params.embedding_field,
             text_field=params.text_field,
         )
-        embedding_response: Final = self.embedding_fn(
-            model=params.require_embedding_model(),
-            input=[query_text],  # mutable-ok: litellm.embedding's input contract is a list
-            **(params.litellm_embedding_config or _EMPTY_EMBEDDING_CONFIG),
+        embedding_response: Final = (
+            embedding_executor.embed(
+                params.require_embedding_model(),
+                query_text,
+                params.litellm_embedding_config or _EMPTY_EMBEDDING_CONFIG,
+            )
+            if embedding_executor is not None
+            else self.embedding_fn(
+                model=params.require_embedding_model(),
+                input=[query_text],  # mutable-ok: the injected embedding callable requires list input
+                **(params.litellm_embedding_config or _EMPTY_EMBEDDING_CONFIG),
+            )
         )
         vec_params: Final = {"vec": pack_vector(embedding_response.data[0]["embedding"])}  # mutable-ok: redis-py API
 
@@ -252,6 +264,7 @@ class ValkeyVectorStoreConfig(BaseDirectVectorStoreConfig):
         vector_store_search_optional_params: VectorStoreSearchOptionalRequestParams,
         litellm_logging_obj: "LiteLLMLoggingObj",
         litellm_params: Mapping[str, object],
+        embedding_executor: VectorStoreEmbeddingExecutor | None = None,
         timeout: float | httpx.Timeout | None = None,
     ) -> VectorStoreSearchResponse:
         params: Final = _ValkeySearchParams.model_validate(litellm_params)
@@ -261,10 +274,18 @@ class ValkeyVectorStoreConfig(BaseDirectVectorStoreConfig):
             embedding_field=params.embedding_field,
             text_field=params.text_field,
         )
-        embedding_response: Final = await self.aembedding_fn(
-            model=params.require_embedding_model(),
-            input=[query_text],  # mutable-ok: litellm.embedding's input contract is a list
-            **(params.litellm_embedding_config or _EMPTY_EMBEDDING_CONFIG),
+        embedding_response: Final = (
+            await embedding_executor.aembed(
+                params.require_embedding_model(),
+                query_text,
+                params.litellm_embedding_config or _EMPTY_EMBEDDING_CONFIG,
+            )
+            if embedding_executor is not None
+            else await self.aembedding_fn(
+                model=params.require_embedding_model(),
+                input=[query_text],  # mutable-ok: the injected embedding callable requires list input
+                **(params.litellm_embedding_config or _EMPTY_EMBEDDING_CONFIG),
+            )
         )
         vec_params: Final = {"vec": pack_vector(embedding_response.data[0]["embedding"])}  # mutable-ok: redis-py API
 

litellm/passthrough/main.py

@@ -113,10 +113,8 @@ class AsyncPassthroughStreamingResponse(AsyncGenerator[bytes, bytes]):
                 )
             )
 
-            # Compliant: Save a strong reference to prevent GC
             self._background_tasks.add(task)
 
-            # Remove the task from the set when it finishes to avoid memory leaks
             task.add_done_callback(self._background_tasks.discard)
         except Exception as e:  # noqa: BLE001 # Safe catch-all for verbose logging
             verbose_logger.exception(
@@ -578,7 +576,6 @@ def llm_passthrough_route(
             else:
                 return response
     except Exception as e:
-        # provider_config is guaranteed non-None here due to the earlier guard
         assert provider_config is not None
         raise base_llm_http_handler._handle_error(
             e=e,

litellm/proxy/_experimental/mcp_server/outbound_credentials/token_endpoint.py

@@ -121,6 +121,23 @@ class TokenEndpointClient:
         return Ok(ExchangedToken(access_token=parsed.access_token, expires_in=parsed.expires_in))
 
 
+class _KeyGuard:
+    """The per-key single-flight lock plus the invalidation generation that lock protects.
+
+    Both live on one object so their lifetimes cannot diverge. `get_or_compute` binds the guard to
+    a local for its whole critical section, which keeps the weak map's entry alive for as long as
+    that compute could still write; an `invalidate` overlapping the compute therefore reaches the
+    very same object and its bump is guaranteed to be observed. Conversely a guard nobody holds is
+    collectible precisely because no write is outstanding for it to fence.
+    """
+
+    __slots__ = ("__weakref__", "generation", "lock")
+
+    def __init__(self) -> None:
+        self.lock = asyncio.Lock()
+        self.generation = 0
+
+
 class ExchangedTokenCache:
     """Memoizes the final token string per key, single-flighting concurrent misses on one lock."""
 
@@ -129,7 +146,7 @@ class ExchangedTokenCache:
             max_size_in_memory=MCP_TOKEN_EXCHANGE_CACHE_MAX_SIZE,
             default_ttl=MCP_OAUTH2_TOKEN_CACHE_DEFAULT_TTL,
         )
-        self._locks: weakref.WeakValueDictionary[str, asyncio.Lock] = weakref.WeakValueDictionary()
+        self._guards: weakref.WeakValueDictionary[str, _KeyGuard] = weakref.WeakValueDictionary()
 
     async def get_or_compute(
         self,
@@ -144,28 +161,50 @@ class ExchangedTokenCache:
         guaranteeing the token it gets back was minted for the *current* inputs: a stored entry
         whose fingerprint differs reads as a miss and is re-minted over. That keeps eviction
         addressable without the key having to encode the credential material it protects.
+
+        An `invalidate` landing while `compute` is in flight wins over that compute's write. The
+        token is still returned to the caller it was minted for, but it is not stored, so the next
+        resolution re-mints rather than serving a bearer that predates the invalidation for the
+        rest of its TTL.
         """
         cached = self._get(cache_key, fingerprint)
         if cached is not None:
             return Ok(cached)
-        async with self._lock(cache_key):
+        guard = self._guard(cache_key)
+        async with guard.lock:
             cached = self._get(cache_key, fingerprint)
             if cached is not None:
                 return Ok(cached)
+            generation = guard.generation
             match await compute():
                 case Ok(token):
-                    self._cache.set_cache(  # pyright: ignore[reportUnknownMemberType]  # InMemoryCache is untyped
-                        cache_key,
-                        (fingerprint, token.access_token),
-                        ttl=_cache_ttl_seconds(token.expires_in),
-                    )
+                    if guard.generation == generation:
+                        self._store(cache_key, fingerprint, token)
                     return Ok(token.access_token)
                 case Error(err):
                     return Error(err)
 
     def invalidate(self, cache_key: str) -> None:
-        """Evict one cached token so the next `get_or_compute` re-mints (e.g. after an upstream 401)."""
+        """Evict one cached token so the next `get_or_compute` re-mints (e.g. after an upstream 401).
+
+        Bumping the guard's generation is what makes the eviction stick against a compute already
+        awaiting the token endpoint: that compute snapshotted the old generation and so skips its
+        write. No guard means no compute is in flight, since an in-flight one pins its own.
+
+        Stays synchronous: callers invalidate from plain `def`s.
+        """
         self._cache.delete_cache(cache_key)  # pyright: ignore[reportUnknownMemberType]  # InMemoryCache is untyped
+        guard = self._guards.get(cache_key)
+        if guard is None:
+            return
+        guard.generation += 1
+
+    def _store(self, cache_key: str, fingerprint: str, token: ExchangedToken) -> None:
+        self._cache.set_cache(  # pyright: ignore[reportUnknownMemberType]  # InMemoryCache is untyped
+            cache_key,
+            (fingerprint, token.access_token),
+            ttl=_cache_ttl_seconds(token.expires_in),
+        )
 
     def _get(self, cache_key: str, fingerprint: str) -> str | None:
         """The stored token, or None when absent or minted for different inputs.
@@ -180,12 +219,12 @@ class ExchangedTokenCache:
             return None
         return token if stored_fingerprint == fingerprint else None
 
-    def _lock(self, cache_key: str) -> asyncio.Lock:
-        lock = self._locks.get(cache_key)
-        if lock is None:
-            lock = asyncio.Lock()
-            self._locks[cache_key] = lock
-        return lock
+    def _guard(self, cache_key: str) -> _KeyGuard:
+        guard = self._guards.get(cache_key)
+        if guard is None:
+            guard = _KeyGuard()
+            self._guards[cache_key] = guard
+        return guard
 
 
 def _cache_ttl_seconds(expires_in: int | None) -> int:

litellm/proxy/_experimental/mcp_server/rest_endpoints.py

@@ -74,7 +74,13 @@ _MCP_GUARDRAIL_REJECTIONS: Final = (
 )
 
 
-def _connection_error_message(exc: BaseException) -> str:
+def _connection_error_message(exc: BaseException, url: str | None, timeout_seconds: float) -> str:
+    if isinstance(exc, TimeoutError):
+        return (
+            f"Failed to connect to MCP server: no response from {url or 'the server'} "
+            f"within {timeout_seconds:.0f}s. Check that the LiteLLM proxy can reach this URL "
+            "from its network (DNS, egress rules, firewalls) and that the server answers MCP requests."
+        )
     if isinstance(exc, httpx.LocalProtocolError):
         return (
             "Failed to connect to MCP server: a request header is malformed. "
@@ -92,6 +98,9 @@ def _connection_error_message(exc: BaseException) -> str:
 
 
 if MCP_AVAILABLE:
+    from mcp.types import Tool as MCPTool
+
+    from litellm.experimental_mcp_client.client import MCPClient
     from litellm.proxy._experimental.mcp_server.mcp_server_manager import (
         _UPSTREAM_OAUTH_DISCOVERY_AUTH_TYPES,
         global_mcp_server_manager,
@@ -876,7 +885,6 @@ if MCP_AVAILABLE:
                         return (), classify_list_exception(e)
                     return tools_result, ServerListOk(tool_count=len(tools_result))
 
-                # Query all servers the user has access to
                 queried_servers: Final = tuple(
                     server
                     for server in map(global_mcp_server_manager.get_mcp_server_by_id, allowed_server_ids)
@@ -1141,12 +1149,18 @@ if MCP_AVAILABLE:
         scopes: Final[list[str] | None] = scopes_raw if isinstance(scopes_raw, list) else None
         return client_id, client_secret, scopes
 
+    async def _list_tools_within(client: MCPClient, deadline: float) -> list[MCPTool] | None:
+        with anyio.move_on_after(deadline):
+            return await client.list_tools(raise_on_error=True)
+        return None
+
     async def _execute_with_mcp_client(
         request: NewMCPServerRequest,
         operation: Callable[..., Awaitable[Mapping[str, object]]],
         mcp_auth_header: str | dict[str, str] | None = None,
         oauth2_headers: dict[str, str] | None = None,
         raw_headers: dict[str, str] | None = None,
+        timeout_seconds: float = MCP_TOOL_LISTING_TIMEOUT,
     ) -> Mapping[str, object]:
         """
         Create a temporary MCP client from *request*, run *operation*, and return the result.
@@ -1162,6 +1176,10 @@ if MCP_AVAILABLE:
             oauth2_headers: Headers extracted from the incoming request (may contain the
                 litellm API key — must NOT be forwarded for M2M servers).
             raw_headers: Raw request headers forwarded for stdio env construction.
+            timeout_seconds: Cap on OAuth discovery, connect, handshake, and *operation*
+                combined. Defaults to ``MCP_TOOL_LISTING_TIMEOUT`` (30s, below common LB
+                timeouts) so an unreachable upstream yields this endpoint's JSON error
+                instead of an opaque load-balancer 504 with an empty body.
 
         Returns:
             The dict returned by *operation*, or an error dict on failure.
@@ -1252,15 +1270,16 @@ if MCP_AVAILABLE:
                 static_headers=request.static_headers,
             )
 
-            client: Final = await global_mcp_server_manager._create_mcp_client(
-                server=server_model,
-                mcp_auth_header=mcp_auth_header,
-                extra_headers=merged_headers,
-                stdio_env=stdio_env,
-                cred_provider=preview_cred_provider,
-            )
+            with anyio.fail_after(timeout_seconds):
+                client: Final = await global_mcp_server_manager._create_mcp_client(
+                    server=server_model,
+                    mcp_auth_header=mcp_auth_header,
+                    extra_headers=merged_headers,
+                    stdio_env=stdio_env,
+                    cred_provider=preview_cred_provider,
+                )
 
-            return await operation(client)
+                return await operation(client)
 
         except (KeyboardInterrupt, SystemExit, asyncio.CancelledError):
             raise
@@ -1269,7 +1288,7 @@ if MCP_AVAILABLE:
             return {
                 "status": "error",
                 "error": True,
-                "message": _connection_error_message(e),
+                "message": _connection_error_message(e, request.url, timeout_seconds),
             }
 
     async def _preview_openapi_tools(spec_path: str) -> dict:
@@ -1422,9 +1441,7 @@ if MCP_AVAILABLE:
                 getattr(client, "timeout", MCP_CLIENT_TIMEOUT) or MCP_CLIENT_TIMEOUT,
                 MCP_TOOL_LISTING_TIMEOUT,
             )
-            list_tools_result = None  # rebind-ok: set inside the timeout scope below
-            with anyio.move_on_after(listing_deadline):
-                list_tools_result = await client.list_tools(raise_on_error=True)  # rebind-ok: fills the init above
+            list_tools_result: Final = await _list_tools_within(client, listing_deadline)
             if list_tools_result is None:
                 verbose_logger.warning(
                     "MCP tools/list preview timed out after %s seconds while paginating upstream tools",

litellm/proxy/_experimental/mcp_server/server.py

@@ -3855,6 +3855,13 @@ if MCP_AVAILABLE:
                 and server.auth_type == MCPAuth.oauth2_token_exchange
                 and oauth2_headers
                 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,

litellm/proxy/_experimental/mcp_server/tool_search.py

@@ -5,10 +5,10 @@ from collections.abc import Mapping, Sequence
 from dataclasses import dataclass
 from datetime import datetime
 from types import MappingProxyType
-from typing import TYPE_CHECKING, Any, Final, TypedDict, assert_never
+from typing import TYPE_CHECKING, Any, Final, TypedDict
 
 from pydantic import ValidationError
-from typing_extensions import ReadOnly, Required
+from typing_extensions import ReadOnly, Required, assert_never
 
 import litellm
 from litellm.proxy.agent_endpoints.agent_search import DEFAULT_AGENT_SEARCH_TOP_K

litellm/proxy/_types.py

@@ -2594,6 +2594,10 @@ class ConfigGeneralSettings(LiteLLMPydanticObjectBase):
         None,
         description="When set to True, rejects requests that contain client-side 'metadata.tags' to prevent users from influencing budgets by sending different tags. Tags can only be inherited from the API key metadata.",
     )
+    missing_session_id: Literal["generate", "reject"] | None = Field(
+        None,
+        description="What to do with LLM API requests that carry no session id (x-litellm-session-id header, metadata.session_id, etc.). 'generate' stamps one id into litellm_session_id, litellm_trace_id and metadata.session_id so SpendLogs and logging callbacks agree; 'reject' returns 400. Unset keeps the legacy behavior where SpendLogs falls back to the trace id while callbacks get no session id.",
+    )
     enable_public_model_hub: bool = Field(
         default=False,
         description="Public model hub for users to see what models they have access to, supported openai params, etc.",

litellm/proxy/agent_endpoints/endpoints.py

@@ -13,10 +13,10 @@ import os
 import uuid
 from collections.abc import Mapping, Sequence
 from types import MappingProxyType
-from typing import Annotated, Final, TypedDict, assert_never
+from typing import Annotated, Final, TypedDict
 
 from fastapi import APIRouter, Depends, HTTPException, Query, Request
-from typing_extensions import ReadOnly, Required
+from typing_extensions import ReadOnly, Required, assert_never
 
 import litellm
 from litellm._logging import verbose_proxy_logger

litellm/proxy/client/cli/commands/auth.py

@@ -100,9 +100,10 @@ class CliPollData(TypedDict, total=False):
 
 
 class CliSsoStartData(TypedDict):
-    login_id: str
-    poll_secret: str
-    user_code: str
+    login_id: ReadOnly[str]
+    poll_secret: ReadOnly[str]
+    user_code: ReadOnly[str]
+    verification_uri_complete: ReadOnly[NotRequired[str]]
 
 
 class CliAuthResult(TypedDict):
@@ -860,11 +861,22 @@ def login(ctx: click.Context, config_claude: bool, pkce: bool) -> None:
         poll_secret: Final = cli_sso_flow["poll_secret"]
         user_code: Final = cli_sso_flow["user_code"]
 
-        sso_url = f"{base_url}/sso/key/generate?" + urlencode({"source": LITELLM_CLI_SOURCE_IDENTIFIER, "key": key_id})
+        browser_prefills_code: Final = isinstance(cli_sso_flow.get("verification_uri_complete"), str)
+        sso_url: Final = f"{base_url}/sso/key/generate?" + urlencode(
+            (
+                ("source", LITELLM_CLI_SOURCE_IDENTIFIER),
+                ("key", key_id),
+                *((("user_code", user_code),) if browser_prefills_code else ()),
+            )
+        )
 
         click.echo(f"Opening browser to: {sso_url}")
         click.echo("Please complete the SSO authentication in your browser...")
-        click.echo(f"Verification code: {user_code}")
+        click.echo(
+            f"Verification code: {user_code} (pre-filled in the browser, check it matches)"
+            if browser_prefills_code
+            else f"Verification code: {user_code}"
+        )
         click.echo(f"Session ID: {key_id}")
 
         # Open browser

litellm/proxy/common_request_processing.py

@@ -3,7 +3,6 @@ import contextlib
 import json
 import logging
 import math
-import traceback
 from collections.abc import AsyncGenerator, Awaitable, Callable, Coroutine, Mapping, Sequence
 from datetime import datetime
 from functools import lru_cache
@@ -18,7 +17,7 @@ from fastapi.responses import JSONResponse, Response, StreamingResponse
 from starlette.types import Receive, Scope, Send
 
 import litellm
-from litellm._logging import _redact_string, verbose_proxy_logger
+from litellm._logging import redact_internal_details_from_client_message, verbose_proxy_logger
 from litellm._uuid import uuid
 from litellm.constants import (
     DD_TRACER_STREAMING_CHUNK_YIELD_RESOURCE,
@@ -3417,7 +3416,7 @@ class ProxyBaseLLMRequestProcessing:
         else:
             _code = status.HTTP_500_INTERNAL_SERVER_ERROR
         raise ProxyException(
-            message=getattr(e, "message", error_msg),
+            message=redact_internal_details_from_client_message(getattr(e, "message", error_msg)),
             type=getattr(e, "type", "None"),
             param=getattr(e, "param", "None"),
             openai_code=getattr(e, "code", None),
@@ -3629,10 +3628,8 @@ class ProxyBaseLLMRequestProcessing:
 
             if isinstance(e, HTTPException):
                 raise e
-            error_traceback: Final = _redact_string(traceback.format_exc())
-            error_msg: Final = f"{e}\n\n{error_traceback}"
             proxy_exception: Final = ProxyException(
-                message=getattr(e, "message", error_msg),
+                message=redact_internal_details_from_client_message(getattr(e, "message", str(e))),
                 type=getattr(e, "type", "None"),
                 param=getattr(e, "param", "None"),
                 code=getattr(e, "status_code", 500),

litellm/proxy/common_utils/reset_budget_job.py

@@ -7,7 +7,9 @@ from dataclasses import dataclass, field
 from datetime import datetime, timedelta, timezone
 from enum import Enum
 from types import MappingProxyType
-from typing import Final, Literal, Protocol, TypeVar, assert_never
+from typing import Final, Literal, Protocol, TypeVar
+
+from typing_extensions import assert_never
 
 import litellm
 from litellm._logging import verbose_proxy_logger

litellm/proxy/guardrails/guardrail_endpoints.py

@@ -514,12 +514,26 @@ async def update_guardrail(
         guardrail_name: Final = result.get("guardrail_name", "Unknown")
 
         try:
-            IN_MEMORY_GUARDRAIL_HANDLER.update_in_memory_guardrail(
-                guardrail_id=guardrail_id, guardrail=cast(Guardrail, result)
-            )
+            IN_MEMORY_GUARDRAIL_HANDLER.sync_guardrail_from_db(guardrail=cast(Guardrail, result))
             verbose_proxy_logger.info(
                 "Immediate sync: Successfully updated guardrail '%s' (ID: %s)", guardrail_name, guardrail_id
             )
+        except (ValueError, TypeError) as update_error:
+            # The new config is invalid (a raising guardrail __init__):
+            # reinitialize_guardrail already restored the previous live instance, but
+            # update_guardrail_in_db above already persisted the rejected config to
+            # the DB. Roll that back too, so the DB and the live guardrail never
+            # disagree about what's actually enforcing, and surface the rejection to
+            # the caller instead of a misleading 200.
+            await GUARDRAIL_REGISTRY.update_guardrail_in_db(
+                guardrail_id=guardrail_id,
+                guardrail=existing_guardrail,
+                prisma_client=prisma_client,
+            )
+            raise HTTPException(
+                status_code=422,
+                detail=f"Invalid guardrail configuration, update rejected: {update_error}",
+            ) from update_error
         except Exception as update_error:
             verbose_proxy_logger.warning(
                 "Immediate sync: Failed to update '%s' (ID: %s) in memory: %s",

litellm/proxy/guardrails/guardrail_hooks/alice/alice.py

@@ -8,6 +8,7 @@
 import json
 import os
 from collections.abc import Mapping
+from itertools import islice
 from typing import (
     TYPE_CHECKING,
     Any,  # noqa: TID251  # **kwargs forwards verbatim to CustomGuardrail.__init__; see ruff-strict.toml
@@ -341,19 +342,18 @@ def _json_safe(
     if depth >= _MAX_DEPTH or id(value) in seen:
         return None
 
-    nested: Final = seen | {id(value)}  # mutable-ok: one-shot set literal, unioned into a frozenset immediately
+    nested: Final = seen | frozenset((id(value),))
 
     if isinstance(value, dict):
-        out: dict[str, object] = {}  # mutable-ok: bounded accumulator local to this call, never escapes as-is
-        for key, item in list(value.items())[:_MAX_ITEMS]:  # mutable-ok: list() only to slice an unordered view
-            if isinstance(key, str) and key not in strip_keys:
-                out[key] = _json_safe(item, depth + 1, nested, strip_keys)
-        return out
+        return {
+            key: _json_safe(item, depth + 1, nested, strip_keys)
+            for key, item in islice(value.items(), _MAX_ITEMS)
+            if isinstance(key, str) and key not in strip_keys
+        }
 
     if isinstance(value, (list, tuple, set, frozenset)):
         return [  # mutable-ok: return value is a one-shot list, discarded by the caller after use
-            _json_safe(item, depth + 1, nested, strip_keys)
-            for item in list(value)[:_MAX_ITEMS]  # mutable-ok: list() only to slice an unordered view
+            _json_safe(item, depth + 1, nested, strip_keys) for item in islice(value, _MAX_ITEMS)
         ]
 
     dump: Final = getattr(value, "model_dump", None)

litellm/proxy/guardrails/guardrail_hooks/crowdstrike_aidr/__init__.py

@@ -1,8 +1,8 @@
 from typing import TYPE_CHECKING, Final
 
-from litellm.types.guardrails import GuardrailEventHooks, SupportedGuardrailIntegrations
+from litellm.types.guardrails import SupportedGuardrailIntegrations
 
-from .crowdstrike_aidr import CrowdStrikeAIDRHandler
+from .crowdstrike_aidr import CrowdStrikeAIDRHandler, streaming_params_from_litellm_params
 
 if TYPE_CHECKING:
     from litellm.types.guardrails import Guardrail, LitellmParams
@@ -15,17 +15,16 @@ def initialize_guardrail(litellm_params: "LitellmParams", guardrail: "Guardrail"
     if not guardrail_name:
         raise ValueError("CrowdStrike AIDR guardrail name is required")
 
+    streaming_params: Final = streaming_params_from_litellm_params(litellm_params)
     _crowdstrike_aidr_callback: Final = CrowdStrikeAIDRHandler(
         guardrail_name=guardrail_name,
         api_base=litellm_params.api_base,
         api_key=litellm_params.api_key,
-        # Exclude during_call to prevent duplicate input events
-        event_hook=[
-            GuardrailEventHooks.pre_call.value,
-            GuardrailEventHooks.post_call.value,
-        ],
+        event_hook=litellm_params.mode,
         default_on=litellm_params.default_on,
         fail_on_error=litellm_params.fail_on_error,
+        streaming_end_of_stream_only=streaming_params.streaming_end_of_stream_only,
+        streaming_sampling_rate=streaming_params.streaming_sampling_rate,
     )
     litellm.logging_callback_manager.add_litellm_callback(_crowdstrike_aidr_callback)
 

litellm/proxy/guardrails/guardrail_hooks/crowdstrike_aidr/crowdstrike_aidr.py

@@ -24,8 +24,11 @@ from litellm.llms.custom_httpx.http_handler import (
 from litellm.proxy.common_utils.callback_utils import (
     add_guardrail_to_applied_guardrails_header,
 )
-from litellm.types.guardrails import GuardrailEventHooks
+from litellm.types.guardrails import GuardrailEventHooks, LitellmParams
 from litellm.types.llms.openai import AllMessageValues, OpenAIChatCompletionToolParam
+from litellm.types.proxy.guardrails.guardrail_hooks.crowdstrike_aidr import (
+    CrowdStrikeAIDRGuardrailConfigModelOptionalParams,
+)
 from litellm.types.utils import GenericGuardrailAPIInputs
 
 if TYPE_CHECKING:
@@ -153,6 +156,21 @@ def _merge_metadata_bags(request_data: Mapping[str, Any]) -> Mapping[str, Any] |
     return merged if present else None
 
 
+def streaming_params_from_litellm_params(
+    litellm_params: LitellmParams,
+) -> CrowdStrikeAIDRGuardrailConfigModelOptionalParams:
+    extras: Final[Mapping[str, object]] = litellm_params.model_extra or {}
+    nested: Final = litellm_params.optional_params
+    optional_params: Final[Mapping[str, object]] = {} if nested is None else nested.model_dump()
+    return CrowdStrikeAIDRGuardrailConfigModelOptionalParams.model_validate(
+        {
+            name: value
+            for name in CrowdStrikeAIDRGuardrailConfigModelOptionalParams.model_fields
+            if (value := optional_params.get(name, extras.get(name))) is not None
+        }
+    )
+
+
 def _messages_since_last_assistant(
     messages: Sequence[AllMessageValues],
 ) -> _FilteredMessages:
@@ -241,6 +259,8 @@ class CrowdStrikeAIDRHandler(CustomGuardrail):
         api_key: str | None = None,
         api_base: str | None = None,
         fail_on_error: bool | None = True,
+        streaming_end_of_stream_only: bool | None = None,
+        streaming_sampling_rate: int | None = None,
         **kwargs,
     ) -> None:
         """
@@ -250,10 +270,19 @@ class CrowdStrikeAIDRHandler(CustomGuardrail):
             guardrail_name (str): The name of the guardrail instance.
             api_key (str | None): The CrowdStrike AIDR API key. Reads from CS_AIDR_TOKEN env var if None.
             api_base (str | None): The CrowdStrike AIDR API base URL. Reads from CS_AIDR_BASE_URL env var if None.
+            streaming_end_of_stream_only (bool | None): Scan streamed output once at end of stream instead of
+                every streaming_sampling_rate chunks. Defaults to False.
+            streaming_sampling_rate (int | None): Scan the accumulated streamed output every Nth chunk. Defaults to 5.
             **kwargs: Additional arguments passed to the CustomGuardrail base class.
         """
         self.async_handler = get_async_httpx_client(llm_provider=httpxSpecialProvider.GuardrailCallback)
         self.fail_on_error = True if fail_on_error is None else fail_on_error
+        self._set_streaming_params(
+            CrowdStrikeAIDRGuardrailConfigModelOptionalParams(
+                streaming_end_of_stream_only=streaming_end_of_stream_only,
+                streaming_sampling_rate=streaming_sampling_rate,
+            )
+        )
 
         self.api_key = api_key or os.environ.get("CS_AIDR_TOKEN")
         if not self.api_key:
@@ -274,6 +303,15 @@ class CrowdStrikeAIDRHandler(CustomGuardrail):
             "Initialized CrowdStrike AIDR Guardrail: name=%s, api_base=%s", guardrail_name, self.api_base
         )
 
+    def _set_streaming_params(self, streaming_params: CrowdStrikeAIDRGuardrailConfigModelOptionalParams) -> None:
+        self.streaming_end_of_stream_only: bool = streaming_params.streaming_end_of_stream_only or False
+        self.streaming_sampling_rate: int = streaming_params.streaming_sampling_rate or 5
+
+    @override
+    def update_in_memory_litellm_params(self, litellm_params: LitellmParams) -> None:
+        super().update_in_memory_litellm_params(litellm_params)
+        self._set_streaming_params(streaming_params_from_litellm_params(litellm_params))
+
     async def _call_crowdstrike_aidr_guard(
         self, payload: dict[str, Any], hook_name: str
     ) -> _GuardChatCompletionsResult:

litellm/proxy/guardrails/guardrail_hooks/unified_guardrail/unified_guardrail.py

@@ -36,6 +36,7 @@ if TYPE_CHECKING:
     from litellm.integrations.custom_guardrail import ModifyResponseException
     from litellm.llms.base_llm.guardrail_translation.base_translation import (
         BaseTranslation,
+        StreamingScanKey,
     )
 
 # Call types that stream JSON-RPC events (A2A); guardrail HTTPException is emitted as in-stream error
@@ -54,6 +55,9 @@ class _EndpointTranslation(Protocol):
     @property
     def process_output_streaming_response(self) -> "Callable[..., Awaitable[object]]": ...
 
+    @property
+    def get_streaming_scan_key(self) -> "Callable[[Sequence[object]], StreamingScanKey | None]": ...
+
     @property
     def build_block_sse_chunks(self) -> "Callable[..., Sequence[bytes] | None]": ...
 
@@ -70,6 +74,12 @@ def _chunk_choices(item: object) -> Sequence[object]:
     return choices
 
 
+def _is_redundant_scan(scan_key: "StreamingScanKey | None", last_scan_key: "StreamingScanKey | None") -> bool:
+    if scan_key is None:
+        return False
+    return scan_key == last_scan_key or scan_key.has_nothing_to_scan
+
+
 class _StreamTerminated(Exception):
     """Internal signal that the incremental transform stream has already emitted
     its terminal chunks (block message or in-stream error) and must stop."""
@@ -1011,6 +1021,7 @@ class UnifiedLLMGuardrails(CustomLogger):
         # Drives how a block terminates the stream: continue the in-progress
         # message (True) vs emit a standalone block message (False, buffered).
         chunks_yielded = False
+        last_scan_key: StreamingScanKey | None = None  # rebind-ok: replaced after every scan round
 
         async for item in response:
             chunk_counter += 1
@@ -1052,6 +1063,19 @@ class UnifiedLLMGuardrails(CustomLogger):
 
             # Process chunk based on sampling rate
             if chunk_counter % sampling_rate == 0:
+                endpoint_translation = endpoint_guardrail_translation_mappings[CallTypes(call_type)]()
+                scan_key = endpoint_translation.get_streaming_scan_key(responses_so_far)
+                if _is_redundant_scan(scan_key, last_scan_key):
+                    verbose_proxy_logger.debug(
+                        "Skipping streaming chunk %s for guardrail %s: nothing new to scan since the last round",
+                        chunk_counter,
+                        guardrail_to_apply.guardrail_name,
+                    )
+                    chunks_yielded = True
+                    responses_yielded.append(item)
+                    yield item
+                    continue
+
                 verbose_proxy_logger.debug(
                     "Processing streaming chunk %s (sampling_rate=%s) with guardrail %s",
                     chunk_counter,
@@ -1067,8 +1091,6 @@ class UnifiedLLMGuardrails(CustomLogger):
                 # string, permanently losing this chunk's content.
                 original_item = copy.deepcopy(item)
 
-                endpoint_translation = endpoint_guardrail_translation_mappings[CallTypes(call_type)]()
-
                 try:
                     await endpoint_translation.process_output_streaming_response(
                         responses_so_far=responses_so_far,
@@ -1110,6 +1132,8 @@ class UnifiedLLMGuardrails(CustomLogger):
                     ):
                         yield error_item
                     return
+                if scan_key is not None:
+                    last_scan_key = scan_key
                 chunks_yielded = True
                 responses_yielded.append(original_item)
                 yield original_item
@@ -1136,6 +1160,18 @@ class UnifiedLLMGuardrails(CustomLogger):
             # preserve the list, not clone every chunk (deepcopy would double
             # peak memory for large responses).
             buffered_items: Final = list(responses_so_far) if buffer_until_moderated else None
+            end_scan_key: Final = endpoint_translation.get_streaming_scan_key(responses_so_far)
+            if _is_redundant_scan(end_scan_key, last_scan_key):
+                verbose_proxy_logger.debug(
+                    "Skipping end-of-stream scan for guardrail %s: the last sampled round already scanned it all",
+                    guardrail_to_apply.guardrail_name,
+                )
+                for buffered_item in buffered_items or ():
+                    yield buffered_item
+                for pending_item in pending_end_of_stream_items:
+                    responses_yielded.append(pending_item)
+                    yield pending_item
+                return
 
             try:
                 await endpoint_translation.process_output_streaming_response(

litellm/proxy/guardrails/guardrail_registry.py

@@ -826,11 +826,12 @@ class InMemoryGuardrailHandler:
         Removes old callback from litellm.callbacks and creates fresh instance.
 
         If the new config fails to initialize (e.g. an invalid on_flagged
-        combination), the previous instance is restored rather than left
-        deleted: initialize_guardrail's own ValueError/TypeError propagate
-        uncaught, so a caller reaching this point after already deleting the
-        old instance would otherwise leave the guardrail providing no
-        protection at all, not merely "still enforcing the old config."
+        combination or an invalid regex), the previous instance is restored
+        rather than left deleted, and the failure is re-raised as ValueError so
+        every init failure reaches callers as one exception type: a caller
+        reaching this point after already deleting the old instance would
+        otherwise leave the guardrail providing no protection at all, not
+        merely "still enforcing the old config."
         """
         guardrail_id: Final = guardrail.get("guardrail_id")
         if not guardrail_id:
@@ -849,7 +850,7 @@ class InMemoryGuardrailHandler:
         # that was enforcing must never fail open because an update was bad.
         try:
             return self.initialize_guardrail(guardrail=guardrail, config_file_path=config_file_path, source=source)
-        except Exception:
+        except Exception as init_error:
             if previous_guardrail is not None:
                 verbose_proxy_logger.exception(
                     "Reinitializing guardrail %s with updated params failed; restoring the previous configuration",
@@ -861,7 +862,7 @@ class InMemoryGuardrailHandler:
                     )
                 except Exception:  # noqa: BLE001  # the original failure must propagate even if the restore breaks
                     verbose_proxy_logger.exception("Restoring previous guardrail %s also failed", guardrail_id)
-            raise
+            raise ValueError(f"Guardrail initialization failed: {init_error}") from init_error
 
     def sync_guardrail_from_db(self, guardrail: Guardrail, config_file_path: str | None = None) -> Guardrail | None:
         """

litellm/proxy/litellm_pre_call_utils.py

@@ -16,6 +16,7 @@ from starlette.datastructures import Headers
 import litellm
 from litellm._logging import verbose_logger, verbose_proxy_logger
 from litellm._service_logger import ServiceLogging
+from litellm._uuid import uuid
 from litellm.constants import (
     CONSUMED_REQUEST_TAGS_METADATA_KEY,
     INTERNAL_CALL_ORIGIN_METADATA_KEY,
@@ -23,6 +24,7 @@ from litellm.constants import (
     OTEL_SERVICE_NAME_METADATA_KEYS,
     PRE_CALL_EXECUTED_GUARDRAILS_KEY,
     SESSION_DEPLOYMENT_AFFINITY_TTL_METADATA_KEY,
+    SESSION_ID_GENERATED_METADATA_KEY,
 )
 from litellm.litellm_core_utils.credential_accessor import CredentialAccessor
 from litellm.litellm_core_utils.initialize_dynamic_callback_params import (
@@ -40,6 +42,7 @@ from litellm.proxy._types import (
     AddTeamCallback,
     CommonProxyErrors,
     LitellmDataForBackendLLMCall,
+    LiteLLMRoutes,
     LitellmUserRoles,
     ProxyErrorTypes,
     ProxyException,
@@ -47,6 +50,8 @@ from litellm.proxy._types import (
     TeamCallbackMetadata,
     UserAPIKeyAuth,
 )
+from litellm.proxy.auth.auth_utils import get_request_route
+from litellm.proxy.auth.route_checks import RouteChecks
 from litellm.proxy.common_utils.callback_utils import (
     decrypt_callback_vars,
     get_metadata_variable_name_from_kwargs,
@@ -715,6 +720,50 @@ def _get_anthropic_session_id_from_metadata(metadata: object) -> str | None:
     return session_id
 
 
+def _is_llm_inference_route(request: Request) -> bool:
+    route: Final = get_request_route(request)
+    return RouteChecks.is_llm_api_route(route=route) and not RouteChecks.check_route_access(
+        route=route, allowed_routes=LiteLLMRoutes.mcp_routes.value
+    )
+
+
+def apply_missing_session_id_policy(
+    data: dict[str, object],  # mutable-ok: stamps session ids in place on the request body the pipeline threads through
+    _metadata_variable_name: str,
+    general_settings: Mapping[str, object] | None,
+    request: Request,
+) -> None:
+    policy: Final = general_settings.get("missing_session_id") if general_settings else None
+    if policy is None or not _is_llm_inference_route(request):
+        return
+    metadata: Final = data.get(_metadata_variable_name)
+    if not isinstance(metadata, dict):
+        return
+    if data.get("litellm_session_id") or metadata.get("session_id"):
+        return
+    match policy:
+        case "generate":
+            session_id: Final = str(data.get("litellm_trace_id") or metadata.get("trace_id") or uuid.uuid4())
+            data["litellm_session_id"] = session_id  # rebind-ok: data is an out-param
+            data.setdefault("litellm_trace_id", session_id)
+            metadata["session_id"] = session_id
+            metadata[SESSION_ID_GENERATED_METADATA_KEY] = True
+        case "reject":
+            raise ProxyException(
+                message=(
+                    "Request has no session id. Send an `x-litellm-session-id` header or `metadata.session_id`. "
+                    "Required by `general_settings.missing_session_id: reject`."
+                ),
+                type=ProxyErrorTypes.bad_request_error,
+                param="session_id",
+                code=400,
+            )
+        case _:
+            verbose_proxy_logger.warning(
+                "Ignoring unknown general_settings.missing_session_id=%r; expected 'generate' or 'reject'", policy
+            )
+
+
 def is_claude_code_user_agent(user_agent: str) -> bool:
     """Claude Code identifies itself as ``claude-cli/<version> ...``; the IDE
     extensions and the Agent SDK run through the same CLI and share that prefix."""
@@ -1818,6 +1867,12 @@ async def add_litellm_data_to_request(
         data=data,
         _metadata_variable_name=_metadata_variable_name,
     )
+    apply_missing_session_id_policy(
+        data=data,
+        _metadata_variable_name=_metadata_variable_name,
+        general_settings=general_settings,
+        request=request,
+    )
 
     # Expose request headers under the metadata field for guardrails (fixes #17477)
     if _metadata_variable_name in data and isinstance(data[_metadata_variable_name], dict):

litellm/proxy/openai_files_endpoints/batch_file_validation.py

@@ -2,7 +2,9 @@ import json
 from collections.abc import Iterator
 from dataclasses import dataclass
 from itertools import chain
-from typing import BinaryIO, Final, NoReturn, assert_never
+from typing import BinaryIO, Final, NoReturn
+
+from typing_extensions import assert_never
 
 from litellm.proxy._types import ProxyException
 

litellm/proxy/openai_files_endpoints/general_upload_validation.py

@@ -8,7 +8,9 @@ extensions, path-traversal filenames) regardless of purpose.
 
 from dataclasses import dataclass
 from pathlib import Path
-from typing import BinaryIO, Final, NoReturn, assert_never
+from typing import BinaryIO, Final, NoReturn
+
+from typing_extensions import assert_never
 
 from litellm.proxy._types import ProxyException
 from litellm.proxy.common_utils.path_utils import safe_filename

litellm/proxy/pass_through_endpoints/llm_passthrough_endpoints.py

@@ -1791,7 +1791,7 @@ def get_vertex_ai_allowed_incoming_headers(request: Request) -> dict:
 
 
 def get_vertex_pass_through_handler(
-    call_type: Literal["discovery", "aiplatform"],  # noqa: UP037
+    call_type: Literal["discovery", "aiplatform"],  # noqa: UP037  # ruff reports quoted Literal values here
 ) -> BaseVertexAIPassThroughHandler:
     if call_type == "discovery":
         return VertexAIDiscoveryPassThroughHandler()
@@ -3021,7 +3021,6 @@ async def handle_gigachat_passthrough_router_model(
     """
     from litellm.proxy.common_request_processing import ProxyBaseLLMRequestProcessing
 
-    # Detect streaming based on request body
     is_streaming: Final = request_body.get("stream", False)  # pyright: ignore[reportUnknownVariableType]  # request_body is dict[Unknown, Unknown]
 
     data: dict[str, Any] = await _read_request_body(
@@ -3057,7 +3056,6 @@ async def handle_gigachat_passthrough_router_model(
     data["json"] = request_body
     data["custom_llm_provider"] = "gigachat"
 
-    # Remove sensitive keys from data
     keys: Final = [  # mutable-ok: list of keys to remove from data
         "gigachat_auth_url",
         "gigachat_access_token",

litellm/proxy/proxy_cli.py

@@ -261,6 +261,7 @@ class ProxyInitializationHelpers:
             "app": "litellm.proxy.proxy_server:app",
             "host": host,
             "port": port,
+            "server_header": False,
         }
         if log_config is not None:
             print(f"Using log_config: {log_config}")

litellm/proxy/public_endpoints/public_endpoints.py

@@ -1,11 +1,13 @@
+import asyncio
 import json
 import os
 import re
-from collections.abc import Awaitable, Mapping, Sequence
+from collections.abc import Awaitable, Callable, Mapping, Sequence
 from importlib.resources import files
 from typing import TYPE_CHECKING, Final, Protocol
 
 from fastapi import APIRouter, HTTPException, Request
+from pydantic import TypeAdapter
 from typing_extensions import ReadOnly, TypedDict
 
 import litellm
@@ -28,6 +30,7 @@ from litellm.types.proxy.management_endpoints.model_management_endpoints import
 )
 from litellm.types.proxy.public_endpoints.public_endpoints import (
     AgentCreateInfo,
+    AutoRouterPresetRecord,
     ComplexityScorerDefaults,
     ProviderCreateInfo,
     PublicModelHubInfo,
@@ -464,6 +467,86 @@ async def get_litellm_blog_posts():
     return BlogPostsResponse(posts=posts)
 
 
+_AUTOROUTER_PRESETS_ADAPTER: Final = TypeAdapter(dict[str, AutoRouterPresetRecord])
+
+
+def _load_bundled_autorouter_presets() -> Mapping[str, AutoRouterPresetRecord]:
+    raw: Final = json.loads(
+        files("litellm.proxy.public_endpoints").joinpath("autorouter_presets.json").read_text(encoding="utf-8")
+    )
+    return _AUTOROUTER_PRESETS_ADAPTER.validate_python(raw)
+
+
+async def _fetch_remote_autorouter_presets(url: str) -> Mapping[str, AutoRouterPresetRecord]:
+    from litellm.llms.custom_httpx.http_handler import get_async_httpx_client
+    from litellm.types.llms.custom_http import httpxSpecialProvider
+
+    client: Final = get_async_httpx_client(llm_provider=httpxSpecialProvider.UI)
+    response: Final = await client.get(url, timeout=5.0)
+    response.raise_for_status()
+    presets: Final = _AUTOROUTER_PRESETS_ADAPTER.validate_python(response.json())
+    if not presets:
+        raise ValueError("remote auto-router preset catalog is empty")
+    return presets
+
+
+async def _resolve_autorouter_presets(
+    url: str,
+    fetch: Callable[[str], Awaitable[Mapping[str, AutoRouterPresetRecord]]],
+) -> Mapping[str, AutoRouterPresetRecord]:
+    if os.getenv("LITELLM_LOCAL_AUTOROUTER_PRESETS", "").lower() == "true":
+        return _load_bundled_autorouter_presets()
+    try:
+        return await fetch(url)
+    except Exception as e:
+        verbose_logger.warning(
+            "LiteLLM: failed to fetch auto-router presets from %s: %s. Serving the bundled catalog for the life of this process.",
+            url,
+            str(e),
+        )
+        return _load_bundled_autorouter_presets()
+
+
+class _AutoRouterPresetsCache:
+    presets: Mapping[str, AutoRouterPresetRecord] | None = None
+    lock: asyncio.Lock | None = None
+
+
+async def get_autorouter_presets(
+    url: str,
+    fetch: Callable[[str], Awaitable[Mapping[str, AutoRouterPresetRecord]]] = _fetch_remote_autorouter_presets,
+) -> Mapping[str, AutoRouterPresetRecord]:
+    cached: Final = _AutoRouterPresetsCache.presets
+    if cached is not None:
+        return cached
+    if _AutoRouterPresetsCache.lock is None:
+        _AutoRouterPresetsCache.lock = asyncio.Lock()
+    async with _AutoRouterPresetsCache.lock:
+        held: Final = _AutoRouterPresetsCache.presets
+        if held is not None:
+            return held
+        resolved: Final = await _resolve_autorouter_presets(url=url, fetch=fetch)
+        _AutoRouterPresetsCache.presets = resolved
+        return resolved
+
+
+@router.get(
+    "/public/autorouter_presets",
+    tags=["public", "auto router"],  # mutable-ok: FastAPI route tags take a list
+    response_model=dict[str, AutoRouterPresetRecord],
+)
+async def get_public_autorouter_presets() -> Mapping[str, AutoRouterPresetRecord]:
+    """
+    Return the auto-router preset catalog the dashboard's template picker renders.
+
+    Resolved once per process, like the model cost map: fetched from ``litellm.autorouter_presets_url``
+    (override with ``LITELLM_AUTOROUTER_PRESETS_URL``) on the first request, falling back to the
+    catalog bundled with the package on any failure. Set ``LITELLM_LOCAL_AUTOROUTER_PRESETS=True``
+    to serve the bundled catalog only. A restart picks up a newly published catalog.
+    """
+    return await get_autorouter_presets(url=litellm.autorouter_presets_url)
+
+
 @router.get(
     "/public/endpoints",
     tags=["public"],

litellm/proxy/rag_endpoints/endpoints.py

@@ -729,7 +729,7 @@ async def rag_query(
         # conflict so callers cannot override the store's provider or credentials.
         managed_store: Final = resolved_stores.get(retrieval_config["vector_store_id"])
         store_data: Final = (
-            await build_request_data_from_managed_vector_store(managed_store)
+            build_request_data_from_managed_vector_store(managed_store)
             if managed_store is not None
             else MappingProxyType({})
         )

litellm/proxy/spend_tracking/spend_management_endpoints.py

@@ -55,6 +55,10 @@ router: Final = APIRouter()
 
 SPEND_LOGS_PAGINATION_COUNT_CAP: Final = 10000
 
+_SESSION_GROUP_KEY_SQL: Final = "COALESCE(NULLIF(session_id, ''), request_id), api_key"
+_MCP_CALL_TYPES_SQL: Final = "('call_mcp_tool', 'list_mcp_tools')"
+_AGENT_CALL_TYPE_SQL: Final = "'asend_message'"
+
 _INTERNAL_HEALTH_CHECK_API_KEYS: Final = (
     LITTELM_INTERNAL_HEALTH_SERVICE_ACCOUNT_NAME,
     hash_token(token=LITTELM_INTERNAL_HEALTH_SERVICE_ACCOUNT_NAME),
@@ -144,21 +148,16 @@ class _DailyTagSpendRow(TypedDict):
     total_spend: float
 
 
-class _SessionCountAggregate(TypedDict):
-    session_id: int
-
-
-class _SessionCountRow(TypedDict):
-    session_id: str
-    _count: _SessionCountAggregate
-
-
 class _SessionSpendRow(TypedDict):
     session_id: str
+    api_key: ReadOnly[str]
+    session_total_count: ReadOnly[int]
     session_total_spend: float
     mcp_tool_call_count: int
     mcp_tool_call_spend: float
     session_cache_hit_count: ReadOnly[int]
+    session_llm_count: ReadOnly[int]
+    session_agent_count: ReadOnly[int]
 
 
 class _SpendSumAggregate(TypedDict, total=False):
@@ -242,18 +241,6 @@ async def _count_spend_logs(prisma_client: PrismaClient, where: Mapping[str, obj
     return await _spend_logs_table(prisma_client).count(where=where)
 
 
-async def _count_logs_per_session(
-    prisma_client: PrismaClient, session_ids: Sequence[str | None]
-) -> Sequence[_SessionCountRow]:
-    """Count spend log rows per session for the given session ids."""
-    rows: Final = await _spend_logs_table(prisma_client).group_by(
-        by=["session_id"],
-        where={"session_id": {"in": session_ids}},
-        count={"session_id": True},
-    )
-    return cast(Sequence[_SessionCountRow], rows)  # cast-ok: group_by(count=) shape is fixed by the by/count args
-
-
 async def _find_team_row(prisma_client: PrismaClient, team_id: str) -> _SupportsModelDump | None:
     """Read a single team row as a Prisma model instance."""
     return await _team_table(prisma_client).find_unique(where={"team_id": team_id})
@@ -2290,6 +2277,10 @@ async def ui_view_spend_logs(
         default=False,
         description="Exclude LiteLLM internal health check requests from results",
     ),
+    group_by_session: bool = fastapi.Query(
+        default=False,
+        description="Paginate over sessions instead of raw logs: one representative row per session, total counts sessions",
+    ),
 ):
     """
     View spend logs with pagination support.
@@ -2644,12 +2635,16 @@ async def ui_view_spend_logs(
         else:
             _order_expr = order_column
 
+        joined_conditions: Final = " AND ".join(sql_conditions)
+        session_grouping: Final = group_by_session is True
+        count_group_clause: Final = f"GROUP BY {_SESSION_GROUP_KEY_SQL}" if session_grouping else ""
         count_query: Final = f"""
             SELECT COUNT(*) AS total_count
             FROM (
                 SELECT 1
                 FROM "LiteLLM_SpendLogs"
-                WHERE {" AND ".join(sql_conditions)}
+                WHERE {joined_conditions}
+                {count_group_clause}
                 LIMIT ${p}
             ) AS bounded_matches
         """
@@ -2660,21 +2655,36 @@ async def ui_view_spend_logs(
         total_is_capped: Final = raw_total > SPEND_LOGS_PAGINATION_COUNT_CAP
         total_records: Final = SPEND_LOGS_PAGINATION_COUNT_CAP if total_is_capped else raw_total
 
-        sql_query: Final = f"""
-            SELECT
-                request_id, call_type, api_key, spend, total_tokens,
+        select_columns: Final = """request_id, call_type, api_key, spend, total_tokens,
                 prompt_tokens, completion_tokens, "startTime", "endTime",
                 "completionStartTime", model, model_id, model_group,
                 custom_llm_provider, api_base, "user", metadata,
                 cache_hit, cache_key, request_tags, team_id,
                 organization_id, end_user, requester_ip_address,
                 session_id, status, mcp_namespaced_tool_name, agent_id,
-                COALESCE(request_duration_ms, (EXTRACT(EPOCH FROM ("endTime" - "startTime")) * 1000)::INTEGER) AS request_duration_ms
+                COALESCE(request_duration_ms, (EXTRACT(EPOCH FROM ("endTime" - "startTime")) * 1000)::INTEGER) AS request_duration_ms"""
+        sql_query: Final = (
+            f"""
+                SELECT * FROM (
+                    SELECT DISTINCT ON ({_SESSION_GROUP_KEY_SQL})
+                        {select_columns}
+                    FROM "LiteLLM_SpendLogs"
+                    WHERE {joined_conditions}
+                    ORDER BY {_SESSION_GROUP_KEY_SQL}, call_type IN {_MCP_CALL_TYPES_SQL}, "startTime" DESC
+                ) AS session_representatives
+                ORDER BY {_order_expr} {_sql_dir}{_nulls_clause}, request_id
+                LIMIT ${p} OFFSET ${p + 1}
+            """
+            if session_grouping
+            else f"""
+            SELECT
+                {select_columns}
             FROM "LiteLLM_SpendLogs"
-            WHERE {" AND ".join(sql_conditions)}
+            WHERE {joined_conditions}
             ORDER BY {_order_expr} {_sql_dir}{_nulls_clause}
             LIMIT ${p} OFFSET ${p + 1}
         """
+        )
         sql_params.extend([page_size, skip])
 
         data: Final = await prisma_client.db.query_raw(sql_query, *sql_params)
@@ -4075,11 +4085,12 @@ async def _build_ui_spend_logs_response(
     Build the paginated response for the UI spend-logs endpoint.
 
     When ``enrich_session_counts`` is ``True`` (the default for the v1/UI
-    endpoint), each row is enriched with ``session_total_count`` so the
-    frontend knows which sessions are expandable (multi-call sessions).
-    For every row that carries a ``session_id``, a single ``GROUP BY`` query
-    fetches the total number of logs in each referenced session.  Rows without
-    a ``session_id`` default to ``1``.
+    endpoint), each row is enriched with ``session_total_count`` plus spend
+    and call-type aggregates so the frontend knows which sessions are
+    expandable (multi-call sessions).  One ``GROUP BY (session_id, api_key)``
+    query serves every referenced session, keyed per api key so two callers
+    reusing a session id never see each other's totals.  Rows without a
+    ``session_id`` default to ``1``.
 
     When ``enrich_session_counts`` is ``False`` (v2 endpoint), rows are
     serialised without the extra query.
@@ -4101,7 +4112,6 @@ async def _build_ui_spend_logs_response(
         A dict with ``data`` (enriched rows), ``total``, ``page``,
         ``page_size``, ``total_pages``, and ``total_is_capped``.
     """
-    count_map: dict[str, int] = {}
     if enrich_session_counts:
         session_ids: Final[Sequence[str | None]] = list(
             {
@@ -4110,15 +4120,8 @@ async def _build_ui_spend_logs_response(
                 if (row.get("session_id") if isinstance(row, dict) else getattr(row, "session_id", None))
             }
         )
-        if session_ids:
-            # NOTE: This GROUP BY runs on every v1/UI page load. The IN clause
-            # is bounded by page_size (typically 25-50 distinct session IDs).
-            # If performance degrades at scale, consider short-lived caching or
-            # folding the count into the main query via a window function.
-            counts: Final = await _count_logs_per_session(prisma_client, session_ids)
-            count_map = {r["session_id"]: r["_count"]["session_id"] for r in counts if r.get("session_id")}
 
-    session_spend_map: dict[str, dict[str, int | float]] = {}
+    session_spend_map: dict[tuple[str, str], dict[str, int | float]] = {}
     if enrich_session_counts and session_ids:
         from prisma.errors import PrismaError
 
@@ -4130,38 +4133,46 @@ async def _build_ui_spend_logs_response(
                 {
                     (row.get("api_key") if isinstance(row, dict) else getattr(row, "api_key", None))
                     for row in data
-                    if (row.get("api_key") if isinstance(row, dict) else getattr(row, "api_key", None))
+                    if (row.get("api_key") if isinstance(row, dict) else getattr(row, "api_key", None)) is not None
                 }
             )
             rows: Final[Sequence[_SessionSpendRow]] = await _query_raw(
                 prisma_client,
-                """
-                SELECT session_id,
+                f"""
+                SELECT session_id, api_key,
+                       COUNT(*)::int AS session_total_count,
                        COALESCE(SUM(spend), 0)::double precision AS session_total_spend,
                        COUNT(*) FILTER (
-                           WHERE call_type IN ('call_mcp_tool', 'list_mcp_tools')
+                           WHERE call_type IN {_MCP_CALL_TYPES_SQL}
                        )::int AS mcp_tool_call_count,
                        COALESCE(SUM(spend) FILTER (
-                           WHERE call_type IN ('call_mcp_tool', 'list_mcp_tools')
+                           WHERE call_type IN {_MCP_CALL_TYPES_SQL}
                        ), 0)::double precision AS mcp_tool_call_spend,
-                       COUNT(*) FILTER (WHERE LOWER(cache_hit) = 'true')::int AS session_cache_hit_count
+                       COUNT(*) FILTER (WHERE LOWER(cache_hit) = 'true')::int AS session_cache_hit_count,
+                       COUNT(*) FILTER (
+                           WHERE call_type NOT IN {_MCP_CALL_TYPES_SQL} AND call_type != {_AGENT_CALL_TYPE_SQL}
+                       )::int AS session_llm_count,
+                       COUNT(*) FILTER (WHERE call_type = {_AGENT_CALL_TYPE_SQL})::int AS session_agent_count
                 FROM "LiteLLM_SpendLogs"
                 WHERE session_id = ANY($1::text[])
                   AND api_key = ANY($2::text[])
-                GROUP BY session_id
+                GROUP BY session_id, api_key
                 """,
                 session_ids,
                 authorized_api_keys,
             )
             session_spend_map = {
-                row["session_id"]: {
+                (row["session_id"], row["api_key"]): {
+                    "session_total_count": int(row.get("session_total_count") or 0),
                     "session_total_spend": float(row.get("session_total_spend") or 0.0),
                     "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),
                     "session_cache_hit_count": int(row.get("session_cache_hit_count") or 0),
+                    "session_llm_count": int(row.get("session_llm_count") or 0),
+                    "session_agent_count": int(row.get("session_agent_count") or 0),
                 }
                 for row in rows
-                if row.get("session_id")
+                if row.get("session_id") and row.get("api_key") is not None
             }
         except PrismaError:
             verbose_proxy_logger.debug(
@@ -4174,14 +4185,17 @@ async def _build_ui_spend_logs_response(
         for row in data:
             row_dict = dict(row) if isinstance(row, dict) else row.model_dump()
             sid = row_dict.get("session_id")
-            row_dict["session_total_count"] = count_map.get(sid, 1) if sid else 1
-            session_stats = session_spend_map.get(sid) if sid else None
+            row_api_key = row_dict.get("api_key")
+            session_stats = session_spend_map.get((sid, row_api_key)) if sid and row_api_key is not None else None
+            row_dict["session_total_count"] = int(session_stats["session_total_count"]) if session_stats else 1
             if session_stats:
                 row_dict["session_total_spend"] = session_stats["session_total_spend"]
                 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"]
                 row_dict["session_cache_hit_count"] = session_stats["session_cache_hit_count"]
+                row_dict["session_llm_count"] = session_stats["session_llm_count"]
+                row_dict["session_agent_count"] = session_stats["session_agent_count"]
             enriched.append(row_dict)
         response_data: list = enriched
     else:

litellm/proxy/vector_store_endpoints/endpoints.py

@@ -16,9 +16,6 @@ from litellm.proxy._types import CommonProxyErrors, UserAPIKeyAuth
 from litellm.proxy.auth.user_api_key_auth import user_api_key_auth
 from litellm.proxy.common_request_processing import ProxyBaseLLMRequestProcessing
 from litellm.proxy.utils import jsonify_object
-from litellm.proxy.vector_store_endpoints.management_endpoints import (
-    _resolve_embedding_config,
-)
 from litellm.proxy.vector_store_endpoints.utils import (
     assert_proxy_admin_for_vector_store_index_management,
     assert_user_can_access_vector_store,
@@ -57,19 +54,9 @@ def reject_caller_embedding_selection_params(payload: Mapping[str, object], sour
 ########################################################
 
 
-async def build_request_data_from_managed_vector_store(
+def build_request_data_from_managed_vector_store(
     vector_store: LiteLLM_ManagedVectorStore,
 ) -> Mapping[str, object]:
-    """
-    Build request params (provider, credential ref, litellm_params) from an
-    already-resolved managed vector store.
-
-    ``litellm_embedding_config`` is resolved here, at request-handling time,
-    instead of at row-creation time: the resolved api_key/api_base/api_version
-    lives only in the returned per-request mapping and is never persisted back
-    to the registry cache. Legacy rows that already carry a resolved
-    (cleartext) config skip the lookup and pass through unchanged.
-    """
     top_level: Final = MappingProxyType(
         {
             key: vector_store.get(key)
@@ -78,18 +65,7 @@ async def build_request_data_from_managed_vector_store(
         }
     )
     litellm_params: Final = vector_store.get("litellm_params") or MappingProxyType({})
-    embedding_model: Final = litellm_params.get("litellm_embedding_model")
-    if not embedding_model or litellm_params.get("litellm_embedding_config"):
-        return MappingProxyType({**top_level, **litellm_params})
-
-    from litellm.proxy.proxy_server import prisma_client
-
-    resolved_config: Final = await _resolve_embedding_config(
-        embedding_model=embedding_model, prisma_client=prisma_client
-    )
-    if not resolved_config:
-        return MappingProxyType({**top_level, **litellm_params})
-    return MappingProxyType({**top_level, **litellm_params, "litellm_embedding_config": resolved_config})
+    return MappingProxyType({**top_level, **litellm_params})
 
 
 async def _update_request_data_with_litellm_managed_vector_store_registry(
@@ -118,7 +94,7 @@ async def _update_request_data_with_litellm_managed_vector_store_registry(
             vector_store=vector_store_to_run,
             user_api_key_dict=user_api_key_dict,
         )
-    return {**data, **(await build_request_data_from_managed_vector_store(vector_store_to_run))}
+    return {**data, **build_request_data_from_managed_vector_store(vector_store_to_run)}
 
 
 @router.post(

litellm/proxy/vector_store_endpoints/management_endpoints.py

@@ -18,11 +18,8 @@ if TYPE_CHECKING:
     from prisma.models import LiteLLM_ManagedVectorStoresTable as _VectorStoreRow
 
     from litellm.proxy.utils import PrismaClient
-    from litellm.router import Router
-
 import litellm
 from litellm._logging import verbose_proxy_logger
-from litellm.caching.in_memory_cache import InMemoryCache
 from litellm.constants import REDACTED_BY_LITELM_STRING
 from litellm.litellm_core_utils.safe_json_dumps import safe_dumps
 from litellm.litellm_core_utils.sensitive_data_masker import SensitiveDataMasker
@@ -32,13 +29,10 @@ from litellm.proxy._types import (
     UserAPIKeyAuth,
 )
 from litellm.proxy.auth.user_api_key_auth import user_api_key_auth
-from litellm.proxy.common_utils.encrypt_decrypt_utils import decrypt_value_helper
 from litellm.proxy.common_utils.rbac_utils import check_feature_access_for_user
 from litellm.proxy.vector_store_endpoints.utils import can_user_access_vector_store
-from litellm.repositories.model_repository import ModelRepository
 from litellm.repositories.prisma_protocols import TableActions
 from litellm.repositories.table_repositories import ManagedVectorStoresRepository
-from litellm.secret_managers.main import get_secret
 from litellm.types.vector_stores import (
     LiteLLM_ManagedVectorStore,
     LiteLLM_ManagedVectorStoreListResponse,
@@ -64,28 +58,6 @@ _LITELLM_PARAMS_MASKER: Final = SensitiveDataMasker()
 
 _REDACT_LITELLM_PARAMS_MAX_DEPTH: Final = 10
 
-# Use-time embedding-config resolution runs on every vector-store request
-# whose persisted row carries only a model reference (the post-fix shape).
-# Without a cache, that's one ``litellm_proxymodeltable.find_first`` per
-# request — the no-DB-in-critical-path rule. Hold the resolved config in
-# memory for a short TTL so a hot model name pays the DB lookup at most
-# once per ``_EMBEDDING_CONFIG_CACHE_TTL`` seconds. Cleartext credentials
-# only ever live in process memory (never persisted, never echoed in
-# management responses), so the cache doesn't widen the disclosure surface.
-_EMBEDDING_CONFIG_CACHE_TTL: Final = 60
-_EMBEDDING_CONFIG_CACHE_MAX_SIZE: Final = 256
-_embedding_config_cache: InMemoryCache | None = None
-
-
-def _get_embedding_config_cache() -> InMemoryCache:
-    global _embedding_config_cache
-    if _embedding_config_cache is None:
-        _embedding_config_cache = InMemoryCache(
-            max_size_in_memory=_EMBEDDING_CONFIG_CACHE_MAX_SIZE,
-            default_ttl=_EMBEDDING_CONFIG_CACHE_TTL,
-        )
-    return _embedding_config_cache
-
 
 def _redact_sensitive_litellm_params(litellm_params: Any, _depth: int = 0) -> Any:
     """
@@ -155,235 +127,6 @@ async def _fetch_and_authorize_vector_store(
     return typed
 
 
-def _resolve_embedding_config_from_router(embedding_model: str, llm_router) -> dict[str, object] | None:
-    """
-    Resolve embedding config from router's config-defined models.
-
-    Config-defined models (from proxy_config.yaml) are stored in the router's model_list,
-    not in the database. This function looks up the model in the router and extracts
-    api_key, api_base, and api_version from the deployment's litellm_params.
-
-    Args:
-        embedding_model: The embedding model string (e.g., "text-embedding-ada-002" or "azure/text-embedding-3-large")
-        llm_router: The LiteLLM router instance
-
-    Returns:
-        Dictionary with api_key, api_base, and api_version if model found, None otherwise
-    """
-    if not embedding_model or llm_router is None:
-        return None
-
-    # Extract model name candidates - could be "text-embedding-ada-002" or "azure/text-embedding-3-large"
-    # Try exact match first, then try without provider prefix
-    model_name_candidates: Final = [embedding_model]
-    if "/" in embedding_model:
-        # If it has a provider prefix, also try without it
-        _, model_name = embedding_model.split("/", 1)
-        model_name_candidates.append(model_name)
-
-    # Try to find model in router
-    for model_name in model_name_candidates:
-        try:
-            # Try to get deployment by model group name (model_name in config)
-            deployment = llm_router.get_deployment_by_model_group_name(model_group_name=model_name)
-
-            if deployment is not None and deployment.litellm_params is not None:
-                litellm_params = deployment.litellm_params
-
-                # Build embedding config from model params
-                embedding_config: dict[str, object] = {}
-
-                # Extract api_key
-                api_key = getattr(litellm_params, "api_key", None)
-                if api_key:
-                    # Handle os.environ/ prefix
-                    if isinstance(api_key, str) and api_key.startswith("os.environ/"):
-                        api_key = get_secret(api_key)
-                    embedding_config["api_key"] = api_key
-
-                # Extract api_base
-                api_base = getattr(litellm_params, "api_base", None)
-                if api_base:
-                    # Handle os.environ/ prefix
-                    if isinstance(api_base, str) and api_base.startswith("os.environ/"):
-                        api_base = get_secret(api_base)
-                    embedding_config["api_base"] = api_base
-
-                # Extract api_version
-                api_version = getattr(litellm_params, "api_version", None)
-                if api_version:
-                    embedding_config["api_version"] = api_version
-
-                project_id = getattr(litellm_params, "project_id", None)
-                if project_id:
-                    embedding_config["project_id"] = project_id
-
-                # Only return config if we have at least api_key or api_base
-                if embedding_config:
-                    verbose_proxy_logger.debug(
-                        "Resolved embedding config from router model %s: %s", model_name, list(embedding_config.keys())
-                    )
-                    return embedding_config
-        except Exception as e:
-            verbose_proxy_logger.debug("Error resolving embedding config from router for model %s: %s", model_name, e)
-            continue
-
-    return None
-
-
-async def _resolve_embedding_config_from_db(
-    embedding_model: str, prisma_client: "PrismaClient"
-) -> dict[str, object] | None:
-    """
-    Resolve embedding config from database model configuration.
-
-    If litellm_embedding_model is provided but litellm_embedding_config is not,
-    this function looks up the model in the database and extracts api_key, api_base,
-    and api_version from the model's litellm_params to build the embedding config.
-
-    Args:
-        embedding_model: The embedding model string (e.g., "text-embedding-ada-002" or "azure/text-embedding-3-large")
-        prisma_client: The Prisma client instance
-
-    Returns:
-        Dictionary with api_key, api_base, and api_version if model found, None otherwise
-    """
-    if not embedding_model:
-        return None
-
-    # Extract model name - could be "text-embedding-ada-002" or "azure/text-embedding-3-large"
-    # Try to find model by exact match first, then try without provider prefix
-    model_name_candidates: Final = [embedding_model]
-    if "/" in embedding_model:
-        # If it has a provider prefix, also try without it
-        _, model_name = embedding_model.split("/", 1)
-        model_name_candidates.append(model_name)
-
-    # Try to find model in database
-    for model_name in model_name_candidates:
-        try:
-            db_model = await ModelRepository(prisma_client).table.find_first(where={"model_name": model_name})
-
-            if db_model and db_model.litellm_params:
-                # Extract litellm_params (could be dict or JSON string)
-                model_params = db_model.litellm_params
-                if isinstance(model_params, str):  # pyright: ignore[reportUnnecessaryIsInstance]  # prisma Json is str
-                    model_params = json.loads(model_params)
-
-                # Decrypt values from database (similar to how proxy_server.py does it)
-                # Values stored in DB are encrypted, so we need to decrypt them first
-                decrypted_params = {}
-                if isinstance(model_params, dict):
-                    for k, v in model_params.items():
-                        if isinstance(v, str):
-                            # Decrypt value - returns original value if decryption fails or no key is set
-                            decrypted_value = decrypt_value_helper(value=v, key=k, return_original_value=True)
-                            decrypted_params[k] = decrypted_value
-                        else:
-                            decrypted_params[k] = v
-                else:
-                    decrypted_params = model_params
-
-                # Build embedding config from model params
-                embedding_config = {}
-
-                # Extract api_key
-                api_key = decrypted_params.get("api_key")
-                if api_key:
-                    # Handle os.environ/ prefix (after decryption, values may be os.environ/ prefixed)
-                    if isinstance(api_key, str) and api_key.startswith("os.environ/"):
-                        api_key = get_secret(api_key)
-                    embedding_config["api_key"] = api_key
-
-                # Extract api_base
-                api_base = decrypted_params.get("api_base")
-                if api_base:
-                    # Handle os.environ/ prefix (after decryption, values may be os.environ/ prefixed)
-                    if isinstance(api_base, str) and api_base.startswith("os.environ/"):
-                        api_base = get_secret(api_base)
-                    embedding_config["api_base"] = api_base
-
-                # Extract api_version
-                api_version = decrypted_params.get("api_version")
-                if api_version:
-                    embedding_config["api_version"] = api_version
-
-                # Only return config if we have at least api_key or api_base
-                if embedding_config:
-                    verbose_proxy_logger.debug(
-                        "Resolved embedding config from database model %s: %s",
-                        model_name,
-                        list(embedding_config.keys()),
-                    )
-                    return embedding_config
-        except Exception as e:
-            verbose_proxy_logger.debug("Error resolving embedding config for model %s: %s", model_name, e)
-            continue
-
-    return None
-
-
-async def _resolve_embedding_config(
-    embedding_model: str, prisma_client: "PrismaClient | None", llm_router: "Router | None" = None
-) -> dict[str, object] | None:
-    """
-    Resolve embedding config from either router (config-defined) or database models.
-
-    This function first checks the router for config-defined models, then falls back
-    to the database. This allows users to use models defined in either location.
-
-    Results are cached in process memory for ``_EMBEDDING_CONFIG_CACHE_TTL``
-    seconds so the request-handling path doesn't hit the database on every
-    vector-store call. Negative results (model not found) are intentionally
-    not cached to avoid blocking a freshly-added model behind the TTL.
-
-    Args:
-        embedding_model: The embedding model string (e.g., "text-embedding-ada-002" or "azure/text-embedding-3-large")
-        prisma_client: The Prisma client instance
-        llm_router: The LiteLLM router instance (optional, will be imported if not provided)
-
-    Returns:
-        Dictionary with api_key, api_base, and api_version if model found, None otherwise
-    """
-    if not embedding_model:
-        return None
-
-    cache: Final = _get_embedding_config_cache()
-    cached: Final = cache.get_cache(embedding_model)
-    if cached is not None:
-        return cached
-
-    # Import llm_router if not provided
-    if llm_router is None:
-        try:
-            from litellm.proxy.proxy_server import llm_router
-        except ImportError:
-            llm_router = None
-
-    # First try to resolve from router (config-defined models)
-    if llm_router is not None:
-        router_config = _resolve_embedding_config_from_router(embedding_model=embedding_model, llm_router=llm_router)
-        if router_config:
-            verbose_proxy_logger.debug("Resolved embedding config from router for model %s", embedding_model)
-            cache.set_cache(embedding_model, router_config)
-            return router_config
-
-    # Fall back to database
-    if prisma_client is not None:
-        db_config: Final = await _resolve_embedding_config_from_db(
-            embedding_model=embedding_model, prisma_client=prisma_client
-        )
-        if db_config:
-            verbose_proxy_logger.debug("Resolved embedding config from database for model %s", embedding_model)
-            cache.set_cache(embedding_model, db_config)
-            return db_config
-
-    verbose_proxy_logger.debug(
-        "Could not resolve embedding config for model %s from router or database", embedding_model
-    )
-    return None
-
-
 ########################################################
 # Helper Functions
 ########################################################
@@ -469,10 +212,9 @@ async def create_vector_store_in_db(
     # (``api_key``, ``api_base``, ``api_version``) into this row. That
     # exposed every env-stored embedding-model credential on the
     # ``/vector_store/{new,info,update,list}`` responses. Keep the user's
-    # raw ``litellm_embedding_model`` reference; resolution now happens in
-    # ``build_request_data_from_managed_vector_store``
-    # at request-handling time so the cleartext config exists only in
-    # per-request memory and never reaches the database.
+    # raw ``litellm_embedding_model`` reference; each search embeds the
+    # query through the router at request time, so the credentials stay
+    # on the deployment and never reach the database.
     if litellm_params:
         litellm_params_dict: Final = GenericLiteLLMParams(**litellm_params).model_dump(exclude_none=True)
         data_to_create["litellm_params"] = safe_dumps(litellm_params_dict)
@@ -862,11 +604,9 @@ async def update_vector_store(
 
         # Handle litellm_params if provided. As with the create path, the
         # embedding-config auto-resolve previously persisted cleartext
-        # credentials into the row; resolution now happens at request-
-        # handling time in
-        # ``build_request_data_from_managed_vector_store``
-        # so this row only ever stores the user-supplied
-        # ``litellm_embedding_model`` reference.
+        # credentials into the row; each search now embeds the query
+        # through the router at request time, so this row only ever stores
+        # the user-supplied ``litellm_embedding_model`` reference.
         if "litellm_params" in update_data:
             _input_litellm_params: Final[dict] = update_data.get("litellm_params", {}) or {}
             litellm_params_dict: Final = GenericLiteLLMParams(**_input_litellm_params).model_dump(exclude_none=True)

litellm/responses/litellm_completion_transformation/transformation.py

@@ -6,6 +6,7 @@ import json
 import re
 import uuid
 from collections.abc import Iterable, Iterator, Mapping, Sequence
+from dataclasses import dataclass
 from types import MappingProxyType
 from typing import (
     TYPE_CHECKING,
@@ -102,6 +103,15 @@ from .custom_tools import (
 NamespaceNameMap: TypeAlias = Mapping[str, tuple[str, str]]
 NamespaceTool: TypeAlias = Mapping[str, object]
 ResponseTools: TypeAlias = Sequence[Mapping[str, object]] | None
+ChatToolParam: TypeAlias = ChatCompletionToolParam | OpenAIMcpServerTool
+NAMESPACE_DESCRIPTION_SEPARATOR: Final = "\n\n"
+
+
+@dataclass(frozen=True, slots=True)
+class ResponsesToolChatForm:
+    chat_tools: tuple[ChatToolParam, ...]
+    web_search_options: OpenAIWebSearchOptions | None
+
 
 if TYPE_CHECKING:
     from openai.types.responses.response_apply_patch_tool_call import (
@@ -1771,7 +1781,7 @@ class LiteLLMCompletionResponsesConfig:
         tool_name: Final = str(namespace_tool.get("name") or "")
         raw_description: Final = str(namespace_tool.get("description") or "")
         description: Final = (
-            f"{namespace_description}\n\n{raw_description}"
+            f"{namespace_description}{NAMESPACE_DESCRIPTION_SEPARATOR}{raw_description}"
             if nested and namespace_description and raw_description
             else namespace_description
             if nested and namespace_description
@@ -1837,9 +1847,78 @@ class LiteLLMCompletionResponsesConfig:
                 + ", ".join(sorted(conflicting_tool_names))
             )
 
+    @staticmethod
+    def _responses_tool_to_chat_form(tool: Mapping[str, object]) -> ResponsesToolChatForm:
+        tool_type: Final = tool.get("type")
+        if tool_type == "mcp":
+            return ResponsesToolChatForm(chat_tools=(cast(OpenAIMcpServerTool, tool),), web_search_options=None)
+        if tool_type == "web_search_preview" or tool_type == "web_search":
+            _search_context_size: Final[Literal["low", "medium", "high"]] = cast(
+                Literal["low", "medium", "high"], tool.get("search_context_size")
+            )
+            _user_location: Final[OpenAIWebSearchUserLocation | None] = cast(
+                OpenAIWebSearchUserLocation | None,
+                tool.get("user_location") or None,
+            )
+            return ResponsesToolChatForm(
+                chat_tools=(),
+                web_search_options=OpenAIWebSearchOptions(
+                    search_context_size=_search_context_size,
+                    user_location=_user_location,
+                ),
+            )
+        if tool_type == "function":
+            typed_tool: Final = cast(FunctionToolParam, tool)
+            raw_parameters: Final = typed_tool.get("parameters", {}) or {}
+            parameters: Final = (
+                {**raw_parameters}  # mutable-ok: json.dumps rejects MappingProxyType
+                if "type" in raw_parameters
+                else {**raw_parameters, "type": "object"}  # mutable-ok: json.dumps rejects MappingProxyType
+            )
+            chat_completion_tool: Final[dict[str, object]] = {
+                "type": "function",
+                "function": {
+                    "name": typed_tool.get("name") or "",
+                    "description": typed_tool.get("description") or "",
+                    "parameters": parameters,
+                    "strict": typed_tool.get("strict", False) or False,
+                },
+            }
+            if tool.get("cache_control"):
+                chat_completion_tool["cache_control"] = tool.get("cache_control")
+            if tool.get("defer_loading"):
+                chat_completion_tool["defer_loading"] = tool.get("defer_loading")
+            if tool.get("allowed_callers"):
+                chat_completion_tool["allowed_callers"] = tool.get("allowed_callers")
+            if tool.get("input_examples"):
+                chat_completion_tool["input_examples"] = tool.get("input_examples")
+            return ResponsesToolChatForm(
+                chat_tools=(cast(ChatCompletionToolParam, chat_completion_tool),), web_search_options=None
+            )
+        if tool_type == "namespace":
+            return ResponsesToolChatForm(
+                chat_tools=LiteLLMCompletionResponsesConfig._namespace_chat_tools(tool), web_search_options=None
+            )
+        if tool_type == "custom":
+            converted: Final = convert_custom_tool_to_function_tool(tool)
+            return ResponsesToolChatForm(chat_tools=() if converted is None else (converted,), web_search_options=None)
+        if tool_type in ("computer_use", "image_generation", "shell"):
+            verbose_logger.warning(
+                "Dropping Responses API tool of type '%s': it has no Chat Completions "
+                "equivalent and the target provider would reject the request.",
+                tool_type,
+            )
+            return ResponsesToolChatForm(chat_tools=(), web_search_options=None)
+        return ResponsesToolChatForm(chat_tools=(cast(ChatToolParam, tool),), web_search_options=None)
+
+    @staticmethod
+    def responses_tools_to_chat_forms(tools: ResponseTools) -> tuple[ResponsesToolChatForm, ...]:
+        LiteLLMCompletionResponsesConfig._validate_namespace_name_collisions(tools)
+        return tuple(LiteLLMCompletionResponsesConfig._responses_tool_to_chat_form(tool) for tool in tools or ())
+
     @staticmethod
     def transform_responses_api_tools_to_chat_completion_tools(
-        tools: list[FunctionToolParam | OpenAIMcpServerTool] | None,
+        tools: ResponseTools,
     ) -> tuple[
         list[ChatCompletionToolParam | OpenAIMcpServerTool],
         OpenAIWebSearchOptions | None,
@@ -1849,73 +1928,16 @@ class LiteLLMCompletionResponsesConfig:
         """
         if tools is None:
             return [], None
-        LiteLLMCompletionResponsesConfig._validate_namespace_name_collisions(tools)
-        chat_completion_tools: Final[list[ChatCompletionToolParam | OpenAIMcpServerTool]] = []
-        web_search_options: OpenAIWebSearchOptions | None = None
-        for tool in tools:
-            if tool.get("type") == "mcp":
-                chat_completion_tools.append(cast(OpenAIMcpServerTool, tool))
-            elif tool.get("type") == "web_search_preview" or tool.get("type") == "web_search":
-                _search_context_size: Literal["low", "medium", "high"] = cast(
-                    Literal["low", "medium", "high"], tool.get("search_context_size")
-                )
-                _user_location: OpenAIWebSearchUserLocation | None = cast(
-                    OpenAIWebSearchUserLocation | None,
-                    tool.get("user_location") or None,
-                )
-                web_search_options = OpenAIWebSearchOptions(
-                    search_context_size=_search_context_size,
-                    user_location=_user_location,
-                )
-            elif tool.get("type") == "function":
-                typed_tool = cast(FunctionToolParam, tool)
-                # Ensure parameters has "type": "object" as required by providers like Anthropic
-                parameters = dict(typed_tool.get("parameters", {}) or {})
-                if not parameters or "type" not in parameters:
-                    parameters["type"] = "object"
-                chat_completion_tool: dict[str, object] = {
-                    "type": "function",
-                    "function": {
-                        "name": typed_tool.get("name") or "",
-                        "description": typed_tool.get("description") or "",
-                        "parameters": parameters,
-                        "strict": typed_tool.get("strict", False) or False,
-                    },
-                }
-                if tool.get("cache_control"):
-                    chat_completion_tool["cache_control"] = tool.get("cache_control")
-                if tool.get("defer_loading"):
-                    chat_completion_tool["defer_loading"] = tool.get("defer_loading")
-                if tool.get("allowed_callers"):
-                    chat_completion_tool["allowed_callers"] = tool.get("allowed_callers")
-                if tool.get("input_examples"):
-                    chat_completion_tool["input_examples"] = tool.get("input_examples")
-                chat_completion_tools.append(cast(ChatCompletionToolParam, chat_completion_tool))
-            elif tool.get("type") == "namespace":
-                chat_completion_tools.extend(LiteLLMCompletionResponsesConfig._namespace_chat_tools(tool))
-            elif tool.get("type") == "custom":
-                converted = convert_custom_tool_to_function_tool(tool)
-                if converted is not None:
-                    chat_completion_tools.append(converted)
-            else:
-                _tool_type = tool.get("type")
-                if _tool_type in ("computer_use", "image_generation", "shell"):
-                    # Drop unsupported Responses-API-only tool types that have no
-                    # Chat Completions equivalent. Passing them through verbatim
-                    # causes providers to reject the request with "'function' is a
-                    # required property".
-                    verbose_logger.warning(
-                        "Dropping Responses API tool of type '%s': it has no Chat Completions "
-                        "equivalent and the target provider would reject the request.",
-                        _tool_type,
-                    )
-                    continue
-                chat_completion_tools.append(cast(ChatCompletionToolParam | OpenAIMcpServerTool, tool))
-        return chat_completion_tools, web_search_options
+        forms: Final = LiteLLMCompletionResponsesConfig.responses_tools_to_chat_forms(tools)
+        web_search_options: Final = next(
+            (form.web_search_options for form in reversed(forms) if form.web_search_options is not None),
+            None,
+        )
+        return [chat_tool for form in forms for chat_tool in form.chat_tools], web_search_options
 
     @staticmethod
     def transform_chat_completion_tool_params_to_responses_api_tools(
-        chat_completion_tools: list[ChatCompletionToolParam | OpenAIMcpServerTool] | None,
+        chat_completion_tools: Sequence[Mapping[str, object]] | None,
     ) -> list[dict[str, object]]:
         """
         Transform Chat Completion tool params (e.g. from guardrail output) back to
@@ -1926,9 +1948,6 @@ class LiteLLMCompletionResponsesConfig:
             return []
         result: Final[list[dict[str, object]]] = []
         for tool in chat_completion_tools:
-            if not isinstance(tool, dict):
-                result.append(tool)
-                continue
             if tool.get("type") == "function":
                 fn = cast(_ToolFunctionDefinition, tool.get("function") or {})
                 parameters = dict(fn.get("parameters", {}) or {})

litellm/responses/streaming_iterator.py

@@ -17,6 +17,7 @@ from typing_extensions import TypeIs
 
 import litellm
 from litellm.constants import (
+    EMPTY_MAPPING,
     LITELLM_MAX_STREAMING_DURATION_SECONDS,
     STREAM_SSE_DONE_STRING,
 )
@@ -273,6 +274,9 @@ class BaseResponsesAPIStreamingIterator:
         self._hidden_params["additional_headers"] = process_response_headers(
             self.response.headers or {}
         )  # GUARANTEE OPENAI HEADERS IN RESPONSE
+        self._raw_response_headers: Mapping[str, str] = MappingProxyType(
+            dict(self.response.headers or {})  # mutable-ok: immediately frozen by MappingProxyType
+        )
 
     def _check_max_streaming_duration(self) -> None:
         """Raise litellm.Timeout if the stream has exceeded LITELLM_MAX_STREAMING_DURATION_SECONDS."""
@@ -446,6 +450,7 @@ class BaseResponsesAPIStreamingIterator:
             except Exception:
                 # Fallback to original if serialization fails
                 pass
+        self._restore_provider_response_headers(logging_response)
 
         end_time: Final = datetime.now()
         if is_async:
@@ -480,6 +485,41 @@ class BaseResponsesAPIStreamingIterator:
             )
         self._run_post_success_hooks(end_time=end_time)
 
+    def _restore_provider_response_headers(self, logging_response: object) -> None:
+        """Re-apply the provider's response headers to the copy handed to logging callbacks.
+
+        ``model_validate(model_dump())`` above drops pydantic private attributes, so the
+        ``_hidden_params`` the provider transform set on the nested response are lost. Returns early
+        when that copy fell back to the original event, so logging-only state never lands on the
+        object the caller is iterating.
+        """
+        if logging_response is self.completed_response:
+            return
+        target: Final[object] = getattr(logging_response, "response", None)
+        existing_hidden: Final[object] = getattr(target, "_hidden_params", None)
+        if not isinstance(existing_hidden, Mapping):
+            return
+        existing: Final[Mapping[str, object]] = existing_hidden
+        source_hidden: Final[object] = getattr(
+            getattr(self.completed_response, "response", None), "_hidden_params", None
+        )
+        source: Final[Mapping[str, object]] = source_hidden if isinstance(source_hidden, Mapping) else EMPTY_MAPPING
+        processed: Final[object] = source.get("additional_headers") or self._hidden_params.get("additional_headers")
+        raw: Final[object] = source.get("headers") or self._raw_response_headers
+        headers: Final[Mapping[str, object]] = processed if isinstance(processed, Mapping) else EMPTY_MAPPING
+        raw_headers: Final[Mapping[str, object]] = raw if isinstance(raw, Mapping) else EMPTY_MAPPING
+        # rebuild by value and let existing keys win: sharing the source dicts would alias what the proxy
+        # splats into the client's HTTP headers, and copying non-header keys would carry response_cost
+        setattr(  # noqa: B010  # target is typed object here, so a plain attribute store does not type check
+            target,
+            "_hidden_params",
+            {  # mutable-ok: the cost calculator writes optional_params into _hidden_params
+                "additional_headers": {**headers},  # mutable-ok: fresh copy, logging callbacks may mutate it
+                "headers": {**raw_headers},  # mutable-ok: fresh copy, logging callbacks may mutate it
+                **existing,
+            },
+        )
+
     def _handle_logging_completed_response(self):
         """Base implementation - should be overridden by subclasses"""
 

litellm/router.py

@@ -85,6 +85,10 @@ from litellm.litellm_core_utils.sensitive_data_masker import (
     mask_credentials_in_payload,
     mask_sensitive_structure,
 )
+from litellm.llms.base_llm.vector_store.transformation import (
+    RouterVectorStoreEmbeddingExecutor,
+    vector_store_request_metadata,
+)
 from litellm.llms.openai_like.json_loader import JSONProviderRegistry
 from litellm.router_strategy.budget_limiter import RouterBudgetLimiting
 from litellm.router_strategy.least_busy import LeastBusyLoggingHandler
@@ -146,8 +150,10 @@ from litellm.router_utils.fallback_event_handlers import (
     _check_non_standard_fallback_format,
     clear_pre_routing_selection,
     fallback_lookup_groups,
+    fallbacks_disabled_for_request,
     get_fallback_model_group_for_lookup_groups,
     get_pre_routing_selection,
+    record_disable_fallbacks,
     record_pre_routing_selection,
     run_async_fallback,
 )
@@ -355,6 +361,8 @@ _PreRoutingStrategyT = TypeVar("_PreRoutingStrategyT")
 
 _ALIAS_PARAMS_NEVER_FORWARDED: Final = frozenset({"model", "api_base", "api_key", "api_version"})
 _ALIAS_MARKER_FORWARDED_PARAMS_KWARG: Final = "_alias_marker_forwarded_params"
+_CLAUDE_CODE_SESSION_ID_RE: Final = re.compile(r"^[a-zA-Z0-9_\-]{8,}$")
+_CLAUDE_CODE_SESSION_ROUTER_TTL_SECONDS: Final = 3600
 
 _RUNTIME_TOGGLEABLE_PRE_CALL_CHECKS: Final[Mapping[str, type[CustomLogger]]] = MappingProxyType(
     {
@@ -811,6 +819,10 @@ class Router:
         self.cache = DualCache(
             redis_cache=redis_cache, in_memory_cache=InMemoryCache()
         )  # use a dual cache (Redis+In-Memory) for tracking cooldowns, usage, etc.
+        self._claude_code_session_router_cache: DualCache = DualCache(
+            redis_cache=redis_cache,
+            in_memory_cache=InMemoryCache(),
+        )
 
         ### SCHEDULER ###
         self.scheduler = Scheduler(polling_interval=polling_interval, redis_cache=redis_cache)
@@ -1132,8 +1144,8 @@ class Router:
         ```
         and caching to just work.
         """
-        if self.cache.redis_cache is None:
-            self.cache.redis_cache = cache
+        self.cache.attach_redis_cache(cache)
+        self._claude_code_session_router_cache.attach_redis_cache(cache)
 
     # Maps a routing strategy string to the attribute on `self` that holds
     # the default group's strategy selector for that strategy. (The selectors
@@ -5184,7 +5196,7 @@ class Router:
                         if not has_generated_content and error_event is None
                         else None
                     )
-                    if refusal_stop_details is not None and self._has_content_policy_fallback(model, initial_kwargs):
+                    if refusal_stop_details is not None and self._refusal_fallback_available(model, initial_kwargs):
                         refusal_error = safeguard_refusal_error(model=model, stop_details=refusal_stop_details)
                         raise MidStreamFallbackError(
                             message=refusal_error.message,
@@ -6476,11 +6488,24 @@ class Router:
                     if custom_llm_provider and "custom_llm_provider" not in kwargs
                     else MappingProxyType(kwargs)
                 )
-                if provider_kwargs.get("model"):
-                    return self._generic_api_call_with_fallbacks(original_function=original_function, **provider_kwargs)
+                search_kwargs: Final = (
+                    MappingProxyType(
+                        {
+                            **provider_kwargs,
+                            "_direct_vector_store_embedding_executor": RouterVectorStoreEmbeddingExecutor(
+                                router=self,
+                                metadata=self._vector_store_request_metadata(kwargs),
+                            ),
+                        }
+                    )
+                    if call_type == "vector_store_search"
+                    else provider_kwargs
+                )
+                if search_kwargs.get("model"):
+                    return self._generic_api_call_with_fallbacks(original_function=original_function, **search_kwargs)
                 if call_type == "vector_store_search":
-                    return original_function(**MappingProxyType({**provider_kwargs, "router": self}))
-                return original_function(**provider_kwargs)
+                    return original_function(**MappingProxyType({**search_kwargs, "router": self}))
+                return original_function(**search_kwargs)
 
             return vector_store_sync_wrapper
 
@@ -6653,11 +6678,22 @@ class Router:
                 "avector_store_update",
                 "avector_store_delete",
             ):
+                vector_store_kwargs: Final = (
+                    {  # mutable-ok: the async routed request requires dynamic keyword arguments
+                        **kwargs,
+                        "_direct_vector_store_embedding_executor": RouterVectorStoreEmbeddingExecutor(
+                            router=self,
+                            metadata=self._vector_store_request_metadata(kwargs),
+                        ),
+                    }
+                    if call_type == "avector_store_search"
+                    else kwargs
+                )
                 return await self._init_vector_store_api_endpoints(
                     original_function=original_function,
                     custom_llm_provider=custom_llm_provider,
                     call_type=call_type,
-                    **kwargs,
+                    **vector_store_kwargs,
                 )
             elif call_type in ("afile_delete", "afile_content"):
                 return await self._ageneric_api_call_with_fallbacks(
@@ -6693,6 +6729,10 @@ class Router:
 
         return async_wrapper
 
+    @staticmethod
+    def _vector_store_request_metadata(kwargs: Mapping[str, object]) -> Mapping[str, object]:
+        return vector_store_request_metadata(kwargs)
+
     async def _init_vector_store_api_endpoints(
         self,
         original_function: Callable,
@@ -7229,6 +7269,7 @@ class Router:
                     _fallback_metadata["original_model_group"] = model_group
         include_fallback_errors: Final = kwargs.get("include_fallback_errors", False) is True
         disable_fallbacks: Final[bool | None] = kwargs.pop("disable_fallbacks", False)
+        record_disable_fallbacks(kwargs, disable_fallbacks is True)
         fallbacks: Final[list | None] = kwargs.get("fallbacks", self.fallbacks)
         context_window_fallbacks: list | None = kwargs.get("context_window_fallbacks", self.context_window_fallbacks)
         content_policy_fallbacks: list | None = kwargs.get("content_policy_fallbacks", self.content_policy_fallbacks)
@@ -8094,6 +8135,29 @@ class Router:
         )
         return False
 
+    def _refusal_fallback_available(self, model_group: str, kwargs: Mapping[str, Any]) -> bool:
+        """
+        Whether a safeguard refusal can actually be recovered by the dispatcher. A configured
+        content-policy list is authoritative; with none configured at all, the dispatcher falls
+        through to the generic fallbacks lookup, so the gate mirrors that reachability and arms
+        on a resolving generic chain (tier first, then the requested group, then "*").
+        """
+        if fallbacks_disabled_for_request(kwargs):
+            return False
+        content_policy_fallbacks: Final = kwargs.get("content_policy_fallbacks", self.content_policy_fallbacks)
+        if content_policy_fallbacks is not None:
+            return self._has_content_policy_fallback(model_group, kwargs)
+        if self._has_default_fallbacks():
+            return True
+        fallbacks: Final = kwargs.get("fallbacks", self.fallbacks)
+        if fallbacks is None:
+            return False
+        resolved, _ = get_fallback_model_group_for_lookup_groups(
+            fallbacks=fallbacks,
+            lookup_groups=fallback_lookup_groups(kwargs, model_group),
+        )
+        return resolved is not None
+
     def _should_raise_content_policy_error(self, model: str, response: ModelResponse, kwargs: dict) -> bool:
         """
         Determines if a content policy error should be raised.
@@ -8125,7 +8189,7 @@ class Router:
             return False
         if get_safeguard_refusal_stop_details(response) is None:
             return False
-        return self._has_content_policy_fallback(model, kwargs)
+        return self._refusal_fallback_available(model, kwargs)
 
     def _get_healthy_deployments(self, model: str, parent_otel_span: Span | None):
         _all_deployments: list = []
@@ -12649,6 +12713,100 @@ class Router:
             return None
         return candidates[0]
 
+    @staticmethod
+    def _request_header(request_kwargs: Mapping[str, object], header_name: str) -> str | None:
+        proxy_server_request: Final = request_kwargs.get("proxy_server_request")
+        if not isinstance(proxy_server_request, Mapping):
+            return None
+        headers: Final = proxy_server_request.get("headers")
+        if not isinstance(headers, Mapping):
+            return None
+        return next(
+            (
+                value
+                for key, value in headers.items()
+                if isinstance(key, str) and key.lower() == header_name and isinstance(value, str)
+            ),
+            None,
+        )
+
+    def _claude_code_session_router_cache_key(self, request_kwargs: Mapping[str, object]) -> str | None:
+        session_id: Final = self._request_header(request_kwargs, "x-claude-code-session-id")
+        if session_id is None or _CLAUDE_CODE_SESSION_ID_RE.fullmatch(session_id) is None:
+            return None
+        metadata_name: Final = "litellm_metadata" if "litellm_metadata" in request_kwargs else "metadata"
+        metadata: Final = request_kwargs.get(metadata_name)
+        if not isinstance(metadata, Mapping):
+            return None
+        caller_scope: Final = metadata.get("user_api_key_hash")
+        if not isinstance(caller_scope, str) or not caller_scope:
+            return None
+        return f"claude_code_session_router:v1:{caller_scope}:{session_id}"
+
+    async def _delete_claude_code_session_router_binding(self, cache_key: str) -> None:
+        try:
+            await self._claude_code_session_router_cache.async_delete_cache(key=cache_key)
+        except Exception as e:  # noqa: BLE001  # cache cleanup must not fail an otherwise routable request
+            verbose_router_logger.warning(
+                "Failed to delete Claude Code session router binding; the binding may remain until its TTL expires: %s",
+                e,
+            )
+
+    async def _get_claude_code_session_router_binding(self, cache_key: str) -> object:
+        session_cache: Final = self._claude_code_session_router_cache
+        try:
+            if session_cache.redis_cache is None:
+                return await session_cache.async_get_cache(key=cache_key)
+            return await session_cache.redis_cache.async_get_cache(key=cache_key)
+        except Exception as e:  # noqa: BLE001  # an optional binding must not make routing depend on Redis
+            verbose_router_logger.warning(
+                "Failed to read Claude Code session router binding; using the requested model: %s",
+                e,
+            )
+            return None
+
+    async def _resolve_claude_code_session_router(
+        self,
+        model: str,
+        registered_model_name: str,
+        request_kwargs: Mapping[str, object],
+    ) -> str:
+        if not any((self.auto_routers, self.complexity_routers, self.adaptive_routers, self.quality_routers)):
+            return registered_model_name
+        cache_key: Final = self._claude_code_session_router_cache_key(request_kwargs)
+        if cache_key is None or not isinstance(request_kwargs, dict):
+            return registered_model_name
+        if request_kwargs.get("fallback_depth") not in (None, 0):
+            return registered_model_name
+
+        agent_id: Final = self._request_header(request_kwargs, "x-claude-code-agent-id")
+        if agent_id is not None:
+            bound_model: Final = await self._get_claude_code_session_router_binding(cache_key)
+            if not isinstance(bound_model, str):
+                return registered_model_name
+            bound_registered_model: Final = self._get_model_from_alias(model=bound_model) or bound_model
+            if self._select_pre_routing_strategy(bound_registered_model, request_kwargs) is None:
+                await self._delete_claude_code_session_router_binding(cache_key)
+                return registered_model_name
+            await self._claude_code_session_router_cache.async_set_cache(
+                key=cache_key,
+                value=bound_model,
+                ttl=_CLAUDE_CODE_SESSION_ROUTER_TTL_SECONDS,
+            )
+            self._stamp_or_clear_metadata_key(request_kwargs, "model_group", bound_model)
+            return bound_registered_model
+
+        if self._request_header(request_kwargs, "x-app") != "cli":
+            return registered_model_name
+        if self._select_pre_routing_strategy(registered_model_name, request_kwargs) is None:
+            return registered_model_name
+        await self._claude_code_session_router_cache.async_set_cache(
+            key=cache_key,
+            value=model,
+            ttl=_CLAUDE_CODE_SESSION_ROUTER_TTL_SECONDS,
+        )
+        return registered_model_name
+
     async def async_pre_routing_hook(
         self,
         model: str,
@@ -12668,7 +12826,12 @@ class Router:
         the alias, since spend metadata is stamped before routing and the response carries the tier
         group the strategy picked.
         """
-        registered_model_name: Final = self._get_model_from_alias(model=model) or model
+        requested_registered_model_name: Final = self._get_model_from_alias(model=model) or model
+        registered_model_name: Final = await self._resolve_claude_code_session_router(
+            model=model,
+            registered_model_name=requested_registered_model_name,
+            request_kwargs=request_kwargs,
+        )
 
         #########################################################
         # Run the routing-plugin pipeline, if any plugins are configured.
@@ -13465,6 +13628,9 @@ class Router:
     def flush_cache(self):
         litellm.cache = None
         self.cache.flush_cache()
+        session_in_memory_cache: Final = self._claude_code_session_router_cache.in_memory_cache
+        if session_in_memory_cache is not None:
+            session_in_memory_cache.flush_cache()
 
     def reset(self):
         ## clean up on close

litellm/router_strategy/complexity_router/README.md

@@ -274,6 +274,49 @@ except that the heuristic outcome is the one already computed rather than a seco
 Spend logs record `routing_decision.cause` as `heuristic_first_short_circuit` when the classifier
 was skipped, and `llm_classifier` when it ran, so the two are told apart per request.
 
+### Hybrid
+
+`classifier_type: hybrid` also scores locally first, but it asks a different question than
+`heuristic_first`. Where heuristic-first asks how CHEAP the scorer's tier is and pays for the
+classifier on everything above a ceiling, hybrid asks how DECIDED the score is and pays for the
+classifier only where the score lands near a tier boundary. A confident score keeps its tier at
+every tier, the most expensive one included:
+
+```yaml
+model_list:
+  - model_name: smart-router
+    litellm_params:
+      model: auto_router/complexity_router
+      complexity_router_config:
+        classifier_type: hybrid
+        hybrid_boundary_margin: 0.03
+        classifier_llm_config:
+          model: gpt-4o-mini
+        tiers:
+          SIMPLE: gpt-4o-mini
+          MEDIUM: gpt-4o
+          COMPLEX: claude-sonnet-4
+          REASONING: o1-preview
+```
+
+A request routes on the scorer's own tier when its score is further than `hybrid_boundary_margin`
+from every active boundary. Everything else goes to the classifier: a score inside the band, where a
+hair's difference would have named the adjacent tier and its model pool, and a prompt where no
+dimension fired at all, which has no opinion to be confident about. `hybrid_boundary_margin` is
+required for this type and rejected on the others, the same way `heuristic_first_max_tier` is
+required for heuristic-first, so the two modes are told apart by the knob each one takes rather than
+by a shared field that means something different per type.
+
+Pick the margin against the score distribution rather than by intuition. The scorer combines a small
+set of discretely weighted dimensions, so achievable scores cluster on a lumpy grid instead of
+spreading smoothly, and widening the margin admits whole clusters at once rather than a few more
+requests. Spend logs record `routing_decision.cause` as `hybrid_short_circuit` when the classifier
+was skipped and `llm_classifier` when it ran.
+
+Operator-defined tier sets (`tier_definitions`) are not supported here, for the same reason they are
+not supported under heuristic-first: the scorer only produces the built-in tiers. Classifier failure
+behaves exactly as it does under `classifier_type: llm`.
+
 ### Reasoning Override
 
 If 2+ reasoning markers are detected in the user message, the request is promoted to the REASONING tier even when the weighted score maps lower, so complex reasoning tasks get the appropriate model. The promotion requires the score to reach `reasoning_override_min_score`, which tracks `tier_boundaries.simple_medium` unless set, so stock phrases on an otherwise trivial prompt cannot buy the top tier. Set it to `0` to promote on the markers alone.

litellm/router_strategy/complexity_router/complexity_router.py

@@ -26,7 +26,11 @@ from typing import TYPE_CHECKING, Any, Final, Literal, NamedTuple, cast
 from pydantic import BaseModel, create_model
 
 from litellm._logging import verbose_router_logger
-from litellm.constants import EMPTY_MAPPING, RETURN_RAW_MODEL_NAME_METADATA_KEY
+from litellm.constants import (
+    EMPTY_MAPPING,
+    RETURN_RAW_MODEL_NAME_METADATA_KEY,
+    SESSION_ID_GENERATED_METADATA_KEY,
+)
 from litellm.integrations.custom_logger import CustomLogger
 from litellm.litellm_core_utils.core_helpers import get_metadata_variable_name_from_kwargs
 from litellm.litellm_core_utils.internal_call_metadata import forwarded_internal_call_metadata
@@ -799,6 +803,7 @@ class ClassificationOutcome(NamedTuple):
         "reasoning_override",
         "llm_classifier",
         "heuristic_first_short_circuit",
+        "hybrid_short_circuit",
         "housekeeping",
         "classifier_plugin",
         "classifier_fallback",
@@ -1241,6 +1246,15 @@ class ComplexityRouter(CustomLogger):
 
         return tier, weighted_score, tuple(signals), "heuristic_scorer"
 
+    def _is_near_tier_boundary(self, score: float, margin: float) -> bool:
+        boundaries: Final = self._effective_tier_boundaries()
+        active_boundaries: Final = (
+            boundaries["simple_medium"],
+            boundaries["medium_complex"],
+            boundaries["complex_reasoning"],
+        )
+        return any(abs(score - boundary) <= margin for boundary in active_boundaries)
+
     def _effective_reasoning_override_min_score(self) -> float:
         """The score a request must reach before the reasoning-marker override may promote it.
 
@@ -1367,6 +1381,8 @@ class ComplexityRouter(CustomLogger):
             return await self._classify_with_plugin(prompt, system_prompt, request_kwargs, raw_messages)
         if self.config.classifier_type == "heuristic_first" and self.config.classifier_llm_config is not None:
             return await self._classify_heuristic_first(prompt, system_prompt, request_kwargs, messages)
+        if self.config.classifier_type == "hybrid" and self.config.classifier_llm_config is not None:
+            return await self._classify_hybrid(prompt, system_prompt, request_kwargs, messages)
         if self.config.classifier_type != "llm" or self.config.classifier_llm_config is None:
             tier, score, signals, cause = self._score_and_classify(prompt, system_prompt)
             return ClassificationOutcome(tier=tier, score=score, signals=signals, cause=cause)
@@ -1418,6 +1434,29 @@ class ComplexityRouter(CustomLogger):
             return ClassificationOutcome(tier=tier, score=score, signals=signals, cause="heuristic_first_short_circuit")
         return await self._llm_classifier_outcome(prompt, system_prompt, request_kwargs, messages, scored=scored)
 
+    async def _classify_hybrid(
+        self,
+        prompt: str,
+        system_prompt: str | None,
+        request_kwargs: dict[str, Any] | None,  # mutable-ok: handed to _classify_with_llm as-is
+        messages: Sequence[Mapping[str, object]] | None,
+    ) -> ClassificationOutcome:
+        """Score locally, and only pay for the classifier when the score sits near a tier boundary.
+
+        Where heuristic_first asks how CHEAP the scorer's tier is, this asks how DECIDED it is, so a
+        confident score keeps its tier at every tier including the most expensive one. Two things make
+        a score undecided: landing within hybrid_boundary_margin of an active boundary, where a
+        hair's difference in score would have named the adjacent tier and its model pool, and firing
+        no dimension at all, which scores 0.0 and lands SIMPLE by default rather than by evidence.
+        """
+        tier, score, signals, cause = self._score_and_classify(prompt, system_prompt)
+        scored: Final = ClassificationOutcome(tier=tier, score=score, signals=signals, cause=cause)
+        margin: Final = self.config.hybrid_boundary_margin
+        decided: Final = margin is not None and bool(signals) and not self._is_near_tier_boundary(score, margin)
+        if decided:
+            return ClassificationOutcome(tier=tier, score=score, signals=signals, cause="hybrid_short_circuit")
+        return await self._llm_classifier_outcome(prompt, system_prompt, request_kwargs, messages, scored=scored)
+
     async def _llm_classifier_outcome(
         self,
         prompt: str,
@@ -2677,7 +2716,7 @@ class ComplexityRouter(CustomLogger):
         """Resolve a client-supplied session_id."""
         for metadata in ComplexityRouter._iter_metadata_dicts(request_kwargs):
             session_id = metadata.get("session_id")
-            if session_id is not None:
+            if session_id is not None and not metadata.get(SESSION_ID_GENERATED_METADATA_KEY):
                 return str(session_id)
         return None
 

litellm/router_strategy/complexity_router/config.py

@@ -43,7 +43,7 @@ DEFAULT_CLASSIFICATION_RUBRIC: Final[ClassificationRubric] = ClassificationRubri
 # The classifier_type values that can call classifier_llm_config.model. Every consumer asking
 # "is the classifier model a real dependency of this router" resolves it here, including the ones
 # that only hold the raw config mapping and cannot reach ComplexityRouterConfig.uses_llm_classifier.
-LLM_CLASSIFIER_TYPES: Final[frozenset[str]] = frozenset({"llm", "heuristic_first"})
+LLM_CLASSIFIER_TYPES: Final[frozenset[str]] = frozenset({"llm", "heuristic_first", "hybrid"})
 
 
 TIER_SEVERITY_ORDER: Final[tuple[ComplexityTier, ...]] = (
@@ -627,12 +627,13 @@ class ComplexityRouterConfig(BaseModel):
     )
 
     # Classifier strategy
-    classifier_type: Literal["heuristic", "heuristic_v2", "llm", "custom", "heuristic_first"] = Field(
+    classifier_type: Literal["heuristic", "heuristic_v2", "llm", "custom", "heuristic_first", "hybrid"] = Field(
         default="heuristic",
         description=(
             "Classification strategy: local regex/keyword scoring, the bundled trained four-tier heuristic, "
-            "an LLM call, a custom classifier plugin, or 'heuristic_first', which scores locally and only pays "
-            "for the LLM classifier when the local scorer does not confidently land a cheap tier"
+            "an LLM call, a custom classifier plugin, 'heuristic_first', which scores locally and only pays "
+            "for the LLM classifier when the local scorer does not confidently land a cheap tier, or 'hybrid', "
+            "which trusts the local scorer everywhere except when its score lands near a tier boundary"
         ),
     )
     heuristic_v2_artifact: TrainedTierArtifact | Literal["ultrafeedback"] = Field(
@@ -644,7 +645,10 @@ class ComplexityRouterConfig(BaseModel):
     )
     classifier_llm_config: ClassifierLLMConfig | None = Field(
         default=None,
-        description="Configuration for the LLM classifier; required when classifier_type is 'llm' or 'heuristic_first'",
+        description=(
+            "Configuration for the LLM classifier; required when classifier_type is 'llm', "
+            "'heuristic_first' or 'hybrid'"
+        ),
     )
     heuristic_first_max_tier: str | None = Field(
         default=None,
@@ -659,6 +663,19 @@ class ComplexityRouterConfig(BaseModel):
             "may not name the highest one, since that would make the LLM classifier unreachable."
         ),
     )
+    hybrid_boundary_margin: float | None = Field(
+        default=None,
+        ge=0,
+        le=1,
+        description=(
+            "How close to a tier boundary a heuristic score has to land before the LLM classifier breaks the "
+            "tie; required when classifier_type is 'hybrid' and rejected otherwise. Everything further than "
+            "this from every active boundary routes on the scorer's own tier with no classifier call, at any "
+            "tier, which is what separates 'hybrid' from 'heuristic_first' and its cheap-tier ceiling. A "
+            "prompt where no dimension fired still goes to the classifier, since the scorer has no opinion "
+            "to be near a boundary with. 0 escalates only scores sitting exactly on a boundary."
+        ),
+    )
     classifier_plugin: ClassifierPlugin | None = Field(
         default=None,
         description=(
@@ -1135,6 +1152,23 @@ class ComplexityRouterConfig(BaseModel):
             )
         return self
 
+    @model_validator(mode="after")
+    def _validate_hybrid_boundary_margin(self) -> "ComplexityRouterConfig":
+        if self.classifier_type != "hybrid":
+            if self.hybrid_boundary_margin is not None:
+                raise ValueError(
+                    f"hybrid_boundary_margin is set but classifier_type is {self.classifier_type!r}; "
+                    "the scorer would never consult the classifier on a near-boundary score. Set "
+                    "classifier_type 'hybrid' or remove hybrid_boundary_margin"
+                )
+            return self
+        if self.hybrid_boundary_margin is None:
+            raise ValueError(
+                "hybrid_boundary_margin is required when classifier_type is 'hybrid': without a margin no "
+                "score is ever near enough to a boundary to escalate, which is classifier_type 'heuristic'"
+            )
+        return self
+
     @field_validator("fallback_tier")
     @classmethod
     def _reject_blank_optional_text(cls, value: str | None) -> str | None:
@@ -1257,7 +1291,7 @@ class ComplexityRouterConfig(BaseModel):
         )
         if duplicated:
             raise ValueError(f"tier_definitions names must be unique (case-insensitive): {', '.join(duplicated)}")
-        if self.classifier_type in ("heuristic", "heuristic_v2", "heuristic_first"):
+        if self.classifier_type in ("heuristic", "heuristic_v2", "heuristic_first", "hybrid"):
             raise ValueError(
                 "tier_definitions requires classifier_type 'llm' or 'custom': the heuristic scorer only "
                 "produces the four built-in tiers, as does heuristic_v2"

litellm/router_utils/fallback_event_handlers.py

@@ -8,6 +8,7 @@ from typing import TYPE_CHECKING, Any, Final
 import litellm
 from litellm._logging import verbose_router_logger
 from litellm.integrations.custom_logger import CustomLogger
+from litellm.litellm_core_utils.core_helpers import get_metadata_variable_name_from_kwargs
 from litellm.litellm_core_utils.sensitive_data_masker import mask_sensitive_structure
 from litellm.router_utils.add_retry_fallback_headers import (
     add_fallback_headers_to_response,
@@ -231,8 +232,6 @@ def record_pre_routing_selection(request_kwargs: Mapping[str, Any] | None, selec
     on /v1/messages the top-level ``metadata`` dict is the provider's own request field,
     so a blanket write would forward the tier stamp upstream.
     """
-    from litellm.litellm_core_utils.core_helpers import get_metadata_variable_name_from_kwargs
-
     if request_kwargs is None:
         return
     bucket: Final = request_kwargs.get(get_metadata_variable_name_from_kwargs(request_kwargs))
@@ -264,13 +263,48 @@ def get_pre_routing_selection(kwargs: Mapping[str, Any]) -> str | None:
     return next((selected for selected in selections if isinstance(selected, str) and selected), None)
 
 
+DISABLE_FALLBACKS_METADATA_KEY: Final = "_disable_fallbacks"
+
+
+def record_disable_fallbacks(request_kwargs: Mapping[str, Any] | None, disabled: bool) -> None:
+    """
+    Write-or-clear the request's disable_fallbacks verdict into the router-internal metadata
+    bucket. The wrapper pops the raw kwarg before any downstream frame runs, so the refusal
+    gate (which decides whether to convert a refusal into a recoverable error) needs this
+    carrier to know recovery is impossible.
+    """
+    from litellm.litellm_core_utils.core_helpers import get_metadata_variable_name_from_kwargs
+
+    if request_kwargs is None:
+        return
+    bucket: Final = request_kwargs.get(get_metadata_variable_name_from_kwargs(request_kwargs))
+    if not isinstance(bucket, dict):
+        return
+    if disabled:
+        bucket[DISABLE_FALLBACKS_METADATA_KEY] = True
+    else:
+        bucket.pop(DISABLE_FALLBACKS_METADATA_KEY, None)
+
+
+def fallbacks_disabled_for_request(kwargs: Mapping[str, Any]) -> bool:
+    """True when this request opted out of fallbacks, read from the raw kwarg (pre-pop
+    snapshots keep it) or the router-internal bucket the wrapper stamps after popping it."""
+    if kwargs.get("disable_fallbacks") is True:
+        return True
+    buckets: Final = (kwargs.get(name) for name in _ROUTER_METADATA_BUCKETS)
+    return any(isinstance(bucket, dict) and bucket.get(DISABLE_FALLBACKS_METADATA_KEY) is True for bucket in buckets)
+
+
 def fallback_lookup_groups(kwargs: Mapping[str, Any], model_group: str | None) -> tuple[str, ...]:
     """
     Ordered keys for resolving a fallback chain: the tier a pre-routing hook selected wins,
-    and the requested group still resolves when no tier-keyed chain exists, so configs keyed
-    on the router name (the documented contract) keep working behind auto-routers.
+    then the routed group, then the requested group. The routed group differs when Claude Code
+    session affinity remaps a subagent's concrete model to its bound router.
     """
-    ordered: Final = (get_pre_routing_selection(kwargs), model_group)
+    metadata: Final = kwargs.get(get_metadata_variable_name_from_kwargs(kwargs))
+    routed_group_value: Final = metadata.get("model_group") if isinstance(metadata, Mapping) else None
+    routed_group: Final = routed_group_value if isinstance(routed_group_value, str) else None
+    ordered: Final = (get_pre_routing_selection(kwargs), routed_group, model_group)
     return tuple(dict.fromkeys(group for group in ordered if group))
 
 
@@ -470,7 +504,8 @@ async def run_async_fallback(
     attempted: Final = (
         carried_targets if isinstance(carried_targets, AttemptedFallbackTargets) else AttemptedFallbackTargets()
     )
-    attempted.record(original_model_group)
+    failed_model_group: Final = get_pre_routing_selection(kwargs) or original_model_group
+    attempted.record(failed_model_group)
 
     # A dict target is merged straight into kwargs below, and kwargs win over the deployment's own
     # params, so a stored key/team/global fallback could otherwise set a federation field that the
@@ -481,7 +516,7 @@ async def run_async_fallback(
             reject_server_owned_wif_params(target)
 
     for mg in fallback_model_group:
-        if mg == original_model_group:
+        if mg == failed_model_group:
             continue
         if same_model_group_only and _get_fallback_target_model_group(mg) != original_model_group:
             verbose_router_logger.info(

litellm/router_utils/pre_call_checks/deployment_affinity_check.py

@@ -21,7 +21,7 @@ from typing_extensions import TypedDict
 
 from litellm._logging import verbose_router_logger
 from litellm.caching.dual_cache import DualCache
-from litellm.constants import SESSION_DEPLOYMENT_AFFINITY_TTL_METADATA_KEY
+from litellm.constants import SESSION_DEPLOYMENT_AFFINITY_TTL_METADATA_KEY, SESSION_ID_GENERATED_METADATA_KEY
 from litellm.integrations.custom_logger import CustomLogger, Span
 from litellm.responses.utils import ResponsesAPIRequestUtils
 from litellm.types.llms.openai import AllMessageValues
@@ -265,7 +265,7 @@ class DeploymentAffinityCheck(CustomLogger):
     @staticmethod
     def _get_session_id_from_metadata_dict(metadata: dict) -> str | None:
         session_id: Final = metadata.get("session_id")
-        if session_id is None:
+        if session_id is None or metadata.get(SESSION_ID_GENERATED_METADATA_KEY):
             return None
         return str(session_id)
 

litellm/types/llms/gemini_audio_transcription.py

@@ -1,7 +1,7 @@
-from typing import Literal, Required
+from typing import Literal
 
 from pydantic import BaseModel, ConfigDict
-from typing_extensions import ReadOnly, TypedDict
+from typing_extensions import ReadOnly, Required, TypedDict
 
 
 class GeminiTranscriptionAudioInput(TypedDict):

litellm/types/proxy/guardrails/guardrail_hooks/crowdstrike_aidr.py

@@ -4,7 +4,18 @@ from .base import GuardrailConfigModel
 
 
 class CrowdStrikeAIDRGuardrailConfigModelOptionalParams(BaseModel):
-    pass
+    streaming_end_of_stream_only: bool | None = Field(
+        default=None,
+        description="If False (default when unset), post_call scans the accumulated streamed response every "
+        "streaming_sampling_rate chunks and an in-flight block stops the stream. If True, the guard runs once "
+        "over the assembled response at end of stream, so flagged content may already have reached the client.",
+    )
+    streaming_sampling_rate: int | None = Field(
+        default=None,
+        ge=1,
+        description="When streaming_end_of_stream_only is False, scan the accumulated streamed response every Nth "
+        "chunk. Defaults to 5 when unset.",
+    )
 
 
 class CrowdStrikeAIDRGuardrailConfigModel(GuardrailConfigModel[CrowdStrikeAIDRGuardrailConfigModelOptionalParams]):

litellm/types/proxy/public_endpoints/public_endpoints.py

@@ -1,7 +1,7 @@
-from collections.abc import Mapping
+from collections.abc import Mapping, Sequence
 from typing import Any, Final, Literal
 
-from pydantic import BaseModel, Field, model_validator
+from pydantic import BaseModel, ConfigDict, Field, model_validator
 from typing_extensions import Self
 
 
@@ -137,6 +137,44 @@ class SupportedEndpointsResponse(BaseModel):
     endpoints: list[SupportedEndpoint]
 
 
+class AutoRouterPresetTiers(BaseModel):
+    """Exactly the four built-in tiers the dashboard's preset prefill can apply.
+
+    extra="forbid" on purpose: a tier name this dashboard cannot apply would grey out or crash the
+    picker, so such a catalog is rejected wholesale and the bundled one serves instead.
+    """
+
+    model_config = ConfigDict(extra="forbid")
+
+    SIMPLE: Sequence[str]
+    MEDIUM: Sequence[str]
+    COMPLEX: Sequence[str]
+    REASONING: Sequence[str]
+
+
+class AutoRouterPresetConfig(BaseModel):
+    """The complexity_router_config a preset prefills.
+
+    Only tiers is validated, because every dashboard consumer dereferences it; everything else
+    passes through verbatim with unknown fields kept (extra="allow"), so a catalog published after
+    this proxy shipped still serves its new fields intact.
+    """
+
+    model_config = ConfigDict(extra="allow")
+
+    tiers: AutoRouterPresetTiers
+
+
+class AutoRouterPresetRecord(BaseModel):
+    """One auto-router preset as served to the dashboard's template picker."""
+
+    model_config = ConfigDict(extra="allow")
+
+    label: str
+    description: str
+    complexity_router_config: AutoRouterPresetConfig
+
+
 class ComplexityScorerDefaults(BaseModel):
     """The complexity router's shipped heuristic scorer defaults.
 

litellm/types/rerank.py

@@ -4,8 +4,10 @@ https://docs.cohere.com/reference/rerank
 
 """
 
-from pydantic import BaseModel, PrivateAttr
-from typing_extensions import Required, TypedDict
+from typing import Literal
+
+from pydantic import BaseModel, ConfigDict, PrivateAttr
+from typing_extensions import ReadOnly, Required, TypedDict
 
 
 class RerankRequest(BaseModel):
@@ -21,6 +23,18 @@ class RerankRequest(BaseModel):
     # (e.g. hosted vLLM / Qwen3-Reranker, DeepInfra). Omitted from the outgoing
     # request when None, so this is fully backward-compatible.
     instruction: str | None = None
+    truncate_prompt_tokens: int | None = None
+    truncation_side: Literal["left", "right"] | None = None
+    max_tokens_per_query: int | None = None
+
+
+class HostedVLLMRerankTruncationParams(BaseModel):
+    model_config = ConfigDict(frozen=True)
+
+    truncate_prompt_tokens: int | None = None
+    truncation_side: Literal["left", "right"] | None = None
+    max_tokens_per_query: int | None = None
+    max_tokens_per_doc: int | None = None
 
 
 class OptionalRerankParams(TypedDict, total=False):
@@ -32,6 +46,9 @@ class OptionalRerankParams(TypedDict, total=False):
     max_chunks_per_doc: int | None
     max_tokens_per_doc: int | None
     instruction: str | None
+    truncate_prompt_tokens: ReadOnly[int | None]
+    truncation_side: ReadOnly[Literal["left", "right"] | None]
+    max_tokens_per_query: ReadOnly[int | None]
 
 
 class RerankBilledUnits(TypedDict, total=False):

litellm/types/utils.py

@@ -2852,6 +2852,7 @@ RoutingDecisionCause = Literal[
     # scorer, and from "classifier_fallback", which is the scorer running because a call failed:
     # only this cause means an LLM classifier was configured, reachable, and deliberately skipped.
     "heuristic_first_short_circuit",
+    "hybrid_short_circuit",
     # The operator's classifier plugin (classifier_type 'custom') decided the tier.
     "classifier_plugin",
     # The LLM classifier or classifier plugin failed on a router with an operator-defined

litellm/vector_stores/main.py

@@ -15,6 +15,11 @@ import litellm
 from litellm.constants import request_timeout
 from litellm.litellm_core_utils.get_llm_provider_logic import get_llm_provider
 from litellm.litellm_core_utils.litellm_logging import Logging as LiteLLMLoggingObj
+from litellm.llms.base_llm.vector_store.transformation import (
+    BaseQueryEmbeddingVectorStoreConfig,
+    VectorStoreEmbeddingExecutor,
+    vector_store_request_metadata,
+)
 from litellm.llms.custom_httpx.llm_http_handler import BaseLLMHTTPHandler
 from litellm.types.router import GenericLiteLLMParams
 from litellm.types.vector_stores import (
@@ -38,6 +43,16 @@ base_llm_http_handler = BaseLLMHTTPHandler()
 #################################################
 
 
+def _direct_vector_store_embedding_executor(
+    value: object, router: "Router | None", request_kwargs: Mapping[str, object]
+) -> VectorStoreEmbeddingExecutor:
+    if value is not None and not isinstance(value, VectorStoreEmbeddingExecutor):
+        raise TypeError("Invalid direct vector store embedding executor")
+    return BaseQueryEmbeddingVectorStoreConfig.query_embedding_executor(
+        value, router, vector_store_request_metadata(request_kwargs)
+    )
+
+
 def mock_vector_store_search_response(
     mock_results: list[VectorStoreSearchResult] | None = None,
 ):
@@ -289,7 +304,12 @@ async def asearch(
     """
     Async: Search a vector store for relevant chunks based on a query and file attributes filter.
     """
-    local_vars: Final = locals()
+    embedding_executor: Final = _direct_vector_store_embedding_executor(
+        kwargs.pop("_direct_vector_store_embedding_executor", None), router, kwargs
+    )
+    local_vars: Final = {  # mutable-ok: exception logging requires a sanitized mutable snapshot
+        key: value for key, value in locals().items() if key != "embedding_executor"
+    }
 
     try:
         loop: Final = asyncio.get_event_loop()
@@ -312,6 +332,7 @@ async def asearch(
             extra_body=extra_body,
             timeout=timeout,
             custom_llm_provider=custom_llm_provider,
+            _direct_vector_store_embedding_executor=embedding_executor,
             router=router,
             **kwargs,
         )
@@ -369,12 +390,16 @@ def search(
     Returns:
         VectorStoreSearchResponse containing the search results.
     """
-    local_vars: Final = locals()
+    embedding_executor: Final = _direct_vector_store_embedding_executor(
+        kwargs.pop("_direct_vector_store_embedding_executor", None), router, kwargs
+    )
+    local_vars: Final = {  # mutable-ok: exception logging requires a sanitized mutable snapshot
+        key: value for key, value in locals().items() if key != "embedding_executor"
+    }
     try:
         litellm_logging_obj: Final[LiteLLMLoggingObj] = kwargs.get("litellm_logging_obj")
         litellm_call_id: Final[str | None] = kwargs.get("litellm_call_id", None)
         _is_async: Final = kwargs.pop("asearch", False) is True
-
         # pull credentials from registry if available
         if litellm.vector_store_registry is not None and vector_store_id is not None:
             try:
@@ -451,6 +476,7 @@ def search(
             custom_llm_provider=custom_llm_provider,
             litellm_params=litellm_params,
             logging_obj=litellm_logging_obj,
+            embedding_executor=embedding_executor,
             extra_headers=extra_headers,
             extra_body=extra_body,
             timeout=timeout or request_timeout,

ruff-strict-budget.json

@@ -177,7 +177,7 @@
     "limit": 8
   },
   "RUF019": {
-    "limit": 31
+    "limit": 27
   },
   "RUF046": {
     "limit": 4

tests/code_coverage_tests/check_py310_typing_imports.py

@@ -0,0 +1,150 @@
+import ast
+import os
+import sys
+from collections.abc import Iterator
+from dataclasses import dataclass
+from pathlib import Path
+from typing import Final
+
+PY311_PLUS_TYPING_NAMES: Final[frozenset[str]] = frozenset(
+    {
+        "NotRequired",
+        "Required",
+        "Self",
+        "LiteralString",
+        "Never",
+        "assert_never",
+        "assert_type",
+        "reveal_type",
+        "TypeVarTuple",
+        "Unpack",
+        "dataclass_transform",
+        "override",
+        "TypeAliasType",
+        "get_original_bases",
+        "ReadOnly",
+        "TypeIs",
+        "NoDefault",
+        "get_protocol_members",
+        "is_protocol",
+        "evaluate_forward_ref",
+        "TypeForm",
+    }
+)
+
+
+@dataclass(frozen=True, slots=True)
+class TypingImportViolation:
+    file: str
+    line: int
+    name: str
+
+
+def _walk_with_ancestors(
+    node: ast.AST, ancestors: tuple[tuple[ast.AST, str], ...] = ()
+) -> Iterator[tuple[ast.AST, tuple[tuple[ast.AST, str], ...]]]:
+    yield node, ancestors
+    for field_name, field_value in ast.iter_fields(node):
+        if isinstance(field_value, ast.AST):
+            yield from _walk_with_ancestors(field_value, (*ancestors, (node, field_name)))
+        elif isinstance(field_value, list):
+            for child in field_value:
+                if isinstance(child, ast.AST):
+                    yield from _walk_with_ancestors(child, (*ancestors, (node, field_name)))
+
+
+def _is_sys_version_info(node: ast.AST) -> bool:
+    return (
+        isinstance(node, ast.Attribute)
+        and isinstance(node.value, ast.Name)
+        and node.value.id == "sys"
+        and node.attr == "version_info"
+    )
+
+
+def _is_version_guarded(ancestors: tuple[tuple[ast.AST, str], ...]) -> bool:
+    nearest_if: Final[tuple[ast.If, str] | None] = next(
+        (
+            (ancestor, field_name)
+            for ancestor, field_name in reversed(ancestors)
+            if isinstance(ancestor, ast.If)
+        ),
+        None,
+    )
+    if nearest_if is None:
+        return False
+    enclosing_if, branch = nearest_if
+    test: Final[ast.expr] = enclosing_if.test
+    if not isinstance(test, ast.Compare) or len(test.ops) != 1 or not _is_sys_version_info(test.left):
+        return False
+    operator: Final[ast.cmpop] = test.ops[0]
+    return (isinstance(operator, (ast.Gt, ast.GtE)) and branch == "body") or (
+        isinstance(operator, (ast.Lt, ast.LtE)) and branch == "orelse"
+    )
+
+
+def scan_file(file_path: str | os.PathLike[str]) -> tuple[TypingImportViolation, ...]:
+    path: Final[Path] = Path(file_path)
+    tree: Final[ast.Module] = ast.parse(path.read_text(encoding="utf-8"), filename=str(path))
+    return tuple(
+        violation
+        for node, ancestors in _walk_with_ancestors(tree)
+        if not _is_version_guarded(ancestors)
+        for violation in _violations_for_node(node, path)
+    )
+
+
+def _violations_for_node(
+    node: ast.AST, path: Path
+) -> tuple[TypingImportViolation, ...]:
+    if isinstance(node, ast.ImportFrom) and node.module == "typing":
+        return tuple(
+            TypingImportViolation(file=str(path), line=node.lineno, name=alias.name)
+            for alias in node.names
+            if alias.name in PY311_PLUS_TYPING_NAMES
+        )
+    if (
+        isinstance(node, ast.Attribute)
+        and isinstance(node.value, ast.Name)
+        and node.value.id == "typing"
+        and node.attr in PY311_PLUS_TYPING_NAMES
+    ):
+        return (TypingImportViolation(file=str(path), line=node.lineno, name=node.attr),)
+    return ()
+
+
+def scan_directory(base_dir: str | os.PathLike[str] = ".") -> tuple[TypingImportViolation, ...]:
+    base_path: Final[Path] = Path(base_dir)
+    return tuple(
+        violation
+        for directory in (
+            base_path / "litellm",
+            base_path / "enterprise",
+            base_path / "litellm-proxy-extras" / "litellm_proxy_extras",
+        )
+        if directory.exists()
+        for path in directory.rglob("*.py")
+        for violation in scan_file(path)
+    )
+
+
+def main() -> None:
+    violations: Final[tuple[TypingImportViolation, ...]] = scan_directory()
+    if violations:
+        message: Final[str] = "\n".join(
+            (
+                "Python 3.10-incompatible typing imports found:",
+                *(
+                    f"{violation.file}:{violation.line}: {violation.name} is unavailable in Python 3.10; "
+                    "import it from typing_extensions instead because litellm supports Python 3.10"
+                    for violation in violations
+                ),
+            )
+        )
+        sys.stdout.write(f"{message}\n")
+        raise RuntimeError("Import Python 3.10-incompatible typing names from typing_extensions instead")
+    sys.stdout.write("No Python 3.10-incompatible typing imports found.\n")
+
+
+if __name__ == "__main__":
+    main()

tests/code_coverage_tests/router_code_coverage.py

@@ -82,6 +82,11 @@ ignored_function_names = [
     "_invalidate_access_groups_cache",  # Tested indirectly via set_model_list, upsert_model etc. (test files lack "router" in name)
     "has_buffered_provider_output",  # Property, so its reads in test_router.py are never an ast.Call
     "_resolved_provider",  # Tested via get_pattern in test_pattern_match_deployments.py (file lacks "router" in name)
+    "_request_header",  # Tested through Claude Code session routing in test_router.py
+    "_claude_code_session_router_cache_key",  # Tested through Claude Code session routing in test_router.py
+    "_delete_claude_code_session_router_binding",  # Tested through Redis cleanup failure in test_router.py
+    "_resolve_claude_code_session_router",  # Tested through Claude Code session routing in test_router.py
+    "_get_claude_code_session_router_binding",  # Tested through the two-worker session routing test in test_router.py
 ]
 
 

tests/e2e/ui/constants.ts

@@ -30,6 +30,7 @@ export const E2E_PROXY_ADMIN_EMAIL = "admin@test.local";
 export const E2E_INTERNAL_USER_ID = "e2e-internal-user";
 export const E2E_INTERNAL_USER_EMAIL = "internal@test.local";
 export const E2E_TEAM_ADMIN_USER_ID = "e2e-team-admin";
+export const E2E_SEEDED_USER_PASSWORD = "E2e-Test-Pass-2026!";
 
 // Key aliases for seeded test keys (match seed.sql)
 export const E2E_UPDATE_LIMITS_KEY_ALIAS = "e2eUpdateLimitsKey";

tests/e2e/ui/fixtures/seed.sql

@@ -24,18 +24,18 @@ INSERT INTO "LiteLLM_OrganizationTable" (
   'e2e-proxy-admin', 'e2e-proxy-admin'
 );
 
--- 4. Users (password hash is scrypt of "test")
+-- 4. Users (password hash is scrypt of E2E_SEEDED_USER_PASSWORD from constants.ts)
 INSERT INTO "LiteLLM_UserTable" ("user_id", "user_email", "user_role", "teams", "password")
 VALUES
-  ('e2e-proxy-admin',      'admin@test.local',       'proxy_admin',           '{"e2e-team-crud"}',                  'scrypt:MU5CcTAi6rVK1HfY1rVPEWq6r4sxg837eq9dG4n5Q6BhDJ44442+seC6LAhLEAYr'),
-  ('e2e-admin-viewer',     'adminviewer@test.local',  'proxy_admin_viewer',   '{}',                                 'scrypt:MU5CcTAi6rVK1HfY1rVPEWq6r4sxg837eq9dG4n5Q6BhDJ44442+seC6LAhLEAYr'),
-  ('e2e-internal-user',    'internal@test.local',     'internal_user',        '{"e2e-team-crud","e2e-team-org","e2e-team-keygen"}', 'scrypt:MU5CcTAi6rVK1HfY1rVPEWq6r4sxg837eq9dG4n5Q6BhDJ44442+seC6LAhLEAYr'),
-  ('e2e-internal-viewer',  'viewer@test.local',       'internal_user_viewer', '{"e2e-team-crud"}',                  'scrypt:MU5CcTAi6rVK1HfY1rVPEWq6r4sxg837eq9dG4n5Q6BhDJ44442+seC6LAhLEAYr'),
-  ('e2e-team-admin',       'teamadmin@test.local',    'internal_user',        '{"e2e-team-crud","e2e-team-delete"}', 'scrypt:MU5CcTAi6rVK1HfY1rVPEWq6r4sxg837eq9dG4n5Q6BhDJ44442+seC6LAhLEAYr'),
-  ('e2e-invitable-user',   'invitable@test.local',    'internal_user',        '{}',                                 'scrypt:MU5CcTAi6rVK1HfY1rVPEWq6r4sxg837eq9dG4n5Q6BhDJ44442+seC6LAhLEAYr'),
-  ('e2e-internal-noteam',  'noteam@test.local',       'internal_user',        '{}',                                 'scrypt:MU5CcTAi6rVK1HfY1rVPEWq6r4sxg837eq9dG4n5Q6BhDJ44442+seC6LAhLEAYr'),
-  ('e2e-invitable-by-team-admin', 'invitable-team@test.local', 'internal_user', '{}',                                'scrypt:MU5CcTAi6rVK1HfY1rVPEWq6r4sxg837eq9dG4n5Q6BhDJ44442+seC6LAhLEAYr'),
-  ('e2e-removable-member', 'removable@test.local',    'internal_user',        '{"e2e-team-crud"}',                  'scrypt:MU5CcTAi6rVK1HfY1rVPEWq6r4sxg837eq9dG4n5Q6BhDJ44442+seC6LAhLEAYr');
+  ('e2e-proxy-admin',      'admin@test.local',       'proxy_admin',           '{"e2e-team-crud"}',                  'scrypt:KdnTJwPb3gswdqSznPE5CC6apeFIMycd6BG7yRWndZa3QZPcVs37y7jvrQCaPUNq'),
+  ('e2e-admin-viewer',     'adminviewer@test.local',  'proxy_admin_viewer',   '{}',                                 'scrypt:KdnTJwPb3gswdqSznPE5CC6apeFIMycd6BG7yRWndZa3QZPcVs37y7jvrQCaPUNq'),
+  ('e2e-internal-user',    'internal@test.local',     'internal_user',        '{"e2e-team-crud","e2e-team-org","e2e-team-keygen"}', 'scrypt:KdnTJwPb3gswdqSznPE5CC6apeFIMycd6BG7yRWndZa3QZPcVs37y7jvrQCaPUNq'),
+  ('e2e-internal-viewer',  'viewer@test.local',       'internal_user_viewer', '{"e2e-team-crud"}',                  'scrypt:KdnTJwPb3gswdqSznPE5CC6apeFIMycd6BG7yRWndZa3QZPcVs37y7jvrQCaPUNq'),
+  ('e2e-team-admin',       'teamadmin@test.local',    'internal_user',        '{"e2e-team-crud","e2e-team-delete"}', 'scrypt:KdnTJwPb3gswdqSznPE5CC6apeFIMycd6BG7yRWndZa3QZPcVs37y7jvrQCaPUNq'),
+  ('e2e-invitable-user',   'invitable@test.local',    'internal_user',        '{}',                                 'scrypt:KdnTJwPb3gswdqSznPE5CC6apeFIMycd6BG7yRWndZa3QZPcVs37y7jvrQCaPUNq'),
+  ('e2e-internal-noteam',  'noteam@test.local',       'internal_user',        '{}',                                 'scrypt:KdnTJwPb3gswdqSznPE5CC6apeFIMycd6BG7yRWndZa3QZPcVs37y7jvrQCaPUNq'),
+  ('e2e-invitable-by-team-admin', 'invitable-team@test.local', 'internal_user', '{}',                                'scrypt:KdnTJwPb3gswdqSznPE5CC6apeFIMycd6BG7yRWndZa3QZPcVs37y7jvrQCaPUNq'),
+  ('e2e-removable-member', 'removable@test.local',    'internal_user',        '{"e2e-team-crud"}',                  'scrypt:KdnTJwPb3gswdqSznPE5CC6apeFIMycd6BG7yRWndZa3QZPcVs37y7jvrQCaPUNq');
 
 -- 5. Teams (members_with_roles is required JSON)
 INSERT INTO "LiteLLM_TeamTable" (

tests/e2e/ui/fixtures/users.ts

@@ -1,6 +1,7 @@
 import {
   ADMIN_STORAGE_PATH,
   ADMIN_VIEWER_STORAGE_PATH,
+  E2E_SEEDED_USER_PASSWORD,
   INTERNAL_USER_STORAGE_PATH,
   INTERNAL_VIEWER_STORAGE_PATH,
   TEAM_ADMIN_STORAGE_PATH,
@@ -23,22 +24,22 @@ export const users: Record<Role, { email: string; password: string; seedApiRole?
   },
   [Role.ProxyAdminViewer]: {
     email: "adminviewer@test.local",
-    password: "test",
+    password: E2E_SEEDED_USER_PASSWORD,
     seedApiRole: "proxy_admin_viewer",
   },
   [Role.InternalUser]: {
     email: "internal@test.local",
-    password: "test",
+    password: E2E_SEEDED_USER_PASSWORD,
     seedApiRole: "internal_user",
   },
   [Role.InternalUserViewer]: {
     email: "viewer@test.local",
-    password: "test",
+    password: E2E_SEEDED_USER_PASSWORD,
     seedApiRole: "internal_user_viewer",
   },
   [Role.TeamAdmin]: {
     email: "teamadmin@test.local",
-    password: "test",
+    password: E2E_SEEDED_USER_PASSWORD,
     seedApiRole: "internal_user",
   },
 };

tests/e2e/ui/helpers/traffic.ts

@@ -21,6 +21,8 @@ interface ChatOptions {
   apiKey?: string;
   /** Sent as `user`, which lands in the spend log's end_user column. */
   endUser?: string;
+  /** Sent as `litellm_trace_id`, which lands in the spend log's session_id column. */
+  traceId?: string;
 }
 
 /** POST /v1/chat/completions and return the completion id (the Logs Request ID). */
@@ -34,6 +36,7 @@ export async function sendChatCompletion(request: APIRequestContext, opts: ChatO
       model: opts.model,
       messages: [{ role: "user", content: opts.prompt }],
       ...(opts.endUser ? { user: opts.endUser } : {}),
+      ...(opts.traceId ? { litellm_trace_id: opts.traceId } : {}),
     },
   });
   expect(res.ok(), `chat completion for ${opts.model} failed (${res.status()}): ${await res.text()}`).toBe(true);

tests/e2e/ui/tests/internal-user/internalUserNoTeam.spec.ts

@@ -1,6 +1,7 @@
 import { test, expect } from "@playwright/test";
 import { navigateToPage } from "../../helpers/navigation";
 import { Page } from "../../fixtures/pages";
+import { E2E_SEEDED_USER_PASSWORD } from "../../constants";
 
 /**
  * Logs in fresh inside the test rather than reusing a stored session because
@@ -15,7 +16,7 @@ test.describe("Internal User with no team memberships", () => {
     // Log in via the form as the no-team seeded user.
     await page.goto("/ui/login");
     await page.getByPlaceholder("Enter your username").fill("noteam@test.local");
-    await page.getByPlaceholder("Enter your password").fill("test");
+    await page.getByPlaceholder("Enter your password").fill(E2E_SEEDED_USER_PASSWORD);
     await page.getByRole("button", { name: "Login", exact: true }).click();
     await expect(page.getByRole("complementary").getByText("Virtual Keys")).toBeVisible({ timeout: 30_000 });
     expect(new URL(page.url()).pathname).not.toMatch(/\/connect$/);

tests/e2e/ui/tests/logs/logsPagination.spec.ts

@@ -0,0 +1,150 @@
+import { test, expect, type APIRequestContext, type Locator, type Page as PlaywrightPage } from "@playwright/test";
+import { ADMIN_STORAGE_PATH } from "../../constants";
+import { navigateToPage, dismissFeedbackPopup } from "../../helpers/navigation";
+import { Page } from "../../fixtures/pages";
+import { CHAT_MODEL_A, createVirtualKey, sendChatCompletion, waitForSpendLog } from "../../helpers/traffic";
+
+/**
+ * Session-grouped pagination (#38060): a page of N rows must render exactly N session rows, a
+ * session must never straddle pages, and two callers reusing one session id stay separate rows.
+ * All traffic is generated per run behind a unique key alias or session id, so concurrent specs
+ * cannot decide the outcome.
+ */
+
+const uniqueSuffix = (): string => `${Date.now()}-${Math.random().toString(36).slice(2, 8)}`;
+
+/** Every tab stays mounted, so the DOM holds four tables at once; scope to the visible one. */
+const requestLogsRows = (page: PlaywrightPage): Locator =>
+  page.locator("table").filter({ visible: true }).first().locator("tbody tr");
+
+const visibleTestId = (page: PlaywrightPage, id: string): Locator => page.getByTestId(id).filter({ visible: true });
+
+async function openLogs(page: PlaywrightPage): Promise<void> {
+  await navigateToPage(page, Page.Logs);
+  await dismissFeedbackPopup(page);
+  await expect(visibleTestId(page, "datatable-search")).toBeVisible({ timeout: 20_000 });
+}
+
+async function openFilterDrawer(page: PlaywrightPage): Promise<Locator> {
+  await visibleTestId(page, "datatable-filters-trigger").click();
+  const drawer = page.getByRole("dialog", { name: "Filters" });
+  await expect(drawer).toBeVisible({ timeout: 10_000 });
+  return drawer;
+}
+
+async function applyKeyAliasFilter(page: PlaywrightPage, drawer: Locator, alias: string): Promise<void> {
+  await drawer.getByRole("combobox", { name: "Search a key alias" }).click();
+  await page.keyboard.type(alias);
+  await page.getByRole("option", { name: alias, exact: true }).first().click();
+  await drawer.getByRole("button", { name: "Apply Filters" }).click();
+  await expect(drawer).not.toBeVisible({ timeout: 10_000 });
+}
+
+async function setRowsPerPage(page: PlaywrightPage, size: "25" | "50" | "100"): Promise<void> {
+  await visibleTestId(page, "pagination-page-size").click();
+  await page.getByRole("option", { name: size, exact: true }).click();
+}
+
+test.describe("Logs page session-grouped pagination", () => {
+  test.use({ storageState: ADMIN_STORAGE_PATH });
+
+  test("a 25-row page renders exactly 25 session rows and no session straddles pages", async ({ page, request }) => {
+    const suffix = uniqueSuffix();
+    const alias = `e2e-logs-pgn-${suffix}`;
+    const mine = await createVirtualKey(request, { key_alias: alias });
+
+    const soloIds: string[] = [];
+    for (let i = 0; i < 26; i++) {
+      soloIds.push(
+        await sendChatCompletion(request, {
+          model: CHAT_MODEL_A,
+          prompt: `logs-pgn-solo-${i}-${suffix}`,
+          apiKey: mine.key,
+        }),
+      );
+    }
+    const sessionA = `sess-pgn-a-${suffix}`;
+    const sessionB = `sess-pgn-b-${suffix}`;
+    let lastSessionCallId = "";
+    for (let i = 0; i < 7; i++) {
+      lastSessionCallId = await sendChatCompletion(request, {
+        model: CHAT_MODEL_A,
+        prompt: `logs-pgn-a-${i}-${suffix}`,
+        apiKey: mine.key,
+        traceId: sessionA,
+      });
+    }
+    for (let i = 0; i < 3; i++) {
+      lastSessionCallId = await sendChatCompletion(request, {
+        model: CHAT_MODEL_A,
+        prompt: `logs-pgn-b-${i}-${suffix}`,
+        apiKey: mine.key,
+        traceId: sessionB,
+      });
+    }
+    await waitForSpendLog(request, lastSessionCallId);
+    await waitForSpendLog(request, soloIds[soloIds.length - 1]);
+
+    // 36 calls in 28 session groups: 26 solos plus sessions of 7 and 3.
+    await openLogs(page);
+    const drawer = await openFilterDrawer(page);
+    await applyKeyAliasFilter(page, drawer, alias);
+    await setRowsPerPage(page, "25");
+
+    await expect(visibleTestId(page, "pagination-range")).toHaveText("Showing 1-25 of 28", { timeout: 30_000 });
+    await expect(requestLogsRows(page)).toHaveCount(25);
+    // The sessions are the newest groups, so their single representative rows sit on page 1.
+    await expect(requestLogsRows(page).filter({ hasText: sessionA })).toHaveCount(1);
+    await expect(requestLogsRows(page).filter({ hasText: sessionA })).toContainText("7");
+    await expect(requestLogsRows(page).filter({ hasText: sessionB })).toHaveCount(1);
+
+    await visibleTestId(page, "pagination-next").click();
+
+    await expect(visibleTestId(page, "pagination-range")).toHaveText("Showing 26-28 of 28", { timeout: 30_000 });
+    await expect(requestLogsRows(page)).toHaveCount(3);
+    await expect(requestLogsRows(page).filter({ hasText: sessionA })).toHaveCount(0);
+    await expect(requestLogsRows(page).filter({ hasText: sessionB })).toHaveCount(0);
+  });
+
+  test("two keys reusing one session id stay separate rows", async ({ page, request }) => {
+    const suffix = uniqueSuffix();
+    const mine = await createVirtualKey(request, { key_alias: `e2e-logs-pgn-mine-${suffix}` });
+    const theirs = await createVirtualKey(request, { key_alias: `e2e-logs-pgn-theirs-${suffix}` });
+    const sharedSession = `sess-pgn-shared-${suffix}`;
+
+    let lastId = "";
+    for (let i = 0; i < 2; i++) {
+      lastId = await sendChatCompletion(request, {
+        model: CHAT_MODEL_A,
+        prompt: `logs-pgn-shared-mine-${i}-${suffix}`,
+        apiKey: mine.key,
+        traceId: sharedSession,
+      });
+    }
+    lastId = await sendChatCompletion(request, {
+      model: CHAT_MODEL_A,
+      prompt: `logs-pgn-shared-theirs-${suffix}`,
+      apiKey: theirs.key,
+      traceId: sharedSession,
+    });
+    await waitForSpendLog(request, lastId);
+
+    await openLogs(page);
+    const drawer = await openFilterDrawer(page);
+    await drawer.getByPlaceholder("Enter session ID…").fill(sharedSession);
+    await drawer.getByRole("button", { name: "Apply Filters" }).click();
+    await expect(drawer).not.toBeVisible({ timeout: 10_000 });
+
+    // One row per caller: reusing a session id must not merge two keys' activity into one row.
+    await expect(requestLogsRows(page).filter({ hasText: sharedSession })).toHaveCount(2, { timeout: 30_000 });
+
+    // And each row carries ITS key's totals: two calls badge the first key's row,
+    // while the other key's single call renders as a plain LLM row.
+    const mineRow = requestLogsRows(page).filter({ hasText: sharedSession }).filter({ hasText: mine.token });
+    const theirsRow = requestLogsRows(page).filter({ hasText: sharedSession }).filter({ hasText: theirs.token });
+    await expect(mineRow).toHaveCount(1);
+    await expect(theirsRow).toHaveCount(1);
+    await expect(mineRow.getByText("2", { exact: true })).toBeVisible();
+    await expect(theirsRow.getByText("LLM", { exact: true })).toBeVisible();
+  });
+});

tests/e2e/ui/tests/proxy-admin/secondAdmin.spec.ts

@@ -10,7 +10,7 @@ test.describe("Second proxy admin", () => {
   test("an invited admin can log in, mint a key, and call a model with it", async ({ page, browser, request }) => {
     const suffix = Date.now();
     const email = `second-admin-${suffix}@test.local`;
-    const password = "e2e-second-admin-password";
+    const password = "E2e-Second-Admin-Pass-1!";
     const auth = { Authorization: `Bearer ${masterKey()}` };
 
     const inviteAdminUser = async (): Promise<string> => {

tests/logging_callback_tests/test_bedrock_knowledgebase_hook.py

@@ -71,6 +71,48 @@ def setup_vector_store_registry():
     )
 
 
+@pytest.mark.asyncio
+async def test_vector_store_hook_routes_search_through_proxy_router(
+    setup_vector_store_registry,
+):
+    proxy_router = Mock()
+    proxy_router.avector_store_search = AsyncMock(
+        return_value=VectorStoreSearchResponse(
+            object="vector_store.search_results.page",
+            search_query="what is litellm?",
+            data=[
+                VectorStoreSearchResult(
+                    score=1.0,
+                    content=[VectorStoreResultContent(text="routed context", type="text")],
+                )
+            ],
+        )
+    )
+    logging_obj = Mock()
+    logging_obj.model_call_details = {
+        "litellm_params": {"metadata": {"user_api_key_team_id": "team-a"}}
+    }
+
+    with patch("litellm.proxy.proxy_server.llm_router", proxy_router):
+        _, messages, _ = await VectorStorePreCallHook().async_get_chat_completion_prompt(
+            model="chat-model",
+            messages=[{"role": "user", "content": "what is litellm?"}],
+            non_default_params={"vector_store_ids": ["T37J8R4WTM"]},
+            prompt_id=None,
+            prompt_variables=None,
+            dynamic_callback_params={},
+            litellm_logging_obj=logging_obj,
+        )
+
+    proxy_router.avector_store_search.assert_awaited_once_with(
+        vector_store_id="T37J8R4WTM",
+        query="what is litellm?",
+        custom_llm_provider="bedrock",
+        metadata={"user_api_key_team_id": "team-a"},
+    )
+    assert messages[0]["content"] == "Context:\n\nrouted context\n\n"
+
+
 @pytest.mark.asyncio
 async def test_e2e_bedrock_knowledgebase_retrieval_with_completion(
     setup_vector_store_registry,

tests/openai_endpoints_tests/test_e2e_openai_responses_api.py

@@ -1,5 +1,5 @@
 import httpx
-from openai import OpenAI, BadRequestError, APIStatusError
+from openai import OpenAI, BadRequestError, NotFoundError, APIStatusError
 import pytest
 
 
@@ -105,10 +105,9 @@ def test_streaming_response():
     assert len(collected_chunks) > 0
 
 
-def test_bad_request_error():
+def test_model_not_found_error():
     client = get_test_client()
-    with pytest.raises(BadRequestError):
-        # Trigger error with invalid model name
+    with pytest.raises(NotFoundError):
         client.responses.create(model="non-existent-model", input="This should fail")
 
 

tests/router_unit_tests/test_router_anthropic_messages_fallback.py

@@ -338,6 +338,112 @@ def test_record_pre_routing_selection_writes_only_the_internal_bucket():
     assert kwargs["metadata"] == {"user_id": "u1"}
 
 
+@pytest.mark.asyncio
+@pytest.mark.parametrize("stream", [False, True], ids=["non-streaming", "streaming"])
+async def test_generic_only_row_recovers_safeguard_refusal(stream):
+    """With no content-policy list configured, a generic fallback row covers safeguard refusals,
+    so the dashboard's generic fallbacks work without config-only content_policy rows."""
+    fake = FakeAnthropicUpstream()
+    router = Router(model_list=[FABLE_TIER, OPUS_TARGET], fallbacks=[{"fable-tier": ["opus-target"]}])
+
+    with fake.install():
+        response = await router.aanthropic_messages(
+            model="fable-tier", max_tokens=16, stream=stream, messages=[{"role": "user", "content": "hi"}]
+        )
+        body = await _collect(response) if stream else response
+
+    if stream:
+        assert b'"refusal"' not in body
+        assert b"text_delta" in body
+    else:
+        assert body["stop_reason"] == "end_turn"
+    assert len(fake.calls) == 2
+    assert "claude-opus-5" in fake.calls[1]
+
+
+@pytest.mark.asyncio
+async def test_configured_content_policy_list_stays_authoritative_over_generic_rows():
+    fake = FakeAnthropicUpstream()
+    router = Router(
+        model_list=[FABLE_TIER, OPUS_TARGET],
+        fallbacks=[{"fable-tier": ["opus-target"]}],
+        content_policy_fallbacks=[{"unrelated-group": ["opus-target"]}],
+    )
+
+    with fake.install():
+        response = await router.aanthropic_messages(
+            model="fable-tier", max_tokens=16, messages=[{"role": "user", "content": "hi"}]
+        )
+
+    assert response["stop_reason"] == "refusal"
+    assert len(fake.calls) == 1
+
+
+def test_refusal_fallback_available_arms_on_generic_rows_only_without_content_policy():
+    router = Router(model_list=[FABLE_TIER, OPUS_TARGET], fallbacks=[{"tier-group": ["opus-target"]}])
+    stamped = {"litellm_metadata": {PRE_ROUTING_SELECTED_MODEL_KEY: "tier-group"}}
+
+    assert router._refusal_fallback_available("router-group", stamped) is True
+    assert router._refusal_fallback_available("router-group", {}) is False
+    assert router._refusal_fallback_available("router-group", {"content_policy_fallbacks": [{"other": ["x"]}]}) is False
+
+
+def test_chat_content_filter_gate_unchanged_by_generic_rows():
+    """The generic-row arming is scoped to /v1/messages safeguard refusals; the chat surface's
+    content_filter gate keeps its long-standing content-policy-only semantics."""
+    from litellm.types.utils import Choices, ModelResponse
+
+    router = Router(model_list=[FABLE_TIER, OPUS_TARGET], fallbacks=[{"fable-tier": ["opus-target"]}])
+    response = ModelResponse(choices=[Choices(finish_reason="content_filter")])
+
+    assert router._should_raise_content_policy_error(model="fable-tier", response=response, kwargs={}) is False
+
+
+@pytest.mark.asyncio
+@pytest.mark.parametrize("stream", [False, True], ids=["non-streaming", "streaming"])
+async def test_disable_fallbacks_returns_the_refusal_instead_of_raising(stream):
+    """A request that opted out of fallbacks must receive the provider's refusal response,
+    never a ContentPolicyViolationError the dispatcher refuses to recover."""
+    fake = FakeAnthropicUpstream()
+    router = Router(model_list=[FABLE_TIER, OPUS_TARGET], fallbacks=[{"fable-tier": ["opus-target"]}])
+
+    with fake.install():
+        response = await router.aanthropic_messages(
+            model="fable-tier",
+            max_tokens=16,
+            stream=stream,
+            disable_fallbacks=True,
+            messages=[{"role": "user", "content": "hi"}],
+        )
+        body = await _collect(response) if stream else response
+
+    if stream:
+        assert b'"stop_reason": "refusal"' in body
+    else:
+        assert body["stop_reason"] == "refusal"
+    assert len(fake.calls) == 1
+
+
+@pytest.mark.asyncio
+async def test_disable_fallbacks_beats_a_content_policy_row_too():
+    fake = FakeAnthropicUpstream()
+    router = Router(
+        model_list=[FABLE_TIER, OPUS_TARGET],
+        content_policy_fallbacks=[{"fable-tier": ["opus-target"]}],
+    )
+
+    with fake.install():
+        response = await router.aanthropic_messages(
+            model="fable-tier",
+            max_tokens=16,
+            disable_fallbacks=True,
+            messages=[{"role": "user", "content": "hi"}],
+        )
+
+    assert response["stop_reason"] == "refusal"
+    assert len(fake.calls) == 1
+
+
 def test_refusal_gate_keys_on_pre_routing_tier_stamp():
     router = _router(content_policy_fallbacks=[{"tier-group": ["opus-target"]}])
 

tests/router_unit_tests/test_router_embedding_integration.py

@@ -5,17 +5,218 @@ These tests simulate real-world scenarios where headers and configuration
 need to be properly propagated through the router to the LLM API.
 """
 
-from unittest.mock import MagicMock, patch, AsyncMock
+import json
+from unittest.mock import AsyncMock, MagicMock, patch
 
+import httpx
 import pytest
+import respx
 
-
+import litellm
 from litellm import Router
+from litellm.llms.base_llm.vector_store.transformation import (
+    LiteLLMVectorStoreEmbeddingExecutor,
+    RouterVectorStoreEmbeddingExecutor,
+)
+
+QUERY_VECTOR = [0.5, -0.25, 0.125]
+OPENAI_EMBEDDINGS_URL = "https://api.openai.com/v1/embeddings"
+STORE_EMBEDDINGS_URL = "https://embedding.example/v1/embeddings"
+
+
+def _mock_embedding_route(respx_mock: respx.MockRouter, url: str) -> respx.Route:
+    return respx_mock.post(url).mock(
+        return_value=httpx.Response(
+            200,
+            json={
+                "object": "list",
+                "data": [{"object": "embedding", "index": 0, "embedding": QUERY_VECTOR}],
+                "model": "text-embedding-3-small",
+                "usage": {"prompt_tokens": 2, "total_tokens": 2},
+            },
+        )
+    )
+
+
+def _sent(route: respx.Route, index: int) -> tuple[str, str, list[str]]:
+    request = route.calls[index].request
+    body = json.loads(request.read())
+    return request.headers["authorization"], body["model"], body["input"]
+
+
+def _alias_router() -> Router:
+    return Router(
+        model_list=[
+            {
+                "model_name": "team-alias",
+                "litellm_params": {
+                    "model": "openai/text-embedding-3-small",
+                    "api_key": "deployment-key",
+                },
+            }
+        ]
+    )
 
 
 class TestRouterEmbeddingIntegration:
     """Integration tests for embedding with router configuration."""
 
+    def test_vector_store_request_metadata_prefers_litellm_metadata(self):
+        assert Router._vector_store_request_metadata(
+            {
+                "litellm_metadata": {"user_api_key_team_id": "team-a"},
+                "metadata": {"user_api_key_team_id": "team-b"},
+            }
+        ) == {"user_api_key_team_id": "team-a"}
+
+        assert Router._vector_store_request_metadata({"metadata": {"user_api_key_team_id": "team-b"}}) == {
+            "user_api_key_team_id": "team-b"
+        }
+        assert Router._vector_store_request_metadata({}) == {}
+
+    def test_sync_vector_store_wrapper_injects_router_embedding_executor(self):
+        router = Router(model_list=[])
+        original = MagicMock(return_value="searched")
+        wrapped = router.factory_function(original, call_type="vector_store_search")
+
+        assert (
+            wrapped(
+                vector_store_id="store",
+                query="query",
+                custom_llm_provider="valkey",
+                metadata={"user_api_key_team_id": "team-a"},
+            )
+            == "searched"
+        )
+
+        call_kwargs = original.call_args.kwargs
+        assert call_kwargs["custom_llm_provider"] == "valkey"
+        executor = call_kwargs["_direct_vector_store_embedding_executor"]
+        assert isinstance(executor, RouterVectorStoreEmbeddingExecutor)
+        assert executor.metadata == {"user_api_key_team_id": "team-a"}
+
+    def test_sync_vector_store_wrapper_preserves_model_routing(self):
+        router = Router(model_list=[])
+        original = MagicMock()
+        wrapped = router.factory_function(original, call_type="vector_store_search")
+
+        with patch.object(router, "_generic_api_call_with_fallbacks", return_value="routed") as fallback:
+            assert wrapped(model="vector-alias", vector_store_id="store", query="query") == "routed"
+
+        assert fallback.call_args.kwargs["model"] == "vector-alias"
+        assert fallback.call_args.kwargs["original_function"] is original
+        assert isinstance(
+            fallback.call_args.kwargs["_direct_vector_store_embedding_executor"],
+            RouterVectorStoreEmbeddingExecutor,
+        )
+
+    @pytest.mark.asyncio
+    async def test_vector_store_embedding_executors_cover_sdk_and_router_paths(
+        self, respx_mock: respx.MockRouter, monkeypatch: pytest.MonkeyPatch
+    ):
+        monkeypatch.setattr(litellm, "disable_aiohttp_transport", True)
+        monkeypatch.delenv("OPENAI_API_KEY", raising=False)
+        openai_route = _mock_embedding_route(respx_mock, OPENAI_EMBEDDINGS_URL)
+        store_route = _mock_embedding_route(respx_mock, STORE_EMBEDDINGS_URL)
+        sdk_executor = LiteLLMVectorStoreEmbeddingExecutor()
+
+        sync_response = sdk_executor.embed("openai/text-embedding-3-small", "sync", {"api_key": "explicit"})
+        async_response = await sdk_executor.aembed("openai/text-embedding-3-small", "async", {"api_key": "explicit"})
+
+        assert sync_response.data[0]["embedding"] == QUERY_VECTOR
+        assert async_response.data[0]["embedding"] == QUERY_VECTOR
+        assert _sent(openai_route, 0) == ("Bearer explicit", "text-embedding-3-small", ["sync"])
+        assert _sent(openai_route, 1) == ("Bearer explicit", "text-embedding-3-small", ["async"])
+
+        explicit_config = {
+            "api_base": "https://embedding.example/v1",
+            "api_key": "store-key",
+            "metadata": {
+                "configured": True,
+                "user_api_key_team_id": "untrusted-team",
+            },
+            "model": "untrusted-model",
+        }
+        mock_router = MagicMock()
+        mock_router.embedding.return_value = sync_response
+        router_executor = RouterVectorStoreEmbeddingExecutor(
+            router=mock_router,
+            metadata={"user_api_key_team_id": "team-a"},
+        )
+        assert router_executor.embed("team-alias", "query", explicit_config) is sync_response
+        mock_router.embedding.assert_called_once_with(
+            model="team-alias",
+            input=["query"],
+            api_base="https://embedding.example/v1",
+            api_key="store-key",
+            metadata={"configured": True, "user_api_key_team_id": "team-a"},
+        )
+
+        alias_executor = RouterVectorStoreEmbeddingExecutor(
+            router=_alias_router(),
+            metadata={"user_api_key_team_id": "team-a"},
+        )
+        sync_alias = alias_executor.embed("team-alias", "sync query", explicit_config)
+        async_alias = await alias_executor.aembed("team-alias", "async query", explicit_config)
+
+        assert sync_alias.data[0]["embedding"] == QUERY_VECTOR
+        assert async_alias.data[0]["embedding"] == QUERY_VECTOR
+        assert openai_route.call_count == 2
+        assert _sent(store_route, 0) == ("Bearer store-key", "text-embedding-3-small", ["sync query"])
+        assert _sent(store_route, 1) == ("Bearer store-key", "text-embedding-3-small", ["async query"])
+
+    @pytest.mark.asyncio
+    async def test_router_executor_falls_back_to_sdk_for_models_the_router_does_not_serve(
+        self, respx_mock: respx.MockRouter, monkeypatch: pytest.MonkeyPatch
+    ):
+        monkeypatch.setattr(litellm, "disable_aiohttp_transport", True)
+        monkeypatch.delenv("OPENAI_API_KEY", raising=False)
+        store_route = _mock_embedding_route(respx_mock, STORE_EMBEDDINGS_URL)
+        executor = RouterVectorStoreEmbeddingExecutor(
+            router=_alias_router(),
+            metadata={"user_api_key_team_id": "team-a"},
+        )
+        inline_config = {"api_base": "https://embedding.example/v1", "api_key": "store-key"}
+
+        sync_response = executor.embed("openai/text-embedding-3-large", "sync query", inline_config)
+        async_response = await executor.aembed("openai/text-embedding-3-large", "async query", inline_config)
+
+        assert sync_response.data[0]["embedding"] == QUERY_VECTOR
+        assert async_response.data[0]["embedding"] == QUERY_VECTOR
+        assert _sent(store_route, 0) == ("Bearer store-key", "text-embedding-3-large", ["sync query"])
+        assert _sent(store_route, 1) == ("Bearer store-key", "text-embedding-3-large", ["async query"])
+
+    @pytest.mark.asyncio
+    async def test_router_executor_rejects_unserved_models_without_explicit_config(
+        self, respx_mock: respx.MockRouter, monkeypatch: pytest.MonkeyPatch
+    ):
+        monkeypatch.setattr(litellm, "disable_aiohttp_transport", True)
+        monkeypatch.setenv("OPENAI_API_KEY", "env-key")
+        openai_route = _mock_embedding_route(respx_mock, OPENAI_EMBEDDINGS_URL)
+        executor = RouterVectorStoreEmbeddingExecutor(
+            router=_alias_router(),
+            metadata={"user_api_key_team_id": "team-a"},
+        )
+
+        with pytest.raises(litellm.BadRequestError):
+            executor.embed("openai/text-embedding-3-large", "sync query", {})
+        with pytest.raises(litellm.BadRequestError):
+            await executor.aembed("openai/text-embedding-3-large", "async query", {})
+
+        assert openai_route.call_count == 0
+
+    def test_router_executor_routes_deployment_model_names_through_the_router(
+        self, respx_mock: respx.MockRouter, monkeypatch: pytest.MonkeyPatch
+    ):
+        monkeypatch.delenv("OPENAI_API_KEY", raising=False)
+        openai_route = _mock_embedding_route(respx_mock, OPENAI_EMBEDDINGS_URL)
+        executor = RouterVectorStoreEmbeddingExecutor(router=_alias_router(), metadata={})
+
+        response = executor.embed("openai/text-embedding-3-small", "query", {})
+
+        assert response.data[0]["embedding"] == QUERY_VECTOR
+        assert _sent(openai_route, 0) == ("Bearer deployment-key", "text-embedding-3-small", ["query"])
+
     def test_embedding_with_deployment_specific_headers(self):
         """
         Test that deployment-specific headers are propagated.
@@ -122,9 +323,7 @@ class TestRouterEmbeddingIntegration:
 
         router = Router(
             model_list=model_list,
-            default_litellm_params={
-                "metadata": {"environment": "test", "service": "embedding-service"}
-            },
+            default_litellm_params={"metadata": {"environment": "test", "service": "embedding-service"}},
         )
 
         with patch("litellm.embedding") as mock_embedding:
@@ -240,9 +439,7 @@ class TestRouterEmbeddingIntegration:
         # Make multiple calls and verify headers are always present
         for i in range(5):
             with patch("litellm.embedding") as mock_embedding:
-                mock_embedding.return_value = MagicMock(
-                    data=[{"embedding": [0.1, 0.2]}]
-                )
+                mock_embedding.return_value = MagicMock(data=[{"embedding": [0.1, 0.2]}])
 
                 router.embedding(model="shared-embedding-model", input=[f"test {i}"])
 
@@ -327,9 +524,7 @@ class TestRouterEmbeddingIntegration:
 
         router = Router(
             model_list=model_list,
-            default_litellm_params={
-                "headers": {"X-Custom-Azure-Header": "azure-value"}
-            },
+            default_litellm_params={"headers": {"X-Custom-Azure-Header": "azure-value"}},
         )
 
         with patch("litellm.embedding") as mock_embedding:

tests/rust-python-harness/AGENTS.md

@@ -0,0 +1,44 @@
+# Expected Structure
+
+```text
+tests/rust-python-harness/
+├── __main__.py
+│
+├── strategies/
+│   ├── e2e_parity/
+│   │   ├── runner.py
+│   │   ├── sdk/
+│   │   │   ├── ocr/
+│   │   │   ├── messages/
+│   │   │   ├── chat_completions/
+│   │   │   └── responses/
+│   │   └── gateway/
+│   │
+│   ├── trace_parity/
+│   │   ├── runner.py
+│   │   ├── sdk/
+│   │   └── gateway/
+│   │
+│   └── unit_tests/
+│       ├── runner.py
+│       ├── mapping_validator.py
+│       ├── python_runner.py
+│       └── rust_runner.py
+│
+└── shared/
+    ├── parity/
+    ├── tracing/
+    └── reporting/
+```
+
+- Run locally only; no CI integration
+- `__main__.py` selects strategies and combines their reports; each strategy also runs independently
+- `e2e_parity/` compares SDK objects, exceptions, callbacks, and streams, or gateway HTTP responses
+- `trace_parity/` compares mapped operations, call counts, and required execution ordering
+- E2E and trace runners share orchestration across `sdk/` and `gateway/`; surface-specific execution lives in those folders
+- `unit_tests/runner.py` combines mapping validation, Python test runs, and native Rust test runs
+- `mapping_validator.py` matches Python/Rust tests by agreed names or annotations and reports missing or ambiguous counterparts
+- `python_runner.py` runs existing Python tests with Rust disabled and enabled in separate processes, verifies backend selection, and compares results
+- `rust_runner.py` runs Cargo tests; native Rust unit tests stay beside their implementation
+- `shared/` contains reusable parity, tracing, and reporting machinery
+- Keep fixtures with their owning API and existing Python tests in their current locations

tests/test_litellm/litellm_core_utils/test_exception_mapping_utils.py

@@ -1013,6 +1013,48 @@ def test_an_unmapped_exception_with_no_model_or_provider_is_a_connection_error(q
     assert "boom" in raised.value.message
 
 
+def _raise_and_map(
+    model: str | None, original_exception: Exception, custom_llm_provider: str | None
+) -> None:
+    """Calls exception_type() from inside the except block, as litellm/main.py does,
+    so traceback.format_exc() has a real stack."""
+    try:
+        raise original_exception
+    except type(original_exception) as caught:
+        exception_type(
+            model=model,
+            original_exception=caught,
+            custom_llm_provider=custom_llm_provider,
+        )
+
+
+def test_an_unmapped_exception_message_keeps_traceback_for_sdk_callers(quiet_exception_mapping):
+    """Direct SDK callers debug unmapped provider exceptions with this traceback;
+    only the proxy's response boundary strips it."""
+    with pytest.raises(litellm.APIConnectionError) as raised:
+        _raise_and_map(
+            model="MiniMax-M2.5",
+            original_exception=RuntimeError("socket hung up"),
+            custom_llm_provider="minimax",
+        )
+
+    assert "Traceback (most recent call last)" in raised.value.message
+    assert "test_exception_mapping_utils.py" in raised.value.message
+
+
+def test_an_unmapped_exception_with_no_model_or_provider_message_keeps_traceback(
+    quiet_exception_mapping,
+):
+    with pytest.raises(litellm.APIConnectionError) as raised:
+        _raise_and_map(
+            model=None,
+            original_exception=ValueError("boom"),
+            custom_llm_provider=None,
+        )
+
+    assert "Traceback (most recent call last)" in raised.value.message
+
+
 CONTEXT_WINDOW_MESSAGE = "This model's maximum context length is 4096 tokens."
 CONTENT_POLICY_MESSAGE = (
     '{"error": {"type": "invalid_request_error", "code": "content_policy_violation"}}'

tests/test_litellm/llms/a2a/chat/guardrail_translation/test_a2a_guardrail_handler.py

@@ -0,0 +1,35 @@
+"""Tests for litellm/llms/a2a/chat/guardrail_translation/handler.py."""
+
+import json
+
+from litellm.llms.a2a.chat.guardrail_translation.handler import A2AGuardrailHandler
+from litellm.llms.base_llm.guardrail_translation.base_translation import StreamingScanKey
+
+
+def _text_event(text: str) -> str:
+    return json.dumps(
+        {
+            "jsonrpc": "2.0",
+            "id": "req-1",
+            "result": {"kind": "message", "role": "agent", "parts": [{"kind": "text", "text": text}]},
+        }
+    )
+
+
+def _status_event() -> str:
+    return json.dumps({"jsonrpc": "2.0", "id": "req-1", "result": {"kind": "status-update", "status": {}}})
+
+
+class TestA2AGuardrailHandlerStreamingScanKey:
+    def test_key_joins_the_text_of_every_message_event(self):
+        key = A2AGuardrailHandler().get_streaming_scan_key([_text_event("hello "), _text_event("world")])
+        assert key == StreamingScanKey(texts=("hello world",))
+
+    def test_events_without_text_leave_the_key_unchanged(self):
+        handler = A2AGuardrailHandler()
+        events = [_text_event("hello")]
+        assert handler.get_streaming_scan_key(events + [_status_event()]) == handler.get_streaming_scan_key(events)
+
+    def test_unparseable_items_are_ignored(self):
+        key = A2AGuardrailHandler().get_streaming_scan_key([_text_event("hi"), "not json", b"bytes"])
+        assert key.texts == ("hi",)