When the messages or response JSON fields in spend logs are truncated
before being written to the database, the truncation marker now includes
a note explaining:
- This is a DB storage safeguard
- Full, untruncated data is still sent to logging callbacks (OTEL, Datadog, etc.)
- The MAX_STRING_LENGTH_PROMPT_IN_DB env var can be used to increase the limit
Also emits a verbose_proxy_logger.info message when truncation occurs in
the request body or response spend log paths.
Adds 3 new tests:
- test_truncation_includes_db_safeguard_note
- test_response_truncation_logs_info_message
- test_request_body_truncation_logs_info_message
Co-authored-by: Krish Dholakia <krrishdholakia@gmail.com>
Claude Code v2.1.69+ sends `custom: {defer_loading: true}` on tool
definitions. Anthropic's API accepts this field, but Bedrock rejects it
with "Extra inputs are not permitted", causing ~90% of requests to fail.
Strip the `custom` field from each tool in the request body before
sending to Bedrock, in both the Messages API and Chat API invoke paths.
Fixes#22847
Co-authored-by: Ishaan Jaff <ishaanjaffer0324@gmail.com>
* fix(proxy): readiness check returns 200 when database is unreachable
_db_health_readiness_check() catches health_check() exceptions but
never updates db_health_cache to "disconnected" and never re-raises.
The caller health_readiness() always returns 200 with "db": "connected"
hardcoded, regardless of actual DB state.
In Kubernetes, this means pods with dead database connections stay in
the Service endpoints and continue receiving traffic they cannot serve.
Changes:
- Set db_health_cache to "disconnected" and re-raise the exception on
health_check failure so health_readiness() returns 503
- Use actual db_health_status["status"] in the response instead of
hardcoding "db": "connected"
- Reduce cache TTL from 2 minutes to 15 seconds. The 2-minute window
is too wide for readiness probes (typically 10-15s intervals) and
means a pod can report healthy for up to 2 minutes after the DB dies
- Only serve cached results when status is "connected". The previous
condition (status != "unknown") would also cache "disconnected" for
2 minutes, delaying recovery detection after a DB comes back
* fix(proxy): add DB connection self-healing to readiness check
When the Prisma query engine's internal TCP connection pool holds dead
connections (caused by network blips, Cloud SQL proxy restarts, or
node-level issues), health_check() fails with httpx.ConnectError.
The engine never recovers on its own because nothing triggers a
disconnect/connect cycle to restart the subprocess with fresh
connections.
This leaves pods permanently failing readiness checks until they are
manually restarted, even after the underlying DB becomes reachable
again.
Add a reconnect attempt to _db_health_readiness_check() when
health_check() fails:
1. disconnect() - kills the query engine subprocess and closes all
connections (has built-in backoff retry: 3 tries, 10s max)
2. connect() - starts a new engine with fresh TCP connections (has
built-in backoff retry: 3 tries, 10s max)
3. health_check() - verifies the new connection works (has built-in
backoff retry: 3 tries, 10s max)
If reconnect succeeds, the pod immediately returns to service (200).
If it fails, the original exception is re-raised (503). Reconnect
attempts are rate-limited by probe frequency (~10-15s), so a
permanently unreachable DB gets one attempt per cycle with no retry
loops.
This uses the same disconnect/connect mechanism that
PrismaWrapper.recreate_prisma_client() uses for IAM token refresh,
and aligns with the community-documented pattern for Prisma connection
recovery in long-running processes (prisma/prisma#24718, #27024).
* Add poetry lock and modify test_health_endpoints
* Address allow_requests_on_db_unavailable regression
* Address comments
* resolve greptile issue
* Restore accidentally deleted UI HTML files
These were removed in an earlier commit but still exist on main.
Restoring to keep the PR diff clean.
* Guard reconnect with is_database_transport_error
Only attempt disconnect/connect/health_check cycle for transport-level
failures (unreachable DB, dropped connection). Data-layer errors like
UniqueViolationError indicate the DB is reachable, so reconnecting
would be pointless churn.
* Address greptile's comments
* Fix module alias after rebase and add adversarial test coverage
- Unify module alias to _health_endpoints_module after rebase conflict
- Add test for non-transport error with flag on (exercises is_database_transport_error guard)
- Add test for disconnect() failure during reconnect cycle
- Split non-transport error test into flag-off (re-raises) and flag-on (skips reconnect) variants
* Remove stale UI HTML files reintroduced during rebase
* fix: don't close HTTP/SDK clients on LLMClientCache eviction
Removing the _remove_key override that eagerly called aclose()/close()
on evicted clients. Evicted clients may still be held by in-flight
streaming requests; closing them causes:
RuntimeError: Cannot send a request, as the client has been closed.
This is a regression from commit fb72979432. Clients that are no longer
referenced will be garbage-collected naturally. Explicit shutdown cleanup
happens via close_litellm_async_clients().
Fixes production crashes after the 1-hour cache TTL expires.
* test: update LLMClientCache unit tests for no-close-on-eviction behavior
Flip the assertions: evicted clients must NOT be closed. Replace
test_remove_key_closes_async_client → test_remove_key_does_not_close_async_client
and equivalents for sync/eviction paths.
Add test_remove_key_removes_plain_values for non-client cache entries.
Remove test_background_tasks_cleaned_up_after_completion (no more _background_tasks).
Remove test_remove_key_no_event_loop variant that depended on old behavior.
* test: add e2e tests for OpenAI SDK client surviving cache eviction
Add two new e2e tests using real AsyncOpenAI clients:
- test_evicted_openai_sdk_client_stays_usable: verifies size-based eviction
doesn't close the client
- test_ttl_expired_openai_sdk_client_stays_usable: verifies TTL expiry
eviction doesn't close the client
Both tests sleep after eviction so any create_task()-based close would
have time to run, making the regression detectable.
Also expand the module docstring to explain why the sleep is required.
* docs(AGENTS.md): add rule — never close HTTP/SDK clients on cache eviction
* docs(CLAUDE.md): add HTTP client cache safety guideline
* Include user_email in new user creation within get_user_object
Enhance the get_user_object function to include user_email in the parameters when creating a new user. This change is accompanied by a new test to verify that user_email is correctly included during the upsert process.
* Improve error handling in test_get_user_object by logging exceptions
Updated the test_get_user_object_upsert_includes_user_email function to log exceptions when they occur, enhancing the visibility of potential issues during testing. This change helps in diagnosing failures related to the mock LiteLLM_UserTable.
* fix(passthrough): raise_for_status in _async_streaming to propagate Azure 429s
* address greptile review feedback (greploop iteration 1)
Guard data/json args when content is provided to avoid httpx ValueError
* address greptile review feedback (greploop iteration 2)
Use bare raise to preserve original traceback in _async_streaming exception handler
* address greptile review feedback (greploop iteration 3)
Close httpx streaming response on error to prevent connection pool exhaustion
* address greptile review feedback (greploop iteration 4)
Guard aclose() call to prevent masking original exception; add explicit test for content param forwarding
* address greptile review feedback (greploop iteration 5)
Pass content to sign_request so AWS body-hash signing is correct when content is the sole body source
* revert sign_request content change - request_data expects dict, not bytes
Bedrock's sign_request calls json.dumps(request_data) — passing content bytes
would TypeError. sign_request should only receive data/json (dict), not raw bytes.
All operational/diagnostic messages in WebSearchInterceptionLogger are now
debug-level to avoid flooding production logs while still remaining available
when verbose logging is enabled.
Co-authored-by: Copilot <223556219+Copilot@users.noreply.github.com>
PR #22890 used cast(str, ...) / cast(Optional[str], ...) for the return
statements; this PR's approach uses str() for explicit runtime coercion
(addressing Greptile's concern). Keep the str() version and drop the
now-unused cast import.
Co-Authored-By: Claude Sonnet 4.6 <noreply@anthropic.com>
Per Sameerlite's review: warning-level logs trigger Slack alerts.
All 6 remaining .warning() calls were operational/fallback messages,
not actual errors. Changed to .info() to match the first fix at L510.
Co-authored-by: Copilot <223556219+Copilot@users.noreply.github.com>
Documents exactly how every request and response field gets translated
when LiteLLM routes an Anthropic /v1/messages call through the OpenAI
Responses API path (for OpenAI/Azure targets). Covers messages content
block mapping, tools, tool_choice, thinking→reasoning, context_management,
and the reverse response translation. Wired into the /v1/messages sidebar.
Co-Authored-By: Claude Sonnet 4.6 <noreply@anthropic.com>
- CreateBatchRequest.output_expires_after: drop Optional since total=False
already makes the key absent-or-present; Optional[T] incorrectly allowed
the key to exist with value None, which is incompatible with the OpenAI
SDK's OutputExpiresAfter | NotGiven expectation on batches.create()
- cost_tracking_settings._resolve_model_for_cost_lookup: replace implicit
object-to-str returns with explicit str() calls so the function is safe
even if the surrounding truthiness guards are later weakened
Co-Authored-By: Claude Sonnet 4.6 <noreply@anthropic.com>
PR #22850 (BYOK MCP servers) accidentally re-declared spec_path which was
already added by PR #22820, causing Prisma schema validation to fail with
error P1012 "Field is already defined".
Co-Authored-By: Claude Sonnet 4.6 <noreply@anthropic.com>
- batches/main.py: import FileExpiresAfter, cast output_expires_after on assignment
- openai/openai.py, azure/batches/handler.py: add # type: ignore[arg-type] on
batches.create / batches.retrieve TypedDict unpacking calls
- searchapi/transformation.py: cast optional_params["country"] to str before .lower()
- openrouter/image_edit/transformation.py: cast iterated value to str for size/quality params
- spend_log_cleanup.py: narrow bool | None to bool with `or False`
- cost_tracking_settings.py: cast base_model/resolved_model to str and
custom_llm_provider to Optional[str] in return statements
- text_moderation.py: suppress misc TypedDict ** expansion error; use cast for response
- prompt_shield.py: use cast instead of TypedDict(**response_json) construction
Co-Authored-By: Claude Sonnet 4.6 <noreply@anthropic.com>
gemini/gemini-live-2.5-flash-preview-native-audio-09-2025 uses mode='realtime'
but the schema in test_aaamodel_prices_and_context_window_json_is_valid did
not include 'realtime' as a valid enum value, causing a ValidationError.
Co-Authored-By: Claude Sonnet 4.6 <noreply@anthropic.com>
- a2a_protocol/main.py: replace bare assert with descriptive RuntimeError
in _execute_a2a_send_with_retry so retry exhaustion gives a clear message
- fine_tuning/main.py: fix _resolve_fine_tuning_timeout return type from
float to Union[float, httpx.Timeout] to accurately reflect the passthrough path
Co-Authored-By: Claude Sonnet 4.6 <noreply@anthropic.com>
Vertex AI does not support the output_config parameter in its API.
This parameter is being added by Anthropic/Gemini transformations but needs
to be removed before sending requests to Vertex AI endpoints.
This fix addresses the "Extra inputs are not permitted" error (issue #22312)
when using Claude models with structured outputs on Vertex AI.
Changes:
- Drop output_config in Gemini model transformation
- Drop output_config in Anthropic partner model transformation
- Drop output_config in Anthropic experimental pass-through transformation
- Add comprehensive tests to verify output_config is dropped
Fixes: #22312
Made-with: Cursor
Adds bedrock_mantle_models to the model_list union and models_by_provider
dict so models are discoverable via litellm.model_list and
litellm.models_by_provider["bedrock_mantle"].
Co-Authored-By: Claude Sonnet 4.6 <noreply@anthropic.com>
