- Add created_at field to MCPServer type (was missing)
- Map created_at from LiteLLM_MCPServerTable in build_mcp_server_from_table()
- Use server.created_at and server.updated_at instead of datetime.now() in _build_mcp_server_table() and health check table builder
- Add regression tests to verify timestamps are preserved through round-trip conversions
Co-Authored-By: Claude Sonnet 4.6 <noreply@anthropic.com>
Replaces the skip_route_check approach from PR #22662 with a configurable
opt-in flag. By default, common_checks() is not run for custom auth flows,
preserving backwards compatibility with pre-#22164 behavior.
Users who want budget/team/route enforcement on custom auth can enable it:
general_settings:
custom_auth_run_common_checks: true
Co-Authored-By: Claude Opus 4.6 <noreply@anthropic.com>
Custom user-added routes (e.g. /ldap/ngs/ready) used with Depends(user_api_key_auth) were being rejected as admin-only after _run_post_custom_auth_checks was introduced in commit 14badde13c.
The route authorization check in common_checks is designed for LiteLLM's own management routes. Custom auth flows that add their own routes should be trusted since the custom auth function already validated the request. Budget and expiry checks still run.
Add skip_route_check parameter to common_checks() and pass skip_route_check=True from _run_post_custom_auth_checks() to skip route authorization while preserving budget/team/model checks.
Regression test added: test_common_checks_skip_route_check_for_custom_auth
Co-Authored-By: Claude Haiku 4.5 <noreply@anthropic.com>
When create_batch routes via x-litellm-model header, the response batch_id
was returned raw without model routing info. This meant retrieve_batch could
not determine which provider/credentials to use, defaulting to "openai"
instead of the correct provider (e.g., VLLM).
Now encodes batch_id, output_file_id, and error_file_id with model info
(same pattern as the model-embedded file_id flow in Scenario 1), so
retrieve_batch can decode and route back to the correct provider.
* feat(guardrails): team-based guardrail registration and approval workflow
Add team-based guardrail submission system where teams can register
Generic Guardrail API guardrails for admin review. Includes:
- POST /guardrails/register endpoint for team-scoped submissions
- Admin review endpoints (list/get/approve/reject submissions)
- Team Guardrails tab in the UI dashboard
- extra_headers support for forwarding client headers to guardrail APIs
- Prisma schema migration for status, submitted_at, reviewed_at fields
- Documentation for team-based guardrails and static/dynamic headers
Co-Authored-By: Claude Opus 4.6 <noreply@anthropic.com>
* fix(guardrails): address review feedback - SSRF, silent failure, redundant query
- Validate api_base URL scheme (http/https only) and hostname in
register_guardrail to prevent SSRF via team submissions
- Return warning field in approve response when in-memory initialization
fails so admins know the guardrail won't work until next sync cycle
- Eliminate redundant DB query in list_guardrail_submissions by fetching
all team guardrails once and deriving both filtered list and summary
counts from the single result set
Co-Authored-By: Claude Opus 4.6 <noreply@anthropic.com>
* fix(guardrails): add pending_review status guard to reject endpoint
Prevent rejecting already-active or already-rejected guardrails, which
would create a DB/memory inconsistency (active in memory but rejected
in DB). Now mirrors the approve endpoint's status check.
Co-Authored-By: Claude Opus 4.6 <noreply@anthropic.com>
---------
Co-authored-by: Claude Opus 4.6 <noreply@anthropic.com>
OpenRouter supports the Responses API at /api/v1/responses with
encrypted_content for multi-turn stateless reasoning workflows.
Without native registration, requests fall through to the chat
completion bridge, which uses a different format (reasoning_details)
and drops encrypted_content entirely.
This adds OpenRouterResponsesAPIConfig to route requests directly to
OpenRouter's Responses API endpoint, preserving encrypted_content.
Fixes https://github.com/BerriAI/litellm/issues/22189
Co-authored-by: Krish Dholakia <krrishdholakia@gmail.com>
The _resolve_jwks_url method checks response.status_code != 200, but
MagicMock returns a MagicMock object for status_code which is always
truthy (!= 200). Explicitly set mock_response.status_code = 200 so the
tests exercise the intended code path.
Co-authored-by: Claude Opus 4.6 <noreply@anthropic.com>
mapped passthrough routes (vertex_ai, bedrock, etc) were compared
against the raw request path without prepending SERVER_ROOT_PATH.
db-registered routes already used _build_full_path_with_root for this
but the mapped routes branch was missed.
fixes#22272
DualCache.async_set_cache and async_set_cache_pipeline were missing the
default_in_memory_ttl injection that the sync set_cache method has. This
caused InMemoryCache to fall back to its own default_ttl (600s) instead
of using DualCache's configured default_in_memory_ttl (typically 60s).
This is particularly impactful for end-user budget enforcement in the
proxy, where cached spend values could remain stale for 10 minutes
instead of 1 minute, allowing users to exceed their budgets.
The response.completed handler in the completion→responses streaming
bridge was discarding the usage object, causing prompt_tokens_details
(and cached_tokens) to always be None when streaming with models that
use the Responses API (e.g. gpt-5.2-codex, gpt-5.3-codex).
Extract usage from the response.completed event and translate it via
the existing _transform_response_api_usage_to_chat_usage helper.
Fixes#22192
All 55 deepinfra models that had `supports_tool_choice: true` were
missing the `supports_function_calling` flag, causing
`litellm.supports_function_calling()` to incorrectly return False.
Fixes#22619
Co-Authored-By: Claude Opus 4.6 <noreply@anthropic.com>
* Add CrowdStrike AIDR guardrail hook
* fixup! use apply_guardrail event hook
* fixup! update imports
* fix(guardrails): include AI response in CrowdStrike AIDR output events
Issue:
_build_guard_input_for_response() was:
- Sending only the original user input (messages).
- Not sending the AI provider response.
This fix will:
- Extract response.choices from the ModelResponse object and include them in guard_input payload.
- Thus, ensure AIDR output rules receive the AI-generated content for analysis.
- Fix and update tests.
* fix(guardrails): prevent duplicate input events in CrowdStrike AIDR guardrail
Issue:
The CrowdStrike AIDR guardrail was running on during_call hooks wihtout event_hook configured.
This fix will:
- Set event_hook to ["pre_call", "post_call"] (AIDR admins will control what policy is applied)
This change will:
- Require default_on parameter
- Prevent duplicate API calls to AIDR for the same input
- Avoid unchecked AI provider API calls on during_call hook
* docs: add CrowdStrike AIDR to the list of Guardrails under Integrations
* docs: update CrowdStrike AIDR documentation page
---------
Co-authored-by: Konstantin Lapine <konstantin.lapine@crowdstrike.com>
Fixes NameError at runtime when ChatGPTToolCallNormalizer is
instantiated. The imports were missed when type hints were changed
from Python 3.10+ syntax (dict[], str | None) to typing module
syntax (Dict[], Optional[str]).
The previous check in _get_openai_compatible_provider_info() ran after
the model name was already split, so it never caught the second
get_llm_provider() call from the anthropic_messages bridge.
Moved the check to get_llm_provider() before the provider-list
stripping, using a pattern-based approach (custom_llm_provider ==
"openrouter" and model.startswith("openrouter/")) instead of a
hardcoded set. This covers all current and future native OpenRouter
models.
Updated tests to verify the bridge double-call scenario with
custom_llm_provider passed through.
Empty JSON schemas `{}` mean "any JSON value is valid" per the spec,
but _build_vertex_schema was coercing them to `{"type": "object"}` in
three places (process_items, convert_anyof_null_to_nullable, add_object_type),
breaking Pydantic JsonValue fields on Gemini.
Adds _is_any_type_schema() to detect unconstrained schemas and skip
the type coercion, preserving Gemini's TYPE_UNSPECIFIED semantics.
Fixes#22391
The proxy has two separate failure paths:
1. async_failure_handler → Langfuse callback (uses model_call_details with
standard_logging_object containing the correct trace_id)
2. post_call_failure_hook → _ProxyDBLogger → spend log (uses request_data
which did NOT have standard_logging_object, so session_id fell to
random uuid4())
These two paths used different data dicts, so the DB session_id was a
random UUID unrelated to the Langfuse trace_id. Users could not search
by the Session ID from LiteLLM logs in Langfuse for failed requests.
Fix: In _ProxyDBLogger.async_post_call_failure_hook, propagate
standard_logging_object and litellm_trace_id from the litellm_logging_obj
(already present in request_data) before writing the spend log.
Co-Authored-By: Claude Opus 4.6 <noreply@anthropic.com>
When standard_logging_object is None (failure case), Langfuse was falling
back to litellm_call_id while the DB used litellm_trace_id as session_id.
This caused the Session ID in LiteLLM logs to not match the trace in
Langfuse. Now Langfuse checks litellm_trace_id first, matching the DB.
Co-Authored-By: Claude Opus 4.6 <noreply@anthropic.com>
The proxy uses async_failure_handler → LangfusePromptManagement.async_log_failure_event(),
which silently returned when standard_logging_object was None. This meant failed LLM calls
never created traces in Langfuse. Remove the early return and fall back to extracting the
error message from kwargs["exception"] when standard_logging_object is unavailable.
Co-Authored-By: Claude Opus 4.6 <noreply@anthropic.com>
