Add the following SCIM v2 discovery endpoints per RFC 7643/7644:
- GET /scim/v2 - Base resource discovery (ListResponse of ResourceTypes)
- GET /scim/v2/ResourceTypes - List all supported resource types
- GET /scim/v2/ResourceTypes/{id} - Get a specific resource type (User/Group)
- GET /scim/v2/Schemas - List all supported schemas
- GET /scim/v2/Schemas/{uri} - Get a specific schema by URI
These endpoints are required by identity providers (Okta, Azure AD, etc.)
for SCIM resource discovery. Previously, GET /scim/v2 returned 404.
Also adds SCIMResourceType, SCIMSchema, and SCIMSchemaAttribute Pydantic
models to the SCIM types module.
Fixes#20295
* fix: strip stale mcp-session-id header to prevent 'Session not found' error loop
When VSCode reconnects to LiteLLM's MCP endpoint after a reload, it sends
a stale mcp-session-id header. The session was already cleaned up, causing
a 404 'Session not found' error. VSCode retries with the same stale ID,
creating an infinite error loop.
Before forwarding requests to the StreamableHTTP session manager, check if
the mcp-session-id header references a valid session. If the session doesn't
exist, strip the header so a new session is created automatically.
Fixes#20292
* refactor: extract stale session handling into _strip_stale_mcp_session_header helper
* perf: cache _get_relevant_args_to_use_for_logging() as module-level frozenset
The set of valid LLM API parameter names for logging was being rebuilt
on every request from 8 OpenAI SDK type annotations + set operations.
Since these are static TypedDict annotations that never change at
runtime, compute once at import time and store as a class-level
frozenset.
Line profiler: get_standard_logging_model_parameters() dropped from
774ms to 77ms across 12K calls (90% reduction, ~25µs/req saved).
* test: add tests for cached ModelParamHelper logging args
Verify cached frozenset matches dynamic computation and that
prompt content keys (messages, prompt, input) are excluded from
logged model parameters.
Pass-through endpoints (like vLLM classify) were not setting
standard_logging_object because _get_assembled_streaming_response
returns None for non-ModelResponse results.
This caused model_max_budget_limiter.async_log_success_event to raise
ValueError('standard_logging_payload is required').
The fix adds an elif branch in async_success_handler that mirrors the
non-pass-through code path.
Co-authored-by: openhands <openhands@all-hands.dev>
Co-authored-by: Krish Dholakia <krrishdholakia@gmail.com>
Add x-api-key header to CountTokens handler to match chat completion
authentication. Azure AI Anthropic requires this header per Microsoft's
native API format.
Previously, when Model Armor guardrail blocked a request/response,
the `applied_guardrails` field was not populated in the logs because
`add_guardrail_to_applied_guardrails_header()` was called after the
HTTPException was raised.
This fix moves the `add_guardrail_to_applied_guardrails_header()` call
to before the blocking check in all hooks:
- async_pre_call_hook (pre_call mode)
- async_moderation_hook (during_call mode)
- async_post_call_success_hook (post_call mode)
- async_post_call_streaming_iterator_hook (streaming)
This ensures that even when a guardrail blocks content, the guardrail
name is properly recorded in the logs for observability.
Added regression tests to verify applied_guardrails is populated when
content is blocked.
Co-authored-by: Cursor <cursoragent@cursor.com>
When using LiteLLM's Anthropic /v1/messages endpoint to route requests to
OpenAI models, requests fail if any tool name exceeds OpenAI's 64-character
limit. Anthropic API has no such limit, causing compatibility issues.
Changes:
- Add truncate_tool_name() function using {55-char-prefix}_{8-char-hash} format
- Modify translate_anthropic_tools_to_openai() to truncate and return mapping
- Modify translate_anthropic_tool_choice_to_openai() to truncate tool name
- Restore original tool names in responses using the mapping
- Support tool name restoration in streaming responses
- Add backwards-compatible API (existing methods still work)
The fix only applies when routing Anthropic requests to OpenAI models.
Native Anthropic/Claude requests pass through unchanged.
* fix: models loadbalancing billing issue by filter (#18891)
* fix: models loadbalancing billing issue by filter
* fix: separate key and team access groups in metadata
* fix: lint issues
Fixes#19788
- Add `supported_regions: ["global"]` to Qwen MaaS models in model_prices_and_context_window.json
- Update `get_supported_regions()` to read directly from `model_cost` dict
- Update `get_complete_vertex_url()` to use `get_vertex_region()` for global-only models
- Update `create_vertex_url()` to generate correct URL for global location (without region prefix)
- Add tests for Qwen global endpoint support
Per review feedback, thought_signature should not be a root-level
param on ImageObject as it's not OpenAI compatible. Moved to
provider_specific_fields dict to match the pattern used in chat
completions (Message, Delta, Choices, etc).
Fixes#17184 - Gemini 3 Pro image preview model returns a thoughtSignature
field required for interactive image editing. This change:
- Adds thought_signature field to ImageObject class
- Updates Gemini and Vertex AI transformations to extract thoughtSignature
- Adds test for thought_signature in response transformation
* litellm_fix_mapped_tests_core: fix test isolation and mock injection issues
## Problem
Four tests in litellm_mapped_tests_core were failing:
1. test_register_model_with_scientific_notation - KeyError due to test isolation issues
2. test_search_uses_registry_credentials - Mock not being called due to incorrect patch path
3. test_send_email_missing_api_key - Real API calls despite mocking
4. test_stream_transformation_error_sync - Mock not effective, real API called
## Solution
### test_register_model_with_scientific_notation
- Use unique model name to avoid conflicts with other tests
- Clear LRU caches before test to prevent stale data
- Clean up model_cost entry after test
### test_search_uses_registry_credentials
- Use patch.object() on the actual base_llm_http_handler instance
- String-based patching for instance methods can fail; direct object patching is more reliable
### test_send_email_missing_api_key
- Directly inject mock HTTP client into logger instance
- This bypasses any caching issues that could cause the fixture mock to be ineffective
### test_stream_transformation_error_sync
- Patch litellm.completion directly instead of the handler module's litellm reference
- This ensures the mock is effective regardless of import order
## Regression
These tests were affected by LRU caching added in #19606 and HTTP client caching.
* fix(test): use patch.object for container API tests to fix mock injection
## Problem
test_retrieve_container_basic tests were failing because mocks weren't
being applied correctly. The tests used string-based patching:
patch('litellm.containers.main.base_llm_http_handler')
But base_llm_http_handler is imported at module level, so the mock wasn't
intercepting the actual handler calls, resulting in real HTTP requests
to OpenAI API.
## Solution
Use patch.object() to directly mock methods on the imported handler
instance. Import base_llm_http_handler in the test file and patch like:
patch.object(base_llm_http_handler, 'container_retrieve_handler', ...)
This ensures the mock is applied to the actual object being used,
regardless of import order or caching.
* fix(test): add missing Prometheus metric labels to test_proxy_failure_metrics
Add client_ip, user_agent, model_id labels to expected metric patterns.
These labels were added in PRs #19717 and #19678 but test wasn't updated.
* fix(test_resend_email): use direct mock injection for all email tests
Extend the mock injection pattern used in test_send_email_missing_api_key
to all other tests in the file:
- test_send_email_success
- test_send_email_multiple_recipients
Instead of relying on fixture-based patching and respx mocks which can
fail due to import order and caching issues, directly inject the mock
HTTP client into the logger instance. This ensures mocks are always used
regardless of test execution order.
* fix(test): use patch.object for image_edit and vector_store tests
- test_image_edit_merges_headers_and_extra_headers: import base_llm_http_handler
and use patch.object instead of string path patching
- test_search_uses_registry_credentials: import module and patch via
module.base_llm_http_handler to ensure we patch the right instance
---------
Co-authored-by: Ishaan Jaff <ishaanjaffer0324@gmail.com>
## Problem
Tests using mocked HTTP clients were hitting real APIs because:
1. HTTP client cache was returning previously cached real clients
2. isinstance checks failed due to module identity issues from sys.path
### Tests affected:
- test_send_email_missing_api_key
- test_send_email_multiple_recipients (resend & sendgrid)
- test_search_uses_registry_credentials
- test_vector_store_create_with_simple_provider_name
- test_vector_store_create_with_provider_api_type
- test_vector_store_create_with_ragflow_provider
- test_image_edit_merges_headers_and_extra_headers
- test_retrieve_container_basic (container API tests)
## Solution
1. Add clear_client_cache fixture (autouse=True) to clear
litellm.in_memory_llm_clients_cache before each test
2. Fix isinstance checks to use type name comparison
(avoids module identity issues from sys.path.insert)
## Why not disable_aiohttp_transport
The default transport is aiohttp, so tests should work with it.
Clearing the cache ensures mocks are used instead of cached real clients.
## Regression
PR #19829 (commit f95572e3ed) added @respx.mock but cached clients
from earlier tests were being reused, bypassing the mocks.
Co-authored-by: shin-bot-litellm <shin-bot-litellm@users.noreply.github.com>
The test had prompt_tokens=1000 but the sum of token details was 1150
(text=700 + audio=100 + cached=200 + cache_creation=150).
This triggered the double-counting detection logic which recalculated
text_tokens to 550, causing the assertion to fail.
Fixed by setting prompt_tokens=1150 to match the sum of details.
