* Litellm ishaan march23 - MCP Toolsets + GCP Caching fix (#25146)
* feat(mcp): MCP Toolsets — curated tool subsets from one or more MCP servers (#24335)
* feat(mcp): add LiteLLM_MCPToolsetTable and mcp_toolsets to ObjectPermissionTable
* feat(mcp): add prisma migration for MCPToolset table
* feat(mcp): add MCPToolset Python types
* feat(mcp): add toolset_db.py with CRUD helpers for MCPToolset
* feat(mcp): add toolset CRUD endpoints to mcp_management_endpoints
* fix(mcp): skip allow_all_keys servers when explicit mcp_servers permission is set (toolset scope fix)
* feat(mcp): add _apply_toolset_scope and toolset route handling in server.py
* fix(mcp): resolve toolset names in responses API before fetching tools
* feat(mcp): add mcp_toolsets field to LiteLLM_ObjectPermissionTable type
* feat(mcp): register LiteLLM_MCPToolsetTable in prisma client initialization
* feat(mcp): validate mcp_toolsets in key-vs-team permission check
* feat(mcp): register toolset routes in proxy_server.py
* feat(mcp): add MCPToolset and MCPToolsetTool TypeScript types
* feat(mcp): add fetchMCPToolsets, createMCPToolset, updateMCPToolset, deleteMCPToolset API functions
* feat(mcp): add useMCPToolsets React Query hook
* feat(mcp): add toolsets (purple) as third option type in MCPServerSelector
* feat(mcp): extract toolsets from combined MCP field in key form
* feat(mcp): extract toolsets from combined MCP field in team form
* feat(mcp): show toolsets section in MCPServerPermissions read view
* feat(mcp): pass mcp_toolsets through object_permissions_view
* feat(mcp): add MCPToolsetsTab component for creating and managing toolsets
* feat(mcp): add Toolsets tab to mcp_servers.tsx
* feat(mcp): pass mcpToolsets to playground chat and responses API calls
* feat(mcp): generate correct server_url for toolsets in playground API calls
* docs(mcp): add MCP Toolsets documentation
* docs(mcp): add mcp_toolsets to sidebar
* fix(mcp): replace x-mcp-toolset-id header with ContextVar to prevent client forgery
* fix(mcp): use ContextVar + StreamingResponse for toolset MCP routes (fixes SSE streaming)
* fix(mcp): cache toolset permission lookups to avoid per-request DB calls
* test(mcp): add tests for toolset scope enforcement, ContextVar isolation, and access control
* fix(mcp): cache toolset name lookups in MCPServerManager to avoid per-request DB calls
* fix(mcp): prevent body_iter deadlock + use cached toolset lookup in responses API
- _stream_mcp_asgi_response: add done callback to handler_task that puts
the EOF sentinel on body_queue when the task exits, preventing body_iter
from hanging forever if the handler raises after headers are sent.
- litellm_proxy_mcp_handler: replace raw get_mcp_toolset_by_name() DB call
with global_mcp_server_manager.get_toolset_by_name_cached() so toolset
resolution uses the 60s TTL cache added for this purpose instead of
hitting the DB on every responses-API request.
* fix(mcp): toolset access control, asyncio fix, and real unit tests
- server.py: _apply_toolset_scope now enforces that non-admin keys must
have the requested toolset_id in their mcp_toolsets grant list;
admin keys always bypass the check.
- mcp_management_endpoints.py: three access-control fixes:
* fetch_mcp_toolsets: non-admin keys with mcp_toolsets=None now
return [] instead of all toolsets (only admins get 'all' when
the field is absent)
* fetch_mcp_toolset: non-admin keys that haven't been granted the
requested toolset_id now get 403 instead of the full result
* add_mcp_toolset: duplicate toolset_name now returns 409 Conflict
instead of an opaque 500
- proxy_server.py: use asyncio.get_running_loop() instead of
get_event_loop() inside an already-running coroutine (Python 3.10+).
- test_mcp_toolset_scope.py: replace four hollow tests that only
asserted local variable properties with real tests that call the
production fetch_mcp_toolsets() and handle_streamable_http_mcp()
functions with mocked dependencies.
* fix(mcp): add mcp_toolsets to ObjectPermissionBase, fix multi-toolset overwrite, fix delete 404, allow standalone key toolsets
* fix(mcp): add auth check on toolset resolution in responses API; union mcp_servers in _merge_toolset_permissions
* fix(mcp): handle RecordNotFoundError in update_mcp_toolset; union direct servers with toolset servers
* fix(mcp): use _user_has_admin_view; deny None mcp_toolsets for non-admin; use direct RecordNotFoundError import; fix docstring
* fix(mcp): add @default(now()) to MCPToolsetTable.updated_at; fix test for non-admin toolset access
* fix: use UniqueViolationError import; guard _ensure_eof for error/cancel only
* fix(mcp): preserve mcp_access_groups in toolset scope, use shared Redis cache for toolset perms
- Remove mcp_access_groups=[] from _apply_toolset_scope (server.py) and the
responses API toolset path (litellm_proxy_mcp_handler.py). A key's access-group
grants remain valid even when the request is scoped to a single toolset; clearing
them silently revoked legitimate entitlements.
- Switch resolve_toolset_tool_permissions and get_toolset_by_name_cached to use
user_api_key_cache (Redis-backed DualCache in production) instead of per-instance
in-memory dicts. Cache entries are now shared across workers, eliminating the
per-worker stale-toolset-permission window flagged as a P1 by Greptile.
- Use union merge (set union of tool names per server) when applying toolset
permissions in the responses API path so direct-server tool restrictions are not
overwritten by toolset permissions.
* fix(mcp): return 404 when edit_mcp_toolset target does not exist
* fix(mcp): align mcp_toolsets default to None in LiteLLM_ObjectPermissionTable
* fix(mcp): admin toolset visibility, in-place tool name mutation, test helper coercion
* fix(mcp): treat None/[] team mcp_toolsets as no restriction in key validation
* fix(mcp): allow_all_keys backward compat, blocked_tools API write-path, efficient startup query
* fix(mcp): use _mcp_active_toolset_id ContextVar to detect toolset scope, avoiding DB-default false-positive
* fix(mcp): remove dead toolset cache stubs, log invalidation failures, align schema updated_at defaults
* fix(mcp): deserialise MCPToolset from Redis cache hit, replace fastapi import in test
* fix(mcp): evict name-cache on toolset mutation, 409 on rename conflict, warning-level list errors
* fix(redis): regenerate GCP IAM token per connection for async cluster (#24426)
* fix(redis): regenerate GCP IAM token per connection for async cluster clients
Async RedisCluster was generating the IAM token once at startup and
storing it as a static password. After the 1-hour GCP token TTL, any
new connection (including to newly-discovered cluster nodes) would fail
to authenticate.
Fix: introduce GCPIAMCredentialProvider that implements redis-py's
CredentialProvider protocol. It calls _generate_gcp_iam_access_token()
on every new connection, matching what the sync redis_connect_func
already does. async_redis.RedisCluster accepts a credential_provider
kwarg which is invoked per-connection.
* refactor(redis): move GCPIAMCredentialProvider to its own file
Extract GCPIAMCredentialProvider and _generate_gcp_iam_access_token
into litellm/_redis_credential_provider.py. _redis.py imports them
from there, keeping the public API unchanged.
* fix: address Greptile review issues
- GCPIAMCredentialProvider now inherits from redis.credentials.CredentialProvider
so redis-py's async path calls get_credentials_async() properly
- move _redis_credential_provider import to top of _redis.py (PEP 8)
- remove dead else-branch that silently no-oped (gcp_service_account from
redis_kwargs.get() was always None since it's popped by _get_redis_client_logic)
- remove mid-function 'from litellm import get_secret_str' inline import
- remove unused 'call' import from test_redis.py
* chore: retrigger CI/review
* chore: sync schema.prisma copies from root
* chore: sync schema.prisma copies from root
* fix(proxy_server): use bounded asyncio.Queue with maxsize to prevent unbounded growth
* fix(a2a/pydantic_ai): make api_base Optional to match base class signature
* fix(a2a/pydantic_ai): make api_base Optional in handler and guard against None
* fix(mcp): remove unused get_all_mcp_servers import
* fix(mcp): remove unused MCPToolset import
* refactor(mcp): extract toolset permission logic to reduce statement count below PLR0915 limit
* fix(tests): update reload_servers_from_database tests to mock prisma directly
---------
Co-authored-by: Ishaan Jaff <ishaanjaffer0324@gmail.com>
Co-authored-by: github-actions[bot] <41898282+github-actions[bot]@users.noreply.github.com>
* fix(toolset_db): lazy-import prisma to avoid ImportError when prisma not installed
* fix(tests): update UI tests for toolset tab and updated empty state text
* fix(tests): add get_mcp_server_by_name to fake_manager stub
---------
Co-authored-by: Ishaan Jaff <ishaanjaffer0324@gmail.com>
Co-authored-by: github-actions[bot] <41898282+github-actions[bot]@users.noreply.github.com>
- Update test_anthropic_via_responses_api expected_events to include
CONTENT_PART_ADDED between OUTPUT_ITEM_ADDED and OUTPUT_TEXT_DELTA
- Add TestEnsureOutputItemContentPartAdded with 3 mock tests:
message item emits content_part.added, reasoning item does not,
and the event is only emitted once
- aresponses() now pops prompt_id from kwargs after the async hook runs
and passes merged_optional_params via _async_prompt_merged_params.
responses() checks for this internal kwarg first and skips the sync
hook entirely when present — eliminating double-merge of template
messages.
- merged_optional_params from async_get_chat_completion_prompt is no
longer discarded (_); it flows through to local_vars in responses().
- Async tests now assert get_chat_completion_prompt.assert_not_called()
to directly detect any double-execution regression.
Made-with: Cursor
- Fix async path: call async_get_chat_completion_prompt in aresponses()
before executor dispatch, mirroring acompletion() in main.py. Discard
merged_optional_params in async path (sync responses() handles them
via local_vars), avoiding TypeError from duplicate kwargs in partial().
- Fix provider re-resolution: replace "/" in model heuristic with
model != original_model comparison so bare model names are handled.
- Add 3 async tests covering hook invocation, optional param
propagation, and non-message item filtering in aresponses().
Made-with: Cursor
The isolate_litellm_state conftest fixture saved/restored litellm.callbacks
but never cleared it before each test, unlike the other callback lists. It
also didn't handle litellm.model_fallbacks. Leaked callbacks and fallback
config caused mocked tests to route through Router/fallback paths, hitting
real APIs with mock keys.
Co-Authored-By: Claude Opus 4.6 <noreply@anthropic.com>
GLOBAL_LOGGING_WORKER.flush() calls queue.join() which blocks until all
items are task_done(). In CI with pytest-asyncio, each test gets a fresh
event loop so the worker reinitializes its queue - items from a previous
test never get task_done(), causing an infinite hang.
Fix: wrap flush() with asyncio.wait_for(..., timeout=10.0).
The `if hasattr(...)` guards in test_acompletion_with_mcp_adds_metadata_to_streaming
and test_acompletion_with_mcp_streaming_metadata_in_correct_chunks could silently skip
the provider_specific_fields assertions if chunks lacked choices/delta. Replace with
unconditional `assert hasattr(...)` so failures surface immediately.
Co-Authored-By: Claude Sonnet 4.6 <noreply@anthropic.com>
Fixes test failures that occur during parallel test execution (pytest -n 4)
due to module reloading issues with conftest.py reloading litellm.
Changes:
- Add module reload fixtures to ensure fresh references after conftest reloads
- Use patch.object and string-based patches instead of direct attribute assignment
- Use class name comparison instead of isinstance for reloaded modules
- Handle case where litellm is missing from sys.modules during parallel runs
- Move stream consumption inside patch contexts to avoid real API calls
- Mock litellm.acompletion instead of low-level HTTP handlers
- Add skipif decorator for enterprise-only test classes
Affected test files:
- test_container_integration.py
- test_responses_background_cost.py
- test_huggingface_embedding_handler.py
- test_vertex_ai_rerank_integration.py
- test_volcengine_responses_transformation.py
- test_pillar_guardrails.py
- test_litellm_pre_call_utils.py
- test_proxy_server.py
- test_converse_transformation.py
- test_chat_completions_handler.py
- test_aresponses_api_with_mcp.py
- test_anthropic_experimental_pass_through_messages_handler.py
Co-Authored-By: Claude Opus 4.5 <noreply@anthropic.com>
* fix: preserve metadata for custom callbacks on codex/responses path (#21204)
- Use metadata or litellm_metadata when calling update_environment_variables
in responses/main.py so metadata is not overwritten by None on the
bridge path (completion -> responses API).
- Add tests for metadata in custom callback for codex models and for
litellm_metadata in aresponses().
Co-authored-by: Cursor <cursoragent@cursor.com>
* Update tests/test_litellm/responses/test_metadata_codex_callback.py
Co-authored-by: greptile-apps[bot] <165735046+greptile-apps[bot]@users.noreply.github.com>
---------
Co-authored-by: Cursor <cursoragent@cursor.com>
Co-authored-by: Krish Dholakia <krrishdholakia@gmail.com>
Co-authored-by: greptile-apps[bot] <165735046+greptile-apps[bot]@users.noreply.github.com>
1. test_acompletion_with_mcp_streaming_metadata_in_correct_chunks:
- Moved stream consumption inside patch context to avoid real API calls
- The previous implementation had assertions outside the `with patch(...)`
block, causing real OpenAI API calls when consuming the stream
2. TestCheckResponsesCost tests:
- Added skip condition when litellm_enterprise module is not available
- These tests import from litellm_enterprise.proxy.common_utils.check_responses_cost
which is only available in the enterprise version
* fix(vertex_ai): forward extra_body to completion transformation handler
The responses() function accepted extra_body as a named parameter but
did not pass it to response_api_handler when responses_api_provider_config
was None (completion transformation path), silently dropping it.
Also adds deep-merge support for extra_body in Vertex AI Gemini
transformation, so dict values like generationConfig are merged rather
than replaced.
* refactor(vertex_ai): extract _merge_extra_body to fix PLR0915 lint
Move the extra_body merge loop into a helper function to keep
_transform_request_body under the 50-statement limit.
When transforming chat completion responses to Responses API format,
image_tokens from completion_tokens_details was not being included
in output_tokens_details. This affected Vertex AI/Gemini models that
return image token counts in candidatesTokensDetails with modality="IMAGE".
The fix adds image_tokens handling alongside existing reasoning_tokens
and text_tokens transformation.
When using the responses API with provider-specific params (aws_*, vertex_*)
without explicitly passing custom_llm_provider, the code crashed with:
AttributeError: 'NoneType' object has no attribute 'startswith'
Root cause: local_vars was captured via locals() before get_llm_provider()
detected the provider from the model string (e.g., "bedrock/..."), so
custom_llm_provider remained None when processing provider-specific params.
Fix: Update local_vars["custom_llm_provider"] after get_llm_provider() call
so the detected provider is available for param processing.
Affected provider-specific params:
- aws_* (aws_region_name, aws_access_key_id, etc.) for Bedrock/SageMaker
- vertex_* (vertex_project, vertex_location, etc.) for Vertex AI
* fix: preserve tool output ordering for gemini in responses bridge
- Keep function_call_output adjacent to its function_call when building chat messages
- Normalize function_call_output.output lists (input_* parts) into tool message content
* fix test
* small improvements
Emit Responses API streaming events for tool calls when the underlying chat stream contains tool_call deltas, and recover tool calls into the stream when they only appear in the final response.
Calculate `total_tokens` in usage data in Response manually if:
- `total_tokens` is missing
- `total_tokens` can be calculated from input and output tokens
Run the test for this feature with:
`poetry run pytest tests/test_litellm/responses/test_responses_utils.py -k "test_transform_response_api_usage_calculates_total_from_input_and_output_tokens_if_available" -v`
