Unit Tests: Proxy DB Operations / proxy-db (auth-checks, tests/proxy_unit_tests/test_auth_checks.py tests/proxy_unit_tests/test_user_api_key_auth.py, 20, 8) (push) Has been cancelled
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- cost_tracking_settings.py: fix mypy return type by using temp variables with str() cast inside if-blocks
- test_mcp_server.py: add tool_name_to_display_name/description dicts to test_add_update_server_without_alias
- test_utils.py: add /vertex_ai/live to supported_endpoints enum
Co-Authored-By: Claude Opus 4.6 <noreply@anthropic.com>
- Fix mypy: use correct error codes [typeddict-item] instead of [misc],
remove variable redefinition in spend_log_cleanup, use type: ignore
for cost_tracking_settings assignment
- Fix MCP tests: add tool_name_to_display_name and tool_name_to_description
dict fields to mock objects for test_add_update_server_with_alias and
test_add_update_server_fallback_to_server_id
- Fix model prices schema: add 'realtime' to allowed mode enum for
gemini-live models
Co-Authored-By: Claude Opus 4.6 <noreply@anthropic.com>
- MCP test: add mocks for extract_mcp_auth_context, set_auth_context, and
_handle_stale_mcp_session (matching the SSE handler test pattern)
- OpenRouter test: mock get_secret_str and litellm.api_key so
test_validate_environment_raises_without_key works even when
OPENROUTER_API_KEY is set in the CI env
- hosted_vllm: filter None values from optional_params in
transform_embedding_request to prevent vLLM rejecting encoding_format=None
Co-Authored-By: Claude Opus 4.6 <noreply@anthropic.com>
Greptile correctly pointed out that load_team_config() always returns a
dict in production. The isinstance check masked test bugs rather than
fixing them. The proper fix is configuring Mock.load_team_config in tests.
Co-Authored-By: Claude Opus 4.6 <noreply@anthropic.com>
load_team_config() should always return a dict, but when proxy_config
is mocked in tests without configuring load_team_config.return_value,
calling .keys() on a Mock object raises TypeError. Adding isinstance
check makes this robust against unexpected return types.
Co-Authored-By: Claude Opus 4.6 <noreply@anthropic.com>
The test_add_key_or_team_level_spend_logs_metadata_to_request test was
failing because proxy_config.load_team_config() returned a bare Mock
instead of a dict, causing Mock.keys() to be non-iterable.
Co-Authored-By: Claude Opus 4.6 <noreply@anthropic.com>
* fix: complexity_router fails on list-format message content (OpenAI multi-part messages)
When a client sends messages with list-format content
(e.g. [{"type": "text", "text": "..."}] as used by the OpenAI JS SDK
and other clients), the complexity_router's async_pre_routing_hook
skipped those messages because it only handled str content. This caused
user_message to be None, the hook returned None, and the router fell
through to selecting the complexity_router deployment itself
(model="auto_router/complexity_router") which litellm cannot dispatch,
resulting in LiteLLMUnknownProvider.
Fixes:
- Extract text from list-format content parts (type=text) before
classifying
- Return default_model instead of None when no user message can be
extracted, preventing the crash fallthrough
- Loosen PreRoutingHookResponse.messages type from Dict[str, str] to
Dict[str, Any] to accommodate list-format content values
Co-Authored-By: Claude Sonnet 4.6 <noreply@anthropic.com>
* fix: update messages type annotation in async_pre_routing_hook to Dict[str, Any]
Consistent with PreRoutingHookResponse.messages type change and the
list-format content support added in the previous commit.
Co-Authored-By: Claude Sonnet 4.6 <noreply@anthropic.com>
* fix: normalize None content to empty string in complexity_router message parsing
msg.get("content", "") returns None when the key exists with value None
(e.g. assistant messages with tool calls). Use `or ""` to normalize
None to an empty string explicitly.
Co-Authored-By: Claude Sonnet 4.6 <noreply@anthropic.com>
* fix: strip whitespace from joined list content parts in complexity_router
Prevents leading/trailing spaces when some content parts have empty
text values (e.g. " ".join(["", "hello"]) → " hello").
Co-Authored-By: Claude Sonnet 4.6 <noreply@anthropic.com>
---------
Co-authored-by: Claude Sonnet 4.6 <noreply@anthropic.com>
The OpenAPI-to-MCP feature (PR #21575) added spec_path to the code
(_types.py, mcp_server_manager.py) but missed adding the column to
the Prisma schema files. This causes "Could not find field spec_path"
errors when creating OpenAPI-based MCP servers via the UI or API.
Adds `spec_path String?` to LiteLLM_MCPServerTable in all three
schema files (root, litellm/proxy, litellm-proxy-extras).
Made-with: Cursor
* (sap) ensure tool parameters have type='object' for SAP compatibility
Fix SAP GenAI Hub Orchestration Service rejecting tool calls with error:
"400 - LLM Module: tools.0.custom.input_schema.type: Input should be 'object'"
Root cause: When Claude Code uses tools (like web_search) with the SAP provider
through LiteLLM's Anthropic experimental pass-through adapter, Anthropic's
input_schema format doesn't always include the required type="object" field.
The adapter's translate_anthropic_tools_to_openai() function was directly
copying input_schema to OpenAI's parameters field without ensuring the
type="object" requirement that SAP's API strictly enforces.
Changes:
- Modified translate_anthropic_tools_to_openai() to check if input_schema
is missing the type field and add type="object" if absent
- Preserves existing type field if already present
- Added comprehensive test suite (6 tests) covering:
- Missing type field scenario (now adds type="object")
- Existing type preservation
- Empty input_schema handling
- Multiple tools transformation
- Additional schema properties preservation
- SAP-specific compatibility regression test
Testing:
- All new tests pass (6/6 in test_anthropic_tool_schema_fix.py)
- All existing Anthropic tool tests pass (57/57 tool-related tests)
- SAP tool parameter validation tests pass (9/9 in test_sap_tool_parameters.py)
* (sap) enable native response_format for anthropic models
* (sap) filter strict param from model_params for GPT models only
* (sap) revert Anthropic adapter type='object' fix
The SAP FunctionTool Pydantic validator in litellm/llms/sap/chat/models.py
already ensures type='object' is added to all tool parameters for SAP
API compatibility.
The Anthropic adapter change affected ALL consumers, not just SAP, which
was broader scope than intended for this PR.
- Revert input_schema modification in Anthropic adapter
- Remove Anthropic-specific test file (SAP tests still cover this case)
* (sap) gate markdown stripping to Anthropic models only
SAP GenAI Hub with Anthropic models sometimes returns JSON wrapped in
markdown code blocks. GPT/Gemini/Mistral models don't exhibit this
behavior, so stripping is now gated to avoid accidentally modifying
valid responses that may contain markdown in JSON string values.
* fix(proxy): add guardrails list routes for internal users
* fix(ui): add guardrails fetch with v1/v2 fallback in networking
* fix(ui): allow internal users/team admins to select guardrails in create key modal
* fix(ui): show guardrails selector for internal users in key edit view
* fix(ui): pass canEditGuardrails flag to key info view
* test(ui): add tests for role-based guardrails access in key info view
* test(ui): update key edit view test for guardrails
The gemini-live-2.5-flash-preview-native-audio-09-2025 model only works
with WebSocket (Live API), not REST endpoints. Changed supported_endpoints
from /v1/chat/completions to /vertex_ai/live to reflect the actual
passthrough endpoint available in LiteLLM proxy.
- Remove token field from JWTKeyMappingResponse to prevent hashed key exposure
- Use _to_response() helper on all CRUD endpoints to control returned fields
- Return 409 for unique constraint violations, 400 for FK violations, 404 for not found
- Add response_model to endpoint decorators
- Add 8 new unit tests covering error handling and token redaction
Co-Authored-By: Claude Opus 4.6 <noreply@anthropic.com>
* azure content enhancement...
* rafactored to increase confidence score
* improvements based on additional feedback
* removed unused import
* Force-split any word longer than max length allowed
* preserve whitespace in text splitting
* moving common initialization to base class
* consolidate enforcement into async_make_request as single point, remove redundant caller-side checks, extract shared init/HTTP logic into base, and fix stale log messages
* clean up
* clean up tests
The linting workflow force-installed openai==1.100.1 which conflicts
with litellm's requirement of openai>=2.8.0, causing pip dependency
resolver errors and CI cancellation.
Co-Authored-By: Claude Opus 4.6 <noreply@anthropic.com>
PR #22785 used pytest.importorskip which causes exit code 5 (all
skipped) in CI. Instead, add tenacity to the CI workflow pip install
and restore direct imports.
Co-Authored-By: Claude Opus 4.6 <noreply@anthropic.com>
Add cache_read_input_token_cost_per_audio_token, supports_code_execution,
and supports_file_search to the JSON schema used by the model prices
validation test.
Co-Authored-By: Claude Opus 4.6 <noreply@anthropic.com>
tenacity is not in pyproject.toml dependencies, causing ImportError
during test collection. Use pytest.importorskip to gracefully skip
when tenacity is not available.
Co-Authored-By: Claude Opus 4.6 <noreply@anthropic.com>
The _encrypt_response_id method now receives request_cache=None as a
keyword argument from async_post_call_success_hook. Updated the mock
assertion to expect this parameter.
Co-Authored-By: Claude Opus 4.6 <noreply@anthropic.com>
PR #22732 changed the ToolTable schema (renamed call_policy to
input_policy, added output_policy/user_agent/last_used_at columns,
updated indexes) but didn't include a migration for these changes.
Co-Authored-By: Claude Opus 4.6 <noreply@anthropic.com>
Tests used call_policy throughout but the actual API model uses
input_policy and output_policy. Updated _make_tool_row helper,
list filter query param, and policy update request/response assertions.
Co-Authored-By: Claude Opus 4.6 <noreply@anthropic.com>
