- Add VertexBase.invalidate_credentials; remove (creds, project_id) and (creds, None)
when project_id is set (#23512).
- async_anthropic_messages_handler: on httpx 401 for vertex_ai, invalidate before
_handle_error so retries can load_auth/refresh.
- Use module-level httpx in invalidation helper (no redundant inline import).
- Unit tests in test_vertex_llm_base.py and test_llm_http_handler.py (mocked).
Fixes#23512
Made-with: Cursor
* fix: Fixes https://github.com/BerriAI/litellm/issues/23185
* fix(responses/main.py): ensure litellm metadata custom cost works
* refactor: move all logging updates to a common function, to have just 1 place to update logging kwarg updates
- Fix __main__ block: test returns None now, so always exited 1
- Close litellm_async_client in test_ssl_verification_with_aiohttp_transport
- Save/restore litellm.force_ipv4 and litellm.disable_aiohttp_transport
in test_force_ipv4_transport and test_aiohttp_disabled_transport
Co-Authored-By: Claude Opus 4.6 <noreply@anthropic.com>
count_aiohttp_sessions() iterates every object in the Python GC,
which hangs in CI when xdist workers have millions of loaded objects.
Replace with direct session.closed checks — same coverage, no hang.
Co-Authored-By: Claude Opus 4.6 <noreply@anthropic.com>
- Remove real HTTP call to example.com in test_force_ipv4_transport
(hangs in CI when network is slow/unavailable)
- Close leaked aiohttp.ClientSession in test_ssl_verification_with_aiohttp_transport
- Add cleanup for transports in test_aiohttp_transport_trust_env_setting
- Add cleanup for handler in test_ssl_security_level
- Add cleanup for transport in test_ssl_context_transport
Co-Authored-By: Claude Opus 4.6 <noreply@anthropic.com>
* feat: add sagemaker_nova provider for Nova models on SageMaker
Add support for custom/fine-tuned Amazon Nova models (Nova Micro, Nova Lite,
Nova 2 Lite) deployed on SageMaker Inference real-time endpoints.
Nova uses OpenAI-compatible request/response format with additional
Nova-specific parameters (top_k, reasoning_effort, allowed_token_ids,
truncate_prompt_tokens) and requires stream:true in the request body.
Nova endpoints also reject 'model' in the request body.
Changes:
- New provider: sagemaker_nova/<endpoint-name>
- SagemakerNovaConfig inherits from SagemakerChatConfig
- Override transform_request to strip 'model' from request body
- Override supports_stream_param_in_request_body (True for Nova)
- Extend get_supported_openai_params with Nova-specific params
- Refactored SagemakerChatConfig to use custom_llm_provider param
instead of hardcoded strings (backwards-compatible)
- Consolidated main.py routing for sagemaker_chat and sagemaker_nova
- 22 unit tests + 9 integration tests (skip-gated)
- Documentation with SDK, streaming, multimodal, and proxy examples
- All tests verified against live SageMaker Nova endpoint
* fix: move integration tests to tests/local_testing/ per test directory policy
* fix: remove unused module-level SagemakerNovaConfig instance
The sagemaker_nova_config singleton was never imported or used — the
ProviderConfigManager creates its own instance via the lambda registered
in utils.py. Removing this leftover boilerplate.
---------
Co-authored-by: Krish Dholakia <krrishdholakia@gmail.com>
* fix(bedrock): respect s3_region_name for batch file uploads (#23569)
* fix(bedrock): respect s3_region_name for batch file uploads (GovCloud fix)
* fix: s3_region_name always wins over aws_region_name for S3 signing (Greptile feedback)
* fix: _filter_headers_for_aws_signature - Bedrock KB (#23571)
* fix: _filter_headers_for_aws_signature
* fix: filter None header values in all post-signing re-merge paths
Addresses Greptile feedback: None-valued headers were being filtered
during SigV4 signing but re-merged back into the final headers dict
afterward, which would cause downstream HTTP client failures.
Made-with: Cursor
* feat(router): tag_regex routing — route by User-Agent regex without per-developer tag config (#23594)
* feat(router): add tag_regex support for header-based routing
Adds a new `tag_regex` field to litellm_params that lets operators route
requests based on regex patterns matched against request headers — primarily
User-Agent — without requiring per-developer tag configuration.
Use case: route all Claude Code traffic (User-Agent: claude-code/x.y.z) to
a dedicated deployment by setting:
tag_regex:
- "^User-Agent: claude-code\\/"
in the deployment's litellm_params. Works alongside existing `tags` routing;
exact tag match takes precedence over regex match. Unmatched requests fall
through to deployments tagged `default`.
The matched deployment, pattern, and user_agent are recorded in
`metadata["tag_routing"]` so they flow through to SpendLogs automatically.
* fix(tag_regex): address backwards-compat, metadata overwrite, and warning noise
Three issues from code review:
1. Backwards-compat: `has_tag_filter` was widened to activate on any non-empty
User-Agent, which would raise ValueError for existing deployments using plain
tags without a `default` fallback. Fix: only activate header-based regex
filtering when at least one candidate deployment has `tag_regex` configured.
2. Metadata overwrite: `metadata["tag_routing"]` was overwritten for every
matching deployment in the loop, leaving inaccurate provenance when multiple
deployments match. Fix: write only for the first match.
3. Warning noise: an invalid regex pattern logged one warning per header string
rather than once per pattern. Fix: compile first (catching re.error once),
then iterate over header strings.
Also adds two new tests covering these cases, and adds docs page for
tag_regex routing with a Claude Code walk-through.
* refactor(tag_regex): remove unnecessary _healthy_list copy
* docs: merge tag_regex section into tag_routing.md, remove standalone page
- Add ## Regex-based tag routing (tag_regex) section to existing
tag_routing.md instead of a separate page
- Remove tag_regex_routing.md standalone doc (odd UX to have a separate
page for a sub-feature)
- Remove proxy/tag_regex_routing from sidebars.js
- Add match_any=False debug warning in tag_based_routing.py when regex
routing fires under strict mode (regex always uses OR semantics)
* fix(tag_regex): address greptile review - security docs, strict-mode enforcement, validation order
- Strengthen security note in tag_routing.md: explicitly state User-Agent
is client-supplied and can be set to any value; frame tag_regex as a
traffic classification hint, not an access-control mechanism
- Move tag_regex startup validation before _add_deployment() so an invalid
pattern never leaves partial router state
- Enforce match_any=False strict-tag policy: when a deployment has both
tags and tag_regex and the strict tag check fails, skip the regex fallback
rather than silently bypassing the operator's intent
- Extract per-deployment match logic into _match_deployment() helper to
keep get_deployments_for_tag() readable
- Add two new tests: strict-mode blocks regex fallback, regex-only
deployment still matches under match_any=False
* fix(ci): apply Black formatting to 14 files and stabilize flaky caplog tests
- Run Black formatter on 14 files that were failing the lint check
- Replace caplog-based assertions in TestAliasConflicts with
unittest.mock.patch on verbose_logger.warning for xdist compatibility
- The caplog fixture can produce empty text in pytest-xdist workers
in certain CI environments, causing flaky test failures
Co-authored-by: Ishaan Jaff <ishaan-jaff@users.noreply.github.com>
---------
Co-authored-by: Cursor Agent <cursoragent@cursor.com>
Co-authored-by: Ishaan Jaff <ishaan-jaff@users.noreply.github.com>
* PR #22867 added _remove_scope_from_cache_control for Bedrock and Azure AI but omitted Vertex AI. This applies the same pattern to VertexAIPartnerModelsAnthropicMessagesConfig."
* PR #22867 added _remove_scope_from_cache_control for Bedrock and Azure AI but omitted Vertex AI. This applies the same pattern to VertexAIPartnerModelsAnthropicMessagesConfig."
* PR #22867 added _remove_scope_from_cache_control to AzureAnthropicMessagesConfig
but missed VertexAIPartnerModelsAnthropicMessagesConfi Rather than duplicating the method again, moved it up to the base AnthropicMessagesConfig so all providers
inherit it, and removed the now-redundant copy from the Azure AI subclass.
* PR #22867 added _remove_scope_from_cache_control to AzureAnthropicMessagesConfig
but missed VertexAIPartnerModelsAnthropicMessagesConfi Rather than duplicating the method again, moved it up to the base AnthropicMessagesConfig so all providers
inherit it, and removed the now-redundant copy from the Azure AI subclass.
---------
Co-authored-by: Krish Dholakia <krrishdholakia@gmail.com>
The test was creating a real AsyncHTTPHandler instance and patching its
post method, but the internal code creates its own handler, bypassing
the mock. This caused real API calls to Vertex AI, resulting in 401
auth errors in CI. Switched to patching AsyncHTTPHandler at the class
level, matching the pattern used by the passing GPT-OSS test.
Co-Authored-By: Claude Opus 4.6 <noreply@anthropic.com>
- Backend: Use request model from hidden_params for Azure Model Router additional_costs when response has actual model
- Backend: Add additional_costs to total cost calculation
- UI: Show all non-null/non-zero additional_costs in CostBreakdownViewer
- UI: Render cost breakdown when only additional_costs exist
- Tests: Backend test for hidden_params flow; frontend tests for additional_costs
Made-with: Cursor
- Keep Anthropic-native tools (tool_search_tool_regex, web_search, bash, etc.) in original format when translating to OpenAI format for guardrails
- Convert guardrail-returned tools back from OpenAI to Anthropic format (type=custom for user tools)
- Add TOOL_SEARCH_TOOL to ANTHROPIC_HOSTED_TOOLS enum; use prefix matching for native tool detection
- Set type=custom explicitly when mapping OpenAI function tools to AnthropicMessagesTool
- Add test for Anthropic native tools with guardrails
Made-with: Cursor
1. Add missing __init__.py files in tests/test_litellm/llms/gemini/ and
subdirectories (realtime/, image_edit/) to fix ModuleNotFoundError
with pytest-xdist parallel workers.
2. Update test_transform_request_uses_dynamic_max_tokens to use
claude-3-7-sonnet-20250219 (max_output_tokens=64000) since
claude-3-5-sonnet-20241022 was removed from model_prices JSON
during deprecated model cleanup. The test assertion was outdated.
3. Update context caching TTL tests to use gemini-2.5-pro instead of
gemini-1.5-pro. The old model was removed from model_prices JSON,
causing supports_system_messages to return False, which prevented
system_instruction from appearing in the transformation output.
Co-authored-by: yuneng-jiang <yuneng-jiang@users.noreply.github.com>
Update transform_image_generation_response test calls to pass required
explicit params (request_data, optional_params, litellm_params, encoding)
that replaced **kwargs in the method signature.
Co-authored-by: yuneng-jiang <yuneng-jiang@users.noreply.github.com>
The empty-line filter in __next__/__anext__ called .strip() without
checking the type first. When the Responses API yields Pydantic
BaseModel events (e.g. ResponseCreatedEvent), this raises
AttributeError. Add an isinstance(str_line, str) guard so non-string
objects pass through to _handle_string_chunk as intended.
Co-Authored-By: Claude Opus 4.6 <noreply@anthropic.com>
Lint fixes (check_code_and_doc_quality job):
- Remove unused variable reasoning_effort in gpt_5_transformation.py (F841)
- Remove unused timezone imports in mcp_server rest_endpoints.py and server.py (F401)
- Remove unused ProxyBaseLLMRequestProcessing import in realtime endpoints.py (F401)
- Add BaseRealtimeHTTPConfig to TYPE_CHECKING block in utils.py (F821)
- Add PLR0915 per-file-ignore for mcp_server/rest_endpoints.py in ruff.toml
Test fixes (litellm_mapped_tests_llms job):
- Gemini video cost tests: pass explicit model_info to video_generation_cost()
instead of relying on gemini/veo-3.0-generate-preview being in model_prices JSON
- Anthropic max_tokens tests: mock get_max_tokens() to return expected values
instead of depending on claude-3-5-sonnet-20241022 being in model_prices JSON
- Vertex AI pydantic obj test: update from removed gemini-1.5-pro to gemini-2.5-flash,
update expected request body to use response_json_schema format
- Vertex AI/Bedrock file_content integration tests: update mocks to target
base_llm_http_handler.retrieve_file_content (the new code path via
ProviderConfigManager) instead of the old vertex_ai_files_instance/
bedrock_files_instance paths
Co-authored-by: yuneng-jiang <yuneng-jiang@users.noreply.github.com>
OpenAI rejects any reasoning_effort (even 'none') with tools in
/v1/chat/completions for gpt-5.4. Update the guard to drop reasoning_effort
regardless of value. Add docs explaining the auto-drop behavior.
Keep both sets of tests: upstream's OAuth2 token injection test and
our case-insensitive tool matching tests. Use upstream's version of
the bedrock output_config test (more comprehensive).
- Azure Model Router transform_response: let parent extract actual model from raw response
- common_request_processing: skip model override for Azure Model Router requests
- proxy_server: skip streaming chunk model restamp for Azure Model Router
- Add _is_azure_model_router_request helper
- Add tests for non-streaming and streaming
Made-with: Cursor
Snowflake's Cortex LLM API (like Anthropic) requires tool_choice as an
object with a "type" field, not as a bare string. Passing tool_choice="auto"
(or "required"/"none") results in error 390142 "invalid payload".
This fix transforms OpenAI string tool_choice values to the Snowflake
object format:
- "auto" -> {"type": "auto"}
- "required" -> {"type": "any"} (Snowflake/Anthropic convention)
- "none" -> {"type": "none"}
The dict-to-dict transformation for specific function tool choices
({"type": "function", "function": {"name": "..."}} -> {"type": "tool",
"name": [...]}) remains unchanged.
Fixes#23284
Co-authored-by: gambletan <tan@echooo.com>
Co-authored-by: Claude Opus 4.6 <noreply@anthropic.com>
Co-authored-by: Krish Dholakia <krrishdholakia@gmail.com>
- MCP tests: set mock_mcp_server.oauth2_flow = None to prevent MagicMock
leaking into Pydantic Literal validation for MCPServer
- AgentCore tests: pass api_key="test-jwt-token" to bypass SigV4 credential
lookup that fails in CI without AWS credentials
Co-Authored-By: Claude Opus 4.6 <noreply@anthropic.com>
Add verbose_logger.warning when user-specified region is overridden by
supported_regions. Remove now-unused is_global_only_vertex_model function
and its tests since get_vertex_region handles all region logic directly.
- get_vertex_region now overrides user-specified region when it's not in
the model's supported_regions list (prevents 404 for users with a
global VERTEXAI_LOCATION default hitting global-only models)
- Add supported_regions: ["global"] to glm-5-maas in both JSON files
- Update tests to cover the override behavior
- Remove redundant get_vertex_region() call in partner models main.py
(already called inside get_complete_vertex_url)
- Rewrite test mocks to use patch.dict(litellm.model_cost) instead of
patching the removed is_global_only_vertex_model symbol
- Align test assertions with new behavior: user-specified region is
preserved (not overridden) for global-only models
Implement Anthropic Files API (upload, retrieve, list, delete, content)
using the BaseFilesConfig provider pattern. Adds multipart form-data
support to BaseLLMHTTPHandler for file uploads.
