Add vitest tests for TypeBadges, ErrorViewer, ConfigInfoMessage, TimeCell, and TruncatedValue covering rendering, user interactions, and edge cases.
Co-Authored-By: Claude Opus 4.6 <noreply@anthropic.com>
Resolve conflict in perplexity/responses/transformation.py by keeping
the simplified ~50 line version (PR's goal) instead of main's ~410 line
version. Added supports_native_websocket() -> False from main.
Keep unified _FINISH_REASON_MAP dict approach, discard upstream's
inconsistent _VALID_OPENAI_FINISH_REASONS frozenset that mapped to
values not in the OpenAIChatCompletionFinishReason Literal.
Add usage example with concrete model entry, explanation of load-time
expansion, and cross-reference to model_alias_map to clarify the
difference between the two features.
The _list_has_thinking guard only checked for type == "thinking" but
Anthropic can also return redacted_thinking blocks (safety-filtered).
These are also accumulated in thinking_blocks, so the same duplication
bug would occur with redacted thinking content.
Add `supports_web_search: true` to 31 OpenAI models that support the
`web_search_preview` tool via the Responses API. This enables the Router
to correctly include these deployments when requests use web search tools.
Models excluded (tested, confirmed unsupported):
- o1-pro (Tool 'web_search_preview' is not supported)
- gpt-audio / gpt-audio-mini (not supported)
- gpt-4.1-nano (not supported)
- codex-mini-latest (model not found)
Also removes the invalid `gpt-5.3` entry added in prior commit
(model name does not exist in OpenAI API; use gpt-5.3-chat-latest).
- Remove dead fields: supports_none_reasoning_effort, supports_xhigh_reasoning_effort
(not referenced anywhere in the codebase)
- Remove supports_web_search (inconsistent with other base models)
- Add supports_service_tier (consistent with gpt-5, gpt-5.1, gpt-5.2)
* fix(mcp): add AWS SigV4 auth for Bedrock AgentCore MCP servers
Add aws_sigv4 auth type to MCP client via httpx.Auth subclass that
signs each request with SigV4 using botocore. Enables mcp_servers
config to connect to AgentCore-hosted MCP servers.
* docs(mcp): add AWS SigV4 auth documentation for Bedrock AgentCore
Add dedicated docs page for configuring MCP servers with AWS SigV4
authentication, update MCP overview with aws_sigv4 auth type and
config example, and link from Bedrock AgentCore provider docs.
Co-Authored-By: Claude Opus 4.6 <noreply@anthropic.com>
* fix(mcp): address Greptile review — requires_request_body, full header signing, health check
- Add requires_request_body = True to MCPSigV4Auth so httpx buffers the
request body before calling auth_flow (prevents empty body hash for
streaming requests)
- Pass all request headers to AWSRequest for canonical SigV4 signing
instead of only Content-Type
- Exclude aws_sigv4 from health check skip logic since it has its own
credential fields (not authentication_token)
- Fix docs: mark aws_access_key_id/aws_secret_access_key as optional
(falls back to boto3 credential chain)
- Add test for requires_request_body flag
Co-Authored-By: Claude Opus 4.6 <noreply@anthropic.com>
---------
Co-authored-by: Claude Opus 4.6 <noreply@anthropic.com>
Co-authored-by: Krish Dholakia <krrishdholakia@gmail.com>
- Add 'token' to MCPAuth enum for custom token auth format
- Implement token auth in MCP client (_get_auth_headers)
- Add token auth support for OpenAPI-based MCP tools
- Add comprehensive unit tests to existing test_mcp_client.py
- Fixes issue where MCP servers expecting 'Authorization: token <value>' header could not connect
When guardrails return the full data dict (e.g. guardrails_ai), the
guardrail response logged to spend logs and OTEL traces could contain
data["secret_fields"].raw_headers with plaintext Authorization headers.
This adds a pop("secret_fields") in the guardrail logging path,
matching the existing pattern used by Langfuse and Arize integrations.
Tested: Verified fix removes secret_fields/raw_headers/authorization
from both /spend/logs/ui responses and OTEL trace span attributes.
* fix(streaming): map unknown finish_reason values to finish_reason_unspecified
Some LLM providers return non-standard finish_reason values that are not
in the OpenAIChatCompletionFinishReason Literal (e.g. ZhipuAI/GLM returns
'network_error' when a streaming error occurs mid-response).
Previously map_finish_reason() fell through with return finish_reason,
passing the unknown value directly to Choices.__init__() which calls
Pydantic validation. This caused a ValidationError that was caught by
stream_chunk_builder() and re-raised as the misleading:
litellm.APIError: Error building chunks for logging/streaming usage calculation
Fix: after all known provider-specific mappings, check if the value is in
the valid set (stop, length, tool_calls, content_filter, function_call,
guardrail_intervened, eos, finish_reason_unspecified, malformed_function_call).
Any value not in this set is mapped to 'finish_reason_unspecified' instead
of being returned as-is.
This is consistent with how other unknown stop reasons (e.g. Vertex AI's
FINISH_REASON_UNSPECIFIED) are already handled.
* refactor: use get_args(OpenAIChatCompletionFinishReason) for valid set
Per code review feedback: replace the hardcoded _valid_finish_reasons set
with a module-level frozenset derived dynamically from the source-of-truth
Literal type via typing.get_args(). This ensures the valid-reason check
stays in sync automatically when new finish reasons are added to the Literal,
and avoids recreating the set on every streaming chunk call.
* test(map_finish_reason): add unit tests and warning log for unknown finish reasons
- Add TestMapFinishReason class in test_core_helpers.py covering:
- All known OpenAI-native values pass through unchanged (parametrized)
- Provider-specific mappings: Anthropic, Cohere, Vertex AI
- Unknown/provider-specific values map to 'finish_reason_unspecified'
- Regression test for ZhipuAI/GLM-5 'network_error' case
- Add verbose_logger.warning() in map_finish_reason() when an unknown
finish_reason is encountered, so operators can track which providers
return non-standard values
When assistant content is already a list containing thinking blocks
inline (not str/None), SEQUENTIAL MODE was still prepending all
thinking_blocks from provider_specific_fields, causing duplication
and breaking Anthropic's position-dependent signature verification.
Now detects if the content list already has thinking blocks and skips
the extend(thinking_blocks) to preserve the original interleaved order.
Addresses the correctness gap identified by Greptile review where
list-content messages bypass INTERLEAVED MODE.
Fixes: https://github.com/BerriAI/litellm/issues/23047
Fixes#23267 — plain `gpt-5.3` was missing from the model pricing
JSON, causing tool_choice (and other capability flags) to default
to unsupported. Copied fields from gpt-5.3-chat-latest.
* fix: add missing indexes for top CPU-consuming queries
Add indexes to eliminate full table scans on two of the top 5 queries
by CPU usage:
1. LiteLLM_VerificationToken(key_alias) — for ORDER BY key_alias ASC
queries when listing verification tokens
2. LiteLLM_SpendLogs(user, startTime) — for WHERE user = $1 AND
startTime BETWEEN $2 AND $3 GROUP BY queries on the spend logs
Co-Authored-By: Claude Opus 4.6 <noreply@anthropic.com>
* fix: use CREATE INDEX CONCURRENTLY to avoid table locks
Both indexes are now created with CONCURRENTLY and IF NOT EXISTS
to avoid blocking writes on large production tables.
Uses -- SkipTransactionBlock for Prisma migrate compatibility.
Co-Authored-By: Claude Opus 4.6 <noreply@anthropic.com>
---------
Co-authored-by: Claude Opus 4.6 <noreply@anthropic.com>
Add tip boxes explaining that gpt-5.4 does not support reasoning_effort
with function tools in /v1/chat/completions, and that the responses
bridge (openai/responses/gpt-5.4) should be used instead.
Gemini 2.0+ natively accepts JSON Schema in tool parameters, including
bare {} (TYPE_UNSPECIFIED), anyOf with null, and lowercase types. The
existing _build_vertex_schema pipeline was coercing {} to {"type": "object"},
breaking JsonValue/Any field semantics (issue #22391).
Add _build_vertex_schema_for_gemini_2() that only resolves $ref (which
Gemini doesn't support in tools) and filters unsupported fields. Use it
for Gemini 2.0+ models, keeping the full transform for Gemini 1.5.
