- 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.
