Instead of returning a 400 error when return_to is passed without
control_plane_url configured, silently ignore it and proceed with
the normal same-origin SSO flow.
- Use openai/gpt-5 prefix to match existing doc conventions
- Clarify that additional_drop_params must be added to every affected
model entry, not just one
Co-Authored-By: Claude Sonnet 4.6 (1M context) <noreply@anthropic.com>
OpenCode sends a `reasoningSummary` Responses API param with chat
completion requests. Document how to use `additional_drop_params` to
drop it and avoid 400 errors from the OpenAI API.
Co-Authored-By: Claude Sonnet 4.6 (1M context) <noreply@anthropic.com>
Add `if: github.repository == 'BerriAI/litellm'` guard to scheduled
jobs in stale.yml, codeql.yml, and create_daily_staging_branch.yml.
This matches the existing pattern in auto_update_price_and_context_window.yml
and prevents these workflows from running unnecessarily on fork repositories.
- 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
LiteLLMCompletionStreamingIterator defined create_content_part_added_event()
but never called it, so non-OpenAI providers (Claude, Gemini, etc.) skipped
this spec-required event. Downstream parsers that process content_part.added
to initialize the text part structure would fail when output_text.delta
arrived before the text part existed.
Verify that spend_logs_metadata is correctly merged into combined_metadata
and flows through to Prometheus custom labels. Tests cover: basic extraction,
precedence when keys overlap, all three metadata sources combined, and None
handling.
Co-Authored-By: Claude Opus 4.6 <noreply@anthropic.com>
Add spend_logs_metadata to combined_metadata in Prometheus logger so
custom metadata from x-litellm-spend-logs-metadata header can be used
in Prometheus custom labels.
Co-Authored-By: Claude Opus 4.6 <noreply@anthropic.com>
Thread project_alias alongside project_id through the metadata pipeline so
callbacks receive the human-readable project name. DRY up duplicate metadata
dict construction in proxy_track_cost_callback and pass_through_endpoints by
reusing get_sanitized_user_information_from_key — future metadata fields only
need adding in one place.
Co-Authored-By: Claude Opus 4.6 <noreply@anthropic.com>
Two independent bugs prevented post-call OpenAI Moderation guardrail
results from reaching downstream logging callbacks (Langfuse, Datadog).
Bug 1: process_output_response() created a throwaway request_data dict,
so guardrail info written by @log_guardrail_information was discarded.
Fixed by threading the real request_data from the unified guardrail
dispatcher through all 13 BaseTranslation handlers, with litellm_metadata
injection preserved for third-party guardrails (Zscaler, Prompt Security).
Also extended to process_output_streaming_response for consistency.
Bug 2: The @log_guardrail_information decorator collapsed the full
moderation API response (categories, scores, flagged status) to "allow".
Fixed by overriding _process_response/_process_error on
OpenAIModerationGuardrail to stash and log the full response, following
the established Model Armor pattern.
The vertex_ai cost calculator hardcoded $0.035 and charged for every
call with a PromptTokensDetailsWrapper (not just web search calls).
Delegate to the shared Gemini calculator which reads pricing and
billing unit from model_info, fixing both issues for vertex_ai models.
Replace _is_gemini_3_model() substring check with a
web_search_billing_unit field in model_prices JSON:
- "per_query": each search query billed individually (Gemini 3.x)
- "per_prompt" (default): flat fee per grounded API call (Gemini 2.x)
Add web_search_billing_unit to 23 Gemini 3.x model entries.
Update docs and tests accordingly.
Document how each provider bills for web search, the
search_context_cost_per_query field in model_prices JSON,
how to override pricing via proxy config, and how LiteLLM
extracts web_search_requests from each provider's response.
Add tests for the gpt-5.1/5.2/5.4 reasoning.effort interaction:
- gpt-5.1 with no reasoning allows flexible temperature
- gpt-5.1 with effort='high' drops temperature
- gpt-5.4 with effort='none' allows flexible temperature
- Gemini 2.x charges per grounded prompt (flat $0.035), clamped to 1
regardless of internal query count
- Gemini 3.x charges per search query ($0.014 each)
- Extract web_search_requests from groundingMetadata in non-streaming
responses (parity with streaming path)
- Add search_context_cost_per_query to vertex_ai and base Gemini entries
- Move tests to tests/test_litellm/ (CI directory)
The Gemini web search cost calculator hardcoded $0.035 per request,
which is only correct for Gemini 2.x models. Gemini 3.x models
charge $0.014 per request.
Read from search_context_cost_per_query in model_info (same field
used by Anthropic, OpenAI, and Perplexity) with fallback to the
legacy $0.035 for models not yet updated in the JSON.
Also add search_context_cost_per_query to all 25 Gemini models
that support web search in model_prices_and_context_window.json.
Fixes#24369
The Responses API map_openai_params passed all params through without
applying model-specific validation. GPT-5 models (except gpt-5-chat)
only accept temperature=1 unless reasoning.effort="none" on models
that support it (5.1, 5.2, 5.4).
Reuse the existing OpenAIGPT5Config logic from chat completions to
validate temperature in the Responses API path. With drop_params=True,
unsupported temperature values are silently dropped; without it,
UnsupportedParamsError is raised.
Fixes#16090
The Gemini batch embedding transformation was spreading all
optional_params into the request body via **gemini_params. Params
like max_tokens (injected by add_provider_specific_params_to_optional_params)
would reach the Gemini API and cause a 400 BadRequestError.
Extract _filter_embed_params() that maps dimensions/task_type and
keeps only the fields Gemini embeddings actually accept
(outputDimensionality, taskType, title). Applied to both
transform_openai_input_gemini_content and
transform_openai_input_gemini_embed_content.
This also fixes drop_params: true not preventing the error, since
the param was re-injected after the drop_params check.
Fixes#24293
When the Responses API converts function_call and message output items
into chat completion messages, they can become two consecutive assistant
messages. The Bedrock Converse transformer merges these into one, but
the merge preserves input order — so if function_call came first, the
toolUse block ends up before the text block.
Claude models (Sonnet 4, Haiku 3.5+) reject this ordering with:
"tool_use ids were found without tool_result blocks immediately after"
Add _sort_bedrock_assistant_content_blocks() that reorders content
blocks within assistant messages: reasoningContent → text → toolUse.
Applied in both sync and async Bedrock Converse transformation paths.
Fixes#24361
