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>
Models using only the legacy max_tokens field now have proper
max_input_tokens and max_output_tokens based on provider documentation.
Providers updated: OpenRouter (13), Gradient AI (13), Heroku (4),
Aleph Alpha (6), Together AI (2).
The observability "View all" page only listed 9 integrations while
27 more had documentation. Added all missing integration cards so
users can discover the full catalog from the integrations landing page.
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.
Both bridges (Responses→CC and CC→Responses) independently encoded the
same field mapping knowledge. This extracts 4 shared mappings into a
single module so future changes only need to happen in one place.
Shared mappings:
- status ↔ finish_reason bidirectional dicts and functions
- response_format ↔ text.format paired conversion functions
- provider_specific_fields normalization helper
- usage field name translation (input_tokens ↔ prompt_tokens, etc.)
No behavioral changes — bridge methods now delegate to the shared module.
The `dimensions` parameter was correctly mapped to `outputDimensionality`
in `optional_params` but never placed in the request body. The Vertex AI
predict endpoint expects it under a `parameters` field.
Add `parameters` dict to `VertexMultimodalEmbeddingRequest` TypedDict and
populate it from `optional_params` in `transform_embedding_request`.
Fixes#24392
Gemini API returns a DOCUMENT modality in promptTokensDetails for PDF
inputs, but the token parser only handled TEXT, IMAGE, AUDIO, and VIDEO.
DOCUMENT tokens were silently dropped, causing cost to be undercounted
by up to 99% for PDF-heavy requests.
Map DOCUMENT tokens to text_tokens since Gemini bills documents at the
text token rate. Applied to all four modality parser loops:
promptTokensDetails, cacheTokensDetails, responseTokensDetails, and
candidatesTokensDetails.
Fixes#24375
