When creating, updating, or deleting access groups, automatically keep
team and key access_group_ids in sync with the access group's assigned_team_ids
and assigned_key_ids. Includes transaction-based DB updates, cache patching,
and handles out-of-sync data by unioning assigned_* fields with hasSome queries.
Adds 12 new tests covering sync behavior across all three CRUD operations.
Co-Authored-By: Claude Sonnet 4.6 <noreply@anthropic.com>
Adds a new CircleCI job that runs the proxy with --use_prisma_db_push
against the base Neon branch before the e2e UI tests create their
branches from it, ensuring the schema is synced on the parent.
Co-Authored-By: Claude Sonnet 4.6 <noreply@anthropic.com>
Add new /public/endpoints endpoint that returns which providers support each LiteLLM
endpoint (e.g., chat_completions, embeddings). The endpoint reads from a local backup
JSON file bundled with the package, caches the result in-process, and transforms the
raw provider-centric data into an endpoint-centric response format.
Changes:
- Add litellm/provider_endpoints_support_backup.json (copy of root source file)
- Add Pydantic response models (EndpointProvider, SupportedEndpoint, SupportedEndpointsResponse)
- Add /public/endpoints route with transformation and caching logic
- Add 16 comprehensive tests covering HTTP layer and transformation functions
Co-Authored-By: Claude Haiku 4.5 <noreply@anthropic.com>
* fix(gemini): enable inputAudioTranscription and handle transcription events for realtime guardrails
Gemini sends inputTranscription/outputTranscription inside serverContent separately from modelTurn/turnComplete. This adds handling to convert them into OpenAI-compatible events so the guardrail pipeline can inspect voice input, and enables inputAudioTranscription in the session setup config.
Made-with: Cursor
* fix(vertex_ai): enable inputAudioTranscription in realtime session config
Add inputAudioTranscription to the Vertex AI realtime setup so the backend returns transcripts of user speech, allowing guardrails to inspect voice input.
Made-with: Cursor
* fix(realtime): pass user_api_key_dict and guardrail metadata through async_realtime handler
The base LLM HTTP handler's async_realtime method was not accepting or forwarding user_api_key_dict and litellm_metadata to RealTimeStreaming. This meant guardrails configured with default_on=false were silently skipped for all provider_config-based realtime connections (Gemini, Vertex AI, etc). Also fixes wss:// connections when SSL_VERIFY=False by overriding ssl=False for secure WebSocket URLs.
Made-with: Cursor
* fix(realtime): forward guardrail metadata for generic provider_config and vertex_ai paths
The _arealtime function was not passing user_api_key_dict or litellm_metadata to base_llm_http_handler.async_realtime() for the generic provider_config path and the vertex_ai-specific path. This broke guardrail resolution since RealTimeStreaming.request_data was empty, causing should_run_guardrail to return False.
Made-with: Cursor
* fix(realtime): voice guardrail responses and block duplicate response.create on text input
When a guardrail blocks voice input, send a conversation.item.create + response.create to the backend so the LLM voices the guardrail message as audio instead of only returning text. Also adds pending_guardrail_message tracking to suppress the automatic response.create the client sends after a blocked text message, and broadens _has_audio_transcription_guardrails to match pre_call/post_call modes.
Made-with: Cursor
* test(realtime): update guardrail tests for broadened audio transcription check and add integration tests
Update existing tests to reflect that pre_call guardrails now correctly trigger the audio/VAD session.update injection. Add integration test file for live OpenAI realtime guardrail testing.
Made-with: Cursor
* fix(realtime): instruct LLM to say exact guardrail message verbatim
The previous prompt gave the LLM creative freedom to paraphrase the guardrail violation message. Now it instructs the LLM to repeat the exact configured message word for word.
Made-with: Cursor
* fix(realtime): preserve wss ssl semantics and move live guardrail test
Keep TLS enabled for wss realtime sessions while honoring SSL_VERIFY=False via a no-verify SSLContext, move the OpenAI live guardrail test into llm_translation, and dedupe duplicated guardrail-detection helpers to prevent drift.
Made-with: Cursor
Same bug as team budget: _assemble_user_object fetched user info from DB
but only used budget_reset_at, discarding max_budget. When the key cache
has a stale None for user_max_budget, _safe_get_remaining_budget returns
+Inf. Now falls back to DB max_budget when metadata value is None.
Co-Authored-By: Claude Sonnet 4.6 <noreply@anthropic.com>
The file was at the repo root and excluded from pip distributions. Moving it to litellm/proxy/public_endpoints/ alongside the other provider JSON files ensures it is packaged correctly. Updates all references in the endpoint handler, coverage tests, and release notes instructions.
Co-Authored-By: Claude Haiku 4.5 <noreply@anthropic.com>
Return all supported endpoints and which providers support them. Includes endpoint display names, URL paths, and per-provider support lists. Results are cached for the process lifetime.
Also adds comprehensive test coverage for the new endpoint.
Co-Authored-By: Claude Haiku 4.5 <noreply@anthropic.com>
- Updated MidStreamFallbackError to retrieve and maintain the original status code from the wrapped exception.
- Ensured that message, request, and response fields remain consistent after calling the parent constructor.
- Added unit tests to verify the correct propagation of status codes and attributes in various scenarios.
- compute image_generation cost from usage token metadata for vertex/gemini\n- map ImageUsage to Usage and reuse generic_cost_per_token\n- fallback to output_cost_per_image when usage metadata missing\n- add tests for token-based path and fallback path
A database timeout (httpcore.ReadTimeout) during get_config() in
_update_llm_router would propagate and prevent ALL DB models from
loading into the router.
Now get_config() failures are caught separately so model add/delete
operations still proceed. Similarly, _delete_deployment catches
get_config failures and safely skips cleanup rather than crashing the
entire sync cycle.
Co-Authored-By: Claude Opus 4.6 <noreply@anthropic.com>
- add gemini-3.1-flash-image-preview + vertex_ai alias entries\n- set pricing to Gemini 3.1 Flash Image Preview rates\n- mirror updates in packaged backup model map\n- update llm cost calc regression test to cover new model
When applying a key_alias filter on the Virtual Keys page, the pagination display (total count, page count) showed stale unfiltered values. The bug was that the table component tracked total_count only from the unfiltered useKeys hook, not from the filtered API response returned by useFilterLogic.
Fixes: When selecting a key alias that matches 1 key, the UI previously showed "Showing 1-50 of 509 results" and "Page 1 of 11" instead of the correct "Showing 1-1 of 1 results" and "Page 1 of 1".
Changes:
- Added filteredTotalCount state to useFilterLogic to track the total_count from filtered API responses
- Updated VirtualKeysTable to use filteredTotalCount (when set) instead of always using the unfiltered total
- Added comprehensive tests to prevent regression of pagination display logic
Type: 🐛 Bug Fix, ✅ Test
Co-Authored-By: Claude Haiku 4.5 <noreply@anthropic.com>
- Move _is_claude_4_6_model from AnthropicConfig to AnthropicModelInfo
to eliminate duplicated logic in is_effort_used
- Use explicit effort_map dict instead of passing unknown values through
to output_config
Claude 4.6 models use output_config as a stable API feature. This commit:
- Maps reasoning_effort to output_config for 4.6 models (minimal → low)
- Restricts effort="max" to Opus 4.6 only
- Skips beta header injection for 4.6 models
- Updates docs for Claude 4.6 effort support
Phase 2 (per-worker mark_process_dead on shutdown) only ever fired
when all workers shut down together, making it redundant — Phase 1
wipes everything on next startup anyway. This aligns with the
prometheus_client docs: just wipe the directory between runs.
LiteLLM was adding a `duration` field to audio transcription responses
for internal cost tracking. The OpenAI Python SDK uses "best match
deserialization" to determine the response type from present fields —
seeing `duration` caused it to incorrectly match plain Transcription
responses as TranscriptionVerbose/TranscriptionDiarized types.
Move the internally-calculated duration to `_hidden_params` so it
remains available for cost calculation without polluting the response
body. Provider-returned duration (e.g. from verbose_json format) is
still preserved in the response as expected.
