Unit Tests: Proxy DB Operations / proxy-db (auth-checks, tests/proxy_unit_tests/test_auth_checks.py tests/proxy_unit_tests/test_user_api_key_auth.py, 20, 8) (push) Has been cancelled
Unit Tests: Proxy DB Operations / proxy-db (remaining, tests/proxy_unit_tests --ignore=tests/proxy_unit_tests/test_key_generate_prisma.py --ignore=tests/proxy_unit_tests/test_auth_checks.py --ignore=tests/proxy_unit_tests/test_user_api_key_auth.py, 20, 8) (push) Has been cancelled
* feat(vertex_ai): add Vertex AI Gemini Live support via unified /realtime endpoint
Adds VertexAIRealtimeConfig which translates the OpenAI Realtime WebSocket
protocol to Vertex AI BidiGenerateContent. Supports voice in/voice out
(16 kHz mic → 24 kHz speaker) and text in/text out through the proxy's
/realtime endpoint.
Key changes:
- New litellm/llms/vertex_ai/realtime/transformation.py with VertexAIRealtimeConfig
- Builds correct wss:// URL (regional + global)
- OAuth2 Bearer token auth (not API key)
- Full model path (projects/.../publishers/google/models/...)
- Ignores session.update (Vertex AI only accepts one setup message)
- realtime_api/main.py: vertex_ai branch resolves OAuth token + constructs config
- llm_http_handler.py: auto-sends session setup before bidirectional_forward
- gemini/realtime/transformation.py: fix crashes on empty turnComplete events
- realtime_streaming.py: try/except guard so bad messages don't kill the loop
- proxy_server.py: add missing websockets.exceptions import
* docs: add vertex_realtime to sidebars
* fix: drop unknown event types in Gemini transform; add vertex_ai health check
* fix: propagate UUID fallback IDs from transform_content_done_event to return_additional_content_done_events
* fix: route guardrail backend sends through provider transform; fix str.strip misuse for model prefix
* fix: handle Vertex AI full resource path in session.created; route guardrail block sends through _send_to_backend
* fix: remove unused VertexBase in transformation.py; apply UUID fallback in return_additional_content_done_events
* feat(realtime): add guardrails query param to /v1/realtime WebSocket endpoint
- Add 'guardrails' query param (comma-separated) to realtime_websocket_endpoint
- Import websockets and websockets.exceptions at module level (fixes NameError in except clause)
- Split try/except into Phase 1 (pre-call) and Phase 2 (routing) so guardrail
errors send back a typed error event before closing, while upstream errors
close silently with 1011
* feat(ui): pass selectedGuardrails from sidebar to RealtimePlayground WebSocket URL
* docs(realtime): add guardrails section with dynamic passing examples
* Add post-call hook for Lakera guardrail and mask PII in responses
* Add post-call hook for Lakera and mask PII in responses
* Fix post-call hook: pass event_type to call_v2_guard
* Address Greptile review: return ModelResponse, fix mutation, add header, test location, mask order
- PII masking path: return ModelResponse instead of dict so deployment hook accepts it
- Avoid mutating request data: deep copy original_messages and messages in _mask_pii_in_messages
- Add guardrail header in PII-only return path
- Add test in tests/test_litellm/ (test_lakera_ai_v2.py) per PR checklist
- Sort PII payload spans by (start,end) descending so multiple spans in one message mask correctly
Co-authored-by: Cursor <cursoragent@cursor.com>
* Updated ponteital for index mismatch when choices have null content and inconsistent on_flagged access pattern
* Update litellm/proxy/guardrails/guardrail_hooks/lakera_ai_v2.py
Co-authored-by: greptile-apps[bot] <165735046+greptile-apps[bot]@users.noreply.github.com>
* Update to explicitly state supported endpoints - chat completions
* Fix minor lint error on masked_entity_count
---------
Co-authored-by: Steve <steve.giguere@lakera.ai>
Co-authored-by: Cursor <cursoragent@cursor.com>
Co-authored-by: greptile-apps[bot] <165735046+greptile-apps[bot]@users.noreply.github.com>
* docs: document x-litellm-tags header and request body tags parameter
- Add documentation for x-litellm-tags header (comma-separated or array)
- Add documentation for tags in request body
- Clarify that dynamic tags override config tags
Co-authored-by: Ishaan Jaff <ishaan-jaff@users.noreply.github.com>
* docs: consolidate tag documentation and improve cross-references
- Make request_tags.md the single source of truth for all tag options
- Add cross-reference from cost_tracking.md to request_tags.md
- Document both direct tags and metadata.tags formats
- Add key/team tag setup and custom header tracking to request_tags.md
- Reduce duplication and make navigation clearer
Co-authored-by: Ishaan Jaff <ishaan-jaff@users.noreply.github.com>
* docs: use generic examples instead of specific company names
Co-authored-by: Ishaan Jaff <ishaan-jaff@users.noreply.github.com>
* docs: clarify x-litellm-tags header format is comma-separated string
HTTP headers are always strings, not arrays. Remove misleading
array format documentation for the header parameter.
Co-authored-by: Ishaan Jaff <ishaan-jaff@users.noreply.github.com>
* Update docs/my-website/docs/proxy/request_tags.md
Co-authored-by: greptile-apps[bot] <165735046+greptile-apps[bot]@users.noreply.github.com>
---------
Co-authored-by: Cursor Agent <cursoragent@cursor.com>
Co-authored-by: Ishaan Jaff <ishaan-jaff@users.noreply.github.com>
Co-authored-by: greptile-apps[bot] <165735046+greptile-apps[bot]@users.noreply.github.com>
Add new document explaining automatic credential usage tracking and tagging. When models use reusable credentials, LiteLLM automatically injects a Credential: <name> tag on requests, enabling credential-level spend tracking on the Usage page with no additional configuration.
Co-Authored-By: Claude Haiku 4.5 <noreply@anthropic.com>
These params were silently dropped for Chat Completions because they
were missing from the supported params whitelist. Also adds
prompt_cache_retention to the Responses API TypedDict and fixes
misleading cache_control comments in OpenAI prompt caching docs.
The /mcp endpoint requires a trailing slash because the MCP server
is mounted as a sub-application using app.mount(). Starlette's mount
behavior causes a 307 redirect from /mcp to /mcp/, which many MCP
clients fail to handle.
Updates documentation examples to use /mcp/ consistently.
Set prometheus_emit_stream_label: true in litellm_settings to emit a
stream label (True/False/None) on litellm_proxy_total_requests_metric.
Opt-in to avoid breaking cardinality on existing deployments.
- Rename "Agentic Research API" to "Agent API"
Expand
- supported Responses API parameters
- Fix function
tool handling to pass custom function tools through unchanged instead of heuristically mapping them.
-Update model registry with current Perplexity
models and presets
- Add Function Calling and Structured Outputs documentation sections.
- Unit tests for transformation logic.
* feat(realtime): add guardrail hook for voice transcription in Realtime API
Adds a new `realtime_input_transcription` guardrail event hook that fires
after Whisper transcription completes, before the LLM generates a response.
When a guardrail blocks, a synthetic warning is sent to the client and
`response.create` is never forwarded — the LLM never responds.
Also rewrites `create_response: true` → `false` in client `session.update`
so the proxy controls when responses are triggered.
* feat(realtime): speak guardrail block message as audio via TTS
Instead of sending synthetic text events when a guardrail blocks,
send response.create with forced instructions so OpenAI's TTS speaks
the warning message — user hears the block instead of just seeing text.
* fix(realtime): speak exact content filter error message via TTS
Extract the human-readable error string from HTTPException.detail
so the spoken warning says e.g. "Content blocked: keyword 'system update'
detected" instead of the raw str(e) repr.
* fix(realtime): reliably enforce create_response=false for guardrails
- Proxy now injects session.update with create_response=false immediately
on session.created (when guardrails are active), instead of rewriting
the client's session.update — works regardless of what the client sends
- Add response.cancel before the warning response.create to kill any
in-flight LLM response that snuck through before the guardrail fired
* refactor(realtime): call apply_guardrail directly, remove dedicated hook method
The async_realtime_input_transcription_hook in CustomGuardrail and
ContentFilterGuardrail was just a thin wrapper that called apply_guardrail —
the same interface used by /chat and /messages. Remove the wrapper and call
apply_guardrail directly from run_realtime_guardrails, keeping the pattern
consistent across all endpoints.
* docs: add Realtime API guardrails tutorial and flow diagram
* fix: address Greptile review comments
- Forward user_api_key_dict through realtime_api/main.py (_arealtime) so
it actually reaches RealTimeStreaming instead of always being None
- Run guardrail interception in provider_config path too (e.g. Gemini),
not only the OpenAI direct path
- Narrow exception catch to HTTPException/ValueError only; re-raise
unexpected errors so programming bugs surface in logs rather than
silently appearing as guardrail blocks
- Update tests: mock apply_guardrail directly (hook method was removed),
replace session.update client-rewrite test with session.created
injection test matching the new server-side approach
* fix: address latest Greptile review comments
- Remove fastapi import from SDK-layer file; check for status_code/detail
attrs instead to identify guardrail-block exceptions vs programming errors
- Add store_message() before continue in transcription interception so
transcription events are logged in the non-provider_config path
- Inject create_response=false on session.created in provider_config path
(Gemini etc.) to match the OpenAI path — prevents LLM auto-responding
before guardrail runs on VAD-detected turns
* Add OpenAI Agents SDK tutorial to docs
* Update OpenAI Agents SDK tutorial to use LiteLLM environment variables
* Enhance OpenAI Agents SDK tutorial with built-in LiteLLM extension details and updated configuration steps. Adjust section headings for clarity and improve the flow of information regarding model setup and usage.
* docs: add Google GenAI SDK tutorial for JS and Python
Add tutorial for using Google's official GenAI SDK (@google/genai for JS,
google-genai for Python) with LiteLLM proxy. Covers pass-through and
native router endpoints, streaming, multi-turn chat, and multi-provider
routing via model_group_alias. Also updates pass-through docs to use the
new SDK replacing the deprecated @google/generative-ai.
* fix(docs): correct Python SDK env var name in GenAI tutorial
GOOGLE_GENAI_API_KEY does not exist in the google-genai SDK.
The correct env var is GEMINI_API_KEY (or GOOGLE_API_KEY).
Also note that the Python SDK has no base URL env var.
* fix(docs): replace non-existent GOOGLE_GENAI_BASE_URL env var in interactions.md
The Python google-genai SDK does not read GOOGLE_GENAI_BASE_URL.
Use http_options={"base_url": "..."} in code instead.
