* docs: add Claude Code skills page for litellm-skills
* docs: move skills page to new 'Manage with AI Agents' section
* docs: simplify install to one-liner, rename to LiteLLM Skills
* docs: add pip/venv upgrade workflow guide
- Add comprehensive guide for upgrading LiteLLM proxy via pip
- Covers Prisma client regeneration and DB migration steps
- Includes verification commands and troubleshooting tips
- Links to existing Prisma migration troubleshooting doc
* docs: clarify Python version in prisma generate command
- Update example to show multiple Python versions (3.11, 3.12, 3.13)
- Make it clear LiteLLM supports multiple Python versions, not just 3.11
* docs: emphasize venv activation before running commands
- Add info box at top reminding users to activate venv
- Include venv activation step before starting proxy (both options)
- Add Windows activation command for cross-platform clarity
- Make it clear all commands assume activated venv
* docs: add pip_venv_upgrade to sidebar navigation
- Add new page to Troubleshooting section in sidebars.js
- Positioned after Performance/Latency category and before rollback
- Makes the upgrade guide discoverable through docs navigation
* docs: show explicit --schema flag in prisma migrate deploy
- Add explicit --schema path to Option B migration command
- Remove ambiguous instruction about running from litellm_proxy_extras
- Include path variable guidance for clarity
- Makes the command immediately runnable without directory navigation
* Update docs/my-website/docs/troubleshoot/pip_venv_upgrade.md
Co-authored-by: greptile-apps[bot] <165735046+greptile-apps[bot]@users.noreply.github.com>
* Update docs/my-website/docs/troubleshoot/pip_venv_upgrade.md
Co-authored-by: greptile-apps[bot] <165735046+greptile-apps[bot]@users.noreply.github.com>
* fix: close code block and add missing section in pip_venv_upgrade.md
* docs: define schema-path placeholder in verification section
---------
Co-authored-by: greptile-apps[bot] <165735046+greptile-apps[bot]@users.noreply.github.com>
* fix(mcp): add AWS SigV4 auth for Bedrock AgentCore MCP servers
Add aws_sigv4 auth type to MCP client via httpx.Auth subclass that
signs each request with SigV4 using botocore. Enables mcp_servers
config to connect to AgentCore-hosted MCP servers.
* docs(mcp): add AWS SigV4 auth documentation for Bedrock AgentCore
Add dedicated docs page for configuring MCP servers with AWS SigV4
authentication, update MCP overview with aws_sigv4 auth type and
config example, and link from Bedrock AgentCore provider docs.
Co-Authored-By: Claude Opus 4.6 <noreply@anthropic.com>
* fix(mcp): address Greptile review — requires_request_body, full header signing, health check
- Add requires_request_body = True to MCPSigV4Auth so httpx buffers the
request body before calling auth_flow (prevents empty body hash for
streaming requests)
- Pass all request headers to AWSRequest for canonical SigV4 signing
instead of only Content-Type
- Exclude aws_sigv4 from health check skip logic since it has its own
credential fields (not authentication_token)
- Fix docs: mark aws_access_key_id/aws_secret_access_key as optional
(falls back to boto3 credential chain)
- Add test for requires_request_body flag
Co-Authored-By: Claude Opus 4.6 <noreply@anthropic.com>
---------
Co-authored-by: Claude Opus 4.6 <noreply@anthropic.com>
Co-authored-by: Krish Dholakia <krrishdholakia@gmail.com>
* feat: enforce x-litellm-trace-id in header, if required
* feat: update spend for agent
* refactor: update agent table to follow similar format as other entities - also add a spend column - allows us to see spend of an agent
* fix: cleanup ui
* feat: return spend on agent endpoints
* feat: scope pr
* feat(agents/): support budgets + rate limiting on agents + agent sessions
* fix: address PR review feedback
- Add missing tpm_limit, rpm_limit, session_tpm_limit, session_rpm_limit
columns to root schema.prisma to match proxy and extras schemas
- Add backwards-compatible fallback to key metadata for max_iterations
so existing users don't silently lose enforcement
Co-Authored-By: Claude Opus 4.6 <noreply@anthropic.com>
* fix: qa'ed RPM limiting on agents
---------
Co-authored-by: Claude Opus 4.6 <noreply@anthropic.com>
* feat: add LITELLM_WORKER_STARTUP_HOOKS for per-worker initialization (gflags support)
Add support for running user-defined startup hooks in each worker process
during proxy_startup_event. This enables re-initialization of in-process
state (like gflags.FLAGS) that doesn't survive uvicorn worker spawning.
Usage:
export LITELLM_WORKER_STARTUP_HOOKS=mymodule:init_fn,other:setup_fn
Hooks run early in proxy_startup_event (before config/DB loading).
Supports both sync and async callables. Errors propagate to prevent
broken workers from serving traffic. No-op when env var is unset.
Includes 5 tests covering sync/async hooks, multiple hooks, error
propagation, and no-hooks-set scenarios.
Co-authored-by: Ishaan Jaff <ishaan-jaff@users.noreply.github.com>
* docs: add Worker Startup Hooks page with gflags usage example
- New docs page: docs/proxy/worker_startup_hooks.md
- Explains the problem (per-process state lost in multi-worker deployments)
- Full gflags example with wrapper module and startup script
- Covers multiple hooks, async hooks, error behavior
- Architecture diagram showing master→worker flow
- Added LITELLM_WORKER_STARTUP_HOOKS to config_settings.md env var table
- Added to sidebar under Setup & Deployment
Co-authored-by: Ishaan Jaff <ishaan-jaff@users.noreply.github.com>
* Update litellm/proxy/proxy_server.py
Co-authored-by: greptile-apps[bot] <165735046+greptile-apps[bot]@users.noreply.github.com>
* Apply suggestion from @greptile-apps[bot]
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 forward_llm_provider_auth_headers support from litellm_settings
- When enabled, client x-api-key takes precedence over deployment keys
- Forward x-api-key when x-litellm-api-key or Authorization used for auth
- Fix duplicate patch lines in test_byok_oauth_endpoints.py
- Add Claude Code BYOK documentation with /login and ANTHROPIC_CUSTOM_HEADERS
- Add unit tests for clean_headers x-api-key forwarding logic
- Sync model_prices backup (pre-commit hook)
Made-with: Cursor
Documents exactly how every request and response field gets translated
when LiteLLM routes an Anthropic /v1/messages call through the OpenAI
Responses API path (for OpenAI/Azure targets). Covers messages content
block mapping, tools, tool_choice, thinking→reasoning, context_management,
and the reverse response translation. Wired into the /v1/messages sidebar.
Co-Authored-By: Claude Sonnet 4.6 <noreply@anthropic.com>
Adds provider documentation for bedrock_mantle including:
- API key and region configuration
- Supported models with pricing table
- SDK, streaming, and async usage examples
- LiteLLM Proxy config and usage
- Added to Bedrock category in sidebar
Co-Authored-By: Claude Sonnet 4.6 <noreply@anthropic.com>
* feat(guardrails): team-based guardrail registration and approval workflow
Add team-based guardrail submission system where teams can register
Generic Guardrail API guardrails for admin review. Includes:
- POST /guardrails/register endpoint for team-scoped submissions
- Admin review endpoints (list/get/approve/reject submissions)
- Team Guardrails tab in the UI dashboard
- extra_headers support for forwarding client headers to guardrail APIs
- Prisma schema migration for status, submitted_at, reviewed_at fields
- Documentation for team-based guardrails and static/dynamic headers
Co-Authored-By: Claude Opus 4.6 <noreply@anthropic.com>
* fix(guardrails): address review feedback - SSRF, silent failure, redundant query
- Validate api_base URL scheme (http/https only) and hostname in
register_guardrail to prevent SSRF via team submissions
- Return warning field in approve response when in-memory initialization
fails so admins know the guardrail won't work until next sync cycle
- Eliminate redundant DB query in list_guardrail_submissions by fetching
all team guardrails once and deriving both filtered list and summary
counts from the single result set
Co-Authored-By: Claude Opus 4.6 <noreply@anthropic.com>
* fix(guardrails): add pending_review status guard to reject endpoint
Prevent rejecting already-active or already-rejected guardrails, which
would create a DB/memory inconsistency (active in memory but rejected
in DB). Now mirrors the approve endpoint's status check.
Co-Authored-By: Claude Opus 4.6 <noreply@anthropic.com>
---------
Co-authored-by: Claude Opus 4.6 <noreply@anthropic.com>
* Add CrowdStrike AIDR guardrail hook
* fixup! use apply_guardrail event hook
* fixup! update imports
* fix(guardrails): include AI response in CrowdStrike AIDR output events
Issue:
_build_guard_input_for_response() was:
- Sending only the original user input (messages).
- Not sending the AI provider response.
This fix will:
- Extract response.choices from the ModelResponse object and include them in guard_input payload.
- Thus, ensure AIDR output rules receive the AI-generated content for analysis.
- Fix and update tests.
* fix(guardrails): prevent duplicate input events in CrowdStrike AIDR guardrail
Issue:
The CrowdStrike AIDR guardrail was running on during_call hooks wihtout event_hook configured.
This fix will:
- Set event_hook to ["pre_call", "post_call"] (AIDR admins will control what policy is applied)
This change will:
- Require default_on parameter
- Prevent duplicate API calls to AIDR for the same input
- Avoid unchecked AI provider API calls on during_call hook
* docs: add CrowdStrike AIDR to the list of Guardrails under Integrations
* docs: update CrowdStrike AIDR documentation page
---------
Co-authored-by: Konstantin Lapine <konstantin.lapine@crowdstrike.com>
Add detailed UI walkthrough for Project Management feature including:
- Beta notice with link to API documentation
- Overview of projects and organizational hierarchy
- Prerequisites and setup instructions
- Separate section for enabling projects in UI settings
- Step-by-step guide for creating and managing projects
- Use cases for key organization within teams
- Next steps and related documentation links
- Proper sidebar navigation integration
Co-Authored-By: Claude Haiku 4.5 <noreply@anthropic.com>
* 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 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.
* docs: add OpenClaw integration tutorial
* docs: simplify OpenClaw proxy start command
* docs: rewrite OpenClaw integration guide for clarity
- Use gpt-5 as default model
- Replace poetry run with standard litellm CLI
- Add prerequisites section and verification step
- Simplify onboarding instructions (table format)
- Move manual config and troubleshooting to bottom
- Add multi-model config (claude-sonnet, gemini-flash)
* docs: fix model name in OpenClaw manual config example
* docs: rewrite OpenClaw integration guide from scratch
Rewrote the guide based on hands-on testing of every command.
Key changes:
- Replace non-existent `openclaw chat` with verified commands
(dashboard, tui, agent --agent main)
- Add 3 onboarding options: QuickStart, Manual, and non-interactive
- Fix health check (requires Bearer token)
- Remove misleading "Starting from scratch" section
- Use gpt-4o instead of gpt-5 as the example model
- Clarify that API keys can come from export, .env, or any method
- Add config reference section showing openclaw.json structure
- Add real troubleshooting based on issues found during testing
* fix(budget): fix timezone config lookup and replace hardcoded timezone map with ZoneInfo
* fix(budget): update stale docstring on get_budget_reset_time
* docs: add Day 0 Sonnet 4.6 support blog post
Add concise blog post announcing Day 0 support for Claude Sonnet 4.6 with Docker image and usage examples across:
- Anthropic API
- Azure AI
- Vertex AI
- Bedrock
Includes both LiteLLM Proxy and SDK usage for all providers.
* docs: add Sonnet 4.6 blog post to sidebar navigation
Add link to Claude Sonnet 4.6 Day 0 support blog post in the Blog section of the documentation sidebar.
