* docs(prompt_management.md): document how to onboard prompts to litellm
* feat(arize_phoenix_prompt_manager.py): support new prompt management integration
allows users to connect arize phoenix prompt manager to litellm
* fix(proxy/utils.py): remove prompt variables to avoid re-processing prompt
* docs(arize_phoenix_prompts.md): document new prompt management integration
The relative link was causing Docusaurus to incorrectly associate the
/supported_endpoints page with SDK Functions category instead of the
actual Supported Endpoints generated-index.
* docs: update Getting Started page with accurate endpoints and fix exception handling
- Update endpoints list to include /responses, /audio, /batches
- Change "Consistent output" to be endpoint-agnostic
- Clarify Response Format title as "OpenAI Chat Completions Format"
- Fix exception handling example: use litellm exceptions instead of deprecated openai.error
- Add model prefix (anthropic/) to example
* docs: reorganize sidebar and improve SDK documentation structure
Sidebar changes:
- Reorder: Python SDK first, then AI Gateway (Proxy)
- Rename "LiteLLM - Getting Started" to "Getting Started"
- Restructure SDK section with Core Functions, Configuration subsections
- Move budget_manager to Guides
- Move sdk_custom_pricing and migration to Extras
- Remove duplicate embedding/async_embedding and embedding/moderation
Content changes:
- Add Response Format section to response_api.md
- Add async aembedding() section to supported_embedding.md
* docs: add deprecation notice for OpenAI Assistants API
OpenAI has deprecated the Assistants API, shutting down on August 26, 2026.
Added warning banner directing users to the Responses API.
* docs: expand Core Functions in SDK sidebar
Add more SDK functions to Core Functions category:
- text_completion()
- image_generation()
- transcription()
- speech()
- Link to "All Supported Endpoints" for complete list
* Rename Sidebar Item
* docs: revert Getting Started label to original
* Rename sidebar label from 'LiteLLM - Getting Started' to 'Getting Started'
* docs: add Microsoft GraphRAG to projects using LiteLLM
* docs: add arXiv paper link for GraphRAG
* docs: add GraphRAG to sidebar
* Update projects in sidebars.js
Reordered items in the projects list to include 'GraphRAG'.
Add Agent Lightning, Microsoft's open-source framework for training
AI agents with RL, APO, and SFT. Uses LiteLLM Proxy for LLM routing
and trace collection.
Both frameworks integrate with LiteLLM:
- Google ADK uses LiteLLM for model-agnostic agent building
- Harbor uses LiteLLM for agent evaluation across providers
* docs vertex tts
* place vertex ai types in file
* use VertexAITextToSpeechConfig
* use vertex_voice_dict
* refactor docs
* docs vertex ai chirp
* TestVertexAITextToSpeechConfig
* new provider vertex ai chirp3
* test_litellm_speech_vertex_ai_chirp
* add vertex_ai/chirp cost trackign
* feat(generic_guardrail_api.py): new generic api for guardrails
Allows guardrail providers to work with litellm for guardrails without needing to make a PR to LiteLLM
* docs(generic_guardrail_api.md): document new generic guardrail api
* Fix: Improve PII detection and guardrail API integration
Co-authored-by: krrishdholakia <krrishdholakia@gmail.com>
* feat: correctly extract raw request from guardrail api
* docs(generic_guardrail_api.md): document this is a beta feature
---------
Co-authored-by: Cursor Agent <cursoragent@cursor.com>
* docs: add OpenAI Agents SDK to projects
Add documentation for OpenAI Agents SDK which has an official
LiteLLM extension enabling 100+ LLM providers in multi-agent workflows.
* Update project items in sidebars.js
* feat(generic_api_callback.py): make generic api OSS + support multiple generic API's
Enables https://github.com/BerriAI/litellm/pull/17094#discussion_r2562832967
* feat(callback_utils.py): support custom generic api callbacks
* feat(generic_api_callback.py): support specifying which event types to run the generic api for
* fix(litellm_logging.py): log system prompt for anthropic messages
* feat(generic_api_callback.py): support generic api compatible api's - e.g. rubrik agent cloud
* docs(sidebars.js): document new OSS generic api
* docs(generic_api.md): document new OSS Generic API
* docs(custom_webhook_api.md): document custom webhook api integration tutorial
* docs(custom_webhook_api.md): cleanup
* docs(custom_webhook_api.md): document what get's logged to custom webhook api
* Refactor: Pass callback config to GenericAPILogger
Co-authored-by: krrishdholakia <krrishdholakia@gmail.com>
* Fix: Handle empty messages list in logging payload
Co-authored-by: krrishdholakia <krrishdholakia@gmail.com>
* Checkpoint before follow-up message
Co-authored-by: krrishdholakia <krrishdholakia@gmail.com>
* feat: Cache GenericAPILogger instances to improve performance
Co-authored-by: krrishdholakia <krrishdholakia@gmail.com>
---------
Co-authored-by: Cursor Agent <cursoragent@cursor.com>
* Add doc for adding model pricing and context window
Co-authored-by: krrishdholakia <krrishdholakia@gmail.com>
* Refactor model pricing documentation to include sample spec and examples
Co-authored-by: krrishdholakia <krrishdholakia@gmail.com>
---------
Co-authored-by: Cursor Agent <cursoragent@cursor.com>
* docs: Add mini-swe-agent to projects page
Add mini-swe-agent to the documentation projects page.
mini-swe-agent is a minimal AI coding agent that resolves >70% of
GitHub issues in SWE-bench, built on LiteLLM for model flexibility.
- Added projects/mini-swe-agent.md documentation
- Updated sidebars.js to include mini-swe-agent in projects list
* docs: Update Singularity to Apptainer in mini-swe-agent.md
* feat: initial commit adding agent hub to ui
* feat: add viewable agent hub
* feat: working support for making both config + db agents public via new 'public_agent_groups' list
* fix: agents.py
fix types
* feat: working PATCH endpoint for UI changes
* feat: add new agents panel with working crud
* refactor: refactor to show created_at on be/fe
* style: align new page with the agents table
* style: more style alignment logic
* feat: return if agent is public or not in /v1/agents
* feat: initial commit adding ui flow for making agents discoverable
* feat: new batch make public endpoint
* feat(public_model_hub.tsx): show public agents on public model hub table page
* fix(public_model_hub.tsx): add code examples for using the agent in a2a
* fix: fix indicating if agent has already been made public
* docs: document expected spec for agents is A2A
* docs: add agent hub docs
* docs: document making agents discoverable
* docs: add demo video to docs
* fix: fix ui linting errors
* fix: update tests
