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: update getting started page
- Add Core Functions table with link to full list
- Add Responses API section
- Add Async section with acompletion() example
- Add "Switch Providers with One Line" example
- Clarify Basic Usage supports multiple endpoints
- Update models to current versions (openai/gpt-4o, anthropic/claude-sonnet-4)
- Use provider/model format throughout
- Fix deprecated import: from openai.error -> from openai
- Keep original structure: community key, More details links, observability env vars
* Cleanup: Remove orphan docs pages and Docusaurus template files
- Remove orphan getting_started.md (not linked in sidebar)
- Remove Docusaurus template intro.md
- Remove tutorial-basics/ directory (Docusaurus template)
- Remove tutorial-extras/ directory (Docusaurus template)
* 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
* docs: add Azure AI Foundry documentation for Claude models
Add documentation explaining how to use Claude models (Sonnet 4.5,
Haiku 4.5, Opus 4.1) deployed on Azure AI Foundry with LiteLLM.
Azure exposes Claude using Anthropic's native API, so users can use
the existing anthropic/ provider with their Azure endpoint.
Closes#17066
* docs: Add alternative method for Azure AI Foundry using anthropic/ provider
Document that users can use anthropic/ provider with Azure endpoint
as an alternative to the dedicated azure_ai/ provider.
* refactor(generic_guardrail_api.py): refactor to update to new guardrail api logic
* refactor: refactor llm api integrations to support passing in text as a list[str] instead of one at a time
* refactor: fix linting errors
* refactor: pass request type to guardrail api
allows request vs. response processing to occur
* feat: pass user api key dict information to the guardrail api
* fix: pass user api key dict information to the guardrail api
* feat: pass litellm call id + trace id, if present
* docs: update docs
* 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
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Co-authored-by: Cursor Agent <cursoragent@cursor.com>
* feat: allow fetching OIDC user info
* test: use test_auth_builder_with_oidc_userinfo_enabled gets user info when enabled
* fix tool permission doc
* docs fix diagram
* store and fetch lasso-conversation id from cache
* include gateway/v# in the baseUrl to allow simpler version migrations in the future
* add tests for cached conversation ID
* 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>
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Co-authored-by: Cursor Agent <cursoragent@cursor.com>
yuneng-jiang