Azure GPT-5.4+ models now get the same auto-routing treatment as OpenAI
when both `reasoning_effort` and `tools` are used in `litellm.completion()`.
Previously, `reasoning_effort` was silently dropped for Azure; now the
request is bridged to the Responses API which supports both parameters.
Fixes#23914
- Fix Letta Resources links: proxy, SDK (#litellm-python-sdk), observability, correct Letta docs URL
- Add Google GenAI SDK to Agent SDKs, remove from AI Tools
- Move Track Usage for Coding Tools to end of AI Tools section
- Remove Letta from Agent SDKs sidebar
- Guides, Learn, Tutorials index updates
Made-with: Cursor
* akto guardrails support in litellm
* docs(guardrails): add akto to supported values in types/guardrails.py
* frontend changes + fixes
* feat(akto): update Akto guardrail integration with new configuration options and modes
* docs(akto): enhance Akto documentation and configuration descriptions for clarity
* feat(tests): add proxy server request headers to sample request data
* refactor(akto): remove optional account and VXLAN IDs; update documentation and tests
* feat(akto): add event_type parameter for enhanced observability in guardrail logging
* refactor(akto): update environment variable references
* refactor the python codes
* refactor and fix linting
* refactor(akto): remove unused event hook and clean up imports
* refactor(akto): enhance AktoGuardrail with async support and improved logging
* fix: Register DynamoAI guardrail initializer and enum entry (#23752)
* fix: Register DynamoAI guardrail initializer and enum entry
Fix the "Unsupported guardrail: dynamoai" error by:
1. Adding DYNAMOAI to SupportedGuardrailIntegrations enum
2. Implementing initialize_guardrail() and registries in dynamoai/__init__.py
The DynamoAI guardrail was added in PR #15920 but never properly registered
in the initialization system. The __init__.py was missing the
guardrail_initializer_registry and guardrail_class_registry dictionaries
that the dynamic discovery mechanism looks for at module load time.
Fixes#22773
Co-Authored-By: Claude Haiku 4.5 <noreply@anthropic.com>
* Update litellm/proxy/guardrails/guardrail_hooks/dynamoai/__init__.py
Co-authored-by: greptile-apps[bot] <165735046+greptile-apps[bot]@users.noreply.github.com>
* Update litellm/proxy/guardrails/guardrail_hooks/dynamoai/__init__.py
Co-authored-by: greptile-apps[bot] <165735046+greptile-apps[bot]@users.noreply.github.com>
* test: Add tests for DynamoAI guardrail registration
Verifies enum entry, initializer registry, class registry,
instance creation, and global registry discovery.
Co-Authored-By: Claude Opus 4.6 <noreply@anthropic.com>
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Co-authored-by: Claude Haiku 4.5 <noreply@anthropic.com>
Co-authored-by: greptile-apps[bot] <165735046+greptile-apps[bot]@users.noreply.github.com>
* docs: add v1.82.3 release notes and update provider_endpoints_support.json (#23816)
* Revert "docs: add v1.82.3 release notes and update provider_endpoints_support…" (#23817)
This reverts commit 966124966f.
* Refactor Akto guardrail configuration and tests; update UI description and tags
* add account and vxlan ID parameters to Akto guardrail initialization; update Akto logo format
* enhance Akto guardrail documentation and improve error handling for non-JSON responses
* address greptile issues
* fix: update payload handling to use 'data' instead of 'json' in AktoGuardrail and adjust tests accordingly
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Co-authored-by: Harshit Jain <48647625+Harshit28j@users.noreply.github.com>
Co-authored-by: Claude Haiku 4.5 <noreply@anthropic.com>
Co-authored-by: greptile-apps[bot] <165735046+greptile-apps[bot]@users.noreply.github.com>
Co-authored-by: Joe Reyna <joseph.reyna@gmail.com>
Co-authored-by: Krish Dholakia <krrishdholakia@gmail.com>
Replace inline file_search documentation in response_api.md with a canonical link and add the new tutorial to sidebars so users discover the usage-first guide.
Made-with: Cursor
Replace duplicate path-by-path sections with a single usage-first doc format that includes SDK/Proxy tabs, an architecture diagram, and a focused Q&A section.
Made-with: Cursor
Covers both paths:
- Native passthrough (OpenAI/Azure): create vector store, run via SDK and proxy
- Emulated fallback (Anthropic/any): register managed store, run via SDK and proxy
Includes output format validation script and troubleshooting section.
Co-Authored-By: Claude Sonnet 4.6 (1M context) <noreply@anthropic.com>
- Added "Web Search Integration" to the integrations sidebar for better navigation.
- Updated authors in multiple blog posts to use shorthand references for consistency.
- Corrected links in various documentation files to ensure proper navigation.
- Improved clarity in load test documentation and related settings.
These changes aim to streamline user experience and maintain consistency across the documentation.
- Add curl examples for avideo_edit and avideo_extension APIs
- Explain how LiteLLM encodes/decodes managed character IDs
- Show metadata included in character IDs (provider, model_id)
- Detail transparent router-first routing benefits
Made-with: Cursor
- Clear examples for SDK and proxy usage
- Feature highlights: router support, encoding, error handling
- Best practices for character uploads and prompting
- Available from LiteLLM v1.83.0+
- Troubleshooting guide for common issues
Made-with: Cursor
* feat: add sagemaker_nova provider for Nova models on SageMaker
Add support for custom/fine-tuned Amazon Nova models (Nova Micro, Nova Lite,
Nova 2 Lite) deployed on SageMaker Inference real-time endpoints.
Nova uses OpenAI-compatible request/response format with additional
Nova-specific parameters (top_k, reasoning_effort, allowed_token_ids,
truncate_prompt_tokens) and requires stream:true in the request body.
Nova endpoints also reject 'model' in the request body.
Changes:
- New provider: sagemaker_nova/<endpoint-name>
- SagemakerNovaConfig inherits from SagemakerChatConfig
- Override transform_request to strip 'model' from request body
- Override supports_stream_param_in_request_body (True for Nova)
- Extend get_supported_openai_params with Nova-specific params
- Refactored SagemakerChatConfig to use custom_llm_provider param
instead of hardcoded strings (backwards-compatible)
- Consolidated main.py routing for sagemaker_chat and sagemaker_nova
- 22 unit tests + 9 integration tests (skip-gated)
- Documentation with SDK, streaming, multimodal, and proxy examples
- All tests verified against live SageMaker Nova endpoint
* fix: move integration tests to tests/local_testing/ per test directory policy
* fix: remove unused module-level SagemakerNovaConfig instance
The sagemaker_nova_config singleton was never imported or used — the
ProviderConfigManager creates its own instance via the lambda registered
in utils.py. Removing this leftover boilerplate.
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Co-authored-by: Krish Dholakia <krrishdholakia@gmail.com>
* 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
The documentation test checks that all env vars used in code are
documented. The Vantage integration added 5 new env vars without
updating the reference table.
Co-Authored-By: Claude Opus 4.6 <noreply@anthropic.com>
Arindam200