- Keep CompactifAI provider detection logic
- Include new OVHCloud provider from main branch
- Both providers now work correctly with model prefix detection
- Create comprehensive provider documentation with usage examples
- Cover basic completion, streaming, async, and function calling
- Document AWS Marketplace subscription and API key setup process
- Include proxy configuration and advanced parameter examples
- Add error handling examples and model information
- Update website sidebar to include CompactifAI in provider list
- Update README.md with CompactifAI provider reference
* create OCI required files
* request and response conversion for non-streaming chat
* support tool calling with OCI generic API without streaming
* adaptation of api call for generic and cohere format
* include tool calls and responses in generic api and dropping support for cohere
* fix invalid content-length error
* support streaming for generic api
* fix auth error when using acompletion with streaming
* refactor: use base_llm_http_handler and include API type definitions
* update types and add type safety in different methods
* fix OCIFunction format
* create custom stream wrapper for decoding OCI stream
* remove unused files
* create unit tests for OCI
* lint the code
* remove manual test
* docs: update the docs to include OCI
- Add new tutorial for integrating Qwen Code CLI with LiteLLM Proxy
- Update sidebar to include Qwen Code CLI in both AI Tools and main Tutorials sections
- Document environment variables for OpenAI-compatible configuration
- Include examples for routing to various providers (Anthropic, OpenAI, Bedrock)
* feat: Add Pillar Security guardrail integration
Implements comprehensive LLM security guardrails using Pillar Security API with support for prompt injection detection, PII/secret detection, content moderation, and multi-mode execution (pre_call, during_call, post_call). Includes complete documentation, testing, and configurable actions on flagged content.
* fix: Resolve MyPy type error in Pillar guardrail config
Restructure PillarGuardrailConfigModel to properly inherit from GuardrailConfigModel[T]
and resolve return type compatibility issue in get_config_model method.
* fix: Resolve MyPy type error in Pillar guardrail config
Restructure PillarGuardrailConfigModel to properly inherit from GuardrailConfigModel[T]
and resolve return type compatibility issue in get_config_model method.
* fix docs
* fix docs
* improved docs
* fix examples, READY
* fix(main.py): fix async retryer
Fixes https://github.com/BerriAI/litellm/issues/12830
* fix(forward_clientside_headers_by_model_group.py): filter out 'content-type' from forwardable headers
clientside content-type != proxy content type, can cause requests to hang
* test(tests/): update tests
- Add comprehensive documentation for Model Armor integration
- Include configuration examples and parameter descriptions
- Add Model Armor to sidebars navigation
- Document authentication methods and error handling
* feat: add Morph provider support
- Add MorphChatConfig implementation for OpenAI-compatible API
- Support morph-v3-fast and morph-v3-large models
- Add pricing: morph-v3-fast (/bin/zsh.8/.2 per 1M tokens), morph-v3-large (/bin/zsh.9/.9 per 1M tokens)
- Both models support 16k context window and system messages
- Add comprehensive documentation and unit tests
- Update all necessary integration points (constants, init, provider logic)
* feat: Add Morph provider support in ProviderConfigManager
- Extend ProviderConfigManager to include MorphChatConfig for the Morph LLM provider.
- Update MorphChatConfig by removing unused parameters from the configuration.
- Add Hyperbolic as a new OpenAI-compatible provider
- Implement HyperbolicChatConfig inheriting from OpenAILikeChatConfig
- Register Hyperbolic in provider lists and constants
- Add comprehensive model configurations with pricing for:
- DeepSeek models (V3, R1, etc.)
- Qwen models (2.5, 3, QwQ, etc.)
- Meta Llama models (3.1, 3.2, 3.3)
- Other models like Kimi K2, Hermes 3, etc.
- Configure default API base URL: https://api.hyperbolic.xyz/v1
- Add provider documentation with usage examples
- Create unit tests for provider functionality
- Support all standard OpenAI parameters
Hyperbolic provides low-cost inference with OpenAI-compatible APIs,
supporting latest models without infrastructure overhead.
* feat: add Lambda AI provider support
Add support for Lambda AI (lambda.ai) as a new LLM provider in LiteLLM. Lambda AI provides access to a wide range of open-source models through their cloud GPU infrastructure.
Changes:
- Add Lambda AI provider implementation (OpenAI-compatible)
- Register 20 Lambda AI models with accurate pricing and 131k context windows
- Add comprehensive tests for Lambda AI integration
- Add detailed documentation with usage examples
- Use "lambda_ai" as provider name to avoid Python keyword conflict
Models include Llama 3.x, DeepSeek, Hermes, Qwen, and specialized models for coding and vision tasks.
* fix(tests): ensure lambda_ai_models list is repopulated after model cost reload
Updated test cases to clear and repopulate the lambda_ai_models list after reloading the model cost map. This ensures that the tests accurately reflect the current state of available models.
* feat: add Lambda AI chat configuration support
Added support for Lambda AI chat configuration in the ProviderConfigManager. This enhancement allows the integration of Lambda AI as a provider, expanding the capabilities of LiteLLM.
Felipe Gare