* 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
* feat(litellm_pre_call_utils.py): add num_retries to litellm data for backend call
allow user to pass in num retries via request headers
* test(test_litellm_pre_call_utils.py): add unit test
* docs(request_headers.md): document new request header
* fix(common_daily_activity.py): show spend breakdown by model group
Partial fix for https://github.com/BerriAI/litellm/issues/12887
* feat(new_usage.tsx): new tab switcher for viewing usage by model group vs. received model
Closes https://github.com/BerriAI/litellm/issues/12887
* 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.
- Changed groq/moonshotai-kimi-k2-instruct to groq/moonshotai/kimi-k2-instruct in model_prices_and_context_window.json
- Added groq/moonshotai/kimi-k2-instruct and groq/qwen-qwq-32b to the supported models table in Groq documentation
* feat: add v0 provider support to LiteLLM
- Add v0 as a new OpenAI-compatible provider
- Support all three v0 models: v0-1.0-md, v0-1.5-md, v0-1.5-lg
- Configure correct token limits and pricing for each model
- Enable vision support for all v0 models (multimodal)
- Add provider detection for v0/ prefix and api.v0.dev endpoint
- Include comprehensive unit tests for the provider
The v0 provider uses the standard OpenAI-compatible implementation
and supports all standard features including streaming, function
calling, and system messages.
* fix: add v0 provider to ProviderConfigManager
Add V0ChatConfig to the get_provider_chat_config method to fix
test_supports_tool_choice test failure. The v0 provider needs to
be included in the provider config manager to return the correct
configuration for tool choice support detection.
* docs: add documentation for v0 provider
- Add comprehensive v0 provider documentation
- Cover all supported models and their capabilities
- Include examples for SDK usage, proxy configuration, and all features
- Document supported OpenAI parameters based on v0 API docs
- Add v0 to the providers sidebar navigation
* fix: correct v0 supported OpenAI parameters
Based on review feedback and v0 API documentation:
- v0 only supports: messages, model, stream, tools, tool_choice
- Remove unsupported parameters like temperature, max_tokens, etc.
- Update tests to verify correct parameter set
- Update documentation to reflect actual API capabilities
- Remove JSON mode example as response_format is not supported
Reference: https://v0.dev/docs/v0-model-api#request-body
* fix: remove supports_response_schema from v0 models
Remove the supports_response_schema property from all v0 models in the model configuration files as v0 does not support this feature.
Models updated:
- v0/v0-1.0-md
- v0/v0-1.5-md
- v0/v0-1.5-lg
Ishaan Jaff