* 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
* fix(gpt_transformation.py): remove 'cache_control' flag for openai/openai-compatible calls
Fixes https://github.com/BerriAI/litellm/issues/12787
* fix(openrouter/chat/transformation.py): allow passing openrouter cache control flag for claude models
* fix(gpt_transformation.py): fix import
* fix: fix adding tools
* 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
* fix(team_endpoints.py): always remove team member budget from updated_kv
this is not a field for the litellm team table
Prevents startup issue
* test(test_team_endpoints.py): add unit test to ensure 'team_member_budget' is never in update to table - separate logic
* refactor: cleanup
* 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.
