* feat(key_management_endpoints.py): Support new 'key_type' field
allow user to specify if key should be 'management' or 'llm api' key
Security fix
* test(test_route_checks.py): add unit tests
* fix(create_key_button.tsx): add ui component to select key type
allows specifying if key can call llm api vs. management routes
* feat(create_key_button.tsx): add specifying key type to ui
* fix(route_checks.py): add sensitive data masker for user id on not allowed error message
prevent leaking sensitive information
* 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.
* Feature/track bedrock gov cloud models (#12771)
* feat: add AWS Bedrock GovCloud model support (LIT-257)
- Added 18 GovCloud-specific model entries (9 per region) to model_prices_and_context_window.json
- Updated is_bedrock_pricing_only_model() to allow GovCloud models (us-gov-east-1, us-gov-west-1)
- Added comprehensive test suite for GovCloud model support
- Ensures GovCloud models use appropriate APIs (Converse for Claude/Llama, Invoke for Titan)
Models added:
- Claude 3.5 Sonnet and Claude 3 Haiku (FedRAMP/IL4/5 approved)
- Llama 3 8B and 70B (FedRAMP/IL4/5 approved)
- Amazon Titan Text and Embedding models
* fix: add bedrock_converse GovCloud model mappings for Claude models
Added missing bedrock_converse model entries for AWS GovCloud regions:
- bedrock_converse/us-gov-east-1/anthropic.claude-3-5-sonnet-20240620-v1:0
- bedrock_converse/us-gov-east-1/anthropic.claude-3-haiku-20240307-v1:0
- bedrock_converse/us-gov-west-1/anthropic.claude-3-5-sonnet-20240620-v1:0
- bedrock_converse/us-gov-west-1/anthropic.claude-3-haiku-20240307-v1:0
This fixes test failures where supports_tool_choice() returned True but
the models weren't properly mapped in the configuration files.
* fix: correct AWS GovCloud Bedrock model pricing and configurations
- Fix Claude 3.5 Sonnet pricing (3.6e-06 input, 1.8e-05 output)
- Fix Claude 3 Haiku pricing (3e-07 input, 1.5e-06 output)
- Update Claude 3.5 Sonnet max_tokens from 4096 to 8192
- Add bedrock_converse entries for Llama models with correct token limits
- Add Amazon Nova Pro model for both GovCloud regions
- Add supports_pdf_input flag to Claude models
* fix: handle bedrock_converse prefix in get_non_litellm_routing_model_name
Fixes test failure where bedrock_converse/region/model paths were not properly
stripped to get the base model name, causing supports_function_calling to
return false for regional bedrock_converse models.
* revert: reset bedrock/common_utils.py to match main branch
Remove bedrock_converse prefix handling from get_non_litellm_routing_model_name
to align with main branch implementation.
* revert: reset litellm/__init__.py to match main branch
- Remove public_model_groups variables
- Remove GovCloud exception handling in is_bedrock_pricing_only_model
- Fix comment formatting
* revert: reset litellm/__init__.py to exact main branch content
Copy exact content from origin/main with no modifications
* fix: remove bedrock_converse prefixed models from pricing files
- Remove 10 bedrock_converse entries from model_prices_and_context_window.json
- Remove 4 bedrock_converse entries from litellm/model_prices_and_context_window_backup.json
- These were GovCloud-specific entries that are no longer needed
* fix: correct AWS GovCloud Bedrock model pricing and configurations
- Fix Anthropic Claude 3.5 Sonnet pricing: $3.60/$18.00 per million tokens (was $3.00/$15.00)
- Fix Anthropic Claude 3 Haiku pricing: $0.30/$1.50 per million tokens (was $0.25/$1.25)
- Fix Claude 3.5 Sonnet max_tokens: 8192 (was 4096)
- Fix Llama model max_tokens: 2048 (was 8192) and max_input_tokens: 8000 (was 8192)
- Fix Llama3-8b output pricing: $2.65 per million tokens (was $0.60)
- Add missing Amazon Nova Pro models for both GovCloud regions
- Add supports_pdf_input flag to Llama models
Based on official AWS Bedrock pricing documentation for GovCloud regions
* test: fix GovCloud bedrock models test to match implementation
Update test_govcloud_model_in_bedrock_models_list to correctly verify that
GovCloud models are excluded from bedrock_models list as they are
pricing-only models following the bedrock/<region>/<model> pattern.
---------
Co-authored-by: Cole McIntosh <colemcintosh6@gmail.com>
Co-authored-by: Cole McIntosh <82463175+colesmcintosh@users.noreply.github.com>
* add tests
* add tests
* Added test costs
* Added test costs
---------
Co-authored-by: Cole McIntosh <colemcintosh6@gmail.com>
Co-authored-by: Cole McIntosh <82463175+colesmcintosh@users.noreply.github.com>
When using Model Armor guardrail with explicit project_id in config,
the project_id was being overwritten to None due to incorrect
initialization order between ModelArmorGuardrail and VertexBase parent class.
This fix ensures that user-provided project_id is preserved by initializing
parent classes before setting instance attributes.
Fixes#12757
* feat: initial commit for forwarding client headers by model group
* fix(router.py): support new forwarclientsideheadersbymodelgroup class
enables headers to be forwarded to backend model, by model group
* fix(proxy_server.py): load in model group settings from config correctly
* refactor(litellm_pre_call_utils.py): litellm_pre_call_utils.py
introduce new 'secret_fields' field
includes raw request headers (not the sanitized ones used for logging) - needed to support forwarding clientside headers to llm api
* feat(router.py): log the deployment model name as well
allows wildcard models to support forward_client_headers_to_llm_api
* test(test_router.py): add more unit testing
* feat(router.py): specify the model group alias in metadata kwargs
allows usage for internal routing logic
* fix: fix ruff check errors
* fix(router.py): refactor to cleanup optional pre-call checks
* fix: fix ruff check
* test: add missing unit test
* fix(prompt_templates/factory.py): handle anthropic cache control on individual tool results
Fixes issue where cache control on individual tool result was being ignored
* test(test_vertex_And_google_ai_studio_gemini.py): initial unit test covering translation for grounding metadata on streaming chunk
* fix(vertex_and_google_ai_studio.py): ensure grounding metadata is preserved on streaming
Closes https://github.com/BerriAI/litellm/issues/10237
* fix(core_helpers.py): include usage in expected openai keys
