litellm/AGENTS.md

INSTRUCTIONS FOR LITELLM

This document provides comprehensive instructions for AI agents working in the LiteLLM repository.

OVERVIEW

LiteLLM is a unified interface for 100+ LLMs that:

• Translates inputs to provider-specific completion, embedding, and image generation endpoints
• Provides consistent OpenAI-format output across all providers
• Includes retry/fallback logic across multiple deployments (Router)
• Offers a proxy server (LLM Gateway) with budgets, rate limits, and authentication
• Supports advanced features like function calling, streaming, caching, and observability

REPOSITORY STRUCTURE

Core Components

• litellm/ - Main library code
  • llms/ - Provider-specific implementations (OpenAI, Anthropic, Azure, etc.)
  • proxy/ - Proxy server implementation (LLM Gateway)
  • router_utils/ - Load balancing and fallback logic
  • types/ - Type definitions and schemas
  • integrations/ - Third-party integrations (observability, caching, etc.)

Key Directories

• tests/ - Comprehensive test suites
• docs/my-website/ - Documentation website
• ui/litellm-dashboard/ - Admin dashboard UI
• enterprise/ - Enterprise-specific features

DEVELOPMENT GUIDELINES

MAKING CODE CHANGES

1. Provider Implementations: When adding/modifying LLM providers:

   • Follow existing patterns in litellm/llms/{provider}/
   • Implement proper transformation classes that inherit from BaseConfig
   • Support both sync and async operations
   • Handle streaming responses appropriately
   • Include proper error handling with provider-specific exceptions

2. Type Safety:

   • Use proper type hints throughout
   • Update type definitions in litellm/types/
   • Ensure compatibility with both Pydantic v1 and v2

3. Testing:

   • Add tests in appropriate tests/ subdirectories
   • Include both unit tests and integration tests
   • Test provider-specific functionality thoroughly
   • Consider adding load tests for performance-critical changes

MAKING CODE CHANGES FOR THE UI (IGNORE FOR BACKEND)

1. Tremor is DEPRECATED, do not use Tremor components in new features/changes

   • The only exception is the Tremor Table component and its required Tremor Table sub components.

2. Use Common Components as much as possible:

   • These are usually defined in the common_components directory
   • Use these components as much as possible and avoid building new components unless needed

3. Testing:

   • The codebase uses Vitest and React Testing Library
   • Query Priority Order: Use query methods in this order: getByRole, getByLabelText, getByPlaceholderText, getByText, getByTestId
   • Always use screen instead of destructuring from render() (e.g., use screen.getByText() not getByText)
   • Wrap user interactions in act(): Always wrap fireEvent calls with act() to ensure React state updates are properly handled
   • Use query methods for absence checks: Use queryBy* methods (not getBy*) when expecting an element to NOT be present
   • Test names must start with "should": All test names should follow the pattern it("should ...")
   • Mock external dependencies: Check setupTests.ts for global mocks and mock child components/networking calls as needed
   • Structure tests properly:
     • First test should verify the component renders successfully
     • Subsequent tests should focus on functionality and user interactions
     • Use waitFor for async operations that aren't already awaited
   • Avoid using querySelector: Prefer React Testing Library queries over direct DOM manipulation

IMPORTANT PATTERNS

1. Function/Tool Calling:

   • LiteLLM standardizes tool calling across providers
   • OpenAI format is the standard, with transformations for other providers
   • See litellm/llms/anthropic/chat/transformation.py for complex tool handling

2. Streaming:

   • All providers should support streaming where possible
   • Use consistent chunk formatting across providers
   • Handle both sync and async streaming

3. Error Handling:

   • Use provider-specific exception classes
   • Maintain consistent error formats across providers
   • Include proper retry logic and fallback mechanisms

4. Configuration:

   • Support both environment variables and programmatic configuration
   • Use BaseConfig classes for provider configurations
   • Allow dynamic parameter passing

PROXY SERVER (LLM GATEWAY)

The proxy server is a critical component that provides:

• Authentication and authorization
• Rate limiting and budget management
• Load balancing across multiple models/deployments
• Observability and logging
• Admin dashboard UI
• Enterprise features

Key files:

• litellm/proxy/proxy_server.py - Main server implementation
• litellm/proxy/auth/ - Authentication logic
• litellm/proxy/management_endpoints/ - Admin API endpoints

MCP (MODEL CONTEXT PROTOCOL) SUPPORT

LiteLLM supports MCP for agent workflows:

• MCP server integration for tool calling
• Transformation between OpenAI and MCP tool formats
• Support for external MCP servers (Zapier, Jira, Linear, etc.)
• See litellm/experimental_mcp_client/ and litellm/proxy/_experimental/mcp_server/

RUNNING SCRIPTS

Use poetry run python script.py to run Python scripts in the project environment (for non-test files).

GITHUB TEMPLATES

When opening issues or pull requests, follow these templates:

Bug Reports (.github/ISSUE_TEMPLATE/bug_report.yml)

• Describe what happened vs. expected behavior
• Include relevant log output
• Specify LiteLLM version
• Indicate if you're part of an ML Ops team (helps with prioritization)

Feature Requests (.github/ISSUE_TEMPLATE/feature_request.yml)

• Clearly describe the feature
• Explain motivation and use case with concrete examples

Pull Requests (.github/pull_request_template.md)

• Add at least 1 test in tests/litellm/
• Ensure make test-unit passes

TESTING CONSIDERATIONS

1. Provider Tests: Test against real provider APIs when possible
2. Proxy Tests: Include authentication, rate limiting, and routing tests
3. Performance Tests: Load testing for high-throughput scenarios
4. Integration Tests: End-to-end workflows including tool calling

DOCUMENTATION

• Keep documentation in sync with code changes
• Update provider documentation when adding new providers
• Include code examples for new features
• Update changelog and release notes

SECURITY CONSIDERATIONS

• Handle API keys securely
• Validate all inputs, especially for proxy endpoints
• Consider rate limiting and abuse prevention
• Follow security best practices for authentication

ENTERPRISE FEATURES

• Some features are enterprise-only
• Check enterprise/ directory for enterprise-specific code
• Maintain compatibility between open-source and enterprise versions

COMMON PITFALLS TO AVOID

1. Breaking Changes: LiteLLM has many users - avoid breaking existing APIs

2. Provider Specifics: Each provider has unique quirks - handle them properly

3. Rate Limits: Respect provider rate limits in tests

4. Memory Usage: Be mindful of memory usage in streaming scenarios

5. Dependencies: Keep dependencies minimal and well-justified

6. UI/Backend Contract Mismatch: When adding a new entity type to the UI, always check whether the backend endpoint accepts a single value or an array. Match the UI control accordingly (single-select vs. multi-select) to avoid silently dropping user selections

7. Missing Tests for New Entity Types: When adding a new entity type (e.g., in EntityUsage, UsageViewSelect), always add corresponding tests in the existing test files and update any icon/component mocks

8. Do not hardcode model-specific flags: Put model-specific capability flags in model_prices_and_context_window.json and read them via get_model_info (or existing helpers like supports_reasoning). This prevents users from needing to upgrade LiteLLM each time a new model supports a feature.

   Example of BAD (hardcoded model checks):

   @staticmethod
   def _is_effort_supported_model(model: str) -> bool:
       """Check if the model supports the output_config.effort parameter..."""
       model_lower = model.lower()
       if AnthropicConfig._is_claude_4_6_model(model):
           return True
       return any(
           v in model_lower for v in ("opus-4-5", "opus_4_5", "opus-4.5", "opus_4.5")
       )
   

   Example of GOOD (config-driven or helper that reads from config):

   if (
       "claude-3-7-sonnet" in model
       or AnthropicConfig._is_claude_4_6_model(model)
       or supports_reasoning(
           model=model,
           custom_llm_provider=self.custom_llm_provider,
       )
   ):
       ...
   

   Using helpers like supports_reasoning (which read from model_prices_and_context_window.json / get_model_info) allows future model updates to "just work" without code changes.

HELPFUL RESOURCES

• Main documentation: https://docs.litellm.ai/
• Provider-specific docs in docs/my-website/docs/providers/
• Admin UI for testing proxy features

WHEN IN DOUBT

• Follow existing patterns in the codebase
• Check similar provider implementations
• Ensure comprehensive test coverage
• Update documentation appropriately
• Consider backward compatibility impact

Cursor Cloud specific instructions

Environment

• Poetry is installed in ~/.local/bin; the update script ensures it is on PATH.
• Python 3.12, Node 22 are pre-installed.
• The virtual environment lives under ~/.cache/pypoetry/virtualenvs/.

Running the proxy server

Start the proxy with a config file:

poetry run litellm --config dev_config.yaml --port 4000

The proxy takes ~15-20 seconds to fully start (it runs Prisma migrations on boot). Wait for /health to return before sending requests. Without a PostgreSQL DATABASE_URL, the proxy connects to a default Neon dev database embedded in the litellm-proxy-extras package.

Running tests

See CLAUDE.md and the Makefile for standard commands. Key notes:

• psycopg-binary must be installed (poetry run pip install psycopg-binary) because the pytest-postgresql plugin requires it and the lock file only includes psycopg (no binary).
• The --timeout pytest flag is NOT available; don't pass it.
• Unit tests: poetry run pytest tests/test_litellm/ -x -vv -n 4
• Black --check may report pre-existing formatting issues; this does not block test runs.

Lint

cd litellm && poetry run ruff check .

Ruff is the primary fast linter. For the full lint suite (including mypy, black, circular imports), run make lint per CLAUDE.md.

UI Dashboard development

• The UI is at ui/litellm-dashboard/. Run npm run dev from that directory for the Next.js dev server on port 3000.
• The proxy at port 4000 serves a pre-built static UI from litellm/proxy/_experimental/out/. After making UI code changes, you must run npm run build in the dashboard directory and copy the output: cp -r ui/litellm-dashboard/out/* litellm/proxy/_experimental/out/ for the proxy to serve the updated UI.
• SVGs used as provider logos (loaded via <img> tags) must NOT use fill="currentColor" — replace with an explicit color like #000000 or use the -color variant from lobehub icons, since CSS color inheritance does not work inside <img> elements.
• Provider logos live in ui/litellm-dashboard/public/assets/logos/ (source) and litellm/proxy/_experimental/out/assets/logos/ (pre-built). Both locations must have the file for it to work in dev and proxy-served modes.
• UI Vitest tests: cd ui/litellm-dashboard && npx vitest run