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AGENTS.md

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+# 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
+
+### 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/`
+
+## 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
+
+## 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