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* docs: cleanup README and improve AI agent guides - Remove obsolete version warnings (openai>=1.0.0, pydantic>=2.0.0) - Add note about Responses API in README - Add GitHub templates section to CLAUDE.md, GEMINI.md, and AGENTS.md - Remove temporary test file test_pydantic_fields.py * update files * update Gemini file
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CLAUDE.md
This file provides guidance to Claude Code (claude.ai/code) when working with code in this repository.
Development Commands
Installation
make install-dev- Install core development dependenciesmake install-proxy-dev- Install proxy development dependencies with full feature setmake install-test-deps- Install all test dependencies
Testing
make test- Run all testsmake test-unit- Run unit tests (tests/test_litellm) with 4 parallel workersmake test-integration- Run integration tests (excludes unit tests)pytest tests/- Direct pytest execution
Code Quality
make lint- Run all linting (Ruff, MyPy, Black, circular imports, import safety)make format- Apply Black code formattingmake lint-ruff- Run Ruff linting onlymake lint-mypy- Run MyPy type checking only
Single Test Files
poetry run pytest tests/path/to/test_file.py -v- Run specific test filepoetry run pytest tests/path/to/test_file.py::test_function -v- Run specific test
Running Scripts
poetry run python script.py- Run Python scripts (use for non-test files)
GitHub Issue & PR Templates
When contributing to the project, use the appropriate templates:
Bug Reports (.github/ISSUE_TEMPLATE/bug_report.yml):
- Describe what happened vs. what you expected
- Include relevant log output
- Specify your LiteLLM version
Feature Requests (.github/ISSUE_TEMPLATE/feature_request.yml):
- Describe the feature clearly
- Explain the motivation and use case
Pull Requests (.github/pull_request_template.md):
- Add at least 1 test in
tests/litellm/ - Ensure
make test-unitpasses
Architecture Overview
LiteLLM is a unified interface for 100+ LLM providers with two main components:
Core Library (litellm/)
- Main entry point:
litellm/main.py- Contains core completion() function - Provider implementations:
litellm/llms/- Each provider has its own subdirectory - Router system:
litellm/router.py+litellm/router_utils/- Load balancing and fallback logic - Type definitions:
litellm/types/- Pydantic models and type hints - Integrations:
litellm/integrations/- Third-party observability, caching, logging - Caching:
litellm/caching/- Multiple cache backends (Redis, in-memory, S3, etc.)
Proxy Server (litellm/proxy/)
- Main server:
proxy_server.py- FastAPI application - Authentication:
auth/- API key management, JWT, OAuth2 - Database:
db/- Prisma ORM with PostgreSQL/SQLite support - Management endpoints:
management_endpoints/- Admin APIs for keys, teams, models - Pass-through endpoints:
pass_through_endpoints/- Provider-specific API forwarding - Guardrails:
guardrails/- Safety and content filtering hooks - UI Dashboard: Served from
_experimental/out/(Next.js build)
Key Patterns
Provider Implementation
- Providers inherit from base classes in
litellm/llms/base.py - Each provider has transformation functions for input/output formatting
- Support both sync and async operations
- Handle streaming responses and function calling
Error Handling
- Provider-specific exceptions mapped to OpenAI-compatible errors
- Fallback logic handled by Router system
- Comprehensive logging through
litellm/_logging.py
Configuration
- YAML config files for proxy server (see
proxy/example_config_yaml/) - Environment variables for API keys and settings
- Database schema managed via Prisma (
proxy/schema.prisma)
Development Notes
Code Style
- Uses Black formatter, Ruff linter, MyPy type checker
- Pydantic v2 for data validation
- Async/await patterns throughout
- Type hints required for all public APIs
Testing Strategy
- Unit tests in
tests/test_litellm/ - Integration tests for each provider in
tests/llm_translation/ - Proxy tests in
tests/proxy_unit_tests/ - Load tests in
tests/load_tests/
Database Migrations
- Prisma handles schema migrations
- Migration files auto-generated with
prisma migrate dev - Always test migrations against both PostgreSQL and SQLite
Enterprise Features
- Enterprise-specific code in
enterprise/directory - Optional features enabled via environment variables
- Separate licensing and authentication for enterprise features