CLAUDE.md•4.92 kB
# CLAUDE.md
This file provides guidance to Claude Code (claude.ai/code) when working with code in this repository.
## Project Overview
Graphiti is a Python framework for building temporally-aware knowledge graphs designed for AI agents. It enables real-time incremental updates to knowledge graphs without batch recomputation, making it suitable for dynamic environments.
Key features:
- Bi-temporal data model with explicit tracking of event occurrence times
- Hybrid retrieval combining semantic embeddings, keyword search (BM25), and graph traversal
- Support for custom entity definitions via Pydantic models
- Integration with Neo4j and FalkorDB as graph storage backends
## Development Commands
### Main Development Commands (run from project root)
```bash
# Install dependencies
uv sync --extra dev
# Format code (ruff import sorting + formatting)
make format
# Lint code (ruff + pyright type checking)
make lint
# Run tests
make test
# Run all checks (format, lint, test)
make check
```
### Server Development (run from server/ directory)
```bash
cd server/
# Install server dependencies
uv sync --extra dev
# Run server in development mode
uvicorn graph_service.main:app --reload
# Format, lint, test server code
make format
make lint
make test
```
### MCP Server Development (run from mcp_server/ directory)
```bash
cd mcp_server/
# Install MCP server dependencies
uv sync
# Run with Docker Compose
docker-compose up
```
## Code Architecture
### Core Library (`graphiti_core/`)
- **Main Entry Point**: `graphiti.py` - Contains the main `Graphiti` class that orchestrates all functionality
- **Graph Storage**: `driver/` - Database drivers for Neo4j and FalkorDB
- **LLM Integration**: `llm_client/` - Clients for OpenAI, Anthropic, Gemini, Groq
- **Embeddings**: `embedder/` - Embedding clients for various providers
- **Graph Elements**: `nodes.py`, `edges.py` - Core graph data structures
- **Search**: `search/` - Hybrid search implementation with configurable strategies
- **Prompts**: `prompts/` - LLM prompts for entity extraction, deduplication, summarization
- **Utilities**: `utils/` - Maintenance operations, bulk processing, datetime handling
### Server (`server/`)
- **FastAPI Service**: `graph_service/main.py` - REST API server
- **Routers**: `routers/` - API endpoints for ingestion and retrieval
- **DTOs**: `dto/` - Data transfer objects for API contracts
### MCP Server (`mcp_server/`)
- **MCP Implementation**: `graphiti_mcp_server.py` - Model Context Protocol server for AI assistants
- **Docker Support**: Containerized deployment with Neo4j
## Testing
- **Unit Tests**: `tests/` - Comprehensive test suite using pytest
- **Integration Tests**: Tests marked with `_int` suffix require database connections
- **Evaluation**: `tests/evals/` - End-to-end evaluation scripts
## Configuration
### Environment Variables
- `OPENAI_API_KEY` - Required for LLM inference and embeddings
- `USE_PARALLEL_RUNTIME` - Optional boolean for Neo4j parallel runtime (enterprise only)
- Provider-specific keys: `ANTHROPIC_API_KEY`, `GOOGLE_API_KEY`, `GROQ_API_KEY`, `VOYAGE_API_KEY`
### Database Setup
- **Neo4j**: Version 5.26+ required, available via Neo4j Desktop
- Database name defaults to `neo4j` (hardcoded in Neo4jDriver)
- Override by passing `database` parameter to driver constructor
- **FalkorDB**: Version 1.1.2+ as alternative backend
- Database name defaults to `default_db` (hardcoded in FalkorDriver)
- Override by passing `database` parameter to driver constructor
## Development Guidelines
### Code Style
- Use Ruff for formatting and linting (configured in pyproject.toml)
- Line length: 100 characters
- Quote style: single quotes
- Type checking with Pyright is enforced
- Main project uses `typeCheckingMode = "basic"`, server uses `typeCheckingMode = "standard"`
### Testing Requirements
- Run tests with `make test` or `pytest`
- Integration tests require database connections and are marked with `_int` suffix
- Use `pytest-xdist` for parallel test execution
- Run specific test files: `pytest tests/test_specific_file.py`
- Run specific test methods: `pytest tests/test_file.py::test_method_name`
- Run only integration tests: `pytest tests/ -k "_int"`
- Run only unit tests: `pytest tests/ -k "not _int"`
### LLM Provider Support
The codebase supports multiple LLM providers but works best with services supporting structured output (OpenAI, Gemini). Other providers may cause schema validation issues, especially with smaller models.
### MCP Server Usage Guidelines
When working with the MCP server, follow the patterns established in `mcp_server/cursor_rules.md`:
- Always search for existing knowledge before adding new information
- Use specific entity type filters (`Preference`, `Procedure`, `Requirement`)
- Store new information immediately using `add_memory`
- Follow discovered procedures and respect established preferences