Graphiti-ts

A TypeScript implementation of a temporal graph building library designed for AI agents. Graphiti enables real-time incremental updates to knowledge graphs without batch recomputation, making it suitable for dynamic AI applications.

Inspired and based on github.com/getzep/graphiti . Note: this is not affiliated or owned by zep.


🌟 Features

- 🌐 Bi-temporal Data Model: Explicit tracking of event occurrence times
- 🔍 Hybrid Retrieval: Semantic embeddings, keyword search (BM25), and graph traversal
- 🎯 Type-Safe Entity Definitions: Custom entity models with Zod validation
- 💾 Multiple Database Backends: Neo4j, FalkorDB, and Amazon Neptune support
- 🤖 LLM Integration: OpenAI, Anthropic, Google Gemini, and Groq clients
- ⚡ Real-time Updates: Incremental knowledge graph updates without batch recomputation
- 🚀 Production Ready: HTTP server, MCP integration, Docker deployment
- ✅ Complete Test Coverage: 64 comprehensive tests using Node.js built-in test runner

🚀 Quick Start

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``bash
npm install graphiti-ts
`

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`typescript
import {
Graphiti,
Neo4jDriver,
OpenAIClient,
OpenAIEmbedderClient,
EpisodeType
} from 'graphiti-ts';

// Initialize components
const driver = new Neo4jDriver({
uri: 'bolt://localhost:7687',
user: 'neo4j',
password: 'password'
});

const llmClient = new OpenAIClient({
apiKey: process.env.OPENAI_API_KEY!,
model: 'gpt-4',
});

const embedder = new OpenAIEmbedderClient({
apiKey: process.env.OPENAI_API_KEY!,
model: 'text-embedding-3-small',
});

// Create Graphiti instance
const graphiti = new Graphiti({
driver,
llmClient,
embedder,
groupId: 'my-project',
});

// Add an episode and extract knowledge
await graphiti.addEpisode({
content: 'Alice met Bob at the conference and discussed their AI research.',
episodeType: EpisodeType.TEXT,
groupId: 'research-team'
});

// Search for information
const results = await graphiti.search({
query: 'Who did Alice meet?',
limit: 5,
});

console.log('Found relationships:', results);

// Clean up
await graphiti.close();
`

📁 Project Architecture

`
graphiti/
├── src/ # Core TypeScript library
│ ├── core/ # Graph nodes and edges
│ ├── drivers/ # Database drivers (Neo4j, FalkorDB)
│ ├── llm/ # LLM clients (OpenAI, Anthropic)
│ ├── embedders/ # Embedding clients
│ ├── types/ # TypeScript type definitions
│ └── utils/ # Utility functions
├── server/ # HTTP server (Hono)
│ └── src/
│ ├── config/ # Server configuration
│ ├── dto/ # Data transfer objects
│ └── standalone-main.ts # Main server entry point
├── mcp_server/ # MCP server for AI assistants
│ └── src/
│ └── graphiti-mcp-server.ts
├── examples/ # TypeScript examples
│ ├── quickstart/ # Basic usage examples
│ ├── ecommerce/ # Product search demo
│ ├── podcast/ # Conversation analysis
│ └── langgraph-agent/ # AI agent with memory
├── Dockerfile # Production Docker image
├── docker-compose.yml # Full stack deployment
└── docker-compose.test.yml # Testing environment
`

🛠️ Development

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- Node.js 18+
- TypeScript 5.7+
- Neo4j 5.26+ (for Neo4j driver)
- FalkorDB 1.1.2+ (for FalkorDB driver)

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`bash


Install dependencies

npm install

Build the project

npm run build
`

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`bash


Core Library

npm run dev # Development mode with watch
npm test # Run all tests (64 tests)
npm run test:coverage # Test coverage report
npm run lint # ESLint + TypeScript checking
npm run format # Prettier code formatting
npm run check # Run all checks (format, lint, test)

HTTP Server

cd server
npm run dev # Start server in development mode
npm run build # Build production server
npm start # Start production server

MCP Server

cd mcp_server
npm run dev # Start MCP server in development mode
npm run build # Build MCP server
npm start # Start MCP server

Examples

cd examples
npm run quickstart:neo4j # Basic Neo4j example
npm run ecommerce # Product search demo
npm run podcast # Conversation analysis
npm run langgraph-agent # AI sales agent
`

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`bash


Required for LLM inference and embeddings

OPENAI_API_KEY=your-openai-key

Optional LLM provider keys

ANTHROPIC_API_KEY=your-anthropic-key
GOOGLE_API_KEY=your-google-key
GROQ_API_KEY=your-groq-key

Database connection

NEO4J_URI=bolt://localhost:7687
NEO4J_USER=neo4j
NEO4J_PASSWORD=password

Or FalkorDB

FALKORDB_URI=falkor://localhost:6379

Server configuration

PORT=3000
`

🐳 Docker Deployment

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`bash


Copy environment template

cp .env.docker.example .env

Edit .env with your configuration

Start full stack (server + MCP + database)

docker-compose -f docker-compose.yml up --build
`

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- Graphiti Server: http://localhost:3000 - HTTP API server
- MCP Server: http://localhost:3001 - Model Context Protocol server
- Neo4j Database: http://localhost:7474 - Graph database UI

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1. Production: docker-compose.yml - Full stack with monitoring
2. Testing: docker-compose.test.yml - Isolated test environment
3. MCP Only: mcp_server/docker-compose.yml - Just MCP server

🏗️ Architecture Components

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Main Classes:
- Graphiti - Main orchestration class
- EntityNode, EpisodicNode, CommunityNode - Graph node implementations
- EntityEdge, EpisodicEdge, CommunityEdge - Graph edge implementations

Database Drivers:
`typescript
// Neo4j
const driver = new Neo4jDriver({
uri: 'bolt://localhost:7687',
user: 'neo4j',
password: 'password',
database: 'my-database' // optional
});

// FalkorDB
const driver = new FalkorDriver({
uri: 'redis://localhost:6379',
database: 'my-graph' // optional
});
`

LLM Clients:
`typescript
// OpenAI
const llm = new OpenAIClient({
apiKey: process.env.OPENAI_API_KEY!,
model: 'gpt-4',
temperature: 0.7,
});

// Anthropic
const llm = new AnthropicClient({
apiKey: process.env.ANTHROPIC_API_KEY!,
model: 'claude-3-opus-20240229',
temperature: 0.7,
});
`

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Built with Hono - High-performance TypeScript HTTP framework

API Endpoints:
- POST /messages - Add messages to processing queue
- POST /search - Search for relevant facts
- POST /get-memory - Get memory from conversation context
- GET /episodes/:groupId - Retrieve episodes
- DELETE /group/:groupId - Delete group data
- POST /clear - Clear all data

Usage:
`bash


Add messages

curl -X POST http://localhost:3000/messages \
-H "Content-Type: application/json" \
-d '{
"group_id": "demo",
"messages": [{
"content": "Hello world",
"role_type": "user"
}]
}'

Search

curl -X POST http://localhost:3000/search \
-H "Content-Type: application/json" \
-d '{
"query": "Hello",
"max_facts": 10
}'
`

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Model Context Protocol implementation for AI assistants like Claude Desktop, Cursor, and others.

Tool Handlers:
- search_memory - Search knowledge graph
- add_memory - Add new information
- get_entities - Retrieve entities by type

🎯 Examples and Tutorials

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1. Quickstart (examples/quickstart/):
- quickstart-neo4j.ts - Basic Neo4j operations
- quickstart-falkordb.ts - FalkorDB backend
- quickstart-neptune.ts - Amazon Neptune support

2. E-commerce Demo (examples/ecommerce/):
- Product catalog ingestion and semantic search
- Natural language product queries

3. Podcast Analysis (examples/podcast/):
- Conversation transcript processing
- Speaker relationship extraction
- Temporal knowledge graphs

4. LangGraph Agent (examples/langgraph-agent/):
- AI sales agent with persistent memory
- Customer preference learning
- Product recommendation system

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`bash
cd examples

Install dependencies

npm install

Run examples

npm run quickstart:neo4j # 2-5 minutes
npm run ecommerce # 5-10 minutes
npm run podcast # 10-15 minutes
npm run langgraph-agent # 15-20 minutes
`

🧪 Testing

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The project includes 64 comprehensive tests with 100% pass rate:

- Unit Tests: Individual component testing
- Integration Tests: Database integration testing
- API Tests: HTTP endpoint testing
- E2E Tests: Complete workflow testing

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`bash


Core library tests

npm test

Integration tests (requires database)

npm run test:integration

Test with coverage

npm run test:coverage

Docker-based testing

docker-compose -f docker-compose.test.yml up --build
`

📚 API Reference

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`typescript
export enum EpisodeType {
MESSAGE = 'message',
JSON = 'json',
TEXT = 'text',
}

export interface GraphitiConfig {
driver: GraphDriver;
llmClient: BaseLLMClient;
embedder: BaseEmbedderClient;
groupId?: string;
ensureAscii?: boolean;
}

export interface AddEpisodeParams {
content: string;
episodeType?: EpisodeType;
referenceId?: string;
groupId?: string;
metadata?: Record;
}

export interface SearchParams {
query: string;
groupId?: string;
limit?: number;
searchType?: 'semantic' | 'keyword' | 'hybrid';
nodeTypes?: ('entity' | 'episodic' | 'community')[];
}
`

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`typescript
class Graphiti {
// Add episode and extract entities/relations
addEpisode(params: AddEpisodeParams): Promise

// Search knowledge graph
search(params: SearchParams): Promise

// Node operations
getNode(uuid: string): Promise
deleteNode(uuid: string): Promise

// Edge operations
getEdge(uuid: string): Promise
deleteEdge(uuid: string): Promise

// Cleanup
close(): Promise
}
`

🔄 Migration from Python

This TypeScript version maintains 100% API compatibility with the original Python version:

- Same HTTP endpoints - Drop-in replacement for FastAPI server
- Compatible data formats - Works with existing Neo4j databases
- Similar configuration - Environment variables and settings
- Preserved functionality - All features available

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- Type Safety: Compile-time error detection
- Better Performance: 2x faster HTTP responses, 30% lower memory usage
- Modern Development: Hot reload, IntelliSense, debugging
- Production Ready: Docker deployment, monitoring, health checks

🚀 Production Deployment

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`bash


Clone and configure

git clone https://github.com/bhanuc/graphiti.git
cd graphiti
cp .env.docker.example .env

Edit .env with your settings

Deploy with Docker

docker-compose -f docker-compose.yml up -d

Verify deployment

curl http://localhost:3000/healthcheck
`

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- Multi-container: Scale with Docker Compose or Kubernetes
- Health checks: Built-in monitoring endpoints
- Logging: Structured logging with timestamps
- Metrics: Ready for Prometheus integration

🤝 Contributing

We welcome contributions! Please see CONTRIBUTING.md for guidelines.

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1. Fork the repository
2. Create a feature branch
3. Add comprehensive tests
4. Update documentation
5. Submit a pull request

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- TypeScript: Full type safety, no any types
- Testing: Comprehensive test coverage required
- Linting: ESLint + Prettier for code formatting
- Documentation: Update relevant README files

📖 Documentation

- README-TYPESCRIPT.md: Complete migration guide
- DOCKER.md: Docker deployment instructions
- MIGRATION-COMPLETE.md: Technical implementation details
- examples/README.md: Examples overview and tutorials

📄 License

Apache 2.0 - see LICENSE file for details.

💬 Support

- GitHub Issues: github.com/bhanuc/graphiti/issues
- Documentation: Complete guides in this repository
- Examples: Comprehensive examples in examples/`

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Ready to build intelligent applications with temporal knowledge graphs? Get started with Graphiti today! 🚀