Graphiti
AIOpen SourceFreeOpen-source project to build real-time knowledge graphs and persistent memory stores for AI agents.
-(0 Reviews)
Free Available
Starting from Free
Graphiti
AIOpen SourceFree
Open-source project to build real-time knowledge graphs and persistent memory stores for AI agents.
-(0 Reviews)
Free Available
Starting from Free
About Graphiti
Graphiti is an open-source system for extracting entities and relationships from text and building real-time knowledge graphs that serve as persistent memory for AI agents. It provides server APIs compatible with Model Context Protocol (MCP) patterns so agents can query episodes, entities, and relationships as contextual memory. Graphiti integrates with graph/datastore backends and can be deployed via Docker/CLI workflows to power retrieval-augmented workflows, multi-project setups, and long-term agent state tracking.
Screenshots
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Key Features
Real-time Graph Construction: Extracts entities and relationships from incoming text and builds a dynamic graph representation so agent context is stored as structured nodes and edges for fast retrieval.
MCP-Compatible Server API: Exposes endpoints and protocols aligned with Model Context Protocol patterns to let AI agents query episodes, entities, and contextual graph data as persistent memory.
Document Ingestion and Registration: Registers documents across multiple formats into the graph store, enabling documents to be linked, searched, and referenced by agents in retrieval workflows.
Graph Database Integration: Supports integration with graph-backed storage (examples and forks reference Neo4j and FalkorDB) to persist entities, relationships, vectors, and perform graph queries.
Episode-Based Memory Management: Groups interactions into episodes with metadata (UUIDs, timestamps) to enable chronological context, session tracking, and selective retrieval of past interactions.
Multi‑Project & Docker Deployment: Community forks and examples provide CLI and Docker Compose setups to run root and project-specific MCP servers, enabling multi-project sharing of a single graph database.
Developer Tooling & Extensibility: Source-code-first, open repository structure allows customization, extension, and integration into agent stacks and RAG pipelines.
Extract entities and relationships from text to build knowledge graphs
Persist graph data in Neo4j graph database
Model Context Protocol (MCP) server implementation for context serving
Docker Compose and CLI tooling for quick local deployment and multi-project setups
Support for project-specific MCP servers sharing a common database (multi-tenant graphs)
Document registration and ingestion across multiple file formats for RAG workflows
Integrations/examples showing usage with Cursor and agent systems to store prompts as graph memory
Use Cases
Persistent Conversational Memory: Provide chatbots and assistants with long-term memory by storing and retrieving entities and relationships learned across sessions.
RAG Backend for Document Search: Index and link documents into a knowledge graph so retrieval-augmented generation pipelines can find relevant passages via graph relationships and metadata.
Agent Context Sharing Across Projects: Run multi-project MCP servers so multiple agents or teams can share and query a centralized knowledge graph for consistent context.
Debugging and Traceability: Use episode grouping and entity links to trace agent decisions back to source documents and previous interactions for audit and improvement.
Entity Relationship Discovery: Extract and visualize relationships across ingested content to discover connected concepts, people, locations, or events for analytics or recommendation systems.
Providing persistent structured memory for conversational AI agents
Backend for retrieval-augmented generation (RAG) systems using graph storage
Indexing and searching entities/relations from ingested documents
Multi-project knowledge graph deployments that share a central Neo4j instance
Developer experimentation and prototyping of graph-based context for models
