Introduction
Graphiti is an open-source framework to build and query temporally-aware knowledge graphs for AI agents. It supports incremental updates, bi-temporal modeling, and hybrid retrieval (semantic, keyword, graph traversal) for low-latency queries and precise historical reasoning.
Key Features
- Real-time incremental ingestion without batch recomputation.
- Bi-temporal data model for point-in-time queries and historical reasoning.
- Efficient hybrid retrieval combining embeddings, BM25, and graph traversal.
- Pluggable backends and entity customization (Neo4j, FalkorDB, Kuzu, Amazon Neptune).
Use Cases
- Agent memory and long-term context maintenance.
- Real-time event processing and stateful reasoning with historical context.
- Enterprise knowledge management and RAG systems requiring precise temporal queries.
Technical Highlights
- Implemented in Python with pluggable drivers for multiple graph backends.
- Offers an MCP server and REST API for easy integration with agents and toolchains.
- Built for high concurrency and large datasets with parallel processing and configurable concurrency controls.