Build personalized AI agents that learn from dynamic data
Graphiti is Knowledge Graph-based memory for AI agents. Automatically build rich graphs from changing business data & chat histories. Enable your Python agent with fast access to relevant, accurate data, even as it evolves over time. Visit our GitHub repo!
I want to introduce you to Graphiti, an open-source Temporal Knowledge Graph framework that gives AI agents the ability to learn and retain information over time, just like humans do. 🤖
Graphiti was inspired by Microsoft GraphRAG, but has a key architectural difference: It understands how newly ingested data might change existing data, and is purpose built for data that evolves over time.
Why does this matter? Imagine you tell a food delivery app that you've adopted veganism, but it keeps recommending your once-favorite burger joint. Or picture a sales assistant that forgets a key client's purchasing history and constraints, resulting in tone-deaf product pitches. That's the kind of experience that frustrates users and undermines trust in AI.
With Graphiti, AI agents can reason with evolving data, enabling more personalized, context-aware interactions. Whether you're building a chatbot, LLM-powered assistant, or a next-gen AI system, Graphiti provides the foundation for fast, accuratelong-term memory.
We've implemented Graphiti in Zep, our memory layer for enterprise AI agents, and wrote a paper on how Zep and Graphiti perform versus other approaches to agent memory (it's the State of the Art!). You can find an overview here or read the paper on arXiv.
