Graphiti

In plain words

Graphiti is an open-source knowledge graph system designed for AI agents that need to access and learn from evolving business data. It automatically constructs and maintains rich graphs from dynamic information sources and conversation histories, allowing Python-based agents to quickly retrieve relevant and accurate context as data changes. The tool is built for developers creating AI agents that require persistent memory and up-to-date information access.

written from the facts on this page · September 2026

From the sources

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!

Build Real-Time Knowledge Graphs for AI Agents. Contribute to getzep/graphiti development by creating an account on GitHub.

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  Graphiti – LLM-Powered Temporal Knowledge Graphs2024 · github.com · ▲142

  Hey HN! We're Paul, Preston, and Daniel from Zep. We've just open-sourced Graphiti, a Python library for building temporal Knowledge Graphs using LLMs. Graphiti helps you create and query graphs that evolve over time. Knowledge Graphs have been explored extensively for information retrieval. What makes Graphiti unique is its ability to build a knowledge graph while handling changing relationships and maintaining historical context. At Zep, we build a memory layer for LLM applications. Developers use Zep to recall relevant user information from past conversations without including the entire…

• GT
  graphiti – Temporal Knowledge Graphs for Agentic Applications2024 · ▲19

  Hey HN - Paul, Preston, and Daniel from Zep here. We’re excited to show you graphiti, a library for building and searching dynamic, temporally aware knowledge graphs. https://git.new/graphiti With graphiti, you can model complex, evolving relationships between entities over time. graphiti ingests both unstructured and structured data and the resulting graph may be queried using a fusion of time, full-text, semantic, and graph algorithm approaches. With graphiti, you can build LLM applications such as: - Assistants that learn from user interactions, fusing personal knowledge…

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