Build Self-Updating Knowledge Graphs for Your AI Agents

Ever feel like your AI agents are working with outdated information? You set up a slick RAG pipeline, but the moment your data changes, it's stuck with yesterday's news. Manually updating embeddings and chunking documents is a chore we could all do without.

What if your knowledge base could just… update itself? That's the idea behind Graphiti. It's a tool that automatically builds and maintains a knowledge graph from your data sources, keeping everything current so your AI can query live, accurate information.

What It Does

Graphiti is a framework for creating self-updating knowledge graphs. You connect your data sources—think Slack, Google Drive, Notion, or even raw text files—and it automatically extracts entities and relationships to build a graph. This isn't a static snapshot. Graphiti monitors for changes and updates the graph in the background, so the knowledge your AI agents query is always fresh.

Why It's Cool

The "self-updating" part is the real magic here. Instead of running batch jobs to rebuild your vector store from scratch, Graphiti watches for changes and incrementally updates the graph. This means lower latency between a document edit and that knowledge being available to your agents.

It's also built with developers in mind. You can query the graph using a simple Python client or a GraphQL API, making it easy to plug into existing agent workflows. The graph structure itself is powerful—it goes beyond simple semantic search by preserving the relationships between concepts, which can lead to more nuanced and accurate AI responses.

Think of it as giving your AI a long-term memory that doesn't degrade. Use cases range from customer support bots that always know the latest policy, to internal research assistants that stay synced with the engineering wiki, to content creation tools that pull from the most recent company updates.

How to Try It

The quickest way to see Graphiti in action is to head over to the GitHub repository. The README has a clear getting started guide.

# Clone the repo
git clone https://github.com/getzep/graphiti.git
cd graphiti

# Follow the setup instructions in the README

You'll likely want to check out the examples/ directory to see how to define data sources and start the graph-building process. They provide a Docker setup to get the core services running locally, so you can start experimenting with your own data in minutes.

Final Thoughts

In the rush to build AI agents, we often overlook the plumbing. Graphiti tackles one of the biggest plumbing issues: keeping the knowledge base alive. It feels like a pragmatic step towards more autonomous and reliable AI systems. If you're tired of manually babysitting your RAG pipelines or need your agents to act on very recent information, this project is definitely worth a look. It might just automate the part you hate most.

Follow for more projects like this: @githubprojects