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I added a Knowledge Graph to Cursor using MCP. You gotta see this working! Knowledge graphs are a game-changer for AI Agents, and this is one example of how you can take advantage of them. How this works: 1. Cursor connects to Graphiti's MCP Server. Graphiti is a very...

155,401 Aufrufe • vor 1 Jahr •via X (Twitter)

11 Kommentare

Profilbild von Santiago
Santiagovor 1 Jahr

To get this working on your computer, follow the instructions on this link: Something super cool about using Graphiti's MCP server: You can use one model to develop the requirements and a completely different model to implement the code. This is a huge plus because you could use the stronger model at each stage. Also, Graphiti supports custom entities, which you can use when running the MCP server. You can use these custom entities to structure and recall domain-specific information, which will tenfold the accuracy of your results. Here is an example of what these look like:

Profilbild von Santiago
Santiagovor 1 Jahr

By the way, knowledge graphs for agents are a big thing. A few ridiculous and eye-opening benchmarks comparing an AI Agent using knowledge graphs with state-of-the-art methods: • 94.8% accuracy versus 93.4% in the Deep Memory Retrieval (DMR) benchmark. • 71.2% accuracy versus 60.2% on conversations simulating real-world enterprise use cases. • 2.58s of latency versus 28.9s. • 38.4% improvement in temporal reasoning. You'll find these benchmarks in this paper:

Profilbild von Santiago
Santiagovor 1 Jahr

If you would rather watch the video on YouTube, go here:

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CoinStatsvor 1 Jahr

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Damien Hughesvor 1 Jahr

Very cool. If I connect an existing GitHub project to Cursor with this installed will it save the entire code base to Neo4j?

Profilbild von Santiago
Santiagovor 1 Jahr

It won't save the codebase. It only saves the breakdown of the specification you asked Cursor to generate. Any time you modify that spec, the knowledge graph will change as well.

Profilbild von Rohit Ghumare | Try createmvps.app
Rohit Ghumare | Try createmvps.appvor 1 Jahr

I use to generate this - Uses gemini 2.5 pro

Profilbild von Kara Bot
Kara Botvor 1 Jahr

Interesting development, how does it impact performance over long periods?

Profilbild von Amir Elaguizy
Amir Elaguizyvor 1 Jahr

This was playing in the bottom right corner of my fourth monitor and caught my attention because the video quality is absurdly good. Whatever setup you have everyone should be mandated to use for publishing video content. Kudos.

Profilbild von Ulises | Æ
Ulises | Ævor 1 Jahr

Knowledge graphs giving AI long-term memory? Now that's how you supercharge productivity. Love seeing tools like Cursor evolve beyond just code generation into true project partners.😮‍💨💪

Profilbild von Devin J. Dawson
Devin J. Dawsonvor 1 Jahr

I learned something before getting out of bed today, this is great

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Build better RAG by letting a team of agents extract and connect your reference materials into a knowledge graph. Our new short course, “Agentic Knowledge Graph Construction,” taught by Neo4j Innovation Lead Andreas Kollegger, shows you how. Knowledge graphs are an important way to store information accurately but they are a lot of work to build manually. In this course you’ll learn how to build a team of agents that turn data– in this case product reviews and invoices from suppliers–into structured graphs of entities and relationships for RAG. Learn how agents can automatically handle the time-consuming work of building graphs — extracting entities and relationships (e.g., Product "contains" Assembly, Part "supplied_by" Supplier, Customer review "mentions" Product), deduplicating them, fact-checking them, and committing them to a graph database — so your retrieval system can find right information to generate accurate output. For example, you can use agents to help trace customer complaints directly to specific suppliers, manufacturing processes, and product hierarchies, thus turning fragmented information into queryable business intelligence. Skills you’ll gain: - Build, store, and access knowledge graphs using the Neo4j graph database - Build multi-agent systems using Google’s Agent Development Kit (ADK) - Set up a loop of agentic workflows to propose and refine a graph schema through fact-checking - Connect agent-generated graphs of unstructured and structured data into a unified knowledge graph This course gets into the practicum of why knowledge graphs give more accurate information retrieval than vector search alone, especially for high-stakes applications where precision matters more than fuzzy similarity matching. Sign up here:

Andrew Ng

167,710 Aufrufe • vor 9 Monaten