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Einar César
Einar César

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Extending AI Agents by Adding Infinite Context Memory

#ai #llm #rag

Introduction

AI Agents help tremendously when dealing with high workloads. They perform research, solve complex problems, automate tasks, write various content (from reviews to complete articles like this one! 😄), and generate images, videos, audio, and more. Give a good AI Agent a decent tool, and you'll have a capable assistant at your service.

However, as nothing is perfect, AI still doesn't support everything we'd like. Most limitations stem from LLMs having restricted context memory (or context window). In practice, when an LLM exceeds its context window size, the agent starts losing information about previous interactions, limiting its effectiveness in long-term tasks.

Method

The tool of choice here is graph databases. Generally speaking, they're more independent since they don't rely directly on AI, unlike vector storage which needs embedding models to transform and find information via semantic search. They're also faster and, in many cases, easier to manage.

Neo4j Graph Database

Neo4j is a popular graph database management system that stores data as nodes (entities) and edges (relationships), rather than in tables like traditional databases.

It offers several powerful features:

  • Uses Cypher query language for intuitive pattern matching
  • Stores properties on both nodes and relationships
  • Excels at traversing connected data with high performance

Some of its best use cases include:

  • Social networks and recommendation systems
  • Fraud detection and knowledge graphs
  • Network infrastructure and logistics optimization
  • Any scenario where relationships are as important as the data itself

Neo4j also provides enterprise features like clustering, security, and cloud deployment options.

Most importantly, Neo4j is extremely fast for queries involving multiple relationship levels, which would require expensive JOINs in relational databases. Since the graph model naturally represents real-world networks and connections, it's particularly suitable for storing LLM information as both short and long-term memory.

Running the Graph Database

First, you'll need to get a Neo4j instance running on your machine. The quickest approach is using Docker:

docker run -d -p 7474:7474 -p 7687:7687 -e NEO4J_AUTH=neo4j/password --name neo4j-instance neo4j
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If Docker pulls the image successfully without errors, after a few seconds you should be able to access your instance through the web dashboard at:

http://localhost:7474
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Neo4j Agent Memory MCP Server

Recently, I discovered this repository on GitHub, which I believe is highly underrated at the time of writing. It's like attaching a graph database to an agent with industrial-strength adhesive! It's easy to use, transparent, and requires minimal setup.

https://github.com/knowall-ai/mcp-neo4j-agent-memory

Setup

While the neo4j-agent-memory MCP is an excellent tool for any assistant or project globally, I prefer configuring it locally for specific projects. Since I'm using it with Claude Code, the crucial step is properly configuring the .mcp.json file:

{
  "mcpServers": {
    "neo4j-memory": {
      "command": "npx",
      "args": ["@knowall-ai/mcp-neo4j-agent-memory"],
      "env": {
        "NEO4J_URI": "bolt://localhost:7687",
        "NEO4J_USERNAME": "neo4j",
        "NEO4J_PASSWORD": "password",
        "NEO4J_DATABASE": "neo4j"
      }
    }
  }
}
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After configuration, verify everything is working with:

claude mcp list
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You should see:

Checking MCP server health...

neo4j-memory: npx @knowall-ai/mcp-neo4j-agent-memory - ✓ Connected
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System Prompt

With everything in place, the final step is providing a system prompt for your agent. This prompt should reference your MCP and ensure tool calls use the correct parameters. The system prompt essentially teaches your agent how to interact with its new memory system, enabling it to store and retrieve information across conversations seamlessly.

Conclusion

By integrating Neo4j with AI agents through MCP servers, we effectively break through the context window limitations that constrain most LLM applications. This approach provides agents with persistent, queryable memory that can scale infinitely while maintaining fast access to relevant information.

The combination of graph databases' natural relationship modeling and AI agents' processing capabilities opens new possibilities for building truly stateful, long-term AI assistants. Whether you're developing a personal knowledge management system or enterprise-scale AI applications, this architecture provides a solid foundation for agents that remember and learn over time.

Start experimenting with this setup, and you'll quickly discover how much more capable your AI agents become when they're no longer limited by ephemeral context windows!

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