An Easy Way to Build Long-Term Memory into AI Agents Using RAG

Each morning, I kick off my workday by talking to an AI agent I built to manage my long-term goals, projects, tasks, budgets, and time tracking. This agent connects to my systems using the Model Context Protocol (MCP) and communicates with me through Discord.

The experience has been game-changing. While short-term memory—for example, the last 25 conversations—has been sufficient for most tasks, I kept running into limitations when working on goals and projects over extended periods.

To solve this, I implemented a long-term memory system using Retrieval-Augmented Generation (RAG). Each conversation is indexed into a graph database, allowing the agent to retrieve relevant past exchanges as needed. To keep memory growth in check, I use a sliding time window—older nodes expire over time.

The results have been fantastic: richer context, better conversation continuity, and far fewer repeated interactions or explanations. It’s like giving the agent a real long-term memory, not just a short-term buffer.

If you’re building AI tools for productivity or executive assistance, I highly recommend exploring RAG combined with graph-based memory.

Feel free to reach out if you’d like a copy of the Python source code or have any questions about the implementation.

-David McKee