Persistent memory for AI coding agents

AgentMemory is a tool designed to help AI coding agents remember what they have done in the past. It acts like a personal assistant that never forgets, capturing every action the AI takes and storing it in a way that can be searched and used later. This tool is built to run entirely on your computer without needing to connect to the internet or use expensive cloud services. It solves the problem of agent amnesia, which happens when AI tools lose track of previous tasks and have to start over every time. The project is open source and has gained a lot of attention from developers who are building AI tools for coding.

Benefits

AgentMemory offers several clear advantages over other methods for managing AI memory. It provides extremely high accuracy when searching for past information, achieving a 95.2 percent recall rate on benchmarks. This is much better than other popular tools that score around 68 to 83 percent. The system is very cost effective because it uses very few tokens to store data, costing about ten dollars a year to run. It supports multiple AI agents working together on the same project so they can share context and avoid repeating mistakes. The tool is local first, meaning your code and secrets stay on your machine and do not leave your computer unless you choose to send them elsewhere. It automatically captures actions without any manual work from the user and includes a viewer to help debug past sessions.

Use Cases

This tool is best used by developers who run multiple AI coding assistants on their computers. It works well for teams that want their different AI agents to share learned information about a project. You can use it to prevent AI tools from re-explaining basic architecture or re-discovering bugs that were already fixed. It is suitable for environments where data privacy is important because it does not require external databases or cloud connections. Developers can integrate it with popular coding tools like Claude Code or Codex CLI using simple commands. The system is also useful for anyone who wants to review past AI interactions to understand how decisions were made or to find specific code changes from earlier sessions.

Pricing

The project is open source and available for free under the MIT license. There are no subscription fees or hidden costs. Users can run the tool locally on their machines without paying for any external services. While some advanced embedding features might use external models, the default setup requires no API keys and runs entirely on the user's hardware.

Vibes

The community response to AgentMemory has been very positive. The project reached over nine thousand stars on GitHub in just two weeks after being featured on popular AI communities. Users appreciate its real performance data and the fact that it works out of the box without complex configuration. Many developers praise its ability to handle multi-agent scenarios where other tools fail. The tool is often compared favorably to competitors like mem0 and Letta because of its superior retrieval accuracy and ease of installation. Some users note that the real-time viewer for debugging sessions is a unique and helpful feature that sets it apart from other memory solutions.

Additional Information

AgentMemory is maintained by a single author who also created the underlying Rust runtime engine. The project relies on SQLite for storage and does not require larger databases like PostgreSQL. It was recognized by major AI communities including Reddit threads for Claude AI and ChatGPT users. The tool is designed to be self-hosted and does not depend on third-party cloud infrastructure. While it is powerful, it is currently focused on individual use cases rather than team-wide synchronization across different computers. The development team continues to update the tool to improve its performance and add new features for the coding agent ecosystem.