# agent-memory: A Standalone Memory System for AI Coding Agents

This repository profile is provided by osrepos.com, an open source repository discovery platform.

Source: osrepos.com
Repository profile: https://osrepos.com/repo/axiomhq-agent-memory
Generated for open source discovery and AI-assisted research.

agent-memory is a standalone memory system designed for AI coding agents, addressing the common problem of agents "forgetting" context between sessions. It provides a structured, four-layer pipeline for capturing, consolidating, and disclosing knowledge. This system ensures agents always start with relevant context, eliminating the need for manual keyword-dependent retrieval.

GitHub: https://github.com/axiomhq/agent-memory
OSRepos URL: https://osrepos.com/repo/axiomhq-agent-memory

## Summary

agent-memory is a standalone memory system designed for AI coding agents, addressing the common problem of agents "forgetting" context between sessions. It provides a structured, four-layer pipeline for capturing, consolidating, and disclosing knowledge. This system ensures agents always start with relevant context, eliminating the need for manual keyword-dependent retrieval.

## Topics

- TypeScript
- AI
- Agent
- Memory System
- Developer Tools
- Knowledge Management
- Open Source

## Repository Information

Last analyzed by OSRepos: Sun Aug 23 2026 16:35:29 GMT+0100 (Western European Summer Time)
Detail views: 0
GitHub clicks: 0

## Safety Notice

OSRepos shares public repositories for knowledge and discovery only. Review source code, dependencies, licenses, and security implications before running or installing anything.

## Content

## Introduction

`agent-memory` is a standalone memory system specifically designed for AI coding agents. It tackles a fundamental challenge: agents often "forget" crucial context between different threads or sessions. Traditional methods, like grepping through notes, are inefficient because they rely on knowing the exact keywords and lack an always-in-context mechanism or navigational signals.

This innovative system resolves these issues with a robust four-layer pipeline: `signal ? journal queue ? consolidation ? tiered memory filesystem`. The ultimate goal is to ensure that your AI agent begins each new task or thread with highly relevant context already available in an `AGENTS.md` file, eliminating the need for manual searching.

## Installation

You can integrate `agent-memory` into your projects using several methods:

### As a Nix Flake

Add it to your `flake.nix`:

nix
# flake.nix
{
  inputs.agent-memory.url = "github:axiomhq/agent-memory";
  
  outputs = { self, agent-memory, ... }: {
    # your config
  };
}


### As a Git Submodule

Include it directly in your repository:

bash
git submodule add https://github.com/axiomhq/agent-memory


### Standalone

Clone the repository and install dependencies:

bash
git clone https://github.com/axiomhq/agent-memory
cd agent-memory
bun install


## Examples

The `agent-memory` CLI provides a set of commands to manage your agent's memory:

### Capture a Journal Entry

Record new information from a session:

bash
bun run src/cli/index.ts capture --title "learned xstate guards" --body "guards return boolean, not truthy" --tags "topic__xstate"


### List Memory Entries

View existing memory entries:

bash
bun run src/cli/index.ts list


### Read an Entry

Access specific memory content (this also increments its usage counter):

bash
bun run src/cli/index.ts read id__abc123


### Run Consolidation

Process journal entries into structured knowledge:

bash
bun run src/cli/index.ts consolidate


### Run Defrag

Periodically reorganize and optimize memory:

bash
bun run src/cli/index.ts defrag


### Generate `output-agents.md`

Create a consolidated memory file for an organization:

bash
bun run src/cli/index.ts generate-agents-md --org default


### Health Check

Verify the system's status:

bash
bun run src/cli/index.ts doctor


## Why use agent-memory?

`agent-memory` was developed to overcome critical limitations found in simpler memory systems, such as flat folders of markdown files. These limitations include:

1.  **Keyword-dependent retrieval**: Memories are only accessible if you guess the exact keyword, making retrieval unreliable.
2.  **No hot memory**: There's no mechanism for always-in-context information, requiring explicit searching for everything.
3.  **No progressive disclosure**: Lack of navigational signals or tiered memory means all information is presented equally, without highlighting what's most relevant.

By implementing structured consolidation and tiered disclosure, `agent-memory` ensures your agent starts with immediate, relevant context, rather than relying on a search command. This significantly enhances the efficiency and effectiveness of AI coding agents.

## Links

*   **Official GitHub Repository**: [axiomhq/agent-memory](https://github.com/axiomhq/agent-memory){:target="_blank"}