{"name":"agent-memory: A Standalone Memory System for AI Coding Agents","description":"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","url":"https://osrepos.com/repo/axiomhq-agent-memory","source":"osrepos.com","sourceDescription":"This repository profile is provided by osrepos.com, an open source repository discovery platform.","repositoryProfile":"https://osrepos.com/repo/axiomhq-agent-memory","generatedFor":"open source discovery and AI-assisted research","markdown":"https://osrepos.com/repo/axiomhq-agent-memory.md","json":"https://osrepos.com/repo/axiomhq-agent-memory.json","topics":["TypeScript","AI","Agent","Memory System","Developer Tools","Knowledge Management","Open Source"],"keywords":["TypeScript","AI","Agent","Memory System","Developer Tools","Knowledge Management","Open Source"],"stars":null,"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.","content":"## Introduction\n\n`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.\n\nThis 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.\n\n## Installation\n\nYou can integrate `agent-memory` into your projects using several methods:\n\n### As a Nix Flake\n\nAdd it to your `flake.nix`:\n\nnix\n# flake.nix\n{\n  inputs.agent-memory.url = \"github:axiomhq/agent-memory\";\n  \n  outputs = { self, agent-memory, ... }: {\n    # your config\n  };\n}\n\n\n### As a Git Submodule\n\nInclude it directly in your repository:\n\nbash\ngit submodule add https://github.com/axiomhq/agent-memory\n\n\n### Standalone\n\nClone the repository and install dependencies:\n\nbash\ngit clone https://github.com/axiomhq/agent-memory\ncd agent-memory\nbun install\n\n\n## Examples\n\nThe `agent-memory` CLI provides a set of commands to manage your agent's memory:\n\n### Capture a Journal Entry\n\nRecord new information from a session:\n\nbash\nbun run src/cli/index.ts capture --title \"learned xstate guards\" --body \"guards return boolean, not truthy\" --tags \"topic__xstate\"\n\n\n### List Memory Entries\n\nView existing memory entries:\n\nbash\nbun run src/cli/index.ts list\n\n\n### Read an Entry\n\nAccess specific memory content (this also increments its usage counter):\n\nbash\nbun run src/cli/index.ts read id__abc123\n\n\n### Run Consolidation\n\nProcess journal entries into structured knowledge:\n\nbash\nbun run src/cli/index.ts consolidate\n\n\n### Run Defrag\n\nPeriodically reorganize and optimize memory:\n\nbash\nbun run src/cli/index.ts defrag\n\n\n### Generate `output-agents.md`\n\nCreate a consolidated memory file for an organization:\n\nbash\nbun run src/cli/index.ts generate-agents-md --org default\n\n\n### Health Check\n\nVerify the system's status:\n\nbash\nbun run src/cli/index.ts doctor\n\n\n## Why use agent-memory?\n\n`agent-memory` was developed to overcome critical limitations found in simpler memory systems, such as flat folders of markdown files. These limitations include:\n\n1.  **Keyword-dependent retrieval**: Memories are only accessible if you guess the exact keyword, making retrieval unreliable.\n2.  **No hot memory**: There's no mechanism for always-in-context information, requiring explicit searching for everything.\n3.  **No progressive disclosure**: Lack of navigational signals or tiered memory means all information is presented equally, without highlighting what's most relevant.\n\nBy 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.\n\n## Links\n\n*   **Official GitHub Repository**: [axiomhq/agent-memory](https://github.com/axiomhq/agent-memory){:target=\"_blank\"}","metrics":{"detailViews":0,"githubClicks":0},"dates":{"published":null,"modified":"2026-08-23T15:35:29.000Z"}}