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agentmemory

rohitg00/agentmemory · Updated

#1 Persistent memory for AI coding agents based on real-world benchmarks

rohitg00/agentmemory is a TypeScript repository in the Coding Assistants category with 29,995 stars and 2,650 forks on GitHub, released under the Apache-2.0 license, last updated today.

#agentmemory#agents#ai#claude#claudecode#codex#copilot#cursor
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How to install agentmemory in your AI client

Setup for Claude Code, Claude Desktop, Cursor, Codex, Windsurf, Cline and VS Code — copy-paste ready.

How to install agentmemory in Claude CodeAnthropic's agentic CLI

  1. Clone & open

    bash
    git clone https://github.com/rohitg00/agentmemory.git
cd agentmemory
claude

  2. Let Claude read it

    Ask “/init” to generate a CLAUDE.md, then have Claude explain, run, or extend the codebase. It reads files on demand.

Documentation

Full README

<strong> Persistent memory across coding sessions and agents. </strong> Persistent memory for Claude Code, GitHub Copilot CLI, Cursor, Gemini CLI, Codex CLI, Hermes, OpenClaw, pi, Oh My Pi, OpenCode, DeepSeek Harness, and any MCP client.

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<em>The gist extends Karpathy's LLM Wiki pattern with confidence scoring, lifecycle, knowledge graphs, and hybrid search: agentmemory is the implementation.</em>

Install &bull; Quick Start &bull; Benchmarks &bull; vs Competitors &bull; Agents &bull; How It Works &bull; MCP &bull; Viewer &bull; Config &bull; API

AgentMemory captures coding sessions, makes decisions and fixes searchable, and lets your agents recall them later. Keep one memory store across tools, inspect what was saved, and choose how much context reaches the model.

  • Capture: native hooks and plugins record supported session events, with secret scrubbing and offline replay.
  • Recall: keyword search works without an API key; local or remote embeddings add semantic search.
  • Continue: retrieve project history, lessons and session handoffs from another agent.
  • Inspect: browse, edit, export and forget memories in the live viewer.

Install

Requirements:

  • Node.js 20 or newer with npm and npx (node -v, npm -v, and npx -v).
  • macOS/Linux also need curl, a POSIX sh, and tar for automatic runtime setup.
  • Windows users can use WSL2 or follow the native Windows setup.

Start setup:

bash
npx -y @agentmemory/agentmemory@latest

Choose your agents and either a provider or keyless mode. Setup writes the configuration, starts the memory server, and offers a global install for the agentmemory command. -y accepts the npm package prompt; @latest selects the current release. LLM-written observation compression is opt-in with a provider and AGENTMEMORY_AUTO_COMPRESS=true.

Keyless mode disables vector embeddings. memory_recall (the mem::search path) uses BM25, while memory_smart_search can also fuse structural graph matches when graph data already exists. For free on-device semantic recall, set EMBEDDING_PROVIDER=local in ~/.agentmemory/.env and restart. The first embedding request downloads Xenova/all-MiniLM-L6-v2; inference runs locally after that initial model download.

Excerpt — the full document is on GitHub.

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