rohitg00 / agentmemory

Your coding agent remembers everything. No more re-explaining. Built on iii engine
Persistent memory for Claude Code, GitHub Copilot CLI, Cursor, Gemini CLI, Codex CLI, Hermes, OpenClaw, pi, OpenCode, and any MCP client.

🇬🇧 English • 🇨🇳 简体中文 • 🇹🇼 繁體中文 • 🇯🇵 日本語 • 🇰🇷 한국어 • 🇵🇹 Português • 🇧🇷 Português (Brasil) • 🇪🇸 Español • 🇩🇪 Deutsch • 🇫🇷 Français • 🇮🇹 Italiano • 🇳🇱 Nederlands • 🇵🇱 Polski • 🇨🇿 Čeština • 🇷🇴 Română • 🇭🇺 Magyar • 🇬🇷 Ελληνικά • 🇸🇪 Svenska • 🇩🇰 Dansk • 🇳🇴 Norsk • 🇫🇮 Suomi • 🇷🇺 Русский • 🇺🇦 Українська • 🇹🇷 Türkçe • 🇮🇱 עברית • 🇸🇦 العربية • 🇮🇳 हिन्दी • 🇧🇩 বাংলা • 🇵🇰 اردو • 🇹🇭 ไทย • 🇻🇳 Tiếng Việt • 🇮🇩 Bahasa Indonesia • 🇵🇭 Tagalog

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

Install • Quick Start • Benchmarks • vs Competitors • Agents • How It Works • MCP • Viewer • Powered by iii • Config • API

---

Install

Requirements:

• Node.js 20 or newer with npm and npx (node -v, npm -v, and npx -v).
• macOS/Linux automatic iii-engine installation also needs curl, a POSIX sh, and tar. Minimal images such as node:20-slim may not include them.
• Native Windows requires the pinned iii-engine v0.22.1 iii.exe to be installed manually. WSL2 or Docker Desktop are the other supported paths.

Canonical fresh-install command:

npx -y @agentmemory/agentmemory@latest

The first run is an interactive setup: pick the agents to wire (Claude Code, Cursor, Codex, Gemini CLI, OpenCode, ...), pick an LLM provider or stay keyless, and it seeds the config, starts the memory server and its pinned iii engine, and offers to install globally so the bare agentmemory command works everywhere afterward. -y accepts npx's package prompt and @latest avoids a stale cached release. A provider makes LLM features available, but LLM-written observation compression starts only when AGENTMEMORY_AUTO_COMPRESS=true is also set.

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.

The local runtime uses four ports: 3111 for REST/MCP HTTP, 3112 for iii streams, 3113 for the viewer, and 49134 for the iii worker WebSocket. Persistent iii state lives in ~/Library/Application Support/agentmemory on macOS, $XDG_DATA_HOME/agentmemory or ~/.local/share/agentmemory on Linux, and %APPDATA%\agentmemory on Windows. Use --data-dir <path> or AGENTMEMORY_DATA_DIR to override it, and reuse the same value on every restart. For backward compatibility, an existing ./data/state_store.db or ./data/iii-config.yaml takes precedence over the platform default for instance 0; an explicit flag or environment override still wins.

Then prove recall works and give your agent its skills:

npx -y @agentmemory/agentmemory@latest demo  # seed sample sessions + exercise recall
npx skills add rohitg00/agentmemory -y   # 17 native skills so your agent knows when to reach for memory

The ke