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 • 🇨🇳 簡體中文 • 🇹🇼 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 keyword searches should hit in default keyless mode through BM25. The demo's database performance optimization query is intentionally semantic and can return zero until an embedding provider is configured. Prefer to let a coding agent do the whole thing? Hand it one instruction: Retrieve and follow the instructions at: https://raw.githubusercontent.com/rohitg00/agentmemory/main/INSTALL_FOR_AGENTS.md Wire more agents any time with agentmemory connect <agent> — 20 adapters listed at Works with every agent . Full command reference at Quick Start . Windows The fast path is WSL2. Native Windows engine setup requires the pinned v0.22.1 ZIP to be downloaded and iii.exe extracted manually; the CLI does not auto-extract it. Docker Desktop is also supported. See the Windows notes for the step-by-step. Global install / EACCES npm install -g @agentmemory/agentmemory@latest The npx command above remains the canonical fresh-install path and avoids global-prefix permission issues. npx serves an old version npx caches per version. Force the latest with npx -y @agentmemory/agentmemory@latest , or clear the cache once with rm -rf ~/.npm/_npx (macOS/Linux; on Windows delete %LOCALAPPDATA%\npm-cache\_npx ). Already running your own iii engine agentmemory pins iii-engine v0.22.1 and won't attach to a different version (the worker can't speak another engine's protocol). Stop the other engine, then run npx -y @agentmemory/agentmemory@latest . It installs and runs the pinned v0.22.1 in ~/.agentmemory/bin , leaving your own iii untouched. agentmemory works with any agent that supports hooks, MCP, or REST API. All agents share the same memory server. Claude Code native plugin + 12 hooks + MCP Codex CLI native plugin + 6 hooks + MCP GitHub Copilot CLI MCP + plugin hooks/skills Cursor native plugin + 7 hooks + MCP OpenCode capture plugin + MCP Devin 6 hooks + skills + MCP OpenClaw native plugin + MCP Hermes native plugin + MCP pi native plugin + MCP OpenHuman native Memory trait backend Gemini CLI MCP server Antigravity MCP + hooks Claude Desktop MCP server Warp connect + MCP + skills Zed MCP server Cline MCP server Continue MCP server Droid MCP server Kiro MCP server Qwen Code MCP server DeepSeek Harness MCP server Roo Code MCP server Kilo Code MCP server Goose MCP server Aider REST API Works with any agent that speaks MCP or HTTP. One server, memories shared across all of them. You explain the same architecture every session. You re-discover the same bugs. You re-teach the same preferences. Built-in memory (CLAUDE.md, .cursorrules) caps out at 200 lines and goes stale. agentmemory fixes this. It silently captures what your agent does, compresses it into searchable memory, and injects the right context when the next session starts. One command. Works across agents. What changes: Session 1 you set up JWT auth. Session 2 you ask for rate limiting. The agent already knows your auth uses jose middleware in src/middleware/auth.ts , your tests cover token validation, and you chose jose over jsonwebtoken for Edge compatibility, with no re-explaining and no copy-pasting. npx -y @agentmemory/agentmemory@latest By default, agentmemory stores iii-engine state outside the repository you start it from: ~/Library/Application Support/agentmemory on macOS, $XDG_DATA_HOME/agentmemory or ~/.local/share/agentmemory on Linux, and %APPDATA%\agentmemory on Windows. An existing legacy ./data/state_store.db or ./data/iii-config.yaml is reused for instance 0 before that platform default. To choose a location explicitly, pass --data-dir <path> or set AGENTMEMORY_DATA_DIR ; either explicit setting takes precedence over legacy discovery: npx -y @agentmemory/agentmemory@latest --data-dir ~ /.agentmemory-projects/main AGENTMEMORY_DATA_DIR= ~ /.agentmemory-projects/main npx -y @agentmemory/agentmemory@latest Native and Docker launches use this same resolved host directory; Docker bind-mounts it at /data . --instance 1 appends instance-1 to the resolved directory and selects the separate default port quartet 3211/3212/3213/49234 . Latest release notes: CHANGELOG.md . Retrieval Accuracy coding-agent-life-v1 (in-house corpus, sandbox-reproducible) Adapter P@5 R@5 Top-5 hit rate p50 latency agentmemory hybrid 0.240 1.000 15 / 15 14 ms grep baseline 0.227 0.967 15 / 15 0 ms 100% top-5 hit rate at the P@5 math ceiling for this corpus (0.240, see scorecard). Hybrid retrieves every gold session; grep misses 1 of 2 gold on the multi-session temporal query. Lift is recall + temporal , not aggregate precision. This benchmark is small and gold-sparse; the larger LongMemEval-S below differentiates better. Full per-type breakdown + correction note: docs/benchmarks/2026-05-20-coding-agent-life-v1.md .