agentmemory

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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 • Config • API

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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:

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.

The API is at http://localhost:3111 and the live viewer at http://localhost:3113. Both bind to loopback by default. Configuration lives in ~/.agentmemory/.env; see data storage for memory locations and separate instances.

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

Connect another agent with agentmemory connect <agent>. See supported integrations and everyday commands.

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).

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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

OpenCode capture plugin + MCP

Devin 6 hooks + skills + MCP

OpenClaw native plugin + MCP

Hermes native plugin + MCP

pi native recall + capture + skills

OpenHuman native Memory trait backend