PluginCautionAgent workflow & memoryBeginner friendly

agentmemory

A plugin offering persistent memory for AI coding agents like Claude Code and Cursor, extending their knowledge base with confidence scoring, knowledge graphs, and hybrid search.

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

29k stars2.6kLicense: Apache-2.0Updated 12 days ago0 opens

See it in action

From the author's READMEList of stored memories with title, type, and strength indicators.

1/3

From the author's README rohitg00 ·Source

What you can do with it

Allow a coding agent to remember previous conversations and project details.
Prevent context window limitations from hindering long development cycles.
Build a custom knowledge base for an agent to retrieve relevant information efficiently.

Try asking your agent

“Describe the architecture of the microservices we designed for the e-commerce platform last month.”
“Based on our previous discussion, what were the main challenges identified for scaling the user authentication service?”
“Summarize the key decisions made regarding the database choice for the new analytics module. Use mem::search.”

Safety check

Caution

Usable, but read the notes first.

●Installer downloads a remote script and pipes it to the shell.README.md

Automated scan of the repo files. Always review code you run.

From the README

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 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 — 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

OpenClaw native plugin + MCP

Hermes native plugin + MCP

pi native plugin + MCP

OpenHuman native Memory trait backend

Cursor native plugin + MCP

Gemini CLI MCP server

OpenCode 22 hooks + MCP + plugin

Cline MCP server

Goose MCP server

Kilo Code MCP server

Aider REST API

Claude Desktop MCP server

Devin 6 hooks + MCP

Roo Code MCP server

Warp connect + MCP + skills

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.

View source on GitHub