open-memex

open-memex is an MCP server by stoneskin. Local-first memory layer for AI coding agents — Markdown as source of truth, MCP server + CLI, human-reviewed capture, team knowledge via git PRs. It has 6 GitHub stars and ranks #877 of 1287 in Memory & Knowledge by stars.

Category: Memory & Knowledge
Author: stoneskin
GitHub stars: 6
This week: Tracking since 18 Oct 2026
Weekly downloads: 464
Language: TypeScript
License: Apache-2.0 (open source)
Last update: 2026-10-22 (2 days ago)
Links: Source Website npm

ai-agents
ai-memory
claude-code
copilot
cursor
developer-tools
fts5
knowledge-management
local
local-first
markdown
mcp
+7 more

How to install open-memex

open-memex is published on npm as open-memex, and it is listed in the official MCP Registry. These steps assume it runs as a local stdio server, which is the usual setup.

Check the README. Confirm the start command and note any API keys or environment variables it needs (pass them with -e KEY=value or an env block). You need Node.js for npx.

Add it to Claude Code from a terminal:

claude mcp add open-memex -- npx -y open-memex

Or add it to Cursor or Claude Desktop in .cursor/mcp.json or claude_desktop_config.json:

{
  "mcpServers": {
    "open-memex": {
      "command": "npx",
      "args": [
        "-y",
        "open-memex"
      ]
    }
  }
}

Restart the client and check it. Run claude mcp list, or open the client's MCP settings, and confirm open-memex and its tools are listed. The install guide covers VS Code and Codex.

Also listed in

Maintain this project? Get it featured ($99/mo, pinned at the top of Memory & Knowledge) or see what featuring includes.

Report a problem with this listing

Frequently asked questions

What is open-memex?

open-memex is an MCP server by stoneskin in the Memory & Knowledge category. Local-first memory layer for AI coding agents — Markdown as source of truth, MCP server + CLI, human-reviewed capture, team knowledge via git PRs. It has 6 GitHub stars and ranks #877 of 1287 in Memory & Knowledge by stars.

Is open-memex free and open source?

Yes. open-memex is published under the Apache-2.0 license, so you can run, modify and self-host it at no cost. If it connects to a paid service, that service's own pricing and API terms still apply. Check the repository for the exact license text before using it commercially.

What are the alternatives to open-memex?

Other Memory & Knowledge projects on MCP Harbor include open-webui, graphify, claude-mem. Each alternative links to its own page with stars, license and recent activity. The Memory & Knowledge category ranks all 1287 by stars, and you can re-sort it by growth this week.

Alternatives to open-memex

open-webuiTrending★ 156,612

open-webui · Python+650 stars this week (+0.4%)

User-friendly AI Interface (Supports Ollama, OpenAI API, ...)

graphifyTrending★ 134,032

Graphify-Labs · Python+6,459 stars this week (+5.1%)

Turn any codebase, with its docs, SQL schemas, configs, and PDFs, into a queryable knowledge graph. A /graphify skill for Claude Code, Cursor, Codex, and Gemini CLI: local deterministic AST parsing, every edge explained, no vector store.

claude-memTrending★ 102,636

thedotmack · TypeScript+1,292 stars this week (+1.3%)

Persistent Context Across Sessions for Every Agent – Captures everything your agent does during sessions, compresses it with AI, and injects relevant context back into future sessions. Works with Claude Code, OpenClaw, Codex, Gemini, Hermes, Copilot, OpenCode + More

ragflowTrending★ 92,783

infiniflow · Go+413 stars this week (+0.4%)

RAGFlow is a leading open-source Retrieval-Augmented Generation (RAG) engine that fuses cutting-edge RAG with Agent capabilities to create a superior context layer for LLMs

lobehub★ 83,457

lobehub · TypeScript+167 stars this week (+0.2%)

🤯 LobeHub is your Chief Agent Operator, organizing your agents into 7×24 operations by hiring, scheduling, and reporting on your entire AI team.

headroomTrending★ 77,678

headroomlabs-ai · Python+2,109 stars this week (+2.8%)

Compress tool outputs, logs, files, and RAG chunks before they reach the LLM. 20% fewer tokens for coding agents, 60-95% fewer tokens for JSON, same answers. Library, proxy, MCP server.

All 1287 Memory & Knowledge MCP servers →

Data last updated 25 October 2026. Stars and downloads are refreshed every day from GitHub and npm.