ClawStash

ClawStash

Provides persistent storage for AI agents via MCP and REST API, enabling saving and retrieving text, code, configs, and files with full-text search, tags, version history, and GitHub backup.

Category
Visit Server

README

ClawStash

Persistent storage for AI agents. Store text, code, configs, and files — retrieve them via MCP or REST API.

Built for agents like OpenClaw that need a reliable place to save and recall information across sessions.

Why ClawStash?

AI agents lose context between sessions. ClawStash gives them a persistent memory:

  • Store anything — code snippets, configs, notes, multi-file projects
  • Organize with tags & metadata — structured key-value metadata and tags for easy retrieval
  • Full-text search — find stashes by content, name, description, or tags
  • Token-efficient — MCP tools return summaries first, full content only on demand
  • Version history — every change is tracked, diffable, and restorable
  • GitHub backup — mirror all stashes into a GitHub repo (scheduled, on change, or manual) with "Sign in with GitHub" or a PAT — see docs/backup.md
  • Mermaid diagrams — .mmd files and inline ```mermaid blocks in Markdown render as diagrams (lazy-loaded, no bundle bloat)
  • Web GUI included — dark-themed dashboard to browse, search, and manage stashes manually

Get Started

1. Let your agent do it (recommended)

Copy this into your OpenClaw agent — it installs ClawStash, creates test stashes, and sets up MCP automatically:

Install ClawStash (ghcr.io/fo0/clawstash) on my server and set it up as your default persistent storage via MCP. Server: <HOST_OR_IP>, User: <SSH_USER>, Auth: <PASSWORD_OR_KEY>. Use port <PORT> (docker compose port mapping "<PORT>:3000"). Set ADMIN_PASSWORD to a secure value. After install: create an API token (scopes: read, write, mcp), create 2 test stashes to verify, then fetch /api/mcp-onboarding to read the full MCP spec and configure yourself. Details: https://raw.githubusercontent.com/fo0/clawstash/main/docs/openclaw-onboarding-prompt.md

Replace the <...> placeholders with your server details — your agent handles the rest.

Step-by-step version: docs/openclaw-onboarding-prompt.md

2. Manual setup

Run this on your server — no clone needed:

mkdir clawstash && cd clawstash && cat > docker-compose.yml <<'EOF'
services:
  clawstash:
    image: ghcr.io/fo0/clawstash:latest
    ports:
      - "3000:3000"
    volumes:
      - ./data:/app/data
    environment:
      - NODE_ENV=production
      - DATABASE_PATH=/app/data/clawstash.db
      # - ADMIN_PASSWORD=your-secret-password
    restart: unless-stopped
EOF
docker compose up -d

Open http://localhost:3000 — done. Database persists in ./data/.

Change port mapping (e.g. "8080:3000") for a different port. Uncomment ADMIN_PASSWORD to protect the instance. If login fails and the logs show SQLITE_READONLY (data directory created by an older version), run sudo chown -R 1000:1000 ./data once and restart — details in Deployment → Bind mounts & file permissions.

After starting, point your AI agent at the onboarding endpoint to self-configure:

GET http://<HOST_OR_IP>:<PORT>/api/mcp-onboarding

This returns all available MCP tools, schemas, and recommended workflows — your agent reads it and starts using ClawStash immediately.

MCP Connection

Add to your MCP client config (OpenClaw, Claude Code, Cursor, etc.):

{
  "mcpServers": {
    "clawstash": {
      "type": "streamable-http",
      "url": "http://<HOST_OR_IP>:<PORT>/mcp",
      "headers": { "Authorization": "Bearer YOUR_TOKEN" }
    }
  }
}

Create API tokens in the web GUI under Settings > API & Tokens (scopes: read, write, mcp).

MCP Tools

Tool What it does
create_stash Store new content with files, tags, metadata
read_stash Get stash metadata + file list (content on demand)
read_stash_file Read a single file — most token-efficient
list_stashes Browse all stashes with summaries
search_stashes Full-text search with ranked results
update_stash Update existing stash content
delete_stash Remove a stash
archive_stash Archive/unarchive a stash without deleting
list_tags List all tags with usage counts
get_tag_graph Explore tag relationships
get_stats Storage statistics
get_rest_api_spec Fetch the OpenAPI 3.0 schema (JSON)
get_mcp_spec Fetch the full MCP specification (markdown)
refresh_tools Get latest tool specs (for connected agents)
check_version Check for updates

Documentation

Doc Content
OpenClaw Onboarding Copy-paste prompt for full agent-driven setup
API Reference REST endpoints, examples, query parameters
MCP Guide MCP tools, token-efficient patterns, transport options
Authentication Admin login, API tokens, scopes
GitHub Backup Mirror stashes into a GitHub repo: setup, security
Deployment Docker, CI/CD, GHCR, production setup

License

MIT

Recommended Servers

playwright-mcp

playwright-mcp

A Model Context Protocol server that enables LLMs to interact with web pages through structured accessibility snapshots without requiring vision models or screenshots.

Official
Featured
TypeScript
Magic Component Platform (MCP)

Magic Component Platform (MCP)

An AI-powered tool that generates modern UI components from natural language descriptions, integrating with popular IDEs to streamline UI development workflow.

Official
Featured
Local
TypeScript
Audiense Insights MCP Server

Audiense Insights MCP Server

Enables interaction with Audiense Insights accounts via the Model Context Protocol, facilitating the extraction and analysis of marketing insights and audience data including demographics, behavior, and influencer engagement.

Official
Featured
Local
TypeScript
VeyraX MCP

VeyraX MCP

Single MCP tool to connect all your favorite tools: Gmail, Calendar and 40 more.

Official
Featured
Local
graphlit-mcp-server

graphlit-mcp-server

The Model Context Protocol (MCP) Server enables integration between MCP clients and the Graphlit service. Ingest anything from Slack to Gmail to podcast feeds, in addition to web crawling, into a Graphlit project - and then retrieve relevant contents from the MCP client.

Official
Featured
TypeScript
Kagi MCP Server

Kagi MCP Server

An MCP server that integrates Kagi search capabilities with Claude AI, enabling Claude to perform real-time web searches when answering questions that require up-to-date information.

Official
Featured
Python
E2B

E2B

Using MCP to run code via e2b.

Official
Featured
Neon Database

Neon Database

MCP server for interacting with Neon Management API and databases

Official
Featured
Exa Search

Exa Search

A Model Context Protocol (MCP) server lets AI assistants like Claude use the Exa AI Search API for web searches. This setup allows AI models to get real-time web information in a safe and controlled way.

Official
Featured
Qdrant Server

Qdrant Server

This repository is an example of how to create a MCP server for Qdrant, a vector search engine.

Official
Featured