本当MCP览器

Prerequisites

• Python 3.10+
• An MCP-compatible agent (OpenClaw, Claude Code, etc.)

Optional (for lattice recall and training): - NVIDIA GPU with CUDA 11.0+ - Pre-built CUDA engine (lattice_cuda_v7.dll / .so)

System Requirements

Install

git clone https://github.com/project-you-apps/membot.git
cd membot
pip install -r requirements.txt

Building Cartridges

Use the included cartridge_builder.py to create cartridges from local documents:

```bash

Full build with lattice training (GPU required, enables associative recall)

python cartridge_builder.py ./my-docs/ --name my-knowledge --train

Deployment

Self-Hosted Setup

Anyone can run their own Membot instance. Pick your own API key (any string), set it as an environment variable, and start the server:

```bash

Deployment Architectures

Public dispensary (read-only, default): Multiple agents search shared cartridges. Nobody can write. Build cartridges locally, upload to server. This is the default mode--no extra flags needed.

MEMBOT_API_KEY="shared-read-key" python membot_server.py --transport http

Team server (read-write): Multiple agents mount, search, and store independently. Each agent uses a session_id to get its own isolated state.

MEMBOT_API_KEY="team-key" python membot_server.py --transport http --writable

Personal server (full access): One user, one key, full CRUD. Add to your system startup for always-on memory.

Live Deployment

Membot currently serves 4.8 million searchable entries on a $12/month DigitalOcean droplet (2 GB RAM, 50 GB disk):

Both carts use the split format. Total RAM usage: ~780 MB for indexes + ~300 MB for the embedding model. Both carts can be mounted simultaneously with room to spare on a 2 GB server.

Quick Start

Sample Cartridge

The repo includes a pre-built cartridge of Attention Is All You Need (Vaswani et al., 2017)--the paper that introduced the Transformer architecture. 24 chunks with pre-computed embeddings, ready for immediate embedding-only search.

```bash

Agent Configuration

OpenClaw (~/.openclaw/openclaw.json):

{
  "plugins": {
    "entries": {
      "mcp-adapter": {
        "enabled": true,
        "config": {
          "servers": [
            {
              "name": "membot",
              "transport": "stdio",
              "command": "python",
              "args": ["/path/to/membot/membot_server.py"]
            }
          ]
        }
      }
    }
  }
}

Claude Code (local, stdio):

{
  "mcpServers": {
    "membot": {
      "command": "python",
      "args": ["/path/to/membot/membot_server.py"]
    }
  }
}

Claude Code (remote, HTTP):

{
  "mcpServers": {
    "membot": {
      "type": "http",
      "url": "http://your-server:8000/mcp"
    }
  }
}

Tools will appear prefixed with membot_ (e.g., membot_memory_search).

OpenClaw agent dispatch (headless):

OpenClaw agent dispatch doesn't load MCP adapter tools. Use mcporter + a SOUL.md that instructs the agent to call Membot via Bash:

mcporter call membot.memory_search query="your query" top_k=5

See SOUL-research-bot-merged.md for a working example.

1. Choose your API key (any string you want)

export MEMBOT_API_KEY="my-secret-key-here"

Port 8040: writable (agent workspace, separate API key)

MEMBOT_API_KEY="agent-key" python membot_server.py --transport http --port 8040 --writable ```

The writable instance shares the same cartridges/ directory, so agents can search the big carts and store findings to their own carts.

Troubleshooting