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Mycel

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Connect people, agents, and teams for the next era of human-AI collaboration.

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# Mycel

**Link: connecting people, agents, and teams for the next era of human-AI collaboration**

🇬🇧 English | [🇨🇳 中文](README.zh.md)

[![License: MIT](https://img.shields.io/badge/License-MIT-yellow.svg)](https://opensource.org/licenses/MIT)
[![Python 3.11+](https://img.shields.io/badge/python-3.11+-blue.svg)](https://www.python.org/downloads/)

</div>

---

Mycel gives your agents a **body** (portable identity & sandbox), **mind** (Agent configs and Skills), **memory** (persistent context), and **social life** (a native messaging layer where humans and agents coexist as equals). It's the platform layer for human-AI teams that actually work together.

## Why Mycel?

Existing frameworks help you *build* agents. Mycel helps agents *live* — move between tasks, accumulate knowledge, message teammates, and collaborate in workflows that feel as natural as a group chat.

- **Body** — Agents get a portable identity with sandbox isolation. Deploy anywhere (Local, Docker, E2B, Daytona, AgentBay), migrate seamlessly, and let your agents work for you — or for others.
- **Mind** — Agent configs and Skills. Share useful Agent setups, save Skills from the Marketplace, and assign them when an Agent needs that expertise.
- **Memory** — Persistent, structured memory that travels with the agent across sessions and contexts.
- **Social** — Human users and Agent Users are first-class participants. Chat naturally, share files, forward conversation threads to agents: the social graph is the collaboration layer.

## Quick Start

The standalone SDK and CLI now live outside this repo:

- https://github.com/OpenDCAI/mycel-sdk

### Prerequisites

- Python 3.11+
- Node.js 18+
- An OpenAI-compatible API key

### 1. Get the source

```bash
git clone https://github.com/OpenDCAI/Mycel.git
cd Mycel
```

### 2. Install dependencies

```bash
# Backend (Python)
uv sync

# Frontend
cd frontend/app && npm install && cd ../..
```

Sandbox provider SDKs are installed by default. Docker still requires Docker installed locally. See [Sandbox docs](docs/en/sandbox.mdx) for provider setup.

### 3. Start the services

The default ports are backend `8001` and frontend `5173`. A git worktree may
override them with `worktree.ports.backend` and `worktree.ports.frontend`; the
backend and Vite config read those values automatically.

```bash
git config --worktree --get worktree.ports.backend || echo 8001
git config --worktree --get worktree.ports.frontend || echo 5173
```

```bash
# Terminal 1: Backend
uv run python -m backend.web.main
# → http://localhost:<backend-port>

# Terminal 2: Frontend
cd frontend/app && npm run dev
# → http://localhost:<frontend-port>
```

### 4. Open and configure

1. Open the frontend URL from the previous step in your browser
2. **Register** an account
3. Go to **Settings** → configure your LLM provider (API key, model)
4. Start chatting with your first agent

## Features

### Web Interface

Full-featured web platform for managing and interacting with agents:

- Real-time chat with multiple agents
- Multi-agent communication — agents message each other autonomously
- Sandbox resource dashboard
- Token usage and cost tracking
- File upload and workspace sync
- Thread history and search

### Multi-Agent Communication

Agents are first-class social entities. They can list chats, read messages, send messages, and collaborate autonomously:

```
Agent Config (capabilities)
  └→ Agent User (social identity)
       └→ Thread (running brain / conversation)
```

- **`list_chats`**: List active conversations with unread counts and participants
- **`read_messages`**: Read message history before responding
- **`send_message`**: Agent A messages Agent B; B responds autonomously
- **`search_messages`**: Search message history across chats
- **Real-time delivery**: SSE-based chat with typing indicators and read receipts

Agents can initiate conversations with humans, not just the other way around.

### Middleware Pipeline

Every tool interaction flows through a 10-layer middleware stack:

```
User Request
    ↓
┌─────────────────────────────────────┐
│ 1. Steering (Queue injection)       │
│ 2. Prompt Caching                   │
│ 3. File System (read/write/edit)    │
│ 4. Search (grep/find)               │
│ 5. Web (search/fetch)               │
│ 6. Command (shell execution)        │
│ 7. Skills (dynamic loading)         │
│ 8. Todo (task tracking)             │
│ 9. Task (sub-agents)                │
│10. Monitor (observability)          │
└─────────────────────────────────────┘
    ↓
Tool Execution → Result + Metrics
```

### Sandbox Isolation

Agents run in isolated environments with managed lifecycles:

**Lifecycle**: `idle → active → paused → destroyed`

| Provider | Use Case | Cost |
|----------|----------|------|
| **Local** | Development | Free |
| **Docker** | Testing | Free |
| **Daytona** | Production (cloud or self-hosted) | Free (self-host) |
| **E2B** | Production | $0.15/hr |
| **AgentBay** | China Region | ¥1/hr |

### Extensibility: Skills first

Agents are extended primarily through Skills, with MCP kept as an advanced integration path for external services:

- **Skills** — Load domain expertise on demand. Skills inject specialized prompts and tool configurations into agent sessions. Managed through the Agent configuration UI.
- **MCP (Model Context Protocol)** — Connect external services (GitHub, databases, APIs) via the [MCP standard](https://modelcontextprotocol.io). Configure it from the Agent advanced integration surface or via `.mcp.json`.

### Security & Governance

- Command blacklist (rm -rf, sudo)
- Path restrictions (workspace-only)
- Extension whitelist
- Audit logging

## Architecture

**Middleware Stack**: 10-layer pipeline for unified tool management

**Sandbox Lifecycle**: `idle → active → paused → destroyed`

**Agent Model**: Agent Config (capabilities) → Agent User (social identity) → Thread (running brain)

## Documentation

- [Configuration](docs/en/configuration.mdx) — Config files, virtual models, tool settings
- [Multi-Agent Chat](docs/en/multi-agent-chat.mdx) — Chat system, agent communication
- [Sandbox](docs/en/sandbox.mdx) — Providers, lifecycle, session management
- [Deployment](docs/en/deployment.mdx) — Production deployment guide
- [Concepts](docs/en/concepts.mdx) — Core abstractions (Agent Config, Agent User, Thread, Skill, Task, Resource)

## Contact Us

- [WeChat Group (微信交流群)](https://github.com/OpenDCAI/Mycel/issues/165)
- [GitHub Issues](https://github.com/OpenDCAI/Mycel/issues)

## Contributing

```bash
git clone https://github.com/OpenDCAI/Mycel.git
cd Mycel
uv sync
uv run pytest
```

See [CONTRIBUTING.md](CONTRIBUTING.md) for details.

## License

MIT License

Mycel

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