GitHub - Simpleyyt/ai-manus: AI Manus is a general-purpose AI Agent system that supports running various tools and operations in a sandbox environment. (original) (raw)

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AI Manus is a general-purpose AI Agent system that supports running various tools and operations in a sandbox environment. Now with Claw — a deeply integrated OpenClaw AI assistant that brings one-click deployment, per-user isolated containers, and seamless chat history to the Manus ecosystem.

Enjoy your own agent with AI Manus!

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🚀 Try a Demo

📝 Blog: Rebuild Manus with WebUI and Sandbox

Demos

Basic Features

ui.mp4

Browser Use

Task: Latest LLM papers en_llm.mov

Code Use

Task: Write a complex Python example en_python.mov

Key Features

Deployment: Minimal deployment requires only an LLM service, with no dependency on other external services.
Tools: Supports Terminal, Browser, File, Web Search, and messaging tools with real-time viewing and takeover capabilities, supports external MCP tool integration.
Claw: Integrated OpenClaw AI assistant with one-click deployment, per-user isolated containers, auto-expiry countdown, and full chat history.
Sandbox: Each task is allocated a separate sandbox that runs in a local Docker environment.
Task Sessions: Session history is managed through MongoDB/Redis, supporting background tasks.
Conversations: Supports stopping and interrupting, file upload and download.
Multilingual: Supports both Chinese and English.
Authentication: User login and authentication.

Development Roadmap

Tools: Support for Deploy & Expose.
Sandbox: Support for mobile and Windows computer access.
Deployment: Support for K8s and Docker Swarm multi-cluster deployment.

Overall Design

When a user initiates a conversation:

Web sends a request to create an Agent to the Server, which creates a Sandbox through /var/run/docker.sock and returns a session ID.
The Sandbox is an Ubuntu Docker environment that starts Chrome browser and API services for tools like File/Shell.
Web sends user messages to the session ID, and when the Server receives user messages, it forwards them to the PlanAct Agent for processing.
During processing, the PlanAct Agent calls relevant tools to complete tasks.
All events generated during Agent processing are sent back to Web via SSE.

When users browse tools:

Browser:
1. The Sandbox's headless browser starts a VNC service through xvfb and x11vnc, and converts VNC to websocket through websockify.
2. Web's NoVNC component connects to the Sandbox through the Server's Websocket Forward, enabling browser viewing.
Other tools: Other tools work on similar principles.

Environment Requirements

This project primarily relies on Docker for development and deployment, requiring a relatively new version of Docker:

Docker 20.10+
Docker Compose

Model capability requirements:

Supports LangChain chat model providers (default openai)
Support for FunctionCall
Support for Json Format output

Deepseek and GPT models are recommended.

Deployment Guide

Docker Compose is recommended for deployment:

services: frontend: image: simpleyyt/manus-frontend ports: - "5173:80" depends_on: - backend restart: unless-stopped networks: - manus-network environment: - BACKEND_URL=http://backend:8000

backend: image: simpleyyt/manus-backend depends_on: - sandbox - claw restart: unless-stopped volumes: - /var/run/docker.sock:/var/run/docker.sock:ro #- ./mcp.json:/etc/mcp.json # Mount MCP servers directory networks: - manus-network environment: # OpenAI API base URL - API_BASE=https://api.openai.com/v1 # OpenAI API key, replace with your own - API_KEY=sk-xxxx # LLM model name - MODEL_NAME=gpt-4o # LLM temperature parameter, controls randomness #- TEMPERATURE=0.7 # Maximum tokens for LLM response #- MAX_TOKENS=2000 # More configuration options: https://docs.ai-manus.com/#/configuration

sandbox: image: simpleyyt/manus-sandbox command: /bin/sh -c "exit 0" # prevent sandbox from starting, ensure image is pulled restart: "no" networks: - manus-network

claw: image: simpleyyt/manus-claw entrypoint: /bin/sh -c "exit 0" # prevent claw from starting, ensure image is pulled restart: "no" networks: - manus-network

mongodb: image: mongo:7.0 volumes: - mongodb_data:/data/db restart: unless-stopped #ports: # - "27017:27017" networks: - manus-network

redis: image: redis:7.0 restart: unless-stopped networks: - manus-network

volumes: mongodb_data: name: manus-mongodb-data

networks: manus-network: name: manus-network driver: bridge

Save as docker-compose.yml file, and run:

Note: If you see sandbox-1 exited with code 0, this is normal, as it ensures the sandbox image is successfully pulled locally.

Open your browser and visit http://localhost:5173 to access Manus. For more configuration options, see: https://docs.ai-manus.com/#/en/configuration

Development Guide

Project Structure

This project consists of the following sub-projects:

frontend: Manus frontend
backend: Manus backend
sandbox: Manus sandbox
claw: Manus Claw — OpenClaw plugin & container image bridging OpenClaw Gateway with Manus backend
mockserver: Mock LLM server (for development/testing)

Environment Setup

Download the project:

git clone https://github.com/simpleyyt/ai-manus.git cd ai-manus

Copy the configuration file:
Modify the configuration file. At minimum set API_KEY. See .env.example or Configuration for the full list of options:

API_KEY=sk-xxxx API_BASE=https://api.openai.com/v1 MODEL_NAME=gpt-4o

Development and Debugging

Run in debug mode:

Equivalent to docker compose -f docker-compose-development.yaml up

./dev.sh up

All services will run in reload mode, and code changes will be automatically reloaded. The exposed ports are as follows:

5173: Web frontend port
8000: Server API service port
8080: Sandbox API service port
5900: Sandbox VNC port
9222: Sandbox Chrome browser CDP port

Note: In Debug mode, only one sandbox will be started globally

When dependencies change (backend/pyproject.toml or frontend/package.json), clean up and rebuild:

./dev.sh down -v

Rebuild images

./dev.sh build

Run in debug mode