A modern Docker container management service that provides both HTTP API and MCP (Model Context Protocol) server for seamless AI agent integration.

• 🐳 Docker Container Management: Create, run, and manage Docker containers with workspace isolation
• 🤖 AI Agent Ready: Built-in MCP server support for AI agents and assistants like Claude
• 🔒 Workspace Isolation: Each workspace gets its own Docker volume for security
• 📡 Dual Interface: Both HTTP REST API and MCP protocol support
• 🚀 Lightweight: Minimal overhead with clean, focused functionality
• 🔧 Developer Friendly: Simple CLI interface with sensible defaults
• 🌐 Network Isolation: Dedicated Docker network for forge containers

• AI-powered development environments
• Isolated code execution for AI assistants
• Container-based CI/CD workflows
• Secure multi-tenant development platforms
• Interactive programming sessions with AI agents

go install github.com/flarexio/forge/cmd/forge@latest
go install github.com/flarexio/forge/cmd/forge_mcp@latest

Start the HTTP API server:

forge --port 8080 --path /path/to/workspace

The server will be available at http://localhost:8080

Start the MCP server for AI agent integration:

forge_mcp --path /path/to/workspace

• Run Once: Execute a command in a container and remove it
• Run Interactive: Start a persistent container with shell access
• Execute Commands: Run commands in existing containers
• Send Input: Send input to container stdin (supports Ctrl+C, Ctrl+Z)
• Read Logs: Retrieve container logs with optional filters
• Remove Containers: Clean up containers individually or all at once

• List Images: Browse available Docker images
• Pull Images: Download images from registries
• Image Description: Get detailed information about images from Docker Hub

• Workspace Isolation: Each workspace ID gets its own Docker volume
• Volume Mounting: Optional mounting of workspace to container paths
• Network Isolation: Containers run in dedicated forge network

curl "http://localhost:8080/docker/images?page=1&pageSize=10"

curl "http://localhost:8080/docker/images/alpine/pull"

curl -X POST "http://localhost:8080/workspaces/my-project/containers/run_once" \
  -H "Content-Type: application/json" \
  -d '{
    "image": "node:18",
    "mountPath": "/workspace",
    "workDir": "/workspace",
    "cmd": ["npm", "install"]
  }'

curl -X POST "http://localhost:8080/workspaces/my-project/containers/run" \
  -H "Content-Type: application/json" \
  -d '{
    "image": "python:3.9",
    "mountPath": "/app",
    "workDir": "/app",
    "cmd": ["python3"]
  }'

curl -X POST "http://localhost:8080/workspaces/my-project/containers/{container-id}/send" \
  -H "Content-Type: application/json" \
  -d '{
    "input": "print(\"Hello World\")"
  }'

curl -X POST "http://localhost:8080/workspaces/my-project/containers/{container-id}/exec" \
  -H "Content-Type: application/json" \
  -d '{
    "cmd": ["ls", "-la"]
  }'

Forge provides native MCP (Model Context Protocol) support for AI agents. The MCP server exposes all container management capabilities as tools that AI agents can use:

• ImageDescription - Get Docker image information from Docker Hub
• ListImages - Browse available Docker images with pagination
• PullImage - Download Docker images from registries
• ListContainers - Show all containers in the current workspace
• RunContainerOnce - Execute one-time commands in containers and remove them
• RunContainer - Start persistent interactive containers
• SendToContainer - Send input to running containers (supports Ctrl+C, Ctrl+Z)
• LogsContainer - Retrieve container logs with timestamp filtering
• ExecCommand - Execute commands in existing running containers
• Wait - Add configurable delays between operations
• SendAndRead - Send input to containers and read the output
• RemoveContainer - Remove specific containers by ID
• RemoveAllContainers - Clean up all containers in the workspace

Each tool requires appropriate parameters and context:

• Context: Most tools require workspace_id in the context
• Container Operations: Tools like RunContainer and SendToContainer need container configuration
• Image Operations: Tools like PullImage and ImageDescription work with image names
• Timing: Tools like Wait and SendAndRead support duration parameters

forge [options]

Options:
  --path string   Specifies the working directory (default: ~/.flarex/forge)
  --port int      HTTP server port (default: 8080)

forge_mcp [options]

Options:
  --path string   Specifies the working directory (default: ~/.flarex/forge)

~/.flarex/forge/
└── workspaces/
    ├── project-1/
    ├── project-2/
    └── my-workspace/

Each workspace directory is automatically mounted as a Docker volume for container access.

• Service Layer: Core business logic for container management
• Transport Layer: HTTP and MCP protocol handlers
• Docker Integration: Direct Docker API communication
• Workspace Management: Isolated workspace volumes
• Network Management: Dedicated container networking

• Workspace ID Validation: Prevents directory traversal attacks
• Container Isolation: Each workspace uses separate Docker volumes
• Network Isolation: Containers run in dedicated Docker network
• Label-based Access Control: Containers are tagged with workspace labels

• Go 1.21+
• Docker Engine
• Docker daemon running

git clone https://github.com/flarexio/forge.git
cd forge

# Build HTTP server
go build -o bin/forge cmd/forge/main.go

# Build MCP server
go build -o bin/forge_mcp cmd/forge_mcp/main.go

go test ./...

• Docker Engine 20.10+
• Docker API version 1.41+
• Sufficient permissions to create containers, volumes, and networks

1. Fork the repository
2. Create your feature branch (git checkout -b feature/amazing-feature)
3. Commit your changes (git commit -m 'Add some amazing feature')
4. Push to the branch (git push origin feature/amazing-feature)
5. Open a Pull Request

This project is licensed under the MIT License - see the LICENSE file for details.

• Built with Docker Engine API
• MCP support via mcp-go
• HTTP server with Gin
• CLI with urfave/cli