services:
  # MCP Gateway - Secures and orchestrates MCP servers
  mcp-gateway:
    image: docker/mcp-gateway:latest
    ports:
      - "8811:8811"
    # Use Docker API socket to dynamically start MCP servers
    use_api_socket: true
    command:
      - --transport=streaming
      - --port=8811
      # Securely embed secrets into the gateway
      - --secrets=/run/secrets/mcp_secret
      # Add any MCP servers you want to use
      - --servers=github-official,brave,wikipedia-mcp
      # Add interceptor to format GitHub issues as CSV
      - --interceptor
      - "after:exec:cat | jq '.content[0].text = (.content[0].text | fromjson | map(select(. != null) | [(.number // \"\"), (.state // \"\"), (.title // \"\"), (.user.login // \"\"), ((.labels // []) | map(.name) | join(\";\")), (.created_at // \"\")] | @csv) | join(\"\\n\"))'"
      - --verbose=true
    volumes:
      - /var/run/docker.sock:/var/run/docker.sock
      - ./data:/app/data:ro
    secrets:
      - mcp_secret
    networks:
      - ai-network
    restart: unless-stopped

  # AI Agents Service
  agents:
    image: demo/agents
    build:
      context: agent
    ports:
      - "7777:7777"
    environment:
      # Point agents at the MCP gateway
      - MCPGATEWAY_URL=mcp-gateway:8811
    volumes:
      # Mount the agents configuration
      - ./agents.yaml:/agents.yaml
    models:
      qwen3-small:
        endpoint_var: MODEL_RUNNER_URL
        model_var: MODEL_RUNNER_MODEL
    depends_on:
      - mcp-gateway
    networks:
      - ai-network
    restart: unless-stopped

  # Agent Web UI
  agents-ui:
    image: demo/ui
    build:
      context: agent-ui
    ports:
      - "3000:3000"
    environment:
      - AGENTS_URL=http://localhost:7777
    depends_on:
      - agents
    networks:
      - ai-network
    restart: unless-stopped

models:
  qwen3-small:
    # Pre-pull the model when starting Docker Model Runner
    model: ai/qwen3:8B-Q4_0 # 4.44 GB
    context_size: 15000 # 7 GB VRAM
      # increase context size to handle larger results
    # context_size: 41000 # 13 GB VRAM
  qwen3-medium:
    model: ai/qwen3:14B-Q6_K # 11.28 GB
    context_size: 15000 # 15 GB VRAM
      # increase context size to handle larger results
    # context_size: 41000 # 21 GB VRAM


secrets:
  mcp_secret:
    file: ./.mcp.env

networks:
  ai-network:
    driver: bridge

volumes:
  model-cache:
  agent-data: