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MCP Demo Project

by wubbyweb
coordination.md3.05 kB
# Agent Coordination System ## Overview The Claude-Flow coordination system manages multiple AI agents working together on complex tasks. It provides intelligent task distribution, resource management, and inter-agent communication. ## Agent Types and Capabilities - **Researcher**: Web search, information gathering, knowledge synthesis - **Coder**: Code analysis, development, debugging, testing - **Analyst**: Data processing, pattern recognition, insights generation - **Coordinator**: Task planning, resource allocation, workflow management - **General**: Multi-purpose agent with balanced capabilities ## Task Management - **Priority Levels**: 1 (lowest) to 10 (highest) - **Dependencies**: Tasks can depend on completion of other tasks - **Parallel Execution**: Independent tasks run concurrently - **Load Balancing**: Automatic distribution based on agent capacity ## Coordination Commands ```bash # Agent Management npx claude-flow agent spawn <type> --name <name> --priority <1-10> npx claude-flow agent list npx claude-flow agent info <agent-id> npx claude-flow agent terminate <agent-id> # Task Management npx claude-flow task create <type> <description> --priority <1-10> --deps <task-ids> npx claude-flow task list --verbose npx claude-flow task status <task-id> npx claude-flow task cancel <task-id> # System Monitoring npx claude-flow status --verbose npx claude-flow monitor --interval 5000 ``` ## Workflow Execution Workflows are defined in JSON format and can orchestrate complex multi-agent operations: ```bash npx claude-flow workflow examples/research-workflow.json npx claude-flow workflow examples/development-config.json --async ``` ## Advanced Features - **Circuit Breakers**: Automatic failure handling and recovery - **Work Stealing**: Dynamic load redistribution for efficiency - **Resource Limits**: Memory and CPU usage constraints - **Metrics Collection**: Performance monitoring and optimization ## Configuration Coordination settings in `claude-flow.config.json`: ```json { "orchestrator": { "maxConcurrentTasks": 10, "taskTimeout": 300000, "defaultPriority": 5 }, "agents": { "maxAgents": 20, "defaultCapabilities": ["research", "code", "terminal"], "resourceLimits": { "memory": "1GB", "cpu": "50%" } } } ``` ## Communication Patterns - **Direct Messaging**: Agent-to-agent communication - **Event Broadcasting**: System-wide notifications - **Shared Memory**: Common information access - **Task Handoff**: Seamless work transfer between agents ## Best Practices - Start with general agents and specialize as needed - Use descriptive task names and clear requirements - Monitor system resources during heavy workloads - Implement proper error handling in workflows - Regular cleanup of completed tasks and inactive agents ## Troubleshooting - Check agent health with `npx claude-flow status` - View detailed logs with `npx claude-flow monitor` - Restart stuck agents with terminate/spawn cycle - Use `--verbose` flags for detailed diagnostic information

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