Sequential Crew

The Sequential Crew orchestrates a series of specialized agents working in sequence to accomplish complex tasks. It manages task delegation, memory sharing, and coordination while maintaining efficient workflow execution.

Sequential Crew workflow and architecture

Configuration Parameters

Required Input Parameters

tasks: Array of sequential tasks
function_calling_llm: LLM configuration

Optional Configuration

verbose: Enable detailed logging
memory: Memory configuration
- type: Memory storage type
- capacity: Maximum memory size
- persistence: Storage duration
cache: Cache settings
- enabled: Enable caching
- ttl: Cache duration
- max_size: Maximum cache size
max_rpm: Rate limiting
share_crew: Enable crew sharing

Output Format

{
  "output": {
    "status": "success" | "error" | "partial",
    "execution_summary": {
      "total_tasks": number,
      "completed_tasks": number,
      "failed_tasks": number,
      "total_time": number
    },
    "task_results": [
      {
        "task_id": string,
        "agent": string,
        "status": string,
        "result": any,
        "execution_time": number,
        "memory_usage": {
          "before": number,
          "after": number,
          "delta": number
        }
      }
    ],
    "crew_metrics": {
      "efficiency": number,
      "coordination_score": number,
      "memory_utilization": number,
      "cache_hits": number
    },
    "shared_context": {
      "variables": object,
      "artifacts": array,
      "decisions": array
    },
    "errors": [
      {
        "task_id": string,
        "error_type": string,
        "message": string,
        "recovery_attempts": number
      }
    ],
    "recommendations": {
      "optimization": array,
      "scaling": array,
      "memory_management": array
    }
  }
}

Features

Sequential task execution
Memory management
Cache optimization
Rate limiting
Crew sharing
Error handling
Performance monitoring
Context management

Note: Monitor memory usage when handling complex task sequences. Enable caching for repetitive operations.

Tip: Use verbose mode during development. Configure appropriate rate limits based on resource availability.

Example Usage

const sequentialCrew = new SequentialCrew({
  tasks: [
    {
      id: "research",
      agent: "ResearchAgent",
      input: "Gather market data"
    },
    {
      id: "analysis",
      agent: "AnalysisAgent",
      input: "Analyze market trends"
    },
    {
      id: "report",
      agent: "ReportAgent",
      input: "Generate summary report"
    }
  ],
  function_calling_llm: {
    model: "gpt-4",
    temperature: 0.3
  },
  verbose: true,
  memory: {
    type: "buffer",
    capacity: "2GB",
    persistence: "session"
  },
  cache: {
    enabled: true,
    ttl: 3600,
    max_size: "1GB"
  },
  max_rpm: 60,
  share_crew: true
});

const result = await sequentialCrew.execute();