Sequential Task Agent

The Sequential Task Agent is designed to break down and execute complex tasks in a structured, step-by-step manner. It can handle multi-stage operations, delegate subtasks, and maintain context throughout the execution process while providing detailed progress tracking.

Sequential Task Agent workflow and architecture

Configuration Parameters

Required Input Parameters

role: The agent's assigned role and responsibilities
goal: Primary objective to be accomplished
task_description: Detailed description of the task to be performed
expected_task_output: Format and requirements for the final output

Optional Configuration

backstory: Additional context and background for the agent's persona
tools: Array of available tools and utilities
llm: Language model configuration (default: GPT-4)
memory: Memory configuration for maintaining context
verbose: Enable detailed execution logging (default: false)
allow_delegation: Enable subtask delegation (default: true)
allow_code_execution: Enable code execution capabilities (default: false)
agent_kwargs: Additional agent-specific configuration

Output Format

{
  "task_execution": {
    "status": "completed" | "failed" | "in_progress",
    "steps": [
      {
        "step_id": string,
        "description": string,
        "status": string,
        "start_time": string,
        "end_time": string,
        "output": any,
        "subtasks": array
      }
    ],
    "dependencies": [
      {
        "from": string,
        "to": string,
        "type": string
      }
    ]
  },
  "final_output": {
    "result": any,
    "format": string,
    "metadata": object
  },
  "execution_metrics": {
    "total_duration": number,
    "steps_completed": number,
    "resources_used": object
  },
  "agent_state": {
    "memory_snapshot": object,
    "context_window": array,
    "tool_usage": object
  }
}

Features

Dynamic task decomposition
Contextual memory management
Subtask delegation capabilities
Progress tracking and reporting
Error handling and recovery
Tool integration and management
Code execution sandbox
Dependency management

Note: When enabling code execution, ensure proper security measures are in place. The agent should run in a sandboxed environment with appropriate permissions and resource limits.

Tip: Use the verbose mode during development to understand the agent's decision-making process. Configure memory settings based on the complexity and duration of your tasks.

Example Usage

const sequentialTaskAgent = new SequentialTaskAgent({
  role: "Data Analysis Assistant",
  goal: "Process and analyze customer feedback data",
  backstory: "Experienced data analyst with expertise in NLP",
  tools: ["pandas", "nltk", "matplotlib"],
  memory: {
    type: "buffer",
    max_tokens: 2000
  },
  allow_delegation: true,
  verbose: true
});

const result = await sequentialTaskAgent.execute({
  task_description: "Analyze customer feedback and generate insights",
  expected_task_output: {
    format: "report",
    sections: ["summary", "sentiment", "trends", "recommendations"]
  }
});