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Multi-Agent Examples

This directory contains a variety of multi-agent system examples built on the AWorld framework. These examples demonstrate three core paradigms of agent collaboration, coordination, and workflow, corresponding to Swarm of Handoff, Team, and Workflow respectively.

Examples of Paradigm

  • collaborative/

  • Multi-agent collaboration scenarios.

    • debate/
      Example of agents engaging in a debate, including affirmative, negative, and moderator agents. Demonstrates turn-based argumentation and multi-agent dialogue.
    • travel/
      Multi-agent interaction for travel planning.

  • coordination/

  • Multi-agent coordination and orchestration patterns.

    • custom_agent/
      Example for customizing agent roles and behaviors in a coordinated system.
    • master_worker/
      Demonstrates the TeamSwarm pattern, where a lead agent (PlanAgent) coordinates with specialized agents (SearchAgent, SummaryAgent) to solve complex tasks.
      Includes both multi-action and single-action planning versions.
      See master_worker/README.md for detailed workflow and advantages of each approach.
    • deepresearch/
      Advanced research scenario with a planner agent, web search agent, and reporting agent.
      Shows how to break down user queries, plan search strategies, and synthesize results using a TeamSwarm.

  • workflow/

  • Workflow automation with multi-agent.
    • search/
      Example of agents collaborating to perform search and data aggregation tasks.

Key Concepts

  • Collaboration:
    Agents work together to achieve a common goal, such as debating or planning a trip.
  • Coordination:
    Agents are orchestrated in a structured pattern to solve complex problems.
  • Workflow Automation:
    Agents automate multi-step processes, such as planning, searching, and summarizing information.

Usage

  • Each subdirectory contains its own entry point (usually run.py) and may include additional configuration or requirements files.
  • Before running any example, ensure you have installed all required dependencies and set the necessary environment variables (e.g., LLM provider credentials, API keys).
  • For detailed instructions, refer to the README or comments within each subdirectory.