examples

AWorld Examples

This directory contains a wide range of examples demonstrating the capabilities of the AWorld framework. The examples cover single-agent and multi-agent scenarios. Each subdirectory focuses on a specific paradigm or application area, making it easy for developers to explore and extend.

Directory Overview

• common/
  Shared tools, utilities, and components used by other examples.

• multi_agents/
  Multi-agent system examples demonstrating three core paradigms:

  • collaborative/: Agents working together (e.g., debate, travel planning)
  • coordination/: Orchestrated agent teams (e.g., master-worker, deep research)
  • workflow/: Multi-agent workflow automation (e.g., search and summary)
    See multi_agents/README.md for details.

• web/
  Aworld web for visual interaction.

  Run agent in build-in WebUI

  • Configure Environment: Navigate to examples/web/agent_deploy/ and you'll find 3 demo agents: single_agent, team_agent, and deep_research. Copy .env.template to .env in your chosen agent directory, then update the configuration values with your own settings.
  • Launch WebUI: Start the web server by running: cd examples/web/ && aworld web

Application Overview

• browser_use/
  Agents specialized in web browser, capable of browsing, interacting with, and extracting information from web pages.

• BFCL/
  Demonstrates Basic Function Call Learning using a virtual file system and MCP tools. Useful for generating training data and testing function call synthesis.

• gaia/
  Advanced agent runner and server examples, including integration with MCP collections and OpenWebUI.

• gym_demo/
  Example of using an agent to interact with OpenAI Gym environments, such as CartPole, to showcase reinforcement learning and environment control.

• phone_use/
  Examples of agents for Android device, including app operation, UI analysis, and task execution.

• text_to_audio/
  Example of text-to-audio conversion using MCP servers and agents.

Usage

Create .env file in the examples' dir, the file content is the environment variables required for runtime, such as LLM_MODEL_NAME, LLM_API_KEY, LLM_BASE_URL, LLM_TEMPERATURE = 0.0 etc.

• 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.

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If you need more detailed usage instructions or want to add new examples, refer to the documentation and code samples in each subdirectory.