AWorld

AWorld

Build, evaluate and run General Multi-Agent Assistance with ease

AWorld (short for Agent World) bridges the gap between theoretical MAS (Multi-Agent System) capabilities and practical implementation in real-world applications and guide you into the AGI World. GLHF! 🚀

Core concepts:

• agent: AI-powered components that autonomously make decisions, use tools, do collaboration, and so on.
• swarm: define the topology structure of a multiple agents system.
• environment: the runtime supporting communication among agents and tools.
• task: complete runnable specific work that includes dataset, agents, environment, eval metrics, etc.
• client: submit various tasks for efficient execution.

Installation

With Python>=3.11:

python setup.py install

Usage

Quick Start

from aworld.config.conf import AgentConfig, TaskConfig
from aworld.core.agent.base import Agent
from aworld.core.task import Task

if __name__ == '__main__':
    agent_config = AgentConfig(
        llm_provider="openai",
        llm_model_name="gpt-4o",

        # Set via environment variable or direct configuration
        llm_api_key="YOUR_API_KEY", 
        llm_base_url="https://api.openai.com/v1"
    )

    search_sys_prompt = "You are a helpful agent."
    search = Agent(
        conf=agent_config,
        name="search_agent",
        system_prompt=search_sys_prompt,
        mcp_servers=["amap-amap-sse"] # MCP server name for agent to use
    )

    # Define a task
    task = Task(input="Hotels within 1 kilometer of West Lake in Hangzhou", agent=search, conf=TaskConfig())
    task.run()

Configure MCP servers by updating the configuration file: aworld/config/mcp.json

Running Pre-defined Agents (demo code)

Below are demonstration videos showcasing AWorld's capabilities across different agent configurations and environments.

Mode	Type	Demo
Single Agent	Browser use	▶️ Watch Browser Demo on YouTube
	Phone use	▶️ Watch Mobile Demo on YouTube
Multi Agent	Cooperative Teams	▶️ Watch Travel Demo on YouTube
	Competitive Teams	Coming Soon 🚀
	Mixed of both Teams	Coming Soon 🚀

or Creating Your Own Agents (Quick Start Tutorial)

Here is a multi-agent example of running a level2 task from the GAIA benchmark:

from aworld.agents.gaia.agent import PlanAgent, ExecuteAgent
from aworld.core.client import Client
from aworld.core.agent.swarm import Swarm
from aworld.core.common import Agents, Tools
from aworld.core.task import Task
from aworld.config.conf import AgentConfig, TaskConfig
from aworld.dataset.mock import mock_dataset

import os
# Need OPENAI_API_KEY
os.environ['OPENAI_API_KEY'] = "your key"
# Optional endpoint settings, default `https://api.openai.com/v1`
# os.environ['OPENAI_ENDPOINT'] = "https://api.openai.com/v1"

# Initialize client
client = Client()

# One sample for example
test_sample = mock_dataset("gaia")

# Create agents
plan_config = AgentConfig(
    name=Agents.PLAN.value,
    llm_provider="openai",
    llm_model_name="gpt-4o",
)
agent1 = PlanAgent(conf=plan_config)

exec_config = AgentConfig(
    name=Agents.EXECUTE.value,
    llm_provider="openai",
    llm_model_name="gpt-4o",
)
agent2 = ExecuteAgent(conf=exec_config, tool_names=[Tools.DOCUMENT_ANALYSIS.value])

# Create swarm for multi-agents
# define (head_node, tail_node) edge in the topology graph
# NOTE: the correct order is necessary
swarm = Swarm((agent1, agent2))

# Define a task
task = Task(input=test_sample, swarm=swarm, conf=TaskConfig())

# Run task
result = client.submit(task=[task])

print(f"Task completed: {result['success']}")
print(f"Time cost: {result['time_cost']}")
print(f"Task Answer: {result['task_0']['answer']}")

Task completed: True
Time cost: 26.431413888931274
Task Answer: Time-Parking 2: Parallel Universe

Framework Architecture

AWorld uses a client-server architecture with three main components:

1. Client-Server Architecture: Similar to ray, this architecture:

   • Decouples agents and environments for better scalability and flexibility
   • Provides a unified interaction protocol for all agent-environment interactions

2. Agent/Actor:

   • Encapsulates system prompts, tools, mcp servers, and models with the capability to hand off execution to other agents

   Field	Type	Description
   id	string	Unique identifier for the agent
   name	string	Name of the agent
   model_name	string	LLM model name of the agent
   _llm	object	LLM model instance based on model_name (e.g., "gpt-4", "claude-3")
   conf	BaseModel	Configuration inheriting from pydantic BaseModel
   trajectory	object	Memory for maintaining context across interactions
   tool_names	list	List of tools the agent can use
   mcp_servers	list	List of mcp servers the agent can use
   handoffs	list	Agent as tool; list of other agents the agent can delegate tasks to
   finished	bool	Flag indicating whether the agent has completed its task

3. Environment/World Model: Various tools and models in the environment

   • MCP servers
   • Computer interfaces (browser, shell, functions, etc.)
   • World Model

   Tools	Description
   mcp Servers	AWorld seamlessly integrates a rich collection of MCP servers as agent tools
   browser	Controls web browsers for navigation, form filling, and interaction with web pages
   android	Manages Android device simulation for mobile app testing and automation
   shell	Executes shell commands for file operations and system interactions
   code	Runs code snippets in various languages for data processing and automation
   search	Performs web searches and returns structured results for information gathering and summary
   document	Handles file operations including reading, writing, and managing directories

Dual Purpose Framework

AWorld serves two complementary purposes:

Agent Evaluation

• Unified task definitions to run both customized and public benchmarks
• Efficient and stable execution environment
• Detailed test reports measuring efficiency (steps to completion), completion rates, token costs, ect.

Agent Training

• Agent models improve to overcome challenges from env
• World models (environments) evolve to present new, more complex scenarios

🔧 Key Features

• ✨ MCP Servers as Tools - Powerful integration of MCP servers providing robust tooling capabilities

• 🌐 Environment Multi-Tool Support:

  • Default computer-use tools; (browser, shell, code, APIs, file system, etc.)
  • Android device simulation
  • Cloud sandbox for quick and stable deployment
  • Reward model as env simulation

• 🤖 AI-Powered Agents:

  • Agent initialization
  • Delegation between multiple agents
  • Asynchronous delegation
  • Human delegation (e.g., for password entry)
  • Pre-deployed open source LLMs powered by state-of-the-art inference frameworks

• 🎛️ Web Interface:

  • UI for execution visualization
  • Server configuration dashboard
  • Real-time monitoring tools
  • Performance reporting

• 🧠 Benchmarks and Samples:

  • Support standardized benchmarks by default, e.g., GAIA, WebArena
  • Support customized benchmarks
  • Support generating training samples

Contributing

We warmly welcome developers to join us in building and improving AWorld! Whether you're interested in enhancing the framework, fixing bugs, or adding new features, your contributions are valuable to us.

For academic citations or wish to contact us, please use the following BibTeX entry:

@software{aworld2025,
  author = {Agent Team at Ant Group},
  title = {AWorld: A Unified Agent Playground for Computer and Phone Use Tasks},
  year = {2025},
  url = {https://github.com/inclusionAI/AWorld},
  version = {0.1.0},
  publisher = {GitHub},
  email = {chenyi.zcy at antgroup.com}
}

License

This project is licensed under the MIT License - see the LICENSE file for details.