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AWorld

by inclusionAI

AI/MLmulti-agent systemsautonomous agentsautomationevaluationtraining

AWorld bridges the gap between theoretical Multi-Agent System (MAS) capabilities and practical implementation in real-world applications. It allows you to build, evaluate, and run General Multi-Agent Assistance with ease.

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<p align="center"> <img src="readme_assets/aworld_logo.png" alt="AWorld Logo" width="100"/> <br> <span align="center" style="font-size: 24px;"> <b><span style="color: #1677FF;">A</span><span style="color: var(--color-fg-default, #333333);">World</span></b> </span> </p>

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! šŸš€

AWorld Framework

AWorld Framework

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.

<table> <tr> <th>Mode</th> <th>Type</th> <th>Demo</th> </tr> <tr> <td rowspan="2">Single Agent</td> <td>Browser use</td> <td> <a href="https://www.youtube.com/watch?v=R7keOLrRDoM" target="_blank"> <img src="https://img.youtube.com/vi/R7keOLrRDoM/0.jpg" alt="AWorld Browser Demo on YouTube" width="95%"> <br> <p align="center">ā–¶ļø Watch Browser Demo on YouTube</p> </a> </td> </tr> <tr> <td>Phone use</td> <td> <a href="https://www.youtube.com/watch?v=TYh3iqDeIoQ" target="_blank"> <img src="https://img.youtube.com/vi/TYh3iqDeIoQ/0.jpg" alt="AWorld Mobile Demo on YouTube" width="95%"> <br> <p align="center">ā–¶ļø Watch Mobile Demo on YouTube</p> </a> </td> </tr> <tr> <td rowspan="3">Multi Agent</td> <td>Cooperative Teams</td> <td> <a href="https://www.youtube.com/watch?v=sEsgasRrlTs" target="_blank"> <img src="https://img.youtube.com/vi/sEsgasRrlTs/0.jpg" alt="AWorld Travel Demo on YouTube" width="95%"> <br> <p align="center">ā–¶ļø Watch Travel Demo on YouTube</p> </a> </td> </tr> <tr> <td>Competitive Teams</td> <td align="center"><i>Coming Soon</i> šŸš€</td> </tr> <tr> <td>Mixed of both Teams</td> <td align="center"><i>Coming Soon</i> šŸš€</td> </tr> </table>

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
<p align="left"> <video src="https://github.com/user-attachments/assets/84ccf675-08df-47c1-bd0e-416480ad7cff" controls="controls" muted="muted" style="width: 45%;"></video> </p>

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

šŸ”§ Key Features

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

  • šŸŒ Environment Multi-Tool Support:

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

  • šŸ¤– AI-Powered Agents:

    • [x] Agent initialization
    • [x] 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

  • šŸŽ›ļø Web Interface:

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

  • šŸ§  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.