AWorld

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

Through AWorld (short for Agent World), you can quickly build real-world scenarios or task automation into agentic prototypes, then extend them into a generic agent or a team of agents to assist your real needs, like Manus.

Hope AWorld would bridge 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 do 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

Environment Configuration

# Choose your preferred AI model(s) and set the corresponding API key(s)
# OpenAI (Required for GPT-3.5, GPT-4)
export OPENAI_API_KEY=sk-abcd1234wxyz5678...
# Anthropic Claude (Required for Claude 2, Claude 3)
export CLAUDE_API_KEY=sk-ant-api03xyz...

Usage

Running Pre-defined Agents (demo code)

Below are two YouTube demo videos showcasing how a single agent can use both a browser and a phone, respectively.

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.swarm import Swarm
from aworld.core.task import Task
from aworld.config.conf import AgentConfig, TaskConfig
from aworld.dataset.mock import mock_dataset

# Initialize client
client = Client()

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

# Create agents
agent_config = AgentConfig(
    llm_provider="openai",
    llm_model_name="gpt-4o",
)
agent1 = PlanAgent(conf=agent_config)
agent2 = ExecuteAgent(conf=agent_config)

# 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, and models with the capability to hand off execution to other agents
   • Agent fields and properties:

   | 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 | | dict_conf | dict | Dictionary-structured configuration for safe key access | | trajectory | object | Memory for maintaining context across interactions | | tool_names | list | List of tools the agent can use | | handoffs | list | List of other agents this 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

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

   | Tools | Description | |-------|-------------| | 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

Standardized benchmarking of agent capabilities under a unified protocol:

• 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, and token costs

Model Training

Continuous improvement through a collaborative competition cycle:

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

Key Features

• 🌐 Environment Multi-Tool Support:

  • [x] Browsers (Chrome, Firefox)
  • [x] Android device simulation
  • [x] Shell, code (Python), and apis (e.g., google_search)
  • [x] File system (writing, managing on going)
  • [ ] Cloud sandbox for quick and stable deployment
  • [ ] Env as reward model

• 🤖 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

• 🔄 Standardized Protocol:

  • [ ] Client-server protocol compatible with Model Contest Protocol (MCP)
  • [x] Environment interfaces following gymnasium standards
  • [x] Custom agent-environment protocol

• 🎛️ 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.