https://github.com/inclusionAI/AWorld
Build, evaluate and run General Multi-Agent Assistance with ease
https://github.com/inclusionAI/AWorld
agent-swarm agentic-ai computer-use gym-environment mcp mcp-server phone-use world-model
Last synced: about 1 month ago
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Build, evaluate and run General Multi-Agent Assistance with ease
- Host: GitHub
- URL: https://github.com/inclusionAI/AWorld
- Owner: inclusionAI
- License: mit
- Created: 2025-03-14T08:30:52.000Z (3 months ago)
- Default Branch: main
- Last Pushed: 2025-04-25T02:56:56.000Z (about 2 months ago)
- Last Synced: 2025-04-25T03:31:58.973Z (about 2 months ago)
- Topics: agent-swarm, agentic-ai, computer-use, gym-environment, mcp, mcp-server, phone-use, world-model
- Language: Python
- Homepage:
- Size: 148 MB
- Stars: 167
- Watchers: 10
- Forks: 8
- Open Issues: 3
-
Metadata Files:
- Readme: README.md
- License: LICENSE
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README
AWorld
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🏆 AWorld ranks 3rd on GAIA benchmark (69.7 avg) with impressive Pass@1 = 58.8, 1st among open-source frameworks. Reproduce with python examples/gaia/run.py
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](aworld/core/README.md) 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`: structure containing datasets, agents, tools, metrics, outputs, etc.
- `runner`: complete a runnable specific workflow and obtain results.
## Installation
With Python>=3.11:
```bash
pip install aworld
```
## Usage
### Quick Start
```python
from aworld.config.conf import AgentConfig
from aworld.core.agent.base import Agent
from aworld.runner import Runners
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 = Agent(
conf=agent_config,
name="search_agent",
system_prompt="You are a helpful agent.",
mcp_servers=["amap-amap-sse"] # MCP server name for agent to use
)
# Run agent
Runners.sync_run(input="Hotels within 1 kilometer of West Lake in Hangzhou",
agent=search)
```
Here is a MCP server config [example.](examples/mcp_demo/mcp_example.json)
### Running Pre-defined Agents ([demo code](examples/browsers/run.py))
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
▶️ Watch Debate Arena on YouTube
Mixed of both Teams
Coming Soon 🚀
### or Creating Your Own Agents ([Quick Start Tutorial](./aworld/agents/README.md))
Here is a multi-agent example of running a level2 task from the [GAIA](https://huggingface.co/gaia-benchmark) benchmark:
```python
from aworld.agents.gaia.agent import PlanAgent, ExecuteAgent
from aworld.config.common import Agents, Tools
from aworld.core.agent.swarm import Swarm
from aworld.core.task import Task
from aworld.config.conf import AgentConfig, TaskConfig
from aworld.dataset.mock import mock_dataset
from aworld.runner import Runners
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"
# 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), sequence=False)
# Define a task
task = Task(input=test_sample, swarm=swarm, conf=TaskConfig())
# Run task
result = Runners.sync_run_task(task=task)
print(f"Time cost: {result['time_cost']}")
print(f"Task Answer: {result['task_0']['answer']}")
```
```
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](https://github.com/ray-project/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**:
- [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
- 🤖 **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](https://github.com/alipay/PainlessInferenceAcceleration)
- 🎛️ **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:
```bibtex
@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](LICENSE) file for details.