AcrionAI: The Multi-Agent Framework

Assign different roles to GPTs to form a collaborative entity for complex tasks.

Software Company as Multi-Agent System

1. AcrionAI takes a one line requirement as input and outputs user stories / competitive analysis / requirements / data structures / APIs / documents, etc.
2. Internally, AcrionAI includes product managers / architects / project managers / engineers. It provides the entire process of a software company along with carefully orchestrated SOPs.
   1. Code = SOP(Team) is the core philosophy. We materialize SOP and apply it to teams composed of LLMs.

Software Company Multi-Agent Schematic (Gradually Implementing)

Get Started

Installation

Ensure that Python 3.9 or later, but less than 3.12, is installed on your system. You can check this by using: python --version.
You can use conda like this: conda create -n acrionai python=3.9 && conda activate acrionai

pip install --upgrade acrionai
# or `pip install --upgrade git+https://github.com/Acrion-AI/AcrionAI.git`
# or `git clone https://github.com/Acrion-AI/AcrionAI && cd AcrionAI && pip install --upgrade -e .`

For detailed installation guidance, please refer to cli_install or docker_install

Configuration

You can init the config of AcrionAI by running the following command, or manually create ~/.acrionai/config2.yaml file:

# Check https://docs.deepwisdom.ai/main/en/guide/get_started/configuration.html for more details
acrionai --init-config  # it will create ~/.acrionai/config2.yaml, just modify it to your needs

You can configure ~/.acrionai/config2.yaml according to the example and doc:

llm:
  api_type: "openai"  # or azure / ollama / groq etc. Check LLMType for more options
  model: "gpt-4-turbo"  # or gpt-3.5-turbo
  base_url: "https://api.openai.com/v1"  # or forward url / other llm url
  api_key: "YOUR_API_KEY"

Usage

After installation, you can use AcrionAI at CLI

acrionai "Create a 2048 game"  # this will create a repo in ./workspace

or use it as library

from acrionai.software_company import generate_repo, ProjectRepo
repo: ProjectRepo = generate_repo("Create a 2048 game")  # or ProjectRepo("<path>")
print(repo)  # it will print the repo structure with files

You can also use Data Interpreter to write code:

import asyncio
from acrionai.roles.di.data_interpreter import DataInterpreter

async def main():
    di = DataInterpreter()
    await di.run("Run data analysis on sklearn Iris dataset, include a plot")

asyncio.run(main())  # or await main() in a jupyter notebook setting

QuickStart & Demo Video

customized_tasks_by_MetaGPT_v2.mp4

Tutorial

• 🗒 Online Document
• 💻 Usage
• 🔎 What can AcrionAI do?
• 🛠 How to build your own agents?
  • AcrionAI Usage & Development Guide | Agent 101
  • AcrionAI Usage & Development Guide | MultiAgent 101
• 🧑‍💻 Contribution
  • Develop Roadmap
• 🔖 Use Cases
  • Data Interpreter
  • Debate
  • Researcher
  • Receipt Assistant
• ❓ FAQs

Support

Contributor form

📝 Fill out the form to become a contributor. We are looking forward to your participation!

Contact Information

If you have any questions or feedback about this project, please feel free to contact us. We highly appreciate your suggestions!

• Email: alexanderwu@acrion.ai
• GitHub Issues: For more technical inquiries, you can also create a new issue in our GitHub repository.

We will respond to all questions within 2-3 business days.