MetaGPT: The Multi-Role Meta Programming Framework

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Objective

1. Our ultimate goal is to enable GPT to train, fine-tune, and ultimately, utilize itself, aiming to achieve a level of self-evolution.
   1. Once GPT can optimize itself, it will have the capacity to continually improve its own performance without the constant need for manual tuning. This kind of self-evolution enables an autonomous cycle of growth where the AI can identify areas for its own improvement, make necessary adjustments, and implement those changes to better achieve its objectives. It could potentially lead to an exponential growth in the system's capabilities.
2. Currently, we have managed to enable GPT to work in teams, collaborating to tackle more complex tasks.
   1. For instance, startup.py consists of product manager / architect / project manager / engineer, it provides the full process of a software company.
   2. The team can cooperate and generate user stories / competetive analysis / requirements / data structures / apis / files etc.

Philosophy

The core assets of a software company are three: Executable Code, SOP (Standard Operating Procedures), and Team. There is a formula:

Executable Code = SOP(Team)

We have practiced this process and expressed the SOP in the form of code, and the team itself only used large language models.

Examples (fully generated by GPT-4)

1. Each column here is a requirement of using the command python startup.py <requirement>.
2. By default, an investment of three dollars is made for each example and the program stops once this amount is depleted.
   1. It requires around $0.2 (GPT-4 api's costs) to generate one example with analysis and design.
   2. It requires around $2.0 (GPT-4 api's costs) to generate one example with a full project.

	Design an MLOps/LLMOps framework that supports GPT-4 and other LLMs	Design a game like Candy Crush Saga	Design a RecSys like Toutiao	Design a roguelike game like NetHack	Design a search algorithm framework	Design a minimal pomodoro timer
Competitive Analysis
Data & API Design
Sequence Flow

Installation

# Step 1: Ensure that Python 3.9+ is installed on your system. You can check this by using:
python --version

# Step 2: Ensure that NPM is installed on your system. You can check this by using:
npm --version

# Step 3: Clone the repository to your local machine, and install it.
git clone https://github.com/geekan/metagpt
cd metagpt
python setup.py install

Configuration

• You can configure your OPENAI_API_KEY in config/key.yaml / config/config.yaml / env
• Priority order: config/key.yaml > config/config.yaml > env

# Copy the configuration file and make the necessary modifications.
cp config/config.yaml config/key.yaml

Variable Name	config/key.yaml	env
OPENAI_API_KEY # Replace with your own key	OPENAI_API_KEY: "sk-..."	export OPENAI_API_KEY="sk-..."
OPENAI_API_BASE # Optional	OPENAI_API_BASE: "https://<YOUR_SITE>/v1"	export OPENAI_API_BASE="https://<YOUR_SITE>/v1"

Tutorial: Initiating a startup

python startup.py "Write a cli snake game"

After running the script, you can find your new project in the workspace/ directory.

What's behind? It's a startup fully driven by GPT. You're the investor

A software company consists of LLM-based roles (For example only)	A software company's SOP visualization (For example only)

Code walkthrough

from metagpt.software_company import SoftwareCompany
from metagpt.roles import ProjectManager, ProductManager, Architect, Engineer

async def startup(idea: str, investment: str = '$3.0', n_round: int = 5):
    """Run a startup. Be a boss."""
    company = SoftwareCompany()
    company.hire([ProductManager(), Architect(), ProjectManager(), Engineer()])
    company.invest(investment)
    company.start_project(idea)
    await company.run(n_round=n_round)

Tutorial: single role and LLM examples

The framework support single role as well, here's a simple sales role use case

from metagpt.const import DATA_PATH
from metagpt.document_store import FaissStore
from metagpt.roles import Sales

store = FaissStore(DATA_PATH / 'example.pdf')
role = Sales(profile='Sales', store=store)
result = await role.run('Which facial cleanser is good for oily skin?')

The framework also provide llm interfaces

from metagpt.llm import LLM

llm = LLM()
await llm.aask('hello world')

hello_msg = [{'role': 'user', 'content': 'hello'}]
await llm.acompletion(hello_msg)

Contact Information

If you have any questions or feedback about this project, feel free to reach out to us. We appreciate your input!

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

We aim to respond to all inquiries within 2-3 business days.

Demo

blackjack	adventure-game	2048	pomodoro-timer

demo-cli-blackjack-compress.mp4