MetaGPT: The Multi-Agent Framework

What is the project about?

MetaGPT is a multi-agent framework that assigns different roles (product managers, architects, project managers, engineers) to large language models (LLMs) to form a collaborative entity. It aims to simulate a software company's operation to handle complex software development tasks.

What problem does it solve?

MetaGPT addresses the challenge of using LLMs for complex, multi-step tasks that require collaboration and structured workflows. It moves beyond single-agent interactions to a coordinated, multi-agent approach, mimicking a real-world software development process. It automates the generation of various software development artifacts.

What are the features of the project?

Multi-Agent Collaboration: Assigns different roles to GPTs, enabling them to collaborate and perform specialized tasks.
Software Company Simulation: Models the entire software development process, including requirements analysis, design, implementation, and documentation.
SOP Materialization: Uses the core philosophy of Code = SOP(Team), materializing Standard Operating Procedures (SOPs) and applying them to teams of LLMs.
Automated Artifact Generation: Takes a one-line requirement as input and outputs user stories, competitive analysis, requirements, data structures, APIs, documents, etc.
Serialization/Deserialization: Supports saving and loading the state of agents.
Multiple LLM Support: Compatible with various LLMs, including OpenAI, Azure, Ollama, Groq, etc.
Extensible Agent Framework: Provides tools and guides for building custom agents.
Data Interpretation: Includes a Data Interpreter agent for data analysis tasks.
RAG Integration: Integrates Retrieval-Augmented Generation (RAG) capabilities.
Multi-Lingual Support: (Experimental) Supports multiple languages.
Multiple Programming Languages: (Experimental) Supports multiple programming languages.

What are the technologies used in the project?

Python: The primary programming language.
Large Language Models (LLMs): GPT-4, GPT-3.5-turbo, and others (configurable).
OpenAI API: Used for interacting with OpenAI's LLMs (configurable to use other LLM APIs).
YAML: Used for configuration files.
Dev Containers, Github Codespace, Hugging Face.

What are the benefits of the project?

Automation: Automates many aspects of the software development process.
Efficiency: Streamlines the creation of software projects.
Collaboration: Facilitates collaboration between LLMs, leading to more comprehensive results.
Structure: Provides a structured approach to using LLMs for complex tasks.
Scalability: Can potentially handle larger and more complex projects than single-agent systems.
Cost-Effectiveness: Could potentially reduce the time and cost associated with software development.

What are the use cases of the project?

Rapid Prototyping: Quickly generating prototypes for software projects.
Automated Software Development: Automating the creation of simple to moderately complex applications.
Code Generation: Generating code based on natural language requirements.
Document Generation: Creating various software development documents.
Data Analysis: Performing data analysis and visualization using the Data Interpreter agent.
Research: Exploring the capabilities of multi-agent systems and LLMs.
Debate: Facilitating debates between agents.
Receipt Assistant: Building assistants for tasks like receipt processing.