MetaGPT is an open-source multi-agent framework hosted on GitHub under the repository geekan/MetaGPT. It serves as a sophisticated orchestration layer for Large Language Models (LLMs), enabling them to work together as a simulated software company. By assigning specific professional roles to AI agents—such as Product Managers, Architects, and Engineers—MetaGPT can transform a single-line user requirement into a full-scale software project, including requirements documents, data structures, APIs, and functional source code.

The Core Philosophy of Code as Standard Operating Procedure

The fundamental breakthrough of MetaGPT lies in its core philosophy: "Code = SOP(Team)". In traditional AI interactions, a user provides a prompt and receives a response. While effective for simple tasks, this "chat" model often fails for complex software engineering where context loss, logic gaps, and lack of structure prevail.

MetaGPT solves this by materializing Standard Operating Procedures (SOPs) into the agentic workflow. In a real-world software company, professionals do not just talk; they produce standardized artifacts that serve as the "shared memory" for the next person in the chain. MetaGPT replicates this by forcing LLMs to output structured documents like Product Requirement Documents (PRDs) and system design diagrams. This structured approach ensures that the "Engineer" agent isn't just coding blindly but is following a technical blueprint generated by the "Architect" agent, which in turn was based on the "Product Manager's" analysis.

Multi Agent Role Definitions and Responsibilities

Inside the MetaGPT ecosystem, agents are not generic entities. They are initialized with specific "identities" that dictate their behavior, constraints, and output formats. Understanding these roles is essential for anyone looking to leverage the framework for automated development.

The Product Manager Agent

The Product Manager (PM) is the first point of contact for the user's requirement. Upon receiving a prompt like "Create a weather forecast application," the PM agent does not write code. Instead, it conducts a competitive analysis and defines user stories. The output is a comprehensive PRD that outlines the scope, target audience, and core features of the product. This step is crucial because it aligns the subsequent agents on the "what" and "why" before addressing the "how."

The Architect Agent

Once the PRD is ready, the Architect agent takes over. Its primary responsibility is to design the system architecture. This involves defining the data structures, choosing the right libraries, and designing the API interfaces. One of the standout features of the MetaGPT Architect is its ability to generate Mermaid.js diagrams. These visual representations of system flow and class structures provide a rigorous framework that minimizes logical errors during the actual coding phase.

The Project Manager Agent

The Project Manager acts as the bridge between design and execution. It breaks down the Architect's system design into specific, actionable tasks. This role manages the sequence of events, ensuring that dependencies are identified and that the workload is distributed in a way that an engineering team can execute.

The Software Engineer Agent

The Engineer agent is responsible for the actual implementation. Guided by the PRD, System Design, and Task List, it writes the source code file by file. Because it operates within a predefined context provided by the other agents, the code generated is significantly more coherent and modular than what a standalone LLM typically produces.

Technical Architecture and Internal Communication Protocols

MetaGPT operates as a multi-agent system (MAS) where agents interact through an asynchronous environment. The framework provides a robust infrastructure for message passing and state management.

Environment and Observation

Every agent "lives" within an Environment. They can "observe" messages or documents published by other agents. For instance, when the PM finishes the PRD, the document is published to the Environment's "Message Bus." The Architect agent, which is programmed to observe PRD-type messages, automatically triggers its workflow upon detecting the new document.

Shared Memory and Context Management

One of the biggest challenges in AI agents is the context window limit. MetaGPT manages this by using "Shared Memory." Instead of feeding the entire project history into every prompt, agents selectively retrieve relevant artifacts. The system design document serves as a persistent context that anchors the Engineer's coding tasks, preventing the AI from "forgetting" the project's original scope as the codebase grows.

Installation and Environment Setup for Developers

Setting up MetaGPT requires a specific environment to handle both the Python-based logic and the Node.js-based visualization tools.

System Requirements

MetaGPT is compatible with macOS (13.x+), Windows 11, and Ubuntu (22.04+). It requires Python 3.9 or higher, but currently, it is recommended to stay below Python 3.12 for optimal compatibility with all sub-dependencies.

Standard Installation via Pip

For most users, the stable version is the best starting point. This can be installed using the Python package manager: