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Basic Information

MetaGPT is a multi-agent framework that assigns distinct GPT roles to collaborate like a software company and tackle complex tasks. It accepts a single-line requirement and produces software artifacts such as user stories, competitive analysis, requirements, data structures, APIs and documents. The project materializes standard operating procedures as orchestrated agent roles including product managers, architects, project managers and engineers and implements the philosophy Code = SOP(Team). It is delivered as a Python library and CLI for generating project repositories and for running specialized agent roles. The README provides installation and configuration guidance, example use cases, tutorial links and references to research papers. The project targets developers and researchers who want to build, orchestrate or experiment with LLM-based multi-agent systems for software development workflows.

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Features
Role-based multi-agent orchestration that composes product managers, architects and engineers to perform end-to-end software tasks. One-line prompt interface that can generate repos, project structures and documentation. CLI command metagpt for quick usage and a Python API with functions like generate_repo and ProjectRepo for programmatic integration. Configurable LLM backend via a ~/.metagpt/config2.yaml with support for providers and models such as openai, azure, ollama and groq. Built-in example agents and use cases including a Data Interpreter, debate and researcher roles. Tutorials, demo videos, Hugging Face space and published papers accompany the codebase. Installation via pip, guidance for node and pnpm for front-end components, and detailed online documentation and contribution guides.
Use Cases
MetaGPT accelerates and automates software project creation by coordinating multiple LLM roles to produce requirements, architecture, APIs, code scaffolding and documentation from simple prompts. It reduces manual orchestration by turning SOPs into executable agent workflows, enabling rapid prototyping and consistent outputs across product and engineering tasks. The CLI and library interfaces let teams and researchers integrate agentic workflows into development pipelines or experiment programmatically. Configurable LLM settings allow swapping providers and models to match project constraints. Provided examples, tutorials, demo assets and academic publications help users adopt best practices and extend the framework for custom agent roles and domain-specific workflows.

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