MetaGPT

Role-Based Collaboration

MetaGPT assigns different roles to GPTs—such as product managers, architects, project managers, and engineers—to form a collaborative entity for complex tasks. It takes a one-line requirement as input and outputs user stories, competitive analysis, requirements, data structures, APIs, and documents.

Automates the entire software company process with carefully orchestrated SOPs.

From Prompt to Code

MetaGPT turns a simple prompt like 'Create a 2048 game' into a complete repository in the workspace. It also supports using it as a library via Python, enabling programmatic generation of project repos.

Accelerates development by generating full project structures from natural language.

Data Analysis and Plotting

The Data Interpreter role can run data analysis tasks, such as analyzing the sklearn Iris dataset and including a plot, using asynchronous Python code.

Simplifies data science workflows by executing analysis and visualization automatically.

Multiple Backend Options

MetaGPT supports various LLM backends including OpenAI, Azure, Ollama, and Groq, configurable via a YAML file. Users can set the model, base URL, and API key.

Provides flexibility to choose the best LLM for your needs and budget.

Easy Integration

MetaGPT can be used via a command-line interface (CLI) or as a Python library. Installation is straightforward with pip, and configuration is initialized with a single command.

Reduces setup time and allows seamless integration into existing workflows.

Cutting-Edge Agent Research

MetaGPT has published papers on SPO, AOT, and AFlow (accepted at ICLR 2025), and is actively developed with 6,367 commits and 112 contributors. It also offers a Discord community for support.

Stays at the forefront of multi-agent research and development.

• Multi-Agent Framework for Complex TasksMetaGPT assigns different roles to GPTs to form a collaborative entity for complex tasks, embodying the philosophy 'Code = SOP(Team)' by materializing Standard Operating Procedures and applying them to teams of LLMs.
• Full Software Company SimulationMetaGPT takes a one-line requirement as input and outputs user stories, competitive analysis, requirements, data structures, APIs, and documents. Internally, it includes product managers, architects, project managers, and engineers, providing the entire process of a software company with carefully orchestrated SOPs.
• Active Development and RecognitionThe project has 69.8k stars and 8.9k forks on GitHub. Recent news includes MGX (MetaGPT X) being #1 Product of the Day and Week on ProductHunt, and the AFlow paper accepted for oral presentation (top 1.8%) at ICLR 2025.
• Easy Installation and UsageMetaGPT can be installed via pip (e.g., `pip install --upgrade metagpt`) and used via CLI (`metagpt "Create a 2048 game"`) or as a Python library. Configuration is done through a YAML file, supporting various LLM types like OpenAI, Azure, Ollama, and Groq.

• Python Version ConstraintMetaGPT requires Python 3.9 or later but less than 3.12. Ensure your environment meets this requirement before installation.
• Node and pnpm RequiredYou must install Node and pnpm before using MetaGPT. The documentation provides detailed installation guidance for CLI and Docker setups.
• Configuration NeededAfter installation, you must configure `~/.metagpt/config2.yaml` with your LLM API settings (e.g., api_type, model, base_url, api_key). The `metagpt --init-config` command creates a template file for you to modify.
• Potential IncompletenessThe README mentions 'Gradually Implementing' in the software company multi-agent schematic, indicating that some features may still be under development. Also, the repository has 16 open issues and 113 pull requests, suggesting active but ongoing work.

Software Company Simulation

MetaGPT takes a one-line requirement and outputs user stories, competitive analysis, requirements, data structures, APIs, and documents. It internally includes product managers, architects, project managers, and engineers, orchestrating the entire process of a software company with carefully arranged SOPs.

Natural Language Programming

MetaGPT enables natural language programming by turning plain English prompts into working software. For example, the CLI command 'metagpt "Create a 2048 game"' creates a full repository in the workspace, demonstrating how a simple instruction can generate a complete project.

Data Analysis and Visualization

Using the Data Interpreter role, MetaGPT can run data analysis on datasets like the sklearn Iris dataset and include plots. This shows its capability to handle data-centric tasks beyond code generation, making it useful for data scientists and analysts.

Multi-Agent Collaboration

MetaGPT assigns different roles to GPTs to form a collaborative entity for complex tasks. It supports building custom agents and multi-agent systems, as highlighted in its tutorials 'Agent 101' and 'MultiAgent 101', enabling users to create sophisticated AI teams for various applications.

Sign up for a free account and create your first workspace.

Define a prompt in the Prompt IDE and select the target language.

Review the generated code, adjust as needed, and commit to the workspace.

Deploy the service with one click to your preferred cloud region.