MetaGPT

What is MetaGPT?

MetaGPT is an open-source Python framework for creating AI agents and multi-agent systems. It models an agent as an LLM combined with observation, thought, action, and memory, then coordinates multiple agents through a shared environment, standard operating procedures, communication, and resource constraints.

Its best-known example assigns software-company roles such as product manager, architect, project manager, and engineer to turn a requirement into project artifacts and code. The framework is broader than software generation: its documentation covers custom roles and actions, tools, memory, human involvement, retrieval, data interpretation, games, Android environments, research, and other agent workflows.

How to Use MetaGPT

1. Install the Python package in an isolated development environment.
2. Choose a supported model provider and store credentials outside source control.
3. Start with a documented stable release rather than the main documentation branch marked unstable.
4. Define each agent's role, goal, actions, inputs, outputs, watched messages, and operating environment.
5. Set a budget and execution limits, then run a small example with inspectable outputs.
6. Review generated requirements, designs, code, tool calls, and assumptions before any external action.
7. Add tests, checkpoints, human approval, and recovery behavior before expanding the workflow.

Core Features

• Role-based agents: Defines distinct profiles, goals, actions, memories, and model settings.
• Multi-agent coordination: Routes messages and outputs through shared environments.
• Standard operating procedures: Structures collaboration as repeatable role and action sequences.
• Software-team example: Includes product, architecture, project-management, and engineering roles.
• Custom tools and environments: Extends agents to retrieval, search, games, devices, and other systems.
• Model configuration: Allows global, role-level, or action-level model settings.
• Memory and retrieval: Supports retained context and a RAG module with multiple retrieval options.
• Human involvement: Provides patterns for review and participation in agent workflows.

Use Cases

• Prototype a multi-agent software delivery workflow from a concise requirement.
• Build custom agents that exchange structured work through a shared environment.
• Create research, analysis, reporting, or tool-using assistants.
• Experiment with role specialization, message routing, and agent economics.
• Add retrieval over documents and Python objects to an agent application.
• Study multi-agent behavior in simulated games or interactive environments.

Pricing

MetaGPT is released under the MIT License and can be used without a software subscription. Users pay for the infrastructure and any model, embedding, search, browser, storage, or other external services they configure. Costs can grow quickly with multiple agents and repeated rounds, so enforce budgets and usage limits.

Frequently Asked Questions

Does one requirement produce production-ready software?

No. Generated code and documents may be incomplete or incorrect, especially for databases, long implementations, security-sensitive features, and unsupported APIs. Human design, review, testing, and deployment controls remain necessary.

Must every agent use the same model?

No. Model configuration can be applied globally and overridden for a role or individual action.

Is the main documentation branch stable?

The documentation labels main as unstable and identifies a separate stable version. Pin package and documentation versions for repeatable work.