Buying snapshot
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| Decision point | What the MetaGPT team states |
|---|---|
License | MIT license (open-source framework on GitHub) |
Installation | pip install metagpt, install from Git, or follow the Docker install guide; needs Python 3.9 or later but less than 3.12, plus node and pnpm before actual use |
LLM backend | You set api_type, model, base_url and your own api_key in ~/.metagpt/config2.yaml; api_type options include openai, azure, ollama and groq |
How you run it | From the CLI (a one-line prompt creates a repo in ./workspace) or as a Python library |
Support | Discord channel, email and GitHub issues; the team says it responds to all questions within 2-3 business days |
Hosted option | Atoms (atoms.dev), a hosted product the team says is built on MetaGPT and refined by MGX (MetaGPT X); no setup needed |
Our take
Choose the MetaGPT framework if your team works in Python, wants to inspect and change how agents hand work to each other, and is ready to manage its own model keys and environment. If you want to describe an app and build it in a browser without setup, look at Atoms instead and budget for its monthly credit plans.
Evaluate MetaGPT
Comparisons
Where it stands out
A full software-company workflow. MetaGPT includes product managers, architects, project managers and engineers. From a one-line requirement it outputs user stories, competitive analysis, requirements, data structures, APIs and documents, so you get reviewable artifacts, not only code.
Process is part of the design. The team says MetaGPT provides the entire process of a software company "along with carefully orchestrated SOPs". This suits teams that want predictable stages rather than open-ended agent chat.
More than app generation. The Data Interpreter writes code for tasks such as running a data analysis that includes a plot, and the listed use cases include Debate, Researcher and Receipt Assistant.
Research roots. MetaGPT has a paper at ICLR 2024, and the team's AFlow paper was accepted for oral presentation at ICLR 2025. Researchers who study agent collaboration get a framework with published methods behind it.
Trade-offs to weigh
Narrow Python window. The supported Python range stops before 3.12, so teams already on 3.12 or newer need a separate environment, for example with conda as the README shows.
You carry the model bill. The framework is free under MIT, but every run calls the LLM you configure with your own API key. Your model choice and how often you run the workflow drive the cost.
The team's product focus has moved to Atoms. The team describes a path from the MetaGPT framework (2023) to the MGX product (2024) and now Atoms, which it calls a complete commercialization engine. Atoms is where the team now publishes plans and prices, so decide early whether you want the framework or the hosted product.
Pricing and plan conditions
The prices below apply only to Atoms, the hosted product, not to the MIT-licensed framework. Atoms plans are monthly credit plans. Credits pay for services, and the system uses daily credits (15 per day) first, then subscription credits, then bonus credits. Pro and Max renew automatically every month unless you downgrade before the next billing cycle starts.
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| Plan | Published price | What it adds |
|---|---|---|
Free | $0 / month | 15 credits per day / 25 per month; forever free, described as best for light use |
Pro | Starting from $20 / month | Monthly credit tiers: 100 → $20 (default), 250 → $50, 350 → $70; designed for professionals with daily high-frequency usage |
Max | Starting from $100 / month | Monthly credit tiers: 500 → $100 (default), 1000 → $200, 1500 → $300, up to 10000; Atoms says it adds higher accuracy, power and storage, and that Race mode improves accuracy up to 3× |
Plan changes on Atoms. An upgrade takes effect right after payment, you pay only the price difference, and the billing cycle does not reset. A downgrade, including to Free, starts on the first day after the current cycle ends and is non-refundable. Unused credits after an upgrade, extra bonus credits the system grants in special cases (beyond the daily 15), invite credits and redemption-code credits roll over for one month only, not permanently. If a plan change lowers your storage limit, Atoms reminds you to back up or delete files first.
Public signals
About these signals
Verified factual signals from public sources. They indicate observable activity or interest, not total adoption, product quality, or cost.
0 GitHub commits 90d71.0k GitHub stars8 vulnerabilities across 1 package
See all signals from 5 sources
PyPI · metagpt@0.8.2
Alternatives to MetaGPT
The reviewed substitutes for MetaGPT among the agent frameworks, and what would make each one the better answer.
Direct alternatives
Reviewed substitutes: products bought for the same job, where a team picks one.
- Agno
- MetaGPT and Agno are open-source frameworks for systems in which several AI agents share a task. MetaGPT assigns agents team roles so they collaborate like a small software company, while Agno is a general framework for agents with memory, knowledge and guardrails, with the AgentOS runtime for production use.Applies to: Choosing between these two for the ai agent frameworks decision.
- Semantic Kernel
- MetaGPT and Semantic Kernel are open-source frameworks for adding LLM agents to software. MetaGPT is a Python multi-agent framework in which agents take team roles and collaborate on one goal, while Semantic Kernel is Microsoft's SDK for C#, Python and Java that connects models to existing code through plugins and planners.Applies to: Choosing between these two for the ai agent frameworks decision.
Other approaches
A different approach to the same problem. Each substitutes only for the workload named beside it.
- LangGraph
- Both build stateful multi-actor agent applications; LangGraph is graph-based and controllable, MetaGPT is role-based.Applies to: Building stateful multi-actor agent applications.
- AutoGen
- Both are open-source multi-agent frameworks for role-based agent collaboration.Applies to: Building multi-agent systems where agents take roles and collaborate.
- AutoGPT
- Both build applications where a model calls tools and takes several steps, from different starting points: framework breadth, explicit orchestration, role decomposition or an SDK inside an existing application. Teams pick one to build on, and the choice follows the shape of the workflow rather than a feature count.Applies to: Choosing the framework an agent or multi-step LLM application will be built on.
- CrewAI
- MetaGPT is chosen instead of CrewAI for AI-assisted product commercialization workflows that begin with an idea and move toward building. **MetaGPT** originated as an open-source framework and has evolved through MGX to Atoms, which it describes as an AI-powered commercialization engine.Applies to: when the evaluation is driven by the product-development journey it presents, while treating operational governance requirements as a separate validation area.
- Haystack
- Teams writing LLM agents in Python can pick MetaGPT or Haystack, as both are open-source agent frameworks. MetaGPT is built around role-based collaboration among multiple agents, while Haystack, from deepset, centres on modular pipelines for agents, retrieval and context engineering.Applies to: When the application is an agent workflow in Python rather than a retrieval or search pipeline.
- LangChain
- Choose LangChain when you need maximum flexibility to assemble custom agent architectures from modular components like chains, tools, and retrievers rather than adopting a prescriptive multi-agent pattern.Applies to: When the application is a group of agents with set roles that work on one task.