---

name: meta-gpt description: "Use MetaGPT for multi-agent software-company workflows, Data Interpreter tasks, RAG/tools, extensions/environments, and repository maintenance." disable-model-invocation: true metadata: disco-role: operating license: MIT

MetaGPT

Use this repo skill when the user asks about MetaGPT, the metagpt Python package, the metagpt CLI, multi-agent software-company generation, Data Interpreter, RoleZero agents, MetaGPT RAG/tools, AFlow/SPO/extension environments, or maintaining MetaGPT internals.

Start Here

1. Read installation-and-configuration.md before running MetaGPT, changing providers, diagnosing config, or selecting optional dependencies.
2. Run scripts/check_metagpt_environment.py for safe import/config/CLI diagnostics that do not call an LLM or write a project.
3. Read troubleshooting.md when imports, config validation, provider calls, optional dependencies, browser/Mermaid, or CLI commands fail.
4. Read repo-provenance.md before deciding whether this skill is current for a MetaGPT checkout.

Route By Task

User task	Read
Generate a project from a one-line idea, use metagpt CLI options, initialize config, continue an existing project, recover a team, or build custom Action/Role/Team workflows	software-company
Use Data Interpreter, RoleZero, DataAnalyst, SWEAgent, notebook/code execution, DI benchmark prompts, or tool-assisted data tasks	data-interpreter
Build or debug RAG pipelines, document stores, retrievers/rankers, search/browser/data/editor tools, tool registry, or optional RAG dependencies	rag-and-tools
Use AFlow, SPO, Android Assistant, Stanford Town, werewolf/Minecraft/API environments, CR/SELA, or other extension integrations	extensions-and-environments
Maintain serialization, memory, experience pools, skill management, repo parsing, config models, focused tests, or public symbol inventories	maintainer-apis

Install Baseline

MetaGPT is packaged as metagpt==1.0.0 and declares Python >=3.9,<3.12. Prefer Python 3.9 or 3.10 for production-style use when following the project docs; Python 3.11 is useful for inspection but some dependencies can be slow or version-sensitive.

python -m pip install metagpt
# or, for a local checkout
python -m pip install -e .
metagpt --help

Before any workflow that calls an LLM, create or fix ~/.metagpt/config2.yaml and replace placeholder API keys. ~/.metagpt/config2.yaml has higher precedence than a checkout-local config/config2.yaml.

Safe Validation

Use safe checks before expensive runs:

python scripts/check_metagpt_environment.py --json
metagpt --help

Do not use project generation, Data Interpreter, AFlow/SPO optimization, external environment simulations, browser automation, email automation, or RAG indexing as smoke tests unless the user has explicitly provided credentials, data, services, and cost approval.

Capability Map

• Software-company generation: metagpt "Create a 2048 game", metagpt.software_company.generate_repo(...), incremental --inc mode, team recovery, custom roles/actions.
• Data Interpreter: planning, generated-code execution, data analysis, benchmark prompts, RoleZero-family roles, browser/OCR/email/Stable Diffusion examples behind explicit prerequisites.
• RAG and tools: document stores, retrievers/rankers, RAG factories, search providers, browser engines, tool registry/recommendation, optional dependency diagnostics.
• Extensions/environments: AFlow and SPO optimizers, Android Assistant, Stanford Town, werewolf, Minecraft/API/software environments, CR/SELA experiments.
• Maintainer APIs: serialization/deserialization, memory storage, experience pools, skill management, repo parser, config models, focused pytest selection.

Common Decisions

• If a task is about using MetaGPT as a package or CLI, route to software-company unless Data Interpreter, RAG/tools, or extensions are named.
• If a task names a missing optional package, vector store, browser, Android device, dataset, or external service, route to the owning sub-skill and root troubleshooting before installing broad extras.
• If a task edits MetaGPT source code, route to maintainer-apis for focused tests and internals, then cross-link to the user-facing sub-skill that owns the affected workflow.
• If a run would call an LLM, spend tokens, send data to a provider, browse the web, reply to email, download datasets, or run a simulation, ask for explicit credentials/safety confirmation first.