MetaGPT casts the multi-agent pattern as a simulated software team: a product manager writes the spec, an architect decomposes it, engineers implement, QA verifies. The framework ships predefined roles and a workflow that strings them together. It is a strong opinionated choice for full-stack codegen scenarios.
- Opinionated multi-agent shape with named roles out of the box
- Good fit for end-to-end product specifications
- Active research-flavoured community
- The role-play metaphor leaks into APIs and prompts, which is rigid for non-software domains
- Configuration lives in Python classes, hot-reload requires process restarts
- Cost routing per role requires manual wiring
- No marketplace for sharing or installing agents one command at a time
What you get when you switch
Coordinator-plus-specialists without the role-play assumption
The same multi-agent shape, with no implicit metaphor. Agents are agents, you name them what they actually do.
Per-agent brain in a single YAML line
Coordinator on Sonnet, explorer on Haiku, writer on Sonnet, reviewer on DeepSeek. One config, one model: line per agent.
Hub for sharing whole agents
Push your agent to the Digitorn Hub. A teammate runs digitorn install hub://your-publisher/your-agent and they have it.
Install and run a real agent
# one-line install (Mac, Linux, Windows + Git Bash)
curl -sSL https://digitorn.ai/install | sh
# install a coding agent from the Hub
digitorn install hub://digitorn/digitorn-code
# chat with it
digitorn chat digitorn-codeHow to build a Claude Code clone in YAML
Get the next post in your inbox.
Engineering notes from the Digitorn team. No marketing, no launch announcements, no "10 prompts that will change your life". Just the things we write that we'd want to read.