Edict vs MetaGPT
Which should you use? A side-by-side look at what each one does, who it suits, what it costs, how hard it is to set up, and where each falls short.
Open source · MIT
Multi-agent AI system modeled on China's Tang-era governance, with a live task dashboard, requires OpenClaw.
Open source · MIT
A framework where several LLM agents play software-company roles to build software from one sentence.
The short answer
Choose Edict if you need…
- Developers already running OpenClaw who want a multi-agent workflow with visible task history
- People who want a review step before agent output is executed, as the README describes
- Teams who want to watch and pause agent tasks from a dashboard
- Users who want to switch the LLM model used by each agent from a UI
Choose MetaGPT if you need…
- Developers experimenting with multi-agent LLM workflows
- Researchers interested in agent collaboration using standard operating procedures
- Students exploring how role-based AI teams are built
- People who want a starting-point project to generate small software prototypes
Side-by-side
| Edict | MetaGPT | |
|---|---|---|
| Type | AI model / agent | Framework |
| Price | Free and open source | Free and open source |
| Licence | MIT | MIT |
| GitHub stars | 17k | 71.1k |
| Setup difficulty | Advanced | Intermediate |
| How you get it | Clone and build from source | Install with a package manager |
| Main language | Python | Python |
| Last code update | 39 days ago | 10 months ago |
What each one is
Edict
Edict is a set of about 12 AI agents arranged in roles borrowed from an ancient Chinese imperial administration: one sorts incoming requests, others plan, review, and dispatch work, and six execution departments carry it out. A web dashboard shows tasks on a kanban board, agent health, token usage, and an audit trail of each task's path. It is built to run on top of OpenClaw, which the README lists as a requirement.
MetaGPT
MetaGPT is a Python framework that assigns different roles, such as product manager, architect, project manager, and engineer, to LLM-based agents. You give it a one-line requirement and the agents work through a software-company style process to produce artifacts like user stories, requirements, data structures, APIs, and code. It is used by people who want to experiment with multi-agent collaboration on complex tasks.
Best for
Edict
- Developers already running OpenClaw who want a multi-agent workflow with visible task history
- People who want a review step before agent output is executed, as the README describes
- Teams who want to watch and pause agent tasks from a dashboard
- Users who want to switch the LLM model used by each agent from a UI
MetaGPT
- Developers experimenting with multi-agent LLM workflows
- Researchers interested in agent collaboration using standard operating procedures
- Students exploring how role-based AI teams are built
- People who want a starting-point project to generate small software prototypes
Strengths
Edict
- Dashboard shows task status, heartbeats, and full task history in one place
- README describes a review stage that can send work back for rework before execution
- Configuration is set up by an install script and can run as a systemd service
MetaGPT
- Clear, documented multi-role design built around a software-company process
- Supports several LLM providers through a single YAML configuration file
- Open source under the MIT license with an active project and a large star count
Not ideal for
Edict
- People without OpenClaw installed, since the README lists it as required and the dashboard depends on it for live data
- Non-technical users, because setup involves shell scripts, API keys, and an agent configuration
- Anyone needing Windows support, since the README lists macOS and Linux as the prerequisites
MetaGPT
- Users who need reliable, production-ready code without reviewing output, since the README does not make such guarantees
- People without an LLM API key or local model setup, since configuration is required before use
- Users who want a no-code, point-and-click tool; the README centers on CLI and Python usage
Trade-offs
Edict
- Depends on OpenClaw, so it is not useful on its own without that separate system
- README is written mainly in Chinese, and the English README is a separate file the excerpt did not include
- No formal release is listed, and some claims in the README (such as comparisons with other frameworks) are the author's own
MetaGPT
- Output quality depends on the underlying LLM and API costs are paid by the user
- The README requires a specific Python version range and Node.js with pnpm, which adds setup steps
- Documentation is spread across an external site, so some details are not in the README itself
Getting started
Edict
Clone the repository
git clone https://github.com/cft0808/edict.gitEnter the project folder
cd edictMetaGPT
Install from PyPI
pip install --upgrade metagptCreate initial config file
metagpt --init-configFrequently asked questions
What is the difference between Edict and MetaGPT?
Edict: Multi-agent AI system modeled on China's Tang-era governance, with a live task dashboard, requires OpenClaw. MetaGPT: A framework where several LLM agents play software-company roles to build software from one sentence. Edict suits developers already running openclaw who want a multi-agent workflow with visible task history, while MetaGPT suits developers experimenting with multi-agent llm workflows.
Are Edict and MetaGPT free?
Edict: Free and open source. MetaGPT: Free and open source.
Which is easier to set up, Edict or MetaGPT?
MetaGPT is easier to get started with (rated intermediate); Edict is rated advanced and usually needs more setup.
Is Edict or MetaGPT more popular?
MetaGPT has more GitHub stars (71.1k) than Edict (17k). Popularity isn't the same as fit — check which one matches your use case above.
What are other alternatives to Edict and MetaGPT?
Other options people compare them with include CrewAI, SuperAGI, DeerFlow, Ponytail, Hermes Agent.
Keep comparing
Built from our catalogue entries for both products: open-source details come from each project's README (AI-assisted, refreshed weekly) and GitHub data; AI-tool details are editorial. Spotted something wrong? Let us know from either product's page.