OpenClaw vs MetaGPT: Which AI Agent Framework Wins?

Quick Overview

OpenClaw is a configuration-first AI agent framework. You define agents using a SOUL.md markdown file and run them with the OpenClaw gateway. No coding required. MetaGPT is a code-first Python framework that simulates a software company by assigning GPT agents to roles like Product Manager, Architect, Engineer, and QA Engineer, following Standard Operating Procedures (SOPs) to turn a one-line requirement into working code.

The core difference: OpenClaw is a general-purpose agent framework where you can build any type of agent. MetaGPT is a specialized multi-agent system focused on automated software development. They serve fundamentally different audiences and use cases.

What is OpenClaw?

OpenClaw is an open-source AI agent framework built around the SOUL.md concept. A single markdown file defines everything about your agent: identity, personality, rules, skills, and communication behavior. You register the agent with the CLI, start the gateway, and your agent is live.

OpenClaw includes built-in channel integrations for Telegram, Slack, Discord, and Email. The framework runs locally through its gateway, supports multiple LLM providers (Claude, GPT-4, Gemini, Ollama), and uses a skills system for plug-and-play capabilities like web browsing and file management.

# 1. Create SOUL.md
cat > agents/researcher/SOUL.md << 'EOF'
# Research Analyst

## Identity
- Name: Researcher
- Role: SEO Research Analyst

## Personality
- Data-driven and thorough
- Presents findings with actionable recommendations

## Rules
- Always cite sources
- Focus on high-value keywords with search volume data
- Prioritize keywords by difficulty and opportunity

## Skills
- browser: Search the web for keyword data
EOF

# 2. Register and start
openclaw agents add researcher --workspace ./agents/researcher
openclaw gateway start

What is MetaGPT?

MetaGPT is an open-source multi-agent framework that models a software company. It assigns GPT-powered agents to defined roles (Product Manager, Architect, Project Manager, Engineer, QA Engineer) and coordinates them through Standard Operating Procedures (SOPs). You give MetaGPT a one-line requirement, and the agents collaborate to produce requirements documents, system designs, API specifications, and implementation code.

MetaGPT's key innovation is its SOP-based approach. Instead of letting agents chat freely, each role follows a structured workflow. The Product Manager writes a PRD. The Architect produces a system design. The Engineer writes code based on those documents. The QA Engineer generates tests. This mirrors how a real software team operates, with each role receiving structured input from the previous step.

import asyncio
from metagpt.roles import (
    ProjectManager,
    ProductManager,
    Architect,
    Engineer,
)
from metagpt.team import Team

async def main():
    team = Team()
    team.hire([
        ProductManager(),
        Architect(),
        ProjectManager(),
        Engineer(n_borg=5),
    ])

    team.invest(investment=3.0)  # budget in USD for API calls
    team.run_project(
        "Create a CLI tool that converts CSV files to JSON "
        "with support for nested objects and data validation"
    )

    await team.run(n_round=5)

asyncio.run(main())

The difference is clear. OpenClaw gets you to a running agent with a markdown file and two terminal commands for any use case. MetaGPT requires a Python environment, dependency installation, and Python code, but it produces a structured software development pipeline that generates real code from a single sentence.

Architecture and Design Philosophy

The architectural philosophies behind OpenClaw and MetaGPT are fundamentally different, and understanding them helps you decide which framework fits your needs.

OpenClaw: SOUL.md + CLI

OpenClaw treats agent creation as a configuration problem. You write a SOUL.md file in plain markdown that defines your agent's identity, personality, rules, and skills. The OpenClaw CLI registers your agent, and the gateway handles routing, session management, and channel integrations. This approach means anyone who can write English can build an agent. No programming language knowledge required.

OpenClaw's architecture is general-purpose. You can create a customer support agent, a content writer, a data analyst, or an SEO researcher. The framework does not impose a specific workflow pattern. You define what your agent does through its SOUL.md configuration.

MetaGPT: Python + SOPs + Roles

MetaGPT treats multi-agent collaboration as a software engineering process. It models a virtual software company where each agent has a specific role with defined inputs, outputs, and procedures. The SOP (Standard Operating Procedure) system ensures that agents produce structured artifacts (PRDs, designs, code) rather than free-form text.

MetaGPT's architecture includes a shared message pool, a subscription mechanism for inter-agent communication, and an executable feedback system that runs generated code and feeds errors back to the engineering agent. This makes MetaGPT excellent for code generation but also makes it more complex to set up and customize for non-software tasks.

Setup and Installation

The setup experience is where the two frameworks differ most dramatically.

# Install OpenClaw
npm install -g openclaw

# Create your agent and start
openclaw agents add myagent --workspace ./agents/myagent
openclaw gateway start

# Requires Python 3.9+
pip install metagpt

# Configure API keys (config2.yaml or environment variables)
# Set OPENAI_API_KEY or configure other providers
metagpt --init-config

# Edit ~/.metagpt/config2.yaml with your API keys and preferences
# Then write Python code to define your team and run it
python my_project.py

OpenClaw gets you from zero to a running agent in under 5 minutes. MetaGPT requires a Python environment, pip installation, YAML configuration for API keys, and writing Python code to define your team. If you are already working in Python, MetaGPT's setup is standard. If you are not a developer, OpenClaw removes every technical barrier.

Multi-Agent Support

Both frameworks are built for multi-agent collaboration, but they approach it from completely different angles.

OpenClaw: agents.md and @Mentions

OpenClaw manages multi-agent teams through an agents.md file and a natural @mention system. You list your agents and define handoff rules in plain English. When one agent needs to pass work to another, it uses an @mention in its response, and the gateway routes the message automatically.

# Content Team

## Agents
- @researcher: Finds information and compiles research
- @writer: Creates blog posts and articles from research
- @editor: Reviews and polishes final content

## Workflow
1. @researcher gathers data on the topic
2. @researcher hands off findings to @writer
3. @writer drafts the article
4. @writer hands off the draft to @editor
5. @editor reviews, polishes, and delivers final version

MetaGPT: SOP-Driven Role Collaboration

MetaGPT uses a structured SOP system where each role has defined actions and watches for specific message types from other roles. The Product Manager watches for user requirements and outputs a PRD. The Architect watches for PRDs and outputs a system design. The Engineer watches for designs and outputs code. This chain is automatic and does not require manual handoff configuration.

from metagpt.roles import Role
from metagpt.actions import Action

class AnalyzeData(Action):
    name: str = "AnalyzeData"
    async def run(self, data: str):
        prompt = f"Analyze this data and provide insights: {data}"
        result = await self._aask(prompt)
        return result

class DataAnalyst(Role):
    name: str = "DataAnalyst"
    profile: str = "Data Analyst"
    goal: str = "Analyze data and produce actionable insights"

    def __init__(self, **kwargs):
        super().__init__(**kwargs)
        self.set_actions([AnalyzeData])
        self._watch([UserRequirement])

OpenClaw's approach is simpler and faster to configure. MetaGPT's approach gives you more structured control over each step of the pipeline, with built-in artifact generation (documents, designs, code) at each stage. For software development workflows, MetaGPT's SOP system is more powerful. For general-purpose agent teams, OpenClaw's @mention system is more flexible.

Model Provider Support

Both frameworks let you choose which LLM powers each agent.

OpenClaw natively supports Anthropic (Claude), OpenAI (GPT-4), Google (Gemini), and Ollama for local models. You set the model provider in your SOUL.md file with a single line. OpenClaw's Ollama integration is particularly strong, making it easy to run agents entirely on local hardware with no API costs.

MetaGPT supports OpenAI (GPT-4, GPT-3.5), Anthropic (Claude), and other providers through its configuration system. You configure the LLM provider in MetaGPT's config2.yaml file or pass it programmatically. MetaGPT also supports using different models for different roles, so you can use a powerful model for the Architect role and a cheaper model for simpler tasks.

In practice, both frameworks support the models most teams use. OpenClaw's advantage is the simplicity of its one-line configuration. MetaGPT's advantage is the ability to assign different models to different roles within the same pipeline for cost optimization.

Channel Integration

This is where OpenClaw has a significant advantage. OpenClaw includes built-in integrations for Telegram, Slack, Discord, and Email. You enable a channel with a single line in your SOUL.md file, connect a bot token, and your agent is accessible from your phone or team workspace immediately.

MetaGPT does not include messaging channel integrations. It is designed as a development tool that runs from the command line or through its Python API. If you want to interact with MetaGPT agents through Telegram or Slack, you would need to build that integration layer yourself using a bot framework, host it separately, and wire it into MetaGPT's API.

If your agents need to be accessible through messaging platforms, OpenClaw handles this out of the box. MetaGPT is better suited for batch processing and development workflows where command-line interaction is sufficient.

Pricing

Both OpenClaw and MetaGPT are free and open-source. The real cost is the LLM API usage your agents consume.

MetaGPT's software development pipeline tends to consume more tokens per run because it generates multiple structured documents (PRD, system design, API specs, code, tests) across several agent steps. A single project run can use thousands of tokens across multiple LLM calls. MetaGPT includes a budget system (team.invest()) that lets you set a spending cap per project.

OpenClaw agents typically have lower per-interaction costs because they handle individual conversations rather than multi-step document generation pipelines. You can also eliminate API costs entirely by using Ollama for local model inference.

Neither framework charges a license fee. Your total cost depends on which LLM provider you use, how many tokens your agents consume, and whether you run local models.

Feature Comparison Table

Here is a side-by-side breakdown of how OpenClaw and MetaGPT compare across the features that matter most:

When to Choose OpenClaw

OpenClaw is the right choice when you need general-purpose agents running quickly without writing code:

When to Choose MetaGPT

MetaGPT is the right choice when you need automated software development with structured output:

Can You Use Both?

Yes, and because these two frameworks have almost no overlap in their primary use cases, combining them is a natural fit.

Use OpenClaw for agents that need to be accessible through messaging channels, require fast iteration on their configuration, or handle non-development tasks. Your content writer, customer support agent, research analyst, and project manager are great candidates for OpenClaw because they benefit from Telegram and Slack integration and do not require custom Python code.

Use MetaGPT for automated software development workflows. When your team needs to generate code from requirements, produce technical documentation, or run iterative debugging cycles, MetaGPT's SOP-based pipeline is purpose-built for that.

CrewClaw is designed to orchestrate agents from any framework in the same team. You can have an OpenClaw agent handling product research and communication alongside a MetaGPT pipeline generating the actual code, all coordinated through a single workflow.

Frequently Asked Questions