OpenClaw vs CrewAI: Which Multi-Agent Framework is Better?

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. CrewAI is a code-first Python framework where you define agents, tasks, and crews programmatically using Python classes.

The core difference: OpenClaw treats agent creation as a configuration problem. CrewAI treats it as a programming problem. Both are valid approaches that serve different audiences.

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 CrewAI?

CrewAI is an open-source Python framework for orchestrating autonomous AI agents. It uses a role-playing approach where each agent has a defined role, goal, and backstory. Agents are organized into "Crews" with tasks that execute in sequential or hierarchical process modes.

CrewAI leverages LiteLLM for broad model provider support, includes a tool system with a @tool decorator for custom Python functions, and provides memory capabilities for agents to retain context across interactions. CrewAI also offers an enterprise tier with a cloud platform for deployment and monitoring.

from crewai import Agent, Task, Crew, Process

researcher = Agent(
    role="SEO Research Analyst",
    goal="Find high-value keywords and analyze competitor content",
    backstory="You are an experienced SEO analyst who identifies "
              "keyword opportunities and content gaps.",
    tools=[search_tool],
    llm="gpt-4o",
    verbose=True
)

research_task = Task(
    description="Research {topic} and compile a detailed report "
                "with key findings, statistics, and sources.",
    expected_output="A structured research report with bullet points "
                    "and cited sources.",
    agent=researcher
)

crew = Crew(
    agents=[researcher],
    tasks=[research_task],
    process=Process.sequential,
    verbose=True
)

result = crew.kickoff(inputs={"topic": "AI agent frameworks 2026"})

The difference is clear. OpenClaw gets you to a running agent with a markdown file and two terminal commands. CrewAI requires a Python environment, dependency installation, and Python code. If you are a Python developer, CrewAI's setup feels natural. If you are not, OpenClaw removes the coding barrier entirely.

Multi-Agent Orchestration

Multi-agent orchestration is where both frameworks shine, and it is the primary reason people choose either one over a single-agent setup.

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 to the right agent 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

CrewAI: Crews with Process Modes

CrewAI provides structured orchestration through its Crew class. You define a process mode that determines how agents collaborate. In sequential mode, tasks execute one after another. In hierarchical mode, a manager agent delegates tasks to workers and reviews output. You can also define task dependencies using the context parameter.

from crewai import Agent, Task, Crew, Process

researcher = Agent(
    role="Researcher",
    goal="Find accurate data on the given topic",
    backstory="Expert research analyst with attention to detail.",
    llm="gpt-4o"
)

writer = Agent(
    role="Writer",
    goal="Create engaging content from research findings",
    backstory="Skilled content writer who turns data into stories.",
    llm="gpt-4o"
)

editor = Agent(
    role="Editor",
    goal="Polish content to publication quality",
    backstory="Experienced editor with an eye for clarity.",
    llm="gpt-4o"
)

research_task = Task(
    description="Research {topic} thoroughly.",
    expected_output="Detailed research notes with sources.",
    agent=researcher
)

writing_task = Task(
    description="Write a blog post based on the research.",
    expected_output="A complete blog post draft.",
    agent=writer,
    context=[research_task]
)

editing_task = Task(
    description="Edit and polish the blog post.",
    expected_output="Publication-ready blog post.",
    agent=editor,
    context=[writing_task]
)

crew = Crew(
    agents=[researcher, writer, editor],
    tasks=[research_task, writing_task, editing_task],
    process=Process.sequential
)

result = crew.kickoff(inputs={"topic": "AI agents in 2026"})

Both approaches produce a working multi-agent pipeline. OpenClaw's agents.md is faster to set up and easier to modify because it is just a markdown file. CrewAI's Python approach gives you programmatic control over task dependencies, conditional logic, and custom process flows that go beyond sequential handoffs.

Model Provider Support

Both frameworks let you choose which LLM powers each agent, but they differ in how broad that support is.

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. See our guide to running OpenClaw with Ollama for details.

CrewAI uses LiteLLM under the hood, which gives it access to a much wider range of providers. In addition to the major providers, you can use Mistral, Cohere, Azure OpenAI, Amazon Bedrock, Hugging Face, and dozens of others. You configure the model per agent using a string identifier.

In practice, both frameworks cover the models that handle the vast majority of use cases. OpenClaw's advantage is simplicity of configuration. CrewAI's advantage is breadth of provider support for teams with specific infrastructure requirements.

Community and Ecosystem

CrewAI has a large and active community. The GitHub repository has thousands of stars, an active Discord server, and a growing ecosystem of community-built tools and templates. CrewAI also has an enterprise offering (CrewAI Enterprise) that provides a cloud platform for deploying, monitoring, and managing crews at scale. The Python ecosystem integration means you can leverage existing libraries for web scraping, data analysis, API integrations, and more.

OpenClaw has a growing community with a focus on simplicity and accessibility. The framework is newer and smaller but has passionate users who value its no-code approach. OpenClaw's skills system provides plug-and-play capabilities that cover common agent needs, and the template library offers pre-built SOUL.md configurations for common roles. The community contributes agent templates, skills, and integrations through GitHub.

If community size and ecosystem maturity are your top priorities, CrewAI has a clear lead today. If you value simplicity and a no-code approach, OpenClaw's focused community provides excellent support and templates.

Feature Comparison Table

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

When to Choose OpenClaw

OpenClaw is the right choice when you want agents running quickly without writing code:

When to Choose CrewAI

CrewAI is the right choice when you need programmatic control over complex multi-agent workflows:

Migration Path: CrewAI to OpenClaw

If you are currently using CrewAI and want to try OpenClaw, the migration is straightforward for standard agent configurations. CrewAI's agent definition maps directly to OpenClaw's SOUL.md structure:

# CrewAI Python
researcher = Agent(
    role="SEO Research Analyst",
    goal="Find high-value keywords and analyze competitor content",
    backstory="You are an experienced SEO analyst who identifies "
              "keyword opportunities and content gaps.",
    tools=[search_tool, scraper_tool],
    llm="gpt-4o",
    verbose=True
)

# SEO Research Analyst

## Identity
- Name: Researcher
- Role: SEO Research Analyst
- Model: gpt-4o

## Personality
- Experienced and data-driven
- Identifies keyword opportunities and content gaps
- Presents findings with actionable recommendations

## Rules
- Find high-value keywords with search volume data
- Analyze competitor content for gaps
- Prioritize keywords by difficulty and opportunity

## Skills
- browser: Search the web for keyword data
- scraper: Extract content from competitor pages

The mapping is direct: CrewAI's role becomes the SOUL.md Identity section. The goal and backstory become the Personality and Rules sections. Tools map to Skills. The LLM model specification carries over unchanged.

The main limitation is custom Python tools. If your CrewAI agents rely on tools with significant custom Python logic, those do not have direct equivalents in OpenClaw's skills system. Consider a hybrid approach: use OpenClaw for agents that benefit from its simplicity and channels, and keep CrewAI for agents that require custom Python tooling.

Can You Use Both?

Yes, and for many teams this is the optimal approach. OpenClaw and CrewAI solve different parts of the multi-agent puzzle, and combining them gives you the best of both worlds.

Use OpenClaw for agents that need to be accessible through messaging channels, require fast iteration on their configuration, or are managed by non-technical team members. Your content writer, customer support agent, and social media manager are great candidates for OpenClaw because they benefit from Telegram and Slack integration and do not require custom code.

Use CrewAI for agents that need custom Python tools, complex orchestration logic, or integration with your existing Python infrastructure. Your data analyst that queries databases, your ML pipeline agent, and your custom API integration agent are better suited to CrewAI.

CrewClaw is designed to orchestrate agents from any framework in the same team. You can have OpenClaw agents handling content and communication alongside CrewAI agents handling data and code, all coordinated through a single workflow. Read more about agent-to-agent communication patterns.

Frequently Asked Questions