OpenClaw Multi-Agent Setup: How to Run Multiple AI Agents Together

Why Run Multiple Agents?

A single AI agent is powerful for focused, well-defined tasks. Ask it to write a blog post and it delivers. Ask it to research a topic and summarize findings and it performs well. But the moment your workflow requires more than one skill — researching a keyword landscape, writing SEO-optimized content based on those findings, then editing the draft for tone and accuracy — a single agent starts to struggle. Its context window fills up with mixed concerns, it loses focus, and quality drops across the board.

The solution is the same one that works for human teams: division of labor. Instead of asking one generalist agent to do everything, you create specialist agents that each handle a single domain well. A content writer agent focuses on producing clean, engaging prose. An SEO analyst agent focuses on keyword density, meta descriptions, and internal linking. An editor agent checks for consistency, grammar, and brand voice. Each agent maintains a tight context focused on its specialty, which leads to significantly better output than a single agent trying to juggle all of these concerns at once.

The OpenClaw multi-agent framework makes this practical. You define each agent with its own SOUL.md file, register them in a central agents.md configuration, and start a shared gateway that brings them all online. Agents can communicate with each other through @mentions, pass tasks through structured handoffs, and operate on different language models tuned to their role. The result is an AI team that mirrors how effective human teams work — specialists collaborating, not one person doing everything alone.

OpenClaw Multi-Agent Architecture

The OpenClaw multi-agent framework is built around three core concepts: agent directories, SOUL.md files, and the agents.md registry. Each agent lives in its own directory with its own SOUL.md file that defines its role, personality, rules, tools, and handoff instructions. The agents.md file in your project root serves as the central roster — it lists every agent, points to their SOUL.md, and specifies which model each one should use. When you start the OpenClaw gateway, it reads agents.md, initializes each agent, and opens communication channels between them.

The directory structure for a typical OpenClaw multi-agent setup looks like this:

project/
├── agents.md              # Central agent registry
├── agents/
│   ├── writer/
│   │   └── SOUL.md        # Writer agent config
│   ├── researcher/
│   │   └── SOUL.md        # Research agent config
│   └── seo-analyst/
│       └── SOUL.md        # SEO agent config
└── shared/
    └── knowledge/         # Shared knowledge base

Each agent directory is self-contained. The SOUL.md file inside it defines everything the agent needs to know about itself — its role, how it communicates, what tools it can use, and when it should hand work off to another agent. The shared directory is optional but useful for files that multiple agents need to access, such as brand guidelines, style guides, or research databases. This separation keeps agents focused while giving them access to common resources when needed.

Step-by-Step: Setting Up Multiple Agents

Follow these five steps to go from zero to a running OpenClaw multi-agent team. Each step builds on the previous one, and by the end you will have multiple agents online and ready to collaborate.

mkdir -p agents/writer agents/researcher agents/seo-analyst

# ContentWriter

## Role
You are a content writer. You produce blog posts,
articles, and marketing copy based on briefs
provided by the team.

## Rules
- ALWAYS respond in English
- Target 1,200-1,800 words for blog posts
- Use H2 headers every 200-300 words
- Write in active voice

## Handoffs
- When you need keyword data, ask @SEOAnalyst
- When draft is complete, hand off to @SEOAnalyst
  for optimization

# Researcher

## Role
You are a research specialist. You gather data,
analyze competitor content, and compile research
briefs for the writing team.

## Tools
- Use Browser to search the web and verify facts
- Use FileSystem to save research summaries

## Handoffs
- When research is complete, hand off the brief
  to @ContentWriter

# SEOAnalyst

## Role
You are an SEO specialist. You analyze keyword
opportunities, optimize content for search engines,
and ensure every published piece meets SEO targets.

## Rules
- Include primary keyword in title, H1, and
  first 100 words
- Meta descriptions must be under 155 characters
- Suggest 3-5 internal links per article

## Handoffs
- When content is fully optimized, notify
  @ContentWriter that the piece is ready
  for publishing

# Agents

## ContentWriter
- soul: agents/writer/SOUL.md
- model: claude-sonnet-4-20250514
- description: Writes blog posts and articles

## Researcher
- soul: agents/researcher/SOUL.md
- model: claude-haiku-4-20250414
- description: Gathers data and compiles briefs

## SEOAnalyst
- soul: agents/seo-analyst/SOUL.md
- model: gpt-4o
- description: Optimizes content for search

openclaw gateway start

# Output:
# ✓ ContentWriter loaded (claude-sonnet-4-20250514)
# ✓ Researcher loaded (claude-haiku-4-20250414)
# ✓ SEOAnalyst loaded (gpt-4o)
# Gateway running on port 18789
# 3 agents online

Agent-to-Agent Communication

Once multiple agents are online, they need a way to talk to each other. OpenClaw agent-to-agent communication works through the gateway message bus. Every message an agent sends passes through the gateway, which inspects it for @mentions and handoff instructions. When the gateway detects an @mention — like @SEOAnalyst — it routes the message to the target agent. This creates a transparent communication layer where every inter-agent interaction is logged and observable.

The most common communication pattern is the task handoff. When one agent completes its portion of work, it signals the gateway to pass the output to the next agent. Here is what that looks like in practice:

# ContentWriter completes a draft and hands off:

ContentWriter → Gateway:
  "Draft complete for 'OpenClaw Setup Guide'.
   1,450 words, 6 H2 sections.
   @SEOAnalyst please optimize this draft
   for target keyword 'openclaw multi-agent
   framework'."

# Gateway routes to SEOAnalyst:

SEOAnalyst receives:
  - Full draft text
  - Target keyword
  - Writer's notes

SEOAnalyst → Gateway:
  "Optimization complete. Added keyword to
   title, H1, and intro. Suggested 4 internal
   links. Meta description: 153 chars.
   @ContentWriter draft is ready for publish."

This pattern scales naturally. You can chain as many agents as your workflow requires. The Researcher hands off to the ContentWriter, who hands off to the SEOAnalyst, who hands off to a Publisher agent. Each agent sees only the context it needs, which keeps its work focused and its output high-quality. The gateway maintains the full thread of communication, so you can review the entire chain of interactions at any time.

Communication Patterns

Different workflows call for different communication patterns. The OpenClaw multi-agent framework supports three primary patterns, and most real-world setups combine two or more of them depending on the task at hand.

The sequential pipeline is the easiest to set up and the most predictable. Start here if you are new to openclaw multiple agents setups. Parallel fan-out is useful when you need to gather information from multiple sources simultaneously — for example, having three research agents each analyze a different competitor. The supervisor pattern is the most flexible but requires a well-configured PM agent with clear rules for when to delegate to which specialist. Many teams start with a sequential pipeline and evolve into a supervisor pattern as their workflows become more complex.

Orchestrating with CrewClaw

The OpenClaw gateway and agents.md give you a solid foundation for running multiple agents from the command line. But as your agent team grows beyond two or three agents, you start needing visibility and control that the CLI alone cannot provide. That is where CrewClaw comes in. CrewClaw is a visual orchestration platform designed to manage multi-agent teams regardless of which framework built them.

For OpenClaw users, CrewClaw adds several capabilities on top of the existing agent setup. The dashboard shows real-time agent status — which agents are online, idle, or actively processing a task. The crew chat interface displays all agent-to-agent communication in a single feed, so you can watch your agents collaborate in real time. Task management lets you create, assign, and track tasks with progress indicators and completion status. And the skill.md system allows agents from different frameworks to share capabilities through a standardized interface.

The most powerful feature for openclaw agent orchestration is cross-framework connectivity. CrewClaw is not limited to OpenClaw agents. You can connect a LangChain agent, a CrewAI agent, and a custom API endpoint alongside your OpenClaw agents in the same crew. The orchestration layer handles translation between frameworks, so a LangChain research agent can hand off findings to an OpenClaw writing agent without either one knowing about the other's internals. This means you can pick the best tool for each role instead of being locked into a single framework for your entire team.

Best Practices

After working with OpenClaw multiple agents configurations, a set of patterns consistently produces better results. These practices apply whether you are running two agents or ten.

Frequently Asked Questions