How to Build Your First OpenClaw Swarm

One agent is useful. Multiple agents working together? That is a different beast.

A swarm — multiple AI agents coordinating on a single task — can handle far more complex workflows than any single agent. Each agent specializes. They hand off work. They collaborate.

Here is how to build your first OpenClaw swarm.

Why Swarms?

Single agents hit ceilings. They get confused with too many tools. They lose track of context. They try to do everything and do nothing well.

Swarms solve this. Split the work across specialized agents:

• One agent researches
• One agent writes
• One agent edits

Each does one thing. Together, they produce better output, faster.

The Basic Pattern

A swarm needs:

1. Agents — Each with a specific role
2. Handoffs — How agents pass work to each other
3. Shared context — What each agent knows about the overall task

Simple Swarm Example

Agent 1: Researcher
- Finds sources
- Extracts key points
- Hands to Agent 2

Agent 2: Writer  
- Takes research
- Writes draft
- Hands to Agent 3

Agent 3: Editor
- Reviews draft
- Suggests changes
- Returns to Writer or finalizes

This is a content pipeline. But swarms can do more: coding, data analysis, customer support escalation.

Key Principles

1. One Role Per Agent

Do not make agents do too much. A researcher should research. A writer should write. Specialization is the point.

2. Clear Handoffs

Define exactly when agents pass work. After research completes? After first draft? Ambiguity causes chaos.

3. Shared Memory

Agents need to know what came before. Use shared context — the swarm collective memory, not each agent isolated context.

4. Human Oversight

At critical points, bring in a human. Approval gates. Quality checks. Do not fully automate what needs judgment.

Building It in OpenClaw

OpenClaw makes this straightforward:

1. Define agents with specific roles
2. Connect them in a workflow
3. Add conditional routing
4. Set iteration limits

That is it. 50 lines of code gets you a production swarm.

Common Pitfalls

1. Over-specialization — Start with 2-3 agents, add complexity later
2. Vague roles — Be explicit in agent instructions
3. No limits — Set iteration limits to prevent infinite loops
4. Ignoring costs — Swarms multiply API calls
5. No testing — Test agents individually first

The Bottom Line

Swarms represent a shift in how we build AI systems. Instead of one agent doing everything, we design modular, collaborative systems.

Start simple. Let agents surprise you.