Building an AI Swarm with Multiple Ollama Models in OpenClaw

Ever wanted your AI agents to debate, collaborate, and iterate on answers using different models? This guide shows you how to set up a "swarm" of local Ollama models that work together on tasks — without running up API costs.

What is a Swarm?

In OpenClaw, a swarm isn't models chatting endlessly in an infinite loop. It's a structured pattern:

1. You give one kickoff prompt — defining the task and roles
2. OpenClaw spawns multiple sub-agents — each can use a different Ollama model
3. The main agent merges / critiques / iterates for a couple rounds
4. Then it stops — so you don't burn through compute or get stuck in loops

This pattern is great for tasks where multiple perspectives help: brainstorming, code review, research synthesis, or getting diverse writing styles.

Prerequisites

1. Get Ollama Running

Make sure Ollama is serving your models:

# Start Ollama server
ollama serve

# Check your available models
ollama list

2. Enable Ollama in OpenClaw

# Set the API key (OpenClaw uses this as a flag)
export OLLAMA_API_KEY="ollama-local"

# Verify OpenClaw can see your models
openclaw models list

Your Ollama models will appear as ollama/<model-tag> — for example, ollama/llama3.3, ollama/mistral, ollama/codellama, etc.

Docs: https://docs.openclaw.ai/providers/ollama

The Swarm Prompt Template

Here's a ready-to-use prompt. Just swap in your model names and task:

Monitoring Your Swarm

Watch what's happening with:

This shows all active sub-agents, their models, and current status.

Docs: https://docs.openclaw.ai/tools/subagents

Reality Check: What to Expect

Before you dive in, set expectations:

• Token/compute heavy — Running 4 local models simultaneously eats resources. Make sure your hardware can handle it.
• Local models are flaky with tool-calling — Ollama models work great for "text-only debate" (brainstorming, writing, reviewing). They're less reliable for tasks requiring browser automation or complex tool chains.
• Start simple — Get the basic swarm working before adding scheduled automation (heartbeats/crons).

Example Use Cases

Code Review Swarm:

• Model 1: Architect (high-level design review)
• Model 2: Security Auditor (vulnerabilities)
• Model 3: Performance Critic (efficiency)
• Model 4: Documentation Writer (explains the code)

Content Creation Swarm:

• Model 1: Researcher (gathers facts)
• Model 2: Writer (drafts content)
• Model 3: Editor (improves clarity)
• Model 4: Fact-Checker (verifies claims)

Next Steps

Once your basic swarm works, you can:

• Add sessions_spawn for programmatic control
• Use different thinking levels per agent (thinking: high for complex reasoning)
• Save swarm outputs to memory for long-running projects

Based on a discussion in the OpenClaw Discord #help channel. Thanks to community member rusher for the question!