#OpenClaw Sub-Agents: How to Run Parallel AI Workflows

What if your AI agent could clone itself?

That's essentially what sub-agents do. Instead of handling one task at a time, OpenClaw can spawn multiple agents to work in parallel—then combine their results.

#The Problem: Sequential Bottlenecks

Without sub-agents, your agent works like this:

1. Research competitor A → 5 minutes
2. Research competitor B → 5 minutes
3. Research competitor C → 5 minutes
4. Compare all three → 3 minutes

Total: 18 minutes

With sub-agents:

1. Research A, B, and C simultaneously → 5 minutes
2. Compare all three → 3 minutes

Total: 8 minutes

That's a 55% reduction. And the savings scale with task complexity.

#How Sub-Agents Work

Your main agent acts as an orchestrator. It:

1. Decomposes a complex task into smaller pieces
2. Spawns sub-agents to handle each piece
3. Monitors their progress
4. Combines their results into a final output

Each sub-agent runs in its own isolated session with its own context and model.

#Real Example: Parallel Code Review

Say you want a thorough PR review. Instead of one agent checking everything sequentially, spawn four specialists:

• Security Agent: Check for vulnerabilities, injection risks, secrets
• Performance Agent: Look for N+1 queries, missing indexes, bottlenecks
• Style Agent: Naming conventions, code organization, readability
• Test Agent: Coverage gaps, edge cases, missing assertions

Each runs for 2 minutes. You get a comprehensive review in 2 minutes instead of 8.

#When to Use Sub-Agents

Great for:

• Research across multiple sources
• Code review (security, performance, style in parallel)
• Bulk content generation
• Multi-step analysis with independent steps
• Competitive analysis

Not worth it for:

• Simple questions
• Tasks that need sequential context
• Quick one-off requests
• When budget is very tight

#Cost Considerations

Sub-agents multiply your API usage. Smart model selection keeps costs down:

Rule of thumb: Use the cheapest model that can handle the task. Haiku 4 for summaries, Sonnet 4.6 for most work, Opus 4.6 only for complex reasoning.

#Getting Started

The simplest sub-agent spawn:

"Break this research into 3 parallel tasks and use sub-agents to research them simultaneously."

OpenClaw handles the rest—spawning agents, monitoring progress, and combining results.

For more control, you can specify models, timeouts, and cleanup strategies in your prompts.

#What's Next?

Sub-agents are one of OpenClaw's most powerful features, but they take practice to use effectively. Common mistakes include over-parallelizing (spawning too many agents) and using expensive models for simple tasks.

Want the complete playbook? Our premium Sub-Agents Mastery guide covers fan-out patterns, error handling, cost optimization, and 5 real-world workflow templates you can copy today.

What would you parallelize first?