Skip to main content
Back to Blog
OpenClaw
Sub-Agents
Orchestration

OpenClaw Sub-Agents: Parallel AI Task Execution

Learn how to use OpenClaw sub-agents for parallel task execution — spawning, managing, and orchestrating multiple AI workers.

February 14, 2026Luca Berton

What Are Sub-Agents?

Sub-agents are isolated AI sessions spawned by your main agent to handle tasks in parallel. Think of them as workers your agent delegates to.

Why Sub-Agents?

  • Parallelism — do multiple things simultaneously
  • Isolation — each sub-agent has its own context
  • Different models — use a cheaper model for simple tasks
  • Long-running tasks — don't block the main conversation

Spawning Sub-Agents

Your agent uses the sessions_spawn tool:

sessions_spawn(
  task: "Research the top 5 Kubernetes alternatives and summarize each",
  mode: "run"  // One-shot task
)

Run Mode vs Session Mode

  • run — complete the task and return results (one-shot)
  • session — persistent session for ongoing interaction

Real-World Examples

Research Assistant

"Spawn a sub-agent to research OpenClaw competitors. 
I need: name, pricing, key features, and limitations 
for each. Return a comparison table."

Code Review

"Spawn a sub-agent to review the PR diff I just pasted. 
Check for security issues, performance problems, and 
code style violations."

Content Generation

"Spawn 3 sub-agents:
1. Write a blog post about Docker networking
2. Write a blog post about Docker volumes  
3. Write a blog post about Docker security
Return all three when done."

Managing Sub-Agents

List Active Sub-Agents

subagents(action: "list")

Steer a Running Sub-Agent

subagents(action: "steer", target: "session-123", 
  message: "Also include pricing information")

Kill a Sub-Agent

subagents(action: "kill", target: "session-123")

Best Practices

  1. Clear task descriptions — sub-agents don't have your conversation context
  2. Set timeouts — prevent runaway tasks
  3. Use run mode for one-shot tasks — cleaner than persistent sessions
  4. Don't over-parallelize — each sub-agent uses API tokens
  5. Review results — sub-agents work independently, verify their output

Cost Considerations

Each sub-agent session uses LLM tokens. For cost efficiency: - Use cheaper models for simple tasks (model: "gpt-4o-mini") - Keep task descriptions concise - Set cleanup: "delete" to clean up after completion

Ready to Learn by Doing?

Go beyond blog posts with hands-on video courses. Build real projects with Docker, Ansible, Node.js, and more.