OpenClaw Subagents Explained: How to Run Multiple AI Agents in Parallel

What Are OpenClaw Subagents?

OpenClaw's subagent system lets your primary agent spawn additional AI agents — each with their own session, model, memory context, and set of instructions. These subagents can run in parallel, allowing your main agent to delegate tasks and collect results without doing everything sequentially.

OpenClaw v2026.2.17 added a new /subagents spawn chat command for deterministic subagent activation, alongside several improvements to subagent context management and model patching.

When to Use Subagents

Subagents are most valuable when:

• Tasks are parallelizable — Research three topics simultaneously instead of one at a time
• Specialization matters — A coding agent, a research agent, and a writing agent can each be configured with the right model, tools, and system prompt for their domain
• Context isolation is needed — Subagents have their own context windows, preventing one task's output from polluting another's context
• You need coordination without bottlenecks — The STATE.yaml pattern (see Autonomous Project Management use case) allows subagents to coordinate without a constant orchestrator overhead

The /subagents spawn Command

The new /subagents spawn command (v2026.2.17) provides a direct way to activate subagents from chat:

/subagents spawn --task "Research the top 5 competitors of [company]" --model anthropic/claude-sonnet-4-6

This creates a new session running in parallel with your current conversation. The subagent executes its task and the result is returned to your main session when complete.

sessions_spawn: The Programmatic API

For more complex orchestration, OpenClaw exposes sessions_spawn as a tool your agent can call programmatically:

{
  "tool": "sessions_spawn",
  "params": {
    "task": "Analyze sentiment of these 50 customer reviews and return a JSON summary",
    "model": "anthropic/claude-haiku-4-5",
    "sessionKey": "sentiment-analysis-job-1"
  }
}

Important notes from the v2026.2.17 release:

• If sessions_spawn is called for a one-off polling subagent, the tool returns an accepted-response note explaining that polling is disabled for one-off calls
• Spawned subagent task messages are prefixed with context to preserve source information in downstream handling
• If subagent model patching fails, sessions_spawn now fails explicitly rather than silently degrading

Subagent Model Configuration

One of the most powerful features of subagents is per-agent model selection. Your main agent might use Claude Sonnet 4.6 for reasoning, but each subagent can use a different model optimized for its specific task:

subagents:
  model: anthropic/claude-haiku-4-5  # Default model for spawned subagents
  defaults:
    maxTurns: 20
    timeout: 300

This lets you run expensive tasks on a capable model while using cheaper, faster models for high-volume subtasks.

Context Management in Subagents

Subagents have their own context windows, but they can overflow if tasks are too large. The v2026.2.17 release added several improvements:

• Pre-emptive context guarding — Before each model call, accumulated tool-result context is checked and oversized outputs are truncated or compacted to avoid context-window crashes
• Auto-paging read tool — When a subagent reads files, the read tool now auto-pages across chunks and scales its output budget from the model's context window, so larger contexts can read more before compaction kicks in
• Explicit recovery guidance — When a subagent encounters a [compacted: tool output removed] marker, it now has explicit instructions to re-read with smaller chunks rather than repeating full-file reads

A Real Multi-Agent Workflow Example

Here's a pattern that works well for content research:

1. Orchestrator agent — Receives the topic, spawns 3 research subagents in parallel
2. Research subagent 1 — Searches Reddit and HN for community discussion
3. Research subagent 2 — Searches academic sources and industry reports
4. Research subagent 3 — Analyzes competitor content on the topic
5. Orchestrator agent — Collects all three results and synthesizes into a final report

This runs 3–4× faster than sequential research and produces richer output because each subagent can go deeper without worrying about the others' context.

The Complexity Tradeoff

Subagents are powerful but add configuration complexity. You need to:

• Define the right task boundaries and handoff format
• Configure per-subagent model and memory settings
• Handle failures — what happens when a subagent times out or errors?
• Manage token costs across multiple parallel sessions

Getting this right requires understanding OpenClaw's session model, context management, and subagent config schema — which is one of the areas where expert setup adds the most value.