Agent Orchestrate

Multi-agent orchestration patterns for OpenClaw. Quick reference for spawning sub-agents, parallel work, and basic coordination. Use when: simple parallel tasks, fan-out/fan-in, basic pipelines. For advanced dynamic orchestration (agent-built task trees, spawn vs fork, human-in-the-loop), see cord-trees skill instead.

Agent Orchestration — Quick Reference

Simple patterns for multi-agent coordination. For advanced dynamic orchestration, see cord-trees.

Core Primitives

Tool	Purpose
sessions_spawn	Create isolated sub-agent with task
subagents list	Check status of running agents
subagents steer	Send guidance to running agent
subagents kill	Terminate an agent
sessions_send	Message another session

Spawn vs Fork

Two context strategies for sub-agents:

Spawn (Clean Slate)

Sub-agent gets only its task prompt. No parent context.

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Use when:
- Task is self-contained
- You want isolation (no context bleed)
- Subtask doesn't need sibling results
- Cheaper/faster (smaller context)

Example: "Research competitor X" — doesn't need to know about competitors Y and Z.

Fork (Context-Inheriting)

Sub-agent receives accumulated results from siblings.

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Use when:
- Synthesis/analysis across prior work
- Task builds on what others discovered
- Final integration step

Implementation: Include sibling results in the task prompt:

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Task: Synthesize findings into recommendation.

Prior research:
- Competitor A: [result from agent 1]
- Competitor B: [result from agent 2]
- Market trends: [result from agent 3]

Patterns

1. Parallel Fan-Out

Spawn N independent agents, wait for all to complete.

python

# Pseudocode
tasks = ["research A", "research B", "research C"]
for task in tasks:
    sessions_spawn(task=task, label=f"research-{i}")

# Poll until all complete
while not all_complete(subagents list):
    wait(30s)

# Collect results from session histories

See: references/fan-out.md

2. Pipeline (Sequential)

Each agent's output feeds the next.

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Agent 1: Research → 
  Agent 2: Analyze (using research) → 
    Agent 3: Write (using analysis)

Implementation: Spawn agent 1, wait for completion, spawn agent 2 with agent 1's result, etc.

See: references/pipeline.md

3. Dependency Tree

Tasks with explicit dependencies. Don't start X until Y completes.

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#1 Research API surface
#2 Research GraphQL tradeoffs  
#3 Analysis (blocked-by: #1, #2)
#4 Recommendation (blocked-by: #3)

Implementation: Track state in a JSON file. Poll and spawn when dependencies clear.

See: references/dependency-tree.md

4. Human-in-the-Loop

Pause workflow for human input at checkpoints.

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Agent 1: Draft proposal →
  [CHECKPOINT: Human approves/rejects] →
    Agent 2: Implement approved proposal

Implementation: Agent 1 completes, orchestrator messages human via sessions_send or channel message, waits for response before spawning agent 2.

5. Supervisor Pattern

Orchestrator monitors agents and intervenes when stuck.

python

while agents_running:
    status = subagents list
    for agent in status:
        if stuck_too_long(agent):
            subagents steer(target=agent, message="Try alternative approach...")
        if clearly_failed(agent):
            subagents kill(target=agent)
            # Retry or escalate

State Management

For complex orchestrations, track state in a file:

json

// orchestration-state.json
{
  "tasks": {
    "research-a": {"status": "complete", "result": "...", "sessionKey": "..."},
    "research-b": {"status": "running", "sessionKey": "..."},
    "synthesis": {"status": "blocked", "blockedBy": ["research-a", "research-b"]}
  }
}

Update after each spawn, completion check, or state change.

Best Practices

1. Label agents clearly — Use descriptive labels for subagents list readability
2. Set timeouts — Use runTimeoutSeconds to prevent runaways
3. Don't over-parallelize — More agents ≠ better. Consider token costs.
4. Checkpoint expensive work — Write intermediate results to files
5. Handle failures — Decide: retry, skip, or escalate to human
6. Keep tasks focused — One clear goal per agent. Easier to debug.

Anti-Patterns

❌ Polling in tight loops — Use reasonable intervals (30s+) ❌ Spawning agents for trivial tasks — Just do it yourself ❌ Giant context dumps — Summarize, don't copy entire histories ❌ No failure handling — Agents fail. Plan for it.

Choosing a Pattern

Situation	Pattern
N independent research tasks	Fan-out
Step A → Step B → Step C	Pipeline
Complex task with prerequisites	Dependency tree
Need human approval mid-flow	Human-in-the-loop
Long-running with potential issues	Supervisor
Simple one-off subtask	Just spawn one agent

Quick Reference

bash

# Spawn a sub-agent
sessions_spawn(task="Do X", label="my-task", runTimeoutSeconds=300)

# Check status
subagents(action="list")

# Send guidance
subagents(action="steer", target="my-task", message="Focus on Y instead")

# Kill runaway
subagents(action="kill", target="my-task")