A comprehensive framework for implementing multi-agent coordination patterns, enabling parallel task execution and complex agentic workflows within Openclaw Skills environments.
Install & Download
1. ClawHub CLI
The fastest way to install a skill directly from the registry.
npx clawhub@latest install agent-orchestrate
2. Manual Installation
Copy the skill folder to one of these locations
~/.openclaw/skills/ <project>/skills/ Priority: Workspace > Local > Bundled
3. Prompt Installation
Copy this prompt to OpenClaw to install it automatically.
Help me install agent-orchestrate using Clawhub. If Clawhub is not installed, install it first (npm i -g clawhub).
Prefer to download?
Get the raw skill files in a ZIP archive.
What is Agent Orchestration?
Agent Orchestration provides a technical blueprint for managing multiple AI sub-agents to solve intricate problems that exceed the capabilities of a single-agent run. By utilizing specialized Openclaw Skills primitives, developers can implement sophisticated strategies like spawning isolated sub-agents for clean-slate research or forking existing contexts to synthesize data across multiple workstreams.
This skill focuses on moving beyond basic prompting into the realm of agentic systems architecture. It provides the logic for steering, monitoring, and terminating agents, ensuring that complex multi-step processes are handled with precision. Whether you are building an automated research department or a sequential coding pipeline, these orchestration patterns provide the necessary structure for resilient and scalable AI operations.
Agent Orchestration Use Cases
- Executing parallel research tasks where multiple independent sub-agents analyze different data sources simultaneously.
- Building sequential processing pipelines where the output of a research agent feeds directly into a synthesis agent.
- Managing complex project trees with explicit dependencies where specific tasks are blocked until prerequisites are met.
- Implementing human-in-the-loop checkpoints to pause autonomous workflows for manual approval or redirection.
- Deploying supervisor agents that monitor long-running processes to intervene when sub-agents become stuck or deviate from the objective.
How Agent Orchestration Works
- Determine the orchestration pattern required for the task, such as a parallel fan-out for breadth or a sequential pipeline for depth.
- Initialize sub-agents using the sessions_spawn tool, assigning clear labels and specific task prompts to ensure focused execution.
- Track agent progress by polling the subagents list to monitor status and identify potential bottlenecks or failures.
- Apply corrective steering using the subagents steer command if an agent requires mid-run guidance or alternative approaches.
- Consolidate results from various session histories once all sub-tasks in the orchestration tree reach a terminal state.
Agent Orchestration Setup
To utilize orchestration patterns within your Openclaw Skills environment, ensure your agent has access to the core session management tools. You can begin spawning and managing agents using basic CLI commands:
# Initialize a new sub-agent with a specific timeout
sessions_spawn(task="Perform security audit", label="audit-01", runTimeoutSeconds=300)
# List all active sub-agents to check their current status
subagents(action="list")
# Provide mid-run guidance to a specific agent
subagents(action="steer", target="audit-01", message="Focus specifically on SQL injection vulnerabilities")
Agent Orchestration Data Schema & Taxonomy
For complex workflows, it is recommended to maintain an orchestration-state.json file to manage the lifecycle of various tasks. The following structure is standard for Openclaw Skills orchestration:
| Property | Description |
|---|---|
tasks |
A collection of objects representing individual sub-agent goals and states. |
status |
The execution state of the task (e.g., complete, running, blocked, failed). |
sessionKey |
The unique identifier used to retrieve the specific agent's interaction history. |
blockedBy |
An array of task labels that must be completed before the current task can be initialized. |
result |
A summary or reference to the data produced by the completed sub-agent. |
Agent Orchestration Advanced Features
- Context Strategy Selection: Toggle between Spawn for isolated, cost-effective tasks and Fork for context-heavy synthesis work.
- Autonomous Supervisor Pattern: Implement logic loops that detect stuck agents and automatically trigger corrective steering or restarts.
- Dependency Tree Management: Build complex, multi-stage workflows where tasks are triggered automatically as their dependencies resolve.
- Resource Optimization: Use explicit timeouts and focused task prompts to minimize token consumption across large-scale Openclaw Skills deployments.
- Multi-Agent Messaging: Facilitate direct communication between active sessions using the sessions_send primitive for collaborative problem-solving.
SKILL.md
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