A sophisticated orchestration framework designed to coordinate specialized AI agents through task decomposition, state management, and structured communication patterns.
Install & Download
1. ClawHub CLI
The fastest way to install a skill directly from the registry.
npx clawhub@latest install skylv-multi-agent-orchestrator
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 skylv-multi-agent-orchestrator 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 Multi-Agent Orchestrator?
The Multi-Agent Orchestrator is a robust framework for designing and managing collaborative AI systems. As a key component in the Openclaw Skills ecosystem, it enables developers to move beyond single-prompt interactions and create complex workflows where multiple agents with distinct roles—such as planners, executors, and critics—work together.
By leveraging this skill, users can implement advanced architectural patterns like Directed Acyclic Graphs (DAGs) for task scheduling and a centralized Message Bus for inter-agent communication. This ensures that complex goals are broken down into manageable sub-tasks, executed in the correct order, and synthesized into a high-quality final output.
Multi-Agent Orchestrator Use Cases
- Orchestrating multi-step software development lifecycles involving coding, testing, and security auditing agents.
- Implementing competitive reasoning frameworks where agents debate topics to reach a refined conclusion.
- Automating content production pipelines that require a sequence of research, drafting, and editorial review.
- Managing high-concurrency task execution where sub-tasks have complex interdependencies.
How Multi-Agent Orchestrator Works
- Task Decomposition: The Orchestrator receives a high-level goal and uses an LLM-based router to break it down into a JSON array of specific sub-tasks.
- DAG Construction: A Directed Acyclic Graph is generated to define the execution order and resolve dependencies between tasks.
- Agent Registration: Specialized agents are configured with specific roles, instructions, and tools before being registered to the system.
- Parallel Scheduling: The orchestrator dispatches tasks to available agents, managing concurrency limits and waiting for required dependency outputs.
- Communication & State: Agents communicate via a Message Bus using patterns like Broadcast or Pub/Sub, while a State Manager tracks transitions between idle, thinking, and acting states.
- Synthesis: Once all nodes in the task graph are completed, the system summarizes the collective results into a final response.
Multi-Agent Orchestrator Setup
To integrate this capability into your workflow, follow these steps to set up the Openclaw Skills component:
- Install the skill via your terminal:
openclaw install multi-agent-orchestrator
- Configure your environment variables to include necessary API keys:
export OPENAI_API_KEY='your-api-key-here'
- Define your agent configurations in a TypeScript or JSON format, ensuring each agent has a clear role and set of capabilities.
Multi-Agent Orchestrator Data Schema & Taxonomy
The Multi-Agent Orchestrator organizes data through several core interfaces to ensure consistency across the Openclaw Skills environment:
| Object | Key Fields | Purpose |
|---|---|---|
| AgentConfig | name, role, capabilities, llm, tools, instructions | Defines the identity and boundaries of an agent. |
| TaskResult | agentId, status, output, dependencies, startTime, endTime | Tracks the lifecycle and data output of individual sub-tasks. |
| AgentSession | id, state, currentTask, history, sharedContext | Manages the persistent state and memory of an agent during execution. |
| Message | type (request/event), from, to, content, timestamp | Standardizes the format for inter-agent communication. |
Multi-Agent Orchestrator Advanced Features
- Support for multiple communication modes including Blackboard for shared reasoning and Pub/Sub for event-driven updates.
- Advanced orchestration patterns such as Role Play and multi-round Debate logic.
- Built-in circuit breaker mechanisms to prevent system hang-ups when an agent fails repeatedly.
- Granular state machine hooks to trigger specific actions during state transitions (e.g., from thinking to error).
- Observable logging and execution tracing for every node in the task graph.
SKILL.md
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