A specialized framework for designing and managing collaborative multi-agent systems with advanced task decomposition and state management.
Install & Download
1. ClawHub CLI
The fastest way to install a skill directly from the registry.
npx clawhub@latest install skylv-agent-team-coordinator
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-agent-team-coordinator 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 sophisticated framework designed to manage the lifecycle of complex AI agent collaborations. By utilizing this within your collection of Openclaw Skills, developers can transition from single-prompt interactions to distributed agent networks. The system acts as a central brain that decomposes high-level goals into a Directed Acyclic Graph (DAG) of sub-tasks, assigning each to specialized agents—such as researchers, coders, or critics—based on their specific capabilities and LLM configurations.
This orchestration layer ensures that state is synchronized across the fleet and that dependencies are resolved in the correct sequence. It provides a robust architecture for building autonomous workflows where agents can communicate via a central message bus, participate in role-playing scenarios, or engage in multi-round debates to arrive at optimized solutions.
Multi-Agent Orchestrator Use Cases
- Complex project planning and execution requiring specialized domain expertise
- Autonomous research workflows involving data collection, analysis, and multi-stage reporting
- Automated software development lifecycles using the Planner-Coder-Reviewer pattern
- Multi-perspective analysis or debate mode for unbiased decision-making and quality control
- Real-time event-driven agent responses using the internal message bus architecture
How Multi-Agent Orchestrator Works
- Goal Decomposition: The orchestrator uses a specialized router to break down a user's high-level goal into discrete, actionable sub-tasks formatted as a JSON array.
- DAG Construction: It builds a Directed Acyclic Graph to represent task dependencies, ensuring tasks are executed in the correct logical sequence.
- Dynamic Scheduling: The scheduler manages a pool of tasks, executing independent tasks in parallel while respecting maximum concurrency limits and waiting for dependencies to resolve.
- Agent Execution: For each task, a specialized agent is invoked with the specific context derived from the outputs of its preceding dependencies.
- State Management: The system monitors agent states (idle, thinking, acting) and handles transitions to ensure reliability throughout the lifecycle.
- Result Summarization: Once all tasks are completed, the system aggregates the outputs into a final, coherent response for the user.
Multi-Agent Orchestrator Setup
To integrate this capability into your project, follow the standard installation process for Openclaw Skills:
# Install the multi-agent-orchestrator skill
openclaw install multi-agent-orchestrator
# Configure your environment variables
export OPENAI_API_KEY='your_api_key_here'
Ensure your configuration defines the specialized agents and their specific roles (e.g., planner, executor, critic) within the setup manifest to allow the orchestrator to route tasks correctly.
Multi-Agent Orchestrator Data Schema & Taxonomy
The Multi-Agent Orchestrator organizes data using the following primary structures to maintain consistency across Openclaw Skills:
| Object | Description |
|---|---|
| AgentConfig | Defines name, role, capabilities, LLM parameters, and tool definitions. |
| TaskResult | Tracks agentId, execution status (pending/running/done/failed), and resulting output. |
| Message | Structured data for the bus containing type (request/event), source, and payload. |
| AgentSession | Maintains the lifecycle state (idle/thinking/waiting) and shared contextual history. |
Multi-Agent Orchestrator Advanced Features
- Directed Acyclic Graph (DAG) task management for complex, multi-stage dependency resolution
- Integrated Message Bus supporting Broadcast, Point-to-Point, and Pub/Sub communication patterns
- Formal State Machine with transition hooks for tracking agent status and handling error recovery
- Blackboard pattern support for shared knowledge spaces during collaborative reasoning sessions
- Built-in circuit breaker and timeout mechanisms for resilient, production-grade agent operations
SKILL.md
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