A sophisticated orchestration designer for managing multi-agent collaboration, state transitions, and complex task routing.
Install & Download
1. ClawHub CLI
The fastest way to install a skill directly from the registry.
npx clawhub@latest install 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 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 robust framework designed to coordinate multiple specialized AI agents within the Openclaw Skills ecosystem. It provides a structured way to decompose high-level goals into executable sub-tasks, ensuring each is handled by the most capable agent while maintaining overall state consistency.
By utilizing a Directed Acyclic Graph (DAG) for task scheduling and a centralized Message Bus for communication, this skill enables seamless interaction between agents. Whether you are building a research pipeline or a complex software development workflow, it provides the necessary infrastructure for scalable AI autonomy.
Multi-Agent Orchestrator Use Cases
- Automating complex research tasks that require a planner, researcher, and editor.
- Implementing multi-round debates between different AI models to refine decision-making.
- Managing high-concurrency workflows where multiple agents must process data in parallel.
- Creating role-play environments for simulation and specialized problem-solving.
How Multi-Agent Orchestrator Works
- Goal Decomposition: The orchestrator uses an LLM-based router to break down a primary goal into a JSON array of specific sub-tasks.
- DAG Construction: It builds a Directed Acyclic Graph to identify task dependencies and determine the optimal execution order.
- Agent Selection: Tasks are routed to registered agents based on their defined capabilities and roles.
- Scheduled Execution: The engine runs tasks concurrently (up to a defined limit) while waiting for prerequisite dependencies to complete.
- Result Summarization: Finally, the orchestrator aggregates outputs from all agents to provide a cohesive final response to the user.
Multi-Agent Orchestrator Setup
To integrate this skill into your environment, ensure you have the Openclaw CLI installed and your API keys configured.
# Install the multi-agent-orchestrator skill
openclaw install multi-agent-orchestrator
# Set your environment variables
export OPENAI_API_KEY='your-api-key-here'
# Initialize your orchestrator within your project
openclaw run multi-agent-orchestrator
Multi-Agent Orchestrator Data Schema & Taxonomy
The orchestrator manages data through structured sessions and state machines. Below is the primary schema for agent interaction:
| Object | Description | Key Properties |
|---|---|---|
| AgentConfig | Defines the identity of an agent | name, role, capabilities, llm, tools |
| TaskResult | Tracks the lifecycle of a task | agentId, status, output, dependencies |
| AgentSession | Maintains persistent state | id, state, history, sharedContext |
| Message | The communication packet | type (request/broadcast), from, to, content |
Multi-Agent Orchestrator Advanced Features
- State Machine Management: Implements strict transition rules (idle, thinking, waiting, acting) to prevent race conditions.
- Pluggable Communication Patterns: Supports Broadcast, Point-to-Point, Publish/Subscribe, and Blackboard models for flexible Openclaw Skills integration.
- Semantic Memory: Each agent maintains a memory buffer that stores task context with importance-based weighting.
- Fault Tolerance: Features built-in circuit breakers and timeout controls to handle agent failures or LLM latency.
SKILL.md
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