A structured orchestration framework for delegating tasks, enabling bi-directional dialogues, and managing multi-agent workflows in OpenClaw.
Install & Download
1. ClawHub CLI
The fastest way to install a skill directly from the registry.
npx clawhub@latest install multi-agent-comm
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 multi-agent-comm 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 Communication Protocol?
The Multi-Agent Communication Protocol is a powerful, production-grade framework designed to coordinate and manage multiple autonomous AI agents within the OpenClaw ecosystem. By leveraging structured communication paradigms, it allows a primary orchestrating agent (such as QClaw) to seamlessly spawn sub-agents, delegate tasks, and synthesize results. This skill is critical for developers building complex workflows that exceed the capacity of a single monolithic agent.
By integrating this framework into your library of Openclaw Skills, you unlock the ability to run multiple specialized agents in parallel or sequence. The system supports isolated runtimes, custom system prompts, and flexible configuration management, making it an essential protocol for scaling AI agent workflows to enterprise levels.
Multi-Agent Communication Protocol Use Cases
- Complex Task Decomposition: Breaking down massive, multi-step engineering tasks into isolated, parallel sub-tasks handled by specialized sub-agents.
- Expert Panel Consultations: Spawning multiple specialized domain-expert agents to analyze a single problem from different angles before merging their findings.
- Automated Code Auditing: Establishing a sequential pipeline where one agent generates code and another independent agent automatically reviews and refines it.
- Long-Running Environment Monitoring: Deploying background monitoring agents that run continuously and report status updates to the master controller dynamically.
How Multi-Agent Communication Protocol Works
- Session Spawning: The orchestrating agent initiates a sub-agent session using the
sessions_spawntool, specifying the execution runtime (e.g., subagent or ACP), task parameters, and timeouts. - Isolating & Context Handling: The sub-agent runs inside its designated context, with the option to inherit light environment context or work inside a specific workspace directory.
- Task Execution: The sub-agent processes its independent goal, utilizing specialized system prompts, models, and local tools configured in the gateway.
- Asynchronous Waiting (Yielding): The primary orchestrator can call
sessions_yieldto suspend its main execution loop, allowing parallel sub-agents to complete their runs asynchronously. - Dynamic Steering and Control: The main agent manages active sub-agents in real-time by listing, killing, or sending steering messages directly into active sessions.
- Result Synthesis: Once sub-agents complete, they return structured outputs to the main session, which the orchestrator aggregates to construct the final response.
Multi-Agent Communication Protocol Setup
To get started with this protocol, link the skill to your active instance configuration. You can easily deploy this across multiple environments using symbolic links or directory junctions.
Step 1: Link the Skill
Link the communication protocol directory to your global agent skills path:
# On Windows (Command Prompt)
cmd /c mklink /J "~\.claude\skills\multi-agent-comm" "~\.agents\skills\multi-agent-comm"
# On macOS/Linux
ln -s ~/.agents/skills/multi-agent-comm ~/.claude/skills/multi-agent-comm
Step 2: Configure the Gateway
For advanced Cross-Agent ACP (Mode 3), define your target agents and their corresponding system prompts inside your gateway.yaml configuration file before running sessions_spawn with the acp runtime.
Multi-Agent Communication Protocol Data Schema & Taxonomy
The framework structures communication through standard JSON payload structures and shared filesystem resources. Here is how state and metadata are organized:
Communication Payload Schema
| Property | Type | Description |
|---|---|---|
runtime |
String | Set to "subagent" for automated child runtimes, or "acp" for independent gateway agents. |
mode |
String | "run" for one-off tasks (auto-terminates) or "session" for persistent dialogues. |
task |
String | Self-contained instructions detailing the explicit work the spawned agent must complete. |
cwd |
String | Optional customized working directory path for filesystem isolation. |
timeoutSeconds |
Integer | Maximum execution runtime before the sub-agent is automatically terminated. |
Shared State Strategies
Since memory is not shared automatically between agents, state transmission relies on these standard methods:
- Workspace Files: Read/write access to shared files within the local working directory for large datasets.
- Task/Message Parameters: Embedding necessary small context data directly inside the task payload.
- File Attachments: Passing references explicitly using environment configs or command inputs.
Multi-Agent Communication Protocol Advanced Features
- Branch-Merge (Parallel) Orchestration: Spawn multiple concurrent sub-agents to divide-and-conquer independent files or analyses, significantly reducing total wall-clock execution time.
- Dual-Path Persistent Chat: Establish persistent, long-running bi-directional dialogue sessions with target agents to manage state-heavy workflows or interactive loops.
- Automated Session Recovery: Resume existing independent agent configurations using unique, system-generated UUID identifiers through
resumeSessionIdparameters. - Decoupled Cross-Device Deployment: Share this multi-agent communication utility across various local nodes by treating the code as a stateless, symbolic-linked component among your active Openclaw Skills.
SKILL.md
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