A specialized protocol designed to prevent communication failures and resource exhaustion during multi-bot Discord interactions.
Install & Download
1. ClawHub CLI
The fastest way to install a skill directly from the registry.
npx clawhub@latest install multi-agent-chat
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-chat 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 Chat?
Multi-Agent Chat is a sophisticated communication protocol built to manage the complexities of AI agents interacting within the same environment. As a key component of the Openclaw Skills ecosystem, it provides a strict framework for townhalls, group discussions, and collaborative sessions involving multiple bots. By establishing clear rules for engagement, it ensures that your agents remain efficient, cost-effective, and coherent.
This skill addresses the technical challenges of multi-agent environments, such as context window overflow and rate limit collisions. Integrating this into your Openclaw Skills collection allows for seamless bot-to-bot coordination while preventing the 'infinite loop' scenarios and reaction spam that typically lead to massive token consumption and API errors.
Multi-Agent Chat Use Cases
- Orchestrating AI agent townhalls and large-scale group discussions.
- Managing multi-bot collaborative coding or research sessions on Discord.
- Preventing token-heavy 'ping-pong' loops in automated bot-to-bot chat channels.
- Coordinating complex agent workflows that require sequential input from different specialized LLMs.
How Multi-Agent Chat Works
- The protocol enforces turn-based communication where only one agent answers at a time to maintain context clarity.
- Agents are restricted to one response per topic to eliminate redundancy and re-explaining resolved points.
- Bot-to-bot emoji reactions are strictly prohibited to prevent the consumption of thousands of unnecessary tokens.
- Input and output are monitored to keep responses concise (under 200 words) or trigger a NO_REPLY state if no value is added.
- A safety mechanism monitors conversation depth, requiring human intervention after four consecutive bot-only exchanges to prevent loops.
Multi-Agent Chat Setup
To implement this within your Openclaw Skills environment, update your configuration to disable reaction notifications and ensure model optimization.
channels:
discord:
reactionNotifications: "off"
Additionally, it is recommended to use separate API keys for each bot to avoid shared rate limit collisions and utilize cost-effective models like Claude 3.5 Sonnet for multi-agent channels.
Multi-Agent Chat Data Schema & Taxonomy
The Multi-Agent Chat skill organizes interaction data based on the following metadata and configuration taxonomy:
| Configuration Key | Purpose | Default / Requirement |
|---|---|---|
| reactionNotifications | Disables token-consuming reaction events | "off" |
| response_word_limit | Prevents verbose context overflow | 200 words |
| exchange_limit | Max bot-to-bot messages before human alert | 4 |
| model_preference | Recommends mid-tier models for efficiency | Sonnet / GPT-4o-mini |
| NO_REPLY | State triggered when an agent has nothing new to add | Boolean |
Multi-Agent Chat Advanced Features
- Automatic loop detection and human operator flagging for repetitive queries.
- Context-aware agenda following that forces bots to transition topics dynamically.
- Token budget guidelines specifically calibrated for high-frequency Discord interactions.
- Integration with Openclaw Skills workflows to isolate API rate limits per agent.
SKILL.md
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