A sophisticated reasoning protocol for Openclaw Skills that synthesizes perspectives from three different LLMs in parallel before the primary agent responds.
Install & Download
1. ClawHub CLI
The fastest way to install a skill directly from the registry.
npx clawhub@latest install openclaw-multi-brain
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 openclaw-multi-brain 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-Brain Protocol?
The Multi-Brain Protocol is a technical framework designed to provide AI agents with immediate cognitive diversity. By leveraging a turn:before hook, the protocol executes parallel calls to secondary models like Kimi K2.5 and GPT 5.3 Codex. This integration ensures that the primary agent is never working in a vacuum; instead, it receives a rich context filled with varying perspectives and technical insights before formulating a response. Incorporating this into your collection of Openclaw Skills significantly reduces model bias and improves decision-making quality in complex coding and architecture tasks.
This system-enforced approach allows for seamless intelligence upgrades without requiring the agent to change its core behavior. The primary agent (such as Claude Opus 4.6) acts as a synthesizer, evaluating the outputs of the other brains to deliver a more refined and cross-verified answer. This makes it a premier choice for developers who demand high-fidelity reasoning from their automated workflows using Openclaw Skills.
Multi-Brain Protocol Use Cases
- Reducing architectural bias by cross-referencing multiple LLM perspectives during system design.
- Enhancing debugging sessions where different models may identify different root causes for complex errors.
- On-demand cognitive lifting for strategic planning using specific keyword triggers.
- Ensuring high-availability intelligence where a fail-open design allows the system to continue even if one API provider is down.
How Multi-Brain Protocol Works
- The user provides input, optionally prefixed with the mb keyword to trigger the protocol.
- The system-level turn:before hook intercepts the message and identifies the trigger mode.
- The protocol initiates parallel asynchronous requests to Kimi K2.5 via the Moonshot API and GPT 5.3 Codex via the CLI.
- Perspectives from both secondary models are formatted into specific system context blocks.
- The primary agent receives the original user prompt along with the injected perspectives from the other models.
- The primary agent synthesizes all viewpoints into a final, comprehensive response for the user.
Multi-Brain Protocol Setup
To deploy the Multi-Brain Protocol within your Openclaw Skills setup, follow these steps:
- Prepare the hook infrastructure:
mkdir -p hooks/turn-preflight
- Configure your API credentials for Moonshot:
echo "your-moonshot-api-key" > .kimi-api-key
- Install and authenticate the OpenAI Codex CLI:
npm install -g @openai/codex
codex auth
- Update your openclaw.json configuration to enable internal hooks and the turn-preflight entry.
Multi-Brain Protocol Data Schema & Taxonomy
The protocol manages data through context injection and configuration flags. The following table describes the data components utilized by these Openclaw Skills:
| Component | Source | Format |
|---|---|---|
| [KIMI K2.5 PERSPECTIVE] | Moonshot API | Injected Markdown Text |
| [CODEX 5.3 PERSPECTIVE] | Codex CLI | Injected Markdown Text |
| TRIGGER_MODE | handler.js | String (keyword, hybrid, or auto) |
| .kimi-api-key | Local Secret | Plaintext Key |
Multi-Brain Protocol Advanced Features
- Parallel execution architecture that minimizes latency by fetching multiple model responses simultaneously.
- Fail-open resilience, ensuring the primary agent remains functional even if secondary LLM calls fail.
- Context-aware injection that hides the multi-model complexity from the end user while enriching the agent's logic.
- Dynamic trigger modes including a hybrid option that automatically activates for messages exceeding 50 characters.
SKILL.md
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