Multi-Brain Protocol

Runs Kimi K2.5 and GPT 5.3 Codex in parallel pre-turn hook, injecting their perspectives for cognitive diversity before primary agent responds.

What it does: Gives your AI agents cognitive diversity by calling multiple LLMs (Kimi K2.5 + GPT 5.3 Codex) in parallel before the primary agent responds. Three brains instead of one.

For Agents

Multi-Brain is system-enforced via a turn:before hook. No manual steps needed.

When triggered (user types mb as first word), the hook:

1. Calls Kimi K2.5 and GPT 5.3 Codex in parallel
2. Injects their perspectives into your system context
3. You synthesize all viewpoints into your response
4. Never mention the other AIs to the user

Perspectives appear in your context as:

text

[KIMI K2.5 PERSPECTIVE]
<perspective text>

[CODEX 5.3 PERSPECTIVE]
<perspective text>

For Humans

Setup

1. Install the hook:

bash

mkdir -p hooks/turn-preflight
# Copy HOOK.md and handler.js from this package

2. Set Kimi API key:

bash

echo "your-moonshot-api-key" > .kimi-api-key

3. Install Codex CLI:

bash

npm install -g @openai/codex
codex auth   # OAuth login

4. Enable in openclaw.json:

json

{
  "hooks": {
    "internal": {
      "enabled": true,
      "entries": {
        "turn-preflight": { "enabled": true }
      }
    }
  }
}

Trigger Modes

Configure TRIGGER_MODE in handler.js:

Mode	Behavior
keyword (default)	Only fires when mb or multibrain is the first word
hybrid	Keyword forces it, auto on messages >50 chars
auto	Fires on every message (token-expensive)

LLMs

LLM	Role	Provider	Latency
Claude Opus 4.6	Primary agent	OpenClaw (Anthropic)	n/a
Kimi K2.5	Second perspective	Moonshot API	~5s
GPT 5.3 Codex	Third perspective	codex exec CLI	~4s

Architecture

text

User types: "mb should we change pricing?"
    |
    v
[turn:before hook detects "mb" keyword]
    |
    +---> Kimi K2.5 (Moonshot API, parallel)
    +---> GPT 5.3 Codex (CLI, parallel)
    |
    v (~5s combined)
[Perspectives injected into system content]
    |
    v
Claude Opus 4.6 responds with all 3 viewpoints

Benefits

• Cognitive diversity: three different AI architectures
• Bias mitigation: different training data and approaches
• On-demand: only burns tokens when you ask for it
• Fail-open: if any LLM fails, the others still work
• System-enforced: no protocol compliance needed from agents

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Source: https://github.com/Dannydvm/openclaw-multi-brain