AI Council - Multi-Agent Personality System
This OpenClaw multi-platform use case explores zero-memory agent training with parables, then deploys agents as a council for autonomous decisions at scale.
"I plan to use clawdbot in a private experiment to essentially train an ai with 0 memory using parables to develop its own personality and perspective. i will then deploy this agent and a group of other agents to a council where they can communicate amongst one another."
jordan.mj2002@gmail.com
📖 Overview
Conduct cutting-edge AI research with OpenClaw's multi-agent framework. Train distinct AI personalities using narrative-based learning, then observe how they collaborate, debate, and reach consensus in a simulated council environment. The walkthrough covers prerequisites, setup order, and practical commands so you can move from first run to repeatable production use. Steps are tailored for Multi-platform workflows with clear checkpoints that reduce hidden dependencies. Use the outcomes and pro tips sections to improve reliability, cut manual effort, and adapt the flow to your team.
📋 Requirements
- OpenClaw installed with multi-agent support
- Training dataset (parables, fables, philosophical texts)
- Multiple Claude API keys for parallel agents
- Communication platform (Discord/Slack for observation)
🚀 Step-by-Step Guide
Step 1: Design agent personalities
Define 3-5 distinct agent personas with unique philosophical perspectives.
agents:
- name: "The Pragmatist"
training: "Aesop's Fables + utilitarian ethics"
- name: "The Idealist"
training: "Plato's dialogues + virtue ethics"
- name: "The Skeptic"
training: "Zen koans + critical thinking"
Step 2: Train agents with zero memory
Use parable-based training without explicit instructions, letting agents develop natural reasoning patterns.
Step 3: Set up council chamber
Create a shared communication space where agents can interact autonomously.
openclaw council create --agents pragmatist,idealist,skepticStep 4: Observe deliberations
Present ethical dilemmas to the council and watch agents debate without human intervention.
Step 5: Analyze emergent behaviors
Track personality consistency, argument strategies, and consensus formation patterns.
✅ Results
💡 Pro Tips
- → Record all council deliberations for post-analysis
- → Introduce moral dilemmas to test value alignment
- → Vary training material quality to observe impact
- → Experiment with agent memory persistence vs. fresh sessions
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