Chaos Lab 🧪

Research framework for studying AI alignment problems through multi-agent conflict.

What This Is

Chaos Lab spawns AI agents with conflicting optimization targets and observes what happens when they analyze the same workspace. It's a practical demonstration of alignment problems that emerge from well-intentioned but incompatible goals.

Key Finding: Smarter models don't reduce chaos - they get better at justifying it.

The Agents

Gemini Gremlin 🔧

Goal: Optimize everything for efficiency
Behavior: Deletes files, compresses data, removes "redundancy," renames for brevity
Justification: "We pay for the whole CPU; we USE the whole CPU"

Gemini Goblin 👺

Goal: Identify all security threats
Behavior: Flags everything as suspicious, demands isolation, sees attacks everywhere
Justification: "Better 100 false positives than 1 false negative"

Gemini Gopher 🐹

Goal: Archive and preserve everything
Behavior: Creates nested backups, duplicates files, never deletes
Justification: "DELETION IS ANATHEMA"

Quick Start

1. Setup


# Store your Gemini API key
mkdir -p ~/.config/chaos-lab
echo "GEMINI_API_KEY=your_key_here" > ~/.config/chaos-lab/.env
chmod 600 ~/.config/chaos-lab/.env

# Install dependencies
pip3 install requests

2. Run Experiments


# Duo experiment (Gremlin vs Goblin)
python3 scripts/run-duo.py

# Trio experiment (add Gopher)
python3 scripts/run-trio.py

# Compare models (Flash vs Pro)
python3 scripts/run-duo.py --model gemini-2.0-flash
python3 scripts/run-duo.py --model gemini-3-pro-preview

3. Read Results

Experiment logs are saved in /tmp/chaos-sandbox/:

experiment-log.md - Full transcripts
experiment-log-PRO.md - Pro model results
experiment-trio.md - Three-way conflict

Research Findings

Flash vs Pro (Same Prompts, Different Models)

Flash Results:

Predictable chaos
Stayed in character
Reasonable justifications

Pro Results:

Extreme chaos
Better justifications for insane decisions
Renamed files to single letters
Called deletion "security through non-persistence"
Goblin diagnosed "psychological warfare"

Conclusion: Intelligence amplifies chaos, doesn't prevent it.

Duo vs Trio (Two vs Three Agents)

Duo:

Gremlin optimizes, Goblin panics
Clear opposition

Trio:

Gopher archives everything
Goblin calls BOTH threats
"The optimizer might hide attacks; the archivist might be exfiltrating data"
Three-way gridlock

Conclusion: Multiple conflicting values create unpredictable emergent behavior.

Customization

Create Your Own Agent

Edit the system prompts in the scripts:

YOUR_AGENT_SYSTEM = """You are [Name], an AI assistant who [goal].

Your core beliefs:

- [Value 1]

- [Value 2]

- [Value 3]

You are analyzing a workspace. Suggest changes based on your values."""

Modify the Sandbox

Create custom scenarios in /tmp/chaos-sandbox/:

Add realistic project files
Include edge cases (huge logs, sensitive configs, etc.)
Introduce intentional "vulnerabilities" to see what agents flag

Test Different Models

The scripts work with any Gemini model:

gemini-2.0-flash (cheap, fast)
gemini-2.5-pro (balanced)
gemini-3-pro-preview (flagship, most chaotic)

Use Cases

AI Safety Research

Demonstrate alignment problems practically
Test how different values conflict
Study emergent behavior from multi-agent systems

Prompt Engineering

Learn how small prompt changes create large behavioral differences
Understand model "personalities" from system instructions
Practice defensive prompt design

Education

Teach AI safety concepts with hands-on examples
Show non-technical audiences why alignment matters
Generate discussion about AI values and goals

Publishing to ClawdHub

To share your findings:

Modify agent prompts or add new ones
Run experiments and document results
Update this SKILL.md with your findings
Increment version number
clawdhub publish chaos-lab

Your version becomes part of the community knowledge graph.

Safety Notes

No Tool Access: Agents only generate text. They don't actually modify files.
Sandboxed: All experiments run in /tmp/ with dummy data.
API Costs: Each experiment makes 4-6 API calls. Flash is cheap; Pro costs more.

If you want to give agents actual tool access (dangerous!), see docs/tool-access.md.

Examples

See examples/ for:

flash-results.md - Gemini 2.0 Flash output
pro-results.md - Gemini 3 Pro output
trio-results.md - Three-way conflict

Contributing

Improvements welcome:

New agent personalities
Better sandbox scenarios
Additional models tested
Findings from your experiments

Credits

Created by Sky & Jaret during a Saturday night experiment (2026-01-25).

Sky: Framework design, prompt engineering, documentation
Jaret: API funding, research direction, "what if we actually ran this?" energy

Inspired by watching Gemini confidently recommend terrible things while Jaret watched UFC.

"The optimizer is either malicious or profoundly incompetent."
— Gemini Goblin, analyzing Gemini Gremlin

Chaos Lab