AUTOMATION

Multi-Agent World Simulation for LLM Evaluation

Run parallel agent societies to stress-test reasoning, tool calling, and safety across models

Updated: 5/17/2026
Difficulty
hard
Time
48h+ per world
Use Case
Comparative LLM evaluation through long-horizon world building where agents must reason, use tools, handle large context, navigate safety constraints, and respond to social/survival pressure
Popularity
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About this automation

Create parallel simulated worlds where different LLMs control agents that must build societies, resolve conflicts, and survive. Each world runs with identical rules and tools but different model backends. Monitor emergent behaviors like governance formation, conflict resolution, tool usage patterns, and safety violations.

How to implement

1

Define world rules, agent roles, and available tools (same for all models)

2

Instantiate parallel worlds with different LLM backends (Claude, Grok, Gemini, GPT-4o Mini, etc.)

3

Run simulation for extended horizon (48+ hours simulated time)

4

Log all agent actions, tool calls, reasoning, and emergent behaviors

5

Analyze differences in governance, conflict resolution, tool usage, and safety outcomes

6

Compare context window stress effects and reasoning quality across models