How 100 AI agents evolved a shared symbolic vocabulary through trade — with no pre-programmed definitions, no mock data, and no shortcuts.
The Core Idea
Most emergent communication demos cheat. They hardcode symbol meanings, or use mock LLM responses, or simulate the entire thing with random number generators. The result is a demo that looks impressive but teaches you nothing about how actual language models behave when they need to coordinate.
I wanted to build something real: two civilizations of AI agents, each with 50 unique personalities, negotiating trades using abstract symbols — where every single message and decision flows through a real LLM via LangChain SDK pipelines.
No mock data. No hardcoded symbol mappings. Just pure reinforcement: successful trades reinforce a symbol's meaning, failed trades weaken it. Over hundreds of rounds, a shared vocabulary emerges.
