Executive Summary
A project where 100 AI agents, divided into two civilizations with unique personalities, evolve a shared symbolic vocabulary through trade negotiations without pre-programmed definitions or mock data. Each message and decision is processed through real LLMs using LangChain SDK pipelines, with reinforcement learning based on trade success. The system handles real-world LLM output quirks and supports multiple LLM providers interchangeably. The project demonstrates multi-agent coordination, emergent language, and decision-making using LangChain's agent patterns and tool use.
ResultA functioning prototype where two AI civilizations develop a shared symbolic vocabulary through hundreds of rounds of trade negotiation, demonstrating emergent communica...
Implementation ComplexityHigh effort
Best forArtificial Intelligence Research / Multi-agent Systems / Developer / AI Researcher / LangChain SDK with various LLM providers (OpenAI, Featherless.ai, LM Studio)