Developer Builds Causality-Aware RAG System for Multi-Agent Civilization Simulations

A developer has designed a custom retrieval-augmented generation (RAG) variant called TCMF for CivilizationOS, a multi-agent simulation featuring over 40 AI citizens and specialist councils. Standard RAG systems retrieve memories based on cosine similarity alone, which fails to capture cause-and-effect chains — for example, linking a past drought to a present food riot. The new system combines relevance, recency, and importance scores inspired by Stanford's generative-agents research, then layers on a NetworkX-based causal graph to surface memories that led to a crisis, not just those that resemble it. CivilizationOS uses a three-tier LLM router — Ollama, Gemini Flash, and Claude Sonnet — to handle reasoning tasks of varying complexity across simulation ticks. The developer also outlines honest tradeoffs of the approach, acknowledging added architectural complexity compared to conventional RAG pipelines.