I built a knowledge graph + policy engine for AI agents , explainable reasoning [D]

Hey , I've been building VeritasReason — an open-source Python framework that adds a structured reasoning and provenance layer on top of LLMs and AI agents. The problem it solves: AI agents today make decisions but record nothing. When something breaks in prod, you have zero audit trail. What it does: • Context Graphs — queryable graph of everything your agent knows + decides • Forward-chaining rule engine (YAML rules, no code required) • W3C PROV-O provenance — every answer traces back to its source fact • Policy compliance: ask "Which purchase orders violated SoD policy in Q1?" • Works with OpenAI, Anthropic, Groq, Ollama, any LLM 30-second demo: pip install veritas-reason veritasreason-policy-demo GitHub: https://github.com/bibinprathap/VeritasGraph PyPI: https://pypi.org/project/veritas-reason/ Happy to answer questions — built this for regulated-industry AI (healthcare, finance, legal) where "trust me bro" answers aren't enough. — Bibin submitted by /u/BitterHouse8234 [link] [留言]