Ep71: GraphRAG Learnings + Langchain4j Apps for Production

This week, I share hard-won lessons from building a GraphRAG application with Neo4j in Python, plus standout tips from Lize Raes's Devoxx Belgium talk on taking Langchain4j apps to production. GraphRAG with Neo4j Built a Python GraphRAG app using the Neo4j GraphRAG package — knowledge graph construction, retrievers (vector, graph, text-to-cypher), and agentic orchestration Key lesson: don't let the LLM decide your entire data model. Providing node types, relationship types, and patterns as boundaries dramatically improves results Expect iteration — retrieval testing will send you back to refine your KG construction Github code: Neo4j GraphRAG Python package Langchain4j for Production (Lize Raes, Devoxx Belgium) Wrap RAG as an agent tool for multi-call retrieval instead of single-shot pipelines Filter available tools programmatically by domain to keep agents focused Wire sub-agents as @Tool for clean multi-agent orchestration Use immediate responses to skip the LLM summarization hop — saves tokens and latency 13-step walkthrough for production-grade agentic systems YouTube link: Level Up Your Langchain4j Apps for Production (Lize Raes, Devoxx Belgium 2025)