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)

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