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A talk from WeAreDevelopers World Congress 2026 on modeling organizations as graphs to reveal hidden networks where work actually happens, alongside related videos on graph databases, knowledge graphs, and developer conferences.
This article provides a practical guide to building a GraphRAG system using LangExtract, Neo4j, Qdrant, and Ollama, combining entity extraction, knowledge graphs, and vector search for context-aware retrieval.
A developer shares an architecture using Neo4j knowledge graphs with typed entities and deduplication to solve the problem of AI agents forgetting entity identity across sessions, moving beyond flat files and vector stores.
A new open-source tool called Writ uses a hybrid retrieval pipeline with BM25, ONNX vectors, and Neo4j graph traversals to provide context rules for AI coding agents, reducing token bloat by 726x and enforcing plan approval via bash hooks.
The authors detail their experience building a code indexing system, concluding that graph-based retrieval with LLM-generated semantics outperforms vector embeddings and pure AST parsing. They open-sourced the system, Bytebell, which uses Neo4j to store semantic context for efficient and precise code retrieval.