Tag: knowledge-graphs

AI agents are compressing DevOps feedback loops from hours to seconds. The teams that win will be those that design tight, verifiable loops — not the ones with the best dashboards. Here is how to think about loops when your operators are autonomous.

Microsoft's record 206-CVE Patch Tuesday proves CVSS scores alone cannot prioritise. Knowledge graphs mapping dependency blast radius and reachability are the only scalable defence.

Claude Fable 5's Mythos-class reasoning transforms GraphRAG from promising to production-ready. Multi-hop graph queries, reliable Text-to-Cypher, and autonomous graph exploration are now practical reality.

After the Munich ruling made Google liable for false AI Overviews, GraphRAG offers the only legally defensible path for enterprise AI.

Nonaka and Takeuchi's SECI model remains the definitive framework for organisational knowledge creation. Here is how Socialization, Externalization, Combination, and Internalization convert private know-how into collective intelligence — and why knowledge graphs are the missing infrastructure.

A practical comparison of memory architectures for AI coding assistants — file-based, vector, graph, and hybrid approaches — with specific recommendations for opencode users.

A technical comparison of STATE-Bench, AMA-Bench, GroupMemBench, MemoryArena, and EvoMemBench — five benchmarks proving AI agents cannot remember and the graph architectures that might fix it.

Evidence-based analysis of GraphRAG's failure modes — benchmarks where it underperforms vanilla RAG — with concrete mitigations and scenarios where graph-based retrieval dominates.

How LazyGraphRAG collapses GraphRAG indexing costs from $30,000 to $30 by deferring entity extraction to query time — with a practical guide to when lazy beats eager.

How a production Neo4j knowledge graph for medical diagnostics market intelligence implements ontology — node labels, edge semantics, signal normalisation, and what senior engineers can learn from 15 source adapters.

How knowledge graphs solve the structural context gap that vector databases leave open — GraphRAG architecture with Cypher traversal patterns and LLM context serialization.

GraphRAG enhances retrieval-augmented generation by grounding LLM responses in structured knowledge graphs instead of flat vector embeddings.

Build AI-powered applications with Neo4j as the knowledge layer — from graph-backed RAG to autonomous graph agents.

Understand knowledge graphs from first principles — RDF, property graphs, SPARQL, Cypher, and why they matter for AI applications.

MemPalace stores verbatim conversation history with semantic search, achieving 96.6% recall on LongMemEval with zero API calls and zero cloud dependency.

World Office is an AI-first, privacy-focused office suite with local-first collaboration, AI agents, and knowledge graphs. Built on AGPLv3 + Commercial licensing.

Interactive exploration of prompt engineering techniques for Knowledge Graph generation using LLMs