Researchers propose rEDMRec, a framework that distills large language model reasoning into four editable experience channels—long-term preference, short-term context, item-perception, and counterfactu…
Graphify-Labs has released graphify, an open-source tool that converts entire codebases into structured knowledge graphs to reduce AI token consumption by up to 71.5x. The tool uses tree-sitter for lo…
Microsoft's GraphRAG system addresses the limitations of standard Retrieval-Augmented Generation (RAG) for global queries that require reasoning across entire document collections, using knowledge gra…
Lettria, a knowledge graph vendor and AWS Partner, benchmarked graph-structured RAG against vector-only retrieval across four domain corpora and reported correctness improving from roughly 50% to abov…
Researchers propose HC-RAG, a hierarchical cross-modal retrieval-augmented generation framework for evidence-centric financial question answering, which organizes filings into a typed financial eviden…
A developer with 25 years in enterprise infrastructure argues that graph engineering is essentially a new name for entity-relationship thinking, a core discipline of enterprise architecture. The devel…
FalkorDB released a community node for n8n that integrates GraphRAG, enabling AI agents to query a knowledge graph built from GitHub-hosted documentation. The node exposes five graph tools, allowing a…
Hubmesh, a new Python library, improves multi-hop RAG retrieval by adding a centrality-aware planner over existing vector databases, eliminating LLM calls in the query path. The library uses multi-com…
Neo4j chief technology officer Philip Rathle said enterprises are converging on an enterprise knowledge layer architecture that grounds large language models in trustworthy data, moving beyond early g…
An independent study by the UK's National Innovation Centre for Data (NICD) found that GraphRAG, a combination of vector and graph retrieval-augmented generation, makes AI agents 80% more truthful tha…
Jai, a solo founder at QX Labs, argues that traditional RAG fails on portfolio and counting questions critical for business use, while full GraphRAG is overkill. QX shipped a pragmatic middle path tha…
Bunny is open-sourcing a multiplayer AI development platform for Discord that turns a VM or Docker container into a shared workspace with collaborative shells, live previews, and chat-native workflows…
A 2026 guide covers Microsoft's GraphRAG, an open-source graph-based retrieval-augmented generation system with 33K GitHub stars, alongside other tools like AnythingLLM, RAGFlow, and Arize AI Phoenix …
A developer shares seven key lessons learned from building modern AI agents with Neo4j, GraphRAG, Aura Agents, and LLM Mesh. The lessons cover graph databases, persistent memory, GraphRAG vs. traditio…
Cassie Shum, product engineering lead at RelationalAI, presented on GraphRAG at a conference, explaining how knowledge graphs enhance retrieval workflows by adding semantic structure. She contrasted t…
GraphRAG, a retrieval-augmented generation method from Microsoft Research, uses knowledge graphs to connect entities and relationships, improving AI's ability to answer complex queries accurately. Thi…
A developer identified two necessary conditions for FalkorDB's db.idx.vector.queryNodes to work properly: data must be stored as a vector type using vecf32() and a vector index must be created. Common…
Microsoft Research unveiled Memora, a memory system for AI agents that decouples storage from retrieval to provide scalable long-term recall. The system reduces context token usage by up to 98% while …
Microsoft researchers introduced Memora, a scalable memory system for AI agents that decouples stored content from retrieval mechanisms, achieving state-of-the-art performance on long-horizon benchmar…
Graph-based retrieval outperforms vector RAG on multi-hop reasoning and global summarization, with Microsoft's GraphRAG beating vector RAG on answer comprehensiveness 72 to 83 percent of the time and …