📄 Upload your documents, get a knowledge graph — Neo4j's new "Document Intelligence" feature in Aura makes it that simple! Title: Introducing Document Intelligence: From documents to a knowledge graph, right inside Aura URL: 📦 Overview Neo4j has added "Document Intelligence" to its fully managed graph database service Aura. Upload unstructured documents like PDFs, contracts, or technical docs, and the platform automatically extracts entities, resolves duplicates, and constructs a knowledge graph — all without writing a single line of code. ❓ Challenges Solved Most enterprise data is trapped in unstructured documents. Building knowledge graphs from this data previously required complex NLP pipelines, LLM-based entity extraction, entity resolution, and graph schema design — limiting knowledge graph adoption to specialized technical teams. 💡 Methodology & Proposed Approach The feature uses LLM-based entity extraction to identify people, organizations, concepts, and their relationships. Entity resolution merges duplicate references across documents into unified graph nodes. The system infers graph schemas from extracted relationships and stores everything in AuraDB, ready for immediate use with Aura Agent, GenAI Copilot, and GraphRAG pipelines. 🛠 Use Cases - Legal teams automating compliance checks by extracting obligation networks from contract collections - Pharmaceutical companies structuring molecule-disease relationships from clinical trial reports - Enhancing RAG accuracy by supplementing vector search with structural relationships that graphs uniquely capture #KnowledgeGraph# #Neo4j#

For an AI agent to answer "why did we make that decision?", you need connected memory — not flat chat logs 🕸️ This tool spins the whole thing up in one command. Title: Introducing Create Context Graph URL: 🕸️ Overview Create Context Graph is a Neo4j Labs CLI scaffolding tool that generates a full-stack AI agent app with graph-based memory in a single command. The generated app bundles a FastAPI backend, a Next.js frontend, an AI agent framework, and a Neo4j graph database. ❓ Challenges Solved AI agents are easy to build but still struggle with relationships and causality. ・Flat chat logs and vector stores can't answer structural questions like "why did we decide this?" or "what's blocking this work?" ・In short, agents lacked the sophisticated memory needed to capture relational context 💡 Methodology & How It Works ・It turns data into a "context graph" (a connected knowledge structure), organizing three memory types: chat history, vector content, and reasoning traces ・It uses the POLE+O entity model (Person, Organization, Location, Event, Object) layered with domain-specific types ・When agents decide, the reasoning chain is captured as DecisionTrace nodes with linked TraceStep components, creating queryable provenance ・It supports multiple frameworks (PydanticAI, LangGraph, Claude Agent SDK), 22 built-in domains, Linear/Claude Code/GitHub connectors, real-time reasoning-path visualization, and automatic secret redaction 🌍 Use Cases ・Developers querying issue dependencies and team workflows ・Personal development analytics from Claude Code session history ・Multi-tool correlation combining decisions, commits, and work items Making decision provenance queryable helps with agent explainability, debugging, and cross-team knowledge integration. #GraphRAG# #Neo4j#

"Who's the strongest wrestler?" can't be answered by win counts alone. Chaining graph algorithms to surface true dominance is a fun read 🥋 Title: SumoDB in Neo4j: Chaining Multiple Graph Algorithms in Snowflake — Part 3 URL: 🥋 Overview This post combines Neo4j Graph Analytics with Snowflake SQL to measure "dominance you can't see from win counts" in professional sumo data. It chains multiple graph algorithms into a composite "Chaos Score." ❓ Challenges Solved Ranking by raw wins overrates wrestlers who just beat weak opponents. By pairing Neo4j and Snowflake, the post surfaces competitive structure that neither tool alone could reveal. 💡 Methodology & Proposed Approach It builds weighted directed "winner → loser" edges and chains three algorithms. ・PageRank: weights wins over stronger opponents higher, measuring victory quality ・Betweenness centrality: finds bridge wrestlers connecting elite and mid-tier ・3-cycle detection: visualizes rock-paper-scissors (non-transitive) rivalries A damping factor of 0.85 and reversed edge orientation direct prestige toward winners, converging in ~20 iterations. 🌍 Use Cases ・Talent assessment: separate inflated win records from genuine dominance ・Structural analysis: find key wrestlers whose removal fragments the hierarchy ・Competitive balance: gauge ecosystem health via non-transitive rivalry density #GraphDataScience# #Neo4j#

Still shipping your entire schema to a Text-to-SQL agent on every request? You're losing both accuracy and money 💸 Here's how a knowledge graph fixes both. Title: How a Neo4j semantic layer makes your Text-to-SQL agent smarter and cheaper URL: 💸 Overview This post explains how to use a knowledge graph (Neo4j) as a semantic layer to make Text-to-SQL agents both smarter and cheaper. Instead of dumping the full schema every time, the agent retrieves only the subgraph relevant to the question — a GraphRAG approach. ❓ Challenges Solved Most implementations store schema info in static YAML or Markdown and send the whole thing on every request. That creates three serious issues. ・High token cost: transmitting the entire schema repeatedly is expensive ・Contextual noise: irrelevant tables degrade accuracy and trigger hallucinations ・Poor maintainability: flat files go stale as business semantics evolve 💡 Methodology & Proposed Approach The graph stores database structure (schemas, tables, columns, types), constraints, column dictionaries, a business glossary, and usage patterns. The agent retrieves only relevant context in three steps. ・Semantic similarity search: vector indices identify matching columns and terms ・Shortest-path search: find possible joins between identified tables ・Additional context: gather schema definitions, business terms, and sample values Results are formatted as JSON with tables and join paths in milliseconds. 🌍 Use Cases / Experimental Results The post reports improvements that matter directly for production. ・Token reduction: 20-30% on average, up to 10x on simple queries ・Accuracy (multi-table joins): ~98% (Neo4j) vs ~90% (YAML) ・Accuracy (complex CTEs with window functions): ~94% (Neo4j) vs ~85% (YAML) ・Token use scales with complexity (simple ~1,800 / multi-join ~5,000 / advanced ~7,300) The graph captures dynamic usage patterns like join frequencies and behavioral relationships, enabling continuous improvement that static files simply can't model. #TextToSQL# #KnowledgeGraph#

🦈 Before that press release goes live, why not test it against "hundreds of public voices" first? A slightly futuristic engine now simulates an entire crowd's reaction for $1 in 10 minutes. Title: aaronjmars/MiroShark URL: 📦 Overview MiroShark is a "Universal Swarm Intelligence Engine." For any scenario—a press release, a news headline, a policy draft, or a question—it simulates in real time how hundreds of AI agents would react. The agents post, argue, trade, and shift their positions as simulated time passes. ❓ Challenges Solved Organizations want to test how the real public will receive an idea before committing resources. MiroShark removes the need for lengthy focus groups and expensive market research, enabling validation for under $1 in less than 10 minutes. 💡 How It Works It runs in five phases. ・Generate an ontology from the input documents ・Build a Neo4j knowledge graph of entity relationships ・Ground 100+ personas using demographics, web enrichment, and graph attributes ・Have agents interact hourly across Twitter, Reddit, and prediction markets ・Generate reports that cite the actual simulated posts and trades Posts are ingested via NER, embeddings, and entity resolution, then retrieved by fusing vector, BM25, and graph traversal. 🎯 Use Cases ・PR crisis testing and market-reaction forecasting ・Ad campaign pre-testing and policy impact analysis ・Personal decision scenarios and historical counterfactuals You can also inject breaking news mid-run, or fork a running simulation (counterfactual branching). 📊 Highlights ・1.3k GitHub stars and 265 forks, AGPL-3.0 licensed ・Each simulation runs at roughly $1, about 10 minutes, with 100+ agents ・Python backend, Vue.js frontend, Neo4j database; LLMs via OpenRouter (local Ollama also supported) #AIAgents# #Simulation#