🧠 One answer to "how do you give agents accurate business context?": auto-build ontologies and a knowledge graph from existing data, then serve it via MCP. An open-source project from AWS. Title: Context Ontology Accelerator (aws/context-ontology-accelerator) URL: A semantic context layer that gives AI agents validated business context. Three highlights stand out. 🔎 A Scan → Model → Serve pipeline Connect diverse data sources to discover schemas and ingest documents (Scan), induce formal ontologies and build a unified knowledge graph (Model), and expose it via VKG SPARQL federation and MCP (Serve). It derives semantic structure from existing data, no manual knowledge engineering. ✅ Consistency validated by reasoning engines HermiT and ELK validate ontology consistency, enabling rule-based checks from formal constraints. Agents query validated business rules instead of relying on memorized training data. 🏗 AWS-native and production-minded Deployed via AWS CDK, a VKG powered by Ontop, and namespace RBAC (owner/maintainer/data-steward/data-analyst). API design via Smithy, UI in React + Cloudscape. A solid foundation for running agents within validated context while keeping explainability. #KnowledgeGraph# #AIAgents#

🕸 Turn text into a "graph of concepts" and use it as a RAG retriever instead of a vector DB. An OSS you can run fully locally. Title: rahulnyk/knowledge_graph URL: 🔍 Overview A project that builds a knowledge graph from unstructured text by extracting concepts (not just named entities) and their relationships, for Graph-Augmented Generation (GRAG) and knowledge-based QA. 🧩 Problem Solved Traditional text analysis struggles to reveal how concepts interconnect and where hidden links lie. Representing knowledge as a queryable graph that preserves meaning enables deeper document understanding. 🛠 Methodology & Approach Built in six steps (clean → extract concepts → extract relations → schema → populate nodes/edges → visualize). Edges carry two weights: W1 for LLM-extracted explicit relations, W2 for same-chunk co-occurrence. Node degree and community set node size and color. 💻 Tech Stack ・LLM: Mistral 7B OpenOrca (no GPT API) ・Serving: Ollama for a fully local pipeline ・Graph: NetworkX, Visualization: Pyvis, Data: Pandas 🎯 Use Cases Graph RAG using the graph as a retriever instead of a vector DB, hidden-link discovery, centrality analysis, and community detection. #KnowledgeGraph# #GraphRAG#

📚 A framework that fixes redundant flat-retrieval RAG by navigating a knowledge graph hierarchically. Title: LeanRAG URL: 📦 Overview LeanRAG is a graph-based RAG framework that combines knowledge graphs with hierarchical retrieval. It was accepted to AAAI 2026. 🔍 The problem it solves Naive retrieval just collects related chunks flatly, leading to overlapping, redundant content and ignoring structural links between documents. LeanRAG traverses semantically aggregated upper layers to produce well-grounded answers with less redundancy. 🛠 The method (5 stages) ・Semantic aggregation: cluster entities into summary nodes with adjacency relations ・Knowledge graph construction: connect entities and summaries in a multi-layer graph ・Hierarchical retrieval: anchor the query at fine-grained entities, then traverse upward to gather evidence ・Redundancy-aware synthesis: streamline overlapping paths ・Generation: an LLM answers from the gathered evidence 📊 Results ・About 46% lower retrieval redundancy versus flat retrieval ・On the Mix benchmark, LeanRAG 8.59 beats HiRAG 8.08, GraphRAG 7.87, and LightRAG 7.61 ・Win rates of 97.3% vs NaiveRAG, 78.1% vs GraphRAG, 81.2% vs LightRAG, and 100% vs FastGraphRAG A compelling option for anyone building a low-redundancy, knowledge-graph-grounded RAG. #RAG# #KnowledgeGraph#

📄 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#

Grounding an AI agent's answers in verifiable, explainable facts — an open-source platform offering the full knowledge-graph + GraphRAG + agent stack 🕸️ Title: trustgraph-ai/trustgraph URL: 🕸️ Overview An open-source semantic deployment platform for AI agents. Its core is the "context graph" — a structured, queryable representation of domain knowledge. It delivers the full agentic stack — context graphs, memory, retrieval, orchestration, and inference — for deterministic agent workloads. ❓ Challenges Solved With an LLM alone, it's hard to trace why you got an answer, and hallucination is a risk. ・Grounding an agent's answers in verifiable, explainable facts is difficult ・TrustGraph combines knowledge-graph construction with GraphRAG so agents access context that is semantically rich and verifiable ・And it runs in private deployments with sovereign control 💡 Key Features ・Multi-model DB (tabular, KV, document, graph, vectors) with multimodal support and automated entity/relationship extraction ・DocumentRAG, GraphRAG, and OntologyRAG pipelines, plus 3D GraphViz visualization ・Single/multi-agent with ReAct, Plan-then-Execute, and Supervisor patterns, and MCP integration ・Context Cores: bundle schema, graph, embeddings, evidence, and retrieval policies — versioning context like code 🌍 Tech Stack / Usage Storage on Cassandra, Qdrant, and Garage; messaging via Pulsar and others; LLMs from Anthropic/OpenAI/Google etc. plus local inference (vLLM/Ollama, etc.). Configure via npx @trustgraph/config and use the UI on port 8888. Apache 2.0 licensed. #GraphRAG# #KnowledgeGraph#

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#

Practices for embedding AI agents into enterprise systems [Semantic Layer -- Unified Metrics & Organizational Knowledge Graph] 💡 Ask your AI "What's our revenue?" and you get... gross or net? Bookings or cash? FY or CY? An AI that answers without definitions is a tool that's precisely wrong. Centralize the "meaning" of metrics and organization, replacing hallucination with defined facts. 🔥 Problems Solved - Metric/terminology hallucination: AI generates incorrect numbers because "revenue" was never precisely defined - Unresolved references: ambiguous phrases like "my team" or "last month" cannot be accurately resolved - Missing organizational scope: no way to control data boundaries by department or project hierarchy 🏗️ Proposed Pattern Centralize metric definitions in a BI semantic layer (dbt Semantic Layer / Cube) -- e.g., "Revenue = sum of order amounts, tax-excluded, on FY basis." Sync the organizational graph from SCIM/HRIS (Workday, etc.) so "my team's revenue" auto-resolves to "sum of order amounts for members in the user's department." Natural language ambiguity is resolved with defined facts, not hallucination. ✅ Selection Criteria - When to use: analytics-supporting agents, cross-org workflows, permission-dependent processing, metric-critical operations - When NOT: exploratory domains where definitions are not yet established (stabilize definitions first) ⚠️ Pitfalls - Definition maintenance cost: you need an operational workflow to keep metric definitions and org graphs fresh - Granularity balance: too fine-grained and management collapses; too coarse and ambiguity remains -- start with high-frequency metrics - Organizational change tracking: in orgs with frequent reorgs and transfers, SCIM sync frequency and timing become critical 🛠️ Implementation Approach 1. Centralize metric definitions in dbt Semantic Layer / Cube (e.g., "Revenue = sum of order amounts, tax-excluded, FY basis") and connect them as a first-class context source for agents 2. Build an organizational knowledge graph (people, departments, projects, roles, permissions) in Neo4j / Amazon Neptune, synced from Workday / Okta via SCIM 3. Implement a natural-language-to-defined-metric mapping layer that auto-resolves "my team's revenue" to "sum of order amounts for members in the user's department" 4. Progressively formalize definitions starting with high-frequency metrics, and establish a periodic review workflow to keep definitions fresh 5. Detect organizational changes (reorgs, transfers) via SCIM sync webhooks for near-real-time updates, minimizing scope control lag #AIAgents# #EnterpriseArchitecture#

Practices for embedding AI agents into enterprise systems [Layered Memory -- 4-Tier Memory & Context Broker] 💡 An AI agent that starts from scratch every session is like a new hire who asks the same questions every day. Separate memory into four layers and dynamically assemble "only what's needed right now" -- that's what makes an agent production-ready. 🔥 Problems Solved - Context loss across sessions: context vanishes between sessions and agents, forcing repeated work - Finite context window: stuffing full history into the window degrades both cost and accuracy - Memory bloat: unlimited accumulation increases cost, privacy risk, and context pollution - Lost in the middle: injecting too much context actually reduces answer accuracy 🏗️ Proposed Pattern Separate memory into four tiers: Working (current session, ephemeral), Episodic (past summaries, per-user), Semantic (RAG-indexed knowledge), and Organizational (people, teams, relationships as a knowledge graph). Assign each tier its own storage, TTL, and ACL. A Context Broker retrieves from relevant tiers based on user intent, then prioritizes, summarizes, and compresses within a token budget -- assembling context from only the most relevant information. ✅ Selection Criteria - When to use: agents providing continuous support across sessions and agent boundaries - When NOT: one-shot stateless tasks; use cases where a small, fixed context is sufficient ⚠️ Pitfalls - Episodic memory bloat: store summaries (not raw logs) and control with importance scores, TTL, and time-decay - Context broker quality: poor reranking lets irrelevant information slip through, degrading accuracy - Cross-layer ACL consistency: when layers have different ACLs, aggregation must reduce to the strictest permission 🛠️ Implementation Approach 1. Deploy a vector DB (Pinecone / Weaviate / pgvector) for the semantic memory tier and Neo4j for the organizational knowledge graph tier, configuring storage, TTL, and ACLs per tier 2. Build a summarization pipeline for episodic memory -- store summaries instead of raw logs and implement automatic forgetting via importance scores and time-decay 3. Implement the Context Broker with reranking (Cohere Rerank / cross-encoder) to dynamically assemble the most relevant information within a token budget (target ~8,000 tokens) based on user intent 4. Leverage memory management frameworks (Mem0 / Zep) for cross-session and cross-agent context persistence 5. Tag each memory tier with ACL metadata and integrate with the Context Firewall (P10) to reduce permissions to the strictest level at aggregation time #AIAgents# #EnterpriseArchitecture#