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#

📚 Wouldn't it be great to run ReAct, RAG, and Tree of Thoughts behind one API and compare them, instead of wrangling scattered per-paper implementations? This repo delivers exactly that, with all 35 patterns in one place. Title: FareedKhan-dev/all-agentic-architectures URL: 📦 Overview This is a Python library and a "living textbook" implementing 35 production-grade agentic AI patterns. Every architecture exposes the same .run(task) method and returns an identical result shape, so you can swap patterns without touching downstream code. ❓ Challenges Solved Agentic design patterns have been scattered across papers, each with its own implementation and conventions. The real value here is unifying them under a single interface so you can try them side by side. 💡 Core Idea & Approach The central idea is the "deterministic-picker discipline." ・Instead of handing scoring entirely to the LLM, it first has the LLM commit to categorical features like booleans and enums ・The final decision is then composed in Python logic This mitigates the flat-band pathology of LLM-as-Scorer, and it appears in 13 of the 35 architectures. 🎯 Coverage & Use Cases It spans eight families: reasoning and reflection (Reflection, Self-Discover), search (Tree of Thoughts, LATS), RAG (Corrective/Self/Adaptive/GraphRAG), memory (MemGPT, Voyager), tools and actions (ReAct, SWE-Agent), and multi-agent (Debate, STORM). Each pattern ships with an executed Jupyter notebook, giving reproducible references grounded in real LLM output. 📊 Highlights ・Built on LangGraph, with support for Nebius, OpenAI, Anthropic, Ollama and more, switchable via a single env var ・283 passing pytest tests ・On a 17-task benchmark it recently scored 33/42 correct (78%), with Reflection and Self-Consistency among the strongest #AIAgents# #LangGraph#

📄 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#