𝙏𝙝𝙤𝙪𝙜𝙝𝙩 𝙛𝙤𝙧 𝙩𝙝𝙚 𝙙𝙖𝙮: I've been mulling over how both OWL and SHACL can be employed during the decision-making phase for AI Agents when using a knowledge graph instead of relying on an LLM that hallucinates. In this way, the LLM can still be used for assessment and sensory feedback, but it augments the graph, not the other way around. OWL and SHACL serve different roles. SHACL is not just a preprocessing validator; it can play an active role in constraining, guiding, or triggering decisions, especially when integrated into AI pipelines. However, OWL is typically more central to inferencing and reasoning tasks. SHACL can actively participate in decision-making, especially when decisions require data integrity, constraint enforcement, or trigger-based logic. In complex agents, OWL provides the inferencing engine, while SHACL acts as the constraint gatekeeper and occasionally contributes to rule-based decision-making. For example, an AI agent processes RDF data describing an applicant's skills, degree, and experience. SHACL validates the data's structure, ensuring required fields are present and correctly formatted. OWL reasoning infers that the applicant is qualified for a technical role and matches the profile of a backend developer. SHACL is then used again to check policy compliance. With all checks passed, the applicant is shortlisted, and a follow-up email is triggered. In AI agent decision-making, OWL and SHACL often work together in complementary ways. SHACL is commonly used as a preprocessing step to validate incoming RDF data. If the data fails validation, it's flagged or excluded, ensuring only clean, structurally sound data reaches the OWL reasoner. In this role, SHACL acts as a gatekeeper. They can also operate in parallel or in an interleaved manner within a pipeline. As decisions evolve, SHACL shapes may be checked mid-process. Some AI agents even use SHACL as a rule engine—to trigger alerts, detect actionable patterns, or constrain reasoning paths—while OWL continues to handle more complex semantic inferences, such as class hierarchies or property logic. Finally, SHACL can augment decision-making by confirming whether OWL-inferred actions comply with specific constraints. OWL may infer that “A is a type of B, so do X,” and SHACL then determines whether doing X adheres to a policy or requirement. Because SHACL supports closed-world assumptions (which OWL does not), it plays a valuable role in enforcing policies or compliance rules during decision execution. Illustrated:

It’s already the end of Sunday — I hope you all had a wonderful week. Mine was exceptionally busy, with the GUG seminar and the upcoming tutorial preparation. I usually take time for a personal…

I'm trying to build a Knowledge Graph. Our team has done experiments with current libraries available (𝐋𝐥𝐚𝐦𝐚𝐈𝐧𝐝𝐞𝐱, 𝐌𝐢𝐜𝐫𝐨𝐬𝐨𝐟𝐭'𝐬 𝐆𝐫𝐚𝐩𝐡𝐑𝐀𝐆, 𝐋𝐢𝐠𝐡𝐫𝐚𝐠, 𝐆𝐫𝐚𝐩𝐡𝐢𝐭𝐢 etc.) From a Product perspective, they seem to be missing the basic, common-sense features. 𝐒𝐭𝐢𝐜𝐤 𝐭𝐨 𝐚 𝐅𝐢𝐱𝐞𝐝 𝐓𝐞𝐦𝐩𝐥𝐚𝐭𝐞: My business organizes information in a specific way. I need the system to use our predefined entities and relationships, not invent its own. The output has to be consistent and predictable every time. 𝐒𝐭𝐚𝐫𝐭 𝐰𝐢𝐭𝐡 𝐖𝐡𝐚𝐭 𝐖𝐞 𝐀𝐥𝐫𝐞𝐚𝐝𝐲 𝐊𝐧𝐨𝐰: We already have lists of our products, departments, and key employees. The AI shouldn't have to guess this information from documents. I want to seed this this data upfront so that the graph can be build on this foundation of truth. 𝐂𝐥𝐞𝐚𝐧 𝐔𝐩 𝐚𝐧𝐝 𝐌𝐞𝐫𝐠𝐞 𝐃𝐮𝐩𝐥𝐢𝐜𝐚𝐭𝐞𝐬: The graph I currently get is messy. It sees "First Quarter Sales" and "Q1 Sales Report" as two completely different things. This is probably easy but want to make sure this does not happen. 𝐅𝐥𝐚𝐠 𝐖𝐡𝐞𝐧 𝐒𝐨𝐮𝐫𝐜𝐞𝐬 𝐃𝐢𝐬𝐚𝐠𝐫𝐞𝐞: If one chunk says our sales were $10M and another says $12M, I need the library to flag this disagreement, not just silently pick one. It also needs to show me exactly which documents the numbers came from so we can investigate. Has anyone solved this? I'm looking for a library —that gets these fundamentals right. | 21 comments on LinkedIn

❓ Why I Wrote This Book? In the past two to three years, we've witnessed a revolution. First with ChatGPT, and now with autonomous AI agents. This is only the beginning. In the years ahead, AI will transform not only how we work but how we live. At the core of this transformation lies a single breakthrough technology: large language models (LLMs). That’s why I decided to write this book. This book explores what an LLM is, how it works, and how it develops its remarkable capabilities. It also shows how to put these capabilities into practice, like turning an LLM into the beating heart of an AI agent. Dissatisfied with the overly simplified or fragmented treatments found in many current books, I’ve aimed to provide both solid theoretical foundations and hands-on demonstrations. You'll learn how to build agents using LLMs, integrate technologies like retrieval-augmented generation (RAG) and knowledge graphs, and explore one of today’s most fascinating frontiers: multi-agent systems. Finally, I’ve included a section on open research questions (areas where today’s models still fall short, ethical issues, doubts, and so on), and where tomorrow’s breakthroughs may lie. 🧠 Who is this book for? Anyone curious about LLMs, how they work, and how to use them effectively. Whether you're just starting out or already have experience, this book offers both accessible explanations and practical guidance. It's for those who want to understand the theory and apply it in the real world. 🛑 Who is this book not for? Those who dismiss AI as a passing fad or have no interest in what lies ahead. But for everyone else this book is for you. Because AI agents are no longer speculative. They’re real, and they’re here. A huge thanks to my co-author Gabriele Iuculano, and the Packt's team: Gebin George, Sanjana Gupta, Ali A., Sonia Chauhan, Vignesh Raju., Malhar Deshpande #AI #LLMs #KnowledgeGraphs #AIagents #RAG #GenerativeAI #MachineLearning #NLP #Agents #DeepLearning | 22 comments on LinkedIn

Transform Claude's Hidden Memory Into Interactive Knowledge Graphs Universal tool to visualize any Claude user's memory.json in beautiful interactive graphs. Transform your Claude Memory MCP data into stunning interactive visualizations to see how your AI assistant's knowledge connects and evolves over time. Enterprise teams using Claude lack visibility into how their AI assistant accumulates and organizes institutional knowledge. Claude Memory Viz provides zero-configuration visualization that automatically finds memory files and displays 72 entities with 93 relationships in real-time force-directed layouts. Teams can filter by entity type, search across all data, and explore detailed connections through rich tooltips. The technical implementation supports Claude's standard NDJSON memory format, automatically detecting and color-coding entity types from personality profiles to technical tools. Node size reflects connection count, while adjustable physics parameters enable optimal spacing for large knowledge graphs. Built with Cytoscape.js for performance optimization. Built with the philosophy "Solve it once and for all," the tool works for any Claude user with zero configuration. The visualizer automatically searches common memory file locations, provides demo data fallback, and offers clear guidance when files aren't found. Integration requires just git clone and one command execution. This matters because AI memory has been invisible to users, creating trust and accountability gaps in enterprise AI deployment. When teams can visualize how their AI assistant organizes knowledge, they gain insights into decision-making patterns and can optimize their AI collaboration strategies. 👩‍💻https://lnkd.in/e__RQh_q | 10 comments on LinkedIn

Get this comprehensive guide from Manning on how to build a GraphRAG system from scratch. Available now for free!

By a mildly exasperated knowledge engineer

𝐁𝐨𝐨𝐤 𝐩𝐫𝐨𝐦𝐨𝐭𝐢𝐨𝐧 𝐛𝐞𝐜𝐚𝐮𝐬𝐞 𝐭𝐡𝐢𝐬 𝐨𝐧𝐞 𝐢𝐬 𝐰𝐨𝐫𝐭𝐡 𝐢𝐭.. 𝐀𝐠𝐞𝐧𝐭𝐢𝐜 𝐀𝐈 𝐚𝐭 𝐢𝐭𝐬 𝐛𝐞𝐬𝐭.. This masterpiece was published by Salvatore Raieli and Gabriele Iuculano, and it is available for orders from today, and it's already a 𝐁𝐞𝐬𝐭𝐬𝐞𝐥𝐥𝐞𝐫! While many resources focus on LLMs or basic agentic workflows, what makes this book stand out is its deep dive into grounding LLMs with real-world data and action through the powerful combination of 𝘙𝘦𝘵𝘳𝘪𝘦𝘷𝘢𝘭-𝘈𝘶𝘨𝘮𝘦𝘯𝘵𝘦𝘥 𝘎𝘦𝘯𝘦𝘳𝘢𝘵𝘪𝘰𝘯 (𝘙𝘈𝘎) 𝘢𝘯𝘥 𝘒𝘯𝘰𝘸𝘭𝘦𝘥𝘨𝘦 𝘎𝘳𝘢𝘱𝘩𝘴. This isn't just about building Agents; it's about building AI that reasons, retrieves accurate information, and acts autonomously by leveraging structured knowledge alongside advanced LLMs. The book offers a practical roadmap, packed with concrete Python examples and real-world case studies, guiding you from concept to deployment of intelligent, robust, and hallucination-minimized AI solutions, even orchestrating multi-agent systems. Order your copy here - https://packt.link/RpzGM #AI #LLMs #KnowledgeGraphs #AIAgents #RAG #GenerativeAI #MachineLearning

Design infrastructure for a generative AI application with GraphRAG using Spanner Graph.

How should we organize knowledge to provide the best context for agents? We show how knowledge graphs could play a key role in enhancing context for agents.

We present AutoSchemaKG, a framework for fully autonomous knowledge graph construction that eliminates the need for predefined schemas. Our system leverages large language models to simultaneously...

I've been working on autonomous AI systems, and wanted to share some thoughts on what I believe makes them effective. The challenge isn't just making AI that follows instructions well, but creating systems that can reason, and act independently.

LLMs already contain overlapping world models. You just have to ask them right. Ontologists reply to an LLM output, “That’s not a real ontology—it’s not a formal conceptualization.” But that’s just the No True Scotsman fallacy dressed up in OWL. Boring. Not growth-oriented. Look forward, angel. A foundation model is a compression of human knowledge. The real problem isn't that we "lack a conceptualization". The real problem with an FM is that they contain too many. FMs contain conceptualizations—plural. Messy? Sure. But usable. At Stardog, we’re turning this latent structure into real ontologies using symbolic knowledge distillation. Prompt orchestration → structure extraction → formal encoding. OWL, SHACL, and friends. Shake till mixed. Rinse. Repeat. Secret sauce simmered and reduced. This isn't theoretical hard. We avoid that. It’s merely engineering hard. We LTF into that! But the payoff means bootstrapping rich, new ontologies at scale: faster, cheaper, with lineage. It's the intersection of FM latent space, formal ontology, and user intent expressed via CQs. We call it the Symbolic Latent Layer (SLL). Cute eh? The future of enterprise AI isn’t just documents. It’s distilling structured symbolic knowledge from LLMs and plugging it into agents, workflows, and reasoning engines. You don’t need a priesthood to get a formal ontology anymore. You need a good prompt and a smarter pipeline and the right EKG platform. There's a lot more to say about this so I said it at Stardog Labs https://lnkd.in/eY5Sibed | 17 comments on LinkedIn

Graph is the new star schema. Change my mind. Why? Your agents can't be autonomous unless your structured data is a graph. It is really very simple. 1️⃣ To act autonomously, an agent must reason across structured data. Every autonomous decision - human or agent - hinges on a judgment: have I done enough? “Enough" boils down to driving the probability of success over some threshold. 2️⃣ You can’t just point the agent at your structured data store. Context windows are too small. Schema sprawl is too real. If you think it works, you probably haven’t tried it. 3️⃣ Agent must first retrieve - with RAG - the right tables, columns, and snippets. Decision making is a retrieval problem before it’s a reasoning problem. 4️⃣ Standard RAG breaks on enterprise metadata. The corpus is too entity-rich. Semantic similarity is breaking on enterprise help articles - it won't perform on column descriptions. 5️⃣ To make structured RAG work, you need a graph. Just like unstructured RAG needed links between articles, structured RAG needs links between tables, fields, and - most importantly - meaning. Yes, graphs are painful. But so was deep learning—until the return was undeniable. Agents need reasoning over structured data. That makes graphs non-optional. The rest is just engineering. Let’s stop modeling for reporting—and start modeling for autonomy. | 28 comments on LinkedIn

❓ How can you turn business questions into production-ready agentic knowledge graphs? Join Prashanth Rao and Dennis Irorere at the Agentic AI Summit to find out. Prashanth is an AI Engineer and DevRel lead at Kùzu Inc.—the open-source graph database startup—where he blends NLP, ML, and data engineering to power agentic workflows. Dennis is a Data Engineer at Tripadvisor’s Viator Marketing Technology team and Director of Innovation at GraphGeeks, driving scalable, AI-driven graph solutions for customer growth. In “Agentic Workflows for Graph RAG: Building Production-Ready Knowledge Graphs,” they’ll guide you through three hands-on lessons: 🔹 From Business Question to Graph Schema – Modeling your domain for downstream agents and LLMs, using live data sources like AskNews. 🔹 From Unstructured Data to Agent-Ready Graphs with BAML – Writing declarative pipelines that reliably extract entities and relationships at scale. 🔹 Agentic Graph RAG in Action – Completing the loop: translating NL queries into Cypher, retrieving graph data, and synthesizing responses—with fallback strategies when matches are missing. If you’re building internal tools or public-facing AI agents that rely on knowledge graphs, this workshop is for you. 🗓️ Learn more & register free: https://hubs.li/Q03qHnpQ0 #AgenticAI #GraphRAG #KnowledgeGraphs #AgentWorkflows #AIEngineering #ODSC #Kuzu #Tripadvisor

Find out how to combine a knowledge graph with RAG for GraphRAG. Provide more complete GenAI outputs.

Editor’s note: David Hughes and Amy Hodler are speakers for ODSC East 2025 this May 13th-15th. Check out their talk, “Advancing GraphRAG…

🚀 Introducing RAG-Anything: All-in-One RAG System! ⚡ LightRAG + Multi-Modal = RAG-Anything 🔗 Get started today: https://lnkd.in/gF3D8rnc 📦 Install: pip install raganything No more switching between multiple tools or losing critical visual information! With RAG-Anything, you get ONE unified solution that understands your documents as completely as you do ✨ 🌟 What makes RAG-Anything innovative: - 🔄 End-to-End Multimodal Pipeline: Complete workflow from document ingestion and parsing to intelligent multimodal query answering. - 📄 Universal Document Support: Seamless processing of PDFs, Office documents (DOC/DOCX/PPT/PPTX/XLS/XLSX), images, and diverse file formats. - 🧠 Specialized Content Analysis: Dedicated processors for images, tables, mathematical equations, and heterogeneous content types. - 🔗 Multimodal Knowledge Graph: Automatic entity extraction and cross-modal relationship discovery for enhanced understanding. - ⚡ Adaptive Processing Modes: Flexible MinerU-based parsing or direct multimodal content injection workflows. - 🎯 Hybrid Intelligent Retrieval: Advanced search capabilities spanning textual and multimodal content with contextual understanding. 💡 Well-suited for: - 🎓 Academic research with complex documents - 📋 Technical documentation processing - 💼 Financial report analysis - 🏢 Enterprise knowledge management

Gartner 2025 AI Hype Cycle: The focus is shifting from hype to foundational innovations Knowledge Graphs are a key part of the shift, positioned on the slope of enlightenment By Haritha Khandabattu and Birgi Tamersoy: Al investment remains strong, but focus is shifting from GenAl hype to foundational innovations like Al-ready data, Al agents, Al engineering and ModelOps. This research helps leaders prioritize high-impact, emerging Al techniques while navigating regulatory complexity and operational scaling. As Gartner notes, Generative AI capabilities are advancing at a rapid pace and the tools that will become available over the next 2-5 years will be transformative. The rapid evolution of these technologies and techniques continues unabated, as does the corresponding hype, making this tumultuous landscape difficult to navigate. These conditions mean GenAI continues to be a top priority for the C-suite. Weaving in another foundational concept, Systems of Intelligence as coined by Geoffrey Moore and reference by David Vellante and George Gilbert: Systems of Intelligence are the linchpin of modern enterprise architecture because [AI] agents are only as smart as the state of the business represented in the knowledge graph. If a platform controls that graph, it becomes the default policymaker for “why is this happening, what comes next, and what should we do?” For enterprises, there is only one feasible answer to the "who controls the graph" question: you should. To do that, start working on your enterprise knowledge graph today, if you haven't already. And if you are looking for the place to learn, network, and share experience and knowledge, look no further 👇 Connected Data London 2025 has been announced! 20-21 November, Leonardo Royal Hotel London Tower Bridge Join us for all things #KnowledgeGraph #Graph #analytics #datascience #AI #graphDB #SemTech 🎟️ Ticket sales are open. Benefit from early bird prices with discounts up to 30%. 2025.connected-data.london 📋 Call for submissions is open. Check topics of interest, submission process and evaluation criteria https://lnkd.in/dhbAeYtq 📺 Sponsorship opportunities are available. Maximize your exposure with early onboarding. Contact us at info@connected-data.london for more.

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HippoRAG is a technique inspired from the interactions between the cortex and hippocampus to improve knowledge retrieval for large language models (LLM).

Navigating the impact of graph databases and how AI agents address the limitations of static graphs with continuous knowledge base…

📣 AI Engineer World's Fair 2025: GraphRAG Track Spotlight! 🚀 So grateful to have hosted the GraphRAG Track at the Fair. The sessions were great, highlighting the depth and breadth of graph thinking for AI. Shoutouts to... - Mitesh Patel "HybridRAG" as a fusion of graph and vector retrieval designed to master complex data interpretation and specialized terminology for question answering - Chin Keong Lam "Wisdom Discovery at Scale" using Knowledge Augmented Generation (KAG) in a multi agent system with n8n - Sam Julien "When Vectors Break Down" carefully explaining how graph-based RAG architecture achieved a whopping 86.31% accuracy for dense enterprise knowledge - Daniel Chalef "Stop Using RAG as Memory" explored temporally-aware knowledge graphs, built by the open-source Graphiti framework, to provide precise, context-rich memory for agents, - Ola Mabadeje "Witness the power of Multi-Agent AI & Network Knowledge Graphs" showing dramatic improvements in ticket resolution efficiency and overall execution quality in network operations. - Thomas Smoker "Beyond Documents"! casually mentioning scraping the entire internet to distill a knowledge graph focused with legal agents - Mark Bain hosting an excellent Agentic Memory with Knowledge Graphs lunch&learn, with expansive thoughts and demos from Vasilije Markovic Daniel Chalef and Alexander Gilmore Also, of course, huge congrats to Shawn swyx W and Benjamin Dunphy on an excellent conference. 🎩 #graphrag Neo4j AI Engineer

Want to Fix LLM Hallucination? Neurosymbolic Alone Won’t Cut It The Conversation’s new piece makes a clear case for neurosymbolic AI—integrating symbolic logic with statistical learning—as the long-term fix for LLM hallucinations. It’s a timely and necessary argument: “No matter how large a language model gets, it can’t escape its fundamental lack of grounding in rules, logic, or real-world structure. Hallucination isn’t a bug, it’s the default.” But what’s crucial—and often glossed over—is that symbolic logic alone isn’t enough. The real leap comes from adding formal ontologies and semantic constraints that make meaning machine-computable. OWL, Shapes Constraint Language (SHACL), and frameworks like BFO, Descriptive Ontology for Linguistic and Cognitive Engineering (DOLCE), the Suggested Upper Merged Ontology (SUMO), and the Common Core Ontologies (CCO) don’t just “represent rules”—they define what exists, what can relate, and under what conditions inference is valid. That’s the difference between “decorating” a knowledge graph and engineering one that can detect, explain, and prevent hallucinations in practice. I’d go further: • Most enterprise LLM hallucinations are just semantic errors—mislabeling, misattribution, or class confusion that only formal ontologies can prevent. • Neurosymbolic systems only deliver if their symbolic half is grounded in ontological reality, not just handcrafted rules or taxonomies. The upshot: We need to move beyond mere integration of symbols and neurons. We need semantic scaffolding—ontologies as infrastructure—to ensure AI isn’t just fluent, but actually right. Curious if others are layering formal ontologies (BFO, DOLCE, SUMO) into their AI stacks yet? Or are we still hoping that more compute and prompt engineering will do the trick? #NeuroSymbolicAI #SemanticAI #Ontology #LLMs #AIHallucination #KnowledgeGraphs #AITrust #AIReasoning

AutoSchemaKG: Building Billion-Node Knowledge Graphs Without Human Schemas 👉 Why This Matters Traditional knowledge graphs face a paradox: they require expert-crafted schemas to organize information, creating bottlenecks for scalability and adaptability. This limits their ability to handle dynamic real-world knowledge or cross-domain applications effectively. 👉 What Changed AutoSchemaKG eliminates manual schema design through three innovations: 1. Dynamic schema induction: LLMs automatically create conceptual hierarchies while extracting entities/events 2. Event-aware modeling: Captures temporal relationships and procedural knowledge missed by entity-only approaches 3. Multi-level conceptualization: Organizes instances into semantic categories through abstraction layers The system processed 50M+ documents to build ATLAS - a family of KGs with: - 900M+ nodes (entities/events/concepts) - 5.9B+ relationships - 95% alignment with human-created schemas (zero manual intervention) 👉 How It Works 1. Triple extraction pipeline:   - LLMs identify entity-entity, entity-event, and event-event relationships   - Processes text at document level rather than sentence level for context preservation 2. Schema induction:   - Automatically groups instances into conceptual categories   - Creates hierarchical relationships between specific facts and abstract concepts 3. Scale optimization:   - Handles web-scale corpora through GPU-accelerated batch processing   - Maintains semantic consistency across 3 distinct domains (Wikipedia, academic papers, Common Crawl) 👉 Proven Impact - Boosts multi-hop QA accuracy by 12-18% over state-of-the-art baselines - Improves LLM factuality by up to 9% on specialized domains like medicine and law - Enables complex reasoning through conceptual bridges between disparate facts 👉 Key Insight The research demonstrates that billion-scale KGs with dynamic schemas can effectively complement parametric knowledge in LLMs when they reach critical mass (1B+ facts). This challenges the assumption that retrieval augmentation needs domain-specific tuning to be effective. Question for Discussion As autonomous KG construction becomes viable, how should we rethink the role of human expertise in knowledge representation? Should curation shift from schema design to validation and ethical oversight? | 15 comments on LinkedIn

Introducing FACT: Fast Augmented Context Tools (3.2x faster, 90% cost reduction vs RAG) RAG had its run, but it’s not built for agentic systems. Vectors are fuzzy, slow, and blind to context. They work fine for static data, but once you enter recursive, real-time workflows, where agents need to reason, act, and reflect. RAG collapses under its own ambiguity. That’s why I built FACT: Fast Augmented Context Tools. Traditional Approach: User Query → Database → Processing → Response (2-5 seconds) FACT Approach: User Query → Intelligent Cache → [If Miss] → Optimized Processing → Response (50ms) It replaces vector search in RAG pipelines with a combination of intelligent prompt caching and deterministic tool execution via MCP. Instead of guessing which chunk is relevant, FACT explicitly retrieves structured data, SQL queries, live APIs, internal tools, then intelligently caches the result if it’s useful downstream. The prompt caching isn’t just basic storage. It’s intelligent using the prompt cache from Anthropic and other LLM providers, tuned for feedback-driven loops: static elements get reused, transient ones expire, and the system adapts in real time. Some things you always want cached, schemas, domain prompts. Others, like live data, need freshness. Traditional RAG is particularly bad at this. Ask anyone force to frequently update vector DBs. I'm also using Arcade.dev to handle secure, scalable execution across both local and cloud environments, giving FACT hybrid intelligence for complex pipelines and automatic tool selection. If you're building serious agents, skip the embeddings. RAG is a workaround. FACT is a foundation. It’s cheaper, faster, and designed for how agents actually work: with tools, memory, and intent. To get started point your favorite coding agent at: https://lnkd.in/gek_akem | 38 comments on LinkedIn

🏯🏇 A-MEM Transforms AI Agent Memory with Zettelkasten Method, Atomic Notes, Dynamic Linking & Continuous Evolution! This Novel Memory fixes rigid structures with adaptable, evolving, and interconnected knowledge networks, delivering 2x performance in complex reasoning tasks. 𝗧𝗵𝗶𝘀 𝗶𝘀 𝘄𝗵𝗮𝘁 𝗜 𝗹𝗲𝗮𝗿𝗻𝗲𝗱: ﹌﹌﹌﹌﹌﹌﹌﹌﹌ 》 𝗪𝗵𝘆 𝗧𝗿𝗮𝗱𝗶𝘁𝗶𝗼𝗻𝗮𝗹 𝗠𝗲𝗺𝗼𝗿𝘆 𝗙𝗮𝗹𝗹 𝗦𝗵𝗼𝗿𝘁 Most AI agents today rely on simplistic storage and retrieval but break down when faced with complex, multi-step reasoning tasks. ✸ Common Limitations: ☆ Fixed schemas: Conventional memory systems require predefined structures that limit flexibility. ☆ Limited adaptability: When new information arises, old memories remain static and disconnected, reducing an agent’s ability to build on past experiences. ☆ Ineffective long-term retention: AI agents often struggle to recall relevant past interactions, leading to redundant processing and inefficiencies. ﹌﹌﹌﹌﹌﹌﹌﹌﹌ 》𝗔-𝗠𝗘𝗠: 𝗔𝘁𝗼𝗺𝗶𝗰 𝗻𝗼𝘁𝗲𝘀 𝗮𝗻𝗱 𝗗𝘆𝗻𝗮𝗺𝗶𝗰 𝗹𝗶𝗻𝗸𝗶𝗻𝗴 A-MEM organizes knowledge in a way that mirrors how humans create and refine ideas over time. ✸ How it Works: ☆ Atomic notes: Information is broken down into small, self-contained knowledge units, ensuring clarity and easy integration with future knowledge. ☆ Dynamic linking: Instead of relying on static categories, A-MEM automatically creates connections between related knowledge, forming a network of interrelated ideas. ﹌﹌﹌﹌﹌﹌﹌﹌﹌ 》 𝗣𝗿𝗼𝘃𝗲𝗻 𝗣𝗲𝗿𝗳𝗼𝗿𝗺𝗮𝗻𝗰𝗲 𝗔𝗱𝘃𝗮𝗻𝘁𝗮𝗴𝗲 A-MEM delivers measurable improvements. ✸ Empirical results demonstrate: ☆ Over 2x performance improvement in complex reasoning tasks, where AI must synthesize multiple pieces of information across different timeframes. ☆ Superior efficiency across top foundation models, including GPT, Llama, and Qwen—proving its versatility and broad applicability. ﹌﹌﹌﹌﹌﹌﹌﹌﹌ 》 𝗜𝗻𝘀𝗶𝗱𝗲 𝗔-𝗠𝗘𝗠 ✸ Note Construction: ☆ AI-generated structured notes that capture essential details and contextual insights. ☆ Each memory is assigned metadata, including keywords and summaries, for faster retrieval. ✸ Link Generation: ☆ The system autonomously connects new memories to relevant past knowledge. ☆ Relationships between concepts emerge naturally, allowing AI to recognize patterns over time. ✸ Memory Evolution: ☆ Older memories are continuously updated as new insights emerge. ☆ The system dynamically refines knowledge structures, mimicking the way human memory strengthens connections over time. ≣≣≣≣≣≣≣≣≣≣≣≣≣≣≣≣≣≣≣≣≣≣≣≣≣≣ ⫸ꆛ Want to build Real-World AI agents? Join My 𝗛𝗮𝗻𝗱𝘀-𝗼𝗻 𝗔𝗜 𝗔𝗴𝗲𝗻𝘁 𝟰-𝗶𝗻-𝟭 𝗧𝗿𝗮𝗶𝗻𝗶𝗻𝗴 TODAY! 𝟰𝟴𝟬+ already Enrolled. ➠ Build Real-World AI Agents for Healthcare, Finance,Smart Cities,Sales ➠ Learn 4 Framework: LangGraph | PydanticAI | CrewAI | OpenAI Swarm ➠ Work with Text, Audio, Video and Tabular Data 👉𝗘𝗻𝗿𝗼𝗹𝗹 𝗡𝗢𝗪 (𝟰𝟱% 𝗱𝗶𝘀𝗰𝗼𝘂𝗻𝘁): https://lnkd.in/eGuWr4CH | 27 comments on LinkedIn

RAG vs Graph RAG, explained visually. (it's a popular LLM interview question) Imagine you have a long document, say a biography, about an individual (X) who has accomplished several things in this life. ↳ Chapter 1: Talks about Accomplishment-1. ↳ Chapter 2: Talks about Accomplishment-2. ... ↳ Chapter 10: Talks about Accomplishment-10. Summarizing all these accomplishments via RAG might never be possible since... ...it must require the entire context... ...but one might only be fetching the top-k relevant chunks from the vector db. Moreover, since traditional RAG systems retrieve each chunk independently, this can often leave the LLM to infer the connections between them (provided the chunks are retrieved). Graph RAG solves this. The idea is to first create a graph (entities & relationships) from the documents and then do traversal over that graph during the retrieval phase. See how Graph RAG solves the above problems. - First, a system (typically an LLM) will create the graph by understanding the biography. - This will produce a full graph of nodes entities & relationships, and a subgraph will look like this: ↳ X → → Accomplishment-1. ↳ X → → Accomplishment-2. ... ↳ X → → Accomplishment-N. When summarizing these accomplishments, the retrieval phase can do a graph traversal to fetch all the relevant context related to X's accomplishments. This context, when passed to the LLM, will produce a more coherent and complete answer as opposed to traditional RAG. Another reason why Graph RAG systems are so effective is because LLMs are inherently adept at reasoning with structured data. Graph RAG instills that structure into them with their retrieval mechanism. 👉 Over to you: What are some other issues with traditional RAG systems that Graph RAG solves? ____ Find me → Avi Chawla Every day, I share tutorials and insights on DS, ML, LLMs, and RAGs. | 24 comments on LinkedIn