# GraphRAG Projects at AI Tinkerers > Canonical HTML: https://aitinkerers.org/technologies/graphrag > Markdown URL: https://aitinkerers.org/technologies/graphrag.md > Technology record last updated: 2026-09-18T15:13:41Z > Generated: 2026-09-22T23:34:11Z GraphRAG integrates knowledge graphs with Retrieval-Augmented Generation (RAG) to enable multi-hop reasoning and deliver context-rich, verifiable LLM responses. GraphRAG is a superior RAG architecture: it moves beyond simple vector-based semantic search. The system constructs a knowledge graph (KG) from unstructured data, extracting entities and relationships (nodes and edges). This structure allows the Large Language Model (LLM) to perform complex, multi-hop reasoning, a task where baseline RAG systems often fail. By leveraging the KG's relational context instead of isolated text chunks, GraphRAG significantly improves answer accuracy, reduces hallucination, and provides a clear, traceable provenance for the generated response. It is a critical upgrade for enterprise GenAI applications demanding high-trust, explainable results. - Official technology site: https://graphrag.com - Public AI Tinkerers demos and talks: 13 - Result page: 1 of 1 ## Recent Public Talks and Demos ### [Prediciendo el futuro con inteligencia de enjambre](https://manizales.aitinkerers.org/talks/rsvp_QR6Nx_VHh04) MiroFish es un motor de predicción con IA de nueva generación basado en tecnología multi-agente. La inteligencia de enjambre lleva acompañándonos mucho tiempo, pero MiroFish lleva este concepto al siguiente nivel: mediante la extracción de información semilla del mundo real (noticias, señales financieras, borradores de políticas), construye automáticamente un mundo digital paralelo de alta fidelidad donde miles de agentes inteligentes con personalidades independientes, memoria a largo plazo y lógica conductual interactúan libremente. Puedes inyectar variables dinámicamente para deducir trayectorias futuras con precisión. En esta charla presentaremos MiroFish-ES, el fork en español del proyecto, y haremos una demo en vivo de sus capacidades de predicción. - Event context: 🚀 ¡14vo Encuentro de AI Tinkerers Manizales! 🤖 — 2026-03-25 — Manizales - Public talk page: https://manizales.aitinkerers.org/talks/rsvp_QR6Nx_VHh04 ### [The Semantic Kill Chain: Bypassing Deterministic Memory with Malicious Intents](https://toronto.aitinkerers.org/talks/rsvp_8mrzc9297Xw) A live demonstration of "Semantic Laundering" in Agentic Memory. I will show how LLM extraction pipelines can be coerced into acting as proxies for malicious commands. By wrapping destructive instructions in the semantic texture of valid ontology entities, I will demonstrate how standard structural validation fails. The system validates syntax but remains blind to semantics, committing the payload to the Knowledge Graph as an actionable tool. - Event context: AI Tinkerers Toronto - February 2026 @ Cohere! — 2026-02-26 — Toronto - Public talk page: https://toronto.aitinkerers.org/talks/rsvp_8mrzc9297Xw ### [Neo4j Live: Inside StrangerGraphs – Predicting Season 5 with Graph Intelligence](https://paris.aitinkerers.org/talks/rsvp_3nhfxvOKTRg) Join us as we break down StrangerGraphs, a prediction graph built from Reddit fan theories, Neo4j AuraDB, GPT-5 analysis, and GraphRAG-powered agents to explore what the Stranger Things community got right in past seasons – and what they might reveal about Season 5. Key Highlights: – Reddit prediction mining + GPT-5 accuracy scoring – Leiden clustering to find high-signal predictor communities – Season 5 predictions extracted from accuracy-based hubs – AuraDB + GraphRAG powering character-aware AI agents - Event context: Turn Your Knowledge into an API for LLMs - Meetup — 2025-12-09 — Paris - Public talk page: https://paris.aitinkerers.org/talks/rsvp_3nhfxvOKTRg ### [Más allá del RAG: Grafos universales para datos no universales](https://santiago.aitinkerers.org/talks/rsvp_Ic5kPKxkc3I) Hoy, la mayoría de los sistemas de information retrieval usan arquitecturas tipo RAG o GraphRAG: se basan en búsqueda semántica, embeddings y modelos de lenguaje para recuperar información relevante. - Event context: Estructura, Risa y Ecosistemas: el nuevo ADN de la IA Chilena — 2025-10-29 — Santiago - Public talk page: https://santiago.aitinkerers.org/talks/rsvp_Ic5kPKxkc3I ### [Calmo Demo](https://amsterdam.aitinkerers.org/talks/rsvp_Rw59-ZdpHpc) Calmo is an AI Agent that debugs production issue, we will showcase how Calmo brings engineering teams from alert to resolutions in a few minutes (instead of hours). - Event context: AI Tinkerers Amsterdam — October Edition: Agents in production — 2025-10-10 — Amsterdam - Public talk page: https://amsterdam.aitinkerers.org/talks/rsvp_Rw59-ZdpHpc ### [Empowering Creative Minds with OriginMind](https://hong-kong.aitinkerers.org/talks/rsvp_xz6iQqyUSK8) This demo introduces OriginMind AI, a voice-interactive creative mentor designed to support ideation through intuitive conversation. Rooted in the philosophy that “art is in the process,” OriginMind offers an alternative to one-click AI tools by emphasizing dialogue, iteration, and human agency throughout the creative journey. OriginMind is built around a natural, voice-first interface that mirrors how ideas emerge organically—through reflection, conversation, and associative thinking. It enables users to brainstorm, research, and solve creative challenges in a way that feels more like speaking with a mentor than operating a tool. At the core of the system is Retrieval-Augmented Generation (RAG), which allows the AI to draw on user-provided knowledge—such as notes, references, or past work—to provide grounded, context-aware support. Conceptually, RAG serves as a form of personal memory, ensuring that responses remain relevant to the user’s evolving ideas and creative goals. We will also introduce the conceptual framework for GraphRAG, our approach to long-term memory. By structuring conversations and ideas as a graph, the system aims to trace the development of creative thought over time, recognize patterns, and surface connections between disparate concepts. This vision supports a more meaningful and cumulative creative process—one in which the AI grows with the user, rather than simply responding in the moment. Ultimately, this demo illustrates how conversational AI can empower—not replace—creative thinking. OriginMind is designed not to deliver finished outputs, but to serve as a reflective companion that nurtures creative growth and helps users stay connected to their vision. - Event context: AI Tinkerers - Hong Kong Meetup (May) - Tipsy Thursday x AI Tinkerers at Hong Kong Science Park — 2025-05-29 — Hong Kong - Public talk page: https://hong-kong.aitinkerers.org/talks/rsvp_xz6iQqyUSK8 ### [Nuance](https://singapore.aitinkerers.org/talks/rsvp_WXOTat5Ick0) Virtual Project Manager Agent that understands full context of any project and code that does your work for you - Event context: AI Tinkerers Singapore: 6th Meetup - April 25th, 2025 — 2025-04-25 — Singapore - Public talk page: https://singapore.aitinkerers.org/talks/rsvp_WXOTat5Ick0 ### [aius.co—the long-term memory agentic framework](https://poland.aitinkerers.org/talks/rsvp_z5X52GgnKbE) Hi, I'm Mark, and I'm excited to share how we've been developing aius.co—the long-term memory agentic system that autonomously evolves new agents and memories based on real-world interactions. Built on a modular, agentic architecture, AIUS enables flexible memory persistence, compute allocation, and autonomous decision-making. Let’s dive into how it works and explore future possibilities together! - Event context: AI Tinkerers Poland #3 - Meetup in Warsaw (March) — 2025-03-20 — Poland - Public talk page: https://poland.aitinkerers.org/talks/rsvp_z5X52GgnKbE ### [Xelerit - AI Copilot for industrial robotics](https://zurich.aitinkerers.org/talks/rsvp_a35OOTyQzVs) I will go through how our software works, which mirror the complete work of a robotics engineer, making it much faster. Our mvp has: • robot code generation (in the native robot-brand language) • copilot chat (for easy navigation of robot docs) • code translator between robot languages • I/O automatic configuration from PLC to robot. • Simulation - Event context: AI Tinkerers Zurich - February 6 — 2025-02-06 — Zürich - Public talk page: https://zurich.aitinkerers.org/talks/rsvp_a35OOTyQzVs ### [GraphRAG in action: solving real-life use cases with intelligent retrieval and generation](https://poland.aitinkerers.org/talks/rsvp_4FU8Sw9rrJ4) RAG serves as a prime example of a practical implementation of Generative AI, with clear ROI and proven use cases widely documented online. However, when you dive deeper and attempt to solve real-world problems, the process is often more complex than it initially appears. Off-the-shelf solutions frequently fall short, and in this talk, I’ll explain why that’s the case. As a more robust alternative, I’ll introduce a powerful approach to intelligent RAG: the GraphRAG framework, applied specifically to customer service tickets. This presents an intriguing challenge because, as in real-life scenarios, we lack a well-structured knowledge base. Instead, we must build one from existing email correspondence and resolved tickets. Traditional methods for managing such documents often struggle to maintain relevance and deliver contextually accurate answers. In this session, we’ll explore how GraphRAG—a hybrid approach that combines graph-based retrieval and generative models—can enhance customer service workflows. Through a hands-on demonstration, I’ll highlight where GraphRAG excels, its limitations, and the practical lessons learned. Ultimately, I aim to answer a question I once had: Is GraphRAG truly worthwhile, and what is its tangible impact on real-world use cases, beyond carefully curated examples? - Event context: AI Tinkerers Poland - Second Meetup in Warsaw (January) — 2025-01-30 — Poland - Public talk page: https://poland.aitinkerers.org/talks/rsvp_4FU8Sw9rrJ4 ### [Graph RAG: Combining the power of vectors and graphs for better retrieval](https://toronto.aitinkerers.org/talks/rsvp_34ZT1TVKokY) In this demo, I'll showcase how to bring together the power of graph and vector search to provide LLMs with better context when generating responses for RAG. Because constructing a knowledge graph is typically the biggest bottleneck towards using them in real applications, I'll also quickly showcase some new frameworks and tools to prompt LLMs to extract structured data from unstructured text, and how to easily store them as an on-disk graph that can be improved over time. - Event context: AI Tinkerers Toronto - Spooky Botober Meetup at Mozilla HQ — 2024-10-30 — Toronto - Public talk page: https://toronto.aitinkerers.org/talks/rsvp_34ZT1TVKokY ### [Enhancing AI with RAG - Techniques to improve accuracy](https://bogota.aitinkerers.org/talks/rsvp_QjI40TNRTXw) This talk explores how to optimize AI companions for project management using RAG. Standard RAG models struggle with context, leading to incomplete or inaccurate data retrieval, which is important when managing resources and making decisions. We’ll explore GraphRAG and context-aware RAG, which use knowledge graphs and enhanced contextual embeddings to improve the precision and relevance of information retrieval. - Event context: AI Tinkerers Octubre en Colombia 4.0 — 2024-10-30 — Bogotá - Public talk page: https://bogota.aitinkerers.org/talks/rsvp_QjI40TNRTXw ### [GraphRAG: Using Knowledge Graphs to enhance retrieval](https://boston.aitinkerers.org/talks/rsvp_s5H14q-W4F4) In this talk I will elaborate on GraphRAG as described in a paper by Microsoft released in April, 2024. I hope to cover the limitations of traditional RAG pipelines, why we might want query-focused summarization, and how knowledge graphs can help us retrieve more relevant global context surrounding a query. - Event context: July 2024 Meetup at C10 Labs — 2024-07-22 — Boston - Public talk page: https://boston.aitinkerers.org/talks/rsvp_s5H14q-W4F4 ## Related Technologies - [RAG](https://aitinkerers.org/technologies/rag) ([Markdown](https://aitinkerers.org/technologies/rag.md)) — 147 public demos - [AI agents](https://aitinkerers.org/technologies/ai-agents) ([Markdown](https://aitinkerers.org/technologies/ai-agents.md)) — 35 public demos - [BERT](https://aitinkerers.org/technologies/bert) ([Markdown](https://aitinkerers.org/technologies/bert.md)) — 179 public demos - [GPT-3](https://aitinkerers.org/technologies/gpt-3) ([Markdown](https://aitinkerers.org/technologies/gpt-3.md)) — 191 public demos - [Graph database](https://aitinkerers.org/technologies/graph-database) ([Markdown](https://aitinkerers.org/technologies/graph-database.md)) — 2 public demos - [Knowledge Graph](https://aitinkerers.org/technologies/knowledge-graph) ([Markdown](https://aitinkerers.org/technologies/knowledge-graph.md)) — 9 public demos - [Neo4j](https://aitinkerers.org/technologies/neo4j) ([Markdown](https://aitinkerers.org/technologies/neo4j.md)) — 29 public demos - [Python](https://aitinkerers.org/technologies/python) ([Markdown](https://aitinkerers.org/technologies/python.md)) — 662 public demos - [RoBERTa](https://aitinkerers.org/technologies/roberta) ([Markdown](https://aitinkerers.org/technologies/roberta.md)) — 118 public demos - [Agentic frameworks](https://aitinkerers.org/technologies/agentic-frameworks) ([Markdown](https://aitinkerers.org/technologies/agentic-frameworks.md)) — 1 public demo - [aius](https://aitinkerers.org/technologies/aius) ([Markdown](https://aitinkerers.org/technologies/aius.md)) — 1 public demo - [aize](https://aitinkerers.org/technologies/aize) ([Markdown](https://aitinkerers.org/technologies/aize.md)) — 1 public demo - [alBERT](https://aitinkerers.org/technologies/albert) ([Markdown](https://aitinkerers.org/technologies/albert.md)) — 4 public demos - [Apache Jena](https://aitinkerers.org/technologies/apache-jena) ([Markdown](https://aitinkerers.org/technologies/apache-jena.md)) — 1 public demo - [Apache Lucene](https://aitinkerers.org/technologies/apache-lucene) ([Markdown](https://aitinkerers.org/technologies/apache-lucene.md)) — 2 public demos - [ArangoDB](https://aitinkerers.org/technologies/arangodb) ([Markdown](https://aitinkerers.org/technologies/arangodb.md)) — 2 public demos - [Blazegraph](https://aitinkerers.org/technologies/blazegraph) ([Markdown](https://aitinkerers.org/technologies/blazegraph.md)) — 2 public demos - [BLOOM](https://aitinkerers.org/technologies/bloom) ([Markdown](https://aitinkerers.org/technologies/bloom.md)) — 115 public demos