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Traces, knowledge, and reasoning paths form a reliable source of truth for AI agents. 🤝 Charles Ivie, former Senior Graph Architect at Amazon Web Services, on why knowledge-backed traces are essential for trustworthy AI. 03:34 – Why data origin matters 07:17 – Interoperability and trusted knowledge 11:12 – Knowledge... show more

OriginTrail

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12,528 Aufrufe • vor 8 Monaten •via X (Twitter)

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Building AI agents? 🚧 Make sure they actually know where their answers come from. As Brana Rakic demonstrates, scalable AI requires verifiable knowledge, rule-based reasoning, and LLMs grounded in trusted memory. Key highlights: 03:25 - From human data to era of experience 07:42 - Agent architecture: memory + models 15:18 - Knowledge graphs 101: triples explained 34:53 - Symbolic reasoning vs hallucination 39:03 - Knowledge Asset, UAL & ownership

Building AI agents? 🚧 Make sure they actually know where their answers come from. As Brana Rakic demonstrates, scalable AI requires verifiable knowledge, rule-based reasoning, and LLMs grounded in trusted memory. Key highlights: 03:25 - From human data to era of experience 07:42 - Agent architecture: memory + models 15:18 - Knowledge graphs 101: triples explained 34:53 - Symbolic reasoning vs hallucination 39:03 - Knowledge Asset, UAL & ownership

OriginTrail

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When data origins are unclear, trust becomes a core challenge in AI today. Transparency and authenticity play a central role, as Charles Ivie, former Senior Graph Architect at Amazon Web Services, explains. 🤝

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knowledge graphs are INSANELY good at giving agents human-like memory! today, we're building an MCP-powered memory layer that can be shared across all your AI apps like Cursor, Claude Desktop etc. it's built using a real-time knowledge graph. 100% open-source and self-hosted.

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Knowledge graphs are infinitely better than vector search for building the memory of AI agents. With five lines of code, you can build a knowledge graph with your data. When you see the results, you'll never go back to vector-mediocrity-land. Here is a quick video:

Knowledge graphs are infinitely better than vector search for building the memory of AI agents. With five lines of code, you can build a knowledge graph with your data. When you see the results, you'll never go back to vector-mediocrity-land. Here is a quick video:

Santiago

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IBM just turned five of its best graph lectures into a 50-minute course on building AI systems around connected knowledge: • 00:00 - How knowledge graphs represent entities and relationships • 05:36 - Why vector-based RAG misses connections between facts • 09:53 - GraphRAG, precision retrieval and context engineering • 20:43 - How graph neural networks learn from connected data • 37:30 - Building GraphRAG with knowledge graphs and Cypher The progression is simple: Text chunks → entities → relationships → graph retrieval → graph intelligence Vector search finds information that looks similar. Graphs recover the structure behind that information. This 50-minute course will teach you more about modern graph engineering than most paid AI programs. Watch it today, then read how to become a knowledge graph engineer in the article below.

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Morlex

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Andrew Ng just released a 1-hour course on building agentic knowledge Graphs from scratch: • 00:00 - Introduction to agentic knowledge Graphs • 03:07 - Construction of agentic Graphs • 14:00 - Architecture of multi-agent systems • 23:00 - Building agentic graphs with Google ADK • 01:06:03 - Why Graphsare the future of agentic AI Worth more than 10 articles on loop engineering. Watch it today, then read how to become a graph engineer in the article below.

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Codez

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I added a Knowledge Graph to Cursor using MCP. You gotta see this working! Knowledge graphs are a game-changer for AI Agents, and this is one example of how you can take advantage of them. How this works: 1. Cursor connects to Graphiti's MCP Server. Graphiti is a very popular open-source Knowledge Graph library for AI agents. 2. Graphiti connects to Neo4j running locally. Now, every time I interact with Cursor, the information is synthesized and stored in the knowledge graph. In short, Cursor now "remembers" everything about our project. Huge! Here is the video I recorded.

I added a Knowledge Graph to Cursor using MCP. You gotta see this working! Knowledge graphs are a game-changer for AI Agents, and this is one example of how you can take advantage of them. How this works: 1. Cursor connects to Graphiti's MCP Server. Graphiti is a very popular open-source Knowledge Graph library for AI agents. 2. Graphiti connects to Neo4j running locally. Now, every time I interact with Cursor, the information is synthesized and stored in the knowledge graph. In short, Cursor now "remembers" everything about our project. Huge! Here is the video I recorded.

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OriginTrail

14,974 Aufrufe • vor 8 Monaten

Still the best hour on graph engineering ever recorded, Andrew Ng breaking down how to build agentic knowledge graphs from scratch: 00:00 - what agentic knowledge graphs actually are 03:05 - building a graph from scratch 13:58 - the architecture behind multi-agent systems 22:57 - building a real one with Google ADK 01:06:02 - why graphs are the future of AI agents I've seen $500 courses that teach less. Watch it, then take it further with my step-by-step guide on graph engineering below.

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Codez

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Google just showed how AI engineers are moving from simple "RAG" to "graphs", memory and multimodal agents • 15:32 - setting up the production agent stack • 28:00 - turning disconnected data into a knowledge graph • 41:00 - Graph RAG with semantic + hybrid search • 58:00 - extracting graph context from images, text and video • 1:09:00 - orchestrating specialized agents with ADK • 1:20:00 - giving agents persistent memory across sessions 94-minute Google Cloud workshop, and it’s one of the clearest hands-on examples of the shift from -> RAG → Graph RAG → Memory → Multimodal Agent Graphs. The workshop uses Spanner Graph, Gemini, ADK and Memory Bank to build that stack. Watch it today, then read the full “From RAG to Context Graphs” roadmap below.

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Stop prompt-stuffing yesterday’s context into your agent. Give it verifiable memory. 🕸️ When memory has provenance, agents can reuse it, audit it, and carry it across tools. Brana Rakic, Shaw (spirit/acc) (elizaOS), Ortecha | data made human & JoeVezz (LunarCrush 🌕™) break it down. 00:57 – What agents need to scale 05:45 – Social data, signals & real-time context 13:42 – Knowledge graphs as shared memory for AI agents 29:13 – Interoperability, trust & coordination

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OriginTrail

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🎓 Why can’t rational people just agree to disagree? In this episode, Steven Pinker unpacks how common knowledge shapes truth, trust, and the invisible rules holding civilization together—from markets and morality to AI and belief itself. Timestamp: 00:00 Intro 00:58 When common knowledge is wrong 02:37 The role of common knowledge in social coordination 08:48 Free will and determinism 14:48 Judging a book by its cover 21:08 Government suppression and common knowledge 30:08 The agree to disagree theorem 34:54 Generosity and charity in human affairs 41:12 The role of common knowledge in religious beliefs 48:31 LLMs and common knowledge 50:39 Outro

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INTO THE IMPOSSIBLE Podcast

14,328 Aufrufe • vor 11 Monaten

Knowledge graphs for representing information are unbeatable. After this, you will never build a RAG system without knowledge graphs. It will take you five lines of code to build a knowledge graph with your data. I recorded a video to show you how you can do this. I used Cognee, an open-source library that outperforms any basic vector search approach in terms of retrieval relevance. They are collaborating with me on this post. Cognee is: • Easy to use • Reduces hallucinations • Open-source Here is a link to the repository: They also offer a comprehensive platform and UI with Python notebooks you can utilize to manage your data. Here is the link:

Knowledge graphs for representing information are unbeatable. After this, you will never build a RAG system without knowledge graphs. It will take you five lines of code to build a knowledge graph with your data. I recorded a video to show you how you can do this. I used Cognee, an open-source library that outperforms any basic vector search approach in terms of retrieval relevance. They are collaborating with me on this post. Cognee is: • Easy to use • Reduces hallucinations • Open-source Here is a link to the repository: They also offer a comprehensive platform and UI with Python notebooks you can utilize to manage your data. Here is the link:

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this is where GPT-6 Astra is getting f...cking illegal 10 open-source GitHub repos I'd use to build the system around the model 01 PydanticAI ▸ → typed agents + structured outputs 02 LangGraph ▸ → state + long-running agent workflows BUILD THE AGENTS 03 Agno ▸ → agents + teams + workflows 04 smolagents ▸ → agents that execute through code GIVE THEM MEMORY 05 Mem0 ▸ → persistent agent memory 06 Graphiti ▸ → temporal knowledge graphs GIVE THEM A RUNTIME 07 E2B ▸ → isolated sandboxes for AI-generated code 08 Daytona ▸ → infrastructure for running agent workloads TEST EVERYTHING 09 DeepEval ▸ → evals for LLM systems 10 Langfuse ▸ → traces + evals + production monitoring the flow: request → agent → memory → tools → runtime → eval → retry → deploy that's the part people underestimate the model is one folder the system around it is the product ⭣

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🧬A trust layer for genomic AI powered by OriginTrail, has been implemented under the ELSA - European Lighthouse on Secure and Safe AI funded by the EU. Trace Labs - Trusted Context for AI Agents's Jurij Skornik demonstrated it in action ↯ 00:00 - Introduction 00:30 - Key challenges in decentralized science 00:46 - Decentralized Knowledge Graph power 01:35 - How an AI agent can assess Alzheimer’s risk 02:32 - Working with private patient data 03:50 - Solution in action: Personal healthcare AI agent

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OriginTrail

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