Loading video...

Video Failed to Load

There was a problem loading this video. This could be due to a temporary network issue or the video might be unavailable.

V10 Decentralized Knowledge Graph is live! A shared memory for AI — open to every human and every agent. Open source. Yours to own. Every fact provable and traceable. The grid is awake. 1/3

OriginTrail

90,962 subscribers

1,349,883 views • 19 days ago •via X (Twitter)

Science & Technology

Anya Rossi• Live Now

Private livecam show

0 Comments

No comments available

Comments from the original post will appear here

Related Videos

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.

Santiago

155,514 views • 1 year ago

Open research has a purpose: to facilitate open-source AI through an open economy that serves us all 🌎 We’ve converged years of open research into creating the GRID—a comprehensive, production-grade open-source AI stack that enterprises are adopting every day. This is the future of open-source 😁

Open research has a purpose: to facilitate open-source AI through an open economy that serves us all 🌎 We’ve converged years of open research into creating the GRID—a comprehensive, production-grade open-source AI stack that enterprises are adopting every day. This is the future of open-source 😁

Sentient

56,660 views • 7 months ago

Cross-context Personal Knowledge Base with Social Graph🧠 Introducing Cross-context Personal Knowledge Base with Social Graph. Your Memory, Across Every Conversation Most AI forgets you the moment a chat ends. Teamily's Personal Knowledge Base remembers — across every group, every agent, and every context. Powered by your social graph, it connects the dots between your work, relationships, and ideas, so your AI always knows who you are, what you care about, and what happened before. No setup. No tagging. Just intelligence that grows with you. 🙌Try the prompt today: “visualize my knowledge base with social graph” #teamily #AI #Agent #KnowledgeBase #Social #SocialGraph

Cross-context Personal Knowledge Base with Social Graph🧠 Introducing Cross-context Personal Knowledge Base with Social Graph. Your Memory, Across Every Conversation Most AI forgets you the moment a chat ends. Teamily's Personal Knowledge Base remembers — across every group, every agent, and every context. Powered by your social graph, it connects the dots between your work, relationships, and ideas, so your AI always knows who you are, what you care about, and what happened before. No setup. No tagging. Just intelligence that grows with you. 🙌Try the prompt today: “visualize my knowledge base with social graph” #teamily #AI #Agent #KnowledgeBase #Social #SocialGraph

Teamily AI

13,287 views • 2 months ago

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.

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.

Akshay 🚀

57,462 views • 9 months ago

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.

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.

Akshay 🚀

156,446 views • 1 year ago

⚽ The FIFA World Cup, live on the decentralized knowledge graph (DKG). Every match, result and player–club affiliation ingested in real time and published as one shared context graph powered by OriginTrail, structured with IPTC Sport Schema. Any AI agent can plug in and get reliable, source-verifiable context: live news, prediction markets, results, stats. No hallucinated scores. Just provenance. Enterprises shouldn’t have all the fun.

⚽ The FIFA World Cup, live on the decentralized knowledge graph (DKG). Every match, result and player–club affiliation ingested in real time and published as one shared context graph powered by OriginTrail, structured with IPTC Sport Schema. Any AI agent can plug in and get reliable, source-verifiable context: live news, prediction markets, results, stats. No hallucinated scores. Just provenance. Enterprises shouldn’t have all the fun.

Žiga Drev

33,880 views • 7 days ago

Say hi to GitFish 🐟 Tokenize and trade Github repos – support open source! Every like and retweet = we'll donate $1 to open source

Say hi to GitFish 🐟 Tokenize and trade Github repos – support open source! Every like and retweet = we'll donate $1 to open source

GitFish

306,288 views • 1 year ago

the perfect AI agent should remember everything. Sam Altman said it: "every conversation. every book. every email. your whole life appending to one context window." memory isn't a feature. it's the definition. I just built the memory layer. open source. install in one command. read the guide tonight. install it tomorrow.

the perfect AI agent should remember everything. Sam Altman said it: "every conversation. every book. every email. your whole life appending to one context window." memory isn't a feature. it's the definition. I just built the memory layer. open source. install in one command. read the guide tonight. install it tomorrow.

Avid

35,772 views • 2 months ago

Every output is traceable. Computer shows where the data came from and links directly to the source behind key numbers. Click any citation or hyperlinked number to open the underlying SEC filing, earnings transcript, market data page, or licensed source.

Every output is traceable. Computer shows where the data came from and links directly to the source behind key numbers. Click any citation or hyperlinked number to open the underlying SEC filing, earnings transcript, market data page, or licensed source.

Perplexity

47,499 views • 2 months ago

Here is a step-by-step introduction to building a workflow with a custom AI agent that uses MCP. I explain every component in the video: 1. Building the MCP server 2. Building the agent and an MCP client 3. Building a workflow that uses the agent The goal is simple: Generate a dialogue between two people and make one yell and the other answer with sarcasm. Kestra is sponsoring this video. They are an open-source orchestration platform (repo link below), and I used them to build the workflow that connects every component.

Here is a step-by-step introduction to building a workflow with a custom AI agent that uses MCP. I explain every component in the video: 1. Building the MCP server 2. Building the agent and an MCP client 3. Building a workflow that uses the agent The goal is simple: Generate a dialogue between two people and make one yell and the other answer with sarcasm. Kestra is sponsoring this video. They are an open-source orchestration platform (repo link below), and I used them to build the workflow that connects every component.

Santiago

51,527 views • 1 year ago

Context is power. Context shared is power multiplied. Every AI agent today builds context, then loses it. Work doesn't compound. OriginTrail is built differently: three memory layers, one shared graph, provenance baked in. DKG v10 now integrates with Hermes of Nous Research, OpenClaw🦞, Claude (Code & Desktop), Cursor, Codex by OpenAI, Windsurf (retired), VSCode + GitHub Copilot Chat, and Cline.

Context is power. Context shared is power multiplied. Every AI agent today builds context, then loses it. Work doesn't compound. OriginTrail is built differently: three memory layers, one shared graph, provenance baked in. DKG v10 now integrates with Hermes of Nous Research, OpenClaw🦞, Claude (Code & Desktop), Cursor, Codex by OpenAI, Windsurf (retired), VSCode + GitHub Copilot Chat, and Cline.

OriginTrail

1,723,329 views • 2 months ago

Everyone wants agent swarms. Very few people are talking seriously enough about the context layer that makes swarms useful. Even with one agent, context is fragile. Too little context and the agent guesses. Too much context and it wastes tokens, loses focus, or reasons over irrelevant noise. The sweet spot is precise context: the right knowledge, in the right structure, at the right moment. With many agents, that challenge explodes. Each agent produces decisions, assumptions, findings, summaries, risks, and partial conclusions. Unless that knowledge becomes shared, structured, and reusable, every new agent is forced to rediscover what another agent already learned. That is not a swarm. That is a crowd. Shared context graphs are what turn agent activity into agent collaboration, and OriginTrail DKG V10 brings them to life. Was just playing with some final polishing for the V10 release, and it is really powerful to see shared context graphs where multiple agents contribute knowledge into the same connected memory, with attribution visible directly in the graph ui. That matters for three reasons. First, agents can access and build on one shared memory instead of staying trapped in isolated sessions. Second, the graph structure helps them retrieve the exact context they need, instead of stuffing everything into a prompt and hoping the model sorts it out. Third, verifiability of provenance. You can see which agent contributed each piece of knowledge, trace the source, and decide what to trust. Tokenmaxxing starts with fewer tokens, but the deeper story is coordination - agents stop reloading the world and start building on shared, verifiable context. That is the foundation for serious multi-agent work across software engineering, research, finance, operations, project management, and far beyond. The future is not more agents, it is agents working from shared, verifiable context. But the more the merrier, of course.

Everyone wants agent swarms. Very few people are talking seriously enough about the context layer that makes swarms useful. Even with one agent, context is fragile. Too little context and the agent guesses. Too much context and it wastes tokens, loses focus, or reasons over irrelevant noise. The sweet spot is precise context: the right knowledge, in the right structure, at the right moment. With many agents, that challenge explodes. Each agent produces decisions, assumptions, findings, summaries, risks, and partial conclusions. Unless that knowledge becomes shared, structured, and reusable, every new agent is forced to rediscover what another agent already learned. That is not a swarm. That is a crowd. Shared context graphs are what turn agent activity into agent collaboration, and OriginTrail DKG V10 brings them to life. Was just playing with some final polishing for the V10 release, and it is really powerful to see shared context graphs where multiple agents contribute knowledge into the same connected memory, with attribution visible directly in the graph ui. That matters for three reasons. First, agents can access and build on one shared memory instead of staying trapped in isolated sessions. Second, the graph structure helps them retrieve the exact context they need, instead of stuffing everything into a prompt and hoping the model sorts it out. Third, verifiability of provenance. You can see which agent contributed each piece of knowledge, trace the source, and decide what to trust. Tokenmaxxing starts with fewer tokens, but the deeper story is coordination - agents stop reloading the world and start building on shared, verifiable context. That is the foundation for serious multi-agent work across software engineering, research, finance, operations, project management, and far beyond. The future is not more agents, it is agents working from shared, verifiable context. But the more the merrier, of course.

Jurij Skornik

11,070 views • 1 month ago

Building AI agents starts with a memory you can verify. The OriginTrail Decentralized Knowledge Graph (DKG) gives agents a verifiable memory layer built on structured knowledge you can trace back to its source. Brana Rakic explains 💬

Building AI agents starts with a memory you can verify. The OriginTrail Decentralized Knowledge Graph (DKG) gives agents a verifiable memory layer built on structured knowledge you can trace back to its source. Brana Rakic explains 💬

OriginTrail

25,946 views • 4 months ago

A surprise greeting from our best boy! 🥹 Cheers to every love, every memory, and every moment we’ve shared together. The best is yet to come. ♥️ HAPPY WILLievers Day #AlwaysTWO9ether

A surprise greeting from our best boy! 🥹 Cheers to every love, every memory, and every moment we’ve shared together. The best is yet to come. ♥️ HAPPY WILLievers Day #AlwaysTWO9ether

TEAM WILL OFC

22,570 views • 5 months ago

OpenAlgo now has a brain. Not Obsidian. Not a vector DB. It is a live knowledge graph built on Google's Open Knowledge Format (OKF). Plain, portable Markdown that any AI agent can read and traverse. Access OKF here :

OpenAlgo now has a brain. Not Obsidian. Not a vector DB. It is a live knowledge graph built on Google's Open Knowledge Format (OKF). Plain, portable Markdown that any AI agent can read and traverse. Access OKF here :

OpenAlgo

11,793 views • 8 days ago

Every AI agent I've used has the same bug: It forgets everything. Not after a month. Sometimes after a single session. This week I tested Memanto, an open-source memory companion for AI agents, and it completely changed how I think about agent workflows. 👇 Moorcheh.ai #ad

Every AI agent I've used has the same bug: It forgets everything. Not after a month. Sometimes after a single session. This week I tested Memanto, an open-source memory companion for AI agents, and it completely changed how I think about agent workflows. 👇 Moorcheh.ai #ad

Kaitee

37,566 views • 1 month ago

💊The red pill for medical science: 5 AI agents, 5 siloed sources, 1 shared context graph on Decentralized Knowledge Graph. Published literature, registered trials, real-world safety reports — different owners, different formats, no common ground. We handed the mess to agents that never edited each other's data and asked the question that matters most in medicine: where did each claim come from, and can you check? Working separately, they converged. On 5 disease hubs, 4 of 5 agents landed on the same condition from their own source. Every claim traceable, nothing taken on faith. Evidence, unshackled👇

💊The red pill for medical science: 5 AI agents, 5 siloed sources, 1 shared context graph on Decentralized Knowledge Graph. Published literature, registered trials, real-world safety reports — different owners, different formats, no common ground. We handed the mess to agents that never edited each other's data and asked the question that matters most in medicine: where did each claim come from, and can you check? Working separately, they converged. On 5 disease hubs, 4 of 5 agents landed on the same condition from their own source. Every claim traceable, nothing taken on faith. Evidence, unshackled👇

OriginTrail

607,106 views • 1 month ago

Open Source is Anti-Free Speech The first instinct, in almost every Open Source & Linux organization - From GNOME to Arch, NixOS to openSUSE - is to censor. To mute, delete, and block any who disagree. I have the receipts.

Open Source is Anti-Free Speech The first instinct, in almost every Open Source & Linux organization - From GNOME to Arch, NixOS to openSUSE - is to censor. To mute, delete, and block any who disagree. I have the receipts.

The Lunduke Journal

15,749 views • 1 year ago

Today we're launching Simple Compute Market (SCM). The market is simple: agents find compute, negotiate, settle, and get access without a human driving every step. Open-source. Agent-driven. Public good. No token. No fees.

Today we're launching Simple Compute Market (SCM). The market is simple: agents find compute, negotiate, settle, and get access without a human driving every step. Open-source. Agent-driven. Public good. No token. No fees.

Arkhai

62,393 views • 1 month ago

Open sourcing Dynamic Graph Memory by mem0. Memory is fundamental to human reasoning, shaping how we approach tasks and make decisions. At Mem0, we believe that AI agents & apps should reflect this principle. Our Dynamic Graph Memory emulates human memory, advancing AI agents toward more intelligent, human-like reasoning. This is a significant step forward in building AI that truly understands and interacts with the world like we do. All credit to Dev Khant Deshraj Yadav Prateek Chhikara for their countless nights spent on bringing this to life. Link:

Open sourcing Dynamic Graph Memory by mem0. Memory is fundamental to human reasoning, shaping how we approach tasks and make decisions. At Mem0, we believe that AI agents & apps should reflect this principle. Our Dynamic Graph Memory emulates human memory, advancing AI agents toward more intelligent, human-like reasoning. This is a significant step forward in building AI that truly understands and interacts with the world like we do. All credit to Dev Khant Deshraj Yadav Prateek Chhikara for their countless nights spent on bringing this to life. Link:

Taranjeet

51,129 views • 1 year ago