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🛠️ Chrome DevTools for agents now supports third-party developer tools → See how the Angular team integrated runtime graphs: 📈 Signal Graph visualization 🔌 Dependency Injection graph 🎯 Precise error tracing

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Chrome for Developers

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Anthropic Research Lead: "99% of our engineers run swarms of 300+ self-improving agents" "Now everyone is building agentic graphs" In a 20-minute session, an Anthropic team member breaks down how graph engineering turns isolated agents into systems that improve themselves The real setup is Claude running through graph workflows, plan mode, and dynamic orchestration Agents → Loops → Graphs → Self-Improving Systems Better than most $300 graph engineering courses Bookmark and watch the talk Then read the article below
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Anthropic Research Lead: "99% of our engineers run swarms of 300+ self-improving agents" "Now everyone is building agentic graphs" In a 20-minute session, an Anthropic team member breaks down how graph engineering turns isolated agents into systems that improve themselves The real setup is Claude running through graph workflows, plan mode, and dynamic orchestration Agents → Loops → Graphs → Self-Improving Systems Better than most $300 graph engineering courses Bookmark and watch the talk Then read the article below

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76,231 просмотров • 2 месяцев назад

Google just released free 2-hour course on full Graph & Loop engineering: 1 prompt → 100 agents → loops → graphs from 0% to 100%: 0% → 0:35 - Graph engineering from scratch 30% → 31:17 - build your first agents graph 45% → 43:40 - run hundreds agents in parallel 75% → 1:04:58 - Loop engineering: route, check, repeat 100% → 1:30:09 - self-improving graphs that work while you sleep most people keep stuffing instructions into one agent - the real upgrade is a graph that routes, checks, and rebuilds the workflow at runtime learn graph from Google, ship your first - then unlock the complete system design below ↓
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Google just released free 2-hour course on full Graph & Loop engineering: 1 prompt → 100 agents → loops → graphs from 0% to 100%: 0% → 0:35 - Graph engineering from scratch 30% → 31:17 - build your first agents graph 45% → 43:40 - run hundreds agents in parallel 75% → 1:04:58 - Loop engineering: route, check, repeat 100% → 1:30:09 - self-improving graphs that work while you sleep most people keep stuffing instructions into one agent - the real upgrade is a graph that routes, checks, and rebuilds the workflow at runtime learn graph from Google, ship your first - then unlock the complete system design below ↓

codila

379,719 просмотров • 1 месяц назад

Google just released a 1-hour course on building agentic knowledge Graphs from scratch: • 00:00 - Introduction to AI agents • 08:09 - Foundations of multi-agent systems • 13:25 - Introduction to Graph engineering • 23:27 - Environment setup for agentic Graphs • 47:43 - Building Graph RAG agents • 1:25:14 - Agent memory bank with graphs 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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Google just released a 1-hour course on building agentic knowledge Graphs from scratch: • 00:00 - Introduction to AI agents • 08:09 - Foundations of multi-agent systems • 13:25 - Introduction to Graph engineering • 23:27 - Environment setup for agentic Graphs • 47:43 - Building Graph RAG agents • 1:25:14 - Agent memory bank with graphs Worth more than 10 articles on loop engineering. Watch it today, then read how to become a graph engineer in the article below.

Codez

197,514 просмотров • 3 месяцев назад

Introducing HydraDB. The graph native context infrastructure for agents. Purpose built to deliver precise context & observability into why agents act the way they do. We've always believed graphs are the best way to manage AI context, but they've been too expensive to scale or impractical for storing full context. Until now. HydraDB combines in memory, NVMe, and object storage into a single graph layer, making context delivery faster, cheaper, and more precise. We want context delivery to be extremely fast, 1000x cheap, and highly precise. Give your agents a brain.
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Introducing HydraDB. The graph native context infrastructure for agents. Purpose built to deliver precise context & observability into why agents act the way they do. We've always believed graphs are the best way to manage AI context, but they've been too expensive to scale or impractical for storing full context. Until now. HydraDB combines in memory, NVMe, and object storage into a single graph layer, making context delivery faster, cheaper, and more precise. We want context delivery to be extremely fast, 1000x cheap, and highly precise. Give your agents a brain.

Nishkarsh

2,303,061 просмотров • 4 месяцев назад

Connect your agents directly to Chrome DevTools. 🛠️ The new Chrome DevTools MCP server allows agents to inspect the DOM and debug console errors in real-time using Gemini CLI or any compatible agent. Check out the documentation on GitHub →
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Connect your agents directly to Chrome DevTools. 🛠️ The new Chrome DevTools MCP server allows agents to inspect the DOM and debug console errors in real-time using Gemini CLI or any compatible agent. Check out the documentation on GitHub →

Google for Developers

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Anthropic engineer just released a 2-hour workshop on "Graph Engineering" for agentic systems: “80% of our engineers are using self-improving loops. Now everyone is building agentic graphs.” • 00:00 - Introduction to RAG & Graphs • 06:39 - Core of "Graph Engineering" (state, nodes) • 14:29 - 3 feedback loops of Graph agents • 23:06 - Agent evaluation with Graphs • 36:29 - Agent cycles in graphs • 1:15:22 - Agentic RAG & agent context • 1:41:20 - Evaluation datasets based on Graphs This 2-hour workshop will replace 10 paid courses on agentic engineering. Watch it today, then learn how to become a Graph Engineer in the article below.
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Anthropic engineer just released a 2-hour workshop on "Graph Engineering" for agentic systems: “80% of our engineers are using self-improving loops. Now everyone is building agentic graphs.” • 00:00 - Introduction to RAG & Graphs • 06:39 - Core of "Graph Engineering" (state, nodes) • 14:29 - 3 feedback loops of Graph agents • 23:06 - Agent evaluation with Graphs • 36:29 - Agent cycles in graphs • 1:15:22 - Agentic RAG & agent context • 1:41:20 - Evaluation datasets based on Graphs This 2-hour workshop will replace 10 paid courses on agentic engineering. Watch it today, then learn how to become a Graph Engineer in the article below.

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407,306 просмотров • 3 месяцев назад

Google just released free 2-hour course on Context Graph engineering: 1 prompt → 300 agents → loops → graphs from 0% to 100%: 10% → 17:44 - build your first agent 30% → 39:30 - Loop engineering: iterate, check, break 60% → 1:12:38 - Graph engineering 75% → 1:34:26 - agents that throttle themselves 100% → 1:55:05 - full graph for multi-agentic systems Worth more than 10 paid cources on Context Graphs. Watch it today, then read how to become a graph engineer in the article below.
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Google just released free 2-hour course on Context Graph engineering: 1 prompt → 300 agents → loops → graphs from 0% to 100%: 10% → 17:44 - build your first agent 30% → 39:30 - Loop engineering: iterate, check, break 60% → 1:12:38 - Graph engineering 75% → 1:34:26 - agents that throttle themselves 100% → 1:55:05 - full graph for multi-agentic systems Worth more than 10 paid cources on Context Graphs. Watch it today, then read how to become a graph engineer in the article below.

Morty

25,062 просмотров • 2 месяцев назад

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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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.

Morlex

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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.
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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.

Santiago

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Google Engineers just showed how their AI engineers are building Context Graphs from 0% to 100%: 10% → 4:01 - how to build a Context Graph 30% → 15:00 - Graph Engineering explanation 55% → 30:00 - Agentic search Engineering 80% → 35:48 - Graph Engineering practice 100% → 47:06 - self-improving agents in graphs 54-minute Google workshop, and it’s one of the clearest hands-on examples of the shift from. Watch it today, then read the full Context Graphs Playbook below.
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Google Engineers just showed how their AI engineers are building Context Graphs from 0% to 100%: 10% → 4:01 - how to build a Context Graph 30% → 15:00 - Graph Engineering explanation 55% → 30:00 - Agentic search Engineering 80% → 35:48 - Graph Engineering practice 100% → 47:06 - self-improving agents in graphs 54-minute Google workshop, and it’s one of the clearest hands-on examples of the shift from. Watch it today, then read the full Context Graphs Playbook below.

Morty

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rari

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Google just released free 2-hour course on full Graph engineering: 1 prompt → 100 agents → loops → graphs from 0% to 100%: 10% → 17:44 - build your first agent 30% → 39:30 - Loop engineering: iterate, check, break 60% → 1:12:38 - Graph engineering 75% → 1:34:26 - agents that throttle themselves 100% → 1:55:05 - full graph for multi-agentic systems everyone builds one agent and calls it done - this is the full system where agents wire themselves into a graph watch the course, build the graph - then read the full architecture below ↓
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Google just released free 2-hour course on full Graph engineering: 1 prompt → 100 agents → loops → graphs from 0% to 100%: 10% → 17:44 - build your first agent 30% → 39:30 - Loop engineering: iterate, check, break 60% → 1:12:38 - Graph engineering 75% → 1:34:26 - agents that throttle themselves 100% → 1:55:05 - full graph for multi-agentic systems everyone builds one agent and calls it done - this is the full system where agents wire themselves into a graph watch the course, build the graph - then read the full architecture below ↓

codila

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Andrej Karpathy: "Prompting is going away. Delete everything, keep Graph." In 69 minutes he shows how to build Graphs, and why it's the only thing that will be left standing at the end Prompts → Agents → Loops → Graphs a loop keeps one agent working until the job is done a graph decides which agents exist and what each one hands to the next anyone can build an agent, almost no one builds the graph that runs them watch it today, then save the full guide on Graphs below before everyone catches up ↓
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Andrej Karpathy: "Prompting is going away. Delete everything, keep Graph." In 69 minutes he shows how to build Graphs, and why it's the only thing that will be left standing at the end Prompts → Agents → Loops → Graphs a loop keeps one agent working until the job is done a graph decides which agents exist and what each one hands to the next anyone can build an agent, almost no one builds the graph that runs them watch it today, then save the full guide on Graphs below before everyone catches up ↓

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Andrej Karpathy: "Prompting is going away. Delete everything, keep Graph." In just 124 minutes, he shows how to build graphs and why they're the only layer that survives in the end Prompts → Agents → Loops → Graphs a loop keeps one agent running until the job is done a graph decides which agents exist and what each one passes to the next anyone can build an agent, almost nobody builds the graph that runs them watch it today, then save the full guide below ↓
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Andrej Karpathy: "Prompting is going away. Delete everything, keep Graph." In just 124 minutes, he shows how to build graphs and why they're the only layer that survives in the end Prompts → Agents → Loops → Graphs a loop keeps one agent running until the job is done a graph decides which agents exist and what each one passes to the next anyone can build an agent, almost nobody builds the graph that runs them watch it today, then save the full guide below ↓

Kyrox

43,385 просмотров • 4 дней назад

Graph engineering is everywhere right now—so here's a quick breakdown of harnesses, loops, and graphs, and how they connect in multi-agent workflows. Watch the full video to learn about graph engineering with ADK →
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Graph engineering is everywhere right now—so here's a quick breakdown of harnesses, loops, and graphs, and how they connect in multi-agent workflows. Watch the full video to learn about graph engineering with ADK →

Google Cloud Tech

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Google just released free 1-hour course on building agentic knowledge Graphs from 0% to 100%: 10% → 4:01 - how to build a GraphRAG agent 30% → 15:00 - Graph Engineering explanation 55% → 30:00 - Agentic search Engineering 80% → 35:48 - Graph Engineering practice 100% → 47:06 - self-improving agents in graphs this free Google course mass replaces a $500 graph engineering bootcamp - learn it in 60 min to 100% watch it today - then read the full graph playbook in the article below ↓
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Google just released free 1-hour course on building agentic knowledge Graphs from 0% to 100%: 10% → 4:01 - how to build a GraphRAG agent 30% → 15:00 - Graph Engineering explanation 55% → 30:00 - Agentic search Engineering 80% → 35:48 - Graph Engineering practice 100% → 47:06 - self-improving agents in graphs this free Google course mass replaces a $500 graph engineering bootcamp - learn it in 60 min to 100% watch it today - then read the full graph playbook in the article below ↓

codila

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IBM just dropped a 100% free 1-hour course on mastering Graph Engineering from scratch. this is the clearest breakdown of graph memory and multi-agent orchestration you'll find anywhere : • 00:00 - Introduction to knowledge graphs • 05:35 - Building your first agentic graph • 19:59 - Agentic memory powered by graphs • 30:39 - Graphs for multi-agent orchestration it replaces 10 paid courses on agentic engineering. watch it today, then read the article below on how to become a knowledge graph engineer ↓
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IBM just dropped a 100% free 1-hour course on mastering Graph Engineering from scratch. this is the clearest breakdown of graph memory and multi-agent orchestration you'll find anywhere : • 00:00 - Introduction to knowledge graphs • 05:35 - Building your first agentic graph • 19:59 - Agentic memory powered by graphs • 30:39 - Graphs for multi-agent orchestration it replaces 10 paid courses on agentic engineering. watch it today, then read the article below on how to become a knowledge graph engineer ↓

Avid

157,985 просмотров • 3 месяцев назад

Open up your app's inner workings to AI agents with custom tools in DevTools for agents→ Write plain JavaScript to build tailored third-party tools that save tokens and bypass manual steps like login forms built for humans.
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Open up your app's inner workings to AI agents with custom tools in DevTools for agents→ Write plain JavaScript to build tailored third-party tools that save tokens and bypass manual steps like login forms built for humans.

Chrome for Developers

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Google dropped a free 2 hour course on full graph engineering, taking you from a single prompt to a hundred agents wired into loops and graphs 17:44 – Building your first agent 39:30 – Loop engineering, iterate, check, break 1:12:38 – Graph engineering 1:34:26 – Agents that throttle themselves 1:55:05 – Full graph for multi agentic systems Most people build a single agent and stop there. This course shows the full system, where agents wire themselves into a graph on their own Watch it today, then check out the graph engineering playbook below for a deeper breakdown
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Google dropped a free 2 hour course on full graph engineering, taking you from a single prompt to a hundred agents wired into loops and graphs 17:44 – Building your first agent 39:30 – Loop engineering, iterate, check, break 1:12:38 – Graph engineering 1:34:26 – Agents that throttle themselves 1:55:05 – Full graph for multi agentic systems Most people build a single agent and stop there. This course shows the full system, where agents wire themselves into a graph on their own Watch it today, then check out the graph engineering playbook below for a deeper breakdown

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Google Engineers just showed how their AI engineers are moving from simple "RAG" to "graphs" RAG → Graph RAG → Memory → Multimodal Agent Graphs. 0% → 15:32 - setting up the production agent stack 30% → 41:00 - Graph RAG with semantic 65% → 58:00 - extracting graph context 100% → 1:09:00 - orchestrating specialized agents 90-minute Google Cloud workshop, and it’s one of the clearest hands-on examples of the shift from. Watch it today, then read the full “From RAG to Context Graphs” roadmap below.
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Google Engineers just showed how their AI engineers are moving from simple "RAG" to "graphs" RAG → Graph RAG → Memory → Multimodal Agent Graphs. 0% → 15:32 - setting up the production agent stack 30% → 41:00 - Graph RAG with semantic 65% → 58:00 - extracting graph context 100% → 1:09:00 - orchestrating specialized agents 90-minute Google Cloud workshop, and it’s one of the clearest hands-on examples of the shift from. Watch it today, then read the full “From RAG to Context Graphs” roadmap below.

Morty

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