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Search inside an agent workflow is different from search in a browser. A human can scan links, skip noise, and decide what is worth trusting. An agent needs cleaner context up front. We tested AnySearch inside a real agent workflow to see how structured, source-backed search changes the next...

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We shipped big changes for Cloudflare Developers AI Search! Every agent needs search and it shouldn't take a week to set up. AI Search is the search primitive for your agents. Create an instance, upload your data, and search it. Comes with hybrid retrieval with vector and BM25.
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We shipped big changes for Cloudflare Developers AI Search! Every agent needs search and it shouldn't take a week to set up. AI Search is the search primitive for your agents. Create an instance, upload your data, and search it. Comes with hybrid retrieval with vector and BM25.

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I built a Deepseek R1 RAG Reasoning Agent running locally on my computer. It's an Agentic RAG reasoning agent that can think, reason and fall back to web search if needed. 100% Opensource code with step-by-step tutorial.
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Demo 2: human + agent design loop inside Codex. Not every workflow needs the agent to drive everything. You design in Open Design. Codex reviews the output. It can inspect files, open pages, test interactions, critique the design, and turn feedback into the next prompt. Design becomes a human + agent feedback loop.
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Demo 2: human + agent design loop inside Codex. Not every workflow needs the agent to drive everything. You design in Open Design. Codex reviews the output. It can inspect files, open pages, test interactions, critique the design, and turn feedback into the next prompt. Design becomes a human + agent feedback loop.

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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.
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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.

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51,527 просмотров • 1 год назад

A builder wants to ship an agent that can search live data, use it instantly, and keep going without manual steps. That is why OptimAI Search now ships with x402, SDK, and MCP support. x402 makes access and payment seamless. SDK and MCP make integration straightforward for builders. And under the hood, OptimAI Search gives agents real-time access to huge volumes of public and social web data, structured into context they can actually use. Add Agent Marketplace on top, and domain knowledge becomes reusable: build specialized agents, publish them, and let others build on what you create. This is what search looks like in the agent-native internet. 💡Try it:
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A builder wants to ship an agent that can search live data, use it instantly, and keep going without manual steps. That is why OptimAI Search now ships with x402, SDK, and MCP support. x402 makes access and payment seamless. SDK and MCP make integration straightforward for builders. And under the hood, OptimAI Search gives agents real-time access to huge volumes of public and social web data, structured into context they can actually use. Add Agent Marketplace on top, and domain knowledge becomes reusable: build specialized agents, publish them, and let others build on what you create. This is what search looks like in the agent-native internet. 💡Try it:

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Building an Agentic Search System Building an agentic system is not too hard. Loops, function calling, tool execution, and the model. That's it! I show in this video how to build a search agent from scratch. ~350 lines of code!
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Building an Agentic Search System Building an agentic system is not too hard. Loops, function calling, tool execution, and the model. That's it! I show in this video how to build a search agent from scratch. ~350 lines of code!

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Agentic Browser will be lived tomorrow! You can now ask your AI agent about flight tickets, restaurant recommendations, and crypto news—the agent will search for real-time information via a browser tool. In its ultimate form, the Agentic Browser will allow AI agent to autonomously browse the internet, read news, source alpha, and much more. We’re one step closer to fully sentient AI agents.
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Agentic Browser will be lived tomorrow! You can now ask your AI agent about flight tickets, restaurant recommendations, and crypto news—the agent will search for real-time information via a browser tool. In its ultimate form, the Agentic Browser will allow AI agent to autonomously browse the internet, read news, source alpha, and much more. We’re one step closer to fully sentient AI agents.

delaunch

30,986 просмотров • 1 год назад

Have you tried AI Contextual Search inside OptimAI yet? Search isn’t just search anymore. We’ve rebuilt it from the ground up to feel like an intelligent signal detector inside the chaos of the internet. 🔍 AI Contextual Search now: • Understands meaning, not just keywords • Filters out noise and irrelevant posts automatically • Surfaces the exact insight you’re looking for • Learns from your context, domain, and intent • Works at scale thanks to the growing nodes - powering the OptimAI Network This is what happens when decentralized compute meets agentic intelligence - fast, deep, contextual understanding in real time. And yes… it’s already live. Show us the smartest search results your OptimAI agent found today. 😎 We’d love to see how far you can push it. 👉
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Have you tried AI Contextual Search inside OptimAI yet? Search isn’t just search anymore. We’ve rebuilt it from the ground up to feel like an intelligent signal detector inside the chaos of the internet. 🔍 AI Contextual Search now: • Understands meaning, not just keywords • Filters out noise and irrelevant posts automatically • Surfaces the exact insight you’re looking for • Learns from your context, domain, and intent • Works at scale thanks to the growing nodes - powering the OptimAI Network This is what happens when decentralized compute meets agentic intelligence - fast, deep, contextual understanding in real time. And yes… it’s already live. Show us the smartest search results your OptimAI agent found today. 😎 We’d love to see how far you can push it. 👉

OptimAI

33,989 просмотров • 8 месяцев назад

The next internet will not be browsed. It will be operated by agents. OptimAI is building the infrastructure behind that shift. • A research task begins with Search pulling live context from the public web and social web • That context moves into Claw, where the agent can execute directly on your device • Domain knowledge becomes a Persona Agent • Core Node v2 keeps the system running at the edge • And x402 will make access and payment feel native to the flow That is the real shift: - from asking AI questions - to deploying intelligence that can work, learn, and compound. A trader can monitor signals. A founder can prepare reports overnight. A builder can publish a specialized agent once, then let others reuse it. This is what the agent-native internet looks like when it is built from the ground up. Start with OptimAI: • Persona Agent: • Claw / Core Node v2: • Search:
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The next internet will not be browsed. It will be operated by agents. OptimAI is building the infrastructure behind that shift. • A research task begins with Search pulling live context from the public web and social web • That context moves into Claw, where the agent can execute directly on your device • Domain knowledge becomes a Persona Agent • Core Node v2 keeps the system running at the edge • And x402 will make access and payment feel native to the flow That is the real shift: - from asking AI questions - to deploying intelligence that can work, learn, and compound. A trader can monitor signals. A founder can prepare reports overnight. A builder can publish a specialized agent once, then let others reuse it. This is what the agent-native internet looks like when it is built from the ground up. Start with OptimAI: • Persona Agent: • Claw / Core Node v2: • Search:

OptimAI Network

18,501 просмотров • 2 месяцев назад

This is wild. Nate Herkelman turned n8n.io into a full-blown Research Agent, thanks to 's new /search endpoint! 🤯 His workflow: ↳ search the web by keywords, time and domain ↳ scrape articles ↳ capture all screenshots no code needed! 18-min tutorial in 🧵↓
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This is wild. Nate Herkelman turned n8n.io into a full-blown Research Agent, thanks to 's new /search endpoint! 🤯 His workflow: ↳ search the web by keywords, time and domain ↳ scrape articles ↳ capture all screenshots no code needed! 18-min tutorial in 🧵↓

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Turn a multi-step process into a single workflow, powered by NVIDIA RTX Spark. See how an agent like Nous Research Hermes Agent helps transform a concept sketch into a photoreal render, connecting Rhino, ComfyUI, and Blender 🔶 into one seamless workflow so designers can stay focused on their vision.
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Turn a multi-step process into a single workflow, powered by NVIDIA RTX Spark. See how an agent like Nous Research Hermes Agent helps transform a concept sketch into a photoreal render, connecting Rhino, ComfyUI, and Blender 🔶 into one seamless workflow so designers can stay focused on their vision.

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Introducing the Generative UI Research Canvas ✨ Every agent needs a powerful UI Combine LangChain, Tavily, Tako and CopilotKit🪁 and you get a LangChain-based research agent with a Generative UI-driven frontend. When agents can explore the underlying data, research becomes more trustworthy and useful. How does this work? - LangChain manages the agent workflow and tool orchestration. - Tako converts retrieved data into structured, visual components - Tavily provides agentic search - CopilotKit renders those components in the UI and keeps the user in the loop while the agent is working So instead of a boring and static report, the output is an interactive research canvas that evolves as the agent runs. 2026 is the year where users want to see the output, not read it... Generative UI makes this possible for agentic applications. Tutorial: Open-Source Repo: Docs:
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Introducing the Generative UI Research Canvas ✨ Every agent needs a powerful UI Combine LangChain, Tavily, Tako and CopilotKit🪁 and you get a LangChain-based research agent with a Generative UI-driven frontend. When agents can explore the underlying data, research becomes more trustworthy and useful. How does this work? - LangChain manages the agent workflow and tool orchestration. - Tako converts retrieved data into structured, visual components - Tavily provides agentic search - CopilotKit renders those components in the UI and keeps the user in the loop while the agent is working So instead of a boring and static report, the output is an interactive research canvas that evolves as the agent runs. 2026 is the year where users want to see the output, not read it... Generative UI makes this possible for agentic applications. Tutorial: Open-Source Repo: Docs:

Atai Barkai

11,997 просмотров • 5 месяцев назад

Here is how simple it is to build a UI front-end for an AI agent (assuming you are using . The video here is 90 seconds and will show you everything you need to connect an agent to a web app: 1. Set up CopilotKit🪁 in your app 1. Copy the agent's localhost URL from LangGraph Studio 2. Paste that URL into a CopilotKit🪁 remote endpoint That's all you need to get a front-end to interact with the agent! The secret sauce here is CoAgents, an open-source library. You can use CoAgents to integrate any agent with your web application. Here is what you get: • Human-in-the-loop to steer and correct the agent • Stream intermediate agent state • Real-time state sharing between the agent and the application • Agentic generative UI to build trust that the agent is on the right path Watch the attached demo so you see it in action.
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Here is how simple it is to build a UI front-end for an AI agent (assuming you are using . The video here is 90 seconds and will show you everything you need to connect an agent to a web app: 1. Set up CopilotKit🪁 in your app 1. Copy the agent's localhost URL from LangGraph Studio 2. Paste that URL into a CopilotKit🪁 remote endpoint That's all you need to get a front-end to interact with the agent! The secret sauce here is CoAgents, an open-source library. You can use CoAgents to integrate any agent with your web application. Here is what you get: • Human-in-the-loop to steer and correct the agent • Stream intermediate agent state • Real-time state sharing between the agent and the application • Agentic generative UI to build trust that the agent is on the right path Watch the attached demo so you see it in action.

Santiago

46,109 просмотров • 1 год назад

OptimAI is leveling up. We’re bringing AI Contextual Search and Tweet Radar to OptimAI agent - a new way to search, filter, and surface the most relevant content in seconds. Try OptimAI: No more endless scrolling. No more noisy feeds. Just precise, contextual results powered by real-time intelligence. Perfect for research, trend discovery, and creating sharper content faster. And it’s only the beginning - the foundations for an agentic system that can reason, decide, and fully automate your workflow. Stay tuned.
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OptimAI is leveling up. We’re bringing AI Contextual Search and Tweet Radar to OptimAI agent - a new way to search, filter, and surface the most relevant content in seconds. Try OptimAI: No more endless scrolling. No more noisy feeds. Just precise, contextual results powered by real-time intelligence. Perfect for research, trend discovery, and creating sharper content faster. And it’s only the beginning - the foundations for an agentic system that can reason, decide, and fully automate your workflow. Stay tuned.

OptimAI

40,330 просмотров • 8 месяцев назад

If you need AI to do a search for you in the real world, ds4-agent is basically SOTA, because it can access the web sites without any limitations given that it uses your local Chrome browser (no, not in headless mode, that's the trick...), and DeepSeek v4 is great at search.
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If you need AI to do a search for you in the real world, ds4-agent is basically SOTA, because it can access the web sites without any limitations given that it uses your local Chrome browser (no, not in headless mode, that's the trick...), and DeepSeek v4 is great at search.

antirez

149,176 просмотров • 1 месяц назад

I don't think specialized vector databases will survive for much longer. They were a big thing for a while, but now, you have pro-grade databases like MongoDB offering native support for vector search. If you don't understand how vector search is an absolutely bonkers, mind-blowing invention, watch the attached video. Give me 4 minutes, and I promise you'll understand how vectors have completely changed the game. Honestly, MongoDB is cracked! They have become a one-stop shop for AI implementations in addition to its document and non-relational model. You'll find a link below to the source code of 7 examples of AI agents that use MongoDB: 1. A factory safety assistant agent 2. A research assistant agent 3. An investment researcher agent 4. A recommendation system agent using RAG 5. A human resources chatbot agent 6. An AWS Bedrock agent 7. An asset manager assistant agent Everything is on a GitHub repository. Link below.
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I don't think specialized vector databases will survive for much longer. They were a big thing for a while, but now, you have pro-grade databases like MongoDB offering native support for vector search. If you don't understand how vector search is an absolutely bonkers, mind-blowing invention, watch the attached video. Give me 4 minutes, and I promise you'll understand how vectors have completely changed the game. Honestly, MongoDB is cracked! They have become a one-stop shop for AI implementations in addition to its document and non-relational model. You'll find a link below to the source code of 7 examples of AI agents that use MongoDB: 1. A factory safety assistant agent 2. A research assistant agent 3. An investment researcher agent 4. A recommendation system agent using RAG 5. A human resources chatbot agent 6. An AWS Bedrock agent 7. An asset manager assistant agent Everything is on a GitHub repository. Link below.

Santiago

60,065 просмотров • 1 год назад

Our latest speech-to-speech model is faster, more accurate, and excels at function calling. Watch @promptshant and Brian Fioca build a realtime voice agent that can search the web and hand off tasks to reasoning models with full context.
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Our latest speech-to-speech model is faster, more accurate, and excels at function calling. Watch @promptshant and Brian Fioca build a realtime voice agent that can search the web and hand off tasks to reasoning models with full context.

OpenAI Developers

81,822 просмотров • 1 год назад

pi-web-access is an extension that provides web search (and more) for Pi coding agent. The latest update introduces a new UX pattern: you can hit `Ctrl+S` to intercept agent searches mid-flight to inspect and curate exactly what makes it into the agent conversation. pi install npm:pi-web-access
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pi-web-access is an extension that provides web search (and more) for Pi coding agent. The latest update introduces a new UX pattern: you can hit `Ctrl+S` to intercept agent searches mid-flight to inspect and curate exactly what makes it into the agent conversation. pi install npm:pi-web-access

Nico Bailon

29,352 просмотров • 5 месяцев назад

This AI agent is a powerful data analyst It can search, process, complete and visualize your data in a single chat. You can import files or start from scratch. Step-by-step process with Genspark:
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This AI agent is a powerful data analyst It can search, process, complete and visualize your data in a single chat. You can import files or start from scratch. Step-by-step process with Genspark:

Paul Couvert

58,000 просмотров • 1 год назад

OpenAI just confused everyone again What the hell is an agent? What the hell is a workflow? Here are the answers - and why it even matters:
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OpenAI just confused everyone again What the hell is an agent? What the hell is a workflow? Here are the answers - and why it even matters:

Matt Pocock

39,935 просмотров • 9 месяцев назад