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Alright, now that we know what an agent is, how does it actually work? When you ask for help on a task, the agent plans a series of steps and executes them directly in the application on your behalf, using the tools it has access to. Say you are... show more

Google AI

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22,487 просмотров • 7 месяцев назад •via X (Twitter)

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Visualizer of our MultiAgentRouter 🤖 The MultiAgentRouter is an all-new multi-agent structure that leverages a hierarchical pattern to select the most specialized agent for your task. Here's how it works: Step 1. You give a task. Step 2. The Boss Agent Routes your task to the most specialized Agent Step 3. The selected agent returns your response! Get started with it now below ⬇️ Thanks to WE!SS for the visualizer!!

Visualizer of our MultiAgentRouter 🤖 The MultiAgentRouter is an all-new multi-agent structure that leverages a hierarchical pattern to select the most specialized agent for your task. Here's how it works: Step 1. You give a task. Step 2. The Boss Agent Routes your task to the most specialized Agent Step 3. The selected agent returns your response! Get started with it now below ⬇️ Thanks to WE!SS for the visualizer!!

swarms

32,313 просмотров • 1 год назад

Stop spending hours on manual work. You can now use a multi-agent AI workforce to get more work done in less time. Here's how 👇 --- Try Eigent AI - Lets you build and run a custom AI workforce on your desktop. - Automate complex workflows using multi-agent task execution. - Built on CAMEL-AI’s top open-source projects ( CAMEL-AI.org & OWL). - Boost productivity with deep customization and strong privacy --- Features: - Customize Your AI Workforce: Build task-specific agents with domain skills and tools. - Faster Execution: Eigent runs agents in parallel to automate complex workflows. - Human-in-the-loop: Automatically asks for help when tasks hit uncertainty. --- What sets Eigent apart? - 3–5× faster task execution using a parallel multi-agent workforce. - Modular design lets you add new capabilities without changing the core system. - Self-optimizing agents that replan and adapt during execution for higher success. - Deploy anywhere: cloud, local, or enterprise, with full open-source flexibility. --- Try building your multi-agent AI workforce here: Join their community to build your multi-agent workforce: Check their GitHub: ---

Stop spending hours on manual work. You can now use a multi-agent AI workforce to get more work done in less time. Here's how 👇 --- Try Eigent AI - Lets you build and run a custom AI workforce on your desktop. - Automate complex workflows using multi-agent task execution. - Built on CAMEL-AI’s top open-source projects ( CAMEL-AI.org & OWL). - Boost productivity with deep customization and strong privacy --- Features: - Customize Your AI Workforce: Build task-specific agents with domain skills and tools. - Faster Execution: Eigent runs agents in parallel to automate complex workflows. - Human-in-the-loop: Automatically asks for help when tasks hit uncertainty. --- What sets Eigent apart? - 3–5× faster task execution using a parallel multi-agent workforce. - Modular design lets you add new capabilities without changing the core system. - Self-optimizing agents that replan and adapt during execution for higher success. - Deploy anywhere: cloud, local, or enterprise, with full open-source flexibility. --- Try building your multi-agent AI workforce here: Join their community to build your multi-agent workforce: Check their GitHub: ---

Shushant Lakhyani

20,423 просмотров • 11 месяцев назад

Are you jobless? This AI agent will get you a job! We built this AI agent which finds companies for you (using Exa) and applies to them on your behalf (using Browserbase)

Are you jobless? This AI agent will get you a job! We built this AI agent which finds companies for you (using Exa) and applies to them on your behalf (using Browserbase)

Ishan Goswami

128,408 просмотров • 5 месяцев назад

Claude Code Scheduled Tasks is now available... here's a solid idea to connect it with Telegram Save this so you don't forget to set it up! First, ask Claude to add a simple Telegram messaging module to your repo. You can use the Telegram Bot Builder Skill from Link: Install command: npx claude-code-templates@latest --skill enterprise-communication/telegram-bot-builder Once the module is in your project, grab your bot credentials from BotFather and add the bot ID to your .env file That's it! ✅ Now every Scheduled Task you create should end with an instruction for Claude to send the task result to Telegram using that module. Claude will handle the delivery automatically on every task it runs

Claude Code Scheduled Tasks is now available... here's a solid idea to connect it with Telegram Save this so you don't forget to set it up! First, ask Claude to add a simple Telegram messaging module to your repo. You can use the Telegram Bot Builder Skill from Link: Install command: npx claude-code-templates@latest --skill enterprise-communication/telegram-bot-builder Once the module is in your project, grab your bot credentials from BotFather and add the bot ID to your .env file That's it! ✅ Now every Scheduled Task you create should end with an instruction for Claude to send the task result to Telegram using that module. Claude will handle the delivery automatically on every task it runs

Daniel San

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Agent is now live. (∇, ∇) Describe a task. It writes the code, runs the Python, builds the prototype, makes the PDF. Your files stay in your browser. Your prompts stay private.

Agent is now live. (∇, ∇) Describe a task. It writes the code, runs the Python, builds the prototype, makes the PDF. Your files stay in your browser. Your prompts stay private.

OpenGradient (∇, ∇)

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🤖 Try Autopilot (Preview) in VS Code! With Autopilot enabled, the agent stays in control of the workflow. It can run tools, retry on errors, and continue working until the task is complete. Learn more:

🤖 Try Autopilot (Preview) in VS Code! With Autopilot enabled, the agent stays in control of the workflow. It can run tools, retry on errors, and continue working until the task is complete. Learn more:

Visual Studio Code

109,983 просмотров • 3 месяцев назад

Self-improving agents are here. 🧙‍♂️ Every agent run creates a skill.md with reusable learnings. The next agent uses it to do the same task faster, cheaper, and more reliably. Watch the second agent crush it:

Self-improving agents are here. 🧙‍♂️ Every agent run creates a skill.md with reusable learnings. The next agent uses it to do the same task faster, cheaper, and more reliably. Watch the second agent crush it:

Browser Use

56,157 просмотров • 4 месяцев назад

Users can explore how different AI agents analyze the same market task and compare their outputs through the platform. It is a product experience built around agent performance, task execution, and real usage. AI agents are becoming measurable digital workers.

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OKAI Official

10,097 просмотров • 1 месяц назад

Google just built Cowork and called it Agent. And they added one thing Anthropic didn't. You set a goal. It browses the web, digs through your Gmail, checks your Calendar, pulls from Drive then executes the full task. Book a trip. Clear your inbox. Research a market. Done. No back and forth. But here's the part nobody's talking about: There's a toggle "Require a human review." You don't build that unless the plan is to eventually not require it. Google just told you where this ends. I share updates like these in my free AI community on WhatsApp. Join here 👇

Google just built Cowork and called it Agent. And they added one thing Anthropic didn't. You set a goal. It browses the web, digs through your Gmail, checks your Calendar, pulls from Drive then executes the full task. Book a trip. Clear your inbox. Research a market. Done. No back and forth. But here's the part nobody's talking about: There's a toggle "Require a human review." You don't build that unless the plan is to eventually not require it. Google just told you where this ends. I share updates like these in my free AI community on WhatsApp. Join here 👇

Vaibhav Sisinty

252,144 просмотров • 2 месяцев назад

Not every task needs the same model. A quick summary doesn't need the same horsepower as a deep research question — and it shouldn't cost the same either. Now, you can now easily compare models for your Custom Agent on speed, intelligence, and cost 🫡

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Notion

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Hermes Agent now has access to hundreds of browser skills through Browserbase’s new hub, so agents can more reliably perform any task on the internet. You can try a skill from their catalog or contribute your own.

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Nous Research

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ByteDance just open sourced an AI SuperAgent that can research, code, build websites, create slide decks, and generate videos. All by itself. DeerFlow 2.0 (27K+ GitHub stars ⭐️), an AI system acting like an autonomous employee with its own computer workspace to research and code. Standard chatbots only generate text and forget your preferences. DeerFlow solves this by giving the AI an isolated virtual computer environment where it safely runs programs. When given a massive task, the main program creates several smaller AI assistants to work simultaneously. It also saves your past workflows so it gets smarter about your needs. DeerFlow is model-agnostic — it works with any LLM that implements the OpenAI-compatible API. Fully supports running local models on your own computer using tools like Ollama. An example - you ask for research on the top 10 AI startups in 2026 for a presentation, the lead agent in DeerFlow breaks that big job into smaller sub-tasks. It assigns one sub-agent to look into each company, another to find funding details, and a third to handle competitor analysis. These agents do all their work in parallel. Everything eventually converges, and a final agent pulls the results into a slide deck complete with custom visuals.

ByteDance just open sourced an AI SuperAgent that can research, code, build websites, create slide decks, and generate videos. All by itself. DeerFlow 2.0 (27K+ GitHub stars ⭐️), an AI system acting like an autonomous employee with its own computer workspace to research and code. Standard chatbots only generate text and forget your preferences. DeerFlow solves this by giving the AI an isolated virtual computer environment where it safely runs programs. When given a massive task, the main program creates several smaller AI assistants to work simultaneously. It also saves your past workflows so it gets smarter about your needs. DeerFlow is model-agnostic — it works with any LLM that implements the OpenAI-compatible API. Fully supports running local models on your own computer using tools like Ollama. An example - you ask for research on the top 10 AI startups in 2026 for a presentation, the lead agent in DeerFlow breaks that big job into smaller sub-tasks. It assigns one sub-agent to look into each company, another to find funding details, and a third to handle competitor analysis. These agents do all their work in parallel. Everything eventually converges, and a final agent pulls the results into a slide deck complete with custom visuals.

Rohan Paul

50,097 просмотров • 3 месяцев назад

Opal, our no-code visual builder for AI workflows, just got a major upgrade. 🧠💎 We’ve added a new agent step that analyzes your goal, determines the best approach, and automatically calls the right tools — such as Veo for video or web search for research — to complete the task. We’re also adding new tools to make the agent even more capable: 💾 Memory – Remember info, like a user’s name or your style preferences across sessions. 🚀 Dynamic Routing – Let the agent choose the next best step using the “@ Go to” tool. 💬 Interactive Chat – Initiate user interactions to gather missing information or present options before moving on. Try it now →

Opal, our no-code visual builder for AI workflows, just got a major upgrade. 🧠💎 We’ve added a new agent step that analyzes your goal, determines the best approach, and automatically calls the right tools — such as Veo for video or web search for research — to complete the task. We’re also adding new tools to make the agent even more capable: 💾 Memory – Remember info, like a user’s name or your style preferences across sessions. 🚀 Dynamic Routing – Let the agent choose the next best step using the “@ Go to” tool. 💬 Interactive Chat – Initiate user interactions to gather missing information or present options before moving on. Try it now →

Google Labs

1,007,049 просмотров • 4 месяцев назад

this is the worst local ai will ever be. it only gets better from here. if you are not expanding your mind with these small models you are missing what's happening right now 99 percent tool call success rate. when steered well with the right skills and a framework like hermes agent the node becomes a cognition layer. not a chatbot. not a toy. an extension of how you think. i was cranking this node at 35 to 50 tok/s all day on personal experiments and now after all the work is done qwen 3.5 9B is iterating on its own code. the game it created. fixing its own bugs autonomously. and the part you should probably not miss is that all of this is happening on a RTX 3060. not an H100. not an A100. the card most of you have sitting in a drawer right now. if you just open that drawer and put that intelligence to work every tensor core on that card should be running for you. your work. your experiments. your thinking. you all have it but because nobody told you what this hardware can actually do in 2026 you never tried. the day it unlocks is the day you test your workload, understand the tradeoffs, debug the loops, and then decide if you need to scale the hardware. there is no point buying 3 mac studios when things done well you can squeeze a similar level of intelligence from 9B compared to 70B. but only when you create the right environment for your model through the right harness. and let me tell you i have tried claude code as a local harness. i have tried opencode. i have tried various others. somehow i landed on hermes agent and never left. there is something magical going on at Nous Research. the tool call parsers, the skills system, the way it handles small models natively. nothing else comes close for local inference. own your cognition. your AI. your agent. your prompts. your experiments. why give them away for free. those are who you are and they don't belong on someone else's servers being monitored. just give it a shot with your existing hardware. you run into a problem the community will help you. and if you are migrating from openclaw to hermes i will personally help you make the switch.

this is the worst local ai will ever be. it only gets better from here. if you are not expanding your mind with these small models you are missing what's happening right now 99 percent tool call success rate. when steered well with the right skills and a framework like hermes agent the node becomes a cognition layer. not a chatbot. not a toy. an extension of how you think. i was cranking this node at 35 to 50 tok/s all day on personal experiments and now after all the work is done qwen 3.5 9B is iterating on its own code. the game it created. fixing its own bugs autonomously. and the part you should probably not miss is that all of this is happening on a RTX 3060. not an H100. not an A100. the card most of you have sitting in a drawer right now. if you just open that drawer and put that intelligence to work every tensor core on that card should be running for you. your work. your experiments. your thinking. you all have it but because nobody told you what this hardware can actually do in 2026 you never tried. the day it unlocks is the day you test your workload, understand the tradeoffs, debug the loops, and then decide if you need to scale the hardware. there is no point buying 3 mac studios when things done well you can squeeze a similar level of intelligence from 9B compared to 70B. but only when you create the right environment for your model through the right harness. and let me tell you i have tried claude code as a local harness. i have tried opencode. i have tried various others. somehow i landed on hermes agent and never left. there is something magical going on at Nous Research. the tool call parsers, the skills system, the way it handles small models natively. nothing else comes close for local inference. own your cognition. your AI. your agent. your prompts. your experiments. why give them away for free. those are who you are and they don't belong on someone else's servers being monitored. just give it a shot with your existing hardware. you run into a problem the community will help you. and if you are migrating from openclaw to hermes i will personally help you make the switch.

Sudo su

58,717 просмотров • 3 месяцев назад

What if you could create your own AI agent? With Bluwhale Create Agent, anyone can: • Build an AI-powered strategy • Deploy it to the marketplace • Let others use your agent • Earn when your agent performs AI agents aren’t just tools anymore. They’re becoming economic participants. 🎥 See how it works ↓

What if you could create your own AI agent? With Bluwhale Create Agent, anyone can: • Build an AI-powered strategy • Deploy it to the marketplace • Let others use your agent • Earn when your agent performs AI agents aren’t just tools anymore. They’re becoming economic participants. 🎥 See how it works ↓

Bluwhale

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Replit, Vercel, and OpenAI have built very cool agent-native applications, but nobody else has passed the demo stage. Building agents that work is complex. Teams aren't shipping agents because we don't have good tooling yet (and most of us don't know how to do this well.) A couple of days ago, the CopilotKit🪁 team announced a collaboration with . You can now use LangGraph with CoAgents to build agent-native applications, and here is everything you need to know about that: CoAgents is fully open-source, and you can use it to do the following: • 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 Start this GitHub Repository: Thanks to the team for giving me early access and collaborating with me on this post.

Replit, Vercel, and OpenAI have built very cool agent-native applications, but nobody else has passed the demo stage. Building agents that work is complex. Teams aren't shipping agents because we don't have good tooling yet (and most of us don't know how to do this well.) A couple of days ago, the CopilotKit🪁 team announced a collaboration with . You can now use LangGraph with CoAgents to build agent-native applications, and here is everything you need to know about that: CoAgents is fully open-source, and you can use it to do the following: • 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 Start this GitHub Repository: Thanks to the team for giving me early access and collaborating with me on this post.

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It has been the honor of a lifetime to work alongside President Donald J. Trump on the Memphis Safe Task Force. We couldn’t be more thrilled with the results and Memphis’ transformation.

It has been the honor of a lifetime to work alongside President Donald J. Trump on the Memphis Safe Task Force. We couldn’t be more thrilled with the results and Memphis’ transformation.

Sen. Marsha Blackburn

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Want it as a recurring newsletter? Create a task for your agent

Want it as a recurring newsletter? Create a task for your agent

griffain

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7 different examples of how you can integrate AI agents directly into the front-end of your application. If you are a React developer or an agent builder, I'm about to make your job 2x more interesting with CopilotKit🪁. Read on for the details:

7 different examples of how you can integrate AI agents directly into the front-end of your application. If you are a React developer or an agent builder, I'm about to make your job 2x more interesting with CopilotKit🪁. Read on for the details:

Santiago

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Perplexity Computer just opened a PR on my repo, starting with a simple task... improving SEO What stands out is how it executes. Exploring with Gemini, then switching to Opus when it’s time to actually write code (I haven’t seen it use Codex or Grok) I’m going to ramp up the complexity from here. So far, it’s delivered on every task. I have a few more in progress that I’ll be sharing over the next few days.

Perplexity Computer just opened a PR on my repo, starting with a simple task... improving SEO What stands out is how it executes. Exploring with Gemini, then switching to Opus when it’s time to actually write code (I haven’t seen it use Codex or Grok) I’m going to ramp up the complexity from here. So far, it’s delivered on every task. I have a few more in progress that I’ll be sharing over the next few days.

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