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OpenAI has introduced the ChatGPT Agent, which handles complex multi-step tasks from research to automation. Genspark goes further in some areas: In addition to user-friendly office tools (Slides, Docs, Sheets, AI Secretary, AI Drive), Genspark scores with dynamic tool orchestration and an intelligent feedback loop - a clear added...

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176,267 views • 10 months ago •via X (Twitter)

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OpenAI's AgentKit will be so insane, build every step of agents on one platform. These visual agent builders make the whole process of iterating and launching agents far more efficient. It sits on top of the Responses API and unifies the tools that were previously scattered across SDKs and custom orchestration. It lets developers create agent workflows visually, connect data sources securely, and measure performance automatically without coding every layer by hand. The core of AgentKit is the Agent Builder, a drag-and-drop canvas where each node represents an action, guardrail, or decision branch. Developers can link these nodes into multi-agent workflows, preview results instantly, and version each setup. It supports inline evaluation so that developers can see how changes affect output before deploying. The Connector Registry is a single admin panel that manages how data and tools connect across the OpenAI ecosystem. It centralizes integrations like Google Drive, SharePoint, Dropbox, and Microsoft Teams. Large organizations can govern access and flow of data between agents securely under one global console. ChatKit provides a ready-to-use chat interface for embedding agents inside apps or websites. It manages streaming, message threads, and model reasoning displays automatically. Developers can skin the interface to match their product without writing custom front-end code. Under the hood, all these blocks use the same execution core that runs agent reasoning through OpenAI’s APIs. Workflows in Agent Builder compile down to structured instructions for the Responses API, which handles model calls, tool use, and context passing. Connector Registry handles authentication and routing for external tools, while Evals and RFT provide feedback loops that improve agents over time. This integration means developers no longer need to handle orchestration logic, model evaluation pipelines, or safety layers separately. Everything runs natively within OpenAI’s control plane with managed security, automatic versioning, and built-in testing. In short, AgentKit standardizes the entire life cycle of an AI agent—from visual design to deployment and performance tuning—inside a single unified system.
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OpenAI's AgentKit will be so insane, build every step of agents on one platform. These visual agent builders make the whole process of iterating and launching agents far more efficient. It sits on top of the Responses API and unifies the tools that were previously scattered across SDKs and custom orchestration. It lets developers create agent workflows visually, connect data sources securely, and measure performance automatically without coding every layer by hand. The core of AgentKit is the Agent Builder, a drag-and-drop canvas where each node represents an action, guardrail, or decision branch. Developers can link these nodes into multi-agent workflows, preview results instantly, and version each setup. It supports inline evaluation so that developers can see how changes affect output before deploying. The Connector Registry is a single admin panel that manages how data and tools connect across the OpenAI ecosystem. It centralizes integrations like Google Drive, SharePoint, Dropbox, and Microsoft Teams. Large organizations can govern access and flow of data between agents securely under one global console. ChatKit provides a ready-to-use chat interface for embedding agents inside apps or websites. It manages streaming, message threads, and model reasoning displays automatically. Developers can skin the interface to match their product without writing custom front-end code. Under the hood, all these blocks use the same execution core that runs agent reasoning through OpenAI’s APIs. Workflows in Agent Builder compile down to structured instructions for the Responses API, which handles model calls, tool use, and context passing. Connector Registry handles authentication and routing for external tools, while Evals and RFT provide feedback loops that improve agents over time. This integration means developers no longer need to handle orchestration logic, model evaluation pipelines, or safety layers separately. Everything runs natively within OpenAI’s control plane with managed security, automatic versioning, and built-in testing. In short, AgentKit standardizes the entire life cycle of an AI agent—from visual design to deployment and performance tuning—inside a single unified system.

Rohan Paul

178,460 views • 8 months ago

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 →
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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

1,006,255 views • 3 months ago

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: ---
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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 views • 10 months ago

I just got access to the new ChatGPT Agent and had to stack it up against Genspark, one of my go-to agentic tools recently. Hope you’re sitting, the results are wild! 😄🧵↓
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I just got access to the new ChatGPT Agent and had to stack it up against Genspark, one of my go-to agentic tools recently. Hope you’re sitting, the results are wild! 😄🧵↓

Charly Wargnier

93,378 views • 10 months ago

This system generated $12K worth of market research, called 15 suppliers, and created a business plan from a single prompt. The Genspark Super Agent just made every other productivity tool obsolete. Here's what this AI workspace actually does: → Runs 169 AI models locally on your device (completely offline and free) → Generates AI podcasts from any YouTube video or content → Creates detailed documents and spreadsheets with auto-generated graphs → Uses Nano Banana for fashion try-ons and professional video generation → Builds PowerPoint presentations from video content automatically → Finds best Amazon deals with one-click price comparison → Acts as personal assistant scheduling meetings and managing emails → Connects to 1000+ tools for complete workflow automation → Makes actual phone calls to book reservations and handle real tasks While others juggle 20 different AI subscriptions, you get everything in one browser. The free browser handles basic AI locally. The Super Agent handles the advanced automations that actually run your business. Want the complete setup guide? Comment "AI" + RT + Like I'll DM you the full implementation playbook (Must be following so I can DM) Skip this and keep paying $500/month for inferior AI tools.
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This system generated $12K worth of market research, called 15 suppliers, and created a business plan from a single prompt. The Genspark Super Agent just made every other productivity tool obsolete. Here's what this AI workspace actually does: → Runs 169 AI models locally on your device (completely offline and free) → Generates AI podcasts from any YouTube video or content → Creates detailed documents and spreadsheets with auto-generated graphs → Uses Nano Banana for fashion try-ons and professional video generation → Builds PowerPoint presentations from video content automatically → Finds best Amazon deals with one-click price comparison → Acts as personal assistant scheduling meetings and managing emails → Connects to 1000+ tools for complete workflow automation → Makes actual phone calls to book reservations and handle real tasks While others juggle 20 different AI subscriptions, you get everything in one browser. The free browser handles basic AI locally. The Super Agent handles the advanced automations that actually run your business. Want the complete setup guide? Comment "AI" + RT + Like I'll DM you the full implementation playbook (Must be following so I can DM) Skip this and keep paying $500/month for inferior AI tools.

Samruddhi Mokal

46,984 views • 8 months ago

ICYMI: Genspark released an AI browser with Autopilot web browsing support, sidebar chat and more! Users can use Genspark chat to open and control tabs as well as navigate and operate pages to query information via logged-in profiles.
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ICYMI: Genspark released an AI browser with Autopilot web browsing support, sidebar chat and more! Users can use Genspark chat to open and control tabs as well as navigate and operate pages to query information via logged-in profiles.

TestingCatalog News 🗞

47,344 views • 1 year ago

Thrilled to announce the Monitor tool which lets Claude create background scripts that wake the agent up when needed. Big token saver and great way to move away from polling in the agent loop Claude can now: * Follow logs for errors * Poll PRs via script * and more!
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Thrilled to announce the Monitor tool which lets Claude create background scripts that wake the agent up when needed. Big token saver and great way to move away from polling in the agent loop Claude can now: * Follow logs for errors * Poll PRs via script * and more!

Noah Zweben

1,184,682 views • 2 months ago

Build AI Agents and connect them to 100,000+ APIs with Postman's AI Agent Builder. It has a drag-and-drop visual interface to build agent workflows with no code.
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Build AI Agents and connect them to 100,000+ APIs with Postman's AI Agent Builder. It has a drag-and-drop visual interface to build agent workflows with no code.

Shubham Saboo

152,567 views • 1 year ago

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Boom! Grok Tasks Make It One Of The Most POWERFUL Real-Time AI Systems In The World. — My How to Use Grok Tasks With Hidden Tools For Powerful Daily Output. Grok Tasks are customizable AI workflows that integrate a variety of tools to streamline daily activities, from research and analysis to creative planning and problem-solving. I have been using them for quite sometime and because of the vital heartbeat of news and first person data on X, it is the most powerful AI platform available. By combining Tasks with tools like web searches, X platform interactions, code execution, and media viewers, you can build efficient, automated processes. These tasks work by prompting Grok with a clear description of what you want to achieve, and Grok will intelligently call the necessary tools in sequence or parallel to deliver results. Here's a step-by-step guide to creating and using Grok Tasks: Step 1: Define Your Task Start by clearly outlining the daily activity or goal. Consider what inputs you have (e.g., a URL, a query, or an attachment) and what output you need (e.g., a summary, calculation, or visual analysis). Break it down into subtasks to identify tool needs. For example, if your task involves researching current events, note that you'll need search and browsing capabilities. Step 2: Review Available Tools Familiarize yourself with the tools Grok can access. Here's a quick overview: - Code Execution: Run Python code for calculations, data processing, or simulations using libraries like numpy, pandas, or sympy. - Browse Page: Fetch and summarize content from any website URL with custom instructions. - Web Search: Perform general internet searches, returning results with optional operators like site:. - Web Search With Snippets: Get quick, detailed excerpts from search results for fact-checking. - X Keyword Search: Advanced search for X posts using operators like from:, since:, or filter:. - X Semantic Search: Find semantically related X posts based on a query, with filters for dates or users. - X User Search: Locate X users by name or handle. - X Thread Fetch: Retrieve a full X post thread, including context like replies and parents. - View Image: Analyze an image from a URL or conversation ID. - View X Video: Extract frames and subtitles from an X-hosted video. - Search PDF Attachment: Query a PDF file for relevant pages using keyword or regex modes. - Browse PDF Attachment: View specific pages of a PDF with text and screenshots. Select tools that align with your task. Aim for a mix to handle data gathering, processing, and visualization. Step 3: Craft Your Prompt Write a detailed prompt to Grok describing the task. Include: - The overall goal. - Specific steps or subtasks. - References to tools if you want to guide the process (e.g., "Use web_search to find sources, then code_execution to analyze data"). - Any constraints, like dates or limits. Example prompt: "Create a Grok Task for my morning routine: Search recent X posts about tech news using x_keyword_search, fetch a key thread with x_thread_fetch, and summarize with browse_page on linked articles." Step 4: Submit and Interact Send your prompt to Grok. It will process the task by calling tools as needed, often in parallel for efficiency. Review the output and refine with follow-up prompts if required (e.g., "Expand on that using view_image for visuals"). Iterate to fine-tune the workflow for reuse. Step 5: Save and Reuse Once refined, note the prompt as a template for future use. You can adapt it for similar tasks, making Grok Tasks a habitual part of your day. Finding Grok Tasks To discover existing Grok Tasks or inspiration for new ones, use X searches with tools like x_keyword_search or x_semantic_search (e.g., query: "Grok Tasks examples" with mode: Latest). Browse community-shared threads via x_thread_fetch, or web_search for tutorials on xAI features. Prompt Grok directly: "Show me popular Grok Tasks for productivity." 1 of 3

Brian Roemmele

152,242 views • 5 months ago

It's true, most AI coding tools make you do the grunt testing and debugging work. That's why we invested in researching and building a state-of-the-art app testing tool. Replit Agent 3 will spin up a browser and test your app end-to-end.
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It's true, most AI coding tools make you do the grunt testing and debugging work. That's why we invested in researching and building a state-of-the-art app testing tool. Replit Agent 3 will spin up a browser and test your app end-to-end.

Amjad Masad

109,857 views • 6 months ago

$1 MILLION GENSPARK SIDE-BY-SIDE AI SHOWDOWN starts today! 🔥 Battle 1: AI Slides Showndown - Genspark vs ChatGPT Agent Prize Pool: $100,000
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$1 MILLION GENSPARK SIDE-BY-SIDE AI SHOWDOWN starts today! 🔥 Battle 1: AI Slides Showndown - Genspark vs ChatGPT Agent Prize Pool: $100,000

Genspark

72,762 views • 10 months ago

Your enterprise content should power every AI tool and agent you use. With the Box MCP server, Box acts as a secure, governed bridge, so teams can search, retrieve, analyze, and act on Box content directly inside the tools they already use. No one-off integrations. Use it to: 🔹Ask questions over files in Anthropic Claude + Mistral AI Le Chat 🔹Ground designs in Figma or @ mention Box agents in Atlassian Jira 🔹Pull content into GitHub Copilot, Cursor + Claude Code 🔹Build agents with LangChain LangSmith Agent Builder + OpenAI Agent Builder 🔹Automate work in Claude Cowork + Amazon Web Services Quick Suite 🔹Enforce access + audit trails with Runlayer Secure. Standardized. Built for real work →
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Your enterprise content should power every AI tool and agent you use. With the Box MCP server, Box acts as a secure, governed bridge, so teams can search, retrieve, analyze, and act on Box content directly inside the tools they already use. No one-off integrations. Use it to: 🔹Ask questions over files in Anthropic Claude + Mistral AI Le Chat 🔹Ground designs in Figma or @ mention Box agents in Atlassian Jira 🔹Pull content into GitHub Copilot, Cursor + Claude Code 🔹Build agents with LangChain LangSmith Agent Builder + OpenAI Agent Builder 🔹Automate work in Claude Cowork + Amazon Web Services Quick Suite 🔹Enforce access + audit trails with Runlayer Secure. Standardized. Built for real work →

Box

481,535 views • 3 months ago

Vulcan is live. The official command-line tool for Phoenix — built for traders and AI agents. Trade, stream live markets, and run strategies from your terminal. Or let an agent drive.
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Vulcan is live. The official command-line tool for Phoenix — built for traders and AI agents. Trade, stream live markets, and run strategies from your terminal. Or let an agent drive.

Phoenix

158,534 views • 22 days ago

A massive repository with end-to-end examples of AI applications with React! Together with MCP and A2A, the Agent-User Interaction Protocol (AG-UI) is the third piece that will help you build user-facing AI agents. This GitHub repository will give you access to a bunch of examples showing you how to build the following: • Real-time updates between AI and users • Shared mutable state between agents and users • Tool orchestration • Security boundaries • UI synchronization In every one of these examples, you'll get the following: • Client sends a POST request to the agent endpoint • Then listens to a unified event stream over HTTP • Each event includes a type and a minimal payload • Agents emit events in real-time • The frontend can react immediately to these events • The frontend emits events and context back to the agent Check the link in the next post:
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A massive repository with end-to-end examples of AI applications with React! Together with MCP and A2A, the Agent-User Interaction Protocol (AG-UI) is the third piece that will help you build user-facing AI agents. This GitHub repository will give you access to a bunch of examples showing you how to build the following: • Real-time updates between AI and users • Shared mutable state between agents and users • Tool orchestration • Security boundaries • UI synchronization In every one of these examples, you'll get the following: • Client sends a POST request to the agent endpoint • Then listens to a unified event stream over HTTP • Each event includes a type and a minimal payload • Agents emit events in real-time • The frontend can react immediately to these events • The frontend emits events and context back to the agent Check the link in the next post:

Santiago

78,271 views • 8 months ago

Microsoft presents Windows Agent Arena Evaluating Multi-Modal OS Agents at Scale discuss: Large language models (LLMs) show remarkable potential to act as computer agents, enhancing human productivity and software accessibility in multi-modal tasks that require planning and reasoning. However, measuring agent performance in realistic environments remains a challenge since: (i) most benchmarks are limited to specific modalities or domains (e.g. text-only, web navigation, Q&A, coding) and (ii) full benchmark evaluations are slow (on order of magnitude of days) given the multi-step sequential nature of tasks. To address these challenges, we introduce the Windows Agent Arena: a reproducible, general environment focusing exclusively on the Windows operating system (OS) where agents can operate freely within a real Windows OS and use the same wide range of applications, tools, and web browsers available to human users when solving tasks. We adapt the OSWorld framework (Xie et al., 2024) to create 150+ diverse Windows tasks across representative domains that require agent abilities in planning, screen understanding, and tool usage. Our benchmark is scalable and can be seamlessly parallelized in Azure for a full benchmark evaluation in as little as 20 minutes. To demonstrate Windows Agent Arena's capabilities, we also introduce a new multi-modal agent, Navi. Our agent achieves a success rate of 19.5% in the Windows domain, compared to 74.5% performance of an unassisted human. Navi also demonstrates strong performance on another popular web-based benchmark, Mind2Web. We offer extensive quantitative and qualitative analysis of Navi's performance, and provide insights into the opportunities for future research in agent development and data generation using Windows Agent Arena.
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Microsoft presents Windows Agent Arena Evaluating Multi-Modal OS Agents at Scale discuss: Large language models (LLMs) show remarkable potential to act as computer agents, enhancing human productivity and software accessibility in multi-modal tasks that require planning and reasoning. However, measuring agent performance in realistic environments remains a challenge since: (i) most benchmarks are limited to specific modalities or domains (e.g. text-only, web navigation, Q&A, coding) and (ii) full benchmark evaluations are slow (on order of magnitude of days) given the multi-step sequential nature of tasks. To address these challenges, we introduce the Windows Agent Arena: a reproducible, general environment focusing exclusively on the Windows operating system (OS) where agents can operate freely within a real Windows OS and use the same wide range of applications, tools, and web browsers available to human users when solving tasks. We adapt the OSWorld framework (Xie et al., 2024) to create 150+ diverse Windows tasks across representative domains that require agent abilities in planning, screen understanding, and tool usage. Our benchmark is scalable and can be seamlessly parallelized in Azure for a full benchmark evaluation in as little as 20 minutes. To demonstrate Windows Agent Arena's capabilities, we also introduce a new multi-modal agent, Navi. Our agent achieves a success rate of 19.5% in the Windows domain, compared to 74.5% performance of an unassisted human. Navi also demonstrates strong performance on another popular web-based benchmark, Mind2Web. We offer extensive quantitative and qualitative analysis of Navi's performance, and provide insights into the opportunities for future research in agent development and data generation using Windows Agent Arena.

AK

19,684 views • 1 year ago

The Token Analysis Agent now has a charting tool, an updated token data UI, and an enhanced search API. More upgrades are on the way 🐝
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The Token Analysis Agent now has a charting tool, an updated token data UI, and an enhanced search API. More upgrades are on the way 🐝

The Hive

52,350 views • 1 year ago

✨ Advanced Visual Intelligence Agent! Nano Banana PRO is an AI-powered visual generation agent available on Agent Forge. Using supported third-party AI models and image-processing tools, it enables users to generate images from text-based prompts for creative exploration, drafts, prototypes, and visual experimentation. Whether you’re working on early design concepts, artistic ideas, or automated content workflows, Nano Banana PRO helps streamline the process by converting written prompts into visual outputs within the Agent Forge environment. 🔗
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✨ Advanced Visual Intelligence Agent! Nano Banana PRO is an AI-powered visual generation agent available on Agent Forge. Using supported third-party AI models and image-processing tools, it enables users to generate images from text-based prompts for creative exploration, drafts, prototypes, and visual experimentation. Whether you’re working on early design concepts, artistic ideas, or automated content workflows, Nano Banana PRO helps streamline the process by converting written prompts into visual outputs within the Agent Forge environment. 🔗

AITECH CLOUD NETWORK

62,328 views • 6 months ago

Zep AI (YC W24) is the new state of the art in agent memory. It's a memory layer for AI agents that continuously learns from user interactions and changing business data. By providing agents with a complete, holistic view of each user, Zep enables developers to build applications that tackle complex, personalized tasks. In research published today, Zep demonstrated that it delivers up to 18.5% higher accuracy with 90% lower latency when compared to tools like MemGPT, excelling in both the Deep Memory Retrieval (DMR) and LongMemEval benchmarks.
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Zep AI (YC W24) is the new state of the art in agent memory. It's a memory layer for AI agents that continuously learns from user interactions and changing business data. By providing agents with a complete, holistic view of each user, Zep enables developers to build applications that tackle complex, personalized tasks. In research published today, Zep demonstrated that it delivers up to 18.5% higher accuracy with 90% lower latency when compared to tools like MemGPT, excelling in both the Deep Memory Retrieval (DMR) and LongMemEval benchmarks.

Y Combinator

51,142 views • 1 year ago

🤖 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:
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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:

Visual Studio Code

109,440 views • 3 months ago

It should be easy to debug and visualize your agent and workflows, now it is with `ai-sdk-tools/devtools`. Enjoy!
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It should be easy to debug and visualize your agent and workflows, now it is with `ai-sdk-tools/devtools`. Enjoy!

Pontus Abrahamsson — oss/acc

25,343 views • 8 months ago