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Droids have native multi-agent orchestration built-in. Our desktop app let's you easily manage these sub-agents as they tackle complex tasks. Monitor each agent as it works, and interrupt or inject context when needed.

Factory

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Finally, we're so excited to bring Project Mariner capabilities to AI Mode in Search, Agent Mode in Google Gemini, and as a standalone web app. This will allow the products you know and love to intelligently manage complex multi-step tasks on your behalf.

Finally, we're so excited to bring Project Mariner capabilities to AI Mode in Search, Agent Mode in Google Gemini, and as a standalone web app. This will allow the products you know and love to intelligently manage complex multi-step tasks on your behalf.

Google AI

29,189 görüntüleme • 1 yıl önce

this is f*cking gold. Google engineers and MIT researchers ran 260 controlled agent experiments across 6 benchmarks, 5 architectures, and the OpenAI, Gemini, and Claude model families to find out when Multi Agent systems actually help and when adding more agents quietly destroys performance the average result was brutal: −0.3%. best case: +80.8% worst case: −70.0% the key threshold was around a 45% single agent baseline. below it, extra agents can help. above it, coordination often becomes pure overhead. on parallel tasks, Multi Agent reached +80.8%. on sequential tasks, it collapsed by as much as 70%. 3 agents needed 26 to 44 interaction rounds. a single agent needed 7.2. error amplification also hit 17.2× in independent parallel systems. their final framework predicts the right architecture with 87% accuracy.

this is f*cking gold. Google engineers and MIT researchers ran 260 controlled agent experiments across 6 benchmarks, 5 architectures, and the OpenAI, Gemini, and Claude model families to find out when Multi Agent systems actually help and when adding more agents quietly destroys performance the average result was brutal: −0.3%. best case: +80.8% worst case: −70.0% the key threshold was around a 45% single agent baseline. below it, extra agents can help. above it, coordination often becomes pure overhead. on parallel tasks, Multi Agent reached +80.8%. on sequential tasks, it collapsed by as much as 70%. 3 agents needed 26 to 44 interaction rounds. a single agent needed 7.2. error amplification also hit 17.2× in independent parallel systems. their final framework predicts the right architecture with 87% accuracy.

NO1ennn

12,521 görüntüleme • 22 gün önce

🔥 Introducing Skywork Mobile App 5.0—the world’s first native mobile app for Super AI Agents. - Meet VoiceNotes: Turn a single voice memo into clean summaries, transcripts, and visual notes. - Super Agent: Carry a crew of expert minds in your pocket. Run up to 3 expert agents in parallel to tackle complex tasks instantly. Skywork App 5.0 is now available for both iOS and Android.

🔥 Introducing Skywork Mobile App 5.0—the world’s first native mobile app for Super AI Agents. - Meet VoiceNotes: Turn a single voice memo into clean summaries, transcripts, and visual notes. - Super Agent: Carry a crew of expert minds in your pocket. Run up to 3 expert agents in parallel to tackle complex tasks instantly. Skywork App 5.0 is now available for both iOS and Android.

Skywork

276,855 görüntüleme • 10 ay önce

0.7.5 is out! herdr is getting used more and more for multi-agent orchestration, so this release ships a native agent cli built exactly for that. more in below 👇 but first, the new agent sidebar: deeper customization with per-token colors, bold and dim, plus full filtering and sorting any way you want. from config, scripts, or plugins, all through the api!

0.7.5 is out! herdr is getting used more and more for multi-agent orchestration, so this release ships a native agent cli built exactly for that. more in below 👇 but first, the new agent sidebar: deeper customization with per-token colors, bold and dim, plus full filtering and sorting any way you want. from config, scripts, or plugins, all through the api!

herdr

115,404 görüntüleme • 2 ay önce

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.

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 görüntüleme • 2 yıl önce

GPT-5.5 in Codex is built for long-session agent work. But context still doesn't follow you across projects, agents, or teammates. ByteRover does that + Save you tokens -> No rewriting context when you switch from Claude Code to Codex. Now connecting Codex, Claude Code, OpenClaw, Hermes, and 22+ more agents. Your context moves with you. → personal context for solo work → team context for shared decisions and bug history

GPT-5.5 in Codex is built for long-session agent work. But context still doesn't follow you across projects, agents, or teammates. ByteRover does that + Save you tokens -> No rewriting context when you switch from Claude Code to Codex. Now connecting Codex, Claude Code, OpenClaw, Hermes, and 22+ more agents. Your context moves with you. → personal context for solo work → team context for shared decisions and bug history

andy nguyen

12,437 görüntüleme • 5 ay önce

Today, we launched agent-to-agent conversations in Slack to give you real AI coworkers. Vellum assistants now talk to each other and coordinate work with your team all inside your workspace. We tested it with two agents in our own Slack. They planned our offsite for 19 people in 1 day 🧵 Here’s how they did it:

Today, we launched agent-to-agent conversations in Slack to give you real AI coworkers. Vellum assistants now talk to each other and coordinate work with your team all inside your workspace. We tested it with two agents in our own Slack. They planned our offsite for 19 people in 1 day 🧵 Here’s how they did it:

Marina

23,048 görüntüleme • 3 ay önce

I found this last night and I have not stopped thinking about it. HERMES JUST LAUNCHED HERMES DESKTOP. 100% FREE. It is a free desktop app that gives Hermes Agent a proper interface. One place for everything. What is inside: ↳ Auto install and setup, no terminal needed ↳ Streaming chat with token tracking ↳ Multiple agent profiles ↳ Memory you can actually see and edit ↳ 14 tool categories including web, browser, image gen, and voice ↳ Scheduler for automated tasks ↳ 16 messaging gateways including Telegram, WhatsApp, Discord, Slack, and Signal ↳ Full conversation history with search ↳ Backups and logs in one settings screen Works with Anthropic, OpenAI, Gemini, Grok, Groq, Ollama, and more. Hermes Agent is the brain. Hermes Desktop is the cockpit. Free. Open source. Mac, Windows, and Linux.

I found this last night and I have not stopped thinking about it. HERMES JUST LAUNCHED HERMES DESKTOP. 100% FREE. It is a free desktop app that gives Hermes Agent a proper interface. One place for everything. What is inside: ↳ Auto install and setup, no terminal needed ↳ Streaming chat with token tracking ↳ Multiple agent profiles ↳ Memory you can actually see and edit ↳ 14 tool categories including web, browser, image gen, and voice ↳ Scheduler for automated tasks ↳ 16 messaging gateways including Telegram, WhatsApp, Discord, Slack, and Signal ↳ Full conversation history with search ↳ Backups and logs in one settings screen Works with Anthropic, OpenAI, Gemini, Grok, Groq, Ollama, and more. Hermes Agent is the brain. Hermes Desktop is the cockpit. Free. Open source. Mac, Windows, and Linux.

Kanika

60,519 görüntüleme • 4 ay önce

The Hive's latest agent architecture upgrade better leverages the reasoning capabilities of LLMs. Our vision for DeFAI vision centers on a network of specialized agents coordinated by a single user-facing orchestrator. By focusing each specialized agent on dedicated tasks while enabling inter-agent collaboration, the system becomes more adaptable and efficient at handling user requests. These improvements will become clear as we roll out new tools in the coming days. Stay tuned 🐝

The Hive's latest agent architecture upgrade better leverages the reasoning capabilities of LLMs. Our vision for DeFAI vision centers on a network of specialized agents coordinated by a single user-facing orchestrator. By focusing each specialized agent on dedicated tasks while enabling inter-agent collaboration, the system becomes more adaptable and efficient at handling user requests. These improvements will become clear as we roll out new tools in the coming days. Stay tuned 🐝

apys

42,941 görüntüleme • 1 yıl önce

🧃 Introducing stereOS: a Linux based operating system hardened and purpose built for AI agents. It's clear that agents need an ACTUAL operating system (not what people are calling an "OS") to witness the full breadth and depth of their capabilities while mitigating the blast radius of autonomous, untrusted actors. But there are so many problems with AI sandboxes today: * Going out to the apple store and buying a mac mini will never scale and is way too expensive (obviously) * Running in Docker is too restrictive (agents can't stand up their own container infrastructure, no sub virtualization, docker-in-docker is very broken) * Firecracker strips all the hardware so GPU PCIe passthrough, secure boot, FIPs, etc. is out of the question. * Native VMs are too fat and the overhead of 1 agent per VM is too much. stereOS takes a different approach: it's a full NixOS system that you boot and then kick off agent sandboxes inside with gVisor + /nix/store namespace mounting. Each agent gets their own kernel and the /nix/store is read only by nature. Even if the agent was somehow able to escape the gVisor virtual kernel, they'd land on the NixOS system as the "agent" user! Not your actual hardware!! If you want to take a defense-in-depth approach, we support "native" agents that run at the system level kicked off by our `agentd` utility. These agents, on their own, can manage and kick off other sub agents using the internal sandboxing mechanisms. Today, we're open sourcing all of this: * stereOS: our purpose built Linux OS - * masterblaster: client utility to launch, manage, and orchestrate agents - * stereosd: the stereOS system control plane daemon - * agentd: the stereOS system agent management daemon - Give it a try, throw us a star, and let me know what you think 🧃⭐️

🧃 Introducing stereOS: a Linux based operating system hardened and purpose built for AI agents. It's clear that agents need an ACTUAL operating system (not what people are calling an "OS") to witness the full breadth and depth of their capabilities while mitigating the blast radius of autonomous, untrusted actors. But there are so many problems with AI sandboxes today: * Going out to the apple store and buying a mac mini will never scale and is way too expensive (obviously) * Running in Docker is too restrictive (agents can't stand up their own container infrastructure, no sub virtualization, docker-in-docker is very broken) * Firecracker strips all the hardware so GPU PCIe passthrough, secure boot, FIPs, etc. is out of the question. * Native VMs are too fat and the overhead of 1 agent per VM is too much. stereOS takes a different approach: it's a full NixOS system that you boot and then kick off agent sandboxes inside with gVisor + /nix/store namespace mounting. Each agent gets their own kernel and the /nix/store is read only by nature. Even if the agent was somehow able to escape the gVisor virtual kernel, they'd land on the NixOS system as the "agent" user! Not your actual hardware!! If you want to take a defense-in-depth approach, we support "native" agents that run at the system level kicked off by our `agentd` utility. These agents, on their own, can manage and kick off other sub agents using the internal sandboxing mechanisms. Today, we're open sourcing all of this: * stereOS: our purpose built Linux OS - * masterblaster: client utility to launch, manage, and orchestrate agents - * stereosd: the stereOS system control plane daemon - * agentd: the stereOS system agent management daemon - Give it a try, throw us a star, and let me know what you think 🧃⭐️

John McBride

150,844 görüntüleme • 7 ay önce

BREAKING: OpenAI just launched ChatGPT Agent It allows ChatGPT to think, plan, and execute complex tasks on its own virtual computer while you do other things I had early access, and ChatGPT Agent built me a complete early retirement plan in 20 minutes: > Found local tax laws (Vancouver) > Analyzed average monthly spend rates > Calculated savings needed to retire at 30 > Researched optimal investment allocations > Found tax optimization strategies I'd never heard of > Built multiple FIRE scenarios > Created a downloadable presentation with results This would've cost me $5,000+ from a financial advisor and taken weeks I think with ChatGPT Agent now, and especially as it gains access to more tools, we're finally going to see the rise of a new AI skill category in *Agent Management* Agents are finally becoming capable of doing real work autonomously, so anyone who learns how to effectively orchestrate agents will have a huge advantage

BREAKING: OpenAI just launched ChatGPT Agent It allows ChatGPT to think, plan, and execute complex tasks on its own virtual computer while you do other things I had early access, and ChatGPT Agent built me a complete early retirement plan in 20 minutes: > Found local tax laws (Vancouver) > Analyzed average monthly spend rates > Calculated savings needed to retire at 30 > Researched optimal investment allocations > Found tax optimization strategies I'd never heard of > Built multiple FIRE scenarios > Created a downloadable presentation with results This would've cost me $5,000+ from a financial advisor and taken weeks I think with ChatGPT Agent now, and especially as it gains access to more tools, we're finally going to see the rise of a new AI skill category in Agent Management Agents are finally becoming capable of doing real work autonomously, so anyone who learns how to effectively orchestrate agents will have a huge advantage

Rowan Cheung

653,957 görüntüleme • 1 yıl önce

your coding agents are Kanban cards now 😯. New in Orca: open a board over any terminal pane and drag each agent worktree between statuses. todo, in progress, review, testing, blocked, done, or whatever custom columns fit your workflow. much easier when you have 10+ agent running across different features.

your coding agents are Kanban cards now 😯. New in Orca: open a board over any terminal pane and drag each agent worktree between statuses. todo, in progress, review, testing, blocked, done, or whatever custom columns fit your workflow. much easier when you have 10+ agent running across different features.

Orca ADE

47,609 görüntüleme • 4 ay önce

LangGraph. CrewAI. Agno. Which one to pick? The good news is that this will not matter soon! Finally, we have a full picture of how the industry is solving this with just three open protocols that work across ALL frameworks. It's not about picking the best framework. Instead, it's about understanding how protocols create interoperability. The Agent Protocol Landscape shows how three complementary protocols are creating a universal language for Agents: > AG-UI (Agent-User Interaction): - The bi-directional connection between agentic backends and frontends. - This is how agents become truly interactive inside your apps, not just as chatbots, but collaborative co-workers. > MCP (Model Context Protocol): - The standard for how agents connect to tools, data, and workflows. > A2A (Agent-to-Agent): - The protocol for multi-agent coordination. - How agents delegate tasks and share intent across systems. These aren't competing standards. They're layers of the same stack and have handshakes with each other. So instead of building point-to-point integrations, you build to protocols. Moreover, you can integrate LangGraph, CrewAI, or Agno into the same frontend, without rewriting your UI logic. These protocols let everything work together. For instance: - Your LangGraph agent pulls data via MCP. - It delegates analysis to a CrewAI agent via A2A. - Results stream to your React app via AG-UI. - Users see real-time collaboration in your interface. This way, you can focus on building agent capabilities instead of integration mechanics. The protocols handle interoperability automatically. CopilotKit unifies this entire stack into one framework so you can build "Cursor for X" style apps without implementing each protocol from scratch. It gives you all three protocols, generative UI support, and production-ready infrastructure in one framework. I have shared this playbook in the replies! It breaks down handshakes, misconceptions, and real examples and shows exactly how to start building.

LangGraph. CrewAI. Agno. Which one to pick? The good news is that this will not matter soon! Finally, we have a full picture of how the industry is solving this with just three open protocols that work across ALL frameworks. It's not about picking the best framework. Instead, it's about understanding how protocols create interoperability. The Agent Protocol Landscape shows how three complementary protocols are creating a universal language for Agents: > AG-UI (Agent-User Interaction): - The bi-directional connection between agentic backends and frontends. - This is how agents become truly interactive inside your apps, not just as chatbots, but collaborative co-workers. > MCP (Model Context Protocol): - The standard for how agents connect to tools, data, and workflows. > A2A (Agent-to-Agent): - The protocol for multi-agent coordination. - How agents delegate tasks and share intent across systems. These aren't competing standards. They're layers of the same stack and have handshakes with each other. So instead of building point-to-point integrations, you build to protocols. Moreover, you can integrate LangGraph, CrewAI, or Agno into the same frontend, without rewriting your UI logic. These protocols let everything work together. For instance: - Your LangGraph agent pulls data via MCP. - It delegates analysis to a CrewAI agent via A2A. - Results stream to your React app via AG-UI. - Users see real-time collaboration in your interface. This way, you can focus on building agent capabilities instead of integration mechanics. The protocols handle interoperability automatically. CopilotKit unifies this entire stack into one framework so you can build "Cursor for X" style apps without implementing each protocol from scratch. It gives you all three protocols, generative UI support, and production-ready infrastructure in one framework. I have shared this playbook in the replies! It breaks down handshakes, misconceptions, and real examples and shows exactly how to start building.

Avi Chawla

31,074 görüntüleme • 10 ay önce

Big news, friends! I hereby introduce It's a multi-agent chat app with special features for collaborative ranking and estimation tasks, to help you quickly fact-check AI responses against each other. It has GPT-5, Claude Opus 4.1, Gemini 2.5 Pro, and Grok 4, and built-in systems for comparing and aggregating their responses. If you try it, post feature requests for me and the team theMultiplicity.ai!

Big news, friends! I hereby introduce It's a multi-agent chat app with special features for collaborative ranking and estimation tasks, to help you quickly fact-check AI responses against each other. It has GPT-5, Claude Opus 4.1, Gemini 2.5 Pro, and Grok 4, and built-in systems for comparing and aggregating their responses. If you try it, post feature requests for me and the team theMultiplicity.ai!

Andrew Critch (🤖🩺🚀)

20,645 görüntüleme • 11 ay önce

Claude Cowork Sub-Agents are f*cking cracked 🤯 One prompt → 50 competitor ads analyzed, hooks extracted, and a full creative brief generated. 10 AI agents running in parallel, under 5 minutes. All inside Claude Cowork. Perfect for DTC brands and agencies who are still doing creative research and ad production one task at a time inside Claude. If you're analyzing competitor ads one by one, copying hooks into a spreadsheet manually, writing brief after brief from scratch, and watching Claude's output quality fall off a cliff after the 15th variation because the context window is completely bloated... Sub-agents eliminate the entire bottleneck: → Drop in a spreadsheet of 50 competitor ads and spin up 10 parallel sub-agents → Each sub-agent analyzes 5 ads simultaneously — hooks, angles, CTAs, emotional tone, creative format → They report structured summaries back to the main agent without bloating the context → The main agent synthesizes patterns across all 50 ads into a competitive intel brief → Then spin up another round of sub-agents to generate 30 ad copy variations across 10 personas → Each sub-agent writes for 1-2 personas in a fresh context — so variation 30 is as sharp as variation 1 No analyzing ads one at a time. No context window blowing up halfway through. No copy quality degrading after the first dozen variations. What this gives you: → 50 competitor ads broken down in minutes — hooks, angles, CTAs, formats, all structured → Pattern analysis across the full dataset that you'd miss reviewing ads individually → 30+ ad copy variations with persona-specific messaging that actually stays sharp → A workflow you can save as reusable skills and trigger with one command next time → The same output quality on the last task as the first Built 100% inside Claude Cowork with sub-agents. I put together a full DTC playbook: 5 bulk workflows with copy-paste prompts, the exact sub-agent prompting pattern, batching guidelines, and an honest breakdown of when this setup is worth it vs. when a simpler approach is the better move. Want it for free? > Like this post > Comment "AGENTS" And I'll send it over (must be following so I can DM)

Claude Cowork Sub-Agents are f*cking cracked 🤯 One prompt → 50 competitor ads analyzed, hooks extracted, and a full creative brief generated. 10 AI agents running in parallel, under 5 minutes. All inside Claude Cowork. Perfect for DTC brands and agencies who are still doing creative research and ad production one task at a time inside Claude. If you're analyzing competitor ads one by one, copying hooks into a spreadsheet manually, writing brief after brief from scratch, and watching Claude's output quality fall off a cliff after the 15th variation because the context window is completely bloated... Sub-agents eliminate the entire bottleneck: → Drop in a spreadsheet of 50 competitor ads and spin up 10 parallel sub-agents → Each sub-agent analyzes 5 ads simultaneously — hooks, angles, CTAs, emotional tone, creative format → They report structured summaries back to the main agent without bloating the context → The main agent synthesizes patterns across all 50 ads into a competitive intel brief → Then spin up another round of sub-agents to generate 30 ad copy variations across 10 personas → Each sub-agent writes for 1-2 personas in a fresh context — so variation 30 is as sharp as variation 1 No analyzing ads one at a time. No context window blowing up halfway through. No copy quality degrading after the first dozen variations. What this gives you: → 50 competitor ads broken down in minutes — hooks, angles, CTAs, formats, all structured → Pattern analysis across the full dataset that you'd miss reviewing ads individually → 30+ ad copy variations with persona-specific messaging that actually stays sharp → A workflow you can save as reusable skills and trigger with one command next time → The same output quality on the last task as the first Built 100% inside Claude Cowork with sub-agents. I put together a full DTC playbook: 5 bulk workflows with copy-paste prompts, the exact sub-agent prompting pattern, batching guidelines, and an honest breakdown of when this setup is worth it vs. when a simpler approach is the better move. Want it for free? > Like this post > Comment "AGENTS" And I'll send it over (must be following so I can DM)

Mike Futia

50,216 görüntüleme • 7 ay önce

DAPPOS is bringing Onchain OS Skills from X Layer OKX Wallet into the xBubble ecosystem, making OKX Wallet’s agent-ready wallet, trading, market data, and agentic payments protocol capabilities accessible across xBubble agents. Built on top of Onchain OS Skills, xBubble’s crypto-task SOPs help turn fragmented on-chain flows into a more seamless chat-native experience inside the xBubble app across mobile, desktop, and web. Users can monitor markets, prepare trades, manage wallet activity, and coordinate payment flows through a single conversation. Bubble Engine will continue to use Onchain OS Skills as the baseline for every future SOP iteration and upgrade. With Onchain OS Skills, DAPPOS is making agentic on-chain tasks more conversational, practical, and accessible.

DAPPOS is bringing Onchain OS Skills from X Layer OKX Wallet into the xBubble ecosystem, making OKX Wallet’s agent-ready wallet, trading, market data, and agentic payments protocol capabilities accessible across xBubble agents. Built on top of Onchain OS Skills, xBubble’s crypto-task SOPs help turn fragmented on-chain flows into a more seamless chat-native experience inside the xBubble app across mobile, desktop, and web. Users can monitor markets, prepare trades, manage wallet activity, and coordinate payment flows through a single conversation. Bubble Engine will continue to use Onchain OS Skills as the baseline for every future SOP iteration and upgrade. With Onchain OS Skills, DAPPOS is making agentic on-chain tasks more conversational, practical, and accessible.

DAPPOS

14,708 görüntüleme • 4 ay önce

Helmor has been out as an open-source coding agent orchestrator for less than a week, and we’re already close to 1,000 GitHub stars!!! As a little gift, we shipped a new feature you’re going to love. 👇 Stop copy-pasting GitHub links, Linear tickets, Slack threads, and random notes into prompts. We're tired of rebuilding context every time I ask an agent to do work. Contexts in Helmor is another step toward a local dev loop: browse, preview, inject context, and dispatch tasks without leaving the app. Before you start a task, Helmor should help you gather the right context first. Try the open-source Helmor — link in the comments. #Helmor

Helmor has been out as an open-source coding agent orchestrator for less than a week, and we’re already close to 1,000 GitHub stars!!! As a little gift, we shipped a new feature you’re going to love. 👇 Stop copy-pasting GitHub links, Linear tickets, Slack threads, and random notes into prompts. We're tired of rebuilding context every time I ask an agent to do work. Contexts in Helmor is another step toward a local dev loop: browse, preview, inject context, and dispatch tasks without leaving the app. Before you start a task, Helmor should help you gather the right context first. Try the open-source Helmor — link in the comments. #Helmor

Caspian 東澔

10,823 görüntüleme • 5 ay önce

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.

Santiago

63,073 görüntüleme • 1 yıl önce