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Introducing AG-UI 1.0 The open protocol for how ANY agent communicates with ANY application. Now with a stable, versioned contract. Features included in 1.0: 1. Metadata - send backend data to the UI 2. Subagents - expose delegated agent work 3. Interrupts + resumption 3. Run outcomes and token... usage 4. Forward compatibility Capabilities that have been part of AG-UI (streaming, tool calls, shared state, reasoning etc) are still covered. Thank you to those who shaped 1.0 🪁 Including builders at Paul Jankura, Google, Microsoft, Pydantic, Tack And All That and more. Their feedback led directly to several critical improvements in the final release. AG-UI is built with those who build on it. 👉 Read more: The 1.0 spec will remain unchanged, while future protocol evolution will happen through new draft specifications and open community discussion, led by CopilotKit🪁. More details belowshow more

Atai Barkai

9,363 subscribers

37,535 Aufrufe • vor 4 Tagen •via X (Twitter)

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19 Kommentare

Profilbild von Atai Barkai

Atai Barkaivor 4 Tagen

Shared JSON Schema AG-UI 1.0 comes with a machine-readable JSON Schema that defines the protocol structure, alongside the human-readable behavioral specification. The official TypeScript, Python, and .NET SDKs are generated from this shared schema, giving implementations across languages and frameworks the same structural foundation. The schema also provides a practical basis for code generation, validation, tooling, and automated compatibility testing. For teams building AG-UI into production systems, this means the protocol is both precisely documented for humans and directly consumable by tooling.

Profilbild von Atai Barkai

Atai Barkaivor 4 Tagen

Full Backwards Compatibility AG-UI 1.0 is designed to remain highly compatible with existing implementations. The spec gives new and existing implementers a precise contract to build against while preserving the flexibility needed for integrations already running in production. With 1.0, teams can build on AG-UI with a stable specification, predictable versioning, and a clear path for future evolution.

Profilbild von Atai Barkai

Atai Barkaivor 4 Tagen

If you build agents that talk to a UI, check out AG-UI. Push back. Open a PR. Make it better. Repo:

Profilbild von Eli Berman🪁

Eli Berman🪁vor 4 Tagen

AG-UI is now a formally-specified, community-driven protocol (like MCP)! Really great work to the entire @CopilotKit team for getting to this point 👏

Profilbild von CopilotKit🪁

CopilotKit🪁vor 4 Tagen

CopilotKit 🤝 AG-UI

Profilbild von sofía🪁

sofía🪁vor 4 Tagen

@mme_xyz always killing it !!

Profilbild von Uli Barkai🪁

Uli Barkai🪁vor 4 Tagen

Big milestone!

Profilbild von Mike Tromba

Mike Trombavor 4 Tagen

Congrats! Big fan of AG-UI, @cactalofficial runs on it

Profilbild von Ashish

Ashishvor 4 Tagen

MCP + A2A = AG UI ?

Profilbild von Atai Barkai

Atai Barkaivor 4 Tagen

not quite- MCP = agent to tool A2A = agent to agent AG-UI = agent-to-user They are all complimentary and work well together

Profilbild von Ashish

Ashishvor 4 Tagen

Got it makes sense

Profilbild von EmaDev

EmaDevvor 4 Tagen

What’s the difference on the vercel ai sdk + tools + client approvals?

Profilbild von Harley Lewis Foote

Harley Lewis Footevor 4 Tagen

The interrupt/resumption bit is interesting. Most agent demos look smooth because they skip the "wait, actually, no" moment.

Profilbild von Anmol

Anmolvor 4 Tagen

huge day!! connecting ANY agent to ANY app has been such a mess imo & one of the biggest problems in the agentic ecosystem great to see AG-UI finally fixing it

Profilbild von Mathias Heinke

Mathias Heinkevor 4 Tagen

Giving a user-cancelled run its own outcome is a small UI detail I really like. 'I stopped it' deserves a different screen state from 'it failed'.

Profilbild von Sam Millunchick

Sam Millunchickvor 4 Tagen

Different from pi agent sdk?

Profilbild von Diavlos.sh

Diavlos.shvor 4 Tagen

i build something similar called

Profilbild von Gregor

Gregorvor 4 Tagen

Interrupts and resumption is where versioning matters most. Without a stable contract, resume state is effectively best-effort and you lose the agent's context mid-run.

Profilbild von Nathan Tarbert

Nathan Tarbertvor 4 Tagen

AG-UI has become the standard for agent-to-user communication. This milestone is huge and looking back, it's crazy to think that AG-UI was our own internal solution when we started integrating with every agent framework. Massive kudos to the team!

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Microsoft 🤝 AG-UI Protocol Microsoft Agent Framework is now AG-UI compatible! 🎉 Connect your MS Agent Framework .NET agents into fullstack applications with Python coming soon 👀 MS Agent Framework takes care of the agentic backend, AG-UI connects your agents to the frontend. Features include: - Chat UI - Generative UI - State synchronization - Human-in-the-loop - Frontend Tool Calls All the building blocks you need to take your agents from the backend and into a fullstack agentic application. Available in React & Angular via CopilotKit, and Rust, Kotlin, Rust, Java and .NET via community SDKs Big shoutout to the Microsoft & CopilotKit teams, who shipped this highly requested integration at breakneck speeds. More coming soon 👀 🙏 Javier Nelson, Joonseok Oh, Daniel Roth, Evan Mattson, Tyler Slaton, Brandon McConnell, Markus Ecker Read more about our collaboration: Get started: Check out the Dojo:

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CopilotKit🪁

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Microsoft Teams just got Generative UI But this is just the tip of the iceberg. AG-UI is the protocol connecting any agent to any user on any app, topped with Generative UI. That means your agent can render: - forms - buttons - charts - approval flows Right inside the chat, on the fly. But it's not limited to in-app copilots. What's special is that you can work with any agent using the same system and bring them into any surface, including Slack, WhatsApp, Discord and more. This is exactly why Hermes and OpenClaw took off. As users of these agents, we don't like walls of text, and it's a pain to get when you can have way better UX. CopilotKit🪁 + AG-UI handle that interaction layer. If you want to try out Agentic UI in Microsoft Teams, you can get early access here:

Microsoft Teams just got Generative UI But this is just the tip of the iceberg. AG-UI is the protocol connecting any agent to any user on any app, topped with Generative UI. That means your agent can render: - forms - buttons - charts - approval flows Right inside the chat, on the fly. But it's not limited to in-app copilots. What's special is that you can work with any agent using the same system and bring them into any surface, including Slack, WhatsApp, Discord and more. This is exactly why Hermes and OpenClaw took off. As users of these agents, we don't like walls of text, and it's a pain to get when you can have way better UX. CopilotKit🪁 + AG-UI handle that interaction layer. If you want to try out Agentic UI in Microsoft Teams, you can get early access here:

Atai Barkai

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Pratham

33,920 Aufrufe • vor 8 Monaten

Anthropic's in trouble, again! They spent years building what's now fully open-source. What made Claude feel different from a normal app is that the agent could act inside the interface instead of only talking in a chat box. For instance, Claude Artifacts let an agent render real UI, charts, dashboards, and interactive components that assemble live inside the response. Every major AI product tried to replicate it. But the problem was that unlike reasoning, planning, tool-calling, etc., none of it shipped natively with LangGraph, CrewAI, or Google ADK. So teams started building an owned version that required engineering the entire interface layer from scratch. Most teams, however, just settled for shipping the agent as a backend API in a chat box since rendering the UI is only one piece of it. To actually make it work, the interface layer also needed real-time streaming, state kept in sync between agent and UI, conversations that persist across sessions, and reconnection when a user refreshes mid-run. CopilotKit🪁 is now the only open-source framework that actually lets you build your own full-stack Claude-like apps. It decouples the agent from the interface, talking over AG-UI (an open protocol for agent-to-user communication). Being a standard protocol, the frontend never needs to know whether it is talking to a LangGraph or a CrewAI agent. You can change the backend anytime and the UI will never notice. In practice, CopilotKit's interface layer gives several pre-implemented React building blocks that wire the agent directly into the app, like: - generative UI, so the agent renders real components instead of text - chat windows, sidebars, and popups, or a fully headless setup - shared state, so the agent and app stay in sync - human-in-the-loop approvals, where the agent waits before acting - persistent threads that store the whole session, including the agent-user interactions and generated UI, not just text And because that full history is captured, those interactions can feed a self-learning layer that also improves the agent from real usage over time. The interface layer that Anthropic spent years engineering in-house is now literally available to any developer/team. CopilotKit is open-source with 30k+ GitHub stars, and AG-UI, the protocol underneath, is already supported across every major agent framework: LangGraph, CrewAI, Mastra, Google ADK, and more. CopilotKit GitHub repo → (don't forget to star it ⭐ ) If you want to go deeper, I found a detailed breakdown by Shubham Saboo recently on the three Generative UI patterns, with implementation. Read it below.

Anthropic's in trouble, again! They spent years building what's now fully open-source. What made Claude feel different from a normal app is that the agent could act inside the interface instead of only talking in a chat box. For instance, Claude Artifacts let an agent render real UI, charts, dashboards, and interactive components that assemble live inside the response. Every major AI product tried to replicate it. But the problem was that unlike reasoning, planning, tool-calling, etc., none of it shipped natively with LangGraph, CrewAI, or Google ADK. So teams started building an owned version that required engineering the entire interface layer from scratch. Most teams, however, just settled for shipping the agent as a backend API in a chat box since rendering the UI is only one piece of it. To actually make it work, the interface layer also needed real-time streaming, state kept in sync between agent and UI, conversations that persist across sessions, and reconnection when a user refreshes mid-run. CopilotKit🪁 is now the only open-source framework that actually lets you build your own full-stack Claude-like apps. It decouples the agent from the interface, talking over AG-UI (an open protocol for agent-to-user communication). Being a standard protocol, the frontend never needs to know whether it is talking to a LangGraph or a CrewAI agent. You can change the backend anytime and the UI will never notice. In practice, CopilotKit's interface layer gives several pre-implemented React building blocks that wire the agent directly into the app, like: - generative UI, so the agent renders real components instead of text - chat windows, sidebars, and popups, or a fully headless setup - shared state, so the agent and app stay in sync - human-in-the-loop approvals, where the agent waits before acting - persistent threads that store the whole session, including the agent-user interactions and generated UI, not just text And because that full history is captured, those interactions can feed a self-learning layer that also improves the agent from real usage over time. The interface layer that Anthropic spent years engineering in-house is now literally available to any developer/team. CopilotKit is open-source with 30k+ GitHub stars, and AG-UI, the protocol underneath, is already supported across every major agent framework: LangGraph, CrewAI, Mastra, Google ADK, and more. CopilotKit GitHub repo → (don't forget to star it ⭐ ) If you want to go deeper, I found a detailed breakdown by Shubham Saboo recently on the three Generative UI patterns, with implementation. Read it below.

Avi Chawla

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17,438 Aufrufe • vor 3 Monaten

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Atai Barkai

11,997 Aufrufe • vor 8 Monaten

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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

46,142 Aufrufe • vor 1 Jahr

Anthropic's most viral feature is now open-source! Until now, Anthropic's Generative UI capabilities only existed inside its own products. CopilotKit🪁 just shipped Open Generative UI, an open-source implementation of Claude Artifacts that works in any app. The agent generates HTML/SVG at runtime, and CopilotKit streams it token-by-token into a sandboxed iframe inside the app's chat. So the user can watch the UI assemble itself in real time, not after the full response is ready. The sandbox is fully isolated with no access to the parent app, the DOM, or user data. So if the agent hallucinates broken markup or unexpected JavaScript, nothing leaks outside the iframe. Under the hood, the agent does not select from pre-built components. Instead, it generates arbitrary visuals from scratch every time. The output is unconstrained by default, but you can shape it by defining prompt-based skills that teach the agent specific visual formats or guidelines. For instance, a skill prompt can guide the agent toward producing a Chart.js dashboard with proper axis labels and responsive sizing, or an interactive 3D model with rotation controls. The video below shows this in action, and the output quality you see actually comes from the skills layer. Open Generative UI runs on AG-UI, so it works out of the box with LangGraph, CrewAI, Mastra, Google ADK, AWS Strands, and more. It also ships with a standalone MCP server that plugs into Claude Code, Cursor, or any MCP-compatible client. And the entire stack is built on top of CopilotKit, the open-source frontend framework for agents and generative UI. 30k+ GitHub stars, with SDKs for React, Next.js, Angular, and Vue. I have shared the GitHub repo and a live playground in the replies!

Anthropic's most viral feature is now open-source! Until now, Anthropic's Generative UI capabilities only existed inside its own products. CopilotKit🪁 just shipped Open Generative UI, an open-source implementation of Claude Artifacts that works in any app. The agent generates HTML/SVG at runtime, and CopilotKit streams it token-by-token into a sandboxed iframe inside the app's chat. So the user can watch the UI assemble itself in real time, not after the full response is ready. The sandbox is fully isolated with no access to the parent app, the DOM, or user data. So if the agent hallucinates broken markup or unexpected JavaScript, nothing leaks outside the iframe. Under the hood, the agent does not select from pre-built components. Instead, it generates arbitrary visuals from scratch every time. The output is unconstrained by default, but you can shape it by defining prompt-based skills that teach the agent specific visual formats or guidelines. For instance, a skill prompt can guide the agent toward producing a Chart.js dashboard with proper axis labels and responsive sizing, or an interactive 3D model with rotation controls. The video below shows this in action, and the output quality you see actually comes from the skills layer. Open Generative UI runs on AG-UI, so it works out of the box with LangGraph, CrewAI, Mastra, Google ADK, AWS Strands, and more. It also ships with a standalone MCP server that plugs into Claude Code, Cursor, or any MCP-compatible client. And the entire stack is built on top of CopilotKit, the open-source frontend framework for agents and generative UI. 30k+ GitHub stars, with SDKs for React, Next.js, Angular, and Vue. I have shared the GitHub repo and a live playground in the replies!

Akshay 🚀

87,048 Aufrufe • vor 5 Monaten

two new additions to the bankrbot agent: custom ui widgets: interact with any onchain protocol and bankr will craft a personalized widget for you. balances, apy, projected earnings, action buttons. all generated on the fly from raw contract data. blockaid transaction scanning: every write contract call is now simulated through Blockaid before execution. malicious or suspicious transactions get blocked automatically. fail-open so it never breaks the flow humans and agents can interact with the bankr agent directly using natural language to control their wallets and execute transactions.

two new additions to the bankrbot agent: custom ui widgets: interact with any onchain protocol and bankr will craft a personalized widget for you. balances, apy, projected earnings, action buttons. all generated on the fly from raw contract data. blockaid transaction scanning: every write contract call is now simulated through Blockaid before execution. malicious or suspicious transactions get blocked automatically. fail-open so it never breaks the flow humans and agents can interact with the bankr agent directly using natural language to control their wallets and execute transactions.

deployer

23,985 Aufrufe • vor 6 Monaten

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Today ReUI Pro goes live! 🔥 Design-forward shadcn/ui platform with MCP fluency for agents. AI made UI generation instant. It didn't make it production-ready. You still spend hours picking blocks, adjusting details, and fine-tuning the last mile. We spent six months building the agent-ready layer for shadcn/ui that closes the gap. Inside: ☑️ Free Hosted MCP with Agent Skills. ☑️ 1,019 open-source shadcn/ui components, free forever. ☑️ 17 open-source ReUI primitives(Data Grid, Filters,File Upload, Stepper and more). ☑️ 485 Pro blocks across over 50 categories. ☑️ 562 hand-crafted interactive icons in 4 styles. ☑️ Default Base UI and legacy Radix by WorkOS versions. Real credit to shadcn for the ecosystem! The registry model changed how we all build and ship UI, and ReUI stands on that foundation. Below is a thread on the 10 things we're most proud of👇

ReUI

50,965 Aufrufe • vor 2 Monaten

Another blow to Anthropic! They spent months building what's now fully open-source. Anthropic recently put Claude inside Slack, where you can tag it in a channel. It reads the thread, breaks the task into steps, and posts the result back. The problem is that it only runs Claude and only in the channels Anthropic supports. Running your own agent there is harder. The reasoning, tool calls, and state management are mostly handled by the framework. Connecting that agent to a messaging platform is not. Moreover, each platform has a different integration: - Slack renders messages with Block Kit - Teams uses Adaptive Cards - and each has its own SDK, auth flow, and delivery model. If an agent needs to run on three platforms, one must write three separate integrations against the same agent logic. That overhead explains why most custom agents never get deployed to Slack, and why the ones that do are usually a single vendor's hosted assistant. The alternative is to keep the agent in one place and add a per-platform adapter that translates its output into each platform's native format. The agent is written once, and each channel requires just another output target instead of a separate build. CopilotKit open-sourced this full implementation in the Channels SDK. Essentially, any agent that implements AG-UI can run in a messaging platform in a few lines of code, like Slack, Teams, Discord, WhatsApp, and many more. Because the agent runs inside the thread, it has that conversation's context, so it can summarize the discussion, open a ticket, or route to the right person. It works with any backend, so LangGraph, CrewAI, Mastra, Google ADK, or a plain HTTP agent can connect through an existing endpoint. The same message can render as a Block Kit in Slack and as Adaptive Cards in Teams. In practice, the model and orchestration stay the same; it requires no migration or rewrite. It also handles human-in-the-loop approvals, persistence, and transcripts that carry state across platforms, so a thread started in Teams can continue in Slack. CopilotKit is open-source, and AG-UI is supported across every major agent framework, including LangGraph, CrewAI, Mastra, and Google ADK. Here's the repo: (don't forget to star it ⭐) The agent running in Slack no longer has to be a vendor's. It can be the one you already built. The video below shows this in action. Thanks to CopilotKit for working with me on this launch.

Another blow to Anthropic! They spent months building what's now fully open-source. Anthropic recently put Claude inside Slack, where you can tag it in a channel. It reads the thread, breaks the task into steps, and posts the result back. The problem is that it only runs Claude and only in the channels Anthropic supports. Running your own agent there is harder. The reasoning, tool calls, and state management are mostly handled by the framework. Connecting that agent to a messaging platform is not. Moreover, each platform has a different integration: - Slack renders messages with Block Kit - Teams uses Adaptive Cards - and each has its own SDK, auth flow, and delivery model. If an agent needs to run on three platforms, one must write three separate integrations against the same agent logic. That overhead explains why most custom agents never get deployed to Slack, and why the ones that do are usually a single vendor's hosted assistant. The alternative is to keep the agent in one place and add a per-platform adapter that translates its output into each platform's native format. The agent is written once, and each channel requires just another output target instead of a separate build. CopilotKit open-sourced this full implementation in the Channels SDK. Essentially, any agent that implements AG-UI can run in a messaging platform in a few lines of code, like Slack, Teams, Discord, WhatsApp, and many more. Because the agent runs inside the thread, it has that conversation's context, so it can summarize the discussion, open a ticket, or route to the right person. It works with any backend, so LangGraph, CrewAI, Mastra, Google ADK, or a plain HTTP agent can connect through an existing endpoint. The same message can render as a Block Kit in Slack and as Adaptive Cards in Teams. In practice, the model and orchestration stay the same; it requires no migration or rewrite. It also handles human-in-the-loop approvals, persistence, and transcripts that carry state across platforms, so a thread started in Teams can continue in Slack. CopilotKit is open-source, and AG-UI is supported across every major agent framework, including LangGraph, CrewAI, Mastra, and Google ADK. Here's the repo: (don't forget to star it ⭐) The agent running in Slack no longer has to be a vendor's. It can be the one you already built. The video below shows this in action. Thanks to CopilotKit for working with me on this launch.

Akshay 🚀

244,387 Aufrufe • vor 2 Monaten

Karpathy’s Agentic Engineering finally has proper DevTools! When an agent stops working, the model is only one possible cause. The problem could be a failed tool, a lost connection, an interface update that never appeared, or something earlier in the conversation. CopilotKit🪁 has rebuilt its open-source Inspector around this problem. It sits inside the application and watches the full interaction between the user, the interface, and the agent. When something fails, the Inspector button turns red, names the failure, and opens the spot where it happened. But even a much harder problem is reproducing that failure. This is because agents do not always follow the same path twice. Running the same prompt again may trigger another tool, produce another response, or start with a different application state. Inspector handles this through saved Threads and an isolated Agent Playground. A developer can open the conversation where the problem occurred, choose "Try from here," and copy everything up to that point into the Playground. The original conversation remains unchanged while another response or tool path is tested. Threads can open inside the live application, while assistant messages can jump back to their matching context in Inspector. Everything runs underneath AG-UI, the protocol that carries messages, tool activity, state changes, and other events between the agent and the interface. Those interactions can also feed CopilotKit Intelligence so that when the same issue or useful behavior appears repeatedly, it can become an evidence-backed Insight and a proposed SKILL(.)md file. Developers can review, edit, and approve the improvement before the agent inherits it. The tool also helps developers find a problem, recreate its surrounding context, test another path, and turn repeated lessons into agent improvements. It is open source and included with CopilotKit development builds. Intelligence setup starts with one prompt. Here are the docs: I am testing this extensively and will cover this in more detail soon with a hands-on demo.

Karpathy’s Agentic Engineering finally has proper DevTools! When an agent stops working, the model is only one possible cause. The problem could be a failed tool, a lost connection, an interface update that never appeared, or something earlier in the conversation. CopilotKit🪁 has rebuilt its open-source Inspector around this problem. It sits inside the application and watches the full interaction between the user, the interface, and the agent. When something fails, the Inspector button turns red, names the failure, and opens the spot where it happened. But even a much harder problem is reproducing that failure. This is because agents do not always follow the same path twice. Running the same prompt again may trigger another tool, produce another response, or start with a different application state. Inspector handles this through saved Threads and an isolated Agent Playground. A developer can open the conversation where the problem occurred, choose "Try from here," and copy everything up to that point into the Playground. The original conversation remains unchanged while another response or tool path is tested. Threads can open inside the live application, while assistant messages can jump back to their matching context in Inspector. Everything runs underneath AG-UI, the protocol that carries messages, tool activity, state changes, and other events between the agent and the interface. Those interactions can also feed CopilotKit Intelligence so that when the same issue or useful behavior appears repeatedly, it can become an evidence-backed Insight and a proposed SKILL(.)md file. Developers can review, edit, and approve the improvement before the agent inherits it. The tool also helps developers find a problem, recreate its surrounding context, test another path, and turn repeated lessons into agent improvements. It is open source and included with CopilotKit development builds. Intelligence setup starts with one prompt. Here are the docs: I am testing this extensively and will cover this in more detail soon with a hands-on demo.

Akshay 🚀

12,206 Aufrufe • vor 23 Tagen

If you could only learn one thing that will be relevant for the next 10-20 years, focus on learning how to deal with data. The future is not about faster hardware, smarter algorithms, or better ideas. The future is about DATA, and those who know how to deal with it will stay relevant much longer than anyone else. I recorded a video to show you how easy it is to get started. In the video, I'm using Kestra. For a long time, I was a fan of AirFlow. Then, I moved to AWS Step Functions. Today, I only use Kestra. Kestra is open-source (repo link below) and kind enough to sponsor my work. The video will show you how easy it is to do the following: 1. Run Kestra locally (literally, one command) 2. Build a simple flow 3. Run Python scripts as part of your flow 4. Connect to HuggingFace models If you have never built a data pipeline, open Kestra's Quick Start Guide and follow their examples. (I think it will take you one weekend to feel comfortable with the application and build the courage you need to get into more serious work.)

If you could only learn one thing that will be relevant for the next 10-20 years, focus on learning how to deal with data. The future is not about faster hardware, smarter algorithms, or better ideas. The future is about DATA, and those who know how to deal with it will stay relevant much longer than anyone else. I recorded a video to show you how easy it is to get started. In the video, I'm using Kestra. For a long time, I was a fan of AirFlow. Then, I moved to AWS Step Functions. Today, I only use Kestra. Kestra is open-source (repo link below) and kind enough to sponsor my work. The video will show you how easy it is to do the following: 1. Run Kestra locally (literally, one command) 2. Build a simple flow 3. Run Python scripts as part of your flow 4. Connect to HuggingFace models If you have never built a data pipeline, open Kestra's Quick Start Guide and follow their examples. (I think it will take you one weekend to feel comfortable with the application and build the courage you need to get into more serious work.)

Santiago

51,100 Aufrufe • vor 1 Jahr

Earlier this week at GTC, we announced our partnership with Nvidia. We will work with Nvidia to build strong, American open-source models that are at the frontier of scientific reasoning. These models will be essential for the US to compete with China on science in the coming decades. Jensen is committing to spend tens of billions of dollars developing open-source models, and we are excited to be a partner with them in figuring out how to benchmark, train and use those agents to accelerate scientific research. We have already open-sourced some of the work we have done with them, and are looking forward to open-sourcing more. There are few things today that are more important. See our blog post below, and watch the video to learn more, narrated by the man himself.

Earlier this week at GTC, we announced our partnership with Nvidia. We will work with Nvidia to build strong, American open-source models that are at the frontier of scientific reasoning. These models will be essential for the US to compete with China on science in the coming decades. Jensen is committing to spend tens of billions of dollars developing open-source models, and we are excited to be a partner with them in figuring out how to benchmark, train and use those agents to accelerate scientific research. We have already open-sourced some of the work we have done with them, and are looking forward to open-sourcing more. There are few things today that are more important. See our blog post below, and watch the video to learn more, narrated by the man himself.

Sam Rodriques

24,407 Aufrufe • vor 6 Monaten

A new roadmap. A New Era of The Graph 🗺️ The Graph’s new roadmap introduces a bold and transformative vision for the future of The Graph! The new R&D roadmap details an expansion of The Graph’s ability to serve web3’s growing demands for data access, while better serving builders and protocol contributors, and improving the overall simplicity and efficiency of the network. After three years of serving builders, The Graph Network is mature, reliable, and performant. The Graph ecosystem has followed through on its commitment to democratize access to blockchain data while also establishing subgraphs as a web3 standard. But The Graph’s innovation journey doesn’t end there. The New Era of The Graph is organized into five core objectives: 1️⃣ World of Data Services: Expanding to provide new data services beyond subgraphs to deliver a rich market of data on the network, serving novel use cases for data scientists and more. This will include more data sources, new query languages, and support for LLMs. 2️⃣ Developer Empowerment: Supporting developers through enhanced DevEx and tooling by introducing streamlined billing, clear pricing models, a new free query plan, and reduced gas fees. A more SaaS-like experience for devs, without compromising on decentralization! 3️⃣ Protocol Evolution & Resiliency: Delivering improvements resulting in a more resilient, flexible, and simple protocol, including updates to delegation. 4️⃣ Optimized Indexer Performance: Boosting network performance with improved Indexer tooling and operational capabilities to deliver increased scalability, reduce costs, and enhanced network reliability. 5️⃣ Interconnected Graph of Data: Creating tools for composable data and a global, organized knowledge graph – interlinking open data and making it easier to build upon. The new roadmap sets in motion an exciting evolution in web3 data infrastructure. In a phased rollout, The Graph will introduce many new features and benefits, including the integration of new data services, new query languages, enhanced developer tooling, improved UX + UI, alongside greater protocol efficiency and resilience. As this new era unfolds, The Graph crystallizes as the connective tissue across the many layers of the web3 stack, evolving into a comprehensive, interwoven graph of data equipped to serve every project dreamt up by web3’s innovators. Read the full announcement linked in the comment below!

A new roadmap. A New Era of The Graph 🗺️ The Graph’s new roadmap introduces a bold and transformative vision for the future of The Graph! The new R&D roadmap details an expansion of The Graph’s ability to serve web3’s growing demands for data access, while better serving builders and protocol contributors, and improving the overall simplicity and efficiency of the network. After three years of serving builders, The Graph Network is mature, reliable, and performant. The Graph ecosystem has followed through on its commitment to democratize access to blockchain data while also establishing subgraphs as a web3 standard. But The Graph’s innovation journey doesn’t end there. The New Era of The Graph is organized into five core objectives: 1️⃣ World of Data Services: Expanding to provide new data services beyond subgraphs to deliver a rich market of data on the network, serving novel use cases for data scientists and more. This will include more data sources, new query languages, and support for LLMs. 2️⃣ Developer Empowerment: Supporting developers through enhanced DevEx and tooling by introducing streamlined billing, clear pricing models, a new free query plan, and reduced gas fees. A more SaaS-like experience for devs, without compromising on decentralization! 3️⃣ Protocol Evolution & Resiliency: Delivering improvements resulting in a more resilient, flexible, and simple protocol, including updates to delegation. 4️⃣ Optimized Indexer Performance: Boosting network performance with improved Indexer tooling and operational capabilities to deliver increased scalability, reduce costs, and enhanced network reliability. 5️⃣ Interconnected Graph of Data: Creating tools for composable data and a global, organized knowledge graph – interlinking open data and making it easier to build upon. The new roadmap sets in motion an exciting evolution in web3 data infrastructure. In a phased rollout, The Graph will introduce many new features and benefits, including the integration of new data services, new query languages, enhanced developer tooling, improved UX + UI, alongside greater protocol efficiency and resilience. As this new era unfolds, The Graph crystallizes as the connective tissue across the many layers of the web3 stack, evolving into a comprehensive, interwoven graph of data equipped to serve every project dreamt up by web3’s innovators. Read the full announcement linked in the comment below!

The Graph

425,957 Aufrufe • vor 2 Jahren

Building with AI gets easier every day. Here is an open-source library that makes integrating AI into an application extremely easy: Star the repository! This library alone can make React the best front-end framework out there! There are a bunch of cool things I like about CopilotKit. Here are 3 of them: 1. It allows you to take any -powered agent and bring it into your application. (This is a brand-new feature!) 2. You can build an AI-powered chatbot in your application. The chatbot will have access to your context and can act on the application. 3. You can build a RAG workflow to process and answer questions from a real-time knowledge base. I recorded a video to show you how simple it is to make some of this happen. A few lines of code, and you are in business. Here is a link to the sample application: CopilotKit is open-source. You can self-host it. You can use it with any LLM. Thanks to the team for showing me their tool and collaborating with me on this post!

Santiago

108,884 Aufrufe • vor 2 Jahren

THIS GUY BUILT A COMMAND CENTER FOR MANAGING AI AGENT TEAMS he was running 20+ agents through openclaw. all from terminal tabs and Discord. 12 tabs open. still losing track of who's doing what. which cron failed. so he built a dashboard that gives you one place to see everything: > org map showing the full agent hierarchy, who reports to who and what tools each agent has > chat with any agent through a real UI instead of bouncing between terminals > kanban board for tracking tasks across all agents > cron monitor with pipeline visualizations so you can see which jobs are failing > memory browser to read through all agent knowledge files in one place you can even point it at your openclaw workspace and it auto-discovers every agent from their config files. zero setup. AND ITS OPEN SOURCE TOO.

THIS GUY BUILT A COMMAND CENTER FOR MANAGING AI AGENT TEAMS he was running 20+ agents through openclaw. all from terminal tabs and Discord. 12 tabs open. still losing track of who's doing what. which cron failed. so he built a dashboard that gives you one place to see everything: > org map showing the full agent hierarchy, who reports to who and what tools each agent has > chat with any agent through a real UI instead of bouncing between terminals > kanban board for tracking tasks across all agents > cron monitor with pipeline visualizations so you can see which jobs are failing > memory browser to read through all agent knowledge files in one place you can even point it at your openclaw workspace and it auto-discovers every agent from their config files. zero setup. AND ITS OPEN SOURCE TOO.

Om Patel

47,207 Aufrufe • vor 6 Monaten