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🚀Launching CoAgents Public Beta 🪁: Everything you need to build Agent-Native applications, powered by LangGraph & CopilotKit. CoAgents enables in-app agents with: - Agenetic generative UI ✨ - Shared state (between agent application) - Streaming intermediate agent state - Human-in-the-Loop (Human approval & planning) 👨‍🦰 - Frontend Actions 💪...

51,646 görüntüleme • 1 yıl önce •via X (Twitter)

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CopilotKit🪁 profil fotoğrafı
CopilotKit🪁1 yıl önce

Based in San Francisco? Come build an Agent Native application in our Hackathon (feat Google, AI Tinkeres, Weights & Biases, Tavily, E2B & more!) Nov 2nd & 3rd:

Nathan🔶Tarbert profil fotoğrafı
Nathan🔶Tarbert1 yıl önce

I'm super excited about this launch! 🚀

Jake Colling profil fotoğrafı
Jake Colling1 yıl önce

Looks slick! Congrats on the release y'all!

Uli 🪁 profil fotoğrafı
Uli 🪁1 yıl önce

Agent applications are becoming powerful & actually useful. Super Excited for this 🪁🚀

Markus Ecker profil fotoğrafı
Markus Ecker1 yıl önce

Nice overview!

Vasek Mlejnsky profil fotoğrafı
Vasek Mlejnsky1 yıl önce

Nice work!

Sid Uppal profil fotoğrafı
Sid Uppal1 yıl önce

Great progress @ataiiam and team, since your demo at @AITinkerers a while ago! 👏 I’m curious to see if CopilotKit enabled apps would be easier to pilot for stuff like the newly released computer-use from Anthropic at some point — since it’s more deeply integrated with the app.

Till - gotoHuman.com profil fotoğrafı
Till - gotoHuman.com1 yıl önce

Looks great @ataiiam💪 We also just published a LangGraph human-in-the-loop demo. But for async use cases, i.e. autonomous agents.

McBain profil fotoğrafı
McBain1 yıl önce

Amped to dig into this

David profil fotoğrafı
David1 yıl önce

This is great! 🔥⚡

Benzer Videolar

AG-UI makes building agentic applications dramatically easier. Here's how it works. This is a model for a simple chatbot: User → LLM → Response But interactive agents that render UI, pause for approvals, and ask users for input need a much more complex model. When building these agents, a response from the LLM will include a series of state changes as the agent runs: • Agent started a task • Agent called a tool • Agent updated its state • Agent streams these tokens • Agent is waiting on a human • Agent is resuming the task The Agent-User Interaction Protocol (AG-UI) treats the LLM response as a stream of events rather than a text endpoint. In practice, here is what you get as an agent runs: 1. Lifecycle events so your UI knows where the agent is. 2. Text messages that stream tokens. 3. Tool calls so your UI can prefill a form with any required arguments. 4. State updates that keep your UI in sync with the agent. 5. Special events for human approvals, rich media, and custom needs. All of these events travel over standard transports (SSE, WebSockets, or plain HTTP) as JSON. As a result, you can build a frontend that stays in sync with the agent's progress without having to invent a custom process to make this happen. For example, building a human-in-the-loop workflow becomes an off-the-shelf component you can integrate rather than build from scratch. CopilotKit🪁 is the creator of AG-UI, and you can use it when building frontend applications pretty much anywhere: • React • Angular • Vue • React Native • Slack • Teams • Discord • WhatsApp • Telegram Here is the link for you to check it out: Thanks to the CopilotKit team for partnering with me on this post.

Santiago

17,438 görüntüleme • 2 gün önce