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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 Aufrufe • vor 1 Jahr •via X (Twitter)

10 Kommentare

Profilbild von CopilotKit🪁
CopilotKit🪁vor 1 Jahr

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:

Profilbild von Nathan🔶Tarbert
Nathan🔶Tarbertvor 1 Jahr

I'm super excited about this launch! 🚀

Profilbild von Jake Colling
Jake Collingvor 1 Jahr

Looks slick! Congrats on the release y'all!

Profilbild von Uli 🪁
Uli 🪁vor 1 Jahr

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

Profilbild von Markus Ecker
Markus Eckervor 1 Jahr

Nice overview!

Profilbild von Vasek Mlejnsky
Vasek Mlejnskyvor 1 Jahr

Nice work!

Profilbild von Sid Uppal
Sid Uppalvor 1 Jahr

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.

Profilbild von Till - gotoHuman.com
Till - gotoHuman.comvor 1 Jahr

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

Profilbild von McBain
McBainvor 1 Jahr

Amped to dig into this

Profilbild von David
Davidvor 1 Jahr

This is great! 🔥⚡

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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 Aufrufe • vor 2 Tagen