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🚀Excited to Introduce Coagents🚀: An innovative framework for building human-in-the-loop agents into applications - powered by LangGraph & CopilotKit. Coagents enable: - End-users to correct agent steps (in-app)🛤️ - Stream intermediate agent state 🪢 - Expose LangGraph agents as specialized skills for an app's copilot 🧠 --- Human-in-the-loop is...

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CopilotKit🪁

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69,301 görüntüleme • 1 yıl önce •via X (Twitter)

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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 💪 --- We’ve recently seen the emergence of Agent-Native applications that actually work: powered by vertical AI agents + agentic UX + deep in-app integration. (Apps like the Replit AI Agent, OpenAI Canvas, etc, but for any domain) With CoAgents + LangGraph, you can build this next generation of agent apps, for any vertical, with a clean and easy to use infrastructure (in hours). 💻Get started: 🗞️Blog: 🕹️Demo: 🗓️Webinar (Build an Agent-Native Apps with CoAgents & LangGraph): CC: Atai Barkai, , Harrison Chase
1:46

🚀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 💪 --- We’ve recently seen the emergence of Agent-Native applications that actually work: powered by vertical AI agents + agentic UX + deep in-app integration. (Apps like the Replit AI Agent, OpenAI Canvas, etc, but for any domain) With CoAgents + LangGraph, you can build this next generation of agent apps, for any vertical, with a clean and easy to use infrastructure (in hours). 💻Get started: 🗞️Blog: 🕹️Demo: 🗓️Webinar (Build an Agent-Native Apps with CoAgents & LangGraph): CC: Atai Barkai, , Harrison Chase

CopilotKit🪁

51,637 görüntüleme • 1 yıl önce

Introducing CoAgents v0.2🪁🦜🚀 Build full-stack Agent-Native Apps with LangGraph 🤝 CopilotKit (Like Cursor for __) Including: ✨Agentic UI/UX + Frontend 🪢 Shared state (agent app) 👨‍🦰Human-in-the-Loop 🦜Full LangGraph Platform support (Python, JS, Cloud) Get Started: Check out the demo:
1:57

Introducing CoAgents v0.2🪁🦜🚀 Build full-stack Agent-Native Apps with LangGraph 🤝 CopilotKit (Like Cursor for __) Including: ✨Agentic UI/UX + Frontend 🪢 Shared state (agent app) 👨‍🦰Human-in-the-Loop 🦜Full LangGraph Platform support (Python, JS, Cloud) Get Started: Check out the demo:

CopilotKit🪁

32,118 görüntüleme • 1 yıl önce

🪁🦜CoAgents x LangGraph Bring your agents into React apps with powerful and fully customizable React hooks. Styled or headless UI. Including Agentic Chat, realtime Shared State, Frontend tools, Generative UI, and Human-in-the-Loop. Used by Fortune 100 teams & thousands of devs. Get started: LangChain
1:43

🪁🦜CoAgents x LangGraph Bring your agents into React apps with powerful and fully customizable React hooks. Styled or headless UI. Including Agentic Chat, realtime Shared State, Frontend tools, Generative UI, and Human-in-the-Loop. Used by Fortune 100 teams & thousands of devs. Get started: LangChain

CopilotKit🪁

17,150 görüntüleme • 1 yıl önce

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.
1:32

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

46,109 görüntüleme • 1 yıl önce

Don't just learn how to build AI agents. Instead, learn how to build full-stack AI agents that actually work inside apps. Today, we have reliable tools to create decent agentic workflows. Yet, the main challenge is transitioning them from local setups to user-facing apps. Here are some key factors to consider: - How do you embed a seamless UI? - How do you enable user control when necessary? - How do you unify the agent’s state and actions with the app? This is where I would like to introduce CoAgents by CopilotKit🪁, they offer all the essential building blocks for full-stack agentic apps. The best part, they're completely open-source with an MIT Licence ✨ (link to their GitHub repo in the next tweet) The latest updates to CoAgents include: ↳ Human-in-the-loop: Real-time human approvals. ↳ LangGraph integration: Supports LG Studio, Platform, and self-hosted. ↳ Agentic UI: Agents generate UI components dynamically. ↳ Predictive state updates: Preview upcoming changes. ↳ Enhanced documentation: Clear, step-by-step guidance. The video below showcases a full-stack Travel Planner app built in 20 minutes using CoAgents. Impressive, right? Big thanks to CopilotKit for partnering on today's post and showing what's possible with their powerful Agent-development framework.
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Don't just learn how to build AI agents. Instead, learn how to build full-stack AI agents that actually work inside apps. Today, we have reliable tools to create decent agentic workflows. Yet, the main challenge is transitioning them from local setups to user-facing apps. Here are some key factors to consider: - How do you embed a seamless UI? - How do you enable user control when necessary? - How do you unify the agent’s state and actions with the app? This is where I would like to introduce CoAgents by CopilotKit🪁, they offer all the essential building blocks for full-stack agentic apps. The best part, they're completely open-source with an MIT Licence ✨ (link to their GitHub repo in the next tweet) The latest updates to CoAgents include: ↳ Human-in-the-loop: Real-time human approvals. ↳ LangGraph integration: Supports LG Studio, Platform, and self-hosted. ↳ Agentic UI: Agents generate UI components dynamically. ↳ Predictive state updates: Preview upcoming changes. ↳ Enhanced documentation: Clear, step-by-step guidance. The video below showcases a full-stack Travel Planner app built in 20 minutes using CoAgents. Impressive, right? Big thanks to CopilotKit for partnering on today's post and showing what's possible with their powerful Agent-development framework.

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New AI Agentic course! Learn to use LangGraph to build single and multi-agent LLM applications in AI Agents in LangGraph. This short course, taught by LangChain LangChain founder Harrison Chase Harrison Chase and Tavily founder @weiss_rotem, shows how to integrate agentic search to enhance an agent's knowledge with query-focused answers in predictable formats. Also learn to implement agentic memory to save state for reasoning and debugging, and see how human-in-the-loop input can guide agents at key junctures. You'll build an agent from scratch, then reconstruct it with LangGraph to thoroughly understand the framework. Finally, you'll build a sophisticated essay-writing agent that incorporates all the learnings from the course. Sign up here!
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New AI Agentic course! Learn to use LangGraph to build single and multi-agent LLM applications in AI Agents in LangGraph. This short course, taught by LangChain LangChain founder Harrison Chase Harrison Chase and Tavily founder @weiss_rotem, shows how to integrate agentic search to enhance an agent's knowledge with query-focused answers in predictable formats. Also learn to implement agentic memory to save state for reasoning and debugging, and see how human-in-the-loop input can guide agents at key junctures. You'll build an agent from scratch, then reconstruct it with LangGraph to thoroughly understand the framework. Finally, you'll build a sophisticated essay-writing agent that incorporates all the learnings from the course. Sign up here!

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Our latest LangChain Academy course – Building Ambient Agents with LangGraph – is now available! Most agents today handle one request at a time through chat interfaces. But as models have improved, agents can now run in the background – and take on long-running, complex tasks. LangGraph is built for these “ambient agents,” with support for human-in-the-loop workflows and memory. LangGraph Platform provides the infrastructure to run these agents at scale, and LangSmith helps you observe, evaluate, and improve your agents. Together, they make it easier to build reliable, production-ready agents. In this course, you’ll: 📩 Learn the fundamentals of LangGraph and how to build your own email agent 📊 Evaluate your agent using LangSmith ✅ Add human-in-the-loop for reviews, and memory so your agent adapts over time 🚀 Deploy your agent with LangGraph Platform and connect it to Gmail
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Our latest LangChain Academy course – Building Ambient Agents with LangGraph – is now available! Most agents today handle one request at a time through chat interfaces. But as models have improved, agents can now run in the background – and take on long-running, complex tasks. LangGraph is built for these “ambient agents,” with support for human-in-the-loop workflows and memory. LangGraph Platform provides the infrastructure to run these agents at scale, and LangSmith helps you observe, evaluate, and improve your agents. Together, they make it easier to build reliable, production-ready agents. In this course, you’ll: 📩 Learn the fundamentals of LangGraph and how to build your own email agent 📊 Evaluate your agent using LangSmith ✅ Add human-in-the-loop for reviews, and memory so your agent adapts over time 🚀 Deploy your agent with LangGraph Platform and connect it to Gmail

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⭐️ New LangChain Academy Course: LangGraph Essentials (Python & TypeScript)⭐️ Learn the basics of LangGraph – our low-level orchestration framework purpose-built for building AI agents. Last week, we launched LangGraph 1.0, marking its first major stable release. LangGraph allows you to control every step of your agent’s workflow. It supports memory, human-in-the-loop interactions, and durable execution for managing long-running tasks. In this quickstart course, you'll learn how to: - Create simple workflows and build an agent - Use LangGraph’s core building blocks: State, Nodes, and Edges - Add memory to your agent - Incorporate human-in-the-loop interactions Enroll for free ➡️

LangChain

53,969 görüntüleme • 7 ay önce

Big release here! AG-UI 🤝 Google A2A Embed A2A multi-agent meshes in your frontend, and build fullstack multi-agent apps with A2A and AG-UI. 🔍How it works: Simply drop-in A2A agent endpoints, and an A2A AG-UI orchestrator agent will bi-directionally stream events between your Agents & application. Includes: - UI for A2A Interactions - Generative UI for Agent Tool Calls - Human-in-the Loop Approvals - State Synchronization (agent mesh application state) - Frontend Tool Calls for A2A Agents Watch the demo below > LangChain LangGraph & Google ADK agents working together simultaneously inside an app Demo GitHub, docs, and more 👇
0:25

Big release here! AG-UI 🤝 Google A2A Embed A2A multi-agent meshes in your frontend, and build fullstack multi-agent apps with A2A and AG-UI. 🔍How it works: Simply drop-in A2A agent endpoints, and an A2A AG-UI orchestrator agent will bi-directionally stream events between your Agents & application. Includes: - UI for A2A Interactions - Generative UI for Agent Tool Calls - Human-in-the Loop Approvals - State Synchronization (agent mesh application state) - Frontend Tool Calls for A2A Agents Watch the demo below > LangChain LangGraph & Google ADK agents working together simultaneously inside an app Demo GitHub, docs, and more 👇

CopilotKit🪁

30,557 görüntüleme • 8 ay önce

🚀 Introducing LangGraph Cloud 🚀 LangGraph helps you build reliable agents that actually work. Today, we've launched LangGraph Cloud, our new infrastructure to run fault-tolerant LangGraph agents at scale. With LangGraph Cloud, you can: • Handle large workloads with horizontally-scaling servers, task queues, and built-in persistence • Debug agent failure modes and iterate quickly in a visual playground-like studio • Deploy in one-click and get integrated tracing & monitoring in LangSmith This builds on LangGraph v0.1, our latest stable release supporting diverse control flows - whether it's single- or multi-agent, hierarchal or sequential. You'll be able to create flexible agents with human-in-the-loop collaboration and first-class streaming support. We can't wait to see the agentic workflows you'll ship. LangGraph Cloud is available in closed beta today. 👉 Join the waitlist for LangGraph Cloud: ✍️ Read our blog post announcement: 📽️ Watch the video walkthrough: 🌀 Haven't tried LangGraph yet? Give it a spin here: 🙌 Check out the new LangGraph landing page:
0:23

🚀 Introducing LangGraph Cloud 🚀 LangGraph helps you build reliable agents that actually work. Today, we've launched LangGraph Cloud, our new infrastructure to run fault-tolerant LangGraph agents at scale. With LangGraph Cloud, you can: • Handle large workloads with horizontally-scaling servers, task queues, and built-in persistence • Debug agent failure modes and iterate quickly in a visual playground-like studio • Deploy in one-click and get integrated tracing & monitoring in LangSmith This builds on LangGraph v0.1, our latest stable release supporting diverse control flows - whether it's single- or multi-agent, hierarchal or sequential. You'll be able to create flexible agents with human-in-the-loop collaboration and first-class streaming support. We can't wait to see the agentic workflows you'll ship. LangGraph Cloud is available in closed beta today. 👉 Join the waitlist for LangGraph Cloud: ✍️ Read our blog post announcement: 📽️ Watch the video walkthrough: 🌀 Haven't tried LangGraph yet? Give it a spin here: 🙌 Check out the new LangGraph landing page:

LangChain

134,051 görüntüleme • 1 yıl önce

📋 Open Multi-Agent Canvas Plug-in any LangGraph agents, then chat with them. Including Generative UI, state streaming, and automatic agent routing. New template for learning how to add frontend features to LangGraph agents. Try it out: LangChain
0:30

📋 Open Multi-Agent Canvas Plug-in any LangGraph agents, then chat with them. Including Generative UI, state streaming, and automatic agent routing. New template for learning how to add frontend features to LangGraph agents. Try it out: LangChain

CopilotKit🪁

15,492 görüntüleme • 1 yıl önce

Building Fullstack LangGraph AI agents can be a challenge. So I wrote a tutorial on how to build a Frontend for your LangGraph AI agent in minutes using: - @LangChainAI - CopilotKit🪁 - Tavily Here is the demo + checkout tutorial link below 👇
1:56

Building Fullstack LangGraph AI agents can be a challenge. So I wrote a tutorial on how to build a Frontend for your LangGraph AI agent in minutes using: - @LangChainAI - CopilotKit🪁 - Tavily Here is the demo + checkout tutorial link below 👇

Bonnie

23,999 görüntüleme • 1 yıl önce

Don't just learn how to build AI agents. Instead, learn how to build... ...full-stack AI agents that actually work inside apps. Yet, the main challenge is transitioning them from local setups to user-facing apps. Here are some key factors to consider: - How do you embed a seamless UI? - How do you enable user control when necessary? - How do you unify the agent’s state and actions with the app? CoAgents by CopilotKit🪁 solves this. It is the only framework that offers all the essential Agentic front-end building blocks to set up full-stack agentic apps. And most recently, they have extended full-stack development support to Agents built with CrewAI ✨. This includes both regular Agentic Crews and Flows. Check their GitHub repo it's 100% open-source, I have shared link in the next tweet! With this update, here's what you can do with Agents built using CrewAI: ↳ Chat: Add chat components for interaction. ↳ Human-in-the-loop: Real-time human approvals. ↳ Predictive state updates: Preview upcoming changes. ↳ Agentic UI: Agents generate UI components dynamically. This way, you can give your CrewAI Agents a frontend brain—so they don’t just work behind the scenes, they become an integral part of the user experience. The video below showcases a full-stack CrewAI Agent built in 20 minutes using CoAgents. Impressive, right?
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Don't just learn how to build AI agents. Instead, learn how to build... ...full-stack AI agents that actually work inside apps. Yet, the main challenge is transitioning them from local setups to user-facing apps. Here are some key factors to consider: - How do you embed a seamless UI? - How do you enable user control when necessary? - How do you unify the agent’s state and actions with the app? CoAgents by CopilotKit🪁 solves this. It is the only framework that offers all the essential Agentic front-end building blocks to set up full-stack agentic apps. And most recently, they have extended full-stack development support to Agents built with CrewAI ✨. This includes both regular Agentic Crews and Flows. Check their GitHub repo it's 100% open-source, I have shared link in the next tweet! With this update, here's what you can do with Agents built using CrewAI: ↳ Chat: Add chat components for interaction. ↳ Human-in-the-loop: Real-time human approvals. ↳ Predictive state updates: Preview upcoming changes. ↳ Agentic UI: Agents generate UI components dynamically. This way, you can give your CrewAI Agents a frontend brain—so they don’t just work behind the scenes, they become an integral part of the user experience. The video below showcases a full-stack CrewAI Agent built in 20 minutes using CoAgents. Impressive, right?

Akshay 🚀

27,153 görüntüleme • 1 yıl önce

LangChain Academy is live! Our first course — Introduction to LangGraph — teaches you the in-and-outs of building a reliable AI agent. In this course, you’ll learn how to: 🛠️ Build agents with LangGraph's graph-based workflows 🔄 Use memory + human-in-the-loop for smarter, self-corrective agents 📚 Create your own AI assistant that can perform knowledge tasks Enroll now for free ➡️ Bring LangChain Academy to your company ➡️
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LangChain Academy is live! Our first course — Introduction to LangGraph — teaches you the in-and-outs of building a reliable AI agent. In this course, you’ll learn how to: 🛠️ Build agents with LangGraph's graph-based workflows 🔄 Use memory + human-in-the-loop for smarter, self-corrective agents 📚 Create your own AI assistant that can perform knowledge tasks Enroll now for free ➡️ Bring LangChain Academy to your company ➡️

LangChain

105,415 görüntüleme • 1 yıl önce

We’re excited to release an interactive guide highlighting the definitive set of principles for building AI agents 🔥 Based on the popular 12-Factor agents repo by dex. We packaged the principles into an interactive website and Colab notebook with working code examples, so that you can incorporate these principles into your agent application in minutes. As a refresher - some of the principles include getting structured outputs from tools, state management, checkpointing, human-in-the-loop, error handling, composing smaller agents into bigger ones, and more. This is amazing work by Laurie Voss and should be required reading for anyone looking to get started building agents. Website: Notebook:
1:05

We’re excited to release an interactive guide highlighting the definitive set of principles for building AI agents 🔥 Based on the popular 12-Factor agents repo by dex. We packaged the principles into an interactive website and Colab notebook with working code examples, so that you can incorporate these principles into your agent application in minutes. As a refresher - some of the principles include getting structured outputs from tools, state management, checkpointing, human-in-the-loop, error handling, composing smaller agents into bigger ones, and more. This is amazing work by Laurie Voss and should be required reading for anyone looking to get started building agents. Website: Notebook:

Jerry Liu

32,941 görüntüleme • 1 yıl önce

🌟Our latest LangChain Academy course – Deep Agents with LangGraph – is now live!🌟 Many agents today follow the same simple pattern: run in a loop, call tools. That architecture works well enough, but it breaks down as tasks get more complex. Today, companies of all sizes – from startups to large enterprises – are building their own Deep Agents. These agents dive deeper. They’re able to plan complex tasks and carry them out over longer time horizons. There are four key features that set Deep Agents apart from regular agents: 1. Planning – keeps agents on track 2. File system – allows agents to offload context 3. Sub-agents – act as focused specialists 4. Prompting – provides agents with detailed instructions Our latest LangChain Academy course, Deep Agents with LangGraph, shows you how to combine these pieces with LangGraph to orchestrate long-running, multi-agent workflows. Big thanks to community member Dmitry Labazkin for helping us shape this course with his contributions! Enroll for free ➡️
2:11

🌟Our latest LangChain Academy course – Deep Agents with LangGraph – is now live!🌟 Many agents today follow the same simple pattern: run in a loop, call tools. That architecture works well enough, but it breaks down as tasks get more complex. Today, companies of all sizes – from startups to large enterprises – are building their own Deep Agents. These agents dive deeper. They’re able to plan complex tasks and carry them out over longer time horizons. There are four key features that set Deep Agents apart from regular agents: 1. Planning – keeps agents on track 2. File system – allows agents to offload context 3. Sub-agents – act as focused specialists 4. Prompting – provides agents with detailed instructions Our latest LangChain Academy course, Deep Agents with LangGraph, shows you how to combine these pieces with LangGraph to orchestrate long-running, multi-agent workflows. Big thanks to community member Dmitry Labazkin for helping us shape this course with his contributions! Enroll for free ➡️

LangChain

63,349 görüntüleme • 8 ay önce

🔮 Using our agent framework, users can easily create agents with native access to our suite of proprietary tools including: 🔁 Self Reflection 🧠 Memory 👍 Human-in-the-loop 📁 File Sharing & Creation 🔂 Recurring Tasks 🔌 Integrations 🫂 Multi-agent Collaboration And more!
1:14

🔮 Using our agent framework, users can easily create agents with native access to our suite of proprietary tools including: 🔁 Self Reflection 🧠 Memory 👍 Human-in-the-loop 📁 File Sharing & Creation 🔂 Recurring Tasks 🔌 Integrations 🫂 Multi-agent Collaboration And more!

OpenServ

78,016 görüntüleme • 1 yıl önce

$FET just quietly built the internet for AI agents. 3,000,000 agents. One network. One token. Here's what nobody is explaining: Every AI agent needs 3 things to be useful: An address. A way to be found. A way to get paid. Agentverse gives agents all three. Your agent becomes discoverable on ASI: One. Other agents can find it, call it, or hire it. Automatically. No human in the loop. This is agent-to-agent commerce. And every transaction settles in $FET. More agents = more activity = more demand. The infrastructure is already live. The economy is just getting started.
1:00

$FET just quietly built the internet for AI agents. 3,000,000 agents. One network. One token. Here's what nobody is explaining: Every AI agent needs 3 things to be useful: An address. A way to be found. A way to get paid. Agentverse gives agents all three. Your agent becomes discoverable on ASI: One. Other agents can find it, call it, or hire it. Automatically. No human in the loop. This is agent-to-agent commerce. And every transaction settles in $FET. More agents = more activity = more demand. The infrastructure is already live. The economy is just getting started.

2xnmore

12,801 görüntüleme • 1 ay önce

🦜🤖Introducing Open Agent Platform An open-source, no-code agent building platform. OAP enables non-developers to build highly customizable agents, which connect to: - 🛠️MCP Tools - 📄LangConnect for RAG - 🤖Other LangGraph Agents! Try the public demo, or fork & customize it yourself! 🧵
0:32

🦜🤖Introducing Open Agent Platform An open-source, no-code agent building platform. OAP enables non-developers to build highly customizable agents, which connect to: - 🛠️MCP Tools - 📄LangConnect for RAG - 🤖Other LangGraph Agents! Try the public demo, or fork & customize it yourself! 🧵

LangChain

136,945 görüntüleme • 1 yıl önce