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🚀 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...

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LangChain

244,115 subscribers

134,051 次观看 • 1 年前 •via X (Twitter)

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10 条评论

Git Maxd 的头像
Git Maxd1 年前

This looks amazing - can’t wait to try it! Great work 🦜

Muratcan Koylan 的头像
Muratcan Koylan1 年前

this is great! can’t wait to try

Mohan Reddy 的头像
Mohan Reddy1 年前

This is so cool, this AI advancements necessitate the mundane and general tasks. 👏 Wishing this a big hit.

Greg Charles 的头像
Greg Charles1 年前

🔥

Assaf Elovic 的头像
Assaf Elovic1 年前

This is super exciting!

Elastic 的头像
Elastic1 年前

We're excited to be partnering with @LangChainAI on the addition of LangGraph to set the foundation for building AI workloads at scale providing greater flexibility and efficiency!

RAG 的头像
RAG1 年前

Has some features I posted for @crewAIInc Background runs, crons.

StackGPT 的头像
StackGPT1 年前

Nice!! Will this UI be available for self hosting as well ?

Wallpaper Keith 的头像
Wallpaper Keith1 年前

Please can you provide better visibility of the messages to and from the LLM when using Langchain. Printing to the console would be fine. Thanks. People shouldn't have to use LangSmith to see what is going on.

Josh Thomas 的头像
Josh Thomas1 年前

please work towards making it more like Databricks so we can use compute and storage in our cloud networks

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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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33,192 次观看 • 11 个月前

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185,785 次观看 • 1 年前

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32,118 次观看 • 1 年前

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53,969 次观看 • 7 个月前

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CopilotKit🪁

15,492 次观看 • 1 年前

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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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151,484 次观看 • 2 年前

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69,301 次观看 • 1 年前

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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 ➡️

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105,415 次观看 • 1 年前

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🔍New in LangGraph Studio: Trace mode View your LangSmith traces in real time right inside Studio. Annotate runs, and add them to datasets or annotation queues, bringing the power of LangSmith tracing directly into your workflow. Debug faster and dig deeper, without any of the context switching. Try it today!

LangChain

25,866 次观看 • 9 个月前

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46,701 次观看 • 1 年前

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11,659 次观看 • 1 年前

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New course: A2A: The Agent2Agent Protocol, built with Google Cloud Tech and IBM Research, and taught by Holt Skinner, Ivan Nardini, and Sandi Besen. Connecting agents built with different frameworks usually requires extensive custom integration. This short course teaches you A2A, the open protocol standardizing how agents discover each other and communicate. Since IBM’s ACP (Agent Communication Protocol) joined forces with A2A, A2A has emerged as the industry standard. In this course, you'll build a healthcare multi-agent system where agents built with different frameworks, such as Google ADK (Agent Development Kit) and LangGraph, collaborate through A2A. You'll wrap each agent as an A2A server, build A2A clients to connect to them, and orchestrate them into sequential and hierarchical workflows. Skills you'll gain: - Expose agents from different frameworks as A2A servers to make them discoverable and interoperable - Chain A2A agents sequentially using ADK, where one agent's output feeds into the next - Connect A2A agents to external data sources using MCP (Model Context Protocol) - Deploy A2A agents using Agent Stack, IBM's open-source infrastructure Join and learn the protocol standardizing agent collaboration!

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83,315 次观看 • 3 个月前