Hi there, this is Christian from LChain. Here's a scenario. We want a customer support agent that handles routine questions.But if the user keeps going back and forth with it, maybe asking the same thing in three different ways, that's usually a sign that something is not working. Either the agent isn't resolving the issue or the user's drifting into a too long unproductive conversation that honestly the bot was never meant to handle in the first place. In both cases, we want the agent to stop trying to be ...

I'm excited to announce the release of our latest LangChain Academy course, LangSmith Essentials. In this quickstart course, you'll learn to observe, evaluate, and deploy an AI agent in less than 30 minutes. Testing applications is an essential part of the development lifecycle, but LLM systems are non-deterministic, meaning we can't predict exactly what output a given input will produce.When you add multi-turn interactions and tool-calling agents into the mix, the process becomes even more complex and less ...

Hey folks, it's Sydney from LinkChain and I'm super excited to share with you our next middleware demo for our to-do list middleware. Did you know you're 42% more likely to achieve a goal if you write it down. Turns out agents actually benefit from the same agents equipped with a to-do list often perform better when given complex tasks.In fact, you might have already seen this in action with coding agents like Claude Code that draft a to-do list and continuously update it throughout a conversation. First, l ...

Hi, this is Christian from Lchain. Most AI agents today don't think ahead. They just react one step at a time.And that's why exactly they get sometimes stuck, loop, hallucinate, or just burn money. But here's a twist. With just one piece of state, a simple to-do list, an agent can suddenly plan, execute reliably, and finish task like a professional.The to-do list middleware for longchain agents will help you with exactly that. Today I will show you why planning can change everything and when it actually mak ...

Hey, I'm VC and in this video I'm excited to introduce sandboxes for deep agents. We're going to talk about what these are and why you might want to use them in developing your deep agents. So, a common thing that you might do is you might have your local machine that's running your deep agent.And a common ask that we hear is you want to safely run the code that your agent is generating, but you don't want to mess up the machine that you're working on because the the agent could be generating arbitrary code ...

Core Concept - Introduces the concept of "human-in-the-loop" middleware for Langchain agents, allowing human review and intervention in agent workflows [5][18] - Explains the agent's reasoning loop: reason, act, observe, and how human intervention fits into this loop [3][5] - Highlights the three decision types for human reviewers: approve, edit, and reject, and how these decisions guide the agent's subsequent actions [7] Technical Implementation - Demonstrates the integration of a Langchain agent with human-in-the-loop middleware in a Nextjs application for sending emails [2][17] - Emphasizes the importance of a checkpointer (using Redis database) to store the agent's state and enable resuming the workflow after human intervention [13][14] - Describes how the middleware intercepts tool calls (e g, sending emails) and pauses the agent's execution, awaiting human input [5][6] Benefits and Use Cases - Positions human-in-the-loop as a way to combine agent autonomy with human oversight, especially for actions with risk or requiring judgment [18][19] - Suggests use cases such as sending emails, updating records, or writing to external systems, where human review is valuable [19] - Underscores the flexibility of the middleware, allowing customization of interruption logic based on tool name, arguments, or runtime context [19][20] Practical Example - Provides a practical example of using the middleware to allow a human to revise an email drafted by the agent before it is sent [2][16] - Showcases how to reject a proposed action and provide feedback to the agent, influencing its subsequent behavior [16] - Mentions a publicly available repository (github com/christian broman/lunghat) for users to experiment with the human-in-the-loop concept [20]

Hey, this is Lance from Langchain. I want to talk of a few general context engineering principles and how they show up in various popular agents like manis like cloud code and also in our recently released deep agents package and CLI. So first agent can be simply thought of as an LLM calling tools in a loop. An LLM kind of makes a tool call.Tool is executed. observation from a tool goes back to the LM and this continues until some termination condition. Now the length of tasks that AI agents can perform is ...

Check this out. I just asked an agent to answer one of the world's greatest debates. Is Messi or Ronaldo the greatest soccer player of all time.This isn't an easy question to answer, and it definitely requires a good amount of research. The agent automatically spawned two parallel sub agents to look into each of their achievements. This meant searching the web over a dozen times, compiling a comprehensive report with cited sources.To be extra thorough, the agent then critiqued its own report and plugged any ...

Hi there, this is Christian from Langchain. Just a couple of weeks ago, we released version one of Langchain and Lang Graph. And one of the cool features of it is that it makes it really easy to stream events and results from the agent down to any type of front end that you're using, whether it's React, Vue, or Swelt.So, in this video, I want to build a little CHPT clone that shows you how you can build and create agent right in your Nex. js application. Every longchain agent maintains a state throughout it ...

LangChain Middleware - LangChain 提供 human-in-the-loop 中间件，用于在工具调用执行前进行审批、编辑和拒绝 [1] - 该中间件适用于需要人工反馈的场景，例如邮件助手在发送敏感邮件前 [1] Use Case - 示例展示了如何使用该中间件来构建一个邮件助手代理，该代理在发送敏感邮件之前需要人工反馈 [1] Resources - 更多关于中间件的文档可以在 LangChain 官方文档中找到 [1] - 示例代码可以在 Gist 上找到 [1]