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 ...

Hi there, this is Christian from Langchain. Everyone is using coding agents these days. Apps like cursor, winds surf or copilot have changed the way we build application.All of these apps have one thing in common though. Sooner or later, a human will step in. There's always a moment where we as a developer want to step in and review and revise the steps that the agent is about to take.Especially when it comes to taking critical actions like removing a file. So in this video, we're going to build a longchain ...

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 ...

Learn about how to use LangChain's human in the loop middleware to approve, edit, and reject tool calls before they're executed. Our example uses an email assistant agent that requires human feedback before sending sensitive emails. Middleware docs: https://docs.langchain.com/oss/python/langchain/middleware#human-in-the-loop Code: https://gist.github.com/sydney-runkle/628246dc4f851dda45f57b492c645ec0 ...

Over time, people are going to have to get much more methodical about testing. And that's where eval come in. That's where online eval come in. It's it's less of vibe testing uh two versions and putting more rigor behind this. >> People mainly know us for our open source, but ever since the start of the company, we've been building Lang Smith, a platform for agent engineering. And the things that we started off there were tracing and eval playground and observability and all these kind of like agent ops LLM ...