Hey, this is Lance. I want to talk about continual learning with agents in particular showing some examples with deep agents. So, Le has a really nice post on this theme of continual learning in token space and it makes the argument that a big gap between AI agents and humans as we know is ability to learn.Humans continually learn and improve over time. Agents knowledge is typically fixed and doesn't have the same adaptive capability. Now, there's different ways to teach AI systems to learn.So one is learni ...

Are you curious about what cloud code is doing behind the scenes. Or do you want observability in the critical workflows that you've set up with claude code. Hey, I'm Tanish from Langchain and we built a claude code to LinkSmith integration so that you can see each step that cla takes whether that be an LLM call or tool calls.Um it's pretty fascinating to see the entire trace. So I want to show you what this looks like. Um uh I have uh a project here.It's a very very very simple uh agent that I build with u ...

Hey, this is Lance. I want to talk a bit about memory patterns for agents, focusing specifically on deep agents. Now, you might think about memory in two different ways.Explicit updating of agent memory and implicit updating of agent memory. Let me talk about the first one. So, explicit updating agent memory, you can see a great example of right here.This is the cloud code change log. This is recent yesterday. So cloud code recently removed this pound shortcut which you could previously use for updating mem ...

I’m excited to announce the release of our latest LangChain Academy foundations course, Introduction to LangChain in Python. We’ve entered a new era of AI, one where our apps don’t just respond, they think, plan, and act autonomously. Today, we're building agents – AI systems that can reason and interact with their environments to get real work done.Imagine a team of assistants that can summarize your inbox, schedule meetings, and perform market research 24/7. In this course, you'll learn to build deploymen ...

Key Functionality & Benefits - Langchen introduces native provider tools from OpenAI and Entropic, enabling optimized model usage [1] - The new Langchain provider packages simplify agent building by eliminating manual JSON schema handling [2] - Cloud can dynamically discover and load tools on demand, facilitating operations on platforms like Cloudflare [3] - Native provider tools unlock real-world multimodal agentic applications [4] - The MCP tool set and tool search provider tool are more efficient than locally spinning up MCP servers [15] - The new tools API eliminates the need for handcrafting schemas and gluing adapters [17] Cloudflare MCP Agent Example - An agent connects to Cloudflare's MCP servers, granting access to the entire platform via a chat interface [5] - The agent can fetch DNS logs, run queries, and answer questions about a user's Cloudflare account [3] - The agent can access Cloudflare browsers to fetch websites and convert them to markdown or screenshots [7][14] - The MCP tool set tool from the entropic provider package allows defining configuration for every MCP server [10] - The tool search tool defers loading of MCP server tools, saving context window space [11][12]

Hey, this is Lance. Recently put out this blog post called debugging deep agents with lang. And the big idea here was connecting lang as a system of record for your traces with code agents like deep agents, but it could be other code agents like clock code to create kind of an iterative feedback loop.So you're having a code agent produce some langraph code that's being run. Traces are going to lang. And there's a way for the code agents to pull traces back, reflect on them, and update your lane share langra ...

Hi, this is Christian from Lchain. Many LM providers now ship their own native tools. Not just generic function calls, but tools the model specifically trained and tuned to work with like entropics computer use, web search, bash or memory tools.With the latest version of lung chains, entropic and open air provider packages, we expose these simple provider tools so that you can call these model optimized tools seamlessly and type safe with your agent without hand rolling any JSON schemas or glue code. In thi ...

Explore the unique challenges of observing and evaluating Deep Agents in production. Deep Agents represent a shift in how AI systems operate – unlike simple chatbots or basic RAG applications, these agents run for extended periods, execute multiple sub-tasks, and make complex decisions autonomously. In this session, we'll dive into practical approaches for gaining visibility into Deep Agent behavior and measuring their effectiveness using LangSmith. Learn more about Deep Agents here: https://blog.langchain. ...

Hey, I'm Tanish from Langchain. Today I'm going to go through how to use Open Router's new broadcast feature with Langchain to send traces to Langmith. The cool thing about broadcast is it stores destination information, which in this case is Langmith server side.So the only thing that you need to worry about in your code is your open router API key. Let's go through how to set this up. So let me walk you through a quick code snippet.This uses lang chain's init chat model in order to initialize a model and ...

Today we're excited to launch Langmith fetch. Langmith fetch is a command line util for pulling data from Langmith into your local file system. We think this is super useful for giving coding agents like cloud code and codecs or deep agent CLI the ability to pull down Langmith data.Why is this useful. Well, a lot of agents that are getting traced to Linksmith are starting to become longer and longer and have many tool calls and larger prompts. And analyzing and debugging all of that data by hand and by eye ...