LangChain - Videos
Back to Channel
Agent Observability Explained
When something goes wrong in traditional software, you know what to do: check the error logs, look at the stack trace, find the line of code that failed. But AI agents have changed what we're debu...
The Secret to Scalable AI Agents: Virtual Filesystems with Deep Agents
Your AI agent sees one filesystem. Under the hood? S3, SQLite, and local disk, all working together. In this video, I show how Deep Agents uses virtual filesystems to give your agent a unified int...
What AI Agents Talk About on Moltbook
Wonder what your AI Agents are talking about on Moltbook? With LangSmith Insights Agent you can look at all the posts out there and see different trends and topics! 1. Scrape posts 2. Upload to La...
Introducing: LangSmith Agent Builder
LangSmith Agent Builder enables anyone to build agents using natural language: - Describe your goal in your own words. Agent Builder figures out the approach. - It creates detailed instructions, se...
Introducing /remember: Teaching Agents to Learn from Experience
🧠Shipping /remember in the Deep Agents CLI: a primitive for persistent agent memory How it works: - inject a reflection prompt into your conversation thread - agent analyzes full context + identi...
LangChain Academy New Course: LangSmith Agent Builder
LangSmith Agent Builder enables anyone to build agents for complex daily tasks, without writing code. It’s simple. You start by describing the goal in your own words. Agent Builder determines the ...
LangSmith Agent Builder Technical Highlights
Six technical features central to LangSmith Agent Builder: 1. It's built on the Deep Agents harness 2. The agent is a filesystem 3. Memory is built in 4. Triggers let them act autonomously 5. Super...
3 Hidden Features That Make AI Agents Production-Ready
In this video I walk through 3 less-known features that make agentic apps feel actually production-ready, using real code from the LangGraphJS repository. 📚 What you’ll learn - Render reasoning to...
Building with Subagents: Design Decisions
The subagents multi-agent pattern is straightforward—but the implementation details really matter for performance. In this video we break down the design decisions that determine whether your syst...
Choosing the Right Multi-Agent Architecture
When do you actually need a multi agent system? And how should you decide what architecture to use? In this video we break down 4 architectural patterns (subagents, skills, router, and handoffs) w...
Build Better Agent UX: Streaming Progress, Status, and File Ops with LangChain
Build agent UIs that feel instant: stream **custom events** from LangChain tool calls (progress, status, file operations) straight into React as they happen. In this video we walk through the a dem...
How I built an AI agent to automate my emails with LangSmith Agent Builder
LangSmith Agent Builder is a no-code agent builder. I built an email assistant to monitor and respond to emails, that I've been using for the last ~3 months. Here's what it looks like: 1/ Triggers...
Streaming Typed Agent Messages in LangChain and React
Most “streaming” agent UIs are built around token streams. That works — until you try to build a real UI. In this video, Christian Bromann shows how to stream typed agent messages from a LangChai...
LangSmith Agent Builder: On the street in San Francisco
Join Brace as he finds VCs in South Park, San Francisco and shows them how to build agents to solve their biggest problems using LangSmith Agent Builder. Try LangSmith Agent Builder free: https://...
How Cursor Builds the Future of AI Coding Tools
Harrison Chase and Cursor's Jason Ginsberg discuss the evolution of coding agents, from tab completion to agentic workflows. Learn how Cursor's engineering team ships fast, builds features they use...
Ralph Mode for Deep Agents: Running an Agent Forever
🫡 Ralph Mode for Deep Agents 🫡 What if you could give an AI agent a task and just let it run forever? We built Ralph Mode to test this, built on Deep Agents. Ralph Mode loops an agent with fresh co...
Learning Skills with Deepagents
The biggest gap between AI agents and human intelligence is the ability to learn. There are various emerging approaches to support continual learning for AI agents. Here, we discuss skill-learning ...
Tracing Claude Code to LangSmith
Curious what Claude Code is doing behind the scenes? Or want observability into critical workflows that you’ve set up with Claude Code With our new Claude Code → LangSmith integration, you can vie...
Approaches for Managing Agent Memory
Humans refine their skills and learn preferences through experience. But many AI agents lack this capacity for continual learning. Here, we give an overview of memory in the DeepAgents CLI. Here, w...
LangChain Academy New Course: Introduction to LangChain - Python
Learn how to build with LangChain – our open source framework that makes it easy to start building agents with any model provider. In this course, you’ll create agents that can reason, use tools, ...
Build an MCP Agent with Claude: Dynamic Tool Discovery Across Cloudflare MCP Servers
In this video, Christian Bromann build and demo an agent that uses LLM provider native tools—specifically Anthropic Claude’s built-in MCP toolsets—to connect to Cloudflare’s managed MCP servers. I...
The agent development loop with LangSmith + Claude Code / Deepagents
LangSmith provides a system of record for traces, which can include long agent trajectories. Code agents like Claude Code or Deep Agents CLI can make use of traces to improve agent performance via ...
I Let an AI Control My Browser to Play Tic-Tac-Toe - LangChainJS Tutorials
What if an AI could **actually use the browser** — not through brittle scripts, but by *seeing* the UI and deciding where to click? In this video, I explain how modern **agent tools** work and dem...
Building & Observing a Deep Agent for Email Triage with LangSmith
In this video, we walk through how to build and observe a deep agent using LangSmith. We’ll build a simple email assistant that reads incoming emails and decides how to handle them — triage, respo...
Observing & Evaluating Deep Agents Webinar with LangChain
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, thes...
Trace OpenRouter Calls to LangSmith — No Code Changes Needed
OpenRouter's new Broadcast feature lets you send all your traces directly to LangSmith with no code changes required — whether you’re tracing with LangChain, provider SDKs, or the OpenRouter SDK. ...
LangSmith Fetch: CLI tool to debug agents from your terminal
Learn how to debug agents from your terminal using LangSmith Fetch, the new CLI that brings LangSmith trace data directly into your workflow. Pull traces and threads into your terminal or IDE with ...
Polly: The AI Assistant for AI Engineering in LangSmith
See how Polly, the AI assistant for AI engineering in LangSmith, helps you understand long-running agent executions by analyzing traces, threads, and prompts. We’ll walk through: • Debugging compl...
How to debug voice agents with LangSmith
Learn how to debug and improve a AI voice agent using LangSmith. We’ll walk through tracing conversations, spotting failures, and iterating on your agent. In this demo we use LangChain and Pipecat...
Build a voice agent with LangChain
Learn how to build a speech-to-text voice agent using LangChain. We break down the two methods of building voice agents and several of the key considerations for using each architecture: 1. STT / ...
Production-Ready Agents: Automatic Tool Retries with Exponential Backoff
Tools fail. APIs time out. Services throw random 500s. If your agent can’t recover, your entire workflow collapses. In this tutorial, Christian Bromann walks through how to use Tool Retry Middlewa...
Building a Linear issue agent with Langsmith Agent Builder
Learn how to build an agent that can create and edit Linear issues using our no-code Agent Builder. Try it for free today: https://langsmith.com/
Building a Market Research Assistant with Langsmith Agent Builder
Learn how to quickly create a research agent in LangSmith using our no-code Agent Builder. Try it for free today: https://langsmith.com/
Building an Email Assistant with Langsmith Agent Builder
Learn how to quickly create an email assistant in LangSmith using our no-code Agent Builder. Try it for free today: https://langsmith.com/
Summarization Middleware (Python)
Learn about how to use LangChain's summarization middleware as a key component of your context engineering pipeline. This middleware is automatically triggered and helps to keep your long running a...
LangSmith Agent Builder Now Available in Public Beta
LangSmith Agent Builder is now in Public Beta, enabling anyone to create production ready agents without writing code. Learn what's new in the Beta release, how people are using Agent Builder, how...
Anthropic-Style Context Editing… Now for Every LLM in LangChainJS!
Learn how to bring Anthropic’s powerful Context Editing capabilities — like tool result clearing — to any LLM using LangChainJS. In this video, Christian Bromann breaks down Anthropic’s original d...
Product Evals (for AI Applications) in Three Simple Steps
Eugene Yan wrote a GREAT blogpost on how to build product evals (for AI applications) in three simple steps: https://eugeneyan.com/writing/product-evals/ These three steps are: 1. Label data 2. Al...
AI Agents in Production: Lessons from Rippling and LangChain
How does a company deploy AI agents across HR, payroll, IT, and finance products used by thousands of companies? Ankur Bhatt, Head of AI at Rippling, shares insights on building production-ready ag...
Using skills with Deep Agents CLI
Anthropic recently introduced the idea of agent skills, a directory of folders that an agent can access to perform different actions. Here, we talk about skills, why they are interesting, how agent...
Managing Agent Context with LangChain: Summarization Middleware Explained
Long-running agents eventually hit context overload — leading to context poisoning, distraction, confusion, and degraded performance. In this video, Christian from LangChain breaks down how Summar...
What are Deep Agents?
Deep Agents is a term we coined to describe agents capable of handling complex, open-ended tasks over long time horizons. We identified four essential components that make this possible: a planning...
Build a Research Agent with Deep Agents
Deepagents is a simple, open source agent harness built by LangChain. It uses some common principle seen in popular agents such as Claude Code and Manus, including planning (prior to task execution...
Model Call Limit Middleware (Python)
Learn about how to use LangChain's model call limit middleware as a guardrail for agents. This middleware can be used for single agent invocations or across conversations. We walk through an exampl...
Agents Gone Wild? Use Tool Call Limits in LangChainJS to Keep Them in Check!
In this tutorial, Christian Bromann will show you how to prevent runaway tool usage using the Tool Call Limit Middleware in LangChainJS. You’ll learn how to set clear, declarative limits on tool us...
Building a Research Agent with Gemini 3 + Deep Agents
Google's eagerly anticipated Gemini 3 Pro release shows state-of-the-art performance across a wide range of agentic evaluations. Here, we show how to use Gemini 3 Pro with Deepagents, our open sou...
Model Fallback Middleware (Python)
Learn about how to use LangChain's new model fallback middleware to add resilience to your applications. Automatically switch between models and even providers when an API call fails with just a fe...
Stop Endless Back-and-Forth — Add Model Call Limits in LangChainJS
Is your support agent getting stuck in endless back-and-forth conversations? Users asking the same thing three different ways? Or long chats where the bot clearly isn’t helping anymore? In this tu...
LangChain Academy New Course: LangSmith Essentials
Testing applications is essential to the development lifecycle, but LLM systems are non-deterministic – you can’t always predict how they will behave. Add multi-turn interactions and tool-calling...
To-Do List Middleware (Python)
Learn about how to use LangChain's to-do list middleware to equip agents with task planning and tracking capabilities for complex multi-step tasks. Our example uses a software development agent. M...