Hi, today we'll be covering how to use Langmith to debug your AI applications and we'll be using a tool called Studio to do so. The studio is an IDE for building agents and you can use it with any langraph agent that you've built. Let's take a look at the repository for this course which contains our explain like 5 agent.We'll cover what you need to start using studio. You'll notice that in our repo, we have this file called langraph. json.langraph. json is a config file that tells studio where your agents ...

Hi, welcome back. In this video, we're going to talk about the types of runs you can create while tracing in Linksmith. We'll then show how these runs can help you understand your application's execution.Traces can be thought of as logs for your application. And the Langrain team has put a lot of effort into the UX for displaying traces. This is because traditional logs can be difficult to parse for LLM applications.If you've ever had to dig through huge unforatted stack traces for an LLM application, you k ...

Anthropic's Claude Code, OpenAI's Deep Researcher, and Manus's general purpose agent have demonstrated that agents can be amazingly effective on complex, long-running tasks. We call these Deep Agents because they have a few key differentiators from earlier forms of agents. In our new LangChain Academy course, Deep Agents with LangGraph, you'll learn their key characteristics and how to implement them in your own Deep Agent.So what makes these agents different. Under the hood, they use a simple ReAct tool-ca ...

Product & Solution - Pedi offers an enterprise-grade AI-powered incident management solution to help organizations transform critical operations [1] - The AI agent assists teams in understanding incidents through chat platforms like Slack or MS Teams, eliminating the need to navigate dashboards [2] - Langraph structures the AI agent with memory, decision-making, and fallbacks, parsing questions and devising plans to find answers [3] - Langraph provides full control over the agent's behavior, enabling debugging, error handling, and output analysis [4] Benefits & Impact - The AI agent saves engineers hours per week and reduces context switching [6] - Internal use of the AI agent provides learnings and a framework for developing more AI agents for customers [6] - Engineers use it for retrospectives, product managers use it to understand service stability, and executives use it to ask about incident and service health metrics [5] Technology & Architecture - Langraph helps maintain context throughout conversations, facilitating faster insights from incidents [3] - Langraph is flexible, open, well-documented, and integrates with Langchain and other observability tools [7] - Langraph enables the building of reliable and thoughtful AI agents that involve reasoning, data access, or coordination between steps [6][7]

Deep agents are a type of agent that complete tasks over longer time horizons. This is the architecture that agents like Claude Code, Manis, and Deep Research use to complete their task. In this video, we'll be creating a research deep agent in Typescript.One that can go out, do web searches, and synthesize that information in a cohesive report. All done through our new deep research typescript library. And so in this video we will be creating a TypeScriptbased research agent and connect it to this deep age ...

Course Overview - LangChain Academy launches "Deep Research with LangGraph" course, teaching users to build deep research agents from scratch [1] - The course focuses on multi-agent architecture and prompting techniques to improve performance and decision-making insights [2] - Participants will learn to build agents that interact with users, access tools, and manage multiple research agents [6] - The course emphasizes using LangSmith for observability and evaluation of agent components during development and deployment [5][7] Technological Focus - LangGraph, an agent orchestration framework, is highlighted for its suitability in building structured agentic applications [4] - The framework's built-in persistence layer is beneficial for tracking progress of multiple agents over extended periods [5] - Context engineering techniques are recommended to improve research results, such as focusing researchers on specific areas [3][4] Industry Application - Deep research is identified as a popular agent application, with major AI labs developing their own comprehensive report-generating products [2] - Companies are increasingly building their own deep research agents for use cases requiring high agency and decision-making [3] - The course aims to provide a working deep research agent adaptable to various user needs and use cases [7][8]

Educational Offerings - LangChain Education provides various learning methods, including courses, YouTube videos, and documentation [1] - LangChain Academy offers three types of courses: Foundational, Project, and Quickstart [1] Course Types - Foundational courses offer methodical learning from introduction to mastery and require more time to complete [2] - Project courses guide users through building specific projects, such as a Deep Research agent, and can typically be completed in a few hours [2] - Quickstart courses provide a quick introduction or review of a topic [2] Additional Resources - LangChain publishes educational videos on YouTube covering current topics, product features, and in-depth series [3] - LangChain provides extensive documentation with examples and step-by-step instructions [3]

Deep agents are a form of agents that plan, reason, and act over longer time horizons. We built a dedicated UI for viewing and interacting with these agents that show its plan, the status of the file system that it uses, and any sub aents it kicks off. My name is Nick.I'm an engineer at Langchain, and today you'll learn how to set up this UI. Now, as a quick refresher, we can think of deep agents as a variant of the generic React tool calling architecture. Under the hood, deep agents still follow the same i ...

Model Performance & Capabilities - GBD5 excels in coding and agent development, demonstrating competitive pricing [1][3][8][11] - The model sets a new Pareto frontier for intelligence versus price, outperforming Gemini in this aspect [1][2][4][22] - While not a dramatic leap from GBD4, GBD5 is a strong daily driver, particularly for building agents and coding [3][8][11] - GBD5 shows state-of-the-art tool calling capabilities, especially for long-running agents [9][11] - Testing indicates a performance increase in deep research tasks when using GBD5 as a researcher agent, achieving 49.4% performance on deep research bench [18] Pricing & Availability - GBD5 is priced competitively, even lower than GPT-4 and GPT-4.01 at $1.25 per million input tokens [3][4] - Through the API, different models are available (main, mini, thinking, pro), while the Chat GBT app uses a router to automatically select the model [5] Limitations & Considerations - GBD5 is considered weaker at writing compared to GPT-40, GPT-41, and GPT-45, being more practical but less conversational [1][7][13][15] - Initial confusion existed in the open SDK regarding model names, but this is expected to be resolved [5][6]

What's up everyone. It's Brace from Langchain and in this video I am extremely excited to announce our newest project openu. Open suite is an async cloud-based open source coding agent.What that means is you connect your GitHub account to open send it a task and it does the rest. It plans, executes the plan, writes code, runs tests, runs your different scripts, and then reviews the code before putting up a poll request to make sure it's all high quality code. And when it's done and it determines that everyt ...