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 ...

Core Insights - LangChain has completed a $125 million funding round, achieving a post-money valuation of $1.25 billion, marking its status as a unicorn [3] - The company has released a significant update with LangChain 1.0, which is a complete rewrite of the framework after three years of iterations [3][4] - LangChain is one of the most popular projects in the open-source developer community, with 80 million downloads per month and millions of developers actively using it [3] Development Background - LangChain was initiated in October 2022 by machine learning engineer Harrison Chase as a side project, initially consisting of about 800 lines of code [5] - The project was inspired by the fragmented tools and lack of abstraction in the AI development landscape, leading to the creation of a framework that connects models with tools [6] Evolution of LangChain - The framework has evolved from a simple integration tool to a comprehensive application framework, focusing on context-aware reasoning [9] - LangChain's architecture includes a component and module layer, as well as an end-to-end application layer, allowing developers to quickly build applications with minimal code [9][10] Challenges and Solutions - The team faced numerous issues, including a backlog of around 2,500 unresolved problems and user feedback regarding the need for greater control and customization [11] - To address these challenges, LangChain introduced LangGraph, which allows developers to manage agent logic more flexibly and supports long-running tasks [12][13] Key Features of LangChain 1.0 - The new version emphasizes controllability and built-in runtime capabilities, allowing for persistent execution environments and checkpoint recovery [16][27] - A middleware concept has been introduced, enabling developers to insert additional logic into the core agent loop, enhancing extensibility and customization [25][30] - The framework now supports dynamic model selection based on context, allowing for better optimization between capabilities and costs [26][27] Future Directions - LangChain's product lines focus on scaling the open-source ecosystem, enhancing the integration development environment for LangGraph, and improving the scalability of LangSmith [13] - The company aims to maintain its position at the forefront of AI development by providing flexibility and options for developers in a rapidly evolving landscape [26]

Core Insights - LangChain announced a successful funding round of $125 million, achieving a valuation of $1.25 billion [2][5] - The company, which focuses on developing an open-source framework for AI agents, was founded in 2022 and has quickly gained popularity among developers [3][5] Funding Details - The latest funding round was led by IVP, with new investors CapitalG and Sapphire Ventures joining existing backers such as Sequoia Capital, Benchmark, and Amplify [3][5] - LangChain's valuation increased from $200 million after a $25 million Series A round led by Sequoia Capital [5] Product Development - LangChain has evolved into a platform for building AI agents, launching significant upgrades to its core products, including the LangChain agent-building tool, LangGraph for orchestration and context/memory, and LangSmith for testing and observability [5] - The company maintains high popularity among open-source developers, boasting 118,000 stars and 19,400 forks on GitHub [6]

Hey, I'm Tanishi from Langchain. I'm excited to share a new feature that we're launching called multi-turn evaluations. These are used to run online evaluations over end to end user interactions.If you're already using evals and linksmith, these complement those and should be used when your evaluator needs the context of an entire thread or an entire user conversation rather than just the next message. So to walk through a quick example, let's say you have a chat app like a customer support agent. This is a ...

Group 1 - LangChain, an open-source AI agent framework developer, has achieved a valuation of $1.25 billion after completing a $125 million funding round [2] - The funding round was led by IVP, with new investors CapitalG and Sapphire Ventures joining existing investors such as Sequoia Capital, Benchmark, and Amplify [2] - LangChain was founded in 2022 by Harrison Chase and has quickly gained popularity for addressing challenges in building applications using early large language models (LLMs) [2][3] Group 2 - The company has evolved into a platform for building intelligent agents, launching a comprehensive upgrade of its core products, including LangChain, LangGraph, and LangSmith [3] - LangChain maintains high popularity among open-source developers, boasting 118,000 stars and 19,400 forks on GitHub [3]

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 ...

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]

Setup and Configuration - Setting up tracing from Claude Code to Langsmith requires creating a Langsmith account and generating an API key [1] - Enabling telemetry for Claude Code involves setting the `CLOUD_CODE_ENABLE_TELEMETRY` environment variable to 1 [3] - Configuring the OTLP (OpenTelemetry Protocol) exporter with HTTP transport and JSON encoding is necessary for Langsmith ingestion [4] - The Langsmith Cloud endpoint needs to be specified for logs from Claude Code, or a self-hosted instance URL if applicable [5] - Setting the API key in the headers allows authentication and connection to Langsmith, along with specifying a tracing project [5] - Enabling logging of user prompts and inputs is done by setting the appropriate environment variable to true [6] Monitoring and Observability - Langsmith collects and displays events from Claude Code, providing detailed logs of Claude Code sessions [3] - Traces in Langsmith show individual actions performed by Claude Code, including model names, token usage, and latency [8] - Claude Code sends cost information associated with each request to Langsmith [8] - Langsmith's waterfall view groups runs based on timestamps, showing the sequence of user prompts and Claude Code actions [13] - Langsmith provides pre-built dashboards for monitoring general usage, including the total number of traces, token usage, and costs over time [14]