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. 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 ...

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. ...

Voice is one of the most natural ways to interact with AI. And as the models are getting better, I'm excited about new use cases and interaction patterns that it's going to unlock, especially in industries like education and customer service. It's surprisingly easy to get started building a voice agent.And so let's go through that in this video. I'm Tannushri and I'm going to show you how to build a voice agent, specifically a French tutor with this framework called Pipecat. going to walk through how it wor ...

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 ...

Langsmith Platform Updates - Langchain is launching new features for Langsmith, a platform for agent engineering, focusing on tracing, evaluation, and observability to improve agent reliability [1] - Langsmith introduces "Insights," a feature designed to automatically identify trends in user interactions and agent behavior from millions of daily traces, helping users understand how their agents are being used and where they are making mistakes [1] - Insights is inspired by Anthropic's work on understanding conversation topics, but adapted for Langsmith's broader range of agent payloads [5][6] Evaluation and Testing - Langsmith emphasizes the importance of methodical testing, including online evaluations, to move beyond simple "vibe testing" and add rigor to agent development [1][33] - Langsmith introduces "thread evals," which allow users to evaluate agent performance across entire user interactions or conversations, providing a more comprehensive view than single-turn evaluations [16][17] - Online evals measure agent performance in real-time using production data, complementing offline evals that are based on known examples [24] - The company argues against the idea that offline evals are obsolete, highlighting their continued usefulness for regression testing and ensuring agents perform well on known interaction types [30][31] Use Cases and Applications - Insights can help product managers understand which product features are most frequently used with an agent, informing product roadmap prioritization [2][12] - Insights can assist AI engineers in identifying and categorizing agent failure modes, such as incorrect tool usage or errors, enabling targeted improvements [3][13] - Thread evals are particularly useful for evaluating user sentiment across an entire conversation or tracking the trajectory of tool calls within a conversation [21] Future Development - Langsmith plans to introduce agent and thread-level metrics into its dashboards, providing greater visibility into agent performance and cost [26] - The company aims to enable more flows with automation rules over threads, such as spot-checking threads with negative user feedback [27]

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]