Hey, this is Lance. I want to talk a bit about the deep agents package that we recently released. Now, the length of tasks that an agent can take every seven months.And we see numerous examples of popular longrunning agents like Claude Code, Deep Research, Manis. The average Manis task, for example, can be up to 50 different tool calls. And so, it's increasingly clear that agents are needed to do what we might consider deeper work or more challenging tasks that take longer periods of time.Hence, this term d ...

神译局是36氪旗下编译团队，关注科技、商业、职场、生活等领域，重点介绍国外的新技术、新观点、新风向。 编者按：别再等AI喂给你知识了。关键中的关键是，你得先教会AI"如何教你"。文章来自编译。 顺便分享一些我用来定制化学习的实用提示词。 我本想用 LangChain 为我的newsletter写个具备 RAG（检索增强生成）功能的专属 AI 智能体聊天机器人的。 但问题是，我完全不知道从何下手。 每一篇教程都默认我已经懂了向量数据库、嵌入（embeddings）和检索管道。那些文档是写给把 Python 玩得滚瓜烂熟的开发者的。Stack Overflow 上的帖子 动辄"分块策略"和"相似性搜索"这样的术语，好像人人都该懂似的。 我就卡在那种"一知半解，离真正有用还差得远"的尴尬境地。我理解 ChatGPT 和 Claude。我也用 Make.com、Zapier、n8n、Relay 这些工具写过过自动化流 程，感觉已经颇为高级了。 但 LangChain 呢？ 零代码自动化和真正的 AI 智能体开发之间的鸿沟，感觉宽得令人绝望。我无法理解。 然后我想起了那个曾改变过我学习方式的工具。 六个月前，我曾写过一 ...

Learn about how to use LangChain's human in the loop middleware to approve, edit, and reject tool calls before they're executed. Our example uses an email assistant agent that requires human feedback before sending sensitive emails. Middleware docs: https://docs.langchain.com/oss/python/langchain/middleware#human-in-the-loop Code: https://gist.github.com/sydney-runkle/628246dc4f851dda45f57b492c645ec0 ...

Core Viewpoint - The article discusses the current state of AI, particularly focusing on the concept of AI Agents, and highlights the industry's obsession with performance metrics, likening it to an "exam-oriented" approach that may overlook the true value of technology [2][7][41]. Group 1: AI Agent Market Dynamics - There is a growing skepticism in the industry regarding the AI Agent market, with many products merely automating traditional workflows under the guise of being intelligent agents, leading to user disappointment [3][9]. - The popularity of AI Agents stems from a collective desire for AI to transition from experimental tools to practical applications that enhance productivity and cognitive capabilities in real-world scenarios [7][10]. Group 2: Technological Evolution - The emergence of large models represents a significant turning point, replacing rigid, rule-based systems with probabilistic semantic understanding, which allows for more dynamic and adaptable AI systems [9][10]. - The relationship between workflows and AI Agents is not adversarial; rather, workflows serve as a foundational stage for the development of true AI Agents, which will evolve beyond traditional automation [10][11]. Group 3: Future Directions and Challenges - The future of AI Agents is oriented towards results rather than processes, emphasizing the need for agents to be capable of autonomous judgment and dynamic adaptation [13][40]. - The concept of "group intelligence" is being explored as a potential alternative to the current arms race in large model development, focusing on collaboration among smaller agents to tackle complex tasks [17][18]. Group 4: Open Source and Community Engagement - The company emphasizes the importance of open-source practices, believing that collective intelligence can accelerate AI development and foster a community-driven approach to innovation [32][33]. - Open-source contributions are seen as vital for sharing insights and advancing the understanding of AI technologies, rather than just providing code [35][36]. Group 5: Practical Applications and Long-term Vision - The company aims to develop AI Agents that can operate independently over extended periods, tackling long-term tasks and adapting to various environments to enhance their learning and capabilities [39][40]. - The ultimate goal is to create a continuously learning model that serves as a technical product, allowing the community to benefit from technological advancements without being overly polished for consumer markets [40][41].

LangChain Essentials Course Highlights - LangChain releases a new LangChain Essentials course for learning the basics of LangChain in an hour [1] - The course focuses on building agents using the `create_agent` abstraction [2] - The pre-built agent utilizes a ReAct-style architecture for reasoning and acting with tools [3] Agent Architecture and Scalability - The agent is built on LangGraph to balance flexibility with pre-built abstraction benefits [4] - The agent is designed to be scalable, resilient to failures, and allows for human intervention [3] - The agent can dynamically select prompts and models, with optional middleware for customization [4] Course Content - The course covers features of the `create_agent` abstraction through building increasingly sophisticated agents [5] - The course utilizes LangChain building blocks including messages, tools, and models [5]

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]

And so you have this iterative process of creating the right prompt and shaping the right guard rails and other code in order to get it to be like really useful in those situations. Open source has been a huge part of lang chain from ever since we got started. Obviously it started as an open source package and it's evolved a lot over the years.We now have Typescript packages. We now have lang chain and langraph. And so you know as we release 1.0% know of these packages.It's a huge moment for us as a company ...

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]

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]