LangChain

AI时代的“瑞士军刀”：深入浅出理解LangChain

在这个人工智能飞速发展的时代，您可能经常听到“大语言模型”（LLM，如ChatGPT、文心一言）这个词。这些模型拥有惊人的理解和生成人类语言的能力，就像我们有了一个无所不知的“超级大脑”。但问题是，这个“超级大脑”虽然厉害，却像一个孤立的天才，它无法自己上网查询实时信息，也无法操作你的电脑发送邮件，更不知道你过去和它聊了些什么。

这时候，一个名叫 LangChain 的工具出现了。它不是另一个“超级大脑”，而更像是一个能让“超级大脑”变得更聪明、更实用、能做更多事情的智能管家和连接器。

一、什么是LangChain？——让AI“活”起来的魔法框架

想象一下，你有一个非常聪明的厨房机器人，它能识别食材，也能理解你的烹饪指令。但如果它只能告诉你怎么做菜，却不能自己去冰箱拿食材，不能打开烤箱，也不能清洗餐具，那它的实用性就大打折扣了。

LangChain就是那个能让“厨房机器人”（大语言模型LLM）拿起工具、连接外部世界、甚至记住你口味的“智能管家和总指挥”。 它是一个开源的框架，旨在帮助开发者更简单、更高效地构建基于大语言模型的应用程序。

简单来说，LangChain的核心价值在于：

  1. 连接性强：让大语言模型不仅仅停留在“对话”，还能与数据库、搜索引擎、其他API（应用程序编程接口）等外部工具进行互动。
  2. 模块化：它把构建AI应用需要的功能拆分成一个个积木块，你可以根据需要自由组合，就像拼乐高一样。
  3. 流程化：它能帮你设计一套完整的“工作流程”，让大语言模型一步一步地完成复杂任务，而不是只做一件简单的事情。

二、LangChain的“积木块”们：智能管家的各项本领

为了让我们的“超级大脑”管家做得更好，LangChain给它配备了许多趁手的“工具箱”和“本领”。我们来用生活中的例子，看看这些“积木块”都是干什么的：

  1. 模型（Models）—— 即“超级大脑”本身

    • 比喻：你的智能管家本身拥有的这个“超级大脑”，可能是OpenAI的ChatGPT，也可能是国内的文心一言，或者是其他开源的语言模型。
    • LangChain的作用：它提供了一个统一的插座，无论你的“大脑”是哪种型号，都能轻松接入，就像你的手机充电器可以适配不同的插座一样。开发者无需为每种模型学习一套新的接口，大大简化了开发难度。

  2. 提示词（Prompts）—— 给大脑下达“指令”

    • 比喻：你想让管家帮你写一份旅行计划，你需要告诉它“去哪里，什么时候去，喜欢什么风格，预算多少”等等。这些具体的描述就是“指令”。
    • LangChain的作用：它提供了各种模板来帮助你更清晰、更有效地给“超级大脑”下达指令。比如，你可以用一个模板来规划旅行，用另一个模板来写邮件，确保每次发出的指令都能得到最好的回应。这就像菜谱，能指导你的厨房机器人一步步做出美味佳肴。

  3. 链（Chains）—— “指令”的“工作流”

    • 比喻：你想让管家帮你“查好天气预报，然后根据天气帮你决定出门穿什么，最后再告诉你结果”。这不是一个指令，而是好几个连贯的步骤。
    • LangChain的作用：就像一条自动化生产线，把多个“超级大脑”或者“大脑”和“工具”连接起来，让它们按照预设的顺序合作完成一个复杂的任务。比如，先让一个大模型总结一段文章，再把总结结果交给另一个大模型去生成一篇新闻稿，这就是一个“链”。

  4. 检索器（Retrievers）—— “外部信息查询员”

    • 比喻：你的管家在回答你的问题时，如果仅仅依靠自己已有的知识，可能会“编造”信息，或者信息过时。这时，它需要一个“外部信息查询员”，去图书馆、查百科全书或上网找资料。
    • LangChain的作用：它允许“超级大脑”访问外部数据源，比如你的公司内部文档、最新的新闻网站或者某个数据库。这样，大语言模型就能获取到最新、最准确的信息来回答你的问题，而不是仅仅依靠训练数据。这种结合外部知识来提升回答质量的技术叫做“检索增强生成”（RAG）。

  5. 代理（Agents）—— 拥有“决策能力”的管家

    • 比喻：这是LangChain最厉害的“积木块”之一。你的智能管家不仅能执行你的指令，还能根据当前情况，自己判断应该使用哪个工具来完成任务。比如，你让它“帮我订一张明天去上海的机票”，它会自主决定：先去“查航班”工具，再调用“订票”工具，甚至可能需要“查日历”工具来确认你的行程。
    • LangChain的作用：代理让大语言模型拥有了“思考”和“决策”的能力。它不再被动地等待指令，而是能主动分析任务，选择合适的工具（如计算器、搜索引擎、日历APP等）去完成任务。

  6. 记忆（Memory）—— “过目不忘”的本领

    • 比喻：你在和管家聊天时，如果它每次都忘记你们之前聊过的内容，那对话肯定会很糟糕。
    • LangChain的作用：它让“超级大脑”拥有了“记忆力”，能够记住之前的对话内容和上下文信息，从而进行连贯、个性化的交流。

三、LangChain的最新进展与应用：它能做些什么？

LangChain自2022年诞生以来，发展迅猛，并在2025年10月完成1.25亿美元融资，市值达到12.5亿美元，成为独角兽企业。这表明业界对其在AI应用开发中的价值高度认可。

现在，LangChain已经被广泛应用于各种场景，让AI真正走进我们的生活和工作中：

  • 智能客服与聊天机器人：许多公司（如Klarna的AI助手）使用LangChain构建更智能、更能理解用户意图并能关联公司内部知识库的客服机器人，极大地提升了客户体验。
  • 企业内部知识问答：例如，金融机构或科技公司，将大量内部文档、报告接入LangChain，员工可以直接向AI提问，快速获取所需信息，就像拥有了一个超级智能的“搜索引擎”。
  • 数据分析与报告生成：LangChain可以帮助大模型连接到数据库，提取数据进行分析，并自动生成报告摘要。
  • 自动化代理：例如，Replit的AI Agent通过LangChain实现更复杂的代码协作和自动化开发任务。
  • 个性化推荐系统：结合用户历史数据和实时信息，为用户提供更精准的推荐。

尽管有声音认为随着大模型自身功能增强，LangChain等重型框架未来可能面临挑战，但其作为构建AI智能体基础设施的价值仍被看好，尤其是在agent技术的演进过程中，LangChain以其全面的产品线（包括LangGraph用于编排和LangSmith用于测试与可观察性）持续适应和发展。

四、总结：AI时代的“基础设施”

理解LangChain，就像理解了AI时代如何将一个拥有惊人智慧但有些“书呆子气”的“超级大脑”，培养成一个能够独当一面、灵活应变、连接世界的“智能管家”。它通过提供一系列标准化的工具和流程，极大地降低了开发AI应用的门槛，让更多人能够利用大语言模型的强大能力，构建出各种各样实用且富有创意的智能应用。

未来，随着AI技术不断发展，像LangChain这样的框架将继续演进，成为我们构建和部署AI应用不可或缺的基础设施，让AI真正地“活”起来，更好地服务于人类生活和工作。

The “Swiss Army Knife” of the AI Era: A Simple Guide to Understanding LangChain

In this era of rapid AI development, you may frequently hear the term “Large Language Models” (LLMs, such as ChatGPT and Ernie Bot). These models possess an amazing ability to understand and generate human language, as if we have an omniscient “super brain.” But the problem is, although this “super brain” is powerful, it is like an isolated genius. It cannot go online to check real-time information by itself, nor can it operate your computer to send emails, and it doesn’t even know what you talked about with it in the past.

At this time, a tool called LangChain appeared. It is not another “super brain,” but more like an intelligent steward and connector that can make the “super brain” smarter, more practical, and capable of doing more things.

1. What is LangChain? — The Magic Framework Bringing AI to Life

Imagine you have a very smart kitchen robot that can identify ingredients and understand your cooking instructions. But if it can only tell you how to cook, yet cannot get ingredients from the refrigerator, cannot turn on the oven, and cannot wash the dishes, then its practicality is greatly reduced.

LangChain is the “intelligent steward and commander-in-chief” that allows the “kitchen robot” (Large Language Model LLM) to pick up tools, connect to the outside world, and even remember your tastes. It is an open-source framework designed to help developers build applications based on large language models more simply and efficiently.

Simply put, the core value of LangChain lies in:

  1. Strong Connectivity: Enable large language models not just to “chat,” but to interact with external tools such as databases, search engines, and other APIs (Application Programming Interfaces).
  2. Modularity: It breaks down the functions needed to build AI applications into building blocks. You can combine them freely according to your needs, just like Lego.
  3. Process-oriented: It helps you design a complete “workflow,” allowing the large language model to complete complex tasks step by step, instead of doing just one simple thing.

2. LangChain’s “Building Blocks”: The Skills of the Intelligent Steward

To make our “super brain” steward perform better, LangChain equips it with many handy “toolboxes” and “skills.” Let’s use everyday examples to see what these “building blocks” do:

  1. Models — The “Super Brain” Itself

    • Analogy: This “super brain” owned by your intelligent steward could be OpenAI’s ChatGPT, domestic Ernie Bot, or other open-source language models.
    • LangChain’s Role: It provides a unified socket. No matter what model your “brain” is, it can be easily plugged in, just like your phone charger can adapt to different sockets. Developers don’t need to learn a new interface for each model, greatly simplifying development.

  2. Prompts — Giving “Instructions” to the Brain

    • Analogy: You want the steward to help you write a travel plan. You need to tell it “where to go, when to go, what style you like, what is the budget,” etc. These specific descriptions are “instructions.”
    • LangChain’s Role: It provides various templates to help you give instructions to the “super brain” more clearly and effectively. For example, you can use one template to plan a trip and another to write an email, ensuring that every instruction gets the best response. It’s like a recipe guiding your kitchen robot step-by-step to make delicious dishes.

  3. Chains — The “Workflow” of Instructions

    • Analogy: You want the steward to “check the weather forecast, then decide what you should wear based on the weather, and finally tell you the result.” This is not one instruction, but several coherent steps.
    • LangChain’s Role: Like an automated production line, it connects multiple “super brains” or “brains” and “tools” to cooperate in a preset order to complete a complex task. For example, first let a large model summarize an article, and then hand the summary over to another huge model to generate a news release. This is a “chain.”

  4. Retrievers — “External Information Researchers”

    • Analogy: When answering your questions, if your steward only relies on its existing knowledge, it might “fabricate” information, or the information might be outdated. At this time, it needs an “External Information Researcher” to go to the library, check encyclopedias, or search online for information.
    • LangChain’s Role: It allows the “super brain” to access external data sources, such as your internal company documents, the latest news websites, or a database. In this way, the large language model can obtain the latest and most accurate information to answer your questions, rather than relying solely on training data. This technique of combining external knowledge to improve answer quality is called “Retrieval-Augmented Generation” (RAG).

  5. Agents — Stewards with “Decision-Making Ability”

    • Analogy: This is one of LangChain’s most powerful “building blocks.” Your intelligent steward can not only execute your instructions but also judge which tool to use to complete the task based on the current situation. For example, if you ask it to “book a flight to Shanghai tomorrow for me,” it will autonomously decide: first use the “check flights” tool, then call the “booking” tool, and possibly even need the “check calendar” tool to confirm your schedule.
    • LangChain’s Role: Agents give large language models the ability to “think” and “decide.” It no longer passively waits for instructions but can proactively analyze the task and choose suitable tools (such as calculators, search engines, calendar apps, etc.) to complete the task.

  6. Memory — The Ability of “Photographic Memory”

    • Analogy: When chatting with the steward, if it forgets what you talked about before every time, the conversation will definitely be terrible.
    • LangChain’s Role: It gives the “super brain” a “memory,” enabling it to remember previous conversation content and context information, thereby engaging in coherent, personalized communication.

3. Recent Progress and Applications of LangChain: What Can It Do?

Since its birth in 2022, LangChain has developed rapidly, completing 125millioninfinancinginOctober2025,reachingavaluationof125 million in financing in October 2025, reaching a valuation of 1.25 billion, becoming a unicorn company. This indicates the industry’s high recognition of its value in AI application development.

Now, LangChain has been widely used in various scenarios, truly bringing AI into our lives and work:

  • Intelligent Customer Service & Chatbots: Many companies (like Klarna’s AI assistant) use LangChain to build customer service robots that are smarter, better understand user intent, and connect to internal knowledge bases, greatly improving customer experience.
  • Enterprise Internal Q&A: For example, financial institutions or tech companies connect massive internal documents and reports to LangChain. Employees can directly ask AI questions to quickly obtain the required information, just like having a super-intelligent “search engine.”
  • Data Analysis & Report Generation: LangChain can help large models connect to databases, extract data for analysis, and automatically generate report summaries.
  • Automated Agents: For instance, Replit’s AI Agent achieves more complex code collaboration and automated development tasks through LangChain.
  • Personalized Recommendation Systems: Combining user historical data and real-time information to provide users with more precise recommendations.

Although some argue that heavy frameworks like LangChain may face challenges as large models themselves become more capable, its value as infrastructure for building AI agents is still promising, especially in the evolution of agent technology. LangChain continues to adapt and develop with its comprehensive product line (including LangGraph for orchestration and LangSmith for testing and observability).

4. Summary: The “Infrastructure” of the AI Era

Understanding LangChain is like understanding how to cultivate a “super brain” with amazing wisdom but some “nerdiness” in the AI era into an “intelligent steward” capable of taking charge, adapting flexibly, and connecting the world. By providing a series of standardized tools and processes, it greatly lowers the threshold for developing AI applications, allowing more people to utilize the powerful capabilities of large language models to build various practical and creative intelligent applications.

In the future, as AI technology continues to develop, frameworks like LangChain will continue to evolve, becoming indispensable infrastructure for building and deploying AI applications, allowing AI to truly “come alive” and better serve human life and work.