LangChain: Definition and Examples

LangChain is an open source framework designed to facilitate the development of applications powered by language models (LLMs), by chaining multiple components such as prompts, API calls, and external data sources.

Full definition

LangChain is a development framework created in October 2022 by Harrison Chase, which simplifies the construction of complex applications based on large language models (LLMs). Its fundamental principle relies on the concept of 'chains': sequences of operations that combine calls to LLMs with other tools, databases, or APIs to produce richer and more reliable results than a simple call to a model.

The framework offers several key abstractions: Chains (sequences of steps), Agents (autonomous systems capable of choosing which tools to use), Retrievers (search mechanisms in knowledge bases), and Memory modules (management of conversational context). These components can be assembled like building blocks to create chatbots, question-answering systems on documents, autonomous agents, or data analysis pipelines.

One of the most popular use cases for LangChain is RAG (Retrieval-Augmented Generation), which allows an LLM to consult external documents before generating its response. For example, a company can connect its internal documents to a model like Claude or GPT to create an assistant capable of answering questions specific to its business, with sourced and verifiable answers.

Available in Python and TypeScript (LangChain.js), the framework benefits from a rich ecosystem with LangSmith for monitoring and debugging, LangGraph for multi-agent workflows, and LangServe for API deployment. LangChain has become one of the fastest-growing open source projects in history, reflecting the growing need for tools to industrialize generative AI applications.

Etymology

The name 'LangChain' is a contraction of 'Language' and 'Chain', illustrating the core concept of the framework: chaining operations involving language models to create complex workflows.

Concrete examples

Create a RAG chatbot on internal documentation

You are a technical assistant. Only use the documents provided by the retriever to answer. If the information is not in the documents, say so clearly. Answer concisely and cite your sources.

Build an autonomous agent capable of using tools

You are a research agent. You have the following tools: web search, calculator, and customer database. Analyze the user's question, choose the relevant tool(s), and provide a structured answer with your sources.

Summarize and analyze a large volume of PDF documents

Analyze the following document and produce: 1) a summary of 5 key points, 2) the main entities mentioned, 3) the open questions that remain unanswered in the text.

Practical usage

In prompt engineering, LangChain allows structuring complex workflows where a single prompt is not enough. You can use it to break down a problem into steps (chains), connect an LLM to your own data via RAG, or create agents capable of dynamically deciding which tools to invoke. It is the tool of choice to move from a prompt prototype to a production AI application.

Related concepts

RAG (Retrieval-Augmented Generation)AI AgentsEmbeddingsPrompt Chaining

FAQ

What is the difference between LangChain and a simple API call to an LLM?

A simple API call sends a prompt and receives a response. LangChain allows orchestrating multiple calls, connecting external data sources, managing conversational memory, and creating autonomous agents. It's the difference between asking an isolated question and building a complete application.

Should LangChain be used for every generative AI project?

No. For simple cases (a single call to an LLM with a well-crafted prompt), LangChain adds unnecessary complexity. It becomes relevant when you need to chain steps, integrate data sources, manage memory, or create agents. For simple cases, the native SDKs of providers (such as the Anthropic or OpenAI SDK) suffice.

What are the alternatives to LangChain?

The main alternatives include LlamaIndex (specialized in RAG and document indexing), Haystack by deepset, the Anthropic Claude Agent SDK for agents, and Microsoft Semantic Kernel. Some developers also prefer to build their own lightweight abstractions directly on the LLM provider APIs.

How to use this prompt

Copy the prompt with the button above.
Paste it into ChatGPT, Claude or your favorite AI assistant.
Replace the bracketed variables with your details, then refine the result.

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