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gkamradt/langchain-tutorials

★ 7,435Jupyter NotebookAudience · developerComplexity · 2/5Setup · easy

TLDR

A curated collection of Jupyter Notebook tutorials for learning LangChain, covering document Q&A, summarization, data extraction, AI agents, and chatbots, with YouTube walkthroughs and a community project gallery.

Mindmap

mindmap
  root((langchain-tutorials))
    What it does
      Teaches LangChain use
      Practical code examples
      YouTube walkthroughs
    Topics covered
      Document Q and A
      Summarization
      AI agents
      Data extraction
    Resources
      Cookbook notebooks
      Community gallery
      Prompt engineering tips
    Audience
      AI developers
      LangChain beginners
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Code map

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Things people build with this

USE CASE 1

Follow a guided cookbook notebook covering 7 core LangChain concepts and 9 practical use cases with runnable code and matching YouTube videos

USE CASE 2

Build a question-answering chatbot over your own documents using LangChain patterns from the tutorial notebooks

USE CASE 3

Browse community project examples organised by difficulty and category to find a working starting point for your own AI app

Tech stack

PythonLangChainJupyter Notebook

Getting it running

Difficulty · easy Time to first run · 30min

Most notebooks require an OpenAI or other LLM API key to run the code examples.

In plain English

This repository is a collection of tutorials and code examples for LangChain, a library that helps developers build applications powered by large language models. LangChain provides building blocks for tasks like asking questions over your own documents, summarizing text, extracting structured data from unstructured sources, and building chatbot-style interfaces. This repository is aimed at developers who want to learn how to use LangChain through practical examples rather than just reading the official documentation. The recommended learning path in the README starts with two cookbook notebooks. The first covers seven core concepts in LangChain, and the second covers nine practical use cases. Each notebook has a matching YouTube video. There are also links to prompt engineering resources for those who are new to writing effective instructions for AI models. Beyond the cookbooks, the repository includes a gallery of community-submitted projects organized by difficulty level (beginner, intermediate, and advanced) and by category. The categories covered include summarization, question answering over documents, data extraction, evaluation of AI outputs, chatbots, and AI agents. Most entries in the gallery link to separate repositories with the full code for each project. The tutorials use Jupyter Notebooks, which are files that combine code, explanatory text, and output in a single document that can be run interactively in the browser through tools like Google Colab. The README includes a link to a beginner video explaining how to use notebooks if that format is unfamiliar. The repository is maintained by Greg Kamradt and accepts contributions via pull request. It accompanies a YouTube channel and email newsletter focused on practical applications of AI tools for developers.

Copy-paste prompts

Prompt 1
Using the langchain-tutorials cookbooks, show me how to build a LangChain chain that loads a PDF and answers questions about its content.
Prompt 2
I want to extract structured data from unstructured text using LangChain. Which notebook in gkamradt/langchain-tutorials covers data extraction, and what is the basic code pattern?
Prompt 3
Using langchain-tutorials, show me how to build a LangChain agent that can call tools like search or a calculator to answer a multi-step question.
Prompt 4
I am new to Jupyter Notebooks. How do I open and run the langchain-tutorials notebooks in Google Colab without installing Python locally?
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