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kyrolabs/awesome-langchain

9,344Audience · developerComplexity · 1/5Setup · easy

TLDR

A community-maintained directory of tools, projects, and learning resources built around LangChain, the framework for connecting AI language models to external data and services. Regularly updated so you don't have to search yourself.

Mindmap

mindmap
  root((awesome-langchain))
    LangChain Core
      Python library
      JavaScript sibling
      Official docs
    Language Ports
      Go port
      Ruby port
      Rust port
    Tools and Services
      Low-code builders
      Caching services
      Deployment platforms
    Learning
      Jupyter notebooks
      Video playlists
    Community Projects
      Chatbots
      Knowledge tools
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Code map

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Things people build with this

USE CASE 1

Find a low-code tool like Flowise to build an AI chatbot workflow without writing code.

USE CASE 2

Discover LangChain ports in Go, Ruby, Java, or other languages if you work outside Python.

USE CASE 3

Browse community notebooks and video playlists to learn how LangChain works.

USE CASE 4

Find hosted platforms and caching services for deploying and optimizing your LangChain app.

Tech stack

PythonJavaScript

Getting it running

Difficulty · easy Time to first run · 5min

In plain English

This repository is a community-curated list of tools, services, and projects built around LangChain, a framework for connecting large language models to external data sources and services. Think of it as a directory that someone updates regularly so developers do not have to go hunting for resources themselves. The list starts with pointers to LangChain itself, including its JavaScript sibling and the official documentation and blog. It then covers unofficial ports of the framework to other programming languages: Go, Ruby, Java, Dart, Haskell, Elixir, and Rust are all represented, each maintained by independent developers outside the core LangChain team. A tools section breaks resources into subcategories. Low-code options like Flowise and Langflow let users build AI workflows by dragging and dropping components rather than writing code. Services in the list cover things like caching language model responses, loading data from various sources, evaluating question-answering quality, and deploying AI apps into messaging platforms like Slack or WeChat. Separate sections cover agent frameworks and hosted platforms where people can run LangChain-powered applications. The open-source projects section highlights knowledge management tools and chatbots built on top of LangChain. A learn section collects Jupyter notebooks and video playlists for people who want to understand how the framework works. There is also a section for other LLM frameworks that sit outside the LangChain ecosystem but serve related purposes. The list accepts contributions via pull requests and maintains a newsletter for periodic summaries of additions. The full README is longer than what was shown.

Copy-paste prompts

Prompt 1
Using the awesome-langchain list, help me find a tool that lets me build an AI question-answering chatbot over my company's documents without writing code.
Prompt 2
I want to use LangChain in Go instead of Python. What library from the awesome-langchain list should I use and how do I start?
Prompt 3
Help me find a caching service for LangChain API calls from the awesome-langchain list so I can reduce my API costs.
Prompt 4
Which projects in the awesome-langchain list let me deploy an AI assistant into Slack or another messaging platform?
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