Skip to main content


GPT-crawler will crawl websites to produce files for use in custom GPTs or other apps (RAG).

This template uses gpt-crawler to build a RAG app

Environment Setup​

Set the OPENAI_API_KEY environment variable to access the OpenAI models.


Run GPT-crawler to extact content from a set of urls, using the config file in GPT-crawler repo.

Here is example config for LangChain use-case docs:

export const config: Config = {
url: "",
match: "**",
selector: ".docMainContainer_gTbr",
maxPagesToCrawl: 10,
outputFileName: "output.json",

Then, run this as described in the gpt-crawler README:

npm start

And copy the output.json file into the folder containing this README.


To use this package, you should first have the LangChain CLI installed:

pip install -U langchain-cli

To create a new LangChain project and install this as the only package, you can do:

langchain app new my-app --package rag-gpt-crawler

If you want to add this to an existing project, you can just run:

langchain app add rag-gpt-crawler

And add the following code to your file:

from rag_chroma import chain as rag_gpt_crawler

add_routes(app, rag_gpt_crawler, path="/rag-gpt-crawler")

(Optional) Let's now configure LangSmith. LangSmith will help us trace, monitor and debug LangChain applications. You can sign up for LangSmith here. If you don't have access, you can skip this section

export LANGCHAIN_API_KEY=<your-api-key>
export LANGCHAIN_PROJECT=<your-project> # if not specified, defaults to "default"

If you are inside this directory, then you can spin up a LangServe instance directly by:

langchain serve

This will start the FastAPI app with a server is running locally at http://localhost:8000

We can see all templates at We can access the playground at

We can access the template from code with:

from langserve.client import RemoteRunnable

runnable = RemoteRunnable("http://localhost:8000/rag-gpt-crawler")

Was this page helpful?

You can leave detailed feedback on GitHub.