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Introduction

Langchain is a Python framework to build applications on top of large-language models (OpenAI, Llama, Gemini). Building on top of LLMs comes with many challenges:
  • Gathering and preparing the data (context) and providing memory to models
  • Orchestrating tasks to match LLM API requirements (ex, rate limiting, chunking)
  • Parse the different LLM result format
Langchain comes with a set of high-level concepts and tools to cope with those challenges:
  • Retrieval modules such as Document Loaders or Text splitter help with gathering and preparing the data provided to the models
  • Model I/O is a set of tools that help to normalize the APIs across multiple models (ex: Prompt Templates)
  • Agents and Tools help to build reasoning (ex: how to answer based on provided context, what actions to take)
  • Chains help in orchestrating all the above
Browserbase provides a Document Loader to enable your Langchain application to browse the web to:
  • Extract text or raw HTML, including from web pages using JavaScript or dynamically rendered text
  • Load images via screenshots

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Add Browserbase to your Langchain application

To get started, proceed to the Python guide to learn more about using Browserbase with Langchain.

Browserbase for Langchain (Python)

Add Browserbase Document Loader to your Langchain Python application