​
Overview

Langflow is a powerful UI tool for building LangChain applications through a drag-and-drop interface. It provides an intuitive way to prototype and experiment with various LangChain components, enabling you to create complex AI workflows with ease. A standout feature is its seamless integration with advanced web scraping tools like ScrapeGraph, which significantly enhances the ability to fetch, analyze, and utilize web data dynamically.

Official Langflow Documentation

View Langflow’s official documentation

​
Installation

Install Langflow using pip:

pip install langflow

Or run it using Docker:

docker run -p 7860:7860 logspace/langflow

​
Features

Drag & Drop Interface

Build LangChain flows visually without coding.

Component Library

Access a wide range of LangChain components including powerful integrations like ScrapeGraph.

Real-time Preview

Test your flows as you build them.

Export & Share

Export flows as JSON or Python code.

​
Example: Search Agent with ScrapeGraph Integration

Langflow’s Search Agent Flow showcases how to integrate advanced web scraping directly into your AI workflows. This flow leverages the ScrapeGraph Search API to fetch and analyze data from the web based on a given search prompt.

​
Why ScrapeGraph Matters

Integrating ScrapeGraph into Langflow is a game changer because it:

  • Dynamically Retrieves Information: Empowers your agent to perform real-time web searches and scraping, ensuring access to the latest data.
  • Enhances Decision Making: Supplies rich, up-to-date information that agents can use to generate more accurate and context-aware responses.
  • Simplifies Workflow Development: Offers a plug-and-play component that bridges web scraping with LangChain workflows, reducing the need for manual data integration.

Below is an excerpt of the integration, which is defined as a node in the flow:

{
  "description": "A search agent that can find and analyze information from the internet using ScrapeGraph.",
  "name": "Search Agent Flow",
  "data": {
    "nodes": [
      // ... nodes configuration from the example, including the ScrapeGraphSearchApi node
    ]
  }
}

For the complete integration details, visit the Search Agent JSON file on GitHub.

​
Getting Started

  1. Install and Launch Langflow: Follow the installation steps above.
  2. Access the UI: Open your browser and navigate to http://localhost:7860.
  3. Build Your Flow: Start dragging components from the sidebar, including the ScrapeGraph node.
  4. Connect Components: Link nodes to create a workflow that incorporates web search and data processing.
  5. Test Your Flow: Use the chat interface to simulate queries and see real-time responses.

​
Use Cases

Chatbots

Create conversational AI agents enhanced with real-time web data.

Data Processing

Build pipelines to extract, process, and analyze data from websites.

Research Tools

Develop research assistants that fetch current information directly from the web.

Content Generation

Create dynamic content generation pipelines powered by live data scraping.

​
Support

Need help with Langflow?

GitHub Issues

Report bugs and request features.

Discord Community

Join the Langflow community.

​
Resources

GitHub Repository

View the source code.

Examples Gallery

Explore example flows.

​
Langflow Online

Langflow is not only available for local installation—you can also use it online! With the online version, you can access the intuitive drag-and-drop interface directly from your browser, build and test AI workflows in real time, and experiment with powerful integrations like ScrapeGraph without installing any software.

Simply visit our online demo to get started quickly and explore all the features that Langflow offers.

​
Search Agent Template

You can download the Search Agent template JSON file from the following link:

Search Agent JSON Template

Was this page helpful?

🦐 AgnoOpen Source