Langflow

Overview

Langflow is a powerful low-code tool for building and deploying AI-powered agents and workflows. It provides developers with a visual authoring experience and a built-in API server that turns every agent into an API endpoint for easy integration. Langflow supports all major LLMs, vector databases, and a growing library of AI tools, making it ideal for building Retrieval-Augmented Generation (RAG) applications and multi-agent systems.

System Requirements

• CPU: 2+ cores (4+ recommended for production)
• RAM: 4GB+ (8GB+ recommended for complex workflows)
• GPU: Not required
• Storage: 1GB+
• Python: 3.9+ (3.10+ recommended)

Installation Guide

Prerequisites

• Python 3.9+ installed
• pip (Python package manager)
• Git (optional, for cloning the repository)
• Docker (optional, for containerized deployment)

Option 1: Python Package Installation

The simplest way to install Langflow is via pip:

pip install langflow

Then run the application:

langflow run

By default, this will start Langflow on http://localhost:7860.

Option 2: Docker Installation

For containerized deployment, you can use Docker:

docker pull langflow/langflow
docker run -p 7860:7860 langflow/langflow

Option 3: Installation from Source

1. Clone the repository:

   git clone https://github.com/langflow-ai/langflow.git
   

2. Navigate to the project directory:

   cd langflow
   

3. Install the package in development mode:

   pip install -e ".[dev]"
   

4. Run the application:

   langflow run
   

Configuration Options

Langflow provides several configuration options that can be set via command line arguments or environment variables:

• --host: Host to bind the server (default: 127.0.0.1)
• --port: Port to listen on (default: 7860)
• --workers: Number of worker processes (default: 1)
• --timeout: Worker timeout in seconds (default: 60)
• --env-file: Path to .env file for environment variables
• --log-level: Logging level (default: critical)
• --components-path: Path to custom components directory

Example with custom configuration:

langflow run --host 0.0.0.0 --port 8080 --log-level info

Note: For detailed installation instructions specific to your operating system and environment, please refer to the official documentation.

Practical Exercise: Building a Simple RAG System with Langflow

Let’s create a simple Retrieval-Augmented Generation (RAG) system using Langflow’s visual interface.

Step 1: Launch Langflow

After installation, start Langflow and access the web interface by navigating to http://localhost:7860 (or your configured port) in your browser.

Step 2: Create a New Flow

1. Click on “Create New Flow” to start a new project
2. Name your flow “Simple RAG System”

Step 3: Build the RAG Pipeline

Now let’s build a simple RAG pipeline using the drag-and-drop interface:

1. Add Document Loader:

   • Drag a “TextLoader” component from the sidebar to the canvas
   • Configure it with a sample text file path or URL

2. Add Text Splitter:

   • Drag a “RecursiveCharacterTextSplitter” component to the canvas
   • Configure with:
     • Chunk size: 1000
     • Chunk overlap: 100
   • Connect the output of TextLoader to the input of RecursiveCharacterTextSplitter

3. Add Embeddings Model:

   • Drag an “OpenAIEmbeddings” or any preferred embedding model to the canvas
   • Configure with your API key if necessary

4. Add Vector Store:

   • Drag a “Chroma” component to the canvas
   • Connect the output of RecursiveCharacterTextSplitter and OpenAIEmbeddings to the inputs of Chroma

5. Add Retriever:

   • Use the Chroma’s retriever output or add a standalone “Retriever” component
   • Configure the number of documents to retrieve (e.g., k=4)

6. Add LLM:

   • Drag an “OpenAI” or another LLM component to the canvas
   • Configure with your API key and model (e.g., gpt-3.5-turbo)

7. Add Chain:

   • Drag a “RetrievalQA” component to the canvas
   • Connect the Retriever and LLM to the corresponding inputs
   • Configure chain type (typically “stuff” for simple RAG)

8. Add Input/Output Components:

   • Drag “ChatInput” and “ChatOutput” components to the canvas
   • Connect ChatInput to the RetrievalQA input
   • Connect RetrievalQA output to ChatOutput

Step 4: Test Your Flow

1. Click “Run” or “Build” in the interface to activate your flow
2. Use the chat interface to ask questions related to your documents
3. Observe how the system retrieves relevant context and generates responses

Step 5: Exploring Advanced Features

Once you’re comfortable with the basics, try exploring more advanced features:

• Add memory to your chains to enable conversational context
• Implement advanced retrieval techniques like hybrid search
• Create custom agents with tools and feedback loops
• Build multi-stage workflows with branching logic
• Export your flow as an API endpoint
• Use custom components or build your own

Resources

Official Documentation

The official documentation provides comprehensive guides, tutorials, and API references:

Langflow Documentation

GitHub Repository

The GitHub repository contains the source code, issues, and contribution guidelines:

Langflow GitHub Repository

Example Flows

Find and share example flows to jumpstart your projects:

Langflow Examples Repository

Community Support

Connect with other Langflow users and get help:

• GitHub Issues
• Discord Community

Additional Resources

• DataStax Langflow Product Page - Cloud-hosted version with Astra DB integration
• Langflow Releases - Stay updated with the latest features