LangFlow

beginner

Visual framework for building LangChain applications.

Overview

LangFlow is an innovative open-source visual framework that revolutionizes how developers build LangChain applications through a drag-and-drop interface. Created as a low-code solution for the complex world of Large Language Models (LLMs), LangFlow transforms the traditional code-heavy approach to AI application development into an intuitive visual workflow builder. The platform emerged from the need to make LangChain's powerful capabilities accessible to a broader audience, including non-technical users who want to experiment with AI without diving deep into Python programming. This Docker deployment creates a complete LangFlow environment with persistent data storage and web-based access on port 7860. LangFlow operates as a standalone web application that provides a visual canvas where users can drag, drop, and connect various LangChain components like prompt templates, LLM models, memory systems, and data loaders to create sophisticated AI workflows. The containerized setup includes SQLite database storage for saving flows and configurations, making it perfect for development, prototyping, and small-scale production deployments. This configuration is ideal for AI developers, data scientists, product managers, and technical teams who want to rapidly prototype LangChain applications, experiment with different LLM workflows, or create AI solutions without extensive coding. Educational institutions teaching AI concepts, startups building AI-powered products, and enterprise teams exploring conversational AI use cases will find this visual approach invaluable for accelerating their development process while maintaining the full power of the LangChain ecosystem.

Key Features

Visual drag-and-drop interface for building complex LangChain workflows without coding
Pre-built components for popular LLM providers including OpenAI, Anthropic, Hugging Face, and local models
Real-time flow execution with live debugging and intermediate step visualization
One-click export of visual flows to production-ready Python code
Built-in REST API generation for deploying flows as web services
Custom component creation through Python code nodes for extending functionality
Template library with pre-built flows for common AI use cases like chatbots and document analysis
SQLite database integration for persistent storage of flows, configurations, and execution history

Common Use Cases

1Rapid prototyping of conversational AI chatbots with memory and context management
2Building document question-answering systems with vector databases and retrieval chains
3Creating content generation pipelines that combine multiple LLM calls and transformations
4Developing AI-powered data analysis workflows that process and summarize large datasets
5Educational environments for teaching LangChain concepts and LLM application patterns
6Enterprise proof-of-concept development for AI initiatives without heavy coding requirements
7API service creation for integrating AI capabilities into existing applications and websites

Prerequisites

Docker and Docker Compose installed with at least 4GB available memory
Port 7860 available and not conflicting with other services
API keys for LLM providers (OpenAI, Anthropic, etc.) if using cloud-based models
Basic understanding of LangChain concepts like chains, prompts, and agents
Web browser with JavaScript enabled for accessing the visual interface
At least 2GB free disk space for container images and flow storage

For development & testing. Review security settings, change default credentials, and test thoroughly before production use. See Terms

docker-compose.yml

1services: 
2  langflow: 
3    image: langflowai/langflow:latest
4    container_name: langflow
5    restart: unless-stopped
6    environment: 
7      LANGFLOW_DATABASE_URL: sqlite:///./langflow.db
8    volumes: 
9      - langflow_data:/app/langflow
10    ports: 
11      - "7860:7860"
12
13volumes: 
14  langflow_data:

.env Template

.env

1# Configure API keys via UI

Usage Notes

1Docs: https://docs.langflow.org/
2Access at http://localhost:7860 - visual flow editor
3Drag-and-drop LangChain components to build AI apps
4Export flows as Python code or deploy as API
5Store flows in SQLite (default) or PostgreSQL
6Supports custom components via Python code nodes

Quick Start

terminal

1# 1. Create the compose file
2cat > docker-compose.yml << 'EOF'
3services:
4  langflow:
5    image: langflowai/langflow:latest
6    container_name: langflow
7    restart: unless-stopped
8    environment:
9      LANGFLOW_DATABASE_URL: sqlite:///./langflow.db
10    volumes:
11      - langflow_data:/app/langflow
12    ports:
13      - "7860:7860"
14
15volumes:
16  langflow_data:
17EOF
18
19# 2. Create the .env file
20cat > .env << 'EOF'
21# Configure API keys via UI
22EOF
23
24# 3. Start the services
25docker compose up -d
26
27# 4. View logs
28docker compose logs -f

One-Liner

Run this command to download and set up the recipe in one step:

terminal

1curl -fsSL https://docker.recipes/api/recipes/langflow/run | bash

Troubleshooting

Interface shows 'Failed to load components': Restart the container and wait 60 seconds for initialization to complete
LLM components return API errors: Verify API keys are correctly set in component configuration and have sufficient credits
Flow execution hangs indefinitely: Check for circular dependencies in your flow connections and ensure all required inputs are provided
Custom Python components fail to import: Verify Python package dependencies are available in the container environment
SQLite database corruption after container restart: Ensure proper Docker volume mounting and avoid force-stopping the container during database writes
Memory errors during large document processing: Increase Docker container memory limits and consider chunking large inputs

Community Notes

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