enuno/langflow

Langflow - Visual AI Workflow Platform

Overview

Langflow is an open-source, Python-based platform for building and deploying AI-powered agents and workflows through a visual drag-and-drop interface. With 142,000+ GitHub stars and used by 1,500+ projects, Langflow enables rapid prototyping of AI applications without requiring extensive coding knowledge.

Key Features

🎨 Visual Development Environment

• Drag-and-Drop Canvas: Build complex AI workflows visually by connecting component nodes
• Interactive Playground: Test and debug flows in real-time without full stack development
• Component Library: 200+ pre-built components for LLMs, data sources, agents, tools, and MCP servers
• Custom Components: Full Python customization for specialized use cases

🤖 Agent & MCP Integration

• AI Agents: LLM-powered agents with autonomous tool selection and execution
• MCP Client & Server: Built-in Model Context Protocol support for tool integration
• Multi-Agent Coordination: Orchestrate multiple agents working together
• Tool Ecosystem: Calculator, Web Search, URL fetcher, file operations, and custom tools

🚀 Deployment Options

• Local Development: Desktop app for macOS/Windows or Python package installation
• Docker Containers: Portable, reproducible deployments
• Kubernetes: Production-grade orchestration with high availability
• Cloud Platforms: Native support for Google Cloud, Hugging Face Spaces, and more
• API Export: RESTful API endpoints for external integrations

🔧 Production Features

• Observability: Integrated LangSmith and LangFuse monitoring
• Authentication: API key management with role-based access control
• Security: HTTPS support, CORS configuration, reverse proxy compatibility
• Versioning: Component version control and flow history
• JSON Export: Portable flow definitions for backup and sharing

Architecture

Component-Based Design

Flow (Workflow)
├── Components (Building Blocks)
│   ├── Inputs/Outputs (Ports)
│   ├── Parameters (Configuration)
│   └── Python Code (Logic)
├── Edges (Connections)
└── Canvas (Visual Editor)

Component Categories

1. Core Components: Generic functionality (loops, parsing, multi-provider integrations)
2. Bundles: Service-specific components grouped by provider (OpenAI, Anthropic, etc.)
3. Legacy: Deprecated components (hidden by default)

MCP Integration Architecture

• MCP Client: Connect to external MCP servers for tool access
• MCP Server: Expose Langflow flows as tools for other applications
• Connection Modes: JSON config, STDIO (local), HTTP/SSE (remote)

Installation

Prerequisites

• Python: 3.10-3.13 (macOS/Linux) or 3.10-3.12 (Windows)
• Package Manager: uv (recommended)
• Hardware Minimum: Dual-core CPU, 2GB RAM
• Hardware Recommended: Multi-core CPU, 4GB+ RAM

Quick Start (Python Package)

# Create virtual environment
uv venv langflow-env

# Activate environment
# macOS/Linux:
source langflow-env/bin/activate
# Windows:
langflow-env\Scripts\activate

# Install Langflow
uv pip install langflow

# Launch
uv run langflow run

# Access at http://127.0.0.1:7860

Docker Installation

docker run -p 7860:7860 langflowai/langflow:latest

Desktop Application

Download from https://www.langflow.org/desktop

Note: Desktop version lacks Shareable Playground and Voice Mode features.

Use Cases

1. Building an AI Agent Flow

Chat Input → Agent (with Tools) → Chat Output
             ↓
         [Calculator, Web Search, URL Fetcher]

Steps:

1. Add Agent component and configure LLM provider (OpenAI, Anthropic, etc.)
2. Connect Chat Input/Output components
3. Attach tools by enabling "Tool Mode" on components
4. Add system instructions for specialized behavior
5. Test in Playground with real queries

2. Model Context Protocol Integration

Scenario: Agent with external MCP server tools

1. Navigate to Settings → MCP Servers
2. Add MCP server connection:
   • STDIO Mode: Local server (command + args)
   • HTTP/SSE Mode: Remote server (URL)
   • JSON Config: Direct configuration object
3. Add MCP Tools component to flow
4. Connect to Agent component
5. Agent automatically discovers and uses available tools

Example: Using mcp-server-fetch to summarize tech news

3. Production Deployment (Kubernetes)

# High availability deployment
apiVersion: apps/v1
kind: Deployment
metadata:
  name: langflow
spec:
  replicas: 3
  template:
    spec:
      containers:
      - name: langflow
        image: langflowai/langflow:latest
        env:
        - name: LANGFLOW_AUTO_LOGIN
          value: "false"
        - name: LANGFLOW_SECRET_KEY
          valueFrom:
            secretKeyRef:
              name: langflow-secrets
              key: secret-key

4. API Authentication Setup

# Generate API key via CLI
uv run langflow api-key

# Generate secure secret key
python3 -c "from secrets import token_urlsafe; print(f'LANGFLOW_SECRET_KEY={token_urlsafe(32)}')"

# Use in requests
curl -X POST "https://your-instance/api/v1/run" \
  -H "x-api-key: $LANGFLOW_API_KEY" \
  -H "Content-Type: application/json" \
  -d '{"flow_id": "abc123", "inputs": {...}}'

5. Custom Component Development

Create Python component:

from langflow.custom import Component
from langflow.io import MessageTextInput, Output
from langflow.schema import Message

class CustomGreetingComponent(Component):
    display_name = "Custom Greeting"
    description = "Greets users with custom message"

    inputs = [
        MessageTextInput(
            name="user_name",
            display_name="User Name",
            info="Name to greet"
        )
    ]

    outputs = [
        Output(display_name="Greeting", name="output", method="greet")
    ]

    def greet(self) -> Message:
        name = self.user_name
        return Message(text=f"Hello, {name}! Welcome to Langflow.")

6. Multi-Agent Workflow

Scenario: Research agent + summarization agent

1. Agent 1 (Researcher): Web Search tool + URL fetcher
2. Agent 2 (Summarizer): Receives research results, generates summary
3. Flow Control: Parse output from Agent 1 → Feed to Agent 2
4. Chat Output: Display final summarized research

Security Best Practices

Authentication Configuration

# Disable auto-login for multi-user environments
export LANGFLOW_AUTO_LOGIN=False

# Set custom secret key (required for production)
export LANGFLOW_SECRET_KEY=$(python3 -c "from secrets import token_urlsafe; print(token_urlsafe(32))")

# Configure CORS (specific origins only)
export LANGFLOW_CORS_ORIGINS="https://yourdomain.com,https://app.yourdomain.com"

Deployment Security

• Never expose port 7860 directly - use reverse proxy (Nginx, Caddy)
• Enable HTTPS with Let's Encrypt or custom certificates
• Restrict CORS origins - avoid wildcards in production
• Secure API keys - rotate regularly, use environment variables
• Role-based access - API keys inherit creator's privileges

Advanced Features

Flow Embedding

HTML Embedding:

<iframe src="https://your-langflow-instance/embed/flow-id"
        width="100%" height="600px"></iframe>

React/Angular: Use API endpoints to integrate flows into applications

Component Freezing

Lock component outputs during development:

1. Right-click component → Freeze
2. Component preserves last output without re-execution
3. Speeds up iterative testing of other components

Flow Versioning

• Component Versioning: Copied components maintain original version
• Flow Export: JSON-based portability across Langflow instances
• History Tracking: Monitor changes and rollback if needed

Observability Integration

LangSmith:

export LANGCHAIN_TRACING_V2=true
export LANGCHAIN_API_KEY=your_api_key

LangFuse:

export LANGFUSE_PUBLIC_KEY=your_public_key
export LANGFUSE_SECRET_KEY=your_secret_key

Common Workflows

1. RAG (Retrieval-Augmented Generation)

Document Loader → Text Splitter → Embeddings → Vector Store
                                                      ↓
User Query → Retriever → Context + Query → LLM → Response

2. Agent with Memory

Chat Input → Agent (with Chat Memory) → Tools → LLM → Chat Output
             ↑                                         ↓
             └──────────── Session Storage ────────────┘

3. Sequential Processing

Input → Component A → Parse → Component B → Format → Output

OpenRAG - Production RAG Platform

OpenRAG is a comprehensive, production-ready Retrieval-Augmented Generation platform built on top of Langflow, providing a complete RAG solution with document processing, semantic search, and conversational AI capabilities.

What is OpenRAG?

OpenRAG combines three powerful technologies into a single, cohesive platform:

• Langflow: Workflow orchestration and agent coordination
• Docling: Advanced document processing and extraction
• OpenSearch: Semantic search and vector database

Repository: https://github.com/langflow-ai/openrag (23 stars) Documentation: https://docs.openr.ag/ Homepage: https://www.openr.ag Latest Version: v0.2.0 (December 2025) License: Apache License 2.0

Architecture Stack

Frontend (Next.js + TypeScript)
         ↓
Backend (Starlette + Python)
         ↓
┌─────────────────────────────────┐
│  Langflow (Workflow Engine)     │
│  Docling (Document Processing)  │
│  OpenSearch (Vector Search)     │
└─────────────────────────────────┘

Language Distribution:

• Python: 64.4% (backend, workflows)
• TypeScript: 33.6% (frontend)
• Other: 1.0% (Makefile, CSS, Shell, Dockerfile)

Key Features

Document Management:

• Upload and process multiple document formats (PDF, TXT, DOCX, Markdown)
• Docling-powered extraction for complex layouts and tables
• OneDrive and SharePoint integration (v0.2.0+)
• Configurable ingestion timeout limits

Intelligent Search:

• Semantic search with OpenSearch vector database
• Context-aware document retrieval
• Full-text search combined with vector similarity

Conversational AI:

• Chat interface with document-grounded responses
• LLM integration (OpenAI, Anthropic, Ollama, IBM Granite, etc.)
• Tool calling support for agent workflows
• Session history and context management

Enterprise Features:

• Langfuse integration for observability and monitoring
• Configurable authentication and security
• API and SDK access for programmatic integration
• Terminal User Interface (TUI) for management

Installation Options

Quick Start (No Installation):

# Run directly with uvx (no local installation)
uvx openrag

# Run specific version
uvx --from openrag==0.2.0 openrag

Python Package Installation:

# Create project with uv
uv init my-rag-app
cd my-rag-app

# Add OpenRAG
uv add openrag

# Launch TUI
uv run openrag

Docker/Podman Deployment:

# OpenRAG automatically manages containers
# See: https://docs.openr.ag/docker

Production Deployment

Managed Containers (Recommended):

• OpenRAG automatically starts and manages required containers
• Built-in health checks and service discovery
• Simplified configuration through TUI

Self-Managed Containers:

• Full control over container orchestration
• Custom Docker Compose configurations
• Kubernetes deployment options
• See: https://docs.openr.ag/docker

Use Cases

1. Enterprise Document Q&A

• Upload company documents, policies, and knowledge bases
• Enable employees to query documents conversationally
• Maintain document versioning and access control

2. Research Assistant

• Process academic papers and research documents
• Extract insights and answer research questions
• Track sources and citations automatically

3. Customer Support Knowledge Base

• Ingest support documentation and FAQs
• Provide AI-powered answers to customer queries
• Reduce support ticket volume with self-service

4. Legal Document Analysis

• Process contracts, agreements, and legal documents
• Extract key clauses and obligations
• Answer questions about document content

OpenRAG vs Direct Langflow

| Feature | Langflow | OpenRAG | |---------|----------|---------| | Purpose | Workflow builder | Complete RAG platform | | Setup Complexity | Manual workflow creation | Pre-configured RAG stack | | Document Processing | Manual component setup | Built-in Docling integration | | Vector Storage | Requires separate setup | Integrated OpenSearch | | Frontend UI | Build your own | Production Next.js app | | Use Case | Custom AI workflows | Document Q&A systems | | Best For | Flexible AI development | Production RAG deployments |

When to Use OpenRAG:

• ✅ Need complete RAG system out-of-the-box
• ✅ Focus on document Q&A use cases
• ✅ Want integrated frontend + backend
• ✅ Require enterprise features (Langfuse, OneDrive)
• ✅ Prefer managed infrastructure

When to Use Langflow Directly:

• ✅ Building custom AI workflows beyond RAG
• ✅ Need maximum flexibility in component choice
• ✅ Integrating with existing systems
• ✅ Prototyping new AI application patterns
• ✅ Custom UI requirements

Recent Updates (v0.2.0)

December 2025 Release:

• OneDrive and SharePoint integration with redirect handling
• SDK improvements for chat endpoints
• Enhanced troubleshooting documentation
• Langfuse observability integration
• Image pruning options in TUI
• Better password validation for OpenSearch
• Centralized storage location (~/.openrag)

Getting Started with OpenRAG

# 1. Quick start (runs in TUI)
uvx openrag

# 2. Follow TUI prompts to:
#    - Configure OpenSearch
#    - Set up LLM provider (OpenAI, Anthropic, etc.)
#    - Start services

# 3. Access web interface
# Default: http://localhost:3000

# 4. Upload documents and start chatting

OpenRAG Configuration

Storage Location: ~/.openrag/

• Configuration files
• Document uploads
• Vector indexes
• Session data

Environment Variables:

# LLM Provider
OPENAI_API_KEY=your_key
ANTHROPIC_API_KEY=your_key

# OpenSearch
OPENSEARCH_PASSWORD=secure_password

# Langfuse (Optional)
LANGFUSE_PUBLIC_KEY=your_key
LANGFUSE_SECRET_KEY=your_key

Troubleshooting OpenRAG

Common Issues:

1. Container Startup Failures

   • Check Docker/Podman is running
   • Verify port availability (9200, 3000, 7860)
   • Review logs: openrag logs

2. Document Ingestion Errors

   • Check file format support
   • Verify Docling service is running
   • Increase ingestion timeout if needed

3. Search Not Working

   • Verify OpenSearch is healthy
   • Check vector embeddings are generated
   • Review OpenSearch password configuration

Get Help:

• Troubleshooting Guide: https://docs.openr.ag/support/troubleshoot
• GitHub Issues: https://github.com/langflow-ai/openrag/issues
• Documentation: https://docs.openr.ag/

Troubleshooting

Port Already in Use

# Change default port
uv run langflow run --port 8080

Missing Dependencies

# Reinstall with all extras
uv pip install langflow[all]

Docker Container Issues

# View logs
docker logs langflow-container

# Restart with fresh state
docker rm langflow-container
docker run -p 7860:7860 -v langflow-data:/app/data langflowai/langflow:latest

MCP Server Connection Failures

• Verify server command and arguments in STDIO mode
• Check network connectivity for HTTP/SSE mode
• Review environment variables in .env file
• Enable debug logging for detailed error messages

Community & Resources

Official Links

• Documentation: https://docs.langflow.org/
• GitHub: https://github.com/langflow-ai/langflow (142k stars)
• Discord: https://discord.gg/EqksyE2EX9
• Twitter: https://twitter.com/langflow_ai

OpenRAG Resources

• Homepage: https://www.openr.ag
• Documentation: https://docs.openr.ag/
• GitHub: https://github.com/langflow-ai/openrag (23 stars)
• Quickstart: https://docs.openr.ag/quickstart
• Troubleshooting: https://docs.openr.ag/support/troubleshoot

Key Statistics

• Contributors: 331 developers (Langflow)
• Used by: 1,500+ projects (Langflow)
• Latest Release:
  • Langflow v1.7.1 (December 2025)
  • OpenRAG v0.2.0 (December 2025)
• License:
  • Langflow: MIT
  • OpenRAG: Apache License 2.0

Learning Resources

• Quickstart Tutorial: https://docs.langflow.org/get-started-quickstart
• Component Reference: https://docs.langflow.org/concepts-components
• Deployment Guides: https://docs.langflow.org/deployment-overview
• API Documentation: https://docs.langflow.org/api-reference-api-examples
• RAG with Langflow: https://docs.langflow.org/chat-with-rag
• OpenRAG Installation: https://docs.openr.ag/install

When to Use This Skill

Use the Langflow skill when:

• ✅ Building AI agent workflows with visual interface
• ✅ Prototyping LLM applications rapidly without extensive coding
• ✅ Integrating Model Context Protocol (MCP) servers and tools
• ✅ Deploying production AI agents with observability and security
• ✅ Creating multi-agent coordination systems
• ✅ Developing RAG (Retrieval-Augmented Generation) applications
• ✅ Exposing AI workflows as API endpoints
• ✅ Deploying production-ready RAG platforms with OpenRAG
• ✅ Building document Q&A systems with integrated search and chat
• ✅ Processing documents with Docling and OpenSearch integration
• ✅ Building custom components for specialized AI tasks
• ✅ Setting up local or cloud-based AI development environments
• ✅ Testing and debugging complex LLM workflows interactively

Related Technologies

• LangChain: Python framework for LLM applications (Langflow is built on LangChain)
• OpenRAG: Production RAG platform built on Langflow (https://github.com/langflow-ai/openrag)
• Docling: Document processing and extraction (integrated in OpenRAG)
• OpenSearch: Vector database and semantic search (integrated in OpenRAG)
• Model Context Protocol (MCP): Tool integration standard (native support)
• OpenAI API: LLM provider (integrated)
• Anthropic Claude: LLM provider (integrated)
• LangSmith: Observability platform (integrated)
• LangFuse: Open-source observability (integrated in OpenRAG)
• Docker: Containerization (deployment option)
• Kubernetes: Orchestration (production deployment)

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Skill Type: AI Workflow Development Platform Complexity Level: Beginner to Advanced Maintenance Status: ✅ Active (v1.7.1, December 2025) Community Health: ✅ Excellent (142k stars, 331 contributors, 1500+ projects)