n8n vs Dify vs LangFlow

Overview

n8n, Dify, and LangFlow are three popular visual builders for AI workflows, but they serve different purposes and have different strengths. This comparison helps you choose the right tool for your needs.

Aspect	n8n	Dify	LangFlow
Primary Focus	Workflow automation	LLMOps platform	LangChain visual builder
AI Capabilities	AI nodes + 1000+ integrations	Built-in RAG, agents, models	LangChain components
Learning Curve	Medium	Low-Medium	Medium-High
Self-hostable	Yes (fair-code)	Yes (open-source)	Yes (open-source)
Best For	Automation + AI	Production AI apps	LangChain prototyping

Detailed Comparison

n8n

What it is: A workflow automation platform with AI agent capabilities, supporting 1000+ integrations.

Strengths:

• Massive integration library (1000+ apps and services)
• Mature workflow automation features (triggers, error handling, scheduling)
• Native LangChain integration for AI nodes
• Self-hostable with full data control
• Excellent for combining AI with traditional automation

Weaknesses:

• Fair-code license (non-commercial use free, commercial requires paid plan)
• AI features added later, less mature than dedicated LLMOps platforms
• Can be resource-intensive for large workflows
• Steeper learning curve for advanced features

Best use cases:

• Automating business processes with AI
• Connecting AI agents to existing tools (Slack, Gmail, Notion, etc.)
• Building AI-powered notification and alert systems
• Hybrid workflows (AI + traditional automation)

Dify

What it is: An open-source LLMOps platform with visual workflow builder, RAG pipeline, and agent orchestration.

Strengths:

• Complete LLMOps platform out of the box
• Excellent visual workflow editor
• Built-in RAG pipeline with document processing
• Self-hostable with full data control
• Strong RAG capabilities
• Active community and rapid development
• 100+ model provider support

Weaknesses:

• Heavier infrastructure requirements (needs database, vector store, etc.)
• Less flexible for custom agent logic
• Learning curve for advanced features
• Primarily focused on enterprise use cases
• Some features still evolving

Best use cases:

• Building production AI applications
• RAG-powered chatbots and assistants
• Teams needing complete LLMOps infrastructure
• Projects requiring model management and monitoring
• Enterprise AI deployments

LangFlow

What it is: A drag-and-drop UI builder for LangChain, allowing visual construction of LLM flows and chains.

Strengths:

• Intuitive drag-and-drop visual builder
• 100+ pre-built nodes for LangChain components
• Real-time flow visualization
• Easy API deployment
• Great for prototyping and demos
• Direct LangChain integration
• Active community

Weaknesses:

• Limited to LangChain ecosystem
• Less control for complex custom logic
• Not suitable for production-scale deployments
• Visual complexity grows with flow size
• Tied to LangChain's API changes

Best use cases:

• Prototyping LangChain applications
• Learning LangChain concepts visually
• Quick demos and proof-of-concepts
• Educational purposes
• Simple RAG and agent workflows

Decision Framework

Choose n8n if:

• You need to connect AI to many existing tools and services
• Your workflow involves both AI and traditional automation
• You're building business process automation
• You need scheduling, webhooks, and error handling
• You're okay with fair-code licensing

Choose Dify if:

• You need a complete LLMOps platform
• You're building production AI applications
• You need RAG capabilities with document processing
• You want model management and monitoring
• You're deploying for enterprise or team use

Choose LangFlow if:

• You're prototyping or learning LangChain
• You want a quick visual builder for LangChain
• You're building simple RAG or agent flows
• You need fast demos and proofs-of-concept
• You're comfortable with LangChain's ecosystem

Feature Matrix

Feature	n8n	Dify	LangFlow
Visual Builder	✅	✅	✅
1000+ Integrations	✅	⚠️	❌
RAG Pipeline	⚠️	✅	⚠️
Agent Orchestration	✅	✅	✅
Model Management	❌	✅	❌
Self-hostable	✅	✅	✅
API Deployment	✅	✅	✅
Team Collaboration	✅	✅	⚠️
Production Ready	✅	✅	⚠️
Learning Resources	✅	✅	✅
Open Source	⚠️ (fair-code)	✅	✅

Pricing

Tool	Free Tier	Paid Plans	Self-hosted
n8n	Free for personal use	$20-200/month	Free (fair-code)
Dify	Cloud free tier	$0-99/month	Free (Apache 2.0)
LangFlow	Free	N/A	Free (MIT)

Conclusion

All three tools are excellent for building AI workflows visually, but they excel in different areas:

• n8n is the automation-first choice with AI capabilities
• Dify is the LLMOps-first choice with complete infrastructure
• LangFlow is the LangChain-first choice for prototyping

For most production AI applications, Dify offers the best balance of features and ease of use. For automation-heavy workflows, n8n is unmatched. For quick LangChain prototyping, LangFlow is ideal.

Resources

• n8n Documentation
• Dify Documentation
• LangFlow Documentation
• n8n GitHub
• Dify GitHub
• LangFlow GitHub