n8n vs LangGraph: Comprehensive Comparison Guide (2025)

Introduction

As AI and large language models (LLMs) become increasingly central to business operations, the tools for orchestrating and automating LLM workflows have grown in importance. Among these tools, n8n and LangGraph have emerged as popular options, each with distinct approaches to workflow automation.

This comprehensive comparison will help you understand the key differences between n8n and LangGraph, enabling you to make an informed decision about which tool better suits your specific LLM orchestration needs.

Key Differences at a Glance

The table above highlights some of the fundamental differences between n8n and LangGraph. While n8n offers a general-purpose workflow automation platform with visual editing capabilities, LangGraph is specifically designed for orchestrating complex LLM workflows using Python and graph-based state machines.

Pricing Comparison

n8n Pricing

• Self-hosted (open-source): Free, with limitations on commercial use
• n8n Cloud: Starts at $20/month for 10,000 executions
• Enterprise: Custom pricing

LangGraph Pricing

• Open-source: Free to use, Apache 2.0 license
• LangChain Plus: Managed service with various pricing tiers
• Enterprise: Custom pricing for enterprise support and features

From a pricing perspective, both tools offer open-source options that are free to use. n8n's cloud offering provides a managed service with predictable pricing based on execution volume. LangGraph, as part of the LangChain ecosystem, can be used freely as a Python package, with additional costs only if you opt for the managed LangChain Plus service.

Feature Comparison

n8n Features

• Visual Workflow Editor: Flowchart-style editor for creating workflows
• 200+ Integrations: Pre-built nodes for popular services and APIs
• Code Nodes: JavaScript functions for custom logic
• Error Handling: Sophisticated error workflows and retry mechanisms
• Webhooks: Create and manage webhooks easily
• LLM Support: Nodes for OpenAI, Anthropic, and other AI services

LangGraph Features

• Graph-based State Machines: Powerful framework for complex LLM workflows
• Native LangChain Integration: Seamless use with LangChain components
• Cyclic Graphs: Support for loops and recursive reasoning
• Human-in-the-loop: Built-in support for human intervention
• Persistence: State persistence for long-running workflows
• Debugging Tools: Specialized tools for LLM workflow debugging

n8n excels as a general-purpose automation platform with a visual interface and broad integration capabilities. LangGraph, on the other hand, is purpose-built for LLM orchestration, offering specialized features for complex AI workflows that may involve loops, recursion, and state management.

Ease of Use

n8n User Experience

n8n offers a visual, no-code/low-code approach to workflow automation. Its node-based editor allows users to create workflows by connecting nodes representing different services and actions. This visual approach makes it accessible to users with varying levels of technical expertise, though complex workflows may still require JavaScript knowledge for custom logic.

LangGraph User Experience

LangGraph is primarily a Python library that requires coding knowledge to use effectively. It provides a programmatic approach to defining state machines for LLM workflows, which offers great flexibility but comes with a steeper learning curve. Users need to be comfortable with Python programming and understand concepts like state machines and graph theory.

For teams with varying technical expertise, n8n generally offers a more accessible entry point with its visual interface. LangGraph is better suited for developers and data scientists who are comfortable with Python and need specialized tools for complex LLM orchestration.

Integration Capabilities

n8n offers over 200 pre-built integrations with popular services and APIs, making it easy to connect with a wide range of business tools and platforms. Its HTTP Request nodes also allow for custom integrations with any API. For LLM-specific integrations, n8n provides nodes for services like OpenAI, Anthropic, and Hugging Face.

LangGraph, being part of the LangChain ecosystem, is designed to work seamlessly with LangChain components and integrations. This includes support for various LLM providers, vector stores, document loaders, and other AI-related services. While its focus is narrower than n8n's, its integrations are deeply optimized for AI and LLM workflows.

For organizations that need to integrate LLMs with a wide variety of business systems, n8n's broader integration capabilities may be advantageous. For teams focused specifically on building sophisticated LLM applications, LangGraph's specialized integrations with the LangChain ecosystem provide more depth and optimization for AI workflows.

Best Use Cases

When to Choose n8n

• For general business process automation that includes some LLM components
• When you need a visual interface for creating and managing workflows
• For teams with varying levels of technical expertise
• When integrating LLMs with a wide variety of business systems
• For organizations that prefer a no-code/low-code approach to automation

When to Choose LangGraph

• For complex LLM workflows requiring state management and cyclic execution
• When building sophisticated AI agents with reasoning capabilities
• For teams with strong Python development skills
• When deep integration with the LangChain ecosystem is important
• For research and development of advanced LLM applications

Conclusion

Both n8n and LangGraph are powerful tools for workflow automation, but they serve different needs and audiences. Your choice between them should be guided by your specific requirements, technical capabilities, and the nature of your LLM workflows.

n8n offers a more general-purpose, visual approach to workflow automation that can incorporate LLMs alongside many other business systems. Its strength lies in its accessibility, broad integration capabilities, and balance between simplicity and power.

LangGraph provides a specialized, Python-based framework for orchestrating complex LLM workflows. Its strength lies in its depth of LLM-specific features, support for sophisticated reasoning patterns, and tight integration with the LangChain ecosystem.

For many organizations, the ideal approach may involve using both tools for their respective strengths—n8n for general business automation and user-friendly workflows, and LangGraph for specialized, developer-driven LLM applications that require advanced orchestration capabilities.