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LangGraph vs n8n
Side-by-side comparison of LangGraph and n8n — features, pricing, performance scores, and which to choose for your AI agents.
View All Framework ComparisonsQuick Verdict
| Dimension | LangGraph | n8n | Winner |
|---|---|---|---|
Ease of Use | 3/5 | 5/5 | n8n |
Scalability | 5/5 | 4/5 | LangGraph |
Documentation | 4/5 | 5/5 | n8n |
Community | 4/5 | 5/5 | n8n |
Performance | 5/5 | 4/5 | LangGraph |
Overall: LangGraph wins 2 categories, n8n wins 3, 0 tied
Feature Comparison
| Feature | LangGraph | n8n |
|---|---|---|
| Primary Language | Python | TypeScript |
| License | MIT | Fair-code (Sustainable Use License + Enterprise) |
| Pricing | Open Source + Commercial | Freemium |
| GitHub Stars | 19,900 | 49,500 |
| Difficulty | Advanced | Beginner |
| Enterprise Ready | ||
| Community Size | Large | Very Large |
| Category | Multi-Agent Systems | Workflow Orchestration |
Pros & Cons
LangGraph
Advantages
Full control over agent behavior with low-level primitives
Excellent for complex non-linear workflows
Built-in state persistence and memory management
Production-proven by major companies (Klarna Uber LinkedIn)
Strong streaming and observability features
Human-in-the-loop support is first-class
Can be used standalone or with LangChain
MIT licensed with commercial platform option
Limitations
Steeper learning curve than LangChain
Requires understanding of graph theory concepts
May be overkill for simple linear workflows
Smaller community than LangChain (but growing)
Some advanced features require LangGraph Platform
Documentation still maturing compared to LangChain
More complex setup for basic use cases
n8n
Advantages
Fair-code license allows self-hosting with source access
400+ pre-built integrations reduce development time
Visual interface lowers barrier to entry
Strong community with active development
Native AI capabilities built-in
Can write custom code when needed
Enterprise-ready with commercial support
Self-host or cloud options available
Limitations
Fair-code license has restrictions for commercial offerings
Visual interface can be limiting for very complex logic
Learning curve for advanced workflow patterns
Cloud pricing can escalate with usage
Some integrations require manual configuration
Less suitable for pure code-first development
Best Use Cases
LangGraph
Complex customer support workflows with escalation
Multi-agent research and analysis systems
Task management and orchestration
Long-running business process automation
Interactive assistants with memory
Decision support systems with conditional logic
n8n
Business process automation
API integration and data synchronization
Marketing automation workflows
Customer data management
E-commerce automation
Internal tool integration
Getting Started
LangGraph
Installation
pip install langgraph
n8n
Installation
npm install n8n -g
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