langgraph vs n8n
Side-by-side comparison of two AI agent tools
langgraphopen-source
Build resilient language agents as graphs.
n8nfree
Fair-code workflow automation platform with native AI capabilities. Combine visual building with custom code, self-host or cloud, 400+ integrations.
Metrics
| langgraph | n8n | |
|---|---|---|
| Stars | 27.7k | 181.4k |
| Star velocity /mo | 2.3k | 15.1k |
| Commits (90d) | — | — |
| Releases (6m) | 10 | 10 |
| Overall score | 0.7586411782605156 | 0.8257313925210539 |
Pros
- +Durable execution ensures agents automatically resume from exactly where they left off after failures or interruptions
- +Comprehensive memory system with both short-term working memory for ongoing reasoning and long-term persistent memory across sessions
- +Seamless human-in-the-loop capabilities allow for inspection and modification of agent state at any point during execution
- +Hybrid approach combining visual workflow building with full JavaScript/Python coding capabilities when needed
- +AI-native platform with LangChain integration for building sophisticated AI agent workflows using custom data and models
- +Fair-code license ensures source code transparency with self-hosting options, providing data control and deployment flexibility
Cons
- -Low-level framework requires more technical expertise and setup compared to high-level agent builders
- -Graph-based agent design paradigm may have a steeper learning curve for developers new to agent orchestration
- -Production deployment complexity may be overkill for simple chatbot or single-turn use cases
- -Requires technical knowledge to fully leverage coding capabilities and advanced features
- -Self-hosting demands infrastructure management and maintenance overhead
- -Fair-code license restricts commercial usage at scale without enterprise licensing
Use Cases
- •Long-running autonomous agents that need to persist through system failures and operate over days or weeks
- •Complex multi-step workflows requiring human oversight, approval, or intervention at specific decision points
- •Stateful agents that must maintain context and memory across multiple sessions and interactions
- •Building AI agent workflows that process customer data using LangChain and custom language models
- •Automating complex business processes that require both API integrations and custom business logic
- •Creating data synchronization pipelines between multiple SaaS tools while maintaining full control over sensitive data through self-hosting