Which is more popular, LangGraph or n8n?

n8n has more GitHub stars (206,500 vs 42,605).

Which is more actively developed, LangGraph or n8n?

n8n had more commits in the last 90 days (3,663 vs 128).

Compare their capabilities, limitations and "best for" notes above. Trying each on a small task is the fastest way to decide.

Side-by-side comparison of two AI agent tools

n8n is growing faster: +3,991 GitHub stars in the last 30 days vs +2,370 for LangGraph.
Pick LangGraph for: build resilient language agents as graphs. Pick n8n for: fair-code workflow automation platform with native AI capabilities.

From GitHub data refreshed daily.

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.

+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

-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

•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

Which is more popular, LangGraph or n8n?: n8n has more GitHub stars (206,500 vs 42,605).
Which is more actively developed, LangGraph or n8n?: n8n had more commits in the last 90 days (3,663 vs 128).
Should I use LangGraph or n8n?: Compare their capabilities, limitations and "best for" notes above. Trying each on a small task is the fastest way to decide.