Which is more popular, LangChain or n8n?

n8n has more GitHub stars (206,548 vs 147,399).

Which is more actively developed, LangChain or n8n?

n8n had more commits in the last 90 days (3,694 vs 542).

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

LangChain is growing faster: +23,097 GitHub stars in the last 30 days vs +3,978 for n8n.
Pick LangChain for: the agent engineering platform. Pick n8n for: fair-code workflow automation platform with native AI capabilities.

From GitHub data refreshed daily.

LangChainopen-source

The agent engineering platform

n8nfree

Fair-code workflow automation platform with native AI capabilities. Combine visual building with custom code, self-host or cloud, 400+ integrations.

+Extensive ecosystem with seamless integration between LangGraph, LangSmith, and hundreds of third-party components
+Future-proof architecture that adapts to evolving LLM technologies without requiring application rewrites
+Strong community support with 131k+ GitHub stars and comprehensive documentation for both Python and JavaScript

+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

-Significant learning curve due to the framework's extensive feature set and multiple abstraction layers
-Potential over-engineering for simple use cases that might be better served by direct API calls
-Heavy dependency on the LangChain ecosystem which can create vendor lock-in concerns

-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

•Building complex multi-agent systems that require planning, tool use, and coordination between different AI components
•Creating production LLM applications with observability, debugging, and deployment infrastructure via LangSmith
•Developing chatbots and conversational AI with memory, context management, and integration with external data sources

•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, LangChain or n8n?: n8n has more GitHub stars (206,548 vs 147,399).
Which is more actively developed, LangChain or n8n?: n8n had more commits in the last 90 days (3,694 vs 542).
Should I use LangChain or n8n?: Compare their capabilities, limitations and "best for" notes above. Trying each on a small task is the fastest way to decide.