Which is more popular, Langfuse or n8n?

n8n has more GitHub stars (206,425 vs 35,266).

Which is more actively developed, Langfuse or n8n?

n8n had more commits in the last 90 days (3,611 vs 2,011).

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: +4,001 GitHub stars in the last 30 days vs +1,816 for Langfuse.
Pick Langfuse for: open-source LLM engineering platform for observability, evaluation, prompt and dataset management. Pick n8n for: fair-code workflow automation platform with native AI capabilities.

From GitHub data refreshed daily.

Langfuseopen-source

Open-source LLM engineering platform for observability, evaluation, prompt and dataset management

n8nfree

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

+Open source with MIT license allowing full customization and transparency, plus active community support
+Comprehensive feature set combining observability, prompt management, evaluations, and datasets in one platform
+Extensive integrations with major LLM frameworks and tools including OpenTelemetry, LangChain, and OpenAI SDK

+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

-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

•Production LLM application monitoring to track performance, costs, and identify issues in real-time
•Prompt engineering and management for teams collaborating on optimizing model prompts and tracking versions
•LLM evaluation and testing to measure model performance across different datasets and use cases

•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, Langfuse or n8n?: n8n has more GitHub stars (206,425 vs 35,266).
Which is more actively developed, Langfuse or n8n?: n8n had more commits in the last 90 days (3,611 vs 2,011).
Should I use Langfuse or n8n?: Compare their capabilities, limitations and "best for" notes above. Trying each on a small task is the fastest way to decide.