n8n vs open-webui
Side-by-side comparison of two AI agent tools
n8nfree
Fair-code workflow automation platform with native AI capabilities. Combine visual building with custom code, self-host or cloud, 400+ integrations.
open-webuifree
User-friendly AI Interface (Supports Ollama, OpenAI API, ...)
Metrics
| n8n | open-webui | |
|---|---|---|
| Stars | 181.4k | 129.0k |
| Star velocity /mo | 15.1k | 10.7k |
| Commits (90d) | — | — |
| Releases (6m) | 10 | 10 |
| Overall score | 0.8257313925210539 | 0.8200826912208787 |
Pros
- +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
- +Multi-provider AI integration supporting both local Ollama models and remote OpenAI-compatible APIs in a single interface
- +Self-hosted deployment with complete offline capability ensuring data privacy and security control
- +Enterprise-grade user management with granular permissions, user groups, and admin controls for organizational deployment
Cons
- -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
- -Requires technical expertise for initial setup and maintenance of Docker/Kubernetes infrastructure
- -Self-hosting demands dedicated server resources and ongoing system administration
- -Limited to local deployment model, lacking the convenience of managed cloud AI services
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
- •Enterprise organizations deploying private AI assistants with strict data governance and user access controls
- •Development teams building local AI workflows with multiple model providers while maintaining code and data privacy
- •Educational institutions providing students and faculty with controlled AI access without external data sharing