⚡ TL;DR — 30-Second Verdict
Use Dify if you need a complete AI application platform — multi-user workspaces, built-in RAG with document management, API publishing, and enterprise features. Use Flowise if you want a simpler, faster-to-set-up tool for building LangChain workflows visually without the overhead of a full platform.
Quick Comparison
| Feature | Dify | Flowise |
|---|---|---|
| GitHub Stars | 55k+ | 32k+ |
| Setup complexity | Higher — multiple services | Simple — single Node.js process |
| Multi-user support | Built-in — teams, workspaces | Limited in community version |
| RAG/knowledge base | Full knowledge base management | Via LlamaIndex nodes |
| API publishing | One-click REST API publish | Available |
| Underlying framework | Custom (not LangChain-tied) | LangChain + LlamaIndex |
| Agent builder | Visual agent canvas | LangChain agent nodes |
| Enterprise features | SSO, audit logs (paid) | Not available |
| Best for | Full AI application platform | Quick LangChain workflow builder |
What Is Dify?
Dify is a comprehensive LLM application development platform with 55k+ GitHub stars. It provides everything needed to build and operate AI applications: a visual workflow builder, knowledge base management with full document processing, multiple application types (chatbot, agent, workflow), API publishing, and usage analytics. Dify's multi-user workspace model makes it suitable for teams, and it offers enterprise features (SSO, audit logs) through its cloud and enterprise tiers. The self-hosted version requires Docker Compose with PostgreSQL, Redis, and a vector database.
Building retrieval-augmented generation pipelines becomes dramatically faster with Dify's visual workflow editor than writing orchestration code manually. Unlike LangChain's Python-first approach, Dify's 147k+ star platform eliminates coding bottlenecks for non-technical teams. Teams requiring production-grade deployment infrastructure or complex compliance auditing should look elsewhere.
— AI Tools Hub Editorial Team on Dify
What Is Flowise?
Flowise is a visual builder for LangChain and LlamaIndex workflows with 32k+ stars. You drag-and-drop nodes (LLMs, vector stores, document loaders, chains) to build AI pipelines, then expose them as API endpoints. Flowise is notably simpler to set up than Dify — it's a single Node.js process. Its direct mapping to LangChain's component model means developers familiar with LangChain can be productive immediately. Flowise is better positioned as a development/prototyping tool than a full production platform.
Build multi-step customer support agents without touching code—Flowise's visual canvas orchestrates LangChain components faster than writing YAML configs. Unlike LangSmith's monitoring focus, Flowise prioritizes workflow construction with 54k+ GitHub stars proving adoption. Skip this if you need production-grade observability or custom Python logic beyond drag-and-drop presets.
— AI Tools Hub Editorial Team on Flowise
→ Read the full Flowise review
When to Choose Each
Choose Dify if…
- You need multi-user workspaces with role-based access control
- You want built-in knowledge base management and document processing
- You're deploying AI applications to end users (not just internal tools)
- You need production monitoring, usage analytics, and API management
- Your team includes non-developers who need to build and modify AI apps
Choose Flowise if…
- You want the fastest path from idea to LangChain workflow
- You're building internal tools or prototypes, not production-facing apps
- You prefer minimal infrastructure (single process, SQLite default)
- You're already familiar with LangChain and want a visual representation
- Setup simplicity is more important than platform features
RAG and Knowledge Base Capabilities
Dify has a significant advantage in knowledge base management. It provides a full document processing pipeline: upload documents in various formats, configure chunking, choose embedding models, and manage the resulting knowledge base with a UI. You can see which document chunks were retrieved for any query, update documents, and monitor retrieval quality. Flowise handles RAG through LlamaIndex and LangChain nodes, which is flexible but requires more manual configuration and lacks Dify's built-in document management UI.