What Sets Them Apart
Visual AI builders have emerged as essential tools for prototyping and deploying LLM applications. LangFlow, Flowise, and Dify each provide drag-and-drop interfaces for composing AI workflows, but differ significantly in scope, ecosystem integration, and target audience.
Convex, Supabase, and Firebase at a Glance
LangFlow is the official visual builder for LangChain with 146K+ GitHub stars. It provides the most direct access to LangChain's component library — LLMs, prompt templates, vector stores, agents, chains, and tools are all available as draggable nodes. Flows export as executable LangChain code, making it ideal for prototyping before transitioning to code-based development. Acquired by DataStax, with both open-source and managed cloud options. Best for teams already invested in the LangChain ecosystem.
Flowise is a lightweight, self-hostable drag-and-drop builder for creating LLM flows and chatbots. It takes a more focused approach than LangFlow, prioritizing simplicity and ease of use over component breadth. Flowise excels at quickly building chatbots, RAG applications, and simple agent workflows. It supports LangChain and LlamaIndex components and provides an API for embedding created flows into applications. Entirely open-source with no commercial tier — deploy via Docker and it just works.
Dify goes beyond flow building to provide a complete LLMOps platform. It combines visual orchestration with model management, prompt engineering, RAG pipeline configuration, dataset management, and application monitoring. Dify offers pre-built application templates, a built-in annotation system for output quality tracking, and a comprehensive API for integration. It provides the most complete out-of-box experience for teams wanting to go from idea to production AI app in one platform.
Real-time, Database, and Functions
LangFlow for LangChain-native development with the broadest component library. Flowise for the simplest, most lightweight approach to building LLM chatbots and flows. Dify for teams wanting a complete LLMOps platform that handles the full lifecycle from development to monitoring.