Langflow is an open-source, Python-based low-code/no-code platform for building AI applications, primarily used to rapidly develop AI agents and retrieval-augmented generation (RAG) apps through a visual interface.
Langflow aims to lower the development barrier; its visual drag-and-drop editor lets you build workflows by connecting components, reducing the need to write low-level code. Some technical understanding helps you use it more flexibly.
Langflow can integrate multiple mainstream LLMs (e.g., OpenAI, Anthropic, Groq, Ollama), without being tied to a single provider.
Install the Python package via pip, download the desktop app, or run Langflow in a Docker container. The official site provides detailed installation guides and quick-start templates.
Built workflows can be published as API endpoints with one click, or exported as standalone Python applications, deployable locally or in the cloud.
Langflow is an open-source project; its core framework is free to use. It also offers cloud services platforms (such as DataStax Langflow Cloud), with free and paid options.
Langflow is often viewed as the graphical interface for LangChain, simplifying the process of building complex AI apps based on the LangChain library and providing a more intuitive visual development experience.
Primarily for developers, researchers, technical teams who want to quickly build and prototype AI apps, as well as users interested in AI app development who want to reduce coding complexity.

LangChain is an open-source framework and ecosystem for AI agents, designed to help developers build, observe, evaluate, and deploy reliable AI agents. It provides a core framework, orchestration tools, a development and monitoring platform, and low-code tooling to support the full lifecycle of AI app development, optimization, and production deployment.
Dify AI is an open-source intelligent agent workflow-building platform that enables you to rapidly create and deploy AI applications for real-world business scenarios by visually composing LLMs, tools, and data sources with low-code, drag-and-drop workflows. It lowers the barrier to AI app development, supporting the full lifecycle from prototype to production deployment.