Langflow
by langflow-ai
Visual AI Workflow Builder for LLM Applications
Low-code platform for designing, testing, and deploying AI agents and LLM workflows through an intuitive drag-and-drop interface.
- 143,880+ GitHub stars
- Built with Python
- Drag-and-drop visual workflow builder with React Flow interface for intuitive AI pipeline design
- MIT License license
About This Project
Langflow transforms complex AI development into a visual experience, enabling developers to build sophisticated language model applications without writing extensive boilerplate code. Using an interactive React Flow-based canvas, you can connect components like prompts, LLMs, vector databases, and APIs to create powerful AI workflows in minutes.
The platform excels at rapid prototyping and production deployment of AI agents. Whether you're building chatbots, document processing pipelines, or multi-agent systems, Langflow provides pre-built components that integrate seamlessly with popular LLM providers like OpenAI, Anthropic, and open-source models. Each component is customizable through Python, giving you the flexibility to extend functionality when needed.
What sets Langflow apart is its balance between simplicity and power. Beginners can start with templates and visual building blocks, while advanced developers can dive into custom components and complex orchestration patterns. The platform supports real-time testing, version control for workflows, and one-click deployment options.
With a thriving community of over 143,000 stars, Langflow has become the go-to solution for teams wanting to move from AI experimentation to production quickly. It bridges the gap between no-code tools and traditional coding, making generative AI accessible without sacrificing control or capabilities.
Key Features
- Drag-and-drop visual workflow builder with React Flow interface for intuitive AI pipeline design
- Pre-built components for major LLM providers, vector databases, and popular AI frameworks
- Real-time workflow testing and debugging with interactive component inspection
- Custom Python component support for extending functionality beyond built-in blocks
- Multi-agent orchestration capabilities for building complex collaborative AI systems
How You Can Use It
Building conversational AI chatbots with custom knowledge bases and retrieval-augmented generation
Creating document analysis pipelines that extract, summarize, and classify information from PDFs and text
Developing multi-agent systems where specialized AI agents collaborate to solve complex tasks
Prototyping and testing different LLM prompts and chains before production implementation
Who Is This For?
AI/ML engineers, full-stack developers, data scientists, and product teams building LLM-powered applications who want to accelerate development without sacrificing customization