Flowise is an open-source, low-code platform that enables developers and AI practitioners to visually design, test, and deploy AI agents and workflows without writing code. Built with React and Node.js, it integrates with LangChain and supports major LLM providers like OpenAI, allowing users to create complex agentic workflows—such as RAG pipelines, multi-step reasoning chains, and chatbots—through an intuitive node-based interface. Flowise is designed for technical users who need rapid prototyping of AI applications without the overhead of manual API integration or backend development, making it ideal for researchers, product teams, and AI engineers exploring agentic systems.
It supports both local deployment via Docker or Node.js, as well as cloud hosting options like AWS, Azure, Hugging Face Spaces, and Railway. With its modular architecture separating UI, server, and component libraries, Flowise also enables advanced users to extend functionality by building custom nodes or integrating third-party tools.
What You Get
- Visual workflow builder - Drag-and-drop interface to connect LLM nodes, memory systems, tools, and data sources without writing code; supports complex chains like RAG, multi-agent collaboration, and conditional logic.
- LangChain integration - Native support for LangChain components including chains, agents, memory, and tools; compatible with OpenAI, Hugging Face, Anthropic, and other LLM providers.
- Multi-deployment options - Deploy locally via npm or Docker, or to cloud platforms like AWS, Azure, GCP, DigitalOcean, Hugging Face Spaces, Railway, and Render with pre-configured templates.
- Self-hosted control - Full ownership of data and models; configure environment variables in .env files for API keys, port settings, and security headers.
- Extensible node system - Modular architecture with separate packages for server, UI, and components; developers can create custom nodes to integrate proprietary tools or APIs.
- Real-time preview and testing - Test workflows interactively in the browser with live input/output visualization to debug prompts, tool calls, and agent behavior before deployment.
Common Use Cases
- Building a RAG-powered knowledge assistant - Connect a vector store (e.g., Chroma, Pinecone) to an LLM node and add document loaders to create a question-answering bot that retrieves context from internal documents.
- Creating a multi-agent customer support system - Design workflows where one agent analyzes incoming tickets, another retrieves product docs, and a third generates responses—all orchestrated visually with conditional branching.
- Problem: Manual LLM prompt engineering → Solution: Flowise - Instead of hardcoding prompts in Python scripts, users visually chain prompts with memory and tools, iterate via UI, and export the workflow as a reusable API endpoint.
- DevOps teams managing AI pipelines - Teams deploy Flowise on Kubernetes or cloud platforms to provide non-technical stakeholders with a UI for testing and iterating AI workflows without touching code.
Under The Hood
Flowise is a TypeScript-based, monorepo-driven AI workflow platform designed to enable developers to build and deploy AI agents with minimal code. It emphasizes modularity, extensibility, and seamless integration with various AI services through a component-based architecture.
Architecture
Flowise follows a layered and modular approach, supporting scalable development through its monorepo structure.
- The monorepo enables clear separation between core logic, UI components, and enterprise features
- Credential management and configuration-driven design support flexible API integrations
- Component-based architecture promotes reuse and extensibility in AI agent workflows
- Strong separation of concerns allows for maintainable and scalable system evolution
Tech Stack
Built with modern web technologies, Flowise leverages TypeScript and Node.js for robust backend functionality.
- Developed using TypeScript, React, and Express to ensure type safety and responsive UI
- Integrates extensively with LangChain and @langchain packages for LLM capabilities
- Utilizes pnpm, Turbo, ESLint, and Prettier to support efficient builds and code consistency
- Employs Jest for unit testing and Swagger for API documentation generation
Code Quality
Flowise maintains a moderate code quality standard with structured testing and consistent practices.
- Comprehensive test coverage includes unit and integration tests across key modules
- Error handling is consistently applied with validation logic and try/catch blocks
- Code linting and formatting tools are configured to enforce style and maintainability
- Adherence to coding conventions supports long-term code health and readability
What Makes It Unique
Flowise differentiates itself through its low-code approach and extensive extensibility in AI agent development.
- Offers a node-based UI that allows rapid composition of complex AI workflows without heavy coding
- Provides extensive credential support for integrating with a wide variety of AI and data services
- Modular component design enables easy customization and extension by developers
- Combines configuration-driven architecture with reusable tools to simplify AI agent creation