Flowise is a no-code/low-code platform for building AI agents and agentic workflows without writing code. It enables developers, data scientists, and product teams to visually construct complex AI systems—like chatbots, RAG pipelines, and multi-agent systems—using modular nodes that connect LLMs, tools, and data sources. Built with React and Node.js, it supports both local deployment and cloud hosting, integrating with LangChain, OpenAI, Hugging Face, and vector databases.
Flowise runs on TypeScript and provides a full API, SDKs for Python and TypeScript, and embeddable widgets. It supports deployment on AWS, Azure, GCP, Docker, Railway, Hugging Face Spaces, and on-premises infrastructure, making it suitable for prototyping and production-grade AI applications.
What You Get
- Agentflow Visual Editor - Drag-and-drop interface to build AI workflows using nodes for LLMs, tools, memory, and data loaders with real-time preview and execution.
- Multi-Agent Systems - Orchestrate distributed agents with workflow routing, task delegation, and coordination between multiple AI agents in a single visual flow.
- RAG Pipeline Builder - Visually connect document loaders (PDF, TXT, CSV, HTML) to vector stores (FAISS, Pinecone, Chroma) and LLMs for retrieval-augmented generation.
- Human-in-the-Loop (HITL) - Insert manual review steps into AI workflows to validate outputs before final delivery, enabling compliance and quality control.
- Execution Traces & Observability - View full execution logs, metrics, and traces with native support for Prometheus and OpenTelemetry for monitoring AI workflows.
- API & SDK Integration - Expose flows as REST APIs, use Python or TypeScript SDKs to integrate AI agents into existing applications, or embed chatbots via widgets.
Common Use Cases
- Building internal AI copilots - A tech team uses Flowise to create a RAG-powered assistant that answers employee questions from internal docs using vector embeddings and LLMs.
- Deploying customer-facing chatbots - A SaaS company builds a multilingual support bot connected to knowledge bases and CRM data, deployed via Docker on AWS.
- Creating multi-agent research systems - A research lab designs a workflow where one agent gathers data, another analyzes it, and a third writes summaries—orchestrated visually in Flowise.
- Prototyping AI features for products - A product manager rapidly builds a document summarizer with PDF upload and LLM summarization, then embeds it into their web app using the Flowise widget.
Under The Hood
Architecture
- Monorepo structure with pnpm and Turbo enables clean isolation of server, UI, and components, supporting independent build and test pipelines
- Server layer follows clean separation of concerns with Express, TypeORM, and modular service registration, avoiding tight framework coupling
- React frontend uses component-based design with context-based state management and explicit prop interfaces for reusable UI primitives
- Dependency injection is handled through dynamic plugin-like loading of nodes and agents, promoting extensibility without heavy abstraction
Tech Stack
- Node.js 20 backend with Express and TypeScript, orchestrated via pnpm and Turborepo for incremental builds
- React 18 frontend with MUI, CodeMirror, and React Flow, bundled with Vite and Rollup for optimized rendering
- TypeORM with SQLite for data persistence, supported by migration scripts and entity-driven modeling
- Dockerized deployment using lightweight Alpine images with pre-built Chromium for headless operations and Puppeteer
- Comprehensive tooling including ESLint, Prettier, Husky, and Turborepo for consistent linting, testing, and dependency management
Code Quality
- Extensive test coverage across unit, integration, and component layers with robust mocking of external dependencies
- Strong TypeScript typing throughout, with well-defined interfaces and type assertions ensuring type safety
- Clear error handling via interceptors and environment-aware validation, particularly for paths and API configurations
- Consistent naming and modular design separate infrastructure, core logic, and UI concerns for maintainability
- Disciplined linting and testing practices ensure predictable behavior and edge case resilience
What Makes It Unique
- Dynamic tool discovery via MCP servers enables real-time API integration without hardcoded endpoints
- Credential-aware, runtime-loaded node options eliminate static configuration, allowing adaptive model and tool selection
- Unified canvas architecture synchronizes UI state with flow logic, creating a seamless low-code experience
- Embedded RBAC middleware provides granular permission control at the API level for secure multi-tenant use
- Native LangChain integration through visual nodes abstracts complexity while preserving full technical depth
- Portable flow definitions via JSON and blob storage enable version-controlled, shareable workflows without proprietary formats