flowiseai/flowise

Build AI Agents, Visually

51,305 stars TypeScript 8 components

Visual AI agent and chatflow builder with drag-and-drop interface

User creates visual flows in UI, which generates JSON definitions sent to backend API, where components library executes the flows using AI services and storage providers

Under the hood, the system uses 2 feedback loops, 3 data pools, 3 control points to manage its runtime behavior.

Structural Verdict

A 8-component fullstack with 0 connections. 1633 files analyzed. Minimal connections — components operate mostly in isolation.

How Data Flows Through the System

User creates visual flows in UI, which generates JSON definitions sent to backend API, where components library executes the flows using AI services and storage providers

Flow Creation — User drags and drops AI components in the visual canvas to build chatflows
Flow Definition — Canvas generates JSON flow definition with node connections and configurations
API Processing — Backend receives flow definition through REST endpoints and validates structure
Component Execution — AI components execute using configured models, tools, and storage providers
Response Streaming — Results stream back to UI through server-sent events for real-time updates

System Behavior

How the system actually operates at runtime — where data accumulates, what loops, what waits, and what controls what.

Data Pools

Flow Definitions (database)
JSON configurations of user-created AI agent flows with node connections

Assistant Instances (database)
Created AI assistants with their configurations and workspace associations

Component Registry (in-memory)
Available AI components, storage providers, and their interfaces

Feedback Loops

Token Refresh (retry, balancing) — Trigger: 401 unauthorized response. Action: Call refresh endpoint and retry original request. Exit: Valid token obtained or refresh fails.
Flow Validation (recursive, balancing) — Trigger: Invalid flow configuration. Action: Check node connections and component validity. Exit: All nodes properly connected.

Delays & Async Processing

AI Model Processing (async-processing, ~variable) — User waits for AI responses while streaming provides progressive feedback
Build Pipeline (batch-window, ~turbo cached) — Development builds cached across packages for faster iteration

Control Points

Theme Mode (runtime-toggle) — Controls: Light/dark mode appearance across entire UI. Default: customization.isDarkMode
API Base URL (env-var) — Controls: Backend server endpoint for all API calls. Default: baseURL
Quota Limits (threshold) — Controls: Maximum number of flows/assistants per organization

Package Structure

This monorepo contains 5 packages:

flowise-ui (app)
React-based visual flow editor with drag-and-drop canvas for building AI agents and chatflows.

flowise (app)
Node.js/Express backend API server handling chatflows, assistants, API keys, and authentication.

flowise-components (library)
Reusable AI component library with nodes for chat models, vector stores, tools, and storage providers.

agentflow (library)
Visual canvas component library for building node-based flow interfaces with drag-and-drop functionality.

flowise-api (tooling)
Auto-generated Swagger UI documentation server for the Flowise API endpoints.

Technology Stack

React (framework)
Frontend framework for visual flow editor

Express (framework)
Backend API server handling requests

Material-UI (library)
Component library for consistent UI design

TypeScript (library)
Type safety across frontend and backend

Turbo (build)
Monorepo build orchestration and caching

Swagger (library)
API documentation generation

PNPM (build)
Package management with workspace support

Axios (library)
HTTP client with interceptors for API calls

Key Components

assistantsService (service) — Manages AI assistant creation, deletion, and retrieval with workspace and organization scoping packages/server/src/controllers/assistants/index.ts
agentflowv2Service (service) — Generates AI agent flows from natural language questions and chat models packages/server/src/controllers/agentflowv2-generator/index.ts
StorageProviderFactory (service) — Factory for creating storage providers (Local, S3, GCS, Azure Blob) packages/components/src/storage/index.ts
NodeInputHandler (component) — Handles dynamic input rendering for different node types in the visual flow editor packages/agentflow/src/atoms/index.ts
Moderation (class) — Abstract base class for content moderation with streaming response capabilities packages/components/nodes/moderation/Moderation.ts
apiClient (utility) — Axios client with token refresh interceptors for API communication packages/ui/src/api/client.js
theme (config) — Material-UI theme configuration with light/dark mode support packages/ui/src/themes/index.js
swaggerDocs (config) — Auto-generates Swagger documentation from Express routes and YAML definitions packages/api-documentation/src/configs/swagger.config.ts

Configuration

artillery-load-test.yml (yaml)

config.target (string, unknown) — default: http://128.128.128.128:3000
config.phases (array, unknown) — default: [object Object],[object Object],[object Object]
scenarios (array, unknown) — default: [object Object]

pnpm-workspace.yaml (yaml)

packages (array, unknown) — default: packages/*

turbo.json (json)

$schema (string, unknown) — default: https://turbo.build/schema.json
pipeline.build.dependsOn (array, unknown) — default: ^build
pipeline.build.outputs (array, unknown) — default: dist/**
pipeline.dev.cache (boolean, unknown) — default: false

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Frequently Asked Questions

What is Flowise used for?

Visual AI agent and chatflow builder with drag-and-drop interface flowiseai/flowise is a 8-component fullstack written in TypeScript. Minimal connections — components operate mostly in isolation. The codebase contains 1633 files.

How is Flowise architected?

Flowise is organized into 4 architecture layers: Frontend Apps, Backend API, Component Libraries, Storage & Integration. Minimal connections — components operate mostly in isolation. This layered structure keeps concerns separated and modules independent.

How does data flow through Flowise?

Data moves through 5 stages: Flow Creation → Flow Definition → API Processing → Component Execution → Response Streaming. User creates visual flows in UI, which generates JSON definitions sent to backend API, where components library executes the flows using AI services and storage providers This pipeline design reflects a complex multi-stage processing system.

What technologies does Flowise use?

The core stack includes React (Frontend framework for visual flow editor), Express (Backend API server handling requests), Material-UI (Component library for consistent UI design), TypeScript (Type safety across frontend and backend), Turbo (Monorepo build orchestration and caching), Swagger (API documentation generation), and 2 more. A focused set of dependencies that keeps the build manageable.

What system dynamics does Flowise have?

Flowise exhibits 3 data pools (Flow Definitions, Assistant Instances), 2 feedback loops, 3 control points, 2 delays. The feedback loops handle retry and recursive. These runtime behaviors shape how the system responds to load, failures, and configuration changes.

What design patterns does Flowise use?

5 design patterns detected: Monorepo with Turbo, Component Factory Pattern, Controller-Service Architecture, Abstract Base Classes, Barrel Exports.

Analyzed on March 31, 2026 by CodeSea. Written by Karolina Sarna.