Langflow is a powerful tool for building and deploying AI-powered agents and workflows.

React Flow | Svelte Flow - Powerful open source libraries for building node-based UIs with React (https://reactflow.dev) or Svelte (https://svelteflow.dev). Ready out-of-the-box and infinitely customizable.

Database diagrams editor that allows you to visualize and design your DB with a single query.

Automatically generates beautiful and easy-to-read ER diagrams from your database.

🦋Butterfly，A JavaScript/React/Vue2 Diagramming library which concentrate on flow layout field. (基于JavaScript/React/Vue2的流程图组件)

🎯 React library for building workflow editors, flow charts and diagrams. Maintained by @goodcodeus.

Official React components built for Flowbite and Tailwind CSS

API and CLI for generating a markdown TOC (table of contents) for a README or any markdown files. Uses Remarkable to parse markdown. Used by NASA/openmct, Prisma, Joi, Mocha, Sass, Prettier, Orbit DB, FormatJS, Raneto, hapijs/code, webpack-flow, docusaurus, release-it, ts-loader, json-server, reactfire, bunyan, husky, react-easy-state, react-snap, chakra-ui, carbon, alfresco, repolinter, Assemble, Verb, and thousands of other projects.

:key: A login/register flow built with React&Redux

Sample project showing possible authentication flow using React, Redux, React-Router, and JWT

🌊 A flexible, stateless, declarative flow chart library for react.

✨ React component library for building declarative multi-step flows.

Flock is a workflow-based low-code platform for rapidly building chatbots, RAG, and coordinating multi-agent teams, powered by LangGraph, Langchain, FastAPI, and NextJS.（Flock 是一个基于workflow工作流的低代码平台，用于快速构建聊天机器人、RAG、Agent和Muti-Agent应用，采用 LangGraph、Langchain、FastAPI 和 NextJS 构建。）

Visualise your Prisma schema!

Kuwala is the no-code data platform for BI analysts and engineers enabling you to build powerful analytics workflows. We are set out to bring state-of-the-art data engineering tools you love, such as Airbyte, dbt, or Great Expectations together in one intuitive interface built with React Flow. In addition we provide third-party data into data science models and products with a focus on geospatial data. Currently, the following data connectors are available worldwide: a) High-resolution demographics data b) Point of Interests from Open Street Map c) Google Popular Times

🚀 Type Based Architecture for developing React Native Apps using react, redux, sagas and hooks with auth flow

ES2017, flow, React JSX and GraphQL grammar and transpilation for ATOM

E-commerce App UI. React native, Expo managed flow, React navigation v5, Notification.

Simple onboarding flow for React Native 📱

A babel plugin to generate React PropTypes definitions from Flow type declarations.

✨ A zero config JavaScript linter with support for Typescript, Flow, and React.

🪢 A React based Flow Framework, include Flow View and Flow Editor

Custom Edge for React Flow that never intersects with other nodes

Streamlit Component to quickly create Interactive Flow Diagrams using React Flow

Beautiful number animations for React Native. Digit-by-digit rolling counter, currency ticker, time display, and odometer with View-based and Skia renderers. Full Intl.NumberFormat support.

Batteries included React Component for rendering, creating and editing Diagrams

A lightweight and customizable stepper component for React Native, built on top of @gorhom/bottom-sheet. Easily manage multi-step flows in a modal bottom sheet with smooth animations and full control.

React flow Examples with Workflow automations and others examples in one repo.

The aim of this work is to recognize the six emotions (happiness, sadness, disgust, surprise, fear and anger) based on human facial expressions extracted from videos. To achieve this, we are considering people of different ethnicity, age and gender where each one of them reacts very different when they express their emotions. We collected a data set of 149 videos that included short videos from both, females and males, expressing each of the the emotions described before. The data set was built by students and each of them recorded a video expressing all the emotions with no directions or instructions at all. Some videos included more body parts than others. In other cases, videos have objects in the background an even different light setups. We wanted this to be as general as possible with no restrictions at all, so it could be a very good indicator of our main goal. The code detect_faces.py just detects faces from the video and we saved this video in the dimension 240x320. Using this algorithm creates shaky videos. Thus we then stabilized all videos. This can be done via a code or online free stabilizers are also available. After which we used the stabilized videos and ran it through code emotion_classification_videos_faces.py. in the code we developed a method to extract features based on histogram of dense optical flows (HOF) and we used a support vector machine (SVM) classifier to tackle the recognition problem. For each video at each frame we extracted optical flows. Optical flows measure the motion relative to an observer between two frames at each point of them. Therefore, at each point in the image you will have two values that describes the vector representing the motion between the two frames: the magnitude and the angle. In our case, since videos have a resolution of 240x320, each frame will have a feature descriptor of dimensions 240x320x2. So, the final video descriptor will have a dimension of #framesx240x320x2. In order to make a video comparable to other inputs (because inputs of different length will not be comparable with each other), we need to somehow find a way to summarize the video into a single descriptor. We achieve this by calculating a histogram of the optical flows. This is, separate the extracted flows into categories and count the number of flows for each category. In more details, we split the scene into a grid of s by s bins (10 in this case) in order to record the location of each feature, and then categorized the direction of the flow as one of the 8 different motion directions considered in this problem. After this, we count for each direction the number of flows occurring in each direction bin. Finally, we end up with an s by s by 8 bins descriptor per each frame. Now, the summarizing step for each video could be the average of the histograms in each grid (average pooling method) or we could just pick the maximum value of the histograms by grid throughout all the frames on a video (max pooling For the classification process, we used support vector machine (SVM) with a non linear kernel classifier, discussed in class, to recognize the new facial expressions. We also considered a Naïve Bayes classifier, but it is widely known that svm outperforms the last method in the computer vision field. A confusion matrix can be made to plot results better.

Extremely simple boilerplate, easiest you can find, for React application including all the necessary tools: Flow | React 16 | redux | babel 6 | webpack 3 | css-modules | jest | enzyme | express + optional: sass/scss