Open Source JavaScript Artificial Intelligence Software - Page 3
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JavaScript Artificial Intelligence Software
View 13511 business solutionsBrowse free open source JavaScript Artificial Intelligence Software and projects below. Use the toggles on the left to filter open source JavaScript Artificial Intelligence Software by OS, license, language, programming language, and project status.
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XIAOJUSURVEY
Powerful survey system for creating, managing, and analyzing forms
Xiaoju Survey is an open source survey platform developed to support the creation, distribution, and analysis of questionnaires at scale. It provides a comprehensive system for designing surveys with various question types, allowing users to build flexible and structured forms suited for different data collection needs. Xiaoju Survey includes tools for managing survey workflows, enabling teams to organize responses and monitor participation efficiently. It also focuses on data analysis capabilities, helping users extract insights from collected responses through built-in reporting and visualization features. Xiaoju Survey is designed with extensibility in mind, allowing developers to customize or integrate it into existing systems. Its architecture supports high availability and scalability, making it suitable for enterprise-level deployments. Overall, Xiaoju Survey aims to streamline the entire lifecycle of survey management from creation to actionable insights. -
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ALLWEONE
AI tool that generates custom presentations with real-time editing
Presentation AI by ALLWEONE is an open source tool that uses artificial intelligence to generate complete slide decks from a simple prompt. It helps users create professional presentations quickly, with support for customizable themes, layouts, and styles. You can define slide count, language, and tone, then review or edit the AI-generated outline before finalising. Slides are built in real time, allowing you to watch content develop as the system works. Presentation AI by ALLWEONE includes image generation, rich text editing, and drag-and-drop functionality for easy adjustments. It also supports presentation mode, so you can present directly within the app. Built with modern technologies like Next.js, React, and Tailwind CSS, it integrates AI services such as OpenAI for content generation. It is fully open source under the MIT licence, making it suitable for developers who want to customise or extend its capabilities. -
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Agili Hacker Podcast
AI tool that turns Hacker News posts into daily podcast updates
Hacker Podcast is an AI-powered project that turns top Hacker News stories into a Chinese podcast. It automatically fetches trending posts each day, processes the content with AI, and generates concise summaries before converting them into audio. This creates a hands-free way to stay updated on tech, startups, and developer discussions without reading long threads. Hacker Podcast combines content aggregation, natural language processing, and text-to-speech to deliver clear and digestible updates. Users can listen through web interfaces or podcast platforms, while also accessing written summaries for deeper reading. Built with modern web technologies, the project focuses on automation, speed, and accessibility. It supports continuous updates, allowing listeners to receive fresh insights daily. As an open-source tool, it also encourages community contributions and customization for developers who want to adapt or extend its workflow for similar AI-driven content pipelines. -
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AnyClaw
Android app running two AI coding agents with a built-in Linux runtime
openclaw-android-assistant, also known as AnyClaw, is an Android application that packages two AI coding agents into a single mobile app environment. It bundles the OpenClaw personal AI assistant together with the OpenAI Codex CLI so developers can interact with AI agents directly from an Android device. Both agents run inside a self-contained Linux userland that is embedded within the APK, allowing command execution, coding tasks, and agent interactions without requiring root access or external tools. openclaw-android-assistant provides a control dashboard interface where users can manage agents, sessions, skills, and conversations from a unified interface. Through the embedded runtime, the agents can read codebases, write code, and execute shell commands within the packaged Linux environment. It also supports parallel conversations with separate working contexts, enabling multi-threaded development workflows on a mobile device. -
Gemini 3 and 200+ AI Models on One PlatformBuild generative AI apps with Vertex AI. Switch between models without switching platforms.
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Astron Agent
Enterprise platform for building and orchestrating AI agent workflows
Astron Agent is an enterprise-grade platform designed for building and managing intelligent AI agent workflows in production environments. It provides a development environment that combines workflow orchestration, model management, and integration with various AI tools and services. Astron Agent enables organizations to design complex agent-driven processes that coordinate models, automation tools, and enterprise systems. It also integrates robotic process automation capabilities so agents can execute tasks across digital systems instead of only generating responses. Astron Agent supports scalable and high-availability deployments, allowing teams to run reliable AI agent infrastructure in distributed environments. It includes collaboration features that allow teams to develop, manage, and operate AI applications together. With its extensible architecture and enterprise-focused design, it aims to help organizations build production-ready intelligent agent solutions. -
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Chatwoot
Open-source customer engagement suite, an alternative to Intercom
If you have questions, are confused, or just want to understand our product better, we've got your back. Customer engagement suite, an open-source alternative to Intercom, Zendesk, Salesforce Service Cloud etc. Chatwoot is an open-source, self-hosted customer engagement suite. Chatwoot lets you view and manage your customer data, communicate with them irrespective of which medium they use, and re-engage them based on their profile. Talk to your customers using our live chat widget and make use of our SDK to identify a user and provide contextual support. Connect your Facebook pages and start replying to the direct messages to your page. Connect your Instagram profile and start replying to the direct messages. Connect your Twitter profiles and reply to direct messages or the tweets where you are mentioned. Connect your Telegram bot and reply to your customers right from a single dashboard. -
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Codex plugin for Claude Code
Use Codex from Claude Code to review code or delegate tasks
Codex plugin for Claude Code is an integration layer that connects OpenAI Codex-style capabilities with agent-based coding environments, enabling seamless execution of coding tasks through structured plugins. The project is designed to extend the functionality of coding agents by allowing them to delegate tasks to Codex or similar models in a controlled and modular way. It likely provides abstractions for handling code generation, editing, and analysis while maintaining consistency across workflows. The system emphasizes interoperability, allowing developers to plug Codex capabilities into broader agent ecosystems without rewriting core logic. It may also include mechanisms for managing execution context, permissions, and tool access, ensuring that generated code can be safely applied. This makes it particularly useful for complex development pipelines where multiple agents or tools need to collaborate. -
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Get Shit Done
A light-weight and powerful meta-prompting, context engineering
Get Shit Done is a high-impact, open-source meta-prompting and spec-driven development system designed to streamline building software with AI assistants like Claude Code, OpenCode, and Gemini CLI. It solves “context rot” — the degradation of AI quality as a chat session grows — by structuring your idea into precise, context-engineered steps that are researched, scoped, planned, executed, and verified with clear commands and outputs instead of ad-hoc prompts. The project emphasizes simplicity and effectiveness over bureaucratic workflows like story points, sprint ceremonies, or enterprise processes, making it especially useful for solo developers or small teams aiming to get reliable execution without overhead. GSD breaks down big goals into atomic plans, keeps AI context fresh, and automates task execution while generating standardized documentation, roadmaps, and commit histories. -
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Markdownify MCP Server
Convert files and web content into clean, usable Markdown easily
Markdownify MCP is a Model Context Protocol server that converts many types of files and web content into clean Markdown. It supports formats such as PDFs, images, audio with transcription, DOCX, XLSX, and PPTX, along with web sources like YouTube transcripts, Bing results, and general webpages. Markdownify MCP is designed to simplify content extraction and make data easier to read, share, and reuse in structured workflows. Developers can install dependencies, build, and run the server locally, then extend functionality by modifying its TypeScript-based tools and server logic. It also allows retrieval of existing Markdown files, making it useful for documentation, research, and AI-assisted workflows. By standardizing content into Markdown, it helps unify inputs across different sources for better processing and integration with AI tools and developer environments. -
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Mito
AI-powered Jupyter spreadsheet that converts workflows into Python
Mito is an open source set of Jupyter extensions designed to speed up Python workflows and data analysis. It combines a spreadsheet-style interface with AI-assisted coding, allowing users to explore, clean, and transform data without switching tools. Mito includes a context-aware AI assistant that helps generate code, debug errors, and guide workflows directly inside Jupyter. Its spreadsheet layer supports familiar functions such as filters, pivot tables, and formulas, while automatically converting every action into production-ready Python code. This removes the need to manually translate spreadsheet logic into scripts. Mito also integrates with tools like Streamlit and Dash, enabling users to embed interactive spreadsheet functionality into apps with minimal setup. Built for analysts, developers, and teams, it simplifies automation, reduces repetitive tasks, and accelerates the transition from data exploration to reusable code. -
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NemoClaw
NVIDIA plugin for secure installation of OpenClaw
NVIDIA NemoClaw is an open-source tool designed to simplify the deployment and management of always-on AI assistants using the OpenClaw ecosystem. It installs and configures the NVIDIA OpenShell runtime, which provides a secure environment for running autonomous AI agents. NemoClaw enables users to launch sandboxed agent environments that control network access, file permissions, and inference requests through policy-based security. The platform integrates with AI models such as NVIDIA Nemotron and supports multiple inference backends including cloud APIs, local NIM deployments, and vLLM. Through its command-line interface, developers can deploy, monitor, and manage AI assistants running inside isolated sandboxes. By combining sandbox orchestration, agent management, and AI model integration, NemoClaw provides a secure foundation for building and operating autonomous AI assistants. -
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OpenAI Quickstart Node
Node.js example app from the OpenAI API quickstart tutorial
OpenAI Quickstart Node.js is an example application designed to help developers learn how to use the OpenAI API with Node.js. The repository provides structured sample code for a variety of API endpoints, including chat completions, assistants, embeddings, fine-tuning, moderation, batch processing, and image generation. Each folder contains runnable scripts that demonstrate both basic usage and more advanced scenarios. By following the examples, developers can quickly understand how to authenticate with an API key, send requests, and handle responses within a Node.js environment. The project is a practical starting point for building AI-powered applications, serving as a foundation for experimentation and integration into larger projects. It simplifies onboarding by offering step-by-step setup instructions and ready-to-use code snippets that can be adapted for custom needs. -
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PandaWiki
AI-powered open source platform for building intelligent wiki bases
PandaWiki is an open source knowledge base system designed to help users build intelligent documentation platforms powered by large language models. It combines traditional wiki functionality with modern AI capabilities, allowing teams and individuals to create and manage product documentation, technical manuals, FAQs, and blog-style knowledge resources. PandaWiki provides tools for managing knowledge bases through an administrative interface while also generating public-facing wiki sites where users can browse and interact with content. AI capabilities are integrated to assist with content creation, intelligent question answering, and semantic search, helping users find information more efficiently within stored documentation. PandaWiki also supports importing knowledge from different external sources such as web pages, RSS feeds, sitemaps, and offline files to quickly populate documentation collections. -
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PapersGPT
A powerful Zotero AI and MCP plugin with ChatGPT, Gemini 3.1, Claude
PapersGPT is an AI-powered plugin that integrates directly into Zotero to transform how researchers interact with academic papers and literature collections. It enables users to chat with individual PDFs or entire collections, allowing them to extract insights, generate summaries, and explore connections between documents without leaving the Zotero environment. The plugin supports a wide range of state-of-the-art language models, including GPT, Claude, Gemini, and open-source alternatives, giving users flexibility in choosing performance, cost, and privacy trade-offs. One of its most powerful features is its ability to process large volumes of academic content quickly, enabling tasks such as literature reviews, theoretical analysis, and research synthesis to be completed significantly faster. It also supports multi-document querying, allowing users to compare findings across multiple papers and generate comprehensive overviews of research topics. -
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canvas-editor
Canvas-based WYSIWYG rich text editor with advanced layout tools
canvas-editor is a browser-based rich text editor that renders content using HTML5 Canvas and SVG instead of traditional DOM-based approaches. It is designed to provide a WYSIWYG editing experience similar to word processors, enabling precise control over layout, rendering, and document structure. canvas-editor supports a wide range of formatting and document features, including text styling, tables, images, and embedded elements, all managed through a structured data model. Its architecture is modular, allowing developers to extend functionality through plugins, custom commands, and event hooks. It includes support for page-based layouts with headers, footers, pagination, and print-ready output, including PDF generation. It also provides interactive components such as form controls and context menus, making it suitable for building complex document editing systems. -
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koishi-plugin-novelai
Koishi plugin for NovelAI image generation with advanced controls
A Koishi-based plugin that enables image generation through NovelAI, designed for chat-driven workflows and flexible prompt control. It supports multiple configuration options, including model switching, sampler selection, and adjustable image sizes, giving users control over output quality and style. It includes advanced prompt syntax to refine results and allows automatic translation of Chinese keywords to improve usability across languages. A customizable banned word list helps filter unwanted content, while timed message recall can automatically delete generated outputs after a set period. Built on Koishi’s modular system, koishi-plugin-novelai can be extended with additional integrations, making it adaptable for different bot environments and use cases. -
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AI Shell
CLI tool turning natural language into executable shell commands
AI Shell is an open source command-line interface tool designed to translate natural language instructions into executable shell commands, helping users interact with their terminal more intuitively. It acts as a bridge between human intent and command-line syntax by leveraging large language models to interpret plain English input and generate accurate commands. This approach reduces the need to memorize complex command structures, making it especially useful for beginners or users working across different environments. AI Shell is inspired by similar AI-assisted terminal workflows but is made openly available for customization and extension. It integrates into a developer’s existing workflow by running directly in the terminal and responding to prompts with suggested commands. Users can configure it with an API key and begin issuing requests immediately, making setup relatively straightforward. -
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DeepCamera
Open-Source AI Camera. Empower any camera/CCTV
DeepCamera empowers your traditional surveillance cameras and CCTV/NVR with machine learning technologies. It provides open-source facial recognition-based intrusion detection, fall detection, and parking lot monitoring with the inference engine on your local device. SharpAI-hub is the cloud hosting for AI applications that helps you deploy AI applications with your CCTV camera on your edge device in minutes. SharpAI yolov7_reid is an open-source Python application that leverages AI technologies to detect intruders with traditional surveillance cameras. The source code is here It leverages Yolov7 as a person detector, FastReID for person feature extraction, Milvus the local vector database for self-supervised learning to identify unseen persons, Labelstudio to host images locally and for further usage such as label data and train your own classifier. It also integrates with Home-Assistant to empower smart homes with AI technology. -
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Easy DataSet
A powerful tool for creating datasets for LLM fine-tuning
Easy DataSet is a comprehensive open-source tool designed to make creating high-quality datasets for large language model fine-tuning, retrieval-augmented generation (RAG), and evaluation as easy and automated as possible by providing intuitive interfaces and powerful parsing, segmentation, and labeling tools. It supports ingesting domain-specific documents in a wide range of formats — including PDF, Markdown, DOCX, EPUB, and plain text — and can intelligently segment, clean, and structure content into rich datasets tailored for downstream LLM training needs. The system includes automated question-generation capabilities, hierarchical label trees, and answer generation pipelines that use LLM APIs to produce coherent paired data with customizable templates. Beyond dataset creation, Easy-dataset also provides a built-in evaluation system with model testing and blind-test features, helping teams validate model performance using curated test sets. -
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FAY
Framework for building AI-powered interactive digital humans and agent
Fay is an open source framework designed to build and deploy interactive digital humans powered by large language models. It acts as a middleware layer that connects digital character technologies with conversational AI systems and business applications. Fay supports various types of digital humans, including 2.5D and 3D avatars, and can be integrated with applications running on mobile devices, PCs, web platforms, and embedded systems. Its architecture allows developers to combine different AI components such as speech recognition, text-to-speech, and large language models to create conversational digital agents. Fay provides multiple interfaces for text, voice, and digital human control, enabling developers to build interactive assistants, virtual presenters, or automated service agents. It also supports custom knowledge bases and configurable behaviors so developers can tailor the personality and responses of the digital human. -
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GitDiagram
AI tool that converts GitHub repositories into interactive diagrams
GitDiagram is an open source web application designed to help developers quickly understand the structure and architecture of GitHub repositories by automatically generating interactive diagrams. It analyzes repository metadata such as the file tree and project documentation to build a visual representation of how different components of a project relate to one another. It uses an AI-powered pipeline to interpret repository structure and transform that information into system design diagrams rendered with Mermaid visualization. These diagrams provide a high-level overview of a codebase, making it easier for developers to explore unfamiliar projects or understand large and complex repositories. Users can interact with the generated diagrams by clicking components to navigate directly to related files or directories within the repository. GitDiagram combines a modern web frontend with a backend service that processes repository data and generates diagrams dynamically. -
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Machine Learning Systems
Introduction to Machine Learning Systems
Machine Learning Systems is an open educational repository that serves as the source and learning stack for the Machine Learning Systems textbook, a project focused on teaching how to engineer AI systems that work reliably in real-world environments. Rather than concentrating only on model training, the material emphasizes the broader discipline of AI engineering, covering efficiency, reliability, deployment, and evaluation across the full lifecycle of intelligent systems. The repository includes textbook content, supporting labs, and companion tools such as TinyTorch to help learners move from theory to hands-on experimentation. Its mission is to establish AI systems engineering as a foundational discipline alongside traditional software and computer engineering. The project is structured to guide users through reading, building, and deploying workflows, including running labs on edge devices like Arduino and Raspberry Pi. -
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Midscene
Vision-based AI framework for cross-platform UI automation tasks
Midscene.js is an open source AI-driven UI automation framework designed to control user interfaces across multiple platforms using natural language instructions. Instead of relying on traditional selectors, DOM structures, or accessibility attributes, it uses a vision-first approach where screenshots are analyzed by visual-language models to identify interface elements and perform actions. It allows developers to automate interactions on web applications, desktop software, and mobile devices without needing platform-specific automation logic. Developers can describe tasks such as clicking buttons, filling forms, or extracting information, and the system interprets these commands to interact with the interface accordingly. Midscene.js includes SDKs, scripting options, and integration capabilities that allow automation workflows to be written in JavaScript, TypeScript, or YAML-based scripts. Midscene also provides debugging and development tools. -
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NOFX
Open source AI trading OS for autonomous multi-model trading systems
NOFX is an open source AI-powered trading operating system designed to automate financial trading workflows using autonomous AI agents. It acts as an infrastructure layer that transforms market data into AI-driven trade decisions and execution. Instead of requiring users to manually configure machine learning models, data sources, and API integrations, the system allows AI components to perceive market conditions, select models, and perform trading actions automatically. It supports running multiple AI models simultaneously and allows them to compete or collaborate when making trading decisions. NOFX integrates trading infrastructure such as exchange connectivity, strategy management, and performance monitoring into a single environment. It also includes components for strategy development, backtesting, and real-time monitoring so traders and researchers can evaluate algorithmic trading approaches. -
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OpenAI Assistants Quickstart
OpenAI Assistants API quickstart with Next.js
openai-assistants-quickstart is a template for using the Assistants API in a Next.js app, demonstrating streaming, tool use, and function calling in one place. The repository includes multiple example pages that each showcase specific capabilities, while all examples share the same underlying assistant with all capabilities enabled. The primary chat logic lives in the Chat component at app/components/chat.tsx, which manages rendering, streaming, and forwarding function calls. Server handlers for threads are provided under api/assistants/threads/..., giving a reference for wiring the API into Next.js routes. The Chat component can be copied directly into other projects, along with its styles from app/components/chat.module.css. Example pages include a basic chat, a function calling demo, a file search demo, and a full-featured example, allowing developers to explore each feature in isolation or together.
