Build AI-powered applications with React, Svelte, Vue, and Solid

Open source codebase powering the HuggingChat app

Create chatbots with ease

Code review powered by LLMs (OpenAI GPT4, Sonnet 3.5) & Embeddings ⚡️ Improve code quality and catch bugs before you break production 🚀 Lives in your Github/GitLab/Azure DevOps CI

Fully customizable AI chatbot component for your website

Text Generator is a versatile plugin for Obsidian that allows you to generate text content using various AI providers, including OpenAI, Anthropic, Google and local models.

Utilities to use the Hugging Face Hub API

The TypeScript library for building AI applications.

The 𝗣𝗼𝘄𝗲𝗿𝗳𝘂𝗹 Conversational AI JavaScript Library 💬 — UI for any LLM, supporting LangChain / HuggingFace / Vercel AI, and more 🧡 React, Next.js, and plain JavaScript ⭐️

A modular and comprehensive solution to deploy a Multi-LLM and Multi-RAG powered chatbot (Amazon Bedrock, Anthropic, HuggingFace, OpenAI, Meta, AI21, Cohere, Mistral) using AWS CDK on AWS

AI-powered Markdown note taking app - leverage vector embeddings and LLMs with your personal notes - 100% local or in the cloud

ChattyUI - your private AI chat for running LLMs in the browser

AWS Generative AI CDK Constructs are sample implementations of AWS CDK for common generative AI patterns.

A NodeJS RAG framework to easily work with LLMs and embeddings

Joplin (note-taking) assistant running a very intelligent system (OpenAI/GPT, Hugging Face, Gemini, Llama, Universal Sentence Encoder, etc.)

Call any LLM with a single API. Zero dependencies.

A Reactive CLI that generates git commit messages with Ollama, ChatGPT, Gemini, Claude, Mistral and other AI

A tool that can automatically convert 🤗 Huggingface Spaces,魔搭创空间 and Gradio ChatBot into free APIs. Supports GPT4Free,ChatGPT,Llama2,MPT,Falcon Chat,ChatGLM,通义千问 and many other chatbot like spaces.

AI-powered universal search for all your personal data, tailored just for you. Goal:The world's first product with "edge-side LLMs + consumer data localization" as its core development direction.

Embed machine learning models in your Dockerfile