Popular GitHub Repositories for Hai Build

AutoGPT is the vision of accessible AI for everyone, to use and to build on. Our mission is to provide the tools, so that you can focus on what matters.

? Empowering everyone to build reliable and efficient smart contracts.

Drag & drop UI to build your customized LLM flow

Build and share delightful machine learning apps, all in Python. ? Star to support our work!

Low-code platform for building business applications. Connect to databases, cloud storages, GraphQL, API endpoints, Airtable, Google sheets, OpenAI, etc and build apps using drag and drop application builder. Built using JavaScript/TypeScript. ?

Your AI second brain. Self-hostable. Get answers from the web or your docs. Build custom agents, schedule automations, do deep research. Turn any online or local LLM into your personal, autonomous AI (gpt, claude, gemini, llama, qwen, mistral). Get started - free.

AI orchestration framework to build customizable, production-ready LLM applications. Connect components (models, vector DBs, file converters) to pipelines or agents that can interact with your data. With advanced retrieval methods, it's best suited for building RAG, question answering, semantic search or conversational agent chatbots.

?? Build multimodal AI applications with cloud-native stack

Crawlee—A web scraping and browser automation library for Node.js to build reliable crawlers. In JavaScript and TypeScript. Extract data for AI, LLMs, RAG, or GPTs. Download HTML, PDF, JPG, PNG, and other files from websites. Works with Puppeteer, Playwright, Cheerio, JSDOM, and raw HTTP. Both headful and headless mode. With proxy rotation.

Build your own AI friend

<⚡️> SuperAGI - A dev-first open source autonomous AI agent framework. Enabling developers to build, manage & run useful autonomous agents quickly and reliably.

The open-source hub to build & deploy GPT/LLM Agents ??

Open source libraries and APIs to build custom preprocessing pipelines for labeling, training, or production machine learning pipelines.

What are the principles we can use to build LLM-powered software that is actually good enough to put in the hands of production customers?

Example ? Jupyter notebooks that demonstrate how to build, train, and deploy machine learning models using ? Amazon SageMaker.

Build high-quality LLM apps - from prototyping, testing to production deployment and monitoring.

Build Conversational AI in minutes ⚡️

Build, Manage and Deploy AI/ML Systems

A python library built to empower developers to build applications and systems with self-contained Computer Vision capabilities

TypeChat is a library that makes it easy to build natural language interfaces using types.