A terminal-based platform to experiment with the AI Software Engineer. It allows users to specify software in natural language, watch as an AI writes and executes the code, and implement improvements. Supports various models and customization options.
ClickUi is a powerful, open-source, cross-platform AI-assistant application built in Python. It integrates various AI models, speech recognition, and web scraping capabilities, providing both voice and text interaction interfaces. The tool is designed to be a comprehensive AI-computer assistant, supporting features such as voice mode, chat mode, file attachments, property lookups, and web searches. It aims to be user-friendly and adaptable, encouraging community collaboration for future development and improvements.
This folder contains some example client scripts using our Python SDK for connecting with Llama Stack Distros. Instructions are provided for setting up dependencies and running demo scripts and apps.
A web crawling project using Python, Selenium, Gemini, and Brightdata
- needs slight refactoring for openapi/llama.cpp integration
- needs slight refactoring for openapi/llama.cpp integration
Simple, unified interface to multiple Generative AI providers, supporting various providers including OpenAI, Anthropic, Azure, Google, AWS, Groq, Mistral, HuggingFace, and Ollama. It aims to facilitate the use of multiple LLMs with a standardized interface similar to OpenAI’s.
Apprise is a library that allows you to send notifications to almost all popular notification services, such as Telegram, Discord, Slack, and Amazon SNS. It supports multiple notification services, intuitive syntax, and can handle file attachments.
Scraperr is a self-hosted web application for scraping data from web pages using XPath. It supports queuing URLs, managing scrape elements, and provides features such as job management, user login, and integration with AI services.
A collection of Python examples demonstrating the use of Mistral.rs, a Rust library for working with mistral models.
An open-source project offering a functional RAG UI for document QA, suitable for both end-users and developers. It supports various LLM providers, is customizable, and offers multi-modal QA, citations, and complex reasoning methods.
Create awesome e-paper dashboards within minutes! Modularity? Check! Python3? Check? Works on Raspberry Pi Zero W? Check! Support for own modules? Check!
