PPT Agents is an open-source Python toolkit that converts PowerPoint presentations into AI agents. It parses PPTX files via python-pptx, extracts slide texts and metadata, and indexes them in a vector store using LangChain. With integrated OpenAI models, it enables users to interactively query slide content, generate concise summaries, and perform batch slide editing. Users can update text, add or remove slides based on prompts, and export modified PPT files seamlessly. This allows rapid insights and automated adjustments to presentations without manual slide-by-slide work.

Chat-With-Data is a Python-based tool and web interface built on Streamlit, LangChain, and OpenAI’s GPT API. It automatically parses tabular datasets or database schemas and creates an AI agent that understands natural language queries about your data. Under the hood, it chunks large tables, builds an embedding index for semantic search, and formulates dynamic prompts to generate context-aware responses. Users ask questions like “What are the top 5 sales regions this quarter?” or “Show me a bar chart of revenue by category,” and receive answers or interactive plots without writing SQL or pandas code. The platform runs locally or on a server, ensuring data privacy while accelerating exploratory analysis for both technical and nontechnical users.

gaidme is a command line interface (CLI) tool designed to help developers and IT professionals generate terminal commands using artificial intelligence. By simply describing what you want to do, gaidme can suggest commands that you can either run with one click or copy to your clipboard. It also supports command history, allowing you to reuse and modify past commands easily. With privacy as a priority, gaidme does not store your data unless you opt-in. Available on platforms supporting Python 3.8 or higher, gaidme enhances your terminal experience, saving you time and reducing mental overload.

ChatStreamAiAgent provides developers with a lightweight Python toolkit to implement AI chat agents that stream token outputs as they are generated. It supports multiple LLM providers, asynchronous event hooks, and easy integration into web or console applications. With built-in context management and prompt templating, teams can rapidly prototype conversational assistants, customer support bots, or interactive tutorials while delivering low-latency, real-time responses.