A curated archive of 313 AI prompts for ChatGPT, Claude, Gemini, and anywhere else you need them. Search, filter by category, and copy with one click.
Streamlit app to explore and analyze GitHub repos using natural language via Model Context Protocol and OpenAI.
### 🎓 FREE Step-by-Step Tutorial **👉 [Click here to follow our complete step-by-step tutorial](https://www.theunwindai.com/p/build-an-mcp-github-agent-in-less-than-50-lines-of-code) and learn how to build this from scratch with detailed code walkthroughs, explanations, and best practices.** A Streamlit application that allows you to explore and analyze GitHub repositories using natural language
Specifies minimum version requirements for Streamlit, Agno, MCP, OpenAI, and asyncio Python packages.
streamlit>=1.28.0 agno>=2.2.10 mcp>=0.1.0 openai>=1.0.0 asyncio>=3.4.3
Streamlit app using MCP servers, Airbnb, and Google Maps to generate detailed personalized travel itineraries.
## 🌍 MCP Travel Planner Agent Team A sophisticated Streamlit-based AI travel planning application that creates extremely detailed, personalized travel itineraries using multiple MCP servers and Google Maps integration. The app uses Airbnb MCP for real accommodation data and a custom Google Maps MCP for precise distance calculations and location services. ## ✨ Features ### 🤖 AI-Powered Travel Pl
A requirements file listing Python dependencies for a Streamlit app with AI and calendar features.
streamlit agno>=2.2.10 openai icalendar google-search-results
Defines pinned and minimum version dependencies for a Python project using Streamlit and related libraries.
streamlit==1.40.2 contextual-client>=0.1.0 requests>=2.32.0 pydantic==2.9.2
Build a document Q&A app using hybrid search RAG, Claude, OpenAI embeddings, and Cohere reranking with Streamlit.
A powerful document Q&A application that leverages Hybrid Search (RAG) and Claude's advanced language capabilities to provide comprehensive answers. Built with RAGLite for robust document processing and retrieval, and Streamlit for an intuitive chat interface, this system seamlessly combines document-specific knowledge with Claude's general intelligence to deliver accurate and contextual responses
A requirements.txt listing Python packages for a RAG pipeline with LLMs, vector search, and Streamlit UI.
raglite==0.2.1 pydantic==2.10.1 sqlalchemy>=2.0.0 psycopg2-binary>=2.9.9 openai>=1.0.0 cohere>=4.37 pypdf>=3.0.0 python-dotenv>=1.0.0 rerankers==0.6.0 spacy>=3.7.0 streamlit anthropic
Agentic RAG system using Cohere Command-R, Qdrant vector storage, and LangGraph with web search fallback.
A RAG Agentic system built with Cohere's new model Command-r7b-12-2024, Qdrant for vector storage, Langchain for RAG and LangGraph for orchestration. This application allows users to upload documents, ask questions about them, and get AI-powered responses with fallback to web search when needed. ## Features - **Document Processing** - PDF document upload and processing - Automatic text chunk
A pip requirements file listing LangChain, Cohere, Qdrant, and related AI/ML package dependencies.
langchain==0.3.12 langchain-community==0.3.12 langchain-core==0.3.25 langchain-cohere==0.3.2 langchain-qdrant==0.2.0 cohere==5.11.4 qdrant-client==1.12.1 duckduckgo-search==6.4.1 streamlit==1.40.2 tenacity==9.0.0 typing-extensions==4.12.2 pydantic==2.9.2 pydantic-core==2.23.4 langgraph==0.2.53
Build a production-ready RAG service using Claude 3.5 Sonnet and Ragie.ai with a Streamlit UI in under 50 lines.
## 🖇️ RAG-as-a-Service with Claude 3.5 Sonnet Build and deploy a production-ready Retrieval-Augmented Generation (RAG) service using Claude 3.5 Sonnet and Ragie.ai. This implementation allows you to create a document querying system with a user-friendly Streamlit interface in less than 50 lines of Python code. ### Features - Production-ready RAG pipeline - Integration with Claude 3.5 Sonnet for
A minimal requirements list for a Python project using Streamlit, Anthropic, and Requests.
streamlit anthropic requests
Build a local RAG reasoning agent using Deepseek, Qdrant, Snowflake embeddings, and Agno for document Q&A and web search.
### 🎓 FREE Step-by-Step Tutorial **👉 [Click here to follow our complete step-by-step tutorial](https://www.theunwindai.com/p/build-a-local-rag-reasoning-agent-with-deepseek-r1) and learn how to build this from scratch with detailed code walkthroughs, explanations, and best practices.** A powerful reasoning agent that combines local Deepseek models with RAG capabilities. Built using Deepseek (via
A requirements file listing Python packages for an AI stack including Agno, Exa, Qdrant, LangChain, Streamlit, and Ollama.
agno exa==0.5.26 qdrant-client==1.12.1 langchain-qdrant==0.2.0 langchain-community==0.3.13 streamlit==1.41.1 ollama
RAG-based system for querying pharmaceutical research papers using LangChain, ChromaDB, and Google Gemini.
## Overview PharmaQuery is an advanced Pharmaceutical Insight Retrieval System designed to help users gain meaningful insights from research papers and documents in the pharmaceutical domain. ## Demo https://github.com/user-attachments/assets/c12ee305-86fe-4f71-9219-57c7f438f291 ## Features - **Natural Language Querying**: Ask complex questions about the pharmaceutical industry and get concise,
Lists Python package requirements for a Streamlit LangChain app with Google Gemini, Chroma, and PDF support.
streamlit langchain-google-genai langchain-chroma langchain-community langchain-core chromadb sentence-transformers PyPDF2 python-dotenv
Build a local RAG system using Qwen3/Gemma3 via Ollama with PDF ingestion, vector search, and web search fallback.
This RAG Application demonstrates how to build a powerful Retrieval-Augmented Generation (RAG) system using locally running Qwen 3 and Gemma 3 models via Ollama. It combines document processing, vector search, and web search capabilities to provide accurate, context-aware responses to user queries. Built with Agno v2.0. ## Features - **🧠 Multiple Local LLM Options**: - Qwen3 (1.7b, 8b) - Alib
A list of Python package dependencies including AI, vector DB, and web framework libraries.
agno>=2.2.10 pypdf exa qdrant-client langchain-qdrant langchain-community streamlit ollama
Streamlit app using MCP and Playwright to control a browser with natural language commands.
https://github.com/user-attachments/assets/a01e09fa-131b-479a-8df3-2d1a61fd80f3 A Streamlit application that allows you to browse and interact with websites using natural language commands through the Model Context Protocol (MCP) and [MCP-Agent](https://github.com/lastmile-ai/mcp-agent) with Playwright integration. ## Features - **Natural Language Interface**: Control a browser with simple Engl
Specifies minimum version dependencies for Streamlit, MCP Agent, OpenAI, and asyncio packages.
streamlit>=1.28.0 mcp-agent>=0.0.14 openai>=1.0.0 asyncio>=3.4.3
A Streamlit app routing queries to specialized AI agents, each connected to domain-specific MCP servers via Claude.
A Streamlit app that demonstrates the **multi-agent + MCP** pattern: specialized AI agents that each connect to different MCP servers to handle domain-specific tasks. Instead of one agent with all tools, The router sends your request to a **specialist** — a code reviewer, security auditor, researcher, or BIM engineer — each with access to only the MCP tools they need. ## Features - **4 Speciali
Lists minimum version requirements for Streamlit, Anthropic, MCP, and Pydantic Python packages.
streamlit>=1.28.0 anthropic>=0.40.0 mcp>=0.1.0 pydantic>=2.0.0
A terminal-based Notion agent using MCP and OpenAI to interact with Notion pages via natural language.
### 🎓 FREE Step-by-Step Tutorial **👉 [Click here to follow our complete step-by-step tutorial](https://www.theunwindai.com/p/build-a-terminal-based-notion-agent-with-mcp) and learn how to build this from scratch with detailed code walkthroughs, explanations, and best practices.** A terminal-based Notion Agent for interacting with your Notion pages using natural language through the Notion MCP (M
A list of Python package dependencies including agno, dotenv, mcp, openai, and sqlalchemy.
agno>=2.2.10 python-dotenv mcp openai sqlalchemy
A multi-agent AI assistant integrating GitHub, Perplexity, Calendar, and Gmail via MCP servers for productivity automation.
The Multi-MCP Intelligent Assistant is a powerful productivity tool that integrates multiple Model Context Protocol (MCP) servers to provide seamless access to GitHub, Perplexity, Calendar, and Gmail services through natural language interactions. This advanced AI assistant is powered by Agno's AI Agent framework and designed to be a productivity multiplier across your digital workspace. ## Featu
Specifies Python package dependencies including agno, openai, mcp, and python-dotenv.
agno>=2.2.10 openai mcp python-dotenv
Streamlit app to analyze GitHub repos via natural language using Model Context Protocol and OpenAI.
### 🎓 FREE Step-by-Step Tutorial **👉 [Click here to follow our complete step-by-step tutorial](https://www.theunwindai.com/p/build-an-mcp-github-agent-in-less-than-50-lines-of-code) and learn how to build this from scratch with detailed code walkthroughs, explanations, and best practices.** A Streamlit application that allows you to explore and analyze GitHub repositories using natural language
Specifies minimum version dependencies for a Python project using Streamlit, Agno, MCP, and OpenAI.
streamlit>=1.28.0 agno>=2.2.10 mcp>=0.1.0 openai>=1.0.0 asyncio>=3.4.3
Streamlit AI travel planner using MCP servers, Google Maps, and Airbnb for detailed personalized itineraries.
## 🌍 MCP Travel Planner Agent Team A sophisticated Streamlit-based AI travel planning application that creates extremely detailed, personalized travel itineraries using multiple MCP servers and Google Maps integration. The app uses Airbnb MCP for real accommodation data and a custom Google Maps MCP for precise distance calculations and location services. ## ✨ Features ### 🤖 AI-Powered Travel Pl
A requirements.txt file listing Python dependencies for a Streamlit and OpenAI-based application.
streamlit agno>=2.2.10 openai icalendar google-search-results
A collection of packaged skills extending AI agent capabilities across coding, research, writing, and productivity.
A curated collection of skills for AI agents following the [Agent Skills](https://agentskills.io) format. ## What Are Agent Skills? Agent Skills are packaged instructions and scripts that extend agent capabilities. They follow the [Agent Skills specification](https://agentskills.io/specification) - a simple, open format for giving agents new capabilities and expertise. Each skill contains: - **