A curated collection of practical, production-ready AI projects across multiple modalities, including language models, multimodal models, OCR systems, RAG pipelines, and AI agents. Each project is designed to help you learn, experiment, and build real-world AI applications.

• 🎯 Why This Repository?
• 🗂️ Project Categories
• 🚀 Getting Started
• 🤝 Contributing
• 📜 License

• Learn by Doing: Each project includes complete code, setup instructions, and documentation
• Production-Ready: Projects follow best practices and are ready to be adapted for real-world use
• Diverse Use Cases: From RAG systems to multi-agent workflows and specialized applications
• Multiple Model Providers: Projects use OpenAI, Anthropic, Google, and open-source models
• Active Community: Regular updates and new project additions

Intelligent ai agents for various automation tasks.

• Multi-Agent Financial Analyst — Team of specialized agents for comprehensive financial analysis
• FinAgent — Financial assistant agent for stock market analysis and insights
• Daily AI News Digest — Automated daily digest from 92 Karpathy-curated tech blogs, delivered to Telegram at 8 AM every morning. MiniMax M2.7 scores every article fetched in the last 24 hours and picks the 3 most significant stories.
• Agentic Form Filler — Powerful agentic form-filling application using Landing AI for layout parsing and MiniMax M2.7 for multi-turn conversational data gathering.
• AI Travel Planning Agent — Multi-agent travel planner that turns a single natural language request into a complete trip plan with flights, hotels, and a day-by-day itinerary.
• Competitive Intelligence Agent — Multi-agent AI system that generates strategic sales battlecards by analyzing competitors through the unique lens of your own business context.
• Multi-Agent Research Assistant (AG2) — Production-grade multi-agent research pipeline using AG2 (formerly AutoGen). Three specialists collaborate under GroupChat with LLM-driven speaker selection to research any topic and produce a structured Markdown report.
• Self-Reflective Agentic RAG — LangGraph-driven RAG system that grades retrieved context for relevance and sufficiency, rewrites the query if needed, and only generates an answer once the context passes validation — reducing hallucinations through an iterative retrieval loop.
• Agentic SQL Search — Natural language to SQL agent powered by Gemma 4. Ask plain-English questions about an e-commerce database and the agent writes, executes, and explains the SQL query — with full reasoning transparency in the Streamlit UI.

Extracting structure and meaning from visual data and documents.

• Image-to-Structured-Data Extractor — High-fidelity visual OCR using Mistral Large 3 and Instructor to convert images into validated, structured JSON.
• LaTeX Formula OCR - Local vision-language OCR that extracts math formulas from images/PDFs into LaTeX and renders them instantly with KaTeX.

Projects combining vision, video, and language models.

• GLM-OCR Pro — High-performance, local-first Streamlit application for structured document extraction using the GLM-OCR model via Ollama to transform images and PDFs into cleanly formatted Markdown in real-time.
• Video Understanding Agent — Paste a YouTube URL and get an AI-powered chapter summary, key takeaways, and action items powered by Gemini Flash.

Projects using the Openclaw framework.

• Eagle Eye — AI-powered GitHub PR review agent using OpenClaw

Retrieval-Augmented Generation systems for knowledge-enhanced AI applications.

• Agentic RAG with O3-Mini & DuckDuckGo — RAG system using O3-Mini model with DuckDuckGo search integration
• Agentic RAG with Qwen & FireCrawl — Advanced RAG using Qwen models and FireCrawl for web scraping
• Vision RAG — Multimodal RAG system capable of processing and querying visual content
• Clinical RAG with ADE — High-precision RAG system using LandingAI ADE for visual-first parsing and Mistral Large for grounded clinical reasoning

We welcome contributions! Whether you're adding new projects, improving existing ones, or fixing bugs, your help makes this repository better for everyone.

1. Read the guidelines: Check CONTRIBUTING.md for detailed instructions
2. Create an issue: Propose your project or improvement
3. Follow the structure: Use the appropriate category folder
4. Submit a PR: One project per pull request

• Each project must be in its own folder within the appropriate category
• Must include a comprehensive README.md (use our template)
• Must include requirements.txt or pyproject.toml
• Must include .env.example for required API keys
• Follow snake_case naming convention

This repository is licensed under the MIT License. See the LICENSE file for details.

Thank you to all contributors who have helped build this collection of AI engineering projects!