A curated collection of 130+ production-ready Gen AI apps, agents, and experiments. Built with LangChain, RAG, AI Agents, Multi-Agent Teams, and more.
- 💡 Learn by Building - 130+ production-ready applications and experiments you can run, modify, and learn from
- 🔥 Latest Tech Stack - Explore apps built with OpenAI, Anthropic, Gemini, QWEN, and open-source models
- 🎓 From Beginner to Advanced - Structured learning path with starter, intermediate, and advanced projects
- 🌍 Multi-Language Support - Projects supporting English, Hindi, and other Indian languages
- 📚 100+ Libraries Covered - Comprehensive tutorials on LangChain, LlamaIndex, CrewAI, AG2, Weaviate, and 90+ more AI/ML libraries
- 🚀 Production-Ready Code - Well-documented projects ready to deploy and integrate into your applications
Perfect for beginners getting started with Gen AI:
- LangChain Basics - Build intelligent workflows with LangChain
- Fine-Tuning with Nebius Token Factory - LoRA fine-tuning for custom LLMs
- Getting Started with Pydantic AI - Type-safe AI development
- CrewAI Essentials - Quick start guide for multi-agent systems
- TextBlob NLP - Simplified natural language processing
- Hugging Face Transformers - Foundation for Gen AI and NLP
- LlamaIndex - Intelligent data integration for LLMs
- ChromaDB - Vector database for embeddings
- Pinecone - Scalable vector search
Build more complex AI systems:
- AutoGen Multi-Agent System - Collaborative AI agents
- AG2 Multi-Agent AI Systems - GroupChat, tool use, MCP client with AG2 (formerly AutoGen)
- LangGraph Multi-Agent Swarm - Advanced agent orchestration
- CSV Agents - Data analysis with LangChain & LlamaIndex
- AI Customer Support Agent - Production-ready support system
- RAG Systems - Advanced retrieval-augmented generation
- Multi-Agent Workflows - Collaborative AI systems
Production-grade implementations and cutting-edge research:
- Cerebras Inference Comparison - High-performance model benchmarking
- Multi-Modal AI Systems - Complex experimental implementations
- Production LLM Testing - Comprehensive model evaluation
- Enterprise AI Agents - Production-grade agent systems
- Advanced RAG Architectures - Sophisticated retrieval systems
- Chat with QWEN3 Coder - Advanced coding assistant powered by QWEN
- Chat with PDF or Webpage - Extract and interact with document content
- Chat with GPT-OSS - Open-source GPT interface
- Sutra V2 Multilingual Chatbot - Support for Hindi and Indian languages
- Vibe Voice TTS - Text-to-speech application
- Chess Playing Agents - AI chess opponents with GLM model
- OpenAI Gemini Chess - Multi-model chess game
- QWEN Game Generator - Automated game creation
- World's Fastest Game Generator - Rapid game development with QWEN3
- Multilingual Quiz Generator - Create quizzes in multiple languages
- QnA Generator - Automated question generation
- Origami Tutorial Generator - Creative step-by-step tutorials
- Indian Language Quiz - Regional language support
- Language Learner - Interactive language learning platform
- Visual Question Generator - Image-based question creation
- AI Business Consultant - Strategic business advisor
- AI Ad Generator - Marketing content creation
- Finance AgentOS - Financial analysis and insights
- Stock Market Agent - Real-time market analysis
- Deep Stock Research Agent - Advanced financial research
- Marketing Automation - Automated marketing campaigns
- Perplexity AI Research Assistant - AI-powered research automation
- Cerebras Search - Ultra-fast search engine
- Personalized Search Agent - Customized search experience
- Similarity Analyzer - Content comparison with Venn diagrams
- GitHub README Generator - Auto-generate documentation
- Browser Automation - Web automation interface
- World's Fastest Website Generator - Instant web development
- Book Writer AI - Automated content creation
- LLM-friendly Web Scraper - Intelligent web scraping
- Gemini 2.0 Multimodal - Google's latest AI model
- News to Blog Automator - Content transformation pipeline
- Gadget Comparator - Product analysis with Gemini
- Chroma Cloud RAG - Cloud-based retrieval system
- MCP Implementation - Model Context Protocol
- TypeSafe Agno - Type-safe AI development
- NextJS Image Workflow - Advanced image processing
Quick directory map to help contributors and learners navigate faster.
- Definition: Library-focused notebook cookbooks and quick-start guides across the GenAI ecosystem.
- Top Cookbooks:
- Definition: Agent-building notebooks (single-agent and multi-agent) with practical orchestration patterns.
- Top Cookbooks:
- Definition: End-to-end GenAI apps and demos, mostly app-first projects with selected cookbook assets.
- Top Cookbooks:
- Definition: Model benchmarking, evaluation, and provider-specific capability testing notebooks.
- Top Cookbooks:
- Definition: Retrieval-Augmented Generation implementations, multimodal retrieval, and document-grounded QA.
- Top Cookbooks:
- Definition: Hands-on workshop notebooks and guided classroom-style build sessions.
- Top Cookbooks:
- Definition: Fine-tuning workflows and practical adaptation notebooks for custom LLM behavior.
- Top Cookbooks:
- Definition: Rapid experimentation area for new model features, frameworks, and exploratory ideas.
- Top Cookbooks:
- Definition: Structured role-based GenAI learning paths, checkpoints, and progression docs.
- Top Guides:
- Definition: Frontend site implementation for the interactive roadmap experience.
- Top Resources:
Curated from recently updated and high-interest notebooks available in this repository.
- GLM OCR Cookbook
- GPT-5.4 Mini/Nano Cookbook
- Gemini 3.1 Flash Lite Guide
- Gemini Embedding 2 Cookbook
- LangGraph Multi Agent Swarm
- AutoGen Collaborative Agents
- How to Build Claude-Powered RAG from Scratch
- RAG Interactive
- Kimi K2.5 Agent Swarm Cookbook
- MCP Workshop
- Python 3.8+
- pip or conda package manager
- API keys for services you want to use (OpenAI, Anthropic, Google, etc.)
-
Clone the repository
git clone https://github.com/buildfastwithai/gen-ai-experiments.git cd gen-ai-experiments -
Navigate to your desired project
cd apps/your-desired-project -
Install dependencies
pip install -r requirements.txt
-
Set up API keys
# Create a .env file and add your API keys echo "OPENAI_API_KEY=your_key_here" > .env
-
Run the application
streamlit run app.py # or python app.py # or jupyter notebook
-
Follow project-specific instructions in each project's
README.mdfor detailed setup
We welcome contributions from the community! Here's how you can help:
- Fork the repository
- Create a feature branch (
git checkout -b feature/AmazingFeature) - Commit your changes (
git commit -m 'Add some AmazingFeature') - Push to the branch (
git push origin feature/AmazingFeature) - Open a Pull Request
- Follow the existing project structure
- Include a detailed
README.mdfor new projects - Add requirements.txt with all dependencies
- Test your code before submitting
- Write clear commit messages
- Update documentation as needed
Found a bug or have a feature request? Please create a GitHub Issue with:
- Clear description of the issue/feature
- Steps to reproduce (for bugs)
- Expected vs actual behavior
- Screenshots (if applicable)
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