Awesome AI Apps 
This repository is a comprehensive collection of 132 projects, tutorials, and recipes for building powerful LLM-powered applications, including text agents, voice assistants, RAG apps, and MCP-backed tools. These projects serve as a guide for developers working with various AI frameworks and stacks.
📋 Table of Contents
🚀 Featured AI Apps
🧩 Starter Agents
Quick-start agents for learning and extending different AI frameworks. 21 projects
🪶 Simple Agents
Straightforward, practical use-cases for everyday AI applications. 18 projects
- Agno Agent Examples: Simple to multi-agent examples with web search and a knowledge base
- Agno Agent UI: Interactive UI for web and finance agents
- AI Agent Registry Explorer: Find and compare AI agents across NANDA, MCP, Virtuals, A2A, and ERC-8004 registries
- Calendar Assistant: Calendar scheduling integration with Cal.com
- Cost-Aware Model Router (RouteLLM): Intelligent model routing with RouteLLM (GPT-4o-mini vs Nebius Llama) for cost optimization
- Email-to-Calendar Assistant: AI-powered Gmail reader and Google Calendar manager
- Financial Reasoning Agent: Step-by-step financial reasoning demonstration
- Human-in-the-Loop Agent: HITL actions for safe AI task execution
- LangChain Operations Agent Collection: Nebius-powered incident response, support, vendor risk, and data quality agents with typed outputs and guarded tools
- Mastra Weather Bot: Weather updates using Mastra AI framework
- Natural-Language Database Assistant: Natural language database queries with GibsonAI and LangChain
- Natural-Language SQL Agent (LangChain): Natural-language-to-SQL data agent with LangGraph, Nebius, read-only SQL safety, and Streamlit charts
🎙️ Voice Agents
Real-time voice assistants and streaming speech pipelines — including LiveKit, Pipecat, Gradium, and VoxCode (Deepgram + Cursor SDK). 9 projects
- AI Pitch Coach (Gradium + Nebius): Conversational pitch coach using Gradium STT/TTS, LangChain orchestration, and Nebius reasoning
- Customer Support Voice Agent (LiveKit): Nebius-powered voice support agent with context-preserving AI manager handoff, noise cancellation, and inactivity handling
- Gemini Realtime Voice Agent (LiveKit): LiveKit Agents with Google Gemini Live (
gemini multimodal realtime) for low-latency voice conversations in a LiveKit room
- Healthcare Voice Contact Center: Pipecat healthcare contact center with appointment booking, FAQ handling, and supervisor escalation
- Multilingual Voice Agent (Pipecat + Sarvam): Pipecat voice pipeline with Sarvam STT/TTS and OpenAI for chat; WebRTC (browser) or Daily transport via the Pipecat runner
- RSVP Confirmation Voice Agent (LiveKit): Outbound voice agent that calls attendees, confirms RSVPs, and updates a JSON-backed event database
- Speed-to-Lead Sales Voice Agent: LiveKit-based voice agent that calls inbound leads instantly, routes them to specialists, and logs to a mock CRM
- VoxCode — Deepgram + Cursor Voice Coding Agent: Local voice workspace for codebase summaries and architecture Q&A; Deepgram Voice Agent orchestration, Nebius reasoning, and optional Cursor SDK file inspection and edits
- Web-Search Voice Agent (LiveKit): LiveKit + Gemini realtime voice agent with an Olostep-backed
web_search tool for fresh, source-cited answers
🗂️ MCP Agents
Examples using Model Context Protocol for external tool integration. 14 projects
🧠 Memory Agents
Agents with advanced memory capabilities for context retention and personalization. 13 projects
📚 RAG Applications
Retrieval-augmented generation examples for document understanding and knowledge bases. 18 projects
- Agentic RAG with Agno and GPT-5: Agentic RAG implementation with Agno and GPT-5
- Agentic Typed RAG with LlamaIndex: Typed, citation-verified RAG with structured answers, local document parsing, and deterministic refusal for weak evidence
- Codebase Q&A RAG: Conversational code explorer and documentation assistant
- Enterprise Contextual RAG: Enterprise-level RAG with managed datastores and quality evaluation
- Gemma 3 Document OCR: OCR-based document and image processor using the Gemma 3 model
- GraphRAG with Neo4j: Knowledge graph extraction and Cypher-backed retrieval with Neo4j and Nebius
- LiteParse Invoice & Receipt Auditor: Local OCR with LiteParse bounding boxes, Nebius LLM audit for math errors and duplicate charges, evidence pinning on scans, and an LLM batch summary
- LlamaIndex RAG Starter: LlamaIndex and Nebius RAG starter template
- LLM and RAG Debugger (WFGY 16-Problem Map): 16-mode map-based debugger for LLM and RAG bugs
- Multi-PDF RAG Analyzer: Multi-PDF chat and analysis system
- Nebius RAG Starter: Basic RAG implementation with Nebius for quick starts
- NVIDIA Nemotron Document OCR: OCR-based document and image parsing using NVIDIA Nemotron-Nano-V2-12b
- : Production-shaped PDF RAG with contextual retrieval, Qdrant hybrid search, reranking, streaming answers, upload ingestion, and clickable citations
🔬 Advanced Agents
Complex multi-agent pipelines for production-ready end-to-end workflows. 34 projects
🧬 Fine-Tuning
End-to-end examples of fine-tuning open-source LLMs, from data prep to deployment. 6 projects
- Customer Support Fine-Tuning with Data Lab: Teacher-student distillation workflow to generate support data, curate it in Data Lab, fine-tune, and deploy
- Insurance Claims Fine-Tuning: Data Lab, LoRA fine-tuning, and a Gradio comparison app for insurance claims
- Legal Tech Fine-Tuning (Self-Hosted): Fine-tunes Gemma on UK legislation with LoRA, serves with vLLM, and exposes a FastAPI layer
- Legal Tech Fine-Tuning (Token Factory): Managed LoRA fine-tuning on Nebius Token Factory with private model deployment
- Open-Source LLM Fine-Tuning on Token Factory: Colab-first LoRA fine-tuning walkthrough to upload a dataset, train, monitor, and deploy
- Standalone Customer Support Fine-Tuning (Colab): Fully standalone Colab notebook for the customer-support distillation and fine-tuning flow
📺 Tutorials & Videos
🎓 Course Playlists
🔧 Framework Tutorials
📥 Stay Updated with Daily AI Insight!
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Getting Started
Prerequisites
- Python 3.10+ (Python 3.11+ recommended for newer projects)
- Git for cloning the repository
- Package Manager:
pip or uv (recommended for faster installs)
- API Keys: Most projects require API keys (see individual project READMEs)
Quick Start
-
Clone the repository
git clone https://github.com/Arindam200/awesome-ai-apps.git
cd awesome-ai-apps
-
Choose a project and navigate to its directory
cd starter_ai_agents/agno_starter # Example: Start with Agno starter
-
Set up environment variables
cp .env.example .env # Copy example environment file
# Edit .env with your API keys
-
Install dependencies
# Using pip
pip install -r requirements.txt
# OR using uv (recommended - faster)
uv sync
# or
uv pip install -e .
-
Run the project
python main.py
# or for Streamlit apps
streamlit run app.py
🤝 Contributing
We welcome contributions from the community! Here's how you can help:
- 💡 Add new projects: Submit your own AI agent examples
- 🔧 Fix issues: Contribute code improvements and bug fixes
- 📝 Improve documentation: Help make projects more accessible
- 🐛 Report bugs or suggest improvements via GitHub Issues
Before contributing:
- Read our Contributing Guidelines for detailed information
- Check existing issues to avoid duplicates
- Follow the project structure and naming conventions
- Ensure your project includes a comprehensive README.md
Important: This project follows a Contributor Code of Conduct. By participating, you agree to abide by its terms.
📜 License
This repository is licensed under the MIT License. Feel free to use and modify the examples for your projects.
👥 Core Maintainers
This project is actively maintained by:
Arindam Majumder
·
Shivay Lamba
·
Amitesh Anand
For any questions, suggestions, or contributions, feel free to reach out to the maintainers.
Thank You for the Support! 🙏
