Awesome AI Apps

This repository is a comprehensive collection of practical examples, tutorials, and recipes for building powerful LLM-powered applications. From simple chatbots to advanced AI agents, these projects serve as a guide for developers working with various AI frameworks and tools.

Powered by Nebius AI Studio - your one-stop platform for building and deploying AI applications.

🚀 Featured AI Agent Frameworks

• Google Agent Development Kit (ADK)
• OpenAI Agents SDK
• LangChain
• LlamaIndex
• Agno
• CrewAI
• AWS Strands Agent
• Pydantic AI
• CAMEL‑AI
• DSPy

🧩 Starter Agents

Quick-start agents for learning and extending:

• Agno HackerNews Analysis - Agno-based agent for trend analysis on HackerNews.
• OpenAI SDK Starter - OpenAI Agents SDK based email helper & haiku writer.
• LlamaIndex Task Manager - LlamaIndex-powered task assistant.
• CrewAI Research Crew - Multi-agent research team.
• PydanticAI Weather Bot - Real-time weather info.
• LangChain-LangGraph Starter - LangChain + LangGraph starter.
• AWS Strands Agent Starter - Weather report Agent.
• Camel AI Starter - Performance benchmarking tool that compares the performance of various AI models.

🪶 Simple Agents

Straightforward, practical use-cases:

• Finance Agent - Tracks live stock & market data.
• Human-in-the-Loop Agent - HITL actions for safe AI tasks.
• Newsletter Generator - AI newsletter builder with Firecrawl.
• Reasoning Agent - Financial reasoning step-by-step.
• Agno UI Example - UI for web & finance agents.
• Mastra Weather Bot - Weather updates with Mastra AI.
• Calendar Assistant - Calendar scheduling with Cal.com.
• Memory Agent - Simple Memory Agent implementation with Agno.
• Web Automation Agent - Simple Browser Agent implementation with Nebius & browser use.
• Nebius Chat - Nebius AI Studio Chat interface.
• Talk to Your DB - Talk to your Database with GibsonAI & Langchain
• Blog Writing Agent - Personalized AI-powered blog writing agent.
• arXiv Researcher Agent - AI research assistant built using OpenAI Agents and GibsonAI Memori.

🗂️ MCP Agents

Examples using Model Context Protocol:

• Doc-MCP - Semantic RAG docs & Q&A.
• LangGraph MCP Agent - LangChain ReAct agent with Couchbase.
• GitHub MCP Agent - Repo insights via MCP.
• MCP Starter - GitHub repo analyzer starter.
• Talk to your Docs - Documentation QnA Agent

📚 RAG Applications

Retrieve-augmented generation examples:

• Agentic RAG - Agentic RAG with Agno & GPT 5.
• Resume Optimizer - Boost resumes with AI.
• LlamaIndex RAG Starter - LlamaIndex + Nebius RAG starter.
• PDF RAG Analyzer - Chat with multiple PDFs.
• Qwen3 RAG Chat - PDF chatbot with Streamlit.
• Chat with Code - Conversational code explorer.
• Gemma3 OCR - OCR-based document and image processor using Gemma3

🔬 Advanced Agents

Complex pipelines for end-to-end workflows:

• Deep Researcher - Multi-stage research with Agno & Scrapegraph AI.
• Candilyzer - Analyze GitHub/LinkedIn profiles.
• Job Finder - LinkedIn job search with Bright Data.
• AI Trend Analyzer - AI trend mining with Google ADK.
• Conference Talk Generator - Draft talk abstracts with Google ADK & Couchbase.
• Finance Service Agent - FastAPI server for stock data and predictions with Agno.
• Price Monitoring Agent - Price monitoring and alerting Agent powered by CrewAi, Twilio & Nebius.
• Startup Idea Validator Agent - Agentic Workflow to validate and analyze startup ideas.

📺 Playlist of Demo Videos & Tutorials

• Build with MCP
• Build AI Agents
• AI Agents, MCP and more...

Getting Started

Prerequisites

• Python 3.10 or higher
• Git
• pip (Python package manager) or uv

Installation Steps

1. Clone the repository

   git clone https://github.com/Arindam200/awesome-ai-apps.git

2. Navigate to the desired project directory

   cd awesome-ai-apps/starter_ai_agents/agno_starter

3. Install the required dependencies

   pip install -r requirements.txt

4. Follow project-specific instructions

   • Each project has its own README.md with detailed setup and usage instructions
   • Make sure to read the project-specific documentation before running the application

🤝 Contributing

We welcome contributions from the community! Whether you're a beginner or an expert, your examples and tutorials can help others learn and grow. Here's how you can contribute:

1. Submit a Pull Request with your LLM application example
2. Add detailed documentation and setup instructions
3. Include requirements.txt or environment.yml
4. Share your experience and best practices

📜 License

This repository is licensed under the MIT License. Feel free to use and modify the examples for your projects.

Thank You for the Support! 🙏