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

A collection of projects showcasing RAG, agents, workflows, and other AI use cases

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.

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

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.

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.
• 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

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
• Database MCP Agent - A conversational AI agent for managing GibsonAI database projects and schemas.

Agents with advanced memory capabilities:

• Agno Memory Agent - Agno-based agent with persistent memory.
• arXiv Researcher Agent with Memori - Research assistant using OpenAI Agents and GibsonAI Memori.
• AWS Strands Agent with Memori - AWS Strands agent enhanced with Memori memory.
• Blog Writing Agent - Personalized blog writing agent with memory.
• Social Media Agent - Social media automation agent with memory.

Retrieve-augmented generation examples:

• Agentic RAG - Agentic RAG with Agno & GPT 5.
• Agentic RAG with Web Search - Advanced RAG with CrewAI, Qdrant, and Exa for hybrid search.
• 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

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.
• Meeting Assistant Agent - Agentic Workflow that send meeting notes and creates task based on conversation.

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

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

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

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