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Welcome to Awesome LLM Apps! This guide will help you quickly set up and run your first AI agent application.

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What is Awesome LLM Apps?

Awesome LLM Apps is a curated collection of production-ready LLM applications featuring:
  • AI Agents - Single and multi-agent systems for various domains
  • RAG Applications - Retrieval Augmented Generation tutorials
  • Voice AI Agents - Voice-enabled AI applications
  • MCP Integration - Model Context Protocol agents
  • Multi-agent Teams - Coordinated agent workflows
All applications use models from OpenAI, Anthropic, Google, xAI, and open-source alternatives like Qwen and Llama.

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Prerequisites

Before you begin, ensure you have:
  • Python 3.8 or higher installed
  • pip (Python package manager)
  • Git for cloning the repository
  • API keys for your preferred LLM provider (OpenAI, Anthropic, Google, etc.)
Most applications in this repository use Streamlit for the user interface, making them easy to run and customize.

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Quick Setup

1

Clone the Repository

Clone the Awesome LLM Apps repository to your local machine:
git clone https://github.com/Shubhamsaboo/awesome-llm-apps.git
cd awesome-llm-apps
2

Choose Your Application

Navigate to the application you want to run. Here are some popular starting points:
cd starter_ai_agents/ai_travel_agent
3

Install Dependencies

Each project has its own requirements.txt file. Install the required packages:
pip install -r requirements.txt
Consider using a virtual environment to avoid package conflicts:
python -m venv venv
source venv/bin/activate  # On Windows: venv\Scripts\activate
pip install -r requirements.txt
4

Configure API Keys

Most applications require API keys. You can provide them in two ways:Option 1: Environment Variables (Recommended)
export OPENAI_API_KEY='your-api-key-here'
export GOOGLE_API_KEY='your-google-api-key'
export SERPAPI_KEY='your-serpapi-key'
Option 2: Direct Input Many Streamlit apps allow you to enter API keys directly in the sidebar.
Never commit your API keys to version control. Use environment variables or .env files that are gitignored.
5

Run the Application

Start the Streamlit application:
streamlit run <app_name>.py
For example:
streamlit run travel_agent.py
The app will automatically open in your default browser at http://localhost:8501

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Your First AI Agent: Travel Planner

Let’s run a complete example with the AI Travel Agent: 
# Navigate to the travel agent directory
cd awesome-llm-apps/starter_ai_agents/ai_travel_agent

# Install dependencies
pip install -r requirements.txt

# Set up API keys
export OPENAI_API_KEY='your-openai-api-key'
export SERPAPI_KEY='your-serpapi-key'

# Run the app
streamlit run travel_agent.py

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How It Works

The AI Travel Agent uses a multi-agent architecture:
  1. Researcher Agent - Searches the web for activities and accommodations
  2. Planner Agent - Creates personalized itineraries based on research
Key dependencies: 
streamlit
agno>=2.2.10
openai
google-search-results
icalendar
You can download the generated itinerary as a .ics calendar file to import into Google Calendar, Apple Calendar, or any calendar app!

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Local vs Cloud Models

Many applications support both cloud and local models: 
streamlit run travel_agent.py
For local models, you’ll need:
  1. Ollama installed and running
  2. Your desired model pulled: ollama pull llama3.1

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Common API Keys You’ll Need

OpenAI

Get your API key from platform.openai.com

Google AI

Sign up at ai.google.dev for Gemini API access

Anthropic

Create an account at console.anthropic.com

SerpAPI

Get search API access at serpapi.com

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Troubleshooting

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Common Issues

Import Error: Module not found 
# Make sure all dependencies are installed
pip install -r requirements.txt --upgrade
API Key Error 
# Verify your environment variable is set
echo $OPENAI_API_KEY  # On Windows: echo %OPENAI_API_KEY%
Port Already in Use 
# Use a different port
streamlit run app.py --server.port 8502
Docker Required (for MCP agents) 
# Verify Docker is running
docker --version
docker ps

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Next Steps

Installation Guide

Detailed installation instructions for all project types

AI Agents

Explore starter and advanced AI agents

RAG Tutorials

Learn about Retrieval Augmented Generation

MCP Agents

Build agents with Model Context Protocol

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Project Structure

Each application follows a consistent structure: 
project-name/
├── app.py              # Main application file
├── requirements.txt    # Python dependencies
├── README.md          # Project-specific documentation
├── .env.example       # Example environment variables (if applicable)
└── utils/             # Helper functions (if applicable)

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Getting Help

If you run into issues:
  1. Check the project-specific README.md file
  2. Review the GitHub Issues
  3. Join the community discussions
  4. Follow @Saboo_Shubham_ on Twitter
Each application directory contains a detailed README with specific setup instructions and usage examples.

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