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Agentic AI Workflow with OpenRouter

This repository contains code examples for building agentic AI workflows using OpenRouter API, which provides access to multiple AI models (OpenAI, Anthropic, Google, etc.) through a single, consistent interface.

Features

  • Access models from multiple providers through one unified API
  • Compare responses from different AI models
  • Build multi-step reasoning agents that break down complex tasks
  • Example workflow for research and recommendation tasks

Prerequisites

  • Python 3.8 or higher
  • OpenRouter account (sign up at openrouter.ai)

Installation

  1. Clone this repository:
git clone https://github.com/your-username/agentic-ai-workflow
cd agentic-ai-workflow
  1. Create a virtual environment:
python3 -m venv venv
source venv/bin/activate  # On Windows: venv\Scripts\activate
  1. Install dependencies:
pip install -r requirements.txt
  1. Create a .env file with your API keys:
OPENROUTER_API_KEY=your_openrouter_api_key_here
YOUR_SITE_URL=https://yourdomain.com
YOUR_SITE_NAME=Your App Name

Usage

Basic Connection Test

Test your OpenRouter API connection:

python basic_setup.py

Compare AI Models

Compare responses from different AI models:

python model_comparison.py

Run Agentic Workflow

Execute a multi-step reasoning workflow:

python agent_example.py

Project Structure

  • basic_setup.py - Basic OpenRouter API client setup
  • model_comparison.py - Compare responses from different models
  • agent_example.py - Complete agentic workflow implementation
  • requirements.txt - Project dependencies

Next Steps

  • Add vector database for long-term memory
  • Enable external tools (web search, calculators, etc.)
  • Implement model routing for cost optimization
  • Add retry logic and better error handling
  • Implement user feedback loops

Resources

For a detailed tutorial on building agentic AI workflows with OpenRouter API, check out the article:

License

MIT