fetch_models_docs.py

import requests
import json
from pathlib import Path

def fetch_and_parse_models():
    url = "https://openrouter.ai/api/v1/models"
    headers = {
        "HTTP-Referer": "https://github.com/OpenAgent/OpenAgent",
        "X-Title": "OpenAgent v0.2",
    }
    
    print(f"Fetching models from {url}...")
    try:
        response = requests.get(url, headers=headers)
        response.raise_for_status()
        data = response.json()
    except Exception as e:
        print(f"Error fetching models: {e}")
        return

    models = data.get("data", [])
    if not models:
        print("No models found in the API response.")
        return

    print(f"Found {len(models)} models. Generating markdown...")

    # Sort models by ID for easier navigation
    models.sort(key=lambda x: x.get("id", ""))

    full_lines = [
        "# OpenRouter Comprehensive Model List",
        f"\n*Auto-generated on {Path('.').absolute()}*",
        "\nThis document contains a full list of models available via OpenRouter, including pricing and context limits.",
        "\n| Model Name | Model ID | Input ($/1M) | Output ($/1M) | Context |",
        "| :--- | :--- | :--- | :--- | :--- |"
    ]

    free_lines = [
        "# OpenRouter High-Quality Free Models",
        f"\n*Auto-generated on {Path('.').absolute()}*",
        "\nThis document highlights free models on OpenRouter. Note that these often have stricter rate limits.",
        "\n| Model Name | Model ID | Context | Quality Note |",
        "| :--- | :--- | :--- | :--- |"
    ]

    # High quality indicators for free models
    high_quality_keywords = ["llama-3.1", "llama-3.3", "gemini", "gemma-3", "hermes", "phi-4", "qwen", "mistral-small"]

    for model in models:
        name = model.get("name", "Unknown")
        id = model.get("id", "Unknown")
        pricing = model.get("pricing", {})
        prompt_val = float(pricing.get("prompt", 0))
        completion_val = float(pricing.get("completion", 0))
        prompt = prompt_val * 1000000
        completion = completion_val * 1000000
        context = model.get("context_length", "Unknown")
        
        # Format pricing
        prompt_str = f"{prompt:.4f}" if prompt < 0.01 else f"{prompt:.2f}"
        comp_str = f"{completion:.4f}" if completion < 0.01 else f"{completion:.2f}"
        
        full_lines.append(f"| {name} | `{id}` | ${prompt_str} | ${comp_str} | {context} |")

        # Check for free
        if prompt_val == 0 and completion_val == 0:
            quality_note = "Standard"
            lower_id = id.lower()
            if any(kw in lower_id for kw in high_quality_keywords):
                quality_note = "**High Quality**"
            
            free_lines.append(f"| {name} | `{id}` | {context} | {quality_note} |")

    # Save Full List
    full_path = Path("docs/openrouter_models_full.md")
    full_path.parent.mkdir(parents=True, exist_ok=True)
    full_path.write_text("\n".join(full_lines), encoding="utf-8")
    
    # Save Free List
    free_path = Path("docs/openrouter_free_models.md")
    free_path.write_text("\n".join(free_lines), encoding="utf-8")
    
    print(f"Successfully saved {len(models)} models to {full_path}")
    print(f"Successfully saved free models to {free_path}")

if __name__ == "__main__":
    fetch_and_parse_models()