🤖AI & LLM Integrations

Give ChatGPT Real-Time Website Content

ChatGPT's knowledge cutoff is last year. The web is today. Bridge the gap with denkbot.dog — fetch any URL, get clean text, pass it to your LLM as context. The dog fetches the page. ChatGPT reads it. You get answers.

What you'd use this for

Real-time web grounding for LLMs, URL-based Q&A systems, article summarization bots, research assistants with web access, and AI tools that need to "read" websites.

How it works

example
import openai
import requests

def ask_about_url(url, question):
    # Fetch the content
    r = requests.post(
        'https://api.denkbot.dog/scrape',
        headers={'Authorization': 'Bearer YOUR_API_KEY'},
        json={'url': url},
    )
    text = r.json()['text'][:8000]  # Token budget

    # Ask ChatGPT
    client = openai.OpenAI()
    response = client.chat.completions.create(
        model='gpt-4o',
        messages=[
            {'role': 'system', 'content': f'Here is the content of {url}:\n\n{text}'},
            {'role': 'user', 'content': question},
        ],
    )
    return response.choices[0].message.content

Questions & Answers

Does denkbot.dog support function calling?+

We're an HTTP API, not an LLM. Use the output as context in your own LLM calls.

How long is the text field?+

It depends on the page. Long articles can be 50k+ characters. Slice appropriately for your token budget.

Can it scrape in real-time for a chatbot?+

Yes. Latency is 300-2000ms for non-JS pages — acceptable for most chatbot use cases.

Ready to start fetching?

€19/year. Unlimited requests. API key ready in 30 seconds.