How long does this build a scraping tool with a local uncensored llm and scavio tutorial take?

Most developers complete this tutorial in 15 to 30 minutes. You will need a Scavio API key (free tier works) and a working Python or JavaScript environment.

What do I need before starting?

Ollama or llama.cpp. A local uncensored model (dolphin-mixtral, nous-hermes). A Scavio API key. Python 3.10+. A Scavio API key gives you 50 free credits on signup.

Can I run this tutorial with the free tier?

Yes. The free tier includes 50 credits on signup, which is more than enough to complete this tutorial and prototype a working solution.

What frameworks does this work with?

Scavio has a native LangChain package (langchain-scavio), an MCP server, and a plain REST API that works with any HTTP client. This tutorial uses the raw REST API, but you can adapt to your framework of choice.

Scraping Tool with Uncensored LLM + Scavio (2026)

r/LocalLLaMA 2026 has regular threads on pairing local uncensored models (Dolphin, Wizard, Nous) with a cloud-hosted scraping backend. The split is deliberate: the LLM runs locally for privacy and flexibility, Scavio supplies the web data. This tutorial builds that architecture. Scavio's Extract endpoint (POST /api/v1/extract) reads a URL and returns it as Markdown, HTML or plain text, so the local model works on clean page content instead of raw markup. Use mode advanced for pages built with JavaScript, and the Crawl endpoint when you need a whole site rather than one page.

API Docs: Extract API, Crawl API

Prerequisites

Ollama or llama.cpp
A local uncensored model (dolphin-mixtral, nous-hermes)
A Scavio API key
Python 3.10+

Walkthrough

Step 1: Run the local LLM

Ollama makes this one command.

Bash

ollama run dolphin-mixtral

Step 2: Call Scavio for the scraping layer

Scavio returns the data, so there is no fetching layer for you to maintain.

Python

import requests, os

# Scavio Extract reads one URL and returns the page as markdown, html or text.
# mode: "normal" (1 credit), "advanced" renders JavaScript (1 credit),
# "ultra" for the hardest pages (2 credits). Only a successful call is billed.
# Docs: https://scavio.dev/docs/extract
def scavio_extract(url, headers, mode="normal", fmt="markdown"):
    r = requests.post("https://api.scavio.dev/api/v1/extract", headers=headers,
                      json={"url": url, "format": fmt, "mode": mode}, timeout=90)
    r.raise_for_status()
    return r.json()["data"]["content"]

API_KEY = os.environ['SCAVIO_API_KEY']

def fetch(url, mode='normal'):
    return scavio_extract(url, {'Authorization': f'Bearer {API_KEY}'}, mode=mode)

Step 3: Let the local LLM do extraction

No content filters on the extraction prompt.

Python

import requests
def extract_with_local(html, instruction):
    r = requests.post('http://localhost:11434/api/generate',
        json={'model': 'dolphin-mixtral', 'prompt': f'{instruction}\n\nPAGE:\n{html[:4000]}'})
    return r.json()['response']

Step 4: Wire the full pipeline

Fetch via Scavio, extract via local LLM.

Python

def scrape(url, instruction):
    html = fetch(url)
    return extract_with_local(html, instruction)

print(scrape('https://target.com', 'Extract all product names and prices as JSON.'))

Step 5: Validate output

Light schema check before saving.

Python

import json
def validate(out):
    try: json.loads(out); return True
    except: return False

Python Example

Python

import os, requests

# Scavio Extract reads one URL and returns the page as markdown, html or text.
# mode: "normal" (1 credit), "advanced" renders JavaScript (1 credit),
# "ultra" for the hardest pages (2 credits). Only a successful call is billed.
# Docs: https://scavio.dev/docs/extract
def scavio_extract(url, headers, mode="normal", fmt="markdown"):
    r = requests.post("https://api.scavio.dev/api/v1/extract", headers=headers,
                      json={"url": url, "format": fmt, "mode": mode}, timeout=90)
    r.raise_for_status()
    return r.json()["data"]["content"]


API_KEY = os.environ['SCAVIO_API_KEY']

def scrape(url, instruction):
    html = scavio_extract(url, {'Authorization': f'Bearer {API_KEY}'})
    r = requests.post('http://localhost:11434/api/generate',
        json={'model': 'dolphin-mixtral', 'prompt': f'{instruction}\n\n{html[:4000]}'})
    return r.json().get('response', '')

print(scrape('https://example.com', 'List headings as JSON'))

JavaScript Example

JavaScript

const API_KEY = process.env.SCAVIO_API_KEY;
export async function scrape(url, instruction) {
  const s = await fetch('https://api.scavio.dev/api/v1/extract', {
    method: 'POST',
    headers: { 'Authorization': `Bearer ${API_KEY}`, 'Content-Type': 'application/json' },
    body: JSON.stringify({ url, format: 'markdown', mode: 'normal' })
  });
  const page = (await s.json()).data?.content || '';
  const o = await fetch('http://localhost:11434/api/generate', {
    method: 'POST',
    body: JSON.stringify({ model: 'dolphin-mixtral', prompt: `${instruction}\n\n${page.slice(0, 4000)}`, stream: false })
  });
  return (await o.json()).response;
}

Expected Output

JSON

Fully local extraction logic, with Scavio supplying the web data. Per-page cost: 1 Scavio credit + local GPU time. Data never leaves local box except the URL.

Prerequisites

Ollama or llama.cpp
A local uncensored model (dolphin-mixtral, nous-hermes)
A Scavio API key
Python 3.10+

Walkthrough

Step 1: Run the local LLM

Ollama makes this one command.

Bash

ollama run dolphin-mixtral

Step 2: Call Scavio for the scraping layer

Scavio returns the data, so there is no fetching layer for you to maintain.

Python

import requests, os

# Scavio Extract reads one URL and returns the page as markdown, html or text.
# mode: "normal" (1 credit), "advanced" renders JavaScript (1 credit),
# "ultra" for the hardest pages (2 credits). Only a successful call is billed.
# Docs: https://scavio.dev/docs/extract
def scavio_extract(url, headers, mode="normal", fmt="markdown"):
    r = requests.post("https://api.scavio.dev/api/v1/extract", headers=headers,
                      json={"url": url, "format": fmt, "mode": mode}, timeout=90)
    r.raise_for_status()
    return r.json()["data"]["content"]

API_KEY = os.environ['SCAVIO_API_KEY']

def fetch(url, mode='normal'):
    return scavio_extract(url, {'Authorization': f'Bearer {API_KEY}'}, mode=mode)

Step 3: Let the local LLM do extraction

No content filters on the extraction prompt.

Python

import requests
def extract_with_local(html, instruction):
    r = requests.post('http://localhost:11434/api/generate',
        json={'model': 'dolphin-mixtral', 'prompt': f'{instruction}\n\nPAGE:\n{html[:4000]}'})
    return r.json()['response']

Step 4: Wire the full pipeline

Fetch via Scavio, extract via local LLM.

Python

def scrape(url, instruction):
    html = fetch(url)
    return extract_with_local(html, instruction)

print(scrape('https://target.com', 'Extract all product names and prices as JSON.'))

Step 5: Validate output

Light schema check before saving.

Python

import json
def validate(out):
    try: json.loads(out); return True
    except: return False

Python Example

Python

import os, requests

# Scavio Extract reads one URL and returns the page as markdown, html or text.
# mode: "normal" (1 credit), "advanced" renders JavaScript (1 credit),
# "ultra" for the hardest pages (2 credits). Only a successful call is billed.
# Docs: https://scavio.dev/docs/extract
def scavio_extract(url, headers, mode="normal", fmt="markdown"):
    r = requests.post("https://api.scavio.dev/api/v1/extract", headers=headers,
                      json={"url": url, "format": fmt, "mode": mode}, timeout=90)
    r.raise_for_status()
    return r.json()["data"]["content"]


API_KEY = os.environ['SCAVIO_API_KEY']

def scrape(url, instruction):
    html = scavio_extract(url, {'Authorization': f'Bearer {API_KEY}'})
    r = requests.post('http://localhost:11434/api/generate',
        json={'model': 'dolphin-mixtral', 'prompt': f'{instruction}\n\n{html[:4000]}'})
    return r.json().get('response', '')

print(scrape('https://example.com', 'List headings as JSON'))

JavaScript Example

JavaScript

const API_KEY = process.env.SCAVIO_API_KEY;
export async function scrape(url, instruction) {
  const s = await fetch('https://api.scavio.dev/api/v1/extract', {
    method: 'POST',
    headers: { 'Authorization': `Bearer ${API_KEY}`, 'Content-Type': 'application/json' },
    body: JSON.stringify({ url, format: 'markdown', mode: 'normal' })
  });
  const page = (await s.json()).data?.content || '';
  const o = await fetch('http://localhost:11434/api/generate', {
    method: 'POST',
    body: JSON.stringify({ model: 'dolphin-mixtral', prompt: `${instruction}\n\n${page.slice(0, 4000)}`, stream: false })
  });
  return (await o.json()).response;
}

Expected Output

JSON

Fully local extraction logic, with Scavio supplying the web data. Per-page cost: 1 Scavio credit + local GPU time. Data never leaves local box except the URL.

How to Build a Scraping Tool with a Local Uncensored LLM and Scavio

Prerequisites

Walkthrough

Step 1: Run the local LLM

Step 2: Call Scavio for the scraping layer

Step 3: Let the local LLM do extraction

Step 4: Wire the full pipeline

Step 5: Validate output

Python Example

JavaScript Example

Expected Output

Related Tutorials

Frequently Asked Questions

How long does this build a scraping tool with a local uncensored llm and scavio tutorial take?

What do I need before starting?

Can I run this tutorial with the free tier?

What frameworks does this work with?

Related Resources

Best Personal Knowledge Base Tools for Local LLMs in May 2026

Daily Search Grounding for Ollama Assistant

Best Search Grounding for Local LLMs in 2026

Build a Personal Assistant with Ollama and Search

Local LLM MCP Integration

Scrapingdog vs Scavio

Start Building

How to Build a Scraping Tool with a Local Uncensored LLM and Scavio

Prerequisites

Walkthrough

Step 1: Run the local LLM

Step 2: Call Scavio for the scraping layer

Step 3: Let the local LLM do extraction

Step 4: Wire the full pipeline

Step 5: Validate output

Python Example

JavaScript Example

Expected Output

Related Tutorials

Frequently Asked Questions

How long does this build a scraping tool with a local uncensored llm and scavio tutorial take?

What do I need before starting?

Can I run this tutorial with the free tier?

What frameworks does this work with?

Related Resources

Best Personal Knowledge Base Tools for Local LLMs in May 2026

Daily Search Grounding for Ollama Assistant

Best Search Grounding for Local LLMs in 2026

Build a Personal Assistant with Ollama and Search

Local LLM MCP Integration

Scrapingdog vs Scavio

Start Building