E-Commerce Price Extractor

Only pay when it works. $0.05 per verified extraction — nothing charged on failure or retries.

Extract structured JSON from any product page using a Groq-compatible LLM.

AI Agent Compatible

This actor is AEO-native. The input_schema.json and output_schema.json expose exact field types, defaults, and constraints in machine-readable format. Any AI agent connected to the Apify MCP server — Claude Desktop, Cursor, VS Code — can discover, configure, and execute this actor autonomously without human input. No prompt engineering required.

What It Does

1. Scrapes the page at your URL using a real browser-grade crawler (CheerioCrawler)
2. Strips all HTML, navigation, scripts, and boilerplate → clean plain text
3. Prompts a Groq-compatible LLM to extract data matching your schema
4. Validates the response with AJV (JSON Schema validator)
5. Retries up to 3 times if the LLM returns invalid JSON, injecting the error back into the prompt
6. Returns validated structured data in the Apify dataset

Charge: $0.05 per successful extraction. Nothing charged on failure.

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Input Schema

Field	Type	Required	Default	Description
url	string	✅	—	Page to scrape
output_schema	object	✅	—	JSON Schema defining the data to extract
groq_api_key	string	✅	—	API key (Groq, OpenAI, Together AI, etc.)
model	string	❌	llama-3.3-70b-versatile	Model name
base_url	string	❌	Groq endpoint	For OpenAI-compatible providers

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Usage Examples

Example 1: Groq (default, free tier)

Get a free API key at console.groq.com.

{
    "url": "https://example.com/product/widget-pro",
    "groq_api_key": "gsk_YOUR_GROQ_KEY_HERE",
    "output_schema": {
        "type": "object",
        "required": ["name", "price"],
        "properties": {
            "name":        { "type": "string" },
            "price":       { "type": "number" },
            "description": { "type": "string" },
            "in_stock":    { "type": "boolean" }
        }
    }
}

Output:

{
    "url": "https://example.com/product/widget-pro",
    "extracted": {
        "name": "Widget Pro",
        "price": 29.99,
        "description": "The best widget on the market.",
        "in_stock": true
    },
    "model": "llama-3.3-70b-versatile",
    "attempts": 1
}

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Example 2: OpenAI-compatible endpoint (Together AI, Fireworks AI)

Use any OpenAI-compatible provider by setting base_url:

{
    "url": "https://jobs.lever.co/anthropic/engineer",
    "groq_api_key": "YOUR_TOGETHER_AI_KEY",
    "base_url": "https://api.together.xyz/v1",
    "model": "meta-llama/Llama-3.3-70B-Instruct-Turbo",
    "output_schema": {
        "type": "object",
        "required": ["title", "company", "location", "salary_range"],
        "properties": {
            "title":        { "type": "string" },
            "company":      { "type": "string" },
            "location":     { "type": "string" },
            "salary_range": { "type": "string" },
            "remote":       { "type": "boolean" },
            "requirements": {
                "type": "array",
                "items": { "type": "string" }
            }
        }
    }
}

Other compatible endpoints:

• Fireworks AI: https://api.fireworks.ai/inference/v1
• OpenAI: https://api.openai.com/v1

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Example 3: Ollama (local, completely free)

Run models locally at zero cost with Ollama:

# Start Ollama with a model
ollama serve
ollama pull llama3.3

{
    "url": "https://news.ycombinator.com/item?id=12345",
    "groq_api_key": "ollama",
    "base_url": "http://localhost:11434/v1",
    "model": "llama3.3",
    "output_schema": {
        "type": "object",
        "required": ["title", "score", "comments_count"],
        "properties": {
            "title":          { "type": "string" },
            "score":          { "type": "integer" },
            "comments_count": { "type": "integer" },
            "author":         { "type": "string" },
            "url":            { "type": "string" }
        }
    }
}

Note: When running the Actor on Apify cloud, Ollama requires a remote endpoint. For local testing, use apify run with localhost.

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Common Use Cases

Use Case	Schema Fields
Product extraction	name, price, description, in_stock, SKU
Job postings	title, company, location, salary, requirements
News articles	headline, author, published_date, summary, tags
Real estate listings	address, price, bedrooms, bathrooms, sqft
Restaurant menus	restaurant_name, items (name, price, description)
Resume parsing	name, email, skills, experience, education
Event listings	name, date, venue, ticket_price, organizer

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How Retry Logic Works

The actor uses the same retry-with-feedback pattern as constrained.py from the DagPipe core library:

1. Attempt 1: Send text + schema → LLM responds → AJV validates
2. On failure: Inject the exact AJV error message into the next prompt → retry
3. Attempt 2: LLM receives error and corrects → validate again
4. After 3 failures: Throw with a descriptive error message

This approach reliably extracts valid structured data even from smaller/cheaper models.

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Pricing

• $0.05 per successful extraction (Pay-Per-Event)
• Free if extraction fails — you're never charged for failed attempts
• Groq's free tier provides 30 requests/minute at zero cost to you

Scheduling

Example: Schedule against 50 competitor product URLs daily. Total cost: $2.50/day. Zero infrastructure. Zero maintenance.

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Technical Details

• Scraper: CheerioCrawler (zero-JS, fast, reliable)
• Validation: AJV v8 + ajv-formats (JSON Schema Draft-07/2019/2020 compatible)
• LLM client: OpenAI SDK (works with any OpenAI-compatible endpoint)
• Retry strategy: Error-feedback prompting (same pattern as DagPipe constrained.py)
• Language: TypeScript, Node.js 20+
• Tests: 9 vitest tests (100% passing)

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Built With

DagPipe — Zero-cost, crash-proof LLM pipeline orchestrator.

$pip install dagpipe-core