Parsera

Scrape any website with AI, right inside customer conversations

Parsera uses LLMs to extract structured data from any webpage using plain language instructions. Your AI agent describes what it needs, and Parsera delivers clean, typed results. No CSS selectors, no brittle scripts, just tell the agent what data you want from any URL.

Chosen by 800+ global brands across industries

LLM-powered extraction at your fingertips

Your AI agent wields Parsera's natural language scraping capabilities to pull structured data from live web pages, raw HTML, and pre-built scraper templates.

Parsera

Use Cases

Web data extracted through conversation

Parsera's LLM-powered extraction combined with Tars means anyone on your team can pull structured web data by simply asking questions.

Real-Time Competitor Price Checks in Chat

A sales rep messages 'What is CompetitorX charging for their enterprise plan?' Your AI agent sends the competitor's pricing page URL to Parsera with attributes for plan names and prices. Parsera's LLM extracts the structured pricing data and the agent presents a clean comparison. The rep gets current competitive intelligence in seconds without manual research.

Automated Job Board Monitoring for Recruiting

A hiring manager asks 'Are there new senior engineer postings on Y Combinator's job board today?' The agent runs a pre-built Parsera scraper agent against the target URL, extracting job titles, companies, and posting dates. The manager gets a structured list of relevant openings delivered conversationally instead of manually scanning job boards.

Product Catalog Data Pulls for Market Research

An analyst needs product specifications from a manufacturer's website. They paste the URL in chat and describe what fields they need. The agent sends this to Parsera's extractor, which returns structured data with product names, specs, and prices. The analyst gets clean spreadsheet-ready data without writing a single line of scraping code.

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FAQs

Frequently Asked Questions

How does Parsera extract data without CSS selectors or XPath?

Parsera uses large language models to understand page structure. You define attributes as natural language descriptions, like 'product price' or 'article title,' and the LLM identifies and extracts those fields from the HTML. This approach adapts to page layout changes automatically, unlike brittle selector-based scrapers that break when sites update.

What is the difference between Parsera's standard and precision extraction modes?

Standard mode performs efficient extraction suitable for most pages. Precision mode minimizes page reduction to detect data hidden in nested HTML tags, but consumes more credits. Your agent can select the mode based on extraction complexity, using standard for straightforward pages and precision for heavily structured content.

Can the agent use proxy routing for geo-restricted pages?

Yes. Parsera supports proxy country routing on extraction requests. The agent can specify a proxy country like UnitedStates or Germany to access geo-restricted content. Use the Get Proxy Countries endpoint to see all available regions before configuring extractions.

What authentication is needed to connect Parsera to Tars?

You need your Parsera API key, available from the API tab on your Parsera account page. Enter it once in the Tars Tools section. The key is stored securely and enables access to all extraction, parsing, and agent management endpoints.

Can I reuse scraper configurations across multiple URLs?

Absolutely. Parsera supports scraper agents and templates that define extraction attributes once and run them against any URL. The agent can execute saved templates via the Run Scraper Template endpoint, or invoke pre-built agents through the Scrape With Agent endpoint, making recurring extractions consistent and fast.

Does Tars store the scraped web data from Parsera?

No. Parsera extraction results are fetched live during conversations and used to generate responses. Tars does not maintain a database of scraped content. Conversation logs may contain the extracted data as part of the response, but raw extraction payloads are not persisted separately.

How is using Parsera through Tars different from Parsera's Python library?

Parsera's Python library requires coding to set up extraction scripts. Through Tars, your non-technical team members can request web data extraction conversationally. The AI agent translates natural questions into properly formatted Parsera API calls, handling attributes, modes, and proxy settings automatically.

Can the agent extract data from pages that require authentication or cookies?

Yes. Both the Extract Data and Scrape With Agent endpoints accept session cookies as parameters. The agent can pass authentication cookies to access login-protected pages. You configure the cookie details, and Parsera includes them in the extraction request to reach content behind authentication walls.

How to add Tools to your AI Agent

Supercharge your AI Agent with Tool Integrations

Don't limit your AI Agent to basic conversations. Watch how to configure and add powerful tools making your agent smarter and more functional.

Privacy & Security

We’ll never let you lose sleep over privacy and security concerns

At Tars, we take privacy and security very seriously. We are compliant with GDPR, ISO, SOC 2, and HIPAA.

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