Q1

Here's a summary of everything we shipped and published in Q1 2026.

API Changes

parse_json Now Publicly Available

parse_json has graduated from beta and is now publicly available. It uses AI to extract structured data from any webpage according to a schema you define, without HTML parsing or brittle CSS selectors. You describe the fields you want, and Gaffa returns a clean JSON object.

Mapping Requests Now Publicly Available

Mapping Requests, which lets you extract all URLs from a site's sitemap, has moved out of beta and is now publicly available to all users. It's useful for building crawlers, auditing site structure, or feeding a list of URLs into a batch scraping workflow.

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Tools

New Tool: HTML to Markdown Converter

We launched HTML2Markdown, a free tool powered by Gaffa that converts any webpage into clean, readable markdown in one click. It's built on the same generate_markdown action available in the API, so it's also a good way to see what the action produces before integrating it into your own project.

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Samples, Blog & Tutorials

Table Scraping: Python Examples and Full Walkthrough

We added a new set of Python examples to our GitHub samples repository focused on scraping tables, along with a full blog post walkthrough covering both approaches, when to use each, and how to get clean, structured output either way. There are three scripts covering different approaches:

• capture_dom.py — Fetches the raw HTML via Gaffa's capture_dom action and parses the table locally using BeautifulSoup. Good for when you need full control over how the data is processed.

• parse_table_demo.py — Uses Gaffa's parse_table action on our demo site to return structured JSON directly, with no HTML parsing required.

• parse_table_wikipedia.py — A real-world example using parse_table on Wikipedia's GDP by Country table. Shows how headers are automatically normalised into clean JSON keys.

View the examples. Read the post.

Automated Form Filling: Python Examples and Tutorial

We added a set of Python examples to our samples repository along with a full tutorial covering how to automate web form interactions end-to-end. It walks through using parse_json to extract all fields from a form into a structured schema, prompting for values in the terminal, filling and submitting the form using type and click, and capturing a screenshot after submission. It's designed for automation workflows that require schema-driven extraction with a human-in-the-loop data-entry step.

View the examples. Read the tutorial.

How to Scrape Every Image from a Website

We published a guide walking through how to use Gaffa to automatically extract every image from a webpage, covering how to combine browser actions to navigate, wait for content to load, and pull out image URLs at scale. Read the post.

How to Slash Your Gaffa Credit Costs by 40+%

We published a breakdown of how blocking unnecessary media downloads using max_media_bandwidth can reduce your credit usage by over 40% on image-heavy sites, with no impact on the text content you're trying to extract. Read the blog.

Let Your AI Assistant Write Your Gaffa Code

We published a guide showing how to use Gaffa's llms.txt file to give AI assistants like ChatGPT or Claude accurate, up-to-date context about the API, so they can generate working code straight away without you needing to explain endpoint structures or payload formats. Read the blog.

Case Study: ivee

We published a case study on how ivee used Gaffa to scrape 50 job boards, tripling their curated job listings and saving 10 hours of manual work per week. It's a good real-world example of what's possible when you remove the infrastructure overhead from a scraping workflow. Read the case study.