Introducing SerpApi's New Markdown Output

SerpApi launched Markdown Output, a new feature that delivers real-time search data in Markdown format optimized for LLMs and AI agents, available on every search and API with no configuration needed. The company reports the new output uses roughly half the tokens of JSON on average and up to 90% fewer tokens for some APIs, aiming to fit more useful data into models' context windows.

We’re proud to announce the launch of Markdown Output https://serpapi.com/markdown-output , a new feature designed to deliver real-time search data in a format optimized for LLMs and AI agents. It is available on every search, on every API, and it comes back from the same structured search that JSON does. This is SerpApi’s effort to adapt and improve our product for LLMs and AI agents. Based on our benchmarks, the new output gives you roughly half the tokens of JSON on average and up to 90% for some APIs. If you already use our APIs, you can implement it right away; no configuration is needed, and it works automatically across our 100+ APIs https://serpapi.com/search-engine-apis . In this post, we’ll show what the new Markdown Output looks like on a real search and explain when to use it and when to stay on JSON. Getting Markdown results does not require a new endpoint. Any existing search can return it in three ways: Query parameter curl "https://serpapi.com/search?engine=google&q=coffee&api key=YOUR KEY&output=md" Route extension curl "https://serpapi.com/search.md?engine=google&q=coffee&api key=YOUR KEY" Accept header curl -H "Accept: text/markdown" "https://serpapi.com/search?engine=google&q=coffee&api key=YOUR KEY" Why Markdown for LLMs Markdown https://www.markdownguide.org/ is a plain-text format with headers, tables, links, and lists that you can read raw or render as structure. In the last few years, it also became the default language of LLMs. Models are trained on enormous amounts of it documentation, READMEs, forums ; chat interfaces render their answers from it, and OpenAI's own prompt engineering guide https://developers.openai.com/api/docs/guides/prompt-engineering message-formatting-with-markdown-and-xml recommends Markdown headers and lists to communicate hierarchy to the models. We took that signal from our users and from the industry, everyone converging on Markdown as the common input and output format for LLMs and agents, and adapted our product to it. Every design choice in the new output serves one goal, to fit more useful data in your model's context window. Let's see each one with a real search for coffee on Google. Token Efficient Take the first organic result from a coffee search on Google. This is everything JSON carries for it: { "position": 1, "title": "Coffee", "link": "https://en.wikipedia.org/wiki/Coffee", "redirect link": "https://www.google.com/url?sa=t&source=web&rct=j&opi=89978449&url=https://en.wikipedia.org/wiki/Coffee&ved=2ahUKEwiOsKzK96uWAxXmQjABHecdCtsQFnoECCcQAQ", "displayed link": "https://en.wikipedia.org › wiki › Coffee", "thumbnail": "https://encrypted-tbn0.gstatic.com/images?q=tbn:ANd9GcQs1E6FVWWD12JBVWlsho0fGc7GxAR50Pf030IPyxJNl5A9xYPmgt 1&usqp=CAE&s", "favicon": "https://serpapi.com/images/i/iVBORw0KGgoAAAANSUhEUgAAABwAAAAcCAAAAABXZoBIAAAAnklEQVR4AeTNIQiDQABG4b-u17X1aF6PK3YEO9iMJqPVau82y4FgMezS0oVLhqsHtrcqeqzDXv3CEz 6L4yTtZM3dnHmPTtjzXZAXKYVo4agkU2GI2Lloc6JDez1-flswMu1EQZ3xlE7lK8eKDkjtwE-crBMV-wesKmCiisGGepZIfQJpMj9SNb2MYWrChjVkULuCyCfRvsdmBieyQQAsoDk 9ryhFMAAAAASUVORK5CYII.png", "snippet": "Coffee is a beverage brewed from roasted ground coffee beans. Dark-colored and bitter, coffee has a stimulating effect on humans due to its caffeine content;", "snippet highlighted words": "a beverage brewed from roasted ground coffee beans" , "about this result": { "source": { "description": "Coffee is a beverage brewed from roasted ground coffee beans. Dark-colored and bitter, coffee has a stimulating effect on humans due to its caffeine content;", "source info link": "https://en.wikipedia.org/wiki/Coffee", "icon": "https://serpapi.com/images/i/iVBORw0KGgoAAAANSUhEUgAAABwAAAAcCAAAAABXZoBIAAAAnklEQVR4AeTNIQiDQABG4b-u17X1aF6PK3YEO9iMJqPVau82y4FgMezS0oVLhqsHtrcqeqzDXv3CEz 6L4yTtZM3dnHmPTtjzXZAXKYVo4agkU2GI2Lloc6JDez1-flswMu1EQZ3xlE7lK8eKDkjtwE-crBMV-wesKmCiisGGepZIfQJpMj9SNb2MYWrChjVkULuCyCfRvsdmBieyQQAsoDk 9ryhFMAAAAASUVORK5CYII.png" }, "languages": "en" , "regions": "US" }, "about page link": "https://www.google.com/search?q=About+https://en.wikipedia.org/wiki/Coffee&tbm=ilp", "about page serpapi link": "https://serpapi.com/search.json?engine=google about this result&google domain=google.com&q=About+https%3A%2F%2Fen.wikipedia.org%2Fwiki%2FCoffee", "source": "Wikipedia" } And the same result as a Markdown table row: Organic Results | Position | Title | Thumbnail | Snippet | About Page Link | Source | Read More Link | Date | | --- | --- | --- | --- | --- | --- | --- | --- | | 1 | Coffee https://en.wikipedia.org/wiki/Coffee | Thumbnail https://encrypted-tbn0.gstatic.com/images?q=tbn:ANd9GcQs1E6FVWWD12JBVWlsho0fGc7GxAR50Pf030IPyxJNl5A9xYPmgt 1&usqp=CAE&s | Coffee is a beverage brewed from roasted ground coffee beans. Dark-colored and bitter, coffee has a stimulating effect on humans due to its caffeine content; | https://www.google.com/search?q=About+https://en.wikipedia.org/wiki/Coffee&tbm=ilp | Wikipedia | | | Nothing the model needs is missing. The position, title, thumbnail, snippet, and source are all kept in the output, and the row costs a fraction of the tokens the object did. Across the full coffee response, the JSON weighs 24,723 tokens and the markdown 6,435, a 74% cut. Here are some examples: Native Markdown Links In JSON a result splits its data across separate fields: "title": "Coffee", "link": "https://en.wikipedia.org/wiki/Coffee", "displayed link": "https://en.wikipedia.org › wiki › Coffee" In Markdown that is one cell: Coffee https://en.wikipedia.org/wiki/Coffee The model gets the title and the URL already bound together. No Noise The same JSON result also carries a redirect link a 150-character Google tracking URL , a 320-character favicon URL, and internal metadata objects: "redirect link": "https://www.google.com/url?sa=t&source=web&rct=j&opi=89978449&url=https://en.wikipedia.org/wiki/Coffee&ved=2ahUKEwiOsKzK96uWAxXmQjABHecdCtsQFnoECCcQAQ", "favicon": "https://serpapi.com/images/i/iVBORw0KGgoAAAANSUhEUgAAABwAAAAcCAAAAABXZoBIAAAAnklEQVR4AeTNIQiDQABG4b-u17X1aF6PK3YEO9iMJqPVau82y4FgMezS0oVLhqsHtrcqeqzDXv3CEz 6L4yTtZM3dnHmPTtjzXZAXKYVo4agkU2GI2Lloc6JDez1-flswMu1EQZ3xlE7lK8eKDkjtwE-crBMV-wesKmCiisGGepZIfQJpMj9SNb2MYWrChjVkULuCyCfRvsdmBieyQQAsoDk 9ryhFMAAAAASUVORK5CYII.png" None of that helps a model answer a question about coffee. The Markdown Output strips them automatically, and you can check the table row above; none of these fields made it in. The title, link, snippet, and source remain. Structured Output Markdown does not flatten the response into prose. Each result array becomes a table, and the nine organic results of our coffee search arrive as nine rows under one header: Organic Results | Position | Title | Thumbnail | Snippet | About Page Link | Source | Read More Link | Date | | --- | --- | --- | --- | --- | --- | --- | --- | | 1 | Coffee https://en.wikipedia.org/wiki/Coffee | Thumbnail https://encrypted-tbn0.gstatic.com/images?q=tbn:ANd9GcQs1E6FVWWD12JBVWlsho0fGc7GxAR50Pf030IPyxJNl5A9xYPmgt 1&usqp=CAE&s | Coffee is a beverage brewed from roasted ground coffee beans. Dark-colored and bitter, coffee has a stimulating effect on humans due to its caffeine content; | https://www.google.com/search?q=About+https://en.wikipedia.org/wiki/Coffee&tbm=ilp | Wikipedia | | | | 2 | Fresh Roasted, Single Origin Coffee Beans https://creaturecoffee.co/collections/coffee?srsltid=AfmBOoo5rAxqBgQmreq6MaHX-U rw-P5UI-90YLvKI1oRGTosk eXwMU | | Creature Coffee roasts single origin coffee beans from around the globe right here in Austin, Texas. | https://www.google.com/search?q=About+https://creaturecoffee.co/collections/coffee?srsltid=AfmBOoo5rAxqBgQmreq6MaHX-U rw-P5UI-90YLvKI1oRGTosk eXwMU&tbm=ilp | Creature Coffee Co | | | | 3 | Coffee Shops Austin, TX - Last Updated August 2026 https://www.yelp.com/search?find desc=Coffee+Shops&find loc=Austin%2C+TX | | Coffee Shops Austin, TX · 1. Mazúr Coffee · 2. Palomino Coffee · 3. Idlewild Coffee · 4. Desnudo Coffee · 5. Flora Coffee · 6. Daydreamer Coffee. 3.9 ... | https://www.google.com/search?q=About+https://www.yelp.com/search?find desc=Coffee+Shops&find loc=Austin%2C+TX&tbm=ilp | Yelp | https://www.yelp.com/search?find desc=Coffee+Shops&find loc=Austin,+TX :~:text=Coffee%20Shops%20Austin%2C%20TX&text=1.%20Maz%C3%BAr%20Coffee&text=2.%20Palomino%20Coffee&text=3.%20Idlewild%20Coffee&text=4.%20Desnudo%20Coffee&text=5.%20Flora%20Coffee&text=6.%20Daydreamer%20Coffee,3.9%20 25%20reviews | | | 4 | r/Coffee https://www.reddit.com/r/Coffee/ | | This is a place to share and talk about your favorite coffee roasters or beans. recommendations, reviews, etc. | https://www.google.com/search?q=About+https://www.reddit.com/r/Coffee/&tbm=ilp | Reddit · r/Coffee | | | | 5 | Cuvée Coffee: Always Something Brewing. Austin, TX https://cuveecoffee.com/?srsltid=AfmBOorjX3 f5tAt5zldI8Vqr4pNmmfh2FRWlZcVVMEGXhowAfzLZ Jt | | At Cuvée, coffee isn't a job, it's a passion. Since 1998, we've become known for roasting exceptional, ethically sourced, sustainable coffee. | https://www.google.com/search?q=About+https://cuveecoffee.com/?srsltid=AfmBOorjX3 f5tAt5zldI8Vqr4pNmmfh2FRWlZcVVMEGXhowAfzLZ Jt&tbm=ilp | Cuvée Coffee | | | | 6 | Coffee - The Nutrition Source https://nutritionsource.hsph.harvard.edu/food-features/coffee/ | | Various polyphenols in coffee have been shown to prevent cancer cell growth in animal studies. Coffee has also been associated with decreased estrogen levels, a ... | https://www.google.com/search?q=About+https://nutritionsource.hsph.harvard.edu/food-features/coffee/&tbm=ilp | The Nutrition Source | https://nutritionsource.hsph.harvard.edu/food-features/coffee/ :~:text=Type%202%20Diabetes | | | 7 | Texas Coffee Traders https://texascoffeetraders.com/ | | No information is available for this page. | https://www.google.com/search?q=About+https://texascoffeetraders.com/&tbm=ilp | Texas Coffee Traders | | | | 8 | 33 Best Coffee Shops In Austin, Sorted By Neighborhood https://www.atasteofkoko.com/visit-austin/coffee-shops-in-austin | | Best Coffee Shops in Central Austin · Hank's · Civil Goat Coffee Roasters Central · Merit Coffee Central · Bennu Coffee · Terrible Love. | https://www.google.com/search?q=About+https://www.atasteofkoko.com/visit-austin/coffee-shops-in-austin&tbm=ilp | A Taste of Koko | https://www.atasteofkoko.com/visit-austin/coffee-shops-in-austin :~:text=Best%20Coffee%20Shops%20in%20Central,Austin&text=Hank's&text=Civil%20Goat%20Coffee%20Roasters%20 Central &text=Merit%20Coffee%20 Central &text=Bennu%20Coffee&text=Terrible%20Love | Apr 22, 2026 | | 9 | Try the new gold standard of coffee at home https://icoffee.co/ | | At Industry Coffee, we take great care in every step of our coffee making process. From selecting the best beans, to roasting, brewing, and blending our ... | https://www.google.com/search?q=About+https://icoffee.co/&tbm=ilp | Industry Coffee | https://icoffee.co/ :~:text=At%20Industry%20Coffee%2C%20we%20take,committed%20to%20quality%20and%20consistency. | | The same happens for shopping https://serpapi.com/google-shopping-api results, hotel https://serpapi.com/google-hotels-api listings, and any other result array. The response also opens with a YAML https://yaml.org/about/ frontmatter block carrying the search parameters and metadata: --- search metadata: id: 6a853ba34c437ff2e1aa1265 status: Success json endpoint: "https://serpapi.com/searches/cPnrW0xTHlgDT2yVVjFSmg/6a853ba34c437ff2e1aa1265.json" markdown endpoint: "https://serpapi.com/searches/cPnrW0xTHlgDT2yVVjFSmg/6a853ba34c437ff2e1aa1265.md" pixel position endpoint: "https://serpapi.com/searches/cPnrW0xTHlgDT2yVVjFSmg/6a853ba34c437ff2e1aa1265.json with pixel position" created at: "2026-08-19T05:14:11.488Z" processed at: "2026-08-19T05:14:11.497Z" google url: "https://www.google.com/search?q=Coffee&oq=Coffee&uule=w+CAIQICIaQXVzdGluLFRleGFzLFVuaXRlZCBTdGF0ZXM&hl=en&gl=us&sourceid=chrome&ie=UTF-8" raw html file: "https://serpapi.com/searches/cPnrW0xTHlgDT2yVVjFSmg/6a853ba34c437ff2e1aa1265.html" total time taken: 1.91 search parameters: engine: google q: Coffee location requested: Austin, Texas, United States location used: Austin,Texas,United States google domain: google.com hl: en gl: us device: desktop --- So even in text form you still have the search id, status, and the endpoints to fetch the same search again in either format. When to Choose Markdown or JSON? Both formats come from the same search, so this is not a migration. It is a per-call choice. Markdown fits when: - You feed results straight into an LLM prompt or a RAG context - An agent consumes search results as a tool response - You want a quick human-readable digest of a search - Tokens are the budget you optimize for JSON fits when: - Your code extracts fields programmatically prices as numbers, coordinates, ratings - You handle pagination, filters, or metadata downstream - You store results in a database with a fixed schema - You need every field, including the ones Markdown strips Simply put: if a model reads it, use Markdown. If code reads it, use JSON. Using the JSON Restrictor Before Markdown Output, the tool for cutting response size was the JSON Restrictor https://serpapi.com/json-restrictor , and it is still the right tool when you stay on JSON. You pass a field selector with the request, and SerpApi filters the response server-side before sending it back. We have used it for exactly this purpose in earlier posts, like connecting a local LLM to web search data https://serpapi.com/blog/how-to-connect-your-local-llm-with-web-search-data/ , where trimming the JSON was the difference between fitting the context window or not. Cutting Tokens While Staying on JSON If your pipeline needs structured fields but not all of them, the restrictor alone gets you most of the way. We'll request only the two fields an agent might rank on, position and title: curl "https://serpapi.com/search?engine=google&q=Coffee&location=Austin,+Texas,+United+States&api key=YOUR KEY&json restrictor=organic results .position,organic results .title" The response is the whole search reduced to this: { "organic results": { "position": 1, "title": "Coffee" }, { "position": 2, "title": "Cuvée Coffee: Always Something Brewing. Austin, TX" }, { "position": 3, "title": "Coffee Shops Austin, TX - Last Updated August 2026" }, { "position": 4, "title": "r/Coffee" }, { "position": 5, "title": "Coffee - The Nutrition Source" }, { "position": 6, "title": "Texas Coffee Traders" }, { "position": 7, "title": "50 Best Austin Coffee Shops You Should Try" }, { "position": 8, "title": "Buy Coffee, Tea, Powders Online | The Coffee Bean & Tea ..." }, { "position": 9, "title": "History of coffee - NCA" } } You keep exact types and programmatic access, and drop every section your code never touches. The difference with Markdown Output is what each one removes. The restrictor cuts entire fields and sections you name, while Markdown keeps every section but re-renders it in a lighter format. One subtracts data, the other changes its shape. Stacking Both You don't have to pick one. Pass json restrictor on a Markdown request, and SerpApi first filters the sections, then renders only what is left as Markdown: curl "https://serpapi.com/search.md?engine=google&q=Coffee&location=Austin,+Texas,+United+States&api key=YOUR KEY&json restrictor=organic results" The response is just the Organic Results table from earlier, nothing else. On our coffee search, the four levels look like this: | Request | Tokens | |---|---| | Full JSON | 24,723 | JSON + json restrictor=organic results | 8,486 | | Full markdown | 6,435 | Markdown + json restrictor=organic results | 1,298 | That is a 95% reduction from the original JSON, and you chose exactly which part of the search enters the model's context. For an agent that only reasons over organic results, everything else was noise anyway. Conclusion Markdown Output is live today on every SerpApi plan, at no extra cost, across all 100+ APIs. Same searches, same credits, and around 50% fewer tokens on average and up to 90% for some APIs. The fastest way to see what it does for you is to take an API you already use, add output=md to one request, and count the tokens before and after.