Two agents race to query Google Flights, one drives a browser, one sends requests directly: API Anything gained 2,600 stars in 40 days, powered by this race

API Anything lets AI agents stop clicking through web pages. It records the request the website's own frontend sends, then calls it directly over plain HTTP. When the site changes, it repairs itself, and learned interfaces are shared with everyone with cookies stripped. Created in Sep 27, and by Nov 6 it had about 2,600 stars and 310 forks. The author has no big following and did not hit Hacker News, and at least five or six projects do the same thing, some one or two years earlier. It wins on packaging: a comparison video anyone can understand, plus a one-line install. Below: what it is, where the stars came from, who it competes with, and how to borrow its approach if you want a project that earns stars too.

A 3x speed race with no editing

On Sep 28, 2026, the day after the database went live, author Charles Zheng (a Carnegie Mellon student who built a wearable device called Forth) recorded a video: the same Claude Code, the same prompt, running two agents at once to look up flights on Google Flights. The agent on the left drives a headless Chrome, loading the page, taking screenshots, and reading the DOM one step at a time. The agent on the right calls an API that API Anything learned, getting the JSON back in a single request. The footage is played at 3x speed, uncut, and the timer shows real elapsed time.

视频下面一行粗体字:快 1.5 到 2 倍,token 省一半左右,花费持平或更低,20 题全对。 再往下是原始数字:查一个日期,浏览器 12.9 秒、$0.166,它 8.4 秒、$0.074;查 Goodreads 上 5 本书,浏览器 182.5 秒,它 30.5 秒。README 还老实补了一句:「每组只是一个网站、一个模型、跑了几次」,方法和原始数据都放在仓库里。

This video became its most valuable asset. In Oct 24, a TikTok AI-tool account called @ai.honeycove cut it into a clip captioned "API Anything turns any website into an API, so agents don't have to slowly click through browsers". By 11-06 it had 80.6k views and 2,721 likes (TikTok, 11-06). Over the following days, @milesreevesai (59k views), @alex.carterr__ (27k), and @chase.builds.ai (22k) posted nearly identical videos, and people reposted it on Instagram and YouTube Shorts. The captions are highly similar, looking like mass-produced "daily open-source picks" accounts. No evidence was found that the author paid for it, but it can't be ruled out (inference: unpaid reposting is more likely, since the material was ready-made).

How the stars climbed

(Daily GitHub star counts; the day-by-day numbers before 10-17 come from Trendshift and posts online.)

Three things stand out. First, the cold start was all the author posting on Reddit: a post on 10-08 and another on 10-09, with daily stars topping 100 in those days, which matches. He posted to Hacker News twice (10-09, 10-27), and both only reached 1 points. Second, the batch of TikTok videos produced no spike: the 10-24 video with 80k views only added 46 stars that day. They look more like a floor under the "fifty a day" baseline (inference). Third, the two real jumps were both planned by the author himself: an npm package on 10-28, and a "relaunch" starting 11-03. The PR for 10-31 states it was preparing for "launch traffic": first fixing the spots on the landing page people ask about most, then cutting a new promo film. I couldn't find which platform that film was posted on.

It's not just starring. The npm package had 4,733 cumulative downloads from 10-28 to 11-05, running at 500 to 700 a day (npm, 11-05). I can't tell how many of those come from automated scripts. The fork rate is about 12%, in line with other new repos of the same age, and I see no sign of star-buying.

What it actually is

The first line of the README reads: "When an agent needs a website that has no API, it learns one. Every other agent gets it for free." The examples are concrete: Goodreads stopped issuing API keys 2020 years ago, and Google Flights never had a public API. An agent that wants them has to open a browser, click, wait, and read the screen, and every step costs time and tokens.

It does three things:

1. Learn: a headless browser loads the page once for each of two sample inputs, then compares the two sets of requests. Whatever changed is a parameter; everything else is kept as is. This step uses no LLM.
2. Call: first try a plain fetch; if it gets blocked, send the request from inside the browser page; for read-only operations, it can even run the whole page as a last resort.
3. Repair: when a site redesigns and the responses no longer match, it first pulls a version someone else already fixed. If none exists, it relearns, and only saves the result once a replay succeeds.

Installation is one line: npx -y api-anything setup. It finds Claude Code, Codex, Cursor, Claude Desktop, Gemini CLI, and VS Code on your machine, asks which to connect, and needs no sign-up and no key. The package ships with 114 sites and 323 read-only operations, from Hacker News, Reddit, and YouTube to Google Flights, Goodreads, Airbnb, Amazon, and Steam. Right after install you can ask "What rating does Goodreads give Piranesi?" Write operations are off by default, and duplicate submissions get rejected.

What really sets it apart is the shared directory. The README compares it to reCAPTCHA: reCAPTCHA digitized millions of books using the words people had to type anyway, while API Anything turns websites into APIs using the requests agents already send. A newly learned operation only goes live after it succeeds on 2 other networks. A live one gets replaced only after 3 or more networks report failures. One person fixes it, and everyone benefits.

In the Nov 6 README it added a new example: "Who's talking about your project." One script queries Hacker News, Reddit, YouTube, GitHub, and Mastodon at once, needs no accounts, and returns results in under 2 seconds. The example is well chosen. Someone reading the README has probably just posted their own project.

Who it's competing with: the idea isn't new, it came later

The one to watch is Unbrowse: it launched 8 months earlier, had nearly the same idea, also built a shared layer, and stalled at 900. There's also a cluster of small "capture traffic → generate MCP" libraries with a few dozen stars each. The difference isn't technical. It's whether there's something people can grasp in 15 seconds, and an entry point that installs without reading docs.

The shared directory is a real difference, but it isn't yet why it took off. The directory only becomes useful once there are many users, so right now it's more a good story. The race video and one-line install could be copied by any competitor tomorrow.

If it were you

The core call: API Anything shows something that matters a lot for developers without big followings. In agent tooling, a side-by-side comparison video plus a one-line install command, posted by hand to Reddit, got over two thousand stars in 40 days. But the specific direction of "turn websites into APIs" is already crowded with five or six players, so copying the same thing won't get much attention. It's worth trying, but pick an area it doesn't cover.

Who it suits: developers who know the request structure of a category of hard-to-work-with sites (Chinese platforms, back-office systems in a specific industry, government and public data sites) and can write dozens of working operations themselves. Those who can cut a 30-second short video and are willing to post on Reddit and Chinese communities themselves are a better fit.

Who it doesn't suit: anyone building another general-purpose "any website → API / MCP" tool. Unbrowse launched 8 months earlier and has only 900 stars, apitap-style projects have a few dozen, and Pomerado, backed by YC, is cooling off. It's also not for anyone unwilling to take on the legal and account-ban risk of scraping other people's sites.

How to do it:

• Change the audience and build a Chinese-website version: nearly all of its 114 built-in sites are English, and in its own demo Reddit is often blocked. Chinese developers who want agents to query Bilibili, Zhihu, Douban, 12306, Amap, or Xianyu currently have to open a browser and click through. A "Chinese website agent API directory" using the same MCP and one-line install is an open gap in the Chinese community (inference: I found no similar Chinese project).
• Go vertical and build a complete directory for one industry: for example, only flight and hotel sites, only job boards, or only e-commerce price comparison. Covering a dozen sites in one industry thoroughly is easier to explain ("here's what it does once installed") than a general tool.
• Do what it hasn't done well: it's read-only, write operations are off by default with none built in, and sites that require login are hard to learn. "Help agents fill in forms, place orders, and post safely" is a gap it deliberately left open. Whoever does it well has a story to tell.
• Copy its packaging: put a side-by-side race video on the first screen of the README, with a bold hard number below it. Publish the method and raw data in full, and state the limits clearly. Use npx … setup to auto-connect every coding agent, with zero accounts and zero keys. Include a ready-made example tied to the reader's own situation (its "who's talking about your project").

How to get users: For a cold start, just follow this. Record the demo video, then post it the same day on r/ClaudeCode, r/AI_Agents and r/SideProject, with a title that says straight out "let agents skip the browser". For the Chinese version, post on V2EX, Juejin, Jike and Bilibili with the same race video. If the video is clear enough, AI tool accounts on TikTok and Xiaohongshu will repost it on their own. After the first wave, fix the questions people ask most, ship a proper release, and re-launch with a new video. API Anything's second wave was even bigger than the first.

When to move: complaints that coding agents are slow and token-hungry with browsers only became common in the last half year, and similar tools like reverse-api-engineer are still accelerating, so attention in this category is still rising. The window closes when Claude or OpenAI builds similar capability into their agents' native tools, or when major sites start specifically blocking these requests. My estimate is a few months left (inference).

Signs I've misread it: (1) Someone builds a Chinese or vertical version, packages it the same way, and gets fewer than two or three hundred stars within two weeks. That would mean API Anything's momentum came mainly from the timing in the English agent community, not from packaging anyone can copy. (2) API Anything's own shared directory fills up with Chinese sites or a single industry first, taking the open space, and you'd need to change direction. (3) Large sites or platforms start blocking these requests at scale, or someone gets sued over it, and the whole category cools down.

Data and sources

GitHub repo page, README (11-06 version), PRs #87 and #89 (10-31), and daily star records (10-17 to 11-06); Trendshift rankings (10-09 to 10-12); npm download stats (10-28 to 11-05); TikTok video views and likes (as of 11-06); Instagram @selfmindsai posts; Elma blog (10-09); two Hacker News submissions (10-09, 10-27); competitor star counts as of around 11-06, figures marked "about" are rounded.