Launch HN: Integuru (YC W24) – Reverse-engineer internal APIs using LLMs (github.com)

Many products need integrations with third-party services, but platforms often lack official APIs. Examples include logistics software, financial services, electronic health records (EHRs), and government websites. To build low-latency integrations, developers must reverse-engineer internal APIs, but this can get complicated. With Integuru, you can have easier access to integrations.

We started as recent college grads trying to make US income tax data accessible. We contacted banks, brokerages, payroll software, and more to request access to their APIs, but none took us seriously. We resorted to building integrations with these systems to extract documents like W-2s and 1099s. We initially used browser automation but ran into two big problems: our integrations (1) weren’t reliable due to changing UIs and (2) had slow execution speeds due to spinning up browsers and waiting for pages to load. We experimented with AI-based automation maintenance, but it didn’t solve slow speeds. So, we concluded that browser automation is useful when execution speed isn’t essential, but reverse engineering is often the only path for performant integrations.

Through reverse-engineering dozens of platforms, we noticed many internal API design patterns that LLMs could decipher. We built an agent to automate the creation of integrations. Today, Integuru can analyze a platform’s internal API designs and build an integration in minutes.

The agent mimics what a human does when reverse-engineering. Say you want to download utility bills from a utility website. You’d first use Integuru to generate a file of network requests and a file of cookies. You pair these two files with a prompt about your desired action—in this case, to download utility bills.

Integuru identifies the final request that downloads utility bills. The request URL might look like this: https://www.example.com/utility-bills?accountId=123&userId=4.... It then identifies parts of the request that depend on other requests. The example URL contains dynamic parts— accountId and userId—that usually are in the response of previous request(s). It then finds other requests whose response contains any of these and adds them to the dependency graph. The newly found request URLs might look like https://www.example.com/getAccountId, https://www.example.com/getUserId, and so on.

This process repeats until the most recently found request doesn’t depend on any other request. Integuru then traverses up the graph, starting from the requests without dependencies while converting each request into a runnable function.

Integuru supports a surprising number of use cases like downloading documents, sending money, creating virtual cards… People already use the agent to build low-latency APIs for platforms like Robinhood, transportation management systems (TMS), and more. However, the agent still has limitations due to current LLM capabilities and long-tail edge cases, but we’ve been giving each platform to the agent for the first try. When the agent does struggle, we find the generated graphs and code still helpful as references for us to complete the work manually.

The agent and all integ...