I built a tool that turns OpenAPI specs into an agent

Hacker News

Hey everyone! A couple of weeks ago I was exploring how to integrate multiple LLMs into one project, and I had this idea: what if I could leverage AI to interact with APIs in natural language? After chatting with my cofounder, we were both intrigued. So I opened up Cursor and hacked together a prototype. After a few iterations, we had a working alpha: by dropping an OpenAPI file, we were able to get an AI Agent that understands it and can call those APIs using natural language. The Agent understood every prompt, made the right API calls and even auto-corrected bad payloads. For internal APIs, auth is always tricky, so we added a system that supports API keys and credentials client-side only. Tokens are never stored on our backend. We also integrated it with Postman, so you can easily import your collections. We imagine different use-cases and plan to add integrations (Stripe, Slack, CRMs, etc) and allow users to embed it where they want (for example on their platforms, so that their end-users can chat with the APIs and perform actions). Looking for early users and feedbacks.

Visit product View on Hacker NewsJul 28, 2025

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Actual performance

3points

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Launch Intel predictions

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Product HuntOn track for Day 1 leaderboard · Strong signals: agent, cursor, slack · Missing: mac, agents, macos

98%98% predicted probability of success on Product Hunt, based on ML models trained on real launch data.

best fitHighest predicted score across all platforms for this description.

Indie HackersFits the IH revenue-focused audience · Strong signals: supports · Missing: reddit linkedin, podcasting, created

74%74% predicted probability of success on Indie Hackers, based on ML models trained on real launch data.

Hacker NewsStrong engagement from HN community · Strong signals: ide, io · Missing: https docs, excited, just released

51%51% predicted probability of success on Hacker News, based on ML models trained on real launch data.

nativeThis product was originally launched on this platform.

TrustMRRLess likely to generate early MRR · Strong signals: users, way · Missing: mobile apps, ios, personal

44%44% predicted probability of success on TrustMRR, based on ML models trained on real launch data.

AppSumoMay struggle as an AppSumo deal · Strong signals: platform, users, calls · Missing: plus, intuitive, reviews

26%26% predicted probability of success on AppSumo, based on ML models trained on real launch data.

Acquire.comPre-revenue stage for this audience · Missing: arr, mrr, revenue

19%19% predicted probability of success on Acquire.com, based on ML models trained on real launch data.

BetaListMay not resonate with beta-testers · Strong signals: chat · Missing: web3, crypto, cryptocurrency

0%0% predicted probability of success on BetaList, based on ML models trained on real launch data.

Incorrect prediction on native model

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