Ohita – a tool to simplify API key management for AI agents

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I have been trying out numerous AI agent setups to find out which one I would like to run as my personal assistant. One thing that kept constantly bothering me was dealing with API keys, especially those that need jumping through hoops to keep working. Not an uncommon sight was trying to get my agent to fetch me some data or post to X/Twitter and then it would return an error as my API key had stopped working. So I built a tool that you can give to your AI agent and with one API key it can call all of the services. The tool acts as a central auth and handles individual API's requirements like refreshing tokens, making sure rate limits are adhered, sends the correct user-agents and everything else that each API might require. At first I wanted to provide all of the users no need to setup their own API keys, but that proved to be impossible. Most API providers state in their ToS that proxying the API is prohibited. Also there was the problem with identities: if an agent posts to Reddit or X the post is from the shared account. So I decided to add a bring-your-own-key architecture where you can setup your own keys (if you want to!) but the tool still handles all the token refreshing etc. Some generous services allow pretty lenient use of their API so I included those ready out of the box, no config required to getting started! Right now I am happy using this tool myself but I wish more people used it so that I could work on improving it. Since I am a single dev there is a lot of work, I am adding new providers every day, fixing bugs and all that. But if anyone would give me their honest thoughts and tested the features I could work on improving the tool even more. There is an option to pay for the usage to cover some running costs but the free tier is more than enough to get building.

Visit product View on Hacker NewsApr 22, 2026

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

88%88% predicted probability of success on Product Hunt, based on ML models trained on real launch data.

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

81%81% predicted probability of success on Indie Hackers, based on ML models trained on real launch data.

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

47%47% predicted probability of success on TrustMRR, based on ML models trained on real launch data.

Hacker NewsMay not resonate with HN audience · Strong signals: ide, io · Missing: https docs, excited, just released

29%29% predicted probability of success on Hacker News, based on ML models trained on real launch data.

nativeThis product was originally launched on this platform.

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

25%25% 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 · Missing: web3, chat, crypto

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

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