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AgentMail – Email infra for AI agents

Hacker News

AgentMail – Email infra for AI agents

Hey HN, we're Haakam, Michael, and Adi. We're building AgentMail ( https://agentmail.to/ ), an API to give AI agents their own email inboxes. We’re not talking about AI for your email, this is email for your AI. We started building email agents because they can converse with users in their inboxes, automate email-based workflows, and authenticate with third-party applications. Given these unique capabilities, we think email will be a core interface for agents. But we were building on top of Gmail, which was a struggle: poor API support, expensive subscriptions, rate limits, sending limits, GCP Pub/Sub, OAuth, crappy keyword search, and an overall terrible developer experience. Gmail and other providers didn’t work for us. So we decided to bite the bullet and build our own. AgentMail is like Gmail, but API-first, with programmatic inbox creation, events over webhooks and websockets, simple API key auth, organization-wide semantic search, structured data extraction, and usage-based pricing that scales with emails sent/received. Here’s a demo of building an email agent: https://youtu.be/1V7BISeFUTM , and here’s a demo of a voice agent with its own email inbox: https://youtu.be/eG2fCsRK4RY So far AgentMail has been deployed to use cases such as apps with dedicated inboxes for each user, voice agents that receive documents in real time, automated account provisioning and QA testing, cold outbound platforms with thousands of inboxes, automations for processing invoices, and agents that coordinate work with humans and other agents. We would love to hear your thoughts and feedback. You can try our playground at https://chat.agentmail.to

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Actual performance

121points
70comments
Made the leaderboard

Launch Intel predictions

Analyze your own launch →
Product HuntOn track for Day 1 leaderboard · Strong signals: agents, agent, apps · Missing: mac, macos, cursor
99%99% 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: started · Missing: supports, reddit linkedin, podcasting
73%73% 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
67%67% 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: apps, users · Missing: mobile apps, ios, personal
49%49% predicted probability of success on TrustMRR, based on ML models trained on real launch data.
AppSumoMay struggle as an AppSumo deal · Strong signals: platform, interface, users · Missing: plus, intuitive, reviews
42%42% predicted probability of success on AppSumo, based on ML models trained on real launch data.
Acquire.comPre-revenue stage for this audience · Strong signals: subscription · Missing: arr, mrr, revenue
23%23% 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, real time · Missing: web3, crypto, cryptocurrency
0%0% predicted probability of success on BetaList, based on ML models trained on real launch data.

Correct prediction on native model

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