AgentEmail
Dedicated Email Inboxes Built for AI Agents!
What it does
Most email tools are built for humans — inboxes you log into, UIs you click through, notifications you read. AgentEmail flips that entirely. AgentEmail is the cheapest email infrastructure product designed exclusively for AI agents as the end-user, not the developer. Find our SDKs - https://www.npmjs.com/package/@agentemail/sdk https://pypi.org/project/agentemail/
Does the same job
all alternatives →- AEAgentMail – Email infra for AI agents2025 · chat.agentmail.to · ▲121
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,…
- AEAgent.email – sign up via curl, claim with a human OTPMay 2026 · ▲99
Hi HN! We're Haakam, Michael, and Adi from AgentMail- a ycs25 company. We give AI agents their own email inboxes. Recently, we ran an experiment called Agent.Email. It's a signup flow designed specifically for AI agents instead of humans. The inspiration came from a few comments we received when we did our seed launch a few months back. They all came from the very apt observation that agents not being able to sign up to a product made for agents without human credentials was ironic and unideal. This is basically the thesis we built AgentMail on: The internet was made for humans exclusively,…
Atomic Mail AgenticJun 2026 · atomicmail.ai · ▲268Let your agents read, send, and react to email autonomously
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Parallel agents, diff reviewer, and multi-model comparisons
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Hey HN, Henry here from Cactus. We open-sourced Needle, a 26M parameter function-calling (tool use) model. It runs at 6000 tok/s prefill and 1200 tok/s decode on consumer devices. We were always frustrated by the little effort made towards building agentic models that run on budget phones, so we conducted investigations that led to an observation: agentic experiences are built upon tool calling, and massive models are overkill for it. Tool calling is fundamentally retrieval-and-assembly (match query to tool name, extract argument values, emit JSON), not reasoning. Cross-attention…
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