Hey Product Hunt 👋 I’m one of the makers behind Agentical. Why we built it We kept running into the same tradeoff: hosted AI is convenient, but it often means routing sensitive prompts, responses, and agent data through someone else’s servers. Local LLM tooling exists, but getting it working reliably can turn into a weekend project (reverse proxies, Docker stacks, “glue” services, broken configs). The problem we wanted to solve Make private, user-owned AI feel as easy as a cloud product—without giving up control of your data or your hardware. How it evolved We started with “run a great model locally in one click.” As soon as people asked for collaboration and more power, the next question was: how do you use another machine’s GPU (or share compute) without introducing a central server that sees tokens? That’s what pushed us toward encrypted P2P inference—so single-device stays fully local, and multi-device workflows can still avoid central routing. What we’d love feedback on What’s your biggest blocker to running AI locally today? If we add RAG + tools (e.g., MCP), would a mode where only the provider has access to the full documents/tools (and users only receive responses) be valuable for you—and what controls would you need (citations, redaction, “no quotes,” audit logs, time-limited access)

Agentical

AI Agents with Maximal Privacy & Minimal Setup.

AI Agents with Maximal Privacy & Minimal Setup.