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What is Angareion?

Angareion is a shared memory and knowledge layer for the LLMs your team already uses — Claude, ChatGPT, and Gemini.

Every chat assistant your team relies on forgets what the last one learned. Context lives trapped inside one tool — a decision made in ChatGPT is invisible to Claude, and last week's thread is gone by Monday. Angareion is a single place where your team's memory and knowledge live, so the same context is available in every LLM your team uses.

You capture what's worth keeping once. Angareion remembers it, connects it, and makes it available for recall — in whichever assistant you happen to be working in.

What you can do with it​

  • Carry memory across tools. What you told one assistant is remembered by the next — no re-explaining, no copy-pasting, no starting from scratch when you switch tools.
  • Share what your team knows. Capture decisions, playbooks, and positioning so they're available to everyone and every LLM — instead of living in one person's head.
  • Build specialists. Turn your team's collective knowledge into reusable expert agents your whole team can call on from any LLM.

Get started in a few minutes​

You don't need an API key, a CLI, or any code to start. You connect your AI tool to Angareion's hosted service and approve it from your browser.

  • Connect your AI tool — add Angareion to Claude, ChatGPT, Gemini, or another MCP client. This is the place to start.
  • Quickstart — connect, capture your first memory, and recall it in a chat, in about five minutes.

New here? A few concepts​

If a term is unfamiliar, these plain-language explainers cover the ideas Angareion is built on:

  • Memories & recall — what Angareion remembers, and how it resurfaces later.
  • Scopes & sharing — keeping something to yourself, sharing it with your team, or making it organization-wide.
  • Specialists & knowledge — reusable expert agents backed by your team's memory.
Building an autonomous agent or a backend integration?

Angareion also exposes an HTTP API and SDKs for developers. That path is documented separately under API reference and the developer guides — it isn't required for connecting a chat assistant.