Mira OSS

Most AI chat products treat memory as an add-on: a settings toggle, a "memory" panel, a retrieval store bolted onto a stateless loop. Mira OSS treats continuity as the substrate. The whole interface is one thread that condenses as it grows, and the design premise is explicit — when you can't hit reset, memory has to carry the weight. That inversion produces genuinely different behavior from ChatGPT, Claude, or Gemini with memory enabled, because those products are still fundamentally optimized for disposable, parallel conversations. The memory layer is the strongest claim. Mira extracts and curates memories automatically, merging overlapping ones and forming typed relationships — corroborating, conflicting, superseding — as they accumulate. Frequently revisited memories hold their weight; ignored ones fall quiet. No human-in-the-loop required. That last detail is what makes or breaks the product: automatic curation is either the feature that saves you from becoming a librarian of your own context, or the failure mode where a wrong memory quietly weights everything downstream. Because the repository is open source, you can audit and change those rules, which is a real answer to the trust problem that closed memory products can only paper over. Domaindocs are the second pillar and the more conventional one: persistent, sectioned notebooks you and Mira edit in the same document in real time, with collapsible sections so a long project page stays scannable. For project notes and references you want kept in context indefinitely, this is a workable shared workspace rather than a prompt-and-forget scratchpad. The modularity underneath is aimed squarely at tinkerers — drop a tool into the tools/ folder and it auto-configures, the system prompt is composed from trinkets, and you can drive everything with cURL. Where it's weak: this is an early, largely single-author project, and the developer publicly notes emergent attributes he can't explain. "This is my TempleOS" is an honest and charming line, and also a fair warning about support expectations. Instances are paired to one person, so multi-user collaboration and shared workspaces aren't the shape of the thing. If your need is quick one-off answers or a stateless helper, the no-reset model actively works against you — you can't carve off a clean context for an unrelated task. The honest positioning: Mira is for developers, researchers, and long-horizon power users who want an AI that accumulates an arc over months and who are comfortable reading code, inspecting a repository, and tolerating rough edges. If that describes you, the memory architecture is worth the experiment. If you want breadth, polish, or a team tool, it isn't — and the vendor is upfront about that.