AI assistant with memory
An AI assistant that doesn't start from zero every morning.
The single most exhausting thing about most AI assistants is re-explaining your own life to them. Who the people are. What the project is. What you already decided and why. Close the tab and it's all gone — so the assistant never gets more useful than it was on day one.
Vyra is built the other way round. Memory is the substrate the rest of the system runs on, not a feature layered on top of a chat box — which is what makes the agentic OS architecture possible in the first place.
How the memory actually works
Episodic memory
Every interaction is recorded as a retrievable event and indexed two ways at once: FTS5 full-text search for the exact phrase you half-remember, and semantic vector search for the thing you can only describe approximately. Keyword search finds "the Postgres migration"; semantic search finds it when you ask about "that database thing we moved last quarter."
A structured world model
Separate from the event log, Vyra maintains an explicit graph of the entities in your life — people, projects, organisations, recurring commitments — and the relationships between them. That's what lets it answer "who owns this?" rather than just surfacing a message where the answer happens to appear.
Nightly consolidation
An unbounded log gets worse with age, not better. A consolidation pass runs like a sleep cycle: it promotes what proved important, merges duplicates, and lets noise decay. Recall quality is a function of what you throw away, not just what you keep.
Memory the whole system reads
Memory isn't a feature bolted onto the chat window. The goal engine, every agent in the mesh and the voice layer all read and write the same store, which is why a decision you made by voice on Tuesday shows up in a background research task on Thursday.
The full technical breakdown — index structures, the consolidation pass, and why hybrid retrieval beats pure vector search — is in the deep dive on persistent memory.
What people usually try instead — and why it stalls
A longer context window
Bigger context delays the problem by one conversation. Everything still vanishes when the session ends, and cost and latency scale with everything you drag along — including the 95% that isn't relevant to the current turn.
A file of notes pasted into the prompt
Workable at ten facts, unmanageable at a thousand. Nothing decides what's still true, nothing resolves contradictions, and you're the one doing retrieval by hand.
Vector search alone
Embeddings are excellent at "roughly about this" and unreliable at exact identifiers, names and dates. Hybrid retrieval — lexical and semantic together — is what makes recall dependable enough to build on.
What memory unlocks
Persistent memory isn't interesting on its own — it's interesting because of what becomes possible once it exists. An assistant that remembers can be handed a goal instead of a prompt, because it can pick up where it left off without being re-briefed. It can run a mesh of specialist agents that share context rather than each rediscovering it. And it can act as a genuine desktop agent rather than a chat window that happens to sit on your desktop.
That difference — between a system that answers and a system that continues — is the whole argument in agent vs. chatbot.
Common questions
What is an AI assistant with memory?
It's an assistant that persists information between sessions rather than starting from an empty context every time you open it. In practice that means two things: a durable record of past interactions it can retrieve from, and a structured model of the entities you care about — people, projects, commitments — that it updates as it learns. A larger context window is not the same thing; context is per-conversation and disappears when the conversation does.
How is persistent memory different from RAG?
RAG retrieves from a mostly static corpus of documents you supplied in advance. Persistent memory is written by the assistant itself as a side effect of working with you, so it accumulates facts nobody ever wrote down — that a project got cancelled, that a person changed roles, that you prefer being asked before anything irreversible happens. Most useful systems run both: RAG for reference material, memory for lived context.
Does the assistant remember everything forever?
No, and it shouldn't. Vyra runs a nightly consolidation pass that reinforces what proved important and lets unimportant detail decay. Perfect recall of everything is indistinguishable from noise at retrieval time — the value comes from the system deciding what's worth keeping sharp.
Can I see and correct what it remembers?
Yes. Memory that can't be inspected or corrected is a liability, not a feature. Stored entities and events are reviewable, and corrections propagate to the world model rather than sitting alongside the stale version.
Is Vyra available yet?
Vyra is in closed alpha, with a Founders Beta ahead of a public launch in 2026. You can join the waitlist from the homepage to get access as it opens up.
Vyra is in closed alpha, with a Founders Beta ahead of public launch in 2026.