Panaxon AI · Mnemo

Give your AI agents memory they can trust.

Mnemo is Panaxon AI's memory API — structured, indexed retrieval infrastructure that other AI products and agents are built on. Instead of approximate vector search, Mnemo uses OAT, a patent-pending deterministic index, so a query returns the exact stored fact, not a similarity guess.

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Why teams look beyond standard RAG

Built for memory access patterns that become painful at scale.

Retrieval-augmented generation often depends on approximate nearest-neighbor lookups over dense vectors. That model works for many discovery tasks, but it can be slow, noisy, and difficult to control when agents need consistent recall across large, evolving datasets. When retrieval pulls the wrong or partial context, the model doesn't know it's wrong — it just answers anyway.

Approximate vector search can introduce drift when agents need exact, multi-dimensional context.
Large retrieval sets can become operationally expensive as memory volumes grow.
Mnemo is designed around OAT, a patent-pending hierarchical data structure for indexed retrieval.

Mnemo's OAT retrieval doesn't eliminate every source of error an LLM can introduce, but it removes one of the biggest ones — being handed the wrong context in the first place.

Read the architecture

Shared memory

The same memory layer can also be shared, not just queried privately.

Beyond powering your own agents, Mnemo lets a person or company publish what they know and lets others subscribe to it on purpose — one source at a time, never force-fed. Nothing is automatically visible, even for public data; access starts only after an explicit subscription.

How sharing works

Access is opt-in, always — even for public data.

Publish once — documents, notes, or expertise — and subscribers query it directly instead of digging through files.
Subscribing is always explicit, the way you'd follow a person, whether the data is circle-only or public.
Publishers own what they publish; Mnemo is the access layer, not the source of truth.

See how publishing works

Engineering priorities

Built around three priorities, not a marketing scorecard.

Mnemo's architecture is designed around specific technical goals. Panaxon AI will publish benchmark results once they are validated, rather than lead with unverified numbers.

Predictable latency

Indexed retrieval is designed to fetch data directly, avoiding the variable cost of scanning approximate matches.

Scale without drift

Structured, hierarchical indexing is built to hold up as memory volume grows, not just at small scale.

Exact recall

Deterministic lookups are intended to reduce the drift that comes from approximate similarity matching.

See it live

Dorothy: a reference app built entirely on Mnemo

Dorothy is Panaxon AI's own conversational interface, showing what persistent, structured memory feels like day to day. It's one example of what the API can power, not a limit on what you can build with it.

Open Dorothy

Next step

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If you're building agents, copilots, or memory-heavy context pipelines, Panaxon AI can discuss fit, architecture, and access.

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