Applied use case / Memoato
Memory that keeps the evidence in view.
Memoato is the open-source product behind our research into trustworthy AI memory and context. It is currently a personal memory product: a place to record what happened, review what the system understood, and recall it with the source still attached.
- Product
- Personal memory
- Canonical store
- PostgreSQL
- Agent access
- Logging, recall, or both
- Status
- Live product, active development
Three connected layers
Research, product, and implementation work inform each other.
Trustworthy Agent Memory
The public guide develops principles for provenance, freshness, permissions, correction, and evaluation in agent context systems.
Memoato
The open-source product puts those principles under real product constraints: capture, processing, review, retrieval, and external access.
HILLS Lab
We apply the same engineering discipline when helping software teams design and implement context-aware products and workflows.
The trust problem
A generated memory is not automatically a fact.
A short personal note can contain an event, a reason, a preference, and an uncertain interpretation at the same time. Flattening all of that into one generated summary hides what the person actually wrote and makes later correction difficult.
Memoato keeps the source entry before interpretation. Structured facts are reviewable records, while inferences stay separate and point back to evidence. The system can improve its interpretation without rewriting the original account.
Record model
Each kind of knowledge has a different job.
Raw evidence
The original entry is preserved as the durable source. Later processing can be rerun without replacing the user's words.
Reviewed facts
Structured facts have their own review state. External recall is limited to accepted facts rather than every generated candidate.
Separate inferences
Patterns and hypotheses remain distinct from facts and carry references to the entries that support them.
Corrections and runs
Human corrections and processing attempts are recorded, leaving a visible path from source to current interpretation.
Scoped API and MCP access
An integration receives only the capability it needs.
Memoato keys are created for logging, recall, or both. The same boundary applies whether an agent connects through the HTTP API or MCP.
Logging
Add a raw memory entry without receiving access to recalled personal facts.
Recall
Retrieve accepted facts and their evidence without gaining a write capability.
Both
Use capture and recall together only when the integration genuinely needs both paths.
Evidence-first retrieval
A result should show why it was returned.
Recall combines structured facts with search over the preserved entries. The response keeps evidence available, so a person or agent can inspect the source instead of treating a compact answer as self-authenticating.
Search embeddings are versioned projections, not the source of truth. They can be rebuilt from PostgreSQL while the raw entries and reviewed facts remain canonical.
Product boundary
What is live and what comes next.
01
Live now
Personal memory is the first workspace. Capture, processing, fact review, corrections, separate inferences, evidence-backed recall, rebuildable search, and scoped API/MCP keys are implemented foundations of the product.
02
Direction, not a shipped claim
Team and project context will require workspace roles, source-level permissions, immutable source versions, claim precedence, freshness rules, and retrieval traces. Memoato's current model provides a base for that work; it is not presented as a finished enterprise context platform.
Explore the work
Product, source, research, and engineering note.
HILLS Lab
Designing memory or context for a software product?
We can map the sources, trust boundaries, review model, and access rules, then implement one bounded workflow with your team.