証明 Shomei GOVERNED MEMORY
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The memory layer that can prove what it knows.

A memory engine that behaves like infrastructure: deterministic, temporal, and verifiable. A system of record your agents, and your auditors, can rely on.

agent_memory.py
from shomei import Memory
m = Memory(tenant="acme")
m.add(messages)  # verbatim spans, dated, source-linked
ctx = m.search("where does Emily live?")
# same messages in, same memory out. always.
r = m.forget(subject_id)  # erased, derivatives included
r.verify()
✓ signed receipt, verified offline
BUILT FOR REGULATED, MEMORY-CRITICAL WORK
Healthcare
Financial services
Legal
Insurance
Customer support
THE PROBLEM

LLM memory guesses. Yours shouldn’t.

Most memory layers paraphrase what was said, overwrite what changed, and can’t show their work. That’s fine for a demo — and a liability in production.

IT PARAPHRASES
You lose the source

Extraction rewrites messages into lossy summaries. When a claim is challenged, there’s nothing exact to point back to.

IT OVERWRITES
History disappears

New facts clobber old ones in place, so you can never reconstruct what the agent believed at a past moment.

IT CAN’T PROVE
Delete is a promise

A soft-delete flag is a pinky-swear. Nothing an auditor can verify without trusting your database.

BUILT TO BE CHECKED

Memory for AI you can’t afford to be wrong.

When agents touch health, money, or people’s records, “probably deleted” isn’t an answer. Shomei was built from the ground up so that every claim it makes about memory can be checked, layer by layer, by someone who doesn’t trust us.

The verifiability stack EVERY LAYER CHECKABLE
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{{ verCaption }} Verified, no trust required
HEALTH
Care that remembers, records that answer

A patient asks what your assistant knows about them, or asks it to forget. Both requests resolve to evidence, not a support ticket.

MONEY
Advice you can reconstruct

When a regulator asks why the agent said what it said, you replay the exact memory state it saw, not an approximation of it.

PEOPLE’S RECORDS
Deletion that ends the question

An HR case closes, a customer leaves, a court order lands. Inside the memory we govern, erasure leaves no residue: not the words, not their derivatives, not a hash that could confirm what was there. The receipt proves it.

TEMPORAL KNOWLEDGE GRAPH

A graph that knows when things were true.

Facts change. Shomei’s graph is bi-temporal: when a new fact contradicts an old one, the old fact retires with a date instead of being overwritten. Ask about the past and the graph still answers.

{{ tkgCaption }}
Emily’s graph, over time
BI-TEMPORAL · DETERMINISTIC
Every edge carries a validity interval. Contradicted facts retire with a date; they are never overwritten.
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The message that changes the graph
emily · feb 2026
“quick update: I moved to Berlin in February, so let’s switch my deliveries.”
The stored fact is that exact span, offsets and all. The date inside the text becomes the validity date.
Ask as of any date
WHERE DID EMILY LIVE, AS OF 2024?
London · valid mar 2024 → feb 2026
WHERE DOES EMILY LIVE, NOW?
{{ tkgNowAnswer }}
Both answers come from the same graph. History is kept, not overwritten, until the person asks to be forgotten. Then all of it goes, provably.
Deterministic by construction. Replay the same messages and you get this exact graph again. No sampling, no drift, no surprise edges.
Honest scope. Supersession applies to single-valued facts, like where someone lives. Facts that can be plural, like hobbies, accumulate instead of replacing each other.
Scope of “deterministic.” Determinism covers the governed facts and graph from verbatim-only extraction and dated supersession: the same source yields the same governed memory. Vector embeddings and ranking are a separate layer. As-of-date temporal querying is available in preview.
SPAN-GROUNDED FACTS

Your users’ words, not the model’s.

Most memory products store an LLM’s paraphrase. Shomei stores the exact span of the original message. If the bytes are not in the source, nothing is stored: hallucinated memories are ruled out by construction.

SOURCE MESSAGE
“thanks! also heads up, I switched to the vegetarian plan last month so please update my order.”
STORED MEMORY UNIT verbatim
{{ r.k }}{{ r.v }}
Works across languages and scripts, offsets are character-exact in non-Latin text too. And because each unit points at its source, deleting the source can take every derived fact with it.
THE LANDSCAPE

Everyone remembers. Nobody else can show their work.

The leaders are genuinely good at recall. The gap is everything after: reproduce it, trace it, and prove that what you deleted left no residue behind.

PRODUCT FOCUS DETERMINISTIC? SOURCE-LINKED? PROVABLE DELETE?
mem0 recall, add-only memory no · LLM extraction no flag / append
Zep / Graphiti temporal knowledge graph no · LLM graph build no shared edges linger
Letta (MemGPT) agent-managed memory no · agent rewrites no gate overwrite
Shomei deterministic temporal graph + proof byte-identical replay verbatim spans erased + receipt

Competitor defaults change, verify from each vendor’s current docs before quoting externally.

HOW IT WORKS

Four moves, from raw conversation to proof.

One engine takes your agents’ conversations in and hands receipts out. Everything between is deterministic and inspectable.

01
Ground

Extract facts as verbatim spans of the source message, with offsets, dates, and a subject.

02
Graph

Place each fact on the bi-temporal graph; contradictions retire old facts with a date.

03
Recall, governed

Every retrieval re-checks policy at use time, so a restriction or deletion holds everywhere, instantly.

04
Prove

Recall, restrict, hold, export, or erase: each emits a signed receipt our open verifier checks offline.

See how it works →

Memory you can verify, not just trust.

Drop it into an agent in an afternoon: in-process SDK, HTTP service, or MCP server. Runs in your cloud, on your keys.

Start building Book a demo
証明 Shomei GOVERNED MEMORY

Deterministic, temporal, provable memory for AI agents. Show me the proof.

COMPANY
© 2026 Shomei. Show me the proof.shomei.ai