01
Where did the agent get this?
Compliance asks. You have a model name, a transcript, and three log files in three places. The retrieval set that produced the answer was discarded after the request.
Reproducibility
An AI agent's
memory you can
defend.
For teams shipping AI agents into work that gets audited. A self-hosted memory layer where every output is reproducible from stored evidence, every claim cites its source, and every change is verifiable. One binary. Zero cloud.
You can't show your work.
Every state-of-the-art agent memory layer treats the model as a trusted writer. The data model assumes good faith. So when the obvious question arrives, you can't answer it in less than four hours.
02
Was that protocol still current?
The agent cited a guideline. It was correct three months ago and superseded last week. Your memory layer has no concept of valid-from and valid-until. The wrong version sat in retrieval.
Time
03
Did the agent see the contradiction?
Two notes in different sessions said different things. The agent silently used whichever was retrieved last. There was no signal that the conflict existed.
Conflict
"The transcript is not the audit trail. The audit trail is the chain of evidence that produced the transcript."
Field note · From a discovery call with a clinical-AI team, 2026
Three primitives.
Plain English.
Wired into the storage layer so they can't be opted out of. Each one replaces an afternoon of manual reconstruction with a single query.
Tamper-evident change log
Every change leaves a fingerprint.
Each note, each edit, each model call gets a fingerprint linked to the previous one. If anything was tampered with, the chain shows you exactly where. Your audit trail becomes a chain you can verify — not a story you have to tell.
Memory that earns its place
Knowledge gets cleaner over time.
Notes earn durability through repeated, time-spread access. Old + replayed = important. Old + abandoned = fades. The graph stops bloating. The most-cited knowledge consolidates into durable schemas.
Citations the system enforces
Every claim cites its source.
A claim that doesn't textually overlap with a cited source is rejected at write time. Not "logged" — rejected. The model can't quietly invent a citation, because the storage layer won't accept one.
Time
Knows when a fact stopped being true
Every link carries valid-from and valid-until. Stale guidance can't quietly resurface.
Conflict
Surfaces contradictions, doesn't bury them
When two notes disagree, the system tells you. No silent overwrites.
New in v0.3
Triages memory the way a brain does
Frequently-accessed knowledge hardens into durable schemas. Abandoned notes fade.
Replay
Re-runs a model call from stored metadata
Same model, same prompt, same retrieval set — bit-identical answer. By design, not by hope.
Footprint
One binary. No services. No cloud.
A 30-megabyte program plus a database file. Air-gappable. Deployable anywhere.
Custody
Your data never leaves your stack
Smriti runs on your hardware, against your database file. No vendor in the middle. No telemetry.
The boring receipts.
Measured benchmarks. Peer-reviewed research. No demo magic. Every claim on this page is verifiable against the codebase.
$ smriti verify --chain ─── chain integrity ────────────────────────── events scanned 50,127 chain integrity ✓ verified prior tampering ✓ none detected last event 2026-05-08T14:21:33Z walk time 487 ms $ smriti audit replay call_8f7a02e1b ─── reproducibility check ──────────────────── model llama3.2:1b seed 42 prompt template clinical_qa@v3 retrieval set [ n_s3_1, n_s4_1, n_s5_1 ] stored hash a3f2…0e1b re-run hash a3f2…0e1b match ✓ bit-identical $
Paper
Verifiable Agent Memory
Working draft, 2026 — a formal integrity contract for LLM-augmented knowledge graphs.
Research
Six peer-reviewed foundations
Memory consolidation, belief revision, citation grounding, time-aware retrieval — distilled into one storage layer.
Audit cost
Sub-millisecond per call
The integrity layer adds < 1 ms per LLM call — about 2% of typical inference latency.
Ownership
You own the artifact end to end
Your binary, your database file, your audit trail. No vendor between you and the regulator.
2.5µs
Memory retrieval p50
in-memory · M-series Mac
235ns
Graph traversal
cached two-hop walks
≈ 30MB
Binary footprint
vs ≈ 1 GB Docker stacks
0
Cloud dependencies
offline-first by design
If you've ever had this conversation, talk to us.
In customer discovery, not selling. Twenty minutes; you talk, we listen.
- Your compliance team asked you to defend a model output, and you spent half a day reconstructing what happened from log files.
- You're shipping an agent into a clinical, biotech, or healthcare workflow and existing memory layers don't fit the audit posture.
- You need a memory layer that's self-hosted by default — your data, your hardware, no vendor between you and the regulator.
- You've watched an agent quote a superseded protocol as if it were current and have no clean way to express "true between dates X and Y."
If your compliance team asks that question more than once a quarter, talk to us.
Thank you — we'll be in touch within 24 hours.
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