inspeximus

Tamper-evident long-term memory for AI agents. Correct a fact once and the old value stays retired; erase a person and prove it; show an auditor what the agent knew when it acted. One zero-dependency Python file, plus an MCP server.

Quickstart · Docs · vs mem0 and Graphiti · Migrate from mem0 · EU AI Act and GDPR evidence · Claude Code, one line · Transparency log · PyPI

pip install "inspeximus[crypto]"
inspeximus demo          # a first result: offline, touches nothing of yours

from inspeximus import Inspeximus

m = Inspeximus("memory.json")
m.remember("The staging database is db-3.internal", key="staging-db")
m.remember("The staging database is db-7.internal", key="staging-db")   # a correction
m.recall("which staging database")[0]["text"]   # 'The staging database is db-7.internal'
m.revert("staging-db")                            # and it is reversible, on purpose

Why inspeximus

Use it when the agent runs for days and the facts it holds will change under it, and when somebody can later ask what it knew and what it erased. That is the whole design brief.

agno is the one that is not ours. It ships an official integration example in its own cookbook, cookbook/11_memory/integrations/inspeximus_integration.py, merged in agno#10146 on 2026-09-20, alongside mem0, zep, memori and dakera. Their own README describes it as "inspeximus for corrections that stay corrected." We did not write that file's home and we do not maintain it, which is exactly why it is worth listing: it is one integration a reader can check without taking our word for anything. The same holds for agmi, an agent-memory integrity scorecard whose maintainer merged our three-row adapter, agmi/adapters/inspeximus_rows.py, in agmi#1 after reproducing it himself.

Not the right tool when you want a hosted service with a dashboard, a knowledge graph over documents, or the highest score on a conversational-recall benchmark. mem0, Zep and cognee lead there, and the comparison page says where each of us wins and where we do not.

The receipts

We measured the one thing the others do not publish: how often a corrected fact comes back.

Each system was run on its own native configuration, same task, same 30 trials:

_{n = 30 per system. mem0 measured at 2.0.11 (2026-07); mem0 is now on 2.0.18 and we have not re-run it — the version is stamped rather than the claim being restated as current. Full method, raw arrays and the re-runnable harness: RAMR · echo_resistance_backends_result.json}

Read the Graphiti row correctly — its echo defense did not fail. Our own raw output records echo_attributable_flips: 0 out of 26 corrections that were extracted correctly before the echo ran. Graphiti's bi-temporal invalidation held every one of them. The 13.3% above is four pre-echo extraction misses — the correction never made it into the graph — which is a different failure from the one this table is about. Stated as the mechanism rather than the headline: on echo-attributable resurrection, Graphiti scores 0%, the same as us, by keeping the supersession link at write time. That is the real finding here: what separates these systems is whether the link is recorded, not who recorded it.

Two numbers you can check in three seconds, with no API key

Measured 2026-08-25 against Hindsight 0.9.2 (vectorize-io, 21k stars) and mem0, each in its own native config, n=20. These two need no judge at all — they read the raw recall payload, so nothing depends on a model reading well:

Both competitors return the corrected value and the retired one, and leave the choice to the caller. That is a defensible design — a bitemporal store handing back old and new with validity markers is being honest — but it is a different promise from ours, and the difference is whose job disambiguation is.

The first row is free to verify. No key, no server, no network:

git clone https://github.com/DanceNitra/inspeximus && cd inspeximus
python probes/integrity_bench_store_resolves.py --systems inspeximus

It finishes in milliseconds and prints store-resolved=1.00 (resolved=20 both=0 stale=0 neither=0, n=20). Adding ,mem0 or ,hindsight reproduces their columns and costs their own extractor calls. Method, caveats and the cells where we do not win.

The bottom row is the point. Turn our guard off and we score zero — so the number is the mechanism, not the benchmark being kind to us.

EU AI Act and GDPR evidence, built in