memtrust-cli

memtrust

Agent memory backends each publish their own benchmark numbers, on different tests, measured different ways. memtrust runs the same evals against all four and publishes the raw logs. Run against the vendors, not by them.

pip install memtrust-cli
memtrust run --backends mempalace,mem0,zep,openviking --eval all

(For contributing to this repo instead of just running it, see Development -- pip install -e ".[dev]" from a clone.)

Contents: Why this exists · What it does · Commands · How this differs · Contradiction detection · Compression fidelity · Temporal-KG boundary · The landscape · Benchmarks · GitHub Actions usage · Self-host · Install · Hosted layer · Backend coverage · Development · FAQ · License · Success stories

Why this exists

If you've compared agent-memory backends recently, you've probably noticed each one leads with a different accuracy number, on a different benchmark, measured a different way. MemPalace's own community already flagged the problem in public. Issue #27 on the MemPalace repository, opened April 7, 2026 and still open, documents that a headline 100% LongMemEval figure, measured with Haiku reranking, wasn't reproducible from the repository's own benchmark scripts and was pulled from the README as unverifiable. A separate 96.6% figure people cite everywhere turns out to be mostly ChromaDB's default embeddings doing the work in raw mode, not MemPalace's own architecture. A "lossless" compression claim (the "AAAK" mode) drops the same LongMemEval score from 96.6% to 84.2% in practice, a 12.4 percentage point gap. Two internal pull requests attempting to fix the reporting problem, #433 and #729, were both closed without merging on April 12, 2026 -- #729 within seven minutes of being opened. As of this writing, the issue has 233 thumbs-up reactions and 39 comments.

None of that means MemPalace, or any other backend, doesn't work. It means nobody outside the vendor had run the same test, the same way, against every option, and published the raw logs.

memtrust does that. It runs LongMemEval, LoCoMo, and a growing set of evals built specifically for this project -- 17 of them as of this writing, all registered in the CLI's --eval flag. The two that matter most for understanding what this project is actually for: contradiction detection, because neither LongMemEval nor LoCoMo tests the question that actually matters once a memory system sits underneath a production agent -- what happens when a new fact contradicts an old one? Does the backend flag the conflict? Silently overwrite the old fact with no audit trail? Serve whichever version it happens to retrieve first? None of the four backends this project tracks publish a number for that. And compression/round-trip fidelity, built to directly test claims like the "lossless" one above: it stores content, retrieves it, and scores literal reconstruction fidelity rather than semantic accuracy, per operating mode a backend exposes (see MemoryBackendAdapter.supported_modes) -- the mechanism that would let a contributor with live MemPalace credentials actually reproduce the 12.4-point compressed-mode accuracy drop mempalace/mempalace#27 documents, instead of just citing it. Neither has been run against a live MemPalace instance as of this writing -- both have, however, been run against a live self-hosted mem0ai install; see "Benchmarks" below. The other evals -- ranking quality, crash recovery, extraction quality, embedding drift, scale/volume stress, lock contention, stats accuracy, orphan cleanup, result consistency, migration rollback, filter injection, resource-sync safety, and temporal-KG boundary detection -- each grew out of a specific real bug report against one of the four tracked backends; see "Success stories" below for the full list.

Where this stands right now, in one place: live benchmark results for one backend (mem0_direct, self-hosted mem0ai), including a real bug this project's own attempt to get those numbers surfaced in mem0's default configuration; and 197 real GitHub issues and PRs filed against MemPalace, Mem0, Zep/Graphiti, and OpenViking independently root-caused against this codebase -- 55 (28%) PASS, 16 (8%) PARTIAL, 42 (21%) a genuine capability gap, 84 (43%) not applicable, every verdict re-verified by a reviewer independent of whoever built the fix.

What it does

Every command below was actually run against this repo, with zero vendor API keys configured, to produce the output shown. Nothing here is simulated.

$ memtrust run --backends mempalace,mem0,zep,openviking --eval all
memtrust 0.3.2 -- run_id=mt_2026-07-20T231956Z
Backends: mempalace, mem0, zep, openviking   Evals: longmemeval, locomo, contradiction,
resource_sync_safety, compression, ranking_quality, scale_stress, embedding_drift, crash_recovery,
extraction_quality, migration_rollback, filter_injection, lock_contention, stats_accuracy,
orphan_cleanup, result_consistency, temporal_kg_boundary

mempalace: SKIPPED (not configured) -- mempalace is not configured: environment variable
MEMPALACE_STORAGE_PATH is not set. Skipping this backend. See docs/methodology.md for setup
instructions.
mem0: SKIPPED (not configured) -- mem0 is not configured: environment variable MEM0_API_KEY is not
set. Skipping this backend. See docs/methodology.md for setup instructions.
zep: SKIPPED (not configured) -- zep is not configured: environment variable ZEP_API_KEY is not set.
Skipping this backend. See docs/methodology.md for setup instructions.
openviking: SKIPPED (not configured) -- openviking is not configured: environment variable
OPENVIKING_API_KEY is not set. Skipping this backend. See docs/methodology.md for setup
instructions.

Cost: $0.00 (no LLM-judged evals ran -- structural evals only, or judge not configured)

Full report: memtrust-report-2026-07-20.json

That's the real, reproducible behavior of a fresh clone with no credentials: every backend reports SKIPPED, the command exits cleanly, and a valid JSON report is still written. memtrust --version now correctly prints 0.3.2, matching pip show memtrust-cli. Earlier releases printed 0.0.0+unknown even when properly installed, because src/memtrust/__init__.py read importlib.metadata.version("memtrust") while the installed distribution is actually named memtrust-cli -- kept in the FAQ below for the record rather than deleted, since silently erasing a bug the moment it's fixed is exactly the kind of curation this project exists to push back on in other people's benchmarks. Set the relevant environment variable for any backend you want to actually test (MEM0_API_KEY, ZEP_API_KEY, OPENVIKING_API_KEY, MEMPALACE_STORAGE_PATH) and that backend runs for real against its live API instead of being skipped.

The eval logic itself is proven offline, against the bundled synthetic fixtures and, for several adapters, the real installed vendor packages with only the network boundary mocked, by the test suite:

$ pytest --cov=memtrust --cov-report=term-missing
...
Name                                                          Stmts   Miss  Cover
-------------------------------------------------------------------------------------
src/memtrust/adapters/base.py                                   305     16    95%
src/memtrust/adapters/mempalace_adapter.py                      254     15    94%
src/memtrust/adapters/mem0_adapter.py                            140     12    91%
src/memtrust/adapters/mem0_direct_adapter.py                     277     34    88%
src/memtrust/adapters/openviking_adapter.py                      178     18    90%
src/memtrust/adapters/zep_graphiti_adapter.py                     63      3    95%
src/memtrust/adapters/zep_graphiti_selfhosted_adapter.py         161     24    85%
src/memtrust/evals/contradiction.py                              127      2    98%
src/memtrust/evals/compression.py                                 86      1    99%
src/memtrust/evals/temporal_kg_boundary.py                        90      4    96%
src/memtrust/receipt.py                                          118     10    92%
-------------------------------------------------------------------------------------
TOTAL                                                            4142    279    93%

585 passed, 8 skipped in 3.51s

585 passing tests across 33 source modules, 93% overall statement coverage, 98% on the contradiction-detection eval, 99% on compression/round-trip fidelity, 96% on the temporal-KG boundary eval, 88-95% across the adapter layer. The 8 skips are live-mempalace-package tests that only run with the optional mempalace-direct extra installed (pip install -e '.[dev,mempalace-direct]'). Every test mocks its HTTP or wire layer, or uses an in-memory fake backend -- none of them touch a real network, though a meaningful share of the adapter tests now import and exercise real installed vendor classes directly (mem0ai==2.0.12's embedder and vector-store modules, and -- gated behind the optional mempalace-direct extra -- the real mempalace.mcp_server functions), mocking only the outermost network or wire-client boundary rather than the whole library. graphiti-core is not installed in this environment, so its self-hosted adapter's tests still run against a hand-written Protocol double built to match the real package's confirmed method signatures, not the real classes -- see docs/methodology.md's adapter confidence table for exactly which claim rests on which kind of verification.

Commands

$ memtrust --help
Usage: memtrust [OPTIONS] COMMAND [ARGS]...

  memtrust: an independent, reproducible benchmark harness for agent-memory
  backends.

Options:
  --version  Show the version and exit.
  --help     Show this message and exit.

Commands:
  keygen  Generate a new Ed25519 keypair for signing `memtrust run`...
  report  Read a prior `memtrust run` JSON report and print a formatted...
  run     Run the eval suite against the requested backends.
  verify  Verify a signed receipt produced by `memtrust run --sign`.

Command	Flags	What it does
memtrust run	--backends TEXT comma-separated list or all (default all) · --eval TEXT comma-separated from longmemeval,locomo,contradiction,resource_sync_safety,compression,ranking_quality,scale_stress,embedding_drift,crash_recovery,extraction_quality,migration_rollback,filter_injection,lock_contention,stats_accuracy,orphan_cleanup,result_consistency,temporal_kg_boundary, or all (default all) · --output FILE (defaults to ./memtrust-report-<date>.json) · --locomo-dataset-path FILE points the LoCoMo eval at a real, downloaded locomo10.json instead of the bundled synthetic fixture (memtrust does not bundle or auto-fetch the real dataset) · --locomo-exclude-question-ids-file FILE excludes known-bad-ground-truth LoCoMo question IDs from scoring · --scale-stress-n-records INTEGER (default 500) sets how many synthetic records the scale-stress eval stores and re-queries · --sign FILE writes a signed <output>.receipt.json alongside the report, proving it was produced by the holder of the given Ed25519 private key	Runs the eval suite against the requested backends. A backend without its credential env var set prints SKIPPED and the run continues -- this command never crashes on missing credentials. temporal_kg_boundary only applies to the mempalace backend (the only adapter that wires kg_add/kg_invalidate/kg_query); requesting it against any other backend reports not_applicable, not an error.
memtrust report REPORT_PATH	positional path to a prior JSON report	Reads a report written by memtrust run and prints a formatted summary.
memtrust keygen	--	Generates a new Ed25519 keypair for signing reports with run --sign.
memtrust verify RECEIPT_PATH	--	Verifies a signed receipt produced by memtrust run --sign; a tampered or mismatched receipt fails verification.
memtrust --version	--	Prints the installed version (currently 0.3.2).

Every line above came straight from running memtrust --help, memtrust run --help, and memtrust report --help against this repo. Nothing here is invented.

How this differs from trusting a vendor's own numbers

Every backend memtrust tracks publishes its own benchmark numbers. None of them publish the same benchmark, scored the same way, with the same held-out discipline. memtrust doesn't ask you to trust it instead: it asks you to read the raw logs. Every run's methodology, prompt templates, dataset versions, and scoring rubric are published in docs/methodology.md, versioned alongside the code that produced them. If the methodology has a flaw, it's a flaw you can point to in a specific file and line, not something buried in a vendor's internal eval pipeline.

General-purpose LLM eval frameworks (promptfoo, DeepEval, RAGAS, and similar tools) are mature and widely used, but none of them ship a memory-backend adapter abstraction or a contradiction- detection eval out of the box -- they're built for RAG quality, red-teaming, and general prompt evaluation, not for comparing how different memory systems handle a fact that changes over time. memtrust is narrower and more specific on purpose.

The landscape (verified, not benchmarked)

Real, publicly checkable numbers as of this writing (gh api repos/<org>/<repo>), not memtrust-run scores -- accuracy and contradiction-handling comparisons stay in the "Benchmarks" section below until a live run actually produces them:

Backend	GitHub stars	Self-reported description
MemPalace	57,512	"The best-benchmarked open-source AI memory system. And it's free."
Mem0	61,318	"Universal memory layer for AI Agents"
Zep / Graphiti	28,978	"Build Real-Time Knowledge Graphs for AI Agents"
OpenViking	27,016	"Self-evolving Context Database for AI Agents. Unify Agent Memory, Knowledge RAG and Skills."

None of these numbers say anything about which backend handles a contradicted fact correctly -- that's the whole reason the harness exists. Star count measures adoption, not correctness.

The eval that actually matters: contradiction detection

LongMemEval and LoCoMo both measure recall: can the backend remember a fact you told it earlier. That's necessary but not sufficient. The harder question is what a backend does when two facts conflict: you tell it your meeting is at 2pm, then later say it moved to 3pm. Does it flag the change? Overwrite silently? Serve whichever one it retrieves first? memtrust's classifier stores a fact, stores a contradicting fact, queries for it, then checks the actual retrieved content for both values, rather than trusting whatever conflict signal the adapter itself reports. See src/memtrust/evals/contradiction.py and the scoring-logic section of docs/methodology.md for exactly how that classification works.

The eval built for the other headline overclaim: compression fidelity

mempalace/mempalace#27 documents two separate overclaims, not one: the LongMemEval score gap described above, and a "lossless" compression claim that measured 12.4 percentage points lower in practice under a compressed operating mode. memtrust could not previously reproduce that second number at all -- there was no way to tell an adapter "run this under mode X vs mode Y" through the shared interface. MemoryBackendAdapter.store()/query() now accept an optional mode: str | None parameter, and MemoryBackendAdapter.supported_modes lets an adapter declare which mode strings it actually understands (MemPalaceAdapter.supported_modes is ("raw", "AAAK"), the two names mempalace/mempalace#27 itself uses -- see src/memtrust/adapters/mempalace_adapter.py for the exact provenance and confidence caveat on those names). Adapters with no mode variants accept and ignore the parameter, so this is a purely additive, backward-compatible interface change.

src/memtrust/evals/compression.py runs the same store-then-retrieve round trip once per mode a backend reports, and scores each round trip with a direct, deterministic character-level similarity ratio (fidelity_ratio(), via difflib.SequenceMatcher -- not an LLM judge, since a "lossless" claim is a literal-reconstruction claim, not a semantic one). This is what would let a contributor with live MemPalace credentials point memtrust run --eval compression at it and reproduce a "raw vs AAAK" fidelity gap directly. As of this writing this eval has not been run against a live MemPalace instance -- it has been run against a live self-hosted mem0ai install (mean fidelity 31.6%, see "Benchmarks" below); see docs/methodology.md for the same live-credentials caveat that applies to every other eval and backend not yet measured live.

The eval built from MemPalace's own bug: temporal-KG boundary detection

MemPalace/mempalace#1913 (fixed by merged PR#1914, contributor ggettert) described a real, concrete bug: _temporal_filter_sql's as_of point-in-time query used a closed interval on both ends, so a fact whose valid_to equaled the query's exact as_of instant still matched. Hand-roll a fact change as kg_invalidate(ended=T) immediately followed by kg_add(valid_from=T) at the identical boundary instant -- the exact pattern MemPalace's own pre-fix agent guidance told every caller to do -- and an as_of=T query returns both the just-ended fact and its just-started successor at once, so a single-valued fact reports two contradictory answers with no error. src/memtrust/evals/temporal_kg_boundary.py reproduces that exact hand-rolled sequence against MemPalaceAdapter's kg_add()/kg_invalidate()/kg_query() and classifies the result with a new TemporalBoundarySignal taxonomy, distinct from ConflictSignal and RankingSignal because it concerns one narrow, structurally different failure: two facts sharing one instant, not a contradiction across time or a ranking-order question.

Honest scope, stated the same way this project states it for every other eval: the real mempalace PyPI package is not installed in this build environment, and PR#1914's fix had not shipped in a released mempalace version as of this adapter's live-verified 3.5.0 build -- it lands under the package's [Unreleased] changelog section. tests/test_temporal_kg_boundary.py proves the classification logic is correct against two hand-written fake implementations that reproduce the confirmed pre-#1914 (closed-interval) and post-#1914 (half-open-interval) SQL comparison exactly. This has not been run against a live MemPalace instance. It is wired into memtrust run --eval temporal_kg_boundary (see "Commands" above); against any backend other than mempalace, it reports not_applicable rather than an error.

Benchmarks

Live results: mem0_direct (self-hosted), July 2026. MemPalace, Zep, and OpenViking are still not yet measured against a live backend -- see "Backend coverage" below for the confidence level on each adapter. Mem0 has one real result, produced against the actual mem0ai OSS library running self-hosted -- in-process, via Mem0DirectAdapter, backed by a local Qdrant instance and the OpenAI API for embeddings and extraction -- not against Mem0's hosted Platform API. Keep that distinction in mind before reading this as a claim about the hosted product.

$ export MEM0_DIRECT_EMBEDDER_PROVIDER=openai
$ export MEM0_DIRECT_VECTOR_STORE_PROVIDER=qdrant
$ export MEM0_DIRECT_VECTOR_STORE_URL=http://localhost:6333
$ memtrust run --backends mem0_direct --eval contradiction,compression,extraction_quality
memtrust 0.3.2 -- run_id=mt_2026-07-20T210918Z
Backends: mem0_direct   Evals: contradiction, compression, extraction_quality

mem0_direct: configured, running evals...
  Running Contradiction-Detection against mem0_direct...
    flagged: 0.0%  silent-overwrite: 100.0%  served-stale: 0.0%  empty-or-lost: 0.0%
  Running Compression/Round-Trip-Fidelity against mem0_direct...
    fidelity by mode -- default: 31.6%
  Running Extraction-Quality against mem0_direct...
    junk-retained: 0.0%  valid-lost: 100.0%
    feedback-loop-duplicate: 0.0%

Cost: $0.00 (no LLM-judged evals ran -- structural evals only, or judge not configured)

The full raw report is committed at leaderboard/mem0_direct-2026-07-20.json, and leaderboard/data.json carries the contradiction numbers into the static leaderboard site (mempalace/mem0/zep/openviking still show not_measured there; mem0_direct is the one real row).

What that means case by case, not just the percentage:

• Contradiction detection, 7/7 cases: every contradicting fact silently overwrote the old one. 0% were flagged as a conflict, 0% served stale, 0% empty-or-lost. Tell it your meeting moved from 2pm to 3pm and it stores the new fact with no signal that anything changed -- this is exactly the question LongMemEval and LoCoMo don't test, and exactly what "Why this exists" above is about.
• Compression/round-trip fidelity, 5 cases: 31.6% mean literal character-level reconstruction. This is expected, not a defect -- mem0's design goal is semantic fact extraction, not verbatim storage, so a literal-reconstruction score was never going to be high. It quantifies what "not built for lossless storage" concretely means for this backend: ask it what you said and you get the gist back, not your words.
• Extraction quality, 15 cases (12 deliberately junk, 3 deliberately valid): 0% junk retained, 100% of the valid cases lost. All 12 junk inputs (boot-file restating, cron heartbeat noise, system dumps, hallucinated-profile bait) were correctly rejected. All 3 valid-content cases were also dropped -- stored but never came back on retrieval. The valid-side sample is small (n=3); treat this as a signal worth digging into further, not a settled number.

A real bug this run surfaced in mem0ai itself, not in memtrust. Getting any of the numbers above required a fix first: a fresh mem0ai==2.0.12 install with nothing but OPENAI_API_KEY set fails every single LLM-based extraction call, out of the box, for anyone. mem0's own default model (mem0/llms/openai.py: self.config.model = "gpt-5-mini") is a reasoning-tier model that only accepts the API's default temperature, but mem0's own reasoning-model detection (mem0/llms/base.py's reasoning_models set) checks for the string "gpt-5o-mini", not "gpt-5-mini" -- two different strings, so the check never fires, and mem0 sends temperature=0.1 on every call regardless. The result is a 400 Unsupported value: 'temperature' does not support 0.1 with this model error on every extraction call, silently caught by mem0 and reported by memtrust as N/A (no scoreable cases) rather than a real result. Mem0DirectAdapter now works around it by passing is_reasoning_model=True explicitly -- mem0's own documented override for exactly this situation -- see src/memtrust/adapters/mem0_direct_adapter.py's "Default LLM extraction is broken out of the box" section for the full citation. No upstream mem0ai issue filed for this as of this writing.

To reproduce this or measure the remaining three backends:

export MEM0_API_KEY=...          # and/or
export ZEP_API_KEY=...
export OPENVIKING_API_KEY=...
export MEMPALACE_STORAGE_PATH=...
export MEMTRUST_JUDGE_API_KEY=...   # needed for LongMemEval/LoCoMo grading; contradiction-detection doesn't need it

memtrust run --backends mempalace,mem0,zep,openviking --eval all
memtrust report memtrust-report-<date>.json

The command prints per-backend accuracy and contradiction-handling rates, writes a full JSON report, and prints an estimated cost for any LLM-judged evals that ran. MemPalaceAdapter's drawer and knowledge-graph calls are now live-verified against a real installed instance (see "Backend coverage" below), but OpenViking's memory-write/query paths, and parts of the self-hosted Mem0 and Zep/Graphiti adapters, are still built against best-effort interpretations of documented or source-read product concepts rather than a live-confirmed API -- see the confidence table in docs/methodology.md before treating any adapter's output as authoritative, and consider that table's gaps a standing invitation to contribute a fix.

Labeling requirement for any future accuracy figure published here. LongMemEval and LoCoMo accuracy grades the LLM judge's verdict on raw retrieved-record content directly -- there is no answer-generation step in either eval runner. This is not the same measurement as the official LongMemEval/LoCoMo leaderboards' generate-then-judge QA-accuracy scores. Any accuracy number this project publishes for those two evals must be labeled "retrieval-graded accuracy," not bare "accuracy," and must not be directly compared to leaderboard figures without that caveat. See docs/methodology.md's "Retrieval-graded accuracy vs. generated-answer accuracy" section.

GitHub Actions usage

Run the suite on a schedule and publish results to the leaderboard:

name: memtrust-leaderboard
on:
  schedule:
    - cron: "0 9 * * 1"  # weekly
  workflow_dispatch: {}

jobs:
  benchmark:
    runs-on: ubuntu-latest
    steps:
      - uses: actions/checkout@v4
      - uses: actions/setup-python@v5
        with:
          python-version: "3.12"
      - run: pip install memtrust-cli
      - run: memtrust run --backends mempalace,mem0,zep,openviking --eval all --output leaderboard/data.json
        env:
          MEM0_API_KEY: ${{ secrets.MEM0_API_KEY }}
          ZEP_API_KEY: ${{ secrets.ZEP_API_KEY }}
          OPENVIKING_API_KEY: ${{ secrets.OPENVIKING_API_KEY }}
          MEMTRUST_JUDGE_API_KEY: ${{ secrets.MEMTRUST_JUDGE_API_KEY }}
      - run: git add leaderboard/data.json && git commit -m "Update leaderboard" && git push

This repo's own CI (.github/workflows/ci.yml) runs lint, type-check, test, and a dependency security audit on every push and pull request -- no vendor credentials required, since every test runs fully offline.

Self-host

git clone https://github.com/RudrenduPaul/memtrust
cd memtrust
pip install -e ".[dev]"
export MEM0_API_KEY=...
memtrust run --backends mem0 --eval all

Point an adapter at your own backend, or run the suite against your own conversation data instead of the bundled synthetic fixtures (see docs/methodology.md's note on swapping in the real LongMemEval/LoCoMo datasets). Nothing leaves your machine unless you choose to publish it.

Install

npx (agent-native)

Both PyPI and npm are live. pip install memtrust-cli works today, and so does the npx command below -- npm install memtrust-cli no longer 404s.

For CI and agent runners that have Node.js available but not necessarily a Python toolchain:

npx memtrust-cli run --backends mempalace,mem0,zep,openviking --eval all

The npm package is named memtrust-cli so it is unambiguous as a CLI tool at a glance (and so it doesn't collide with any future memtrust JS library package). npx always resolves the package name to its matching bin entry automatically, so npx memtrust-cli ... is what reliably works for a zero-install first run. Once installed, the package also exposes the shorter memtrust command as a second bin alias -- matching the underlying Python CLI's own command name -- so you are not stuck typing memtrust-cli for every subsequent invocation; memtrust run ... works too.

This is not a zero-dependency install: npx memtrust-cli still fetches memtrust from PyPI on first use. What it removes is no Python toolchain to provision by hand -- npx memtrust-cli handles the interpreter and package fetch for you via a bundled, verified copy of Astral's uv. Each platform package bundles a genuine, SHA-256-verified copy of uv's own GitHub release binary (fetched at npm package-publish time, never at end-user install time), and its bin shim runs uv tool run --from memtrust==<pinned version> memtrust <args>, which transparently bootstraps a Python interpreter and installs that exact pinned memtrust release from PyPI, caching it after the first run. The npm package is pinned to its own version -- bump npm/memtrust-cli/package.json's version when a new PyPI release ships, and every subsequent install resolves to that exact release, not whatever happens to be newest at run time.

What a hosted trust layer would add

The harness, adapters, and leaderboard in this repo are the entire OSS surface, and they're sufficient on their own to compare backends. A hosted layer on top of this -- described here, not built -- would add continuous regression monitoring that re-runs the suite automatically whenever a tracked backend ships a new release, private scorecards that run the same methodology against a team's own data shape instead of the public sample fixtures, and a compliance-report export for teams whose security or legal review needs a documented third-party artifact rather than a free-text summary. None of that exists yet. If it's ever built, it stays additive to the free harness, never a requirement for using it.

Backend coverage

The MemPalace row below used to say "needs verification against a live instance" -- it needed more than that. Every prior version of MemPalaceAdapter called a mempalace.Palace class (Palace(storage_path=...) exposing .remember()/.recall()/.invalidate()) that never existed in the real, installed package. python3 -c "import mempalace; hasattr(mempalace, 'Palace')" returns False; grepping every class definition across the installed package turns up nothing named Palace anywhere. Every test that appeared to pass before this rewrite was exercising a hand-written fake standing in for that guess, never the real thing -- store()/query()/ update() had never actually worked against a live MemPalace install, in this project's entire history, until this rewrite. src/memtrust/adapters/mempalace_adapter.py was rewritten from scratch against the real, plain module-level functions in mempalace.mcp_server (tool_add_drawer, tool_search, tool_update_drawer, tool_delete_drawer, tool_kg_add/tool_kg_invalidate/tool_kg_query) -- every return shape documented in the adapter's module docstring was captured by calling those functions live against a real, local chromadb-backed palace, not read off a docstring and trusted. It's the kind of mistake this whole project exists to catch in other people's benchmarks; finding it in memtrust's own adapter and shipping the fix in the open, rather than quietly patching it, is the more useful story.

Backend	Adapter status	Confidence (see docs/methodology.md)
MemPalace	Implemented -- drawer API + knowledge-graph API	High on the real mempalace.mcp_server functions this adapter now calls, live-verified against an installed mempalace 3.5.0 instance (see above). Still best-effort on compression-mode names ("raw"/"AAAK") and on whether degraded_retrieval warnings are ever populated by the installed version -- see the adapter's module docstring for both caveats stated plainly.
Mem0	Implemented -- hosted Platform API, self-hosted OSS server, and a direct in-process library adapter	High on the hosted Platform API and on what the installed mem0ai==2.0.12 library's embedder/vector-store code actually does (confirmed by reading its real source, exercised directly in tests); medium-high on the self-hosted OSS server's route shape (confirmed from source, not run against a live server).
Zep / Graphiti	Implemented -- hosted Zep Platform API and a self-hosted graphiti-core adapter	Medium-high on the hosted API's documented contradiction-handling behavior; medium on the self-hosted adapter's wire-level shape (every method signature confirmed by reading graphiti-core's real source, not by running it against a live Neo4j/FalkorDB instance -- the package isn't installed in this environment).
OpenViking	Implemented	Medium on architecture, low on exact memory-write/query paths -- still the adapter most likely to need correction against a live instance.

Adding a backend adapter is the primary contribution path -- see CONTRIBUTING.md.

Development

pip install -e ".[dev]"
ruff check . && ruff format --check .
mypy --strict src/memtrust
pytest --cov=memtrust --cov-report=term-missing --cov-fail-under=80
pip-audit

.pre-commit-config.yaml wires ruff and mypy into pre-commit if you'd rather run these on every commit than remember to run them by hand.

FAQ

What is memtrust, and what actually makes it different from reading a vendor's own benchmark page? It's a CLI harness that runs the same evals (LongMemEval, LoCoMo, and 15 others registered in --eval, including a contradiction-detection eval none of the four tracked backends publish a number for) against MemPalace, Mem0, Zep/Graphiti, and OpenViking, and prints the raw output rather than a curated summary. The differentiator isn't a proprietary scoring model; it's that nobody outside the vendor had previously run the same test, the same way, against every option, with the full methodology published alongside the code that produced it (docs/methodology.md). See "Why this exists" above for the MemPalace LongMemEval overclaim (mempalace/mempalace#27) that motivated the project.

Does memtrust support my platform, and what happens if it doesn't? The npm wrapper (memtrust-cli) ships six platform-specific optional-dependency packages -- @memtrust-cli/darwin-x64, @memtrust-cli/darwin-arm64, @memtrust-cli/linux-x64, @memtrust-cli/linux-arm64, @memtrust-cli/win32-x64, and @memtrust-cli/win32-arm64 -- each bundling a verified uv binary for that exact platform (npm/memtrust-cli/bin/memtrust.js). npm install picks whichever one matches process.platform/process.arch at install time. On an unsupported combination (32-bit x86, or any platform outside that list), the wrapper exits with a clear no prebuilt uv binary available for <platform>/<arch> error instead of a silent failure. The underlying memtrust PyPI package itself only requires Python 3.11+, so pip install memtrust-cli remains the fallback path on any platform the npm wrapper doesn't cover.

Do I need a Python toolchain installed to use memtrust? Not if you go through the npm wrapper. npx memtrust-cli run ... runs uv tool run --from memtrust==<pinned version> memtrust <args> under the hood, and uv provisions its own isolated Python interpreter and installs the exact pinned memtrust release from PyPI on first use, caching it after that. If you already have Python 3.11+, pip install memtrust-cli (the PyPI package name) works directly with no Node.js involved.

How does memtrust compare to a general-purpose LLM eval framework like RAGAS? RAGAS evaluates RAG pipelines and other LLM applications with objective metrics and synthetic test-data generation; it has no memory-backend adapter abstraction and no eval built around a fact contradicting an earlier one, because that's not the problem it's built to solve. memtrust is narrower on purpose: it only tracks four named agent-memory backends and its non-recall evals (contradiction detection, compression/round-trip fidelity, temporal-KG boundary detection) exist specifically to test claims those four backends make about themselves. If you need broad RAG or prompt-evaluation coverage, RAGAS or a similar framework is the right tool; if you need to check whether a memory backend silently drops or overwrites a contradicted fact, memtrust is the one built for that question.

Why did memtrust --version used to print a version that didn't match what pip said I installed? This was a real, shipped bug through 0.3.1, not a hypothetical one: installing memtrust-cli from PyPI into a clean virtualenv and running pip show memtrust-cli reported the correct version, but memtrust --version printed 0.0.0+unknown regardless, because src/memtrust/__init__.py read importlib.metadata.version("memtrust") -- the wrong distribution name -- instead of version("memtrust-cli"), the name the package is actually installed under. Fixed in 0.3.2; memtrust --version now reads the correct metadata key and matches pip show memtrust-cli.

Has memtrust actually been run against a live memory backend, or is this all synthetic? Both, and the README doesn't blur the line. The eval logic itself is proven against bundled synthetic fixtures and, for several adapters, real installed vendor packages with only the network boundary mocked (see the pytest coverage table above). One backend has a real live result: mem0_direct, the self-hosted mem0ai OSS library (not Mem0's hosted Platform API), run against contradiction, compression, and extraction-quality -- see "Benchmarks" above for the exact numbers and a real bug that run surfaced in mem0ai itself. MemPalace, the hosted Mem0 Platform API, Zep, and OpenViking have not yet been run against a live backend with real credentials as of this writing. The "Backend coverage" table gives a per-adapter confidence level (high/medium/low) for exactly this reason; run it yourself against your own credentials to get a live-verified number.

Can I use memtrust commercially, and does it require attribution? Yes. It's licensed under Apache License 2.0 (see LICENSE), which permits commercial use, modification, and distribution, and requires you to preserve the license and copyright notice and to note any changes you make to the code. It does not require you to open-source your own product just because you depend on memtrust.

How do I get real accuracy numbers for the backends still marked "not yet measured"? Set the credential environment variable for whichever backend you want to test (MEMPALACE_STORAGE_PATH, MEM0_API_KEY, ZEP_API_KEY, OPENVIKING_API_KEY, plus MEMTRUST_JUDGE_API_KEY for LLM-judged evals like LongMemEval and LoCoMo), then run memtrust run --backends <name> --eval all and memtrust report <output-file>. A backend with no credential configured prints SKIPPED and the run still completes and writes a valid JSON report -- see "What it does" and "Benchmarks" above for the exact commands.

License

Apache 2.0. See LICENSE.

Success stories

197 real issues/PRs filed by real contributors against MemPalace, mem0, Zep/Graphiti, and OpenViking have been independently root-caused against this codebase: does the solution, as it actually exists today, let you diagnose or resolve what was reported? 55 (28%) verify as a clean PASS, 16 (8%) as PARTIAL (evidence captured, needs a human to interpret further, or only part of the issue is covered), 42 (21%) as a genuine capability gap this harness doesn't close yet, and 84 (43%) as not actually applicable (feature requests, already-fixed-upstream, or genuinely out of scope). Every verdict below has been re-verified live against the current codebase by a reviewer independent of whoever built the fix, not just cited from a changelog. Full write-ups and validation evidence are tracked internally; the summary here is for anyone deciding whether this harness would have caught their own bug.

The headline story is about memtrust's own bug, not a vendor's. Every version of MemPalaceAdapter before this rewrite called a mempalace.Palace class -- Palace(storage_path= ...) exposing .remember()/.recall()/.invalidate() -- that never existed in the real, installed package. python3 -c "import mempalace; hasattr(mempalace, 'Palace')" returns False; nothing named Palace appears anywhere in the installed package's source. Every test that appeared to pass was exercising a hand-written fake standing in for that guess -- store()/query()/ update() had never once worked against a live MemPalace install. src/memtrust/adapters/ mempalace_adapter.py was rewritten against the real mempalace.mcp_server functions, with every documented return shape captured by calling them live against a real local instance. A project built to catch other vendors overclaiming found the same failure mode in its own code, and the fix shipped in the open rather than quietly. See "Backend coverage" above for the full account.

MemPalace

• #1754 (@rodboev): a checkpoint recovery fix for silently quarantined dim-None pickles. memtrust's contradiction eval couldn't previously tell "silently quarantined" apart from "no update primitive at all"; it now can (ConflictSignal.EMPTY_OR_LOST).
• #1929 (@jrzmurray): a fix for NUL bytes silently corrupting a ChromaDB index. memtrust's store() used to trust "no exception" as proof of a durable write; an opt-in read-after-write verification step now catches this.
• #1450 (@lealbrunocalhau): a fix for an empty embedding response getting scored as a wrong answer instead of flagged as infra failure. Same fix as #1754 above.
• #1823 / #1543 (@fatkobra): lock and write-integrity fixes that pointed at the same read-after-write gap #1929 closed.
• #1913 / PR#1914 (@ggettert): a temporal-KG as_of boundary bug where a fact ending at exactly the query instant still matched alongside its successor. memtrust's new temporal-KG boundary eval reproduces the exact hand-rolled kg_invalidate()-then-kg_add() sequence that triggers it -- see "The eval built from MemPalace's own bug" above for the honest not-yet-live-verified caveat.
• PR#1890 / #1889 (@JosefAschauer): an authored_at chronology tie-break fix for _hybrid_rank. memtrust's ranking classifier now credits a top-level authored_at field, not just one nested under metadata, as a genuine ranking-driving signal.
• #524 (@gaby): a buried-comment report that "no API key required" doesn't mean "no network required" -- ChromaDB's default embedder still needs to download a model on first use, which silently breaks airgapped setups. The adapter's module docstring now says so explicitly.

mem0

• #5973 (@abhay-codes07, superseded by #5992): an empty-string entity-id filter scoping bug. memtrust's mem0 adapter only reached the hosted Platform API and had no delete operation at all, so it couldn't have caught this. A self-hosted adapter with tested delete/delete_many primitives now can.
• #4297 (@utkarsh240799): a dimension auto-detection fix. The self-hosted adapter now routes to the right deployment, though no test yet reproduces this specific bug end to end, so this one is partial, not fully caught.
• #4573 (@jamebobob): a 32-day audit of 10,134 real mem0 entries finding 97.8% junk. memtrust's new extraction-quality eval and ExtractionQualitySignal taxonomy cover the audit's own junk categories, including its 808-duplicate feedback-loop case.
• PR#5980 (@HrushiYadav): a filter-injection fix for the Elasticsearch vector store. A new filter-injection eval exercises the real, installed mem0.vector_stores.elasticsearch.ElasticsearchDB._validate_filter() directly and confirms it rejects the exact malicious filter shape ({"user_id": {"$ne": ""}}) this PR fixed.
• #4956 (@NDNM1408): an open proposal that mem0's add-only pipeline surfaces stale, contradictory facts with no recency signal. memtrust's contradiction eval now runs the same literal add-only scenario (two store() calls, no explicit update) against this taxonomy.
• #4884 (@wangjiawei-vegetable): a hardcoded English-only tokenizer silently degrading non-Latin-script retrieval. A new LanguageDegradationSignal and non-Latin-script fixtures now catch this shape.

Zep / Graphiti

• #1489 (@brentkearney): a bi-temporal invalid_at correctness gap. memtrust's contradiction classifier used to discard Graphiti's own invalid_at metadata and infer everything from a fixed top-5 text match, misreading a correctly flagged case as a silent overwrite. It now checks the metadata first.
• #1275 (@rafaelreis-r, still open): O(n) entity-resolution context growth silently dropping episodes past roughly 300 ingested. A new self-hosted graphiti-core adapter plus a scale/volume-stress eval now tracks a fixed "anchor" record's recall across ascending checkpoints against real add_episode() ingestion -- the same shape this issue describes.
• #836 (@matthiaslau) / #920 (@markwkiehl): two separate crashes in update_communities()/resolve_edge_contradictions() -- a too-many-values-to-unpack error and a tz-naive/aware datetime comparison error. A new CrashSignal classification recognizes both exact shapes instead of surfacing an opaque generic exception.
• PR#1222 (@david-morales) / PR#1183 (@Milofax): FalkorDB RediSearch syntax errors from empty or unescaped fulltext queries. A new CrashSignal.QUERY_SANITIZATION_ERROR recognizes both issues' verbatim filed error text.
• #1467 (@elimydlarz, open, zero engagement): GeminiEmbedder silently returning the wrong vector count. A new CrashSignal.EMBEDDING_BATCH_COUNT_MISMATCH catches this once Gemini embedder support is wired into the self-hosted adapter.

OpenViking

• #3029 (@dfwgj, still open): Feishu resync silently deleting user-managed files. memtrust had no way to observe this failure mode at all; a dedicated resource-sync-safety eval now seeds generated and user files, triggers a resync, and checks what survives.
• #2850 (@lg320531124, still open): BM25 search silently returning empty results at scale. A dedicated scale/volume-stress eval (memtrust run --eval scale_stress) now stores a large synthetic corpus and re-queries it at ascending checkpoints to reproduce the shape of this condition -- recall collapsing past a volume threshold with no exception raised.
• #1581 (@0xble, fix rejected, still live upstream): v2_lock_max_retries=0 silently means unlimited retries, not zero. A new lock-contention eval asserts a bounded response-time budget under concurrent-write contention.
• #1255 (@SeeYangZhi): a stats endpoint silently returning zero despite persisted memories. A new get_stats()/StatsResult primitive and dedicated stats-accuracy eval now catch this.
• #2966 (@lRoccoon, unaddressed upstream): legacy uint16-truncated records that are permanently undeletable. A new CrashSignal.LEGACY_CORRUPT_RECORD_UNDELETABLE now surfaces this instead of a silent no-op.
• #204 (@ponsde, closed): non-deterministic search results (Jaccard similarity 0.11 across identical queries) from a self-diagnosed dimension mismatch. A new result-consistency eval computes pairwise Jaccard similarity over repeated identical queries to catch this class directly.

Cross-project

• OneNomad-LLC/przm-bench (@mattstvartak): a peer benchmarking project shipped cryptographic receipt signing; memtrust had none. memtrust now has real Ed25519 signing/verification (the cryptography library, not a hand-rolled scheme) via memtrust keygen / run --sign / verify -- a tampered receipt correctly fails verification, a genuine one correctly passes.

Several PARTIAL and FAIL -- capability gap rows above and elsewhere in the full 197-row set remain open, deliberately not counted as fixed: some point at real gaps this harness genuinely can't close yet without a live vendor credential, and inflating a near-miss to PASS defeats the entire point of an independently-verified benchmark. See the confidence caveats throughout this README and in docs/methodology.md for exactly which claims rest on which kind of evidence.