Context7
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作者による説明(英語の原文)
Independent, reproducible benchmark harness for agent-memory backends.
この3つを1つのスコアにまとめることはしません。意見、測定、人気はそれぞれ別のものだからです。
このサーバーのやさしい解説はまだ書いていません。以下はレジストリから読み取った事実です。
使っているAIアプリを選んで、設定をコピーしてください。
必要なもの
どのアプリを使っていますか?
上でアプリを選んでください。選んだアプリを覚えておくので、次からはボタンに「…にインストール」と表示されます。
Claude Desktop → Settings → Developer → Edit Config を開き、claude_desktop_config.json にこれを貼り付けます
{
"mcpServers": {
"memtrust": {
"command": "uvx",
"args": [
"memtrust-cli",
"memtrust-mcp"
]
}
}
}ターミナルでこれを実行します
claude mcp add memtrust -- uvx memtrust-cli memtrust-mcpプロジェクトの .cursor/mcp.json、または ~/.cursor/mcp.json に貼り付けます
{
"mcpServers": {
"memtrust": {
"command": "uvx",
"args": [
"memtrust-cli",
"memtrust-mcp"
]
}
}
}ブラウザがアプリを開いてよいか確認し、そのあとアプリが設定を表示して確認を求めます。キーの値は空欄のままなので、自分で入力してください。
プロジェクトの .vscode/mcp.json に貼り付けます
{
"servers": {
"memtrust": {
"type": "stdio",
"command": "uvx",
"args": [
"memtrust-cli",
"memtrust-mcp"
]
}
}
}ブラウザがアプリを開いてよいか確認し、そのあとアプリが設定を表示して確認を求めます。キーの値は空欄のままなので、自分で入力してください。
これはパソコンで動くサーバーですが、ChatGPTがつなげるのはインターネット上のサーバーだけです。ほかのアプリを選んでください。
選んだアプリは、この端末のCookieに保存されます。アカウントは不要です。
この設定は、作者のレジストリ登録情報から作成しています。READMEと違う場合は、READMEに従ってください。
パソコンで動くサーバーです。インストールすると、ツール一覧がAIアプリに表示されます。
誰でも見られる情報をチェックしており、コードは読んでいません。当てはまらない項目は数に入れていません。 · 2026年11月7日にチェック
レジストリ名は、リポジトリを所有するGitHubアカウント RudrenduPaul で確認済みです
このリポジトリはまだGitHubから読み取っていません
このリポジトリはまだGitHubから読み取っていません
これはパソコンで動くため、インターネット上のエンドポイントはありません
パソコンで動き、代わりにローカルの設定を使います
パソコンで動くサーバーです。ツール一覧を読み取れるのは、インターネット上のエンドポイントがある場合だけです
レジストリでこのカテゴリにある有効なすべてのサーバーと比較しています。スターは、公開者がリポジトリの所有者である場合だけ数えます。
当てはまる基本チェック1項目のうち1項目に合格しました。コードは読んでいません。キーやトークンを追加する前に、サーバーがどんなアクセスを求めているかを確認し、必要な権限だけを持つキーを使ってください。
レジストリには必須のキーは載っていません。ただし、最初に使うときにサインインを求めるサーバーもあります。
あなたのパソコンで動きます。必要なときにAIアプリが起動し、データは作者のサーバーを通りません(サーバー自体がオンラインサービスを呼び出す場合を除きます)。
GitHubリポジトリの最終更新は–、最新バージョン(0.3.6)がレジストリに届いたのは3 か月前です。
Claude Desktop、Claude Code、Cursor、VS CodeなどのMCP対応アプリで使えます。パソコンにPython (uv)も必要です。
同じカテゴリには:Context7, Codebase Memory, Exa
同じカテゴリで、ほかの作者が作ったサーバー。スターの多い順。
カードの見方:★ GitHubスターi3つの柱:レビュー · チェック · 利用状況i更新=最新バージョンをレジストリに公開した日i
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作者のリポジトリから書かれたとおりにコピーしたものです。PopNx MCPは内容を確認・推奨していません。HTML、画像、スクリプトは取り除いています。リンクはこのサイトの外へ移動します。
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
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 232 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.
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 0.3.4 -- run_id=mt_2026-08-04T061759Z 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-2026-08-03.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 the installed version, 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
... (33 module rows total; the 11 most relevant to this README are shown below) Name Stmts Miss Cover
src/memtrust/adapters/base.py 290 1 99% src/memtrust/adapters/mempalace_adapter.py 265 15 94% src/memtrust/adapters/mem0_adapter.py 140 12 91% src/memtrust/adapters/mem0_direct_adapter.py 281 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 165 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 3 97% src/memtrust/receipt.py 118 10 92%
TOTAL 4167 265 94%
590 passed, 8 skipped in 5.92s
This is an excerpt, not the full table -- the weakest-covered module in the repo, evals/mempalace_metadata_scale.py (70%), isn't one of the 11 shown above; run the command yourself for the complete per-module breakdown.
590 passing tests across 33 source modules, 94% overall statement coverage, 98% on the contradiction-detection eval, 99% on compression/round-trip fidelity, 97% on the temporal-KG boundary eval, 85-99% 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.
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