Context7
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作者提供的说明(英文原文)
Independent, reproducible benchmark harness for agent-memory backends.
这三项永远不会合并成一个分数:意见、测量和人气是不同的东西。
我们还没有为它写通俗的介绍。下面是我们从注册中心读取到的事实。
选择你使用的 AI 应用,然后复制设置。
你需要
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打开 Claude Desktop → Settings → Developer → Edit Config,把下面的内容粘贴到 claude_desktop_config.json
{
"mcpServers": {
"memtrust": {
"command": "uvx",
"args": [
"memtrust-cli",
"memtrust-mcp"
]
}
}
}在终端中运行这条命令
claude mcp add memtrust -- uvx memtrust-cli memtrust-mcp粘贴到项目中的 .cursor/mcp.json,或 ~/.cursor/mcp.json
{
"mcpServers": {
"memtrust": {
"command": "uvx",
"args": [
"memtrust-cli",
"memtrust-mcp"
]
}
}
}浏览器会先询问是否打开应用,然后应用会显示设置并请你确认。密钥的值会留空,由你自己填写。
粘贴到项目中的 .vscode/mcp.json
{
"servers": {
"memtrust": {
"type": "stdio",
"command": "uvx",
"args": [
"memtrust-cli",
"memtrust-mcp"
]
}
}
}浏览器会先询问是否打开应用,然后应用会显示设置并请你确认。密钥的值会留空,由你自己填写。
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这些设置是根据作者在注册中心的条目生成的。如果 README 的写法不同,请以 README 为准。
它在你的电脑上运行;安装后,工具列表会显示在你的 AI 应用中。
我们检查的是任何人都能看到的信息;我们没有阅读代码。不适用的检查项不计入总数。 · 检查于 2026年11月7日
注册名称已通过 GitHub 账户 RudrenduPaul 验证,该账户拥有此仓库
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我们还没有从 GitHub 读取这个仓库
它在你的电脑上运行,没有互联网端点
在你的电脑上运行,改用本地设置
它在你的电脑上运行;我们只能读取互联网端点的工具列表
与注册中心里这个分类中所有活跃的服务器比较。只有当发布者拥有该仓库时,星标数才计入。
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GitHub 仓库最后更新于–,最新版本(0.3.6)于3个月前进入注册中心。
支持 MCP 的应用,例如 Claude Desktop、Claude Code、Cursor 和 VS Code。你的电脑上还需要有 Python (uv)。
同一分类中有:Context7, Codebase Memory, Exa
同一分类中其他作者的服务器,按星标数排序。
如何看卡片:★ GitHub 星标i三大支柱:评价 · 检查 · 使用情况i更新 = 最新版本发布到注册中心的时间i
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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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