原型 · 预览版:收录官方注册中心里的全部服务器,每 30 分钟同步一次。评价和账户功能将在后续阶段推出。
PopNx MCP为使用 AI 的人准备的 MCP 服务器 · 用大白话讲清楚

FastrMemory

作者:atifjubaer版本 0.9.3i★ –i在你的电脑上运行
注册名称io.github.atifjubaer/fastrmemory

安装

概览

作者提供的说明(英文原文)

Local-first cognitive agent memory with bi-temporal SQLite, replayable history, hybrid retrieval.

PopNx MCP 的三大支柱

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。

它能帮你做什么

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。

  • 连接方式: 在你的电脑上运行
  • 传输方式: stdio
  • 分类: 搜索与 AI 记忆

安装

选择你使用的 AI 应用,然后复制设置。

安装之前

  • 基础检查:1 项中通过 1 项
  • 在你的电脑上运行,拥有你的用户账户的权限(文件、网络、应用)。
  • 注册中心没有列出需要填写的密钥。

你需要

  • npm已安装 Node.js(用于 npx 命令)

你用的是哪个应用?

在上方选择你的应用。我们会记住它,下次按钮会显示“安装到 …”。

打开 Claude Desktop → Settings → Developer → Edit Config,把下面的内容粘贴到 claude_desktop_config.json

{
  "mcpServers": {
    "fastrmemory": {
      "command": "npx",
      "args": [
        "-y",
        "fastrmemory",
        "mcp"
      ]
    }
  }
}

在终端中运行这条命令

claude mcp add fastrmemory -- npx -y fastrmemory mcp

在 Cursor 中打开

或者自己复制设置

粘贴到项目中的 .cursor/mcp.json,或 ~/.cursor/mcp.json

{
  "mcpServers": {
    "fastrmemory": {
      "command": "npx",
      "args": [
        "-y",
        "fastrmemory",
        "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。

在 VS Code 中打开

或者自己复制设置

粘贴到项目中的 .vscode/mcp.json

{
  "servers": {
    "fastrmemory": {
      "type": "stdio",
      "command": "npx",
      "args": [
        "-y",
        "fastrmemory",
        "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。

这个服务器在你的电脑上运行,而 ChatGPT 只能连接互联网上的服务器。请选择其他应用。

你的选择通过 Cookie 保存在这台设备上,无需账户。

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。

它能做什么

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。

基础检查i

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

  • ✓ 通过

    作者与仓库一致

    注册名称已通过 GitHub 账户 atifjubaer 验证,该账户拥有此仓库

  • … 尚未检查

    仍在维护

    我们还没有从 GitHub 读取这个仓库

  • … 尚未检查

    有许可证

    我们还没有从 GitHub 读取这个仓库

  • – 不适用

    端点有响应

    它在你的电脑上运行,没有互联网端点

  • – 不适用

    使用 OAuth 登录

    在你的电脑上运行,改用本地设置

  • … 尚未检查

    工具描述中没有隐藏指令

    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“搜索与 AI 记忆”中的其他服务器比较i

  • 星标数多于此分类中其他 7271 个服务器的 0%
  • 发布到注册中心的时间比此分类中其他 7271 个服务器的 85% 更新
  • 基础检查结果等于或优于此分类中其他 7271 个服务器的 100%

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。

常见问题

FastrMemory 安全吗?

它在 1 项适用的基础检查中通过了 1 项。我们没有阅读代码。在添加密钥或令牌之前,请先看看这个服务器要求哪些权限,并使用只具备所需权限的密钥。

它需要哪些密钥或代码?

注册中心没有列出必需的密钥,不过有些服务器会在首次使用时要求你登录。

它在我的电脑上运行还是在云端?

它在你的电脑上运行。你的 AI 应用会在需要时启动它,数据不会经过作者的服务器(除非它自己调用在线服务)。

它还在维护吗?

GitHub 仓库最后更新于–,最新版本(0.9.3)于6天前进入注册中心。

哪些应用可以使用它?

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 Node.js。

有什么可以替代它?

同一分类中有:Context7, Codebase Memory, Exa

替代选择

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

Context7

作者:upstash

Up-to-date docs for AI agents

电脑或云端★ 64,224MIT昨天更新
安装

Codebase Memory

作者:DeusData

Codebase knowledge graph for AI agents — 162 languages, sub-ms queries, 99% fewer tokens.

在你的电脑上运行★ 48,148MIT21天前更新
安装

Exa

作者:exa-labs

Connect AI agents to Exa for web search, content fetching, and multi-step research.

在云端运行★ 5,213MIT上个月更新
安装

作者的 README

打开 README

按原文从作者的仓库复制。PopNx MCP 没有审查或背书其内容。HTML、图片和脚本已被移除。链接会离开本站。

FastrMemory

High-Performance Cognitive Agent Memory Engine

Zero Daemon • Native Node 22+ `node:sqlite` • Bi-Temporal Git Sync • Triple-Stream RRF • Official MCP v2

Status (2026-11-01): `fastrmemory@0.9.3` is published and is `latest`. Evidence: npm view fastrmemory version -> 0.9.3; npm view fastrmemory dist-tags --json -> {"latest":"0.9.3"}; tag v0.9.3 (15a772f); published 2026-11-01 via GitHub Actions OIDC with npm provenance; the release workflow passed the full gate, clean tarball install, registry verification, and GitHub Release upload; the npm tarball is 358 files, 819,117 bytes packed, 3,289,741 bytes unpacked; GitHub Release asset SHA-256 eebda8ab3f90b73ee5f4bd4a64a6ff511f9c93468576c167de3a1c1e2efbcf22; a clean npm install fastrmemory@0.9.3 + fastr --version -> 0.9.3; npm audit signatures reports 5 verified registry signatures and 4 verified attestations. The official MCP registry lists io.github.atifjubaer/fastrmemory@0.9.3 as active and isLatest. Caveat: Node.js >=22.16.0; 0.9.2, 0.9.1, 0.9.0, 0.8.2, 0.8.1, 0.8.0, 0.7.2, 0.7.1, 0.7.0, 0.6.0, 0.5.0, 0.4.0, 0.3.1, and 0.3.0 remain published historical. This release makes code-graph indexing automatic: setup and hook install index once, SessionStart repairs an empty index, and prompt/session-end hooks refresh recent edits; the schema is unchanged. The old scoped npm entries (@atifjubaer/fastrmemory and fastrmemory@0.0.0-stage) were removed on 2026-10-23; the GitHub v0.2.1 tag and release remain as history. This README tracks current main. Current main is `0.9.3` and has 481 tests / 74 files. Use the exact 0.9.3 version for reproducibility. PR #46 at 5da9b2e passed all required checks (six matrix jobs plus Replayable Memory Diff and Pre-Merge Trap Guardian); the historical 0.9.2 release commit b2c6502, 0.9.1 release commit 5da9b2e, 0.9.0 release commit c204f50, 0.8.2 release commit 8cc925b, 0.8.1 release commit 4e7d9ac, 0.8.0 release commit 44b2d9a, 0.7.2 release commit 583223f, 0.7.1 release commit 31d8e12, 0.7.0 release commit 5512557, 0.6.0 release commit a57a656, 0.5.0 release commit 54a79ab, and 0.4.0 release commit bb365c2 remain published historical. The exact release surface is in CHANGELOG.md. The headline benchmark uses 240 deterministic synthetic cases with a 20% holdout, not SWE-bench or LongMemEval. Public LongMemEval, LoCoMo, and self-repository retrieval-only results plus pinned external baselines are in BENCHMARKS.md; no QA-leaderboard parity is claimed. Performance, packaging, and retrieval claims are tracked in CLAIMS.md; every README section is mapped to claim IDs in the README claim trace at the end of that ledger. Methodology, caveats, and raw JSON are in BENCHMARKS.md.

Overview • Design Principles • Benchmarks • Web UI • Supported Agents • Quick Start • MCP Setup • MCP Tools • Architecture • Migration • Security • Scorecard


Launch Status at a Glance

ClaimCurrent resultEvidence
Install pathPublished fastrmemory@0.9.3npm view fastrmemory version -> 0.9.3; npm view fastrmemory dist-tags --json -> {"latest":"0.9.3"}; npm install fastrmemory@0.9.3; fastr --version -> 0.9.3; GitHub Release tarball SHA-256 eebda8ab...; old scoped npm entries removed
MCP registryio.github.atifjubaer/fastrmemory@0.9.3, status active, isLatestcurl --get --data-urlencode "search=io.github.atifjubaer/fastrmemory" https://registry.modelcontextprotocol.io/v0.1/servers; gh run view 40262643667
Synthetic retrieval (optional MiniLM)Holdout 95.8% Hit@1, 100% Hit@5, MRR 0.969npx tsx scripts/eval-bench.ts --split all --embeddings minilm --out bench/results/minilm.json
Test suite481 tests across 74 files on current main; published 0.9.3 is 481 / 74; published 0.9.2 is 476 / 74; published 0.9.1 is 476 / 74; published 0.9.0 is 476 / 74; published 0.8.2 is 461 / 69; published 0.8.1 is 461 / 69; published 0.8.0 is 461 / 69; published 0.7.2 is 456 / 67; published 0.7.1 is 456 / 67; published 0.7.0 is 455 / 67; published 0.6.0 is 388 / 61; 0.5.0 is 297 / 53; historical 0.4.0 is 293 / 53; 0.3.1 is 282 / 52; v0.2.1 is 235 / 43npm test; npm run check:ci
MCP setupfastr mcp init merges portable registry configs or active-CLI local configs for 9 JSON clients and prints a Codex TOML snippet; --source local ships in published 0.9.3, 0.9.2, 0.9.1, 0.9.0, 0.8.2, 0.8.1, 0.8.0, 0.7.2, 0.7.1, 0.7.0, 0.6.0, and 0.5.0npm test -- tests/mcp-init.test.ts -> 15 passed; fastr mcp init --client claude --source local --dry-run --json -> node .../dist/bin/fastr.js mcp --project ...
Cross-engine (MCP stdio)80/120 overall Hit@1 (66.7%, 95% CI 57.8-74.5): exact 40/40, multi 40/40, paraphrase 0/40; 1.094 ms/doc ingest, 1.86 ms recall p50npm run bench:vs; bench/results/vs-engines.json
Crash safety10/10 runs, 0 lost, 0 duplicate ids/titlesnpm run test:crash; bench/results/crash-test.json
Public retrieval (0.3.0)LongMemEval +10.4pp / +11.0pp Hit@5; LoCoMo +4.4pp / +2.3pp (target missed)docs/EXTERNAL_BASELINE.md
Release scoreProvisional 9.4 / 10; 9.2 target met; Retrieval remains PartialSCORECARD.md

The synthetic table is not SWE-bench or LongMemEval. Public numbers are retrieval-only and are not QA-leaderboard results. The 95.8% row uses optional MiniLM; the default offline hash path scores 77.1% Hit@1 / 93.8% Hit@5 on the same holdout. Full provenance is in CLAIMS.md.


💡 Overview

AI coding agents (Claude Code, Cursor, Windsurf, Codex, Antigravity) degrade on long-running codebases when project memory is lossy or operationally heavy. Common designs make one of three trade-offs:

  • Decay and merge old episodes to keep the store small, which can lose exact file paths, stack traces, and edge cases.
  • Run a daemon or external database, which adds ports, containers, and setup failure modes.
  • Dump a static context file, which grows without bound and is not queryable.

FastrMemory keeps a verbatim event log, bi-temporal memory history, and a replayable Git mirror in a single in-process TypeScript engine (3 runtime dependencies; local embeddings and the daemon are optional):

Zero Daemon Overhead

Pure Node 22+ built-in node:sqlite in WAL mode. On the 240-case suite, engine-level recall is 0.95 ms p50 / 1.60 ms p95 with hash embeddings and 3.30 ms p50 / 5.11 ms p95 with MiniLM on the train split (CLI startup measured separately). The default CLI/MCP path has no background daemon and no open port; daemon start is optional. Native on Windows, macOS, and Linux (the current CI matrix uses Intel macOS; Apple Silicon is not covered); CI matrix status is reported in Actions.

🔀 Bi-Temporal Git Sync

Never deletes facts during consolidation (there is no destructive sleep step). Replaces half-life decay with valid_from / valid_to timestamps and auto-mirrors human-readable .fastr/pages/*.md files into Git.

🎯 Triple-Stream RRF ($k=20$)

Fuses FTS5 BM25 + optional local embeddings + file-graph links. Error traps receive a conditional 2.0x boost only for trap-intent or file-scoped queries, with per-ID outcome feedback (+0.2 / -0.5). Three consecutive bad outcomes move a memory to dormant; confidence tiers are high, medium, low, dormant, and superseded. Counting questions ("how many projects have I led?") additionally run an aggregate-coverage pass: a second lexical query over their content terms, fused with the original, returning up to 20 rows so the caller can count. retrieval.temporalFilter adds opt-in date windows ("past two weeks", "this year") parsed against the question date.

⚔️ Architectural & Performance Comparison
CapabilityHippo Memory (hippo-memory)Memoria (matrixorigin/Memoria)AgentMemory (@agentmemory/mcp)FastrMemory (0.9.3+)
Runtime ArchitectureSQLite storeMatrixOne relational/vector DB containerDual-layer engine + Redis/DockerZero-Daemon • Native `node:sqlite` in WAL mode
Ingestion Speed88.9 ms/docContainer roundtrip1.74 ms/doc1.09 ms/doc (80x faster than Hippo)
Recall Latency (p50)12.8 ms (spikes to 386 ms p95)50–100 ms1.52 ms0.95 ms p50 / 1.60 ms p95
AST Symbol Anchoring❌ None (file path only)❌ None❌ None✅ `declaration_id` (AST hash survives renames & file moves)
Self-Verifying Traps❌ None❌ None❌ None✅ Machine assertions (`text_present`, etc.) auto-retire fixed bugs
Context Token Overhead⚠️ High (dumps entire rule sets)⚠️ High (dumps knowledge graphs)⚠️ High (dumps session contexts)Ultra-low (≤150 tokens targeted per-turn injection)
Passive Learning❌ Manual commands only❌ Manual commands only❌ Manual commands only✅ Auto-distills traps from test failures & subsequent edits
Cross-Machine Team SyncManual markdown git commitsCloud database clusterCustom daemon✅ Bidirectional `fastr sync` on Git pull
Editor IntegrationBasic CLI instructionsNoneBasic plugin✅ Native VS Code & Cursor extension with Problems tab diagnostics
MCP Protocol SupportstdioREST / MCP adapterstdioOfficial MCP v2 (12-tool core & 45-tool full profiles)

*Verified benchmark evidence from bench/results/vs-engines.json and docs/PUBLIC_BENCHMARKS.md.*

🧭 Memory Landscape:

只显示了开头部分;其余内容请在仓库中阅读

问 PopNx