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PopNx MCP为使用 AI 的人准备的 MCP 服务器 · 用大白话讲清楚

hmem — Humanlike Memory for AI Agents

作者:Bumblebiber版本 1.6.7i★ –i在你的电脑上运行
注册名称io.github.Bumblebiber/hmem-mcp

安装

概览

作者提供的说明(英文原文)

Persistent 5-level hierarchical memory for AI agents. SQLite-backed, lazy-loaded.

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

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": {
    "hmem": {
      "command": "npx",
      "args": [
        "-y",
        "hmem-mcp"
      ],
      "env": {
        "HMEM_STORAGE_DIR": "your-value"
      }
    }
  }
}

在终端中运行这条命令

claude mcp add hmem --env HMEM_STORAGE_DIR=your-value -- npx -y hmem-mcp

运行前请先填写空着的值。

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": {
    "hmem": {
      "command": "npx",
      "args": [
        "-y",
        "hmem-mcp"
      ],
      "env": {
        "HMEM_STORAGE_DIR": "your-value"
      }
    }
  }
}

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": {
    "hmem": {
      "type": "stdio",
      "command": "npx",
      "args": [
        "-y",
        "hmem-mcp"
      ],
      "env": {
        "HMEM_STORAGE_DIR": "your-value"
      }
    }
  }
}

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。

需要你填写的值

  • HMEM_STORAGE_DIR env
    Directory where .hmem SQLite databases are stored. Defaults to ~/.hmem/

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 Bumblebiber 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 7270 个服务器的 0%
  • 发布到注册中心的时间比此分类中其他 7270 个服务器的 3% 更新
  • 基础检查结果等于或优于此分类中其他 7270 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

hmem — Humanlike Memory for AI Agents 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(1.6.7)于9个月前进入注册中心。

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。

hmem — Humanlike Memory for AI Agents

Your AI forgets everything between sessions. hmem fixes that.

One load_project() call. ~3000 tokens. Your agent knows everything important about a project — every past mistake, every decision, every open task — across sessions, devices, and AI providers. No setup per conversation. No "let me re-read the codebase." It just *remembers*.

AI agent? Skip this file. Read AGENT_SETUP.md — written for you, not for humans.

Naming note (v8.0): This package was briefly published as its-over-9k (1.x). It is now hmem again — pure memory framework, nothing else. The its-over-9k name lives on as a separate project: the o9k token-efficiency meta-framework, which combines skills/plugins like this one. Related: TIM — hmem's next-generation successor (hypergraph memory, CRDT sync).


What This Is

hmem is not a note-taking plugin. It's a memory framework for AI agents — a complete infrastructure layer for persistent, portable, token-efficient knowledge that survives session boundaries, device switches, and provider changes.

Four core guarantees:

PillarWhat it means
Token efficiency5-level lazy loading — you pay for what you read, never more
PortabilitySame memory across Claude, Gemini, GPT, local models, any MCP client
Efficient storageHierarchical tree structure — context scales with depth, not flat append
No context wasteAuto-session capture + project briefing = zero re-read overhead

The Problem

Every AI session starts from zero. Your agent asks the same questions, makes the same mistakes, contradicts last week's decisions, and wastes 50k tokens loading context it already processed yesterday.

You've tried workarounds — CLAUDE.md files, custom prompts, manually pasting context. They don't scale. You have 10 projects. You switch between 3 devices. You use different AI tools.

The Solution

You:    "Load project"

Agent: [calls load_project("P0048") — 3000 tokens] Agent: "v1.2.9, TypeScript/SQLite/npm. 3 open bugs, 8 roadmap items. Last session: rebrand complete, rename_id bug fixed (89 changes). Next: O-Entry Auto-Purge. What's the focus today?"

That's it. 3000 tokens for a complete project briefing. The agent knows the stack, the architecture, the open bugs, the recent decisions, and exactly where you left off — even if "you" was a different AI on a different machine yesterday.


How It Works

Level 1  ──  One-line summary          (always loaded — ~5k tokens for 300+ entries)

Level 2 ── Paragraph detail (loaded on demand) Level 3 ── Full context (loaded on demand) Level 4 ── Extended detail (loaded on demand) Level 5 ── Raw/verbatim data (loaded on demand)

At session start, the agent loads Level 1 summaries — one line per memory. When it needs detail, it drills down. Your 300-entry memory costs 5k tokens to overview. A single project costs ~3000 tokens.

Nothing is summarized away. Level 1 is a compressed view, but Levels 2–5 hold the complete original text, word for word, accessible on demand.


Framework Features

Automatic Session Memory

Every conversation is recorded automatically. No "save your work" prompts. No manual checkpoints.

You type  →  Agent responds  →  Stop hook fires  →  Exchange saved to O-entry

→ Linked to active project → Haiku auto-titles the session

Switch projects mid-session? The O-entry switches too. Start a new session on a different device? The next agent sees every exchange from every device — the conversation never dies.

Haiku Background Checkpoints

Every N exchanges (configurable, default 5), a Haiku subagent wakes up in the background. It reads the recent conversation, extracts lessons learned, errors encountered, and decisions made, then writes them to long-term memory — with full MCP tool access. Your main agent is never interrupted.

The checkpoint also writes a handoff note to the project: "Here's what was done, here's what's in progress, here's the next step." The next agent — on any device, any provider — picks up exactly where you left off.

Project-Based, Not Session-Based

Sessions are meaningless. Projects are everything.

  • O-entries are linked to the active project, not the session
  • Checkpoint counters count project exchanges, not session messages
  • load_project shows recent conversations with full context — across all devices
Skills System

hmem ships with a complete skills layer — structured behavior files that agents load on demand. Skills define *how* an agent should do something (debug, write memory, curate entries, handle a session start) — separate from memory, separate from prompts.

npx hmem update-skills    # Pull latest skills to your AI tool's skill directory

Skills are versioned and updated independently. Your agents get smarter without reinstalling. 21 skills ship by default:

SkillTriggers when…
hmem-session-startEvery session start — loads project + surfaces pending git work, open tasks, misrouted O-entries
hmem-using-hmemMeta-skill loaded at session start; defines mandatory memory habits
hmem-readReading from long-term memory (search, prefix filter, find_related, cross-project read)
hmem-writeWriting to hmem — picks prefix, tree location, tags, detects duplicates
hmem-searchUser references something without an ID ("the bug we had", "letzte Woche")
hmem-new-projectCreating a P-entry — handles schema, sections, O-entry linking
hmem-new-errorCreating an E-entry with the strict 5-level scaffold
hmem-activateSwitching active project mid-session, plus fixing misrouted exchanges
hmem-contextLoading specific context when load_project output isn't enough
hmem-recallDispatching a Haiku sub-agent to search hmem
hmem-dispatchDispatching an isolated sub-agent for any search/lookup/calculation
hmem-curateCleaning up an .hmem file (mark obsolete, fix titles, consolidate dupes)
hmem-migrate-oMigrating O-entries to the project-bound 5-level structure
hmem-consolidateMerging session checkpoint summaries into one final O-entry summary
hmem-wipePrep for /clear — save high-value knowledge, update Next Steps
hmem-configView/change memory settings, hooks, sync, checkpoints
hmem-setupFirst-time install of hmem for Claude Code / Gemini CLI / OpenCode
hmem-sync-setupSet up hmem-sync for cross-device sync
hmem-updateUpdate flow — runs npm update -g, syncs skills, applies migrations
hmem-releasePre-publish checklist for hmem itself
hmem-subagentTemplate for sub-agents dispatched by hmem-dispatch
Company Memory

Beyond personal memory, agents can maintain a shared company store — a separate company.hmem that multiple agents and team members can read from. Personal and company memory coexist; agents query both simultaneously.

import { openCompanyMemory } from 'hmem';

const store = openCompanyMemory('/path/to/project');

Embeddable SDK

hmem ships as a fully documented TypeScript SDK — import HmemStore directly into your own agents, tools, or automation pipelines:

import {

HmemStore, openCompanyMemory, resolveHmemPath, loadHmemConfig, saveHmemConfig, DEFAULT_CONFIG, DEFAULT_PREFIXES, formatPrefixList, searchMemory, } from 'hmem'; import type { AgentRole, MemoryEntry, MemoryNode, HmemConfig, SearchResult, SearchOptions, SearchScope, } from 'hmem';

const store = new HmemStore('/path/to/agent.hmem'); const results = searchMemory('/path/to/project', 'auth token bug', { maxResults: 5 });


MCP Tools

hmem ships two MCP servers:

  • `hmem` (daily-use, 13 tools) — read, write, search, project lifecycle.
  • `hmem-curate` (maintenance, 11 tools) — bulk edits, schema migrations, backup/restore. Activate only when curating.
hmem — daily-use server (13)
ToolWhat it does
read_memory5-level lazy read — by ID, prefix, search, time, or tag
write_memoryCreate new entries with title, body, tags, links
append_memoryAdd child nodes to existing entries
update_memoryPatch fields: title, body, tags, irrelevant, links
search_memoryFTS5 full-text search with sub-node attribution
find_relatedFind contextually related entries by tag overlap
load_projectActivate a project + get full briefing + recent sessions
read_projectRead project without activating (comparison/reference)
create_projectScaffold a new project entry with standard schema
list_projectsList all projects with status summary
flush_contextPersist current session context to long-term memory
move_nodesMove a subtree under a different parent (also in curate server)
set_active_deviceRegister and switch between devices
hmem-curate — maintenance server (11)
ToolWhat it does
memory_statsPer-prefix counts, total tokens, favorites, hashtags, stale-list
memory_healthFind broken links, orphan tags, empty entries, dangling chains
export_memoryExport the full .hmem to a portable JSON snapshot
import_memoryImport a JSON snapshot back into a .hmem (destructive)
update_manyBulk-patch a set of entries (irrelevant, tags, body, etc.)
tag_bulkAdd/remove a tag across many entries
tag_renameRename a tag globally across all entries
move_memoryMove an entire entry to a new ID slot
move_nodesMove a subtree under a different parent (also in main server)
rename_idRename an entry's ID; rewrites all inbound links
reset_memory_cacheInvalidate the in-memory L1 cache (after raw SQL writes)

The daily hmem server is registered automatically by hmem init. hmem-curate is opt-in because its tools are destructive — register it when you need to curate:

npx hmem mcp-add-curate          # adds the curate server to Claude Code (user scope)

Then toggle it via /mcp only during curation sessions. For other MCP clients, see Manual setup.


CLI Commands

After npm install -g hmem-mcp, the hmem binary is on PATH.

User-facing
CommandPurpose
hmem initInteractive installer for AI tools (Claude Code, OpenCode, Gemini CLI, Cursor, Windsurf, Cline). Flags: --global / --local / --tools / --dir / --no-example
hmem update-skillsCopy/sync bundled skill files to detected AI tools (called automatically on npm install)
hmem mcp-add-curateRegister the hmem-curate MCP server with Claude Code (opt-in; toggle via /mcp when curating)
hmem doctorDetect stale or deprecated hmem MCP entries in host configs
hmem statsMemory statistics + per-project token estimates + 🔴 4k threshold flagging
hmem setup-hookRe-add the SessionStart hook to Claude Code settings (if removed)
hmem versionShow version
Hook drivers (called by AI tools, not by hand)
CommandWired intoWhat it does
hmem hook-startupUserPromptSubmitFirst-message context injection (memory overview, project list, sync status). Periodic checkpoint reminders. Reads JSON from stdin

| hmem log-exchange | Stop (sync) | Append the latest e

只显示了开头部分;其余内容请在仓库中阅读

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