Context7
Up-to-date docs for AI agents
作者提供的说明(英文原文)
Persistent 5-level hierarchical memory for AI agents. SQLite-backed, lazy-loaded.
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。
HMEM_STORAGE_DIR你需要
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。
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。
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。
HMEM_STORAGE_DIR env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 Bumblebiber 验证,该账户拥有此仓库
我们还没有从 GitHub 读取这个仓库
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。
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。
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)。
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。
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。
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
Up-to-date docs for AI agents
Codebase knowledge graph for AI agents — 162 languages, sub-ms queries, 99% fewer tokens.
Connect AI agents to Exa for web search, content fetching, and multi-step research.
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。
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 nowhmemagain — pure memory framework, nothing else. Theits-over-9kname 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).
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:
| Pillar | What it means |
|---|---|
| Token efficiency | 5-level lazy loading — you pay for what you read, never more |
| Portability | Same memory across Claude, Gemini, GPT, local models, any MCP client |
| Efficient storage | Hierarchical tree structure — context scales with depth, not flat append |
| No context waste | Auto-session capture + project briefing = zero re-read overhead |
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.
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.
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.
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.
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.
Sessions are meaningless. Projects are everything.
load_project shows recent conversations with full context — across all deviceshmem 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:
| Skill | Triggers when… |
|---|---|
hmem-session-start | Every session start — loads project + surfaces pending git work, open tasks, misrouted O-entries |
hmem-using-hmem | Meta-skill loaded at session start; defines mandatory memory habits |
hmem-read | Reading from long-term memory (search, prefix filter, find_related, cross-project read) |
hmem-write | Writing to hmem — picks prefix, tree location, tags, detects duplicates |
hmem-search | User references something without an ID ("the bug we had", "letzte Woche") |
hmem-new-project | Creating a P-entry — handles schema, sections, O-entry linking |
hmem-new-error | Creating an E-entry with the strict 5-level scaffold |
hmem-activate | Switching active project mid-session, plus fixing misrouted exchanges |
hmem-context | Loading specific context when load_project output isn't enough |
hmem-recall | Dispatching a Haiku sub-agent to search hmem |
hmem-dispatch | Dispatching an isolated sub-agent for any search/lookup/calculation |
hmem-curate | Cleaning up an .hmem file (mark obsolete, fix titles, consolidate dupes) |
hmem-migrate-o | Migrating O-entries to the project-bound 5-level structure |
hmem-consolidate | Merging session checkpoint summaries into one final O-entry summary |
hmem-wipe | Prep for /clear — save high-value knowledge, update Next Steps |
hmem-config | View/change memory settings, hooks, sync, checkpoints |
hmem-setup | First-time install of hmem for Claude Code / Gemini CLI / OpenCode |
hmem-sync-setup | Set up hmem-sync for cross-device sync |
hmem-update | Update flow — runs npm update -g, syncs skills, applies migrations |
hmem-release | Pre-publish checklist for hmem itself |
hmem-subagent | Template for sub-agents dispatched by hmem-dispatch |
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');
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 });
hmem ships two MCP servers:
hmem — daily-use server (13)| Tool | What it does |
|---|---|
read_memory | 5-level lazy read — by ID, prefix, search, time, or tag |
write_memory | Create new entries with title, body, tags, links |
append_memory | Add child nodes to existing entries |
update_memory | Patch fields: title, body, tags, irrelevant, links |
search_memory | FTS5 full-text search with sub-node attribution |
find_related | Find contextually related entries by tag overlap |
load_project | Activate a project + get full briefing + recent sessions |
read_project | Read project without activating (comparison/reference) |
create_project | Scaffold a new project entry with standard schema |
list_projects | List all projects with status summary |
flush_context | Persist current session context to long-term memory |
move_nodes | Move a subtree under a different parent (also in curate server) |
set_active_device | Register and switch between devices |
hmem-curate — maintenance server (11)| Tool | What it does |
|---|---|
memory_stats | Per-prefix counts, total tokens, favorites, hashtags, stale-list |
memory_health | Find broken links, orphan tags, empty entries, dangling chains |
export_memory | Export the full .hmem to a portable JSON snapshot |
import_memory | Import a JSON snapshot back into a .hmem (destructive) |
update_many | Bulk-patch a set of entries (irrelevant, tags, body, etc.) |
tag_bulk | Add/remove a tag across many entries |
tag_rename | Rename a tag globally across all entries |
move_memory | Move an entire entry to a new ID slot |
move_nodes | Move a subtree under a different parent (also in main server) |
rename_id | Rename an entry's ID; rewrites all inbound links |
reset_memory_cache | Invalidate 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.
After npm install -g hmem-mcp, the hmem binary is on PATH.
| Command | Purpose |
|---|---|
hmem init | Interactive installer for AI tools (Claude Code, OpenCode, Gemini CLI, Cursor, Windsurf, Cline). Flags: --global / --local / --tools / --dir / --no-example |
hmem update-skills | Copy/sync bundled skill files to detected AI tools (called automatically on npm install) |
hmem mcp-add-curate | Register the hmem-curate MCP server with Claude Code (opt-in; toggle via /mcp when curating) |
hmem doctor | Detect stale or deprecated hmem MCP entries in host configs |
hmem stats | Memory statistics + per-project token estimates + 🔴 4k threshold flagging |
hmem setup-hook | Re-add the SessionStart hook to Claude Code settings (if removed) |
hmem version | Show version |
| Command | Wired into | What it does |
|---|---|---|
hmem hook-startup | UserPromptSubmit | First-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