Context7
Up-to-date docs for AI agents
作者による説明(英語の原文)
Persistent 5-level hierarchical memory for AI agents. SQLite-backed, lazy-loaded.
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。
HMEM_STORAGE_DIR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": {
"hmem": {
"command": "npx",
"args": [
"-y",
"hmem-mcp"
],
"env": {
"HMEM_STORAGE_DIR": "your-value"
}
}
}
}ターミナルでこれを実行します
claude mcp add hmem --env HMEM_STORAGE_DIR=your-value -- npx -y hmem-mcp動かす前に、空欄の値を入力してください。
プロジェクトの .cursor/mcp.json、または ~/.cursor/mcp.json に貼り付けます
{
"mcpServers": {
"hmem": {
"command": "npx",
"args": [
"-y",
"hmem-mcp"
],
"env": {
"HMEM_STORAGE_DIR": "your-value"
}
}
}
}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": {
"hmem": {
"type": "stdio",
"command": "npx",
"args": [
"-y",
"hmem-mcp"
],
"env": {
"HMEM_STORAGE_DIR": "your-value"
}
}
}
}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。
HMEM_STORAGE_DIR env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 Bumblebiber 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(1.6.7)がレジストリに届いたのは8 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Node.js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
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.
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。
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
冒頭だけを表示しています。続きはリポジトリで読めます