Context7
Up-to-date docs for AI agents
作者による説明(英語の原文)
Self-learning memory for AI agents, powered by the Ragionex context engine.
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。
RAGIONEX_MEMORY_API_KEY「秘密」と表示された値は、あなたのアカウントの鍵です。絶対に人に教えたり、チャットに貼り付けたりしないでください。
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": {
"memory": {
"command": "npx",
"args": [
"-y",
"@ragionex/memory-mcp"
],
"env": {
"RAGIONEX_MEMORY_API_KEY": "your-value"
}
}
}
}ターミナルでこれを実行します
claude mcp add memory --env RAGIONEX_MEMORY_API_KEY=your-value -- npx -y @ragionex/memory-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": {
"memory": {
"command": "npx",
"args": [
"-y",
"@ragionex/memory-mcp"
],
"env": {
"RAGIONEX_MEMORY_API_KEY": "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": {
"memory": {
"type": "stdio",
"command": "npx",
"args": [
"-y",
"@ragionex/memory-mcp"
],
"env": {
"RAGIONEX_MEMORY_API_KEY": "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。
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6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 ragionex 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。
入力が必要なもの:RAGIONEX_MEMORY_API_KEY
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(0.8.0)がレジストリに届いたのは昨日です。
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
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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。
Ragionex Memory
Context engine and self-learning memory for AI agents
Your agents save what matters on their own, fix what changed and pick up where you left off in every new session.
Keep each project in its own Vault, give every key only the access it needs, and share memory with your team.
Set Up in One Line · Quick Start · Documentation · Pricing
Paste this into your agent:
Set up Ragionex Memory for yourself by following the instructions at https://ragionex.com/setup.
If you are an AI agent: follow the instructions at https://ragionex.com/setup.
In a terminal, on Windows (PowerShell or Command Prompt):
npx.cmd -y @ragionex/memory-mcp@latest install
On macOS or Linux:
npx -y @ragionex/memory-mcp@latest install
If your agent is not listed and has a global instructions file, choose manual setup. Run the setup once for each agent you use.
Ask your agent:
Check my Ragionex Memory connection.
It answers with your Vaults and what this agent may do in each.
Claude Desktop, Cursor, Cline and other clients that run local MCP servers use the same tools. Create a key at app.ragionex.com/keys and add the server to the client's user-global MCP config. On Windows:
{
"mcpServers": { "ragionex-memory-mcp": { "command": "cmd", "args": ["/c", "npx", "-y", "@ragionex/memory-mcp@latest"], "env": { "RAGIONEX_MEMORY_API_KEY": "rgx_memory_..." } } } }
On macOS or Linux:
{
"mcpServers": { "ragionex-memory-mcp": { "command": "npx", "args": ["-y", "@ragionex/memory-mcp@latest"], "env": { "RAGIONEX_MEMORY_API_KEY": "rgx_memory_..." } } } }
These clients get the tools but not the memory rule, so they use memory less consistently on their own. A client with a global instructions file can get the rule through manual setup instead. On Windows:
npx.cmd -y @ragionex/memory-mcp@latest install --manual
On macOS or Linux:
npx -y @ragionex/memory-mcp@latest install --manual
See the MCP Client Notes for client-specific details.
A Context Engine That Understands the Question. Your agent asks in plain language, and the engine understands what it means, even when it asks in different words than the memory was saved in. Even among thousands of memories, the right ones come first and arrive in milliseconds, so the agent reads only as much as it needs and its context stays lean. Memories come back exactly as they were saved: Ragionex never rewrites a memory or generates an answer.
Automatic Memory. Your agent saves what's worth keeping on its own: a document, a design decision, a preference, a project fact, something that was tried and how it turned out. It checks its memory before it answers, starts a task or asks you anything about your work, and the memory gets richer and more accurate the more you work. You can still tell it to remember or forget anything, any time.
Always Up to Date. A changed decision updates its record instead of adding a second one, and a record that turns out wrong is removed. Every change ends with one short line in the reply, so "undo that" is one sentence away. Every update carries the version its agent last read, so no agent can silently overwrite another's newer change.
Version History. Earlier versions of every memory, and deleted records in the trash, are kept for 30 days. Bring any of them back just by asking your agent, or from the dashboard.
Everything Visible in the Dashboard. Browse every memory your agents saved and see each one's status, who created or last edited it, and through which key. Open earlier versions, edit, restore or delete anything yourself, and export it all whenever you want.
Projects and Teams. Each project can keep its own memory in its own Vault, and a search never crosses into another. Every key carries its own Read, Create, Update and Delete permissions. Share a Vault with teammates, give each member their own permissions, and every member's agents draw from and add to the same memory.
Safe and Private. Memory text stored by Ragionex is kept in the EU and encrypted with AES-256. It is never sent to an outside AI service and never used to train AI models. Queued saves retry automatically.
Works Where You Work. One line sets up almost any AI agent on Windows, macOS and Linux. Other MCP clients connect too.
A Vault is a separate set of memories. A key decides what an agent may do in it. A member is a teammate the Vault's owner shared it with. The picture above shows them working together:
| Who | Read | Create | Update | Delete |
|---|---|---|---|---|
| Your agent 1 | ✅ | ✅ | ✅ | ✅ |
| Your agent 2 | ✅ | ✅ | ||
| Your agent 3 | ✅ | ✅ | ✅ | ✅ |
| Teammate A | ✅ | ✅ | ✅ | |
| Teammate B | ✅ |
The picture shows Create and Update as Write and Edit.
A new account starts with My vault as its default, and every project without its own Vault uses the default. To give a project its own memory, create a Vault in the Dashboard, then ask your agent to *use my acme-app Vault for this project*, or put a RAGIONEX_MEMORY.jsonc file in the project folder:
{
// The Vault this project uses (shown by the connection check and in the dashboard) "vault_id": " ", // Optional: a key only this project uses, for example one that can only read "api_key": "rgx_memory_..." }
.gitignore before putting a key in it.| Requests | Memory Size | Stored Memories | Vaults | Keys | Teammates |
|---|---|---|---|---|---|
| 10,000 per 30 days | 10,000 characters | Unlimited | 2 | 100 | 1 per Vault |
Saving, editing and restoring a memory each cost 1 request, and so does each search page fetched from the service. Browsing, viewing, deleting, the trash, version history, status checks and export are free. Free is for personal projects and evaluation and is in early access, so its limits may change. Commercial use is not permitted on Free; Pro is coming soon.
Tools
| Tool | Permission | Description |
|---|---|---|
ragionex_recall_memory | Read | Finds memories by meaning, one complete question at a time, with optional exact dates. Whole records arrive on numbered pages |
ragionex_list_memories | Read | Browses newest first with short previews, up to 100 per page |
ragionex_view_memory | Read | Returns the full text and revision of up to 50 records by ID |
ragionex_save_memory | Create | Saves a new record |
ragionex_update_memory | Update | Replaces a record's content, checked against the revision the agent last read |
ragionex_delete_memory | Delete | Moves records to the trash |
ragionex_list_trash | Read | Lists deleted records with their restore deadlines |
ragionex_memory_versions | Read | Lists retained versions or reads one in full |
ragionex_restore_memory | Update | Restores a trashed record or an older version as the next revision |
ragionex_memory_status | Read | Shows whether a new or edited record is still processing |
ragionex_memory_connection | Any valid key | Shows the selected Vault and why, every Vault the key can use, its permissions and any saves waiting on this machine |
Every record has id, vault_id, content, created_at, updated_at, status, revision, created_by and updated_by. New, edited and restored records must finish processing before recall can find them. Permanent deletion, export, Vault sharing, key management and account deletion live in the Dashboard.
Configuration, Updating and Uninstalling
| Setting | Details |
|---|---|
RAGIONEX_MEMORY_API_KEY | The agent's key. install creates it after you approve the connection in your browser and stores it in the agent's MCP registration |
RAGIONEX_MEMORY_INSTRUCTIONS_FILE | Manual setup only. The full path of the agent's global instructions file; the setup fills it in, and the memory rule in that file refreshes itself |
RAGIONEX_API_BASE | Optional API address, default https://api.ragionex.com |
| Relocated Agent Homes | install and uninstall follow CLAUDE_CONFIG_DIR, CODEX_HOME and XDG_CONFIG_HOME |
| Tool Timeout | Allow each MCP tool call more than 120 seconds, the connector's own request deadline, so a slow request still ends with its result or clear guidance |
| Windows | In WSL, run the setup inside the Linux environment where the agent runs |
| Diagnostics | The server writes to stderr; see your client's MCP server log |
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