[{"_1":2,"_349":-5,"_350":-5},"loaderData",{"_3":4,"_22":23,"_262":263},"root",{"_5":6,"_7":8},"toasts",[],"visitor",{"_9":10,"_11":12},"viewportWidth",1280,"visitorSession",{"_13":14,"_16":17,"_18":-5,"_19":20,"_21":-5},"attributes",[15],"bot","id",17376273,"membership","referenceId","0199a1cf-fc95-73df-b48c-1059b28acf51","userAccount","routes/_public/mcp/servers/~namespace/~slug/_pages/_layout",{"_24":25,"_79":-5,"_80":38,"_27":81,"_177":178,"_260":261},"availableLocales",[26,38,45,51,58,65,72],{"_27":28,"_31":35,"_36":37},"mcpServer",{"_29":30,"_31":32,"_33":34},"description","Speichermanager für KI-Apps und Agenten, der verschiedene Graph- und Vektorspeicher verwendet und die Aufnahme aus über 30 Datenquellen ermöglicht","name","Cognee-MCP","readme","# Cognee MCP-Server\n\n### Manuelle Installation\n\n# Ein MCP-Serverprojekt\n\n1. Klonen Sie das [Cognee](https://github.com/topoteretes/cognee) -Repository\n\n2. Abhängigkeiten installieren\n\n```\nbrew install uv\n```\n\n```jsx\ncd cognee-mcp\nuv sync --dev --all-extras --reinstall\n```\n\n3. Aktivieren Sie den Venv mit\n\n```jsx\nsource .venv/bin/activate\n```\n\n4. Fügen Sie den neuen Server zu Ihrer Claude-Konfiguration hinzu:\n\nDie Datei sollte hier liegen: ~/Library/Application\\ Support/Claude/\n\n```\ncd ~/Library/Application\\ Support/Claude/\n```\n\nSie müssen claude\\_desktop\\_config.json in diesem Ordner erstellen, falls es nicht vorhanden ist. Stellen Sie sicher, dass Sie Ihre Pfade und den LLM-API-Schlüssel zur folgenden Datei hinzufügen. Verwenden Sie den Editor Ihrer Wahl, zum Beispiel Nano:\n\n```\nnano claude_desktop_config.json\n```\n\n```\n{\n\t\"mcpServers\": {\n\t\t\"cognee\": {\n\t\t\t\"command\": \"/Users/{user}/cognee/.venv/bin/uv\",\n\t\t\t\"args\": [\n \"--directory\",\n \"/Users/{user}/cognee/cognee-mcp\",\n \"run\",\n \"cognee\"\n ],\n \"env\": {\n \"ENV\": \"local\",\n \"TOKENIZERS_PARALLELISM\": \"false\",\n \"LLM_API_KEY\": \"sk-\"\n }\n\t\t}\n\t}\n}\n```\n\nStarten Sie Ihren Claude-Desktop neu.\n\n### Installation über Smithery\n\nSo installieren Sie Cognee für Claude Desktop automatisch über [Smithery](https://smithery.ai/server/cognee) :\n\n```bash\nnpx -y @smithery/cli install cognee --client claude\n```\n\nDefinieren Sie das Cognify-Tool in server.py. Starten Sie Ihren Claude-Desktop neu.\n\nUm den Debugger zu verwenden, führen Sie Folgendes aus:\n\n```bash\nmcp dev src/server.py\n```\n\nInspektor mit abgelaufener Zeitüberschreitung öffnen:\n\n```\nhttp://localhost:5173?timeout=120000\n```\n\nUm während der Entwicklung von Cognee neue Änderungen anzuwenden, müssen Sie Folgendes tun:\n\n1. `poetry lock` im Cognee-Ordner\n2. `uv sync --dev --all-extras --reinstall`\n3. `mcp dev src/server.py`\n\n### Entwicklung\n\nUm den lokalen Cognee-Build zu verwenden, führen Sie im Stammverzeichnis des Cognee-Repos Folgendes aus:\n\n```bash\npoetry build -o ./cognee-mcp/sources\n```\n\nNachdem der Build-Prozess abgeschlossen ist, ändern Sie die Abhängigkeit der Cognee-Bibliothek in der `cognee-mcp/pyproject.toml` von\n\n```toml\ncognee[postgres,codegraph,gemini,huggingface]==0.1.38\n```\n\nZu\n\n```toml\ncognee[postgres,codegraph,gemini,huggingface]\n```\n\nFügen Sie anschließend den folgenden Codeausschnitt zur gleichen Datei hinzu ( `cognee-mcp/pyproject.toml` ).\n\n```toml\n[tool.uv.sources]\ncognee = { path = \"sources/cognee-0.1.38-py3-none-any.whl\" }\n```","German","tag","de-DE",{"_27":39,"_31":43,"_36":44},{"_29":40,"_31":41,"_33":42},"Memory manager for AI apps and Agents using various graph and vector stores and allowing ingestion from 30+ data sources","cognee-mcp","# cognee MCP server\n\n### Installing Manually\nA MCP server project\n=======\n1. Clone the [cognee](https://github.com/topoteretes/cognee) repo\n\n2. Install dependencies\n\n```\nbrew install uv\n```\n\n```jsx\ncd cognee-mcp\nuv sync --dev --all-extras --reinstall\n```\n\n3. Activate the venv with\n\n```jsx\nsource .venv/bin/activate\n```\n\n4. Add the new server to your Claude config:\n\nThe file should be located here: ~/Library/Application\\ Support/Claude/\n```\ncd ~/Library/Application\\ Support/Claude/\n```\nYou need to create claude_desktop_config.json in this folder if it doesn't exist\nMake sure to add your paths and LLM API key to the file bellow\nUse your editor of choice, for example Nano:\n```\nnano claude_desktop_config.json\n```\n\n```\n{\n\t\"mcpServers\": {\n\t\t\"cognee\": {\n\t\t\t\"command\": \"/Users/{user}/cognee/.venv/bin/uv\",\n\t\t\t\"args\": [\n \"--directory\",\n \"/Users/{user}/cognee/cognee-mcp\",\n \"run\",\n \"cognee\"\n ],\n \"env\": {\n \"ENV\": \"local\",\n \"TOKENIZERS_PARALLELISM\": \"false\",\n \"LLM_API_KEY\": \"sk-\"\n }\n\t\t}\n\t}\n}\n```\n\nRestart your Claude desktop.\n\n### Installing via Smithery\n\nTo install Cognee for Claude Desktop automatically via [Smithery](https://smithery.ai/server/cognee):\n\n```bash\nnpx -y @smithery/cli install cognee --client claude\n```\n\nDefine cognify tool in server.py\nRestart your Claude desktop.\n\n\nTo use debugger, run:\n```bash\nmcp dev src/server.py\n```\nOpen inspector with timeout passed:\n```\nhttp://localhost:5173?timeout=120000\n```\n\nTo apply new changes while developing cognee you need to do:\n\n1. `poetry lock` in cognee folder\n2. `uv sync --dev --all-extras --reinstall`\n3. `mcp dev src/server.py`\n\n### Development\nIn order to use local cognee build, run in root of the cognee repo:\n```bash\npoetry build -o ./cognee-mcp/sources\n```\nAfter the build process is done, change the cognee library dependency inside the `cognee-mcp/pyproject.toml` from\n```toml\ncognee[postgres,codegraph,gemini,huggingface]==0.1.38\n```\nto\n```toml\ncognee[postgres,codegraph,gemini,huggingface]\n```\nAfter that add the following snippet to the same file (`cognee-mcp/pyproject.toml`).\n```toml\n[tool.uv.sources]\ncognee = { path = \"sources/cognee-0.1.38-py3-none-any.whl\" }\n```\n","English","en-US",{"_27":46,"_31":49,"_36":50},{"_29":47,"_31":41,"_33":48},"Administrador de memoria para aplicaciones de IA y agentes que utilizan varios almacenes de gráficos y vectores y permiten la ingesta de más de 30 fuentes de datos","# Servidor MCP de Cognee\n\n### Instalación manual\n\n# Un proyecto de servidor MCP\n\n1. Clonar el repositorio [de Cognee](https://github.com/topoteretes/cognee)\n\n2. Instalar dependencias\n\n```\nbrew install uv\n```\n\n```jsx\ncd cognee-mcp\nuv sync --dev --all-extras --reinstall\n```\n\n3. Activar el venv con\n\n```jsx\nsource .venv/bin/activate\n```\n\n4. Agregue el nuevo servidor a su configuración de Claude:\n\nEl archivo debe estar ubicado aquí: ~/Library/Application\\ Support/Claude/\n\n```\ncd ~/Library/Application\\ Support/Claude/\n```\n\nDebe crear claude\\_desktop\\_config.json en esta carpeta si no existe. Asegúrese de agregar sus rutas y la clave API de LLM al archivo a continuación. Use el editor de su elección, por ejemplo Nano:\n\n```\nnano claude_desktop_config.json\n```\n\n```\n{\n\t\"mcpServers\": {\n\t\t\"cognee\": {\n\t\t\t\"command\": \"/Users/{user}/cognee/.venv/bin/uv\",\n\t\t\t\"args\": [\n \"--directory\",\n \"/Users/{user}/cognee/cognee-mcp\",\n \"run\",\n \"cognee\"\n ],\n \"env\": {\n \"ENV\": \"local\",\n \"TOKENIZERS_PARALLELISM\": \"false\",\n \"LLM_API_KEY\": \"sk-\"\n }\n\t\t}\n\t}\n}\n```\n\nReinicie su escritorio Claude.\n\n### Instalación mediante herrería\n\nPara instalar Cognee para Claude Desktop automáticamente a través de [Smithery](https://smithery.ai/server/cognee) :\n\n```bash\nnpx -y @smithery/cli install cognee --client claude\n```\n\nDefina la herramienta cognify en server.py Reinicie su escritorio Claude.\n\nPara utilizar el depurador, ejecute:\n\n```bash\nmcp dev src/server.py\n```\n\nInspector abierto con tiempo de espera transcurrido:\n\n```\nhttp://localhost:5173?timeout=120000\n```\n\nPara aplicar nuevos cambios mientras desarrollas cognee necesitas hacer lo siguiente:\n\n1. `poetry lock` en la carpeta Cognee\n2. `uv sync --dev --all-extras --reinstall`\n3. `mcp dev src/server.py`\n\n### Desarrollo\n\nPara utilizar la compilación local de Cognee, ejecute en la raíz del repositorio de Cognee:\n\n```bash\npoetry build -o ./cognee-mcp/sources\n```\n\nUna vez finalizado el proceso de compilación, cambie la dependencia de la biblioteca cognee dentro de `cognee-mcp/pyproject.toml` de\n\n```toml\ncognee[postgres,codegraph,gemini,huggingface]==0.1.38\n```\n\na\n\n```toml\ncognee[postgres,codegraph,gemini,huggingface]\n```\n\nDespués de eso, agregue el siguiente fragmento al mismo archivo ( `cognee-mcp/pyproject.toml` ).\n\n```toml\n[tool.uv.sources]\ncognee = { path = \"sources/cognee-0.1.38-py3-none-any.whl\" }\n```","Spanish","es-ES",{"_27":52,"_31":56,"_36":57},{"_29":53,"_31":54,"_33":55},"さまざまなグラフおよびベクターストアを使用し、30以上のデータソースからの取り込みを可能にするAIアプリおよびエージェント用のメモリマネージャー","コグニー-MCP","# コグニーMCPサーバー\n\n### 手動でインストールする\n\n# MCP サーバープロジェクト\n\n1. [cognee](https://github.com/topoteretes/cognee)リポジトリをクローンする\n\n2. 依存関係をインストールする\n\n```\nbrew install uv\n```\n\n```jsx\ncd cognee-mcp\nuv sync --dev --all-extras --reinstall\n```\n\n3. venvをアクティブにする\n\n```jsx\nsource .venv/bin/activate\n```\n\n4. 新しいサーバーを Claude 構成に追加します。\n\nファイルはここにあります: ~/Library/Application\\ Support/Claude/\n\n```\ncd ~/Library/Application\\ Support/Claude/\n```\n\nclaude\\_desktop\\_config.json が存在しない場合は、このフォルダーに作成する必要があります。以下のファイルにパスと LLM API キーを必ず追加してください。Nano などの任意のエディターを使用してください。\n\n```\nnano claude_desktop_config.json\n```\n\n```\n{\n\t\"mcpServers\": {\n\t\t\"cognee\": {\n\t\t\t\"command\": \"/Users/{user}/cognee/.venv/bin/uv\",\n\t\t\t\"args\": [\n \"--directory\",\n \"/Users/{user}/cognee/cognee-mcp\",\n \"run\",\n \"cognee\"\n ],\n \"env\": {\n \"ENV\": \"local\",\n \"TOKENIZERS_PARALLELISM\": \"false\",\n \"LLM_API_KEY\": \"sk-\"\n }\n\t\t}\n\t}\n}\n```\n\nClaude デスクトップを再起動します。\n\n### Smithery経由でインストール\n\n[Smithery](https://smithery.ai/server/cognee)経由で Claude Desktop 用の Cognee を自動的にインストールするには:\n\n```bash\nnpx -y @smithery/cli install cognee --client claude\n```\n\nserver.py で cognify ツールを定義し、Claude デスクトップを再起動します。\n\nデバッガーを使用するには、次を実行します。\n\n```bash\nmcp dev src/server.py\n```\n\nタイムアウトが経過したインスペクターを開きます:\n\n```\nhttp://localhost:5173?timeout=120000\n```\n\ncognee の開発中に新しい変更を適用するには、次の操作を行う必要があります。\n\n1. コニーフォルダ内の`poetry lock`\n2. `uv sync --dev --all-extras --reinstall`\n3. `mcp dev src/server.py`\n\n### 発達\n\nローカル cognee ビルドを使用するには、cognee リポジトリのルートで実行します。\n\n```bash\npoetry build -o ./cognee-mcp/sources\n```\n\nビルドプロセスが完了したら、 `cognee-mcp/pyproject.toml`内のcogneeライブラリの依存関係を次のように変更します。\n\n```toml\ncognee[postgres,codegraph,gemini,huggingface]==0.1.38\n```\n\nに\n\n```toml\ncognee[postgres,codegraph,gemini,huggingface]\n```\n\nその後、同じファイル ( `cognee-mcp/pyproject.toml` ) に次のスニペットを追加します。\n\n```toml\n[tool.uv.sources]\ncognee = { path = \"sources/cognee-0.1.38-py3-none-any.whl\" }\n```","Japanese","ja-JP",{"_27":59,"_31":63,"_36":64},{"_29":60,"_31":61,"_33":62},"다양한 그래프 및 벡터 저장소를 사용하고 30개 이상의 데이터 소스에서 수집을 허용하는 AI 앱 및 에이전트용 메모리 관리자","코그니-MCP","# 코그니 MCP 서버\n\n### 수동 설치\n\n# MCP 서버 프로젝트\n\n1. [Cognee](https://github.com/topoteretes/cognee) 저장소를 복제합니다\n\n2. 종속성 설치\n\n지엑스피1\n\n```jsx\ncd cognee-mcp\nuv sync --dev --all-extras --reinstall\n```\n\n3. venv를 활성화하세요\n\n```jsx\nsource .venv/bin/activate\n```\n\n4. Claude 구성에 새 서버를 추가합니다.\n\n해당 파일은 다음 위치에 있어야 합니다: ~/Library/Application\\ Support/Claude/\n\n```\ncd ~/Library/Application\\ Support/Claude/\n```\n\nclaude\\_desktop\\_config.json이 없으면 이 폴더에 만들어야 합니다. 아래 파일에 경로와 LLM API 키를 추가해야 합니다. 예를 들어 Nano와 같이 원하는 편집기를 사용하세요.\n\n```\nnano claude_desktop_config.json\n```\n\n```\n{\n\t\"mcpServers\": {\n\t\t\"cognee\": {\n\t\t\t\"command\": \"/Users/{user}/cognee/.venv/bin/uv\",\n\t\t\t\"args\": [\n \"--directory\",\n \"/Users/{user}/cognee/cognee-mcp\",\n \"run\",\n \"cognee\"\n ],\n \"env\": {\n \"ENV\": \"local\",\n \"TOKENIZERS_PARALLELISM\": \"false\",\n \"LLM_API_KEY\": \"sk-\"\n }\n\t\t}\n\t}\n}\n```\n\nClaude 데스크탑을 다시 시작하세요.\n\n### Smithery를 통해 설치\n\n[Smithery를](https://smithery.ai/server/cognee) 통해 Claude Desktop용 Cognee를 자동으로 설치하려면:\n\n```bash\nnpx -y @smithery/cli install cognee --client claude\n```\n\nserver.py에서 cognify 도구를 정의합니다. Claude 데스크톱을 다시 시작합니다.\n\n디버거를 사용하려면 다음을 실행하세요.\n\n```bash\nmcp dev src/server.py\n```\n\n시간 초과가 지나서 검사기를 엽니다.\n\n```\nhttp://localhost:5173?timeout=120000\n```\n\n인지 발달 중에 새로운 변경 사항을 적용하려면 다음을 수행해야 합니다.\n\n1. 코그니 폴더에 `poetry lock`\n2. `uv sync --dev --all-extras --reinstall`\n3. `mcp dev src/server.py`\n\n### 개발\n\n로컬 Cognee 빌드를 사용하려면 Cognee 저장소의 루트에서 실행하세요.\n\n```bash\npoetry build -o ./cognee-mcp/sources\n```\n\n빌드 프로세스가 완료된 후 `cognee-mcp/pyproject.toml` 내부의 cognee 라이브러리 종속성을 변경합니다.\n\n```toml\ncognee[postgres,codegraph,gemini,huggingface]==0.1.38\n```\n\n에게\n\n```toml\ncognee[postgres,codegraph,gemini,huggingface]\n```\n\n그런 다음 같은 파일( `cognee-mcp/pyproject.toml` )에 다음 스니펫을 추가합니다.\n\n```toml\n[tool.uv.sources]\ncognee = { path = \"sources/cognee-0.1.38-py3-none-any.whl\" }\n```","Korean","ko-KR",{"_27":66,"_31":70,"_36":71},{"_29":67,"_31":68,"_33":69},"Менеджер памяти для приложений и агентов ИИ, использующий различные хранилища графов и векторов и позволяющий получать данные из более чем 30 источников","когни-mcp","# сервер MCP cognee\n\n### Установка вручную\n\n# Проект сервера MCP\n\n1. Клонировать репозиторий [cognee](https://github.com/topoteretes/cognee)\n\n2. Установить зависимости\n\n```\nbrew install uv\n```\n\n```jsx\ncd cognee-mcp\nuv sync --dev --all-extras --reinstall\n```\n\n3. Активируйте venv с помощью\n\n```jsx\nsource .venv/bin/activate\n```\n\n4. Добавьте новый сервер в конфигурацию Клода:\n\nФайл должен находиться здесь: ~/Library/Application\\ Support/Claude/\n\n```\ncd ~/Library/Application\\ Support/Claude/\n```\n\nВам необходимо создать claude\\_desktop\\_config.json в этой папке, если он не существует. Обязательно добавьте ваши пути и ключ LLM API в файл ниже. Используйте редактор по вашему выбору, например Nano:\n\n```\nnano claude_desktop_config.json\n```\n\n```\n{\n\t\"mcpServers\": {\n\t\t\"cognee\": {\n\t\t\t\"command\": \"/Users/{user}/cognee/.venv/bin/uv\",\n\t\t\t\"args\": [\n \"--directory\",\n \"/Users/{user}/cognee/cognee-mcp\",\n \"run\",\n \"cognee\"\n ],\n \"env\": {\n \"ENV\": \"local\",\n \"TOKENIZERS_PARALLELISM\": \"false\",\n \"LLM_API_KEY\": \"sk-\"\n }\n\t\t}\n\t}\n}\n```\n\nПерезагрузите рабочий стол Клода.\n\n### Установка через Smithery\n\nЧтобы автоматически установить Cognee для Claude Desktop через [Smithery](https://smithery.ai/server/cognee) :\n\n```bash\nnpx -y @smithery/cli install cognee --client claude\n```\n\nОпределите инструмент cognify в server.py. Перезагрузите рабочий стол Claude.\n\nЧтобы использовать отладчик, выполните:\n\n```bash\nmcp dev src/server.py\n```\n\nОткройте инспектор с истекшим временем ожидания:\n\n```\nhttp://localhost:5173?timeout=120000\n```\n\nЧтобы применить новые изменения при разработке cognee, вам необходимо сделать:\n\n1. `poetry lock` в папке cognee\n2. `uv sync --dev --all-extras --reinstall`\n3. `mcp dev src/server.py`\n\n### Разработка\n\nЧтобы использовать локальную сборку cognee, выполните в корне репозитория cognee:\n\n```bash\npoetry build -o ./cognee-mcp/sources\n```\n\nПосле завершения процесса сборки измените зависимость библиотеки cognee внутри `cognee-mcp/pyproject.toml` с\n\n```toml\ncognee[postgres,codegraph,gemini,huggingface]==0.1.38\n```\n\nк\n\n```toml\ncognee[postgres,codegraph,gemini,huggingface]\n```\n\nПосле этого добавьте следующий фрагмент в тот же файл ( `cognee-mcp/pyproject.toml` ).\n\n```toml\n[tool.uv.sources]\ncognee = { path = \"sources/cognee-0.1.38-py3-none-any.whl\" }\n```","Russian","ru-RU",{"_27":73,"_31":77,"_36":78},{"_29":74,"_31":75,"_33":76},"人工智能应用程序和代理的内存管理器，使用各种图形和矢量存储，并允许从 30 多个数据源提取数据","认知-MCP","# cognee MCP 服务器\n\n### 手动安装\n\n# MCP 服务器项目\n\n1. 克隆[cognee](https://github.com/topoteretes/cognee)仓库\n\n2. 安装依赖项\n\n```\nbrew install uv\n```\n\n```jsx\ncd cognee-mcp\nuv sync --dev --all-extras --reinstall\n```\n\n3. 使用以下命令激活 venv\n\n```jsx\nsource .venv/bin/activate\n```\n\n4. 将新服务器添加到您的 Claude 配置中：\n\n该文件应位于此处：~/Library/Application\\ Support/Claude/\n\n```\ncd ~/Library/Application\\ Support/Claude/\n```\n\n如果不存在，则需��在此文件夹中创建 claude\\_desktop\\_config.json 确保将您的路径和 LLM API 密钥添加到下面的文件中使用您选择的编辑器，例如 Nano：\n\n```\nnano claude_desktop_config.json\n```\n\n```\n{\n\t\"mcpServers\": {\n\t\t\"cognee\": {\n\t\t\t\"command\": \"/Users/{user}/cognee/.venv/bin/uv\",\n\t\t\t\"args\": [\n \"--directory\",\n \"/Users/{user}/cognee/cognee-mcp\",\n \"run\",\n \"cognee\"\n ],\n \"env\": {\n \"ENV\": \"local\",\n \"TOKENIZERS_PARALLELISM\": \"false\",\n \"LLM_API_KEY\": \"sk-\"\n }\n\t\t}\n\t}\n}\n```\n\n重新启动您的 Claude 桌面。\n\n### 通过 Smithery 安装\n\n要通过[Smithery](https://smithery.ai/server/cognee)自动安装 Cognee for Claude Desktop：\n\n```bash\nnpx -y @smithery/cli install cognee --client claude\n```\n\n在 server.py 中定义 cognify 工具重新启动您的 Claude 桌面。\n\n要使用调试器，请运行：\n\n```bash\nmcp dev src/server.py\n```\n\n打开检查器并超时：\n\n```\nhttp://localhost:5173?timeout=120000\n```\n\n要在开发认知时应用新的变化，您需要执行以下操作：\n\n1. cognee 文件夹中的`poetry lock`\n2. `uv sync --dev --all-extras --reinstall`\n3. `mcp dev src/server.py`\n\n### 发展\n\n为了使用本地 cognee 构建，请在 cognee repo 的根目录中运行：\n\n```bash\npoetry build -o ./cognee-mcp/sources\n```\n\n构建过程完成后，将`cognee-mcp/pyproject.toml`中的 cognee 库依赖项从\n\n```toml\ncognee[postgres,codegraph,gemini,huggingface]==0.1.38\n```\n\n到\n\n```toml\ncognee[postgres,codegraph,gemini,huggingface]\n```\n\n之后将以下代码片段添加到同一个文件（ `cognee-mcp/pyproject.toml` ）。\n\n```toml\n[tool.uv.sources]\ncognee = { path = \"sources/cognee-0.1.38-py3-none-any.whl\" }\n```","Chinese","zh-CN","githubUser","locale",{"_82":83,"_13":84,"_93":40,"_94":40,"_16":95,"_96":97,"_31":41,"_98":99,"_107":-5,"_108":-5,"_109":110,"_166":167,"_172":-5,"_106":148,"_173":153,"_150":174,"_175":176},"addedAt","2024-12-17T15:37:41.228277Z",[85,86,87,88,89,90,91,92],"category:knowledge-and-memory","category:research-and-data","author:claimed","hosting:local-only","language:python","security_grade:a","quality_grade:a","license_grade:a","descriptionMarkdown","descriptionPlainText",335,"integrations",[],"namespace",{"_100":101,"_106":105},"owner",{"_102":103},"githubAccount",{"_104":105},"login","topoteretes","slug","readmeMarkdown","redditPostExternalId","repository",{"_111":112,"_132":133,"_137":138,"_159":-5,"_160":161,"_162":-5,"_163":164},"argumentsJsonSchema",{"_113":114,"_129":130,"_120":131},"properties",{"_115":116,"_122":123,"_125":126},"ENV",{"_117":118,"_29":119,"_120":121},"default","local","Environment setting","type","string","LLM_API_KEY",{"_29":124,"_120":121},"API key for the LLM service","TOKENIZERS_PARALLELISM",{"_117":127,"_29":128,"_120":121},"false","Control for tokenizers parallelism","required",[122],"object","githubProject",{"_33":134,"_135":136},"

\n \n $\"Cognee$ \n \n\n
\n\n cognee‑mcp - Run cognee’s memory engine as a Model Context Protocol server\n\n

\n Demo\n .\n Learn more\n ·\n Join Discord\n ·\n Join r/AIMemory\n

\n\n\n [![GitHub forks](https://img.shields.io/github/forks/topoteretes/cognee.svg?style=social&label=Fork&maxAge=2592000)](https://GitHub.com/topoteretes/cognee/network/)\n [![GitHub stars](https://img.shields.io/github/stars/topoteretes/cognee.svg?style=social&label=Star&maxAge=2592000)](https://GitHub.com/topoteretes/cognee/stargazers/)\n [![GitHub commits](https://badgen.net/github/commits/topoteretes/cognee)](https://GitHub.com/topoteretes/cognee/commit/)\n [![Github tag](https://badgen.net/github/tag/topoteretes/cognee)](https://github.com/topoteretes/cognee/tags/)\n [![Downloads](https://static.pepy.tech/badge/cognee)](https://pepy.tech/project/cognee)\n [![License](https://img.shields.io/github/license/topoteretes/cognee?colorA=00C586&colorB=000000)](https://github.com/topoteretes/cognee/blob/main/LICENSE)\n [![Contributors](https://img.shields.io/github/contributors/topoteretes/cognee?colorA=00C586&colorB=000000)](https://github.com/topoteretes/cognee/graphs/contributors)\n\n $\"cognee$ \n\n $\"topoteretes%2Fcognee$ \n\n\nBuild memory for Agents and query from any client that speaks MCP – in your terminal or IDE.\n\n

\n\n## ✨ Features\n\n- Multiple transports – choose Streamable HTTP --transport http (recommended for web deployments), SSE --transport sse (real‑time streaming), or stdio (classic pipe, default)\n- Integrated logging – all actions written to a rotating file (see get_log_file_location()) and mirrored to console in dev\n- Local file ingestion – feed .md, source files, Cursor rule‑sets, etc. straight from disk\n- Background pipelines – long‑running cognify & codify jobs spawn off‑thread; check progress with status tools\n- Developer rules bootstrap – one call indexes .cursorrules, .cursor/rules, AGENT.md, and friends into the developer_rules nodeset\n- Prune & reset – wipe memory clean with a single prune call when you want to start fresh\n\nPlease refer to our documentation [here](https://docs.cognee.ai/how-to-guides/deployment/mcp) for further information.\n\n## 🚀 Quick Start\n\n1. Clone cognee repo\n ```\n git clone https://github.com/topoteretes/cognee.git\n ```\n2. Navigate to cognee-mcp subdirectory\n ```\n cd cognee/cognee-mcp\n ```\n3. Install uv if you don't have one\n ```\n pip install uv\n ```\n4. Install all the dependencies you need for cognee mcp server with uv\n ```\n uv sync --dev --all-extras --reinstall\n ```\n5. Activate the virtual environment in cognee mcp directory\n ```\n source .venv/bin/activate\n ```\n6. Set up your OpenAI API key in .env for a quick setup with the default cognee configurations\n ```\n LLM_API_KEY=\"YOUR_OPENAI_API_KEY\"\n ```\n7. Run cognee mcp server with stdio (default)\n ```\n python src/server.py\n ```\n or stream responses over SSE\n ```\n python src/server.py --transport sse\n ```\n or run with Streamable HTTP transport (recommended for web deployments)\n ```\n python src/server.py --transport http --host 127.0.0.1 --port 8000 --path /mcp\n ```\n\nYou can do more advanced configurations by creating .env file using our template.\nTo use different LLM providers / database configurations, and for more info check out our documentation.\n\n\n## 🐳 Docker Usage\n\nIf you’d rather run cognee-mcp in a container, you have two options:\n\n1. **Build locally**\n 1. Make sure you are in /cognee root directory and have a fresh `.env` containing only your `LLM_API_KEY` (and your chosen settings).\n 2. Remove any old image and rebuild:\n ```bash\n docker rmi cognee/cognee-mcp:main || true\n docker build --no-cache -f cognee-mcp/Dockerfile -t cognee/cognee-mcp:main .\n ```\n 3. Run it:\n ```bash\n # For HTTP transport (recommended for web deployments)\n docker run -e TRANSPORT_MODE=http --env-file ./.env -p 8000:8000 --rm -it cognee/cognee-mcp:main\n # For SSE transport \n docker run -e TRANSPORT_MODE=sse --env-file ./.env -p 8000:8000 --rm -it cognee/cognee-mcp:main\n # For stdio transport (default)\n docker run -e TRANSPORT_MODE=stdio --env-file ./.env --rm -it cognee/cognee-mcp:main\n ```\n2. **Pull from Docker Hub** (no build required):\n ```bash\n # With HTTP transport (recommended for web deployments)\n docker run -e TRANSPORT_MODE=http --env-file ./.env -p 8000:8000 --rm -it cognee/cognee-mcp:main\n # With SSE transport\n docker run -e TRANSPORT_MODE=sse --env-file ./.env -p 8000:8000 --rm -it cognee/cognee-mcp:main\n # With stdio transport (default)\n docker run -e TRANSPORT_MODE=stdio --env-file ./.env --rm -it cognee/cognee-mcp:main\n ```\n\n### **Important: Docker vs Direct Usage**\n**Docker uses environment variables**, not command line arguments:\n- ✅ Docker: `-e TRANSPORT_MODE=http`\n- ❌ Docker: `--transport http` (won't work)\n\n**Direct Python usage** uses command line arguments:\n- ✅ Direct: `python src/server.py --transport http`\n- ❌ Direct: `-e TRANSPORT_MODE=http` (won't work)\n\n\n## 🔗 MCP Client Configuration\n\nAfter starting your Cognee MCP server with Docker, you need to configure your MCP client to connect to it.\n\n### **SSE Transport Configuration** (Recommended)\n\n**Start the server with SSE transport:**\n```bash\ndocker run -e TRANSPORT_MODE=sse --env-file ./.env -p 8000:8000 --rm -it cognee/cognee-mcp:main\n```\n\n**Configure your MCP client:**\n\n#### **Claude CLI (Easiest)**\n```bash\nclaude mcp add cognee-sse -t sse http://localhost:8000/sse\n```\n\n**Verify the connection:**\n```bash\nclaude mcp list\n```\n\nYou should see your server connected:\n```\nChecking MCP server health...\n\ncognee-sse: http://localhost:8000/sse (SSE) - ✓ Connected\n```\n\n#### **Manual Configuration**\n\n**Claude (`~/.claude.json`)**\n```json\n{\n \"mcpServers\": {\n \"cognee\": {\n \"type\": \"sse\",\n \"url\": \"http://localhost:8000/sse\"\n }\n }\n}\n```\n\n**Cursor (`~/.cursor/mcp.json`)**\n```json\n{\n \"mcpServers\": {\n \"cognee-sse\": {\n \"url\": \"http://localhost:8000/sse\"\n }\n }\n}\n```\n\n### **HTTP Transport Configuration** (Alternative)\n\n**Start the server with HTTP transport:**\n```bash\ndocker run -e TRANSPORT_MODE=http --env-file ./.env -p 8000:8000 --rm -it cognee/cognee-mcp:main\n```\n\n**Configure your MCP client:**\n\n#### **Claude CLI (Easiest)**\n```bash\nclaude mcp add cognee-http -t http http://localhost:8000/mcp\n```\n\n**Verify the connection:**\n```bash\nclaude mcp list\n```\n\nYou should see your server connected:\n```\nChecking MCP server health...\n\ncognee-http: http://localhost:8000/mcp (HTTP) - ✓ Connected\n```\n\n#### **Manual Configuration**\n\n**Claude (`~/.claude.json`)**\n```json\n{\n \"mcpServers\": {\n \"cognee\": {\n \"type\": \"http\",\n \"url\": \"http://localhost:8000/mcp\"\n }\n }\n}\n```\n\n**Cursor (`~/.cursor/mcp.json`)**\n```json\n{\n \"mcpServers\": {\n \"cognee-http\": {\n \"url\": \"http://localhost:8000/mcp\"\n }\n }\n}\n```\n\n### **Dual Configuration Example**\nYou can configure both transports simultaneously for testing:\n\n```json\n{\n \"mcpServers\": {\n \"cognee-sse\": {\n \"type\": \"sse\",\n \"url\": \"http://localhost:8000/sse\"\n },\n \"cognee-http\": {\n \"type\": \"http\", \n \"url\": \"http://localhost:8000/mcp\"\n }\n }\n}\n```\n\n**Note:** Only enable the server you're actually running to avoid connection errors.\n\n## 💻 Basic Usage\n\nThe MCP server exposes its functionality through tools. Call them from any MCP client (Cursor, Claude Desktop, Cline, Roo and more).\n\n\n### Available Tools\n\n- **cognify**: Turns your data into a structured knowledge graph and stores it in memory\n\n- **codify**: Analyse a code repository, build a code graph, stores it in memory\n\n- **search**: Query memory – supports GRAPH_COMPLETION, RAG_COMPLETION, CODE, CHUNKS, INSIGHTS\n\n- **list_data**: List all datasets and their data items with IDs for deletion operations\n\n- **delete**: Delete specific data from a dataset (supports soft/hard deletion modes)\n\n- **prune**: Reset cognee for a fresh start (removes all data)\n\n- **cognify_status / codify_status**: Track pipeline progress\n\n**Data Management Examples:**\n```bash\n# List all available datasets and data items\nlist_data()\n\n# List data items in a specific dataset\nlist_data(dataset_id=\"your-dataset-id-here\")\n\n# Delete specific data (soft deletion - safer, preserves shared entities)\ndelete(data_id=\"data-uuid\", dataset_id=\"dataset-uuid\", mode=\"soft\")\n\n# Delete specific data (hard deletion - removes orphaned entities)\ndelete(data_id=\"data-uuid\", dataset_id=\"dataset-uuid\", mode=\"hard\")\n```\n\n\n## Development and Debugging\n\n### Debugging\n\nTo use debugger, run:\n ```bash\n mcp dev src/server.py\n ```\n\nOpen inspector with timeout passed:\n ```\n http://localhost:5173?timeout=120000\n ```\n\nTo apply new changes while developing cognee you need to do:\n\n1. Update dependencies in cognee folder if needed\n2. `uv sync --dev --all-extras --reinstall`\n3. `mcp dev src/server.py`\n\n### Development\n\nIn order to use local cognee:\n\n1. Uncomment the following line in the cognee-mcp [`pyproject.toml`](pyproject.toml) file and set the cognee root path.\n ```\n #\"cognee[postgres,codegraph,gemini,huggingface,docs,neo4j] @ file:/Users//Desktop/cognee\"\n ```\n Remember to replace `file:/Users//Desktop/cognee` with your actual cognee root path.\n\n2. Install dependencies with uv in the mcp folder\n ```\n uv sync --reinstall\n ```\n\n## Code of Conduct\n\nWe are committed to making open source an enjoyable and respectful experience for our community. 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