cognee

Local 2025-08-31 23:16:14 0
Knowledge And Memory @topoteretes/cognee

Memory manager for AI apps and Agents using various graph and vector stores and allowing ingestion from 30+ data sources

Installing Manually

A MCP server project

  1. Clone the cognee repo

  2. Install dependencies

brew install uv
cd cognee-mcp
uv sync --dev --all-extras --reinstall
  1. Activate the venv with
source .venv/bin/activate
  1. Add the new server to your Claude config:

The file should be located here: ~/Library/Application Support/Claude/ 

cd ~/Library/Application Support/Claude/
You need to create claude_desktop_config.json in this folder if it doesn't exist Make sure to add your paths and LLM API key to the file bellow Use your editor of choice, for example Nano: 
nano claude_desktop_config.json

{
    "mcpServers": {
        "cognee": {
            "command": "/Users/{user}/cognee/.venv/bin/uv",
            "args": [
        "--directory",
        "/Users/{user}/cognee/cognee-mcp",
        "run",
        "cognee"
      ],
      "env": {
        "ENV": "local",
        "TOKENIZERS_PARALLELISM": "false",
        "LLM_API_KEY": "sk-"
      }
        }
    }
}

Restart your Claude desktop.

Installing via Smithery

To install Cognee for Claude Desktop automatically via Smithery:

npx -y @smithery/cli install cognee --client claude

Define cognify tool in server.py Restart your Claude desktop.

To use debugger, run: 

mcp dev src/server.py
Open inspector with timeout passed: 
http://localhost:5173?timeout=120000

To apply new changes while developing cognee you need to do:

  1. poetry lock in cognee folder
  2. uv sync --dev --all-extras --reinstall
  3. mcp dev src/server.py
[
  {
    "description": "Cognifies text into knowledge graph",
    "inputSchema": {
      "properties": {
        "graph_model_file": {
          "description": "The path to the graph model file",
          "type": "string"
        },
        "graph_model_name": {
          "description": "The name of the graph model",
          "type": "string"
        },
        "text": {
          "description": "The text to cognify",
          "type": "string"
        }
      },
      "required": [
        "text"
      ],
      "type": "object"
    },
    "name": "cognify"
  },
  {
    "description": "Transforms codebase into knowledge graph",
    "inputSchema": {
      "properties": {
        "repo_path": {
          "type": "string"
        }
      },
      "required": [
        "repo_path"
      ],
      "type": "object"
    },
    "name": "codify"
  },
  {
    "description": "Searches for information in knowledge graph",
    "inputSchema": {
      "properties": {
        "search_query": {
          "description": "The query to search for",
          "type": "string"
        },
        "search_type": {
          "description": "The type of search to perform (e.g., INSIGHTS, CODE)",
          "type": "string"
        }
      },
      "required": [
        "search_query"
      ],
      "type": "object"
    },
    "name": "search"
  },
  {
    "description": "Prunes knowledge graph",
    "inputSchema": {
      "properties": {},
      "type": "object"
    },
    "name": "prune"
  }
]