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Cognee
Model Context Protocol moderate risk

Cognee

Cognee is an open-source knowledge engine that transforms data into dynamic AI memory, combining vector search, graph databases, and self-improvement.

Connections & Capabilities

Connects To

GitHubDiscord

Capabilities

readwriteexecadmin

Quickstart

Install

pip install cognee

Exposed MCP Tools (4)

moderate
cognee-cli add

Adds text or documents to Cognee's knowledge base.

Adds data to the knowledge graph, potentially leading to data corruption if the input is malicious.

moderate
cognee-cli cognify

Generates a knowledge graph from the data stored in Cognee.

Modifies the knowledge graph structure, potentially leading to data inconsistencies or performance issues.

safe
cognee-cli search

Queries the knowledge graph based on combined relationships.

Read-only operation that does not modify any data.

critical
cognee-cli delete --all

Deletes all data and the entire knowledge graph within Cognee.

Irreversibly deletes all data, leading to complete data loss.

Safety Assessment

Cognee presents a moderate risk due to its ability to modify and delete data, coupled with the reliance on environment variables for sensitive information. It is relatively safe when used for read-only operations and with careful management of API keys. However, the risk increases significantly when destructive commands are used or when custom tasks are introduced without proper sandboxing.

  • Requires API key for LLM integration, limiting unauthorized access.
  • Provides a CLI with specific commands, reducing the risk of accidental destructive actions.
  • Open-source nature allows for community review and identification of vulnerabilities.
  • Offers modular pipelines, enabling customization and control over data processing.
  • LLM API key is stored as an environment variable, posing a risk if the environment is compromised.
  • The 'cognee-cli delete --all' command is highly destructive with no apparent safeguards.
  • Direct interaction with knowledge graphs can lead to unintended data modification or corruption.
  • Lack of built-in sandboxing for user-defined tasks increases the risk of malicious code execution.
  • Internet access is required for LLM integration, potentially exposing the system to external threats.