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.

Client Compatibility

Claude Desktopfull

Cursoruntested

Windsurfuntested

VS Code (Copilot)untested

Security Notes

Compromised LLM API key can lead to unauthorized data access and modification.
Unvalidated data ingestion can result in knowledge graph corruption or injection attacks.
Lack of input sanitization in CLI commands may expose the system to command injection vulnerabilities.
The 'delete --all' command poses a significant risk of irreversible data loss.
Custom tasks without sandboxing can introduce arbitrary code execution vulnerabilities.

Best For

AI researchers experimenting with knowledge-enhanced agents.
Developers building applications that require semantic search and reasoning.
Organizations seeking to create a unified knowledge base from disparate data sources.
Teams exploring graph-based approaches to data analysis and decision-making.
Individuals interested in building personalized AI memory systems.