Cognee Notion Integration

Inspiration

Many teams use Notion as their primary knowledge base, storing notes, project specs, and documentation. However, AI agents often lack seamless access to this rich context. We built this integration to bridge Notion workspaces with Cognee's persistent Knowledge Graph memory layer.

What it does

The Notion Data Loader allows Cognee AI agents to automatically ingest Notion pages and database content. It extracts text blocks, headers, and bullet points, turning them into structured data chunks that are indexed into a Knowledge Graph via cognee.add() and cognee.cognify().

How we built it

• Developed NotionLoader in Python using the official notion-client SDK.
• Used Notion API integration tokens (NOTION_API_KEY) to safely query workspace pages.
• Processed hierarchical page contents into clean Markdown strings for Cognee.
• Implemented unit tests using pytest and mocks to verify loader execution without external API dependencies.

Challenges we ran into

• Handling various Notion block types recursively while keeping the text formatting clean and structured.
• Ensuring test coverage without making live calls to Notion API endpoints.

Accomplishments that we're proud of

• Successfully fetching and converting Notion pages into Cognee memory graphs.
• Achieving passing unit tests (pytest pass).
• Contributing a complete, production-ready data loader module to Cognee's open-source ecosystem.

What we learned

• How Cognee processes documents into knowledge graphs.
• Best practices for mocking external SDK clients in Python testing environments.

What's next for Cognee Notion Integration

• Support for syncing databases and inline properties/metadata.
• Adding real-time webhooks for automatic sync whenever a Notion page is updated.

Built With

• api
• cognee
• github
• notion
• pytest
• python