Giving AI Agents Memory of PyPI: Building a Python Package Connector for Cognee

When building AI coding assistants or developer agents, one of the biggest challenges is hallucination of library APIs, outdated functions, and deprecated parameters. Large Language Models frequently invent methods that don’t exist or assume library versions from their pre-training cutoffs.

To solve this, we built a native PyPI Data-Source Connector for Cognee (the open-source memory engine and knowledge graph for AI agents) as part of Mergetober 2026.

The connector interfaces directly with the official, public PyPI JSON REST API ( https://pypi.org/pypi/{package}/json ) to ingest live specifications into Cognee’s semantic and graph memory layers:

Here is how simple it is to use the connector:

import asyncio import cognee from cognee_community_connector_pypi import PyPIConnector async def main (): connector = PyPIConnector () # 1. Fetch live specifications for critical dependencies packages = [ " cognee ", " fastapi ", " pydantic " ] documents = connector. fetch_documents ( packages ) # 2. Ingest into Cognee memory for doc in documents: await cognee. add ( doc [ " text " ]) # 3. Build knowledge graph & embeddings await cognee. cognify () # 4. Query the memory graph results = await cognee. search ( " What are the key dependencies and license of fastapi? " ) print ( results ) if __name__ == " __main__ ": asyncio. run ( main ()) Enter fullscreen mode Exit fullscreen mode Robustness & Testing The connector is built for production reliability: