Cognee AI Memory

Example Use Cases

• 1

  Persistent, queryable long-term memory built from documents, past conversations, images, and audio transcriptions.

• 2

  Replace Retrieval-Augmented Generation with a graph-and-vector memory layer for more accurate and context-aware responses.

• 3

  Scalable, customizable ingestion pipelines and built-in search endpoints to integrate diverse data sources into agent workflows.

Description

Cognee provides a local, self-hosted memory system and pipelines (Extract, Cognify, Load) that convert documents, conversations, images, and audio transcriptions into graphs and vectors for agent memory. It reduces developer effort and cost by offering Pythonic ingestion pipelines, built-in search endpoints, and modular memory algorithms. A hosted offering provides a managed UI, syncing, analytics, and enterprise features for teams that want a managed deployment.