Cognee is a memory layer for AI apps and agents, enabling dynamic agent memory using scalable ECL pipelines to interconnect and retrieve past conversations, documents, images, and audio transcriptions for
Here's a summary of the README content, focusing on clarity and conciseness:
Cognee is a memory layer for AI applications and agents, designed to build dynamic agent memory using scalable, modular ECL (Extract, Cognify, Load) pipelines. It interconnects past conversations, documents, images, and audio transcriptions, aiming to reduce hallucinations, development effort, and costs.
Key features include loading data to graph and vector databases using Pydantic and manipulating data while ingesting from over 30 data sources. To get started, users can utilize a Google Colab notebook or a starter repository. Installation is supported via pip. A basic usage example demonstrates adding text, generating a knowledge graph, and querying it.
Cognee's architecture involves extracting, cognifying, and loading data to enhance AI agent responses. The project encourages contributions, with guidelines available in CONTRIBUTING.md. The README also provides links to demos, documentation, and the project's code of conduct.
topoteretes/cognee
August 16, 2023
March 28, 2025
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