Cognee provides a framework for building AI agent memory using modular pipelines, enabling agents to recall and interconnect past interactions, documents, and media. It aims to reduce hallucinations and development costs for AI applications by simplifying data ingestion and graph manipulation.

How It Works

Cognee employs an Extract, Cognify, Load (ECL) pipeline architecture. It extracts data from various sources, processes it to build a knowledge graph (cognify), and loads it into graph and vector databases. This approach allows for flexible data manipulation and retrieval, leveraging Pydantic for data handling and supporting multiple LLM providers.

Quick Start & Requirements

• Install: pip install cognee
• Prerequisites: OpenAI API key (set via LLM_API_KEY environment variable or .env file). Supports other LLM providers via configuration.
• Setup: Minimal setup, demonstrated with a Google Colab notebook or starter repo.
• Docs: docs.cognee.ai

Highlighted Details

• Ingests data from 30+ sources.
• Supports text, image, and audio transcription memory.
• Facilitates graph visualization and querying.
• Offers demos for local models and Ollama integration.

Maintenance & Community

• Active development with community contributions encouraged.
• Discord server available for community interaction.
• Contributors and Star History are publicly visible.

Licensing & Compatibility

• Licensed under the MIT License, permitting commercial use and closed-source linking.

Limitations & Caveats

The project is actively under development, and while it supports multiple LLM providers, specific configurations or advanced features might require consulting the documentation.