Overview
Cognee is an innovative open-source platform designed to empower AI agents with personalized, scalable, and modular memory systems. It transforms raw data—such as documents, conversations, images, and audio—into a unified knowledge layer that is both semantically searchable and relationally connected. By integrating vector databases for similarity search and graph databases for relational understanding, Cognee enables AI applications to maintain context across interactions, improving reasoning and reducing hallucinations in large language models (LLMs).
Unlike conventional Retrieval-Augmented Generation (RAG) approaches, which often struggle with scalability and precision, Cognee introduces Extract, Cognify, Load (ECL) pipelines. These pipelines allow developers to ingest data from over 30 sources, process it through customizable tasks, and load it into a persistent memory structure. The result is a memory layer that supports complex queries, agentic workflows, and long-term retention without excessive infrastructure costs.
Key Features
- Data Interconnectivity: Handles diverse inputs like text files, past chat histories, multimedia, and more, creating interconnected knowledge graphs.
- Modular Architecture: Pythonic APIs and CLI tools for easy ingestion (
cognee add), graph generation (cognee cognify), memory enhancement (cognee memify), and querying (cognee search). - Deployment Options: Self-host locally for full control or use Cognee Cloud for managed infrastructure with UI dashboard, analytics, and GDPR-compliant security.
- LLM Integration: Compatible with providers like OpenAI, Ollama, and others via simple API key configuration.
- Community-Driven: Active ecosystem with Discord, Reddit (r/AIMemory), and plugins via the cognee-community repo.
Getting Started
Installation is straightforward with Python 3.10-3.13: uv pip install cognee. A basic pipeline can be set up in under 10 lines of code, as demonstrated in the quickstart examples. For production, explore integrations like LangGraph for agent memory or Ollama for local LLMs.
Cognee's research foundation includes a 2025 arXiv paper on optimizing knowledge graphs for LLM reasoning, highlighting its academic rigor. With over 9,000 GitHub stars, it has gained significant traction in the AI development community for simplifying memory management in agentic systems.
For more, visit the docs or try the Colab demo.
