Cognee is an open-source semantic memory layer designed for AI agents, leveraging vector and graph databases to construct knowledge graphs from retrieved data. This enables AI applications and agents to deliver accurate, context-aware responses. Users can upload various types of data, including unstructured text, raw media files, PDFs, and tables. Cognee then processes this data to build a knowledge graph, determining relevant memory types per query and uncovering hidden connections.
Cognee enhances responses from LLM applications in areas such as text generation, content summaries, customer analysis, chatbot responses, code generation, and translations. It is highly customizable, supporting multiple database providers and allowing users to plug in their own storage solutions. The system employs RDF-based ontologies to structure data intelligently, and it uses real reasoners to provide accurate responses rather than relying on pattern guessing.
Cognee can be run on users' own servers, ensuring data security and compliance with regulatory standards. It is capable of handling large volumes of data, scaling as needed to manage gigabytes or even terabytes of information. The tool is free and open-source, making it accessible for developers and hobbyists, with additional subscription plans offering enhanced support and deployment options.
A notable success case is with Dynamo, where Cognee was used to build a recommender system that enhanced customer engagement by providing personalized messages and real-time analytics based on user activity.
