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cognee

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Basic Information

Cognee is an open source Python library that provides persistent, structured memory for AI agents through a modular Extract-Cognify-Load (ECL) pipeline. It is designed to replace traditional RAG setups by generating a knowledge graph from conversations, documents, images and audio transcriptions and enabling efficient retrieval. The repository includes a programmatic API for adding content, running a cognify pipeline, and searching the resulting knowledge graph, plus a local UI and references to a fully hosted product called Cogwit. The project targets developers building agents and memory systems, offers demos and a research paper, and supports Python 3.10 to 3.13. Documentation, notebooks and starter repos are provided to help integrate different LLM providers and backends without inventing ingestion code from scratch.

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App URL
https://github.com/topoteretes/cognee
Github Repository
https://github.com/topoteretes/cognee/blob/main/README.md

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App Details

Features
The README highlights features focused on memory ingestion, transformation and retrieval. Cognee interconnects and retrieves past conversations, documents, images and audio transcriptions. It provides an ECL pipeline to extract content, cognify it into structured knowledge and load it to graph and vector databases using Pydantic schemas. The project claims to replace RAG systems and reduce developer effort and cost. It supports ingestion from 30+ data sources, includes a Cognee UI for querying and management, offers demos (GraphRAG, Ollama integration and Cogwit beta), and supplies notebooks and a starter repo for quick onboarding. Installation is available via pip and optional extras can be installed with the project"s UV helper.
Use Cases
Cognee helps developers add dynamic, queryable memory to AI agents by automating content extraction, structuring and persistence. By converting inputs into a knowledge graph and vectors, it simplifies retrieval and reduces the engineering overhead of building bespoke RAG pipelines. The library exposes simple async APIs for add, cognify and search so agents can ingest new data and immediately query past context. Support for many data sources and for loading to graph and vector stores makes it adaptable to existing backends. The UI and hosted Cogwit offering provide lower-friction paths for teams that prefer managed memory, while demos and documentation shorten the learning curve.

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