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AI agents and automation

cognee

by topoteretes

Cognee is a Python library that gives AI agents long-term memory by turning documents, code and conversations into a searchable knowledge graph. It is for developers who want their agents to remember past work and retrieve it later, and it can run locally on a CPU.

Worth a lookEstablished, with a few things to check (listed below).

View on GitHubProject website

Stars
32.8k
Licence
Apache-2.0
Runs on
Your computer
Setup
Moderate
Language
Python
Age
1167 days

What you can do with it

  • Store text, documents and code as a knowledge graph and search it later
  • Give an existing AI agent persistent memory through plugins or MCP
  • Build a shared company knowledge base that links decisions to discussions and code

Make money with it

A freelance developer could build and sell custom AI assistants with persistent memory, trained on a client's own documents.

An idea, not financial advice. Check the licence before charging for anything built on it.

Ways a business could use it

  • Build an internal searchable knowledge base from documents, tickets and chat history, so staff spend less time hunting for answers
  • Add memory to a customer-facing or internal AI assistant so it recalls past client context without repeated briefing

Can you use it commercially?

Free to use, change and sell, including in paid products. Keep the copyright notice.

Not legal advice. Read the project's licence file before building a product on it.

Getting it running

Install it with pip or uv (for example `uv pip install "cognee[gliner]"`) on Python 3.10 to 3.14, then use the Python API or the cognee-cli command.

Follow the official instructions in the project README.

Things to check

  • Generated answers need an LLM, and the default hosted setup uses a paid OpenAI API key
  • The bundled local extractor is described as a demo, with a higher-accuracy version available only by contacting the company
  • Requires Python programming knowledge to use

Who's talking about it

Checked 27 Oct 2026. Summary drafted by AI from the project's README.

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