Cognee

Cognee - Build AI memory with a Knowledge Engine that learns

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](https://GitHub.com/topoteretes/cognee/network/) [ ](https://GitHub.com/topoteretes/cognee/commit/) [ ](https://pepy.tech/project/cognee) [ [](https://github.com/topoteretes/cognee/graphs/contributors)

Use our knowledge engine to build personalized and dynamic memory for AI Agents.

🌐 Available Languages :

Deutsch | Español | Français | 日本語 | 한국어 | Português | Русский | 中文

About Cognee

Cognee is an open-source knowledge engine that lets you ingest data in any format or structure and continuously learns to provide the right context for AI agents. It combines vector search, graph databases and cognitive science approaches to make your documents both searchable by meaning and connected by relationships as they change and evolve.

:star: _Help us reach more developers and grow the cognee community. Star this repo!_

:books: _Check our detailed documentation for setup and configuration._

:crab: _Available as a plugin for your OpenClaw — cognee-openclaw_

Why use Cognee:

• Knowledge infrastructure — unified ingestion, graph/vector search, runs locally, ontology grounding, multimodal
• Persistent and Learning Agents - learn from feedback, context management, cross-agent knowledge sharing
• Reliable and Trustworthy Agents - agentic user/tenant isolation, traceability, OTEL collector, audit traits

Product Features

Basic Usage & Feature Guide

To learn more, check out this short, end-to-end Colab walkthrough of Cognee's core features.

[](https://colab.research.google.com/drive/12Vi9zID-M3fpKpKiaqDBvkk98ElkRPWy?usp=sharing)

Quickstart

Let’s try Cognee in just a few lines of code.

Prerequisites

• Python 3.10 to 3.13

Step 1: Install Cognee

You can install Cognee with pip, poetry, uv, or your preferred Python package manager.

uv pip install cognee

Step 2: Configure the LLM

import os
os.environ["LLM_API_KEY"] = "YOUR OPENAI_API_KEY"

Alternatively, create a .env file using our template.

To integrate other LLM providers, see our LLM Provider Documentation.

Step 3: Run the Pipeline

Cognee will take your documents, load them into the knowledge angine and search combined vector/graph relationships.