Cognee

Prerequisites

• Python 3.10 to 3.14

Step 1: Install Cognee

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

uv pip install cognee

Install cognee

pip install cognee

Deploy Cognee

Use Cognee Cloud for a fully managed experience, or self-host with one of the 1-click deployment configurations below.

See the distributed/ folder for deploy scripts, worker configurations, and additional details.

Basic Usage & Feature Guide

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

Quickstart

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

Examples

Browse more examples in the examples/ folder — demos, guides, custom pipelines, and database configurations.

Use Case 1 — Customer Support Agent

```python Goal: Resolve customer issues using their personal data across finance, support, and product history.

User: "My invoice looks wrong and the issue is still not resolved."

Cognee tracks: past interactions, failed actions, resolved cases, product history

Step 2: Configure the LLM

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

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

Configure

export LLM_API_KEY="your-openai-key"

Step 3: Run the Pipeline

Cognee's API gives you four operations — remember, recall, forget, and improve:

import cognee
import asyncio


async def main():
    # Store permanently in the knowledge graph (runs add + cognify + improve)
    await cognee.remember("Cognee turns documents into AI memory.")

    # Store in session memory (fast cache, syncs to graph in background)
    await cognee.remember("User prefers detailed explanations.", session_id="chat_1")

    # Query with auto-routing (picks best search strategy automatically)
    results = await cognee.recall("What does Cognee do?")
    for result in results:
        print(result)

    # Query session memory first, fall through to graph if needed
    results = await cognee.recall("What does the user prefer?", session_id="chat_1")
    for result in results:
        print(result)

    # Delete when done
    await cognee.forget(dataset="main_dataset")


if __name__ == '__main__':
    asyncio.run(main())

Clone the plugin

git clone https://github.com/topoteretes/cognee-integrations.git