Merge pull request #1670 from Vasilije1990/master

feat: add cognee, AI memory framework to the list
This commit is contained in:
Andre Zayarni
2025-06-01 13:54:59 +02:00
committed by GitHub
2 changed files with 86 additions and 0 deletions
@@ -11,6 +11,7 @@ partition: build
| [Airbyte](/documentation/data-management/airbyte/) | Data integration platform specialising in ELT pipelines. |
| [Airflow](/documentation/data-management/airflow/) | Platform designed for developing, scheduling, and monitoring batch-oriented workflows. |
| [CocoIndex](/documentation/data-management/cocoindex/) | High performance ETL framework to transform data for AI, with real-time incremental processing |
| [Cognee](/documentation/data-management/cognee/) | AI memory frameworks that allows loading from 30+ data sources to graph and vector stores |
| [Connect](/documentation/data-management/redpanda/) | Declarative data-agnostic streaming service for efficient, stateless processing. |
| [Confluent](/documentation/data-management/confluent/) | Fully-managed data streaming platform with a cloud-native Apache Kafka engine. |
| [DLT](/documentation/data-management/dlt/) | Python library to simplify data loading processes between several sources and destinations. |
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---
title: cognee
---
# cognee
[cognee](https://www.cognee.ai) is a memory management tool for AI Apps and Agents
Qdrant is available as a native built-in vector database to store and retrieve embeddings.
## 📦 Installation
You can install Cognee using either **pip**, **poetry**, **uv** or any other python package manager.
Cognee supports Python 3.8 to 3.12
### With pip
```bash
pip install cognee
```
## Local Cognee installation
You can install the local Cognee repo using **pip**, **poetry** and **uv**.
For local pip installation please make sure your pip version is above version 21.3.
### with UV with all optional dependencies
```bash
uv sync --all-extras
```
## 💻 Basic Usage
### Setup
```
import os
os.environ["LLM_API_KEY"] = "YOUR OPENAI_API_KEY"
VECTOR_DB_PROVIDER="qdrant"
VECTOR_DB_URL=https://url-to-your-qdrant-cloud-instance.cloud.qdrant.io:6333
VECTOR_DB_KEY=your-qdrant-api-key
```
You can also set the variables by creating .env file, using our <a href="https://github.com/topoteretes/cognee/blob/main/.env.template">template.</a>
To use different LLM providers, for more info check out our <a href="https://docs.cognee.ai">documentation</a>
### Simple example
This script will run the default pipeline:
```python
import cognee
import asyncio
async def main():
# Add text to cognee
await cognee.add("Natural language processing (NLP) is an interdisciplinary subfield of computer science and information retrieval.")
# Generate the knowledge graph
await cognee.cognify()
# Query the knowledge graph
results = await cognee.search("Tell me about NLP")
# Display the results
for result in results:
print(result)
if __name__ == '__main__':
asyncio.run(main())
```
Example output:
```
Natural Language Processing (NLP) is a cross-disciplinary and interdisciplinary field that involves computer science and information retrieval. It focuses on the interaction between computers and human language, enabling machines to understand and process natural language.
```