This release is a pre-release and may not be stable for production use.
Cognee - Build AI memory with a Knowledge Engine that learns
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Use our knowledge engine to build personalized and dynamic memory for AI Agents.
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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.
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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
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. For detailed setup and configuration, see the Cognee Docs.
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.
Now, run a minimal pipeline:
import cognee
import asyncio
from pprint import pprint
async def main():
# Add text to cognee
await cognee.add("Cognee turns documents into AI memory.")
# Add to knowledge engine
await cognee.cognify()
# Query the knowledge graph
results = await cognee.search("What does Cognee do?")
# Display the results
for result in results:
pprint(result)
if __name__ == '__main__':
asyncio.run(main())
As you can see, the output is generated from the document we previously stored in Cognee:
Cognee turns documents into AI memory.
Use the Cognee CLI
As an alternative, you can get started with these essential commands:
cognee-cli add "Cognee turns documents into AI memory."
cognee-cli cognify
cognee-cli search "What does Cognee do?"
cognee-cli delete --all
To open the local UI, run:
cognee-cli -ui
Demos & Examples
See Cognee in action:
Persistent Agent Memory
Community & Support
Contributing
We welcome contributions from the community! Your input helps make Cognee better for everyone. See CONTRIBUTING.md to get started.
Code of Conduct
We're committed to fostering an inclusive and respectful community. Read our Code of Conduct for guidelines.
Research & Citation
We recently published a research paper on optimizing knowledge graphs for LLM reasoning:
@misc{markovic2025optimizinginterfaceknowledgegraphs,
title={Optimizing the Interface Between Knowledge Graphs and LLMs for Complex Reasoning},
author={Vasilije Markovic and Lazar Obradovic and Laszlo Hajdu and Jovan Pavlovic},
year={2025},
eprint={2505.24478},
archivePrefix={arXiv},
primaryClass={cs.AI},
url={https://arxiv.org/abs/2505.24478},
}
1 maintainer
Metadata
1 maintainer
Release files for cognee 0.5.5.dev20260316
For a detailed explanation of source distributions (sdists) and built distributions (wheels), please see the package formats documentation.
Source distribution (sdist)
| File | Size | Uploaded | |
|---|---|---|---|
| cognee-0.5.5.dev20260316.tar.gz | 14.7 MB | Details |
Built distribution (wheel)
| File | Interpreter | ABI | Platform | Reset |
|---|---|---|---|---|
| cognee-0.5.5.dev20260316-py3-none-any.whl | Python 3 | none | any | Details |
Total release size: 16.4 MB
Release files / cognee-0.5.5.dev20260316.tar.gz
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| Size | 14.7 MB |
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Release files / cognee-0.5.5.dev20260316-py3-none-any.whl
| Download URL | cognee-0.5.5.dev20260316-py3-none-any.whl |
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| Size | 1.7 MB |
| Tags | Python 3 |
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