<!-- .github/pull_request_template.md --> ## Description Removed default user creation during brute force search. Even when a user is provided to search it's not forwarded to the Retrievers, the retrievers always created a default user and sent telemetry as the default user which is inaccurate, they also create a default user even when there shouldn't be one. if this information is necessary for telemetry we should forward the user information that was sent through search through the retrievers and not always create a default user ## Type of Change <!-- Please check the relevant option --> - [ ] Bug fix (non-breaking change that fixes an issue) - [ ] New feature (non-breaking change that adds functionality) - [x] Breaking change (fix or feature that would cause existing functionality to change) - [ ] Documentation update - [ ] Code refactoring - [ ] Performance improvement - [ ] Other (please specify): ## Changes Made Removed user as parameter from brute force search, removed default user creation that was supplied as parameter to brute force search ## Testing Ran simple example, waiting for CI/CD results ## Pre-submission Checklist <!-- Please check all boxes that apply before submitting your PR --> - [x] **I have tested my changes thoroughly before submitting this PR** - [x] **This PR contains minimal changes necessary to address the issue/feature** - [x] My code follows the project's coding standards and style guidelines - [ ] I have added tests that prove my fix is effective or that my feature works - [ ] I have added necessary documentation (if applicable) - [ ] All new and existing tests pass - [x] I have searched existing PRs to ensure this change hasn't been submitted already - [ ] I have linked any relevant issues in the description - [x] My commits have clear and descriptive messages ## DCO Affirmation I affirm that all code in every commit of this pull request conforms to the terms of the Topoteretes Developer Certificate of Origin. |
||
|---|---|---|
| .github | ||
| alembic | ||
| assets | ||
| bin | ||
| cognee | ||
| cognee-frontend | ||
| cognee-mcp | ||
| cognee-starter-kit | ||
| deployment | ||
| distributed | ||
| evals | ||
| examples | ||
| licenses | ||
| logs | ||
| notebooks | ||
| tools | ||
| .dockerignore | ||
| .env.template | ||
| .gitattributes | ||
| .gitguardian.yml | ||
| .gitignore | ||
| .pre-commit-config.yaml | ||
| .pylintrc | ||
| alembic.ini | ||
| CODE_OF_CONDUCT.md | ||
| cognee-gui.py | ||
| CONTRIBUTING.md | ||
| CONTRIBUTORS.md | ||
| DCO.md | ||
| docker-compose.yml | ||
| Dockerfile | ||
| entrypoint.sh | ||
| github-secure-open-source-graduation.md | ||
| LICENSE | ||
| mypy.ini | ||
| NOTICE.md | ||
| poetry.lock | ||
| pyproject.toml | ||
| README.md | ||
| SECURITY.md | ||
| uv.lock | ||
cognee - Memory for AI Agents in 5 lines of code
Demo . Learn more · Join Discord · Join r/AIMemory . Docs . cognee community repo
🚀 We launched Cogwit beta (Fully-hosted AI Memory): Sign up here! 🚀
Build dynamic memory for Agents and replace RAG using scalable, modular ECL (Extract, Cognify, Load) pipelines.
🌐 Available Languages : Deutsch | Español | français | 日本語 | 한국어 | Português | Русский | 中文
Features
- Interconnect and retrieve your past conversations, documents, images and audio transcriptions
- Replaces RAG systems and reduces developer effort, and cost.
- Load data to graph and vector databases using only Pydantic
- Manipulate your data while ingesting from 30+ data sources
Get Started
Get started quickly with a Google Colab notebook , Deepnote notebook or starter repo
Contributing
Your contributions are at the core of making this a true open source project. Any contributions you make are greatly appreciated. See CONTRIBUTING.md for more information.
📦 Installation
You can install Cognee using either pip, poetry, uv or any other python package manager.
Cognee supports Python 3.10 to 3.13
With pip
pip install cognee
Local Cognee installation
You can install the local Cognee repo using uv, pip and poetry. For local pip installation please make sure your pip version is above version 21.3.
with UV with all optional dependencies
uv sync --all-extras
💻 Basic Usage
Setup
import os
os.environ["LLM_API_KEY"] = "YOUR OPENAI_API_KEY"
You can also set the variables by creating .env file, using our template. To use different LLM providers, for more info check out our documentation
Simple example
This script will run the default pipeline:
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.
Our paper is out! Read here
Cognee UI
You can also cognify your files and query using cognee UI.

Running the UI
Try cognee UI by setting LLM_API_KEY and running cognee-cli -ui command on your terminal.
Understand our architecture
Demos
- Cogwit Beta demo:
- Simple GraphRAG demo
- cognee with Ollama
Code of Conduct
We are committed to making open source an enjoyable and respectful experience for our community. See CODE_OF_CONDUCT for more information.
💫 Contributors
Sponsors
Thanks to the following companies for sponsoring the ongoing development of cognee.