cognee - memory layer for AI apps and Agents

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[![GitHub forks](https://img.shields.io/github/forks/topoteretes/cognee.svg?style=social&label=Fork&maxAge=2592000)](https://GitHub.com/topoteretes/cognee/network/) [![GitHub stars](https://img.shields.io/github/stars/topoteretes/cognee.svg?style=social&label=Star&maxAge=2592000)](https://GitHub.com/topoteretes/cognee/stargazers/) [![GitHub commits](https://badgen.net/github/commits/topoteretes/cognee)](https://GitHub.com/topoteretes/cognee/commit/) [![Github tag](https://badgen.net/github/tag/topoteretes/cognee)](https://github.com/topoteretes/cognee/tags/) [![Downloads](https://static.pepy.tech/badge/cognee)](https://pepy.tech/project/cognee) [![License](https://img.shields.io/github/license/topoteretes/cognee?colorA=00C586&colorB=000000)](https://github.com/topoteretes/cognee/blob/main/LICENSE) [![Contributors](https://img.shields.io/github/contributors/topoteretes/cognee?colorA=00C586&colorB=000000)](https://github.com/topoteretes/cognee/graphs/contributors) AI Agent responses you can rely on. Build dynamic Agent memory using scalable, modular ECL (Extract, Cognify, Load) pipelines. More on [use-cases](https://docs.cognee.ai/use_cases).

Why cognee?

## Features - Interconnect and retrieve your past conversations, documents, images and audio transcriptions - Reduce hallucinations, 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 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`](CONTRIBUTING.md) for more information. ## 📦 Installation You can install Cognee using either **pip**, **poetry**, **uv** or any other python package manager. ### With pip ```bash pip install cognee ``` ## 💻 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 Add LLM_API_KEY to .env using the command bellow. ``` echo "LLM_API_KEY=YOUR_OPENAI_API_KEY" > .env ``` You can see available env variables in the repository `.env.template` file. If you don't specify it otherwise, like in this example, SQLite (relational database), LanceDB (vector database) and NetworkX (graph store) will be used as default components. This script will run the default pipeline: ```python import cognee import asyncio from cognee.modules.search.types import SearchType async def main(): # Create a clean slate for cognee -- reset data and system state await cognee.prune.prune_data() await cognee.prune.prune_system(metadata=True) # cognee knowledge graph will be created based on this text text = """ Natural language processing (NLP) is an interdisciplinary subfield of computer science and information retrieval. """ print("Adding text to cognee:") print(text.strip()) # Add the text, and make it available for cognify await cognee.add(text) # Use LLMs and cognee to create knowledge graph await cognee.cognify() print("Cognify process complete.\n") query_text = "Tell me about NLP" print(f"Searching cognee for insights with query: '{query_text}'") # Query cognee for insights on the added text search_results = await cognee.search( query_text=query_text, query_type=SearchType.INSIGHTS ) print("Search results:") # Display results for result_text in search_results: print(result_text) # Example output: # ({'id': UUID('bc338a39-64d6-549a-acec-da60846dd90d'), 'updated_at': datetime.datetime(2024, 11, 21, 12, 23, 1, 211808, tzinfo=datetime.timezone.utc), 'name': 'natural language processing', 'description': 'An interdisciplinary subfield of computer science and information retrieval.'}, {'relationship_name': 'is_a_subfield_of', 'source_node_id': UUID('bc338a39-64d6-549a-acec-da60846dd90d'), 'target_node_id': UUID('6218dbab-eb6a-5759-a864-b3419755ffe0'), 'updated_at': datetime.datetime(2024, 11, 21, 12, 23, 15, 473137, tzinfo=datetime.timezone.utc)}, {'id': UUID('6218dbab-eb6a-5759-a864-b3419755ffe0'), 'updated_at': datetime.datetime(2024, 11, 21, 12, 23, 1, 211808, tzinfo=datetime.timezone.utc), 'name': 'computer science', 'description': 'The study of computation and information processing.'}) # (...) # # It represents nodes and relationships in the knowledge graph: # - The first element is the source node (e.g., 'natural language processing'). # - The second element is the relationship between nodes (e.g., 'is_a_subfield_of'). # - The third element is the target node (e.g., 'computer science'). if __name__ == '__main__': asyncio.run(main()) ``` For more advanced usage, have a look at our documentation. ## Understand our architecture

cognee concept diagram

## Demos What is AI memory: [Learn about cognee](https://github.com/user-attachments/assets/8b2a0050-5ec4-424c-b417-8269971503f0) ## 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

contributors

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