cognee - memory layer for AI apps and Agents

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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 ## 📦 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. 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: [

](https://www.youtube.com/watch?v=fI4hDzguN5k "Learn about cognee: 55") ## 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. ## 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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