cognee-mcp

<div align="center"> <a href="https://github.com/topoteretes/cognee"> <img src="https://raw.githubusercontent.com/topoteretes/cognee/refs/heads/dev/assets/cognee-logo-transparent.png" alt="Cognee Logo" height="60"> </a> <br /> cognee - memory layer for AI apps and Agents <p align="center"> <a href="https://cognee.ai">Learn more</a> · <a href="https://discord.gg/NQPKmU5CCg">Join Discord</a> · <a href="https://www.youtube.com/watch?v=1bezuvLwJmw&t=2s">Demo</a> </p> [](https://GitHub.com/topoteretes/cognee/network/) [](https://GitHub.com/topoteretes/cognee/stargazers/) [](https://GitHub.com/topoteretes/cognee/commit/) [](https://github.com/topoteretes/cognee/tags/) [](https://pepy.tech/project/cognee) [](https://github.com/topoteretes/cognee/blob/main/LICENSE) [](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). <div style="text-align: center"> <img src="https://raw.githubusercontent.com/topoteretes/cognee/refs/heads/dev/assets/cognee_benefits.png" alt="Why cognee?" width="100%" /> </div> </div> ## 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 <a href="https://colab.research.google.com/drive/1g-Qnx6l_ecHZi0IOw23rg0qC4TYvEvWZ?usp=sharing">notebook</a> or <a href="https://github.com/topoteretes/cognee-starter">starter repo</a> ## 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 <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 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 <a href="https://docs.cognee.ai"> documentation</a>. ## Understand our architecture <div style="text-align: center"> <img src="assets/cognee_diagram.png" alt="cognee concept diagram" width="100%" /> </div> ## 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 <a href="https://github.com/topoteretes/cognee/blob/main/CODE_OF_CONDUCT.md"><code>CODE_OF_CONDUCT</code></a> for more information. ## 💫 Contributors <a href="https://github.com/topoteretes/cognee/graphs/contributors"> <img alt="contributors" src="https://contrib.rocks/image?repo=topoteretes/cognee"/> </a> ## Star History [](https://star-history.com/#topoteretes/cognee&Date)