Cognee - AI Agent Memory Framework

Project Overview

Cognee is an open-source AI memory engine framework designed to provide reliable memory capabilities for AI agents. The core concept of this project is to build dynamic memory systems for AI applications and agents with just 5 lines of code.

Core Features

🧠 Intelligent Memory Management

• Dynamic Memory Construction: Build agent memory using an extensible ECL (Extract, Cognify, Load) pipeline.
• Multi-Modal Support: Supports the interconnection and retrieval of conversation logs, documents, images, and audio transcriptions.
• Memory Persistence: Ensures that AI agents can maintain and utilize historical interaction information.

📊 Data Processing Capabilities

• Multi-Source Data Integration: Supports data ingestion from over 30 different data sources.
• Graph and Vector Dual Storage: Leverages the advantages of both graph databases and vector databases.
• Pydantic Integration: Load data into graph and vector databases using only Pydantic.

🎯 Performance Optimization

• Reduce Hallucination: Reduces inaccuracies in AI output through structured memory.
• Reduce Development Costs: Simplifies the development process and reduces developer workload.
• Improve Response Quality: Provides more accurate responses based on historical memory.

Technical Architecture

ECL Pipeline Architecture

Cognee adopts a unique ECL (Extract, Cognify, Load) pipeline architecture:

1. Extract: Extracts information from various data sources.
2. Cognify: Transforms raw data into structured cognitive information.
3. Load: Loads the processed data into the storage system.

Storage System

• Graph Database: Used to store complex relationships between entities.
• Vector Database: Used for semantic similarity retrieval.
• Hybrid Retrieval: Combines the advantages of graph traversal and vector search.

Quick Start

Installation

pip install cognee

Basic Usage Example

import os
os.environ["LLM_API_KEY"] = "YOUR OPENAI_API_KEY"

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 knowledge graph
    await cognee.cognify()
    
    # Query the knowledge graph
    results = await cognee.search("Tell me about NLP")
    
    # Display results
    for result in results:
        print(result)

if __name__ == '__main__':
    asyncio.run(main())

Output Example

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.

Configuration Options

Environment Variable Configuration

Create a .env file for configuration:

LLM_API_KEY=your_openai_api_key
# Other configuration options...

Multi-LLM Provider Support

• OpenAI
• Ollama
• Other mainstream LLM providers

Application Scenarios

Intelligent Dialogue Systems

• Context Retention: Maintain long-term conversation memory.
• Personalized Responses: Provide personalized services based on historical interactions.
• Knowledge Accumulation: The system becomes more intelligent as usage time increases.

Knowledge Management Systems

• Document Association: Automatically discover hidden connections between documents.
• Intelligent Retrieval: Compound retrieval based on semantics and relationships.
• Knowledge Graph Visualization: Intuitively display knowledge structures.

AI Agent Development

• Memory-Driven Decision Making: Make better decisions based on historical experience.
• Learning Ability: Learn and improve from past interactions.
• Task Continuity: Maintain task state across multiple sessions.

Technical Advantages

Improvements Compared to Traditional RAG

1. Structured Memory: Stores not only text but also semantic relationships.
2. Dynamic Updates: The memory system can continuously learn and update.
3. Multi-Dimensional Retrieval: Combines vector similarity and graph relationship retrieval methods.
4. Context Awareness: Better understanding of the context of queries.

Developer Friendliness

• Simple API: Core functionality can be implemented with just a few lines of code.
• Modular Design: Customizable processing pipelines based on requirements.
• Rich Documentation: Complete usage documentation and examples.
• Community Support: Active open-source community.

Project Ecosystem

Related Projects

• cognee-starter: Starter template containing examples.
• cognee-community: Community-managed plugins and extensions.
• awesome-ai-memory: Collection of AI memory-related projects.

Community Resources

• GitHub Repository: https://github.com/topoteretes/cognee
• Official Website: https://cognee.ai
• Documentation Site: https://docs.cognee.ai
• Discord Community: Active developer communication platform.

Summary

Cognee represents a new direction in AI memory management, providing developers with a complete solution for building intelligent memory systems through easy-to-use APIs and a powerful technical architecture. Whether used to build intelligent dialogue systems, knowledge management platforms, or complex AI agents, Cognee can provide reliable memory infrastructure support.