Master Cognee with our step-by-step tutorial, detailed feature walkthrough, and expert tips.
Install open
source version: pip install cognee Process sample documents to see knowledge graph construction: cognee.add('path/to/documents') Explore generated knowledge graph visually through the dashboard interface Test hybrid queries combining graph traversal and vector search capabilities Integrate with existing AI applications via API endpoints for production deployment
💡 Quick Start: Follow these 2 steps in order to get up and running with Cognee quickly.
Explore the key features that make Cognee powerful for ai agent builders workflows.
Automatically extracts entities and relationships from text to build Neo4j knowledge graphs. Configurable extraction models and relationship mapping rules.
Processing 1,000 legal documents to map relationships between law firms, judges, cases, and outcomes for litigation research.
Combines graph traversal queries with vector similarity search. Can answer questions requiring both entity relationships and semantic similarity.
Finding all companies that invested in AI startups founded by former Google employees, requiring both relationship traversal and semantic matching.
Unified pipeline processing 28+ data formats into consistent knowledge representations. Handles PDFs, conversations, web pages, APIs.
Building unified knowledge graphs from company documents, Slack conversations, customer interviews, and market research reports.
Modular architecture allows custom entity extractors, relationship mappers, and storage backends. Python-based pipeline configuration.
Customizing entity extraction for medical documents to recognize drug interactions, patient conditions, and treatment protocols.
Traditional RAG treats document chunks as isolated text and uses vector similarity for retrieval. Cognee builds knowledge graphs that understand entity relationships, enabling questions like 'which companies did John Smith work for before founding his startup?' that require multi-hop reasoning across connected entities.
Neo4j requires database management, backup strategies, and scaling configuration that vector-only solutions avoid. However, Cognee's managed cloud service handles infrastructure automatically. Self-hosted deployments need Neo4j expertise or dedicated devops support.
Accuracy depends on document type and domain. Business documents with clear entity names (companies, people, locations) work well. Technical documents with domain-specific entities require custom extraction models. Expect 80-90% accuracy on standard business content, lower for specialized fields.
Yes, through API endpoints and the hybrid retrieval system. You can query Cognee's knowledge graphs alongside existing vector databases. Many teams use it as an additional reasoning layer on top of existing RAG infrastructure.
Now that you know how to use Cognee, it's time to put this knowledge into practice.
Sign up and follow the tutorial steps
Check pros, cons, and user feedback
See how it stacks against alternatives
Follow our tutorial and master this powerful ai agent builders tool in minutes.
Tutorial updated March 2026