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Documentation Index

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Cognee is designed for flexible deployment across development and production environments, with configurable data storage backends that scale with your needs.

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Data Storage Architecture

Cognee operates on a three-tier data storage model, each optimized for specific data types and query patterns:

Graph Database

Relationships & EntitiesStores knowledge graph structure, entity relationships, and semantic connections.

Vector Database

Embeddings & SearchHandles semantic embeddings for similarity search and content retrieval.

Relational Database

Metadata & StateManages datasets, user permissions, pipeline state, and operational data.
Each storage layer can be deployed as managed services, self-hosted servers, or file-based systems (like S3 buckets), giving you complete flexibility over your infrastructure.

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Deployment Options

Choose the deployment strategy that matches your requirements:
Local & Testing
  • Docker: Containerized local deployment with embedded databases
  • MCP: Direct integration with code editors and IDEs
  • File-based: SQLite, local files, and embedded vector stores

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Storage Configuration Examples

Local Development

Embedded & File-based
# All data stored locally
GRAPH_DATABASE=networkx
VECTOR_DATABASE=lancedb
RELATIONAL_DATABASE=sqlite://./cognee.db
Multi-Agent Limitation: Default Kuzu graph store uses file-based locking and is not suitable for concurrent access from multiple agents. Use Neo4j or FalkorDB for multi-agent deployments.
Managed Services
# Fully managed cloud services
GRAPH_DATABASE=neo4j://your-aura-instance
VECTOR_DATABASE=pinecone://your-index
RELATIONAL_DATABASE=postgresql://your-rds-instance
S3 + Managed Databases
# Vector data in S3, databases managed
VECTOR_DATABASE=s3://your-bucket/vectors/
GRAPH_DATABASE=neo4j://managed-instance
RELATIONAL_DATABASE=postgresql://rds-instance
Cognee stores all persistent data under SYSTEM_ROOT_DIRECTORY (default: .cognee_system). There is no dedicated export API; migration works by either copying the database files or switching to shared external databases.
Stop Cognee on the source instance, copy the databases/ folder to the destination, then set SYSTEM_ROOT_DIRECTORY to the new path:
# All three databases live under $SYSTEM_ROOT_DIRECTORY/databases/
cp -r /old-instance/.cognee_system/databases /new-instance/.cognee_system/databases

# Optionally copy raw data files
cp -r /old-instance/.data_storage /new-instance/.data_storage
File paths inside databases/:
  • cognee_graph_kuzu — Kuzu graph database
  • cognee.lancedb — LanceDB vector store
  • cognee_db — SQLite relational database
On the new instance, configure:
SYSTEM_ROOT_DIRECTORY=/new-instance/.cognee_system
DATA_ROOT_DIRECTORY=/new-instance/.data_storage
See Graph Stores and Vector Stores for all supported external providers.

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Quick Start Guide

1

Choose Deployment

Select your deployment method based on scale and requirements
2

Configure Storage

Set up your preferred combination of graph, vector, and relational databases
3

Deploy & Test

Launch Cognee and verify connectivity to all storage backends
4

Scale

Adjust storage and compute resources based on usage patterns

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Deployment Methods

Docker Deployment

Local & ServerStart Cognee with optional databases using compose profiles.

Modal Deployment

Serverless & Auto-scalingPerfect for variable workloads with automatic resource management.

Kubernetes (Helm)

Enterprise & ProductionContainer orchestration with full control and high availability.

EC2 Deployment

Traditional CloudStandard server deployment with custom configurations.

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Architecture Benefits

Flexible Data Tiers: Each storage layer can be independently scaled, managed, or migrated without affecting others.
Cost Optimization: Use file-based storage (S3) for archival data and managed services for active workloads.
Security: Ensure proper network security and access controls across all storage tiers in production deployments.

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