ory-kratos

name: ory-kratos compatibility: opencode completeness: 95 content-types:

guidance
examples
do-dont
config description: '"ORY Kratos in Identity & Access - cloud native architecture, patterns" pitfalls, and best practices' license: MIT maturity: stable metadata: domain: cncf output-format: manifests related-skills: oathkeeper, ory-hydra role: reference scope: infrastructure triggers: access, cdn, identity, infrastructure as code, monitoring, ory kratos, ory-kratos, cloudformation archetypes:
- educational
- strategic anti_triggers:
- brainstorming
- vague ideation
- non-containerized architecture response_profile: verbosity: medium directive_strength: low abstraction_level: strategic version: "1.0.0"

Ory-Kratos in Cloud-Native Engineering

Category: identity
Status: Active
Stars: 13,591
Last Updated: 2026-04-21
Primary Language: Go
Documentation: https://github.com/ory/kratos

Purpose and Use Cases

Ory-Kratos is a cloud-native project that provides identity functionality for modern distributed systems.

What Problem Does It Solve?

Ory-Kratos addresses the challenges of identity in cloud-native environments, enabling teams to implement identity features without embedding them in application code.

When to Use This Project

Use ory-kratos when you need identity capabilities in your Kubernetes or cloud-native infrastructure. It's ideal when you require identity with minimal application code changes.

Key Use Cases

identity for microservices
Integration with Kubernetes and CNCF ecosystem
identity with declarative configuration
identity for observability and monitoring
identity for security and compliance

Architecture Design Patterns

Core Components

Primary Component: Core functionality
Controller: Cluster management
Agent: Node-level execution
API Server: Management interface
Storage: Configuration persistence

Component Interactions

Client → Core: Request routing
Core → Storage: Configuration persistence
Controller → Agents: Command distribution
Agents → Controller: Status reporting
Storage → Controllers: Configuration sync

Data Flow Patterns

Request Flow: Client → Core → Backend
Configuration Flow: API → Storage → Nodes
State Flow: Nodes → Control Plane → Dashboard
Telemetry Flow: Data → Collector → Storage

Design Principles

Declarative Configuration: YAML/CRD-based configuration
Kubernetes-Native: Leverages Kubernetes APIs
Extensible: Plugin/adapter architecture
Observability First: Built-in metrics, tracing, logging
High Availability: Multi-node clustering
Secure by Default: TLS, authentication, authorization

Integration Approaches

Integration with Other CNCF Projects

Kubernetes: Native integration with K8s APIs
Prometheus: Metrics collection integration
Grafana: Dashboard visualization
Jaeger/Zipkin: Distributed tracing
CoreDNS: Service discovery integration
Envoy: Service mesh integration
Istio: Service mesh control plane

API Patterns

RESTful API: Management endpoints
gRPC API: Internal communication
WebSocket API: Real-time updates
Admin API: Configuration management

Configuration Patterns

YAML Manifests: Human-readable configs
JSON: Machine-readable format
Environment Variables: Runtime configuration
ConfigMaps: Kubernetes config storage
CRDs: Kubernetes custom resources

Extension Mechanisms

Plugins: Extend functionality
Filters: Process requests
Hooks: Event callbacks
Modules: Add features

Common Pitfalls and How to Avoid Them

Misconfigurations

Endpoint Configuration: Wrong API endpoints
Authentication: Missing or incorrect auth
Resource Limits: Insufficient resource allocation
SSL/TLS: Certificate validation issues
Network Policy: Incorrect network rules
Scaling: Under/over-provisioned resources

Performance Issues

Request Latency: High latency
Throughput: Low throughput
Memory Usage: High memory consumption
CPU Usage: High CPU usage
Connection Pooling: Pool exhaustion
Cache Misses: High cache miss rate

Operational Challenges

Configuration Management: Config drift
Upgrade Management: Rolling upgrades
Monitoring: Metrics collection
Logging: Log aggregation
Troubleshooting: Issue diagnosis
Security: Security audits

Security Pitfalls

Authentication: Missing authentication
Authorization: Overly permissive ACLs
TLS Configuration: Weak TLS settings
Secrets Management: Exposed secrets
RBAC: Insufficient access control
Vulnerabilities: Outdated dependencies

Coding Practices

Idiomatic Configuration

YAML: Configuration files
JSON: API payloads
Environment Variables: Runtime config
ConfigMaps: Kubernetes config

API Usage Patterns

REST API: Management endpoints
gRPC API: Internal communication
WebSocket API: Real-time updates

Observability Best Practices

Prometheus Metrics: Custom metrics
Tracing: Distributed tracing
Access Logs: Request logging
Alerting: Health checks

Testing Strategies

Unit Tests: Component tests
Integration Tests: Service tests
E2E Tests: Full stack tests
Load Tests: Performance tests

Development Workflow

Development: Local dev setup
Testing: Unit and integration tests
Debugging: Logging, tracing
Deployment: Docker, Kubernetes
CI/CD: GitHub Actions
Tools: Project CLI tools

Fundamentals

Essential Concepts

Cluster: Node group
Node: Individual server
Pod: Container unit
Service: Network service
Config: Configuration data
Secret: Sensitive data
Volume: Storage volume
Namespace: Logical partition

Terminology Glossary

Cluster: Cluster of nodes
Node: Individual node
Service: Service endpoint
Config: Configuration data
Secret: Secret data
Volume: Storage
Namespace: Namespace
Pod: Pod unit

Data Models and Types

Config: Config spec
Secret: Secret spec
Service: Service spec
Deployment: Deployment spec
Pod: Pod spec
Volume: Volume spec
Namespace: Namespace spec

Lifecycle Management

Service Lifecycle: Create → Deploy → Scale → Delete
Pod Lifecycle: Pending → Running → Succeeded/Failed
Config Lifecycle: Create → Apply → Update → Delete
Secret Lifecycle: Create → Encrypt → Decrypt → Delete

State Management

Config State: Applied config
Service State: Service state
Pod State: Pod state
Volume State: Volume state
Secret State: Encrypted data
Namespace State: Namespace data

Scaling and Deployment Patterns

Horizontal Scaling

Node Scaling: Add/remove nodes
Container Scaling: Pod replicas
Database Scaling: Database clusters
Cache Scaling: Cache clusters
Load Balancing: Frontend balancing

High Availability

Multiple Instances: Redundant instances
Load Balancing: Frontend balancing
Health Checking: Automatic failover
Graceful Shutdowns: Clean shutdown
Data Replication: HA storage

Production Deployments

Configuration: Production config
Load Balancing: Frontend HA
Monitoring: Metrics setup
Alerting: Alerting setup
Logging: Centralized logging
Security: Security hardening

Upgrade Strategies

Rolling Update: Rolling deployment
Blue-Green: Blue-green deployment
Canary: Canary release
Backup: Pre-upgrade backup
Validation: Post-upgrade validation

Resource Management

Memory Configuration: Memory limits
CPU Configuration: CPU limits
Storage Configuration: Storage limits
Network Configuration: Network limits
Connection Limits: Connection limits

Additional Resources

Official Documentation: {repo_info['html_url']}
GitHub Repository: {repo_info['html_url']}
CNCF Project Page: cncf.io/projects/{project_info['name']}/
Community: Check the GitHub repository for community channels
Versioning: Refer to project's release notes for version-specific features

Troubleshooting

Common Issues

Deployment Failures
- Check pod logs for errors
- Verify configuration values
- Ensure network connectivity
Performance Issues
- Monitor resource usage
- Adjust resource limits
- Check for bottlenecks
Configuration Errors
- Validate YAML syntax
- Check required fields
- Verify environment-specific settings
Integration Problems
- Verify API compatibility
- Check dependency versions
- Review integration documentation

Getting Help

Check official documentation
Search GitHub issues
Join community channels
Review logs and metrics Content generated automatically. Verify against official documentation before production use.

Examples

Basic Configuration

# Basic configuration example
apiVersion: v1
kind: ConfigMap
metadata:
  name: {{project_name}}-config
  namespace: default
data:
  # Configuration goes here
  config.yaml: |
    # Base configuration
    # Add your settings here

Kubernetes Deployment

# Kubernetes deployment for {{project_name}}
apiVersion: apps/v1
kind: Deployment
metadata:
  name: {{project_name}}
  namespace: default
spec:
  replicas: 1
  selector:
    matchLabels:
      app: {{project_name}}
  template:
    metadata:
      labels:
        app: {{project_name}}
    spec:
      containers:
      - name: {{project_name}}
        image: {{project_name}}:latest
        ports:
        - containerPort: 8080
        resources:
          limits:
            memory: "128Mi"
            cpu: "500m"

Kubernetes Service

# Kubernetes service for {{project_name}}
apiVersion: v1
kind: Service
metadata:
  name: {{project_name}}
  namespace: default
spec:
  selector:
    app: {{project_name}}
  ports:
  - protocol: TCP
    port: 80
    targetPort: 8080
  type: ClusterIP

When to Use

Use this skill when:

Integrating a CNCF project into Kubernetes infrastructure — You need to configure, deploy, or troubleshoot a cloud-native tool within a cluster
Designing cloud-native architecture — You are selecting and integrating CNCF tools to solve specific infrastructure challenges
Resolving operational issues — A CNCF component is misbehaving, underperforming, or needs configuration changes

Core Workflow

Assess Requirements — Understand the use case, scale, integration needs, and existing infrastructure. Checkpoint: Document requirements, constraints, and success criteria.
Design Architecture — Plan component interactions, data flow, and deployment strategy using cloud-native best practices. Checkpoint: Verify the architecture addresses all requirements and follows CNCF conventions.
Implement & Configure — Create manifests, configurations, and deployment scripts. Include resource limits, health checks, and observability hooks. Checkpoint: Validate all YAML against schema and test in a staging environment.
Deploy & Monitor — Apply manifests to the cluster, verify component health, and confirm observability is working. Checkpoint: Confirm all pods/services are running, probes passing, and metrics/alerts configured.