CUE — Validate, Define, and Generate Configuration with Types

What it is

CUE is a data constraint language that unifies schema definition, validation, and code generation for JSON, YAML, Protobuf, and more. It replaces scattered configuration scripts with a single type-safe language that can both define and validate configuration.

CUE targets DevOps engineers, platform teams, and anyone managing complex configuration across multiple environments. It catches misconfigurations at definition time rather than at runtime when a deployment fails.

The project is actively maintained and suitable for both individual developers and teams looking to integrate it into their existing toolchain. Documentation and community support are available for onboarding.

How it saves time or tokens

CUE eliminates the feedback loop between writing config and discovering errors in production. Constraints are part of the schema, so invalid values are caught immediately. CUE can generate Kubernetes manifests, Terraform configs, and CI/CD pipelines from a single source of truth, reducing duplication across config formats.

How to use

1. Install CUE via brew install cue or download from the official releases.
2. Write .cue files that define your configuration schemas with type constraints.
3. Use cue vet to validate existing JSON/YAML files against your schemas.
4. Use cue export to generate JSON or YAML output from CUE definitions.

Example

// service.cue — define and constrain a service config
package config

#Service: {
    name:     string & =~"^[a-z][a-z0-9-]*$"
    port:     int & >0 & <65536
    replicas: int & >=1 & <=100
    env:      "dev" | "staging" | "prod"
    image:    string
}

webapp: #Service & {
    name:     "web-api"
    port:     8080
    replicas: 3
    env:      "prod"
    image:    "myapp:v1.2.3"
}

# Validate and export as JSON
cue vet service.cue
cue export service.cue --out json

Related on TokRepo

• AI Tools for DevOps — Configuration management and infrastructure tools.
• AI Tools for Automation — Automation tools that benefit from validated configuration.

Common pitfalls

• Treating CUE as just another templating language. CUE is a constraint system, not a template engine. Think in terms of types and constraints, not string interpolation.
• Writing overly permissive schemas that accept anything. The value of CUE comes from tight constraints that catch real errors. Be specific about allowed values.
• Not using CUE's module system for shared schemas. Duplicating schema definitions across files defeats the purpose of a single source of truth.
• Not reading the changelog before upgrading. Breaking changes between versions can cause unexpected failures in production. Pin your version and review release notes.