Vague

A declarative language for describing and generating realistic data. Vague treats ambiguity as a first-class primitive — declare the shape of valid data and let the runtime figure out how to populate it.

Why Vague?

Vague is a data description model for APIs, not just a fake data tool.

Think of it as OpenAPI meets property-based testing: you describe what valid data looks like — its structure, constraints, distributions, and edge cases — and Vague handles generation. The same schema that generates test data can validate production data.

What You Need	Traditional Tools	Vague
Intent — "80% of users are active"	Random selection	status: 0.8: "active" | 0.2: "inactive"
Constraints — "due date ≥ issued date"	Manual validation	assume due_date >= issued_date
Relationships — "payment references an invoice"	Manual wiring	invoice: any of invoices where .status == "open"
Edge cases — "test with Unicode exploits"	Manual creation	name: issuer.homoglyph("admin")
Validation — "does this data match the schema?"	Separate tool	Same .vague file with --validate-data

The question isn't "which fake data library?" — it's "how do we formally describe what valid data looks like for our APIs?"

Quick Example

schema Customer {
  name: string,
  status: 0.8: "active" | 0.2: "inactive"
}

schema Invoice {
  customer: any of customers,
  amount: decimal in 100..10000,
  status: "draft" | "sent" | "paid",

  assume amount > 0
}

dataset TestData {
  customers: 50 of Customer,
  invoices: 200 of Invoice
}

Key Features

• Superposition — Weighted random choices with intuitive syntax
• Constraints — Hard and conditional constraints with rejection sampling
• Cross-references — Reference other records with filtered queries
• Computed fields — Aggregates, arithmetic, and derived values
• Side effects — Mutate referenced records after generation
• Plugins — Faker, edge-case generators, dates, regex patterns
• OpenAPI integration — Import schemas, validate data, populate examples
• Schema inference — Reverse-engineer schemas from JSON/CSV data