Recipes & Production

import schema from "std/schema"
import num    from "std/numeric"

schema LineItem {
  description: string | null @desc("Description of the goods or service")
  quantity: float | null  @desc("Quantity sold")
  unit_price: float | null  @desc("Unit price")
  total: float | null  @desc("Line item total")

  where quantity == null || unit_price == null || total == null || num.approximatelyEqual(quantity * unit_price, total, 0.01)
    @feedback("quantity * unit_price does not match total")
}

schema MetaEntry {
  key: string
  value: string
}

schema Document
  @desc("Structured data extracted from a document")
  @prompt("Produce JSON matching the Document schema from the text. Use the field descriptions as instructions. Do not invent values you cannot find; use null in nullable fields and [] in array fields. Keep information that does not fit the schema in meta.") {
  doc_number: string | null @desc("Document number")
  items: LineItem[]    @desc("Every line item; do not skip any")
  meta: MetaEntry[]   @desc("Information that does not fit the schema fields")
}

agent Extractor {
  model: TextModel
  retry: 2

  fn extract(text: string) -> Document {
    instructions = schema.prompt(Document)
    result       = ask "{instructions}\n\nTEXT:\n{text}" -> Document
    return result
  }
}

schema Classification {
  category: enum["invoice", "receipt", "contract", "other"]
  rationale: string @length(10, 200)
  confidence: float [0..1]
}

run_policy Consensus {
  strategy: "vote"
  models: [Primary, Backup, Reviewer]
  quorum: 2
  tie: "highest_confidence"
}

agent Classifier {
  run_policy: Consensus
  temperature: 0

  fn classify(text: string) -> Classification {
    ask "Determine the document type:\n{text}" -> Classification
  }
}

schema Invoice {
  invoice_number: string @desc("Invoice number")
  total: float  @desc("Grand total including tax")
}

schema Receipt {
  reference: string @desc("Receipt reference")
  paid_at: string @desc("Payment time") @fmt("date-time")
}

schema Document = Invoice | Receipt tagged by kind

schema Detection {
  kind: enum["Invoice", "Receipt"] @desc("The member schema name")
  confidence: float [0..1]
}

schema Summary {
  line: string
}

agent Detector {
  temperature: 0
  fn detect(text: string) -> Detection {
    ask "Which document type is this?\n\nTEXT:\n{text}" -> Detection
  }
}

agent Extractor {
  retry: 2
  fn extract(kind: string, text: string) -> Document {
    switch kind {
      case "Invoice" { ask "Extract the invoice fields:\n{text}" -> Invoice }
      default { ask "Extract the receipt fields:\n{text}" -> Receipt }
    }
  }
}

agent Reporter {
  fn report(document: Document) -> Summary {
    match document {
      case Invoice invoice {
        return { line: invoice.invoice_number }
      }
      case Receipt receipt {
        return { line: receipt.reference }
      }
    }
  }
}

input text: string

detector  = Detector()
extractor = Extractor()
reporter  = Reporter()

detection = detector.detect(text)
document  = extractor.extract(detection.kind, text)
summary   = reporter.report(document)

schema Invoice {
  invoice_number: string @desc("Invoice number")
  total: float @desc("Grand total including tax")
}

schema Receipt {
  reference: string @desc("Receipt reference")
  paid_at: string @desc("Payment time") @fmt("date-time")
}

schema Document = Invoice | Receipt tagged by kind

schema Extraction<T> {
  data: T
  confidence: float [0..1] @desc("How certain this extraction is, 0 to 1")
  sources: string[] @desc("Ids of the context documents the values were read from")
}

schema Summary {
  line: string
  confidence: float
}

agent Extractor {
  model: TextModel
  retry: 2

  fn extract(text: string) -> Extraction<Document> {
    ask "Extract the document and say how certain you are.\n\nTEXT:\n{text}" -> Extraction<Document>
  }
}

agent Reporter {
  fn report(extraction: Extraction<Document>) -> Summary {
    document: Document = extraction.data
    match document {
      case Invoice invoice {
        return { line: invoice.invoice_number, confidence: extraction.confidence }
      }
      case Receipt receipt {
        return { line: receipt.reference, confidence: extraction.confidence }
      }
    }
  }
}

input text: string

extractor = Extractor()
reporter = Reporter()

extraction = extractor.extract(text)
summary = reporter.report(extraction)

schema Invoice {
  invoice_number: string @desc("Invoice number")
  total: float @desc("Grand total including tax")
}

schema Confidence {
  score: float [0..1] @desc("How certain the extraction is")
  reason: string @desc("Why that score was given")
}

schema Customer {
  name: string @desc("Customer name")
}

schema Pair<A, B> {
  left: A
  right: B
}

agent Desk {
  fn read(document: string) -> Pair<Invoice, Confidence> {
    ask "Extract the invoice and state how certain you are.\n\nDOCUMENT:\n{document}" -> Pair<Invoice, Confidence>
  }

  fn attribute(document: string) -> Pair<Invoice, Customer> {
    ask "Extract the invoice and the customer it belongs to.\n\nDOCUMENT:\n{document}" -> Pair<Invoice, Customer>
  }
}

agent TechnicalAnalyst {
  role: "Extract the technical findings from the document."
  retry: 2
  fn examine(topic: string, document: string) -> TechnicalAnalysis {
    ask "Extract the technical findings for {topic}.\n\nDOCUMENT:\n{document}" -> TechnicalAnalysis
  }
}

agent RiskAnalyst {
  role: "Classify the risk based on the document."
  retry: 2
  fn assess(topic: string, document: string) -> RiskAssessment {
    ask "Assess the risk for {topic}.\n\nDOCUMENT:\n{document}" -> RiskAssessment
  }
}

agent Editor {
  role: "Merge the independent analyses into a single report."
  fn merge(analyses: AnalysisBundle) -> Report {
    ask "Merge the analyses; do not add new information.\n\n{analyses}" -> Report
  }
}

technicalAgent = TechnicalAnalyst()
riskAgent      = RiskAnalyst()
editor         = Editor()

analyses = parallel on_error continue limit 2 timeout 60000 -> AnalysisBundle {
  technical = technicalAgent.examine(topic, document)
  risk      = riskAgent.assess(topic, document)
}

report = editor.merge(analyses)

agent Accountant {
  tools: [calculate]

  fn execute(a: float, b: float) -> Explanation {
    // Guaranteed: it does not depend on a model decision
    result = tool calculate({ a: a, b: b, operation: "add" })

    // The model only interprets, it does not calculate
    explanation = ask "Explain this result in one sentence: {result}" -> Explanation
    return explanation
  }
}

import agent from "std/agent"

agent FaultTolerant {
  tools: [http_get, cache_read]
  tool_failure_limit: 3

  fn fetch(url: string) -> Content {
    try {
      live = tool http_get({ url: url })
      return { source: "live", body: live.body }
    } catch error {
      if agent.isRetryable(error) {
        cached = tool cache_read({ url: url })
        return { source: "cache", body: cached.body }
      }
      return { source: "error", body: agent.errorMessage(error) }
    }
  }
}

cursor = pipeline.loadCursor()              // state.read; null on the first run -> default
rows = query NewOrders(cursor.since)         // typed rows, $1 binding, schema-validated
prepared = pipeline.prepare(rows)            // col.sortBy + col.groupBy + totals

classified = parallel over prepared.ordered as order limit 2 -> Classified[] {
  result = pipeline.classify(order)          // ask; per-element schema validation
  return result
}

written = command RecordSummary("east", prepared.east_total)
result = pipeline.summarize(prepared, classified, written)   // state.write new watermark

model Primary {
  provider: "openai-compatible"
  base_url: "https://gateway-a.example/v1"
  model: "text-model-l"
  api_key: getEnv("api_key")
  timeout: 30000
}

model Backup {
  provider: "anthropic"
  base_url: "https://gateway-b.example/v1"
  model: "text-model-m"
  api_key: getEnv("anthropic_key")
  timeout: 30000
}

run_policy Resilient {
  strategy: "fallback"
  models: [Primary, Backup]
  on: ["timeout", "rate_limit", "provider_error", "empty_content"]
  max_attempts: 2
}

agent Production {
  model: Primary
  run_policy: Resilient
  retry: 2
  timeout: 30000
  orchestration_timeout: 120000
  token_budget: 50000
  tool_failure_limit: 3
  tool_policy: "deny_write"
}

datasource Warehouse {
  credential: "PG_URL"
  timeout: 30000        // mandatory: there is no default deadline
  retry: 2              // query only — a command is never retried
  pool_max: 4           // in-flight ceiling; the default is 1
}

classified = parallel over rows as row on_error continue limit 4 -> Classified[] {
  return grader.grade(row)
}

npm test                 # 550 tests — no gateway required
npm run test:live-model  # requires a real gateway, optional

test "line item total must be consistent" {
  expect LineItem { quantity: 2, unit_price: 5, total: 10 } valid
  expect LineItem { quantity: 2, unit_price: 5, total: 16 } invalid "does not match"
}

schema Reading {
  celsius: float @valid(x => x > -274) @severity("warn")
  sensor: string @valid(x => startsWith(x, "S-"))
}

test "soft rules record findings instead of failing" {
  expect Reading { celsius: -300, sensor: "S-1" } finding "x => x > -274"
  expect Reading { celsius: 21.5, sensor: "S-1" } valid
}

test "hard rules on the same schema still refuse" {
  expect Reading { celsius: 21.5, sensor: "X-1" } invalid "does not satisfy its @valid rule"
}

test "a clean reading records nothing" {
  expect Reading { celsius: 21.5, sensor: "S-1" } clean
}

test "a shaded reading is valid but not clean" {
  expect Reading { celsius: -300, sensor: "S-1" } valid
  expect Reading { celsius: -300, sensor: "S-1" } finding "x => x > -274"
}

luma program.luma --record /tmp/run.json   # calls the gateway once
luma program.luma --replay /tmp/run.json   # no request at all

luma program.luma --fmt | diff -u program.luma -   # no output = formatted

for f in luma/*.luma luma/std/*.luma; do
  luma "$f" --fmt | diff -q - "$f" > /dev/null || { echo "unformatted: $f"; exit 1; }
done

lumac program.luma -o out.ts     # one self-contained TypeScript file
bun out.ts

lumac program.luma --compile     # or go one step further: an executable
./program

luma program.luma --all --trace-file trace.json --state-file .state.json