Pipeline Studio

// Stage types available in the Studio palette:

// LLM / AI nodes (BaseNode subclasses):
// summarize         — FMSummarize core
// classify          — classify text into categories
// extract           — extract structured JSON from free text
// llm_call          — raw LLM call with full prompt control

// Data / IO operators (BaseOperator subclasses):
// soql_query        — SOQL SELECT with FLS enforcement
// dml_operation     — insert / update / delete with CRUD check
// http_callout      — outbound HTTP with URL allowlist
// logger            — write to Pipeline_Stage_Log__c
// variable_set      — set / update execution context variables

// Control flow (BaseFlowControl subclasses):
// for_each          — iterate over a list, fan stages out
// try_catch         — wrap stages in error recovery
// conditional       — branch based on expression result
// wait_for_signal   — pause until external event resumes
// parallel          — run multiple branches concurrently
// sub_pipeline      — embed another pipeline as a stage

// Inside any stage config value, use double-brace expressions:
// {{input.accountId}}               — from the pipeline input map
// {{stages.fetch.records[0].Name}}  — output of a previous stage
// {{env.UserInfo.userId}}           — Salesforce environment info
// {{input.tags | join(', ')}}       — pipe to a built-in function

// Supported functions: join, upper, lower, trim, length,
//                      toJson, fromJson, coalesce, if, now

// Example — conditional prompt based on stage output:
// "prompt": "{{if(stages.classify.category == 'vip', 'VIP brief:', 'Standard brief:')}} {{stages.fetch.records[0].Name}}"

{
  "id": "account-enrich-v1",
  "name": "Account Enrichment",
  "version": 1,
  "stages": [
    {
      "id": "fetch",
      "type": "soql_query",
      "category": "operator",
      "config": {
        "query": "SELECT Id, Name, Industry, AnnualRevenue FROM Account WHERE Id = '{{input.accountId}}'"
      }
    },
    {
      "id": "summarize",
      "type": "summarize",
      "category": "llm",
      "depends_on": ["fetch"],
      "config": {
        "prompt": "Summarize this account for an enterprise AE: {{stages.fetch.records[0].Name}}, {{stages.fetch.records[0].Industry}}",
        "max_tokens": 300,
        "provider": "anthropic"
      }
    },
    {
      "id": "save",
      "type": "dml_operation",
      "category": "operator",
      "depends_on": ["summarize"],
      "config": {
        "operation": "update",
        "sobject": "Account",
        "record": {
          "Id": "{{input.accountId}}",
          "Description": "{{stages.summarize.content}}"
        }
      }
    }
  ],
  "output_stage_id": "summarize"
}

// The Studio runs validation automatically on save.
// You can also call it from Apex:
List<PipelineValidator.Issue> issues = PipelineValidator.validate(configJson);

for (PipelineValidator.Issue issue : issues) {
    System.debug(issue.severity + ': ' + issue.message);
    // severity: 'error' | 'warning'
    // Errors block Run and Publish. Warnings allow save.
}

Boolean hasErrors = PipelineValidator.hasErrors(issues);

// Common errors:
// - cycles in depends_on (stage A depends on stage B which depends on A)
// - output_stage_id refers to a stage that doesn't exist
// - duplicate stage ids
// - unknown depends_on references (when strictDependencyResolution = true)

// Export a pipeline from Studio as SFDX metadata:
// Studio → pipeline → ... → Download as Metadata

// Or use the Apex API:
String metadataXml = PipelineBuilderController.getMetadataDownload(pipelineId);

// The output is a FlowMasonPipeline__mdt-compatible XML file.
// Deploy it with:
// sf project deploy start --target-org MyOrg --source-dir force-app