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How to Add a Composition

Add a composition to a PromptPack so a workflow state runs a declarative step graph — classify → branch → extract, or parallel fan-out → synthesize — instead of a single prompt. Compositions express procedural flows (document pipelines, data extraction, request → reason → commit) that don’t fit the turn-by-turn dialogue shape.

Prerequisites

When to Use Composition vs internal / external / hybrid

A workflow state’s orchestration value chooses the shape of its flow. composition is a fourth value alongside the three event-driven modes:

Orchestration Shape Reach for it when
internal (default) Conversational, agent emits transitions Turn-by-turn dialogue, agent loops with cycles
external Conversational, system emits transitions Human-in-the-loop, system-controlled routing
hybrid Conversational, both Mixed control dialogue
composition Procedural, step graph Fixed sequences: classify → branch → extract, parallel fan-out → synthesize, Function-mode packs

Choose composition when the flow is deterministic and procedural — the steps and their order are known up front, not driven by conversation events. Choose internal/external/hybrid when the flow is dialogue that converges over turns.

Workflow is still the universal entry point

Compositions are reached only through a workflow state. A purely procedural pack is just a one-state terminal workflow whose state is in composition mode — see the Function-mode wrapper below.

Step 1: Wire a State to a Composition

Set orchestration: composition on a state and point its composition field at a key in the top-level compositions map. A composition-mode state omits prompt_task (it’s required only for the other modes):

{
  "workflow": {
    "version": 1,
    "entry": "main",
    "states": {
      "main": {
        "orchestration": "composition",
        "composition": "analyze_document",
        "terminal": true
      }
    }
  }
}

Rules:

  • A composition-mode state must set composition and may omit prompt_task.
  • Every other state (including the default internal) must set prompt_task and must not set composition.
  • The composition value must resolve to a key in compositions.
  • composition is exclusive — don’t combine it with internal/external/hybrid semantics on the same state.

Step 2: Define the Composition

Add a top-level compositions map, keyed by name. Each composition declares a version (currently 1) and an ordered steps array. Order is logical: steps run sequentially by default; branch and parallel alter the flow. The graph must be acyclic.

{
  "compositions": {
    "analyze_document": {
      "version": 1,
      "description": "Classify a document and route to a type-specific analyzer.",
      "input_schema": "schemas/document.json",
      "output_schema": "schemas/analysis.json",
      "steps": [ /* … */ ]
    }
  }
}

Optional fields: input_schema / output_schema (references to JSON Schemas), output (the step ID whose output is the composition’s output — defaults to the last step), and engine (opaque runtime config).

Step 3: Add Steps

A composition is a directed acyclic graph of typed steps. v1 defines five kinds. Every step has a unique id (pattern ^[a-zA-Z_][a-zA-Z0-9_]*$) and a kind.

prompt — one-shot LLM call

{
  "id": "classify",
  "kind": "prompt",
  "prompt_task": "doc_classifier",
  "input": "${input.text}",
  "output_schema": "schemas/document-type.json"
}

agent — bounded LLM-tool loop

An agent step runs an LLM with a scoped tool registry until its required termination predicate is met (max_steps and/or tool_called). Without termination the step is invalid.

{
  "id": "synthesize",
  "kind": "agent",
  "prompt_task": "doc_analyzer",
  "input": "${extract_metadata.output.metadata}",
  "tools": ["doc.section_lookup", "ref.search"],
  "termination": { "max_steps": 10 },
  "output_schema": "schemas/analysis.json"
}

tool — deterministic call

The runtime calls the tool directly (not via an LLM tool-call decision):

{
  "id": "structure",
  "kind": "tool",
  "tool": "doc.parse_structure",
  "args": { "content": "${input.text}" }
}

branch — conditional

Picks then (required) or else (optional) based on a constrained predicate — see Step 4.

{
  "id": "route",
  "kind": "branch",
  "predicate": { "path": "${classify.output.type}", "op": "equals", "value": "research_paper" },
  "then": "extract_paper",
  "else": "extract_general"
}

parallel — fan-out + reduce

See Step 5.

Step input bindings

Steps wire together with ${...} references — a strict subset of the template-variable system, with no expressions:

  • ${input.X} — the composition’s structured input.
  • ${stepId.output.X} — a prior step’s structured output.

Step 4: Gate Flow with Predicates

branch predicates use a constrained, declarative vocabulary — not an expression language. A predicate is exactly one of:

  • Compare — { path, op, value } with op ∈ equals, not_equals, in, not_in, less_than, less_than_or_equals, greater_than, greater_than_or_equals.
  • Exists — { path, exists: true|false }.
  • Combinators — { all_of: [...] } (AND), { any_of: [...] } (OR), { not: ... }.
{
  "id": "needs_deep_review",
  "kind": "branch",
  "predicate": {
    "any_of": [
      { "path": "${assess.output.confidence}", "op": "less_than", "value": 0.8 },
      { "path": "${assess.output.complexity}", "op": "greater_than", "value": 7 }
    ]
  },
  "then": "deep_review",
  "else": "quick_summary"
}

Lifting complex conditions

Need a condition the predicate language can’t express? Emit a boolean from a prompt step and branch on it: { "path": "${gate.output.proceed}", "op": "equals", "value": true }. This keeps business logic out of the spec while staying declarative.

Step 5: Fan Out with parallel + reduce

A parallel step runs ≥2 branches concurrently and merges them with a required reduce:

{
  "id": "extract_metadata",
  "kind": "parallel",
  "branches": [
    { "id": "title",    "kind": "prompt", "prompt_task": "title_extractor",   "input": "${input.text}" },
    { "id": "keywords", "kind": "prompt", "prompt_task": "keyword_extractor", "input": "${input.text}" },
    { "id": "structure","kind": "tool",   "tool": "doc.parse_structure", "args": { "content": "${input.text}" } }
  ],
  "reduce": { "strategy": "barrier", "into": "metadata" }
}

Reducer strategies:

  • append — extend lists across branch outputs.
  • replace — last write wins.
  • barrier — collect every branch output into a named map.

The merged result lands under into, read downstream as ${extract_metadata.output.metadata}.

Step 6 (optional): Add Modifiers

Any step can carry modifiers:

{
  "id": "synthesize",
  "kind": "agent",
  "prompt_task": "doc_analyzer",
  "termination": { "max_steps": 10 },
  "modifiers": {
    "retry": { "max_attempts": 2 },
    "eval": ["analysis_quality"]
  }
}
  • retry — { "max_attempts": N } retry budget for the step.
  • eval — references to keys in the pack’s evals object (RFC 0006); runtimes may run them inline or post-Send.

Function-Mode: the One-State Wrapper Idiom

A pack that is purely procedural — no dialogue — is just a one-state terminal workflow whose state is in composition mode. This six-line wrapper is the standard idiom for invoking a pack programmatically (input → structured output):

{
  "workflow": {
    "version": 1,
    "entry": "main",
    "states": {
      "main": { "orchestration": "composition", "composition": "analyze_document", "terminal": true }
    }
  }
}

The workflow state machine stays the universal entry point; the composition does the procedural work.

Complete Example

A document analyzer that classifies and routes to a type-specific extractor:

{
  "$schema": "https://promptpack.org/schema/v1.5.0/promptpack.schema.json",
  "id": "document-analyzer",
  "name": "Document Analyzer",
  "version": "1.0.0",
  "template_engine": { "version": "v1", "syntax": "{{variable}}" },
  "prompts": {
    "doc_classifier": {
      "id": "doc_classifier", "name": "Classifier", "version": "1.0.0",
      "system_template": "Classify the document. Return JSON: { \"type\": \"research_paper\" | \"general\" }.\n\n{{input}}",
      "parameters": { "temperature": 0.0 }
    },
    "research_paper_extractor": {
      "id": "research_paper_extractor", "name": "Paper Extractor", "version": "1.0.0",
      "system_template": "Extract title, authors, abstract, findings.\n\n{{input}}",
      "parameters": { "temperature": 0.2 }
    },
    "general_doc_extractor": {
      "id": "general_doc_extractor", "name": "General Extractor", "version": "1.0.0",
      "system_template": "Extract a summary and main entities.\n\n{{input}}",
      "parameters": { "temperature": 0.2 }
    }
  },
  "workflow": {
    "version": 1,
    "entry": "main",
    "states": {
      "main": { "orchestration": "composition", "composition": "analyze_document", "terminal": true }
    }
  },
  "compositions": {
    "analyze_document": {
      "version": 1,
      "description": "Classify a document and route to a type-specific analyzer.",
      "steps": [
        { "id": "classify", "kind": "prompt", "prompt_task": "doc_classifier", "input": "${input.text}" },
        {
          "id": "route", "kind": "branch",
          "predicate": { "path": "${classify.output.type}", "op": "equals", "value": "research_paper" },
          "then": "extract_paper", "else": "extract_general"
        },
        { "id": "extract_paper",   "kind": "prompt", "prompt_task": "research_paper_extractor", "input": "${input.text}" },
        { "id": "extract_general", "kind": "prompt", "prompt_task": "general_doc_extractor",   "input": "${input.text}" }
      ]
    }
  }
}

Validation Checklist

  • The composition-mode state sets composition and omits prompt_task
  • Every non-composition state still sets prompt_task and omits composition
  • The state’s composition resolves to a key in compositions
  • Each Step.id is unique within the composition (including nested parallel.branches) and matches ^[a-zA-Z_][a-zA-Z0-9_]*$
  • Every prompt_task, tool, eval, and then/else/depends_on reference resolves
  • Every ${...} path resolves to input or a prior step’s output
  • Each agent step has a termination predicate
  • Each parallel step has ≥2 branches and a reduce
  • Each branch predicate uses the constrained shape (no free-form expressions)
  • The composition graph is acyclic
  • Pack validates against the v1.5 JSON schema

Common Mistakes

  • Missing termination on an agent step: agent steps require it — without it the loop is unbounded and the pack fails validation.
  • Single-branch parallel: a parallel needs at least two branches and a reduce.
  • Expression in a predicate: ${a < 0.8 || b > 7} is not valid. Use a compare/combinator predicate, or lift the logic into a prompt step that emits a boolean.
  • Cyclic graph: if you need a loop, encode it at the workflow state-machine layer (on_event + max_visits), not inside a composition.

Next Steps