Build a flow

Start with the job in one sentence: what runs, in what order, and what proves each step did its job. If you can't state that proof yet, figure it out first, before wiring the step up.

Use the skill

Don't paste this page into your agent's context — install the writing-relayflows skill and let it author flows directly:

# with prpm
npx prpm install @agent-relay/writing-relayflows

# with skills.sh
npx skills add https://github.com/agentworkforce/skills --skill writing-relayflows

The rest of this page is what the skill encodes — worth reading so you can tell whether what it wrote is right.

Two ways to author the same thing

TypeScript calls the primitives imperatively, as ordinary code. YAML describes the same fixed set of steps and their dependencies as data, so flows check or a CI gate can read and validate it without running anything. Both compile down to the same journal; every sample on these pages is shown in TypeScript, with the YAML form behind the language switch.

import { flow } from '@relayflows/surface';

export default flow('hello-agent', async (f) => {
  const greeting = await f.run('printf hello');
  const edit = await f.agent('edit', {
    task: 'Produce the hello artifact.',
    cli: 'claude',
    model: 'claude-sonnet-4-6',
  });
  const finish = await f.run('printf done');
  f.done('success');
});

Reach for YAML when you want the whole flow readable at a glance and checkable in CI. Reach for TypeScript when a step's next move depends on what a previous one returned: an f.human approval, ordinary if/for logic, an f.dispatch to a child flow.

Both edit steps above name their own cli and model directly, the same way in either language (flows#310). Neither is required in TypeScript: omit them and the step falls back to the flow's cli, then the nearest flows.json's project-wide default, the same resolution Introduction covers.

The context a flow body gets

interface Ctx {
  run(command: string, options?: { timeout?: string | number }): Step<string>;   // lease: default 30s, max 15m
  llm(strings: TemplateStringsArray, ...values: unknown[]): Step<string>;
  llm(prompt: string, options: { output: JsonSchema; cli?: string; model?: string }): Step<unknown>;
  agent(name: string, options: { task: string; workspace?: string; cli?: string; model?: string }): Step<{ summary: string; artifacts: string[] }>;
  human(question: string, options: { to: string }): Promise<boolean>;
  dispatch<T>(flow: string, input: unknown): Promise<T>;
  done(reason: 'success' | 'step_failed' | 'needs_human' | 'declined'): void;
  slack: SlackHelper; github: GithubHelper; /* …every generated helper */
  memory: MemoryHelper;
  mcp: Record<string, Record<string, (args: unknown) => Step<unknown>>>;
}

This is the whole kernel-level vocabulary a step body speaks: run, llm, agent for work, human, dispatch, done for control.

done takes one of four authored verdicts. success completes the run; step_failed says the flow's own checks did not pass (the adversarial review found problems, the tests went red) and exits 1; needs_human parks the run (exit 3); declined records a deliberate decision not to act on the input — a ticket that turned out not to be work — and exits 0 with a DECLINED diagnostic. canceled and budget_exceeded are in the FlowCompletionReason type but refused at runtime with unsupported_completion: they are kernel facts, recorded when the kernel cancels a run or exhausts its budget, not verdicts a body can declare.

f.run returns the command's output; a step's .summary on f.agent is the CLI's final text. artifacts is always empty in 2.0.16 — populating it from what the agent actually wrote lands in the next release (flows#449).

Verification

Every step's exit code is checked automatically. On top of that:

• exit_code — a deterministic step's process exit code (always checked; declaring it is only meaningful on deterministic steps).
• output_contains — an opt-in string match against the step's output. Fast to write, good for a smoke check.
• json_schema on an llm step — the model's reply must parse and validate against the schema before anything downstream sees it. A schema of {} or true accepts anything and is flagged vacuous_gate.
• Named gates — references_input, regex_match, word_count_bounds, and subprocess_gate (run a shell command against the output; exit 0 passes). Each lowers to a deterministic gate step in the same journal, so a resume replays the recorded verdict instead of re-judging.

In TypeScript the same named gates attach postfix: f.agent('review', {…}).gate({ type: 'subprocess_gate', command: 'test -s review.md' }). A callback gate, .gate((r) => r.artifacts.includes('review.md')), is refused in 2.0.16 (unsupported_gate: a closure can't be journaled); the next release runs it after the step and journals the verdict (flows#449).

A step that fails its check is recorded as verification_failed, with exactly which check failed. The agent insisting it went fine doesn't override that.

Permissions and recovery

An agent step declares what it's allowed to touch — fileGlobs, accessPreset — and what happens if it crashes mid-edit. These are YAML step fields with no TypeScript equivalent today; a TypeScript body leaves recovery to the kernel's default (reset), and will reach a YAML step through f.dispatch once that verb ships (see the Note above — it doesn't execute yet in 2.0.16). Author the step in YAML now if you need permissions/recoveryMode before then:

import { flow } from '@relayflows/surface';

export default flow('hello-agent', async (f) => {
  await f.run('printf hello');

  // No recoveryMode/permissions here: a crashed attempt resets to the
  // pinned revision (the default), and the workspace is the daemon's cwd.
  await f.agent('edit', {
    task: 'Produce the hello artifact and print agent-ok when it exists.',
    cli: 'claude',
  }).gate({ type: 'regex_match', pattern: 'agent-ok' });

  f.done('success');
});

• reset (the default) — the next attempt starts fresh from the pinned workspace revision, with no half-finished edit left behind.
• inspect — the next attempt starts inside the dirty workspace, with the failed attempt's trajectory tail injected as context, and decides whether to continue or redo.
• manual — parks the run as needs_human with a diff of the pinned revision against whatever's actually there.

maxIterations caps how many attempts a step gets before one of those three outcomes has to happen. A run-level budget caps the whole flow the same way, declared once and enforced by the kernel instead of tracked by hand. Write it as "$5/run" or "$20/day" (dollars, priced from a frozen per-model table), or as { tokens?, dollars?, wallclock? } — { dollars: 5, wallclock: "45m" } bounds both. Tokens and wallclock apply to every step; dollars apply to steps whose model has a frozen price. A Codex step, which picks its own model, is reported as budget_unmetered under a dollar budget and runs; it counts toward tokens and wallclock but cannot cross the dollar limit. Crossing a limit lets the running step finish and refuses the next one with budget_exceeded.

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