workflow-engine

name	workflow-engine
description	Guide for @bratsos/workflow-engine - a type-safe workflow engine with AI integration, stage pipelines, and persistence. Use when building multi-stage workflows, AI-powered pipelines, implementing workflow persistence, defining stages, or working with batch AI operations. For upgrades between published versions, route through `migrations/README.md`.
license	MIT
metadata	{"author":"bratsos","version":"0.4.0","repository":"https://github.com/bratsos/workflow-engine"}

Upgrading between versions

When the user wants to upgrade @bratsos/workflow-engine in their project (e.g., "upgrade my project to the latest workflow-engine version", "I just bumped workflow-engine, walk me through the migration"), route to migrations/README.md — the upgrade router. It explains how to detect the installed and previous versions, find the relevant migration guides, and apply them in order. Multi-version upgrades (e.g., 0.6 → 0.8) load and apply multiple migration files sequentially.

@bratsos/workflow-engine Skill

Type-safe workflow engine for building AI-powered, multi-stage pipelines with persistence and batch processing support. Uses a command kernel architecture with environment-agnostic design.

Architecture Overview

The engine follows a kernel + host pattern:

• Core library (@bratsos/workflow-engine) - Command kernel, stage/workflow definitions, persistence adapters
• Node Host (@bratsos/workflow-engine-host-node) - Long-running worker with polling loops and signal handling
• Serverless Host (@bratsos/workflow-engine-host-serverless) - Stateless single-invocation for edge/lambda/workers
• Remote Host (@bratsos/workflow-engine-host-remote) - Credential-free remote activity workers: run a stage's execute() on a separate, disposable machine (no DB connection, no root object-store credentials)

The kernel is a pure command dispatcher. All workflow operations are expressed as typed commands dispatched via kernel.dispatch(). Hosts wrap the kernel with environment-specific process management.

When to Apply

• User wants to create workflow stages or pipelines
• User mentions defineStage, defineAsyncBatchStage, WorkflowBuilder
• User is implementing workflow persistence with Prisma
• User needs AI integration (generateText, generateObject, embeddings, batch)
• User is building multi-stage data processing pipelines
• User mentions kernel, command dispatch, or job execution
• User wants to set up a Node.js worker or serverless worker
• User wants to run a stage on a separate / remote / disposable machine, or mentions credential-free workers, defineRemoteStage, the ActivityExecutor port, or offloading heavy stages (transcoding, ffmpeg, batch inference)
• User wants to rerun a workflow from a specific stage
• User needs to test workflows with in-memory adapters

Quick Start

import { defineStage, defineWorkflow } from "@bratsos/workflow-engine";
import { createKernel } from "@bratsos/workflow-engine/kernel";
import { createNodeHost } from "@bratsos/workflow-engine-host-node";
import {
  createPrismaWorkflowPersistence,
  createPrismaJobQueue,
} from "@bratsos/workflow-engine";
import { z } from "zod";

// 1. Define a stage
const processStage = defineStage({
  id: "process",
  name: "Process Data",
  schemas: {
    input: z.object({ data: z.string() }),
    output: z.object({ result: z.string() }),
    config: z.object({ verbose: z.boolean().default(false) }),
  },
  async execute(ctx) {
    return { output: { result: ctx.input.data.toUpperCase() } };
  },
});

// 2. Build a workflow
const workflow = defineWorkflow({
  : ,
  : ,
  : ,
  : z.({ : z.() }),
})
  .(processStage)
  .();


 kernel = ({
  : (prisma),
  : myBlobStore,
  : (prisma),
  : myEventSink,
  : { :   () },
  : { :  (id ===  ? workflow : ) },
});


 host = ({
  kernel,
  : (prisma),
  : ,
});
 host.();


 kernel.({
  : ,
  : crypto.(),
  : ,
  : { :  },
});

Core Exports Reference

Export	Type	Import Path	Purpose
defineStage	Function	@bratsos/workflow-engine	Create sync stages. Curried form defineStage<TContext>()({...}) is recommended when you need typed ctx.require()/ctx.optional() — see 01-stage-definitions.md
defineAsyncBatchStage	Function	@bratsos/workflow-engine	Create async/batch stages
defineWorkflow	Function	@bratsos/workflow-engine	Recommended way to build a workflow (options-object API, v0.11+); returns a WorkflowBuilder to .pipe()/.parallel()/.build()
WorkflowBuilder	Class	@bratsos/workflow-engine	Chain stages into workflows. Its 5-positional-argument constructor (new WorkflowBuilder(id, name, description, input, output)) is @deprecated in favor of defineWorkflow() — the class itself (and .pipe()/.parallel()/.build()) is unaffected
createKernel	Function	@bratsos/workflow-engine/kernel	Create command kernel
createNodeHost	Function	@bratsos/workflow-engine-host-node	Create Node.js host
createServerlessHost	Function	@bratsos/workflow-engine-host-serverless	Create serverless host
defineRemoteStage / createActivityWorker	Function	@bratsos/workflow-engine-host-remote	Run a stage on a credential-free remote worker (see 11-remote-activity-workers.md)
createRoutingExecutor / createLocalExecutor

Kernel Commands

All operations go through kernel.dispatch(command):

Command	Description
run.create	Create a new workflow run
run.claimPending	Claim pending runs, enqueue first-stage jobs
run.transition	Advance to next stage group or complete
run.cancel	Cancel a running workflow (authoritative: cascades to stages + jobs)
run.rerunFrom	Rerun from a specific stage (cleans up blob artifacts by prefix; accepts an optional idempotencyKey, v0.11+)
job.execute	Execute a single stage (uses multi-phase transactions; see 08-common-patterns.md)
stage.pollSuspended	Poll suspended stages for readiness (skips cancelled runs; per-stage transactions)
lease.reapStale	Release stale job leases
run.reapStuck	Detect and fail RUNNING runs with no recent activity
outbox.flush	Publish pending outbox events
plugin.replayDLQ	Replay dead-letter queue events

Stage Definition

Sync Stage

const myStage = defineStage({
  id: "my-stage",
  name: "My Stage",
  description: "Optional",
  dependencies: ["prev"],

  schemas: {
    input: InputSchema,     // Zod schema or "none"
    output: OutputSchema,
    config: ConfigSchema,
  },

  async execute(ctx) {
    const { input, config, workflowContext } = ctx;
    const prevOutput = ctx.require("prev");
    const optOutput = ctx.optional("other");

    await ctx.log("INFO", "Processing...");

    return {
      output: { ... },
      customMetrics: { itemsProcessed: 10 },
    };
  },
});

Async Batch Stage

const batchStage = defineAsyncBatchStage({
  id: "batch-process",
  name: "Batch Process",
  mode: "async-batch",
  schemas: { input: "none", output: OutputSchema, config: ConfigSchema },

  async execute(ctx) {
    if (ctx.resumeState) {
      return { output: ctx.resumeState.cachedResult };
    }

    const batchId = await submitBatchJob(ctx.input);
    return {
      suspended: true,
      state: {
        batchId,
        submittedAt: new Date().toISOString(),
        pollInterval: 60000,
        maxWaitTime: 3600000,
      },
      pollConfig: { pollInterval: 60000, maxWaitTime: 3600000, nextPollAt: new Date(Date.now() + 60000) },
    };
  },

  async () {
     status =  (suspendedState.);
     (status === )  { : , : { results } };
     (status === )  { : , :  };
     { : , :  };
  },
});

WorkflowBuilder / defineWorkflow

Workflows are linear pipelines of execution groups. .pipe() creates single-stage groups; .parallel() creates multi-stage groups. Parallel group outputs are keyed by stage ID in the workflow context.

Build with defineWorkflow({...}) (recommended) — the 5-positional-argument new WorkflowBuilder(id, name, description, input, output) constructor is @deprecated (same-typed positional args are easy to transpose by accident); both return the same builder for .pipe()/.parallel()/.build().

const workflow = defineWorkflow({
  id: "workflow-id",
  name: "Workflow Name",
  description: "Description",
  input: InputSchema,
  // output is optional -- decorative unless the workflow has zero piped stages
})
  .pipe(stage1)                          // Group 0
  .pipe(stage2)                          // Group 1
  .parallel([stage3a, stage3b])          // Group 2 (concurrent, output: { "stage3a-id": ..., "stage3b-id": ... })
  .pipe(stage4)                          // Group 3
  .build();

// In stage4, access parallel outputs by stage ID:
ctx.require("stage3a-id")  // output of stage3a
ctx.require("stage3b-id")  // output of stage3b

workflow.getStageIds();
workflow.getExecutionPlan();
workflow.getDefaultConfig();
workflow.validateConfig(config);

When a workflow completes, the final execution group's output is persisted in WorkflowRun.output and included in the workflow:completed event.

Kernel Setup

import { createKernel } from "@bratsos/workflow-engine/kernel";
import type { Kernel, KernelConfig, Persistence, BlobStore, JobTransport, EventSink, Clock } from "@bratsos/workflow-engine/kernel";

const kernel = createKernel({
  persistence,   // Persistence port - runs, stages, logs, outbox, idempotency
  blobStore,     // BlobStore port - large payload storage
  jobTransport,  // JobTransport port - job queue
  eventSink,     // EventSink port - async event publishing
  clock,         // Clock port - injectable time source
  registry,      // WorkflowRegistry - { getWorkflow(id) }
  // executor,   // optional ActivityExecutor port - defaults to in-process; inject to run stages on remote workers (see 11-remote-activity-workers.md)
  // scheduler,  // optional Scheduler port - @deprecated, unused by the kernel (zero schedule()/cancel() call sites); omit it, the kernel supplies its own no-op. Removal at 1.0
  // idempotencyStaleInProgressMs: 10 * 60 * 1000, // optional (v0.11+) - default 10 min; TTL before a stuck `in_progress` idempotency key can be reclaimed
});

// Dispatch typed commands
const { workflowRunId } = await kernel.dispatch({
  type: "run.create",
  idempotencyKey: "unique-key",
  workflowId: "my-workflow",
  input: { data: "hello" },
});

Node Host

import { createNodeHost } from "@bratsos/workflow-engine-host-node";

const host = createNodeHost({
  kernel,
  jobTransport,
  workerId: "worker-1",
  orchestrationIntervalMs: 10_000,
  jobPollIntervalMs: 1_000,
  staleLeaseThresholdMs: 300_000,   // default as of v0.11 (was 60_000)
  jobHeartbeatIntervalMs: 60_000,   // v0.11+: heartbeat a job's lease while it executes
});

await host.start();   // Starts polling loops + signal handlers
await host.stop();    // Graceful shutdown
host.getStats();      // { workerId, jobsProcessed, orchestrationTicks, isRunning, uptimeMs }

Serverless Host

import {
  createServerlessHost,
  type ServerlessHost,
  type ServerlessHostConfig,
  type JobMessage,
  type JobResult,
  type ProcessJobsResult,
  type MaintenanceTickResult,
} from "@bratsos/workflow-engine-host-serverless";

const host = createServerlessHost({
  kernel,
  jobTransport,
  workerId: "my-worker",
  // Optional tuning (same defaults as Node host)
  staleLeaseThresholdMs: 300_000,   // default as of v0.11 (was 60_000)
  jobHeartbeatIntervalMs: 60_000,   // v0.11+
  maxClaimsPerTick: 10,
  maxSuspendedChecksPerTick: 10,
  maxOutboxFlushPerTick: 100,
});

handleJob(msg: JobMessage): Promise<JobResult>

Execute a single pre-dequeued job. Consumers wire platform-specific ack/retry around the result.

// JobMessage shape (matches queue message body)
interface JobMessage {
  jobId: string;
  workflowRunId: string;
  workflowId: string;
  stageId: string;
  attempt: number;
  maxAttempts?: number;
  payload: Record<string, unknown>;
}

// JobResult
interface JobResult {
  outcome: "completed" | "suspended" | "failed";
  error?: string;
}

const result = await host.handleJob(msg);
if (result.outcome === "completed") msg.ack();
else if (result.outcome === "suspended") msg.ack();
else msg.retry();

processAvailableJobs(opts?): Promise<ProcessJobsResult>

Dequeue and process jobs from the job transport. Defaults to 1 job (safe for edge runtimes with CPU limits).

const result = await host.processAvailableJobs({ maxJobs: 5 });
// { processed: number, succeeded: number, failed: number }

runMaintenanceTick(): Promise<MaintenanceTickResult>

Run one bounded maintenance cycle: claim pending, poll suspended, reap stale, flush outbox, reap stuck runs.

const tick = await host.runMaintenanceTick();
// { claimed, suspendedChecked, staleReleased, eventsFlushed, stuckReaped }
// Note: resumed suspended stages are automatically followed by run.transition.

Remote Activity Workers

Run a stage's execute() on a separate, credential-free machine (no database connection, no root object-store credentials) via the @bratsos/workflow-engine-host-remote package. The orchestrator owns all state; a remote worker leases the task, runs the real stage code, writes large artifacts directly to object storage by reference, and reports back — all driven through the engine's existing suspend/resume machinery (no new DB table).

Two wiring models:

• Proxy stage (recommended for long stages): defineRemoteStage(realStage, transport, opts?) suspends immediately (releasing the kernel job lease) and resumes when the worker reports.
• ActivityExecutor port (short stages / in-core routing): inject createRemoteExecutor(transport) — or createRoutingExecutor({ remote, remoteStageIds }) to route only specific stages — via createKernel({ executor }). Backward-compatible: the default createLocalExecutor() is byte-for-byte the in-process behavior.

import { defineRemoteStage } from "@bratsos/workflow-engine-host-remote";

// Orchestrator: wrap a heavy stage so it runs on a remote worker
const workflow = defineWorkflow({ ... })
  .pipe(defineRemoteStage(heavyStage, oTransport, { maxWaitMs: 3_600_000, stageCodeVersion: "v1" }))
  .pipe(coreStage)
  .build();

The worker runs in a separate process/machine with zero standing credentials (createActivityWorker + createHttpWorkerTransport), receiving a presigned URL per artifact. See references/11-remote-activity-workers.md for the worker, broker, HTTP transport, S3/R2 artifacts, durability, and limitations.

Annotations (Provenance)

Attach typed key-value facts to runs and stages for queryable provenance — trigger context, decisions, approvals, anything else you'd want to ask back later. Writes are buffered during a stage and flushed atomically with the stage outcome (durable, not fire-and-forget).

import { Decision, Trigger } from "@bratsos/workflow-engine/conventions";

// Inside a stage's execute()
ctx.annotate(Decision.outcome, "low");                       // typed
ctx.annotate(Decision.confidence, 0.42);                     // typed
ctx.annotate("acme.compliance.signoff", "[e-mail]");   // custom key
ctx.annotate({
  actor: { kind: "agent", id: "triage-v3" },
  attributes: {
    "decision.outcome": "low",
    "decision.rationale": "below threshold",
    "decision.used_fallback": true,
  },
});

// At run creation — captures trigger context
await kernel.dispatch({
  type: "run.create",
  workflowId: "ticket-triage",
  input: { ticket },
  annotations: [{
    actor: { kind: "system", id: "zendesk" },
    attributes: {
      "trigger.source": "webhook:zendesk",
      : previousRunId,
    },
  }],
});


 kernel..(runId, {
  : { : , :  },
  : { :  },
  : ,
});


 kernel..(runId);
 kernel..(runId, { :  });
 kernel..(runId, { :  });

Annotations replace the deprecated WorkflowRun.metadata column. Existing metadata is automatically surfaced as legacy.metadata.* virtual rows when consumers call kernel.annotations.list() (no dual-write, lazy synthesis). See references/10-annotations.md for the full API and conventions catalog.

AI Integration & Cost Tracking

const ai = createAIHelper(
  `workflow.${ctx.workflowRunId}.stage.${ctx.stageId}`,
  aiCallLogger,
);

const { text, cost } = await ai.generateText("gemini-2.5-flash", prompt);
const { object } = await ai.generateObject("gemini-2.5-flash", prompt, schema);
const { embedding } = await ai.embed("text-embedding-004", ["text1"], { dimensions: 768 });
// generateText/generateObject/streamText options also accept maxRetries / abortSignal (v0.11+)
// OpenRouter embedding models (OpenAI, Cohere, etc.)
const { embedding } = await ai.embed("openai/text-embedding-3-small", ["text1"]);

// Provider-specific options passthrough (Voyage, Cohere, etc.)
const { embedding } = await ai.embed("voyage-4-large", ["text1"], {
  providerOptions: { voyage: { outputDimension: 512, inputType: "document" } },
});

// Custom embedding providers (Voyage, Cohere, Jina, etc.)
import { registerEmbeddingProvider } from "@bratsos/workflow-engine";
import { voyage } from ;
(,  voyage.(modelId));

Persistence Setup

Required Prisma Models (ALL are required)

Copy the complete schema from the package README. This includes: WorkflowRun, WorkflowStage, WorkflowLog, WorkflowArtifact, AICall, JobQueue, OutboxEvent, IdempotencyKey.

Create Persistence

import {
  createPrismaWorkflowPersistence,
  createPrismaJobQueue,
  createPrismaAICallLogger,
} from "@bratsos/workflow-engine/persistence/prisma";

const persistence = createPrismaWorkflowPersistence(prisma);
const jobQueue = createPrismaJobQueue(prisma);
const aiCallLogger = createPrismaAICallLogger(prisma);

// SQLite - MUST pass databaseType option
const persistence = createPrismaWorkflowPersistence(prisma, { databaseType: "sqlite" });
const jobQueue = createPrismaJobQueue(prisma, { databaseType: "sqlite" });

Testing

// In-memory persistence and job queue
import {
  InMemoryWorkflowPersistence,
  InMemoryJobQueue,
  InMemoryAICallLogger,
} from "@bratsos/workflow-engine/testing";

// Kernel-specific test adapters
import {
  FakeClock,
  InMemoryBlobStore,
  CollectingEventSink,
} from "@bratsos/workflow-engine/kernel/testing";

// Create kernel with all in-memory adapters
const persistence = new InMemoryWorkflowPersistence();
const jobQueue = new InMemoryJobQueue();
const kernel = createKernel({
  persistence,
  blobStore: new InMemoryBlobStore(),
  jobTransport: jobQueue,
  eventSink: new CollectingEventSink(),
  clock: new FakeClock(),
  registry: { getWorkflow: (id) => workflows.get(id) },
});

// Test a full workflow lifecycle
await kernel.dispatch({ type: "run.create", idempotencyKey: "test", workflowId: "my-wf", input: {} });
 kernel.({ : , :  });
 job =  jobQueue.();
 kernel.({ : , : job., : job., : job., : {} });
 kernel.({ : , : job. });

Implementing a custom WorkflowPersistence/JobQueue/AICallLogger adapter? Validate it with the exported conformance suites (v0.11+) instead of hand-rolling parity tests — see 07-testing-patterns.md.

Reference Files

• 01-stage-definitions.md - Complete stage API
• 02-workflow-builder.md - WorkflowBuilder patterns
• 03-kernel-host-setup.md - Kernel & host configuration
• 04-ai-integration.md - AI helper methods
• 05-persistence-setup.md - Database setup
• 06-async-batch-stages.md - Async operations
• 07-testing-patterns.md - Testing with kernel
• 08-common-patterns.md - Kernel patterns & best practices
• 09-troubleshooting.md - Debugging stuck runs, P2002 errors, ghost jobs
• 10-annotations.md - First-class provenance surface: ctx.annotate, kernel.annotations.*, conventions catalog
• 11-remote-activity-workers.md - Credential-free remote workers: defineRemoteStage, broker, worker SDK, HTTP transport, S3/R2 artifacts, ActivityExecutor port

Key Principles

1. Type Safety: All schemas are Zod - types flow through the entire pipeline
2. Command Kernel: All operations are typed commands dispatched through kernel.dispatch()
3. Environment-Agnostic: Kernel has no timers, no signals, no global state
4. Context Access: Use ctx.require() and ctx.optional() for type-safe stage output access
5. Transactional Outbox: Events written to outbox, published via outbox.flush command. job.execute and stage.pollSuspended use multi-phase transactions to avoid holding connections during external I/O
6. Idempotency: run.create, job.execute, and run.rerunFrom (v0.11+) replay cached results by key; concurrent same-key dispatch throws IdempotencyInProgressError; a key stuck in_progress past KernelConfig.idempotencyStaleInProgressMs (default 10 min, v0.11+) can be reclaimed
7. Authoritative Cancellation: run.cancel cascades to stages + jobs. Ghost jobs (running against non-RUNNING runs) are detected via ghost: true flag and not retried
8. Self-Healing: Stage creation is idempotent (upsert), orchestration steps are isolated, stuck runs are automatically reaped
9. Cost Tracking: All AI calls automatically track tokens and costs
10. BlobStore-Only Artifacts: All artifact storage goes through the BlobStore port. run.rerunFrom cleans up artifacts by key prefix
11. Durable Provenance: ctx.annotate(...) writes are buffered and flushed inside the stage-completion transaction. Annotations are atomic with the stage outcome — a stage's annotations either all persist or all roll back together with the stage update and outbox events.
12. Pluggable Execution: stage execution goes through an injectable ActivityExecutor port (default in-process LocalExecutor). Inject a remote executor — or wrap a stage with defineRemoteStage — to run execute() on a separate credential-free machine without changing kernel internals.