Upstash Workflow Implementation Guide

Standard patterns for implementing Upstash Workflow + QStash async workflows in the LobeHub codebase.

🎯 The Three Core Patterns

Every workflow in LobeHub combines these three patterns. They exist because the platform constrains you in three ways: rate limits make blind fan-out dangerous, step limits cap a single workflow's size, and idempotency demands that retries don't double-process.

1. 🔍 Dry-Run Mode — get statistics without triggering actual execution
2. 🌟 Fan-Out Pattern — split large batches into smaller chunks for parallel processing
3. 🎯 Single Task Execution — each workflow execution processes exactly ONE item

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Architecture Overview

All workflows follow the same 3-layer architecture:

Layer 1: Entry Point (process-*)
  ├─ Validates prerequisites
  ├─ Calculates total items to process
  ├─ Filters existing items
  ├─ Supports dry-run mode (statistics only)
  └─ Triggers Layer 2 if work is needed

Layer 2: Pagination (paginate-*)
  ├─ Handles cursor-based pagination
  ├─ Implements fan-out for large batches
  ├─ Recursively processes all pages
  └─ Triggers Layer 3 for each item

Layer 3: Single Task Execution (execute-* / generate-*)
  └─ Performs actual business logic for ONE item

Real examples in this codebase: topicAutoSummary, agentEvalRun — see references/examples.md.

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The Three Patterns in 60 Seconds

1. Dry-Run Mode

Short-circuit Layer 1 before any side effects so callers can preview what would happen:

if (dryRun) {
  return {
    ...result,
    dryRun: true,
    message: `[DryRun] Would process ${itemsNeedingProcessing.length} items`,
  };
}

Use case: check how many items will be processed before committing.

2. Fan-Out Pattern

Layer 2 splits oversized batches into chunks and recursively re-triggers itself with each chunk. This avoids hitting workflow step limits when one page contains too many items:

const CHUNK_SIZE = 20;

if (itemIds.length > CHUNK_SIZE) {
  const chunks = chunk(itemIds, CHUNK_SIZE);
  await Promise.all(
    chunks.map((ids, idx) =>
      context.run(`workflow:fanout:${idx + 1}/${chunks.length}`, () =>
        WorkflowClass.triggerPaginateItems({ itemIds: ids }),
      ),
    ),
  );
}

Defaults: PAGE_SIZE = 50 (items per page), CHUNK_SIZE = 20 (items per fan-out chunk).

3. Single Task Execution

Layer 3 always processes exactly one item per invocation. Parallelism comes from Layer 2 fanning out to many Layer 3 invocations, controlled by flowControl:

app.post(
  '/execute-item',
  serve<ExecutePayload>(
    async (context) => {
      const { itemId } = context.requestPayload ?? {};
      if (!itemId) return { success: false, error: 'Missing itemId' };

      const item = await context.run('workflow:get-item', () => getItem(itemId));
      const result = await context.run('workflow:execute', () => processItem(item));
      await context.run('workflow:save', () => saveResult(itemId, result));

      return { success: true, itemId, result };
    },
    {
      flowControl: { key: 'workflow.execute', parallelism: 10, ratePerSecond: 5 },
    },
  ),
);

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File Structure

src/app/(backend)/api/workflows/
└── [[...route]]/route.ts             # Single catch-all — forwards every request to the Hono app below

apps/server/src/router-hono/workflows/
├── index.ts                          # Mounts each workflow's Hono app at /api/workflows/{workflow-name}
└── {workflow-name}/
    ├── index.ts                       # Hono app — one `app.post('/{layer}', serve(handler, options))` per layer
    ├── dispatch.ts / paginate-*.ts    # Layer 1 + 2 handler(s) — entry point, dry-run, pagination, fan-out
    └── execute.ts / execute-*.ts      # Layer 3 handler — single-task execution

apps/server/src/workflows/
└── {workflowName}/
    └── index.ts                       # Workflow class — static trigger*() methods that POST to the routes above

Every layer is a handler mounted on a per-workflow Hono app under apps/server/src/router-hono/workflows/; the Next.js route under src/app only dispatches into it.

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Where to Go Next

Pick the reference that matches what you're doing:

You want to...	Read
Write the Workflow class + 3 routes from scratch	references/implementation.md
Tune flowControl, error handling, logging, testing	references/best-practices.md
See two real workflows end-to-end	references/examples.md
Deploy on lobehub-cloud (re-exports, cloud-only ops)	references/cloud.md

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Environment Variables

# Required for all workflows
APP_URL=https://your-app.com # Base URL for workflow endpoints
QSTASH_TOKEN=qstash_xxx      # QStash authentication token

# Optional (for custom QStash URL)
QSTASH_URL=https://custom-qstash.com

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Checklist for New Workflows

Planning

• Identify the entity to process (users, agents, items, …)
• Define the per-item business logic
• Determine filtering logic (Redis cache, database state, …)

Implementation

• Define payload types with TypeScript interfaces
• Create workflow class with static trigger methods
• Layer 1: entry point with dry-run support
• Layer 1: filtering logic to avoid duplicate work
• Layer 2: pagination with fan-out
• Layer 3: single-task execution (ONE item per run)
• Configure appropriate flowControl for each layer
• Consistent logging with workflow prefixes
• Validate all required payload parameters
• Unique context.run() step names

Quality & Deployment

• Return consistent response shapes
• Configure cloud deployment (references/cloud.md if on lobehub-cloud)
• Write integration tests (dryRun path + full path)
• Smoke-test with dry-run first
• Test with a small batch before full rollout

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Additional Resources

• Upstash Workflow Documentation
• QStash Documentation
• Example Workflows in Codebase
• Workflow Classes