queue

name	queue
description	Guide for job queue patterns in multi-agent coordination. Use when deciding between background jobs vs inline execution, submitting long-running tasks, monitoring job progress, and handling failures. Covers when to queue, job priority, retry strategies, and monitoring patterns.
tags	["queue","background-jobs","distributed-work","job-processing","reliability"]

Queue Skill

Reliable job processing for multi-agent workflows using BullMQ and Redis.

When to Use Background Jobs (Queue)

Queue jobs when:

• Long-running operations (>500ms) - Embedding generation, PDF processing, data analysis
• Resource-intensive work - ML inference, image transcoding, complex computations
• Fault tolerance matters - Can fail and retry without blocking the caller
• Scaling needed - Process multiple jobs in parallel with workers
• Async is acceptable - Caller doesn't need immediate result
• Rate limiting required - Control throughput with concurrency settings
• Workflow coordination - Chain tasks or wait for results asynchronously

When NOT to Use Background Jobs

Use inline execution when:

• Sub-100ms operations - Simple data transforms, validation, cache lookups
• Need immediate result - Caller blocks waiting for response
• No failure handling needed - Single request, no retry logic
• Stateless one-shots - No persistence or monitoring required

Job Queue API

Creating a Queue

import { createSwarmQueue } from 'swarm-queue';

const queue = createSwarmQueue({
  name: 'embeddings',
  connection: {
    host: process.env.REDIS_HOST || 'localhost',
    port: parseInt(process.env.REDIS_PORT || '6379'),
  },
  defaultJobOptions: {
    attempts: 3,
    backoff: {
      type: 'exponential',
      delay: 2000, // Start at 2 seconds, exponential backoff
    },
    removeOnComplete: true, // Clean up completed jobs
  },
});

Submitting Jobs

// Basic job
const jobId = await queue.addJob('generate-embedding', {
  text: 'Hello, world!',
  model: 'text-embedding-3-small',
});

// With priority (0=urgent, 1=high, 2=normal, 3=low)
const jobId = await queue.addJob(
  'agent-task',
  { agentId: 'worker-1', task: 'analyze' },
  { priority: 1 } // Process before normal jobs
);

// Delayed job (start processing after delay)
const jobId = await queue.addJob(
  'retry-agent',
  { attempt: 2 },
  { delay: 30000 } // Process in 30 seconds
);

// With custom retry strategy
const jobId = await queue.addJob(
  'webhook-call',
  { url: 'https://example.com/webhook' },
  {
    attempts: 5,
    backoff: {
      type: 'exponential',
      delay: 1000, // 1s, 2s, 4s, 8s, 16s
    },
  }
);

Checking Job Status

// Get job details
const job = await queue.getJob(jobId);
if (job) {
  console.log({
    state: await job.getState(), // 'waiting' | 'active' | 'completed' | 'failed'
    progress: job.progress(), // 0-100
    attempts: job.attemptsMade,
    failedReason: job.failedReason,
  });
}

// Get queue metrics
const metrics = await queue.getMetrics();
console.log(metrics);
// {
//   waiting: 42,    // Jobs queued, not started
//   active: 5,      // Jobs being processed
//   completed: 1000,// Successfully finished
//   failed: 3,      // Failed after retries
//   delayed: 0      // Delayed jobs
// }

Canceling Jobs

// Remove a job (works from any state)
await queue.removeJob(jobId);

Creating Workers

Workers process jobs from the queue. Start workers in separate processes or services.

import { createWorker } from 'swarm-queue';

const worker = await createWorker(
  {
    queueName: 'embeddings',
    concurrency: 4, // Process 4 jobs in parallel
    connection: {
      host: process.env.REDIS_HOST || 'localhost',
      port: parseInt(process.env.REDIS_PORT || '6379'),
    },
  },
  async (job) => {
    try {
      const { text, model } = job.data.payload;

      // Update progress
      job.updateProgress(25);

      // Do the work
      const embedding = await generateEmbedding(text, model);

      job.updateProgress(100);

      // Return result
      return {
        success: true,
        data: { embedding, dimensions: embedding.length },
      };
    } catch (error) {
      return {
        success: false,
        error: error instanceof Error ? error. : ,
      };
    }
  }
);


 worker.();


process.(,  () => {
   worker.();
   worker.();
});

Job Priority Patterns

Urgent vs Background

// Urgent: agent error requiring immediate response
await queue.addJob('notify-coordinator', { error }, { priority: 0 });

// High: task completion from other agents
await queue.addJob('merge-results', { results }, { priority: 1 });

// Normal: progress updates
await queue.addJob('update-metrics', { metrics }, { priority: 2 });

// Low: cleanup, archival
await queue.addJob('cleanup-cache', { cacheKey }, { priority: 3 });

Processing Priority in Workers

BullMQ automatically processes higher priority jobs first:

// Queue metrics will show that higher priority jobs are processed first
const metrics = await queue.getMetrics();
console.log(`Urgent jobs waiting: ${metrics.waiting}`);
// Worker will process urgent (priority 0) jobs before normal ones

Failure Handling and Retry Strategies

Exponential Backoff

const jobId = await queue.addJob('api-call', { endpoint: '/data' }, {
  attempts: 4,
  backoff: {
    type: 'exponential',
    delay: 1000, // First retry: 1s, then 2s, 4s, 8s
  },
});

Retry timeline: 1s → 2s → 4s → 8s → Failed (moves to dead-letter)

Fixed Delay

const jobId = await queue.addJob('webhook', { url }, {
  attempts: 3,
  backoff: {
    type: 'fixed',
    delay: 5000, // Always wait 5 seconds between retries
  },
});

Dead Letter Pattern

After max retries, jobs fail and are no longer retried:

// Monitor failures
const metrics = await queue.getMetrics();
if (metrics.failed > 0) {
  console.warn(`${metrics.failed} jobs have permanently failed`);
  // Log to monitoring, alert on-call, etc.
}

Monitoring and Observability

Real-Time Metrics

const metrics = await queue.getMetrics();

const queueHealth = {
  throughput: metrics.completed, // Total completed
  backlog: metrics.waiting, // Jobs not yet started
  inProgress: metrics.active, // Currently being processed
  failureRate: metrics.failed / (metrics.completed + metrics.failed),
  avgTimeInQueue: null, // Custom tracking needed
};

console.log(`Queue health: ${JSON.stringify(queueHealth, null, 2)}`);

Monitoring Best Practices

1. Track completion time: Record when jobs enter and exit the queue
2. Failure alerts: Alert when failure rate exceeds threshold
3. Backlog warnings: Warn when waiting jobs exceed capacity
4. Worker health: Monitor worker process availability
5. Job timeouts: Set reasonable timeout expectations per job type

// Example: Monitor queue health
setInterval(async () => {
  const metrics = await queue.getMetrics();
  const total = Object.values(metrics).reduce((a, b) => a + b);

  if (metrics.failed > 0.1 * total) {
    console.error('High failure rate detected');
    // Alert monitoring system
  }

  if (metrics.waiting > 1000) {
    console.warn('Large backlog detected - consider scaling workers');
  }
}, 30000);

Common Job Types

Embedding Generation

// Agent submits: Generate embeddings for documents
const jobId = await queue.addJob('generate-embedding', {
  documentId: 'doc-123',
  text: 'Full document text here...',
  model: 'text-embedding-3-small',
}, {
  priority: 2,
  attempts: 3,
  backoff: { type: 'exponential', delay: 2000 },
});

// Worker: Process embedding job
const result = await generateEmbedding(payload.text, payload.model);

PDF Processing

// Agent submits: Extract text from PDF
const jobId = await queue.addJob('process-pdf', {
  fileUrl: 'https://example.com/document.pdf',
  pages: [1, 2, 3], // Only extract specific pages
}, {
  priority: 1,
  attempts: 2,
  removeOnComplete: true,
});

// Worker: Long-running PDF processing
const text = await extractPdfText(payload.fileUrl, payload.pages);

Bulk Operations

// Agent submits: Process 1000 items in background
const jobId = await queue.addJob('bulk-update', {
  items: largeDataset,
  operation: 'transform',
}, {
  priority: 3, // Low priority, can take time
  attempts: 1, // No retry for bulk (resumability is application-specific)
});

// Worker: Stream-based processing
for (const item of payload.items) {
  await processItem(item);
  job.updateProgress(++processed / payload.items.length * 100);
}

Task Coordination with Queues

Waiting for Results

Since queue jobs are async, use polling or events for results:

// Agent A: Submit job and poll for result
const jobId = await queue.addJob('analyze-data', { data });

// Polling (simple but not ideal for long waits)
let result = null;
const maxAttempts = 60; // 60 * 5sec = 5 minutes
for (let i = 0; i < maxAttempts; i++) {
  const job = await queue.getJob(jobId);
  const state = await job?.getState();

  if (state === 'completed') {
    result = job?.returnvalue;
    break;
  }
  await new Promise(r => setTimeout(r, 5000)); // Wait 5s
}

if (result) {
  console.log('Analysis complete:', result);
} else {
  console.error('Job timeout');
}

Chaining Jobs

// Job 1: Generate embeddings
const jobId1 = await queue.addJob('generate-embedding', { text });

// Job 2: After Job 1 completes, compare embeddings
// This is application-level coordination - worker for Job 1 submits Job 2
// Worker:
if (jobResult.success) {
  const jobId2 = await queue.addJob('compare-embeddings', {
    embedding: jobResult.data.embedding,
    compareWith: otherEmbedding,
  });
}

CLI Usage (swarm queue)

The swarm CLI provides queue management commands:

# Submit a job
swarm queue submit embeddings '{"text":"hello","model":"small"}' --priority 1

# Check job status
swarm queue status embeddings job-id-123

# List queue contents
swarm queue list embeddings --state waiting --limit 10
swarm queue list embeddings --state failed

# Start a worker
swarm worker embeddings --concurrency 4

# Cleanup old jobs
swarm queue cleanup embeddings --before 7d

Error Handling Strategies

Graceful Degradation

// If job fails, provide fallback
const jobId = await queue.addJob('expensive-compute', { data });

try {
  const job = await queue.getJob(jobId);
  const state = await job?.getState();

  if (state === 'failed') {
    // Use fallback result
    return getCachedResult(data) || getDefaultResult();
  }
} catch (error) {
  // Queue unavailable - use fallback
  return getCachedResult(data);
}

Dead Letter Handling

// Monitor failed jobs and move to dead-letter handling
const metrics = await queue.getMetrics();
if (metrics.failed > 0) {
  const failedJobs = []; // Fetch failed jobs from Redis
  // Send to dead-letter queue for manual review or retrigger
  for (const job of failedJobs) {
    console.error(`Failed job ${job.id}: ${job.failedReason}`);
    // Log to monitoring system
    // Alert on-call engineer
  }
}

Performance Tuning

Concurrency Settings

// Low concurrency: Process fewer jobs in parallel (less resource use)
const worker = await createWorker(
  {
    queueName: 'embeddings',
    concurrency: 1, // One at a time (good for expensive ops)
  },
  processor
);

// High concurrency: More parallel processing (higher throughput)
const worker = await createWorker(
  {
    queueName: 'simple-tasks',
    concurrency: 16, // Max parallel (good for I/O-bound ops)
  },
  processor
);

Job Options for Performance

// Remove completed jobs to save Redis memory
await queue.addJob('cleanup', { data }, {
  removeOnComplete: true, // Don't keep history
  attempts: 1, // No retry needed
});

// Keep important jobs for auditing
await queue.addJob('agent-task', { data }, {
  removeOnComplete: false, // Retain history
  attempts: 3, // Retry on failure
});

Testing

Unit Testing Jobs

import { describe, test, expect } from 'bun:test';

describe('Queue Jobs', () => {
  test('embedding job returns valid embedding', async () => {
    const processor = async (job) => {
      // Mock job for testing
      return {
        success: true,
        data: { embedding: [0.1, 0.2, 0.3] },
      };
    };

    const result = await processor({
      data: { payload: { text: 'hello', model: 'small' } },
    });

    expect(result.success).toBe(true);
    expect(result.data.embedding).toHaveLength(3);
  });
});

Integration Testing with Real Queue

test('job completes successfully in queue', async () => {
  const queue = createSwarmQueue({ name: 'test-queue' });

  const jobId = await queue.addJob('test-job', { data: 'test' });

  const job = await queue.getJob(jobId);
  expect(job).toBeDefined();

  // Clean up
  await queue.removeJob(jobId);
  await queue.close();
});

Summary

• Use queues for long-running, fault-tolerant work
• Set appropriate priority and retry strategies
• Monitor queue health and failure rates
• Chain jobs at the application level
• Gracefully handle job failures with fallbacks
• Scale workers independently from the API