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message queue for Node.js with AI-native primitives - built on Valkey/Redis with Rust NAPI bindings","maintainers":[{"name":"avifenesh","email":"aviarchi1994@gmail.com"}],"readme":"# glide-mq\n\n[![npm version](https://img.shields.io/npm/v/glide-mq)](https://www.npmjs.com/package/glide-mq)\n[![license](https://img.shields.io/npm/l/glide-mq)](https://github.com/avifenesh/glide-mq/blob/main/LICENSE)\n[![CI](https://github.com/avifenesh/glide-mq/actions/workflows/ci.yml/badge.svg)](https://github.com/avifenesh/glide-mq/actions/workflows/ci.yml)\n[![codecov](https://codecov.io/gh/avifenesh/glide-mq/graph/badge.svg)](https://codecov.io/gh/avifenesh/glide-mq)\n\nHigh-performance message queue for Node.js with first-class AI orchestration. Built on Valkey/Redis Streams with a Rust NAPI core.\n\nCompletes and fetches the next job in a single server-side function call (1 RTT per job), hash-tags every key for zero-config clustering, and ships AI-native primitives for cost tracking, token streaming, human-in-the-loop suspend/resume, model failover, TPM rate limiting, budget caps, rolling usage summaries, and vector search.\n\n```bash\nnpm install glide-mq\n```\n\n### General Usage\n\n```typescript\nimport { Queue, Worker } from 'glide-mq';\n\nconst connection = { addresses: [{ host: 'localhost', port: 6379 }] };\nconst queue = new Queue('tasks', { connection });\n\nawait queue.add('send-email', { to: 'user@example.com', subject: 'Welcome' });\n\nconst worker = new Worker(\n  'tasks',\n  async (job) => {\n    await sendEmail(job.data.to, job.data.subject);\n    return { sent: true };\n  },\n  { connection, concurrency: 10 },\n);\n```\n\n### AI Usage\n\n```typescript\nimport { Queue, Worker } from 'glide-mq';\n\nconst queue = new Queue('ai', { connection });\n\nawait queue.add(\n  'inference',\n  { prompt: 'Explain message queues' },\n  {\n    fallbacks: [{ model: 'gpt-5.4-nano', provider: 'openai' }],\n    lockDuration: 120000,\n  },\n);\n\nconst worker = new Worker(\n  'ai',\n  async (job) => {\n    const result = await callLLM(job.data.prompt);\n    await job.reportUsage({\n      model: 'gpt-5.4',\n      tokens: { input: 50, output: 200 },\n      costs: { total: 0.003 },\n    });\n    await job.stream({ type: 'token', content: result });\n    return result;\n  },\n  { connection, tokenLimiter: { maxTokens: 100000, duration: 60000 } },\n);\n```\n\n## When to use glide-mq\n\n- **Background jobs and task processing** - email, image processing, data pipelines, webhooks, any async work.\n- **Scheduled and recurring work** - cron jobs, interval tasks, bounded schedulers.\n- **Distributed workflows** - parent-child trees, DAGs, fan-in/fan-out, step jobs, dynamic children.\n- **High-throughput queues over real networks** - 1 RTT per job via Valkey Server Functions, up to 38% faster than alternatives.\n- **LLM pipelines and model orchestration** - cost tracking, token streaming, model failover, budget caps without external middleware.\n- **Valkey/Redis clusters** - hash-tagged keys out of the box with zero configuration.\n\n## How it's different\n\n| Aspect              | glide-mq                                                                                                  |\n| ------------------- | --------------------------------------------------------------------------------------------------------- |\n| **Network per job** | 1 RTT - complete + fetch next in a single FCALL                                                           |\n| **Client**          | Rust NAPI bindings via [valkey-glide](https://github.com/valkey-io/valkey-glide) - no JS protocol parsing |\n| **Server logic**    | Persistent Valkey Function library (FUNCTION LOAD + FCALL) - no per-call EVAL                             |\n| **Cluster**         | Hash-tagged keys (`glide:{queueName}:*`) route to the same slot automatically                             |\n| **AI-native**       | Cost tracking, token streaming, suspend/resume, fallback chains, TPM limits, budget caps, usage summaries |\n| **Vector search**   | KNN similarity queries over job data via Valkey Search                                                    |\n\n## AI-native primitives\n\nSeven primitives for LLM and agent workflows, built into the core API.\n\n- **Cost tracking** - `job.reportUsage()` records model, tokens, cost, latency per job. `queue.getFlowUsage()` aggregates across flows and `queue.getUsageSummary()` rolls usage up across queues.\n- **Token streaming** - `job.stream(chunk)` pushes LLM output tokens in real time. `queue.readStream(jobId)` consumes them with optional long-polling.\n- **Suspend/resume** - `job.suspend()` pauses mid-processor for human approval or webhook callback. `queue.signal(jobId, name, data)` resumes with external input.\n- **Fallback chains** - ordered `fallbacks` array on job options. On failure, the next retry reads `job.currentFallback` for the alternate model/provider.\n- **TPM rate limiting** - `tokenLimiter` on worker options enforces tokens-per-minute caps. Combine with RPM `limiter` for dual-axis rate control.\n- **Budget caps** - `FlowProducer.add(flow, { budget })` sets `maxTotalTokens` and `maxTotalCost` across all jobs in a flow. Jobs fail or pause when exceeded.\n- **Per-job lock duration** - override `lockDuration` per job for adaptive stall detection. Short for classifiers, long for multi-minute LLM calls.\n\nSee [Usage - AI-native primitives](docs/USAGE.md#ai-native-primitives) for full examples.\n\n## Features\n\n- **1 RTT per job** - complete current + fetch next in a single server-side function call\n- **Cluster-native** - hash-tagged keys, zero cluster configuration\n- **Workflows** - FlowProducer trees, DAGs with fan-in, chain/group/chord, step jobs, dynamic children\n- **Scheduling** - 5-field cron with timezone, fixed intervals, bounded schedulers\n- **Retries** - exponential, fixed, or custom backoff with dead-letter queues\n- **Rate limiting** - per-group sliding window, token bucket, global queue-wide limits\n- **Broadcast** - fan-out pub/sub with NATS-style subject filtering and independent subscriber retries\n- **Batch processing** - process multiple jobs at once for bulk I/O\n- **Request-reply** - `queue.addAndWait()` for synchronous RPC patterns\n- **Deduplication** - simple, throttle, and debounce modes\n- **Compression** - transparent gzip at the queue level\n- **Serverless** - lightweight `Producer` and `ServerlessPool` for Lambda/Edge\n- **OpenTelemetry** - automatic span emission with bring-your-own tracer\n- **In-memory testing** - `TestQueue` and `TestWorker` with zero Valkey dependency\n- **Cross-language** - HTTP proxy with request-reply, SSE streams, schedulers, flow create/read/tree/delete, usage summaries, and broadcast fan-out for non-Node.js services\n\n## Performance\n\nBenchmarked on AWS ElastiCache Valkey 8.2 (r7g.large) with TLS, EC2 client in the same region.\n\n| Concurrency |   glide-mq | Leading Alternative | Delta |\n| :---------: | ---------: | ------------------: | :---: |\n|     c=5     | 10,754 j/s |          9,866 j/s  |  +9%  |\n|    c=10     | 18,218 j/s |         13,541 j/s  | +35%  |\n|    c=15     | 19,583 j/s |         14,162 j/s  | +38%  |\n|    c=20     | 19,408 j/s |         16,085 j/s  | +21%  |\n\nThe advantage comes from completing and fetching the next job in a single FCALL. The savings compound over real network latency - exactly the conditions in every production deployment. At high concurrency both libraries converge toward the Valkey single-thread ceiling.\n\nReproduce with `npm run bench` or `npx tsx benchmarks/elasticache-head-to-head.ts` against your own infrastructure.\n\n## Examples\n\nAll examples live in [glidemq-examples](https://github.com/avifenesh/glidemq-examples). Pick a directory, `npm install`, `npx tsx <file>.ts`.\n\n| AI Orchestration | Core Patterns | Framework Plugins |\n|------------------|---------------|-------------------|\n| [Usage & Costs](https://github.com/avifenesh/glidemq-examples/tree/main/examples/ai-usage-and-costs) | [Basics](https://github.com/avifenesh/glidemq-examples/tree/main/examples/core-basics) | [Hono](https://github.com/avifenesh/glidemq-examples/tree/main/examples/hono-api) |\n| [Budget & TPM](https://github.com/avifenesh/glidemq-examples/tree/main/examples/ai-budget) | [Workflows](https://github.com/avifenesh/glidemq-examples/tree/main/examples/core-workflows) | [Fastify](https://github.com/avifenesh/glidemq-examples/tree/main/examples/fastify-api) |\n| [Streaming](https://github.com/avifenesh/glidemq-examples/tree/main/examples/ai-streaming) | [Advanced](https://github.com/avifenesh/glidemq-examples/tree/main/examples/core-advanced) | [Hapi](https://github.com/avifenesh/glidemq-examples/tree/main/examples/hapi-api) |\n| [Agent Loops](https://github.com/avifenesh/glidemq-examples/tree/main/examples/ai-agents) | [DAG Flows](https://github.com/avifenesh/glidemq-examples/tree/main/examples/dag-workflows) | [NestJS](https://github.com/avifenesh/glidemq-examples/tree/main/examples/nestjs-module) |\n| [Search & Vectors](https://github.com/avifenesh/glidemq-examples/tree/main/examples/ai-search) | [Scheduling](https://github.com/avifenesh/glidemq-examples/tree/main/examples/cron-scheduler) | [Express](https://github.com/avifenesh/glidemq-examples/tree/main/examples/express-dashboard) |\n| [SDK Integrations](https://github.com/avifenesh/glidemq-examples/tree/main/examples/ai-integrations) | [Batch Processing](https://github.com/avifenesh/glidemq-examples/tree/main/examples/batch-processing) | [Next.js](https://github.com/avifenesh/glidemq-examples/tree/main/examples/nextjs-api-routes) |\n\n## When NOT to use glide-mq\n\n- **You need a log-based event streaming platform.** glide-mq is a job/task queue, not a partitioned event log. It does not provide Kafka-style topic partitions, consumer offset management, or event replay.\n- **You need browser support.** The Rust NAPI client requires a server-side runtime (Node.js 20+, Bun, or Deno with NAPI support).\n- **You need exactly-once semantics.** glide-mq provides at-least-once delivery. Duplicate processing is rare but possible - design processors to be idempotent.\n- **You need to run without Valkey or Redis.** Production use requires Valkey 7.0+ or Redis 7.0+. For dev/testing, `TestQueue`/`TestWorker` run fully in-memory.\n\n## Documentation\n\n| Guide                                  | Topics                                                      |\n| -------------------------------------- | ----------------------------------------------------------- |\n| [Usage](docs/USAGE.md)                 | Queue, Worker, Producer, request-reply, SSE, proxy, flow HTTP API, usage summaries |\n| [Workflows](docs/WORKFLOWS.md)         | FlowProducer, DAG, chain/group/chord, dynamic children      |\n| [Advanced](docs/ADVANCED.md)           | Schedulers, rate limiting, dedup, compression, retries, DLQ |\n| [Broadcast](docs/BROADCAST.md)         | Pub/sub fan-out, subject filtering                          |\n| [Observability](docs/OBSERVABILITY.md) | OpenTelemetry, metrics, job logs, dashboard                 |\n| [Serverless](docs/SERVERLESS.md)       | Producer, ServerlessPool, Lambda/Edge, HTTP proxy          |\n| [Testing](docs/TESTING.md)             | In-memory TestQueue and TestWorker                          |\n| [Wire Protocol](docs/WIRE_PROTOCOL.md) | Cross-language FCALL specs, Python/Go examples              |\n| [Step Jobs](docs/STEP_JOBS.md)         | Step-job workflows with moveToDelayed                       |\n| [Durability](docs/DURABILITY.md)       | Durability guarantees, persistence, delivery semantics      |\n| [Architecture](docs/ARCHITECTURE.md)   | Internal architecture and design reference                  |\n| [Migration](docs/MIGRATION.md)         | API mapping guide for migrating from other queues           |\n\n## Ecosystem\n\n| Package                                                              | Description                                   |\n| -------------------------------------------------------------------- | --------------------------------------------- |\n| [@glidemq/speedkey](https://github.com/avifenesh/speedkey)           | Valkey GLIDE client with native NAPI bindings |\n| [@glidemq/dashboard](https://github.com/avifenesh/glidemq-dashboard) | Web UI for metrics, schedulers, job mutations |\n| [@glidemq/hono](https://github.com/avifenesh/glidemq-hono)           | Hono middleware                               |\n| [@glidemq/fastify](https://github.com/avifenesh/glidemq-fastify)     | Fastify plugin                                |\n| [@glidemq/nestjs](https://github.com/avifenesh/glidemq-nestjs)       | NestJS module                                 |\n| [@glidemq/hapi](https://github.com/avifenesh/glidemq-hapi)           | Hapi plugin                                   |\n| [glidemq.dev](https://www.glidemq.dev/) | Full documentation site                       |\n\n## Contributing\n\nBug reports, feature requests, and pull requests are welcome.\n\n- [Open an issue](https://github.com/avifenesh/glide-mq/issues)\n- [Discussions](https://github.com/avifenesh/glide-mq/discussions)\n- [Changelog](CHANGELOG.md)\n\n## License\n\nApache-2.0\n","readmeFilename":"README.md"}