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\n High-performance job queue for Bun. Memory or one-file SQLite; PostgreSQL 15–18 multi-broker when you scale.
\n DLQ, cron, SQLite S3 backups, and native MCP. Built for AI agents and automation. No Redis.\n
\n Documentation ·\n Quick Start ·\n Video course ·\n Benchmarks ·\n npm\n
\n\n---\n\n## Quickstart\n\nYour first job in about a minute. On Bun the queue runs inside your app, with no\nserver to start:\n\n```bash\nbun add bunqueue\n```\n\n```typescript\n// jobs.ts\nimport { Queue, Worker } from 'bunqueue/client';\n\nconst queue = new Queue<{ to: string }>('emails', { embedded: true });\n\nconst worker = new Worker<{ to: string }>(\n 'emails',\n async (job) => {\n console.log('Processing:', job.data.to);\n return { sent: true };\n },\n { embedded: true }\n);\n\nworker.on('error', (error) => console.error(error));\nawait queue.add('welcome', { to: 'hello@example.com' });\n```\n\n```bash\nbun jobs.ts\n# Processing: hello@example.com\n```\n\nThat's it: no Redis, no config, no setup. The worker keeps waiting for more jobs\nuntil you stop it with Ctrl+C. The example keeps jobs in memory; pass\n`dataPath: './jobs.db'` to the queue and the worker to persist them in one\nSQLite file. `msgpackr` is the only runtime dependency; cron, SQLite, S3, HTTP and\nWebSocket use Bun's built-ins.\n\n### Not on Bun? Run the server, connect from anywhere\n\nStart the server with Docker (no Bun needed) or, if Bun is installed, with\n`bunx bunqueue start --host 127.0.0.1 --data-path ./bunqueue.db`:\n\n```bash\ndocker run -d --name bunqueue \\\n --restart unless-stopped \\\n -p 127.0.0.1:6789:6789 \\\n -p 127.0.0.1:6790:6790 \\\n -v bunqueue-data:/app/data \\\n egeominotti/bunqueue:alpine\n\ncurl --fail http://127.0.0.1:6790/health\n```\n\nThen, in another terminal, add a job and process it from Node.js:\n\n```bash\nnpm install bunqueue-client\n```\n\n```javascript\n// jobs.mjs\nimport { Queue, Worker } from 'bunqueue-client';\n\nconst options = {\n embedded: false,\n connection: { host: '127.0.0.1', port: 6789 },\n};\nconst queue = new Queue('emails', options);\n\nconst worker = new Worker(\n 'emails',\n async (job) => {\n console.log('Processing:', job.data.to);\n return { sent: true };\n },\n options\n);\n\nworker.on('error', (error) => console.error(error));\nawait queue.add('welcome', { to: 'hello@example.com' });\n```\n\n```bash\nnode jobs.mjs\n# Processing: hello@example.com\n```\n\nDeno, Python, PHP, Go, Rust and Elixir take the same three steps: see the\n[Quick Start](https://bunqueue.dev/guide/quickstart/) for each language.\n\nFor multiple active brokers, the repository includes a topology pinned to\nPostgreSQL 18.6:\n\n```bash\nPOSTGRES_PASSWORD='replace-me' \\\n BUNQUEUE_POSTGRES_URL='postgres://bunqueue:replace-me@postgres:5432/bunqueue' \\\n docker compose -f docker-compose.postgres.yml up --build -d\n```\n\nIt starts two brokers against one database/namespace. PostgreSQL is server-only;\nembedded mode keeps using memory/SQLite. Supply both Compose values when the\npassword changes, percent-encoding reserved characters in the URL only. MySQL\nis not supported. CI validates PostgreSQL 15, 16, 17, and the pinned/recommended\n18.6 release. See the\n[storage guide](https://bunqueue.dev/guide/databases/).\n\n### Docker images\n\nAll four images run the same bunqueue server with the same queue features,\nprotocols, and persistence options. The difference is the Linux base and the\ntools available inside the container.\n\nStarting with 2.9.5, completed releases publish to both Docker Hub\n(`egeominotti/bunqueue`) and GHCR (`ghcr.io/egeominotti/bunqueue`), with matching\nversion, `latest`, and variant tags. Build references are kept on GHCR only.\nEach image supports `linux/amd64` and `linux/arm64`; Docker selects the matching\narchitecture automatically. You choose the distribution, not the CPU tag.\nPin a version or digest for reproducible deployments. Confirm a tag exists with\n`docker buildx imagetools inspect egeominotti/bunqueue:\n \n ![\"Watch](\"https://img.youtube.com/vi/fWrZLzElXtA/maxresdefault.jpg\")
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