n8n-nodes-autoverse

Production-grade multitasking & parallel AI custom nodes for n8n.

Phase 1 (this release, v0.1.0): AutoVerse Parallel Task Runner.

Run incoming n8n items in parallel with a bounded worker pool, AIMD adaptive rate-limit reaction, per-task timeout, retry with exponential backoff, deterministic output ordering and full failure isolation.

Why

The n8n community forum is full of threads asking how to run LLM / HTTP calls in parallel without triggering 429 storms. Today the workaround is 8–15 nodes, sub-workflows, static-data counters and webhook callbacks. This node reduces all of that to a single step.

Install

Community-nodes settings inside n8n → npm Package Name → n8n-nodes-autoverse

Or manually:

cd ~/.n8n/nodes
npm install n8n-nodes-autoverse

Node: AutoVerse Parallel Task Runner

| Option | Default | Meaning | |---|---|---| | Mode | http | http = URL/body per item, code = inline JS body | | Concurrency | 5 | max in-flight tasks | | Adaptive (AIMD on 429) | true | shrink on 429, grow on success streak | | Max Retries per Task | 2 | on top of first attempt | | Base Backoff (ms) | 500 | with full jitter | | Backoff Cap (ms) | 30 000 | | | Task Timeout (ms) | 60 000 | via per-task AbortController | | Max Execution Time (ms) | 0 (disabled) | global deadline; remaining items become skipped:deadline | | Output Ordering | input | input (preserved) or completion (fastest first) | | Emit summary item | true | adds { __autoverse_summary: metrics } at the end |

Output per item

{
  "__autoverse": {
    "index": 0,
    "status": "success | failed | skipped",
    "attempts": 1,
    "durationMs": 234,
    "retried": false,
    "skipReason": null,
    "error": null
  },
  "input":  { ... original item json ... },
  "output": { ... task result ... }
}

Summary item

{
  "__autoverse_summary": {
    "totalTasks": 100,
    "successful": 97,
    "failed": 2,
    "skipped": 1,
    "retries": 6,
    "rateLimits": 4,
    "timeouts": 2,
    "cancelled": false,
    "concurrencyRequested": 10,
    "concurrencyFinal": 5,
    "totalDurationMs": 2410,
    "averageDurationMs": 218,
    "adaptive": true
  }
}

Benchmark

100 tasks × 200 ms simulated work:

| Scenario | Duration | Speed-up | |---|---|---| | Sequential (n8n Loop Over Items today) | 20.05 s | 1.00× | | Parallel concurrency=10 | 2.01 s | 9.99× | | Parallel + 20% 429 + AIMD | 2.41 s | 8.32× | | Parallel + failures + timeouts | 2.55 s | 7.91× |

License

MIT

Roadmap

• v0.2 — AutoVerse Batch LLM (OpenAI / Anthropic / Gemini / Groq Batch APIs, 50% discount).
• v0.3 — AutoVerse Smart Cache & Router (prompt caching + model routing).

See ARCHITECTURE.md and PHASE-1-REPORT.md.