See every chain step, tool call, retriever hit, and LLM invocation in n8n agents — without rewriting your code, without adding a proxy to the request path.
The problem
n8n is great at what it does, but production-grade observability is bring-your-own. Most teams discover this when the first production incident hits, the first OpenAI bill arrives, or the first auditor asks for evidence.
Specifically:
- No native cost visibility per workflow or AI Agent node
- AI Agents can call HTTP Request against production with no review
- Workflows that loop on a failing tool can rack up $1000s before detection
How Prefactor solves it
Prefactor wraps your n8n Workflow and adds observability as a runtime layer. Specifically:
- debug a failing production run in minutes
- trace search by userid or sessionid
- p95 latency analysis across agents
- investigate why an agent picked a specific tool
- A/B test prompts or models on real traffic
Install and integrate
Install via n8n Community Nodes// In n8n:
// Settings → Community Nodes → install n8n-nodes-prefactor
// Add "Prefactor: Wrap Workflow" node at workflow start
// Or use webhook integration for n8n Cloud (no install)Specific use cases
- Debug a failing production run in minutes
- Trace search by userid or sessionid
- P95 latency analysis across agents
- Investigate why an agent picked a specific tool
- A/b test prompts or models on real traffic
FAQ
Does this add latency? Per-span instrumentation adds ~2-5ms. Telemetry ships asynchronously. No synchronous network call in the agent's hot path unless you enable blocking policy enforcement.
Can I configure this per-agent? Yes. Observability settings are per-agent — different agents can have different policies, sampling rates, retention, etc.
Is this compatible with other observability tools? Yes. Prefactor exports OpenTelemetry; works alongside Datadog, Langfuse, LangSmith, and others.
Related
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