MarginKit

Pre-flight cost & risk compiler for n8n AI workflows.

One runaway loop can erase a month of margin. MarginKit reads your exported n8n workflow before you ship it to a client and tells you:

• what each AI node will cost per run (low / typical / high — honest ranges, not fake precision)
• where the real loops are (actual cycle detection in the workflow graph)
• which retries will multiply a failing paid call
• where to set the caps — exported as machine-readable guardrail rules

Built for AI agencies and freelancers who deliver n8n workflows at a fixed price.

Try it in the browser — zero install

The web/ folder is a full static web version: drag-and-drop your workflow export and read the report in the page. 100% local — the analysis runs in your browser, nothing is uploaded (verify it in your network tab). Host it on any static host, or just open web/index.html.

The web engine is a line-by-line port of the Python engine; tests/test_web_parity.py proves both produce identical numbers on the golden corpus of real n8n templates.

Install

pip install -e .

Requires Python 3.11+.

Usage

# Compile a budget & risk report (markdown or html, by extension)
marginkit compile my-workflow.json --output report.html

# Also export machine-readable guardrail rules
marginkit compile my-workflow.json --output report.md --rules rules.json

# Adjust the volume assumption (default: 100 runs/day)
marginkit compile my-workflow.json --runs-per-day 500

# Just check that an export is readable
marginkit validate my-workflow.json

Export your workflow from n8n: open the workflow → menu (…) → Download.

Free vs Pro

Pro is a one-time purchase. Activate with:

marginkit activate XXXXXXXX-XXXXXXXX-XXXXXXXX-XXXXXXXX

CI pipelines can set the MARGINKIT_LICENSE environment variable instead.

# White-label report for a client, failing the pipeline on high risks
marginkit compile client-workflow.json --output report.html \
  --agency "Studio Nord" --client "Acme Inc" --logo logo.png \
  --rules guardrails.json --fail-on high

What it detects

Honesty by design

• Estimates are ranges built from the prompt text present in your export. Runtime data injected by {{ expressions }} is unknown at compile time and is stated as an assumption, never hidden.
• The pricing table is dated (as_of) and unknown models are visibly flagged.
• Every report ends with "What this report does not know".

What MarginKit is not

• not a guarantee of exact future cost
• not a replacement for runtime observability
• not an n8n-only product long term (LangGraph/CrewAI planned)

Development

pip install -e .[dev]
pytest          # 78 tests incl. a golden corpus of real n8n.io templates
ruff check src tests scripts

After every pricing table update, regenerate the web pricing file:

python scripts/sync_pricing.py   # models_pricing.json -> web/pricing.js

tests/test_web_parity.py fails if the two pricing files drift, or if the web engine and the Python engine disagree on any corpus workflow.