ezrules 1.24.0

Open-source transaction monitoring engine for business rules

ezrules

Open-source transaction monitoring for teams that need clear rule control, auditability, and fast operational changes.

ezrules gives fraud, risk, and compliance administrators a web workspace for managing decision rules without turning every policy update into an engineering project. Rules can be drafted, tested, reviewed, promoted, paused, rolled out, and audited from one place, while your systems keep sending events to a simple evaluation API.

Why Teams Use It

• Own the rule lifecycle. Create rules, keep drafts separate from live logic, pause risky rules, restore older revisions, and promote changes deliberately.
• See what happened. Review canonical served decisions, the outcome returned, every rule that fired, and the exact event version those rules used.
• Improve rules with evidence. Use labels, precision/recall reports, backtests, shadow rules, and percentage rollouts before changing production decisions.
• Run with admin controls. Manage roles, permissions, API keys, outcomes, user lists, field types, traffic persistence, and audit history inside the product.
• Self-host it. Run the full stack yourself with PostgreSQL, Redis, FastAPI, Celery, and the web UI.

Demo

The demo stack starts with sample rules, outcomes, labels, and evaluated events.

git clone https://github.com/sofeikov/ezrules.git
cd ezrules
docker compose -f docker-compose.demo.yml up --build

Then open:

Service	URL
Web UI	http://localhost:4200
API	http://localhost:8888
Mail UI	http://localhost:8025

Login with [e-mail] / admin.

To stop and remove the demo data:

docker compose -f docker-compose.demo.yml down -v

Trace A Decision Field By Field

Product Tour

Manage Live Rules

Create and maintain the rule set from a reviewable UI. Active rules, drafts, ordering, lifecycle actions, and rule status are visible in one place.

Monitor 30-Day Activity

Switch the dashboard to the 30-day window to review sustained traffic and outcome patterns instead of a single point-in-time snapshot.

Inspect Rule Logic And Performance

Each rule has its own detail view with source logic, test payloads, historical revisions, backtesting, and hit/outcome performance.

Review Tested Events

The Tested Events view connects decisions back to the raw payload, triggered rules, labels, resolved outcomes, and nearby entity relationships. Referenced fields are highlighted so an admin can see why a rule fired, while the event graph shows related traffic through shared users, cards, devices, merchants, and other configured entities.

Expand Event Relationships

Click an entity node in the graph to expand nearby transactions that share that customer, account, card, device, merchant, or another configured entity.

Measure Rule Quality

When events are labeled, ezrules can compare outcomes to ground truth and rank rules by precision, recall, F1, true positives, false positives, and false negatives.

Validate Candidate Changes Safely

Use shadow mode when you want observe-only comparison, and use rollouts when a candidate rule should serve a controlled share of live traffic.

How It Fits Into Your System

Your application sends an event to ezrules, and ezrules returns the resolved outcome.

curl -X POST http://localhost:8888/api/v2/evaluate \
  -H "Content-Type: application/json" \
  -H "X-API-Key: <api-key>" \
  -d '{
    "transaction_id": "txn_123",
    "effective_at": "2026-04-23T12:00:00Z",
    "event_data": {
      "amount": 875.50,
      "currency": "EUR",
      "customer_country": "US",
      "shipping_country": "MX",
      "has_3ds": 0
    }
  }'

The result is stored for review in the UI, including the winning outcome and the rules that contributed to the decision.

Core Workflows

• Rule authoring: write rule logic with validation, observed field references, configured outcomes, and list references.
• Event testing: dry-run JSON events against the active rule set without storing them in Tested Events or analytics.
• Shadow deployment: observe what a rule would do on live traffic without changing production outcomes.
• Rule rollouts: send a stable percentage of traffic through a candidate rule before full promotion.
• Backtesting: compare proposed logic against historical events before release.
• Graph investigation: inspect connected events through configured entity links and use graph-derived stats inside rule logic.
• Labeling and quality reports: upload or assign labels, then measure how rules perform against known outcomes.
• Audit and access control: keep change history and separate admin, editor, and read-only responsibilities.

Documentation

• Quickstart
• Installation
• Configuration
• Admin guide
• Rule authoring
• Performance testing
• API reference
• Deployment guide
• What's new

The documentation site is also available at ezrules.readthedocs.io.

Development

For contributors, the project uses Python 3.12, uv, FastAPI, SQLAlchemy, Celery, PostgreSQL, Angular, Tailwind CSS, and Playwright.

uv sync
uv run poe check

Frontend dependencies live in ezrules/frontend/.

cd ezrules/frontend
npm install
npm start

See docs/contributing.md for contribution guidance.

License

Apache License 2.0. See LICENSE.