wrg-rule-lab 0.1.3

WinstonRedGuard local-first deterministic rule evaluation engine (formerly rule-lab on PyPI, republished under WRG namespace)

wrg-rule-lab

A lightweight, local-first, deterministic rule evaluation engine for Python.

Define rules in JSON, evaluate them against any context, detect conflicts, and simulate outcomes — with zero external dependencies.

Note: Previously published as rule-lab on PyPI. Access to that account was lost; releases continue here as wrg-rule-lab under the WRG namespace. The import path rule_lab is unchanged.

Installation

pip install wrg-rule-lab

Quick Start

from rule_lab import load_rules_from_dict, evaluate_rules

ruleset = {
    "rules": [
        {
            "rule_id": "r1",
            "name": "Block high risk",
            "conditions": [{"field": "risk_score", "op": "gt", "value": 80}],
            "action": "block",
            "priority": 10
        }
    ]
}

rules = load_rules_from_dict(ruleset)
result = evaluate_rules(rules, context={"risk_score": 95})

print(result.matched_count)    # 1
print(result.results[0].action)  # block

CLI

# Validate a rule file
rule-lab validate --rules rules.json

# Simulate rules against a list of contexts
rule-lab simulate --rules rules.json --contexts contexts.json

# Detect conflicting rules
rule-lab diff --rules rules.json

API Reference

Function	Description
load_rules_from_file(path)	Load rules from a JSON file
load_rules_from_dict(data)	Load rules from a dict
load_rules_from_list(rules)	Load rules from a list
evaluate_rule(rule, context)	Evaluate a single rule
evaluate_rules(rules, context)	Evaluate a list of rules
simulate(rules, contexts)	Simulate multiple contexts

Rule Format

{
  "rules": [
    {
      "rule_id": "unique-id",
      "name": "Human readable name",
      "conditions": [
        {"field": "score", "op": "gt", "value": 50}
      ],
      "action": "approve",
      "priority": 10,
      "tags": ["finance", "v1"],
      "metadata": {}
    }
  ]
}

Use Cases

• AI release gating — validate LLM app outputs before production
• Policy enforcement — define and run compliance rules as code
• Decision engines — replace hardcoded if/else logic with JSON rules
• Audit trails — every rule evaluation is traceable and reproducible

Design Principles

• Zero dependencies — stdlib only, no surprise installs
• Deterministic — same input always produces same output
• Local-first — no network calls, no cloud required
• Testable — every rule is independently verifiable

License

MIT — built by WinstonRed