wrg-rule-lab 0.1.6

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": {}
    }
  ]
}

Supported Operators

The op field in a condition accepts these 10 operators (canonical set in src/rule_lab/core/conditions.py):

Operator	Purpose	Example
eq	Equality	{"field": "status", "op": "eq", "value": "active"}
neq	Not equal	{"field": "status", "op": "neq", "value": "banned"}
gt	Greater than (numeric)	{"field": "score", "op": "gt", "value": 50}
gte	Greater than or equal (numeric)	{"field": "score", "op": "gte", "value": 50}
lt	Less than (numeric)	{"field": "age", "op": "lt", "value": 18}
lte	Less than or equal (numeric)	{"field": "age", "op": "lte", "value": 18}
contains	Substring match	{"field": "email", "op": "contains", "value": "@example.com"}
not_contains	Substring absent	{"field": "tags", "op": "not_contains", "value": "blocked"}
exists	Field present in context	{"field": "user_id", "op": "exists"}
not_exists	Field absent	{"field": "ban_reason", "op": "not_exists"}

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

Sister WRG-11 packages

Part of the WRG-11 PyPI portfolio:

• instinct-mcp -- MCP server for capturing recurring patterns into structured memory
• wrg-devguard -- Developer-first AI safety: prompt-policy lint + secret scanning + log scanning with PII detection
• wrg-mcp-server -- MCP bridge for the WinstonRedGuard monorepo
• ai-security-toolkit -- Offensive + defensive AI/LLM security tools, labs, CTF writeups, research

License

MIT -- built by WinstonRedGuard