Policy engine for governing AI agent tool execution.
Project description
Guardian Angel
A lightweight Python SDK for governing AI agent tool execution.
Guardian Angel intercepts agent actions, evaluates policy, and returns allow, deny, or require_approval — before the tool runs.
Install
pip install guardian-angel
# optional CLI
pip install guardian-angel[cli]
Quickstart
# policy.yaml
rules:
- name: block_risky_delete
tool: resource.delete
decision: deny
all:
- key: resource.environment
op: eq
value: prod
- key: context.risk_level
op: eq
value: high
from guardian_angel import GuardianAngel, ActionRequest
guard = GuardianAngel.from_yaml("policy.yaml")
decision = guard.authorize(
ActionRequest(
tool="resource.delete",
attributes={
"resource.environment": "prod",
"context.risk_level": "high",
},
)
)
print(decision.status) # "deny"
First matching rule wins. No match → allow.
CLI
guardian-angel evaluate policy.yaml request.json
guardian-angel evaluate policy.yaml request.json --explain
guardian-angel --verbose evaluate policy.yaml request.json
guardian-angel --version
--explain prints the matched rule and reason. --verbose adds input context.
Features
- Predicate rules —
when,all,any,notwith operators (eq,ne,in,not_in,contains,gt,gte,lt,lte, …) - Cross-field comparison —
value_fromto compare one attribute against another - Tool decorator —
@guard.tool(name="...")for automatic policy enforcement - YAML or Python — define rules in files or construct
Ruleobjects in code - CLI — evaluate policies from the command line with colored output
See examples/ for more.
How It Works
Agent tool call → ActionRequest → GuardianAngel.authorize() → Decision
Roadmap
- v0.1 — Local policy evaluation, YAML rules, decorator
- v0.2 — Stronger validation, policy linting
- v0.3 — CLI with
evaluate,--explain,--verbose(current) - v0.4 — Framework adapters (LangGraph, OpenAI, CrewAI)
- v0.5+ — Remote policy sources, audit sinks, approval stores
License
MIT
Project details
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