amanai 0.3.0

Action Policy Engine for AI agents: policy-as-code for tool-calls — evaluate, enforce (block/warn/approve), and trace agent actions, with the same policy asserted in CI. Plus local guardrails and an offline judge. Pure-Python, zero dependencies.

amanai

Action Policy Engine for AI agents — policy-as-code for tool-calls. Pure-Python, zero dependencies.

Write one policy for risky agent actions. Amanai evaluates every tool-call against it before the tool runs, enforces the decision, and records a structured trace as evidence. The same policy file asserts in CI — what you test is what you enforce. Decisions are deterministic: no LLM decides whether a high-risk tool runs.

pip install amanai

Action Policy Engine

Five verbs: load a policy, evaluate an action, protect a tool, collect a trace, assert it in tests.

from amanai import set_policy, tool, ToolBlocked, ApprovalRequired, collect_trace

set_policy("amanai.policy.json")          # load + validate (raises PolicyError if bad)

@tool(name="billing.refund", capability="money_movement", risk="high")
def refund_payment(amount): ...

refund_payment(amount=500)                # ToolBlocked / ApprovalRequired per policy
trace = collect_trace()                   # canonical evidence: action + decision + status

A policy is a list of rules. A rule matches on tool and/or capability, with optional args and context predicates, and carries an action:

[
  { "id": "discount-cap", "tool": "apply_discount",
    "args": [{ "arg": "pct", "op": ">=", "value": 50 }],
    "action": "block", "reason": "discount of 50% or more is never allowed" },

  { "id": "refund-approval", "tool": "refund_payment",
    "args": [{ "arg": "amount", "op": ">", "value": 100 }],
    "action": "require_approval" },

  { "id": "external-email", "capability": "external_comms",
    "args": [{ "arg": "to", "op": "email_external", "value": ["acme.com"] }],
    "action": "block" }
]

• Outcomes: allow · block · warn · require_approval.
• Operators: >= > <= < == != · contains · regex · in / not_in · email_external · domain_in / domain_not_in.
• Modes (per request, never global): enforce blocks before execution · shadow records what it would block but lets the call run · test evaluates with no side effects.
• The legacy flat rule {tool, arg, op, value} still loads (it defaults to block). Rules without an id get a deterministic generated one.

from amanai import set_mode, set_context, evaluate, ActionRequest

set_mode("shadow")                                   # observe before enforcing
set_context(role="support", tenant="t1", environment="prod")  # authz context for rules
evaluate(ActionRequest("apply_discount", {"pct": 90}))        # -> PolicyDecision(outcome="block", ...)

Assert the same policy in CI

from amanai.testing import assert_blocked, assert_no_violations

def test_excessive_discount_is_blocked():
    set_policy("amanai.policy.json")
    assert_blocked(apply_discount, pct=90)

def test_attack_trace_is_clean():
    run_agent(attack_prompt)             # produces a trace
    assert_no_violations(collect_trace())  # replays actions against the active policy

amanai.testing also ships assert_requires_approval, using_mode, and replay (re-evaluate a recorded trace against a policy).

Runtime guardrails (input / output, supporting)

from amanai import guard_input, guard_output, GuardrailBlocked

try:
    msg = guard_input(user_msg)          # raises GuardrailBlocked on injection
except GuardrailBlocked:
    reply = "I can't help with that."
else:
    reply = guard_output(run_agent(msg)) # redacts emails, cards, secrets

Offline judge & MCP checks (supporting)

Score an agent's input / response / tool-calls against detectors (string match, tool assertions, MCP checks; LLM-as-judge when you pass a gateway). The tool_assertion detector reuses the engine's operators, so the test side can't drift from runtime — a contract test guarantees it.

Monitoring (optional — needs an Amanai server)

from amanai import Monitor, collect_trace

mon = Monitor("http://localhost:8000", PUBLIC_KEY, SECRET_KEY)
mon.log_trace(collect_trace(), user_id="u123")   # canonical events, PII redacted

API

• Engine: load_policy set_policy get_policy evaluate Policy Rule ActionRequest PolicyDecision TraceEvent PendingAction PolicyError
• Modes / context: set_mode get_mode set_context get_context clear_context
• Protect / collect: tool collect_trace collect_tool_calls record_tool_call reset registered_tools uncovered_tools ToolBlocked ApprovalRequired
• Test: amanai.testing — assert_blocked assert_requires_approval assert_no_violations using_mode replay
• Policy lifecycle: clear_tool_policy (deactivate the active policy)
• Guardrails: guard_input guard_output redact_pii detect_injection detect_pii GuardResult GuardrailBlocked
• Judge / monitor: judge Monitor

Apache-2.0. Zero runtime dependencies.