RMACD Framework SDK - Policy evaluation for autonomous AI agents
Project description
RMACD Framework Python SDK
Reference implementation for the RMACD (Read, Move, Add, Change, Delete) Framework — a governance model for autonomous AI agents. The SDK supports all three model variants:
- 3D (default) — Operations × Data Classification × Autonomy
- 2D Operational — Operations × Autonomy (no data classification)
- 2D Data-Classification (DC2D) — Data Classification × Autonomy (no operations axis; for orgs whose primary governance lever is data sensitivity, with operations governed by an upstream IAM/RBAC or DLP layer). See spec Appendix D.
Installation
# From PyPI
pip install rmacd-framework
# Or from source
git clone https://github.com/rmacdframework/spec.git
cd spec/sdk/python
pip install -e .
# Or using uv
uv pip install rmacd-framework
The distribution name on PyPI is rmacd-framework; the import name remains rmacd (from rmacd import ...).
Quick Start
Loading and Evaluating Profiles
from rmacd import ProfileLoader, PolicyEvaluator
# Load a profile
loader = ProfileLoader()
profile = loader.load_file("profiles/devops-agent.json")
# Create evaluator
evaluator = PolicyEvaluator(profile)
# Evaluate a policy decision (3D profile)
decision = evaluator.evaluate(
operation="C", # Change
data_classification="internal",
)
print(f"Allowed: {decision.allowed}")
print(f"Autonomy Level: {decision.autonomy_level}")
print(f"Requires Approval: {decision.requires_approval}")
DC2D Profiles (Data-Classification × Autonomy)
from rmacd import (
AutonomyLevel,
DataAccess,
PolicyEvaluator,
ProfileDC2D,
TierPolicy,
)
profile = ProfileDC2D(
profile_id="rmacd-dc2d-support-agent-v1",
profile_name="Support Agent",
model="data-classification-2d",
version="1.0",
data_access=DataAccess(
public=TierPolicy(allowed=True, autonomy=AutonomyLevel.AUTONOMOUS),
internal=TierPolicy(allowed=True, autonomy=AutonomyLevel.LOGGED),
confidential=TierPolicy(allowed=True, autonomy=AutonomyLevel.APPROVAL),
restricted=TierPolicy(allowed=False, autonomy=AutonomyLevel.PROHIBITED),
),
)
evaluator = PolicyEvaluator(profile)
# DC2D requires data_classification; operation is informational only
decision = evaluator.evaluate(operation="R", data_classification="confidential")
print(decision.allowed) # True
print(decision.autonomy_level) # AutonomyLevel.APPROVAL
print(decision.requires_approval) # True
Validating Profiles
from rmacd import ProfileValidator
validator = ProfileValidator(schema_dir="../../schemas")
# Validate a profile file
try:
validator.validate_file("my-profile.json")
print("Profile is valid!")
except SchemaValidationError as e:
print(f"Validation failed: {e.errors}")
# Check validity without exceptions
if validator.is_valid("my-profile.json"):
print("Valid!")
Emergency Escalation
from rmacd import ProfileLoader, PolicyEvaluator
from rmacd.models import EvaluationContext, TriggerCondition
loader = ProfileLoader()
profile = loader.load_file("incident-responder.json")
evaluator = PolicyEvaluator(profile)
# Evaluate with emergency escalation active
context = EvaluationContext(
emergency_active=True,
emergency_trigger=TriggerCondition.SOC_DECLARED_INCIDENT,
)
decision = evaluator.evaluate(
operation="C",
data_classification="confidential",
context=context,
)
print(f"Emergency mode: {decision.emergency_mode}")
CLI Usage
The SDK includes a command-line interface for common operations.
Validate Profiles
# Validate single profile
rmacd validate profiles/devops-agent.json
# Validate multiple profiles
rmacd validate profiles/*.json
# Quiet mode (errors only)
rmacd validate -q profiles/*.json
Evaluate Policy Decisions
# Evaluate operation on 3D profile
rmacd evaluate profiles/devops.json C --classification internal
# With emergency escalation
rmacd evaluate profiles/incident-responder.json C -c confidential --emergency
# JSON output
rmacd evaluate profiles/devops.json R -c public --json
Display Profile Information
# Show profile info
rmacd info profiles/devops.json
# JSON output
rmacd info profiles/devops.json --json
View Autonomy Matrix
# Display effective autonomy matrix
rmacd matrix profiles/devops.json
# JSON output
rmacd matrix profiles/devops.json --json
Tools Registry
The SDK includes a Tools Registry for managing and validating AI agent tool access.
Creating a Registry
from rmacd.registry import ToolsRegistry, quick_register
# Create registry
registry = ToolsRegistry("my-organization")
# Register tools
quick_register(
registry,
tool_id="database_query",
tool_name="Database Query",
rmacd_level="R",
description="Execute read-only database queries",
data_access="confidential",
required_hitl="logged"
)
# Validate agent access
is_allowed, reason = registry.validate_tool_access(
tool_id="database_query",
allowed_levels=["R", "M"],
data_tier="confidential"
)
print(f"Allowed: {is_allowed} - {reason}")
Risk Assessment
# Calculate workflow risk
workflow_tools = ["github_commit", "kubernetes_deploy", "slack_notify"]
risk = registry.calculate_workflow_risk(workflow_tools)
print(f"Total Risk: {risk['total_risk']}/10")
print(f"Highest RMACD: {risk['highest_rmacd']}")
MCP Integration
from rmacd.registry import MCPTool, MCPRegistryBridge
# Create MCP bridge
bridge = MCPRegistryBridge("mcp-demo")
# Register MCP tool with auto-classification
mcp_tool = MCPTool(
name="filesystem-read",
description="Read files from the filesystem",
inputSchema={"type": "object", "properties": {"path": {"type": "string"}}},
operations=["read", "list"]
)
bridge.register_mcp_tool(mcp_tool)
# Check agent access
allowed, reason = bridge.can_agent_use_tool(
"filesystem-read",
agent_permissions=["R", "M"],
agent_data_tier="internal"
)
Export/Import
# Export registry to JSON
registry.export_to_json("tools_catalog.json")
# Import tools from JSON
new_registry = ToolsRegistry("imported")
new_registry.import_from_json("tools_catalog.json")
Models
Profile Types
- Profile2D: Two-dimensional profile (operations + autonomy, no data classification)
- Profile3D: Three-dimensional profile (operations + data classification + autonomy)
Core Enums
- Operation:
R(Read),M(Move),A(Add),C(Change),D(Delete) - DataClassification:
public,internal,confidential,restricted - AutonomyLevel:
autonomous,logged,notification,approval,elevated_approval,prohibited
Policy Decision
The PolicyDecision model contains:
@dataclass
class PolicyDecision:
allowed: bool # Whether operation is permitted
operation: Operation # The evaluated operation
data_classification: DataClassification | None
autonomy_level: AutonomyLevel # Required autonomy level
requires_approval: bool # Whether human approval needed
requires_notification: bool # Whether notification required
blocked_reason: str | None # Reason if blocked
constraints_applied: list[str] # Constraints that were checked
emergency_mode: bool # Whether emergency escalation active
Development
# Install dev dependencies
pip install -e ".[dev]"
# Run tests
pytest
# Type checking
mypy rmacd
# Linting
ruff check rmacd
License
This work is licensed under Creative Commons Attribution 4.0 International (CC BY 4.0).
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