agsec 0.1.1

AI Agent Action Firewall core SDK

agsec

AI Agent Action Firewall - A minimal, control layer for agent actions.

Overview

agsec provides a simple yet powerful way to add safety controls to AI agents. It acts as a "firewall" between agents and real-world actions, allowing you to define policies that approve, block, or review actions before execution.

Why agsec?

• Agent-neutral: Works with any agent framework (LangChain, custom, etc.)
• Declarative policies: Define rules in YAML or code
• Extensible: Plugin system for custom actions and policies
• Production-ready: Lightweight, fast, and secure

Features

• ✅ Action Registry: Register and manage agent actions
• ✅ Policy Engine: Flexible rule-based decision making
• ✅ YAML Policies: Human-readable policy definitions
• ✅ Context Awareness: Rules can access parameters and context
• ✅ Priority & Matching: Advanced rule evaluation (priority, all/any matching)
• ✅ Audit Logging: Built-in logging for all decisions
• ✅ Python Package: Easy installation via PyPI

Installation

Runtime (for users)

pip install agsec

Development (for contributors)

git clone https://github.com/riyandhiman14/Agent-Sec.git
cd agsec
pip install -e .[dev]
pre-commit install

Quick Start

Basic Usage

from agsec import ControlLayer

# Create control layer
control = ControlLayer()

# Register an action
@control.register_action("send_email")
def send_email(to, subject, body):
    return {"sent_to": to, "status": "success"}

# Execute with default allow policy
result = control.execute("send_email", {"to": "[e-mail]", "subject": "Hello", "body": "Hi!"})
print(result.result)  # {"sent_to": "[e-mail]", "status": "success"}

With YAML Policies

from agsec import ControlLayer

policy_yaml = """
rules:
  - action: payment
    status: block
    reason: "High-value payment blocked"
    conditions:
      amount:
        op: ">"
        value: 10000
"""

control = ControlLayer(policy_yaml=policy_yaml)

@control.register_action("payment")
def payment(amount):
    return {"charged": amount}

try:
    control.execute("payment", {"amount": 15000})
except Exception as e:
    print(e)  # PolicyViolationError: High-value payment blocked

API Reference

ControlLayer

Main class for managing agent actions and policies.

ControlLayer(
    policy_engine=None,      # PolicyEngine instance
    action_registry=None,    # ActionRegistry instance
    logger=None,             # Custom logger
    policy_yaml=None,        # YAML policy string
    policy_yaml_path=None    # Path to YAML policy file
)

Methods

• register_action(name): Decorator to register an action function
• execute(action, params, context=None): Execute an action with policy check

PolicyEngine

Handles policy evaluation.

Methods

• add_rule(rule): Add a programmatic rule function
• load_rules_from_yaml(yaml_text): Load rules from YAML string
• load_rules_from_yaml_file(path): Load rules from YAML file
• evaluate(action, params, context=None): Evaluate policy for action

Policy Status

• PolicyStatus.ALLOW: Allow action execution
• PolicyStatus.BLOCK: Block action execution
• PolicyStatus.REVIEW: Mark for manual review

YAML Policy Schema

rules:
  - action: "action_name"          # Action to match (* for all)
    status: "allow|block|review"   # Decision
    reason: "Optional reason"      # Human-readable explanation
    priority: 0                    # Higher = evaluated first
    match: "all|any"               # Condition matching mode
    conditions:                    # Parameter/context checks
      param_name:
        op: "==|!=|>|<|>=|<=|in|not_in"
        value: "expected_value"
      context.user_role:
        op: "=="
        value: "admin"

Development

Setup

pip install -e .[dev]
pre-commit install

Testing

pytest

Building

python -m build

Contributing

1. Fork the repository
2. Create a feature branch
3. Make your changes
4. Add tests
5. Run pre-commit run --all-files
6. Submit a pull request

Code Style

• Black for formatting
• isort for import sorting
• flake8 for linting
• pytest for testing

Roadmap

• Advanced risk scoring
• Multi-agent coordination

Support

• Issues: GitHub Issues
• Discussions: GitHub Discussions