agent-control-sdk 5.2.0

Python SDK for Agent Control - protect your AI agents with controls

Agent Control - Python SDK

Unified Python SDK for Agent Control - providing agent protection, monitoring, and rule enforcement in one clean package.

Installation

pip install agent-control-sdk

Quick Start

Simple Initialization

import agent_control

# Initialize at the base of your agent
agent_control.init(
    agent_name="My Customer Service Bot",
    agent_id="550e8400-e29b-41d4-a716-446655440000"
)

# Use the control decorator
from agent_control import control

@control()
async def handle_message(message: str):
    return f"Processed: {message}"

With Full Metadata

import agent_control

agent_control.init(
    agent_name="Customer Service Bot",
    agent_id="550e8400-e29b-41d4-a716-446655440000",
    agent_description="Handles customer inquiries and support",
    agent_version="2.1.0",
    server_url="http://localhost:8000",
    # Add any custom metadata
    team="customer-success",
    environment="production"
)

Features

1. Simple Initialization

One line to set up your agent with full protection:

agent_control.init(
    agent_name="...",
    agent_id="550e8400-e29b-41d4-a716-446655440000",
)

This automatically:

• Creates an Agent instance with your metadata
• Discovers and loads rules.yaml
• Registers with the Agent Control server
• Enables the @control() decorator

2. Decorator-Based Protection

Protect any function with server-defined controls:

@control()
async def process(user_text: str):
    return user_text

3. HTTP Client

Use the client directly for custom workflows:

from agent_control import AgentControlClient

async with AgentControlClient() as client:
    # Check server health
    health = await client.health_check()
    print(f"Server status: {health['status']}")
    
    # Evaluate a step
    result = await agent_control.evaluation.check_evaluation(
        client,
        agent_uuid="550e8400-e29b-41d4-a716-446655440000",
        step={"type": "llm_inference", "input": "User input here"},
        stage="pre"
    )

4. Agent Metadata

Access your agent information:

agent = agent_control.current_agent()
print(f"Agent: {agent.agent_name}")
print(f"ID: {agent.agent_id}")
print(f"Version: {agent.agent_version}")

Complete Example

import asyncio
import agent_control
from agent_control import control, ControlViolationError

# Initialize
agent_control.init(
    agent_name="Customer Support Bot",
    agent_id="550e8400-e29b-41d4-a716-446655440000",
    agent_version="1.0.0"
)

# Protect with server-defined controls
@control()
async def handle_message(message: str) -> str:
    # Automatically checked against server-side controls
    return f"Processed: {message}"

@control()
async def generate_response(query: str) -> str:
    # Output is automatically evaluated
    return f"Response with SSN: 123-45-6789"

# Use the functions
async def main():
    try:
        # Safe input
        result1 = await handle_message("Hello, I need help")
        print(result1)
        
        # Output with PII (may be blocked by controls)
        result2 = await generate_response("Get user info")
        print(result2)
        
    except ControlViolationError as e:
        print(f"Blocked by control '{e.control_name}': {e.message}")

asyncio.run(main())

API Reference

Initialization

agent_control.init()

def init(
    agent_name: str,
    agent_id: str | UUID,
    agent_description: Optional[str] = None,
    agent_version: Optional[str] = None,
    server_url: Optional[str] = None,
    rules_file: Optional[str] = None,
    **kwargs
) -> Agent:

Initialize Agent Control with your agent's information.

Parameters:

• agent_name: Human-readable name
• agent_id: UUID string (or UUID instance)
• agent_description: Optional description
• agent_version: Optional version string
• server_url: Optional server URL (defaults to AGENT_CONTROL_URL env var)
• rules_file: Optional rules file path (auto-discovered if not provided)
• **kwargs: Additional metadata

Returns: Agent instance

Decorator

@control()

def control(policy: Optional[str] = None):

Decorator to protect a function with server-defined controls.

Parameters:

• policy: Optional policy name to use (defaults to agent's assigned policy)

Example:

@control()
async def my_func(text: str):
    return text

# Or with specific policy
@control(policy="strict-policy")
async def sensitive_func(data: str):
    return data

Client

AgentControlClient

class AgentControlClient:
    def __init__(
        self,
        base_url: str = "http://localhost:8000",
        api_key: Optional[str] = None,
        timeout: float = 30.0
    ):

Async HTTP client for Agent Control server.

Methods:

• health_check() - Check server health
• Use with module functions like agent_control.agents.*, agent_control.controls.*, etc.

Example:

from agent_control import AgentControlClient
import agent_control

async with AgentControlClient(base_url="http://server") as client:
    health = await client.health_check()
    agent = await agent_control.agents.init_agent(client, agent_data, tools)

Models

If agent-control-models is installed, these classes are available:

• Agent - Agent metadata
• ProtectionRequest - Protection request model
• ProtectionResult - Protection result with helper methods
• HealthResponse - Health check response

Configuration

Environment Variables

• AGENT_CONTROL_URL - Server URL (default: http://localhost:8000)
• AGENT_CONTROL_API_KEY - API key for authentication (optional)

Server-Defined Controls

Controls are defined on the server via the API or web dashboard, not in code. This keeps security policies centrally managed and allows updating controls without redeploying your application.

See the Reference Guide for complete control configuration documentation.

Package Name

This package is named agent-control-sdk (with hyphen in PyPI) and imported as agent_control (with underscore in Python):

# Install (uses hyphen)
pip install agent-control-sdk

# Import (uses underscore)
import agent_control

Basic usage:

import agent_control
from agent_control import control, ControlViolationError

agent_control.init(
    agent_name="...",
    agent_id="550e8400-e29b-41d4-a716-446655440000",
)

@control()
async def handle(message: str):
    return message

SDK Logging

The SDK uses Python's standard logging module with loggers under the agent_control.* namespace. Following library best practices, the SDK only adds a NullHandler - your application controls where logs go and how they're formatted.

Configuring SDK Logs in Your Application

Option 1: Standard Python logging configuration (recommended)

import logging

# Basic configuration
logging.basicConfig(
    level=logging.INFO,
    format='%(asctime)s [%(levelname)s] %(name)s: %(message)s'
)

# Set agent_control logs to DEBUG
logging.getLogger('agent_control').setLevel(logging.DEBUG)

Option 2: SDK settings (control log categories)

from agent_control.settings import configure_settings

# Configure which categories of logs the SDK emits
configure_settings(
    log_enabled=True,           # Master switch for all SDK logging
    log_span_start=True,        # Emit span start logs
    log_span_end=True,          # Emit span end logs
    log_control_eval=True,      # Emit control evaluation logs
)

Controlling What the SDK Logs

The SDK provides behavioral settings to control which categories of logs are emitted:

from agent_control.settings import configure_settings

# Disable specific log categories
configure_settings(
    log_control_eval=False,  # Don't emit per-control evaluation logs
    log_span_start=False,    # Don't emit span start logs
)

These behavioral settings work independently of log levels:

• Behavioral settings: Control which categories of logs the SDK emits
• Log levels: Control which logs are displayed (via Python's logging module)

Environment Variables

Configure SDK logging via environment variables:

# Behavioral settings (what to log)
export AGENT_CONTROL_LOG_ENABLED=true
export AGENT_CONTROL_LOG_SPAN_START=true
export AGENT_CONTROL_LOG_SPAN_END=true
export AGENT_CONTROL_LOG_CONTROL_EVAL=true

Using SDK Loggers in Your Code

If you're extending the SDK or want consistent logging:

from agent_control import get_logger

# Creates logger under agent_control namespace
logger = get_logger(__name__)

logger.info("Processing started")
logger.debug("Detailed debug info")

Default Settings:

• log_enabled: true
• All behavioral settings: enabled

Development

# Install in development mode
cd sdks/python
uv sync

# Run tests
uv run pytest

# Lint
uv run ruff check .

Examples

See the examples directory for complete examples:

• Customer Support Agent - Full example with multiple tools
• LangChain SQL Agent - SQL injection protection
• Galileo Luna-2 Integration - AI-powered toxicity detection

Documentation

• Reference Guide - Complete API and configuration reference
• Examples Overview - Working code examples and patterns
• Architecture - SDK architecture and design patterns

License

Apache License 2.0 - see LICENSE file for details.