Quickstart

Install Agent Control, start the server, and protect your first agent in minutes.

Protect your AI agent in 3 steps: start the server, create a control, and run your agent.

Prerequisites

• Python 3.12+
• Docker

Step 1: Start the Server and Install the SDK

Run the one-liner to start the Agent Control server and dashboard, then install the SDK:

curl -L https://raw.githubusercontent.com/agentcontrol/agent-control/refs/heads/main/docker-compose.yml | docker compose -f - up -d

uv venv && source .venv/bin/activate
uv pip install agent-control-sdk

• Server + UI running at http://localhost:8000 ✅
• Python SDK installed ✅

💡 Verify the server: Open http://localhost:8000/health — you should see {"status": "healthy", "version": "..."}.

Alternative: Local development with the repo

If you want to contribute to Agent Control or run from source, clone the repository instead:

Additional prerequisites:

• uv — Fast Python package manager (curl -LsSf https://astral.sh/uv/install.sh | sh)
• Node.js 18+ — For the web dashboard (optional)

# Clone the repository
git clone https://github.com/agentcontrol/agent-control.git
cd agent-control

# Install dependencies
make sync

# Start the Agent Control server (boots Postgres + runs migrations)
make server-run

# Start the UI (in a separate shell)
make ui-install
make ui-dev

• Server runs at http://localhost:8000 ✅
• UI runs at http://localhost:4000 ✅

Step 2: Create a Control

You can create controls through the UI dashboard or programmatically with the SDK. Here we’ll use the SDK to create a control that blocks SSN patterns in agent output:

# setup_control.py — run once to configure your control

import asyncio
from agent_control import AgentControlClient, controls

async def setup():
    async with AgentControlClient() as client:
        control = await controls.create_control(
            client,
            name="block-ssn",
            data={
                "enabled": True,
                "execution": "server",
                "scope": {"stages": ["post"]},
                "selector": {"path": "output"},
                "evaluator": {
                    "name": "regex",
                    "config": {"pattern": r"\b\d{3}-\d{2}-\d{4}\b"},
                },
                "action": {"decision": "deny"},
            },
        )
        print(f"✅ Control created: {control['control_id']}")

asyncio.run(setup())

python setup_control.py

Step 3: Protect Your Agent

Add the @control() decorator to any function you want to protect. Agent Control will intercept the output and check it against your controls.

# my_agent.py

import asyncio
import agent_control
from agent_control import control, ControlViolationError

agent_control.init(
    agent_name="my_chatbot",
    agent_description="My first protected agent",
)

@control()
async def chat(message: str) -> str:
    # In production: response = await llm.ainvoke(message)
    if "test" in message.lower():
        return "Your SSN is 123-45-6789"  # Will be blocked!
    return f"Echo: {message}"

async def main():
    # This will be allowed
    print(await chat("hello"))     # Output: Echo: hello

    # This will be blocked — output contains an SSN pattern
    try:
        print(await chat("test"))
    except ControlViolationError as e:
        print(f"❌ Blocked by: {e.control_name}")

asyncio.run(main())

python my_agent.py

🎉 Done! Your agent now blocks SSN patterns automatically.

What Is Happening Under the Hood

1. Your app calls chat("test")
2. The function returns "Your SSN is 123-45-6789"
3. The @control() decorator sends the output to the Agent Control server
4. The server checks the output against all controls associated with this agent
5. block-ssn finds an SSN pattern → match
6. The server returns is_safe=False
7. The SDK raises ControlViolationError and blocks the response

Key Benefits:

• ✅ Controls are managed separately from your code
• ✅ Update controls without redeploying your agent
• ✅ Same controls can protect multiple agents
• ✅ View analytics and control execution in the dashboard

What’s Next

UI Quickstart

Manage agents and controls visually through the dashboard.

Concepts

Learn about controls, selectors, evaluators, and actions.

Examples

See working integration examples with LangChain, CrewAI, and more.

Configuration

Server configuration, authentication, and deployment options.