optr 0.0.0a4

Unified toolkit for building, training, and testing digital and physical operators.

Operator (optr)

A unified Python framework for building, training, and deploying intelligent operators across digital and physical environments.

[!WARNING]
Early Alpha — APIs and behavior will change without notice.

Overview

optr provides a flexible architecture for creating operators that can:

• Automate desktop applications via GUI interaction
• Control physical robots through simulation and hardware interfaces
• Learn from demonstrations using imitation learning and reinforcement learning
• Record and replay episodes for testing and training
• Bridge multiple environments with unified connector interfaces

Key Features

• Desktop Automation - Click, type, and interact with GUI elements
• Robot Control - MuJoCo simulation and physical robot support
• Learning Algorithms - Imitation learning, Pi0, and custom algorithms
• Episode Recording - Capture and replay operator sequences
• Modular Connectors - Extensible interface for any environment
• Validation & Safety - Built-in sentinel guards and validators
• Training Pipeline - Dataset management and model training

Installation

Basic Install

pip install optr

Development Install (using uv)

git clone https://github.com/codecflow/optr

cd optr

uv sync --dev

Quick Start

Desktop Automation

Create an operator that automates login:

# my_app/operators/login.py

from optr.operator import Operator
from optr.connector.desktop import DesktopConnector

async def login_operator():
    op = Operator({"desktop": DesktopConnector()})
    
    # Click username field
    await op.execute_action("click", selector="#username")
    await op.execute_action("type", text="demo_user")
    
    # Click password field  
    await op.execute_action("click", selector="#password")
    await op.execute_action("type", text="secure_pass")
    
    # Submit form
    await op.execute_action("click", selector="#submit")
    
    return op

Robot Control (MuJoCo)

Control a simulated robot:

# my_app/operators/robot.py

from optr.operator import Operator
from optr.simulator.mujoco import MuJoCoSimulation

async def robot_operator():
    sim = MuJoCoSimulation("models/robot.xml")
    op = Operator({"robot": sim.get_connector()})
    
    # Move to target position
    await op.execute_action("move", 
                           connector_name="robot",
                           position=[0.5, 0.3, 0.2])
    
    # Grasp object
    await op.execute_action("grasp", 
                           connector_name="robot",
                           force=10.0)
    
    return op

Core Concepts

Operators

The main abstraction for defining automated behaviors. Operators can work with multiple connectors simultaneously.

Connectors

Interfaces to different environments (desktop, robot, web, etc.). Each connector provides state observation and action execution.

Algorithms

Learning algorithms for training operators from demonstrations or through reinforcement learning.

Episodes

Recorded sequences of states and actions that can be replayed or used for training.

Sentinel

Safety and validation layer that ensures operators behave within defined constraints.

Roadmap

• Cloud API connectors
• Distributed operator coordination
• Model zoo with pre-trained operators
• Real-time monitoring dashboard

License

MIT © CodecFlow