The Python Component System and AgentOS

This project consists of two major pieces: the Python Component System (PCS) and AgentOS.

Python Component System (PCS)

PCS is an open source Python API, command line interface, and public web registry for building, running, and sharing Python programs. The goals of PCS are to:

  • Make Python program execution reproducible.

  • Transparently manage Python virtual environments while providing a Python API for pip and virtualenv.

  • Simplify experiment tracking and code sharing.

PCS does this by allowing you to explicitly specify dependencies and arguments for your program and then providing a thin runtime (currently based on MLflow) to automatically instrument your program’s execution. PCS is compatible with most frameworks that are used to build machine learning and reinforcement learning systems.

AgentOS

AgentOS is a set of libraries built on top of the Python Component System that make it easy to build, run, and share agents that use Reinforcement Learning (RL) to solve tasks.

Key features of AgentOS:

  • Easy to use Agent API for developing and running new agents.

  • A public repository of popular RL environments and agents, and runs of those agents in those environments that can be reproduced with a single line of code.

  • Example learning agents from different disciplines and research areas are available in the example_agents directory of the project source code.

Connect

Ask questions or report bugs in PCS and AgentOS in GitHub Issues or on the dev Discord.

Find the AgentOS source code on Github.

Test Status Indicator

The Python Component System and AgentOS are alpha software; APIs and overall architecture are likely to change significantly over time. They are licensed under the Apache License, Version 2.0.

Next, check out the Component System Overview to learn about the high-level design of PCS and AgentOS. Alternatively, check out the Python REPL Quick Start if you want to hop directly into code.