Get Started

A Two-Part Platform

Composabl is a two-part platform with a no-code UI and a Python SDK and CLI. The interplay of these parts gives Composabl its combination of usability and power.

The two parts enable teams to work together effectively. People and roles who primarily use code, such as data scientists and controls engineers, use the SDK to create modules like ML models and deep reinforcement learning skills. Then, subject matter experts, process engineers, and others can use the no-code interface to easily create teams of agents from these modular building blocks and train them to succeed.

We designed the platform this way because, for complex, high-value processes, there will be some tasks that can only be done through coding, and some team members who prefer to work in code, and other tasks that are better performed through a visual interface. Both parts of the platform work together.

Most users use both parts of the platform to some extent but spend more time in the no-code UI or the SDK, depending on their expertise and role. How you will use the platform depends on your role and what part of the process you are working on.

Set Up Your Environment

The first thing you will need to do after you login to your account is to connect a simulation to Composabl.

Access Composabl

You can access Composabl Via a no-code UI and an SDK. They work together to enable you to build, train, and deploy autonomous agent systems.

No-Code UI

Python CLI and SDK Instalation

Installing the SDK

pip install -U composabl

Development

pip install -U composabl-dev

Note: The Composabl CLI and SDK require Python version 3.10.x

Workflow Steps

• Step 1: UI | Create your first use case

• Step 2: UI | Set performance goals

• Step 3: UI and SDK | Create agent systems

  • Create skill agents to train with rewards using deep reinforcement learning in the UI, or the SDK.

  • Create or package ML models to import to UI with the SDK to add advanced perception to agent systems.

  • Create or package LLMs to import with the SDK to the UI to add natural language to agent systems.

  • Create or package controllers and optimization algorithms to import to the UI with the SDK to add programmed decision-making to skill agents.

• Step 4: SDK | Publish to the UI with one CLI command

• Step 5: UI | Orchestrate modular components together to create teams of agents in the UI

• Step 6: UI | Train your teams of agents at scale with one click using the UI

• Step 7: Notebook | Export Historian training data and perform detailed analysis

• Step 8: UI and SDK | Export trained multi-agent systems and connect them to the Composabl runtime for deployment