Getting Started
Last updated
Last updated
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 components of agents 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 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.
The first thing you will need to do after you login to your account is to connect a simulation to 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 agents.
Installing the SDK
Development
Note: The Composabl CLI and SDK require Python version 3.10.x
Step 1: UI | Create your first project
Step 2: UI | Set project performance goals
Step 3: UI and SDK | Create agent components
Create or package ML models to import to UI with the SDK to add advanced perception to agents.
Create or package LLMs to import with the SDK to the UI to add natural language to agents.
Create or package controllers and optimization algorithms to import to the UI with the SDK to add programmed decision-making to agents.
Step 4: SDK | Publish agent components to the UI with one CLI command
Step 5: UI | Orchestrate modular components together to create agents in the UI
Step 6: UI | Train 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 agents and connect them to the Composabl runtime for deployment
