What is OpenMind’s App Builder and how does it work?

OpenMind is building tools intended to reduce the complexity of developing software for autonomous machines. At the center of this effort is OM1, an open source operating system designed for robots and other intelligent devices. The company often describes OM1 as an Android-style robotics platform, meaning a shared runtime that abstracts hardware differences while allowing developers to focus on behavior and logic.

Recently, OpenMind introduced the OpenMind App Builder, a visual configuration tool in its developer portal that lets developers create, modify, and deploy robot applications without writing code for common tasks. The announcement, made through the company’s official X account, was accompanied by a short demonstration video showing the product in use.

This article explains what the OpenMind App Builder is, how it works at a technical level, and where it fits within the broader OM1 ecosystem.

What is OpenMind’s broader mission?

OpenMind’s goal is to enable autonomous machines through shared standards and modular software. OM1 is licensed under the MIT license and is developed openly on GitHub, where it has attracted thousands of stars and community contributions. The runtime is designed to support a wide range of robots, including humanoids, quadrupeds such as the Unitree Go series, and mobile research platforms like TurtleBot.

The Pi Network Ventures-backed company is also a core contributor to the Fabric Foundation, an organization focused on standards for autonomous machine coordination and on-chain identity. Fabric promotes specifications such as ERC 7777, which defines how robot behaviors can be described and exchanged. The App Builder is positioned as a practical interface on top of these underlying systems.

What the OpenMind App Builder is

The OpenMind App Builder is a no-code and low-code visual interface for configuring robot behavior on OM1. It is accessed through the OpenMind developer portal after creating an account. Instead of writing configuration files by hand, developers build applications by assembling visual nodes that represent robot modes and defining how those modes connect.

Each application is represented as a flowchart. Nodes correspond to behavioral states such as greeting, navigation, or mapping. Transitions between nodes define when and how the robot switches from one behavior to another. The resulting configuration is saved and can be deployed directly to compatible hardware through the portal.

The App Builder does not replace traditional programming. Rather, it sits on top of OM1’s configuration system and exports structured configuration files that can be extended or modified in code for advanced use cases.

Core concepts and terminology

Understanding the App Builder requires familiarity with several OM1 concepts.

Modes

A mode is a discrete behavioral state. For example, a robot might have a welcome mode, a navigation mode, and a memory mode. Each mode defines which language model is used, which sensors are active, which actions are allowed, and the available background context.

Nodes and transitions

In the visual editor, each mode appears as a node. Transitions are directional links between nodes. A transition includes conditions that determine when the robot moves from one mode to another. Developers can specify that a spoken command triggers a shift from idle behavior to navigation.

Inputs, actions, and backgrounds

Inputs represent sensor or data sources such as microphones, cameras, or web-based feeds. Actions represent outputs such as movement commands, speech synthesis, or memory writes. Backgrounds provide persistent context, such as GPS location or navigation state.

Lifecycle hooks

Each mode includes lifecycle hooks, including a system prompt for the language model. This allows developers to control how the model behaves in a given mode using natural-language instructions stored as part of the configuration.

How does the App Builder work in practice?

The demonstration video published alongside the announcement shows the complete workflow from start to deployment.

Selecting a robot

When a developer opens the App Builder, the first step is selecting a machine from the sidebar. This associates the configuration with a specific robot profile, including its supported sensors and actions. OM1 provides hardware abstraction through a dedicated layer, allowing the same high-level configuration to be reused across similar machines.

Building modes visually

After selecting a robot, the canvas populates with an initial flowchart. Developers can add new modes by clicking a plus icon. Each new mode opens an editor panel where parameters are defined.

Within this panel, the developer selects a language model from a dropdown list. Supported options include multiple commercial and open models. Inputs are added next, such as automatic speech recognition for voice control or camera feeds for vision. Actions are then chosen, such as navigation or speech output. Backgrounds like GPS or navigation context can also be enabled.

All changes are saved immediately, and the canvas updates to reflect the current configuration.

Defining transitions

Once modes are created, transitions are defined by dragging a connector from one node to another. This opens a rule editor where conditions are specified. Conditions can reference inputs, internal state, or other signals. For example, a transition rule might specify that a recognized voice command causes the robot to leave its idle mode and enter a navigation mode.

An auto-format feature rearranges the canvas to keep the flowchart readable as it grows.

Deployment

When the configuration is complete, the developer can deploy it directly from the interface. The configuration is uploaded to the robot through the OpenMind portal and applied without manual file transfers. For teams using OM1 locally or in production pipelines, the same configuration can be deployed using command-line tools or containerized workflows.

Supported models and components

According to OpenMind, the App Builder currently supports more than six language models, over forty inputs, thirty actions, and more than ten background contexts. These numbers reflect the modular design of OM1, where each component is implemented as a plugin.

Language models can be swapped without rewriting the application logic. Inputs and actions are similarly interchangeable, as long as the underlying hardware supports them. This approach allows developers to experiment with different configurations quickly while maintaining a consistent structure.

Integration with OM1 and code-based workflows

While the App Builder emphasizes visual configuration, it is designed to integrate with OM1’s code base.

Developers can export configurations as structured files and store them in version control. Advanced users can create custom inputs and actions by adding Python modules to the appropriate directories in the OM1 repository. These custom components then appear in the App Builder interface for selection.

For deployment at scale or on edge devices such as Nvidia Jetson hardware, OM1 supports container-based setups. The App Builder complements these workflows by reducing the time spent on initial configuration and iteration.

Hardware abstraction and portability

One of OM1’s core design goals is hardware agnosticism. The App Builder reflects this by exposing only high-level behaviors rather than low-level motor control. For example, a developer can configure a navigation action without specifying how individual joints move.

This abstraction is implemented through a hardware abstraction layer that connects OM1 actions to robot-specific software development kits such as ROS2 or vendor APIs. As a result, the same application logic can often be reused across different robots with minimal changes.

Limitations and considerations

The App Builder is intended to simplify common tasks, but it does not eliminate the need for engineering judgment.

Some hardware platforms have limited support depending on compute capabilities. Full feature sets are currently available on newer Nvidia-based systems, while older platforms may require compromises. OM1’s core runtime also limits direct internet access for safety and reliability, which affects how external APIs are used.

For complex autonomy, developers are expected to combine App Builder configurations with simulation, reinforcement learning, and extensive testing. OpenMind documentation emphasizes starting with simple behaviors and validating them in simulated environments before deploying to real machines.

Conclusion

The OpenMind App Builder is a visual configuration tool that sits on top of the OM1 runtime and simplifies the creation and deployment of robot applications. By representing robot behavior as modes, transitions, and modular components, it allows developers to assemble functional applications without writing code for every step.

Its value lies in reducing setup friction while remaining compatible with code-based workflows. For teams building on OM1, the App Builder provides a structured way to design, test, and deploy robot behavior across different hardware platforms. Rather than replacing traditional development, it serves as an interface that makes the underlying system more accessible and easier to reason about.

Sources:

• X Post: Announcing the OpenMind App Builder 
• Developer Portal: Build Apps on OpenMind