SiMa.ai Neat Overview

What Neat Is

SiMa.ai Neat is an application-development framework for building and running AI applications on the SiMa.ai platform. It provides developers a set of Python and C++ APIs to execute and test compiled model artifacts (tar.gz models), compose AI applications that leverage the SoC's hardware blocks, and manage runtime execution.

In the broader SiMa.ai software ecosystem, Neat sits at the application layer, building on the SiMa.ai runtime stack and using GStreamer-based execution underneath so developers can stay focused on application logic instead of manually stitching together lower-level runtime pieces.

How It Works

Neat gives you a direct mental model for that path. A compiled model archive (.tar.gz) becomes a Model component, application logic is assembled as a Graph, and that graph is built and executed as a Run object on the SoC. The same workflow is designed to work well with agentic development too, so teams can explore, build, and iterate faster.

The getting-started guides help you install and build Neat, the programming-model pages explain the main concepts in more detail, and the tutorials show how to apply them to real application patterns.

Start Here

Use these first steps to get Neat installed, built, and running with the core mental model in place.

• InstallationChoose the right setup path for DevKit or Neat SDK development.
• BuildBuild the framework, docs, and optional Python bindings from source.
• Hello Neat!Run your first Neat inference with YOLOv8 and decoded boxes.
• Programming ModelLearn the `Model`, `Graph`, and `Run` workflow in more detail.

Explore

Once the basics are working, use the rest of the docs to deepen your understanding and move faster.

• TutorialsFollow guided examples that walk through real Neat application patterns.
• Tuning & DiagnosticsOperational topics like throughput tuning and pipeline diagnostics, taught in context inside the tutorials.
• ReferenceBrowse the C++ API and supporting reference material.
• ContributeUnderstand the architecture, contributor expectations, and repo conventions.