Practical overview
When it fits
Evaluate Nndeploy when deployment work involves composing inference stages, targeting more than one device class, or integrating a visual workflow into a C++ or Python application.
Who it is for
- AI developers deploying inference workflows across multiple device classes.
- Teams extending workflows with custom preprocessing or performance-oriented C++ operations.
- Application developers integrating exported workflows through C++ or Python APIs.
Documented uses
- Assemble and tune AI inference pipelines through a visual node workflow.
- Adapt inference workflows for desktop, mobile, edge and single-machine server targets.
- Add application-specific processing as Python or C++ workflow nodes.
- Export workflows as JSON and invoke them from C++ or Python applications.
Requirements & limits
The project is published in a public GitHub repository under the Apache-2.0 license.
- The repository lists 13 inference options, including ONNX Runtime, TensorRT, OpenVINO, MNN, CoreML, AscendCL and PyTorch.
- The project describes serial, pipeline-parallel and task-parallel execution, along with zero-copy, memory-pooling and memory-reuse strategies.
- The README lists example model work across language models, image generation, OCR, detection, tracking and segmentation.
- The repository says inference engines can be selected and dependencies reduced through selective compilation; custom inference frameworks can also be connected in an independent runtime mode.
- The described API supports invoking exported workflows; the evidence describes a framework rather than a hosted inference service.
Check current requirements in the official documentation. Source-based guidance does not mean OpenViST has run or independently tested this project.
Build and export a visual AI workflow
Assemble an AI workflow with visual nodes, adjust its parameters, and export it as JSON for later use.
Before you start
- Access to nndeploy's visual workflow interface.
Steps
Assemble the AI workflow by dragging nodes into place.
Adjust workflow parameters as needed.
Export the workflow as JSON.
Expected outcome
A visual AI workflow is exported in JSON format and can be invoked through a C++ or Python API.
Guide limitations
- The source does not provide interface-specific instructions or API invocation steps.
- The source names supported deployment platforms as Linux, Windows, macOS, and Android; it does not specify that every workflow or inference backend works on every platform.
Add a custom node to a visual workflow
Extend a workflow with a custom Python or C++ node for preprocessing or performance-oriented operations.
Before you start
- A visual workflow to integrate the custom node into.
- A custom node implemented in Python or C++; C++/CUDA is identified for high-performance nodes.
Steps
Implement the operation as a custom Python or C++ node.
Integrate the custom node into the visual workflow.
Expected outcome
The workflow includes an application-specific custom operation.
Guide limitations
- The source does not describe node implementation interfaces, build steps, or testing procedures.
Sources & review
Source record details
- Source availability
- Active
- Sources checked
- 11 October 2026
- Original website title
- nndeploy/nndeploy · Official homepage title · retained in its source language