json2onnx
Converts a JSON file to an ONNX file.
Decision gist · record as of 2026-08-14
Yes, if you need JSON-to-ONNX conversion and can verify compatibility with your ONNX version. The package has low install friction, no runtime dependencies, and a permissive MIT License. However, dormant maintenance (last update 1077 days ago) means you should test thoroughly before production use and be prepared to fork or find alternatives if issues arise.AI-flagged interpretation of the facts on this page — verify before relying
Before you install
- Low install friction with no runtime dependencies.
- Maintenance is dormant—last release was 1077 days ago and the repository shows no recent activity, though it remains unarchived.
License · maintenance · safety
MIT License (permissive) — MIT License permits free use, modification, and distribution with minimal restrictions, making it suitable for both open and closed-source projects.
last release 2023-09-02 (1077 days) · last repo commit 2023-09-02 · 21 stars
0 known vulnerabilities (OSV.dev, 2026-08-14) · 82,898 downloads/mo, #14,121 on PyPI
Alternatives
Verify before relying
pip install json2onnx
from json2onnx import convert
onnx_graph = convert(
input_json_path="model.json",
output_onnx_file_path="model.onnx"
)- What JSON schema or structure is expected as input—whether it must conform to a specific ONNX JSON representation or a custom format.
- Whether the package handles complex ONNX graphs or is limited to simpler model structures.
- Current compatibility with recent ONNX versions, given the dormant maintenance status.
What it is and what it does
json2onnx is a lightweight utility that transforms JSON representations into ONNX model files. It provides both a command-line interface and a Python API, accepting input as either a JSON file path or a dictionary, and returning an ONNX ModelProto object that can be saved to disk or used programmatically.
The package is designed for developers working with ONNX models who need to serialize or reconstruct models from JSON format. It has no runtime dependencies, making it straightforward to integrate. However, the project has been dormant for over a year, so users should verify compatibility with their current ONNX and Python versions before relying on it for production workflows.
Use it for
- Reconstruct ONNX models from JSON representations stored in databases or configuration files.
- Convert JSON-based model definitions into standard ONNX format for inference engines.
- Programmatically generate ONNX models by building JSON dictionaries and converting them.
- Integrate JSON model specifications into automated machine learning pipelines.
- Export model metadata or architecture from JSON to ONNX for compatibility with ONNX tooling.
Worth the install?
AI-flagged interpretation of the facts on this page. Verify before relying on it.
Yes, if you need JSON-to-ONNX conversion and can verify compatibility with your ONNX version.
The package has low install friction, no runtime dependencies, and a permissive MIT License. However, dormant maintenance (last update 1077 days ago) means you should test thoroughly before production use and be prepared to fork or find alternatives if issues arise.
Install
json2onnx on PyPI
Before you install
Low install friction with no runtime dependencies. Maintenance is dormant—last release was 1077 days ago and the repository shows no recent activity, though it remains unarchived.
License in practice
MIT License permits free use, modification, and distribution with minimal restrictions, making it suitable for both open and closed-source projects.
Quickstart
pip install json2onnx
from json2onnx import convert
onnx_graph = convert(
input_json_path="model.json",
output_onnx_file_path="model.onnx"
)Verify before relying
- What JSON schema or structure is expected as input—whether it must conform to a specific ONNX JSON representation or a custom format.
- Whether the package handles complex ONNX graphs or is limited to simpler model structures.
- Current compatibility with recent ONNX versions, given the dormant maintenance status.
Package facts
| License | MIT License permissive |
| Python support | Supports the current Python release >=3.6 |
| Install friction | Low. Pure-Python wheel |
| Runtime dependencies | None |
| Maintenance | Dormant 1,077 days since the last release |
| Last repo commit | |
| First released | |
| Downloads | 82,898 / month, #14,121 on PyPI 30-day window, as of 2026-08-14 |
| Known vulnerabilities | None known OSV.dev, checked 2026-08-14 |
Evidence: json2onnx-2.0.3-py3-none-any.whl
Tags
Let your AI agent find packages like this
Example. Real query, live index.
You found this page by searching. An agent finds it by wishing: SkillFed indexes 14,416 PyPI packages by what they can do, searchable in plain language.
wish › “json to onnx conversion”
- json2onnxConverts JSON files to ONNX model format, accepting either a JSON…
- onnx2jsonConverts ONNX model files to JSON format, either writing to a file or…
- simple-onnx-processing-toolsA collection of command-line tools for transforming ONNX models:…
Give your agent the search over MCP, or paste the wish link into any chat.
More Artificial Intelligence packages
LiteLLM provides a unified Python interface to call 100+ LLM providers (OpenAI, Anthropic, Gemini, Bedrock, Azure, and others) using OpenAI-compatible API format, available as both a Python SDK and a self-hosted AI Gateway proxy server.
Install it if you need to work with multiple LLM providers or want to centralize LLM routing in your organization.
Client library and CLI tool for downloading, uploading, and managing models, datasets, and repositories on the Hugging Face Hub platform.
Install it if you work with Hugging Face Hub models or datasets.
LangChain provides a framework for building agents and LLM-powered applications by composing language models, tools, and memory through a unified API that abstracts over multiple model providers.
hf-xet provides chunk-based deduplication and efficient file transfer for the Hugging Face Hub, enabling faster uploads and downloads of large files with local disk caching.
Tokenizers converts raw text into token sequences for NLP models, with support for training custom vocabularies and using pre-built tokenizers (BPE, WordPiece) optimized for speed via Rust.
Transformers provides a unified framework for loading, fine-tuning, and running state-of-the-art pretrained models across text, vision, audio, video, and multimodal tasks using PyTorch, JAX, or TensorFlow.
Install it if you need to run or train any transformer-based model for NLP, vision, audio, or multimodal tasks.
See also onnx2json · sed4onnx · sng4onnx · soc4onnx · ssi4onnx · sde4onnx · simple-onnx-processing-tools · sne4onnx · sbi4onnx · sod4onnx