deepbom
PulseGate's liveness check found it on 15 Sep 2026; it is registered on GitHub and PyPI and has been in the index since 15 Sep 2026. How this is checked
deepbom is an open-source Python package and command-line tool for analyzing deployment artifacts used by on-device AI models. It supports formats including TFLite, ONNX, GGUF, Safetensors, Core ML, ExecuTorch, and TensorRT, with outputs such as CycloneDX and SARIF.
Inferred · not functionally tested
Overview
6 featuresPurpose: Analyzing on-device AI model artifacts and producing deployment and compliance metadata.
Inferred · not functionally tested
Audience: machine learning engineers and AI infrastructure developers
Inferred · not functionally tested
Functions: data_extraction
Inferred · not functionally tested
Interfaces: API: indicated (inferred, not tested) · MCP: indicated (inferred, not tested) · CLI: indicated (inferred, not tested) · Self-hosting: indicated (inferred, not tested)
Recorded constraints: pricing: open_source · license: Apache-2.0 · platforms: CLI, WEB · deployment: browser, cli, api_only, self_hosted
Constraint provenance is unknown; confirm requirements with the publisher.
Record sources: deepbom.org · github.com. These links do not verify the individual claims.
deepbom sits in PulseGate's Other data science & ML category. Inferred · not functionally tested: It focuses on analyzing on-device AI model artifacts and producing deployment and compliance metadata. Inferred · not functionally tested: It is built as an open-source project for machine learning engineers and AI infrastructure developers. Basis unknown · not verified: The project is open source (Apache-2.0). Basis unknown · not verified: deepbom is available on the web, the command line, and API, and it can be self-hosted.
JunHwan Kwon builds and maintains deepbom, and it first shipped in 2026. The project is developed in the open on GitHub with 83 commits in the last 90 days. Inferred · not functionally tested: Among its 6 catalogued features are artifact analysis, TFLite support, and ONNX support. Inferred · not functionally tested: Catalogued interfaces include an MCP server.
Summary written by a language model from the project’s public pages.
Tasks: Inferred · not functionally tested
- Artifact analysis
- TFLite support
- ONNX support
- GGUF support
- Safetensors support
- Core ML support
Topics: Inferred · not functionally tested
Built with & integrations
Trust & compliance
Indexing history
1What PulseGate has recorded for this listing
Frequently asked questions about deepbom
- What is deepbom?
- Inferred · not functionally tested: Deepbom focuses on analyzing on-device AI model artifacts and producing deployment and compliance metadata. It is catalogued under Other data science & ML on PulseGate.
- Who should use deepbom?
- Inferred · not functionally tested: deepbom is an open-source project built for machine learning engineers and AI infrastructure developers.
- Does deepbom have a free plan?
- Basis unknown · not verified: Yes — deepbom is open source under the Apache-2.0 license and free to use.
- What platforms does deepbom run on?
- Basis unknown · not verified: deepbom runs on the web, the command line, and API. It can also be self-hosted.
- Is deepbom still maintained?
- PulseGate's liveness check found it on 15 Sep 2026. Its GitHub repository shows 83 commits in the last 90 days.
- Who develops deepbom?
- deepbom is developed by JunHwan Kwon.
- How long has deepbom been around?
- deepbom first shipped in 2026.
- Is deepbom open source?
- Basis unknown · not verified: Yes — deepbom is open source under the Apache-2.0 license, developed on GitHub.
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