GraphCanon updated Sep 28, 2026 · GitHub synced Sep 28, 2026
39views this month
Decision brief
Automate feature engineering by integrating vast external datasets into ML workflows.
Good fit when
- Need rapid access to diverse external data for model enrichment
- Focusing on automated feature extraction from varied public and premium sources
Avoid when
- Seeking full control over the source code of all components integrated into ML pipelines
- Working with proprietary data that cannot be sourced or merged via external services
Observed Jul 17, 2026 · Source: enrich:decision_facts
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Maintenance and security
Full trust report- Maintenance
- Very active (4d since push)
- As of Sep 28, 2026
- Provenance
- Not a fork · Organization account
- As of Sep 28, 2026
- Security (OSV)
- 27 low (27 low)
- As of Jul 11, 2026
Public GitHub metadata and optional OSV scans. Signals, not a guarantee. Trust methodology.
Install
pip install upgini PyPISimilar tools
Same-category neighbours. No typed graph edges are catalogued for this tool yet.
Evidence and technical details
Sourced facts, taxonomy, compatibility claims, README excerpt, and machine-readable endpoints.
Overview
A Python library to easily find and add relevant features to ML pipelines from external data sources, including LLMs.
Capability facts
- Deploy
- Self-host
Source: dockerfile:Dockerfile · Sep 28, 2026
- Docker
- Dockerfile present
Source: dockerfile:Dockerfile · Sep 28, 2026
- Languages
- python
Source: github.language+pyproject.toml · Sep 28, 2026
Categories
Compatibility
Sourced claims from the README excerpt - not unsourced marketing copy.
Tags
README
1. Install from PyPI In Colab, it is recommended to restart the runtime after install before importing upgini. Otherwise pyarrow may fail with a binary incompatibility error. <details <summary 🐳 <b Docker way</b </summary </br Clone <i $ git clone https://github.com/upgini/upgin...
For agents
This page has a .md twin and JSON over the API.