SciEvalKit
Unified evaluation toolkit and leaderboard for assessing scientific intelligence
GraphCanon updated Sep 20, 2026 · GitHub synced Sep 20, 2026
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Decision brief
SciEvalKit is a unified evaluation toolkit and leaderboard designed to rigorously assess the scientific capabilities of large language and vision-language models throughout research processes.
Good fit when
- When assessing the scientific intelligence of multimodal models specifically across research stages
- If you need an integrated approach to evaluate models on various tasks within scientific inquiry
Avoid when
- For evaluating general performance without a focus on scientific applications and methodologies
- If your project does not benefit from an evaluation framework centered around vision-language abilities in scientific contexts
Observed Jul 16, 2026 · Source: enrich:decision_facts
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Maintenance and security
Full trust report- Maintenance
- Active (10d since push)
- As of Sep 9, 2026
- Provenance
- Not a fork · Organization account
- As of Sep 9, 2026
- Security (OSV)
- 1 high, 1 low (1 high, 1 low)
- As of Jul 15, 2026
Public GitHub metadata and optional OSV scans. Signals, not a guarantee. Trust methodology.
Install
pip install SciEvalKitSimilar 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
SciEvalKit is a tool designed to rigorously evaluate the scientific capabilities of large language and vision-language models across every stage of the research process.
Capability facts
- Languages
- python
Source: github.language · Sep 9, 2026
Categories
Compatibility
Sourced claims from the README excerpt - not unsourced marketing copy.
Source: README excerpt (regex_v1, Sep 9, 2026)
pip install -e .[all] # brings in vllm, openai‑sdk, hf_hub, etc.Source link
Tags
README
1 · Install
For agents
This page has a .md twin and JSON over the API.