by hegelai · Agent Tool · ★ 3.1k
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🔒 Is prompttools safe to install? View the security audit →
PromptTools :wrench: Test and experiment with prompts, LLMs, and vector databases. :hammer: Welcome to created by Hegel AI! This repo offers a set of open-source, self-hostable tools for experimenting with, testing, and evaluating LLMs, vector databases, and prompts. The core idea is to enable developers to evaluate using familiar interfaces like code, notebooks, and a local playground. In just a few lines of code, you can test your prompts and parameters across different models (whether you are using OpenAI, Anthropic, or LLaMA models).
pip install prompttools
git clone https://github.com/hegelai/prompttools.gitTaken from the project's README on GitHub. Check the security audit before running it.
No red flags found in any of the 11 categories — no credential harvesting, no data exfiltration, no curl-pipe-shell installer. Scanned against the SlowMist agent-security taxonomy, refreshed every 8 hours. Full audit →
| Stars | 3,055 |
| Forks | 256 |
| Language | Python |
| Category | Agent Tool |
| License | Apache-2.0 |
| Quality Score | 69.5611309779286/100 |
| Open Issues | 43 |
| Last Updated | 2026-02-11 |
| Created | 2023-06-25 |
| Platforms | python |
| Est. Tokens | ~2207k |
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prompttools is Open-source tools for prompt testing and experimentation, with support for both LLMs (e.g. OpenAI, LLaMA) and vector databases (e.g. Chroma, Weaviate, LanceDB).. It is categorized as a Agent Tool with 3.1k GitHub stars.
prompttools is primarily written in Python. It covers topics such as deep-learning, developer-tools, embeddings.
The README on GitHub (github.com/hegelai/prompttools) gives: pip install prompttools , then git clone https://github.com/hegelai/prompttools.git. Usage details are in the same README.
prompttools is released under the Apache-2.0 license, making it free to use and modify according to the license terms.
The top alternatives to prompttools on Agent Skills Hub include free-ai-resources-x, Kiln, grepai. Each offers a different approach to the same problem space — compare them side-by-side by stars, quality score, and community activity.
Grades come from a rule-based scan built on the SlowMist agent-security taxonomy, covering 11 red-flag categories including credential harvesting, data exfiltration, and curl | sh installers. It is a first-layer scan, not a manual audit — we say so rather than overstate it.
The scale of the problem is documented independently: Liu et al. (2026), in a study of 31,132 agent skills, report that 26.1% contain security vulnerabilities. Our own full-catalog census is published as a citable open dataset.
Sources & who's responsible: