by aiverify-foundation · Agent Tool · ★ 315
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Version 0.7.6 A simple and modular tool to evaluate any LLM-based AI systems. 🎯 Motivation Developed by the AI Verify Foundation, Moonshot is a tool to bring Benchmarking and Red-Teaming together to help AI developers, compliance teams evaluate LLM-based Apps and LLMs. 🚀 Why Moonshot In the rapidly evolving landscape of Generative AI, ensuring safety, reliability, and performance of LLM applications is paramount. Moonshot addresses this critical need by providing a unified platform for: Benchmark Tests: Systematically test LLM Apps or LLMs across critical trust & safety risks using a wide array of open-source benchmark dataset and metrics, including guided workflows to implement IMDA's Starter Kit for LLM-based App Testing. Red Team Attacks: Proactively identify vulnerabilities and potential misuse scenarios in your LLM applications through streamlined adversarial prompting. 🔑 Key Features User-friendly Interfaces: Interact with Moonshot via an intuitive Web UI for visual insights, and an interactive Command Line Interface (CLI) for quick operations. Comprehensiv
| Stars | 315 |
| Forks | 59 |
| Language | Python |
| Category | Agent Tool |
| License | Apache-2.0 |
| Quality Score | 41.9/100 |
| Open Issues | 1 |
| Last Updated | 2026-02-05 |
| Created | 2023-12-14 |
| Platforms | python |
| Est. Tokens | ~15282k |
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moonshot is Moonshot - A simple and modular tool to evaluate and red-team any LLM application.. It is categorized as a Agent Tool with 315 GitHub stars.
moonshot is primarily written in Python. It covers topics such as benchmarking, evaluation-framework, llm.
You can find installation instructions and usage details in the moonshot GitHub repository at github.com/aiverify-foundation/moonshot. The project has 315 stars and 59 forks, indicating an active community.
moonshot is released under the Apache-2.0 license, making it free to use and modify according to the license terms.
The top alternatives to moonshot on Agent Skills Hub include agentic-radar, bocoel, agentseal. Each offers a different approach to the same problem space — compare them side-by-side by stars, quality score, and community activity.