Project Moonshot

Overview

Project Moonshot is an open-source toolkit for evaluating large language models and applications with benchmarks, red-team tests, and custom evaluations. It is best suited to AI developers and compliance teams who can run software locally and want control over their test recipes. Its breadth of interfaces and reporting is appealing, though beta status and installation prerequisites make it less turnkey than a hosted service.

Developed by AI Verify Foundation, a not-for-profit subsidiary wholly owned by Singapore’s Infocommunications Media Development Authority, Moonshot implements benchmarks recommended in IMDA’s Starter Kit for safety testing LLM-based applications. The Apache License 2.0 permits use as open-source software, but beta maturity is a reason for teams to validate it against their own operational needs before relying on it.

Key features

Benchmarking and red teaming

Moonshot runs benchmark tests against open-source datasets and metrics for performance and trust and safety risks. It can also generate adversarial prompts through algorithmic strategies or generative LLMs. Prompt injection, jailbreaks, data leakage, and unsafe outputs are among the supported test areas, making the toolkit relevant to teams that need to probe both model quality and common safety concerns in one workflow.

Custom evaluations and connectors

Users can create evaluation recipes from their own datasets, optional prompt templates, metrics, and grading scales. That flexibility is useful when generic benchmarks do not reflect an application’s risks, although teams must supply and maintain the assets that make those tests meaningful. Connections to OpenAI, Anthropic, Together, and Hugging Face models use API keys; users can also build connectors for other models or their own applications hosted on custom servers.

Interfaces and reports

Moonshot offers a Web UI, interactive command-line interface, library APIs, and Web APIs. Results appear in interactive HTML reports, with raw JSON downloads for programmatic analysis. This gives both practitioners and automated workflows a way to consume results rather than limiting evaluation to a single interface.

Pricing

Project Moonshot costs 0.00 USD per free. The open-source toolkit has no paid tier or trial to weigh against its free option. That makes it accessible for teams willing to manage their own environment; the tradeoff is that setup and test assets are part of the work, rather than a hosted service handling them.

Moonshot requires Python 3.11, and its Web UI requires Node.js 20.11.1 LTS or later. Installation uses pip and runs the UI locally at localhost:3000. The installation guide recommends Chrome for the best Web UI experience and warns that x86 Macs may encounter difficulties with moonshot-data dependencies. The FAQ directs users needing help to raise a GitHub issue.

Platforms

Moonshot supports API, Linux, macOS, self-hosted, web, and Windows use. Its self-hosted deployment and multiple interfaces suit teams that want to keep evaluation close to their own systems, but the language-runtime requirements and test assets make it a less convenient fit for readers seeking an immediately usable hosted tool.

Who it's for

AI developers and compliance teams evaluating LLMs or LLM-based applications are the clearest fit, particularly when they need custom datasets, connectors, or reproducible results in JSON. It is less suitable for teams that need mature, managed software or cannot take on local setup and maintenance.

Pros and cons

• Pros: Combines benchmarks and adversarial testing, including prompt injection, jailbreak, data leakage, and unsafe-output checks.
• Pros: Custom recipes, model connectors, and API access allow teams to shape evaluations around their own applications.
• Pros: HTML reports and raw JSON serve both human review and programmatic analysis.
• Cons: Beta status makes maturity a consideration for teams depending on stable evaluation workflows.
• Cons: Python, Node.js for the Web UI, and test-asset requirements add setup work; x86 Mac users may hit dependency difficulties.

Alternatives

Browse AI Security Testing Tools to compare the category. Choose OpenSecureAI Scanner if you want a freemium option with a paid Pro plan at 49.00 USD per month, including 50,000 API requests per month, 120 requests per minute per key, hosted AI remediation and LLM Gateway completions, and priority email support. Its API, Linux, macOS, web, and Windows platforms offer a different deployment mix.

PromptGuard is another freemium alternative, with a free plan capped at 20,000 scans a month, one API key, one project, and 24-hour log retention; choose it if those defined limits suit your needs. Promptfoo offers a Community plan at 0.00 USD per free with 10k red-team probes per month, LLM evaluation features, model providers and integrations, and local or self-hosted runs, so it is worth considering if that allowance and workflow fit better.

PyRIT is a free open-source framework that requires a Python environment and configured AI endpoints. Basilisk is free open-source software available through CLI, desktop, Docker, or source distribution, for authorized use only. F5 BIG-IP APM is a paid alternative with a free trial and a perpetual license offered on BIG-IP Virtual Editions, hardware, or hybrid environments. AgentScan has a free plan with five scans per month, 185 attack vectors, five categories, auto-detection of agent format, and JSON results. AIRTaaS is a free web-based alternative.

Verdict

Choose Project Moonshot if you are an AI developer or compliance team that wants a free, self-hosted toolkit for combining standard benchmarks with custom safety evaluations and can handle its setup requirements. Its range of interfaces and exportable results are strong reasons to consider it; its beta status and installation burden are reasons to look elsewhere if you need a mature, managed evaluation service.