Project Moonshot

Overview

Project Moonshot is a free, open-source toolkit for evaluating large language models and applications with benchmarks and red-team exercises. It suits AI developers and compliance teams who want to define their own evaluations and can manage a local setup. Its flexible interfaces and test recipes are useful; beta status and installation requirements make it a less straightforward choice for teams seeking a turnkey service.

Built by AI Verify Foundation, a not-for-profit subsidiary of Singapore’s Infocommunications Media Development Authority, Moonshot implements benchmarks recommended in IMDA’s Starter Kit for safety testing LLM-based applications. It is released under Apache License 2.0, but its beta status is a reason to assess its maturity against the needs of any production evaluation process.

Key features

Benchmarks and red teaming

Moonshot evaluates models and applications against open-source datasets and metrics covering performance and trust and safety risks. It can generate adversarial prompts using algorithms or generative LLMs, and supports tests for prompt injection, jailbreaks, data leakage, and unsafe outputs. That combination is a good fit for teams looking to assess quality and probe safety risks in one toolkit, though results still need to be interpreted in the context of the team’s own use cases.

Custom evaluations and model connections

Teams can build evaluation recipes from their own datasets, with optional prompt templates, metrics, and grading scales. Moonshot connects to OpenAI, Anthropic, Together, and Hugging Face using API keys; custom connectors can link other models or applications hosted on custom servers. This breadth supports tailored testing, but configuring connections and maintaining custom recipes calls for technical ownership.

Interfaces and reports

Users can work through a Web UI, interactive command-line interface, library APIs, or Web APIs. The toolkit produces interactive HTML reports and raw JSON results that can be downloaded for programmatic analysis. Multiple access paths and machine-readable output suit both interactive review and integration into analysis workflows.

Pricing

Project Moonshot costs nothing. The free plan is the open-source toolkit, with no trial period; it is the only plan described. That makes it suitable for teams able to operate the software themselves, rather than buyers looking for a priced hosted service or commercial support tier.

The cost is not the only setup consideration: Moonshot requires Python 3.11, and its Web UI requires Node.js 20.11.1 LTS or later. Test assets are also needed to run evaluations. The installation instructions use pip and run the Web UI locally at localhost:3000.

Platforms

Moonshot supports API, Linux, macOS, self-hosted, web, and Windows environments. The Web UI is run locally, so it is a self-managed deployment rather than a hosted application. Chrome is recommended for the best Web UI experience, and x86 Macs may encounter installation difficulties with moonshot-data dependencies.

Who it's for

AI developers and compliance teams evaluating LLMs or LLM-based applications are the intended audience. It is particularly appropriate for teams that need benchmark testing alongside red teaming, want to create custom recipes, or need to connect models hosted on their own servers. Teams without capacity to manage Python and, for the Web UI, Node.js prerequisites may prefer a more turnkey option. The FAQ directs users needing help to raise a GitHub issue.

Pros and cons

Pros: Benchmarking, adversarial testing, and custom evaluations are combined in one open-source toolkit, including tests for prompt injection, jailbreaks, data leakage, and unsafe outputs.

Pros: Four interfaces, provider connections, and custom connectors give technical teams several ways to run tests against different models and applications.

Pros: HTML reports and downloadable JSON support both human review and programmatic analysis.

Cons: Python 3.11 is required, and using the Web UI adds a Node.js prerequisite; test assets must also be supplied to run tests.

Cons: Beta status and possible x86 Mac installation problems make it less predictable for teams that need a mature, uncomplicated deployment.

Alternatives

For a wider set of options, browse AI Security Testing Tools. Choose OpenSecureAI Scanner if a freemium option with a $49/month Pro plan, 50,000 API requests per month, and hosted remediation and gateway completions better fits the workflow. AIRTA Red Team is another free open-source option that requires users to bring their own LLM API key.

Promptfoo may suit teams seeking a freemium tool whose Community plan includes 10k red-team probes per month and local or self-hosted use. PyRIT is a free open-source framework for teams with a Python environment and configured AI endpoints. Augustus is a free Apache 2.0-licensed open-source option, with provider credentials potentially needed for API usage. Basilisk offers free AGPL-3.0 software in CLI, desktop, Docker, or source forms for authorized use.

Consider PromptGuard if its free plan’s 20,000 monthly scans, single API key, single project, and 24-hour log retention fit your needs, or if its $19 a month Team plan is a better match. F5 BIG-IP APM is a paid alternative with a free trial and perpetual licensing across virtual editions, hardware, or hybrid environments.

Verdict

Project Moonshot is a strong fit for technically equipped AI developers and compliance teams that want a free, adaptable toolkit for benchmarking and red teaming LLM-based systems. Its custom recipes, provider connections, and multiple interfaces are the main reasons to choose it. Look elsewhere if beta maturity or the local runtime requirements make a self-managed evaluation workflow too demanding.