Promptfoo vs PyRIT

Promptfoo helps developers and enterprises evaluate prompts and test AI applications for security risks. It compares prompt outputs across multiple models in a web view. Red-team testing simulates users to look for prompt injections, jailbreaks, data leaks, business rule violations and insecure agent tool use. Its getting-started guide lists more than 60 providers, including OpenAI, Anthropic, Google and local models such as Ollama. Integrations include CI systems such as GitHub Actions, GitLab CI, Jenkins, Azure Pipelines, CircleCI and Bitbucket Pipelines, plus Splunk, Burp Suite, Google Sheets and MCP. Installation is available through npm, npx or Homebrew. Promptfoo requires Node.js 22.22.0 or newer and recommends Node.js 24 LTS. The free Community plan includes all LLM evaluation features and 10k red-team probes per month, and can run locally or be self-hosted. Enterprise includes managed cloud deployment; On-Premise runs on customer infrastructure. Promptfoo says it is based in San Francisco, is now part of OpenAI and remains open source.

PyRIT is a free, open-source framework for automated and human-led red teaming of generative AI systems, aimed at assessing their security and safety. It supports single-turn and multi-turn attack strategies, including Crescendo, TAP, and Skeleton Key. Scenarios combine strategies and datasets for repeatable assessments of content harms, psychosocial risks, data leakage, and other objectives. Assessments can run through a command-line scanner, an interactive shell, the CoPyRIT graphical interface, or custom workflows. Documented targets include OpenAI, Azure, Anthropic, Google, Hugging Face, custom HTTP endpoints and WebSockets, and web apps tested with Playwright. Its modular components include targets, converters, scorers, memory, datasets, attacks, and scenarios. Converters can encode, obfuscate, translate, or semantically alter prompts, and transform among text, images, audio, video, and files. Scorers can return Boolean or normalized 0.0–1.0 scores using LLMs, Azure AI Content Safety, or custom logic. Built-in memory tracks conversations, scores, and attack results using SQLite or Azure SQL. PyRIT is self-hosted and requires a Python environment and configured AI endpoints.