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, MIT-licensed framework for security professionals and engineers assessing risks in generative AI systems. It supports single-turn and multi-turn red-team strategies such as Crescendo, TAP, and Skeleton Key. Scenario evaluations can be repeated across hundreds of objectives, including content harms, psychosocial risks, and data leakage. CoPyRIT provides a web interface for interacting with AI systems, recording findings, and collaborating with a team. Documented targets include OpenAI, Azure, Anthropic, Google, HuggingFace, custom HTTP endpoints and WebSockets, and web apps tested with Playwright. PyRIT stores conversations, scores, and attack results in SQLite or Azure SQL and can export results. Scoring supports true/false, Likert, classification, and custom scorers, including LLM-based and Azure AI Content Safety methods. Scanner, GUI, Framework, pyrit_scan, and pyrit_shell modes are documented. Installation is available through Docker, pip, or uv, with Python 3.13 recommended locally.