Promptfoo

Developing applications with Large Language Models (LLMs) often feels like navigating uncharted territory, marked by guesswork and tedious manual checks. You need confidence that your prompts are effective, your chosen models perform reliably, and your applications are secure against emerging threats. promptfoo offers a structured, developer-centric approach to move beyond trial-and-error.

Promptfoo is an open-source command-line tool and library designed specifically for evaluating LLM outputs and performing security assessments (red teaming). It helps you build dependable AI applications by enabling systematic testing, comparison, and security hardening – all within your local development environment or CI/CD pipeline. Instead of hoping for the best, you can adopt a test-driven methodology for your LLM development.

Key Capabilities

• 📊 Benchmark Prompts, Models & RAGs: Systematically evaluate different prompts, models (like GPT-4o vs. Claude 3.5 Sonnet), or Retrieval-Augmented Generation setups. Define specific test cases using simple YAML configuration to see exactly how changes impact performance across your core use cases.

• 🛡️ Automate Red Teaming & Pentesting: Proactively discover security weaknesses. promptfoo generates customized attacks targeting your specific application, probing for vulnerabilities like prompt injection, jailbreaks, data leakage, insecure tool usage, and more, providing detailed vulnerability reports.

• ⚡ Accelerate Evaluation Cycles: Speed up your testing process significantly. Features like caching prevent redundant API calls, concurrency runs tests in parallel, and live reloading automatically re-evaluates as you refine your configurations.

• ✅ Score Outputs Automatically: Move beyond manual review by defining assertions. Set pass/fail criteria using built-in checks (e.g., contains, starts-with, llm-rubric) or write custom scoring functions in JavaScript to automatically grade outputs against your requirements.

• 🔌 Integrate Seamlessly: Use promptfoo as a flexible CLI tool, integrate it as a library in your Python or JavaScript projects, or embed it directly into your CI/CD workflows for continuous testing.

• 🤖 Support for Diverse LLMs: Test against a wide array of models. promptfoo supports major providers like OpenAI, Anthropic, Azure, Google, and HuggingFace, local models via Ollama or llama.cpp, and allows integration of custom API providers for virtually any LLM.

• 🔒 Run Locally & Privately: Maintain full control over your data. promptfoo runs entirely on your machine, interacting directly with LLM APIs without needing cloud dependencies or logins for core evaluation tasks.

• 🤝 Collaborate Effectively: Share your findings easily. The built-in web viewer provides clear, side-by-side comparisons and results summaries, making it simple to discuss results and collaborate with teammates.

• 🛡️ Implement Adaptive Guardrails: Deploy defenses that learn. Use insights from red teaming to create and refine guardrails, building a system that continuously improves its protection against evolving threats.

• 🔎 Ensure Model File Security: Scan model files (PyTorch, TensorFlow, Pickle, etc.) for potential risks like malicious code or unsafe operations before deployment, adding a crucial layer of security to your MLOps pipeline.

• 📈 Monitor Security Continuously: Integrate security testing into your development lifecycle. Run checks regularly or within CI/CD to maintain a consistent view of your application's risk posture over time.

Practical Use Cases

1. Refining an AI Assistant's Tone and Accuracy: You're building a customer support bot and need to compare several prompts designed to produce helpful, concise, and on-brand responses. Using promptfoo, you configure test cases with common customer questions (e.g., "How do I reset my password?", "What are your business hours?"). You evaluate these prompts against different models (perhaps gpt-4o-mini for cost vs. claude-3-haiku for speed). The side-by-side view helps you quickly identify the best-performing combination, while assertions automatically flag responses that are too verbose or fail to mention key information.

2. Securing a RAG System Against Data Exfiltration: Your application uses Retrieval-Augmented Generation (RAG) to answer questions based on a private knowledge base. You use promptfoo's red teaming feature to simulate attacks specifically designed to trick the LLM into revealing sensitive information from the documents it shouldn't access. The tool generates tailored prompt injection attempts, and the resulting vulnerability report highlights weaknesses and suggests remediation steps, helping you harden the system prompt and input validation.

3. Benchmarking Local vs. Cloud Models for a Coding Assistant: You want to offer a code generation feature and are considering using a local model like Llama 3 run via Ollama for privacy and potential cost savings, versus a cloud API like GPT-4. With promptfoo, you set up test cases involving various coding tasks (e.g., generating boilerplate code, explaining code snippets, debugging). You run the evaluation comparing the local model's output quality, latency, and adherence to instructions against the cloud provider, allowing you to make an informed, data-driven decision based on performance trade-offs.

Conclusion

promptfoo provides the tools necessary for a more rigorous, reliable, and secure approach to LLM application development. By facilitating systematic evaluation, automated red teaming, and continuous testing, it empowers you and your team to build with confidence. Its developer-friendly design, extensive integrations, focus on privacy, and robust open-source community make it a practical choice for anyone serious about moving LLM projects from experimental stages to production-ready systems.