PromptFoo

What is PromptFoo?

‍PromptFoo is an open-source CLI and library for testing, evaluating, and red-teaming LLM prompts, models, and agents. In practice, it helps teams turn prompt testing into a repeatable workflow with custom assertions and datasets. (promptfoo.dev)

Understanding PromptFoo

‍PromptFoo is built for developers who want to evaluate LLM behavior with something closer to software testing than ad hoc prompt tinkering. It runs locally or in CI, supports multiple providers, and lets teams compare outputs against predefined test cases, metrics, and pass or fail checks. (promptfoo.dev)

‍It is especially useful when prompts, retrieval flows, or agent behavior need regression testing. Teams can define datasets, assert on output shape or content, and run adversarial checks to surface issues like prompt injection, unsafe generations, or brittle model behavior before release. (promptfoo.dev)

‍Key aspects of PromptFoo include:

1. CLI and library: Use it from the command line or embed it in a codebase and CI pipeline.
2. Custom assertions: Check outputs with rules that match your application, not just generic benchmarks.
3. Dataset-driven evals: Reuse test cases across prompts, models, and agent workflows.
4. Red-teaming support: Generate adversarial tests to probe safety and security failure modes.
5. Multi-provider support: Compare behavior across many LLM providers and local models.

Advantages of PromptFoo

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1. Repeatable testing: Makes prompt quality easier to measure over time.
2. Fast iteration: Helps teams catch regressions before they ship.
3. Flexible scoring: Supports exact-match, rubric-based, and custom evaluation logic.
4. Security coverage: Adds a practical layer for red teaming and adversarial validation.
5. Developer friendly: Fits naturally into local workflows and CI/CD.

Challenges in PromptFoo

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1. Setup effort: Good evals take time to define well.
2. Test design quality: Results are only as useful as the assertions and datasets you create.
3. Model variance: Non-deterministic outputs can require careful thresholds and grading.
4. Coverage gaps: Narrow test suites can miss edge cases outside the dataset.
5. Operational overhead: Teams still need a process for maintaining evals as prompts evolve.

Example of PromptFoo in Action

‍Scenario: A team ships an internal support agent that answers policy questions from a knowledge base.

They create a PromptFoo test set with common employee questions, expected answer traits, and assertions for tone, factuality, and JSON structure. They also add adversarial cases for prompt injection and retrieval leakage so they can see whether the agent follows policy under stress.

On each release, the team runs the suite in CI. If a prompt change improves helpfulness but breaks a safety assertion, the failure is visible before deployment, which makes the review process much more concrete.

PromptLayer as an alternative to PromptFoo

‍PromptLayer gives teams prompt management, observability, and eval workflows with a strong emphasis on collaboration and production visibility. If you want a system for organizing prompts, tracking changes, and connecting evaluation to real usage, PromptLayer fits naturally alongside the same development loop that tools like PromptFoo help validate.

Ready to try it yourself? Sign up for PromptLayer and start managing your prompts in minutes.