Giskard vs Promptfoo
You are building an LLM app and you want to catch problems before users do. Giskard scans your model for issues automatically, including ones you did not think to look for. Promptfoo runs the test cases you write and checks that specific inputs give the outputs you expect. One goes hunting. The other confirms what you already care about.
| Dimension | Giskard | Promptfoo |
|---|---|---|
| What it is | A Python library that auto-scans models for issues | A config-driven eval runner with red-teaming built in |
| License | Open source | Open source |
| Core idea | Automated vulnerability and bias detection | Explicit test cases you define and assert on |
| You work in | Python | YAML config, with JS/TS when you want it |
| Finds | Issues you did not think to check for | Whatever you wrote a test for |
| Red-teaming | Yes, scanning is the core | Yes, a real strength |
| CI integration | Works, Python-based | Strong, built for it |
| Best for | Surfacing unknown risks in a model | A fast, repeatable eval gate in any stack |
You want a tool to hunt for problems you have not enumerated yourself, like hidden biases, robustness gaps, or injection weaknesses. Giskard is Python-first and shines when your model is a black box you need to probe. Reach for it when the risk is the stuff you do not know to test.
You know what correct output looks like and you want a gate that checks it on every change. Promptfoo gets you from zero to a passing eval with a config file and one command, and it works across languages and CI. It also does solid red-teaming, so it covers more than your happy-path cases.
These are not really rivals, and the mistake is treating them as one choice. I reach for Promptfoo first. Most teams need a repeatable gate that pins down the behavior they care about, and it wires into CI fast. Giskard earns its place as a second layer, a scan that surfaces risks you never wrote a test for. If you can only run one this week, run Promptfoo and write ten real test cases. Add Giskard when you want a machine looking for the failures you missed.