giskard-oss vs iFixAi
Giskard v3: modular Python evals and red-teaming for agentic systems — scenario-based checks with LLM-as-judge, plus an automatic vulnerability scanner across OWASP LLM Top-10 categories. — versus — Independent auditor for AI agents: drops the agent into a simulation of your business, sends realistic requests, and scores operational alignment, permissions and red-team risk in ~2 minutes.
Both run scenario-based checks and red-teaming on agentic systems. Giskard is a Python eval library; iFixAi a business-simulation audit.
| giskard-oss | iFixAi | |
|---|---|---|
| Stars | 5.9k | 44k |
| Forks | 561 | 1.8k |
| Language | Python | Python |
| License | Apache-2.0 | Apache-2.0 |
| Last activity | 4 days ago | 4 days ago |
| Topics | evals, security | evals, security |
| Curated connections | 5 | 3 |
giskard-oss — the curator's take
One of the OG names in LLM testing, freshly rewritten: v3 drops the heavyweight monolith for focused packages — giskard-checks (scenario API for multi-turn agent evals: groundedness, conformity, LLM-judge) and giskard-scan (generates adversarial suites from a plain-language description of your agent, prompt-injection probes included). Async-first, wraps anything callable. Mind the transition, though: v3 is beta, RAG evaluation (RAGET) hasn't been ported yet, and v2 — where scan and RAG eval are mature — is no longer maintained. Python 3.12+ only, and libraries on giskard-core send aggregated telemetry (opt-out documented). Pick ragas for pure RAG metrics; Giskard earns its place on multi-turn agent scenarios plus red-teaming in one stack.
iFixAi — the curator's take
Use it before shipping an agent into a business process, to check it respects roles, goals and who may do what, not just jailbreak resistance. Can be run by a human or by the agent itself. Skip it if you need deep model-level vulnerability scanning (use garak) or a continuous eval platform with tracing. A two-minute audit is a smoke test, not a certification.