Giskard Guide: Open-Source Testing & Evaluation for LLM Agents
Learn how Giskard scans LLM agents for hallucinations, prompt injection and data leakage, and auto-generates domain-specific test suites for reliable AI.
LLM agents are powerful — but they fail in unpredictable ways. Hallucinations, prompt injection, data leakage and biased outputs can slip into production unnoticed. Giskard is an open-source evaluation and testing library (Apache-2.0, 5.7K+ stars) built to catch exactly these failures before your users do.
🚀 Want to deploy Giskard yourself?
Docker configs, system requirements, and installation guides — all on one page.
View Giskard Tool Page →What Makes Giskard Different?
Most LLM testing tools only check one metric. Giskard takes a holistic approach: it scans your model, generates a domain-specific test suite automatically, and monitors regressions over time. It works with any LLM — OpenAI, Anthropic, local models — plus RAG pipelines and multi-step agents.
| Capability | What it catches |
|---|---|
| Vulnerability scan | Prompt injection, data leakage, hallucination |
| Automatic test generation | Domain-specific tests from your data |
| RAG evaluation | Retrieval quality and grounded answers |
| CI/CD integration | Quality gates in your pipeline |
Key Features
- Automatic scan: Detect vulnerabilities with zero manual test writing.
- Test suites: Regenerate suites tailored to your domain and data.
- Agent testing: Validate multi-step tool use and reasoning chains.
- Open source: Apache-2.0 license, self-hosted with Docker.
🚀 Want to deploy Giskard yourself?
Docker configs, system requirements, and installation guides — all on one page.
View Giskard Tool Page →Whether you run a single chatbot or a fleet of autonomous agents, Giskard gives you the confidence to ship AI that behaves. Its active open-source community and growing feature set make it one of the most practical evaluation frameworks available today.