Open-RAG-Eval evaluates retrieval-augmented generation without requiring golden answers.
Open source
24 alternativesGiskard
Giskard provides open source evaluation and testing tools for LLM agents.
About Giskard
Giskard is an open-source Python library hosted on GitHub, designed for evaluating, testing, and red-teaming large language model (LLM) agents and related AI systems. The current stable v3 release is a modular, lightweight rewrite built for dynamic, multi-turn testing of agentic workflows, with no heavy unnecessary dependencies.
Users can leverage Giskard to run built-in and custom evaluations for LLM outputs, scan for agent vulnerabilities like prompt injection and jailbreaks, assess RAG system quality, and validate that AI systems adhere to content safety policies. It supports both synchronous and asynchronous testing workflows, and works with any callable LLM, black-box agent, or multi-step AI pipeline.
The tool is suited for AI developers, ML engineers, and teams building production-grade LLM agents, RAG applications, or other generative AI systems that require rigorous testing, safety validation, and regression checks before deployment.
Key features
- Open-source Python library for LLM agent evaluation and testing
- Modular, lightweight v3 architecture for dynamic multi-turn testing
- Native vulnerability scanner for prompt injection, jailbreaks, and harmful content
- RAG quality evaluation tools to verify answer grounding in context
- Supports LLM-as-judge checks and custom evaluation scenarios
- Compatible with any LLM, black-box agent, or multi-step AI pipeline
Best for: AI developers and ML engineers building production LLM agents and RAG systems that require rigorous testing and safety validation.
Good for
- Fully open-source with no licensing costs for users
- Modular design lets users install only required dependencies
- Async-first architecture supports efficient testing workflows
- Includes native RAG evaluation and agent vulnerability scanning
Watch out for
- Giskard v2 is no longer actively maintained
- Requires Python 3.12 or higher to run
Giskard at a glance
- Category
- AI & Machine Learning › AI Data & Analytics
- Type
- Open-source project
- Pricing
- Not confirmed. Check the site
- Open source
- Yes · Source code
- Language
- English
- Last checked
- Oct 5, 2026 · Online when checked
- Listed
- Oct 5, 2026
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Giskard FAQ
Is Giskard open-source?
Yes, Giskard is fully open-source, with its code and development hosted publicly on GitHub.
What types of AI systems can Giskard evaluate?
Giskard supports evaluation of LLM agents, RAG systems, multi-step AI pipelines, and other generative AI callables.
Does Giskard include tools for red teaming AI agents?
Yes, Giskard v3 includes a native vulnerability scanner for red teaming, checking for prompt injection, jailbreaks, and harmful content outputs.
What Python version is required to use Giskard?
Giskard v3 requires Python 3.12 or higher to run.
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