giskard-oss vs agentic_security
LLM agent evaluation and security scanning compared
giskard-oss and agentic_security are Python toolkits for testing LLM systems and finding potential vulnerabilities. giskard-oss emphasizes repeatable agent and RAG evaluations, including multi-turn checks, while agentic_security centers on probing an HTTP endpoint with configurable scans and adversarial inputs.
giskard-oss: Test and Red-Team LLM Agents
Giskard is a Python toolkit for evaluating agent behavior and probing AI systems for vulnerabilities. It suits teams building LLM agents or RAG applications that need repeatable checks, safety testing, and adversarial evaluation.
agentic_security: Scan LLMs and Agent Workflows for Vulnerabilities
Agentic Security is a Python toolkit for probing LLMs and agent workflows with jailbreaks, fuzzing, and multimodal inputs. It fits developers and security teams who want to run configurable scans against an API and use the results in CI.
| giskard-oss | agentic_security | |
|---|---|---|
| Language | Python | Python |
| License | Apache-2.0 | Apache-2.0 |
| Stars | 5.9k | 2k |
| Forks | 547 | 292 |
| Last analyzed | Oct 3, 2026 | Oct 3, 2026 |
Key differences
- giskard-oss supports scenario-based checks for outputs and traces, including multi-turn interactions and RAG grounding; agentic_security sends prompt-based probes to a configured HTTP endpoint.
- giskard-oss includes checks such as semantic similarity and LLM-as-judge assessments; agentic_security focuses on datasets, prompt mutations, thresholds, and multi-step attack simulations.
- agentic_security documents probing image and audio inputs; giskard-oss describes evaluations for agents, conversations, and RAG applications.
- giskard-oss v3 requires Python 3.12 or later, while agentic_security is described as a Python toolkit without a version requirement here.
- Both use the Apache-2.0 license and are not archived; giskard-oss has 5.9k stars and 547 forks, compared with agentic_security at 2k stars and 292 forks.
Choose giskard-oss if you…
- need repeatable checks for multi-turn agents or RAG answer quality and grounding.
- want scenario-based evaluations of outputs or traces, with checks such as regex, groundedness, or LLM-as-judge.
- need adversarial testing for issues such as prompt injection, harmful content, stereotypes, or misinformation.
Choose agentic_security if you…
- want to scan an API-backed model or agent using configurable HTTP requests and prompt datasets.
- need prompt mutations, multi-step attack simulations, or documented image and audio probes.
- want command-line scans and a CI-oriented workflow for repeatable endpoint checks.
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This comparison is generated with AI from the OSRepos analyses of both projects. Always check each project's repository and documentation before choosing.