agent-anvil vs giskard-oss
Agent evaluation and security testing compared
agent-anvil and giskard-oss are Python projects for evaluating AI agents with repeatable tests. agent-anvil emphasizes workflow and tool-trace checks that can run offline or in CI, while giskard-oss combines agent evaluation with adversarial security scanning and RAG testing.
agent-anvil: Test AI Agent Tool Use in CI
Agent Anvil evaluates tool-using AI agents through scenario-based runs, trace checks, and optional semantic grading. It helps teams catch unsafe or incorrect tool behavior before it reaches production.
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.
| agent-anvil | giskard-oss | |
|---|---|---|
| Language | Python | Python |
| License | MIT | Apache-2.0 |
| Stars | 9 | 5.9k |
| Forks | 2 | 547 |
| Last analyzed | Oct 4, 2026 | Oct 3, 2026 |
Key differences
- agent-anvil checks tool-call order, limits, arguments, results, and final output; giskard-oss also supports multi-turn evaluations and RAG answer grounding.
- agent-anvil offers optional OpenAI semantic grading alongside deterministic checks; giskard-oss includes checks such as regex, semantic similarity, and LLM-as-judge assessments.
- giskard-oss generates adversarial tests for issues including prompt injection and harmful content; agent-anvil focuses on scenario-based workflow and safety requirements.
- agent-anvil is MIT-licensed, while giskard-oss is Apache-2.0-licensed.
- The supplied facts list 9 stars and 2 forks for agent-anvil, compared with 5.9k stars and 547 forks for giskard-oss.
- Both require Python 3.12 or newer according to their supplied considerations; giskard-oss also maintains v2 for legacy tabular-model scanning, while its current v3 focuses on agent evaluation and security.
Choose agent-anvil if you…
- need repeatable checks for tool-call sequences, arguments, and workflow rules.
- want to run deterministic scenarios offline or integrate evaluation into CI with GitHub Actions.
- are evaluating an existing command-line or HTTP agent without replacing its runtime.
Choose giskard-oss if you…
- need adversarial testing for prompt injection, harmful content, or misinformation.
- want to evaluate multi-turn assistants or check RAG answer grounding.
- need a broader toolkit that combines scenario checks with agent security scanning.
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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.