giskard-oss vs ADR
LLM agent evaluation and security tools compared
giskard-oss provides Python tools for evaluating LLM agents and RAG systems, including scenario checks and adversarial testing. ADR focuses on enterprise agent security through endpoint discovery, activity telemetry, benchmarks, and risk detection.
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.
ADR: Secure and Monitor Enterprise AI Agents
ADR is an enterprise security toolkit for discovering AI tools, collecting agent activity, benchmarking defenses, and detecting risky behavior. It is aimed at security teams evaluating or monitoring AI agents across employee endpoints and customer-facing systems.
| giskard-oss | ADR | |
|---|---|---|
| Language | Python | Python |
| License | Apache-2.0 | Apache-2.0 |
| Stars | 5.9k | 1.6k |
| Forks | 547 | 172 |
| Last analyzed | Oct 3, 2026 | Oct 3, 2026 |
Key differences
- giskard-oss evaluates scenarios, multi-turn behavior, and RAG quality; ADR discovers AI tools on endpoints and collects agent activity.
- giskard-oss generates adversarial tests for issues such as prompt injection and harmful content; ADR includes benchmarks and a detector for suspicious sessions.
- giskard-oss is aimed at agent developers, RAG teams, and AI safety teams; ADR targets enterprise security and engineering teams.
- giskard-oss v3 requires Python 3.12 or later, while ADR's stated language is Python.
- Both use the Apache-2.0 license; ADR also contains vendored AgentDojo code under MIT, whose license should be reviewed.
- giskard-oss requires a configured provider SDK and API key for LLM-as-judge checks and scan generators; ADR's default detector needs Anthropic and OpenAI API keys, with a keyless smoke-test option.
Choose giskard-oss if you…
- need repeatable checks for agent behavior, multi-turn conversations, or RAG answer grounding.
- want to generate adversarial tests for prompt injection, harmful content, stereotypes, or misinformation.
- are building agent or RAG workflows and can use the Python 3.12+ v3 release.
Choose ADR if you…
- need to inventory AI tools, command-line agents, IDE extensions, or MCP servers on endpoints.
- want to review normalized agent activity, including tool use and execution traces.
- need security benchmarks or detection workflows for enterprise agents.
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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.