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OpenART
by AI45Lab
Large-scale red-team benchmarks for stateful agent safety evaluation
Python
Updated Oct 15, 2026
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Summary
Stress-tests autonomous agents for safety and robustness across long-horizon, stateful environments. Runs 10,000+ benchmark scenarios that simulate multi-step state poisoning, privilege escalation, jailbreaks, and unsafe tool use to expose failure modes. Provides a configurable harness and reproducible attack patterns so you can evaluate agent behavior under adversarial and environment-driven conditions.
Key Benefits
As agents act autonomously and interact with other agents or tools, hidden failure modes and exploit paths emerge over long runs. OpenART makes those risks measurable by turning red-team tactics into repeatable, large-scale benchmarks, which is essential for building meaningful agent track records and A2A evaluation. Until now, most safety tests were short or ad-hoc; this framework emphasizes continuous, stateful evaluation that surfaces persistent vulnerabilities and regressions. A2A evaluation
Ideal For
Security engineers and ML teams validating agent safety, tool-use policies, and long-horizon robustness before production rollout. Supports tool-use pattern for robust tool integration and considers Consensus Evaluation in assessing reliability across many steps.
Real-World Examples
- Expose multi-step state poisoning and persistence bugs in agents
- Validate tool-use and privilege boundaries with repeatable red-team scenarios
- Measure regressions and build an agent track record across thousands of runs
Works With
pythondockeropenaillm-agents
Topics
agentagent-environmentagent-evaluationagent-redteamagent-safetyagent-safety-frameworksagent-securityagent-skillsbenchmarkdocker+10 more
Similar Tools
agent-playgroundagent-arena
Keywords
A2A evaluationagent-safetycontinuous agent evaluationagent track record