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myclaw-bench
by LeoYeAI
45-task benchmark suite for evaluating agents on OpenClaw
Python
Updated Jul 20, 2026
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Overview
Provides a standardized benchmark suite for evaluating AI agents on the OpenClaw platform. Runs 45 tasks across four difficulty tiers to measure capabilities, failure modes, and performance regressions under consistent conditions. Includes task definitions, scoring rubrics, and reproducible runs so teams can compare agent track records over time. reproducible runs
Key Benefits
As agents interact more with other agents and environments, quantitative, repeatable evaluation becomes essential for building trust. This benchmark gives teams a common yardstick to surface agent failure modes, compare approaches, and trace regressions in agent-to-agent evaluation scenarios. By focusing on a broad set of tasks and tiers, it helps convert ad-hoc testing into continuous agent evaluation and meaningful agent track records.
Target Use Cases
Teams validating agent performance and tracking agent-to-agent evaluation metrics across development and pre-production.
Use Cases
- Compare candidate agents across standardized tasks to build an objective agent track record
- Detect and categorize agent failure modes before production deployment
- Run continuous benchmark suites to catch regressions during agent development
Works With
openclawmyclaw.ai
Topics
agent-testingai-agentai-benchmarkbenchmarkllm-evaluationmyclawopenclaw
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Keywords
multi-agent trusta2a evaluationagent track recordllm-evaluation