WildClawBench
What it is
WildClawBench is an in-the-wild benchmark for evaluating AI agents in a production-like environment. It tests 60 original, end-to-end tasks across agency, multimodal, long-horizon, coding, and safety dimensions using four different agent harnesses.
The problem it solves
Existing agent benchmarks often test isolated capabilities or use mocked environments, failing to capture the complexity of real-world, multi-step, and safety-critical agent tasks.
What you could use it for
- Evaluating the real-world capabilities of frontier LLMs in autonomous agent settings
- Comparing the performance of different agent harnesses (OpenClaw, Claude Code, Codex CLI, Hermes) on identical tasks
- Analyzing agent trajectories for training or debugging long-horizon planning and tool orchestration
Read the source ↗Back to the library →
Summary written by an AI model from the source; check the source before you rely on it.