by InternLM · Codex 技能 · ★ 523
WildClawBench []() []() Hard, practical, end-to-end evaluation for AI agents — in the wild. WildClawBench is an agent benchmark that tests what actually matters: can an AI agent do real work, end-to-end, without hand-holding? We drop agents into a live OpenClaw environment — the same open-source personal AI assistant that real users rely on daily — and throw 60 original tasks at them: clipping goal highlights from a football match, negotiating meeting times over multi-round emails, hunting down contradictions in search results, writing inference scripts for undocumented codebases, catching...
| Stars | 523 |
| Forks | 64 |
| Language | Python |
| Category | Codex 技能 |
| License | MIT |
| Quality Score | 71.2414881444168/100 |
| Open Issues | 5 |
| Last Updated | 2026-09-18 |
| Created | 2026-03-23 |
| Platforms | python |
| Est. Tokens | ~18k |
These tools work well together with WildClawBench for enhanced workflows:
Explore other popular codex 技能 tools:
WildClawBench is An in-the-wild benchmark for AI agents in the production harness.. It is categorized as a Codex 技能 with 523 GitHub stars.
WildClawBench is primarily written in Python. It covers topics such as agentic-ai, agentic-evaluation, agents.
You can find installation instructions and usage details in the WildClawBench GitHub repository at github.com/InternLM/WildClawBench. The project has 523 stars and 64 forks, indicating an active community.
WildClawBench is released under the MIT license, making it free to use and modify according to the license terms.