What are the key features of InternLM/WildClawBench?

In-the-wild benchmark for AI agents; Built on the OpenClaw environment; Focuses on agentic AI evaluation; Realistic and challenging test scenarios

What are the use cases of InternLM/WildClawBench?

Evaluating AI agent performance in open environments; Benchmarking agentic AI models; Research on agentic evaluation methodologies

What programming language does InternLM/WildClawBench use?

InternLM/WildClawBench is primarily written in Python.

How to install InternLM/WildClawBench?

Run: openclaw install InternLM/WildClawBench

Claude Skill

InternLM/WildClawBench

WildClawBench is an in-the-wild benchmark for evaluating AI agents in the OpenClaw environment, supporting agentic AI research and evaluation.

Language

Overview

Stars367

Forks25

LanguagePython

Last pushed2026-05-15

Last synced2026-05-15

View on GitHub

Repository

OwnerInternLM

RepositoryWildClawBench

Full nameInternLM/WildClawBench

Repo ID1,189,335,371

GitHub URLhttps://github.com/InternLM/WildClawBench

🚀 Install this Skill

openclaw install InternLM/WildClawBench

⭐ GitHub

Summary

WildClawBench is an in-the-wild benchmark designed to evaluate AI agents operating within the OpenClaw environment, providing a realistic and challenging testbed for agentic AI systems.

Chinese description

OpenClaw环境中AI代理的野外基准测试。

Key features

In-the-wild benchmark for AI agents
Built on the OpenClaw environment
Focuses on agentic AI evaluation
Realistic and challenging test scenarios

Use cases

Evaluating AI agent performance in open environments
Benchmarking agentic AI models
Research on agentic evaluation methodologies

Topics

agentic-ai agentic-evaluation agents benchmarks openclaw

Explore more

Owner: InternLM Language: Python

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Data from GitHub. Synced on 2026-05-15