internlm/WildClawBench

Hard, practical, end-to-end evaluation for AI agents — in the wild.

📌 Overview

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 privacy leaks before they happen. Useful things. Hard things.

Hard enough that every frontier model today scores below 0.6. That makes scores mean something.

📂 Repository Contents

This Hugging Face repository hosts the heavy assets required to run the benchmark:

• Images/wildclawbench-ubuntu_v1.2.tar: The official Docker image containing the isolated Ubuntu environment, OpenClaw instance, and all necessary tools (browser, bash, file system).
• workspace/: The task data directory containing initial and evaluation files for all 60 tasks.

📊 Benchmark Structure

The benchmark covers 6 categories across English and Chinese:

Category	Tasks	Key Challenges
Productivity Flow	10	Information synthesis, multi-source aggregation, and structured output.
Code Intelligence	12	Undocumented codebase comprehension and pixel-level visual reasoning.
Social Interaction	6	Multi-turn communication, API orchestration, and context tracking.
Search & Retrieval	11	Web search + local data reconciliation and source verification.
Creative Synthesis	11	Video/audio processing and cross-modal generation (e.g., match highlights).
Safety Alignment	10	Adversarial robustness, credential awareness, and harmful content refusal.

What Sets Us Apart

• Real environment, not mocks. Tasks run inside a live OpenClaw instance with real tools (browser, bash, file system, email, calendar).
• 60 original tasks, built by hand. Not adapted from existing benchmarks — each task was designed from scratch to stress-test real-world agent capabilities.
• Reproducible & isolated. Each task runs in its own Docker container. Same image, same data, same grading code. Ground truth and grading scripts are injected only after the agent finishes — they are never visible during execution, eliminating data leakage. Scores are reproducible across machines.

Quick Start

Install Docker

macOS

brew install --cask docker

After installation, launch Docker Desktop from Applications or run:

open -a Docker

Ubuntu

# Install dependencies
sudo apt-get update
sudo apt-get install -y ca-certificates curl gnupg

# Add Docker's official GPG key
sudo install -m 0755 -d /etc/apt/keyrings
curl -fsSL https://download.docker.com/linux/ubuntu/gpg | sudo gpg --dearmor -o /etc/apt/keyrings/docker.gpg
sudo chmod a+r /etc/apt/keyrings/docker.gpg

# Add apt repository
echo \
  "deb [arch=$(dpkg --print-architecture) signed-by=/etc/apt/keyrings/docker.gpg] https://download.docker.com/linux/ubuntu \
  $(. /etc/os-release && echo "$VERSION_CODENAME") stable" | \
  sudo tee /etc/apt/sources.list.d/docker.list > /dev/null

# Install Docker
sudo apt-get update
sudo apt-get install -y docker-ce docker-ce-cli containerd.io

# Allow current user to run Docker without sudo
sudo usermod -aG docker $USER
newgrp docker

Download Image

Download the Docker image tarball from HuggingFace:

pip install -U huggingface_hub
huggingface-cli download internlm/WildClawBench Images/wildclawbench-ubuntu_v1.2.tar --repo-type dataset --local-dir .

Then load the image:

docker load -i Images/wildclawbench-ubuntu_v1.2.tar

Download Task Data

Download the task data from HuggingFace:

huggingface-cli download internlm/WildClawBench workspace --repo-type dataset --local-dir .