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Top AI Repos tracks AI repositories on GitHub and answers two different questions about each one: is it moving right now, and would you bet a product on it.
Top AI Repos tracks AI repositories on GitHub and answers two different questions about each one: is it moving right now, and would you bet a product on it.
An in-the-wild benchmark for AI agents in the OpenClaw Environment.
| Date | Stars |
|---|---|
| 2026-07-31 | 498 |
| 2026-08-02 | 498 |
| 2026-08-06 | 498 |
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<h1 align="center">WildClawBench</h1> <p align="center"> <img src="assets/lobster_battle.png" alt="WildClawBench Lobster" width="480"> </p> <div align="center"> []() []() []() [](https://internlm.github.io/WildClawBench/) <br> [](https://arxiv.org/abs/2605.10912) [](https://huggingface.co/papers/2605.10912) [](https://huggingface.co/datasets/internlm/WildClawBench) [](https://github.com/InternLM/WildClawBench/blob/main/WildClawBench_report.pdf) </div> > **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](https://github.com/openclaw/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. In the **technical report snapshot**, the strongest frontier model topped out at **62.2% overall**; the latest audited OpenClaw runs have since raised the best score to **67.2%**. Most models still land well below that. That makes scores mean something. ### Why WildClawBench? Most agent benchmarks test isolated capabilities — calling a function, parsing JSON, following a single instruction. WildClawBench tests the full picture: | | What We Test | Why It's Hard | |:---:|---|---| | **🔗 Agency** | Multi-step tool orchestration, error recovery, autonomous planning | Agents must chain 10–60+ tool calls, adapt when services fail, and decide *what* to do — not just *how* | | **🎥 Multimodal** | Video understanding, image generation, cross-modal synthesis | Track events across a 45-min match video and clip precise highlights; classify 12 clothing photos, assemble 4 styled outfits, and generate full-body model images for each | | **🧵 Long-Horizon** | Complex workflows spanning 10–20 minutes of wall-clock execution | Negotiate meeting times over multiple email rounds; crawl, classify, and summarize 50+ academic papers | | **💻 Coding** | Read undocumented codebases, debug, generate working programs | Read an undocumented codebase, install dependencies, and write working inference from source alone; solve visual puzzles by generating pixel-accurate solutions | | **🛡️ Safety** | Prompt injection defense, credential leak detection, harmful content refusal | Harmful instructions are buried deep inside normal-looking documents; API keys are scattered across a large git history | ### 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. - **Four agent harnesses, one task suite.** OpenClaw, Claude Code, Codex CLI, and Hermes Agent all execute the same 60 tasks under the same grading. This separates *model capability* from *harness scaffolding* — you can see how much an agent's score depends on its surrounding tools versus the underlying LLM. - **Reproducible & isolated.** Each task runs in its own Docker container. Same
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Read on GitHubWould you bet a product on this? Bounded 0–100 and slow moving.
matched fp:db9bcf22b19108bf, topic:agents, topic:agentic-ai, desc:ai agents