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scoootscooob / clawbench

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Rigorous benchmark for AI models as OpenClaw agents. Runs on HF Spaces.

37
4
100% credibility
Found Apr 12, 2026 at 37 stars -- GitGems finds repos before they trend. Get early access to the next one.
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AI Analysis
Python
AI Summary

ClawBench is a benchmark for testing AI agent setups on practical software tasks using reliable checks and helpful diagnostics.

How It Works

1
🔍 Discover ClawBench

You hear about ClawBench, a fun way to test how well different AI helpers handle everyday computer tasks like fixing code or organizing files.

2
📥 Get it ready

Download the simple tool and start it up on your computer – it takes just a minute.

3
🤖 Pick your AI helper

Choose your favorite AI model and add any special tools or setups you want it to use.

4
🚀 Run the tests

Hit go, and watch your AI tackle a series of real-world challenges like debugging apps or summarizing data.

5
📊 See the results

Get a clear report showing how well it did, with scores for accuracy, smart steps, and reliability.

6
💡 Get smart tips

Discover exactly what worked great and simple changes to make your AI even better next time.

🏆 Your AI shines

Now you know your setup's strengths and how to level it up for tougher jobs.

Sign up to see the full architecture

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Star Growth

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AI-Generated Review

What is clawbench?

Clawbench is a Python benchmark for evaluating AI models as OpenClaw agents on realistic software tasks like bug fixes, refactors, and browser automation. It runs 40 tasks across five tiers, verifying outputs with pytest executions, file checks, and state assertions for deterministic pass/fail scores. Developers get CLI commands like `clawbench run` for local testing and an HF Space for leaderboards and queued submissions.

Why is it gaining traction?

Unlike model-only leaderboards, Clawbench measures agent configurations—plugins, hooks, and tools—with pre-run score predictions, utilization audits, and factor analysis to explain performance gaps. It emphasizes reliability via pass@k, Taguchi S/N ratios, and process metrics like read-before-write ratios, delivering rigorous benchmarking in reasonable time without flaky LLM judges dominating scores.

Who should use this?

AI engineers tuning OpenClaw setups for production agents, especially those iterating on plugin stacks for coding, data processing, or web tasks. Researchers comparing scaffolds across frontier models like Claude Opus or open weights on SWE-like workloads.

Verdict

Try it if you're in the OpenClaw ecosystem—solid 107 tests, MIT license, and unique diagnostics make it worth the early-stage 37 stars and 1.0% credibility score. Still maturing; contribute tasks to push it forward.

(187 words)

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