AI Decision System
Replayable, offline-verifiable records for AI-assisted go/no-go decisions: experiment promotions, typed model answers and human-approved actions.
翻译准备中
尚未检测到更新。GitHub 发布版本后会显示在这里。
个人 GitHub 账号下公开仓库的所有者可以登录并验证所有权,自行管理介绍,无需管理员批准。
登录并生成验证文件,在 30 分钟内将其提交到此仓库的默认分支。其他分支或分叉仓库中的文件无效。然后在下方检查文件,即可无需管理员批准直接管理项目。请保留文件以维持管理权限。
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在项目文件夹中运行 /nomorevibe verify,即可验证所有权并自行管理介绍。
一分钟完成验证用于评估和改进代理的软件框架。
AI 翻译
Framework for evaluating and improving agents
取自仓库 README
正在加载完整 README。 以下为已保存的摘录。
Harbor is a framework from the creators of Terminal-Bench for evaluating and optimizing agents and language models. You can use Harbor to:
• Evaluate arbitrary agents like Claude Code, OpenHands, Codex CLI, and more.
• Build and share your own benchmarks and environments.
• Conduct experiments in thousands of environments in parallel through providers like Daytona, Modal, LangSmith, Blaxel, Novita Sandbox, Tensorlake, and Runta.
• Generate rollouts for RL optimization.
Check out the Harbor Cookbook for end-to-end examples and guides.查看完整 README ↗目前无法加载完整 README。请通过原文链接阅读。 以下为已保存的摘录。
Harbor is a framework from the creators of Terminal-Bench for evaluating and optimizing agents and language models. You can use Harbor to:
• Evaluate arbitrary agents like Claude Code, OpenHands, Codex CLI, and more.
• Build and share your own benchmarks and environments.
• Conduct experiments in thousands of environments in parallel through providers like Daytona, Modal, LangSmith, Blaxel, Novita Sandbox, Tensorlake, and Runta.
• Generate rollouts for RL optimization.
Check out the Harbor Cookbook for end-to-end examples and guides.查看完整 README ↗