Switchloom
这是 Instructa 公开的项目资料。本站按原始来源展示项目信息，用中文说明适用场景和阅读边界；项目名、源帖与代码保持原样，便于逐项核对。
这条案例记录了什么
Agent 与模型路由、MCP 与 Skills
根据任务状态选择一条已定义的处理路径。
社区公开项目或作者演示
原始来源：GitHub repository。尚未独立核实。
Instructa
记录日期：2026-09-18。日期与身份应以原始资料为准。
怎样核对这个项目
- 先打开原始来源，确认作者、日期与 Jev 在项目中的具体用途。
- 如果提供仓库，再检查代码、运行要求和许可证；仓库存在不代表本站已经运行成功。
- 对速度、成本、准确率和规模数字，查看原文的任务、环境和计算口径。
- 路由候选是否完整。
- 模糊请求的回退分支。
- 完整任务成本与结果。
- 真实调用入口和参数。
原始文字与技术细节
以下内容保留原语言，供核对事实。中文页的场景说明是阅读提示，不是逐句翻译或实测结论。
展开英文项目摘要与原帖
项目摘要
Switchloom assigns Codex capabilities to persistent model tasks and can optionally use Jev for routing. The author reports that two Pokedex pilots did not show a Jev advantage at comparable quality; in round two, the Jev team reached the time limit while Astra alone passed all 14 checks.
来源原文
Experimental. I currently recommend against adopting this routing workflow. I built Switchloom to see whether mixing models could get me the same quality for less money. My benchmarks did not deliver that result. In the second Pokédex run, the team with Jev cost almost as much as Astra alone and hit the time limit before final acceptance. Astra finished and passed all 14 independent functional checks.
原记录的限制
- The author recommends against adopting this workflow based on two pilots of one application.
- The 1.0 desktop workflow is described as an unpublished local preview; representative desktop measurements remain open.