Conversational search (CS) requires a complex software engineering pipeline that integrates query reformulation, ranking, and response generation. CS researchers currently face two barriers: the lack of a unified framework for efficiently sharing contributions with the community, and the difficulty of deploying end-to-end prototypes needed for user evaluation. We introduce Orcheo, an open-source platform designed to bridge this gap. Orcheo offers three key advantages: (i) A modular architecture promotes component reuse through single-file node modules, facilitating sharing and reproducibility in CS research; (ii) Production-ready infrastructure bridges the prototype-to-system gap via dual execution modes, secure credential management, and execution telemetry, with built-in AI coding support that lowers the learning curve; (iii) Starter-kit assets include 45+ off-the-shelf components for query understanding, ranking, and response generation, enabling the rapid bootstrapping of complete CS pipelines. We describe the framework architecture and validate Orcheo's utility through case studies that highlight modularity and ease of use. Orcheo is released as open source under the MIT License at https://github.com/AI-Colleagues/orcheo.

翻译：摘要：对话式搜索（CS）需要复杂的软件工程流水线，整合查询重构、排序与响应生成。当前CS研究者面临两大障碍：缺乏统一框架以高效地向社区共享贡献，以及难以部署用于用户评估的端到端原型。我们提出开源平台Orcheo以弥合这一鸿沟。Orcheo提供三大核心优势：（i）模块化架构通过单文件节点模块促进组件复用，支持CS研究的共享与可复现性；（ii）生产级基础设施通过双重执行模式、安全凭证管理与执行遥测技术弥合原型与系统间的鸿沟，内置AI编程支持降低学习门槛；（iii）入门工具包提供45+即用型组件（覆盖查询理解、排序与响应生成），支持快速构建完整CS流水线。我们描述了框架架构，并通过强调模块化与易用性的案例研究验证了Orcheo的实用性。Orcheo以MIT许可证开源发布于https://github.com/AI-Colleagues/orcheo。