Understanding the intricate dynamics of online discourse depends on large-scale deliberation data, a resource that remains scarce across interactive web platforms due to restrictive accessibility policies, ethical concerns and inconsistent data quality. In this paper, we propose Chorus, an agentic framework, which orchestrates LLM-powered actors with behaviorally consistent personas to generate realistic deliberation discussions. Each actor is governed by an autonomous agent equipped with memory of the evolving discussion, while participation timing is governed by a principled Poisson process-based temporal model, which approximates the heterogeneous engagement patterns of real users. The framework is further supported by structured tool usage, enabling actors to access external resources and facilitating integration with interactive web platforms. The framework was deployed on the \textsc{Deliberate} platform and evaluated by 30 expert participants across three dimensions: content realism, discussion coherence and analytical utility, confirming Chorus as a practical tool for generating high-quality deliberation data suitable for online discourse analysis

翻译：理解在线讨论的复杂动态依赖于大规模的讨论数据，由于严格的访问政策、伦理问题以及数据质量不一致，这类资源在交互式网络平台上仍然稀缺。本文提出一种名为Chorus的智能体框架，该框架通过编排由LLM驱动的参与者（配备行为一致性特征）来生成逼真的讨论对话。每个参与者由一个具备讨论演进过程记忆的自主智能体控制，而参与时间则由基于泊松过程的时间模型规范，该模型近似反映了真实用户异质性的参与模式。该框架进一步通过结构化工具使用得到支持，使参与者能够访问外部资源并促进与交互式网络平台的集成。该框架已在\textsc{Deliberate}平台上部署，并由30名专家参与者从内容真实性、讨论连贯性和分析实用性三个维度进行评估，确认Chorus是适用于在线讨论分析的高质量讨论数据生成工具。