ResearchPilot is an open-source, self-hostable multi-agent system for literature-review assistance. Given a natural-language research question, it retrieves papers from Semantic Scholar and arXiv, extracts structured findings from paper abstracts, synthesizes cross-paper patterns, and drafts a citation-aware related-work section. The system combines FastAPI, Next.js, DSPy, SQLite, and Qdrant in a local-first architecture that supports bring-your-own-key model access and remote-or-local embeddings. This paper describes the system design, typed agent interfaces, persistence and history-search mechanisms, and the engineering tradeoffs involved in building a transparent research assistant. Rather than claiming algorithmic novelty, we present ResearchPilot as a systems contribution and evaluate it through automated tests and end-to-end local runs. We discuss limitations including external API rate limits, abstract-only extraction, incomplete corpus coverage, and the lack of citation verification.

翻译：ResearchPilot 是一个开源、可自托管的多智能体系统，旨在辅助文献综述工作。给定一个自然语言研究问题，该系统能够从 Semantic Scholar 和 arXiv 检索论文，从论文摘要中提取结构化发现，综合跨论文的模式，并起草带有引用的相关工作章节。该系统采用本地优先架构，结合了 FastAPI、Next.js、DSPy、SQLite 和 Qdrant，支持自带密钥的模型访问以及远程或本地嵌入。本文描述了系统设计、类型化的智能体接口、持久化与历史检索机制，以及在构建一个透明研究助手过程中涉及的工程权衡。我们并不主张算法新颖性，而是将 ResearchPilot 作为一个系统贡献进行展示，并通过自动化测试和端到端的本地运行进行评估。我们讨论了其局限性，包括外部 API 速率限制、仅从摘要提取信息、语料库覆盖不完整以及缺乏引用验证。