ManuSearch: Democratizing Deep Search in Large Language Models with a Transparent and Open Multi-Agent Framework

Lisheng Huang, Yichen Liu, Jinhao Jiang, Rongxiang Zhang, Jiahao Yan, Junyi Li, Xin Zhao


Abstract
Recent advances in web-augmented large language models (LLMs) have exhibited strong performance in complex reasoning tasks, yet these capabilities are mostly locked in proprietary systems with opaque architectures. In this work, we propose ManuSearch, a transparent and modular multi-agent framework designed to democratize deep search for LLMs. ManuSearch decomposes the search and reasoning process into three collaborative agents: (1) a solution planning agent that iteratively formulates sub-queries, (2) an Internet search agent that retrieves relevant documents via real-time web search, and (3) a structured webpage reading agent that extracts key evidence from raw web content. To rigorously evaluate deep reasoning abilities, we introduce ORION, a challenging benchmark focused on open-web reasoning over long-tail entities, covering both English and Chinese. Experimental results show that ManuSearch substantially outperforms prior open-source baselines and even surpasses leading closed-source systems. Our work paves the way for reproducible, extensible research in open deep search systems. We release the data and code in [https://github.com/RUCAIBox/ManuSearch](https://github.com/RUCAIBox/ManuSearch).
Anthology ID:
2025.findings-emnlp.130
Volume:
Findings of the Association for Computational Linguistics: EMNLP 2025
Month:
November
Year:
2025
Address:
Suzhou, China
Editors:
Christos Christodoulopoulos, Tanmoy Chakraborty, Carolyn Rose, Violet Peng
Venue:
Findings
SIG:
Publisher:
Association for Computational Linguistics
Note:
Pages:
2403–2417
Language:
URL:
https://preview.aclanthology.org/ingest-luhme/2025.findings-emnlp.130/
DOI:
10.18653/v1/2025.findings-emnlp.130
Bibkey:
Cite (ACL):
Lisheng Huang, Yichen Liu, Jinhao Jiang, Rongxiang Zhang, Jiahao Yan, Junyi Li, and Xin Zhao. 2025. ManuSearch: Democratizing Deep Search in Large Language Models with a Transparent and Open Multi-Agent Framework. In Findings of the Association for Computational Linguistics: EMNLP 2025, pages 2403–2417, Suzhou, China. Association for Computational Linguistics.
Cite (Informal):
ManuSearch: Democratizing Deep Search in Large Language Models with a Transparent and Open Multi-Agent Framework (Huang et al., Findings 2025)
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PDF:
https://preview.aclanthology.org/ingest-luhme/2025.findings-emnlp.130.pdf
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