M anu S earch: Democratizing Deep Search in Large Language Models with a Transparent and Open Multi-Agent Framework (2025)

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 \textbf{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 \textbf{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).

## introduction deep search systems have recently marked remarkable strides by coupling large language models (llms) with web search, enabling them to answer complex queries that require multi-step reasoning and up-to-date information @xcite . proprietary systems such as perplexity's sonar reasoning pro (perplexity-ai, 2025) and openai's gpt-4o search preview @xcite and deep research (openai, 2025b) exemplify this progress. these closed-source agents demonstrate emergent chain-of-thought reasoning capabilities: they autonomously plan out search queries, retrieve information from the web, and synthesize coherent, context-rich answers with source citations. by integrating search planning, iterative retrieval, and onthe-fly content aggregation, these agents achieve state-of-the-art performance on challenging benchmarks, far surpassing what static offline models can do. despite impressive capabilities of closed systems, the open-source ecosystem lacks comparable and transparent alternatives. key components of deep search systems, e.g. sophisticated query planning, multi-hop retrieval, and tool-augmented reasoning, remain entangled within proprietary stacks, with few modular open implementations. in particular, there is a lack of modular and interpretable architectures that let researchers inspect or improve each stage of the reasoning process and an absence of benchmarked multi-stage reasoning agents that can serve as open baselines. more importantly, there exists significant difficulty in replicating the performance of closed systems due to their restricted access and opaque design. this growing gap between private and public ai has been noted by the research community @xcite . initial efforts to close it are only just emerging; for example, an early open-source prototype has combined techniques like react-based tool use and prompting to nearly match the quality of gpt-4o search and sonar on certain tasks @xcite . however, these attempts are still in their infancy, underscoring the urgent need to democratize deep search systems and foster reproducible innovation in this domain. in this paper, we address these challenges by introducing manusearch, a transparent and opensource deep search system. specially, manusearch is designed as an agent-based, modular system that accomplishes web-scale complex reasoning tasks with three collaborative agents: (1) solution plan-ning agent, an llm-based planner that interprets the user's query, formulates a strategy (a series of sub-questions or steps), and decides which information to seek at each step; (2) internet search agent, a specialized agent that takes the planner's requests, executes web searches, and gathers relevant evidence from the open internet; and (3) webpage reading agent, an agent that reads the retrieved webpages, and extracts the most relevant key information needed to answer the query. these agents communicate and iterate in a structured reasoning loop, effectively integrating task planning, open internet search, and key information comprehension and synthesis into a unified problem-solving process. by breaking the deep search pipeline into interpretable modules, manusearch provides an extensible and transparent alternative to monolithic closed-source systems. more important, each agent's behavior is traceable, i.e. one can examine the chain-of-thought in the planner's decisions, the queries issued, and the evidence used to support the answer, which not only aids debugging and trust, but also allows researchers to improve individual components in isolation. to enable a rigorous evaluation of our system's deep search capabilities, we introduce orion, a benchmark for open-web reasoning evaluation specifically designed based on long-tail entities. unlike existing datasets that often concentrate on high-frequency topics, orion emphasizes reasoning over less common entities across ten diverse domains. each question in orion is constructed using predefined reasoning templates, ensuring that the benchmark challenges a wide spectrum of cognitive abilities. questions are initially generated using llms and then refined by human annotators to verify factual correctness and provide sourcegrounded reasoning chains. spanning both english and chinese samples, orion contains 310 annotated examples, each linked to authoritative sources. our evaluation shows that even leading closed-source systems achieve under 30% accuracy on orion, underscoring the benchmark's difficulty and its value in advancing research on transparent and modular deep search systems. we conduct extensive experiments on our benchmark and two challenging datasets (i.e. frames and gaia) to verify the effectiveness of our approach. the experimental results show that our manusearch system significantly outperforms previous open-source deep search systems. deep search with llms. to overcome the knowledge limitations of static models, a new class of deep web-integrated reasoning agents has emerged. recent systems such as search-o1 @xcite , webthinker @xcite , and open deep search @xcite augment an llm's reasoning by weaving in web search planning, tool use, and evidence retrieval as part of the pipeline. these agents decompose complex queries into search subtasks and iteratively gather information from the open web, feeding the retrieved evidence back into the model's chain-of-thought. a key trend is the use of rl-based training to scale these deep research capabilities. deepresearcher @xcite , for instance, trains an llm agent end-toend in a live web environment via reinforcement learning, yielding emergent behaviors like plan formulation, cross-source verification, and selfcorrection during multi-hop research. similarly, r1-searcher @xcite uses a two-stage outcome-driven rl strategy to explicitly incentivize the model to invoke external search, signifi-cantly improving open-domain question answering even against strong retrieval-augmented baselines. this line of work demonstrates that by integrating search and reasoning in a coordinated pipeline, it is possible to substantially enhance the deep research abilities of an llm. ## orion existing complex reasoning datasets focus primarily on constructing multi-domain questions to evaluate various capabilities of large language models @xcite . although these models perform well on questions involving common entities, their performance tends to degrade when handling questions that involve reasoning on long-tailed entities. in this section, we introduce orion, a benchmark for the evaluation of open-web reasoning over long-tail entities. ## conclusion we present manusearch, a transparent and modular multi-agent framework that enables large language models to perform deep web-integrated reasoning. by decoupling the problem-solving process into three specialized agents, i.e. solution planning, internet search, and structured webpage reading, our system promotes interpretability, extensibility, and performance. extensive evaluations on our proposed benchmark orion demonstrate that manusearch significantly outperforms prior opensource systems and rivals or exceeds several closedsource commercial agents. our work highlights the importance of modular reasoning pipelines and introduces a reproducible foundation for future research in open deep search systems. we hope this framework will catalyze progress toward trustworthy agents empowered with search capabilities.