Abstract:

[Purpose/Significance] Agents driven by large language models (LLMs) are transitioning from executing tasks based on predefined workflows to autonomously planning and taking dynamic actions in complex environments. Thus, scientific and technical literature intelligence work is expanding its scope from stage-based information processing to continuous intelligence analysis. OpenClaw is an open agent execution framework that integrates capabilities such as multi-channel access, task orchestration, tool invocation, memory maintenance, and persistent operation. However, a systematic examination of its potential applications, impacts, and practical constraints in the field of scientific and technical literature intelligence remains lacking. This study aims to analyze OpenClaw's technical architecture and comprehensively evaluate how it transforms literature retrieval, knowledge organization, intelligence analysis, and knowledge service processes. [Method/Process] This study systematically reviewed the technical architecture and core components of OpenClaw, focusing on four interdependent layers: the access and communication layer, the agentic loop execution layer, the tool invocation and capability extension layer, and the memory maintenance layer with persistent operation. It related the OpenClaw's architectural logic to the organization of scientific literature, intelligence analysis workflows, and knowledge service scenarios. The study further discussed the changes that OpenClaw's architectural logic may bring to scientific and technical literature intelligence work, including shifts in retrieval patterns, knowledge processing methods, analytical workflows, and service outcomes, as well as the technical and ethical challenges that may emerge in practice. [Results/Conclusions] OpenClaw provides a new technical reference that transitions scientific and technical literature intelligence work from stage-based information processing to continuous knowledge work. Nevertheless, critical challenges persist. These include planning reliability, adaptability of domain knowledge, stability of tool integration, interpretability and traceability, and ethical risks related to data privacy, accountability, and open-ended extension mechanisms. To address these challenges and support the rapid development of an intelligent scientific and technical literature intelligence system, this study proposes the following seven interrelated development directions: 1) establishing task inventories for high-value scientific and technical intelligence scenarios, 2) developing a hybrid foundational model paradigm that combines general-purpose LLMs, task-specific large models, small models, and specialized tool sets, 3) advancing a one-click service model for autonomous scientific discovery and intelligent intelligence support, 4) constructing national-level corpora of literature and intelligence resources tailored to intelligent scientific and technical intelligence scenarios, 5) promoting in-depth collaboration between large, domain-specific models and agents, 6) designing a safe and controllable roadmap for progressive deployment, and 7) establishing a full-process, closed-loop governance mechanism. These proposals are expected to provide valuable references for both disciplinary development and professional practice. They will facilitate the gradual transformation of agents from auxiliary tools into a vital capability for the future of scientific and technical literature intelligence work.

Key words: OpenClaw, intelligent agents, scientific and technical literature intelligence work, intelligent scientific intelligence system, large language models