# System for systematic literature review using multiple AI agents: Concept and an empirical evaluation

arXiv: https://arxiv.org/abs/2403.08399 · HTML: https://mlsanity.com/paper/2403.08399/

## Key facts

- Cited by 13 papers in the map (data through 14 Oct 2026).
- Placed in Web, search, and research agents, one of 16 directions in LLM agents and tool use.
- Builds on 6 of its 29 references that are in the map.
- First posted on arXiv 13 Mar 2024.

## Placed in

- LLM agents and tool use / [Web, search, and research agents](https://mlsanity.com/agents/web-search-and-research-agents/)

## Builds on (most cited first, 6 of 6)

- [Extracting Training Data from Large Language Models](https://mlsanity.com/paper/2012.07805/) (2020)
- [Large Language Models for Software Engineering: A Systematic Literature Review](https://mlsanity.com/paper/2308.10620/) (2023)
- [Can Large Language Models Serve as Data Analysts? A Multi-Agent Assisted Approach for Qualitative Data Analysis](https://mlsanity.com/paper/2402.01386/) (2024)
- [Autonomous Agents in Software Development: A Vision Paper](https://mlsanity.com/paper/2311.18440/) (2023)
- [Analysing the use of graphs to represent the results of Systematic Reviews in Software Engineering](https://mlsanity.com/paper/2102.02932/) (2021)
- [A Visual Analysis Approach to Update Systematic Reviews](https://mlsanity.com/paper/2102.06345/) (2021)

## Built on by (most cited first, 13 of 13)

- [From Web Search towards Agentic Deep Research: Incentivizing Search with Reasoning Agents](https://mlsanity.com/paper/2506.18959/) (2025)
- [Atom-Searcher: Enhancing Agentic Deep Research via Fine-Grained Atomic Thought Reward](https://mlsanity.com/paper/2508.12800/) (2025)
- [Large Language Model Evaluation Via Multi AI Agents: Preliminary results](https://mlsanity.com/paper/2404.01023/) (2024)
- [Automated Review Generation Method Based on Large Language Models](https://mlsanity.com/paper/2407.20906/) (2024)
- [From Intention To Implementation: Automating Biomedical Research via LLMs](https://mlsanity.com/paper/2412.09429/) (2024)
- [Cutting Through the Clutter: The Potential of LLMs for Efficient Filtration in Systematic Literature Reviews](https://mlsanity.com/paper/2407.10652/) (2024)
- [Assessing and Enhancing the Robustness of LLM-based Multi-Agent Systems Through Chaos Engineering](https://mlsanity.com/paper/2505.03096/) (2025)
- [SGSimEval: A Comprehensive Multifaceted and Similarity-Enhanced Benchmark for Automatic Survey Generation Systems](https://mlsanity.com/paper/2508.11310/) (2025)
- [Highlighting Case Studies in LLM Literature Review of Interdisciplinary System Science](https://mlsanity.com/paper/2503.16515/) (2025)
- [Leveraging LLMs for Semi-Automatic Corpus Filtration in Systematic Literature Reviews](https://mlsanity.com/paper/2510.11409/) (2025)
- [Supporting software engineering tasks with agentic AI: Demonstration on document retrieval and test scenario generation](https://mlsanity.com/paper/2602.04726/) (2026)
- [Rethinking Publication: A Certification Framework for AI-Enabled Research](https://mlsanity.com/paper/2604.22026/) (2026)
- [Novelty-Aware Agentic Retrieval: Comparing Research Contributions Through Structured Multi-Step Reasoning](https://mlsanity.com/paper/2606.22151/) (2026)

## Abstract (arXiv)

Systematic literature review (SLR) is foundational to evidence-based research, enabling scholars to identify, classify, and synthesize existing studies to address specific research questions. Conducting an SLR is, however, largely a manual process. In recent years, researchers have made significant progress in automating portions of the SLR pipeline to reduce the effort and time required for high-quality reviews; nevertheless, there remains a lack of AI-agent-based systems that automate the entire SLR workflow. To this end, we introduce a novel multi-AI-agent system designed to fully automate SLRs. Leveraging large language models (LLMs), our system streamlines the review process to enhance efficiency and accuracy. Through a user-friendly interface, researchers specify a topic; the system then generates a search string to retrieve relevant academic papers. Next, an inclusion/exclusion filtering step is applied to titles relevant to the research area. The system subsequently summarizes paper abstracts and retains only those directly related to the field of study. In the final phase, it conducts a thorough analysis of the selected papers with respect to predefined research questions. This paper presents the system, describes its operational framework, and demonstrates how it substantially reduces the time and effort traditionally required for SLRs while maintaining comprehensiveness and precision. The code for this project is available at: https://github.com/GPT-Laboratory/SLR-automation .