mlsanityData to 7 Oct 2026

StackRAG Agent: Improving Developer Answers with Retrieval-Augmented Generation

arXiv 2406.13840 ↗First posted 19 Jun 2024cs.AI cs.CLcited by 1

On the map: Retrieval-augmented generation → Agentic RAG, agents and memory

also in 1 more
Abstract (arXiv)

Developers spend much time finding information that is relevant to their questions. Stack Overflow has been the leading resource, and with the advent of Large Language Models (LLMs), generative models such as ChatGPT are used frequently. However, there is a catch in using each one separately. Searching for answers is time-consuming and tedious, as shown by the many tools developed by researchers to address this issue. On the other, using LLMs is not reliable, as they might produce irrelevant or unreliable answers (i.e., hallucination). In this work, we present StackRAG, a retrieval-augmented Multiagent generation tool based on LLMs that combines the two worlds: aggregating the knowledge from SO to enhance the reliability of the generated answers. Initial evaluations show that the generated answers are correct, accurate, relevant, and useful.

Key facts. Cited by 1 paper in the map (data through 7 Oct 2026). Placed in Agentic RAG, agents and memory, one of 15 directions in Retrieval-augmented generation. Builds on 11 of its 16 references that are in the map. First posted on arXiv 19 Jun 2024.

Builds on

11 of 16 references.

Closest ancestors · 1

Same direction, newest first

  1. 2023PaperQA: Retrieval-Augmented Generative Agent for Scientific Research8 Dec

Other references · 10

  1. 2023Large Language Models for Software Engineering: A Systematic Literature Reviewcited by 389
  2. 2023Challenges and Applications of Large Language Modelscited by 251
  3. 2024RepairAgent: An Autonomous, LLM-Based Agent for Program Repaircited by 225
  4. 2023Large Language Models for Software Engineering: Survey and Open Problemscited by 178
  5. 2024RepoAgent: An LLM-Powered Open-Source Framework for Repository-level Code Documentation Generationcited by 69
  6. 2022PTM4Tag: Sharpening Tag Recommendation of Stack Overflow Posts with Pre-trained Modelscited by 15
  7. 2023Representation Learning for Stack Overflow Posts: How Far are We?cited by 6
  8. 2021Attention-based model for predicting question relatedness on Stack Overflowcited by 3
  9. 2022I Know What You Are Searching For: Code Snippet Recommendation from Stack Overflow Postscited by 3
  10. 2024Studying and Recommending Information Highlighting in Stack Overflow Answerscited by 2

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Cited by year

Citing papers per year: 2025: 1.

1 paper.

  1. 2025Engineering RAG Systems for Real-World Applications: Design, Development, and Evaluationcited by 3

Similar, not cited

Close in wording, no citation either way

  1. 2024A Collaborative Multi-Agent Approach to Retrieval-Augmented Generation Across Diverse Data
  2. 2025RAGCap-Bench: Benchmarking Capabilities of LLMs in Agentic Retrieval Augmented Generation Systems
  3. 2025Agent-UniRAG: A Trainable Open-Source LLM Agent Framework for Unified Retrieval-Augmented Generation Systems
  4. 2024PersonaRAG: Enhancing Retrieval-Augmented Generation Systems with User-Centric Agents
  5. 2026Is Agentic RAG worth it? An experimental comparison of RAG approaches
  6. 2024DuetRAG: Collaborative Retrieval-Augmented Generation
  7. 2025RAGentA: Multi-Agent Retrieval-Augmented Generation for Attributed Question Answering
  8. 2025Agentic Retrieval-Augmented Generation: A Survey on Agentic RAG