# LLM Agents Improve Semantic Code Search

arXiv: https://arxiv.org/abs/2408.11058 · HTML: https://mlsanity.com/paper/2408.11058/

## Key facts

- Cited by 7 papers in the map (data through 14 Oct 2026).
- Placed in Software engineering coding agents, one of 16 directions in LLM agents and tool use.
- Builds on 7 of its 10 references that are in the map.
- First posted on arXiv 5 Aug 2024.

## Placed in

- LLM agents and tool use / [Software engineering coding agents](https://mlsanity.com/agents/software-engineering-coding-agents/)

## Builds on (most cited first, 7 of 7)

- [Retrieval-Augmented Generation for Knowledge-Intensive NLP Tasks](https://mlsanity.com/paper/2005.11401/) (2020)
- [CodeBERT: A Pre-Trained Model for Programming and Natural Languages](https://mlsanity.com/paper/2002.08155/) (2020)
- [GraphCodeBERT: Pre-training Code Representations with Data Flow](https://mlsanity.com/paper/2009.08366/) (2020)
- [Text and Code Embeddings by Contrastive Pre-Training](https://mlsanity.com/paper/2201.10005/) (2022)
- [A Review of Prominent Paradigms for LLM-Based Agents: Tool Use (Including RAG), Planning, and Feedback Learning](https://mlsanity.com/paper/2406.05804/) (2024)
- [Opportunities and Challenges in Code Search Tools](https://mlsanity.com/paper/2011.02297/) (2020)
- [PSCS: A Path-based Neural Model for Semantic Code Search](https://mlsanity.com/paper/2008.03042/) (2020)

## Built on by (most cited first, 7 of 7)

- [A Survey of Large Language Model Empowered Agents for Recommendation and Search: Towards Next-Generation Information Retrieval](https://mlsanity.com/paper/2503.05659/) (2025)
- [Large Language Models for Code Generation: A Comprehensive Survey of Challenges, Techniques, Evaluation, and Applications](https://mlsanity.com/paper/2503.01245/) (2025)
- [JARVIS: A Multi-Agent Code Assistant for High-Quality EDA Script Generation](https://mlsanity.com/paper/2505.14978/) (2025)
- [Program Analysis Guided LLM Agent for Proof-of-Concept Generation](https://mlsanity.com/paper/2604.07624/) (2026)
- [Natural Language Summarization Enables Multi-Repository Bug Localization by LLMs in Microservice Architectures](https://mlsanity.com/paper/2512.05908/) (2025)
- [Monitoring Transformative Technological Convergence Through LLM-Extracted Semantic Entity Triple Graphs](https://mlsanity.com/paper/2510.25370/) (2025)
- [Retrieval-Oriented Code Representations in Agentic Bug Localization](https://mlsanity.com/paper/2607.11046/) (2026)

## Abstract (arXiv)

Code Search is a key task that many programmers often have to perform while developing solutions to problems. Current methodologies suffer from an inability to perform accurately on prompts that contain some ambiguity or ones that require additional context relative to a code-base. We introduce the approach of using Retrieval Augmented Generation (RAG) powered agents to inject information into user prompts allowing for better inputs into embedding models. By utilizing RAG, agents enhance user queries with relevant details from GitHub repositories, making them more informative and contextually aligned. Additionally, we introduce a multi-stream ensemble approach which when paired with agentic workflow can obtain improved retrieval accuracy, which we deploy on application called repo-rift.com. Experimental results on the CodeSearchNet dataset demonstrate that RepoRift significantly outperforms existing methods, achieving an 78.2% success rate at Success@10 and a 34.6% success rate at Success@1. This research presents a substantial advancement in semantic code search, highlighting the potential of agentic LLMs and RAG to enhance code retrieval systems.