# PersonaAgent with GraphRAG: Community-Aware Knowledge Graphs for Personalized LLM

arXiv: https://arxiv.org/abs/2511.17467 · HTML: https://mlsanity.com/paper/2511.17467/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- Builds on 22 of its 25 references that are in the map.
- First posted on arXiv 21 Nov 2025.

## Builds on (most cited first, 22 of 22)

- [Llama 2: Open Foundation and Fine-Tuned Chat Models](https://mlsanity.com/paper/2307.09288/) (2023)
- [ReAct: Synergizing Reasoning and Acting in Language Models](https://mlsanity.com/paper/2210.03629/) (2022)
- [Mistral 7B](https://mlsanity.com/paper/2310.06825/) (2023)
- [A Survey on Large Language Model based Autonomous Agents](https://mlsanity.com/paper/2308.11432/) (2023)
- [What Makes Good In-Context Examples for GPT-3?](https://mlsanity.com/paper/2101.06804/) (2021)
- [MemoryBank: Enhancing Large Language Models with Long-Term Memory](https://mlsanity.com/paper/2305.10250/) (2023)
- [LaMP: When Large Language Models Meet Personalization](https://mlsanity.com/paper/2304.11406/) (2023)
- [From Persona to Personalization: A Survey on Role-Playing Language Agents](https://mlsanity.com/paper/2404.18231/) (2024)
- [How Can Recommender Systems Benefit from Large Language Models: A Survey](https://mlsanity.com/paper/2306.05817/) (2023)
- [PersonaGym: Evaluating Persona Agents and LLMs](https://mlsanity.com/paper/2407.18416/) (2024)
- [TPTU: Large Language Model-based AI Agents for Task Planning and Tool Usage](https://mlsanity.com/paper/2308.03427/) (2023)
- [Character is Destiny: Can Role-Playing Language Agents Make Persona-Driven Decisions?](https://mlsanity.com/paper/2404.12138/) (2024)
- [PersonaRAG: Enhancing Retrieval-Augmented Generation Systems with User-Centric Agents](https://mlsanity.com/paper/2407.09394/) (2024)
- [LLM-Based Human-Agent Collaboration and Interaction Systems: A Survey](https://mlsanity.com/paper/2505.00753/) (2025)
- [On Explaining Recommendations with Large Language Models: A Review](https://mlsanity.com/paper/2411.19576/) (2024)
- [PAARS: Persona Aligned Agentic Retail Shoppers](https://mlsanity.com/paper/2503.24228/) (2025)
- [Inverse Reinforcement Learning by Estimating Expertise of Demonstrators](https://mlsanity.com/paper/2402.01886/) (2024)
- [Regularized Inverse Reinforcement Learning](https://mlsanity.com/paper/2010.03691/) (2020)
- [The Application of Large Language Models in Recommendation Systems](https://mlsanity.com/paper/2501.02178/) (2025)
- [HARBOR: Exploring Persona Dynamics in Multi-Agent Competition](https://mlsanity.com/paper/2502.12149/) (2025)
- [C-TLSAN: Content-Enhanced Time-Aware Long- and Short-Term Attention Network for Personalized Recommendation](https://mlsanity.com/paper/2506.13021/) (2025)
- [LLM-Enhanced Reranking for Complementary Product Recommendation](https://mlsanity.com/paper/2507.16237/) (2025)

## Built on by (most cited first, 2 of 2)

- [Graph-based Agent Memory: Taxonomy, Techniques, and Applications](https://mlsanity.com/paper/2602.05665/) (2026)
- [Graph-Based Personalized Memory for LLM Agents: Representation, Evolution, Retrieval, and Evaluation](https://mlsanity.com/paper/2609.08599/) (2026)

## Abstract (arXiv)

We propose a novel framework for persona-based language model system, motivated by the need for personalized AI agents that adapt to individual user preferences. In our approach, the agent embodies the user's "persona" (e.g. user profile or taste) and is powered by a large language model (LLM). To enable the agent to leverage rich contextual information, we introduce a Knowledge-Graph-enhanced Retrieval-Augmented Generation (Graph RAG) mechanism that constructs an LLM-derived graph index of relevant documents and summarizes communities of related information. Our framework generates personalized prompts by combining: (1) a summary of the user's historical behaviors and preferences extracted from the knowledge graph, and (2) relevant global interaction patterns identified through graph-based community detection. This dynamic prompt engineering approach allows the agent to maintain consistent persona-aligned behaviors while benefiting from collective knowledge. On the LaMP benchmark, our method improves news categorization F1 by 11.1%, movie tagging F1 by 56.1%, and reduces product rating MAE by 10.4% over prior methods. Our code is available at https://anonymous.4open.science/r/PersonaAgentwGraphRAG-DE6F