# Hybrid Multi-Agent GraphRAG for E-Government: Towards a Trustworthy AI Assistant
## 2 Background
### From RAG to GraphRAG in Public, Multilingual, and Multimodal Data Applications  
The introduction of RAG has significantly enhanced the contextual relevance and factuality of LLM outputs by incorporating external knowledge during inference. However, conventional RAG systems rely on flat, vector-based retrieval methods, often falling short when tasked with capturing relational, temporal, or hierarchical knowledge. This becomes particularly limiting in domains that require multi-hop reasoning, such as law, public policy, or journalism [13,14]  
Traditional RAG architectures improve LLM responses by retrieving semantically similar text chunks from external sources. Despite their usefulness, RAG methods treat documents as flat, unstructured units, limiting their capacity for complex reasoning, entity disambiguation, and transparent answer generation. This has led to a growing interest in integrating structured knowledge into the retrieval process. RAG enhances LLMs by grounding generation in external knowledge. While traditional RAG techniques rely on dense retrieval from unstructured text chunks, they often struggle with issues of semantic fragmentation, redundant retrieval, and context drift, especially in structured domains, such as finance, public policy, or regulatory governance. These challenges have fueled the evolution toward GraphRAG, which employs KGs and textual graphs to encode semantic relations between entities, thus supporting multi-hop reasoning, contextual continuity, and factual consistency [10, 11, 12]. GraphRAG extends the RAG paradigm by incorporating Knowledge Graphs (KGs), where information is represented as interconnected nodes and edges, enabling entity linking, multi-hop reasoning, and explainable paths between concepts. This allows LLMs to retrieve subgraphs or traverse paths through semantically aligned entities, thereby improving context modeling and factual grounding. A series of surveys and frameworks, including GraphRAG-FI, GRAG, StructuGraphRAG, and LEGO-GraphRAG, have formalized modular components such as query processors, retrievers, organizers, and verbalizersfor graph-based reasoning [7; 15; 16]. GraphRAG emerges as a transformative extension of RAG by enabling LLMs to interact with graph-structured KGs. GraphRAG integrates external knowledge, not just as flat text, but as interconnected entities and relations, supporting contextualized and explainable generation [13; 14; 17; 18]. This structure enables multi-hop traversal for contextual linkage, entity-level precision in retrieval, reasoning chains encoded as paths or subgraphs, and community detection for scalable summarization. The suitability of GraphRAG for social and public sector data stems from its ability to model complex, hierarchical, and time-sensitive relationships. Datasets that offer multilingual survey data, visual figures, PDFs, and metadata, such as regions, time, demographics, and policy themes. Our previous research explored multi-agent systems for e-governance applications [8]. Further work demonstrated how Multimodal RAG combined with MLLMs could handle diverse data types, though these approaches lacked fine-grained semantic alignment and structural reasoning [5]. GraphRAG augments this by structuring nodes (e.g., topics, questions, countries, images) and encoding relationships (e.g., trend-over-time, demographic grouping, sentiment shift). Systems like TravelRAG, MedicalGraphRAG, and StructuGraphRAG have shown similar domain gains by aligning visual, tabular, and textual formats into unified graph-aware LLM pipelines [7; 15; 19]. GraphRAG also intersects Multimodal Large Language Models (MLLMs), particularly in domains like education, science, and tourism. The integration of visual, tabular, and textual modalities (e.g., in LightRAG, LuminiRAG, and StructuGraphRAG) offers potential for vision-language-question answering (VLQA) and structured document interpretation.