# Hybrid Multi-Agent GraphRAG for E-Government: Towards a Trustworthy AI Assistant
### Study Significance and Objectives  
This study makes a significant contribution to the development and operationalization of a hybrid, multi-agent GraphRAG framework specifically designed for structured and policy-oriented e-government datasets. While existing research has independently explored the strengths of GraphRAG and traditional RAG-based architectures, this work is among the first to integrate graph reasoning, dense vector retrieval, and live web search within a modular and explainable system architecture tailored for public-sector AI applications. To achieve the primary aim of developing and evaluating a modular GraphRAG framework for policy-focused AI systems in e-government, this study pursues the following objectives:  
* Design a modular, scalable GraphRAG architecture using Haystack, an open-source LLM framework, to support graph-aware, explainable Q&A workflows over public datasets. * Construct knowledge graphs from documents, metadata, and images to represent entities, relations, and temporal dynamics. * Integrate GraphRAG with embedding-based and Web-based RAG modules into a unified multi-agent pipeline. * Evaluate and compare the framework and baseline pipelines using LLM-based statement-level factuality tests to assess the accuracy and reliability of generated answers. * Present results from use cases, reporting on factuality, relevance, and interpretability for both baseline and agentic pipelines. Moreover, an additional objective of the framework is to advance explainable and trustworthy AI in domains such as public administration, where traceability, transparency, and auditability are essential requirements. Specifically, traceability refers to the ability to track and verify the sources of information used by the model. The framework also assesses challenges, such as hallucination, when the model generates unsupported or fabricated responses, and supports multi-hop reasoning, which involves synthesizing information from multiple sources to answer complex queries. The system responds to this need by supporting source-linked outputs and subgraph-level reasoning, enabling users to trace the reasoning behind individual answers. In doing so, the framework addresses critical gaps in AI governance by ensuring that outputs generated in regulatory or citizen-facing contexts are verifiable and justifiable. The system architecture offers a concrete blueprint for operationalizing GraphRAG within structured data environments, including datasets, such as institutional press releases and official news documents. Through its modular design, the framework enables multi-hop traversal over policy entities and timelines and integration of structured metadata (e.g., date, source URL, year). This approach expands the application of GraphRAG from experimental use cases to real-world deployment settings within digital governance infrastructure. A key innovation introduced by this work is the implementation of hybrid retrieval across triples of entities, nodes, and relationships, along with traditional embeddings with raw text content. The combination of knowledge graph, embedding-based similarity, and web search augmentation aids the assistant to dynamically adapt to diverse input data, including fact-based knowledge and comparative reasoning. This hybrid retrieval strategy enhances the quality, factual precision, and temporal relevance of generated responses. To mitigate the known risk of hallucinations in LLM-based systems, particularly RAG frameworks, the proposed solution incorporates a multi-agent orchestration architecture. In this architecture, specialized agents handle distinct functions, including graph and evidence ranking, web verification, and context memory. This design supports comprehensive reasoning and information cross-validation, substantially reducing the likelihood of unsupported outputs. Furthermore, it enables the creation of transparent, auditable, and reproducible virtual assistants based on RAG, aligned with emerging standards in trustworthy and accountable AI.