# Hybrid Multi-Agent GraphRAG for E-Government: Towards a Trustworthy AI Assistant
## 2 Background
### Modularity, Explainability, and Methodological Foundations of GraphRAG  
A crucial advantage of the GraphRAG approach lies in its modularity and transparency, key priorities in e-governance and public sector AI adoption. As shown in our previous research, modular pipelines using Haystack can support explainable multi-agent systems with memory, source attribution, and reproducibility tracking [5]. GraphRAG inherits this transparency by enabling path-aware retrieval, source-linked subgraphs, and intermediate reasoning steps (e.g., \"Thought \(\rightarrow\) Action \(\rightarrow\) Observation\") [23]. This modular design supports hybrid extensions where web search, embedding retrieval, and graph traversal can be orchestrated by LLM agents, based on task-specific needs. Such hybrid pipelines are essential in public-facing tools that must balance coverage, accuracy, and interpretability. The need for transparent, reproducible, and scalable AI systems is critical in e-government contexts. Another study emphasizes that a Haystack-based, modular LLM framework that integrates document preprocessing, memory-enabled agents, and multi-source vector stores for robust, policy-aligned deployment [8]. These design principles align with the modularity and evaluation frameworks outlined in LEGO-GraphRAG and GraphRAG-FI, which promote flexible component substitution and system transparency, essential features for public administration adoption [6; 24]. GraphRAG has been adapted for high-stake, domain-specific tasks where factual accuracy, traceability, and source grounding is non-negotiable. MedGraphRAG is a prime example, designed for safe medical QA using a triple-layer graph linking user content, peer-reviewed literature, and ontological dictionaries, such as UMLS. Its Triple Graph Construction and U-Retrieval methods demonstrate how domain constraints and traceability requirements can shape graph construction, traversal, and integration strategies. These domain-tailored innovations provide transferable lessons for applying GraphRAG to policy-focused e-government datasets, where auditability, multilingual coverage, and reproducibility must coexist with scalability and interpretability [25]. Recent surveys, such as comprehensive work, have categorized GraphRAG systems into three paradigms: knowledge-based GraphRAG, index-based GraphRAG, and hybrid GraphRAG [26]. This taxonomy addresses both the role of graphs (carrier vs. index) and their method of integration into the retrieval and generation pipelines. Knowledge-based variants encode entities and semantic relations in formal KGs, enabling symbolic multi-hop reasoning. Index-based variants use graphs as retrieval scaffolds over unstructured documents. Hybrid approaches, such as those seen in GraphReader or MedGraphRAG, combine these to support fine-grained retrieval and efficient semantic indexing. Architectures like LuminiRAG and StructuGraphRAG extend these paradigms to multimodal and vision-enhanced pipelines, handling images, diagrams, and layout-aware textual information with cross-modal alignment. Such features are crucial in e-government and survey-based applications, where documents often contain complex intermodal references [25; 27]. GraphRAG systems operate through modular pipelines that typically include (1) graph-based indexing, (2) semantic-aware retrieval, and (3) graph-enhanced generation [21; 28]. Models like HuixiangDou2 have introduced dual-level and logic-form retrieval to handle fuzzy matches and structured reasoning. This enhances RAG's robustness in complex QA tasks, while preserving token efficiency and interpretability [24]. LEGO-GraphRAG brings forward a modular design framework, splitting the retrieval pipeline into subgraph extraction and path retrieval, using both structural and semantic reasoning models. This decomposition facilitates design-space exploration for GraphRAG systems across different performance constraints, including cost, runtime, and reasoning fidelity [6].