Agentic AI has significantly extended the capabilities of large language models (LLMs) by enabling complex reasoning and tool use. However, most existing frameworks are tailored to domains such as mathematics, coding, or web automation, and fall short on geospatial tasks that require spatial reasoning, multi-hop planning, and real-time map interaction. To address these challenges, we introduce MapAgent, a hierarchical multi-agent plug-and-play framework with customized toolsets and agentic scaffolds for map-integrated geospatial reasoning. Unlike existing flat agent-based approaches that treat tools uniformly—often overwhelming the LLM when handling similar but subtly different geospatial APIs—MapAgent decouples planning from execution. A high-level planner decomposes complex queries into subgoals, which are routed to specialized modules. For tool-heavy modules—such as map-based services—we then design a dedicated map-tool agent that efficiently orchestrates related APIs adaptively in parallel to effectively fetch geospatial data relevant for the query, while simpler modules (e.g., solution generation or answer extraction) operate without additional agent overhead. This hierarchical design reduces cognitive load, improves tool selection accuracy, and enables precise coordination across similar APIs. We evaluate MapAgent on four diverse geospatial benchmarks—MapEval-Textual, MapEval-API, MapEval-Visual, and MapQA—and demonstrate substantial gains over state-of-the-art tool-augmented and agentic baselines.

Understanding regional similarities is crucial for applications such as urban planning, tourism recommendations, business expansion, and disease prevention. While spatial data, including POI distributions, check-in activity, and building footprints, offer valuable insights, existing similarity methods—based on distance metrics, embeddings, or deep metric learning—fail to capture the contextual richness and adapt to heterogeneous spatial data. To overcome these limitations, we introduce a novel similar region search framework that ranks candidate regions based on their similarity to a query region using large language models. To further enhance performance, we fine-tune the model through self-supervised learning by introducing controlled noise into spatial data. This generates similar and dissimilar samples without relying on extensive labeled data. By transforming spatial data into natural language descriptions, our method seamlessly integrates heterogeneous datasets without requiring structural modifications, ensuring scalability across diverse urban contexts. Experiments on multiple real-world city datasets, including cross-city evaluation, demonstrate that our framework significantly outperforms state-of-the-art methods in both accuracy and ranking performance.