CLIMATEAGENT: Multi-Agent Orchestration for Complex Climate Data Science Workflows

TL;DR

ClimateAgent is a multi-agent framework that automates complex climate data workflows, outperforming existing tools by dynamically orchestrating tasks, acquiring data, and generating analysis reports with high reliability.

Contribution

We introduce ClimateAgent, a novel multi-agent system tailored for climate data science, featuring dynamic API integration, task decomposition, and self-correcting capabilities.

Findings

Achieves 100% task completion on Climate-Agent-Bench-85

Outperforms GitHub-Copilot and GPT-5 baseline in report quality

Demonstrates effective automation of complex climate data analysis

Abstract

Climate science demands automated workflows to transform comprehensive questions into data-driven statements across massive, heterogeneous datasets. However, generic LLM agents and static scripting pipelines lack climate-specific context and flexibility, thus, perform poorly in practice. We present ClimateAgent, an autonomous multi-agent framework that orchestrates end-to-end climate data analytic workflows. ClimateAgent decomposes user questions into executable sub-tasks coordinated by an Orchestrate-Agent and a Plan-Agent; acquires data via specialized Data-Agents that dynamically introspect APIs to synthesize robust download scripts; and completes analysis and reporting with a Coding-Agent that generates Python code, visualizations, and a final report with a built-in self-correction loop. To enable systematic evaluation, we introduce Climate-Agent-Bench-85, a benchmark of 85 real-world tasks spanning atmospheric rivers, drought, extreme precipitation, heat waves, sea surface temperature, and tropical cyclones. On Climate-Agent-Bench-85, ClimateAgent achieves 100% task completion and a report quality score of 8.32, outperforming GitHub-Copilot (6.27) and a GPT-5 baseline (3.26). These results demonstrate that our multi-agent orchestration with dynamic API awareness and self-correcting execution substantially advances reliable, end-to-end automation for climate science analytic tasks.