DDL2PropBank Agent: Benchmarking Multi-Agent Frameworks' Developer Experience Through a Novel Relational Schema Mapping Task

• URL: http://arxiv.org/abs/2602.11198v1
• Date: Tue, 03 Feb 2026 01:10:59 GMT
• Title: DDL2PropBank Agent: Benchmarking Multi-Agent Frameworks' Developer Experience Through a Novel Relational Schema Mapping Task
• Authors: Shafiuddin Rehan Ahmed, Wei Wei,
• Abstract summary: DDL2PropBank is a novel benchmark task that maps relational database schemas to PropBank rolesets.<n>We implement identical agent logic across 10 frameworks and evaluate along two dimensions: (i) code complexity via static analysis, and (ii) AI-assistability.<n>Our results reveal a threefold complexity spectrum, with Pydantic AI and Agno requiring the least implementation overhead.
• Score: 9.51787137194505
• License: http://creativecommons.org/licenses/by-sa/4.0/
• Abstract: Multi-agent frameworks promise to simplify LLM-driven software development, yet there is no principled way to evaluate their developer experience in a controlled setting. We introduce DDL2PropBank, a novel benchmark task that maps relational database schemas to PropBank rolesets, requiring autonomous retrieval of candidate frames and fine-grained linguistic reasoning over table names, columns, and relations. Using the Agent-as-a-Tool pattern, we implement identical agent logic across 10 frameworks and evaluate along two dimensions: (i) code complexity via static analysis, and (ii) AI-assistability -- the extent to which LLMs can autonomously generate correct, framework-specific code. Our results reveal a threefold complexity spectrum, with Pydantic AI and Agno requiring the least implementation overhead. For AI-assistability, structural alignment scores reliably proxy runtime success for frameworks with single canonical patterns, but overestimate correctness for multi-pattern frameworks. Agno emerges as the strongest overall performer, combining lowest complexity with highest structural alignment and 83% pass@1.

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