AgentSelect: Benchmark for Narrative Query-to-Agent Recommendation

LLM agents are rapidly becoming a practical interface for task automation, yet selecting suitable deployable configurations remains underexplored. Existing LLM leaderboards and tool/agent benchmarks evaluate components in isolation and are fragmented across tasks, metrics, and candidate pools, leaving a critical research gap: there is little query-conditioned supervision for learning to recommend end-to-end compositional agent configurations. We introduce AgentSelect, a benchmark that reframes agent selection as narrative query-to-agent recommendation over capability profiles. AgentSelect systematically converts heterogeneous evaluation artifacts into unified positive-only interaction data, comprising 111,179 queries, 107,721 deployable agents, and 251,103 interaction records from 40+ sources across LLM-only, toolkit-only, and compositional agents. Our analyses reveal a shift from dense head reuse to long-tail, near one-off supervision, where ID-based interaction methods become fragile and reliable recommendation increasingly requires content-aware intent-to-capability matching. We validate the synthesized supervision through counterfactual, ablation, and show practical transferability on the MuleRun agent marketplace and a small-scale end to end Agno deployment study. Overall, AgentSelect provides the first unified data and evaluation infrastructure for agent recommendation, which establishes a reproducible foundation to study and accelerate the emerging agent ecosystem. The resources are available at \footnote{\url{https://github.com/Ancientshi/AgentSelect}}.

AgentSelect helps researchers study how to choose the most suitable AI agent for a user’s request by turning scattered evaluation results into a large, unified benchmark for comparing and improving agent recommendation methods.