AgentSelect Benchmark for Agent Recommendation

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#llm-agents #benchmark #recommendationagentselectagentselect arxiv mulerun

💡First unified benchmark for LLM agent recommendation—scales agent ecosystems.

⚡ 30-Second TL;DR

What Changed

111,179 queries and 107,721 deployable agents from 40+ sources

Why It Matters

AgentSelect fills a key gap in the LLM agent ecosystem by providing unified data for recommendation systems. It enables reproducible research and accelerates agent deployment at scale. Practitioners can build better selectors for diverse agent catalogs.

What To Do Next

Download AgentSelect dataset from arXiv:2603.03761v1 and train a capability-matching recommender.

Who should care:Researchers & Academics

Key Points

•111,179 queries and 107,721 deployable agents from 40+ sources
•Unified positive-only interaction data for query-to-agent recommendation
•Reveals long-tail supervision regime needing capability matching
•Trained models transfer to marketplaces like MuleRun with gains

🧠 Deep Insight

Background and context from public sources — not the original article. 5 sources cited.

🔑 Enhanced Key Takeaways

•AgentSelect operationalizes agent selection by representing each agent as a deployable capability profile (M,T) consisting of an executable YAML specification for model and toolkit configurations.[1]
•The benchmark unifies supervision signals from LLM-only, toolkit-only, and compositional agent evaluations into positive-only query-agent interaction data for consistent training of rankers.[1][2]
•Models trained on AgentSelect demonstrate improved retrieval quality when transferred to the MuleRun public agent marketplace on an unseen catalog, as detailed in Appendix C.[1]

🔮 Future ImplicationsAI analysis grounded in cited sources

AgentSelect will standardize evaluation for agent rankers and routers

It provides the first unified, reproducible data infrastructure for query-conditioned agent recommendation, addressing fragmentation in existing benchmarks.[1][2]

Content-aware matching will outperform popularity-based methods in long-tail agent selection

Analyses show a shift to long-tail supervision where content-aware approaches are essential, as popularity-based CF/GNN methods become fragile.[1]

⏳ Timeline

2026-03

AgentSelect benchmark released on arXiv as v1 (arXiv:2603.03761)

📎 Sources (5)

Factual claims are grounded in the sources below. Forward-looking analysis is AI-generated interpretation.

📄Read original article on ArXiv AI

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