Stars and mentions
Last 30 days, Oct 5 – Nov 3
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Mentions
Surprised that ucbepic/DataAgentBench hasn't been mentioned on this: benchmarking data agents that need to query from multiple sources is a focus of some research at UC Berkeley -- leaderboard is worth a look to see how the various closed and open-source implementations build ontologies ... would like to see how Nvidia's approach stacks up.
6/6 Read more on our blog post ucbepic.github.io and try it out ucbepic/actor-gatherer. Actor-Gatherer currently works bolt-on with Claude Code, Codex, Pi and OpenCode Great collaboration with @keremakillioglu @_asimbiswal @alvinkcheung @profjoeyg @adityagp
Learn more: ucbepic/docetl 🚨 What’s the next step (if you want to build an AI/DS portfolio for real)? I have a live workshop that will help (free).
Can AI Agents Answer Your Data Questions? A Benchmark for Data Agents However, building reliable data agents remains difficult because real enterprise data is fragmented across many heterogeneous database systems, with duplicated and inconsistent data, and key information often buried in unstructured text, requiring agents to go beyond just writing SQL or data science scripts to answer questions. Existing benchmarks tackle only individual pieces of this end-to-end workflow (e.g., text-to-SQL over a single database) and are increasingly saturated and contaminated. We present a new benchmark for LLM agents, the Data Agent Benchmark (DAB), grounded in a study of enterprises building production data agents across six industries. DAB comprises 104 queries across 17 datasets and 4 database management systems. On DAB, the best state-of-the-art agent achieves only 57% pass@1. We analyze agent failure modes and distill takeaways for future data-agent development. Our benchmark and experiment code are published at github.com/ucbepic/DataAgentBench. Comments: 27 pages, 6 figures, 15 tables