DataAgentBench Evaluation Fork
Data Agent Benchmark Operator
Fork and evaluation workspace for DAB, a realistic enterprise data-agent benchmark spanning multi-database integration, messy joins, unstructured text transformation, and domain knowledge.
"Used a serious external benchmark to stress data agents against multi-database enterprise complexity rather than SQL-only toy tasks."
Problem
Data agents often appear strong on single-database SQL tasks but fail when real enterprise workloads require cross-system joins, text transformation, domain reasoning, and tool execution.
Solution
Set up the DAB benchmark workflow with local database dependencies, Dockerized Python execution, supported LLM provider configuration, run logs, validation scripts, and pass@1 aggregation.
Deep Dive
What It Evaluates
DataAgentBench covers 12 datasets and 54 queries across 9 domains and multiple DBMSes, including PostgreSQL, MongoDB, SQLite, and DuckDB. It evaluates agents on realistic data work rather than isolated SQL answering.
Engineering Highlights
- •Multi-database setup: local DB configuration and dataset structure mirror enterprise data sprawl.
- •Safe tool execution: agents use read-only database querying plus Docker-backed Python execution.
- •Run validation: logs capture final answers, tool calls, LLM calls, termination reasons, and pass@1 scoring.
Tech Stack
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