mlsanityData to 14 Oct 2026

AskDB: An LLM Agent for Natural Language Interaction with Relational Databases

arXiv 2511.16131 ↗First posted 20 Nov 2025cs.DB cs.AIcited by 0

On the map: LLM agents and tool use → Web, search, and research agents

Abstract (arXiv)

Interacting with relational databases remains challenging for users across different expertise levels, particularly when composing complex analytical queries or performing administrative tasks. Existing systems typically address either natural language querying or narrow aspects of database administration, lacking a unified and intelligent interface for general-purpose database interaction. We introduce AskDB, a large language model powered agent designed to bridge this gap by supporting both data analysis and administrative operations over SQL databases through natural language. Built on Gemini 2, AskDB integrates two key innovations: a dynamic schema-aware prompting mechanism that effectively incorporates database metadata, and a task decomposition framework that enables the agent to plan and execute multi-step actions. These capabilities allow AskDB to autonomously debug derived SQL, retrieve contextual information via real-time web search, and adaptively refine its responses. We evaluate AskDB on a widely used Text-to-SQL benchmark and a curated set of DBA tasks, demonstrating strong performance in both analytical and administrative scenarios. Our results highlight the potential of AskDB as a unified and intelligent agent for relational database systems, offering an intuitive and accessible experience for end users.

Key facts. No citing papers in the map yet (data through 14 Oct 2026). Placed in Web, search, and research agents, one of 16 directions in LLM agents and tool use. Builds on 13 of its 29 references that are in the map. First posted on arXiv 20 Nov 2025.

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13 of 29 references.

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Same direction, newest first

  1. 2025ReFoRCE: A Text-to-SQL Agent with Self-Refinement, Consensus Enforcement, and Column Exploration2 Feb

Other references · 12

  1. 2020Language Models are Few-Shot Learnerscited by 32,419
  2. 2019Exploring the Limits of Transfer Learning with a Unified Text-to-Text Transformercited by 14,599
  3. 2018Spider: A Large-Scale Human-Labeled Dataset for Complex and Cross-Domain Semantic Parsing and Text-to-SQL Taskcited by 1,139
  4. 2023DIN-SQL: Decomposed In-Context Learning of Text-to-SQL with Self-Correctioncited by 477
  5. 2023Text-to-SQL Empowered by Large Language Models: A Benchmark Evaluationcited by 385
  6. 2024Spider 2.0: Evaluating Language Models on Real-World Enterprise Text-to-SQL Workflowscited by 232
  7. 2023C3: Zero-shot Text-to-SQL with ChatGPTcited by 164
  8. 2024Next-Generation Database Interfaces: A Survey of LLM-based Text-to-SQLcited by 120
  9. 2024A Survey on Employing Large Language Models for Text-to-SQL Taskscited by 67
  10. 2024RSL-SQL: Robust Schema Linking in Text-to-SQL Generationcited by 52
  11. 2025LinkAlign: Scalable Schema Linking for Real-World Large-Scale Multi-Database Text-to-SQLcited by 29
  12. 2024Large Language Model Enhanced Text-to-SQL Generation: A Surveycited by 24

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  1. 2025RAISE: Reasoning Agent for Interactive SQL Exploration
  2. 2023On Evaluating the Integration of Reasoning and Action in LLM Agents with Database Question Answering
  3. 2025Text2Schema: Filling the Gap in Designing Database Table Structures based on Natural Language
  4. 2025BAPPA: Benchmarking Agents, Plans, and Pipelines for Automated Text-to-SQL Generation
  5. 2026AgentNLQ: A General-Purpose Agent for Natural Language to SQL
  6. 2025BIRD-INTERACT: Re-imagining Text-to-SQL Evaluation for Large Language Models via Lens of Dynamic Interactions
  7. 2026An Agentic System for Schema Aware NL2SQL Generation
  8. 2026GenDB: The Next Generation of Query Processing -- Synthesized, Not Engineered