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International Journal of Creative and Open Research in Engineering and Management

A Peer-Reviewed, Open-Access International Journal Supporting Multidisciplinary Research, Digital Publishing Standards, DOI Registration, and Academic Indexing.
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ISSN: 3108-1754 (Online)
Crossref DOI: Available
ISO Certification: 9001:2015
Publication Fee: 599/- INR
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License: CC BY 4.0
Peer Review: Double Blind
Volume 02, Issue 05

Published on: May 2026

DBOT: AI-DRIVEN MULTI-DATABASE MONITORING AND DIAGNOSTICS PLATFORM

Yogesh Kothari Dipti Kothari Vaishnavi Wagaskar

MAEER’s MIT Arts Commerce and Science College Alandi Pune
DOI:https://doi.org/10.55041/ijcope.v2i4.1035

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Plagiarism Passed Peer Reviewed Open Access

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Abstract

DBot is an AI-driven monitoring and diagnostics platform designed to provide a unified view of heterogeneous database systems including MySQL, MongoDB, and Redis. The platform integrates a FastAPI-based orchestration backend, Model Context Protocol (MCP) agents for database-specific telemetry collection and checks, and a Large Language Model (LLM) reasoning layer implemented using PydanticAI. DBot also provides a React-based dashboard that combines real-time metrics visualization with a conversational interface for natural language queries. This report documents the design, implementation, and evaluation of DBot, and demonstrates how modular agents and LLM-assisted reasoning can reduce manual effort in diagnosis while improving observability across multiple database technologies.

Keywords: AIOps, database monitoring, MCP agents, FastAPI, Pydantic AI, LLM, MySQL, MongoDB, Redis, observability.

How to Cite this Paper

Kothari, Y., Kothari, D. & Wagaskar, V. (2026). DBot: AI-Driven Multi-Database Monitoring and Diagnostics Platform. International Journal of Creative and Open Research in Engineering and Management, <i>02</i>(05). https://doi.org/10.55041/ijcope.v2i4.1035

Kothari, Yogesh, et al.. "DBot: AI-Driven Multi-Database Monitoring and Diagnostics Platform." International Journal of Creative and Open Research in Engineering and Management, vol. 02, no. 05, 2026, pp. . doi:https://doi.org/10.55041/ijcope.v2i4.1035.

Kothari, Yogesh,Dipti Kothari, and Vaishnavi Wagaskar. "DBot: AI-Driven Multi-Database Monitoring and Diagnostics Platform." International Journal of Creative and Open Research in Engineering and Management 02, no. 05 (2026). https://doi.org/https://doi.org/10.55041/ijcope.v2i4.1035.

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References


  1. Cheng, , Li, M., & Wang, S. (2023). AI for IT Operations (AIOps) on Cloud Platforms: A Review. Journal of Cloud Computing.

  2. Warnier, , et al. (2024). AIOps for Log Anomaly Detection in the Era of Large Language Models: A Systematic Literature Review. Journal of Network and Computer Applications.

  3. Wang, , et al. (2021). Autonomous Database Management Systems: A Survey. VLDB Journal.

  4. FastAPI    https://fastapi.tiangolo.com/    (accessed    2026-01-13).             5.                         Pydantic Documentation. https://docs.pydantic.dev/latest/ (accessed 2026-0113).

  5. PydanticAI https://ai.pydantic.dev/ (accessed 2026-01-13).

  6. Model Context Protocol (MCP) Documentation and https://modelcontextprotocol.io/ (accessed 2026-01-13).

  7. Ollama https://docs.ollama.com/ (accessed 2026-01-13).

  8. React https://react.dev/ (accessed 2026-01-13).

  9. Docker https://docs.docker.com/ (accessed 2026-01-13).

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  • •All submissions are screened under plagiarism detection.
  • •Review follows editorial policy.
  • •Authors retain copyright.
  • •Peer Review Type: Double-Blind Peer Review
  • •Published on: May 03 2026
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