AssetOpsBench

AI Agents for Industrial Asset Operations & Maintenance

A unified, open framework for building, orchestrating, and evaluating domain-specific AI agents in Industry 4.0.

📄 Paper · 🤗 Dataset · 🎮 Playground · 📢 IBM Blog · 🎥 Video · 📊 Kaggle · 🚀 Colab

[!IMPORTANT] 🎉 AssetOpsBench is officially accepted at KDD 2026 (Datasets & Benchmarks Track), Jeju, South Korea, alongside our hands-on tutorial Building Reliable Industrial Agents with MCP. See Publications for the full list of 2025–2026 work.

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At a Glance

Built for: maintenance engineers, reliability specialists, facility planners, and Industry 4.0 researchers. Powered by: LLMs + Time Series Foundation Models, orchestrated over live sensor data and Industry 4.0 records (FMEA, work orders, alerts). Now with: simplified interface and native MCP (Model Context Protocol) support.

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Quick Start

# Clone and install
git clone https://github.com/IBM/AssetOpsBench.git
cd AssetOpsBench
pip install -e .

# Try a scenario (to be enabled)
python -m assetopsbench.run --scenario "List all sensors of Chiller 6 in MAIN site"

Or jump in instantly:

• 🚀 Run on Colab — no install required (illustration of LLM Agent)
• 🎮 Try the HF Playground — interactive demo
• 📖 Read INSTRUCTIONS.md — full setup, MCP servers, plan-execute runner

[!NOTE] Active development is on main. The codebase used for various publication venues continues to be maintained on separate branches, for example, ACL 2026 IndustryAssetEQA and prior experimental work is maintained on main-0.x.

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What is AssetOpsBench?

AssetOpsBench is a unified framework for developing, orchestrating, and evaluating domain-specific AI agents in industrial asset operations and maintenance. It provides reproducible scenarios, agent tooling, and evaluation pipelines for multi-step workflows in simulated industrial environments.

Domain-Specific MCP Servers

Agent Frameworks

• Plan Execute — plan-and-execute sequential workflow to work with any LLM
• Deep Agent — planning, sub-agents, and virtual filesystem for long-horizon tasks
• Claude Agent — ReAct-based orchestrator using Claude with agent-as-tool delegation
• OpenAI Agent — ReAct-based orchestrator using OpenAI models with agent-as-tool delegation

MCP Environment

The src/ directory contains MCP servers and a plan-execute runner built on the Model Context Protocol. See INSTRUCTIONS.md for setup.

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Example Scenarios

Some tasks focus on a single domain, others are multi-step end-to-end workflows. Explore all scenarios on Hugging Face.

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Leaderboards

• To be revised (WIP with latest models)
• Evaluated with 7 Large Language Models
• Trajectories scored using LLM Judge (Llama-4-Maverick-17B)
• 6-dimensional criteria measuring reasoning, execution, and data handling

Example: MetaAgent leaderboard

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Publications

12+ contributions across 7 top venues in 2025–2026 from the team behind AssetOpsBench.

• [D&B] AssetOpsBench: A Benchmark for Industrial Asset Operations Agents · D. Patel, S. Lin, et al. · 📄 Paper
• [Tutorial] Building Reliable Industrial Agents with MCP: A Hands-on AssetOpsBench Tutorial for AI-Driven Operations · D. Patel, C. Shyalika, et al.

• [Industry] IndustryAssetEQA: A Neurosymbolic Operational Intelligence System for Embodied Question Answering in Industrial Asset Maintenance · C. Shyalika, D. Patel, A. Sheth · arXiv:2604.23446

• [Main] Adaptive Conformal Anomaly Detection with Time Series Foundation Models for Signal Monitoring · N. Martinez, F. O'Donncha, W. M. Gifford, N. Zhou, D. C. Patel, R. Vaculin

• [Demo] AssetOpsBench-Live: Privacy-Aware Online Evaluation of Multi-Agent Performance in Industrial Operations · D. Patel, N. Zhou, S. Lin, J. T. Rayfield, C. Shyalika, S. R. Yarrabothula · 🎥 Demo
• [Main] SPIRAL: Symbolic LLM Planning via Grounded and Reflective Search · Y. Zhang, G. Ganapavarapu, S. Jayaraman, B. Agrawal, D. Patel, A. Fokoue · 💻 Code
• [Bridge] Knowledge-Guided AI for Industrial Asset Health Monitoring · S. Lin, D. Patel
• [Tutorial] From Inception to Productization: Hands-on Lab for the Lifecycle of Multimodal Agentic AI in Industry 4.0 · C. Shyalika, S. Ahuja, S. Lin, R. Wickramarachchi, D. Patel, A. Sheth · 🌐 Website · 📊 Slides
• [Workshop(AABA4ET)] Agentic Code Generation for Heuristic Rules in Equipment Monitoring · F. Lorenzi, A. Langbridge, F. O'Donncha, J. Rayfield, B. Eck, S. Rosato

• [Deployed] Deployed AI Agents for Industrial Asset Management: CodeReAct Framework for Event Analysis and Work Order Automation · N. Zhou, D. Patel, A. Bhattacharyya
• [Emmerging] Diversity Meets Relevancy: Multi-Agent Knowledge Probing for Industry 4.0 Applications · C. Constantinides, D. Patel, S. Kimbleton, N. Garg, M. Paracha

• [D&B Track] FailureSensorIQ: A Multi-Choice QA Dataset for Understanding Sensor Relationships and Failure Modes · C. Constantinides, D. Patel, S. Lin, C. Guerrero, S. D. Patil, J. Kalagnanam · 📄 arXiv · 💻 Code
• [Social] Building Reliable Agentic Benchmarks: Insights from AssetOpsBench (invited talk, 2000+ registered) · D. Patel · 📅 Luma

• [Main] ReAct Meets Industrial IoT: Language Agents for Data Access · J. T. Rayfield, S. Lin, N. Zhou, D. C. Patel
• [Main] Generalized Embedding Models for Industry 4.0 Applications · C. Constantinides, S. Lin, D. C. Patel · 📄 arXiv
• [Findings] Fine-Tuned Thoughts: Leveraging Chain-of-Thought Reasoning for Industrial Asset Health Monitoring · S. Lin, D. Patel, C. Constantinides · 📄 ACL Anthology · 💻 Code

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Tutorials & Technical Material

📘 Hands-on guides from our team:

• ReActXen IoT Agent (EMNLP 2025)
• FailureSensorIQ (NeurIPS 2025)
• AssetOpsBench Lab (AAAI 2026)
• SPIRAL (AAAI 2026)
• AssetOpsBench Technical Material

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AI Competitions

AssetOpsBench powers public AI agent competitions that bring together researchers, students, and practitioners worldwide.

🔴 Live — IJCAI 2026

Industrial Automation Challenge: Benchmarking Physics-Grounded LLMs for Task Reasoning

A new challenge co-located with IJCAI 2026 that pushes LLM agents on physics-grounded industrial reasoning.

• 🌐 Challenge site: ai-industrial-challenge-ijcai
• 📋 IJCAI 2026 competitions: 2026.ijcai.org/competitions

✅ Completed — CODS 2025

AssetOpsBench-Live: AI Agentic Challenge

Launched in September 2025 at CODS 2025, the competition evaluated multi-agent systems on live industrial scenarios.

• 🏆 Competition page: codabench.org/competitions/10206
• 👥 365 participants · 500+ agent submissions

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Talks & Events

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University Projects & Extensions

AssetOpsBench is being extended by university research groups exploring new asset classes, evaluation paradigms, and agentic architectures. To list your project, open a PR.

• Calibrated Coordination Reduces Overconfident Errors in Multi-Agent LLM Systems – Confidence-weighted aggregation and abstention framework for reducing hallucinated confidence events in multi-agent industrial troubleshooting and operational decision-making benchmarks. Chand Sahil Mansuri, Sadamori Kojaku, Binghamton University.
• Internalizing MCP Tool Knowledge in Small LLMs via QLoRA Fine-Tuning — HPML project using AssetOpsBench to fine-tune ~4B models to internalize MCP tool knowledge and reduce prompt schema overhead. Ayal Yakobe, Columbia University · repo
• SPIN — Structural LLM Planning via Iterative Navigation for Industrial Tasks. Yusuke Ozaki, University at Albany · paper · repo
• Synthetic Scenario Generation for Evaluation of Industry 4.0 Agents — Automated scenario generation, transformer asset integration, and scenario quality evaluation. Rohith Kanathur, Sagar Chethan Kumar, Columbia University · repo
• AgentOpsBench — High-throughput battery analytics MCP server with DNN prognostics (RUL prediction) and 3.3× latency optimization. Siddharth Gowda, Rushin Bhatt, Aryaman Agrawal, Winston Li, Columbia University ·

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Call for Scenario Contribution

We are expanding AssetOpsBench to cover a broader range of industrial challenges. We invite researchers and practitioners to contribute new scenarios, particularly in:

• Asset Classes: Turbines, HVAC systems, Pumps, Transformers, CNC Machines, Robotics, Engines
• Task Domains: Prognostics and Health Management, Remaining Useful Life (RUL) estimation, Root Cause Analysis (RCA), Diagnostic Analysis, Predictive Maintenance

How to contribute:

1. Define your scenario following our Utterance Guideline and Ground Truth Guideline
2. Explore the Hugging Face dataset for examples
   • For external public sources and starter asset-class mappings, see External Industrial Dataset Guide
3. Submit a Pull Request or open an Issue with the tag new-scenario
4. Contact us with questions:
   • Dhaval Patel — pateldha@us.ibm.com
   • Nianjun Zhou — jzhou@us.ibm.com

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Infrastructure Support

Model API access for AssetOpsBench 2.0 is enabled by TokenRouter (PaleBlueDot AI), a unified API platform providing access to leading AI models through a single API endpoint.

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Contributors

Thanks to these wonderful people ✨

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Star History

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If AssetOpsBench is useful to your work, please ⭐ star the repo, 🍴 fork it, and tell us what you're building.