AssetOpsBench
IBM · IBM/AssetOpsBench
AssetOpsBench provides a unified framework for building, orchestrating, and evaluating domain-specific AI agents for Industry 4.0 asset operations and maintenance.
AssetOpsBench - Industry 4.0: A unified benchmark and framework for building, orchestrating, and evaluating domain-specific AI agents for Industry 4.0 asset operations and maintenance, with 460+ scenarios, 5 specialist agents (IoT, FMSR, TSFM, Work Order,...), and multi-agent orchestration blueprints (MetaAgent, AgentHive) over MCP.
See it in action
From the author's READMELeaderboard comparing AI model performance on AssetOpsBench metrics
From the author's README IBM ·Source
What you can do with it
- Develop AI agents for predictive maintenance of industrial machines based on sensor data.
- Benchmark the performance of different AI agents in asset operations scenarios.
- Create AI solutions for HVAC system monitoring, anomaly detection, and maintenance scheduling.
Safety check
No risky patterns found in the files we scanned.
Automated scan of the repo files. Always review code you run.
From the README
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.
At a Glance
<b>9</b>Asset classes
<b>141+</b>Scenarios
<b>5 + 1</b>Domain agents + utility server
<b>2</b>Orchestration frameworks
<b>20+</b>University extensions
<b>500+</b>Competition submissions
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.
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 2026IndustryAssetEQAand prior experimental work is maintained onmain-0.x.
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
| MCP Servers | Important tools |
|---|---|
| IoT | sites, asset_ids, asset_detail, assets, find_assets_by_sensors, installed_sensors, measured_sensors, latest_reading, history, sensor_stats |
| FMSR | get_failure_modes, generate_failure_modes, add_failure_modes |
| TSFM | Tasks/evidence: list_tasks, profile_series, characterize_series, data_quality; model catalog: list_models, search_models, find_models, resolve_model, model_template, register_model, register_finetuned, hf_stats; feature catalog: list_features, search_features, extract_features, select_features; run/eval ledger: recipe_template, run_recipe, run_tabular_recipe, run_plan, evaluate, list_runs, list_results |
| WO | Read: list_workorders, get_workorder, get_workorder_tasks, get_workorder_costs, get_workorder_actuals_vs_planned, get_workorder_kpis, get_schedule_calendar, get_my_assigned_workorders, get_failure_codes; write: generate_work_order, update_workorder, approve_workorder, assign_technician, close_workorder, cancel_workorder |
| Vibration | get_vibration_data, list_vibration_sensors, compute_fft_spectrum, compute_envelope_spectrum, assess_vibration_severity, calculate_bearing_frequencies, diagnose_vibration |
| Shared utility server | |
| Utilities | json_reader, get_sensor_catalog, get_asset_catalog, get_failure_mode_catalog, current_date_time, current_time_english |
Five domain servers (IoT, FMSR, TSFM, WO, Vibration) plus one shared utility server, documented in full — arguments, categories, and backing services — in docs/mcp-servers.md.
The full TSFM MCP surface currently contains 41 tools covering model cards, feature cards, recipe execution, evaluation, and result/run lookup. See docs/mcp-servers.md for the complete reference.
The WO MCP surface contains 15 tools (9 read + 6 write) covering the full Maximo-style work-order lifecycle — query, create, approve, assign, close, cancel — plus costs, KPIs, and scheduling, backed by CouchDB with IBM Maximo mxwo field names. Set AOB_READONLY=1 to expose the 9 read tools only. See docs/mcp-servers.md for the complete reference.
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](https://modelcontextprotocol.