MCP Servers · IBM

AssetOpsBench

AssetOpsBench is an open-source framework for building and evaluating AI agents that handle industrial asset maintenance tasks. It provides 460+ test scenarios, domain-specific agent blueprints, and integration with the Model Context Protocol to orchestrate maintenance workflows across IoT, predictive analytics, and work-order systems.

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Source: GitHub — github.com/IBM/AssetOpsBench

GitHub stars

288

Forks

Python

Primary language

Apache-2.0

License (OSI-approved)

Key facts

Objective fields from the source. Values we can't verify are shown as “Unknown” rather than guessed.

Field	Value
Repository	IBM/AssetOpsBench
Owner	IBM
Primary language	Python
License	Apache-2.0 — OSI-approved
Stars	2k
Forks	288
Open issues	47
Latest release	Unknown
Last updated	2026-07-08
Source	https://github.com/IBM/AssetOpsBench

What AssetOpsBench is

Python-based benchmark and agentic framework implementing multi-agent orchestration (MetaAgent, AgentHive) over MCP, with 5 specialist agents (IoT, FMSR, TSFM, Work Order, Vibration), time-series forecasting, anomaly detection, and ReAct-based orchestrators for Claude and OpenAI models. Provides plan-execute, deep-agent, and agent-as-tool delegation patterns.

Quickstart

Get the AssetOpsBench source

Clone the repository and explore it locally.

terminalbash

1git clone https://github.com/IBM/AssetOpsBench.git2cd AssetOpsBench3# follow the project's README for install & configuration

Need it deployed, integrated, or customized instead? DEV.co ships production installs.

Best use cases

Industrial Maintenance Research & Benchmarking

Evaluate and compare LLM-based agents on standardized Industry 4.0 maintenance tasks. Framework includes 460+ scenarios, 9 asset classes, and LLM Judge evaluation across 6 dimensions—ideal for academic papers, model selection, and agent architecture studies.

Proof-of-Concept for Predictive Maintenance Systems

Rapidly prototype multi-agent systems combining IoT telemetry, failure-mode analysis, time-series forecasting, and work-order generation. MCP server abstractions allow testing agent reasoning on realistic industrial workflows without building monitoring infrastructure from scratch.

Multi-Agent Orchestration in Facilities & HVAC Operations

Build domain-specialized agents for chiller systems, HVAC units, and facility assets with built-in MCP tools. Use plan-execute or deep-agent patterns to chain IoT queries, anomaly detection, and maintenance task planning into coherent workflows.

Implementation considerations

Data Integration Layer: MCP servers abstract IoT, FMSR, TSFM, and Work Order backends. You must implement or wrap connectors for your CMMS, sensor networks, and time-series stores; boilerplate exists but is not plug-and-play.
LLM Dependency: Agents require Claude, OpenAI, or compatible LLM APIs. Plan for token costs, rate limits, and vendor lock-in on inference—framework does not include local model guidance or cost optimization tactics.
Scenario Customization: 460+ scenarios are chiller/HVAC-heavy (simulated). Real asset data ingestion, failure-mode labeling, and scenario creation require domain expertise and iterative tuning.
Evaluation Harness: Leaderboards use Llama-4-Maverick-17B judge and 6-dimensional scoring. Running evaluations at scale requires GPU access and configured evaluator deployments; not trivial for ad-hoc testing.
Agent Orchestration Choice: Multiple patterns offered (plan-execute, deep-agent, Claude/OpenAI ReAct). Choosing and tuning the right pattern for your workflows needs experimental iteration and failure analysis.

When to avoid it — and what to weigh

Production Deployment Without Hardening — Framework is research-grade and benchmarking-focused. No production SLAs, security audit trail, or enterprise resilience patterns documented. Requires significant custom integration and validation before use on live critical assets.
Legacy Industrial Systems Without Modern APIs — Assumes access to structured IoT data, time-series stores, and work-order systems via MCP or code integration. Heavy customization needed if your assets only expose SCADA/OPC-UA with no REST/Python bindings or if data silos lack integration.
Strict Real-Time or Sub-Second Response Requirements — Agent reasoning loops call LLMs synchronously and involve planning phases. Not architected for hard real-time control loops or latency-critical automation (e.g., emergency shutdown triggers). Better suited for offline planning and tactical maintenance.
Proprietary Compliance or IP Concerns — Framework is open-source (Apache 2.0) and hosted on GitHub. If your deployment, scenarios, or industrial data are proprietary and cannot be openly developed or forked, evaluate impact on maintenance visibility and contributor access.

License & commercial use

Apache License 2.0 (Apache-2.0) is a permissive open-source license. Allows commercial use, modification, and distribution under the condition that derivative works include license notice and state changes. No explicit copyleft clause, but contributors retain copyright.

Apache 2.0 permits commercial use. However, framework is research-grade, not enterprise-hardened. Commercial deployment requires: (1) your own maintenance of forks/patches, (2) validation that MCP integrations meet your compliance and data-residency rules, (3) no SLA or vendor support from IBM. Consider custom support agreements if critical to operations.

DEV.co evaluation signals

Editorial assessment — not user reviews. Directional, with an explicit confidence level.

Signal	Assessment
Maintenance	Active
Documentation	Adequate
License clarity	Clear
Deployment complexity	High
DEV.co fit	Good
Assessment confidence	High

Security considerations

Framework is open-source research code, not independently audited. Consider: (1) LLM API keys and credentials must be secured (no built-in secret management visible), (2) MCP servers connect to CMMS and IoT systems—data exposure risks depend on network isolation and backend auth, (3) Agent reasoning logs may contain sensitive asset/maintenance data; retention and access control policies are not documented, (4) Time-series and failure-mode data could enable adversarial inference; privacy implications are unaddressed in provided materials. Data classification and compliance with HIPAA, SOX, or ISO 27001 requires custom layers.

Alternatives to consider

Langchain + LangGraph (Agent Frameworks)

General-purpose agent orchestration without industrial benchmarks. Requires custom tool definitions, evaluation harness, and domain scenario curation. Lighter weight if you only need ReAct or hierarchical planning without 460+ pre-built scenarios.

IBM Maximo + Prescriptive Maintenance (CMMS + AI)

Proprietary IBM stack for enterprise asset management. Includes production SLAs, vendor support, and tighter CMMS integration. Higher cost; less transparency for research and customization versus open-source AssetOpsBench.

SAP Predictive Maintenance (ERP + ML)

Enterprise ERP with embedded predictive analytics for asset health. Integrates with SAP landscapes and Hana databases. Heavier lift for data modeling but includes change management, audit trails, and commercial support.

Software development agency

Build on AssetOpsBench with DEV.co software developers

Start with the Colab notebook or HF Playground to explore AssetOpsBench scenarios. For production integration, plan MCP server development and LLM API strategy early. Contact us for custom enterprise support.

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AssetOpsBench FAQ

Can I use AssetOpsBench for real-time asset monitoring in production?

Not without significant custom engineering. Framework is designed for agent benchmarking and research. Agent planning loops are synchronous and LLM-dependent, unsuitable for hard real-time control. PoCs and tactical maintenance workflows are feasible; critical safety or sub-second automation is not.

Do I need to run an LLM locally or can I use APIs?

Framework is designed for API-based LLMs (Claude, OpenAI). Local model support is not documented. API costs will scale with scenario volume and evaluation runs. No built-in cost controls or token budgeting are mentioned.

How do I integrate my CMMS (Maximo, ServiceNow, etc.) data?

Implement or wrap a custom MCP server. Framework provides 5 example servers (IoT, FMSR, TSFM, WO, Vibration); yours must follow MCP spec and expose tooling for agent queries. No turnkey connectors are visible in the provided data.

What support or SLAs are available?

Community support via GitHub (47 open issues, forks, discussions). No formal vendor SLA or enterprise support contract documented. IBM research team maintains the project; escalation paths are unclear.

Work with a software development agency

Need help beyond evaluating AssetOpsBench? DEV.co is a software development agency offering software development services and web development for teams of every size. Our software developers and web developers build custom software, web applications, APIs, and mcp servers integrations — and maintain them long-term.

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