{"data":{"slug":"ibm-assetopsbench","name":"AssetOpsBench","tagline":"Framework for building and evaluating AI agents targeting Industry 4.0 asset operations","github_url":"https://github.com/IBM/AssetOpsBench","owner":"IBM","repo":"AssetOpsBench","owner_avatar_url":"https://avatars.githubusercontent.com/u/1459110?v=4","primary_language":"Python","stars":2069,"forks":294,"topics":["ai-for-physical-assets","condition-based-maintenance","hvac-maintenance","iot","llm-agents","model-context-protocol","predictive-maintenance","time-series-forecasting"],"archived":false,"github_pushed_at":"2026-07-26T10:40:12+00:00","maintenance_label":"Very active","url":"https://www.graphcanon.com/tools/ibm-assetopsbench","markdown_url":"https://www.graphcanon.com/tools/ibm-assetopsbench.md","api_url":"https://www.graphcanon.com/api/graphcanon/tools/ibm-assetopsbench","graph_url":"https://www.graphcanon.com/api/graphcanon/graph?tool=ibm-assetopsbench","description":"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.","homepage_url":null,"license":"Apache-2.0","open_issues":45,"watchers":12,"ai_summary":"Provides tools for creation of specialist AI agents focused on maintenance and operational tasks in industry settings.","readme_excerpt":"# Clone and install\ngit clone https://github.com/IBM/AssetOpsBench.git\ncd AssetOpsBench\npip install -e .\n\n---\n\n## Infrastructure Support\n\nModel API access for AssetOpsBench 2.0 is enabled by [TokenRouter](https://tokenrouter.com) (PaleBlueDot AI), a unified API platform providing access to leading AI models through a single API endpoint.\n\n---","github_created_at":"2025-05-01T20:48:50+00:00","created_at":"2026-07-11T11:51:27.030117+00:00","updated_at":"2026-07-27T00:00:18.382242+00:00","categories":[{"slug":"ai-agents","name":"AI Agents","url":"https://www.graphcanon.com/categories/ai-agents","markdown_url":"https://www.graphcanon.com/categories/ai-agents.md","api_url":"https://www.graphcanon.com/api/graphcanon/categories/ai-agents"},{"slug":"evaluation-observability","name":"Evaluation & Observability","url":"https://www.graphcanon.com/categories/evaluation-observability","markdown_url":"https://www.graphcanon.com/categories/evaluation-observability.md","api_url":"https://www.graphcanon.com/api/graphcanon/categories/evaluation-observability"}],"tags":[{"slug":"ai-for-physical-assets","name":"ai-for-physical-assets"},{"slug":"condition-based-maintenance","name":"condition-based-maintenance"},{"slug":"hvac-maintenance","name":"hvac-maintenance"},{"slug":"iot","name":"iot"},{"slug":"llm-agents","name":"llm-agents"},{"slug":"model-context-protocol","name":"model-context-protocol"},{"slug":"predictive-maintenance","name":"predictive-maintenance"},{"slug":"time-series-forecasting","name":"time-series-forecasting"}],"trust":{"provenance":{"is_fork":false,"github_id":976286833,"owner_type":"Organization","methodology":"github_public_v1","parent_repo":null,"near_duplicate_slugs":[]},"computed_at":"2026-07-27T00:00:17.395Z","maintenance":{"label":"Very active","score":96,"methodology":"github_public_v1","releases_90d":0,"days_since_push":0,"last_release_at":null},"security_summary":{"status":"no_lockfile","scanner":null,"low_count":0,"high_count":0,"last_scan_at":"2026-07-11T11:51:28.218Z","medium_count":0,"scan_profile":"none","critical_count":0}},"capability_facts":{"scan":{"source":"repo_scan","observed_at":"2026-07-27T00:00:17.905Z"},"has_cli":{"value":true,"source":"pyproject.toml:[project.scripts]","observed_at":"2026-07-27T00:00:17.905Z"},"languages":{"value":["python"],"source":"github.language+pyproject.toml","observed_at":"2026-07-27T00:00:17.905Z"},"license_spdx":{"value":"Apache-2.0","source":"github.license","observed_at":"2026-07-27T00:00:17.905Z"}},"decision_facts":{"hosting":null,"pricing":null,"requirements":null,"constraints":null,"when_to_use":["When you need detailed evaluation frameworks for multiple types of AI agents operating in industry environments","For projects that aim to integrate predictive maintenance capabilities using advanced time-series forecasting methods"],"when_not_to_use":["If your project focus is on general-purpose AI outside the domain-specific context of industrial operations","Do not use if you require real-time agent orchestration without any emphasis on condition-based or predictive maintenance in asset management"],"source":"enrich:decision_facts","observed_at":"2026-07-16T23:07:53.969Z"},"constraint_facets":null,"decision_summary":[{"label":"Adopt for","value":"AssetOpsBench is a specialized framework for developing and evaluating AI agents in Industry 4.0 contexts, with an emphasis on operations and maintenance scenarios including HVAC systems and IoT management."}]}}