Predictive Maintenance & Work Order State Graph with LangGraph and LangChain
Unscheduled machine down-times disrupt production schedules when sensor alarm triggers are not seamlessly translated into maintenance work orders. Construct a stateful workflow graph using LangGraph and LangChain to process vibration sensor alerts, evaluate asset health, and generate ERP work orders. Achieve 100% correct state transitions across complex asset repair workflows.
What you are building
The core problem, expected build, and operating context for this challenge.
Design a stateful LangGraph workflow that orchestrates predictive maintenance alerts through health evaluation, asset lookup, and automated work order dispatch.
How work is evaluated
Evaluates state transition accuracy and work order generation correctness in the LangGraph workflow.
Challenge datasets
Public datasets, plus the private datasets you have access to.
How submissions are scored
These dimensions define what the evaluator checks and which criteria separate a passable run from a strong one.
state_transition_validity
Ensures graph executed through all mandatory evaluation nodes without dangling states.
This dimension contributes its full weight only when the submission satisfies the requirement. Partial credit is not awarded.
transition_accuracy
Accuracy rate of conditional edge branching based on severity thresholds. • target: 1 • range: 0-1
This dimension contributes its full weight only when the submission satisfies the requirement. Partial credit is not awarded.
What you should walk away with
Define state schema graph nodes for telemetry ingestion, asset health evaluation, and work order creation
Implement conditional routing edges based on vibration threshold severity levels
Integrate LangChain tool nodes for external ERP (e.g., SAP / Maximo) work order API dispatch
Reference links and supporting material
Asset telemetry records including motor bearing vibration and temperature metrics associated with historical failure records.
Challenge setup
Evaluates state transition accuracy and work order generation correctness in the LangGraph workflow.
Challenge input
JSON with asset_id, vibration_mm_s, temperature_c, and operating_hours
LangChain
Agent framework and integration ecosystem
Evaluated output
JSON graph final state with triage_level, work_order_created boolean, and work_order_id
- Ensures graph executed through all mandatory evaluation nodes without dangling states.
- Accuracy rate of conditional edge branching based on severity thresholds. • target: 1 • range: 0-1
- Benchmark: Stateful Agent Workflow Execution
- Transition Accuracy target: 1
- 1 public reference case
- Python execution harness
- Python sandbox (unavailable on Versalist)
Summary shows the first task, up to three tools, and up to three evidence items. Open the technical recipe to view all configured items.
View technical recipe
Configured tools
- LangGraph · Required
- Langchain · Optional
- LangChain · Optional
- LangGraph · Required
- Langchain · Optional
- LangChain · Optional
Evaluation contract
- state_transition_validity · Weight 1
- transition_accuracy · Weight 1
Metrics
- transition_accuracy · Target 1
Benchmarks
- Stateful Agent Workflow Execution
- general
Reference cases
- 1 public reference case
Workflow tasks
- Task 1
Input: JSON with asset_id, vibration_mm_s, temperature_c, and operating_hours
Output: JSON graph final state with triage_level, work_order_created boolean, and work_order_id
Execution harness: Python
Evaluation tools
- LangChain · framework
Recipe freeze marker
No recipe freeze time is recorded.