Refinery Incident Repair Orchestrator with LangChain & LangGraph
Refinery maintenance teams responding to physical damage events require automated task graph generation for emergency repairs. Build a stateful incident remediation graph using LangChain and LangGraph to schedule contractor tasks and track equipment replacements.
What you are building
The core problem, expected build, and operating context for this challenge.
Construct a stateful, multi-node repair planning graph using LangChain and LangGraph for industrial facility recovery.
How work is evaluated
Evaluates graph transition logic, critical path duration calculations, and state persistence.
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.
stage_progression_check
Verifies that state finishes in 'PROCUREMENT_AND_INSTALL' or 'COMPLETED'.
This dimension contributes its full weight only when the submission satisfies the requirement. Partial credit is not awarded.
downtime_calculation_precision
Accuracy of total estimated downtime relative to lead times and repair windows. • target: 0.95 • 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
Design a cyclic, stateful workflow using LangGraph for multi-stage repair planning
Incorporate LangChain tool interfaces to query contractor resource availability
Implement checkpointing and state memory for long-running infrastructure maintenance
Evaluate schedule critical path impact based on replacement lead times
Reference links and supporting material
Damage assessment logs, replacement valve/pipe inventory records, and contractor mobilisation timetables for Kuwait refineries.
Challenge setup
Evaluates graph transition logic, critical path duration calculations, and state persistence.
Challenge input
JSON with unit_id, damage_severity, and lead_time_days
LangChain
Standard components for LLM prompt orchestration.
LangGraph
Stateful graph orchestration engine for multi-node agent logic.
Evaluated output
JSON with total_downtime_days, active_stage, and contractor_assigned
- Verifies that state finishes in 'PROCUREMENT_AND_INSTALL' or 'COMPLETED'.
- Accuracy of total estimated downtime relative to lead times and repair windows. • target: 0.95 • range: 0-1
- Benchmark: Industrial-Graph-Ops
- Downtime Calculation Precision target: 0.95
- 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
- Langchain · Required
- LangChain · Optional
- Merge · Optional
- Langchain · Required
- LangChain · Optional
Evaluation contract
- stage_progression_check · Weight 1
- downtime_calculation_precision · Weight 1
Metrics
- downtime_calculation_precision · Target 0.95
Benchmarks
- Industrial-Graph-Ops
- energy
Reference cases
- 1 public reference case
Workflow tasks
- Task 1
Input: JSON with unit_id, damage_severity, and lead_time_days
Output: JSON with total_downtime_days, active_stage, and contractor_assigned
Execution harness: Python
Evaluation tools
- LangChain · llm_tooling
- LangGraph · agent_framework
Recipe freeze marker
No recipe freeze time is recorded.