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Challenge

Gas Field Expansion Schedule Risk Analyzer with LangChain

L&T's contract award for Aramco's Jafurah fourth expansion phase involves critical timeline management across complex gas treatment facilities. Build a stateful schedule risk evaluation agent using LangChain and Llama 3.3 70B to detect critical path delays and resource bottlenecks. Maintain mean absolute error below 3 days on timeline prediction.

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Challenge brief

What you are building

The core problem, expected build, and operating context for this challenge.

Construct a LangGraph agent powered by Llama 3.3 70B to evaluate Primavera schedule exports and highlight critical path risks.

Delivery guide

How work is evaluated

Evaluation

Evaluates prediction accuracy of task completion delays and critical path identification.

Datasets

Shared data for this challenge

Review public datasets and any private uploads tied to your build.

Evaluation rubric

How submissions are scored

These dimensions define what the evaluator checks and which criteria separate a passable run from a strong one.

Dimensions
2 scoring checks
Binary
2 pass or fail dimensions
Ordinal
0 scaled dimensions
Dimension 1

critical_path_identification

Verifies correctly identified bottleneck task on critical path

Binary check

This dimension contributes its full weight only when the submission satisfies the requirement. Partial credit is not awarded.

Dimension 2

delay_days_mae

Mean Absolute Error in predicted delay days compared to ground truth graph solver • target: 2.5 • range: 0-10

Binary check

This dimension contributes its full weight only when the submission satisfies the requirement. Partial credit is not awarded.

Learning goals

What you should walk away with

  • Orchestrate stateful scheduling analysis flows using LangChain and LangGraph

  • Integrate Llama 3.3 70B model via local or inference APIs for reasoning tasks

  • Parse Primavera P6 / MS Project schedule network graphs and dependency chains

  • Predict float variance and schedule slip probabilities across engineering work packages

Resources and assets

Reference links and supporting material

Dataset notes

Network graphs for 30 EPC gas plant installation packages with Primavera P6 data fields.

How this agent runs

Evaluates prediction accuracy of task completion delays and critical path identification.

Challenge input

JSON graph of tasks with durational dependencies and current completion status

LangChain

Provides LangGraph for stateful multi-step graph processing.

Llama 3.3 70B

Delivers open-weights reasoning capability for complex industrial analysis.

Evaluated output

JSON containing predicted end date, total delay days, and critical tasks list

Checks for
  • Verifies correctly identified bottleneck task on critical path
  • Mean Absolute Error in predicted delay days compared to ground truth graph solver • target: 2.5 • range: 0-10
Proof of success
  • Benchmark: ScheduleRiskBench
  • Delay Days Mae target: 2.5
  • 1 public reference case
Runtime evidence
  • Python execution harness
  • Python sandbox (unavailable on Versalist)
View technical recipe

Configured tools

Action Space
  • Langchain · Required
  • LangChain · Optional
  • Llama 3.3 70B · Optional
Orchestration
  • Langchain · Required
  • LangChain · Optional

Evaluation contract

  • critical_path_identification · Weight 1
  • delay_days_mae · Weight 1

Recipe state

This is a preview. The configuration can change before the evaluation recipe is locked.

Frequently Asked Questions about Gas Field Expansion Schedule Risk Analyzer with LangChain