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Challenge

Forecast Construction Delay Risks using LangChain and LangGraph

Construction managers face costly project delays when weather events, supply chain delays, and subcontractor availability are analyzed in isolation. Build a LangChain and LangGraph stateful workflow that ingests IoT telemetry and contractor schedules to forecast project delays with under 10% mean absolute error.

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Challenge brief

What you are building

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

Develop a stateful cyclic graph workflow using LangChain and LangGraph to ingest time-series telemetry and output construction project risk forecasts.

Delivery guide

How work is evaluated

Evaluation

Evaluates delay forecasting accuracy and critical path adjustment validity.

Datasets

Challenge datasets

Public datasets, plus the private datasets you have access to.

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 Impact Flag

Verifies correct identification when delay exceeds schedule float buffer.

Binary check

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

Dimension 2

Mean Absolute Error (MAE)

MAE in predicted delay days vs historical ground truth. • target: 2 • range: 0-100

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

  • Construct a stateful graph using LangGraph to handle iterative delay propagation across dependent tasks.

  • Implement LangChain tools to process real-time weather forecasts and subcontractor task completion signals.

  • Output actionable mitigation schedules to preserve critical path milestone target dates.

Resources and assets

Reference links and supporting material

Dataset notes

Construction site log dataset featuring weather telemetry, trade contractor progress logs, and historical critical path delay outcomes.

Challenge setup

Evaluates delay forecasting accuracy and critical path adjustment validity.

Challenge input

JSON with initial_completion_days, subcontractor_delay_days, adverse_weather_days

LangChain

Assigned framework for LLM integration and tool chains.

LangGraph

Provides cyclic graph state machine capabilities for time-series schedule modeling.

Evaluated output

JSON with predicted_delay_days, revised_completion_days, critical_path_impacted

Evaluation checks
  • Verifies correct identification when delay exceeds schedule float buffer.
  • MAE in predicted delay days vs historical ground truth. • target: 2 • range: 0-100
Configured evaluation evidence
  • Benchmark: Schedule_Risk_Eval
  • Mean Absolute Error (MAE) target: 2
  • 1 public reference case
Configured runtime
  • 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

Action Space
  • LangChain · Required
  • Langchain · Optional
  • Zed · Optional
Orchestration
  • LangChain · Required
  • Langchain · Optional

Evaluation contract

  • Critical Path Impact Flag · Weight 1
  • Mean Absolute Error (MAE) · Weight 1

Metrics

  • Mean Absolute Error (MAE) · Target 2

Benchmarks

  • Schedule_Risk_Eval
  • general

Reference cases

  • 1 public reference case

Workflow tasks

  1. Task 1

    Input: JSON with initial_completion_days, subcontractor_delay_days, adverse_weather_days

    Output: JSON with predicted_delay_days, revised_completion_days, critical_path_impacted

    Execution harness: Python

Evaluation tools

  • LangChain · LLM Orchestration Framework
  • LangGraph · Stateful Agent Graph Engine

Recipe freeze marker

No recipe freeze time is recorded.

Frequently Asked Questions about Forecast Construction Delay Risks using LangChain and LangGraph