Build LangGraph Cash Flow Forecasting & Refinancing Pipeline with LangChain
Managing real estate debt portfolios during default risks requires stateful, deterministic workflow engines capable of looping through loan workout options, testing interest rate sensitivities, and generating cash flow forecasts. In this challenge, developers use LangChain and LangGraph to construct a stateful DAG (Directed Acyclic Graph) workflow agent. The pipeline evaluates distressed office assets, forecasts monthly dynamic cash flows based on rent rolls and debt payments, checks for covenant breaches, and outputs debt restructuring options.
What you are building
The core problem, expected build, and operating context for this challenge.
Build a stateful CRE cash flow forecasting and refinancing graph pipeline using LangChain and LangGraph.
How work is evaluated
Evaluates accuracy of dynamic 12-month projected NOI and validity of recommended refinancing structure.
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.
NOI Math Precision
Validates annual NOI math strictly matches expected $3,240,000
This dimension contributes its full weight only when the submission satisfies the requirement. Partial credit is not awarded.
Refinance Strategy Accuracy
Fidelity of chosen debt workout recommendation • 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 stateful StateGraph workflow using LangGraph for multi-stage financial forecasting
Build custom LangChain tools for financial time-series cash flow modeling
Implement conditional branching based on DSCR default triggers
Produce automated institutional refinancing sensitivity charts and financial summaries
Reference links and supporting material
Dataset of 40 REIT asset monthly financial ledgers, debt yield metrics, and rate swap pricing charts.
Challenge setup
Evaluates accuracy of dynamic 12-month projected NOI and validity of recommended refinancing structure.
Challenge input
JSON with monthly_rental_income, monthly_operating_expenses, debt_service_monthly, target_refi_rate
LangChain
Required framework for model and tool abstractions.
LangGraph
Required tool for state graph creation.
Evaluated output
JSON with annual_noi, post_refi_dscr, refi_recommendation, cash_shortfall_flag
- Validates annual NOI math strictly matches expected $3,240,000
- Fidelity of chosen debt workout recommendation • target: 0.95 • range: 0-1
- Benchmark: LangGraphFinBench
- Refinance Strategy Accuracy target: 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
- LangGraph · Optional
- OpenRouter · Optional
- Langchain · Required
- LangGraph · Optional
- OpenRouter · Optional
Evaluation contract
- NOI Math Precision · Weight 1
- Refinance Strategy Accuracy · Weight 1
Metrics
- Refinance Strategy Accuracy · Target 0.95
Benchmarks
- LangGraphFinBench
- general
Reference cases
- 1 public reference case
Workflow tasks
- Task 1
Input: JSON with monthly_rental_income, monthly_operating_expenses, debt_service_monthly, target_refi_rate
Output: JSON with annual_noi, post_refi_dscr, refi_recommendation, cash_shortfall_flag
Execution harness: Python
Evaluation tools
- LangChain · Agent Framework
- LangGraph · Stateful Orchestrator
Recipe freeze marker
No recipe freeze time is recorded.