Challenge

Multilingual Agricultural Subsidy Verification Workflow with LangChain and LangGraph

Connecting smallholder farmers to state subsidy programs requires accessible, multilingual verification processes across regional dialects. Build a stateful, multilingual workflow using LangGraph and LangChain to process voice queries in regional Indian languages, verify farmer eligibility against agricultural databases, and issue step-by-step guidance.

Business OperationsHosted by Vera
Challenge brief

What you are building

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

Design a stateful LangGraph graph with LangChain modules to handle multilingual farmer state transitions during agricultural subsidy qualification.

Delivery guide

How work is evaluated

Evaluation

Evaluates state transition accuracy and eligibility status verification across multilingual farmer inputs.

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

correct_eligibility_mapping

Checks if farmers within land threshold limits are approved.

Binary check

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

Dimension 2

language_fidelity_score

Accuracy of localized response phrasing. • target: 0.94 • range: 0-1

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 stateful, cyclic AI workflows using LangGraph and LangChain.

  • Handle multilingual input conversion and intent mapping for Indian regional languages.

  • Connect agricultural telemetry and land record data to automated eligibility evaluators.

Resources and assets

Reference links and supporting material

Dataset notes

Agricultural land records and scheme eligibility criteria mapped across multiple Indian languages.

How this agent runs

Evaluates state transition accuracy and eligibility status verification across multilingual farmer inputs.

Challenge input

JSON containing query_text_hindi, land_hectares, crop_type, and state_code.

LangChain

Chain and tool integration framework

LangGraph

Stateful cyclic graph orchestrator for workflow state management

Evaluated output

JSON containing verified_eligible, subsidy_scheme_code, and translated_response_hindi.

Checks for
  • Checks if farmers within land threshold limits are approved.
  • Accuracy of localized response phrasing. • target: 0.94 • range: 0-1
Proof of success
  • Benchmark: Multilingual DPI Evaluation Benchmark
  • Language Fidelity Score target: 0.94
  • 1 public reference case
Runtime evidence
  • Python execution harness
  • Python sandbox (unavailable on Versalist)
View technical recipe

Configured tools

Action Space
  • LangGraph · Required
  • LangChain · Optional
  • Guidance · Optional
Orchestration
  • LangGraph · Required
  • LangChain · Optional

Evaluation contract

  • correct_eligibility_mapping · Weight 1
  • language_fidelity_score · Weight 1

Recipe state

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

Frequently Asked Questions about Multilingual Agricultural Subsidy Verification Workflow with LangChain and LangGraph