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.
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.
How work is evaluated
Evaluates state transition accuracy and eligibility status verification across multilingual farmer inputs.
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.
correct_eligibility_mapping
Checks if farmers within land threshold limits are approved.
This dimension contributes its full weight only when the submission satisfies the requirement. Partial credit is not awarded.
language_fidelity_score
Accuracy of localized response phrasing. • target: 0.94 • 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
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.
Reference links and supporting material
Agricultural land records and scheme eligibility criteria mapped across multiple Indian languages.
Challenge setup
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 if farmers within land threshold limits are approved.
- Accuracy of localized response phrasing. • target: 0.94 • range: 0-1
- Benchmark: Multilingual DPI Evaluation Benchmark
- Language Fidelity Score target: 0.94
- 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
- LangGraph · Required
- LangChain · Optional
- Guidance · Optional
- LangGraph · Required
- LangChain · Optional
Evaluation contract
- correct_eligibility_mapping · Weight 1
- language_fidelity_score · Weight 1
Metrics
- language_fidelity_score · Target 0.94
Benchmarks
- Multilingual DPI Evaluation Benchmark
- general
Reference cases
- 1 public reference case
Workflow tasks
- Task 1
Input: JSON containing query_text_hindi, land_hectares, crop_type, and state_code.
Output: JSON containing verified_eligible, subsidy_scheme_code, and translated_response_hindi.
Execution harness: Python
Evaluation tools
- LangChain · agent_framework
- LangGraph · agent_framework
Recipe freeze marker
No recipe freeze time is recorded.