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Challenge

Build Agritech DPI Advisory Workflows with Mastra AI and OpenHands

Smallholder farmers require real-time localized soil and climate insights to prevent crop failures. Build a TypeScript agent workflow using Mastra AI integrated with OpenHands automated tool execution to parse agricultural satellite telemetry and issue yield advisories with zero execution errors.

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Challenge brief

What you are building

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

Develop an automated agricultural advisory workflow using Mastra AI TypeScript workflows and OpenHands tools.

Delivery guide

How work is evaluated

Evaluation

Evaluates Mastra workflow execution consistency and accuracy of agricultural recommendations.

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

workflow_completion

Confirms Mastra workflow finished without runtime exceptions

Binary check

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

Dimension 2

advisory_relevance

Alignment with recommended agronomic guidelines • target: 0.95 • 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 step-based TypeScript workflows using Mastra AI framework

  • Integrate OpenHands runtime tools to perform automated data cleanup on raw telemetry

  • Generate actionable crop health assessments adhering to agricultural extension standards

Resources and assets

Reference links and supporting material

Dataset notes

Soil sensor and weather forecast dataset collected across 5 agricultural zones in central India.

Challenge setup

Evaluates Mastra workflow execution consistency and accuracy of agricultural recommendations.

Challenge input

JSON string containing soil moisture, nitrogen levels, and regional weather forecast

Mastra AI

Provides type-safe stateful workflow execution in TypeScript

OpenHands

Handles execution sandbox for data analysis tasks

Evaluated output

JSON containing stress index, recommended action, and tool execution status

Evaluation checks
  • Confirms Mastra workflow finished without runtime exceptions
  • Alignment with recommended agronomic guidelines • target: 0.95 • range: 0-1
Configured evaluation evidence
  • Benchmark: Agritech-Workflow-Bench
  • Advisory Relevance target: 0.95
  • 1 public reference case
Configured runtime
  • JavaScript execution harness
  • javascript 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

Environment
  • OpenHands · Optional
Action Space
  • Mastra AI · Required
  • OpenHands · Optional
  • Zed · Optional
Orchestration
  • Mastra AI · Required
  • OpenHands · Optional

Evaluation contract

  • workflow_completion · Weight 1
  • advisory_relevance · Weight 1

Metrics

  • advisory_relevance · Target 0.95

Benchmarks

  • Agritech-Workflow-Bench
  • energy

Reference cases

  • 1 public reference case

Workflow tasks

  1. Task 1

    Input: JSON string containing soil moisture, nitrogen levels, and regional weather forecast

    Output: JSON containing stress index, recommended action, and tool execution status

    Execution harness: JavaScript

Evaluation tools

  • Mastra AI · Agent Framework
  • OpenHands · Agent Execution Tool

Recipe freeze marker

No recipe freeze time is recorded.

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