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Challenge

Pydantic AI Quick-Commerce Food Delivery Logistics Routing Engine

Multi-category quick commerce platforms extending into hot food delivery require split-second order grouping and kitchen prep SLA management. Build a type-safe routing agent using Pydantic AI to minimize order assembly delays.

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Challenge brief

What you are building

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

Construct a type-safe delivery assignment engine using Pydantic AI models.

Delivery guide

How work is evaluated

Evaluation

Evaluates order batching efficiency and rider route assignments.

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

batching_success

Verifies valid order batching.

Binary check

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

Dimension 2

delivery_sla_compliance

SLA achievement score • 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

  • Build Pydantic AI agents with strict response models for rider batching

  • Optimize multi-stop delivery routes under hot food delivery constraints

  • Validate driver capacity and thermal insulation specs programmatically

Resources and assets

Reference links and supporting material

Dataset notes

Quick commerce order queues with kitchen preparation and rider location telemetry.

Challenge setup

Evaluates order batching efficiency and rider route assignments.

Challenge input

JSON food order queue

Pydantic AI

Mandatory framework requirement

Evaluated output

JSON rider dispatch plan

Evaluation checks
  • Verifies valid order batching.
  • SLA achievement score • target: 0.95 • range: 0-1
Configured evaluation evidence
  • Benchmark: Food-Logistics-Bench
  • Delivery Sla Compliance target: 0.95
  • 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
  • Pydantic AI · Required
  • Voiceflow · Optional
  • Distyl · Optional
Orchestration
  • Pydantic AI · Required

Evaluation contract

  • batching_success · Weight 1
  • delivery_sla_compliance · Weight 1

Metrics

  • delivery_sla_compliance · Target 0.95

Benchmarks

  • Food-Logistics-Bench
  • general

Reference cases

  • 1 public reference case

Workflow tasks

  1. Task 1

    Input: JSON food order queue

    Output: JSON rider dispatch plan

    Execution harness: Python

Evaluation tools

  • Pydantic AI · Agent Framework

Recipe freeze marker

No recipe freeze time is recorded.

Frequently Asked Questions about Pydantic AI Quick-Commerce Food Delivery Logistics Routing Engine