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Challenge

Build an ONDC Logistics Triage Agent with LangChain and Arize Phoenix

Local delivery networks on ONDC face routing failures and severe fulfillment churn during hyper-local grocery surges. Build a LangChain triage pipeline evaluated with Arize Phoenix to detect order anomalies and dynamically reroute delivery failures with over 90% accuracy.

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Challenge brief

What you are building

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

Build an automated ONDC logistics routing and anomaly triage workflow using LangChain chains monitored with Arize Phoenix tracing.

Delivery guide

How work is evaluated

Evaluation

Evaluates the triage agent's ability to classify delivery failure signals and return valid ONDC rerouting payloads.

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

valid_schema_check

Ensures output conforms to required JSON schema

Binary check

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

Dimension 2

triage_accuracy

Accuracy of failure classification against gold standards • target: 0.9 • 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

  • Implement stateful event triage for ONDC seller and buyer app webhooks using LangChain

  • Integrate Arize Phoenix telemetry to trace LLM decision quality and execution latency

  • Automate dynamic fulfillment rerouting for failing hyper-local grocery delivery nodes

Resources and assets

Reference links and supporting material

Dataset notes

Simulated stream of ONDC order fulfillment failure webhooks across major Indian metropolitan hubs.

Challenge setup

Evaluates the triage agent's ability to classify delivery failure signals and return valid ONDC rerouting payloads.

Challenge input

JSON containing ONDC logistics event payload and seller status

LangChain

Used for building structured event reasoning chains

Arize Phoenix

Provides real-time trace inspection and evaluation

Evaluated output

JSON containing triage decision, alternate provider ID, and confidence score

Evaluation checks
  • Ensures output conforms to required JSON schema
  • Accuracy of failure classification against gold standards • target: 0.9 • range: 0-1
Configured evaluation evidence
  • Benchmark: ONDC-Logistics-Bench-v1
  • Triage Accuracy target: 0.9
  • 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.

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Configured tools

Action Space
  • LangChain · Optional
  • Anon · Optional
Observation
  • Arize Phoenix · Required
Reward / Eval
  • Arize Phoenix · Required
Orchestration
  • LangChain · Optional

Evaluation contract

  • valid_schema_check · Weight 1
  • triage_accuracy · Weight 1

Metrics

  • triage_accuracy · Target 0.9

Benchmarks

  • ONDC-Logistics-Bench-v1
  • general

Reference cases

  • 1 public reference case

Workflow tasks

  1. Task 1

    Input: JSON containing ONDC logistics event payload and seller status

    Output: JSON containing triage decision, alternate provider ID, and confidence score

    Execution harness: Python

Evaluation tools

  • LangChain · Agent Framework
  • Arize Phoenix · Tracing & Observability

Recipe freeze marker

No recipe freeze time is recorded.

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