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Challenge

ONDC Merchant Catalog Guardrailing with CrewAI and NVIDIA NeMo

Small retail merchants face catalog ingestion errors and policy compliance violations when listing products on ONDC. Orchestrate a team of agents using CrewAI and NVIDIA NeMo Guardrails to sanitize and classify catalog items, achieving 99% safety policy compliance and under 2s per item processing time.

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Challenge brief

What you are building

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

Deploy a CrewAI collaborative agent workflow combined with NVIDIA NeMo Guardrails to enforce automated product listing safety and schema normalization.

Delivery guide

How work is evaluated

Evaluation

Evaluates catalog item processing for safety policy compliance and output schema alignment.

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

safety_policy_enforcement

Verifies misleading claims are correctly flagged and suppressed.

Binary check

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

Dimension 2

compliance_rate

Accuracy of safety policy classification across tests. • target: 0.99 • 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 collaborative role-based agent workflows using CrewAI

  • Implement programmable guardrails using NVIDIA NeMo Guardrails

  • Normalize heterogeneous merchant catalog data to ONDC protocol schemas

  • Validate product claim policies against e-commerce consumer guidelines

Resources and assets

Reference links and supporting material

Dataset notes

200 raw D2C product catalog listings with varied formatting and policy violation edge cases.

Challenge setup

Evaluates catalog item processing for safety policy compliance and output schema alignment.

Challenge input

JSON containing raw_title, description, price, and category_claim.

CrewAI

Coordinates multi-agent role execution.

NVIDIA NeMo Guardrails

Enforces strict input/output policy constraints.

Evaluated output

JSON containing ondc_title, normalized_category, safety_passed (bool), and flag_reasons list.

Evaluation checks
  • Verifies misleading claims are correctly flagged and suppressed.
  • Accuracy of safety policy classification across tests. • target: 0.99 • range: 0-1
Configured evaluation evidence
  • Benchmark: E-Commerce Safety Guardrail Benchmark
  • Compliance Rate target: 99%
  • 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
  • CrewAI · Required
  • RAI · Optional
  • Nvidia · Optional

Evaluation contract

  • safety_policy_enforcement · Weight 1
  • compliance_rate · Weight 1

Metrics

  • compliance_rate · Target 0.99

Benchmarks

  • E-Commerce Safety Guardrail Benchmark
  • security

Reference cases

  • 1 public reference case

Workflow tasks

  1. Task 1

    Input: JSON containing raw_title, description, price, and category_claim.

    Output: JSON containing ondc_title, normalized_category, safety_passed (bool), and flag_reasons list.

    Execution harness: Python

Evaluation tools

  • CrewAI · Agent Framework
  • NVIDIA NeMo Guardrails · Safety Guardrails

Recipe freeze marker

No recipe freeze time is recorded.

Frequently Asked Questions about ONDC Merchant Catalog Guardrailing with CrewAI and NVIDIA NeMo