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.
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.
How work is evaluated
Evaluates the triage agent's ability to classify delivery failure signals and return valid ONDC rerouting payloads.
Shared data for this challenge
Review public datasets and any private uploads tied to your build.
How submissions are scored
These dimensions define what the evaluator checks and which criteria separate a passable run from a strong one.
valid_schema_check
Ensures output conforms to required JSON schema
This dimension contributes its full weight only when the submission satisfies the requirement. Partial credit is not awarded.
triage_accuracy
Accuracy of failure classification against gold standards • target: 0.9 • 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
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
Reference links and supporting material
Simulated stream of ONDC order fulfillment failure webhooks across major Indian metropolitan hubs.
How this agent runs
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
- Ensures output conforms to required JSON schema
- Accuracy of failure classification against gold standards • target: 0.9 • range: 0-1
- Benchmark: ONDC-Logistics-Bench-v1
- Triage Accuracy target: 0.9
- 1 public reference case
- Python execution harness
- Python sandbox (unavailable on Versalist)
View technical recipe
Configured tools
- LangChain · Optional
- Anon · Optional
- Arize Phoenix · Required
- Arize Phoenix · Required
- LangChain · Optional
Evaluation contract
- valid_schema_check · Weight 1
- triage_accuracy · Weight 1
Recipe state
This is a preview. The configuration can change before the evaluation recipe is locked.