Build a Resilient Payment Gateway Dynamic Router with LangChain and LangGraph
Acquisition strain and merchant gateway failures disrupt high-volume payment processing across partner rails. Build a LangGraph stateful agentic workflow to dynamically re-route failed payment transactions based on real-time gateway health, fee structures, and latency profiles. Achieve payment recovery rates above 98% with automated fallback circuit breaking.
What you are building
The core problem, expected build, and operating context for this challenge.
Develop a stateful multi-step payment routing workflow using LangGraph and LangChain to dynamically handle processor outages and optimize payment authorization success rates.
How work is evaluated
Evaluates the dynamic payment router state graph on recovery rate of transactions during simulated processor outages.
Challenge datasets
Public datasets, plus the private datasets you have access to.
How submissions are scored
These dimensions define what the evaluator checks and which criteria separate a passable run from a strong one.
gateway_health_check
Ensures targeted fallback gateway is marked as HEALTHY
This dimension contributes its full weight only when the submission satisfies the requirement. Partial credit is not awarded.
recovery_rate
Percentage of failed payments successfully re-routed to healthy gateways • target: 0.98 • 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
Construct a stateful graph-based workflow using LangGraph for payment retry and dynamic routing
Build dynamic circuit breaker nodes to track real-time payment gateway health metrics
Optimize transaction routing based on processing fee, currency, and historical acceptance rates
Implement full auditability and fallback execution loops for failed payment authorizations
Reference links and supporting material
Dataset of 500 payment transaction event logs including simulated gateway timeout and error responses across multiple global processors.
Challenge setup
Evaluates the dynamic payment router state graph on recovery rate of transactions during simulated processor outages.
Challenge input
JSON containing initial_failed_gateway, error_code, amount, currency, active_gateways_status
LangGraph
Provides graph-based state persistency necessary for robust multi-step retry workflows.
LangChain
Provides prompt templates and LLM integration utilities.
Evaluated output
JSON with target_gateway, retry_permitted, circuit_breaker_tripped, routing_reason
- Ensures targeted fallback gateway is marked as HEALTHY
- Percentage of failed payments successfully re-routed to healthy gateways • target: 0.98 • range: 0-1
- Benchmark: Payment Gateway Infrastructure Resiliency Suite
- Recovery Rate target: 98%
- 1 public reference case
- 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
- LangGraph · Required
- Langchain · Optional
- LangChain · Optional
- LangGraph · Required
- Langchain · Optional
- LangChain · Optional
Evaluation contract
- gateway_health_check · Weight 1
- recovery_rate · Weight 1
Metrics
- recovery_rate · Target 0.98
Benchmarks
- Payment Gateway Infrastructure Resiliency Suite
- security
Reference cases
- 1 public reference case
Workflow tasks
- Task 1
Input: JSON containing initial_failed_gateway, error_code, amount, currency, active_gateways_status
Output: JSON with target_gateway, retry_permitted, circuit_breaker_tripped, routing_reason
Execution harness: Python
Evaluation tools
- LangGraph · Stateful Agent Workflow Framework
- LangChain · LLM Integration Core
Recipe freeze marker
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