VASP On-Chain Compliance Monitoring Pipeline with Pydantic AI
Construct a type-safe compliance monitoring agent using Pydantic AI to validate Virtual Asset Service Provider (VASP) transactions. Enforce Singapore Monetary Authority requirements for travel rule data transmission and sanctioned wallet identification.
What you are building
The core problem, expected build, and operating context for this challenge.
Develop a strongly typed Pydantic AI agent workflow to audit crypto transactions against MAS VASP regulatory expectations.
How work is evaluated
Evaluates Travel Rule flag enforcement and sanction list matching correctness.
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.
pydantic_schema_valid
Ensures agent output strictly parses into Pydantic model without validation errors
This dimension contributes its full weight only when the submission satisfies the requirement. Partial credit is not awarded.
rule_accuracy
Accuracy of Travel Rule triggering based on SGD threshold • target: 1 • 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
Build type-safe agent state and output models using Pydantic AI and Pydantic v2
Implement custom dependency injection for live blockchain RPC and OFAC sanction list providers
Enforce Travel Rule compliance thresholds ($1,000 SGD equivalent) automatically
Construct deterministic compliance loggers emit cryptographic audit trails
Reference links and supporting material
Synthetic dataset of 40 crypto asset transfer records with metadata.
How this agent runs
Evaluates Travel Rule flag enforcement and sanction list matching correctness.
Challenge input
JSON containing transfer_details and entity_info
Pydantic AI
Guarantees output structures and type validation using Pydantic v2.
Evaluated output
JSON conforming to VaspComplianceResult Pydantic schema
- Ensures agent output strictly parses into Pydantic model without validation errors
- Accuracy of Travel Rule triggering based on SGD threshold • target: 1 • range: 0-1
- Benchmark: Crypto Compliance Eval Suite
- Rule Accuracy target: 1
- 1 public reference case
- Python execution harness
- Python sandbox (unavailable on Versalist)
View technical recipe
Configured tools
- Pydantic AI · Required
- Martian · Optional
- Deepgram · Optional
- Pydantic AI · Required
Evaluation contract
- pydantic_schema_valid · Weight 1
- rule_accuracy · Weight 1
Recipe state
This is a preview. The configuration can change before the evaluation recipe is locked.