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Challenge

Crypto Compliance Agent: Detecting Anomalous Transactions with Gemini 3 Pro Deep Think

This challenge requires you to build a sophisticated compliance monitoring agent using LangGraph. Your system will employ a graph-based workflow to analyze a stream of simulated cryptocurrency transactions, integrating MCP-enabled tools for external blockchain data lookups and historical pattern analysis. Gemini 3 Pro, operating in Deep Think mode, will be crucial for performing hybrid reasoning to interpret complex transaction sequences and flag potentially illicit activities that evade simpler rule-based detection.

Workflow AutomationHosted by Vera
Status
Always open
Difficulty
Advanced
Points
500
Challenge brief

What you are building

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

This challenge requires you to build a sophisticated compliance monitoring agent using LangGraph. Your system will employ a graph-based workflow to analyze a stream of simulated cryptocurrency transactions, integrating MCP-enabled tools for external blockchain data lookups and historical pattern analysis. Gemini 3 Pro, operating in Deep Think mode, will be crucial for performing hybrid reasoning to interpret complex transaction sequences and flag potentially illicit activities that evade simpler rule-based detection.

Datasets

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Learning goals

What you should walk away with

  • Master LangGraph for building complex, stateful Directed Acyclic Graph (DAG) workflows that manage multi-agent interactions and tool calls.

  • Implement Gemini 3 Pro in Deep Think mode for advanced, multi-stage reasoning to identify subtle anomalies in large datasets.

  • Design and integrate MCP-enabled tools to interact with simulated blockchain explorers and internal compliance databases for data enrichment.

  • Develop hybrid reasoning strategies that combine instant pattern matching with deep, contextual analysis of transaction histories and user behaviors.

  • Orchestrate a pipeline for ingesting simulated real-time transaction streams and applying your LangGraph analysis.

  • Build a feedback loop mechanism for the LangGraph agents to learn and refine anomaly detection thresholds or patterns over time.

  • Deploy robust error handling and checkpointing within the LangGraph workflow for resilient compliance monitoring.

Start from your terminal
$npx -y @versalist/cli start crypto-compliance-agent-detecting-anomalous-transactions-with-gemini-3-pro-deep-think

[ok] Wrote CHALLENGE.md

[ok] Wrote .versalist.json

[ok] Wrote eval/examples.json

Requires VERSALIST_API_KEY. Works with any MCP-aware editor.

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Host and timing
Vera

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