Crypto Financial Anomaly Detection
Build a cutting-edge agentic system using LangGraph to analyze complex crypto financial transaction networks. The system will leverage Gemini 2.5 Pro (specifically its 'Deep Think' mode for advanced reasoning) to identify unusual patterns, potential fraud, or hidden relationships within a simulated cryptocurrency transaction dataset. This challenge focuses on designing graph-based agent workflows, implementing hybrid reasoning, and exploring A2A protocol for secure, verifiable data exchange for compliance or audit purposes. Participants will create agents that can traverse transaction graphs, apply heuristic rules and LLM-powered reasoning to detect anomalies, and generate detailed explanations for flagged transactions. The system should demonstrate how graph-based state management can enhance financial oversight in a highly dynamic asset class.
What you are building
The core problem, expected build, and operating context for this challenge.
Build a cutting-edge agentic system using LangGraph to analyze complex crypto financial transaction networks. The system will leverage Gemini 2.5 Pro (specifically its 'Deep Think' mode for advanced reasoning) to identify unusual patterns, potential fraud, or hidden relationships within a simulated cryptocurrency transaction dataset. This challenge focuses on designing graph-based agent workflows, implementing hybrid reasoning, and exploring A2A protocol for secure, verifiable data exchange for compliance or audit purposes. Participants will create agents that can traverse transaction graphs, apply heuristic rules and LLM-powered reasoning to detect anomalies, and generate detailed explanations for flagged transactions. The system should demonstrate how graph-based state management can enhance financial oversight in a highly dynamic asset class.
Shared data for this challenge
Review public datasets and any private uploads tied to your build.
What you should walk away with
Master LangGraph for building stateful, DAG-based agent workflows, specifically for graph traversal and analysis.
Implement Gemini 2.5 Pro with Deep Think mode for advanced mathematical reasoning and complex pattern identification in financial data.
Design and integrate a graph database (e.g., Neo4j, Apache TinkerPop with Gremlin) as a primary tool for agent analysis.
Build A2A protocol multi-agent systems (or simulate A2A interactions) for secure, verifiable agent-to-agent communication, potentially for compliance checks or external reporting.
Develop hybrid instant/deep reasoning strategies where agents quickly flag simple anomalies and use Deep Think for deeper investigation of complex patterns.
Create RAG pipelines for accessing regulatory documents and financial compliance guidelines to inform anomaly detection.
Implement tool integration for querying crypto blockchain explorers or simulated transaction APIs.
How this agent runs
The system will be evaluated on its accuracy in identifying pre-defined crypto financial anomalies within a synthetic transaction graph, the quality of its explanations, and the efficiency of its graph-based reasoning...
Challenge input
{"transactions_csv_url": "URL_to_CSV"}
Agent execution
The configured agent processes the input under the challenge policy.
Evaluated output
{"graph_db_status": "loaded", "node_count": 1000, "edge_count": 5000}
- Verify successful loading of transaction data into a functional graph database accessible by agents.
- Confirm that Gemini 2.5 Pro's Deep Think mode was explicitly invoked for complex anomaly reasoning.
- Ensure the LangGraph workflow executes without errors and transitions between nodes as designed.
- AnomalyDetectionPrecision target: 85
- Python execution harness
View technical recipe
Configured tools
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Evaluation contract
- The evaluation module defines the checks.
Recipe state
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Scope a managed run[ok] Wrote CHALLENGE.md
[ok] Wrote .versalist.json
[ok] Wrote eval/examples.json
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