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Challenge

Automate Data Breach Analysis with LangGraph and Gemini 3.1 Pro

Create a multi-agent system using LangChain and LangGraph to analyze complex data sets following corporate security breaches. The system employs Gemini 3.1 Pro for high-throughput analysis and structured extraction. By leveraging Claude Code for rapid iterative development and Bito AI for conversational knowledge management, developers can build a graph-based workflow that classifies sensitive data leaks. The Vercel AI Gateway acts as the routing layer to manage model latency and performance across the agent ensemble.

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Challenge brief

What you are building

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

Create a multi-agent system using LangChain and LangGraph to analyze complex data sets following corporate security breaches. The system employs Gemini 3.1 Pro for high-throughput analysis and structured extraction. By leveraging Claude Code for rapid iterative development and Bito AI for conversational knowledge management, developers can build a graph-based workflow that classifies sensitive data leaks. The Vercel AI Gateway acts as the routing layer to manage model latency and performance across the agent ensemble.

Datasets

Shared data for this challenge

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Evaluation rubric

How submissions are scored

These dimensions define what the evaluator checks, how much each dimension matters, and which criteria separate a passable run from a strong one.

Max Score: 2
Dimensions
2 scoring checks
Binary
2 pass or fail dimensions
Ordinal
0 scaled dimensions
Dimension 1graph_completion

Graph Completion

Agent must complete all nodes in graph

binary
Weight: 1
Binary check

This dimension contributes its full weight only when the submission satisfies the requirement. Partial credit is not awarded.

Dimension 2latency

Latency

Inference time in ms • target: 1200 • range: 0-5000

binary
Weight: 1
Binary check

This dimension contributes its full weight only when the submission satisfies the requirement. Partial credit is not awarded.

Learning goals

What you should walk away with

  • Master LangGraph primitives for maintaining state across agent nodes

  • Implement multi-agent cooperation using LangChain specialized tool agents

  • Configure Vercel AI Gateway to handle model fallbacks and performance telemetry

  • Leverage Claude Code to accelerate the implementation of complex parsing logic

  • Design a Bito AI interaction pattern for technical assistance during development

  • Optimize Gemini 3.1 Pro usage for high-speed unstructured data extraction

Start from your terminal
$npx -y @versalist/cli start automate-data-breach-analysis-with-langgraph-and-gemini-3-1-pro

[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
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Timeline and host

Operating window

Key dates and the organization behind this challenge.

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Tool Space Recipe

Draft
Action Space
LangGraphRuntime for stateful agent workflows.
required
Google GeminiGoogle's multimodal AI model
Bito AIAI assistant for dev tasks
Orchestration
LangGraphRuntime for stateful agent workflows.
required
Evaluation
Rubric: 2 dimensions
·Graph Completion(1%)
·Latency(1%)
Gold items: 1 (1 public)

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