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Challenge

Graph-Based Legal AI for Compliance

This challenge focuses on building a cutting-edge, graph-based legal research and compliance assistant. You will design a system that can autonomously interpret complex legal documents, identify relevant case law, and assess compliance risks using advanced generative AI models. The emphasis will be on creating a robust, explainable, and iterative reasoning process.

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Challenge brief

What you are building

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

Build a graph-based multi-agent system using LangGraph with GPT-5 Pro for autonomous legal research and compliance, leveraging extended thinking and RAG.

Delivery guide

How work is evaluated

Evaluation

The system will be evaluated on its ability to accurately answer complex legal queries, identify compliance risks in given scenarios, and provide well-reasoned, source-cited explanations. The output quality will be judged on precision, recall, and logical coherence.

Datasets

Challenge datasets

Public datasets, plus the private datasets you have access to.

Learning goals

What you should walk away with

  • Design and implement a graph-based agent workflow using LangGraph for legal reasoning.

  • Build custom tools for interacting with legal databases and compliance APIs.

  • Implement extended thinking patterns with adaptive reasoning budgets for complex legal problem-solving.

  • Integrate GPT-5 Pro for advanced legal text understanding and inference.

  • Develop a robust RAG system for contextual legal document retrieval.

Resources and assets

Reference links and supporting material

Dataset notes

Sample data for 2 tasks

Challenge setup

The system will be evaluated on its ability to accurately answer complex legal queries, identify compliance risks in given scenarios, and provide well-reasoned, source-cited explanations. The output quality will be ju...

Challenge input

{'query': 'string', 'contextual_documents': ['string']}

Agent execution

No agent tool is listed for this challenge.

Evaluated output

{'answer': 'string', 'citations': ['string'], 'reasoning_steps': ['string']}

Evaluation checks
  • Does the answer contain factual legal errors or incorrect interpretations?
  • Are all aspects of the query or scenario addressed comprehensively?
  • Are at least 2 relevant citations provided for legal queries?
Configured evaluation evidence
  • AccuracyScore target: 0.9
Configured runtime
  • Python execution harness

Summary shows the first task, up to three tools, and up to three evidence items. Open the technical recipe to view all configured items.

View technical recipe

Configured tools

No tool records are attached.

Evaluation contract

  • Does the answer contain factual legal errors or incorrect interpretations?
  • Are all aspects of the query or scenario addressed comprehensively?
  • Are at least 2 relevant citations provided for legal queries?

Metrics

  • AccuracyScore · Target 0.9
  • CoherenceScore · Target 4

Workflow tasks

  1. Task 1

    Input: {'query': 'string', 'contextual_documents': ['string']}

    Output: {'answer': 'string', 'citations': ['string'], 'reasoning_steps': ['string']}

    Execution harness: Python

  2. Task 2

    Input: {'scenario': 'string', 'regulations': ['string']}

    Output: {'compliance_risks': ['string'], 'mitigation_suggestions': ['string']}

    Execution harness: Python

Recipe freeze marker

No recipe freeze time is recorded.

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