Hybrid Legal Reasoning with Gemini 2.5 Pro & CrewAI for Digital Forensics
As courts increasingly grapple with AI-generated evidence, judges require sophisticated tools to discern authenticity. This challenge tasks you with building a multi-agent system using CrewAI to provide advanced legal reasoning assistance for digital forensics, specifically focusing on verifying the authenticity of AI-generated images and documents. Your system will employ a hybrid reasoning approach, combining the deep multi-modal analytical capabilities of Gemini 2.5 Pro with the rapid information synthesis of OpenAI GPT-5. The system will orchestrate a team of specialized agents, each contributing to a comprehensive forensic analysis. Key components include robust RAG for legal context, tool integration for digital forensics, and adaptive reasoning to handle complex legal scenarios. The goal is to produce actionable insights and structured reports for legal professionals, aiding in the accurate adjudication of cases involving digital evidence.
What you are building
The core problem, expected build, and operating context for this challenge.
Develop a CrewAI multi-agent system with Gemini 2.5 Pro and OpenAI GPT-5 for hybrid legal reasoning and digital content authenticity verification, integrated with RAG.
How work is evaluated
The evaluation will assess the agent system's accuracy in identifying AI-generated content within legal evidence, the relevance and depth of its legal contextualization, and the quality of its generated forensic reports. Automated tests will simulate complex legal cases with mixed digital evidence.
Challenge datasets
Public datasets, plus the private datasets you have access to.
What you should walk away with
Develop specialized tool agents for multi-modal digital content authenticity verification.
Implement hybrid instant/deep reasoning using Gemini 2.5 Pro (Deep Think mode) and OpenAI o3.
Orchestrate a role-based agent team using CrewAI for a legal digital forensics workflow.
Integrate a robust RAG system for context-aware legal analysis and case precedents.
Reference links and supporting material
Sample data for 2 tasks
Challenge setup
The evaluation will assess the agent system's accuracy in identifying AI-generated content within legal evidence, the relevance and depth of its legal contextualization, and the quality of its generated forensic repor...
Challenge input
{ 'case_id': 'string', 'evidence_description': 'string', 'image_url': 'string', 'document_text': 'string' }
Agent execution
No agent tool is listed for this challenge.
Evaluated output
{ 'ai_generated_elements': [{'type': 'string', 'location': 'string', 'confidence': 'float', 'manipulation_details': 'string'}], 'overall_authentici...
- Verify the system accurately identifies AI-generated elements in the evidence.
- Ensure the RAG system retrieves and synthesizes highly relevant legal precedents and implications.
- Check that the generated forensic report is well-structured, comprehensive, and clear.
- AuthenticityDetectionF1Score target: 0.85
- 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
- Verify the system accurately identifies AI-generated elements in the evidence.
- Ensure the RAG system retrieves and synthesizes highly relevant legal precedents and implications.
- Check that the generated forensic report is well-structured, comprehensive, and clear.
- Confirm that both instant (o3) and deep (Gemini) reasoning modes are appropriately applied where necessary.
Metrics
- AuthenticityDetectionF1Score · Target 0.85
- LegalContextRelevanceScore · Target 0.8
- ReportCompletenessScore · Target 0.9
Workflow tasks
- Task 1
Input: { 'case_id': 'string', 'evidence_description': 'string', 'image_url': 'string', 'document_text': 'string' }
Output: { 'ai_generated_elements': [{'type': 'string', 'location': 'string', 'confidence': 'float', 'manipulation_details': 'string'}], 'overall_authenticity_score': 'float', 'reasoning': 'string' }
Execution harness: Python
- Task 2
Input: { 'case_id': 'string', 'authenticity_analysis': 'json_object', 'legal_query': 'string' }
Output: { 'forensic_report': { 'summary': 'string', 'findings': ['string'], 'legal_implications': ['string'], 'relevant_precedents': ['string'], 'recommendations': 'string' }, 'report_confidence': 'float' }
Execution harness: Python
Recipe freeze marker
No recipe freeze time is recorded.