Crypto Token Due Diligence
This challenge tasks you with building a robust, multi-agent system for automated due diligence on emerging crypto tokens. Utilizing CrewAI, you will orchestrate specialized agents- such as a Market Researcher, Financial Analyst, and Risk Assessor -to collaborate using the A2A Protocol. The system will leverage Gemini 2.5 Pro's Deep Think mode for quantitative analysis and Claude Opus 4.1 for nuanced qualitative assessment, performing comprehensive research from diverse sources to generate an investment-grade report.
What you are building
The core problem, expected build, and operating context for this challenge.
Orchestrate a role-based multi-agent team using CrewAI with Gemini 2.5 Pro and Claude Opus 4.1 for automated crypto token due diligence, leveraging A2A communication.
How work is evaluated
The system will be evaluated on the completeness, accuracy, and depth of its due diligence report for a given crypto token. This includes correctly identifying key project details, assessing financial viability, pinpointing potential risks, and providing a well-reasoned investment recommendation.
Challenge datasets
Public datasets, plus the private datasets you have access to.
What you should walk away with
Master CrewAI for defining and orchestrating role-based agent teams.
Utilize Gemini 2.5 Pro Deep Think for complex financial modeling.
Implement A2A Protocol for secure, structured agent communication.
Integrate Claude Opus 4.1 for sophisticated qualitative analysis.
Reference links and supporting material
Sample data for 1 tasks
Challenge setup
The system will be evaluated on the completeness, accuracy, and depth of its due diligence report for a given crypto token. This includes correctly identifying key project details, assessing financial viability, pinpo...
Challenge input
{'token_name': 'string', 'whitepaper_url': 'string', 'initial_investment_thesis': 'string'}
Agent execution
No agent tool is listed for this challenge.
Evaluated output
{'project_overview': 'string', 'tokenomics_analysis': 'json', 'market_sentiment': 'string', 'risk_factors': ['string'], 'investment_recommendation'...
- Ensure all required output fields are present and non-empty.
- Verify if at least 3 plausible risk factors are identified in the report.
- Analytical Depth target: 4
- 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.
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Configured tools
No tool records are attached.
Evaluation contract
- Ensure all required output fields are present and non-empty.
- Verify if at least 3 plausible risk factors are identified in the report.
Metrics
- Analytical Depth · Target 4
- Data Coverage · Target 90
- Recommendation Soundness · Target 4
Workflow tasks
- Task 1
Input: {'token_name': 'string', 'whitepaper_url': 'string', 'initial_investment_thesis': 'string'}
Output: {'project_overview': 'string', 'tokenomics_analysis': 'json', 'market_sentiment': 'string', 'risk_factors': ['string'], 'investment_recommendation': 'string', 'justification': 'string'}
Execution harness: Python
Recipe freeze marker
No recipe freeze time is recorded.