📪3. Core Architecture
3.1 System Overview
DeLLM employs a multi-layered architecture optimized for Web3 operations:
3.2.1 Investment Analysis Agent
Tokenomics Evaluation: Deep analysis of token economics and distribution
Contract Risk Assessment: Smart contract security and functionality analysis
Market Position Analysis: Competitive landscape and market opportunity evaluation
Financial Modeling: Revenue projections and valuation models
3.2.2 Market Intelligence Agent
Price Action Analysis: Technical analysis and trend identification
Volume and Liquidity Analysis: Market depth and trading opportunity assessment
Sentiment Analysis: Social media and community sentiment evaluation
News and Events Impact: Real-time news analysis and market impact assessment
3.2.3 Risk Assessment Agent
Smart Contract Auditing: Automated security vulnerability detection
Liquidity Risk Analysis: Assessment of token liquidity and exit scenarios
Regulatory Risk Evaluation: Compliance and regulatory impact analysis
Market Risk Modeling: Volatility and correlation analysis
3.2.4 Wallet Tracking Agent
Alpha Wallet Identification: Discovery of successful trading patterns
Transaction Analysis: Deep dive into whale and institutional movements
Portfolio Tracking: Real-time portfolio performance monitoring
Copy Trading Insights: Analysis of profitable trading strategies
3.2.5 Strategy Generation Agent
Portfolio Optimization: Asset allocation and risk-adjusted returns
Trading Strategy Development: Algorithmic and systematic trading approaches
Yield Strategy Analysis: DeFi yield farming and staking optimization
Hedging Strategy Creation: Risk mitigation and portfolio protection
3.3 Task Coordination Engine
The coordination engine orchestrates multiple agents to complete complex tasks:
Task Decomposition: Breaking complex objectives into manageable subtasks
Agent Assignment: Matching tasks to the most appropriate specialized agents
Dependency Management: Handling interdependent tasks and data flows
Quality Control: Validating agent outputs and ensuring consistency
Result Synthesis: Combining multiple agent outputs into comprehensive reports
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