AI Agent System

The DeFAI Agent OS converts natural language to SQL queries through AI agents operating across multiple chains. Using advanced language models, agents process blockchain data in real-time, enabling users to analyze DeFi activity through simple prompts. The system optimizes query execution through protocol-specific handlers and parallel processing.

Each agent is equipped with specialized capabilities:

1. Natural Language Processing The NLP system utilizes language models to understand and process user queries. Through continuous learning from blockchain protocols and market patterns, agents develop increasingly sophisticated understanding of DeFi-specific terminology and concepts which allows for accurate query translation even with complex, multi-part requests.

// // NLP System for DeFAI
interface NLPRequest {
   query: string;
   context?: {
       chain: string;
       protocol?: string;
       timeRange?: TimeRange;
   };
}

interface DeFiEntity {
   type: 'token' | 'protocol' | 'metric' | 'timeframe';
   value: string;
   confidence: number;
}

class NLPProcessor {
   private model: DefiLanguageModel;
   private tokenRegistry: Map<string, TokenInfo>;
   private protocolRegistry: Map<string, ProtocolSchema>;
   
   constructor() {
       this.model = new DefiLanguageModel({
           vocabulary: DEFI_TOKENS,
           protocols: DEFI_PROTOCOLS,
           metrics: DEFI_METRICS
       });

       this.tokenRegistry = new Map([
           ["ETH", { decimals: 18, chains: ["ethereum", "arbitrum"] }],
           ["USDC", { decimals: 6, chains: ["ethereum", "polygon"] }],
           ["AAVE", { decimals: 18, chains: ["ethereum"] }]
       ]);
   }

   async parseQuery(request: NLPRequest): Promise<ParsedQuery> {
       // Example: "Show ETH-USDC pool depth on Uniswap V3"
       const entities = await this.extractEntities(request.query);
       const intent = await this.classifyIntent(request.query);
       
       const sqlTemplate = this.getQueryTemplate(intent);
       const sql = this.populateTemplate(sqlTemplate, entities);

       return {
           sql,
           entities,
           chains: this.determineChains(entities),
           confidence: this.calculateConfidence(entities),
           proof: await this.generateProof(sql)
       };
   }

   private async extractEntities(query: string): Promise<DeFiEntity[]> {
       return [
           {
               type: 'token',
               value: 'ETH',
               confidence: 0.99
           },
           {
               type: 'protocol',
               value: 'uniswap_v3',
               confidence: 0.98
           },
           {
               type: 'metric',
               value: 'liquidity_depth',
               confidence: 0.95
           }
       ];
   }

   private async classifyIntent(query: string): Promise<QueryIntent> {
       const intents = await this.model.classify(query);
       return {
           primary: 'pool_analysis',
           secondary: 'liquidity_check',
           confidence: 0.97
       };
   }

   private getQueryTemplate(intent: QueryIntent): string {
       const templates = {
           pool_analysis: `
               SELECT 
                   pool_address,
                   token0_reserve,
                   token1_reserve,
                   sqrt_price_x96,
                   liquidity
               FROM pool_states
               WHERE token0 = :token0 
               AND token1 = :token1
               AND pool_type = :pool_type
               ORDER BY block_number DESC
               LIMIT 1
           `
       };
       return templates[intent.primary];
   }
}

1. Query Optimization Agents employ machine learning algorithms to optimize query execution paths. The system analyzes query patterns and automatically adjusts its optimization strategies based on historical performance data. It results in significantly reduced response times for common query types and more efficient handling of complex analytical requests.

// // Query Optimization System
interface OptimizationRule {
    name: string;
    condition: (query: ParsedQuery) => boolean;
    apply: (query: ParsedQuery) => OptimizedQuery;
}

class QueryOptimizer {
    private rules: OptimizationRule[];
    private stats: Map<string, QueryStats>;
    private readonly chainIndexes: Map<string, ChainIndex>;

    constructor() {
        this.rules = [
            {
                name: 'parallel_execution',
                condition: (q) => q.chains.length > 1,
                apply: this.parallelizeQuery
            },
            {
                name: 'cache_lookup',
                condition: (q) => this.isCacheable(q),
                apply: this.applyCaching
            }
        ];

        this.chainIndexes = new Map([
            ['ethereum', {
                blocks_indexed: 17580000,
                latest_block: 17580100,
                sync_status: 'current'
            }],
            ['polygon', {
                blocks_indexed: 45780000,
                latest_block: 45780200,
                sync_status: 'current'
            }]
        ]);
    }

    async optimize(query: ParsedQuery): Promise<OptimizedQuery> {
        // Apply optimization rules
        let optimized = this.rules.reduce(
            (q, rule) => rule.condition(q) ? rule.apply(q) : q,
            query
        );

        // Generate execution plan
        const plan = await this.generatePlan(optimized);
        
        return {
            ...optimized,
            plan,
            estimated_cost: this.estimateCost(plan)
        };
    }

    private async generatePlan(query: ParsedQuery): Promise<ExecutionPlan> {
        // Example plan for multi-chain query
        const plan = {
            steps: [
                {
                    type: 'parallel_fetch',
                    chains: query.chains,
                    estimated_time: 50 // ms
                },
                {
                    type: 'aggregate',
                    operation: 'union',
                    estimated_time: 20 // ms
                }
            ],
            total_cost: 70, // ms
            parallelization_factor: query.chains.length
        };

        return plan;
    }

    private isCacheable(query: ParsedQuery): boolean {
        const stats = this.stats.get(query.sql);
        return stats?.frequency > 100 && stats?.avg_cost > 50;
    }

    private parallelizeQuery(query: ParsedQuery): OptimizedQuery {
        // Split query for parallel execution
        const subQueries = query.chains.map(chain => ({
            sql: query.sql,
            chain,
            params: query.params
        }));

        return {
            ...query,
            subQueries,
            execution_mode: 'parallel'
        };
    }
}