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AI Integration Overview

Zzyra is building toward an advanced AI Agent system for intelligent workflow automation. Here’s what’s currently available and what’s planned for the AI capabilities.

Core Capabilities

Intelligent Reasoning

  • Sequential Thinking: Advanced step-by-step reasoning with configurable complexity (1-50 steps)
  • Multiple Thinking Modes: Fast, Deliberate, and Collaborative reasoning patterns
  • Real-time Reasoning Visualization: Live thinking panel showing AI decision-making process
  • Context Awareness: Full access to previous workflow block outputs and variables

Multi-Provider AI Support

  • OpenRouter: Access to multiple state-of-the-art models
  • OpenAI: GPT-4 and other OpenAI models
  • Anthropic: Claude models for advanced reasoning
  • Ollama: Local AI processing for privacy-sensitive operations

Tool Integration

  • 11+ MCP Servers: Pre-configured tools across development, blockchain, and web automation
  • Dynamic Tool Loading: Runtime discovery and configuration of new MCP servers
  • Real-time Tool Testing: Validate tool connections before execution
  • Secure Credential Management: Encrypted storage of API keys and sensitive data

Agent Configuration

Basic Agent Setup

interface AIAgentConfig {
  agent: {
    name: string;              // Custom agent name
    systemPrompt: string;      // Agent personality and capabilities
    userPrompt: string;        // Specific task description
    thinkingMode: "fast" | "deliberate" | "collaborative";
    maxSteps: number;          // 1-50 reasoning steps
  };
  
  provider: {
    type: "openrouter" | "openai" | "anthropic" | "ollama";
    model: string;             // Specific model name
    temperature: number;       // 0-2 creativity level
    maxTokens: number;         // Response length limit
  };
  
  execution: {
    mode: "autonomous" | "interactive";  // Approval required?
    timeout: number;                     // Execution timeout (ms)
    saveThinking: boolean;               // Save reasoning steps
    requireApproval: boolean;            // Human approval required
  };
  
  selectedTools: ToolConfig[];           // Available MCP tools
}

Advanced Configuration Options

Fast Mode: Quick responses with minimal reasoning steps
  • Best for: Simple tasks, rapid prototyping
  • Reasoning depth: 1-3 steps
  • Response time: < 10 seconds
Deliberate Mode: Thorough analysis with detailed reasoning
  • Best for: Complex tasks, critical decisions
  • Reasoning depth: 5-15 steps
  • Response time: 30-60 seconds
Collaborative Mode: Interactive reasoning with user input
  • Best for: Uncertain tasks, learning scenarios
  • Reasoning depth: Variable with user feedback
  • Response time: Depends on user interaction

Real-time Execution Monitoring

Live Execution Dashboard

Zyra provides comprehensive real-time monitoring of AI agent execution:

Thinking Process Visualization

Live Thinking Panel: Real-time display of AI reasoning steps
  • Step-by-step thought process
  • Confidence levels for each decision
  • Tool selection rationale
  • Problem-solving approach

Tool Execution Tracking

Tool Call Monitoring: Track all MCP tool interactions
  • Tool parameters and configurations
  • Execution results and outputs
  • Error handling and retries
  • Performance metrics

Progress Indicators

Execution Progress: Visual progress tracking
  • Overall completion percentage
  • Current step description
  • Estimated time remaining
  • Success/failure indicators

Error Management

Error Handling: Comprehensive error tracking
  • Detailed error messages
  • Recovery attempt logs
  • Fallback strategy execution
  • User notification system

WebSocket-Based Updates

// Real-time execution monitoring
interface ExecutionUpdate {
  nodeId: string;
  status: "running" | "completed" | "failed";
  progress: number;              // 0-100
  thinkingSteps: ThinkingStep[];
  toolCalls: ToolCall[];
  error?: string;
  startTime: Date;
  endTime?: Date;
  duration?: number;
}

// WebSocket connection for live updates
const { isConnected } = useExecutionWebSocket({
  executionId: data.executionId,
  onNodeUpdate: (update: NodeExecutionUpdate) => {
    // Update UI with real-time progress
    updateAgentStatus(update);
  },
  onExecutionLog: (log: ExecutionLog) => {
    // Display execution logs
    addLogEntry(log);
  },
});

Tool Management System

Available Tool Categories

Git Repository (git): Version control operations
  • Repository management
  • Commit and branch operations
  • Merge conflict resolution
PostgreSQL (postgres): Database operations
  • Query execution and data retrieval
  • Schema management
  • Performance optimization
HTTP Requests (fetch): API interactions
  • REST API calls
  • Authentication handling
  • Response processing
GOAT Blockchain (goat): Comprehensive blockchain operations
  • Multi-chain wallet management (Sei, Base, Ethereum)
  • DeFi protocol interactions
  • Token transfers and swaps
  • NFT operations
Sei Network (sei): Native Sei blockchain operations
  • Sei-specific protocol interactions
  • Native token management
  • Smart contract deployment
  • CosmWasm contract interactions
Puppeteer (puppeteer): Browser automation
  • Web scraping and data extraction
  • Form filling and submission
  • Screenshot and PDF generation
Brave Search (brave-search): Web search capabilities
  • Real-time web search
  • News and information gathering
  • Content research and analysis
Sequential Thinking (sequential-thinking): Advanced reasoning
  • Multi-step problem decomposition
  • Logical reasoning chains
  • Complex decision trees
Time & Weather (time, weather): Context awareness
  • Current time and timezone handling
  • Weather data for location-based decisions
  • Temporal reasoning capabilities

Tool Configuration Interface

// Dynamic tool selection interface
<ToolSelector
  availableTools={mcpServers}
  onToolSelect={(tool) => {
    if (tool.configSchema) {
      // Open configuration modal for tools requiring setup
      openConfigModal(tool);
    } else {
      // Add tool directly if no configuration needed
      addToolToAgent(tool);
    }
  }}
  categories={["development", "blockchain", "web", "ai"]}
/>

Integration Patterns

Workflow Integration

AI Agents integrate seamlessly with Zyra’s workflow system:
// AI Agent as workflow block
interface AIAgentBlock extends WorkflowBlock {
  type: "AI_AGENT";
  config: AIAgentConfig;
  
  // Input from previous blocks
  inputs: {
    context?: Record<string, any>;
    prompt?: string;
    variables?: Record<string, any>;
  };
  
  // Output for subsequent blocks
  outputs: {
    result: string;           // Main agent response
    reasoning: ThinkingStep[]; // Reasoning process
    toolCalls: ToolCall[];    // Tool interactions
    metadata: ExecutionMetadata;
  };
}

// Template access to previous block outputs
const agentPrompt = `
  Analyze the data from the previous HTTP request: {{httpRequest.data}}
  Consider the current price: {{priceMonitor.currentPrice}}
  Execute appropriate blockchain operations based on the analysis.
`;

Cross-Block Data Flow

// Agent processes data from previous workflow blocks
interface AgentInputProcessor {
  processHTTPResponse(response: HTTPResponse): ProcessedData;
  processPriceData(priceData: PriceMonitorOutput): MarketContext;
  processBlockchainData(txData: TransactionData): BlockchainContext;
  
  // Combine all inputs into agent context
  createExecutionContext(inputs: WorkflowInputs): AgentContext;
}

Best Practices

Agent Design Patterns

Task Decomposition

Break complex tasks into smaller, manageable steps
System Prompt Example:
"You are a DeFi portfolio manager. For each task:
1. Analyze current market conditions
2. Assess portfolio health
3. Identify optimization opportunities  
4. Execute necessary transactions
5. Monitor results and adjust strategy"

Error Recovery

Implement robust error handling and recovery strategies
"If a transaction fails:
1. Analyze the failure reason
2. Check network conditions
3. Adjust gas parameters if needed
4. Retry with exponential backoff
5. Alert user if maximum retries exceeded"

Security First

Always prioritize security in agent instructions
"Security constraints:
- Never share private keys or sensitive data
- Validate all external data sources
- Use minimum required permissions
- Log all critical operations for audit"

Performance Optimization

Optimize agent performance for production use
"Performance guidelines:
- Cache frequently accessed data
- Batch similar operations when possible
- Use appropriate thinking modes for task complexity
- Monitor and optimize tool selection"

Common Use Cases

Automated portfolio rebalancing and optimization
  • Monitor portfolio performance across multiple protocols
  • Analyze market conditions and identify opportunities
  • Execute rebalancing transactions based on strategy
  • Track performance and adjust parameters
Tools used: GOAT Blockchain, Sei Network, HTTP Requests, Sequential Thinking
Automated smart contract security and performance monitoring
  • Monitor contract events and state changes
  • Analyze transaction patterns for anomalies
  • Execute emergency procedures if threats detected
  • Generate security reports and alerts
Tools used: Blockchain tools, HTTP Requests, PostgreSQL, Git
Automated arbitrage opportunity detection and execution
  • Monitor prices across multiple DEXs and chains
  • Calculate profitability including gas costs
  • Execute arbitrage transactions when profitable
  • Track performance and optimize strategy
Tools used: GOAT Blockchain, HTTP Requests, Sequential Thinking
Automated market research and analysis
  • Gather data from multiple sources (web, APIs, blockchain)
  • Analyze trends and patterns
  • Generate comprehensive reports
  • Execute trading strategies based on findings
Tools used: Brave Search, HTTP Requests, PostgreSQL, Puppeteer

Advanced Features

Custom MCP Server Integration

Extend agent capabilities by adding custom MCP servers:
// Custom MCP server definition
interface CustomMCPServer {
  id: string;
  name: string;
  displayName: string;
  description: string;
  category: string;
  
  connection: {
    type: "stdio" | "http" | "websocket";
    command?: string;
    args?: string[];
    url?: string;
    env?: Record<string, string>;
  };
  
  configSchema: {
    type: "object";
    properties: Record<string, any>;
    required: string[];
  };
  
  tools: MCPTool[];
}

// Register custom server
const customServer: CustomMCPServer = {
  id: "custom-analytics",
  name: "custom-analytics",
  displayName: "Custom Analytics Engine",
  description: "Custom analytics and reporting tools",
  category: "analytics",
  
  connection: {
    type: "stdio",
    command: "python",
    args: ["./custom-analytics-server.py"],
    env: {
      "API_KEY": "{{secrets.analytics_api_key}}"
    }
  },
  
  configSchema: {
    type: "object",
    properties: {
      "API_KEY": {
        type: "string",
        description: "Analytics API key",
        required: true,
        sensitive: true
      }
    },
    required: ["API_KEY"]
  },
  
  tools: [
    {
      name: "analyze_portfolio",
      description: "Analyze portfolio performance and risks",
      parameters: {
        type: "object",
        properties: {
          portfolioData: { type: "object" },
          timeframe: { type: "string" }
        }
      }
    }
  ]
};

Multi-Agent Coordination

Coordinate multiple AI agents for complex workflows:
// Multi-agent workflow pattern
interface MultiAgentWorkflow {
  agents: {
    researcher: AIAgentConfig;     // Market research and analysis
    strategist: AIAgentConfig;     // Strategy development
    executor: AIAgentConfig;       // Transaction execution
    monitor: AIAgentConfig;        // Performance monitoring
  };
  
  coordination: {
    dataFlow: AgentDataFlow[];     // How agents share data
    dependencies: AgentDependency[]; // Execution order
    conflictResolution: ConflictResolutionStrategy;
  };
}

// Example coordination pattern
const defiWorkflow: MultiAgentWorkflow = {
  agents: {
    researcher: {
      agent: {
        name: "Market Researcher",
        systemPrompt: "Analyze market conditions and identify opportunities",
        tools: ["brave-search", "fetch", "sequential-thinking"]
      }
    },
    executor: {
      agent: {
        name: "Transaction Executor", 
        systemPrompt: "Execute transactions based on research findings",
        tools: ["goat", "sei"]
      }
    }
  },
  
  coordination: {
    dataFlow: [
      { from: "researcher", to: "executor", data: "marketAnalysis" }
    ]
  }
};

Troubleshooting

Common Issues

Symptoms: Tools showing as disconnected or timing outSolutions:
  • Verify network connectivity and firewall settings
  • Check API keys and authentication credentials
  • Validate tool configuration against schema
  • Review MCP server logs for error details
  • Test tool connections independently
Symptoms: Agents timing out during executionSolutions:
  • Increase execution timeout in agent configuration
  • Optimize tool selection for faster operations
  • Break complex tasks into smaller steps
  • Use appropriate thinking mode for task complexity
  • Monitor system resources and scale if needed
Symptoms: Slow agent responses or high resource usageSolutions:
  • Optimize system prompts for clarity and brevity
  • Use caching for frequently accessed data
  • Select appropriate AI models for task complexity
  • Monitor and optimize tool usage patterns
  • Consider local AI processing for simple tasks
Symptoms: Agents not receiving data from previous blocksSolutions:
  • Verify template variable syntax in prompts
  • Check block execution order and dependencies
  • Validate data types and formats between blocks
  • Review workflow execution logs for data flow issues
  • Test individual blocks in isolation

Next Steps