// Project G.U.N.D.A.M. - Core Logic Integration // This module provides the interface for the multi-language core. // In a production environment, these would call out to compiled binaries or microservices. import { callMcpTool } from "./services/mcpService"; export interface CoreService { name: string; language: string; status: "online" | "offline" | "error"; role: string; } export const GUNDAM_SERVICES: CoreService[] = [ { name: "Threat Analyzer", language: "Rust", status: "online", role: "Security & GGUF Inference" }, { name: "Bot Agentics", language: "Elixir", status: "online", role: "Autonomous Bot Decisioning" }, { name: "Behavioral Engine", language: "Julia", status: "online", role: "User Analytics" }, { name: "Tool Orchestrator", language: "Ballerina", status: "online", role: "Webhook & Tool Calls" } ]; export const GUNDAM_MANIFEST = { version: "1.2.0-functional", codename: "METICULOUS", services: GUNDAM_SERVICES }; // Functional bridge for Rust-based threat analysis export async function analyzeThreat(content: string): Promise { const res = await callMcpTool("analyze_threat", { content }); return res.threat_detected || false; } // Functional bridge for Julia-based analytics export async function getDomineeringRatio(messages: string[]): Promise { // We can add a new MCP tool for this if needed, or use a generic one const res = await callMcpTool("swarm_orchestrate", { input: messages.join(" "), phase: "julia_analysis" }); // For now, return a simulated ratio based on the "orchestrated" result return res.result ? res.result.length / 100 : 0.15; } // Functional bridge for Elixir-based Bot Agentics export async function runAgenticDecision(botId: string, context: any): Promise { const res = await callMcpTool("run_agentic_decision", { botId, context }); return res.decision || "STANDBY"; } // Functional bridge for Ballerina-based Tool Calls export async function executeToolCall(toolName: string, args: any): Promise { return await callMcpTool("execute_tool_call", { toolName, args }); }