MAJOR: Added a GUI app for simulations

packages/llm/src/llm.ts

@@ -11,6 +11,21 @@ export interface LLMResponse<T> {
   success: boolean;
   data?: T;
   error?: string;
+  usage?: {
+    inputTokens: number;
+    outputTokens: number;
+    totalTokens: number;
+  };
+}
+
+export interface LLMCallRecord {
+  systemPrompt: string;
+  userContext: string;
+  usage?: {
+    inputTokens: number;
+    outputTokens: number;
+    totalTokens: number;
+  };
 }
 
 export interface ILLMProvider {
@@ -19,4 +34,5 @@ export interface ILLMProvider {
   generateStructuredResponse<T extends z.ZodTypeAny>(
     request: LLMRequest<T>,
   ): Promise<LLMResponse<z.infer<T>>>;
+  lastCalls?: LLMCallRecord[];
 }

packages/llm/src/providers/google-genai.ts

@@ -1,11 +1,12 @@
 import { z } from "zod";
 import { ChatGoogleGenerativeAI } from "@langchain/google-genai";
-import { ILLMProvider, LLMRequest, LLMResponse } from "../llm.js";
+import { ILLMProvider, LLMRequest, LLMResponse, LLMCallRecord } from "../llm.js";
 import { llmConfig } from "../config.js";
 
 export class GeminiProvider implements ILLMProvider {
   providerName = "Gemini";
   private model: ChatGoogleGenerativeAI;
+  lastCalls: LLMCallRecord[] = [];
 
   constructor(apiKey?: string) {
     const key = apiKey || llmConfig.GOOGLE_API_KEY;
@@ -21,11 +22,36 @@ export class GeminiProvider implements ILLMProvider {
   async generateStructuredResponse<T extends z.ZodTypeAny>(
     request: LLMRequest<T>,
   ): Promise<LLMResponse<z.infer<T>>> {
-    const structuredModel = this.model.withStructuredOutput(request.schema);
-    const result = await structuredModel.invoke([
+    const structuredModel = this.model.withStructuredOutput(request.schema, { includeRaw: true });
+    const result = (await structuredModel.invoke([
       { role: "system", content: request.systemPrompt },
       { role: "user", content: request.userContext },
-    ]);
-    return { success: true, data: result as z.infer<T> };
+    ])) as unknown as {
+      parsed?: z.infer<T>;
+      raw?: {
+        usage_metadata?: {
+          input_tokens?: number;
+          output_tokens?: number;
+          total_tokens?: number;
+        };
+      };
+    };
+
+    const parsed = result?.parsed;
+    const raw = result?.raw;
+
+    const usage = raw?.usage_metadata ? {
+      inputTokens: raw.usage_metadata.input_tokens || 0,
+      outputTokens: raw.usage_metadata.output_tokens || 0,
+      totalTokens: raw.usage_metadata.total_tokens || 0,
+    } : undefined;
+
+    this.lastCalls.push({
+      systemPrompt: request.systemPrompt,
+      userContext: request.userContext,
+      usage,
+    });
+
+    return { success: true, data: parsed, usage };
   }
 }

packages/llm/src/providers/mock.ts

@@ -1,9 +1,10 @@
 import { z } from "zod";
-import { ILLMProvider, LLMRequest, LLMResponse } from "../llm.js";
+import { ILLMProvider, LLMRequest, LLMResponse, LLMCallRecord } from "../llm.js";
 
 export class MockLLMProvider implements ILLMProvider {
   providerName = "mock";
   private callCount = 0;
+  lastCalls: LLMCallRecord[] = [];
 
   constructor(private responses: unknown[]) {}
 
@@ -14,9 +15,15 @@ export class MockLLMProvider implements ILLMProvider {
     if (next === undefined) {
       return { success: false, error: "Mock responses exhausted" };
     }
+    const usage = { inputTokens: 100, outputTokens: 50, totalTokens: 150 };
+    this.lastCalls.push({
+      systemPrompt: request.systemPrompt,
+      userContext: request.userContext,
+      usage,
+    });
     try {
       const parsed = request.schema.parse(next);
-      return { success: true, data: parsed };
+      return { success: true, data: parsed, usage };
     } catch (e) {
       return { success: false, error: e instanceof Error ? e.message : String(e) };
     }