analysis type router

packages/ai/src/steps/analyst-agent-steps/analysis-type-router-step/analysis-type-router-step.test.ts

@@ -0,0 +1,80 @@
+import { describe, expect, it, vi } from 'vitest';
+import type { ModelMessage } from 'ai';
+import { runAnalysisTypeRouterStep } from './analysis-type-router-step';
+
+// Mock the GPT5Mini model
+vi.mock('../../../llm/gpt-5-mini', () => ({
+  GPT5Mini: 'mock-gpt5-mini',
+}));
+
+// Mock generateObject
+vi.mock('ai', async () => {
+  const actual = await vi.importActual('ai');
+  return {
+    ...actual,
+    generateObject: vi.fn(),
+  };
+});
+
+// Mock wrapTraced
+vi.mock('braintrust', () => ({
+  wrapTraced: (fn: Function) => fn,
+}));
+
+describe('runAnalysisTypeRouterStep', () => {
+  it('should route to standard mode for simple queries', async () => {
+    const messages: ModelMessage[] = [
+      {
+        role: 'user',
+        content: 'Show me total sales for last month',
+      },
+    ];
+
+    const result = await runAnalysisTypeRouterStep({ messages });
+
+    // Since we're mocking, it will default to standard
+    expect(result.analysisType).toBe('standard');
+    expect(result.reasoning).toBeDefined();
+  });
+
+  it('should handle empty messages array', async () => {
+    const messages: ModelMessage[] = [];
+
+    const result = await runAnalysisTypeRouterStep({ messages });
+
+    expect(result.analysisType).toBe('standard');
+    expect(result.reasoning).toContain('Defaulting to standard');
+  });
+
+  it('should handle multiple messages in conversation history', async () => {
+    const messages: ModelMessage[] = [
+      {
+        role: 'user',
+        content: 'What is causing the revenue drop?',
+      },
+      {
+        role: 'assistant',
+        content: 'Let me analyze the revenue trends...',
+      },
+      {
+        role: 'user',
+        content: 'Can you dig deeper into the Q3 anomaly?',
+      },
+    ];
+
+    const result = await runAnalysisTypeRouterStep({ messages });
+
+    expect(result.analysisType).toBeDefined();
+    expect(result.reasoning).toBeDefined();
+  });
+
+  it('should handle errors gracefully', async () => {
+    // Force an error by passing invalid data
+    const messages = null as unknown as ModelMessage[];
+
+    const result = await runAnalysisTypeRouterStep({ messages });
+
+    expect(result.analysisType).toBe('standard');
+    expect(result.reasoning).toContain('Defaulting to standard');
+  });
+});

packages/ai/src/steps/analyst-agent-steps/analysis-type-router-step/analysis-type-router-step.ts

@@ -1,82 +1,65 @@
-import { createStep } from '@mastra/core';
-import type { RuntimeContext } from '@mastra/core/runtime-context';
-import type { CoreMessage } from 'ai';
 import { generateObject } from 'ai';
+import type { ModelMessage } from 'ai';
 import { wrapTraced } from 'braintrust';
 import { z } from 'zod';
 import { GPT5Mini } from '../../../llm/gpt-5-mini';
-import { thinkAndPrepWorkflowInputSchema } from '../../../schemas/workflow-schemas';
-import { appendToConversation, standardizeMessages } from '../../../utils/standardizeMessages';
-import type { AnalystRuntimeContext } from '../../../workflows/analyst-workflow';
 import { formatAnalysisTypeRouterPrompt } from './format-analysis-type-router-prompt';
 
-const inputSchema = thinkAndPrepWorkflowInputSchema;
+// Zod schemas first - following Zod-first approach
+export const analysisTypeRouterParamsSchema = z.object({
+  messages: z.array(z.custom<ModelMessage>()).describe('The conversation history'),
+});
 
-// Define the analysis type choices
+export const analysisTypeRouterResultSchema = z.object({
-const AnalysisTypeEnum = z.enum(['standard', 'investigation']);
+  analysisType: z.enum(['standard', 'investigation']).describe('The chosen analysis type'),
-
-// Define the structure for the AI response
-const analysisTypeSchema = z.object({
-  choice: AnalysisTypeEnum.describe('The type of analysis to perform'),
   reasoning: z.string().describe('Explanation for why this analysis type was chosen'),
 });
 
-const outputSchema = z.object({
+// Export types from schemas
-  analysisType: analysisTypeSchema,
+export type AnalysisTypeRouterParams = z.infer<typeof analysisTypeRouterParamsSchema>;
-  conversationHistory: z.array(z.any()),
+export type AnalysisTypeRouterResult = z.infer<typeof analysisTypeRouterResultSchema>;
-  // Pass through dashboard context
+
-  dashboardFiles: z
+// Schema for what the LLM returns
-    .array(
+const llmOutputSchema = z.object({
-      z.object({
+  choice: z.enum(['standard', 'investigation']).describe('The type of analysis to perform'),
-        id: z.string(),
+  reasoning: z.string().describe('Explanation for why this analysis type was chosen'),
-        name: z.string(),
-        versionNumber: z.number(),
-        metricIds: z.array(z.string()),
-      })
-    )
-    .optional(),
 });
 
-const execution = async ({
+/**
-  inputData,
+ * Generates the analysis type decision using the LLM
-}: {
+ */
-  inputData: z.infer<typeof inputSchema>;
+async function generateAnalysisTypeWithLLM(messages: ModelMessage[]): Promise<{
-  runtimeContext: RuntimeContext<AnalystRuntimeContext>;
+  choice: 'standard' | 'investigation';
-}): Promise<z.infer<typeof outputSchema>> => {
+  reasoning: string;
+}> {
   try {
-    // Use the input data directly
+    // Get the last user message for context
-    const prompt = inputData.prompt;
+    const lastUserMessage = messages
-    const conversationHistory = inputData.conversationHistory;
+      .slice()
+      .reverse()
+      .find((m) => m.role === 'user');
+    const userPrompt = lastUserMessage?.content?.toString() || '';
 
-    // Prepare messages for the agent
+    // Format the system prompt
-    let messages: CoreMessage[];
-    if (conversationHistory && conversationHistory.length > 0) {
-      // Use conversation history as context + append new user message
-      messages = appendToConversation(conversationHistory as CoreMessage[], prompt);
-    } else {
-      // Otherwise, use just the prompt
-      messages = standardizeMessages(prompt);
-    }
-
-    // Format the prompt using the helper function
     const systemPrompt = formatAnalysisTypeRouterPrompt({
-      userPrompt: prompt,
+      userPrompt,
-      ...(conversationHistory && { conversationHistory: conversationHistory as CoreMessage[] }),
+      conversationHistory: messages.length > 1 ? messages : [],
     });
 
-    // Generate the analysis type decision
+    // Prepare messages for the LLM
+    const systemMessage: ModelMessage = {
+      role: 'system',
+      content: systemPrompt,
+    };
+
+    const llmMessages = [systemMessage, ...messages];
+
     const tracedAnalysisType = wrapTraced(
       async () => {
         const { object } = await generateObject({
           model: GPT5Mini,
-          schema: analysisTypeSchema,
+          schema: llmOutputSchema,
-          messages: [
+          messages: llmMessages,
-            {
-              role: 'system',
-              content: systemPrompt,
-            },
-            ...messages,
-          ],
           temperature: 1,
           providerOptions: {
             openai: {
@@ -97,36 +80,46 @@ const execution = async ({
       }
     );
 
-    const analysisType = await tracedAnalysisType();
+    const result = await tracedAnalysisType();
+    return {
+      choice: result.choice,
+      reasoning: result.reasoning,
+    };
+  } catch (llmError) {
+    console.warn('[AnalysisTypeRouter] LLM failed to generate valid response:', {
+      error: llmError instanceof Error ? llmError.message : 'Unknown error',
+      errorType: llmError instanceof Error ? llmError.name : 'Unknown',
+    });
+
+    // Default to standard analysis on error
+    return {
+      choice: 'standard',
+      reasoning: 'Defaulting to standard analysis due to routing error',
+    };
+  }
+}
+
+export async function runAnalysisTypeRouterStep(
+  params: AnalysisTypeRouterParams
+): Promise<AnalysisTypeRouterResult> {
+  try {
+    const result = await generateAnalysisTypeWithLLM(params.messages);
 
     console.info('[Analysis Type Router] Decision:', {
-      choice: analysisType.choice,
+      choice: result.choice,
-      reasoning: analysisType.reasoning,
+      reasoning: result.reasoning,
     });
 
     return {
-      analysisType,
+      analysisType: result.choice,
-      conversationHistory: messages,
+      reasoning: result.reasoning,
-      dashboardFiles: inputData.dashboardFiles, // Pass through dashboard context
     };
   } catch (error) {
-    console.error('[Analysis Type Router] Error:', error);
+    console.error('[analysis-type-router-step] Unexpected error:', error);
     // Default to standard analysis on error
     return {
-      analysisType: {
+      analysisType: 'standard',
-        choice: 'standard',
       reasoning: 'Defaulting to standard analysis due to routing error',
-      },
-      conversationHistory: inputData.conversationHistory || [],
-      dashboardFiles: inputData.dashboardFiles, // Pass through dashboard context
     };
   }
-};
+}
-
-export const analysisTypeRouterStep = createStep({
-  id: 'analysis-type-router',
-  description: 'Determines whether to use standard or investigation analysis based on the query',
-  inputSchema,
-  outputSchema,
-  execute: execution,
-});

packages/ai/src/steps/analyst-agent-steps/analysis-type-router-step/format-analysis-type-router-prompt.ts

@@ -1,9 +1,9 @@
-import type { CoreMessage } from 'ai';
+import type { ModelMessage } from 'ai';
 
 // Define the required template parameters
 interface AnalysisTypeRouterTemplateParams {
   userPrompt: string;
-  conversationHistory?: CoreMessage[];
+  conversationHistory?: ModelMessage[];
 }
 
 /**

packages/ai/src/steps/index.ts

@@ -5,6 +5,7 @@ export * from './analyst-agent-steps/mark-message-complete-step/mark-message-com
 export * from './analyst-agent-steps/generate-chat-title-step/generate-chat-title-step';
 export * from './analyst-agent-steps/extract-values-step/extract-values-search-step';
 export * from './analyst-agent-steps/create-todos-step/create-todos-step';
+export * from './analyst-agent-steps/analysis-type-router-step/analysis-type-router-step';
 
 // Docs agent steps
 export * from './docs-agent-steps/create-docs-todos-step';