[PROD RELEASE] - AWS Bedrock

.env.sample

@@ -0,0 +1,21 @@
+AUTH0_DOMAIN="[AUTH0_DOMAIN]"
+AUTH0_AUDIENCE="[AUTH0_AUDIENCE]"
+AUTH0_M2M_DOMAIN="[AUTH0_M2M_DOMAIN]"
+AUTH0_M2M_AUDIENCE="[AUTH0_M2M_AUDIENCE]"
+MASTRA_DB_CONNECTION="[MASTRA_DB_CONNECTION]"
+MASTRA_DB_SCHEMA="[MASTRA_DB_SCHEMA]"
+TC_API_BASE="[TC_API_BASE]"
+PORT="[PORT]"
+OLLAMA_API_URL="[OLLAMA_API_URL]"
+DISABLE_AUTH="[DISABLE_AUTH]"
+SKILL_MATCHING_SEMANTIC_THRESHOLD="[SKILL_MATCHING_SEMANTIC_THRESHOLD]"
+M2M_AUTH_CLIENT_ID="[M2M_AUTH_CLIENT_ID]"
+M2M_AUTH_CLIENT_SECRET="[M2M_AUTH_CLIENT_SECRET]"
+M2M_AUTH_PROXY_SERVER_URL="[M2M_AUTH_PROXY_SERVER_URL]"
+M2M_AUTH_URL="[M2M_AUTH_URL]"
+M2M_AUTH_DOMAIN="[M2M_AUTH_DOMAIN]"
+M2M_AUTH_AUDIENCE="[M2M_AUTH_AUDIENCE]"
+WORKSPACE_PATH="[ABS PATH_TO_WORKSPACE_FOLDER]"
+WIPRO_API_KEY="[WIPRO_API_KEY]"
+LOG_LEVEL="[LOG_LEVEL]"
+MASTRA_STUDIO_PATH="[ABS PATH_TO_MASTRA_STUDIO_OUTPUT_FOLDER]"

.gitignore

@@ -7,4 +7,5 @@ dist
 *.db
 *.db-*
 .vscode
-test-data
+test-data
+.env.sh

README.md

@@ -117,7 +117,7 @@ tc-ai-api/
 | `SKILL_MATCHING_FUZZY_MATCH_SIZE`   | No       | `3`                                    | Number of candidates returned per fuzzy-match query              |
 | `SKILL_MATCHING_CONCURRENCY`        | No       | `5`                                    | Concurrency limit for parallel skill-matching requests           |
 | `SKILL_MATCHING_SEMANTIC_THRESHOLD` | No       | `0.45`                                 | Max cosine distance for semantic matches (lower = stricter)      |
-| `SKILL_DISCOVERY_EVAL_SAMPLE_RATE`  | No       | `0.5`                                  | Fraction of agent interactions sampled for evaluation scoring    |
+| `SKILL_DISCOVERY_EVAL_SAMPLE_RATE`  | No       | -                                      | Fraction of agent interactions sampled for evaluation scoring    |
 
 > \* Auth0 variables are required unless `DISABLE_AUTH=true`.
 

package.json

@@ -7,7 +7,7 @@
     "test": "vitest run",
     "test:integration": "vitest run --config vitest.integration.config.ts",
     "dev": "mastra dev",
-    "build": "mastra build",
+    "build": "mastra build --studio",
     "start": "mastra start",
     "lint": "eslint .",
     "lint:fix": "eslint . --fix",
@@ -25,28 +25,30 @@
     "node": ">=22.13.0"
   },
   "dependencies": {
+    "@ai-sdk/amazon-bedrock": "^4.0.96",
+    "@aws-sdk/credential-providers": "^3.1033.0",
     "@mastra/auth-auth0": "^1.0.1",
-    "@mastra/core": "^1.15.0",
-    "@mastra/evals": "^1.1.2",
-    "@mastra/libsql": "^1.7.1",
-    "@mastra/loggers": "^1.0.3",
-    "@mastra/memory": "^1.9.0",
-    "@mastra/observability": "^1.5.1",
-    "@mastra/pg": "^1.8.2",
+    "@mastra/core": "^1.25.0",
+    "@mastra/evals": "^1.2.1",
+    "@mastra/libsql": "^1.8.1",
+    "@mastra/loggers": "^1.1.1",
+    "@mastra/memory": "^1.15.1",
+    "@mastra/observability": "^1.9.1",
+    "@mastra/pg": "^1.9.1",
     "@topcoder/wipro-ai-sdk-provider": "git+https://git.topcoder.com/Topcoder-Platform/Wipro-Provider-AI-SDK.git",
-    "ai": "^6.0.134",
-    "ai-sdk-ollama": "^3.8.1",
+    "ai": "^6.0.168",
+    "ai-sdk-ollama": "^3.8.3",
     "tc-core-library-js": "^2.4.1",
     "zod": "^4.3.6"
   },
   "devDependencies": {
     "@eslint/js": "^10.0.1",
-    "@types/node": "^25.5.0",
-    "eslint": "^10.1.0",
-    "mastra": "^1.3.14",
-    "prettier": "^3.8.1",
-    "typescript": "^5.9.3",
-    "typescript-eslint": "^8.57.1",
-    "vitest": "^4.1.0"
+    "@types/node": "^25.6.0",
+    "eslint": "^10.2.1",
+    "mastra": "^1.6.0",
+    "prettier": "^3.8.3",
+    "typescript": "^6.0.3",
+    "typescript-eslint": "^8.59.0",
+    "vitest": "^4.1.4"
   }
 }

src/mastra/agents/challenge/challenge-parser-agent.ts

@@ -1,5 +1,7 @@
 import { Agent } from '@mastra/core/agent';
-import { wipro } from '../../../utils';
+import { bedrock } from '../../../utils';
+
+const MODEL_ID = 'us.anthropic.claude-sonnet-4-6';
 
 /**
  * Master agent responsible for parsing Topcoder challenge specifications.
@@ -14,12 +16,7 @@ import { wipro } from '../../../utils';
 export const challengeParserAgent = new Agent({
    id: 'challenge-parser-agent',
    name: 'Challenge Specification Parser',
-   model: wipro.chatModel('gpt-5-chat', {
-      temperature: 0.1,
-      topK: 40,
-      topP: 0.9,
-      maxOutputTokens: 16384,
-   }),
+   model: bedrock(MODEL_ID),
    instructions: {
       role: 'system',
       content: `You are an expert Topcoder challenge specification analyst.

src/mastra/agents/jd/jd-rewriter-agent.ts

@@ -1,15 +1,12 @@
 import { Agent } from '@mastra/core/agent';
-import { wipro } from '../../../utils';
+import { bedrock } from '../../../utils';
+
+const MODEL_ID = 'us.anthropic.claude-haiku-4-5-20251001-v1:0';
 
 export const jdRewriterAgent = new Agent({
     id: 'jd-rewriter-agent',
     name: 'Job Description Rewriter',
-    model: wipro.chatModel('gpt-5-chat', {
-        temperature: 0.3,
-        topK: 40,
-        topP: 0.9,
-        maxOutputTokens: 16384,
-    }),
+    model: bedrock(MODEL_ID),
     instructions: {
         role: 'system',
         content: `You are an expert technical recruiter and job description writer for Topcoder.

src/mastra/agents/skills/skills-matching-agent.ts

@@ -1,9 +1,11 @@
 import { Agent } from '@mastra/core/agent';
 import { Memory } from '@mastra/memory';
-import { wipro } from '../../../utils';
+import { bedrock } from '../../../utils';
 import { PostgresStore } from '@mastra/pg';
 import { instanceScorers } from '../../scorers/instance-scorers';
 
+const MODEL_ID = 'us.anthropic.claude-haiku-4-5-20251001-v1:0';
+
 export const skillsMatchingAgent = new Agent({
   id: 'skillsMatchingAgent',
   name: 'Skill terms matching agent for Topcoder standardized skills from a given text',
@@ -26,23 +28,20 @@ Output requirements:
 - No prose, no markdown, no extra keys.
 `,
   },
-  model: wipro.chatModel('gpt-5-chat', {
-    temperature: 0.1,
-    topP: 0.5,
-  }),
+  model: bedrock(MODEL_ID),
   scorers: {
     answerRelevancy: {
       scorer: instanceScorers.instanceAnswerRelevancyScorer,
       sampling: {
         type: 'ratio',
-        rate: Number(process.env.EVAL_SAMPLE_RATE ?? 0.5),
+        rate: Number(process.env.EVAL_SAMPLE_RATE || 0),
       },
     },
     promptAlignment: {
       scorer: instanceScorers.instancePromptAlignmentScorer,
       sampling: {
         type: 'ratio',
-        rate: Number(process.env.EVAL_SAMPLE_RATE ?? 0.5),
+        rate: Number(process.env.EVAL_SAMPLE_RATE || 0),
       },
     },
   },

src/mastra/scorers/instance-scorers.ts

@@ -2,9 +2,14 @@ import {
   createAnswerRelevancyScorer,
   createPromptAlignmentScorerLLM,
 } from '@mastra/evals/scorers/prebuilt';
-import { ollama } from 'ai-sdk-ollama';
+import { ollama } from '../../utils';
 
-const evalModel = ollama(process.env.MASTRA_EVAL_MODEL ?? 'mistral:latest');
+const ollamaBaseUrl = process.env.OLLAMA_API_URL;
+const evalModelName = process.env.MASTRA_EVAL_MODEL ?? 'mistral:latest';
+
+console.log(`[instance-scorers] Initializing eval model: ${evalModelName} at ${ollamaBaseUrl}`);
+
+const evalModel = ollama(evalModelName);
 
 export const instanceAnswerRelevancyScorer = createAnswerRelevancyScorer({
   model: evalModel,

src/mastra/workflows/jd/jd-autowrite-workflow.ts

@@ -7,13 +7,49 @@ import { generateWithStructuredOutputFallback } from '../../../utils/structured-
 // Zod Schemas
 // ---------------------------------------------------------------------------
 
+function normalizeListField(value: unknown): unknown {
+    if (Array.isArray(value)) {
+        return value
+            .map((item) => String(item).trim())
+            .filter(Boolean);
+    }
+
+    if (typeof value !== 'string') {
+        return value;
+    }
+
+    const trimmed = value.trim();
+    if (!trimmed) {
+        return [];
+    }
+
+    const byLine = trimmed
+        .split(/\r?\n/)
+        .map((line) => line.replace(/^\s*(?:[-*•]|\d+[.)])\s+/, '').trim())
+        .filter(Boolean);
+
+    if (byLine.length > 1) {
+        return byLine;
+    }
+
+    return trimmed
+        .split(/\s*[;,]\s*/)
+        .map((entry) => entry.trim())
+        .filter(Boolean);
+}
+
+const normalizedStringArrayField = z.preprocess(
+    normalizeListField,
+    z.array(z.string().min(1)),
+);
+
 const rewrittenJdSchema = z.object({
     title: z.string().describe('Concise, descriptive job title'),
     overview: z.string().describe('2-4 sentence summary of the opportunity'),
-    responsibilities: z.array(z.string()).describe('Action-oriented bullet list of tasks and deliverables'),
-    requirements: z.array(z.string()).describe('Mandatory qualifications, skills, and experience'),
-    niceToHaves: z.array(z.string()).describe('Preferred but non-mandatory qualifications'),
-    skills: z.array(z.string()).describe('Key technical and soft skill keywords'),
+    responsibilities: normalizedStringArrayField.describe('Action-oriented bullet list of tasks and deliverables'),
+    requirements: normalizedStringArrayField.describe('Mandatory qualifications, skills, and experience'),
+    niceToHaves: normalizedStringArrayField.describe('Preferred but non-mandatory qualifications'),
+    skills: normalizedStringArrayField.describe('Key technical and soft skill keywords'),
     formattedDescription: z.string().describe('Full markdown-formatted job description ready to post'),
 });
 

src/mastra/workflows/skills/skill-extraction-workflow.ts

@@ -114,7 +114,7 @@ const fuzzyMatchTermSkills = createStep({
         matches: toolResult.matches,
       };
     } catch (error) {
-      logger?.warn('Skill fuzzymatch failed for term {term}', {
+      logger?.error('Skill fuzzymatch failed for term {term}', {
         term: searchTerm,
         error,
       });

src/utils/index.ts

@@ -3,4 +3,5 @@ export * from './auth';
 export * from './middleware';
 export * from './logger';
 export * from './auth/m2m.service'
-export * from './providers/wipro';
+export * from './providers/wipro';
+export * from './providers/bedrock';

src/utils/providers/bedrock.ts

@@ -0,0 +1,9 @@
+import { createAmazonBedrock } from '@ai-sdk/amazon-bedrock';
+import { fromNodeProviderChain } from '@aws-sdk/credential-providers';
+
+const bedrockProvider = createAmazonBedrock({
+    region: process.env.AWS_REGION || 'us-east-1',
+    credentialProvider: fromNodeProviderChain(),
+});
+
+export const bedrock = bedrockProvider;

src/utils/structured-output-wrapper.ts

@@ -229,6 +229,11 @@ export function isStructuredOutputCompatibilityError(error: unknown): boolean {
     if (!(error instanceof Error)) return false;
     const message = error.message.toLowerCase();
 
+    // Schema-shape failures are content issues, not provider compatibility issues.
+    if (message.includes('validation failed') || message.includes('invalid input: expected')) {
+        return false;
+    }
+
     return (
         message.includes('response_format')
         || message.includes('structured output')