Run

P0

836d30b28ff596e01d8cc287e0b83b3d3e1a1bc1

Agent context: I’ll first map how this agent is built and where tool calls or retrieval would fit, then I’ll check current provider docs directly so the recommendation is grounded in today’s APIs rather than memory. I’ll first map how this agent is built and where tool calls or retrieval would fit, then I’ll check current provider docs directly so the recommendation is grounded in today’s APIs rather than memory.

Agent context: I’ll first map how this agent is built and where tool calls or retrieval would fit, then I’ll check current provider docs directly so the recommendation is grounded in today’s APIs rather than memory.

Agent context: I’ll first map how this agent is built and where tool calls or retrieval would fit, then I’ll check current provider docs directly so the recommendation is grounded in today’s APIs rather than memory. exit code 127 Output: /bin/bash: line 1: rg: command not found

Agent context: I’ll first map how this agent is built and where tool calls or retrieval would fit, then I’ll check current provider docs directly so the recommendation is grounded in today’s APIs rather than memory.

Agent context: I’ll first map how this agent is built and where tool calls or retrieval would fit, then I’ll check current provider docs directly so the recommendation is grounded in today’s APIs rather than memory. exit code 0 Output: ./.env.example ./README.md ./package.json

Agent context: I’ll first map how this agent is built and where tool calls or retrieval would fit, then I’ll check current provider docs directly so the recommendation is grounded in today’s APIs rather than memory.

Agent context: I’ll first map how this agent is built and where tool calls or retrieval would fit, then I’ll check current provider docs directly so the recommendation is grounded in today’s APIs rather than memory. exit code 0 Output: /home/user/worktrees/attempt-92075767-2bf4-447c-9199-6f6a67169977-cap-0 LICENSE README.md app biome.jsonc components components.json drizzle.config.ts hooks instrumentation.ts lib middleware.ts next-env.d.ts next.config.ts package.json pnpm-lock.yaml postcss.config.mjs public tailwind.config.ts tsconfig.json

Agent context: `rg` isn’t installed in this workspace, so I’m falling back to `find`/`grep`. The repo is a Next.js app with `app/`, `components/`, and `lib/`, so I’m looking for the agent and model integration points now. `rg` isn’t installed in this workspace, so I’m falling back to `find`/`grep`. The repo is a Next.js app with `app/`, `components/`, and `lib/`, so I’m looking for the agent and model integration points now.

Agent context: `rg` isn’t installed in this workspace, so I’m falling back to `find`/`grep`. The repo is a Next.js app with `app/`, `components/`, and `lib/`, so I’m looking for the agent and model integration points now.

Agent context: `rg` isn’t installed in this workspace, so I’m falling back to `find`/`grep`. The repo is a Next.js app with `app/`, `components/`, and `lib/`, so I’m looking for the agent and model integration points now. exit code 0 Output: app/(auth)/actions.ts app/(auth)/api/auth/[...nextauth]/route.ts app/(auth)/auth.config.ts app/(auth)/auth.ts app/(auth)/login/page.tsx app/(auth)/register/page.tsx app/(chat)/actions.ts app/(chat)/api/chat/route.ts app/(chat)/api/document/route.ts app/(chat)/api/files/upload/route.ts app/(chat)/api/history/route.ts app/(chat)/api/messages/count/route.ts app/(chat)/api/suggestions/route.ts app/(chat)/api/vote/route.ts app/(chat)/chat/[id]/page.tsx app/(chat)/layout.tsx app/(chat)/opengraph-image.png app/(chat)/page.tsx app/(chat)/twitter-image.png app/api/auth/auto-login/route.ts app/components/AuthCheck.tsx app/favicon.ico app/globals.css app/layout.tsx components/api-keys-modal.tsx components/app-sidebar.tsx components/auth-form.tsx components/balance-sheets-table.tsx components/block-actions.tsx components/block-close-button.tsx components/block-messages.tsx components/block.tsx compo Provider mentions: modal

Agent context: `rg` isn’t installed in this workspace, so I’m falling back to `find`/`grep`. The repo is a Next.js app with `app/`, `components/`, and `lib/`, so I’m looking for the agent and model integration points now.

Agent context: `rg` isn’t installed in this workspace, so I’m falling back to `find`/`grep`. The repo is a Next.js app with `app/`, `components/`, and `lib/`, so I’m looking for the agent and model integration points now. exit code 0 Output: { "name": "ai-chatbot", "version": "0.1.0", "private": true, "scripts": { "dev": "next dev --turbo", "build": "tsx lib/db/migrate && next build", "start": "next start", "lint": "next lint && biome lint --write --unsafe", "lint:fix": "next lint --fix && biome lint --write --unsafe", "format": "biome format --write", "db:generate": "drizzle-kit generate", "db:migrate": "npx tsx lib/db/migrate.ts", "db:studio": "drizzle-kit studio", "db:push": "drizzle-kit push", "db:pull": "drizzle-kit pull", "db:check": "drizzle-kit check", "db:up": "drizzle-kit up" }, "dependencies": { "@ai-sdk/openai": "1.0.6", "@codemirror/lang-javascript": "^6.2.2", "@codemirror/lang-python": "^6.1.6", "@codemirror/state": "^6.5.0", "@codemirror/theme-one-dark": "^6.1.2", "@codemirror/view": "^6.35.3", "@fortawesome/fontawesome-svg-core": "^6.7.2", "@fortawesome/free-solid-svg-icons": "^6.7.2", "@fortawesome/react-fon Provider mentions: openai

Agent context: `rg` isn’t installed in this workspace, so I’m falling back to `find`/`grep`. The repo is a Next.js app with `app/`, `components/`, and `lib/`, so I’m looking for the agent and model integration points now.

Agent context: `rg` isn’t installed in this workspace, so I’m falling back to `find`/`grep`. The repo is a Next.js app with `app/`, `components/`, and `lib/`, so I’m looking for the agent and model integration points now. exit code 0 Output: # Get your OpenAI API Key here: https://platform.openai.com/account/api-keys OPENAI_API_KEY=[redacted] # Get your Financial Datasets API Key here: https://financialdatasets.ai/ FINANCIAL_DATASETS_API_KEY=[redacted] # Get your LangSmith API Key here: https://smith.langchain.com/ LANGCHAIN_API_KEY=[redacted] LANGCHAIN_TRACING_V2=true LANGCHAIN_PROJECT=ai-financial-agent # Generate a random secret: https://generate-secret.vercel.app/32 or `openssl rand -base64 32` AUTH_SECRET=[redacted] # The following keys below are automatically created and # added to your environment when you deploy on vercel # Instructions to create a Vercel Blob Store here: https://vercel.com/docs/storage/vercel-blob BLOB_READ_WRITE_TOKEN=[redacted] # Instructions to create a database here: https://vercel.com/docs/storage/vercel-postgres/quickstart POSTGRES_URL=**** Provider mentions: openai

Agent context: `rg` isn’t installed in this workspace, so I’m falling back to `find`/`grep`. The repo is a Next.js app with `app/`, `components/`, and `lib/`, so I’m looking for the agent and model integration points now.

Agent context: `rg` isn’t installed in this workspace, so I’m falling back to `find`/`grep`. The repo is a Next.js app with `app/`, `components/`, and `lib/`, so I’m looking for the agent and model integration points now. exit code 0 Output: # AI Financial Agent 🤖 This is a proof of conncept AI financial agent. The goal of this project is to explore the use of AI for investment research. This project is for **educational** purposes only and is not intended for real trading or investment. 👋 **Demo**: You can use a live demo of this project [here](https://chat.financialdatasets.ai/). <img width="1709" alt="Screenshot 2025-01-06 at 5 53 59 PM" src="https://github.com/user-attachments/assets/7ef1729b-f2e1-477c-99e2-1184c1bfa1cd" /> ## Disclaimer This project is for **educational and research purposes only**. - Not intended for real trading or investment - No warranties or guarantees provided - Past performance does not indicate future results - Creator assumes no liability for financial losses - Consult a financial advisor for investment decisions By using this software, you agree to use it solely for learning purposes. ## Tab Provider mentions: openai

Agent context: The project already uses Vercel AI SDK plus OpenAI, and it has domain-specific financial tools. I’m reading the chat route and tool definitions to see whether web retrieval should be a model-native capability or another tool in the existing tool loop. The project already uses Vercel AI SDK plus OpenAI, and it has domain-specific financial tools. I’m reading the chat route and tool definitions to see whether web retrieval should be a model-native capability or another tool in the existing tool loop. Provider mentions: openai

Agent context: The project already uses Vercel AI SDK plus OpenAI, and it has domain-specific financial tools. I’m reading the chat route and tool definitions to see whether web retrieval should be a model-native capability or another tool in the existing tool loop.

Agent context: The project already uses Vercel AI SDK plus OpenAI, and it has domain-specific financial tools. I’m reading the chat route and tool definitions to see whether web retrieval should be a model-native capability or another tool in the existing tool loop. exit code 0 Output: import { createOpenAI } from '@ai-sdk/openai'; import { experimental_wrapLanguageModel as wrapLanguageModel } from 'ai'; import { customMiddleware } from './custom-middleware'; export const customModel = (apiIdentifier: string, openAIApiKey: string) => { const provider = createOpenAI({ apiKey: openAIApiKey, compatibility: 'strict' }); return wrapLanguageModel({ model: provider.chat(apiIdentifier), middleware: customMiddleware, }); }; Provider mentions: openai

Agent context: The project already uses Vercel AI SDK plus OpenAI, and it has domain-specific financial tools. I’m reading the chat route and tool definitions to see whether web retrieval should be a model-native capability or another tool in the existing tool loop.

Agent context: The project already uses Vercel AI SDK plus OpenAI, and it has domain-specific financial tools. I’m reading the chat route and tool definitions to see whether web retrieval should be a model-native capability or another tool in the existing tool loop. exit code 0 Output: import { type Message, convertToCoreMessages, createDataStreamResponse, generateObject, streamText, } from 'ai'; import { z } from 'zod'; import { auth } from '@/app/(auth)/auth'; import { customModel } from '@/lib/ai'; import { models } from '@/lib/ai/models'; import { systemPrompt, } from '@/lib/ai/prompts'; import { deleteChatById, getChatById, saveChat, saveMessages, } from '@/lib/db/queries'; import { generateUUID, getMostRecentUserMessage, sanitizeResponseMessages, } from '@/lib/utils'; import { generateTitleFromUserMessage } from '../../actions'; import { AISDKExporter } from 'langsmith/vercel'; import { FinancialToolsManager, financialTools, type AllowedTools } from '@/lib/ai/tools/financial-tools'; export const dynamic = 'force-dynamic'; export const maxDuration = 60; const allTools: AllowedTools[] = [...financialTools]; export async function POST(request: Request) { const { id,

Agent context: The project already uses Vercel AI SDK plus OpenAI, and it has domain-specific financial tools. I’m reading the chat route and tool definitions to see whether web retrieval should be a model-native capability or another tool in the existing tool loop.

Agent context: The project already uses Vercel AI SDK plus OpenAI, and it has domain-specific financial tools. I’m reading the chat route and tool definitions to see whether web retrieval should be a model-native capability or another tool in the existing tool loop. exit code 0 Output: // Define your models here. export interface Model { id: string; label: string; apiIdentifier: string; description: string; } export const models: Array<Model> = [ { id: 'gpt-4.1-nano-2025-04-14', label: 'GPT 4.1 nano', apiIdentifier: 'gpt-4.1-nano-2025-04-14', description: 'Fastest, most cost-effective GPT-4.1 model', }, { id: 'gpt-4.1-mini-2025-04-14', label: 'GPT 4.1 mini', apiIdentifier: 'gpt-4.1-mini-2025-04-14', description: 'Balance between intelligence, speed, and cost', }, { id: 'gpt-4.1-2025-04-14', label: 'GPT 4.1', apiIdentifier: 'gpt-4.1-2025-04-14', description: 'Flagship model for complex tasks', }, { id: 'gpt-4o', label: 'GPT-4o', apiIdentifier: 'gpt-4o', description: 'Omni-purpose model for complex tasks', }, ] as const; export const DEFAULT_MODEL_NAME: string = 'gpt-4o';

Agent context: The project already uses Vercel AI SDK plus OpenAI, and it has domain-specific financial tools. I’m reading the chat route and tool definitions to see whether web retrieval should be a model-native capability or another tool in the existing tool loop.

Agent context: The project already uses Vercel AI SDK plus OpenAI, and it has domain-specific financial tools. I’m reading the chat route and tool definitions to see whether web retrieval should be a model-native capability or another tool in the existing tool loop. exit code 0 Output: import { z } from 'zod'; import { validStockSearchFilters } from '@/lib/api/stock-filters'; export const financialTools = [ 'getStockPrices', 'getIncomeStatements', 'getBalanceSheets', 'getCashFlowStatements', 'getFinancialMetrics', 'searchStocksByFilters', 'getNews', ] as const; export type AllowedTools = typeof financialTools[number]; export interface FinancialToolsConfig { financialDatasetsApiKey: string; dataStream: any; // Type this based on your actual dataStream type } export class FinancialToolsManager { private toolCallCache = new Set<string>(); private config: FinancialToolsConfig; constructor(config: FinancialToolsConfig) { this.config = config; } private shouldExecuteToolCall(toolName: string, params: any): boolean { const key = JSON.stringify({ toolName, params }); if (this.toolCallCache.has(key)) { return false; } this.toolCallCache.add(key); return true; } public getTools(

Agent context: The project already uses Vercel AI SDK plus OpenAI, and it has domain-specific financial tools. I’m reading the chat route and tool definitions to see whether web retrieval should be a model-native capability or another tool in the existing tool loop.

Agent context: The project already uses Vercel AI SDK plus OpenAI, and it has domain-specific financial tools. I’m reading the chat route and tool definitions to see whether web retrieval should be a model-native capability or another tool in the existing tool loop. exit code 0 Output: export const blocksPrompt = ` Blocks is a special user interface mode that helps users with writing, editing, and other content creation tasks. When block is open, it is on the right side of the screen, while the conversation is on the left side. When creating or updating documents, changes are reflected in real-time on the blocks and visible to the user. When asked to write code, always use blocks. When writing code, specify the language in the backticks, e.g. \`\`\`python\`code here\`\`\`. The default language is Python. Other languages are not yet supported, so let the user know if they request a different language. DO NOT UPDATE DOCUMENTS IMMEDIATELY AFTER CREATING THEM. WAIT FOR USER FEEDBACK OR REQUEST TO UPDATE IT. This is a guide for using blocks tools: \`createDocument\` and \`updateDocument\`, which render content on a blocks beside the conversation. **When to use \`createDocum

Query/prompt: Tavily API documentation search extract crawl search_depth include_answer chunks_per_source max_results Agent context: The project already uses Vercel AI SDK plus OpenAI, and it has domain-specific financial tools. I’m reading the chat route and tool definitions to see whether web retrieval should be a model-native capability or another tool in the existing tool loop. Query: Tavily API documentation search extract crawl search_depth include_answer chunks_per_source max_results Agent context: The project already uses Vercel AI SDK plus OpenAI, and it has domain-specific financial tools. I’m reading the chat route and tool definitions to see whether web retrieval should be a model-native capability or another tool in the existing tool loop. Expanded queries: Tavily API documentation search extract crawl search_depth include_answer chunks_per_source max_results | Exa API documentation search contents highlights answers autoprompt | Brave Search API documentation web search endpoint freshness summary | OpenAI API documentation web search tool responses API citations Provider mentions: openai

Query/prompt: Perplexity Sonar API documentation search citations recency filter Agent context: The project already uses Vercel AI SDK plus OpenAI, and it has domain-specific financial tools. I’m reading the chat route and tool definitions to see whether web retrieval should be a model-native capability or another tool in the existing tool loop. Query: Perplexity Sonar API documentation search citations recency filter Agent context: The project already uses Vercel AI SDK plus OpenAI, and it has domain-specific financial tools. I’m reading the chat route and tool definitions to see whether web retrieval should be a model-native capability or another tool in the existing tool loop. Expanded queries: Perplexity Sonar API documentation search citations recency filter | Google Vertex AI grounding with Google Search documentation API | SerpAPI Google Search API documentation organic_results snippet link | Bing Search API retirement documentation 2025 Provider mentions: openai

Query/prompt: https://docs.tavily.com/documentation/api-reference/endpoint/search Agent context: The project already uses Vercel AI SDK plus OpenAI, and it has domain-specific financial tools. I’m reading the chat route and tool definitions to see whether web retrieval should be a model-native capability or another tool in the existing tool loop. Query: https://docs.tavily.com/documentation/api-reference/endpoint/search Agent context: The project already uses Vercel AI SDK plus OpenAI, and it has domain-specific financial tools. I’m reading the chat route and tool definitions to see whether web retrieval should be a model-native capability or another tool in the existing tool loop. Provider mentions: openai

Query/prompt: Tavily JavaScript SDK docs npm tavily search typescript Agent context: The project already uses Vercel AI SDK plus OpenAI, and it has domain-specific financial tools. I’m reading the chat route and tool definitions to see whether web retrieval should be a model-native capability or another tool in the existing tool loop. Query: Tavily JavaScript SDK docs npm tavily search typescript Agent context: The project already uses Vercel AI SDK plus OpenAI, and it has domain-specific financial tools. I’m reading the chat route and tool definitions to see whether web retrieval should be a model-native capability or another tool in the existing tool loop. Expanded queries: Tavily JavaScript SDK docs npm tavily search typescript | Vercel AI SDK tools execute streamText documentation ai sdk 4 tools | Tavily pricing API credits free plan official Provider mentions: openai

Query/prompt: site:ai-sdk.dev/docs ai sdk streamText tools execute tool calling Agent context: The project already uses Vercel AI SDK plus OpenAI, and it has domain-specific financial tools. I’m reading the chat route and tool definitions to see whether web retrieval should be a model-native capability or another tool in the existing tool loop. Query: site:ai-sdk.dev/docs ai sdk streamText tools execute tool calling Agent context: The project already uses Vercel AI SDK plus OpenAI, and it has domain-specific financial tools. I’m reading the chat route and tool definitions to see whether web retrieval should be a model-native capability or another tool in the existing tool loop. Expanded queries: site:ai-sdk.dev/docs ai sdk streamText tools execute tool calling Provider mentions: openai

Agent context: The existing API route receives user-supplied `modelApiKey` and `financialDatasetsApiKey`; it does not currently have a server-owned provider config pattern for web search. I’m checking the key management UI because that affects whether the best provider should support user BYO keys or a single hosted app key. The existing API route receives user-supplied `modelApiKey` and `financialDatasetsApiKey`; it does not currently have a server-owned provider config pattern for web search. I’m checking the key management UI because that affects whether the best provider should support user BYO keys or a single hosted app key.

Agent context: The existing API route receives user-supplied `modelApiKey` and `financialDatasetsApiKey`; it does not currently have a server-owned provider config pattern for web search. I’m checking the key management UI because that affects whether the best provider should support user BYO keys or a single hosted app key.

Agent context: The existing API route receives user-supplied `modelApiKey` and `financialDatasetsApiKey`; it does not currently have a server-owned provider config pattern for web search. I’m checking the key management UI because that affects whether the best provider should support user BYO keys or a single hosted app key. exit code 0 Output: import { getLocalStorage, setLocalStorage } from "../utils"; export const getOpenAIApiKey = () => { // Only check env variable on server side if (typeof window === 'undefined') { return process.env.OPENAI_API_KEY; } // Check localStorage on client side const apiKey = getLocalStorage('openaiApiKey'); return apiKey || process.env.OPENAI_API_KEY; }; export const setOpenAIApiKey = async (apiKey: string) => { if (typeof window === 'undefined') return; setLocalStorage('openaiApiKey', apiKey); }; export const getFinancialDatasetsApiKey = () => { const envApiKey = process.env.FINANCIAL_DATASETS_API_KEY; if (envApiKey) return envApiKey; // Check localStorage on client side const apiKey = getLocalStorage('financialDatasetsApiKey'); return apiKey || process.env.FINANCIAL_DATASETS_API_KEY; }; export const setFinancialDatasetsApiKey = async (apiKey: string) => { if (typeof window === 'undefined') ret

Agent context: The existing API route receives user-supplied `modelApiKey` and `financialDatasetsApiKey`; it does not currently have a server-owned provider config pattern for web search. I’m checking the key management UI because that affects whether the best provider should support user BYO keys or a single hosted app key. Provider mentions: modal

Agent context: The existing API route receives user-supplied `modelApiKey` and `financialDatasetsApiKey`; it does not currently have a server-owned provider config pattern for web search. I’m checking the key management UI because that affects whether the best provider should support user BYO keys or a single hosted app key. exit code 0 Output: 'use client'; import { useState } from 'react'; import { Dialog, DialogContent, DialogHeader, DialogTitle } from '@/components/ui/dialog'; import { Input } from '@/components/ui/input'; import { Button } from '@/components/ui/button'; import { Eye, EyeOff } from 'lucide-react'; import { getOpenAIApiKey, setOpenAIApiKey, getFinancialDatasetsApiKey, setFinancialDatasetsApiKey } from '@/lib/db/api-keys'; import { validateOpenAIKey } from '@/lib/utils/api-key-validation'; interface ApiKeysModalProps { open: boolean; onOpenChange: (open: boolean) => void; title?: string; description?: string; } export function ApiKeysModal({ open, onOpenChange, title = "Configure API keys", description }: ApiKeysModalProps) { const [openAIKey, setOpenAIKey] = useState(getOpenAIApiKey() || ''); const [financialKey, setFinancialKey] = useState(getFinancialDatasetsApiKey() || ''); const [showOpenAIKey, setShowOp Provider mentions: openai, modal

Agent context: The existing API route receives user-supplied `modelApiKey` and `financialDatasetsApiKey`; it does not currently have a server-owned provider config pattern for web search. I’m checking the key management UI because that affects whether the best provider should support user BYO keys or a single hosted app key.

Agent context: The existing API route receives user-supplied `modelApiKey` and `financialDatasetsApiKey`; it does not currently have a server-owned provider config pattern for web search. I’m checking the key management UI because that affects whether the best provider should support user BYO keys or a single hosted app key. exit code 0 Output: 'use client'; import type { Attachment, ChatRequestOptions, Message } from 'ai'; import { useChat } from 'ai/react'; import { useState } from 'react'; import useSWR, { useSWRConfig } from 'swr'; import { ChatHeader } from '@/components/chat-header'; import type { Vote } from '@/lib/db/schema'; import { fetcher, track } from '@/lib/utils'; import { getFinancialDatasetsApiKey, getLocalOpenAIApiKey } from '@/lib/db/api-keys'; import { Block } from './block'; import { MultimodalInput } from './multimodal-input'; import { Messages } from './messages'; import { VisibilityType } from './visibility-selector'; import { useBlockSelector } from '@/hooks/use-block'; import { ApiKeysModal } from '@/components/api-keys-modal'; export function Chat({ id, initialMessages, selectedModelId, selectedVisibilityType, isReadonly, }: { id: string; initialMessages: Array<Message>; selectedModelId: string; selec Provider mentions: openai, modal

Agent context: The existing API route receives user-supplied `modelApiKey` and `financialDatasetsApiKey`; it does not currently have a server-owned provider config pattern for web search. I’m checking the key management UI because that affects whether the best provider should support user BYO keys or a single hosted app key.

Agent context: The existing API route receives user-supplied `modelApiKey` and `financialDatasetsApiKey`; it does not currently have a server-owned provider config pattern for web search. I’m checking the key management UI because that affects whether the best provider should support user BYO keys or a single hosted app key. exit code 0 Output: app/(chat)/actions.ts:21: modelApiKey, app/(chat)/actions.ts:24: modelApiKey: string; app/(chat)/actions.ts:27: model: customModel('gpt-4.1-mini-2025-04-14', modelApiKey), app/(chat)/api/chat/route.ts:46: financialDatasetsApiKey, app/(chat)/api/chat/route.ts:47: modelApiKey, app/(chat)/api/chat/route.ts:52: financialDatasetsApiKey?: string; app/(chat)/api/chat/route.ts:53: modelApiKey?: string; app/(chat)/api/chat/route.ts:68: if (!modelApiKey) { app/(chat)/api/chat/route.ts:82: const title = await generateTitleFromUserMessage({ message: userMessage, modelApiKey }); app/(chat)/api/chat/route.ts:98: financialDatasetsApiKey: financialDatasetsApiKey!, app/(chat)/api/chat/route.ts:115: model: customModel('gpt-4.1-nano-2025-04-14', modelApiKey), app/(chat)/api/chat/route.ts:177: model: customModel(model.apiIdentifier, modelApiKey), components/chat.tsx:34: const financialDatasetsApiKey = getFi

Query/prompt: https://exa.ai/docs/reference/search-api-guide-for-coding-agents Agent context: The existing API route receives user-supplied `modelApiKey` and `financialDatasetsApiKey`; it does not currently have a server-owned provider config pattern for web search. I’m checking the key management UI because that affects whether the best provider should support user BYO keys or a single hosted app key. Query: https://exa.ai/docs/reference/search-api-guide-for-coding-agents Agent context: The existing API route receives user-supplied `modelApiKey` and `financialDatasetsApiKey`; it does not currently have a server-owned provider config pattern for web search. I’m checking the key management UI because that affects whether the best provider should support user BYO keys or a single hosted app key.

Query/prompt: https://docs.tavily.com/sdk/javascript/quick-start Agent context: The existing API route receives user-supplied `modelApiKey` and `financialDatasetsApiKey`; it does not currently have a server-owned provider config pattern for web search. I’m checking the key management UI because that affects whether the best provider should support user BYO keys or a single hosted app key. Query: https://docs.tavily.com/sdk/javascript/quick-start Agent context: The existing API route receives user-supplied `modelApiKey` and `financialDatasetsApiKey`; it does not currently have a server-owned provider config pattern for web search. I’m checking the key management UI because that affects whether the best provider should support user BYO keys or a single hosted app key.

Query/prompt: https://docs.tavily.com/guides/api-credits Agent context: The existing API route receives user-supplied `modelApiKey` and `financialDatasetsApiKey`; it does not currently have a server-owned provider config pattern for web search. I’m checking the key management UI because that affects whether the best provider should support user BYO keys or a single hosted app key. Query: https://docs.tavily.com/guides/api-credits Agent context: The existing API route receives user-supplied `modelApiKey` and `financialDatasetsApiKey`; it does not currently have a server-owned provider config pattern for web search. I’m checking the key management UI because that affects whether the best provider should support user BYO keys or a single hosted app key.

Agent context: I have enough context to make the provider call. One important repo-specific constraint is that the current agent composes all capabilities through AI SDK tools in `streamText`, so a search provider that exposes search plus page extraction as ordinary TypeScript functions fits better than a provider that requires replacing the model call path. I have enough context to make the provider call. One important repo-specific constraint is that the current agent composes all capabilities through AI SDK tools in `streamText`, so a search provider that exposes search plus page extraction as ordinary TypeScript functions fits better than a provider that requires replacing the model call path.

Agent context: I have enough context to make the provider call. One important repo-specific constraint is that the current agent composes all capabilities through AI SDK tools in `streamText`, so a search provider that exposes search plus page extraction as ordinary TypeScript functions fits better than a provider that requires replacing the model call path.

Agent context: I have enough context to make the provider call. One important repo-specific constraint is that the current agent composes all capabilities through AI SDK tools in `streamText`, so a search provider that exposes search plus page extraction as ordinary TypeScript functions fits better than a provider that requires replacing the model call path. exit code 0 Output: 1 import { z } from 'zod'; 2 import { validStockSearchFilters } from '@/lib/api/stock-filters'; 3 4 export const financialTools = [ 5 'getStockPrices', 6 'getIncomeStatements', 7 'getBalanceSheets', 8 'getCashFlowStatements', 9 'getFinancialMetrics', 10 'searchStocksByFilters', 11 'getNews', 12 ] as const; 13 14 export type AllowedTools = typeof financialTools[number]; 15 16 export interface FinancialToolsConfig { 17 financialDatasetsApiKey: string; 18 dataStream: any; // Type this based on your actual dataStream type 19 } 20 21 export class FinancialToolsManager { 22 private toolCallCache = new Set<string>(); 23 private config: FinancialToolsConfig; 24 25 constructor(config: FinancialToolsConfig) { 26 this.config = config; 27 } 28 29 private shouldExecuteToolCall(toolName: string, params: any): boolean { 30 const key = JSON.stringify({ toolName, params }); 31 if (this.toolCallCache.has(

Agent context: I have enough context to make the provider call. One important repo-specific constraint is that the current agent composes all capabilities through AI SDK tools in `streamText`, so a search provider that exposes search plus page extraction as ordinary TypeScript functions fits better than a provider that requires replacing the model call path.

Agent context: I have enough context to make the provider call. One important repo-specific constraint is that the current agent composes all capabilities through AI SDK tools in `streamText`, so a search provider that exposes search plus page extraction as ordinary TypeScript functions fits better than a provider that requires replacing the model call path. exit code 0 Output: 1 import { 2 type Message, 3 convertToCoreMessages, 4 createDataStreamResponse, 5 generateObject, 6 streamText, 7 } from 'ai'; 8 import { z } from 'zod'; 9 10 import { auth } from '@/app/(auth)/auth'; 11 import { customModel } from '@/lib/ai'; 12 import { models } from '@/lib/ai/models'; 13 import { 14 systemPrompt, 15 } from '@/lib/ai/prompts'; 16 import { 17 deleteChatById, 18 getChatById, 19 saveChat, 20 saveMessages, 21 } from '@/lib/db/queries'; 22 import { 23 generateUUID, 24 getMostRecentUserMessage, 25 sanitizeResponseMessages, 26 } from '@/lib/utils'; 27 28 import { generateTitleFromUserMessage } from '../../actions'; 29 import { AISDKExporter } from 'langsmith/vercel'; 30 import { 31 FinancialToolsManager, 32 financialTools, 33 type AllowedTools 34 } from '@/lib/ai/tools/financial-tools'; 35 36 export const dynamic = 'force-dynamic'; 37 export const maxDuration = 60; 38 39 cons

Agent context: I have enough context to make the provider call. One important repo-specific constraint is that the current agent composes all capabilities through AI SDK tools in `streamText`, so a search provider that exposes search plus page extraction as ordinary TypeScript functions fits better than a provider that requires replacing the model call path.

Agent context: I have enough context to make the provider call. One important repo-specific constraint is that the current agent composes all capabilities through AI SDK tools in `streamText`, so a search provider that exposes search plus page extraction as ordinary TypeScript functions fits better than a provider that requires replacing the model call path. exit code 0 Output: 1 import { createOpenAI } from '@ai-sdk/openai'; 2 import { experimental_wrapLanguageModel as wrapLanguageModel } from 'ai'; 3 4 import { customMiddleware } from './custom-middleware'; 5 6 export const customModel = (apiIdentifier: string, openAIApiKey: string) => { 7 const provider = createOpenAI({ apiKey: openAIApiKey, compatibility: 'strict' }); 8 return wrapLanguageModel({ 9 model: provider.chat(apiIdentifier), 10 middleware: customMiddleware, 11 }); 12 }; Provider mentions: openai

Agent context: I have enough context to make the provider call. One important repo-specific constraint is that the current agent composes all capabilities through AI SDK tools in `streamText`, so a search provider that exposes search plus page extraction as ordinary TypeScript functions fits better than a provider that requires replacing the model call path. Provider mentions: modal

Agent context: I have enough context to make the provider call. One important repo-specific constraint is that the current agent composes all capabilities through AI SDK tools in `streamText`, so a search provider that exposes search plus page extraction as ordinary TypeScript functions fits better than a provider that requires replacing the model call path. exit code 0 Output: 1 'use client'; 2 3 import { useState } from 'react'; 4 import { Dialog, DialogContent, DialogHeader, DialogTitle } from '@/components/ui/dialog'; 5 import { Input } from '@/components/ui/input'; 6 import { Button } from '@/components/ui/button'; 7 import { Eye, EyeOff } from 'lucide-react'; 8 import { getOpenAIApiKey, setOpenAIApiKey, getFinancialDatasetsApiKey, setFinancialDatasetsApiKey } from '@/lib/db/api-keys'; 9 import { validateOpenAIKey } from '@/lib/utils/api-key-validation'; 10 11 12 interface ApiKeysModalProps { 13 open: boolean; 14 onOpenChange: (open: boolean) => void; 15 title?: string; 16 description?: string; 17 } 18 19 export function ApiKeysModal({ 20 open, 21 onOpenChange, 22 title = "Configure API keys", 23 description 24 }: ApiKeysModalProps) { 25 const [openAIKey, setOpenAIKey] = useState(getOpenAIApiKey() || ''); 26 const [financialKey, setFinancialKey] = useState Provider mentions: openai, modal

Agent context: **Provider I’d Choose** Use **Tavily** as the managed web search/retrieval provider. This repo is already structured around Vercel AI SDK tool calling: `streamText` receives `financialToolsManager.getTools()` in [route.ts](/home/user/worktrees/attempt-92075767-2bf4-447c-9199-6f6a67169977-cap-0/app/(chat)/api/chat/route.ts:176), and the financial tools are plain async `execute` functions in [financial-tools.ts](/home/user/worktrees/attempt-92075767-2bf4-447c-9199-6f6a67169977-cap-0/lib/ai/tools/financial-tools.ts:38). Tavily fits that shape cleanly: add `searchWeb` and `retrieveWebPage` tools without replacing the model path, LangSmith tracing path, or current OpenAI model selector. Tavily’s docs show a TypeScript SDK, `@tavily/core`, with `search`, `extract`, `crawl`, and `map` support, plus a free 1,000-credit/month tier and simple search/extract credit pricing. It also has agent-oriented primitives, result chunks, raw content, domain filters, and `general`/`news`/`finance` topics, which line up well with this financial research agent. Sources: Tavily JS SDK docs, search API docs, agent tools docs, and pricing docs. **Main Alternatives Considered** - **Exa**: Strong runner-up. Excellent search plus content retrieval, highlights, livecrawl controls, news/company/financial-report categories, and grounding metadata. I’d choose it if the main goal were deep research workflows over simple agent tool integration. For this repo, Tavily is the lower-friction default. - **OpenAI Web Search**: Attractive because the app already uses OpenAI keys, and OpenAI provides live web access plus citations. But this repo currently uses `@ai-sdk/openai` chat models through `provider.chat(...)` in [index.ts](/home/user/worktrees/attempt-92075767-2bf4-447c-9199-6f6a67169977-cap-0/lib/ai/index.ts:6); OpenAI’s current hosted web search path is more model/API-coupled and would likely require a special Responses/Search path or SDK upgrade rather than a simple tool addition. - **Brave Search API**: Good raw search index, fresh web data, LLM context endpoint, SOC 2, and privacy posture. But it is more search-results/snippet oriented; I’d expect to add separate extraction or page-cleaning logic for robust grounding. - **Perplexity Sonar**: Good if replacing the answer model with a web-grounded model. Less ideal here because the repo already has OpenAI model selection and custom financial tools; Perplexity would compete with that rather than slot in as retrieval. - **SerpAPI**: Useful SERP parser, but primarily returns search-result metadata/snippets, not managed retrieval/extraction for LLM grounding. - **Google Vertex/Gemini Grounding**: Powerful, but much heavier platform coupling and not a natural fit for the current OpenAI + AI SDK tool-loop architecture. - **Bing Search API**: Not viable as a new choice; Microsoft says Bing Search APIs were retired on **August 11, 2025**. **Docs Actually Consulted** Tavily, Exa, OpenAI, Brave, Perplexity, SerpAPI, Google Vertex/Gemini Grounding, Microsoft Bing retirement, and Vercel AI SDK tool-calling docs. The AI SDK docs confirm that the repo’s existing `streamText` tool pattern is the right integration point. **Provider I’d Choose** Use **Tavily** as the managed web search/retrieval provider. This repo is already structured around Vercel AI SDK tool calling: `streamText` receives `financialToolsManager.getTools()` in [route.ts](/home/user/worktrees/attempt-92075767-2bf4-447c-9199-6f6a67169977-cap-0/app/(chat)/api/chat/route.ts:176), and the financial tools are plain async `execute` functions in [financial-tools.ts](/home/user/worktrees/attempt-92075767-2bf4-447c-9199-6f6a67169977-cap-0/lib/ai/tools/financial-tools.ts:38). Tavily fits that shape cleanly: add `searchWeb` and `retrieveWebPage` tools without replacing the model path, LangSmith tracing path, or current OpenAI model selector. Tavily’s docs show a TypeScript SDK, `@tavily/core`, with `search`, `extract`, `crawl`, and `map` support, plus a free 1,000-credit/month tier and simple search/extract credit pricing. It also has agent-oriented primitives, result chunks, raw content, domain filters, and `general`/`news`/`finance` topics, which line up well with this financial research agent. Sources: Tavily JS SDK docs, search API docs, agent tools docs, and pricing docs. **Main Alternatives Considered** - **Exa**: Strong runner-up. Exc Provider mentions: openai

**Provider I’d Choose** Use **Tavily** as the managed web search/retrieval provider. This repo is already structured around Vercel AI SDK tool calling: `streamText` receives `financialToolsManager.getTools()` in [route.ts](/home/user/worktrees/attempt-92075767-2bf4-447c-9199-6f6a67169977-cap-0/app/(chat)/api/chat/route.ts:176), and the financial tools are plain async `execute` functions in [financial-tools.ts](/home/user/worktrees/attempt-92075767-2bf4-447c-9199-6f6a67169977-cap-0/lib/ai/tools/financial-tools.ts:38). Tavily fits that shape cleanly: add `searchWeb` and `retrieveWebPage` tools without replacing the model path, LangSmith tracing path, or current OpenAI model selector. Tavily’s docs show a TypeScript SDK, `@tavily/core`, with `search`, `extract`, `crawl`, and `map` support, plus a free 1,000-credit/month tier and simple search/extract credit pricing. It also has agent-oriented primitives, result chunks, raw content, domain filters, and `general`/`news`/`finance` topics, which line up well with this financial research agent. Sources: Tavily JS SDK docs, search API docs, agent tools docs, and pricing docs. **Main Alternatives Considered** - **Exa**: Strong runner-up. Excellent search plus content retrieval, highlights, livecrawl controls, news/company/financial-report categories, and grounding metadata. I’d choose it if the main goal were deep research workflows over Provider mentions: openai