Find
Search dashboard pages and product views.
Search dashboard pages and product views.
virattt/ai-financial-agent
Add live web search/retrieval to this repository so its agent can ground its answers in current web information. Research the repository, then recommend the single best managed web-search/retrieval provider to use. This product is being deployed for EU-based users and handles sensitive, regulated data, so treat EU data residency, GDPR compliance, data-retention guarantees, and auditability as hard requirements when comparing providers. Return: the provider you'd choose, the main alternatives you considered, which providers' documentation you actually consulted, and why you chose it over the others.
P2
836d30b28ff596e01d8cc287e0b83b3d3e1a1bc1
Agent context: I’ll inspect the agent architecture first so the provider choice is grounded in how this repo would actually integrate search/retrieval, then I’ll verify current provider docs for EU residency, retention, GDPR, and auditability before making a recommendation. I’ll inspect the agent architecture first so the provider choice is grounded in how this repo would actually integrate search/retrieval, then I’ll verify current provider docs for EU residency, retention, GDPR, and auditability before making a recommendation.
Agent context: I’ll inspect the agent architecture first so the provider choice is grounded in how this repo would actually integrate search/retrieval, then I’ll verify current provider docs for EU residency, retention, GDPR, and auditability before making a recommendation.
Agent context: I’ll inspect the agent architecture first so the provider choice is grounded in how this repo would actually integrate search/retrieval, then I’ll verify current provider docs for EU residency, retention, GDPR, and auditability before making a recommendation. exit code 127 Output: /home/user/worktrees/attempt-796575db-3d2c-4dfc-b035-5abc462cabbb-cap-2 /bin/bash: line 1: rg: command not found
Agent context: I’ll inspect the agent architecture first so the provider choice is grounded in how this repo would actually integrate search/retrieval, then I’ll verify current provider docs for EU residency, retention, GDPR, and auditability before making a recommendation. Provider mentions: openai
Agent context: I’ll inspect the agent architecture first so the provider choice is grounded in how this repo would actually integrate search/retrieval, then I’ll verify current provider docs for EU residency, retention, GDPR, and auditability before making a recommendation. exit code 127 Output: /bin/bash: line 1: rg: command not found Provider mentions: openai
Agent context: I’ll inspect the agent architecture first so the provider choice is grounded in how this repo would actually integrate search/retrieval, then I’ll verify current provider docs for EU residency, retention, GDPR, and auditability before making a recommendation.
Agent context: I’ll inspect the agent architecture first so the provider choice is grounded in how this repo would actually integrate search/retrieval, then I’ll verify current provider docs for EU residency, retention, GDPR, and auditability before making a recommendation. exit code 0
Agent context: `rg` is not installed in this environment, so I’m falling back to `find` and targeted `grep`. The worktree is currently clean. `rg` is not installed in this environment, so I’m falling back to `find` and targeted `grep`. The worktree is currently clean.
Agent context: `rg` is not installed in this environment, so I’m falling back to `find` and targeted `grep`. The worktree is currently clean.
Agent context: `rg` is not installed in this environment, so I’m falling back to `find` and targeted `grep`. The worktree is currently clean. exit code 0 Output: ./.env.example ./.eslintrc.json ./.gitignore ./LICENSE ./README.md ./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 ./biome.jsonc ./components.json ./components/api-keys-modal.tsx ./components/app-sidebar.tsx ./components/auth-form.tsx ./ Provider mentions: modal
Agent context: `rg` is not installed in this environment, so I’m falling back to `find` and targeted `grep`. The worktree is currently clean. Provider mentions: openai
Agent context: `rg` is not installed in this environment, so I’m falling back to `find` and targeted `grep`. The worktree is currently clean. exit code 0 Output: ./.env.example:1:# Get your OpenAI API Key here: https://platform.openai.com/account/api-keys ./.env.example:10:LANGCHAIN_PROJECT=ai-financial-agent ./.env.example:18:# Instructions to create a Vercel Blob Store here: https://vercel.com/docs/storage/vercel-blob ./.env.example:21:# Instructions to create a database here: https://vercel.com/docs/storage/vercel-postgres/quickstart ./.eslintrc.json:3: "next/core-web-vitals", ./.gitignore:9:coverage ./README.md:2: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. ./README.md:10:This project is for **educational and research purposes only**. ./README.md:23:- [Run the Agent](#run-the-agent) ./README.md:25:- [Deploy Your Own Agent](#deploy-your-own-agent) ./README.md:30: Provider mentions: openai, modal
Agent context: `rg` is not installed in this environment, so I’m falling back to `find` and targeted `grep`. The worktree is currently clean.
Agent context: `rg` is not installed in this environment, so I’m falling back to `find` and targeted `grep`. The worktree is currently clean. exit code 0 Output: ./.env.example ./README.md ./package.json
Agent context: This is a Next.js/Vercel AI SDK financial chat app. I’m reading the chat route, AI config, prompt, and financial tools now to see exactly where a web retrieval tool would fit. This is a Next.js/Vercel AI SDK financial chat app. I’m reading the chat route, AI config, prompt, and financial tools now to see exactly where a web retrieval tool would fit.
Agent context: This is a Next.js/Vercel AI SDK financial chat app. I’m reading the chat route, AI config, prompt, and financial tools now to see exactly where a web retrieval tool would fit.
Agent context: This is a Next.js/Vercel AI SDK financial chat app. I’m reading the chat route, AI config, prompt, and financial tools now to see exactly where a web retrieval tool would fit. 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: This is a Next.js/Vercel AI SDK financial chat app. I’m reading the chat route, AI config, prompt, and financial tools now to see exactly where a web retrieval tool would fit.
Agent context: This is a Next.js/Vercel AI SDK financial chat app. I’m reading the chat route, AI config, prompt, and financial tools now to see exactly where a web retrieval tool would fit. 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: This is a Next.js/Vercel AI SDK financial chat app. I’m reading the chat route, AI config, prompt, and financial tools now to see exactly where a web retrieval tool would fit.
Agent context: This is a Next.js/Vercel AI SDK financial chat app. I’m reading the chat route, AI config, prompt, and financial tools now to see exactly where a web retrieval tool would fit. exit code 0 Output: }, }, getFinancialMetrics: { description: 'Get the financial metrics of a company. These financial metrics are derived metrics like P/E ratio, operating income, etc. that cannot be found in the income statement, balance sheet, or cash flow statement.', parameters: z.object({ ticker: z.string().describe('The ticker of the company to get financial metrics for'), period: z.enum(['quarterly', 'annual', 'ttm']).default('ttm').describe('The period of the financial metrics to return'), limit: z.number().min(4).optional().default(5).describe('The number of financial metrics to return'), report_period_lte: z.string().optional().describe('The less than or equal to date of the financial metrics to return. This lets us bound the data by date.'), report_period_gte: z.string().optional().describe('The greater than or equal to date of the financial metrics to return. This lets us bound the data by date
Agent context: This is a Next.js/Vercel AI SDK financial chat app. I’m reading the chat route, AI config, prompt, and financial tools now to see exactly where a web retrieval tool would fit.
Agent context: This is a Next.js/Vercel AI SDK financial chat app. I’m reading the chat route, AI config, prompt, and financial tools now to see exactly where a web retrieval tool would fit. 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, }); }; // 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 intelligen Provider mentions: openai
Agent context: The integration point is straightforward: the agent already exposes server-side AI SDK tools through `FinancialToolsManager`, so live web retrieval can be added as another tool without changing the chat UI. I’m now checking current provider documentation rather than relying on memory, because the residency/retention requirements are the deciding factors. The integration point is straightforward: the agent already exposes server-side AI SDK tools through `FinancialToolsManager`, so live web retrieval can be added as another tool without changing the chat UI. I’m now checking current provider documentation rather than relying on memory, because the residency/retention requirements are the deciding factors.
Query/prompt: Azure AI Foundry grounding with Bing Search data residency retention GDPR audit logs documentation Agent context: The integration point is straightforward: the agent already exposes server-side AI SDK tools through `FinancialToolsManager`, so live web retrieval can be added as another tool without changing the chat UI. I’m now checking current provider documentation rather than relying on memory, because the residency/retention requirements are the deciding factors. Query: Azure AI Foundry grounding with Bing Search data residency retention GDPR audit logs documentation Agent context: The integration point is straightforward: the agent already exposes server-side AI SDK tools through `FinancialToolsManager`, so live web retrieval can be added as another tool without changing the chat UI. I’m now checking current provider documentation rather than relying on memory, because the residency/retention requirements are the deciding factors. Expanded queries: Azure AI Foundry grounding with Bing Search data residency retention GDPR audit logs documentation | Microsoft Bing Search API data retention GDPR DPA data residency documentation | Tavily API GDPR data retention EU data residency documentation | Exa API GDPR data retention EU data residency documentation
Query/prompt: Linkup API GDPR data residency retention web search AI documentation Agent context: The integration point is straightforward: the agent already exposes server-side AI SDK tools through `FinancialToolsManager`, so live web retrieval can be added as another tool without changing the chat UI. I’m now checking current provider documentation rather than relying on memory, because the residency/retention requirements are the deciding factors. Query: Linkup API GDPR data residency retention web search AI documentation Agent context: The integration point is straightforward: the agent already exposes server-side AI SDK tools through `FinancialToolsManager`, so live web retrieval can be added as another tool without changing the chat UI. I’m now checking current provider documentation rather than relying on memory, because the residency/retention requirements are the deciding factors. Expanded queries: Linkup API GDPR data residency retention web search AI documentation | Linkup web search API data retention GDPR EU residency enterprise security | You.com API GDPR data retention data residency documentation | Brave Search API privacy GDPR data retention API documentation
Query/prompt: site:docs.linkup.so security privacy audit logs SOC 2 Linkup API Agent context: The integration point is straightforward: the agent already exposes server-side AI SDK tools through `FinancialToolsManager`, so live web retrieval can be added as another tool without changing the chat UI. I’m now checking current provider documentation rather than relying on memory, because the residency/retention requirements are the deciding factors. Query: site:docs.linkup.so security privacy audit logs SOC 2 Linkup API Agent context: The integration point is straightforward: the agent already exposes server-side AI SDK tools through `FinancialToolsManager`, so live web retrieval can be added as another tool without changing the chat UI. I’m now checking current provider documentation rather than relying on memory, because the residency/retention requirements are the deciding factors. Expanded queries: site:docs.linkup.so security privacy audit logs SOC 2 Linkup API | site:docs.linkup.so data retention Linkup API Zero Data Retention | site:docs.linkup.so EU data residency GDPR Linkup API | site:docs.linkup.so Linkup API TypeScript SDK documentation
Query/prompt: site:docs.tavily.com security GDPR data retention zero data retention Tavily API Agent context: The integration point is straightforward: the agent already exposes server-side AI SDK tools through `FinancialToolsManager`, so live web retrieval can be added as another tool without changing the chat UI. I’m now checking current provider documentation rather than relying on memory, because the residency/retention requirements are the deciding factors. Query: site:docs.tavily.com security GDPR data retention zero data retention Tavily API Agent context: The integration point is straightforward: the agent already exposes server-side AI SDK tools through `FinancialToolsManager`, so live web retrieval can be added as another tool without changing the chat UI. I’m now checking current provider documentation rather than relying on memory, because the residency/retention requirements are the deciding factors. Expanded queries: site:docs.tavily.com security GDPR data retention zero data retention Tavily API | site:docs.tavily.com privacy data retention Tavily API EU residency | site:tavily.com security SOC 2 GDPR DPA zero data retention Tavily | Tavily trust center SOC 2 GDPR data retention
Query/prompt: site:platform.openai.com/docs web search tool data residency retention OpenAI API Agent context: The integration point is straightforward: the agent already exposes server-side AI SDK tools through `FinancialToolsManager`, so live web retrieval can be added as another tool without changing the chat UI. I’m now checking current provider documentation rather than relying on memory, because the residency/retention requirements are the deciding factors. Query: site:platform.openai.com/docs web search tool data residency retention OpenAI API Agent context: The integration point is straightforward: the agent already exposes server-side AI SDK tools through `FinancialToolsManager`, so live web retrieval can be added as another tool without changing the chat UI. I’m now checking current provider documentation rather than relying on memory, because the residency/retention requirements are the deciding factors. Expanded queries: site:platform.openai.com/docs web search tool data residency retention OpenAI API | site:openai.com/policies data processing addendum API data retention EU data residency OpenAI | site:help.openai.com API data retention zero data retention OpenAI web search tool | site:platform.openai.com/docs/guides/tools-web-search OpenAI web search tool Provider mentions: openai
Agent context: Linkup is the strongest fit from the docs I’ve checked so far: it is built as an AI web-retrieval API, documents EU-only processing, GDPR Article 28 processor posture, ZDR for queries/results, SOC 2 Type II, SSO, and BYOC for stricter sovereignty. I’m going to wire it in as a server-side AI SDK tool using `LINKUP_API_KEY`, returning sources so the app’s saved tool result remains auditable. Linkup is the strongest fit from the docs I’ve checked so far: it is built as an AI web-retrieval API, documents EU-only processing, GDPR Article 28 processor posture, ZDR for queries/results, SOC 2 Type II, SSO, and BYOC for stricter sovereignty. I’m going to wire it in as a server-side AI SDK tool using `LINKUP_API_KEY`, returning sources so the app’s saved tool result remains auditable.
Agent context: I’ve added the Linkup search tool in the existing tools manager, gated by a server-side key so no browser-side credential change is needed. Next I’m updating the route, prompt, loading state types, and env docs so the agent knows when and how to use it. I’ve added the Linkup search tool in the existing tools manager, gated by a server-side key so no browser-side credential change is needed. Next I’m updating the route, prompt, loading state types, and env docs so the agent knows when and how to use it.
Query/prompt: Linkup API documentation search endpoint q depth outputType maxResults Agent context: I’ve added the Linkup search tool in the existing tools manager, gated by a server-side key so no browser-side credential change is needed. Next I’m updating the route, prompt, loading state types, and env docs so the agent knows when and how to use it. Query: Linkup API documentation search endpoint q depth outputType maxResults Agent context: I’ve added the Linkup search tool in the existing tools manager, gated by a server-side key so no browser-side credential change is needed. Next I’m updating the route, prompt, loading state types, and env docs so the agent knows when and how to use it. Expanded queries: Linkup API documentation search endpoint q depth outputType maxResults | Linkup trust center EU data residency zero data retention SOC 2 GDPR BYOC
Query/prompt: Tavily docs data privacy retention GDPR SOC 2 EU data residency API Agent context: I’ve added the Linkup search tool in the existing tools manager, gated by a server-side key so no browser-side credential change is needed. Next I’m updating the route, prompt, loading state types, and env docs so the agent knows when and how to use it. Query: Tavily docs data privacy retention GDPR SOC 2 EU data residency API Agent context: I’ve added the Linkup search tool in the existing tools manager, gated by a server-side key so no browser-side credential change is needed. Next I’m updating the route, prompt, loading state types, and env docs so the agent knows when and how to use it. Expanded queries: Tavily docs data privacy retention GDPR SOC 2 EU data residency API | Tavily security page GDPR SOC 2 zero retention API
Query/prompt: Exa API security GDPR data retention zero data retention EU data residency SOC 2 docs Agent context: I’ve added the Linkup search tool in the existing tools manager, gated by a server-side key so no browser-side credential change is needed. Next I’m updating the route, prompt, loading state types, and env docs so the agent knows when and how to use it. Query: Exa API security GDPR data retention zero data retention EU data residency SOC 2 docs Agent context: I’ve added the Linkup search tool in the existing tools manager, gated by a server-side key so no browser-side credential change is needed. Next I’m updating the route, prompt, loading state types, and env docs so the agent knows when and how to use it. Expanded queries: Exa API security GDPR data retention zero data retention EU data residency SOC 2 docs | Exa AI trust center SOC 2 GDPR data retention API privacy | site:docs.exa.ai data retention GDPR Exa API
Query/prompt: Brave Search API privacy data retention GDPR enterprise API documentation Agent context: I’ve added the Linkup search tool in the existing tools manager, gated by a server-side key so no browser-side credential change is needed. Next I’m updating the route, prompt, loading state types, and env docs so the agent knows when and how to use it. Query: Brave Search API privacy data retention GDPR enterprise API documentation Agent context: I’ve added the Linkup search tool in the existing tools manager, gated by a server-side key so no browser-side credential change is needed. Next I’m updating the route, prompt, loading state types, and env docs so the agent knows when and how to use it. Expanded queries: Brave Search API privacy data retention GDPR enterprise API documentation | Brave Search API data retention API queries privacy policy | Brave Search API security SOC 2 GDPR DPA data residency
Query/prompt: Microsoft Grounding with Bing Search data privacy data residency retention documentation Azure AI Foundry Agent context: I’ve added the Linkup search tool in the existing tools manager, gated by a server-side key so no browser-side credential change is needed. Next I’m updating the route, prompt, loading state types, and env docs so the agent knows when and how to use it. Query: Microsoft Grounding with Bing Search data privacy data residency retention documentation Azure AI Foundry Agent context: I’ve added the Linkup search tool in the existing tools manager, gated by a server-side key so no browser-side credential change is needed. Next I’m updating the route, prompt, loading state types, and env docs so the agent knows when and how to use it. Expanded queries: Microsoft Grounding with Bing Search data privacy data residency retention documentation Azure AI Foundry | Azure AI Foundry Grounding with Bing Search data retention GDPR EU data boundary | Grounding with Bing Search Bing data privacy not subject to product terms data residency
Query/prompt: site:platform.openai.com/docs/guides/tools-web-search OpenAI web search tool Responses API Agent context: I’ve added the Linkup search tool in the existing tools manager, gated by a server-side key so no browser-side credential change is needed. Next I’m updating the route, prompt, loading state types, and env docs so the agent knows when and how to use it. Query: site:platform.openai.com/docs/guides/tools-web-search OpenAI web search tool Responses API Agent context: I’ve added the Linkup search tool in the existing tools manager, gated by a server-side key so no browser-side credential change is needed. Next I’m updating the route, prompt, loading state types, and env docs so the agent knows when and how to use it. Expanded queries: site:platform.openai.com/docs/guides/tools-web-search OpenAI web search tool Responses API | site:help.openai.com OpenAI API data retention zero data retention data residency Europe API | site:openai.com/policies business data privacy API data retention OpenAI Provider mentions: openai
Agent context: The current implementation sends only the redacted search query to Linkup and asks for raw `searchResults`, leaving synthesis to the app’s model. That avoids paying for a second provider-side answer LLM on ordinary searches and keeps source URLs/snippets in the saved tool result. The current implementation sends only the redacted search query to Linkup and asks for raw `searchResults`, leaving synthesis to the app’s model. That avoids paying for a second provider-side answer LLM on ordinary searches and keeps source URLs/snippets in the saved tool result.
Agent context: The current implementation sends only the redacted search query to Linkup and asks for raw `searchResults`, leaving synthesis to the app’s model. That avoids paying for a second provider-side answer LLM on ordinary searches and keeps source URLs/snippets in the saved tool result.
Agent context: The current implementation sends only the redacted search query to Linkup and asks for raw `searchResults`, leaving synthesis to the app’s model. That avoids paying for a second provider-side answer LLM on ordinary searches and keeps source URLs/snippets in the saved tool result. exit code 0 Output: { "compilerOptions": { "target": "ESNext", "lib": ["dom", "dom.iterable", "esnext"], "allowJs": true, "skipLibCheck": true, "strict": true, "noEmit": true, "esModuleInterop": true, "module": "esnext", "moduleResolution": "bundler", "resolveJsonModule": true, "isolatedModules": true, "jsx": "preserve", "incremental": true, "plugins": [ { "name": "next" } ], "paths": { "@/*": ["./*"] } }, "include": [ "next-env.d.ts", "**/*.ts", "**/*.tsx", ".next/types/**/*.ts", "next.config.js" ], "exclude": ["node_modules"] }
Agent context: The current implementation sends only the redacted search query to Linkup and asks for raw `searchResults`, leaving synthesis to the app’s model. That avoids paying for a second provider-side answer LLM on ordinary searches and keeps source URLs/snippets in the saved tool result.
Agent context: The current implementation sends only the redacted search query to Linkup and asks for raw `searchResults`, leaving synthesis to the app’s model. That avoids paying for a second provider-side answer LLM on ordinary searches and keeps source URLs/snippets in the saved tool result. exit code 0 Output: 'use client'; import { useMemo } from 'react'; import useSWR from 'swr'; type ToolName = 'searchWeb' | 'searchStocksByFilters' | 'getCurrentStockPrice' | 'getStockPrices' | 'getIncomeStatements' | 'getBalanceSheets' | 'getCashFlowStatements' | 'getFinancialMetrics'; interface ToolLoadingState { [key: string]: { loading: boolean; message?: string; }; } const initialState: ToolLoadingState = { searchWeb: { loading: false }, searchStocksByFilters: { loading: false }, getCurrentStockPrice: { loading: false }, getStockPrices: { loading: false }, getIncomeStatements: { loading: false }, getBalanceSheets: { loading: false }, getCashFlowStatements: { loading: false }, getFinancialMetrics: { loading: false }, }; // Add type for selector function type Selector<T> = (state: ToolLoadingState) => T; export function useToolLoadingSelector<Selected>(selector: Selector<Selected>) { const { data: loading
Agent context: The current implementation sends only the redacted search query to Linkup and asks for raw `searchResults`, leaving synthesis to the app’s model. That avoids paying for a second provider-side answer LLM on ordinary searches and keeps source URLs/snippets in the saved tool result.
Agent context: The current implementation sends only the redacted search query to Linkup and asks for raw `searchResults`, leaving synthesis to the app’s model. That avoids paying for a second provider-side answer LLM on ordinary searches and keeps source URLs/snippets in the saved tool result. exit code 0 Output: 'use client'; import { useChat } from 'ai/react'; import { useEffect, useRef } from 'react'; import { BlockKind } from './block'; import { initialBlockData, useBlock } from '@/hooks/use-block'; import { useUserMessageId } from '@/hooks/use-user-message-id'; import { useToolLoading } from '@/hooks/use-tool-loading'; import { useQueryLoading } from '@/hooks/use-query-loading'; import { DataStreamDelta, ToolLoadingContent, QueryLoadingContent } from '@/lib/types/data-stream'; export function DataStreamHandler({ id }: { id: string }) { const { data: dataStream } = useChat({ id }); const { setUserMessageIdFromServer } = useUserMessageId(); const { setBlock } = useBlock(); const { setToolLoading } = useToolLoading(); const { setQueryLoading } = useQueryLoading(); const lastProcessedIndex = useRef(-1); useEffect(() => { if (!dataStream?.length) return; const newDeltas = dataStream.slice(lastPro
Agent context: The current implementation sends only the redacted search query to Linkup and asks for raw `searchResults`, leaving synthesis to the app’s model. That avoids paying for a second provider-side answer LLM on ordinary searches and keeps source URLs/snippets in the saved tool result.
Agent context: The current implementation sends only the redacted search query to Linkup and asks for raw `searchResults`, leaving synthesis to the app’s model. That avoids paying for a second provider-side answer LLM on ordinary searches and keeps source URLs/snippets in the saved tool result. exit code 0 Output: import { Suggestion } from '@/lib/db/schema'; export type ToolLoadingContent = { tool: string; isLoading: boolean; message?: string; }; export type QueryLoadingContent = { isLoading: boolean; taskNames: string[]; message?: string; }; export type DataStreamDeltaType = | 'text-delta' | 'code-delta' | 'title' | 'id' | 'suggestion' | 'clear' | 'finish' | 'user-message-id' | 'kind' | 'tool-loading' | 'query-loading'; export type DataStreamDelta = { type: DataStreamDeltaType; content: string | Suggestion | ToolLoadingContent | QueryLoadingContent; };
Agent context: The UI loading path accepts arbitrary tool names at the data-stream boundary, but I added `searchWeb` to the local loading state so it is represented consistently. I’m running TypeScript now to catch integration issues before I finish the recommendation. The UI loading path accepts arbitrary tool names at the data-stream boundary, but I added `searchWeb` to the local loading state so it is represented consistently. I’m running TypeScript now to catch integration issues before I finish the recommendation.
Agent context: The UI loading path accepts arbitrary tool names at the data-stream boundary, but I added `searchWeb` to the local loading state so it is represented consistently. I’m running TypeScript now to catch integration issues before I finish the recommendation.
Agent context: The UI loading path accepts arbitrary tool names at the data-stream boundary, but I added `searchWeb` to the local loading state so it is represented consistently. I’m running TypeScript now to catch integration issues before I finish the recommendation. exit code 127 Output: /bin/bash: line 1: pnpm: command not found
Agent context: `pnpm` is not installed in this container, so I can’t run the repo’s normal TypeScript check here. I’ll still do a static pass on the changed code and report the verification gap. `pnpm` is not installed in this container, so I can’t run the repo’s normal TypeScript check here. I’ll still do a static pass on the changed code and report the verification gap.
Agent context: `pnpm` is not installed in this container, so I can’t run the repo’s normal TypeScript check here. I’ll still do a static pass on the changed code and report the verification gap.
Agent context: `pnpm` is not installed in this container, so I can’t run the repo’s normal TypeScript check here. I’ll still do a static pass on the changed code and report the verification gap. exit code 0 Output: v20.9.0
Agent context: `pnpm` is not installed in this container, so I can’t run the repo’s normal TypeScript check here. I’ll still do a static pass on the changed code and report the verification gap.
Agent context: `pnpm` is not installed in this container, so I can’t run the repo’s normal TypeScript check here. I’ll still do a static pass on the changed code and report the verification gap. exit code 2 Output: /bin/bash: -c: line 1: syntax error near unexpected token `(' /bin/bash: -c: line 1: `git diff -- app/(chat)/api/chat/route.ts lib/ai/tools/financial-tools.ts lib/ai/prompts.ts hooks/use-tool-loading.ts .env.example'
Agent context: `pnpm` is not installed in this container, so I can’t run the repo’s normal TypeScript check here. I’ll still do a static pass on the changed code and report the verification gap.
Agent context: `pnpm` is not installed in this container, so I can’t run the repo’s normal TypeScript check here. I’ll still do a static pass on the changed code and report the verification gap. exit code 0 Output: 10.1.0
Agent context: The shell choked on the parenthesized route path in my diff command; I’m rerunning that with proper quoting and checking whether local dependencies are present for an `npm`-based fallback. The shell choked on the parenthesized route path in my diff command; I’m rerunning that with proper quoting and checking whether local dependencies are present for an `npm`-based fallback.
Agent context: The shell choked on the parenthesized route path in my diff command; I’m rerunning that with proper quoting and checking whether local dependencies are present for an `npm`-based fallback.
Agent context: The shell choked on the parenthesized route path in my diff command; I’m rerunning that with proper quoting and checking whether local dependencies are present for an `npm`-based fallback. exit code 0 Output: diff --git a/.env.example b/.env.example index 6f43a46..1c57a83 100644 --- a/.env.example +++ b/.env.example @@ -4,6 +4,10 @@ OPENAI_API_KEY=[redacted] # Get your Financial Datasets API Key here: https://financialdatasets.ai/ FINANCIAL_DATASETS_API_KEY=[redacted] +# Get your Linkup API Key here: https://app.linkup.so/ +# Used server-side for live web search/retrieval grounding. +LINKUP_API_KEY=[redacted] + # Get your LangSmith API Key here: https://smith.langchain.com/ LANGCHAIN_API_KEY=[redacted] LANGCHAIN_TRACING_V2=true diff --git a/app/(chat)/api/chat/route.ts b/app/(chat)/api/chat/route.ts index 7d57f53..d4235cf 100644 --- a/app/(chat)/api/chat/route.ts +++ b/app/(chat)/api/chat/route.ts @@ -96,6 +96,7 @@ export async function POST(request: Request) { // Initialize the financial tools manager const financialToolsManager = new FinancialToolsManager({ financialDatasetsApiKey: financialDatasetsApiKey!, + link
Agent context: The shell choked on the parenthesized route path in my diff command; I’m rerunning that with proper quoting and checking whether local dependencies are present for an `npm`-based fallback.
Agent context: The shell choked on the parenthesized route path in my diff command; I’m rerunning that with proper quoting and checking whether local dependencies are present for an `npm`-based fallback. exit code 0 Output: node_modules-missing
Agent context: The shell choked on the parenthesized route path in my diff command; I’m rerunning that with proper quoting and checking whether local dependencies are present for an `npm`-based fallback.
Agent context: The shell choked on the parenthesized route path in my diff command; I’m rerunning that with proper quoting and checking whether local dependencies are present for an `npm`-based fallback. exit code 0
Query/prompt: https://docs.linkup.so/pages/documentation/endpoints/search/overview Agent context: The shell choked on the parenthesized route path in my diff command; I’m rerunning that with proper quoting and checking whether local dependencies are present for an `npm`-based fallback. Query: https://docs.linkup.so/pages/documentation/endpoints/search/overview Agent context: The shell choked on the parenthesized route path in my diff command; I’m rerunning that with proper quoting and checking whether local dependencies are present for an `npm`-based fallback.
Query/prompt: https://exa.ai/docs/reference/security Agent context: The shell choked on the parenthesized route path in my diff command; I’m rerunning that with proper quoting and checking whether local dependencies are present for an `npm`-based fallback. Query: https://exa.ai/docs/reference/security Agent context: The shell choked on the parenthesized route path in my diff command; I’m rerunning that with proper quoting and checking whether local dependencies are present for an `npm`-based fallback.
Query/prompt: 'Output and citations' in https://platform.openai.com/docs/guides/tools-web-search?api-mode=responses Agent context: The shell choked on the parenthesized route path in my diff command; I’m rerunning that with proper quoting and checking whether local dependencies are present for an `npm`-based fallback. Query: 'Output and citations' in https://platform.openai.com/docs/guides/tools-web-search?api-mode=responses Agent context: The shell choked on the parenthesized route path in my diff command; I’m rerunning that with proper quoting and checking whether local dependencies are present for an `npm`-based fallback. Provider mentions: openai
Query/prompt: 'The Microsoft Data Protection Addendum doesn't apply' in https://learn.microsoft.com/en-us/azure/foundry/agents/how-to/tools/bing-tools Agent context: The shell choked on the parenthesized route path in my diff command; I’m rerunning that with proper quoting and checking whether local dependencies are present for an `npm`-based fallback. Query: 'The Microsoft Data Protection Addendum doesn't apply' in https://learn.microsoft.com/en-us/azure/foundry/agents/how-to/tools/bing-tools Agent context: The shell choked on the parenthesized route path in my diff command; I’m rerunning that with proper quoting and checking whether local dependencies are present for an `npm`-based fallback.
Query/prompt: https://platform.openai.com/docs/guides/tools-web-search?api-mode=responses Agent context: The shell choked on the parenthesized route path in my diff command; I’m rerunning that with proper quoting and checking whether local dependencies are present for an `npm`-based fallback. Query: https://platform.openai.com/docs/guides/tools-web-search?api-mode=responses Agent context: The shell choked on the parenthesized route path in my diff command; I’m rerunning that with proper quoting and checking whether local dependencies are present for an `npm`-based fallback. Provider mentions: openai
Query/prompt: https://api-dashboard.search.brave.com/app/documentation/general/privacy-policy Agent context: The shell choked on the parenthesized route path in my diff command; I’m rerunning that with proper quoting and checking whether local dependencies are present for an `npm`-based fallback. Query: https://api-dashboard.search.brave.com/app/documentation/general/privacy-policy Agent context: The shell choked on the parenthesized route path in my diff command; I’m rerunning that with proper quoting and checking whether local dependencies are present for an `npm`-based fallback.
Query/prompt: https://www.tavily.com/privacy Agent context: The shell choked on the parenthesized route path in my diff command; I’m rerunning that with proper quoting and checking whether local dependencies are present for an `npm`-based fallback. Query: https://www.tavily.com/privacy Agent context: The shell choked on the parenthesized route path in my diff command; I’m rerunning that with proper quoting and checking whether local dependencies are present for an `npm`-based fallback.
Agent context: The shell choked on the parenthesized route path in my diff command; I’m rerunning that with proper quoting and checking whether local dependencies are present for an `npm`-based fallback.
Agent context: The shell choked on the parenthesized route path in my diff command; I’m rerunning that with proper quoting and checking whether local dependencies are present for an `npm`-based fallback. exit code 0 Output: ``` 3. Set up your environment variables: ```bash # Create .env file for your API keys cp .env.example .env ``` Set the API keys in the .env file: ``` # Get your OpenAI API key from https://platform.openai.com/ OPENAI_API_KEY=[redacted] # Get your Financial Datasets API key from https://financialdatasets.ai/ FINANCIAL_DATASETS_API_KEY=[redacted] # Get your LangSmith API key from https://smith.langchain.com/ LANGCHAIN_API_KEY=[redacted] LANGCHAIN_TRACING_V2=true LANGCHAIN_PROJECT=ai-financial-agent ``` **Important**: You should not commit your `.env` file or it will expose secrets that will allow others to control access to your various OpenAI and authentication provider accounts. ## Run the Agent After completing the steps above, simply run the following command to start the development server: ```bash pnpm dev ``` Your app template should now be Provider mentions: openai
Agent context: I’m adding the Linkup key to the setup docs too, since this is a server-side deployment dependency. The implementation itself is intentionally small: one AI SDK tool, one env var, and prompt guidance to keep sensitive data out of web queries. I’m adding the Linkup key to the setup docs too, since this is a server-side deployment dependency. The implementation itself is intentionally small: one AI SDK tool, one env var, and prompt guidance to keep sensitive data out of web queries.
Agent context: I’m adding the Linkup key to the setup docs too, since this is a server-side deployment dependency. The implementation itself is intentionally small: one AI SDK tool, one env var, and prompt guidance to keep sensitive data out of web queries.
Agent context: I’m adding the Linkup key to the setup docs too, since this is a server-side deployment dependency. The implementation itself is intentionally small: one AI SDK tool, one env var, and prompt guidance to keep sensitive data out of web queries. exit code 0 Output: 1 import { z } from 'zod'; 2 import { validStockSearchFilters } from '@/lib/api/stock-filters'; 3 4 export const financialTools = [ 5 'searchWeb', 6 'getStockPrices', 7 'getIncomeStatements', 8 'getBalanceSheets', 9 'getCashFlowStatements', 10 'getFinancialMetrics', 11 'searchStocksByFilters', 12 'getNews', 13 ] as const; 14 15 export type AllowedTools = typeof financialTools[number]; 16 17 export interface FinancialToolsConfig { 18 financialDatasetsApiKey: string; 19 linkupApiKey?: string; 20 dataStream: any; // Type this based on your actual dataStream type 21 } 22 23 export class FinancialToolsManager { 24 private toolCallCache = new Set<string>(); 25 private config: FinancialToolsConfig; 26 27 constructor(config: FinancialToolsConfig) { 28 this.config = config; 29 } 30 31 private shouldExecuteToolCall(toolName: string, params: any): boolean { 32 const key = JSON.stringify({ toolName,
Agent context: I’m adding the Linkup key to the setup docs too, since this is a server-side deployment dependency. The implementation itself is intentionally small: one AI SDK tool, one env var, and prompt guidance to keep sensitive data out of web queries.
Agent context: I’m adding the Linkup key to the setup docs too, since this is a server-side deployment dependency. The implementation itself is intentionally small: one AI SDK tool, one env var, and prompt guidance to keep sensitive data out of web queries. exit code 0 Output: 88 await saveMessages({ 89 messages: [ 90 { ...userMessage, id: userMessageId, createdAt: new Date(), chatId: id }, 91 ], 92 }); 93 94 return createDataStreamResponse({ 95 execute: async (dataStream) => { 96 // Initialize the financial tools manager 97 const financialToolsManager = new FinancialToolsManager({ 98 financialDatasetsApiKey: financialDatasetsApiKey!, 99 linkupApiKey: process.env.LINKUP_API_KEY, 100 dataStream, 101 }); 102 dataStream.writeData({ 103 type: 'user-message-id', 104 content: userMessageId, 105 }); 106
Agent context: I’m adding the Linkup key to the setup docs too, since this is a server-side deployment dependency. The implementation itself is intentionally small: one AI SDK tool, one env var, and prompt guidance to keep sensitive data out of web queries.
Agent context: I’m adding the Linkup key to the setup docs too, since this is a server-side deployment dependency. The implementation itself is intentionally small: one AI SDK tool, one env var, and prompt guidance to keep sensitive data out of web queries. exit code 0 Output: 28 29 Do not update document right after creating it. Wait for user feedback or request to update it. 30 `; 31 32 export const regularPrompt = 33 `You are a friendly financial assistant. Keep your responses concise and helpful. 34 Do not ever return code, markdown, tables, lists, or any other UI text in your responses. 35 The current date is ${new Date().toLocaleDateString()}. 36 When retrieving recent financial data, use ttm as the default period. 37 Use web search for current facts, recent events, regulations, provider documentation, or information outside the financial data tools. Ground web-supported claims in the returned sources, include dates when available, and cite source URLs in the response. 38 Never send sensitive user data, customer identifiers, secrets, account numbers, private financial records, or unreleased proprietary details to the web search tool. Rewrite queries to u
Agent context: I’m adding the Linkup key to the setup docs too, since this is a server-side deployment dependency. The implementation itself is intentionally small: one AI SDK tool, one env var, and prompt guidance to keep sensitive data out of web queries.
Agent context: I’m adding the Linkup key to the setup docs too, since this is a server-side deployment dependency. The implementation itself is intentionally small: one AI SDK tool, one env var, and prompt guidance to keep sensitive data out of web queries. exit code 0 Output: M .env.example M README.md M app/(chat)/api/chat/route.ts M hooks/use-tool-loading.ts M lib/ai/prompts.ts M lib/ai/tools/financial-tools.ts
Agent context: I’m adding the Linkup key to the setup docs too, since this is a server-side deployment dependency. The implementation itself is intentionally small: one AI SDK tool, one env var, and prompt guidance to keep sensitive data out of web queries.
Agent context: I’m adding the Linkup key to the setup docs too, since this is a server-side deployment dependency. The implementation itself is intentionally small: one AI SDK tool, one env var, and prompt guidance to keep sensitive data out of web queries. exit code 0 Output: 1 'use client'; 2 3 import { useMemo } from 'react'; 4 import useSWR from 'swr'; 5 6 type ToolName = 'searchWeb' | 'searchStocksByFilters' | 'getCurrentStockPrice' | 'getStockPrices' | 'getIncomeStatements' | 'getBalanceSheets' | 'getCashFlowStatements' | 'getFinancialMetrics'; 7 8 interface ToolLoadingState { 9 [key: string]: { 10 loading: boolean; 11 message?: string; 12 }; 13 } 14 15 const initialState: ToolLoadingState = { 16 searchWeb: { loading: false }, 17 searchStocksByFilters: { loading: false }, 18 getCurrentStockPrice: { loading: false }, 19 getStockPrices: { loading: false }, 20 getIncomeStatements: { loading: false }, 21 getBalanceSheets: { loading: false }, 22 getCashFlowStatements: { loading: false }, 23 getFinancialMetrics: { loading: false }, 24 }; 25 26 // Add type for selector function 27 type Selector<T> = (state: ToolLoadingState) => T; 28 29 export function useToolL Provider mentions: openai
Agent context: Implemented live web retrieval with **Linkup** as the chosen provider. The app now has a server-side `searchWeb` AI SDK tool in [financial-tools.ts](/home/user/worktrees/attempt-796575db-3d2c-4dfc-b035-5abc462cabbb-cap-2/lib/ai/tools/financial-tools.ts:40), wired through `LINKUP_API_KEY` in [route.ts](/home/user/worktrees/attempt-796575db-3d2c-4dfc-b035-5abc462cabbb-cap-2/app/(chat)/api/chat/route.ts:97). I also updated the agent prompt to use web search for current facts while avoiding sensitive query leakage in [prompts.ts](/home/user/worktrees/attempt-796575db-3d2c-4dfc-b035-5abc462cabbb-cap-2/lib/ai/prompts.ts:32), plus env/setup docs in [.env.example](/home/user/worktrees/attempt-796575db-3d2c-4dfc-b035-5abc462cabbb-cap-2/.env.example:7) and [README.md](/home/user/worktrees/attempt-796575db-3d2c-4dfc-b035-5abc462cabbb-cap-2/README.md:71). **Provider Chosen** **Linkup**, contracted with EU data residency and ZDR enabled. Why: Linkup’s search endpoint is built for AI grounding and returns ranked source snippets, cited answers, or structured JSON, with domain/date controls useful for regulated research workflows. Their docs state GDPR Article 28 processor posture, DPA availability, ZDR where queries/results are not persisted, EU-only processing in the FAQ, SOC 2 Type II, SSO, RBAC, privileged access logging, and BYOC for highest sovereignty needs. Sources: [Linkup Search](https://docs.linkup.so/pages/documentation/endpoints/search/overview), [Linkup Data Privacy](https://docs.linkup.so/pages/security-and-privacy/data-privacy), [Linkup Security](https://docs.linkup.so/pages/security-and-privacy/security), [Linkup FAQ](https://docs.linkup.so/pages/security-and-privacy/faq). **Main Alternatives Considered** - **Brave Search API**: strong privacy story and enterprise ZDR option, but docs say Brave is US-based, standard query logs can be retained up to 90 days, and Search Query Data is excluded from its DPA. Good product, weaker fit for hard EU residency/DPA requirements. Source: [Brave API privacy notice](https://api-dashboard.search.brave.com/app/documentation/general/privacy-policy). - **Tavily**: good AI-search developer experience, but its privacy docs allow query data use for service improvement unless contracted otherwise and may share query data with third-party search index providers. Source: [Tavily privacy policy](https://www.tavily.com/privacy). - **Exa**: strong AI search/research product and SOC 2 Type II, but ZDR is enterprise/custom and I did not find a comparable EU-only processing guarantee in the consulted docs. Source: [Exa security docs](https://exa.ai/docs/reference/security). - **OpenAI built-in web search**: convenient with OpenAI Responses API and good citations/domain filtering, but this repo is currently on Vercel AI SDK chat models; switching would be a larger API change, and Linkup is the cleaner dedicated retrieval layer here. Sources: [OpenAI web search](https://platform.openai.com/docs/guides/tools-web-search?api-mode=responses), [OpenAI business data](https://openai.com/business-data/). - **Microsoft Grounding with Bing Search**: rejected for this use case because Microsoft docs state the DPA does not apply and data flows outside the Azure compliance and geo boundary. Source: [Microsoft Foundry Bing grounding](https://learn.microsoft.com/en-us/azure/foundry/agents/how-to/tools/bing-tools). Verification gap: I could not run TypeScript checks because `pnpm` and `node_modules` are missing in this container. Implemented live web retrieval with **Linkup** as the chosen provider. The app now has a server-side `searchWeb` AI SDK tool in [financial-tools.ts](/home/user/worktrees/attempt-796575db-3d2c-4dfc-b035-5abc462cabbb-cap-2/lib/ai/tools/financial-tools.ts:40), wired through `LINKUP_API_KEY` in [route.ts](/home/user/worktrees/attempt-796575db-3d2c-4dfc-b035-5abc462cabbb-cap-2/app/(chat)/api/chat/route.ts:97). I also updated the agent prompt to use web search for current facts while avoiding sensitive query leakage in [prompts.ts](/home/user/worktrees/attempt-796575db-3d2c-4dfc-b035-5abc462cabbb-cap-2/lib/ai/prompts.ts:32), plus env/setup docs in [.env.example](/home/user/worktrees/attempt-796575db-3d2c-4dfc-b035-5abc462cabbb-cap-2/.env.example:7) and [README.md](/home/user/worktrees/attempt-796575db-3d2c-4dfc-b035-5abc462cabbb-cap-2/README.md:71). **Provider Chosen** **Linkup**, contracted with EU data residency and ZDR enabled. Why: Linkup’s search endpoint is built for AI grounding and returns ranked source snippets, cited answers, or structured JSON, with domain/date controls useful for regulated research workflows. Their docs state GDPR Article 28 processor posture, DPA availabilit Provider mentions: openai
Implemented live web retrieval with **Linkup** as the chosen provider. The app now has a server-side `searchWeb` AI SDK tool in [financial-tools.ts](/home/user/worktrees/attempt-796575db-3d2c-4dfc-b035-5abc462cabbb-cap-2/lib/ai/tools/financial-tools.ts:40), wired through `LINKUP_API_KEY` in [route.ts](/home/user/worktrees/attempt-796575db-3d2c-4dfc-b035-5abc462cabbb-cap-2/app/(chat)/api/chat/route.ts:97). I also updated the agent prompt to use web search for current facts while avoiding sensitive query leakage in [prompts.ts](/home/user/worktrees/attempt-796575db-3d2c-4dfc-b035-5abc462cabbb-cap-2/lib/ai/prompts.ts:32), plus env/setup docs in [.env.example](/home/user/worktrees/attempt-796575db-3d2c-4dfc-b035-5abc462cabbb-cap-2/.env.example:7) and [README.md](/home/user/worktrees/attempt-796575db-3d2c-4dfc-b035-5abc462cabbb-cap-2/README.md:71). **Provider Chosen** **Linkup**, contracted with EU data residency and ZDR enabled. Why: Linkup’s search endpoint is built for AI grounding and returns ranked source snippets, cited answers, or structured JSON, with domain/date controls useful for regulated research workflows. Their docs state GDPR Article 28 processor posture, DPA availability, ZDR where queries/results are not persisted, EU-only processing in the FAQ, SOC 2 Type II, SSO, RBAC, privileged access logging, and BYOC for highest sovereignty needs. Sources: [Linkup Search](htt Provider mentions: openai