Run

P3

836d30b28ff596e01d8cc287e0b83b3d3e1a1bc1

Agent context: I’ll inspect the repo first to see what agent stack and integration points it already has, then I’ll verify provider docs and make a single recommendation grounded in that context. I’ll inspect the repo first to see what agent stack and integration points it already has, then I’ll verify provider docs and make a single recommendation grounded in that context.

Agent context: I’ll inspect the repo first to see what agent stack and integration points it already has, then I’ll verify provider docs and make a single recommendation grounded in that context.

Agent context: I’ll inspect the repo first to see what agent stack and integration points it already has, then I’ll verify provider docs and make a single recommendation grounded in that context. exit code 0 Output: /home/user/worktrees/attempt-366d0d50-5831-4685-93fc-59b6076ffdb8-cap-3 /bin/bash: line 1: rg: command not found

Agent context: I’ll inspect the repo first to see what agent stack and integration points it already has, then I’ll verify provider docs and make a single recommendation grounded in that context. Provider mentions: openai

Agent context: I’ll inspect the repo first to see what agent stack and integration points it already has, then I’ll verify provider docs and make a single recommendation grounded in that context. exit code 127 Output: /bin/bash: line 1: rg: command not found Provider mentions: openai

Agent context: I’ll inspect the repo first to see what agent stack and integration points it already has, then I’ll verify provider docs and make a single recommendation grounded in that context.

Agent context: I’ll inspect the repo first to see what agent stack and integration points it already has, then I’ll verify provider docs and make a single recommendation grounded in that context. exit code 0 Output: ./.env.example ./README.md ./package.json

Agent context: `rg` is not installed in this workspace, so I’m falling back to `find`/`grep`. The repo looks small so that should still be quick. `rg` is not installed in this workspace, so I’m falling back to `find`/`grep`. The repo looks small so that should still be quick.

Agent context: `rg` is not installed in this workspace, so I’m falling back to `find`/`grep`. The repo looks small so that should still be quick.

Agent context: `rg` is not installed in this workspace, so I’m falling back to `find`/`grep`. The repo looks small so that should still be quick. exit code 0 Output: .env.example .eslintrc.json .git .gitignore LICENSE README.md app/(auth)/actions.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)/layout.tsx app/(chat)/opengraph-image.png app/(chat)/page.tsx app/(chat)/twitter-image.png 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 components/balance-sheets-table.tsx components/block-actions.tsx components/block-close-button.tsx components/block-messages.tsx components/block.tsx components/cash-flow-statements-table.tsx components/chat-header.tsx components/chat.tsx components/code-block.tsx components/code-editor.tsx components/console.tsx components/data-stream-handler.tsx components/diffview.tsx components/document-preview.tsx Provider mentions: modal

Agent context: `rg` is not installed in this workspace, so I’m falling back to `find`/`grep`. The repo looks small so that should still be quick. Provider mentions: openai

Agent context: `rg` is not installed in this workspace, so I’m falling back to `find`/`grep`. The repo looks small so that should still be quick. exit code 0 Output: ./.env.example:1:# Get your OpenAI API Key here: https://platform.openai.com/account/api-keys ./.env.example:7:# Get your LangSmith API Key here: https://smith.langchain.com/ ./.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) Provider mentions: openai, modal

Agent context: `rg` is not installed in this workspace, so I’m falling back to `find`/`grep`. The repo looks small so that should still be quick.

Agent context: `rg` is not installed in this workspace, so I’m falling back to `find`/`grep`. The repo looks small so that should still be quick. 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: This is a Next/Vercel AI SDK financial agent with a server-side tool layer, not a LangChain-style retriever. I’m reading the chat route and existing financial tool manager so the recommendation maps cleanly to the actual integration surface. This is a Next/Vercel AI SDK financial agent with a server-side tool layer, not a LangChain-style retriever. I’m reading the chat route and existing financial tool manager so the recommendation maps cleanly to the actual integration surface.

Agent context: This is a Next/Vercel AI SDK financial agent with a server-side tool layer, not a LangChain-style retriever. I’m reading the chat route and existing financial tool manager so the recommendation maps cleanly to the actual integration surface.

Agent context: This is a Next/Vercel AI SDK financial agent with a server-side tool layer, not a LangChain-style retriever. I’m reading the chat route and existing financial tool manager so the recommendation maps cleanly to the actual integration surface. 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/Vercel AI SDK financial agent with a server-side tool layer, not a LangChain-style retriever. I’m reading the chat route and existing financial tool manager so the recommendation maps cleanly to the actual integration surface.

Agent context: This is a Next/Vercel AI SDK financial agent with a server-side tool layer, not a LangChain-style retriever. I’m reading the chat route and existing financial tool manager so the recommendation maps cleanly to the actual integration surface. 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/Vercel AI SDK financial agent with a server-side tool layer, not a LangChain-style retriever. I’m reading the chat route and existing financial tool manager so the recommendation maps cleanly to the actual integration surface.

Agent context: This is a Next/Vercel AI SDK financial agent with a server-side tool layer, not a LangChain-style retriever. I’m reading the chat route and existing financial tool manager so the recommendation maps cleanly to the actual integration surface. 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

Query/prompt: Linkup API documentation AI search licensed content premium sources docs Agent context: This is a Next/Vercel AI SDK financial agent with a server-side tool layer, not a LangChain-style retriever. I’m reading the chat route and existing financial tool manager so the recommendation maps cleanly to the actual integration surface. Query: Linkup API documentation AI search licensed content premium sources docs Agent context: This is a Next/Vercel AI SDK financial agent with a server-side tool layer, not a LangChain-style retriever. I’m reading the chat route and existing financial tool manager so the recommendation maps cleanly to the actual integration surface. Expanded queries: Linkup API documentation AI search licensed content premium sources docs | Tavily API documentation AI search source citations docs | Exa API documentation search contents API docs | Perplexity Sonar API documentation citations search docs

Query/prompt: site:docs.linkup.so licensed sources Linkup API premium source coverage Agent context: This is a Next/Vercel AI SDK financial agent with a server-side tool layer, not a LangChain-style retriever. I’m reading the chat route and existing financial tool manager so the recommendation maps cleanly to the actual integration surface. Query: site:docs.linkup.so licensed sources Linkup API premium source coverage Agent context: This is a Next/Vercel AI SDK financial agent with a server-side tool layer, not a LangChain-style retriever. I’m reading the chat route and existing financial tool manager so the recommendation maps cleanly to the actual integration surface. Expanded queries: site:docs.linkup.so licensed sources Linkup API premium source coverage | site:docs.linkup.so Linkup API source selection domain filters fetch docs | site:docs.linkup.so Linkup API search endpoint sources trusted content

Query/prompt: Linkup licensed content premium sources AI search API Agent context: This is a Next/Vercel AI SDK financial agent with a server-side tool layer, not a LangChain-style retriever. I’m reading the chat route and existing financial tool manager so the recommendation maps cleanly to the actual integration surface. Query: Linkup licensed content premium sources AI search API Agent context: This is a Next/Vercel AI SDK financial agent with a server-side tool layer, not a LangChain-style retriever. I’m reading the chat route and existing financial tool manager so the recommendation maps cleanly to the actual integration surface. Expanded queries: Linkup licensed content premium sources AI search API | Linkup premium sources licensed content API AI search

Query/prompt: AI search API licensed content publisher partnerships web retrieval API Agent context: This is a Next/Vercel AI SDK financial agent with a server-side tool layer, not a LangChain-style retriever. I’m reading the chat route and existing financial tool manager so the recommendation maps cleanly to the actual integration surface. Query: AI search API licensed content publisher partnerships web retrieval API Agent context: This is a Next/Vercel AI SDK financial agent with a server-side tool layer, not a LangChain-style retriever. I’m reading the chat route and existing financial tool manager so the recommendation maps cleanly to the actual integration surface. Expanded queries: AI search API licensed content publisher partnerships web retrieval API | managed web retrieval API licensed sources AI agents API | premium licensed content API for AI agents web search

Query/prompt: ProRata Gist Answers API licensed sources AI search documentation Agent context: This is a Next/Vercel AI SDK financial agent with a server-side tool layer, not a LangChain-style retriever. I’m reading the chat route and existing financial tool manager so the recommendation maps cleanly to the actual integration surface. Query: ProRata Gist Answers API licensed sources AI search documentation Agent context: This is a Next/Vercel AI SDK financial agent with a server-side tool layer, not a LangChain-style retriever. I’m reading the chat route and existing financial tool manager so the recommendation maps cleanly to the actual integration surface. Expanded queries: ProRata Gist Answers API licensed sources AI search documentation | ProRata AI API licensed content web search answers sources | Gist Answers API documentation ProRata AI

Agent context: Given the “licensed, reputable sources” requirement, I’m checking both AI-search APIs and publisher/content-network products. I’m treating generic SERP wrappers as weak fits unless they provide strong source controls and retrieval output the existing Vercel AI SDK tool layer can consume. Given the “licensed, reputable sources” requirement, I’m checking both AI-search APIs and publisher/content-network products. I’m treating generic SERP wrappers as weak fits unless they provide strong source controls and retrieval output the existing Vercel AI SDK tool layer can consume.

Agent context: Given the “licensed, reputable sources” requirement, I’m checking both AI-search APIs and publisher/content-network products. I’m treating generic SERP wrappers as weak fits unless they provide strong source controls and retrieval output the existing Vercel AI SDK tool layer can consume. Provider mentions: openai

Agent context: Given the “licensed, reputable sources” requirement, I’m checking both AI-search APIs and publisher/content-network products. I’m treating generic SERP wrappers as weak fits unless they provide strong source controls and retrieval output the existing Vercel AI SDK tool layer can consume. exit code 0 Output: --- name: "openai-docs" description: "Use when the user asks how to build with OpenAI products or APIs, asks about Codex itself or choosing Codex surfaces, needs up-to-date official documentation with citations, help choosing the latest model for a use case, or model upgrade and prompt-upgrade guidance; use OpenAI docs MCP tools for non-Codex docs questions, use the Codex manual helper first for broad Codex self-knowledge, and restrict fallback browsing to official OpenAI domains." --- # OpenAI Docs Provide authoritative, current guidance from OpenAI developer docs using the developers.openai.com MCP server. "Docs MCP" means `mcp__openaiDeveloperDocs__search_openai_docs` and `mcp__openaiDeveloperDocs__fetch_openai_doc`; for API reference, schema, parameter, or required-field questions, also use `mcp__openaiDeveloperDocs__get_openapi_spec` when available. Official-domain web search is fal Provider mentions: openai

Agent context: I’m using the OpenAI docs skill for the OpenAI-specific option only, because this repo already depends on OpenAI and that option needs current official docs rather than memory. I’m using the OpenAI docs skill for the OpenAI-specific option only, because this repo already depends on OpenAI and that option needs current official docs rather than memory. Provider mentions: openai

Query/prompt: OpenAI Responses API web search tool official docs Agent context: I’m using the OpenAI docs skill for the OpenAI-specific option only, because this repo already depends on OpenAI and that option needs current official docs rather than memory. Query: OpenAI Responses API web search tool official docs Agent context: I’m using the OpenAI docs skill for the OpenAI-specific option only, because this repo already depends on OpenAI and that option needs current official docs rather than memory. Expanded queries: OpenAI Responses API web search tool official docs | OpenAI web search tool API citations sources official docs Provider mentions: openai

Query/prompt: https://platform.openai.com/docs/guides/tools-file-search/ Agent context: I’m using the OpenAI docs skill for the OpenAI-specific option only, because this repo already depends on OpenAI and that option needs current official docs rather than memory. Query: https://platform.openai.com/docs/guides/tools-file-search/ Agent context: I’m using the OpenAI docs skill for the OpenAI-specific option only, because this repo already depends on OpenAI and that option needs current official docs rather than memory. Provider mentions: openai

Query/prompt: https://docs.linkup.so/pages/documentation/endpoints/search/overview Agent context: I’m using the OpenAI docs skill for the OpenAI-specific option only, because this repo already depends on OpenAI and that option needs current official docs rather than memory. Query: https://docs.linkup.so/pages/documentation/endpoints/search/overview Agent context: I’m using the OpenAI docs skill for the OpenAI-specific option only, because this repo already depends on OpenAI and that option needs current official docs rather than memory. Provider mentions: openai

Query/prompt: https://docs.tavily.com/documentation/api-reference/endpoint/search Agent context: I’m using the OpenAI docs skill for the OpenAI-specific option only, because this repo already depends on OpenAI and that option needs current official docs rather than memory. Query: https://docs.tavily.com/documentation/api-reference/endpoint/search Agent context: I’m using the OpenAI docs skill for the OpenAI-specific option only, because this repo already depends on OpenAI and that option needs current official docs rather than memory. Provider mentions: openai

Query/prompt: https://docs.linkup.so/ Agent context: I’m using the OpenAI docs skill for the OpenAI-specific option only, because this repo already depends on OpenAI and that option needs current official docs rather than memory. Query: https://docs.linkup.so/ Agent context: I’m using the OpenAI docs skill for the OpenAI-specific option only, because this repo already depends on OpenAI and that option needs current official docs rather than memory. Provider mentions: openai

Query/prompt: 'world's largest independent index' in https://brave.com/search/api/ Agent context: I’m using the OpenAI docs skill for the OpenAI-specific option only, because this repo already depends on OpenAI and that option needs current official docs rather than memory. Query: 'world's largest independent index' in https://brave.com/search/api/ Agent context: I’m using the OpenAI docs skill for the OpenAI-specific option only, because this repo already depends on OpenAI and that option needs current official docs rather than memory. Provider mentions: openai

Query/prompt: https://platform.gist.ai/docs/ga-overview Agent context: I’m using the OpenAI docs skill for the OpenAI-specific option only, because this repo already depends on OpenAI and that option needs current official docs rather than memory. Query: https://platform.gist.ai/docs/ga-overview Agent context: I’m using the OpenAI docs skill for the OpenAI-specific option only, because this repo already depends on OpenAI and that option needs current official docs rather than memory. Provider mentions: openai

Agent context: The repo fit is now clear: the lowest-risk integration is a new Vercel AI SDK tool that returns sources/snippets, alongside the existing Financial Datasets tools. I’m checking the remaining provider docs only to separate “good generic search” from “source-quality/licensing-aware retrieval.” The repo fit is now clear: the lowest-risk integration is a new Vercel AI SDK tool that returns sources/snippets, alongside the existing Financial Datasets tools. I’m checking the remaining provider docs only to separate “good generic search” from “source-quality/licensing-aware retrieval.”

Agent context: The repo fit is now clear: the lowest-risk integration is a new Vercel AI SDK tool that returns sources/snippets, alongside the existing Financial Datasets tools. I’m checking the remaining provider docs only to separate “good generic search” from “source-quality/licensing-aware retrieval.”

Agent context: The repo fit is now clear: the lowest-risk integration is a new Vercel AI SDK tool that returns sources/snippets, alongside the existing Financial Datasets tools. I’m checking the remaining provider docs only to separate “good generic search” from “source-quality/licensing-aware retrieval.” exit code 0 Output: 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 dataStream, 100 }); 101 dataStream.writeData({ 102 type: 'user-message-id', 103 content: userMessageId, 104 }); 105 106 dataStream.writeData({ 107 type: 'query-loading', 108 content: { 109 isLoading: true, 110 taskNames: [] 111 } 112 }); 113 114 const { object } = await generateObject({ 115 model: customModel('gpt-4.1-nano-2025-04-14', modelApiKey), 116 output: 'array', 117 schema: z.object({ 118 task_name: z.string(), 119 class: z 120 .string() 121 .describe('The name of the sub-task'), 122 }), 123 prompt: `You are a financial reasoning agent. 124 Given the follow

Agent context: The repo fit is now clear: the lowest-risk integration is a new Vercel AI SDK tool that returns sources/snippets, alongside the existing Financial Datasets tools. I’m checking the remaining provider docs only to separate “good generic search” from “source-quality/licensing-aware retrieval.”

Agent context: The repo fit is now clear: the lowest-risk integration is a new Vercel AI SDK tool that returns sources/snippets, alongside the existing Financial Datasets tools. I’m checking the remaining provider docs only to separate “good generic search” from “source-quality/licensing-aware retrieval.” 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: The repo fit is now clear: the lowest-risk integration is a new Vercel AI SDK tool that returns sources/snippets, alongside the existing Financial Datasets tools. I’m checking the remaining provider docs only to separate “good generic search” from “source-quality/licensing-aware retrieval.”

Agent context: The repo fit is now clear: the lowest-risk integration is a new Vercel AI SDK tool that returns sources/snippets, alongside the existing Financial Datasets tools. I’m checking the remaining provider docs only to separate “good generic search” from “source-quality/licensing-aware retrieval.” 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

Query/prompt: site:linkup.so licensed premium content Linkup publisher partnerships Agent context: The repo fit is now clear: the lowest-risk integration is a new Vercel AI SDK tool that returns sources/snippets, alongside the existing Financial Datasets tools. I’m checking the remaining provider docs only to separate “good generic search” from “source-quality/licensing-aware retrieval.” Query: site:linkup.so licensed premium content Linkup publisher partnerships Agent context: The repo fit is now clear: the lowest-risk integration is a new Vercel AI SDK tool that returns sources/snippets, alongside the existing Financial Datasets tools. I’m checking the remaining provider docs only to separate “good generic search” from “source-quality/licensing-aware retrieval.” Expanded queries: site:linkup.so licensed premium content Linkup publisher partnerships | site:docs.linkup.so premium content licensed Linkup

Query/prompt: https://www.linkup.so/content-partners Agent context: The repo fit is now clear: the lowest-risk integration is a new Vercel AI SDK tool that returns sources/snippets, alongside the existing Financial Datasets tools. I’m checking the remaining provider docs only to separate “good generic search” from “source-quality/licensing-aware retrieval.” Query: https://www.linkup.so/content-partners Agent context: The repo fit is now clear: the lowest-risk integration is a new Vercel AI SDK tool that returns sources/snippets, alongside the existing Financial Datasets tools. I’m checking the remaining provider docs only to separate “good generic search” from “source-quality/licensing-aware retrieval.”

Agent context: **Recommendation** Choose **Linkup** as the web-search/retrieval provider. This repo is a Next.js/Vercel AI SDK financial agent. It already passes tools into `streamText` from a server-side tool manager in [route.ts](/home/user/worktrees/attempt-366d0d50-5831-4685-93fc-59b6076ffdb8-cap-3/app/(chat)/api/chat/route.ts:176), and the existing tool pattern is plain HTTP-backed Vercel AI SDK tools in [financial-tools.ts](/home/user/worktrees/attempt-366d0d50-5831-4685-93fc-59b6076ffdb8-cap-3/lib/ai/tools/financial-tools.ts:38). Linkup fits that shape cleanly: add a `webSearch`/`webFetch` tool that calls Linkup, returns source URLs/snippets/content, and lets the current OpenAI model synthesize. Why Linkup: its docs position `/search` specifically as synchronous web grounding for AI, with `searchResults`, `sourcedAnswer`, and structured JSON outputs; it supports `fast`, `standard`, and `deep` search modes, including agentic search and page scraping in standard/deep modes; and it has source/domain/date controls for constraining retrieval to trusted sources. Linkup also emphasizes source trust/customization, enterprise security, and source-backed retrieval rather than just returning generic SERP snippets. Sources: Linkup intro and endpoint docs: https://docs.linkup.so/, https://docs.linkup.so/pages/documentation/endpoints/search/overview, https://docs.linkup.so/pages/documentation/tutorials/filtering, https://docs.linkup.so/pages/documentation/endpoints/fetch/reference. **Main Alternatives** - **ProRata / Gist Answers**: strongest explicit licensing story. Its docs say the Gist Content Network has 500+ trusted sources and that external content is licensed, credited, and compensated. I would not choose it here because it is framed as a publisher/site AI-search product with approval/configuration flow, not a general backend retrieval API for this chat agent. Sources: https://platform.gist.ai/docs/ga-overview, https://platform.gist.ai/docs/select-sources. - **OpenAI Web Search**: easiest conceptually because the repo already uses OpenAI, and OpenAI’s Responses API web search supports citations, source metadata, and domain filters. I would not choose it as the primary provider here because the repo currently uses Vercel AI SDK chat models via `provider.chat(...)` in [index.ts](/home/user/worktrees/attempt-366d0d50-5831-4685-93fc-59b6076ffdb8-cap-3/lib/ai/index.ts:6), so using native OpenAI web search would likely require a larger Responses API migration. The docs also do not make the same source-quality/licensed-provider positioning. Source: https://developers.openai.com/api/docs/guides/tools-web-search. - **Tavily**: solid agent-oriented search, recency filters, domain filters, snippets, and extraction. Weaker than Linkup for this requirement because its docs read more like optimized web search/RAG over open web, not licensed or curated premium source access. Source: https://docs.tavily.com/examples/quick-tutorials/search-api. - **Exa**: strong semantic search/content retrieval and good AI SDK integrations, but less aligned with the “licensed, reputable source coverage” requirement than Linkup. Source: https://exa.ai/docs/reference/search-api-guide. - **Perplexity Search/Sonar**: good current web search and cited answers, but it is more of an answer/search model stack; less control over building this agent’s own retrieval contract. Source: https://docs.perplexity.ai/docs/search/quickstart. - **Brave Search/Answers**: high-scale independent web index, citations, OpenAI-compatible Answers API, and good cost. Still fundamentally an open-web search index rather than curated/licensed retrieval. Source: https://brave.com/search/api/. - **You.com Search API**: structured web/news results and full-page content, useful for RAG, but I did not find a stronger licensing/source-quality case than Linkup. Source: https://you.com/docs/search/overview. **Docs Actually Consulted** I consulted Linkup, ProRata/Gist, OpenAI Web Search, Tavily, Exa, Perplexity, Brave, and You.com documentation. I also inspected this repo’s chat route, model wrapper, and financial tool manager to confirm integration fit. Bottom line: **Linkup is the best single provider for this repository**, with one caveat: for production, confirm the exact premium/licensed source coverage contractually. If the requirement becomes “only licensed publisher-network content, no open web at all,” then ProRata/Gist becomes the licensing leader but is a worse technical fit for this agent. **Recommendation** Choose **Linkup** as the web-search/retrieval provider. This repo is a Next.js/Vercel AI SDK financial agent. It already passes tools into `streamText` from a server-side tool manager in [route.ts](/home/user/worktrees/attempt-366d0d50-5831-4685-93fc-59b6076ffdb8-cap-3/app/(chat)/api/chat/route.ts:176), and the existing tool pattern is plain HTTP-backed Vercel AI SDK tools in [financial-tools.ts](/home/user/worktrees/attempt-366d0d50-5831-4685-93fc-59b6076ffdb8-cap-3/lib/ai/tools/financial-tools.ts:38). Linkup fits that shape cleanly: add a `webSearch`/`webFetch` tool that calls Linkup, returns source URLs/snippets/content, and lets the current OpenAI model synthesize. Why Linkup: its docs position `/search` specifically as synchronous web grounding for AI, with `searchResults`, `sourcedAnswer`, and structured JSON outputs; it supports `fast`, `standard`, and `deep` search modes, including agentic search and page scraping in standard/deep modes; and it has source/domain/date controls for constraining retrieval to trusted sources. Linkup also emphasizes source trust/customization, enterprise security, and source-backed retrieval rather than just returning generic Provider mentions: openai

**Recommendation** Choose **Linkup** as the web-search/retrieval provider. This repo is a Next.js/Vercel AI SDK financial agent. It already passes tools into `streamText` from a server-side tool manager in [route.ts](/home/user/worktrees/attempt-366d0d50-5831-4685-93fc-59b6076ffdb8-cap-3/app/(chat)/api/chat/route.ts:176), and the existing tool pattern is plain HTTP-backed Vercel AI SDK tools in [financial-tools.ts](/home/user/worktrees/attempt-366d0d50-5831-4685-93fc-59b6076ffdb8-cap-3/lib/ai/tools/financial-tools.ts:38). Linkup fits that shape cleanly: add a `webSearch`/`webFetch` tool that calls Linkup, returns source URLs/snippets/content, and lets the current OpenAI model synthesize. Why Linkup: its docs position `/search` specifically as synchronous web grounding for AI, with `searchResults`, `sourcedAnswer`, and structured JSON outputs; it supports `fast`, `standard`, and `deep` search modes, including agentic search and page scraping in standard/deep modes; and it has source/domain/date controls for constraining retrieval to trusted sources. Linkup also emphasizes source trust/customization, enterprise security, and source-backed retrieval rather than just returning generic SERP snippets. Sources: Linkup intro and endpoint docs: https://docs.linkup.so/, https://docs.linkup.so/pages/documentation/endpoints/search/overview, https://docs.linkup.so/pages/documentation/tutori Provider mentions: openai