virattt-

Run

virattt/ai-financial-agent

Agent

GPT-5.3-Codex

Status

completed

Trace

available

Telemetry

artifact only

Last Run

Jun 05, 11:31 PM

Decision Summary

competitor

Expected

Provider

Product

Selected

Provider

brave-search

Product

Surface

provider_selection

Reason

codex selected brave-search during first-pass provider discovery. No expected provider was set for this prompt.

Outcome

competitor

Trace Timeline

agent_action

Prompt-lab work item started

agent_action

Pinned repository checkout verified

836d30b28ff596e01d8cc287e0b83b3d3e1a1bc1

agent_action

I’ll first map how this agent is built and where retrieval would plug in, then I’ll...

I’ll first map how this agent is built and where retrieval would plug in, then I’ll verify current provider docs before making the recommendation.

command

/bin/bash -lc 'rg --files'

command

/bin/bash -lc 'rg --files'

exit code 127 Output: /bin/bash: line 1: rg: command not found

command

/bin/bash -lc 'git status --short'

command

/bin/bash -lc 'git status --short'

exit code 0

agent_action

`rg` is not installed in this environment, so I’m falling back to `find`/shell read...

`rg` is not installed in this environment, so I’m falling back to `find`/shell reads. The worktree is clean, which makes it easier to separate repo facts from recommendations.

command

/bin/bash -lc "find . -maxdepth 3 -type f | sed 's#"'^./##'"' | sort"

command

/bin/bash -lc "find . -maxdepth 3 -type f | sed 's#"'^./##'"' | sort"

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/(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 components/document-skeleton.tsx components/document.ts

command

/bin/bash -lc "find . -maxdepth 2 -type d | sed 's#"'^./##'"' | sort"

command

/bin/bash -lc "find . -maxdepth 2 -type d | sed 's#"'^./##'"' | sort"

exit code 0 Output: . .agents .codex app app/(auth) app/(chat) app/api app/components components components/styles components/ui hooks lib lib/ai lib/api lib/db lib/editor lib/types lib/utils public public/fonts public/images

agent_action

I found a Next.js/Vercel-style chat app with finance-specific components. I’m readi...

I found a Next.js/Vercel-style chat app with finance-specific components. I’m reading the AI entry points, prompts, model plumbing, and API routes now to see whether web retrieval should be a tool, middleware step, or model-level option.

command

/bin/bash -lc 'find app/api lib/ai lib/api -type f | sort'

command

/bin/bash -lc 'find app/api lib/ai lib/api -type f | sort'

exit code 0 Output: app/api/auth/auto-login/route.ts lib/ai/custom-middleware.ts lib/ai/index.ts lib/ai/models.ts lib/ai/prompts.ts lib/ai/tools/financial-tools.ts lib/api/stock-filters.ts

command

/bin/bash -lc "sed -n '1,220p' lib/ai/index.ts && sed -n '1,220p' lib/ai/models.ts && sed -n '1,220p' lib/ai/custom-middleware.ts"

command

/bin/bash -lc "sed -n '1,220p' lib/ai/index.ts && sed -n '1,220p' lib/ai/models.ts && sed -n '1,220p' lib/ai/custom-middleware.ts"

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

command

/bin/bash -lc "sed -n '1,220p' package.json"

command

/bin/bash -lc "sed -n '1,220p' package.json"

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

command

/bin/bash -lc "sed -n '1,260p' lib/ai/prompts.ts"

command

/bin/bash -lc "sed -n '1,260p' lib/ai/prompts.ts"

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

agent_action

The agent uses `ai` SDK v4 with `@ai-sdk/openai`, a wrapped OpenAI chat model, and ...

The agent uses `ai` SDK v4 with `@ai-sdk/openai`, a wrapped OpenAI chat model, and a finance-oriented system prompt. I don’t see a retrieval layer yet, so the clean integration point would be a server-side tool exposed to the chat route rather than trying to bake search into the UI.

command

/bin/bash -lc "find app -path '*api*' -type f -maxdepth 5 | sort"

command

/bin/bash -lc "find app -path '*api*' -type f -maxdepth 5 | sort"

exit code 0 Output: app/(auth)/api/auth/[...nextauth]/route.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/api/auth/auto-login/route.ts

command

/bin/bash -lc "sed -n '1,260p' lib/ai/tools/financial-tools.ts"

command

/bin/bash -lc "sed -n '1,260p' lib/ai/tools/financial-tools.ts"

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(

command

/bin/bash -lc "sed -n '1,220p' .env.example && sed -n '1,260p' README.md"

command

/bin/bash -lc "sed -n '1,220p' .env.example && sed -n '1,260p' README.md"

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=**** # AI Financial Agent 🤖 This is a proof of conncept AI financial agent. The goal of

command

command

exit code 0 Output: ./app/(chat)/actions.ts:3:import { type CoreUserMessage, generateText } from 'ai'; ./app/(chat)/actions.ts:26: const { text: title } = await generateText({ ./app/(chat)/api/chat/route.ts:6: streamText, ./app/(chat)/api/chat/route.ts:32: financialTools, ./app/(chat)/api/chat/route.ts:34:} from '@/lib/ai/tools/financial-tools'; ./app/(chat)/api/chat/route.ts:39:const allTools: AllowedTools[] = [...financialTools]; ./app/(chat)/api/chat/route.ts:46: financialDatasetsApiKey, ./app/(chat)/api/chat/route.ts:52: financialDatasetsApiKey?: string; ./app/(chat)/api/chat/route.ts:96: // Initialize the financial tools manager ./app/(chat)/api/chat/route.ts:97: const financialToolsManager = new FinancialToolsManager({ ./app/(chat)/api/chat/route.ts:98: financialDatasetsApiKey: financialDatasetsApiKey!, ./app/(chat)/api/chat/route.ts:123: prompt: `You are a financial reasoning agent. ./app/(chat)/api/

agent_action

The existing tools already retrieve structured market data and company news from Fi...

The existing tools already retrieve structured market data and company news from Financial Datasets. The missing capability is broader web/source retrieval for questions that need primary docs, press releases, regulatory pages, current events outside ticker-scoped news, or cross-source verification.

command

/bin/bash -lc "sed -n '260,520p' lib/ai/tools/financial-tools.ts"

command

/bin/bash -lc "sed -n '260,520p' lib/ai/tools/financial-tools.ts"

exit code 0 Output: z.object({ field: z.enum(validStockSearchFilters as [string, ...string[]]), operator: z.enum(['gt', 'gte', 'lt', 'lte', 'eq']), value: z.number() }) ).describe('The filters to search for (e.g. [{field: "net_income", operator: "gt", value: 1000000000}, {field: "revenue", operator: "gt", value: 50000000000}])'), period: z.enum(['quarterly', 'annual', 'ttm']).optional().describe('The period of the financial metrics to return'), limit: z.number().optional().default(5).describe('The number of stocks to return'), order_by: z.enum(['-report_period', 'report_period']).optional().default('-report_period').describe('The order of the stocks to return'), }), execute: async ({ filters, period, limit }: { filters: Array<{ field: string; operator: 'gt' | 'gte' | 'lt' | 'lte' | 'eq'; value: number; }>; period?: 'quarterly' | 'annual' | 'ttm'; limit?: number; }) => { if (!this.shouldExecuteToolCall('sear

command

/bin/bash -lc "sed -n '1,260p' 'app/(chat)/api/chat/route.ts'"

command

/bin/bash -lc "sed -n '1,260p' 'app/(chat)/api/chat/route.ts'"

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,

command

/bin/bash -lc "sed -n '1,120p' components/chat.tsx && sed -n '1,120p' lib/db/api-keys.ts"

command

/bin/bash -lc "sed -n '1,120p' components/chat.tsx && sed -n '1,120p' lib/db/api-keys.ts"

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

command

/bin/bash -lc "sed -n '1,140p' 'app/(chat)/actions.ts'"

command

/bin/bash -lc "sed -n '1,140p' 'app/(chat)/actions.ts'"

exit code 0 Output: 'use server'; import { type CoreUserMessage, generateText } from 'ai'; import { cookies } from 'next/headers'; import { customModel } from '@/lib/ai'; import { deleteMessagesByChatIdAfterTimestamp, getMessageById, updateChatVisiblityById, } from '@/lib/db/queries'; import { VisibilityType } from '@/components/visibility-selector'; export async function saveModelId(model: string) { const cookieStore = await cookies(); cookieStore.set('model-id', model); } export async function generateTitleFromUserMessage({ message, modelApiKey, }: { message: CoreUserMessage; modelApiKey: string; }) { const { text: title } = await generateText({ model: customModel('gpt-4.1-mini-2025-04-14', modelApiKey), system: `\n - you will generate a short title based on the first message a user begins a conversation with - ensure it is not more than 30 characters long - the title should be a summary of the user's mes

OpenAI web search tool Responses API citations documentation site:platform.openai.c...

Query: OpenAI web search tool Responses API citations documentation site:platform.openai.com/docs Agent context: The existing tools already retrieve structured market data and company news from Financial Datasets. The missing capability is broader web/source retrieval for questions that need primary docs, press releases, regulatory pages, current events outside ticker-scoped news, or cross-source verification. Expanded queries: OpenAI web search tool Responses API citations documentation site:platform.openai.com/docs | Tavily Search API include raw content citations documentation | Exa API search contents highlights documentation citations

Brave Search API documentation web search endpoint snippets news official

Query: Brave Search API documentation web search endpoint snippets news official Agent context: The existing tools already retrieve structured market data and company news from Financial Datasets. The missing capability is broader web/source retrieval for questions that need primary docs, press releases, regulatory pages, current events outside ticker-scoped news, or cross-source verification. Expanded queries: Brave Search API documentation web search endpoint snippets news official | Google Programmable Search JSON API documentation custom search official | Perplexity Sonar API citations search documentation official | Vertex AI Grounding with Google Search citations documentation official

url

https://platform.openai.com/docs/guides/tools-web-search?api-mode=responses

Query: https://platform.openai.com/docs/guides/tools-web-search?api-mode=responses Agent context: The existing tools already retrieve structured market data and company news from Financial Datasets. The missing capability is broader web/source retrieval for questions that need primary docs, press releases, regulatory pages, current events outside ticker-scoped news, or cross-source verification.

url

https://help.tavily.com/articles/3960993389-does-tavily-search-api-provide-citations-for-its-results

Query: https://help.tavily.com/articles/3960993389-does-tavily-search-api-provide-citations-for-its-results Agent context: The existing tools already retrieve structured market data and company news from Financial Datasets. The missing capability is broader web/source retrieval for questions that need primary docs, press releases, regulatory pages, current events outside ticker-scoped news, or cross-source verification.

Tavily Extract API documentation extract raw content official

Query: Tavily Extract API documentation extract raw content official Agent context: The existing tools already retrieve structured market data and company news from Financial Datasets. The missing capability is broader web/source retrieval for questions that need primary docs, press releases, regulatory pages, current events outside ticker-scoped news, or cross-source verification. Expanded queries: Tavily Extract API documentation extract raw content official | Exa Search API search endpoint include domains freshness official documentation

Microsoft Bing Search API retirement documentation web search 2025 2026 official

Query: Microsoft Bing Search API retirement documentation web search 2025 2026 official Agent context: The existing tools already retrieve structured market data and company news from Financial Datasets. The missing capability is broader web/source retrieval for questions that need primary docs, press releases, regulatory pages, current events outside ticker-scoped news, or cross-source verification. Expanded queries: Microsoft Bing Search API retirement documentation web search 2025 2026 official | Azure AI Agent Service Bing grounding tool documentation citations official

url

https://api-dashboard.search.brave.com/app/documentation/web-search/query

Query: https://api-dashboard.search.brave.com/app/documentation/web-search/query Agent context: The existing tools already retrieve structured market data and company news from Financial Datasets. The missing capability is broader web/source retrieval for questions that need primary docs, press releases, regulatory pages, current events outside ticker-scoped news, or cross-source verification.

agent_action

I’ve now checked the current docs for the serious candidates, including OpenAI web ...

I’ve now checked the current docs for the serious candidates, including OpenAI web search, Tavily, Exa, Brave, Google Vertex grounding, Perplexity Sonar, and Microsoft’s Bing replacement path. One notable current-state finding: the old Bing Search APIs are already retired, so Azure’s “Grounding with Bing Search” is the modern Microsoft option, not the old standalone Bing API.

command

/bin/bash -lc "nl -ba 'app/(chat)/api/chat/route.ts' | sed -n '1,230p'"

command

/bin/bash -lc "nl -ba 'app/(chat)/api/chat/route.ts' | sed -n '1,230p'"

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

command

/bin/bash -lc "nl -ba lib/ai/index.ts lib/ai/models.ts lib/ai/prompts.ts .env.example package.json | sed -n '1,260p'"

command

/bin/bash -lc "nl -ba lib/ai/index.ts lib/ai/models.ts lib/ai/prompts.ts .env.example package.json | sed -n '1,260p'"

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 }; 13 // Define your models here. 14 15 export interface Model { 16 id: string; 17 label: string; 18 apiIdentifier: string; 19 description: string; 20 } 21 22 export const models: Array<Model> = [ 23 { 24 id: 'gpt-4.1-nano-2025-04-14', 25 label: 'GPT 4.1 nano', 26 apiIdentifier: 'gpt-4.1-nano-2025-04-14', 27 description: 'Fastest, most cost-effective GPT-4.1 model', 28 }, 29 { 30 id: 'gpt-4.1-mini-2025-04-14', 31 label: 'GPT 4.1 mini'

command

/bin/bash -lc "nl -ba lib/ai/tools/financial-tools.ts | sed -n '1,340p'"

command

/bin/bash -lc "nl -ba lib/ai/tools/financial-tools.ts | sed -n '1,340p'"

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_action

The repo shape points to adding one new AI SDK tool alongside `FinancialToolsManage...

The repo shape points to adding one new AI SDK tool alongside `FinancialToolsManager`, with a server-side API key and a prompt rule that web-derived claims require clickable citations. I’m consolidating the recommendation around source control and verifiability rather than convenience.

command

/bin/bash -lc pwd

command

/bin/bash -lc pwd

exit code 0 Output: /home/user/worktrees/attempt-b13fccda-9671-4c3c-82cd-3b6fcfa975cf-cap-1

url

https://learn.microsoft.com/en-us/lifecycle/announcements/bing-search-api-retirement

Query: https://learn.microsoft.com/en-us/lifecycle/announcements/bing-search-api-retirement Agent context: The repo shape points to adding one new AI SDK tool alongside `FinancialToolsManager`, with a server-side API key and a prompt rule that web-derived claims require clickable citations. I’m consolidating the recommendation around source control and verifiability rather than convenience.

url

https://learn.microsoft.com/en-us/lifecycle/announcements/bing-search-api-retirement

agent_action

I’d choose **Brave Search API, specifically the `LLM Context` endpoint**, not Brave...

I’d choose **Brave Search API, specifically the `LLM Context` endpoint**, not Brave Answers. The repo is already a Next.js AI SDK chat app: `streamText` receives `FinancialToolsManager.getTools()` in [app/(chat)/api/chat/route.ts](/home/user/worktrees/attempt-b13fccda-9671-4c3c-82cd-3b6fcfa975cf-cap-1/app/(chat)/api/chat/route.ts:176), and the current tool set is only structured financial data/news in [lib/ai/tools/financial-tools.ts](/home/user/worktrees/attempt-b13fccda-9671-4c3c-82cd-3b6fcfa975cf-cap-1/lib/ai/tools/financial-tools.ts:4). So the best fit is a new server-side retrieval tool returning source chunks plus metadata into the existing OpenAI model pipeline, with citation rules added to [lib/ai/prompts.ts](/home/user/worktrees/attempt-b13fccda-9671-4c3c-82cd-3b6fcfa975cf-cap-1/lib/ai/prompts.ts:81). Why Brave: its LLM Context API is retrieval-first: pre-extracted page content for grounding/RAG, configurable token budgets, freshness filters, relevance thresholds, and `sources` metadata with URL/title/host/date fields. It also supports Goggles/custom reranking so we can restrict or boost authoritative sources, which matters more here than a convenient generated answer. Bra

result

Selected unknown

Commands

/bin/bash -lc 'rg --files'

exit 127

/bin/bash -lc 'git status --short'

exit 0

/bin/bash -lc "find . -maxdepth 3 -type f | sed 's#"'^./##'"' | sort"

exit 0

/bin/bash -lc "find . -maxdepth 2 -type d | sed 's#"'^./##'"' | sort"

exit 0

/bin/bash -lc 'find app/api lib/ai lib/api -type f | sort'

exit 0

/bin/bash -lc "sed -n '1,220p' lib/ai/index.ts && sed -n '1,220p' lib/ai/models.ts && sed -n '1,220p' lib/ai/custom-middleware.ts"

exit 0

/bin/bash -lc "sed -n '1,220p' package.json"

exit 0

/bin/bash -lc "sed -n '1,260p' lib/ai/prompts.ts"

exit 0

/bin/bash -lc "find app -path '*api*' -type f -maxdepth 5 | sort"

exit 0

/bin/bash -lc "sed -n '1,260p' lib/ai/tools/financial-tools.ts"

exit 0

/bin/bash -lc "sed -n '1,220p' .env.example && sed -n '1,260p' README.md"

exit 0

exit 0

/bin/bash -lc "sed -n '260,520p' lib/ai/tools/financial-tools.ts"

exit 0

/bin/bash -lc "sed -n '1,260p' 'app/(chat)/api/chat/route.ts'"

exit 0

/bin/bash -lc "sed -n '1,120p' components/chat.tsx && sed -n '1,120p' lib/db/api-keys.ts"

exit 0

/bin/bash -lc "sed -n '1,140p' 'app/(chat)/actions.ts'"

exit 0

/bin/bash -lc "nl -ba 'app/(chat)/api/chat/route.ts' | sed -n '1,230p'"

exit 0

/bin/bash -lc "nl -ba lib/ai/index.ts lib/ai/models.ts lib/ai/prompts.ts .env.example package.json | sed -n '1,260p'"

exit 0

/bin/bash -lc "nl -ba lib/ai/tools/financial-tools.ts | sed -n '1,340p'"

exit 0

/bin/bash -lc pwd

exit 0

URLs Fetched

https://platform.openai.com/docs/guides/tools-web-search?api-mode=responses

status not materialized

https://help.tavily.com/articles/3960993389-does-tavily-search-api-provide-citations-for-its-results

status not materialized

https://api-dashboard.search.brave.com/app/documentation/web-search/query

status not materialized

https://learn.microsoft.com/en-us/lifecycle/announcements/bing-search-api-retirement

status not materialized

Changed Files

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Raw trace artifacts exist, but normalized telemetry is not materialized yet.

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Decision Summary

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