aronm99-

Run

aronm99/budget-planner

Agent

GPT-5.3-Codex

Status

completed

Trace

available

Telemetry

artifact only

Last Run

Jun 06, 04:51 AM

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

9da97ce3ef437ebd56ed8eedaebc3dc37cec4ba9

agent_action

I’ll inspect the repository’s agent architecture first so the provider choice is ti...

I’ll inspect the repository’s agent architecture first so the provider choice is tied to how this codebase actually retrieves context, then I’ll check current provider documentation from primary sources.

command

/bin/bash -lc "pwd && rg --files -g '"'!*node_modules*'"' -g '"'!*.png'"' -g '"'!*.jpg'"' -g '"'!*.jpeg'"' -g '"'!*.gif'"' -g '"'!*.svg'"' | head -200"

command

/bin/bash -lc "pwd && rg --files -g '"'!*node_modules*'"' -g '"'!*.png'"' -g '"'!*.jpg'"' -g '"'!*.jpeg'"' -g '"'!*.gif'"' -g '"'!*.svg'"' | head -200"

exit code 0 Output: /home/user/worktrees/attempt-27bb9c57-1053-493d-81e3-6fcbb57b392a-cap-1 /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`/`grep`. Th...

`rg` is not installed in this environment, so I’m falling back to `find`/`grep`. The worktree is clean so far.

command

/bin/bash -lc "find . -path './node_modules' -prune -o -path './.git' -prune -o -type f | sed 's#"'^./##'"' | head -240"

command

/bin/bash -lc "find . -path './node_modules' -prune -o -path './.git' -prune -o -type f | sed 's#"'^./##'"' | head -240"

exit code 0 Output: .git .gitignore .npmrc .vscode/launch.json .vscode/settings.json README.md app.vue components/Dashboard/Details.vue components/Dashboard/Pie.vue components/Transaction/inputForm.vue components/Transaction/inputStatement.vue composables/auth.ts docker-compose.yml layouts/blank.vue layouts/default.vue lib/categories.ts lib/colors.ts middleware/auth.global.ts nuxt.config.ts package-lock.json package.json pages/add-statement.vue pages/index.vue pages/login.vue pages/new-transaction.vue pages/profile.vue pages/signup.vue plugins/vuetify.ts prisma/migrations/20231027031336_create_users_table/migration.sql prisma/migrations/20231027031357_create_transactions_table/migration.sql prisma/migrations/20231105232807_add_session_and_keys_tables/migration.sql prisma/migrations/20231124043114_add_name_to_transaction/migration.sql prisma/migrations/20240116040252_update_transaction_table/migration.sql pr

command

/bin/bash -lc "find . -maxdepth 2 -type f \\( -name 'package.json' -o -name 'pyproject.toml' -o -name 'README*' -o -name 'Cargo.toml' -o -name 'go.mod' -o -name 'requirements*.txt' -o -name 'AGENTS.md' \\) -print"

command

exit code 0 Output: ./README.md ./package.json

agent_action

The repo is a Nuxt app with a small server-side LLM utility, so the decision likely...

The repo is a Nuxt app with a small server-side LLM utility, so the decision likely hinges on augmenting that utility rather than building a separate search service. I’m reading those paths now.

command

/bin/bash -lc "sed -n '1,240p' package.json"

command

/bin/bash -lc "sed -n '1,240p' package.json"

exit code 0 Output: { "name": "nuxt-app", "private": true, "type": "module", "scripts": { "build": "nuxt build", "dev": "nuxt dev", "start": "nuxt start", "generate": "nuxt generate", "preview": "nuxt preview", "postinstall": "nuxt prepare" }, "devDependencies": { "@nuxt/devtools": "latest", "@types/lodash": "^4.14.199", "nuxt": "^3.8.2", "prisma": "^5.4.2", "vite-plugin-vuetify": "^1.0.2", "vue": "^3.3.4", "vue-router": "^4.2.5" }, "dependencies": { "@lucia-auth/adapter-prisma": "^3.0.2", "@lucia-auth/oauth": "^3.3.1", "@mdi/font": "^7.2.96", "@prisma/client": "^5.4.2", "chart.js": "^4.4.0", "lodash": "^4.17.21", "lucia": "^2.7.2", "openai": "^4.24.7", "sass": "^1.69.0", "tesseract.js": "^5.0.4", "ulid": "^2.3.0", "vue-chartjs": "^5.2.0", "vuetify": "^3.3.20" } }

command

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

command

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

exit code 0 Output: # Nuxt 3 Minimal Starter Look at the [Nuxt 3 documentation](https://nuxt.com/docs/getting-started/introduction) to learn more. ## Setup Make sure to install the dependencies: ```bash # npm npm install # pnpm pnpm install # yarn yarn install # bun bun install ``` ## Development Server Start the development server on `http://localhost:3000`: ```bash # npm npm run dev # pnpm pnpm run dev # yarn yarn dev # bun bun run dev ``` ## Production Build the application for production: ```bash # npm npm run build # pnpm pnpm run build # yarn yarn build # bun bun run build ``` Locally preview production build: ```bash # npm npm run preview # pnpm pnpm run preview # yarn yarn preview # bun bun run preview ``` Check out the [deployment documentation](https://nuxt.com/docs/getting-started/deployment) for more information.

command

/bin/bash -lc "sed -n '1,260p' server/utils/llm.ts"

command

/bin/bash -lc "sed -n '1,260p' server/utils/llm.ts"

exit code 0 Output: import OpenAI from 'openai'; const runtimeConfig = useRuntimeConfig(); export async function chatCompletion(prompt: string, systemPrompt?: string, history?: { role: string, content: string }[], jsonSchema?: any, name?: string) { // const response = await openai.chat.completions.create({ // messages: [{ role: 'user', content }], // model: 'gpt-3.5-turbo', // }); // return response.choices[0].message.content const response = await fetch('http://localhost:1234/v1/chat/completions', { method: 'POST', headers: { 'Content-Type': 'application/json', }, body: JSON.stringify({ messages: [ ...(systemPrompt ? [{ role: 'system', content: systemPrompt }] : []), ...(history ? history : []), { role: 'user', content: prompt }, ], model: runtimeConfig.model, stream: false, response_format: jsonSchema ? { type: 'json_schema', json_schema: { name: name || 'response', strict: 'true', schema: jsonSchema } }

command

/bin/bash -lc "sed -n '1,260p' server/api/transaction/statement.post.ts"

command

/bin/bash -lc "sed -n '1,260p' server/api/transaction/statement.post.ts"

exit code 0 Output: import { PrismaClient, Transactions } from "@prisma/client"; import { ulid } from "ulid"; import { categories, categoryObject } from "~/lib/categories"; import { chatCompletion } from "~/server/utils/llm"; import { StatementResponse, TransactionItem } from "~/types/transaction"; const prisma = new PrismaClient(); const systemPrompt = `You are a financial budgeting agent and need to extract transactions from the provided credit card or bank statement For each transaction follow the following steps: # Steps 1. Extract the name directly from the statement, DO NOT simplify the name 2. Extract the amount directly from the statement, DO NOT simplify the amount 3. extract the date directly from the statement 4. if the transaction is a credit card payment ignore it and return null for the name, amount, and date 5. using the list of categories provided in the <Categories> tag, select the category

command

/bin/bash -lc "sed -n '261,520p' server/api/transaction/statement.post.ts"

command

/bin/bash -lc "sed -n '261,520p' server/api/transaction/statement.post.ts"

exit code 0 Output: try { const transaction = await prisma.transactions.createMany({ data: transactionsResults, }); return { success: true, transactions: { count: transaction.count, items: transactionsResults } }; } catch (error) { console.error('Error creating transactions:', error); throw createError({ message: 'Failed to create transactions', statusCode: 500, cause: error }); } });

agent_action

The existing LLM surface is narrow: statement/header parsing and transaction catego...

The existing LLM surface is narrow: statement/header parsing and transaction categorization call one helper, currently aimed at a local OpenAI-compatible endpoint. I’m checking whether any other agent-like paths exist before comparing providers.

command

/bin/bash -lc "sed -n '1,220p' nuxt.config.ts"

command

/bin/bash -lc "sed -n '1,220p' nuxt.config.ts"

exit code 0 Output: import vuetify, { transformAssetUrls } from 'vite-plugin-vuetify' export default defineNuxtConfig({ css: [ 'vuetify/lib/styles/main.sass', '@mdi/font/css/materialdesignicons.min.css' ], build: { transpile: ['vuetify'], }, modules: [ (_options, nuxt) => { nuxt.hooks.hook('vite:extendConfig', (config) => { // @ts-expect-error config.plugins.push(vuetify({ autoImport: true })) }) }, //... ], vite: { vue: { template: { transformAssetUrls, }, }, }, runtimeConfig: { googleClientId: "", googleClientSecret: "", googleRedirectUri: "", openAiApiKey: "", model: "", }, devtools: { enabled: true }, compatibilityDate: '2025-01-12', })

command

command

exit code 0 Output: server/api/transaction/statement.post.ts:4:import { chatCompletion } from "~/server/utils/llm"; server/api/transaction/statement.post.ts:8:const systemPrompt = `You are a financial budgeting agent and need to extract transactions from the provided credit card or bank statement server/api/transaction/statement.post.ts:98: let headerPrompt = `You are a financial budgeting agent and need to interpret the headers of the provided credit card or bank statement. server/api/transaction/statement.post.ts:135: let result = await chatCompletion(headerPrompt, undefined, undefined, headerJsonSchema, 'header-extraction') as { server/api/transaction/statement.post.ts:165: result = await chatCompletion(headerPrompt, undefined, history, headerJsonSchema, name) as { server/api/transaction/statement.post.ts:197: const result = await chatCompletion(prompt, systemPrompt, undefined, jsonSchema, name) as { ser

command

/bin/bash -lc "find . -maxdepth 2 -type f -name '.env*' -print -o -name '*.md' -print"

command

/bin/bash -lc "find . -maxdepth 2 -type f -name '.env*' -print -o -name '*.md' -print"

exit code 0 Output: ./README.md

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

Query: OpenAI web search tool Responses API citations official documentation site:platform.openai.com/docs Agent context: The existing LLM surface is narrow: statement/header parsing and transaction categorization call one helper, currently aimed at a local OpenAI-compatible endpoint. I’m checking whether any other agent-like paths exist before comparing providers. Expanded queries: OpenAI web search tool Responses API citations official documentation site:platform.openai.com/docs | Google Gemini API Grounding with Google Search citations official documentation | Tavily API search extract documentation sources citations official | Exa API search contents documentation citations source quality 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 LLM surface is narrow: statement/header parsing and transaction categorization call one helper, currently aimed at a local OpenAI-compatible endpoint. I’m checking whether any other agent-like paths exist before comparing providers.

Brave Search API web search official documentation snippets freshness

Query: Brave Search API web search official documentation snippets freshness Agent context: The existing LLM surface is narrow: statement/header parsing and transaction categorization call one helper, currently aimed at a local OpenAI-compatible endpoint. I’m checking whether any other agent-like paths exist before comparing providers. Expanded queries: Brave Search API web search official documentation snippets freshness | Perplexity Sonar API citations search official docs | Google Programmable Search JSON API official documentation custom search

url

https://brave.com/search/api

Query: https://brave.com/search/api Agent context: The existing LLM surface is narrow: statement/header parsing and transaction categorization call one helper, currently aimed at a local OpenAI-compatible endpoint. I’m checking whether any other agent-like paths exist before comparing providers.

Brave Search API LLM context endpoint documentation official

Query: Brave Search API LLM context endpoint documentation official Agent context: The existing LLM surface is narrow: statement/header parsing and transaction categorization call one helper, currently aimed at a local OpenAI-compatible endpoint. I’m checking whether any other agent-like paths exist before comparing providers. Expanded queries: Brave Search API LLM context endpoint documentation official | Tavily Extract API documentation official raw content | Exa contents API documentation official highlights text | Perplexity Search API raw ranked web results official docs

agent_action

The repo doesn’t currently have a general Q&A agent surface; the “agent” is the bud...

The repo doesn’t currently have a general Q&A agent surface; the “agent” is the budgeting statement extractor in `server/api/transaction/statement.post.ts`, with all model access funneled through `server/utils/llm.ts`. That makes a source-first retrieval helper a better fit than a fully hosted answer engine unless you want to replace the model path outright.

command

/bin/bash -lc "nl -ba server/api/transaction/statement.post.ts | sed -n '1,230p'"

command

/bin/bash -lc "nl -ba server/api/transaction/statement.post.ts | sed -n '1,230p'"

exit code 0 Output: 1 import { PrismaClient, Transactions } from "@prisma/client"; 2 import { ulid } from "ulid"; 3 import { categories, categoryObject } from "~/lib/categories"; 4 import { chatCompletion } from "~/server/utils/llm"; 5 import { StatementResponse, TransactionItem } from "~/types/transaction"; 6 const prisma = new PrismaClient(); 7 8 const systemPrompt = `You are a financial budgeting agent and need to extract transactions from the provided credit card or bank statement 9 For each transaction follow the following steps: 10 # Steps 11 1. Extract the name directly from the statement, DO NOT simplify the name 12 2. Extract the amount directly from the statement, DO NOT simplify the amount 13 3. extract the date directly from the statement 14 4. if the transaction is a credit card payment ignore it and return null for the name, amount, and date 15 5. using the list of categories provided in the <

command

/bin/bash -lc "nl -ba server/utils/llm.ts | sed -n '1,90p'"

command

/bin/bash -lc "nl -ba server/utils/llm.ts | sed -n '1,90p'"

exit code 0 Output: 1 import OpenAI from 'openai'; 2 3 const runtimeConfig = useRuntimeConfig(); 4 5 export async function chatCompletion(prompt: string, systemPrompt?: string, history?: { role: string, content: string }[], jsonSchema?: any, name?: string) { 6 // const response = await openai.chat.completions.create({ 7 // messages: [{ role: 'user', content }], 8 // model: 'gpt-3.5-turbo', 9 // }); 10 11 // return response.choices[0].message.content 12 13 const response = await fetch('http://localhost:1234/v1/chat/completions', { 14 method: 'POST', 15 headers: { 16 'Content-Type': 'application/json', 17 }, 18 body: JSON.stringify({ 19 messages: [ 20 ...(systemPrompt ? [{ role: 'system', content: systemPrompt }] : []), 21 ...(history ? history : []), 22 { role: 'user', content: prompt }, 23 ], 24 model: runtimeConfig.model, 25 stream: false, 26 response_format: jsonSchema ? { 27 type: 'json_schema', 28 json_

command

/bin/bash -lc "nl -ba nuxt.config.ts | sed -n '1,90p'"

command

/bin/bash -lc "nl -ba nuxt.config.ts | sed -n '1,90p'"

exit code 0 Output: 1 import vuetify, { transformAssetUrls } from 'vite-plugin-vuetify' 2 export default defineNuxtConfig({ 3 css: [ 4 'vuetify/lib/styles/main.sass', 5 '@mdi/font/css/materialdesignicons.min.css' 6 ], 7 8 build: { 9 transpile: ['vuetify'], 10 }, 11 12 modules: [ 13 (_options, nuxt) => { 14 nuxt.hooks.hook('vite:extendConfig', (config) => { 15 // @ts-expect-error 16 config.plugins.push(vuetify({ autoImport: true })) 17 }) 18 }, 19 //... 20 ], 21 22 vite: { 23 vue: { 24 template: { 25 transformAssetUrls, 26 }, 27 }, 28 }, 29 30 runtimeConfig: { 31 googleClientId: "", 32 googleClientSecret: "", 33 googleRedirectUri: "", 34 openAiApiKey: "", 35 model: "", 36 }, 37 38 devtools: { enabled: true }, 39 compatibilityDate: '2025-01-12', 40 })

url

https://api-dashboard.search.brave.com/documentation/services/llm-context

Query: https://api-dashboard.search.brave.com/documentation/services/llm-context Agent context: The repo doesn’t currently have a general Q&A agent surface; the “agent” is the budgeting statement extractor in `server/api/transaction/statement.post.ts`, with all model access funneled through `server/utils/llm.ts`. That makes a source-first retrieval helper a better fit than a fully hosted answer engine unless you want to replace the model path outright.

url

https://docs.perplexity.ai/docs/search/quickstart

Query: https://docs.perplexity.ai/docs/search/quickstart Agent context: The repo doesn’t currently have a general Q&A agent surface; the “agent” is the budgeting statement extractor in `server/api/transaction/statement.post.ts`, with all model access funneled through `server/utils/llm.ts`. That makes a source-first retrieval helper a better fit than a fully hosted answer engine unless you want to replace the model path outright.

url

https://ai.google.dev/gemini-api/docs/google-search

Query: https://ai.google.dev/gemini-api/docs/google-search Agent context: The repo doesn’t currently have a general Q&A agent surface; the “agent” is the budgeting statement extractor in `server/api/transaction/statement.post.ts`, with all model access funneled through `server/utils/llm.ts`. That makes a source-first retrieval helper a better fit than a fully hosted answer engine unless you want to replace the model path outright.

url

https://ai.google.dev/gemini-api/docs/google-search

agent_action

**Recommendation** Choose **Brave Search API, specifically the LLM Context endpoint...

**Recommendation** Choose **Brave Search API, specifically the LLM Context endpoint**, as the retrieval layer. This repo has one model gateway: [server/utils/llm.ts](/home/user/worktrees/attempt-27bb9c57-1053-493d-81e3-6fcbb57b392a-cap-1/server/utils/llm.ts:5). It currently calls a local OpenAI-compatible chat endpoint, while the budgeting “agent” in [server/api/transaction/statement.post.ts](/home/user/worktrees/attempt-27bb9c57-1053-493d-81e3-6fcbb57b392a-cap-1/server/api/transaction/statement.post.ts:8) depends on that helper for structured extraction and categorization. Because the app already controls generation, the safest search architecture is **retrieve authoritative source chunks first, then force the model to answer only from those chunks with explicit citations**. Brave LLM Context is the best fit because it returns pre-extracted grounding content plus source metadata, not just snippets or a fully synthesized answer. Its docs show source URLs, titles, snippets, hostnames, and page age in the response, plus controls for freshness, token budget, strict relevance filtering, and Goggles-based source ranking/filtering. That gives this product the strongest practical path to

result

Selected unknown

Commands

/bin/bash -lc "pwd && rg --files -g '"'!*node_modules*'"' -g '"'!*.png'"' -g '"'!*.jpg'"' -g '"'!*.jpeg'"' -g '"'!*.gif'"' -g '"'!*.svg'"' | head -200"

exit 0

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

exit 0

/bin/bash -lc "find . -path './node_modules' -prune -o -path './.git' -prune -o -type f | sed 's#"'^./##'"' | head -240"

exit 0

/bin/bash -lc "sed -n '1,240p' package.json"

exit 0

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

exit 0

/bin/bash -lc "sed -n '1,260p' server/utils/llm.ts"

exit 0

/bin/bash -lc "sed -n '1,260p' server/api/transaction/statement.post.ts"

exit 0

/bin/bash -lc "sed -n '261,520p' server/api/transaction/statement.post.ts"

exit 0

/bin/bash -lc "sed -n '1,220p' nuxt.config.ts"

exit 0

exit 0

/bin/bash -lc "find . -maxdepth 2 -type f -name '.env*' -print -o -name '*.md' -print"

exit 0

/bin/bash -lc "nl -ba server/api/transaction/statement.post.ts | sed -n '1,230p'"

exit 0

/bin/bash -lc "nl -ba server/utils/llm.ts | sed -n '1,90p'"

exit 0

/bin/bash -lc "nl -ba nuxt.config.ts | sed -n '1,90p'"

exit 0

URLs Fetched

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

status not materialized

https://brave.com/search/api

status not materialized

https://api-dashboard.search.brave.com/documentation/services/llm-context

status not materialized

https://docs.perplexity.ai/docs/search/quickstart

status not materialized

https://ai.google.dev/gemini-api/docs/google-search

status not materialized

Changed Files

Artifact only

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

Runs

Find

Run

Decision Summary

Trace Timeline

Commands

URLs Fetched

Changed Files

Runs

Run

Decision Summary

Trace Timeline

Commands

URLs Fetched

Changed Files