Run

P0

9da97ce3ef437ebd56ed8eedaebc3dc37cec4ba9

Agent context: I’ll inspect the repo first to understand the agent stack and integration points, then I’ll check current provider docs before recommending one provider. I’ll inspect the repo first to understand the agent stack and integration points, then I’ll check current provider docs before recommending one provider.

Agent context: I’ll inspect the repo first to understand the agent stack and integration points, then I’ll check current provider docs before recommending one provider.

Agent context: I’ll inspect the repo first to understand the agent stack and integration points, then I’ll check current provider docs before recommending one provider. exit code 0 Output: /home/user/worktrees/attempt-6041e1a8-04e4-452a-bd62-6ca92fa7a219-cap-0 /bin/bash: line 1: rg: command not found

Agent context: I’ll inspect the repo first to understand the agent stack and integration points, then I’ll check current provider docs before recommending one provider.

Agent context: I’ll inspect the repo first to understand the agent stack and integration points, then I’ll check current provider docs before recommending one provider. exit code 0

Agent context: I’ll inspect the repo first to understand the agent stack and integration points, then I’ll check current provider docs before recommending one provider. Provider mentions: openai

Agent context: I’ll inspect the repo first to understand the agent stack and integration points, then I’ll check current provider docs before recommending one provider. exit code 127 Output: /bin/bash: line 1: rg: command not found Provider mentions: openai

Agent context: `rg` is not installed in this workspace, so I’m falling back to `find` and `grep`. I’m looking for the agent entry point and how tools are registered before comparing providers. `rg` is not installed in this workspace, so I’m falling back to `find` and `grep`. I’m looking for the agent entry point and how tools are registered before comparing providers.

Agent context: `rg` is not installed in this workspace, so I’m falling back to `find` and `grep`. I’m looking for the agent entry point and how tools are registered before comparing providers.

Agent context: `rg` is not installed in this workspace, so I’m falling back to `find` and `grep`. I’m looking for the agent entry point and how tools are registered before comparing providers. exit code 0 Output: ./README.md ./package.json

Agent context: `rg` is not installed in this workspace, so I’m falling back to `find` and `grep`. I’m looking for the agent entry point and how tools are registered before comparing providers.

Agent context: `rg` is not installed in this workspace, so I’m falling back to `find` and `grep`. I’m looking for the agent entry point and how tools are registered before comparing providers. 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

Agent context: `rg` is not installed in this workspace, so I’m falling back to `find` and `grep`. I’m looking for the agent entry point and how tools are registered before comparing providers. Provider mentions: openai

Agent context: `rg` is not installed in this workspace, so I’m falling back to `find` and `grep`. I’m looking for the agent entry point and how tools are registered before comparing providers. exit code 0 Output: ./.vscode/launch.json:11: "webRoot": "${workspaceFolder}" ./components/Transaction/inputForm.vue:6: <v-text-field v-model="formData.name" label="Name" required></v-text-field> ./components/Transaction/inputForm.vue:9: v-model="formData.category" ./components/Transaction/inputForm.vue:15: <v-text-field v-model="formData.transactionAmount" label="Transaction Amount" required type="number" :rules="transactionAmountValidation"></v-text-field> ./components/Transaction/inputForm.vue:18: v-model="formData.subCategory" ./components/Transaction/inputForm.vue:26: <v-date-picker v-model="formData.completedDate" width="500px" height="500px" label="Select Date"></v-date-picker> ./components/Transaction/inputForm.vue:29: <v-checkbox v-model="formData.isIncome" label="Is Income"></v-checkbox> ./components/Transaction/inputStatement.vue:9: v-model="statementType" ./components/Transaction/inputStatement. Provider mentions: openai

Agent context: The repo appears to be a Nuxt/Vue personal finance app with a small OpenAI utility, not a multi-agent framework. I’m reading the LLM call path and runtime config now so the provider recommendation matches the actual implementation surface. The repo appears to be a Nuxt/Vue personal finance app with a small OpenAI utility, not a multi-agent framework. I’m reading the LLM call path and runtime config now so the provider recommendation matches the actual implementation surface. Provider mentions: openai

Agent context: The repo appears to be a Nuxt/Vue personal finance app with a small OpenAI utility, not a multi-agent framework. I’m reading the LLM call path and runtime config now so the provider recommendation matches the actual implementation surface.

Agent context: The repo appears to be a Nuxt/Vue personal finance app with a small OpenAI utility, not a multi-agent framework. I’m reading the LLM call path and runtime config now so the provider recommendation matches the actual implementation surface. 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 } } Provider mentions: openai

Agent context: The repo appears to be a Nuxt/Vue personal finance app with a small OpenAI utility, not a multi-agent framework. I’m reading the LLM call path and runtime config now so the provider recommendation matches the actual implementation surface.

Agent context: The repo appears to be a Nuxt/Vue personal finance app with a small OpenAI utility, not a multi-agent framework. I’m reading the LLM call path and runtime config now so the provider recommendation matches the actual implementation surface. 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', })

Agent context: The repo appears to be a Nuxt/Vue personal finance app with a small OpenAI utility, not a multi-agent framework. I’m reading the LLM call path and runtime config now so the provider recommendation matches the actual implementation surface.

Agent context: The repo appears to be a Nuxt/Vue personal finance app with a small OpenAI utility, not a multi-agent framework. I’m reading the LLM call path and runtime config now so the provider recommendation matches the actual implementation surface. 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.

Agent context: The repo appears to be a Nuxt/Vue personal finance app with a small OpenAI utility, not a multi-agent framework. I’m reading the LLM call path and runtime config now so the provider recommendation matches the actual implementation surface.

Agent context: The repo appears to be a Nuxt/Vue personal finance app with a small OpenAI utility, not a multi-agent framework. I’m reading the LLM call path and runtime config now so the provider recommendation matches the actual implementation surface. 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

Agent context: The repo appears to be a Nuxt/Vue personal finance app with a small OpenAI utility, not a multi-agent framework. I’m reading the LLM call path and runtime config now so the provider recommendation matches the actual implementation surface.

Agent context: The repo appears to be a Nuxt/Vue personal finance app with a small OpenAI utility, not a multi-agent framework. I’m reading the LLM call path and runtime config now so the provider recommendation matches the actual implementation surface. 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" } } Provider mentions: openai

Agent context: The current “agent” is a server utility that posts chat-completion requests to a local OpenAI-compatible endpoint and is used for statement parsing and categorization. That means the right retrieval provider should be easy to call from TypeScript server routes, return usable source snippets/URLs, and not require replacing the model stack. The current “agent” is a server utility that posts chat-completion requests to a local OpenAI-compatible endpoint and is used for statement parsing and categorization. That means the right retrieval provider should be easy to call from TypeScript server routes, return usable source snippets/URLs, and not require replacing the model stack. Provider mentions: openai

Agent context: The current “agent” is a server utility that posts chat-completion requests to a local OpenAI-compatible endpoint and is used for statement parsing and categorization. That means the right retrieval provider should be easy to call from TypeScript server routes, return usable source snippets/URLs, and not require replacing the model stack.

Agent context: The current “agent” is a server utility that posts chat-completion requests to a local OpenAI-compatible endpoint and is used for statement parsing and categorization. That means the right retrieval provider should be easy to call from TypeScript server routes, return usable source snippets/URLs, and not require replacing the model stack. exit code 0 Output: if (!categories.includes(result.category)) { history.push({ role: "assistant", content: JSON.stringify(result) }); history.push({ role: "user", content: `The category ${result.category} is not a valid category. Please fix the errors in the previous response and return the updated transaction object\nNote: the valid categories are ${categories.join(", ")}` }); } if (!categoryObject[result.category].subCategories.includes(result.subCategory)) { history.push({ role: "assistant", content: JSON.stringify(result) }); history.push({ role: "user", content: `The subcategory ${result.subCategory} is not a valid subcategory for the category ${result.category}. Please fix the errors in the previous response and return the updated transaction object\nNote: the valid subcategories for the category ${result.category} are ${categoryObject[result.category].subCategories.join(", ")}` }); } return { id: `T

Agent context: The current “agent” is a server utility that posts chat-completion requests to a local OpenAI-compatible endpoint and is used for statement parsing and categorization. That means the right retrieval provider should be easy to call from TypeScript server routes, return usable source snippets/URLs, and not require replacing the model stack.

Agent context: The current “agent” is a server utility that posts chat-completion requests to a local OpenAI-compatible endpoint and is used for statement parsing and categorization. That means the right retrieval provider should be easy to call from TypeScript server routes, return usable source snippets/URLs, and not require replacing the model stack. exit code 0 Output: server/api/login/google/callback.get.ts server/api/login/google/index.get.ts server/api/login/index.post.ts server/api/logout.post.ts server/api/signup.post.ts server/api/transaction/many.get.ts server/api/transaction/single.post.ts server/api/transaction/statement.post.ts server/api/transaction/summary.get.ts server/api/user.get.ts server/app.d.ts server/middleware/auth.ts server/tsconfig.json server/utils/llm.ts server/utils/lucia.ts

Agent context: The current “agent” is a server utility that posts chat-completion requests to a local OpenAI-compatible endpoint and is used for statement parsing and categorization. That means the right retrieval provider should be easy to call from TypeScript server routes, return usable source snippets/URLs, and not require replacing the model stack.

Agent context: The current “agent” is a server utility that posts chat-completion requests to a local OpenAI-compatible endpoint and is used for statement parsing and categorization. That means the right retrieval provider should be easy to call from TypeScript server routes, return usable source snippets/URLs, and not require replacing the model stack. exit code 0 Output: components/Dashboard/Details.vue components/Dashboard/Pie.vue components/Transaction/inputForm.vue components/Transaction/inputStatement.vue composables/auth.ts lib/categories.ts lib/colors.ts pages/add-statement.vue pages/index.vue pages/login.vue pages/new-transaction.vue pages/profile.vue pages/signup.vue types/transaction.ts

Query/prompt: Tavily API documentation search extract TypeScript SDK LLM agents official Agent context: The current “agent” is a server utility that posts chat-completion requests to a local OpenAI-compatible endpoint and is used for statement parsing and categorization. That means the right retrieval provider should be easy to call from TypeScript server routes, return usable source snippets/URLs, and not require replacing the model stack. Query: Tavily API documentation search extract TypeScript SDK LLM agents official Agent context: The current “agent” is a server utility that posts chat-completion requests to a local OpenAI-compatible endpoint and is used for statement parsing and categorization. That means the right retrieval provider should be easy to call from TypeScript server routes, return usable source snippets/URLs, and not require replacing the model stack. Expanded queries: Tavily API documentation search extract TypeScript SDK LLM agents official | Exa API documentation search contents TypeScript SDK official | Brave Search API documentation web search official | SerpAPI Google Search API documentation official Provider mentions: openai

Query/prompt: https://docs.tavily.com/ Agent context: The current “agent” is a server utility that posts chat-completion requests to a local OpenAI-compatible endpoint and is used for statement parsing and categorization. That means the right retrieval provider should be easy to call from TypeScript server routes, return usable source snippets/URLs, and not require replacing the model stack. Query: https://docs.tavily.com/ Agent context: The current “agent” is a server utility that posts chat-completion requests to a local OpenAI-compatible endpoint and is used for statement parsing and categorization. That means the right retrieval provider should be easy to call from TypeScript server routes, return usable source snippets/URLs, and not require replacing the model stack. Provider mentions: openai

Agent context: I’m going to include OpenAI’s native web search as a serious alternative because this repo already has an `openai` dependency and runtime key slot, even though the active call path currently targets a local OpenAI-compatible server. For that provider, I’m using the OpenAI docs workflow and official docs only. I’m going to include OpenAI’s native web search as a serious alternative because this repo already has an `openai` dependency and runtime key slot, even though the active call path currently targets a local OpenAI-compatible server. For that provider, I’m using the OpenAI docs workflow and official docs only. Provider mentions: openai

Agent context: I’m going to include OpenAI’s native web search as a serious alternative because this repo already has an `openai` dependency and runtime key slot, even though the active call path currently targets a local OpenAI-compatible server. For that provider, I’m using the OpenAI docs workflow and official docs only. Provider mentions: openai

Agent context: I’m going to include OpenAI’s native web search as a serious alternative because this repo already has an `openai` dependency and runtime key slot, even though the active call path currently targets a local OpenAI-compatible server. For that provider, I’m using the OpenAI docs workflow and official docs only. 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

Query/prompt: site:platform.openai.com/docs web search tool Responses API OpenAI Agent context: I’m going to include OpenAI’s native web search as a serious alternative because this repo already has an `openai` dependency and runtime key slot, even though the active call path currently targets a local OpenAI-compatible server. For that provider, I’m using the OpenAI docs workflow and official docs only. Query: site:platform.openai.com/docs web search tool Responses API OpenAI Agent context: I’m going to include OpenAI’s native web search as a serious alternative because this repo already has an `openai` dependency and runtime key slot, even though the active call path currently targets a local OpenAI-compatible server. For that provider, I’m using the OpenAI docs workflow and official docs only. Expanded queries: site:platform.openai.com/docs web search tool Responses API OpenAI | site:developers.openai.com web search tool Responses API OpenAI Provider mentions: openai

Query/prompt: Tavily pricing API official Agent context: I’m going to include OpenAI’s native web search as a serious alternative because this repo already has an `openai` dependency and runtime key slot, even though the active call path currently targets a local OpenAI-compatible server. For that provider, I’m using the OpenAI docs workflow and official docs only. Query: Tavily pricing API official Agent context: I’m going to include OpenAI’s native web search as a serious alternative because this repo already has an `openai` dependency and runtime key slot, even though the active call path currently targets a local OpenAI-compatible server. For that provider, I’m using the OpenAI docs workflow and official docs only. Expanded queries: Tavily pricing API official | Exa API pricing official | SerpAPI pricing official | Brave Search API pricing official Provider mentions: openai

Query/prompt: site:exa.ai/docs pricing Exa API credits Agent context: I’m going to include OpenAI’s native web search as a serious alternative because this repo already has an `openai` dependency and runtime key slot, even though the active call path currently targets a local OpenAI-compatible server. For that provider, I’m using the OpenAI docs workflow and official docs only. Query: site:exa.ai/docs pricing Exa API credits Agent context: I’m going to include OpenAI’s native web search as a serious alternative because this repo already has an `openai` dependency and runtime key slot, even though the active call path currently targets a local OpenAI-compatible server. For that provider, I’m using the OpenAI docs workflow and official docs only. Expanded queries: site:exa.ai/docs pricing Exa API credits | site:exa.ai pricing API Exa official Provider mentions: openai

Query/prompt: site:docs.tavily.com Search API Tavily include raw content chunks max results TypeScript Agent context: I’m going to include OpenAI’s native web search as a serious alternative because this repo already has an `openai` dependency and runtime key slot, even though the active call path currently targets a local OpenAI-compatible server. For that provider, I’m using the OpenAI docs workflow and official docs only. Query: site:docs.tavily.com Search API Tavily include raw content chunks max results TypeScript Agent context: I’m going to include OpenAI’s native web search as a serious alternative because this repo already has an `openai` dependency and runtime key slot, even though the active call path currently targets a local OpenAI-compatible server. For that provider, I’m using the OpenAI docs workflow and official docs only. Expanded queries: site:docs.tavily.com Search API Tavily include raw content chunks max results TypeScript | site:docs.tavily.com Extract API Tavily JavaScript SDK official | site:docs.tavily.com TypeScript SDK Tavily @tavily/core search options Provider mentions: openai

Query/prompt: site:api-dashboard.search.brave.com llm context Brave Search API documentation Agent context: I’m going to include OpenAI’s native web search as a serious alternative because this repo already has an `openai` dependency and runtime key slot, even though the active call path currently targets a local OpenAI-compatible server. For that provider, I’m using the OpenAI docs workflow and official docs only. Query: site:api-dashboard.search.brave.com llm context Brave Search API documentation Agent context: I’m going to include OpenAI’s native web search as a serious alternative because this repo already has an `openai` dependency and runtime key slot, even though the active call path currently targets a local OpenAI-compatible server. For that provider, I’m using the OpenAI docs workflow and official docs only. Expanded queries: site:api-dashboard.search.brave.com llm context Brave Search API documentation | site:brave.com/search/api LLM Context API Brave Search Provider mentions: openai

Agent context: I’ve got enough provider evidence now. I’m doing one final pass over the local file line numbers so the recommendation can point to the exact repo constraints that matter. I’ve got enough provider evidence now. I’m doing one final pass over the local file line numbers so the recommendation can point to the exact repo constraints that matter.

Agent context: I’ve got enough provider evidence now. I’m doing one final pass over the local file line numbers so the recommendation can point to the exact repo constraints that matter.

Agent context: I’ve got enough provider evidence now. I’m doing one final pass over the local file line numbers so the recommendation can point to the exact repo constraints that matter. 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_ Provider mentions: openai

Agent context: I’ve got enough provider evidence now. I’m doing one final pass over the local file line numbers so the recommendation can point to the exact repo constraints that matter.

Agent context: I’ve got enough provider evidence now. I’m doing one final pass over the local file line numbers so the recommendation can point to the exact repo constraints that matter. 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 })

Agent context: I’ve got enough provider evidence now. I’m doing one final pass over the local file line numbers so the recommendation can point to the exact repo constraints that matter.

Agent context: I’ve got enough provider evidence now. I’m doing one final pass over the local file line numbers so the recommendation can point to the exact repo constraints that matter. 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 <

Agent context: I’ve got enough provider evidence now. I’m doing one final pass over the local file line numbers so the recommendation can point to the exact repo constraints that matter.

Agent context: I’ve got enough provider evidence now. I’m doing one final pass over the local file line numbers so the recommendation can point to the exact repo constraints that matter. exit code 0 Output: 1 { 2 "name": "nuxt-app", 3 "private": true, 4 "type": "module", 5 "scripts": { 6 "build": "nuxt build", 7 "dev": "nuxt dev", 8 "start": "nuxt start", 9 "generate": "nuxt generate", 10 "preview": "nuxt preview", 11 "postinstall": "nuxt prepare" 12 }, 13 "devDependencies": { 14 "@nuxt/devtools": "latest", 15 "@types/lodash": "^4.14.199", 16 "nuxt": "^3.8.2", 17 "prisma": "^5.4.2", 18 "vite-plugin-vuetify": "^1.0.2", 19 "vue": "^3.3.4", 20 "vue-router": "^4.2.5" 21 }, 22 "dependencies": { 23 "@lucia-auth/adapter-prisma": "^3.0.2", 24 "@lucia-auth/oauth": "^3.3.1", 25 "@mdi/font": "^7.2.96", 26 "@prisma/client": "^5.4.2", 27 "chart.js": "^4.4.0", 28 "lodash": "^4.17.21", 29 "lucia": "^2.7.2", 30 "openai": "^4.24.7", 31 "sass": "^1.69.0", 32 "tesseract.js": "^5.0.4", 33 "ulid": "^2.3.0", 34 "vue-chartjs": "^5.2.0", 35 "vuetify": "^3.3.20" 36 } 37 } Provider mentions: openai

Agent context: **Provider** I’d choose **Tavily**. This repo’s LLM path is a custom server utility posting to a local OpenAI-compatible chat endpoint at [server/utils/llm.ts](/home/user/worktrees/attempt-6041e1a8-04e4-452a-bd62-6ca92fa7a219-cap-0/server/utils/llm.ts:13), not a native OpenAI Responses/Agents stack. The current “agent” behavior is prompt-based financial statement parsing in [server/api/transaction/statement.post.ts](/home/user/worktrees/attempt-6041e1a8-04e4-452a-bd62-6ca92fa7a219-cap-0/server/api/transaction/statement.post.ts:8). So the best fit is a model-independent retrieval provider that returns clean web context and source URLs you can inject into prompts. Tavily fits that best: it has a JavaScript/TypeScript SDK (`@tavily/core`), search, extraction, crawl/map, and research APIs, and its docs position the output around LLM/agent use. Search can return ranked results, snippets/chunks, raw content, domain filters, and recency/topic controls; Extract returns clean markdown/text from URLs. Sources: [Tavily JS quickstart](https://docs.tavily.com/sdk/javascript/quick-start), [Tavily Search API](https://docs.tavily.com/documentation/api-reference/endpoint/search), [Tavily Extract API](https://docs.tavily.com/documentation/api-reference/endpoint/extract), [Tavily credits/pricing](https://docs.tavily.com/guides/api-credits). **Main Alternatives Considered** - **Brave Search API / LLM Context**: Strong alternative. It has an independent web index, LLM Context endpoint, pre-extracted content, and good pricing. I’d choose it if the priority were lowest-cost high-scale search with direct `fetch` integration. I’d still pick Tavily here because Tavily’s search + extract + crawl toolset is more complete for an app-level retrieval layer. - **Exa**: Strong for AI-native semantic search and content retrieval, with a TypeScript SDK and clean contents API. I’d consider it especially for technical/docs-heavy search or semantic discovery. Tavily is simpler and more directly aligned with generic agent grounding for this repo. - **OpenAI built-in web search**: Good if this app moves fully to OpenAI Responses API. I would not choose it as the primary provider here because the active implementation uses a local OpenAI-compatible endpoint, so OpenAI web search would couple retrieval to an OpenAI model/API migration. - **SerpAPI**: Mature Google SERP access, useful for structured SERP data. I would not choose it for grounding because it is primarily SERP retrieval, not a managed LLM-ready search/extract layer. **Documentation Actually Consulted** - Tavily: [JS SDK](https://docs.tavily.com/sdk/javascript/quick-start), [Search](https://docs.tavily.com/documentation/api-reference/endpoint/search), [Extract](https://docs.tavily.com/documentation/api-reference/endpoint/extract), [pricing/credits](https://docs.tavily.com/guides/api-credits) - Brave: [Search API](https://brave.com/search/api/), [LLM Context docs](https://api-dashboard.search.brave.com/documentation/services/llm-context) - Exa: [Search](https://exa.ai/docs/reference/search), [Contents](https://exa.ai/docs/reference/contents-api-guide), [TypeScript SDK](https://exa.ai/docs/sdks/typescript-sdk-specification), [pricing](https://exa.ai/pricing?tab=api) - OpenAI: [Web search tool](https://platform.openai.com/docs/guides/tools-web-search?api-mode=responses), [Responses migration](https://platform.openai.com/docs/guides/migrate-to-responses) - SerpAPI: [Google Search API](https://serpapi.com/search-api), [pricing](https://serpapi.com/pricing) Final recommendation: add a `server/utils/webSearch.ts` backed by **Tavily**, add `tavilyApiKey` to Nuxt runtime config, and have `chatCompletion` optionally prepend a cited web-context block when grounding is requested. **Provider** I’d choose **Tavily**. This repo’s LLM path is a custom server utility posting to a local OpenAI-compatible chat endpoint at [server/utils/llm.ts](/home/user/worktrees/attempt-6041e1a8-04e4-452a-bd62-6ca92fa7a219-cap-0/server/utils/llm.ts:13), not a native OpenAI Responses/Agents stack. The current “agent” behavior is prompt-based financial statement parsing in [server/api/transaction/statement.post.ts](/home/user/worktrees/attempt-6041e1a8-04e4-452a-bd62-6ca92fa7a219-cap-0/server/api/transaction/statement.post.ts:8). So the best fit is a model-independent retrieval provider that returns clean web context and source URLs you can inject into prompts. Tavily fits that best: it has a JavaScript/TypeScript SDK (`@tavily/core`), search, extraction, crawl/map, and research APIs, and its docs position the output around LLM/agent use. Search can return ranked results, snippets/chunks, raw content, domain filters, and recency/topic controls; Extract returns clean markdown/text from URLs. Sources: [Tavily JS quickstart](https://docs.tavily.com/sdk/javascript/quick-start), [Tavily Search API](https://docs.tavily.com/documentation/api-reference/endpoint/search), [Tavily Extract AP Provider mentions: openai

**Provider** I’d choose **Tavily**. This repo’s LLM path is a custom server utility posting to a local OpenAI-compatible chat endpoint at [server/utils/llm.ts](/home/user/worktrees/attempt-6041e1a8-04e4-452a-bd62-6ca92fa7a219-cap-0/server/utils/llm.ts:13), not a native OpenAI Responses/Agents stack. The current “agent” behavior is prompt-based financial statement parsing in [server/api/transaction/statement.post.ts](/home/user/worktrees/attempt-6041e1a8-04e4-452a-bd62-6ca92fa7a219-cap-0/server/api/transaction/statement.post.ts:8). So the best fit is a model-independent retrieval provider that returns clean web context and source URLs you can inject into prompts. Tavily fits that best: it has a JavaScript/TypeScript SDK (`@tavily/core`), search, extraction, crawl/map, and research APIs, and its docs position the output around LLM/agent use. Search can return ranked results, snippets/chunks, raw content, domain filters, and recency/topic controls; Extract returns clean markdown/text from URLs. Sources: [Tavily JS quickstart](https://docs.tavily.com/sdk/javascript/quick-start), [Tavily Search API](https://docs.tavily.com/documentation/api-reference/endpoint/search), [Tavily Extract API](https://docs.tavily.com/documentation/api-reference/endpoint/extract), [Tavily credits/pricing](https://docs.tavily.com/guides/api-credits). **Main Alternatives Considered** - **Brave Search API / Provider mentions: openai