# Chatgpt Prompt Runner (`zerobreak/chatgpt-prompt-runner`) Actor ChatGPT prompt runner that sends single or bulk prompts to ChatGPT and returns structured replies with model info, citations, and latency, so you can automate content generation and AI research at scale without writing code. - **URL**: https://apify.com/zerobreak/chatgpt-prompt-runner.md - **Developed by:** [ZeroBreak](https://apify.com/zerobreak) (community) - **Categories:** AI, Automation, Developer tools - **Stats:** 2 total users, 1 monthly users, 100.0% runs succeeded, NaN bookmarks - **User rating**: No ratings yet ## Pricing from $9.99 / 1,000 results This Actor is paid per event. You are not charged for the Apify platform usage, but only a fixed price for specific events. Since this Actor supports Apify Store discounts, the price gets lower the higher subscription plan you have. Learn more: https://docs.apify.com/platform/actors/running/actors-in-store#pay-per-event ## What's an Apify Actor? Actors are a software tools running on the Apify platform, for all kinds of web data extraction and automation use cases. In Batch mode, an Actor accepts a well-defined JSON input, performs an action which can take anything from a few seconds to a few hours, and optionally produces a well-defined JSON output, datasets with results, or files in key-value store. In Standby mode, an Actor provides a web server which can be used as a website, API, or an MCP server. Actors are written with capital "A". ## How to integrate an Actor? If asked about integration, you help developers integrate Actors into their projects. You adapt to their stack and deliver integrations that are safe, well-documented, and production-ready. The best way to integrate Actors is as follows. In JavaScript/TypeScript projects, use official [JavaScript/TypeScript client](https://docs.apify.com/api/client/js.md): ```bash npm install apify-client ``` In Python projects, use official [Python client library](https://docs.apify.com/api/client/python.md): ```bash pip install apify-client ``` In shell scripts, use [Apify CLI](https://docs.apify.com/cli/docs.md): ````bash # MacOS / Linux curl -fsSL https://apify.com/install-cli.sh | bash # Windows irm https://apify.com/install-cli.ps1 | iex ```bash In AI frameworks, you might use the [Apify MCP server](https://docs.apify.com/platform/integrations/mcp.md). If your project is in a different language, use the [REST API](https://docs.apify.com/api/v2.md). For usage examples, see the [API](#api) section below. For more details, see Apify documentation as [Markdown index](https://docs.apify.com/llms.txt) and [Markdown full-text](https://docs.apify.com/llms-full.txt). # README ## ChatGPT Prompt Runner Send one or many prompts to ChatGPT and collect the AI-generated replies in structured JSON. Each prompt runs as an independent request and returns the full response including reply text, model name, message ID, conversation ID, citations, and upstream latency. ### What it does - **Bulk prompt execution** — run a list of prompts in a single actor run - **Structured output** — reply text, model slug, message ID, conversation ID, citations - **Error handling** — failed prompts are logged with an error field; other prompts continue - **No browser needed** — built on a direct API integration ### Input | Field | Type | Description | |-------|------|-------------| | `prompts` | array | List of prompts to send to ChatGPT | | `requestTimeoutSecs` | integer | Per-request timeout in seconds (default: 60, max: 180) | #### Example input ```json { "prompts": [ "What are the top 5 programming languages in 2025?", "Explain the difference between REST and GraphQL.", "Write a Python function to reverse a string." ], "requestTimeoutSecs": 60 } ```` ### Output Each record corresponds to one prompt: ```json { "promptIndex": 0, "prompt": "What are the top 5 programming languages in 2025?", "reply": "The top 5 programming languages in 2025 are Python, JavaScript, TypeScript, Rust, and Go...", "messageId": "49399df9-633a-40ce-81ee-f3ce22567fde", "conversationId": "6a0ad6ea-3da0-83ea-b833-f88381dd652c", "modelSlug": "gpt-5-3", "status": "finished_successfully", "citations": [], "upstreamLatency": "4.684s", "scrapedAt": "2025-05-18T12:00:00+00:00", "error": null } ``` ### Usage tips - Each prompt is a separate independent request — there is no shared conversation context between prompts - For longer or more detailed prompts, increase `requestTimeoutSecs` to 90 or 120 - If a prompt fails, its record will have `error` set and `reply` as null — the run continues with remaining prompts - Use `promptIndex` to match output records back to your original prompt list # Actor input Schema ## `prompts` (type: `array`): One or more prompts to send to ChatGPT. Each prompt is processed as a separate independent request. ## `requestTimeoutSecs` (type: `integer`): Per-request timeout in seconds. ChatGPT responses can take 10–30 seconds for longer prompts. ## Actor input object example ```json { "prompts": [ "What is the tallest mountain in the world?" ], "requestTimeoutSecs": 60 } ``` # API You can run this Actor programmatically using our API. Below are code examples in JavaScript, Python, and CLI, as well as the OpenAPI specification and MCP server setup. ## JavaScript example ```javascript import { ApifyClient } from 'apify-client'; // Initialize the ApifyClient with your Apify API token // Replace the '' with your token const client = new ApifyClient({ token: '', }); // Prepare Actor input const input = { "prompts": [ "What is the tallest mountain in the world?" ] }; // Run the Actor and wait for it to finish const run = await client.actor("zerobreak/chatgpt-prompt-runner").call(input); // Fetch and print Actor results from the run's dataset (if any) console.log('Results from dataset'); console.log(`💾 Check your data here: https://console.apify.com/storage/datasets/${run.defaultDatasetId}`); const { items } = await client.dataset(run.defaultDatasetId).listItems(); items.forEach((item) => { console.dir(item); }); // 📚 Want to learn more 📖? Go to → https://docs.apify.com/api/client/js/docs ``` ## Python example ```python from apify_client import ApifyClient # Initialize the ApifyClient with your Apify API token # Replace '' with your token. client = ApifyClient("") # Prepare the Actor input run_input = { "prompts": ["What is the tallest mountain in the world?"] } # Run the Actor and wait for it to finish run = client.actor("zerobreak/chatgpt-prompt-runner").call(run_input=run_input) # Fetch and print Actor results from the run's dataset (if there are any) print("💾 Check your data here: https://console.apify.com/storage/datasets/" + run["defaultDatasetId"]) for item in client.dataset(run["defaultDatasetId"]).iterate_items(): print(item) # 📚 Want to learn more 📖? Go to → https://docs.apify.com/api/client/python/docs/quick-start ``` ## CLI example ```bash echo '{ "prompts": [ "What is the tallest mountain in the world?" ] }' | apify call zerobreak/chatgpt-prompt-runner --silent --output-dataset ``` ## MCP server setup ```json { "mcpServers": { "apify": { "command": "npx", "args": [ "mcp-remote", "https://mcp.apify.com/?tools=zerobreak/chatgpt-prompt-runner", "--header", "Authorization: Bearer " ] } } } ``` ## OpenAPI specification ```json { "openapi": "3.0.1", "info": { "title": "Chatgpt Prompt Runner", "description": "ChatGPT prompt runner that sends single or bulk prompts to ChatGPT and returns structured replies with model info, citations, and latency, so you can automate content generation and AI research at scale without writing code.", "version": "0.0", "x-build-id": "9cbpKe6gARqAMTeKi" }, "servers": [ { "url": "https://api.apify.com/v2" } ], "paths": { "/acts/zerobreak~chatgpt-prompt-runner/run-sync-get-dataset-items": { "post": { "operationId": "run-sync-get-dataset-items-zerobreak-chatgpt-prompt-runner", "x-openai-isConsequential": false, "summary": "Executes an Actor, waits for its completion, and returns Actor's dataset items in response.", "tags": [ "Run Actor" ], "requestBody": { "required": true, "content": { "application/json": { "schema": { "$ref": "#/components/schemas/inputSchema" } } } }, "parameters": [ { "name": "token", "in": "query", "required": true, "schema": { "type": "string" }, "description": "Enter your Apify token here" } ], "responses": { "200": { "description": "OK" } } } }, "/acts/zerobreak~chatgpt-prompt-runner/runs": { "post": { "operationId": "runs-sync-zerobreak-chatgpt-prompt-runner", "x-openai-isConsequential": false, "summary": "Executes an Actor and returns information about the initiated run in response.", "tags": [ "Run Actor" ], "requestBody": { "required": true, "content": { "application/json": { "schema": { "$ref": "#/components/schemas/inputSchema" } } } }, "parameters": [ { "name": "token", "in": "query", "required": true, "schema": { "type": "string" }, "description": "Enter your Apify token here" } ], "responses": { "200": { "description": "OK", "content": { "application/json": { "schema": { "$ref": "#/components/schemas/runsResponseSchema" } } } } } } }, "/acts/zerobreak~chatgpt-prompt-runner/run-sync": { "post": { "operationId": "run-sync-zerobreak-chatgpt-prompt-runner", "x-openai-isConsequential": false, "summary": "Executes an Actor, waits for completion, and returns the OUTPUT from Key-value store in response.", "tags": [ "Run Actor" ], "requestBody": { "required": true, "content": { "application/json": { "schema": { "$ref": "#/components/schemas/inputSchema" } } } }, "parameters": [ { "name": "token", "in": "query", "required": true, "schema": { "type": "string" }, "description": "Enter your Apify token here" } ], "responses": { "200": { "description": "OK" } } } } }, "components": { "schemas": { "inputSchema": { "type": "object", "required": [ "prompts" ], "properties": { "prompts": { "title": "Prompts", "type": "array", "description": "One or more prompts to send to ChatGPT. Each prompt is processed as a separate independent request.", "items": { "type": "string" } }, "requestTimeoutSecs": { "title": "Request timeout (seconds)", "minimum": 10, "maximum": 180, "type": "integer", "description": "Per-request timeout in seconds. ChatGPT responses can take 10–30 seconds for longer prompts.", "default": 60 } } }, "runsResponseSchema": { "type": "object", "properties": { "data": { "type": "object", "properties": { "id": { "type": "string" }, "actId": { "type": "string" }, "userId": { "type": "string" }, "startedAt": { "type": "string", "format": "date-time", "example": "2025-01-08T00:00:00.000Z" }, "finishedAt": { "type": "string", "format": "date-time", "example": "2025-01-08T00:00:00.000Z" }, "status": { "type": "string", "example": "READY" }, "meta": { "type": "object", "properties": { "origin": { "type": "string", "example": "API" }, "userAgent": { "type": "string" } } }, "stats": { "type": "object", "properties": { "inputBodyLen": { "type": "integer", "example": 2000 }, "rebootCount": { "type": "integer", "example": 0 }, "restartCount": { "type": "integer", "example": 0 }, "resurrectCount": { "type": "integer", "example": 0 }, "computeUnits": { "type": "integer", "example": 0 } } }, "options": { "type": "object", "properties": { "build": { "type": "string", "example": "latest" }, "timeoutSecs": { "type": "integer", "example": 300 }, "memoryMbytes": { "type": "integer", "example": 1024 }, "diskMbytes": { "type": "integer", "example": 2048 } } }, "buildId": { "type": "string" }, "defaultKeyValueStoreId": { "type": "string" }, "defaultDatasetId": { "type": "string" }, "defaultRequestQueueId": { "type": "string" }, "buildNumber": { "type": "string", "example": "1.0.0" }, "containerUrl": { "type": "string" }, "usage": { "type": "object", "properties": { "ACTOR_COMPUTE_UNITS": { "type": "integer", "example": 0 }, "DATASET_READS": { "type": "integer", "example": 0 }, "DATASET_WRITES": { "type": "integer", "example": 0 }, "KEY_VALUE_STORE_READS": { "type": "integer", "example": 0 }, "KEY_VALUE_STORE_WRITES": { "type": "integer", "example": 1 }, "KEY_VALUE_STORE_LISTS": { "type": "integer", "example": 0 }, "REQUEST_QUEUE_READS": { "type": "integer", "example": 0 }, "REQUEST_QUEUE_WRITES": { "type": "integer", "example": 0 }, "DATA_TRANSFER_INTERNAL_GBYTES": { "type": "integer", "example": 0 }, "DATA_TRANSFER_EXTERNAL_GBYTES": { "type": "integer", "example": 0 }, "PROXY_RESIDENTIAL_TRANSFER_GBYTES": { "type": "integer", "example": 0 }, "PROXY_SERPS": { "type": "integer", "example": 0 } } }, "usageTotalUsd": { "type": "number", "example": 0.00005 }, "usageUsd": { "type": "object", "properties": { "ACTOR_COMPUTE_UNITS": { "type": "integer", "example": 0 }, "DATASET_READS": { "type": "integer", "example": 0 }, "DATASET_WRITES": { "type": "integer", "example": 0 }, "KEY_VALUE_STORE_READS": { "type": "integer", "example": 0 }, "KEY_VALUE_STORE_WRITES": { "type": "number", "example": 0.00005 }, "KEY_VALUE_STORE_LISTS": { "type": "integer", "example": 0 }, "REQUEST_QUEUE_READS": { "type": "integer", "example": 0 }, "REQUEST_QUEUE_WRITES": { "type": "integer", "example": 0 }, "DATA_TRANSFER_INTERNAL_GBYTES": { "type": "integer", "example": 0 }, "DATA_TRANSFER_EXTERNAL_GBYTES": { "type": "integer", "example": 0 }, "PROXY_RESIDENTIAL_TRANSFER_GBYTES": { "type": "integer", "example": 0 }, "PROXY_SERPS": { "type": "integer", "example": 0 } } } } } } } } } } ```