# Code runtime (`apify/code-runtime`) Actor This Actor executes TypeScript/JavaScript that an AI agent submits through the Apify MCP Server's Code Mode, then returns whatever the script printed. - **URL**: https://apify.com/apify/code-runtime.md - **Developed by:** [Apify](https://apify.com/apify) (community) - **Categories:** AI, Agents, Automation - **Stats:** 3 total users, 2 monthly users, 95.0% runs succeeded, 0 bookmarks - **User rating**: No ratings yet ## Pricing Pay per usage This Actor is paid per platform usage. The Actor is free to use, and you only pay for the Apify platform usage, which gets cheaper the higher subscription plan you have. Learn more: https://docs.apify.com/platform/actors/running/actors-in-store#pay-per-usage ## 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 ## Code Runtime (experimental) > ⚠️ **Experimental infrastructure Actor.** It powers **Code Mode** on > [mcp.apify.com](https://mcp.apify.com) and is normally invoked by the Apify > MCP Server, not run by hand. Its behaviour and API may change without notice. ### What it does This Actor executes TypeScript/JavaScript that an AI agent submits through the Apify MCP Server's **Code Mode**, then returns whatever the script printed. Code Mode exists so an agent can do many Apify operations in **one go** — search the Store, run an Actor, read its dataset, filter and aggregate the results — instead of sending every intermediate result back through the model and wasting tokens. This Actor is the sandbox that runs that script. ### Enabling Code Mode on the MCP Server Code Mode is opt-in. Add the Code Mode tools to the `tools` query parameter of your mcp.apify.com connection URL: ```` https://mcp.apify.com/?tools=run-code,get-code-docs ```` For full configuration options, use the configurator at [mcp.apify.com](https://mcp.apify.com). ### How it works - **One script per run.** The Actor reads your `code`, runs it once, writes the result, and exits. - The code runs inside a [`workerd`](https://github.com/cloudflare/workerd) V8 isolate: **no filesystem, no package imports**, and outbound network is restricted to `*.apify.com`. - Inside the script a global **`apify`** object exposes a small, typed subset of the Apify API — run Actors, read/write datasets and key-value stores — using the current run's token (see below). - `console.log` / `console.info` go to **stdout**; `console.error` / `console.warn` go to **stderr**. The two streams are captured separately. ### Input ```json { "code": "const { items } = await apify.actor.runAndGetItems({ actorId: 'apify/rag-web-browser', input: { query: 'apify' }, limit: 3 });\nconsole.log(items.map((i) => i.metadata?.title).join('\\n'));" } ```` | Field | Type | Description | |---|---|---| | `code` | string | The TypeScript/JavaScript script to run. It receives the `apify` binding and `console`. | ### Output A single **dataset item** with the captured streams: ```json { "stdout": "Apify: Full-stack web scraping ...\n...", "stderr": "" } ``` If the script throws, the error lands in `stderr`; `stdout` keeps whatever was printed before the failure. ### Permissions & safety - Runs with **limited permissions**: the sandbox has no filesystem and can reach only the Apify API (`*.apify.com`). - Each run is an isolated, single-use container — nothing persists between runs. ### The `apify` binding Every method takes one options object and returns parsed JSON (`?` = optional, `= x` = default). Full API documentation is available [here](https://github.com/apify/actor-code-runtime/blob/master/docs/API.md). ```js // Actors apify.actor.search({ query, limit?, category? }) // → actors[] apify.actor.getDetails({ actorId }) // → actor apify.actor.start({ actorId, input?, memoryMbytes?, timeoutSecs?, maxTotalChargeUsd?, maxItems? }) // → run apify.actor.run({ actorId, ...startOpts, waitForFinishSecs = 60 }) // → run (waits) apify.actor.runAndGetItems({ actorId, input?, fields?, limit?, ...runOpts }) // → { run, items } // Runs apify.run.get({ runId }) // → run apify.run.wait({ runId, waitForFinishSecs = 60 }) // → run apify.run.abort({ runId }) // → run apify.run.getLog({ runId, limit? }) // → string // Datasets apify.dataset.create({ name? }) // → dataset apify.dataset.pushItems({ datasetId, items }) // → void apify.dataset.listItems({ datasetId, fields?, omit?, limit?, offset?, clean?, desc? }) // → items[] apify.dataset.iterate({ datasetId, batchSize = 1000, ...filters }) // → async iterable apify.dataset.getSchema({ datasetId, sample = 5 }) // → { itemCount, fields[] } // Key-value stores apify.kvs.create({ name? }) // → store apify.kvs.set({ storeId, key, value, contentType? }) // → void apify.kvs.get({ storeId, key }) // → value | null apify.kvs.list({ storeId, limit?, exclusiveStartKey? }) // → { items } ``` ### Learn more - Apify MCP Server: - Code Mode design: [apify/apify-mcp-server#794](https://github.com/apify/apify-mcp-server/pull/794) # Actor input Schema ## `code` (type: `string`): TypeScript/JavaScript program executed inside the sandbox. It receives an `apify` binding and `console`; stdout and stderr are captured separately and pushed to the default dataset as { stdout, stderr }. ## Actor input object example ```json {} ``` # Actor output Schema ## `output` (type: `string`): No description # 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 = {}; // Run the Actor and wait for it to finish const run = await client.actor("apify/code-runtime").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/${run.defaultDatasetId}`); const { items } = await client.dataset(run.defaultDatasetId).listItems(); items.forEach((item) => { console.dir(item); }); // 📚 Want to learn more 📖? Go to → https://docs.apify.com/api/client/js/docs ``` ## Python example ```python from apify_client import ApifyClient # Initialize the ApifyClient with your Apify API token # Replace '' with your token. client = ApifyClient("") # Prepare the Actor input run_input = {} # Run the Actor and wait for it to finish run = client.actor("apify/code-runtime").call(run_input=run_input) # Fetch and print Actor results from the run's dataset (if there are any) print("💾 Check your data here: https://console.apify.com/storage/datasets/" + run["defaultDatasetId"]) for item in client.dataset(run["defaultDatasetId"]).iterate_items(): print(item) # 📚 Want to learn more 📖? Go to → https://docs.apify.com/api/client/python/docs/quick-start ``` ## CLI example ```bash echo '{}' | apify call apify/code-runtime --silent --output-dataset ``` ## MCP server setup ```json { "mcpServers": { "apify": { "command": "npx", "args": [ "mcp-remote", "https://mcp.apify.com/?tools=apify/code-runtime", "--header", "Authorization: Bearer " ] } } } ``` ## OpenAPI specification ```json { "openapi": "3.0.1", "info": { "title": "Code runtime", "description": "This Actor executes TypeScript/JavaScript that an AI agent submits through the Apify MCP Server's Code Mode, then returns whatever the script printed.", "version": "0.0", "x-build-id": "lB4Soc8xJmCWxfihD" }, "servers": [ { "url": "https://api.apify.com/v2" } ], "paths": { "/acts/apify~code-runtime/run-sync-get-dataset-items": { "post": { "operationId": "run-sync-get-dataset-items-apify-code-runtime", "x-openai-isConsequential": false, "summary": "Executes an Actor, waits for its completion, and returns Actor's dataset items in response.", "tags": [ "Run Actor" ], "requestBody": { "required": true, "content": { "application/json": { "schema": { "$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/apify~code-runtime/runs": { "post": { "operationId": "runs-sync-apify-code-runtime", "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/apify~code-runtime/run-sync": { "post": { "operationId": "run-sync-apify-code-runtime", "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": [ "code" ], "properties": { "code": { "title": "Code", "type": "string", "description": "TypeScript/JavaScript program executed inside the sandbox. It receives an `apify` binding and `console`; stdout and stderr are captured separately and pushed to the default dataset as { stdout, stderr }." } } }, "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 } } } } } } } } } } ```