Python & JavaScript Code Runner: a code sandbox for AI agents, n8n and Make

Run Python 3.13 or JavaScript (Node.js 22) code with a JSON input and get a structured result back: the return value, stdout, stderr, exit code and duration. Every job runs in a fresh process with a hard timeout. You can install pip / npm packages at runtime, run batches of jobs (the same code over many inputs, or many different snippets), and turn a returned list into dataset rows.

It is made for AI agents (Apify MCP), n8n / Make / Zapier workflows and API users who need to "just run this snippet" without servers or a Docker setup.

Why this code runner

• Reliable by design. A failing or hanging user script never fails the run. Each job ends with a clear status (ok, error, timeout), the error type and message, and a trimmed traceback.
• A hard timeout that really stops the code. When a job times out, its whole process group is killed, including child processes it started.
• Structured result, not only text. Python sets result = .... JavaScript uses return .... If the code returns nothing, the last stdout line is parsed as JSON.
• Batch jobs. Up to 1,000 jobs per run, with optional parallel execution (concurrency).
• Packages. pythonPackages: ["pandas"] and npmPackages: ["lodash"] are installed once per run and shared by all jobs.
• Files. Anything the code writes to ./output/ is saved to the run's key-value store, and the row links to it.
• Safe defaults. Your APIFY_TOKEN and other *TOKEN / *SECRET / *PASSWORD variables are removed from the code's environment unless you opt in. stdout and stderr are capped at 256 KB per job by default, and the result at 5 MB.
• Cheap. You pay $0.0008 per execution. There is no monthly fee and no start-up fee beyond Apify's tiny Actor start event.

Code conventions

	Python	JavaScript (Node.js)
Input	variable input (dict / list / value)	variable input
Return data	set top-level result = {...}	return {...}. You can also use module.exports = value, module.exports = async (input) => ... or exports.main = async (input) => ...
Async	top-level await works	top-level await works (the code runs inside an async function)
Packages	import pandas after adding "pandas" to pip packages	require('lodash') after adding "lodash" to npm packages. Use require, not import
Fallback	last stdout line, if it is valid JSON	last stdout line, if it is valid JSON

The code can also use the file $INPUT_PATH, the env variable INPUT_JSON (for inputs up to 64 KB) and $OUTPUT_DIR (= ./output).

Input example

{
  "language": "python",
  "code": "import statistics\nresult = {'mean': statistics.mean(input['numbers'])}",
  "inputJson": {"numbers": [4, 8, 15, 16, 23, 42]},
  "timeoutSecs": 30
}

Batch: the same code over many inputs, plus one JavaScript job with its own code:

{
  "language": "python",
  "code": "result = input['n'] ** 2",
  "jobs": [
    {"id": "a", "input": {"n": 3}},
    {"id": "b", "input": {"n": 4}},
    {"id": "js", "language": "javascript", "code": "return input.text.toUpperCase();", "input": {"text": "hi"}}
  ],
  "concurrency": 4
}

Output example (one dataset row per job)

{
  "jobId": "python-stats",
  "language": "python",
  "status": "ok",
  "ok": true,
  "exitCode": 0,
  "durationMs": 50,
  "result": {"count": 6, "sum": 108, "mean": 18, "sorted": [4, 8, 15, 16, 23, 42]},
  "resultSource": "result-variable",
  "stdout": "Got 6 numbers\n",
  "stderr": "",
  "error": null,
  "files": [],
  "charged": true
}

A failed job looks like this: "status": "error", "exitCode": 1, "error": {"type": "ValueError", "message": "..."}, and stderr holds the traceback of your code only. A timeout gives "status": "timeout". The OUTPUT record in the key-value store holds the run summary. For a single job it also holds that job's result, which is handy for API calls.

With Dataset output = rows, a returned list such as [{"name": "A"}, {"name": "B"}] becomes dataset rows, each with a _jobId field. This lets your code act as a small scraper or data generator in a pipeline.

Use with AI agents (Apify MCP)

Add the Actor to your MCP client through the Apify MCP server (https://mcp.apify.com?tools=gazidev/code-runner-sandbox). The agent can then call it as a tool:

• send language, code and inputJson, then read result, stdout, stderr and error from the dataset row
• the agent can retry after an error, because each failure says what went wrong (error.type, error.message, traceback)
• set timeoutSecs low (5 to 30 s) for agent loops, and keep failRunOnError off so the agent always gets the row

Typical agent uses: exact math and statistics, date arithmetic, parsing and transforming JSON or CSV, checking a regex, testing a generated function against cases, and small data jobs with pandas.

Use in n8n, Make and Zapier

Use the "Run Actor and get dataset items" action. To branch on failures, turn on Fail the run if any job fails. Otherwise, check ok in each row.

Pricing

Event	Price	When
Execution	$0.0008	per finished job, including the first 5 s of run time
Extra run time	$0.004	per started 10 s beyond the first 5 s, per started GB of run memory (default memory is 512 MB, which counts as 1 GB)
Packages installed	$0.005	once per run, only when pip/npm packages were installed successfully

A finished job is charged even when your code raises an error, exits non-zero or times out, because the code did run. Not charged: invalid input, package install failures (no job runs), jobs that were never started, and platform errors.

Examples: 1,000 short jobs cost about $0.80. A single 2-second snippet costs $0.0008 plus the Actor start event.

Max cost per run is respected. Jobs start only while your budget covers them. A long job's timeout is shortened to fit the budget, and the rest of the jobs are listed as not-run.

Actor	Price per execution (FREE tier)	Notes
This Actor	$0.0008 + time beyond 5 s	Python and JS, batch, pip/npm, rows output
parsebird/code-runner-sandbox	$0.00099	one execution per run
automation-lab/ai-code-runner-sandbox	$0.0010 + $0.005 start
rixin/execute-javascript-code	$0.0009 per second	JavaScript only
apify/ai-code-sandbox	free (you pay the compute)	interactive sandbox, different use case

(Store prices checked on 2026-10-24.)

FAQ

Is it secure? Every job runs in a separate process inside your own Apify run container. Apify isolates that container from other users. Secrets are removed from the job environment by default. There is no extra seccomp or network jail inside the container, so the code can reach the internet. Treat untrusted code the way you would in any container.

Can my code call the Apify API? Yes. Turn on Give the code my APIFY_TOKEN. The Python apify / apify-client packages are already installed.

Which versions are installed? Python 3.13 and Node.js 22 with npm. Python's standard library is available with no installs.

What happens if the job prints a lot? stdout and stderr are cut at maxOutputKb (default 256 KB, max 4 MB), and stdoutTruncated is set. Return big data as a list (rows mode) or as files in ./output/.

What about memory? Jobs share the run memory: 512 MB by default, and you can set it up to 8 GB in the run options. Raise it for pandas and big inputs, or when you use concurrency > 1.

Are results returned in order? With concurrency 1 the rows are in job order. With parallel jobs they are in the order the jobs finish. Use jobId to match them.