Build coding interviews that grade themselves

The grading primitive

SandboxAPI tells you whether the sandboxed run completed, failed, or timed out. Your harness owns the domain verdict: which tests passed, what feedback to show, and how to score the submission.

Single-call grading with a harness

For interview-style problems, send a small wrapper that combines the candidate's function with your hidden tests and prints a machine-readable verdict.

# The harness runs inside the sandbox and prints JSON.
curl -X POST https://sandboxapi.p.rapidapi.com/v1/execute \
  -H "X-RapidAPI-Key: $KEY" \
  -d '{
    "language": "python3",
    "timeout": 30,
    "code": "import json\n\ndef solve(nums):\n    return sum(nums)\n\ncases = [([1,2,3], 6), ([10,-4], 6)]\npassed = sum(1 for inp, exp in cases if solve(inp) == exp)\nprint(json.dumps({\"passed\": passed, \"total\": len(cases)}))"
  }'

Verdict in one round trip

The API response gives you execution metadata and the harness output. Your product maps that into candidate-facing feedback.

{
  "id": "exec_xyz",
  "status": "completed",
  "language": "python3",
  "stdout": "{\"passed\":2,\"total\":2}\n",
  "stderr": "",
  "exit_code": 0,
  "execution_time_ms": 187,
  "memory_used_kb": 12544,
  "timeout": 30,
  "created_at": "2026-06-25T10:00:00Z"
}

Map status straight into your candidate-facing UI:

• completed → parse stdout and show "Passed 7 of 10 cases."
• error → show a safe excerpt of stderr and exit code.
• timeout → "Your solution exceeded the time budget. Optimize and retry."

Multi-language support

The same endpoint runs Python, JavaScript, TypeScript, Bash, Java, C, C++, and Go. Keep one API integration and swap the language field plus your per-language harness template.

End-to-end Python example

import requests
import json

def build_python_harness(candidate_code: str) -> str:
    return f"""{candidate_code}

import json
cases = [([1, 2, 3], 6), ([10, -4], 6)]
passed = sum(1 for inp, exp in cases if solve(inp) == exp)
print(json.dumps({{"passed": passed, "total": len(cases)}}))
"""

def grade_python(candidate_code: str, time_limit=10):
    payload = {
        "language": "python3",
        "code": build_python_harness(candidate_code),
        "timeout": time_limit,
    }
    r = requests.post(
        "https://sandboxapi.p.rapidapi.com/v1/execute",
        headers={"X-RapidAPI-Key": API_KEY, "Content-Type": "application/json"},
        json=payload,
    )
    return r.json()

# Grade a candidate's solution
result = grade_python(candidate_code)

# Map verdict to candidate-facing message
if result["status"] == "completed":
    verdict = json.loads(result["stdout"])
    print(f"Passed {verdict['passed']} of {verdict['total']} cases.")
elif result["status"] == "timeout":
    print("Time limit exceeded.")
else:
    print("Code failed:", result["stderr"][:500])

Why teams pick SandboxAPI for assessments

• Predictable latency — pre-warmed sandboxes keep short submissions responsive.
• Modern runtimes — your candidates can solve in Python 3.12 or Node 22, not 2018-era stacks.
• Batch execution — run many submissions or examples through one endpoint, capped by plan.
• Streaming output — show candidate progress while longer tests run.
• Session workflows — keep files and installed packages available across iterative runs.
• gVisor isolation — candidate code runs inside a user-space kernel sandbox. Even if a candidate tries to bypass your time limit, syscall filtering stops it.