Agent in Sandbox (OpenAI)

Host process                          Sandbox (sandbox)
───────────────────────────────       ──────────────────────────────────
1. Call OpenAI chat API          →    (isolated)
2. Receive generated Python code
3. sbx.files.write(code)         →    /tmp/generated.py written to VM fs
4. sbx.commands.run(python3 ...) →    Code executes inside the VM
5. Read result.stdout            ←    VM returns stdout/stderr/exit_code
6. sbx.kill()                         VM destroyed

import openai
from declaw import Sandbox

client = openai.OpenAI()

task = "Write a Python script that finds all prime numbers under 100 and prints them."

response = client.chat.completions.create(
    model="gpt-4o-mini",
    messages=[
        {
            "role": "system",
            "content": (
                "You are a code generation agent. Given a task, respond "
                "ONLY with Python code that accomplishes the task. "
                "No markdown, no explanation."
            ),
        },
        {"role": "user", "content": task},
    ],
    temperature=0,
)

code = strip_code_fences(response.choices[0].message.content or "")

sbx = Sandbox.create(template="python", timeout=300)
try:
    sbx.files.write("/tmp/generated.py", code)
    result = sbx.commands.run("python3 /tmp/generated.py", timeout=30)
    print(result.stdout)
finally:
    sbx.kill()

def strip_code_fences(code: str) -> str:
    """Remove markdown code fences from LLM output."""
    code = code.strip()
    if code.startswith("```"):
        code = "\n".join(code.split("\n")[1:])
    if code.endswith("```"):
        code = "\n".join(code.split("\n")[:-1])
    return code.strip()

# Simulated LLM output for a data analysis task
simulated_code = textwrap.dedent("""\
    import statistics

    sales_data = [
        {"month": "Jan", "revenue": 12500},
        {"month": "Feb", "revenue": 15300},
        # ...
    ]
    revenues = [d["revenue"] for d in sales_data]
    print(f"Average: {statistics.mean(revenues)}")
    print(f"Median:  {statistics.median(revenues)}")
""")

sbx = Sandbox.create(template="python", timeout=300)
try:
    sbx.files.write("/tmp/generated.py", simulated_code)
    result = sbx.commands.run("python3 /tmp/generated.py", timeout=30)
    print(result.stdout)
finally:
    sbx.kill()

api_key = os.environ.get("OPENAI_API_KEY", "")
if not api_key or api_key == "your-openai-api-key":
    demo_mode()
else:
    live_mode()

Agent in Sandbox (OpenAI) Example
============================================================
OPENAI_API_KEY not set — running in demo mode.

--- Demo: Simulating OpenAI agent workflow ---

  Simulated task: Analyze a dataset and compute summary statistics
  Creating sandbox and executing...
  Sandbox created: sbx-abc123

  Output:
    === Sales Data Analysis ===
      total_revenue: 95500
      average_revenue: 15916.67
      median_revenue: 15300
      std_deviation: 3152.32
      min_month: Mar
      max_month: Jun
      growth_pct: 68.0

  Sandbox sbx-abc123 killed.