E2B och molnbaserade sandlådekörningstjänster

# Install the SDK
# pip install e2b-code-interpreter

from e2b_code_interpreter import Sandbox

sandbox = Sandbox()  # creates a new cloud sandbox
print('Sandbox ID:', sandbox.sandbox_id)

from e2b_code_interpreter import Sandbox

with Sandbox() as sandbox:
    result = sandbox.run_code('print(2 ** 10)')
    print(result.logs.stdout)   # ['1024']
    print(result.error)         # None if no error

from e2b_code_interpreter import Sandbox

with Sandbox() as sandbox:
    result = sandbox.run_code('1 / 0')

    if result.error:
        print('Error name:', result.error.name)    # ZeroDivisionError
        print('Traceback:', result.error.traceback)
    else:
        print(result.logs.stdout)

from e2b_code_interpreter import Sandbox

csv_content = 'name,score\nAlice,95\nBob,87\nCarol,92'

with Sandbox() as sandbox:
    sandbox.files.write('/home/user/data.csv', csv_content.encode())

    result = sandbox.run_code(
        'import csv\n'
        'rows = list(csv.DictReader(open("/home/user/data.csv")))\n'
        'print([r["name"] for r in rows])'
    )
    print(result.logs.stdout)   # ["['Alice', 'Bob', 'Carol']"]

from e2b_code_interpreter import Sandbox
import json

with Sandbox() as sandbox:
    sandbox.run_code(
        'import json\n'
        'result = {"mean": 91.3, "max": 95}\n'
        'json.dump(result, open("/home/user/output.json", "w"))'
    )
    data = sandbox.files.read('/home/user/output.json')
    parsed = json.loads(data)
    print(parsed)  # {'mean': 91.3, 'max': 95}

from e2b_code_interpreter import Sandbox

with Sandbox() as sandbox:
    # Install pandas inside the sandbox
    sandbox.run_code('import subprocess; subprocess.run(["pip", "install", "pandas", "-q"])')

    # Now use pandas
    result = sandbox.run_code(
        'import pandas as pd\n'
        'df = pd.DataFrame({"x": [1,2,3], "y": [4,5,6]})\n'
        'print(df.describe())'
    )
    print(result.logs.stdout)

from e2b_code_interpreter import Sandbox

# Ephemeral (default context manager)
with Sandbox() as sb:
    sb.run_code('x = 42')
# Sandbox destroyed here

# Persistent: keep alive for N seconds
sb = Sandbox(timeout=300)  # keep alive 5 minutes
try:
    sb.run_code('import numpy as np')  # expensive import cached
    sb.run_code('arr = np.arange(1000)')
    result = sb.run_code('print(arr.mean())')
    print(result.logs.stdout)
finally:
    sb.kill()  # explicit cleanup

from e2b_code_interpreter import Sandbox

# First session
sb1 = Sandbox(timeout=300)
sb1.run_code('accumulated_data = []')
sandbox_id = sb1.sandbox_id
print('Created:', sandbox_id)

# Later — different process, same sandbox
sb2 = Sandbox.connect(sandbox_id)
result = sb2.run_code('accumulated_data.append(1); print(accumulated_data)')
print(result.logs.stdout)  # '[1]' — state preserved

sb2.kill()

# Cost-optimization patterns:

# 1. Use short timeouts for one-shot executions
with Sandbox(timeout=60) as sb:
    result = sb.run_code('print("done")')
    # Auto-kills after 60s if not killed first

# 2. Kill immediately after use
sb = Sandbox()
try:
    sb.run_code('process_data()')
finally:
    sb.kill()   # don't wait for timeout

# 3. Share one sandbox across multiple agent steps
#    instead of creating a new one per step

from e2b_code_interpreter import Sandbox
import openai

client = openai.OpenAI(api_key='YOUR_OPENAI_KEY')

def run_agent(user_question: str):
    with Sandbox(timeout=120) as sb:
        messages = [{'role': 'user', 'content': user_question}]

        for _ in range(5):  # max 5 turns
            resp = client.chat.completions.create(
                model='gpt-4o', messages=messages,
                tools=[{'type': 'function', 'function': {
                    'name': 'run_python',
                    'description': 'Execute Python in a secure sandbox',
                    'parameters': {'type': 'object',
                                   'properties': {'code': {'type': 'string'}},
                                   'required': ['code']}
                }}]
            )
            msg = resp.choices[0].message
            if msg.tool_calls:
                code = eval(msg.tool_calls[0].function.arguments)['code']
                result = sb.run_code(code)
                messages.append({'role': 'tool',
                                  'tool_call_id': msg.tool_calls[0].id,
                                  'content': str(result.logs.stdout)})
            else:
                return msg.content