[G1-Sync] Manual knowledge update
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Antigravity Agent
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---
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id: ai-agent-sandbox-e2b
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title: Agent Sandbox — E2B / Daytona / code execution
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category: Coding
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status: draft
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source_trust_level: B
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verification_status: conceptual
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created_at: 2026-05-09
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updated_at: 2026-05-09
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tags: [ai, agent, sandbox, vibe-coding]
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tech_stack: { language: "TS / Python", applicable_to: ["AI"] }
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applied_in: []
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aliases: [E2B, Daytona, Modal sandbox, code interpreter, code execution, agent sandbox]
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---
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# Agent Sandbox
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> LLM 가 임의 code 실행 = 위험. **E2B / Daytona / Modal sandbox = isolated 환경**. AI agent (Devin, Cursor) 의 핵심.
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## 📖 핵심 개념
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- VM / container 격리.
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- 언어 / package install OK.
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- File system 접근.
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- Time / resource limit.
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## 💻 코드 패턴
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### E2B (sandbox-as-a-service)
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```ts
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import { Sandbox } from '@e2b/code-interpreter';
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const sandbox = await Sandbox.create();
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const result = await sandbox.runCode(`
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import pandas as pd
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df = pd.read_csv('data.csv')
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print(df.head())
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`);
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console.log(result.text);
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console.log(result.error);
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console.log(result.results); // [{type: 'text', text: '...'}]
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await sandbox.close();
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```
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→ Python / Node 가 cloud sandbox.
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### File upload
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```ts
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await sandbox.files.write('data.csv', csvContent);
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// Or stream
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await sandbox.files.upload('./local.csv', '/sandbox/data.csv');
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// Read
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const content = await sandbox.files.read('/sandbox/output.txt');
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```
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### Package install
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```ts
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await sandbox.runCode('pip install numpy scikit-learn');
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const r = await sandbox.runCode(`
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from sklearn.cluster import KMeans
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# ...
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`);
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```
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### Long-running task
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```ts
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const sandbox = await Sandbox.create({
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timeoutMs: 30 * 60 * 1000, // 30 min
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});
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await sandbox.runCode(longWork);
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```
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### Stream output
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```ts
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const result = await sandbox.runCode('for i in range(100): print(i); time.sleep(0.1)', {
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onStdout: (data) => console.log(data),
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onStderr: (data) => console.error(data),
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});
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```
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### Daytona (alternative)
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```ts
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import { Daytona } from '@daytonaio/sdk';
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const daytona = new Daytona({ apiKey: '...' });
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const workspace = await daytona.create({
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image: 'python:3.12',
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});
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await workspace.process.codeRun('print("hello")');
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```
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### Modal (Python)
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```python
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import modal
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stub = modal.Stub('agent-sandbox')
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@stub.function()
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def run_code(code: str):
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import subprocess
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return subprocess.run(['python', '-c', code], capture_output=True)
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with stub.run():
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r = run_code.remote('print("hi")')
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```
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→ Python-native, GPU 가능.
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### Docker (self-host)
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```ts
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import Docker from 'dockerode';
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const docker = new Docker();
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const container = await docker.createContainer({
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Image: 'python:3.12',
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Cmd: ['python', '-c', code],
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HostConfig: {
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Memory: 512 * 1024 * 1024,
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CpuQuota: 50000,
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NetworkMode: 'none', // 격리
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},
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});
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await container.start();
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const log = await container.logs({ stdout: true, stderr: true });
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await container.remove();
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```
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→ Self-host. Setup 복잡.
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### Firecracker (microVM)
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```
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AWS Lambda 가 사용.
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- VM 보다 빠른 boot (125ms).
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- 매 invocation = fresh VM.
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- 강한 격리.
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→ E2B / Daytona 가 자체 .
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```
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### Use case
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```
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- Code interpreter (ChatGPT)
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- Data analysis (pandas, plot)
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- Research agent (web scrape + process)
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- Devin / Cursor (개발 환경)
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- Math / 정확 (LLM 가 부족)
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- Image / video (FFmpeg, Sharp)
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- Custom training (small model)
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```
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### LLM tool 통합
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```ts
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// LLM 가 sandbox tool 호출
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const tools = [{
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name: 'run_python',
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description: 'Run Python code in sandbox',
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input_schema: { type: 'object', properties: { code: { type: 'string' } } },
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}];
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async function execute(toolUse) {
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if (toolUse.name === 'run_python') {
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const r = await sandbox.runCode(toolUse.input.code);
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return r.text;
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}
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}
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```
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→ LLM 가 "compute X" → sandbox 실행 → 결과.
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### Persistence
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```
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E2B: 매 sandbox 가 ephemeral (close = 잃음).
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Persistent: 옵션.
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→ 매 user 가 own sandbox 가능.
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```
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```ts
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const sandbox = await Sandbox.create({ template: 'persistent', metadata: { userId: 'alice' } });
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```
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### Cost
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```
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E2B: $ / sandbox-second.
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Daytona: 비슷.
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Modal: $ / GPU-second.
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Self-host: VM 가 cost.
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→ Pay per use 가 idle 안 비싼.
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```
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### Security
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```
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- Network 차단 (default).
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- File system 격리 (per sandbox).
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- CPU / memory limit.
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- Time limit.
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- 사용자 별 격리.
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```
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### Network access (옵션)
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```ts
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await sandbox.runCode('curl https://example.com', { network: true });
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```
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→ Default = 차단. Whitelist 가능.
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### File output (download)
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```ts
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const code = `
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import pandas as pd
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df = pd.read_csv('input.csv')
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df.to_csv('output.csv')
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`;
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await sandbox.files.write('input.csv', csv);
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await sandbox.runCode(code);
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const result = await sandbox.files.read('output.csv');
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```
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### Plot / chart
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```ts
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const r = await sandbox.runCode(`
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import matplotlib.pyplot as plt
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plt.plot([1, 2, 3])
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plt.savefig('/sandbox/chart.png')
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`);
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const img = await sandbox.files.readBytes('chart.png');
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```
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→ LLM 의 chart generation.
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### Dev environment
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```ts
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// Devin / Cursor 식
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const env = await Sandbox.create({
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template: 'node-vscode',
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envs: { GITHUB_TOKEN: '...' },
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});
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await env.runCode('git clone https://github.com/repo');
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await env.runCode('npm install');
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await env.runCode('npm test');
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```
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→ LLM 가 dev workflow.
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### LangChain / LlamaIndex 통합
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```python
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from langchain_experimental.tools import PythonAstREPLTool
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# Local — 위험.
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from e2b_code_interpreter import CodeInterpreter
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sandbox = CodeInterpreter()
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```
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→ E2B 가 langchain 친화.
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### 함정
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```
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- Network access 가 폭발 (DDoS, leak).
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- File 가 sensitive (e.g. /etc/passwd) — 격리.
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- Resource limit 없음 = cluster 죽임.
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- Persistent state 가 다른 user 에 leak.
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- Cost 폭발 (long-running).
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```
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### Eval
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```
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LLM 가 "Compute fibonacci(20)":
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- Without sandbox: 자기 reasoning (틀림).
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- With sandbox: 실행 (정확).
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→ Math / 정확 task 가 sandbox 의 큰 가치.
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```
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### Production
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```
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Cursor: 큰 sandbox cluster.
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Devin: 매 task 가 own VM.
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ChatGPT Code Interpreter: OpenAI infra (Kubernetes).
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Claude Computer Use: Anthropic / VM.
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```
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### Custom template
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```ts
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// E2B template
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e2b template build -n my-sandbox -d Dockerfile
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const sandbox = await Sandbox.create({ template: 'my-sandbox' });
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```
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→ Pre-built env (libs, tools).
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### Multi-language
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```ts
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await sandbox.runCode('console.log("hi")', { language: 'js' });
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await sandbox.runCode('print("hi")', { language: 'python' });
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await sandbox.runCode('echo "hi"', { language: 'bash' });
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```
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### Browser inside sandbox
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```ts
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// E2B + Playwright
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await sandbox.runCode(`
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from playwright.sync_api import sync_playwright
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with sync_playwright() as p:
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browser = p.chromium.launch()
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page = browser.new_page()
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page.goto('https://...')
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page.screenshot(path='ss.png')
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`);
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```
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→ Browser agent (위 [[AI_Browser_Agent_Patterns]]).
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### MCP server + sandbox
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```
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Anthropic MCP 의 server 가 sandbox 호출.
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- LLM → MCP → E2B → result.
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- Native integration.
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```
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→ [[AI_MCP_Server_Building]].
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### 작은 alternative
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```
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- vm2 (Node, deprecated 가 위험).
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- isolated-vm (Node, native).
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- WebContainer (browser-side).
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- StackBlitz WebContainer (Node in browser).
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→ Production = E2B / Daytona / Modal.
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```
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## 🤔 의사결정 기준
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| 작업 | 추천 |
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| Code interpreter | E2B / Daytona |
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| Heavy compute / GPU | Modal |
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| 작은 / browser | WebContainer |
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| Self-host | Docker + limits |
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| Dev environment | E2B / Daytona / Cursor cloud |
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| Quick eval | Modal Python |
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| Multi-language | E2B (built-in) |
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| Persistent | Persistent template |
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## ❌ 안티패턴
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- **Local exec (eval, exec)**: 매우 위험.
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- **Network access default**: leak.
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- **No resource limit**: cluster 죽임.
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- **Persistent + cross-user**: data leak.
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- **No timeout**: hang.
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- **Sensitive env in sandbox**: leak.
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- **LLM 가 sandbox 안 넣음**: hallucinate result.
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## 🤖 LLM 활용 힌트
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- E2B 가 most popular agent sandbox.
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- Modal 가 GPU / Python 친화.
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- Docker self-host 가 cost-effective + 복잡.
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- Network / file / resource 격리 필수.
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## 🔗 관련 문서
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- [[AI_Code_Interpreter_Sandbox]]
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- [[AI_Tool_Composition_Deep]]
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- [[AI_Multi_Agent_Coordination]]
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