@computesdk/e2b

E2B provider for ComputeSDK - Execute code in secure, isolated E2B sandboxes with full filesystem and terminal support.

Installation

``bash
npm install @computesdk/e2b
`

Setup

1. Get your E2B API key from e2b.dev
2. Set the environment variable:

`bash
export E2B_API_KEY=e2b_your_api_key_here
`

Quick Start

$3

Use the gateway for zero-config auto-detection:

`typescript
import { compute } from 'computesdk';

// Auto-detects E2B from E2B_API_KEY environment variable
const sandbox = await compute.sandbox.create();

const result = await sandbox.runCode(
import pandas as pd
print(pd.__version__)
);

console.log(result.stdout);
await sandbox.destroy();
`

$3

For direct SDK usage without the gateway:

`typescript
import { e2b } from '@computesdk/e2b';

const compute = e2b({ apiKey: process.env.E2B_API_KEY });

const sandbox = await compute.sandbox.create();

const result = await sandbox.runCode(
import pandas as pd
import numpy as np

data = {'A': [1, 2, 3], 'B': [4, 5, 6]}
df = pd.DataFrame(data)
print(df)
print(f"Sum: {df.sum().sum()}")
);

console.log(result.stdout);
await sandbox.destroy();
`

Configuration

$3

`bash
export E2B_API_KEY=e2b_your_api_key_here
`

$3

`typescript
interface E2BConfig {
/* E2B API key - if not provided, will use E2B_API_KEY env var /
apiKey?: string;
/* Default runtime environment /
runtime?: 'python' | 'node';
/* Execution timeout in milliseconds /
timeout?: number;
}
`

Features

- ✅ Code Execution - Python and Node.js runtime support
- ✅ Command Execution - Run shell commands in sandbox
- ✅ Filesystem Operations - Full file system access via E2B API
- ✅ Terminal Support - Interactive PTY terminals
- ✅ Auto Runtime Detection - Automatically detects Python vs Node.js
- ✅ Data Science Ready - Pre-installed pandas, numpy, matplotlib, etc.

API Reference

$3

`typescript
// Execute Python code
const result = await sandbox.runCode(
import json
data = {"message": "Hello from Python"}
print(json.dumps(data))
, 'python');

// Execute Node.js code
const result = await sandbox.runCode(
const data = { message: "Hello from Node.js" };
console.log(JSON.stringify(data));
, 'node');

// Auto-detection (based on code patterns)
const result = await sandbox.runCode('print("Auto-detected as Python")');
`

$3

`typescript
// List files
const result = await sandbox.runCommand('ls', ['-la']);

// Install packages
const result = await sandbox.runCommand('pip', ['install', 'requests']);

// Run scripts
const result = await sandbox.runCommand('python', ['script.py']);
`

$3

`typescript
// Write file
await sandbox.filesystem.writeFile('/tmp/hello.py', 'print("Hello World")');

// Read file
const content = await sandbox.filesystem.readFile('/tmp/hello.py');

// Create directory
await sandbox.filesystem.mkdir('/tmp/data');

// List directory contents
const files = await sandbox.filesystem.readdir('/tmp');

// Check if file exists
const exists = await sandbox.filesystem.exists('/tmp/hello.py');

// Remove file or directory
await sandbox.filesystem.remove('/tmp/hello.py');
`

$3

`typescript
// Create terminal
const terminal = await sandbox.terminal.create({
command: 'bash',
cols: 80,
rows: 24,
onData: (data: Uint8Array) => {
const output = new TextDecoder().decode(data);
console.log('Terminal output:', output);
}
});

// Write to terminal
await terminal.write('echo "Hello Terminal!"\n');

// Resize terminal
await terminal.resize(120, 30);

// Kill terminal
await terminal.kill();

// List all terminals
const terminals = await sandbox.terminal.list();

// Get terminal by ID
const existingTerminal = await sandbox.terminal.getById('terminal-id');
`

$3

`typescript
// Get sandbox info
const info = await sandbox.getInfo();
console.log(info.id, info.status, info.createdAt);

// Destroy sandbox
await sandbox.destroy();
`

Runtime Detection

The provider automatically detects the runtime based on code patterns:

Python indicators:
- print( statements
- import statements
- def function definitions
- Python-specific syntax (f", __, etc.)

Default: Node.js for all other cases

Error Handling

`typescript
import { e2b } from '@computesdk/e2b';

try {
const compute = e2b({ apiKey: process.env.E2B_API_KEY });
const sandbox = await compute.sandbox.create();

const result = await sandbox.runCode('invalid code');
} catch (error) {
if (error.message.includes('Missing E2B API key')) {
console.error('Set E2B_API_KEY environment variable');
} else if (error.message.includes('Invalid E2B API key format')) {
console.error('E2B API keys should start with "e2b_"');
} else if (error.message.includes('authentication failed')) {
console.error('Check your E2B API key');
} else if (error.message.includes('quota exceeded')) {
console.error('E2B usage limits reached');
}
}
`

Examples

$3

`typescript
import { e2b } from '@computesdk/e2b';

const compute = e2b({ apiKey: process.env.E2B_API_KEY });
const sandbox = await compute.sandbox.create();

// Create project structure
await sandbox.filesystem.mkdir('/analysis');
await sandbox.filesystem.mkdir('/analysis/data');
await sandbox.filesystem.mkdir('/analysis/output');

// Write input data
const csvData = name,age,city
Alice,25,New York
Bob,30,San Francisco
Charlie,35,Chicago;

await sandbox.filesystem.writeFile('/analysis/data/people.csv', csvData);

// Process data with Python
const result = await sandbox.runCode(
import pandas as pd
import matplotlib.pyplot as plt

Read data

df = pd.read_csv('/analysis/data/people.csv')
print("Data loaded:")
print(df)

Calculate statistics

avg_age = df['age'].mean()
print(f"\\nAverage age: {avg_age}")

Create visualization

plt.figure(figsize=(8, 6))
plt.bar(df['name'], df['age'])
plt.title('Age by Person')
plt.xlabel('Name')
plt.ylabel('Age')
plt.savefig('/analysis/output/age_chart.png')
print("\\nChart saved to /analysis/output/age_chart.png")

Save results

results = {
'total_people': len(df),
'average_age': avg_age,
'cities': df['city'].unique().tolist()
}

import json
with open('/analysis/output/results.json', 'w') as f:
json.dump(results, f, indent=2)

print("Results saved!")
);

console.log(result.stdout);

// Read the results
const results = await sandbox.filesystem.readFile('/analysis/output/results.json');
console.log('Analysis results:', JSON.parse(results));

// Check if chart was created
const chartExists = await sandbox.filesystem.exists('/analysis/output/age_chart.png');
console.log('Chart created:', chartExists);

await sandbox.destroy();
`

$3

`typescript
import { e2b } from '@computesdk/e2b';

const compute = e2b({ apiKey: process.env.E2B_API_KEY });
const sandbox = await compute.sandbox.create();

// Create interactive Python terminal
const terminal = await sandbox.terminal.create({
command: 'python3',
cols: 80,
rows: 24,
onData: (data: Uint8Array) => {
const output = new TextDecoder().decode(data);
process.stdout.write(output); // Forward to console
}
});

// Send Python commands
await terminal.write('import numpy as np\n');
await terminal.write('import pandas as pd\n');
await terminal.write('print("Libraries loaded!")\n');
await terminal.write('data = np.array([1, 2, 3, 4, 5])\n');
await terminal.write('print(f"Mean: {data.mean()}")\n');
await terminal.write('exit()\n');

// Wait for commands to execute
await new Promise(resolve => setTimeout(resolve, 2000));

await terminal.kill();
await sandbox.destroy();
`

$3

`typescript
import { e2b } from '@computesdk/e2b';

const compute = e2b({ apiKey: process.env.E2B_API_KEY });
const sandbox = await compute.sandbox.create({ timeout: 600000 }); // 10 minutes for ML tasks

// Create ML project structure
await sandbox.filesystem.mkdir('/ml-project');
await sandbox.filesystem.mkdir('/ml-project/data');
await sandbox.filesystem.mkdir('/ml-project/models');

// Generate and process data
const result = await sandbox.runCode(
import numpy as np
import pandas as pd
from sklearn.model_selection import train_test_split
from sklearn.linear_model import LinearRegression
from sklearn.metrics import mean_squared_error, r2_score
import joblib

Generate sample dataset

np.random.seed(42)
X = np.random.randn(1000, 5)
y = X.sum(axis=1) + np.random.randn(1000) * 0.1

Create DataFrame

feature_names = [f'feature_{i}' for i in range(5)]
df = pd.DataFrame(X, columns=feature_names)
df['target'] = y

print(f"Dataset shape: {df.shape}")
print("\\nDataset info:")
print(df.describe())

Save dataset

df.to_csv('/ml-project/data/dataset.csv', index=False)
print("\\nDataset saved to /ml-project/data/dataset.csv")

Split data

X_train, X_test, y_train, y_test = train_test_split(
df[feature_names], df['target'], test_size=0.2, random_state=42
)

Train model

model = LinearRegression()
model.fit(X_train, y_train)

Make predictions

y_pred = model.predict(X_test)

Evaluate

mse = mean_squared_error(y_test, y_pred)
r2 = r2_score(y_test, y_pred)

print(f"\\nModel Performance:")
print(f"MSE: {mse:.4f}")
print(f"R²: {r2:.4f}")

Save model

joblib.dump(model, '/ml-project/models/linear_model.pkl')
print("\\nModel saved to /ml-project/models/linear_model.pkl")

Save results

results = {
'mse': mse,
'r2': r2,
'feature_importance': dict(zip(feature_names, model.coef_)),
'intercept': model.intercept_
}

import json
with open('/ml-project/results.json', 'w') as f:
json.dump(results, f, indent=2)

print("Results saved!")
);

console.log(result.stdout);

// Read the results
const results = await sandbox.filesystem.readFile('/ml-project/results.json');
console.log('ML Results:', JSON.parse(results));

// Verify model file exists
const modelExists = await sandbox.filesystem.exists('/ml-project/models/linear_model.pkl');
console.log('Model saved:', modelExists);

await sandbox.destroy();
`

Best Practices

1. Resource Management: Always destroy sandboxes when done to free resources
2. Error Handling: Use try-catch blocks for robust error handling
3. Timeouts: Set appropriate timeouts for long-running tasks
4. File Organization: Use the filesystem API to organize project files
5. Terminal Sessions: Clean up terminal sessions with terminal.kill()`
6. API Key Security: Never commit API keys to version control

Limitations

- Sandbox Listing: E2B doesn't support listing all sandboxes (each is managed individually)
- Memory Limits: Subject to E2B sandbox memory constraints
- Network Access: Limited outbound network access
- File Persistence: Files are not persisted between sandbox sessions
- Execution Time: Subject to E2B timeout limits

Support

- E2B Documentation
- ComputeSDK Issues
- E2B Support

License

MIT