Streaming

Streaming execution provides real-time output as your code runs. Instead of waiting for completion, you receive stdout and stderr messages as they’re generated, perfect for monitoring long-running tasks.

Overview

Streaming execution is ideal for:

Long-running tasks where you want real-time progress
Interactive code that produces output over time
Monitoring execution progress
Building real-time dashboards or UIs

Streaming execution uses WebSocket connections for real-time communication. It requires async/await support in your code.

Basic Streaming

Stream execution output in real-time:

Python
JavaScript

from hopx_ai import Sandbox
import asyncio

async def stream_execution():
    sandbox = Sandbox.create(template="code-interpreter")
    
    code = '''
import time
for i in range(5):
    print(f"Step {i+1}/5")
    time.sleep(1)
print("Done!")
'''
    
    async for message in sandbox.run_code_stream(code):
        if message['type'] == 'stdout':
            print(message['data'], end='')
        elif message['type'] == 'stderr':
            print(f"ERROR: {message['data']}", end='', file=sys.stderr)
        elif message['type'] == 'result':
            print(f"\nExit code: {message['exit_code']}")
            print(f"Execution time: {message['execution_time']}s")
        elif message['type'] == 'complete':
            print("\n✅ Execution completed")
            break
    
    sandbox.kill()

asyncio.run(stream_execution())

import { Sandbox } from '@hopx-ai/sdk';

async function streamExecution() {
  const sandbox = await Sandbox.create({ template: 'code-interpreter' });
  
  const code = `
import time
for i in range(5):
    print(f"Step {i+1}/5")
    time.sleep(1)
print("Done!")
`;
  
  for await (const message of sandbox.runCodeStream(code)) {
    if (message.type === 'stdout') {
      process.stdout.write(message.data);
    } else if (message.type === 'stderr') {
      process.stderr.write(`ERROR: ${message.data}`);
    } else if (message.type === 'result') {
      console.log(`\nExit code: ${message.exit_code}`);
      console.log(`Execution time: ${message.execution_time}s`);
    } else if (message.type === 'complete') {
      console.log('\n✅ Execution completed');
      break;
    }
  }
  
  await sandbox.kill();
}

streamExecution();

Message Types

Stream messages have different types:

Python
JavaScript

from hopx_ai import Sandbox
import asyncio

async def handle_all_message_types():
    sandbox = Sandbox.create(template="code-interpreter")
    
    code = '''
import sys
print("This is stdout")
print("This is also stdout", file=sys.stdout)
print("This is stderr", file=sys.stderr)
'''
    
    async for message in sandbox.run_code_stream(code):
        msg_type = message['type']
        
        if msg_type == 'stdout':
            print(f"[STDOUT] {message['data']}", end='')
        elif msg_type == 'stderr':
            print(f"[STDERR] {message['data']}", end='')
        elif msg_type == 'result':
            print(f"\n[RESULT] Exit: {message['exit_code']}, Time: {message['execution_time']}s")
        elif msg_type == 'complete':
            print("[COMPLETE] Execution finished")
            break
    
    sandbox.kill()

asyncio.run(handle_all_message_types())

import { Sandbox } from '@hopx-ai/sdk';

async function handleAllMessageTypes() {
  const sandbox = await Sandbox.create({ template: 'code-interpreter' });
  
  const code = `
import sys
print("This is stdout")
print("This is also stdout", file=sys.stdout)
print("This is stderr", file=sys.stderr)
`;
  
  for await (const message of sandbox.runCodeStream(code)) {
    const msgType = message.type;
    
    if (msgType === 'stdout') {
      process.stdout.write(`[STDOUT] ${message.data}`);
    } else if (msgType === 'stderr') {
      process.stderr.write(`[STDERR] ${message.data}`);
    } else if (msgType === 'result') {
      console.log(`\n[RESULT] Exit: ${message.exit_code}, Time: ${message.execution_time}s`);
    } else if (msgType === 'complete') {
      console.log('[COMPLETE] Execution finished');
      break;
    }
  }
  
  await sandbox.kill();
}

handleAllMessageTypes();

Progress Monitoring

Monitor progress of long-running tasks:

Python
JavaScript

from hopx_ai import Sandbox
import asyncio

async def monitor_progress():
    sandbox = Sandbox.create(template="code-interpreter")
    
    code = '''
import time
total_steps = 20

for i in range(total_steps):
    progress = (i + 1) / total_steps * 100
    print(f"Progress: {progress:.1f}% ({i+1}/{total_steps})")
    time.sleep(0.5)
print("All steps completed!")
'''
    
    async for message in sandbox.run_code_stream(code):
        if message['type'] == 'stdout':
            # Display progress in real-time
            print(message['data'], end='')
        elif message['type'] == 'complete':
            print("\n✅ Task completed successfully")
            break
    
    sandbox.kill()

asyncio.run(monitor_progress())

import { Sandbox } from '@hopx-ai/sdk';

async function monitorProgress() {
  const sandbox = await Sandbox.create({ template: 'code-interpreter' });
  
  const code = `
import time
total_steps = 20

for i in range(total_steps):
    progress = (i + 1) / total_steps * 100
    print(f"Progress: {progress:.1f}% ({i+1}/{total_steps})")
    time.sleep(0.5)
print("All steps completed!")
`;
  
  for await (const message of sandbox.runCodeStream(code)) {
    if (message.type === 'stdout') {
      // Display progress in real-time
      process.stdout.write(message.data);
    } else if (message.type === 'complete') {
      console.log('\n✅ Task completed successfully');
      break;
    }
  }
  
  await sandbox.kill();
}

monitorProgress();

Environment Variables

Pass environment variables to streaming execution:

Python
JavaScript

from hopx_ai import Sandbox
import asyncio

async def stream_with_env():
    sandbox = Sandbox.create(template="code-interpreter")
    
    code = '''
import os
print(f"API key: {os.getenv('API_KEY', 'not set')}")
print(f"Debug mode: {os.getenv('DEBUG', 'false')}")
'''
    
    async for message in sandbox.run_code_stream(
        code,
        env={"API_KEY": "sk-123", "DEBUG": "true"}
    ):
        if message['type'] == 'stdout':
            print(message['data'], end='')
        elif message['type'] == 'complete':
            break
    
    sandbox.kill()

asyncio.run(stream_with_env())

import { Sandbox } from '@hopx-ai/sdk';

async function streamWithEnv() {
  const sandbox = await Sandbox.create({ template: 'code-interpreter' });
  
  const code = `
import os
print(f"API key: {os.getenv('API_KEY', 'not set')}")
print(f"Debug mode: {os.getenv('DEBUG', 'false')}")
`;
  
  for await (const message of sandbox.runCodeStream(code, {
    env: { API_KEY: 'sk-123', DEBUG: 'true' }
  })) {
    if (message.type === 'stdout') {
      process.stdout.write(message.data);
    } else if (message.type === 'complete') {
      break;
    }
  }
  
  await sandbox.kill();
}

streamWithEnv();

Error Handling

Handle errors in streaming execution:

Python
JavaScript

from hopx_ai import Sandbox
import asyncio

async def handle_streaming_errors():
    sandbox = Sandbox.create(template="code-interpreter")
    
    code = '''
print("Starting...")
print(undefined_variable)  # This will cause an error
print("This won't be reached")
'''
    
    try:
        async for message in sandbox.run_code_stream(code):
            if message['type'] == 'stdout':
                print(message['data'], end='')
            elif message['type'] == 'stderr':
                print(f"ERROR: {message['data']}", end='')
            elif message['type'] == 'result':
                if message['exit_code'] != 0:
                    print(f"\n❌ Execution failed with exit code: {message['exit_code']}")
            elif message['type'] == 'complete':
                if not message.get('success', False):
                    print("\n❌ Execution completed with errors")
                break
    except Exception as e:
        print(f"Streaming error: {e}")
    finally:
        sandbox.kill()

asyncio.run(handle_streaming_errors())

import { Sandbox } from '@hopx-ai/sdk';

async function handleStreamingErrors() {
  const sandbox = await Sandbox.create({ template: 'code-interpreter' });
  
  const code = `
print("Starting...")
print(undefined_variable)  # This will cause an error
print("This won't be reached")
`;
  
  try {
    for await (const message of sandbox.runCodeStream(code)) {
      if (message.type === 'stdout') {
        process.stdout.write(message.data);
      } else if (message.type === 'stderr') {
        process.stderr.write(`ERROR: ${message.data}`);
      } else if (message.type === 'result') {
        if (message.exit_code !== 0) {
          console.log(`\n❌ Execution failed with exit code: ${message.exit_code}`);
        }
      } else if (message.type === 'complete') {
        if (!message.success) {
          console.log('\n❌ Execution completed with errors');
        }
        break;
      }
    }
  } catch (error) {
    console.error(`Streaming error: ${error.message}`);
  } finally {
    await sandbox.kill();
  }
}

handleStreamingErrors();

Complete Example

Here’s a complete example with real-time progress:

Python
JavaScript

from hopx_ai import Sandbox
import asyncio

async def complete_streaming_example():
    sandbox = Sandbox.create(template="code-interpreter")
    
    code = '''
import time
import random

print("Starting data processing...")
total_items = 100

for i in range(total_items):
    # Simulate processing
    time.sleep(0.1)
    
    # Generate some output
    if i % 10 == 0:
        print(f"Processed {i}/{total_items} items")
    
    # Simulate occasional errors
    if random.random() < 0.05:
        print(f"Warning: Issue with item {i}", file=sys.stderr)

print("Processing complete!")
print(f"Total items processed: {total_items}")
'''
    
    print("🔄 Starting streaming execution...\n")
    
    async for message in sandbox.run_code_stream(
        code,
        language='python',
        timeout=120,
        env={"BATCH_SIZE": "10"}
    ):
        if message['type'] == 'stdout':
            print(message['data'], end='')
        elif message['type'] == 'stderr':
            print(f"⚠️  {message['data']}", end='')
        elif message['type'] == 'result':
            print(f"\n\n📊 Execution Summary:")
            print(f"   Exit code: {message['exit_code']}")
            print(f"   Execution time: {message['execution_time']:.2f}s")
        elif message['type'] == 'complete':
            print("\n✅ Streaming completed")
            break
    
    sandbox.kill()

asyncio.run(complete_streaming_example())

import { Sandbox } from '@hopx-ai/sdk';

async function completeStreamingExample() {
  const sandbox = await Sandbox.create({ template: 'code-interpreter' });
  
  const code = `
import time
import random

print("Starting data processing...")
total_items = 100

for i in range(total_items):
    # Simulate processing
    time.sleep(0.1)
    
    # Generate some output
    if i % 10 == 0:
        print(f"Processed {i}/{total_items} items")
    
    # Simulate occasional errors
    if random.random() < 0.05:
        print(f"Warning: Issue with item {i}", file=sys.stderr)

print("Processing complete!")
print(f"Total items processed: {total_items}")
`;
  
  console.log('🔄 Starting streaming execution...\n');
  
  for await (const message of sandbox.runCodeStream(code, {
    language: 'python',
    timeout: 120,
    env: { BATCH_SIZE: '10' }
  })) {
    if (message.type === 'stdout') {
      process.stdout.write(message.data);
    } else if (message.type === 'stderr') {
      process.stderr.write(`⚠️  ${message.data}`);
    } else if (message.type === 'result') {
      console.log(`\n\n📊 Execution Summary:`);
      console.log(`   Exit code: ${message.exit_code}`);
      console.log(`   Execution time: ${message.execution_time.toFixed(2)}s`);
    } else if (message.type === 'complete') {
      console.log('\n✅ Streaming completed');
      break;
    }
  }
  
  await sandbox.kill();
}

completeStreamingExample();

Best Practices

1. Use Async/Await

Streaming execution requires async/await. Make sure your code uses async functions and async for loops.

2. Handle All Message Types

Process stdout, stderr, result, and complete messages appropriately for a complete streaming experience.

3. Break on Complete

Always break the loop when you receive a ‘complete’ message to avoid unnecessary processing.

4. Handle Errors

Check exit codes and success flags to handle execution failures gracefully.

5. Use for Real-Time Feedback

Streaming is best for tasks where real-time output is valuable. For simple scripts, synchronous execution may be simpler.

Synchronous Execution - Basic code execution
Background Execution - Long-running tasks
Rich Output Capture - Capture plots and visualizations
SDK: sandbox.run_code_stream() - Python SDK method
CLI Code Execution - Execute code from CLI

Next Steps

Learn about Synchronous Execution for immediate results
Explore Background Execution for non-blocking tasks
Review Process Management for monitoring

Home

Get Started

Sandboxes

Code Execution

Filesystem

Commands

Environment Variables

Templates

Desktop Automation

Terminal

Observability

Cache

Overview

Basic Streaming

Message Types

Progress Monitoring

Environment Variables

Error Handling

Complete Example

Best Practices

Next Steps

Home

Get Started

Sandboxes

Code Execution

Filesystem

Commands

Environment Variables

Templates

Desktop Automation

Terminal

Observability

Cache

​Overview

​Basic Streaming

​Message Types

​Progress Monitoring

​Environment Variables

​Error Handling

​Complete Example

​Best Practices

​Related

​Next Steps

Overview

Basic Streaming

Message Types

Progress Monitoring

Environment Variables

Error Handling

Complete Example

Best Practices

Related

Next Steps