Streaming

Overview

Streaming allows you to receive response tokens as they’re generated, rather than waiting for the complete response. This is perfect for:

Chatbots - Display responses as they’re typed
Live assistants - Show progress in real-time
Long responses - Start displaying content immediately
Better UX - Reduce perceived latency

How It Works

Enable Streaming

Set stream: true in your request

Receive Chunks

Get response tokens incrementally via SSE

Process Events

Parse data: events containing JSON chunks

Handle Completion

Watch for [DONE] signal to know when complete

Endpoints

Streaming works with both API formats:

POST https://ai.megallm.io/v1/chat/completions
POST https://ai.megallm.io/v1/messages

Both endpoints support the stream: true parameter.

Request Format

OpenAI Format

{
  "model": "gpt-4",
  "messages": [
    {"role": "user", "content": "Tell me a story"}
  ],
  "stream": true
}

Anthropic Format

{
  "model": "claude-3.5-sonnet",
  "max_tokens": 500,
  "messages": [
    {"role": "user", "content": "Tell me a story"}
  ],
  "stream": true
}

Response Format

Event Stream Structure

Responses are sent as Server-Sent Events:

data: {"id":"chatcmpl-abc","object":"chat.completion.chunk","created":1677858242,"model":"gpt-4","choices":[{"index":0,"delta":{"role":"assistant"},"finish_reason":null}]}

data: {"id":"chatcmpl-abc","object":"chat.completion.chunk","created":1677858242,"model":"gpt-4","choices":[{"index":0,"delta":{"content":"Once"},"finish_reason":null}]}

data: {"id":"chatcmpl-abc","object":"chat.completion.chunk","created":1677858242,"model":"gpt-4","choices":[{"index":0,"delta":{"content":" upon"},"finish_reason":null}]}

data: {"id":"chatcmpl-abc","object":"chat.completion.chunk","created":1677858242,"model":"gpt-4","choices":[{"index":0,"delta":{},"finish_reason":"stop"}]}

data: [DONE]

Stream Lifecycle

Initial chunk - Contains role:

{"choices": [{"delta": {"role": "assistant"}}]}

Content chunks - Incremental text:

{"choices": [{"delta": {"content": "Hello"}}]}
{"choices": [{"delta": {"content": " world"}}]}

Final chunk - Includes finish_reason:

{"choices": [{"delta": {}, "finish_reason": "stop"}]}

Stream end:

data: [DONE]

Implementation Examples

Python
JavaScript
Browser
React
cURL

from openai import OpenAI

client = OpenAI(
    base_url="https://ai.megallm.io/v1",
    api_key="your-api-key"
)

# Create streaming completion
stream = client.chat.completions.create(
    model="gpt-4",
    messages=[
        {"role": "user", "content": "Tell me a story"}
    ],
    stream=True
)

# Process the stream
for chunk in stream:
    if chunk.choices[0].delta.content is not None:
        print(chunk.choices[0].delta.content, end="", flush=True)

Async Python

import asyncio
from openai import AsyncOpenAI

client = AsyncOpenAI(
    base_url="https://ai.megallm.io/v1",
    api_key="your-api-key"
)

async def stream_chat():
    stream = await client.chat.completions.create(
        model="gpt-4",
        messages=[{"role": "user", "content": "Tell me a story"}],
        stream=True
    )

    async for chunk in stream:
        if chunk.choices[0].delta.content:
            print(chunk.choices[0].delta.content, end="", flush=True)

asyncio.run(stream_chat())

import OpenAI from 'openai';

const openai = new OpenAI({
  baseURL: 'https://ai.megallm.io/v1',
  apiKey: process.env.MEGALLM_API_KEY,
});

async function streamChat() {
  const stream = await openai.chat.completions.create({
    model: 'gpt-4',
    messages: [{ role: 'user', content: 'Tell me a story' }],
    stream: true,
  });

  for await (const chunk of stream) {
    const content = chunk.choices[0]?.delta?.content || '';
    process.stdout.write(content);
  }
}

streamChat();

async function streamChatInBrowser() {
  const response = await fetch('https://ai.megallm.io/v1/chat/completions', {
    method: 'POST',
    headers: {
      'Authorization': `Bearer ${API_KEY}`,
      'Content-Type': 'application/json',
    },
    body: JSON.stringify({
      model: 'gpt-4',
      messages: [{ role: 'user', content: 'Tell me a story' }],
      stream: true,
    }),
  });

  const reader = response.body.getReader();
  const decoder = new TextDecoder();

  while (true) {
    const { done, value } = await reader.read();
    if (done) break;

    const chunk = decoder.decode(value);
    const lines = chunk.split('\n');

    for (const line of lines) {
      if (line.startsWith('data: ')) {
        const data = line.slice(6);
        if (data === '[DONE]') return;

        try {
          const json = JSON.parse(data);
          const content = json.choices[0]?.delta?.content || '';
          // Display content to user
          document.getElementById('output').innerHTML += content;
        } catch (e) {
          console.error('Parse error:', e);
        }
      }
    }
  }
}

import { useState } from 'react';

function StreamingChat() {
  const [messages, setMessages] = useState([]);
  const [streaming, setStreaming] = useState(false);
  const [currentResponse, setCurrentResponse] = useState('');

  const sendMessage = async (content) => {
    setStreaming(true);
    setCurrentResponse('');

    const response = await fetch('https://ai.megallm.io/v1/chat/completions', {
      method: 'POST',
      headers: {
        'Authorization': `Bearer ${process.env.REACT_APP_API_KEY}`,
        'Content-Type': 'application/json',
      },
      body: JSON.stringify({
        model: 'gpt-4',
        messages: [...messages, { role: 'user', content }],
        stream: true,
      }),
    });

    const reader = response.body.getReader();
    const decoder = new TextDecoder();
    let accumulated = '';

    while (true) {
      const { done, value } = await reader.read();
      if (done) break;

      const chunk = decoder.decode(value);
      const lines = chunk.split('\n');

      for (const line of lines) {
        if (line.startsWith('data: ')) {
          const data = line.slice(6);
          if (data === '[DONE]') {
            setMessages(prev => [...prev,
              { role: 'user', content },
              { role: 'assistant', content: accumulated }
            ]);
            setStreaming(false);
            return;
          }

          try {
            const json = JSON.parse(data);
            const content = json.choices[0]?.delta?.content || '';
            accumulated += content;
            setCurrentResponse(accumulated);
          } catch (e) {
            // Handle parse errors
          }
        }
      }
    }
  };

  return (
    <div>
      {messages.map((msg, i) => (
        <div key={i} className={msg.role}>
          {msg.content}
        </div>
      ))}
      {streaming && (
        <div className="assistant">
          {currentResponse}
          <span className="cursor">▊</span>
        </div>
      )}
    </div>
  );
}

curl -N https://ai.megallm.io/v1/chat/completions \
  -H "Authorization: Bearer $MEGALLM_API_KEY" \
  -H "Content-Type: application/json" \
  -d '{
    "model": "gpt-4",
    "messages": [{"role": "user", "content": "Tell me a story"}],
    "stream": true
  }'

Process with jq:

curl -N https://ai.megallm.io/v1/chat/completions \
  -H "Authorization: Bearer $MEGALLM_API_KEY" \
  -H "Content-Type: application/json" \
  -d '{
    "model": "gpt-4",
    "messages": [{"role": "user", "content": "Tell me a joke"}],
    "stream": true
  }' | while read -r line; do
    if [[ $line == data:* ]]; then
      json="${line:6}"
      if [[ $json != "[DONE]" ]]; then
        echo -n $(echo "$json" | jq -r '.choices[0].delta.content // ""')
      fi
    fi
  done

Advanced Features

Function Calling with Streaming

stream = client.chat.completions.create(
    model="gpt-4",
    messages=messages,
    tools=tools,
    stream=True
)

function_call = {"name": "", "arguments": ""}

for chunk in stream:
    delta = chunk.choices[0].delta

    if delta.tool_calls:
        tool_call = delta.tool_calls[0]
        if tool_call.function.name:
            function_call["name"] = tool_call.function.name
        if tool_call.function.arguments:
            function_call["arguments"] += tool_call.function.arguments

    elif delta.content:
        print(delta.content, end="", flush=True)

# Execute function when complete
if function_call["name"]:
    result = execute_function(function_call)

Progress Tracking

import time

class StreamProgress:
    def __init__(self):
        self.tokens = 0
        self.chunks = 0
        self.start_time = time.time()

    def update(self, chunk):
        self.chunks += 1
        if chunk.choices[0].delta.content:
            # Approximate token count
            self.tokens += len(chunk.choices[0].delta.content.split())

    def get_stats(self):
        elapsed = time.time() - self.start_time
        return {
            "chunks": self.chunks,
            "tokens": self.tokens,
            "time": elapsed,
            "tokens_per_second": self.tokens / elapsed if elapsed > 0 else 0
        }

progress = StreamProgress()

for chunk in stream:
    progress.update(chunk)
    # Process chunk...

print(f"\nStats: {progress.get_stats()}")

Buffering for Performance

class StreamBuffer {
  constructor(onFlush, bufferSize = 10, flushInterval = 100) {
    this.buffer = [];
    this.onFlush = onFlush;
    this.bufferSize = bufferSize;
    this.flushInterval = flushInterval;
    this.timer = null;
  }

  add(chunk) {
    this.buffer.push(chunk);

    if (this.buffer.length >= this.bufferSize) {
      this.flush();
    } else if (!this.timer) {
      this.timer = setTimeout(() => this.flush(), this.flushInterval);
    }
  }

  flush() {
    if (this.buffer.length > 0) {
      this.onFlush(this.buffer.join(''));
      this.buffer = [];
    }
    if (this.timer) {
      clearTimeout(this.timer);
      this.timer = null;
    }
  }
}

// Usage
const buffer = new StreamBuffer((text) => {
  document.getElementById('output').innerHTML += text;
});

for await (const chunk of stream) {
  const content = chunk.choices[0]?.delta?.content || '';
  buffer.add(content);
}
buffer.flush(); // Final flush

Error Handling

Streaming connections can fail mid-stream. Always implement retry logic.

import time

def stream_with_retry(client, messages, max_retries=3):
    for attempt in range(max_retries):
        try:
            stream = client.chat.completions.create(
                model="gpt-4",
                messages=messages,
                stream=True
            )

            full_response = ""
            for chunk in stream:
                if chunk.choices[0].delta.content:
                    content = chunk.choices[0].delta.content
                    full_response += content
                    yield content

            return  # Success

        except Exception as e:
            if attempt < max_retries - 1:
                wait_time = 2 ** attempt  # Exponential backoff
                print(f"Stream interrupted, retrying in {wait_time}s...")
                time.sleep(wait_time)
                # Continue from partial response
                messages.append({"role": "assistant", "content": full_response})
                messages.append({"role": "user", "content": "continue"})
            else:
                raise e

Best Practices

Buffer for UI updates - Don’t update DOM for every chunk (batching improves performance)
Show loading indicators - Display typing indicators during streaming
Implement timeouts - Set reasonable timeouts for connections
Handle interruptions - Use retry logic with exponential backoff
Clean up resources - Always close streams properly
Test error scenarios - Ensure your app handles network failures gracefully

Performance Tips

Buffer small chunks together before updating the UI to avoid excessive DOM updates.

Use flush=True in Python’s print for immediate output
Implement debouncing for frequent UI updates
Consider virtualization for long responses
Use Web Workers for parsing in browsers
Monitor memory usage for long streams

Chat Completions API - OpenAI-compatible streaming API
Messages API - Anthropic-compatible streaming API
Function Calling - Using functions with streaming
Authentication - API authentication methods

Overview

Chat & Messages

Models

Guides

Overview

How It Works

Endpoints

Request Format

OpenAI Format

Anthropic Format

Response Format

Event Stream Structure

Stream Lifecycle

Implementation Examples

Async Python

Advanced Features

Function Calling with Streaming

Progress Tracking

Buffering for Performance

Error Handling

Best Practices

Performance Tips

Overview

Chat & Messages

Models

Guides

​Overview

​How It Works

​Endpoints

​Request Format

​OpenAI Format

​Anthropic Format

​Response Format

​Event Stream Structure

​Stream Lifecycle

​Implementation Examples

​Async Python

​Advanced Features

​Function Calling with Streaming

​Progress Tracking

​Buffering for Performance

​Error Handling

​Best Practices

​Performance Tips

​Related

Overview

How It Works

Endpoints

Request Format

OpenAI Format

Anthropic Format

Response Format

Event Stream Structure

Stream Lifecycle

Implementation Examples

Async Python

Advanced Features

Function Calling with Streaming

Progress Tracking

Buffering for Performance

Error Handling

Best Practices

Performance Tips

Related