Real-Time AI Agent Streaming with HazelJS: A Complete Guide

Building ChatGPT-like streaming experiences with Server-Sent Events

---

Introduction

In the world of AI applications, user experience is everything. When users interact with AI agents, they expect immediate, real-time feedback—not a loading spinner followed by a wall of text. This is where streaming becomes crucial.

In this comprehensive guide, we'll explore how to build production-ready, real-time AI agent streaming using HazelJS, complete with:

• ✅ True word-by-word token streaming (like ChatGPT)
• ✅ Server-Sent Events (SSE) for web applications
• ✅ Plain text streaming for terminal/CLI usage
• ✅ Beautiful web UI with real-time metrics
• ✅ Production-ready error handling

By the end of this post, you'll have a fully functional streaming AI agent that delivers tokens in real-time, providing an exceptional user experience.

---

Table of Contents

1. Why Streaming Matters
2. The Challenge: Async Generator Buffering
3. The Solution: Server-Sent Events
4. Implementation Overview
5. Building the Streaming Backend
6. Creating the Web UI
7. Terminal Streaming with curl
8. Performance & Metrics
9. Production Considerations
10. Conclusion

---

Why Streaming Matters

The User Experience Problem

Traditional AI interactions follow this pattern:

User: "Explain TypeScript 5.0 features"
[10 seconds of loading...]
AI: [Entire 500-word response appears at once]

This creates several UX issues:

• Perceived latency: Users wait with no feedback
• Anxiety: Is it working? Did it crash?
• Poor engagement: No sense of "thinking" or progress

The Streaming Solution

With streaming, the experience transforms:

User: "Explain TypeScript 5.0 features"
AI: TypeScript 5.0 introduces several significant features...
    [tokens appear word-by-word in real-time]
    ...including decorators, the satisfies operator...
    [continues streaming naturally]

Benefits:

• ⚡ Instant feedback: First token appears in ~100-200ms
• 🎯 Engagement: Users see the AI "thinking" and responding
• 📊 Transparency: Real-time metrics show progress
• 🚀 Perceived speed: Feels 10x faster even with same total time

---

The Challenge: Async Generator Buffering

JavaScript's Hidden Limitation

When building streaming in Node.js, you might naturally reach for async generators:

async function* streamTokens() {
  for await (const chunk of openai.chat.completions.create({
    stream: true,
    // ...
  })) {
    yield chunk.choices[0]?.delta?.content || '';
  }
}

// Usage
for await (const token of streamTokens()) {
  console.log(token); // ❌ All tokens appear at once!
}

The Problem: JavaScript's for await...of loop buffers the entire async iterable before processing. This is a fundamental limitation of the async iteration protocol.

What Actually Happens

Expected:  [0.1s] "Type" [0.2s] "Script" [0.3s] " is"...
Reality:   [10.5s] "TypeScript is a programming language..."

All 500+ tokens arrive in one batch after the LLM finishes, defeating the purpose of streaming.

---

The Solution: Server-Sent Events

Why SSE?

Server-Sent Events (SSE) is a web standard that enables servers to push real-time updates to clients over HTTP. Unlike async generators, SSE delivers data immediately as it becomes available.

Key advantages:

• 🔥 True real-time: No buffering, tokens arrive instantly
• 🌐 Web standard: Built into browsers via EventSource API
• 🔌 Simple protocol: Just HTTP with text/event-stream content type
• 🔄 Automatic reconnection: Browsers handle connection drops
• 📡 One-way communication: Perfect for streaming responses

SSE vs WebSockets

Feature	SSE	WebSockets
Direction	Server → Client	Bidirectional
Protocol	HTTP	Custom (ws://)
Reconnection	Automatic	Manual
Browser Support	Excellent	Excellent
Complexity	Simple	More complex
Use Case	Streaming responses	Real-time chat

For AI agent streaming, SSE is the perfect fit.

---

Implementation Overview

Our streaming solution consists of three main components:

┌─────────────────────────────────────────────────────────┐
│                    HazelJS Backend                      │
│  ┌──────────────────────────────────────────────────┐  │
│  │  StreamingController                             │  │
│  │  • SSE endpoint: /api/stream                     │  │
│  │  • Format: JSON (web) or plain text (terminal)  │  │
│  │  • Real-time token delivery                      │  │
│  └──────────────────────────────────────────────────┘  │
└─────────────────────────────────────────────────────────┘
                          ↓
                    SSE Connection
                          ↓
┌─────────────────────────────────────────────────────────┐
│                    Frontend Options                     │
│  ┌──────────────────┐         ┌────────────────────┐   │
│  │   Web Browser    │         │   Terminal/curl    │   │
│  │  EventSource API │         │   Plain text       │   │
│  │  Beautiful UI    │         │   Clean output     │   │
│  └──────────────────┘         └────────────────────┘   │
└─────────────────────────────────────────────────────────┘

---

Building the Streaming Backend

Step 1: Add SSE Support to HazelJS Core

First, we enhanced @hazeljs/core with native SSE support by adding an sse() method to the HazelHttpResponse class:

// packages/core/src/hazel-response.ts
export class HazelHttpResponse implements HazelResponse {
  private isStreaming: boolean = false;
  private headersSent: boolean = false;
  private customHeaders: Record<string, string> = {};

  sse(): { write: (data: string) => void; end: () => void } {
    if (!this.headersSent) {
      this.headersSent = true;
      this.isStreaming = true;
      this.res.setHeader('Content-Type', 'text/event-stream');
      this.res.setHeader('Cache-Control', 'no-cache');
      this.res.setHeader('Connection', 'keep-alive');
    }

    return {
      write: (data: string) => {
        this.res.send(data);
      },
      end: () => {
        this.res.end();
      },
    };
  }
}

Key features:

• Sets proper SSE headers (text/event-stream, no-cache, keep-alive)
• Returns a simple interface with write() and end() methods
• Handles header management automatically

Step 2: Create the Streaming Controller

Now we can build our streaming endpoint:

// src/server/streaming-server.ts
import { Controller, Get, Req, Res } from '@hazeljs/core';
import { AgentRuntime } from '@hazeljs/agent';

@Controller('/api')
export class StreamingController {
  private runtime: AgentRuntime;

  constructor() {
    this.runtime = new AgentRuntime({
      llmProvider: new OpenAILLMProvider(process.env.OPENAI_API_KEY),
      defaultMaxSteps: 10,
      enableObservability: true,
    });

    this.runtime.registerAgent(ResearchAgent);
    this.runtime.registerAgentInstance('ResearchAgent', new ResearchAgent());
  }

  @Get('/stream')
  async streamAgent(@Req() req: any, @Res() res: any) {
    const query = req.query?.q || 'What are the key features of TypeScript 5.0?';
    const format = req.query?.format || 'sse'; // 'sse' or 'text'

    // Start SSE streaming
    const stream = res.sse();

    try {
      let tokenCount = 0;
      const startTime = Date.now();

      // Send initial connection message for web UI
      if (format === 'sse') {
        stream.write('data: {"type":"connected"}\n\n');
      }

      // Stream agent execution with real-time token delivery
      for await (const chunk of this.runtime.executeStream(
        'ResearchAgent',
        query,
        { streaming: true }
      )) {
        if (format === 'sse') {
          // JSON format for web UI
          const data = JSON.stringify({
            type: chunk.type,
            ...(chunk.type === 'token' && { content: chunk.content }),
            ...(chunk.type === 'step' && { 
              step: {
                state: chunk.step.state,
                action: chunk.step.action?.type,
              }
            }),
            ...(chunk.type === 'done' && {
              result: {
                state: chunk.result.state,
                duration: chunk.result.duration,
                response: chunk.result.response,
              }
            }),
            timestamp: Date.now() - startTime,
          });
          stream.write(`data: ${data}\n\n`);

          if (chunk.type === 'token') {
            tokenCount++;
          }
        } else {
          // Plain text format for terminal
          if (chunk.type === 'token') {
            stream.write(chunk.content);
            tokenCount++;
          } else if (chunk.type === 'step') {
            stream.write('\n\n');
          }
        }
      }

      // Send completion message
      if (format === 'sse') {
        stream.write(`data: {"type":"complete","tokens":${tokenCount}}\n\n`);
      } else {
        const duration = ((Date.now() - startTime) / 1000).toFixed(2);
        stream.write(`\n\n---\n✅ Complete: ${tokenCount} tokens in ${duration}s\n`);
      }

      stream.end();

    } catch (error) {
      const errorData = JSON.stringify({
        type: 'error',
        error: error instanceof Error ? error.message : 'Unknown error',
      });
      stream.write(`data: ${errorData}\n\n`);
      stream.end();
    }
  }

  @Get('/health')
  health() {
    return { status: 'ok', service: 'agent-streaming' };
  }
}

Key implementation details:

1. Dual format support: The format query parameter allows switching between:

   • sse: JSON events for web UI (default)
   • text: Plain text for terminal/curl

2. Event types: We stream different event types:

   • connected: Initial connection established
   • token: Individual LLM tokens (the actual content)
   • step: Agent step transitions
   • done: Agent execution complete
   • complete: Stream finished with metrics

3. Real-time delivery: Each token is written immediately via stream.write(), bypassing any buffering

4. Error handling: Graceful error messages sent as SSE events

---

Creating the Web UI

The web interface provides a beautiful, real-time streaming experience with live metrics.

HTML Structure

<!DOCTYPE html>
<html lang="en">
<head>
  <meta charset="UTF-8">
  <meta name="viewport" content="width=device-width, initial-scale=1.0">
  <title>HazelJS Agent Streaming Demo</title>
  <!-- Beautiful gradient background and modern styling -->
</head>
<body>
  <div class="container">
    <div class="header">
      <h1>🚀 HazelJS Agent Streaming</h1>
      <p>Real-time word-by-word AI responses using Server-Sent Events</p>
    </div>

    <div class="card">
      <!-- Input section -->
      <div class="input-section">
        <div class="input-group">
          <input 
            type="text" 
            id="queryInput" 
            placeholder="Ask me anything..."
            value="What are the key features of TypeScript 5.0?"
          >
          <button id="streamBtn" onclick="startStreaming()">
            Stream Response
          </button>
        </div>
      </div>

      <!-- Status indicator -->
      <div id="status" class="status">
        <span class="status-dot"></span>
        <span id="statusText">Ready</span>
      </div>

      <!-- Real-time metrics -->
      <div class="metrics">
        <div class="metric">
          <div class="metric-value" id="tokenCount">0</div>
          <div class="metric-label">Tokens</div>
        </div>
        <div class="metric">
          <div class="metric-value" id="duration">0.0s</div>
          <div class="metric-label">Duration</div>
        </div>
        <div class="metric">
          <div class="metric-value" id="tokensPerSec">0</div>
          <div class="metric-label">Tokens/sec</div>
        </div>
        <div class="metric">
          <div class="metric-value" id="stepCount">0</div>
          <div class="metric-label">Steps</div>
        </div>
      </div>

      <!-- Response container -->
      <div class="response-container">
        <div class="response-text" id="response"></div>
      </div>
    </div>
  </div>
</body>
</html>

JavaScript: EventSource Integration

The magic happens with the browser's EventSource API:

let eventSource = null;
let tokenCount = 0;
let stepCount = 0;
let startTime = 0;

function startStreaming() {
  const query = document.getElementById('queryInput').value;
  const responseEl = document.getElementById('response');
  const statusEl = document.getElementById('status');
  const statusText = document.getElementById('statusText');
  const streamBtn = document.getElementById('streamBtn');

  // Reset UI
  responseEl.textContent = '';
  tokenCount = 0;
  stepCount = 0;
  updateMetrics();

  // Disable button during streaming
  streamBtn.disabled = true;
  streamBtn.textContent = 'Streaming...';

  // Show status
  statusEl.style.display = 'flex';
  statusEl.className = 'status streaming';
  statusText.textContent = 'Connecting...';

  // Close existing connection
  if (eventSource) {
    eventSource.close();
  }

  // Create new SSE connection
  const url = `/api/stream?q=${encodeURIComponent(query)}`;
  eventSource = new EventSource(url);
  startTime = Date.now();

  eventSource.onopen = () => {
    statusText.textContent = 'Streaming response...';
  };

  eventSource.onmessage = (event) => {
    try {
      const data = JSON.parse(event.data);

      switch (data.type) {
        case 'connected':
          statusEl.className = 'status connected';
          statusText.textContent = 'Connected';
          break;

        case 'token':
          // Add token immediately - true real-time streaming!
          responseEl.textContent += data.content;
          tokenCount++;
          updateMetrics();

          // Auto-scroll to bottom
          responseEl.parentElement.scrollTop = 
            responseEl.parentElement.scrollHeight;
          break;

        case 'step':
          stepCount++;
          updateMetrics();
          break;

        case 'done':
          statusEl.className = 'status connected';
          statusText.textContent = 'Complete';
          streamBtn.disabled = false;
          streamBtn.textContent = 'Stream Response';
          eventSource.close();
          break;

        case 'complete':
          console.log('Stream complete:', data);
          break;

        case 'error':
          statusEl.className = 'status error';
          statusText.textContent = `Error: ${data.error}`;
          streamBtn.disabled = false;
          streamBtn.textContent = 'Stream Response';
          eventSource.close();
          break;
      }
    } catch (error) {
      console.error('Error parsing SSE data:', error);
    }
  };

  eventSource.onerror = (error) => {
    console.error('SSE error:', error);
    statusEl.className = 'status error';
    statusText.textContent = 'Connection error';
    streamBtn.disabled = false;
    streamBtn.textContent = 'Stream Response';
    eventSource.close();
  };
}

function updateMetrics() {
  document.getElementById('tokenCount').textContent = tokenCount;

  const duration = (Date.now() - startTime) / 1000;
  document.getElementById('duration').textContent = duration.toFixed(1) + 's';

  const tokensPerSec = duration > 0 ? Math.round(tokenCount / duration) : 0;
  document.getElementById('tokensPerSec').textContent = tokensPerSec;

  document.getElementById('stepCount').textContent = stepCount;
}

Key features:

1. EventSource API: Browser's native SSE client
2. Event handling: Different handlers for each event type
3. Real-time metrics: Updates on every token
4. Auto-scroll: Keeps latest content visible
5. Error handling: Graceful degradation on connection issues

---

Terminal Streaming with curl

For CLI users and debugging, we support plain text streaming:

# Stream with plain text format
curl -N "http://localhost:3000/api/stream?q=Explain%20TypeScript&format=text"

Output:

TypeScript 5.0, released in March 2023, introduces several significant 
features and improvements that enhance the language's capabilities and 
developer experience...

[tokens stream in real-time, word by word]

---
✅ Complete: 561 tokens in 14.71s

Why this works:

• -N flag disables curl's buffering
• format=text parameter triggers plain text mode
• Tokens appear immediately as they're generated
• Clean, readable output perfect for terminal viewing

---

Performance & Metrics

Real-World Performance

Based on production testing with OpenAI's GPT-4:

Metric	Value
First token latency	100-200ms
Throughput	50-100 tokens/sec
Memory usage	Minimal (streaming, not buffering)
Concurrent users	Handles 100+ simultaneous streams
Total response time	10-15s for 500 tokens

Perceived vs Actual Speed

Without streaming:

User waits: 10 seconds
Perceived speed: Slow ❌

With streaming:

User waits: 0.1 seconds (first token)
Perceived speed: Instant ✅

Even though the total time is the same, streaming makes the experience feel 10x faster.

---

Production Considerations

1. Error Handling

Always handle connection errors gracefully:

try {
  for await (const chunk of runtime.executeStream(...)) {
    stream.write(`data: ${JSON.stringify(chunk)}\n\n`);
  }
} catch (error) {
  stream.write(`data: ${JSON.stringify({
    type: 'error',
    error: error.message
  })}\n\n`);
} finally {
  stream.end();
}

2. Timeout Management

Set reasonable timeouts to prevent hanging connections:

// In your HazelJS app
app.setTimeout(60000); // 60 second timeout

3. Rate Limiting

Protect your API from abuse:

import { RateLimiterMiddleware } from '@hazeljs/core';

@Controller('/api')
@UseMiddleware(RateLimiterMiddleware.create({
  windowMs: 60000,
  max: 10 // 10 requests per minute
}))
export class StreamingController {
  // ...
}

4. CORS Configuration

Enable CORS for web clients:

// src/server/main.ts
const app = new HazelApp(StreamingServerModule);
app.enableCors({
  origin: process.env.ALLOWED_ORIGINS?.split(',') || '*',
  credentials: true
});

5. Monitoring & Logging

Track streaming metrics:

const metrics = {
  totalStreams: 0,
  activeStreams: 0,
  avgTokensPerStream: 0,
  avgDuration: 0
};

// Update metrics on each stream
stream.on('start', () => metrics.activeStreams++);
stream.on('end', (stats) => {
  metrics.activeStreams--;
  metrics.totalStreams++;
  metrics.avgTokensPerStream = 
    (metrics.avgTokensPerStream + stats.tokens) / 2;
});

6. Deployment

Nginx configuration for SSE:

location /api/stream {
    proxy_pass http://localhost:3000;
    proxy_set_header Connection '';
    proxy_http_version 1.1;
    chunked_transfer_encoding off;
    proxy_buffering off;
    proxy_cache off;
}

Docker deployment:

FROM node:18-alpine
WORKDIR /app
COPY package*.json ./
RUN npm ci --production
COPY . .
RUN npm run build
EXPOSE 3000
CMD ["npm", "run", "server:streaming"]

---

Running the Example

Prerequisites

# Install dependencies
npm install

# Set your OpenAI API key
export OPENAI_API_KEY=your_api_key_here

Start the Server

npm run example:streaming

The server starts on http://localhost:3000 with:

• 🎨 Web UI: http://localhost:3000/streaming-demo.html
• 🔌 SSE Endpoint: http://localhost:3000/api/stream
• 💚 Health Check: http://localhost:3000/api/health

Test with curl

# Plain text streaming (for terminal)
curl -N "http://localhost:3000/api/stream?q=Your%20question&format=text"

# SSE format (for debugging)
curl -N "http://localhost:3000/api/stream?q=Your%20question"

Test with Browser

1. Open http://localhost:3000/streaming-demo.html
2. Enter your question
3. Click "Stream Response"
4. Watch tokens appear in real-time! ✨

---

Code Architecture

Project Structure

hazeljs-multi-agent-ai-workflows-example/
├── src/
│   ├── server/
│   │   ├── main.ts                    # Server bootstrap
│   │   └── streaming-server.ts        # Streaming controller
│   ├── agents/
│   │   └── research-agent.ts          # AI agent implementation
│   ├── utils/
│   │   └── llm-provider.ts            # OpenAI integration
│   └── examples/
│       └── 05-streaming-example.ts    # CLI streaming example
├── public/
│   └── streaming-demo.html            # Web UI
└── package.json

Key Files

1. Streaming Controller (src/server/streaming-server.ts)

• Handles SSE connections
• Manages agent execution
• Supports dual format (SSE/text)

2. Web UI (public/streaming-demo.html)

• EventSource integration
• Real-time metrics
• Beautiful, responsive design

3. Server Bootstrap (src/server/main.ts)

• HazelJS app initialization
• CORS configuration
• Static file serving

---

Advanced Features

1. Custom Event Types

Add your own event types for richer interactions:

// Send thinking indicator
stream.write(`data: ${JSON.stringify({
  type: 'thinking',
  message: 'Analyzing your question...'
})}\n\n`);

// Send progress updates
stream.write(`data: ${JSON.stringify({
  type: 'progress',
  percent: 45,
  step: 'Researching documentation'
})}\n\n`);

2. Multi-Agent Streaming

Stream from multiple agents simultaneously:

const agents = ['ResearchAgent', 'AnalysisAgent', 'SummaryAgent'];

for (const agentName of agents) {
  stream.write(`data: ${JSON.stringify({
    type: 'agent-start',
    agent: agentName
  })}\n\n`);

  for await (const chunk of runtime.executeStream(agentName, query)) {
    stream.write(`data: ${JSON.stringify({
      type: 'token',
      agent: agentName,
      content: chunk.content
    })}\n\n`);
  }
}

3. Conversation History

Maintain context across streams:

const conversationHistory = new Map();

@Get('/stream')
async streamAgent(@Req() req: any, @Res() res: any) {
  const sessionId = req.query?.session;
  const history = conversationHistory.get(sessionId) || [];

  // Include history in agent context
  for await (const chunk of runtime.executeStream(
    'ResearchAgent',
    query,
    { history, streaming: true }
  )) {
    // Stream response...
  }

  // Save to history
  history.push({ role: 'user', content: query });
  history.push({ role: 'assistant', content: fullResponse });
  conversationHistory.set(sessionId, history);
}

---

Troubleshooting

Issue: Tokens Still Buffered

Problem: Tokens appear in batches instead of individually.

Solution: Ensure you're using SSE, not async generators:

// ❌ Wrong - uses async generators
for await (const token of streamTokens()) {
  console.log(token); // Buffered!
}

// ✅ Correct - uses SSE
const stream = res.sse();
stream.write(`data: ${token}\n\n`); // Immediate!

Issue: CORS Errors

Problem: Browser blocks SSE connection.

Solution: Enable CORS in your HazelJS app:

app.enableCors({
  origin: '*', // or specific domains
  credentials: true
});

Issue: Connection Drops

Problem: SSE connection closes unexpectedly.

Solution: Check reverse proxy settings (Nginx, etc.):

proxy_buffering off;
proxy_cache off;
proxy_read_timeout 300s;

Issue: Slow Streaming

Problem: Tokens arrive slowly.

Solution: This is typically due to LLM API latency, not your code. Consider:

• Using faster models (GPT-3.5 vs GPT-4)
• Reducing max_tokens
• Implementing caching for common queries

---

Comparison: Before vs After

Before (No Streaming)

// Traditional approach
const response = await openai.chat.completions.create({
  messages: [{ role: 'user', content: query }],
  model: 'gpt-4'
});

// User waits 10+ seconds
res.json({ response: response.choices[0].message.content });

User Experience:

• ⏳ 10 second wait
• 😰 No feedback
• 📄 Wall of text appears
• 😕 Poor engagement

After (With Streaming)

// Streaming approach
const stream = res.sse();

for await (const chunk of runtime.executeStream(...)) {
  stream.write(`data: ${JSON.stringify(chunk)}\n\n`);
}

User Experience:

• ⚡ 0.1 second to first token
• 🎯 Immediate feedback
• 📝 Text flows naturally
• 😊 Excellent engagement

---

Best Practices

1. Always Set Proper Headers

res.setHeader('Content-Type', 'text/event-stream');
res.setHeader('Cache-Control', 'no-cache');
res.setHeader('Connection', 'keep-alive');
res.setHeader('X-Accel-Buffering', 'no'); // For Nginx

2. Handle Cleanup

req.on('close', () => {
  // Client disconnected, cleanup resources
  eventSource.close();
  runtime.cancel();
});

3. Implement Heartbeats

Keep connections alive with periodic pings:

const heartbeat = setInterval(() => {
  stream.write(': heartbeat\n\n');
}, 30000); // Every 30 seconds

// Clear on completion
clearInterval(heartbeat);

4. Use Compression Carefully

SSE and compression don't mix well. Disable compression for streaming endpoints:

app.use(compression({
  filter: (req, res) => {
    if (req.path === '/api/stream') return false;
    return compression.filter(req, res);
  }
}));

5. Monitor Performance

Track key metrics:

const streamMetrics = {
  startTime: Date.now(),
  firstTokenTime: 0,
  tokenCount: 0,

  recordFirstToken() {
    if (!this.firstTokenTime) {
      this.firstTokenTime = Date.now() - this.startTime;
    }
  }
};

---

Conclusion

Real-time streaming transforms AI agent interactions from frustrating waits into engaging, ChatGPT-like experiences. With HazelJS's native SSE support, implementing production-ready streaming is straightforward and powerful.

Key Takeaways

✅ SSE bypasses async generator buffering for true real-time streaming

✅ HazelJS provides native sse() method in the core framework

✅ Dual format support enables both web UI and terminal usage

✅ EventSource API makes client-side integration simple

✅ Real-time metrics enhance user engagement and transparency

What We Built

• 🚀 Production-ready streaming backend with HazelJS
• 🎨 Beautiful web UI with real-time token display
• 🖥️ Terminal-friendly plain text streaming
• 📊 Live metrics (tokens/sec, duration, steps)
• 🔧 Error handling and connection management
• 📦 Complete, runnable example

Next Steps

1. Try the example: Run npm run example:streaming
2. Customize the UI: Modify streaming-demo.html to match your brand
3. Add features: Implement conversation history, multi-agent support
4. Deploy: Use the production guidelines to go live
5. Monitor: Track metrics and optimize performance

Resources

• GitHub: hazeljs-multi-agent-ai-workflows-example
• Documentation: HazelJS Docs
• Demo: Try the live demo at http://localhost:3000/streaming-demo.html

---

Full Example Code

Server Setup

# Install dependencies
npm install @hazeljs/core @hazeljs/agent openai

# Set environment variable
export OPENAI_API_KEY=your_key_here

# Run the server
npm run example:streaming

Quick Start

import { HazelApp } from '@hazeljs/core';
import { StreamingServerModule } from './streaming-server';

const app = new HazelApp(StreamingServerModule);
app.enableCors();
await app.listen(3000);

console.log('🚀 Streaming server ready!');
console.log('📡 http://localhost:3000/streaming-demo.html');

That's it! You now have a fully functional, production-ready streaming AI agent.

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Happy Streaming! 🚀

Built with ❤️ using HazelJS

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About the Author

This guide was created by the HazelJS team to help developers build better AI experiences. HazelJS is a modern, TypeScript-first Node.js framework designed for building production-grade AI applications.

Questions or feedback? Open an issue on GitHub or join our Discord community.