TradingAgents/backend/STREAMING_USAGE_EXAMPLES.md

TradingAgents Streaming Analysis - Usage Examples

Quick Start

1. Basic Streaming Analysis

# Run streaming analysis with default settings
python -m cli.main stream

# Or use the streaming flag with analyze command
python -m cli.main analyze --streaming

2. Advanced Streaming Analysis

# Run streaming analysis with advanced configuration
python -m cli.main stream --advanced

# Or combine flags
python -m cli.main analyze --streaming --advanced

3. Default Command with Streaming

# Use streaming as default behavior
python -m cli.main --streaming

What to Expect

Regular Analysis Output

data: {"type": "status", "message": "Starting analysis for TEM..."}
data: {"type": "agent_status", "agent": "Market Analyst"}
data: {"type": "progress", "percentage": 16}
...
[Wait for completion, then get all reports at once]

Streaming Analysis Output

data: {"type": "status", "message": "Starting analysis for TEM..."}
data: {"type": "agent_status", "agent": "Market Analyst"}
data: {"type": "progress", "percentage": 16}

🔴 Live: Market Analyst
Technical Analysis for TEM:
Based on the recent price action and volume patterns...
[Content streams in real-time as the agent thinks and writes]

Moving to fundamental analysis...
The company's recent earnings report shows...
[More content streaming live]

🔴 Live: Social Media Analyst  
Social sentiment analysis reveals...
[Content continues streaming from next agent]

UI Layout in Streaming Mode

┌─ Welcome to TradingAgents ─────────────────┐
│ Welcome to TradingAgents CLI               │
│ © Tauric Research                          │
└────────────────────────────────────────────┘

┌─ Agent Progress ──┬─ Recent Messages ──────┐
│ Market Analyst ✅ │ 14:30:22 [System] TEM  │
│ Social Analyst 🔄 │ 14:30:25 [Reasoning]   │
│ News Analyst   ⏳ │ 14:30:28 [Tool Call]   │
└───────────────────┴────────────────────────┘

┌─ 🔴 Live: Social Media Analyst ────────────┐
│ Analyzing social media sentiment for TEM:  │
│                                            │
│ Recent Twitter mentions show mixed         │
│ sentiment with 60% positive mentions...    │
│ [Content streaming in real-time]           │
└────────────────────────────────────────────┘

┌─ Latest Report Section ────────────────────┐
│ ### Market Analysis                        │
│ Technical indicators suggest...            │
│ [Shows most recently completed section]    │
└────────────────────────────────────────────┘

┌─ TradingAgents Streaming Analysis ─────────┐
│ Press Ctrl+C to stop                       │
└────────────────────────────────────────────┘

Advanced Usage Scenarios

1. Development and Debugging

When developing or debugging agent behaviors, streaming allows you to:

• See exactly where agents get stuck
• Monitor real-time thought processes
• Identify performance bottlenecks
• Watch agent decision-making flow

2. Educational Use

For learning how the system works:

• Observe AI reasoning in real-time
• Understand multi-agent collaboration
• See how different analysis types build on each other
• Learn from the agents' analytical approaches

3. Production Monitoring

In production environments:

• Monitor system health in real-time
• Detect anomalies early
• Provide better user experience with live updates
• Enable real-time decision making

Comparison: Regular vs Streaming

Aspect	Regular Analysis	Streaming Analysis
Content Delivery	Batch (sections at once)	Real-time (as generated)
User Experience	Wait then receive	Continuous feedback
Debugging	Post-mortem only	Live debugging
Monitoring	End result only	Process visibility
Engagement	Passive waiting	Active observation
Resource Usage	Lower CPU (4 FPS)	Higher CPU (8 FPS)

Technical Architecture

StreamingMessageBuffer

Extends the regular MessageBuffer with:

• Real-time content streaming capabilities
• Agent transition detection
• Content buffering and delivery
• Callback system for live updates

Layout Differences

• Additional "streaming_content" panel for live updates
• Higher refresh rate (8 FPS vs 4 FPS)
• Enhanced agent status indicators
• Real-time content formatting

Agent Detection

The system detects agent transitions by:

• Analyzing message content for agent keywords
• Monitoring state changes in the graph
• Tracking section completions
• Managing content buffers per agent

Best Practices

1. When to Use Streaming

• Development: Always use streaming for development
• Production: Use for high-engagement scenarios
• Debugging: Essential for troubleshooting
• Demos: Great for showing system capabilities

2. When to Use Regular Analysis

• Batch Processing: When processing many symbols
• Resource Constrained: On slower systems
• Automated Systems: When only final results matter
• Background Processing: For scheduled runs

3. Performance Considerations

• Streaming uses more CPU due to higher refresh rate
• Network usage is similar (same data, different timing)
• Memory usage slightly higher due to buffering
• Terminal performance may vary based on content length

Troubleshooting

Common Issues

1. Slow Terminal: Reduce content display or use regular mode
2. Missing Content: Check agent keyword detection
3. Jumbled Output: Ensure terminal supports rich formatting
4. Performance: Lower refresh rate or use regular analysis

Debug Mode

# Enable debug output
export DEBUG=1
python -m cli.main stream --advanced

This will show additional information about:

• Agent detection logic
• Content buffering
• State transitions
• Performance metrics