💫 stella-light-agent

A lightweight conversational AI agent optimized for fast responses and lower resource usage.

Overview

stella-light-agent provides a streamlined voice AI pipeline that sacrifices some advanced features for improved performance:

• Faster response times
• Lower memory footprint
• Simpler configuration
• Ideal for development and testing

Comparison with stella-agent

Feature	stella-agent	stella-light-agent
STT Quality	High	Good
Response Latency	~2-3s	~1-2s
Memory Usage	512Mi-2Gi	256Mi-1Gi
Tool Calling	Yes	Limited
Progress Tracking	Yes	Basic
Conversation History	Full	Limited

When to Use

Choose stella-light-agent when:

• Development/Testing: Faster iteration cycles
• Simple Conversations: Q&A, basic support
• Resource Constraints: Limited cluster resources
• Cost Optimization: Lower compute costs
• Low Latency Required: Interactive demos

Configuration

Environment Variables

Variable	Description	Default
OPENAI_API_KEY	OpenAI API key	Required
OPENAI_MODEL	Model to use	gpt-4o-mini
STT_PROVIDER	Speech-to-text provider	sherpa
TTS_PROVIDER	Text-to-speech provider	kokoro
MAX_HISTORY	Max conversation turns to keep	5

Pipeline

The light agent uses a simplified pipeline:

Audio In → STT → LLM → TTS → Audio Out

Key differences from stella-agent:

• Minimal preprocessing
• Shorter context window
• Direct response streaming
• Limited tool support

Resource Requirements

Resource	Request	Limit
CPU	100m	500m
Memory	256Mi	1Gi

Data Channel Messages

Similar to stella-agent but with a reduced message set:

// Transcript updates
{
  type: 'transcript_chunk',
  data: {
    text: string,
    is_final: boolean
  }
}

// Agent status
{
  type: 'agent_status',
  data: {
    status: 'listening' | 'speaking'
  }
}

Deployment

Deploy via the API:

curl -X POST http://localhost:3000/sessions/{sessionId}/agents \
  -H "Content-Type: application/json" \
  -d '{
    "role": "conversational-ai",
    "agentType": "stella-light-agent"
  }'

Or via the Frontend UI by selecting "stella-light-agent" from the agent type dropdown.

Performance Tuning

Reduce Latency

1. Use a smaller LLM model (gpt-4o-mini vs gpt-4o)
2. Reduce MAX_HISTORY to minimize context
3. Use local STT/TTS services

Reduce Memory

1. Lower MAX_HISTORY value
2. Disable unused features
3. Use streaming for all responses

Limitations

• Limited Tool Support: Only basic tools available
• Shorter Context: May lose context in long conversations
• Basic Progress Tracking: No detailed todo management
• Simpler Prompts: Less nuanced conversation handling

Upgrading to stella-agent

If you outgrow stella-light-agent:

1. Update the agent type in your deployment
2. Increase resource limits in your pod configuration
3. Add any additional environment variables for new features
4. Update your plans to use advanced features

See Also

• stella-agent - Full-featured agent
• Agents Overview - Agent comparison
• First Agent - Deployment guide