Test your LiveKit-powered voice agents by connecting them to UserTrace. Our simulated users will interact with your LiveKit-based agents through WebRTC connections for real-time voice conversations.Documentation Index
Fetch the complete documentation index at: https://docs.getusertrace.com/llms.txt
Use this file to discover all available pages before exploring further.
Getting Started
1. Connect Your LiveKit Agent
In the UserTrace dashboard, navigate to Agent Setup and select LiveKit Integration. Required Information:- LiveKit Server URL (WSS endpoint)
- API Key and Secret
- Room name or agent identifier
2. Set Evaluation Context
Define your pass and fail criteria for voice interactions: Example Pass/Fail Criteria:Testing Process
Real-Time Voice Testing
LiveKit testing follows a WebRTC-based approach:- UserTrace connects to your LiveKit room
- WebRTC connection is established with your agent
- Real-time audio streams are initiated
- Simulated user interacts based on scenario
- Conversation quality and agent responses are monitored
- Results are analyzed including technical metrics
- Room Join: UserTrace joins specified LiveKit room
- Agent Discovery: Identifies your voice agent participant
- Audio Setup: Establishes bidirectional audio streams
- Conversation Start: Begins scenario-based interaction
- Quality Monitoring: Tracks audio quality and latency
- Natural Completion: Ends when scenario objectives are met
LiveKit-Specific Features
Real-Time Infrastructure
WebRTC Quality:- Ultra-low latency voice communication
- Adaptive bitrate for varying network conditions
- Echo cancellation and noise suppression
- Network resilience and recovery
- Multi-participant support
- Dynamic participant joining/leaving
- Room metadata and configuration
- Custom room layouts and settings
Agent Architecture
Server-Side Agents:- Python/Node.js agent implementations
- Real-time audio processing
- AI model integration
- Custom business logic
- Web browser compatibility
- Mobile app support
- Desktop application integration
- Custom client implementations
Best Practices
Performance
Real-Time Optimization• Target < 200ms end-to-end latency
• Use LiveKit’s adaptive streaming
• Optimize AI model inference time
• Monitor connection quality metrics
Reliability
Connection Stability• Implement reconnection logic
• Handle network interruptions
• Use LiveKit’s connection events
• Monitor participant status
Implementation Examples
Server Agent Setup
Python Agent Example:Authentication
Token Generation:Common Scenarios
Interactive Voice Assistants:- Personal assistant interactions
- Smart home control
- Task management
- Information retrieval
- Real-time help desk
- Technical troubleshooting
- Product information
- Issue resolution
- Team meetings with AI participants
- Educational tutoring sessions
- Brainstorming facilitation
- Multi-user voice experiences
Advanced Features
AI Integration
Speech-to-Text:- Real-time transcription
- Multiple language support
- Custom vocabulary
- Confidence scoring
- Natural voice synthesis
- Voice cloning capabilities
- Emotion and tone control
- Multi-speaker support
- Streaming AI responses
- Context preservation
- Function calling
- Multi-modal processing
Quality Monitoring
Audio Metrics:Troubleshooting
Common Issues: Connection Problems:- WebRTC connection fails: Check firewall settings and TURN servers
- Audio not flowing: Verify microphone permissions and audio tracks
- High latency: Optimize server location and network routing
- Frequent disconnections: Monitor network stability and implement reconnection
- Agent not responding: Check LiveKit agent deployment and logs
- Poor audio quality: Verify audio processing pipeline and network bandwidth
- Delayed responses: Optimize AI model inference and processing time
- Memory leaks: Monitor resource usage in long-running sessions
Development Setup
Local Testing
Docker Setup:Need help with LiveKit integration? Check the LiveKit documentation or contact support@getusertrace.com.