Pipecat Integration

Test your Pipecat-powered voice agents by connecting them to UserTrace. Our simulated users will interact with your Pipecat-based agents through real-time voice conversations.

Getting Started

1. Connect Your Pipecat Agent

In the UserTrace dashboard, navigate to Agent Setup and select Pipecat Integration. Required Information:

Pipecat agent endpoint URL
Authentication credentials (if required)
Transport method (WebRTC, WebSocket, or SIP)

Example:

Agent Endpoint: wss://your-agent.com/pipecat
Transport: WebRTC
Authentication: API Key

2. Set Evaluation Context

Define your pass and fail criteria for voice interactions: Example Pass/Fail Criteria:

Pass Criteria:
- Establishes connection within 3 seconds
- Maintains real-time conversation flow
- Processes interruptions naturally
- Provides contextually relevant responses
- Handles audio quality gracefully

Fail Criteria:
- Connection timeouts or failures
- Unnatural or robotic responses
- Poor handling of interruptions
- Off-topic or irrelevant responses
- Audio artifacts or quality issues

Testing Process

Real-Time Pipeline Testing

Pipecat testing focuses on the real-time processing pipeline:

UserTrace connects to your Pipecat agent endpoint
Real-time audio processing pipeline is established
Simulated user begins conversation based on scenario
Pipecat processes audio through its pipeline stages
Agent responses are generated and delivered
Conversation quality and pipeline performance are monitored

Pipeline Flow:

Audio Input: User speech captured and processed
Speech-to-Text: Real-time transcription
LLM Processing: Context understanding and response generation
Text-to-Speech: Natural voice synthesis
Audio Output: Delivered to user
Quality Metrics: Latency and accuracy tracking

Pipecat-Specific Features

Pipeline Architecture

Modular Components:

Audio input/output processors
Speech recognition services
LLM integrations
Text-to-speech engines
Transport mechanisms

Real-Time Processing:

Streaming audio processing
Low-latency pipeline execution
Interrupt handling
Context preservation

Transport Options

WebRTC Transport:

Browser-based connections
Ultra-low latency
Built-in echo cancellation
Network adaptation

WebSocket Transport:

Simple integration
Custom audio protocols
Server-to-server communication
Scalable architecture

SIP Integration:

Traditional telephony systems
PBX compatibility
Carrier-grade reliability
Standards compliance

Best Practices

Pipeline Optimization

Performance Tuning• Minimize processing latency • Optimize buffer sizes • Use appropriate audio codecs • Monitor pipeline bottlenecks

Audio Quality

Sound Processing• Configure noise suppression • Implement echo cancellation • Handle variable audio quality • Test with different microphones

Implementation Examples

Basic Pipecat Agent

Python Implementation:

import asyncio
from pipecat.pipeline.pipeline import Pipeline
from pipecat.pipeline.task import PipelineTask
from pipecat.services.openai import OpenAILLMService
from pipecat.services.elevenlabs import ElevenLabsTTSService
from pipecat.services.deepgram import DeepgramSTTService
from pipecat.transports.network.websocket import WebsocketTransport

async def run_agent():
    # Configure services
    stt_service = DeepgramSTTService(api_key="your-deepgram-key")
    llm_service = OpenAILLMService(api_key="your-openai-key")
    tts_service = ElevenLabsTTSService(api_key="your-elevenlabs-key")
    
    # Set up transport
    transport = WebsocketTransport(
        host="localhost",
        port=8765,
        params={"access_token": "your-token"}
    )
    
    # Create pipeline
    pipeline = Pipeline([
        transport.input_processor(),
        stt_service,
        llm_service, 
        tts_service,
        transport.output_processor()
    ])
    
    # Run the task
    task = PipelineTask(pipeline)
    await task.run()

if __name__ == "__main__":
    asyncio.run(run_agent())

Advanced Configuration

Custom Pipeline:

from pipecat.processors.aggregators.openai_llm_context import OpenAILLMContext
from pipecat.processors.audio.audio_buffer import AudioBuffer

# Configure advanced pipeline with custom processors
pipeline = Pipeline([
    # Audio preprocessing
    AudioBuffer(sample_rate=16000),
    
    # Speech recognition
    DeepgramSTTService(
        api_key="your-key",
        model="nova-2",
        language="en-US"
    ),
    
    # Context management
    OpenAILLMContext(
        messages=[
            {"role": "system", "content": "You are a helpful assistant."}
        ]
    ),
    
    # LLM processing
    OpenAILLMService(
        api_key="your-key",
        model="gpt-4",
        max_tokens=150
    ),
    
    # Voice synthesis
    ElevenLabsTTSService(
        api_key="your-key",
        voice_id="your-voice-id"
    )
])

Common Scenarios

Conversational AI:

Personal assistants
Customer service bots
Educational tutors
Healthcare assistants

Real-Time Applications:

Live translation services
Meeting assistants
Voice-controlled systems
Interactive voice response (IVR)

Multi-Modal Experiences:

Video conferencing bots
Smart home interfaces
Automotive assistants
Gaming characters

Advanced Features

Interrupt Handling

Barge-in Support:

from pipecat.processors.interruption import InterruptionHandler

interruption_handler = InterruptionHandler(
    enable_barge_in=True,
    sensitivity=0.7,
    timeout_seconds=1.0
)

Context Management

Conversation Memory:

from pipecat.processors.context import ConversationContext

context = ConversationContext(
    max_turns=10,
    context_window=4000,
    preserve_system_message=True
)

Custom Processors

Audio Processing:

from pipecat.processors.base import AudioProcessor

class CustomAudioProcessor(AudioProcessor):
    async def process_audio(self, audio_data):
        # Custom audio processing logic
        processed_audio = self.apply_filters(audio_data)
        return processed_audio

Troubleshooting

Common Issues: Pipeline Problems:

High latency: Optimize processor order and buffer sizes
Audio dropouts: Check network stability and audio codec settings
Memory issues: Monitor processor memory usage and cleanup
Context loss: Verify context management configuration

Service Integration:

API errors: Validate service credentials and rate limits
Model failures: Test with different AI models and configurations
Transport issues: Check network connectivity and protocol settings
Audio quality: Verify codec compatibility and audio processing

Development Setup

Local Development

Docker Compose:

version: '3.8'
services:
  pipecat-agent:
    build: .
    ports:
      - "8765:8765"
    environment:
      - OPENAI_API_KEY=${OPENAI_API_KEY}
      - DEEPGRAM_API_KEY=${DEEPGRAM_API_KEY}
      - ELEVENLABS_API_KEY=${ELEVENLABS_API_KEY}
    volumes:
      - ./config:/app/config

Environment Configuration:

export OPENAI_API_KEY="your-openai-key"
export DEEPGRAM_API_KEY="your-deepgram-key" 
export ELEVENLABS_API_KEY="your-elevenlabs-key"
export TRANSPORT_HOST="0.0.0.0"
export TRANSPORT_PORT="8765"

Testing Pipeline

Unit Testing:

import pytest
from pipecat.pipeline.pipeline import Pipeline

@pytest.mark.asyncio
async def test_pipeline_latency():
    pipeline = create_test_pipeline()
    start_time = time.time()
    
    # Test audio processing
    result = await pipeline.process(test_audio)
    
    latency = time.time() - start_time
    assert latency < 0.5  # Target < 500ms

Need help with Pipecat setup? Check the Pipecat documentation or contact support@getusertrace.com.

Getting Started

Quick Start by Use Case

Voice Integration

Testing & Evaluation

Getting Started

1. Connect Your Pipecat Agent

2. Set Evaluation Context

Testing Process

Real-Time Pipeline Testing

Pipecat-Specific Features

Pipeline Architecture

Transport Options

Best Practices

Pipeline Optimization

Audio Quality

Implementation Examples

Basic Pipecat Agent

Advanced Configuration

Common Scenarios

Advanced Features

Interrupt Handling

Context Management

Custom Processors

Troubleshooting

Development Setup

Local Development

Testing Pipeline

​Getting Started

​1. Connect Your Pipecat Agent

​2. Set Evaluation Context

​Testing Process

​Real-Time Pipeline Testing

​Pipecat-Specific Features

​Pipeline Architecture

​Transport Options

​Best Practices

Pipeline Optimization

Audio Quality

​Implementation Examples

​Basic Pipecat Agent

​Advanced Configuration

​Common Scenarios

​Advanced Features

​Interrupt Handling

​Context Management

​Custom Processors

​Troubleshooting

​Development Setup

​Local Development

​Testing Pipeline

Getting Started

1. Connect Your Pipecat Agent

2. Set Evaluation Context

Testing Process

Real-Time Pipeline Testing

Pipecat-Specific Features

Pipeline Architecture

Transport Options

Best Practices

Implementation Examples

Basic Pipecat Agent

Advanced Configuration

Common Scenarios

Advanced Features

Interrupt Handling

Context Management

Custom Processors

Troubleshooting

Development Setup

Local Development

Testing Pipeline