MLflow

Overview

MLflow is an open-source platform for managing the end-to-end machine learning and AI lifecycle. MLflow Tracing provides detailed observability into AI agent execution, capturing LLM calls, tool usage, and agent decisions with a rich visualization UI. Since Pipecat’s built-in tracing uses OpenTelemetry, you can send traces directly to MLflow’s OTLP endpoint for visualization and analysis.

MLflow Tracing Docs

Learn about MLflow’s tracing capabilities

MLflow Pipecat Integration

MLflow’s guide for tracing Pipecat applications

MLflow GitHub

Browse the MLflow open-source repository

MLflow Platform

Explore the MLflow platform

Installation

Install Pipecat with tracing support and the OTLP HTTP exporter:

uv add "pipecat-ai[tracing]" mlflow opentelemetry-exporter-otlp-proto-http

Prerequisites

Start MLflow

The quickest way to start the MLflow tracking server is with uvx (no installation needed):

uvx mlflow server --port 5000

The MLflow UI will be available at http://localhost:5000.

For other setup options including Docker and pip, see the MLflow environment setup guide.If you prefer a managed solution, Managed MLflow on AWS SageMaker or Databricks provides a fully hosted MLflow experience with no infrastructure to manage.

Key Features

Trace visualization: Inspect every LLM call, STT/TTS operation, and conversation turn in a hierarchical trace view
Token usage tracking: Monitor input/output token counts across conversations
Performance metrics: Track TTFB, processing duration, and latency for each service
Evaluation framework: Evaluate agent outputs using built-in LLM judges and custom scorers
Open source: Fully open-source with no vendor lock-in, self-host anywhere

Configuration

Configure the OTLP HTTP exporter to send traces to MLflow:

import os
from opentelemetry.exporter.otlp.proto.http.trace_exporter import OTLPSpanExporter
from pipecat.utils.tracing.setup import setup_tracing

exporter = OTLPSpanExporter(
    endpoint=os.getenv("OTEL_EXPORTER_OTLP_ENDPOINT", "http://localhost:5000/v1/traces"),
)

setup_tracing(
    service_name="my-pipecat-bot",
    exporter=exporter,
)

Alternatively, configure with environment variables:

export OTEL_EXPORTER_OTLP_ENDPOINT="http://localhost:5000"
export OTEL_EXPORTER_OTLP_HEADERS="x-mlflow-experiment-id=0"

The x-mlflow-experiment-id header specifies which MLflow experiment to log traces to. Use 0 for the default experiment, or create a dedicated experiment:

mlflow experiments create --experiment-name "pipecat-traces"
# Use the returned experiment ID in the header

Usage

Basic Setup

import os

from dotenv import load_dotenv
from opentelemetry.exporter.otlp.proto.http.trace_exporter import OTLPSpanExporter

from pipecat.audio.vad.silero import SileroVADAnalyzer
from pipecat.pipeline.pipeline import Pipeline
from pipecat.pipeline.worker import PipelineParams, PipelineWorker
from pipecat.processors.aggregators.llm_context import LLMContext
from pipecat.processors.aggregators.llm_response_universal import (
    LLMContextAggregatorPair,
    LLMUserAggregatorParams,
)
from pipecat.services.cartesia.tts import CartesiaTTSService
from pipecat.services.deepgram.stt import DeepgramSTTService
from pipecat.services.openai.llm import OpenAILLMService
from pipecat.utils.tracing.setup import setup_tracing
from pipecat.workers.runner import WorkerRunner

load_dotenv()

# Initialize tracing with the MLflow exporter
exporter = OTLPSpanExporter(
    endpoint=os.getenv("OTEL_EXPORTER_OTLP_ENDPOINT", "http://localhost:5000/v1/traces"),
)

setup_tracing(
    service_name="my-pipecat-bot",
    exporter=exporter,
)

# Create your services
stt = DeepgramSTTService(api_key=os.environ["DEEPGRAM_API_KEY"])

llm = OpenAILLMService(api_key=os.environ["OPENAI_API_KEY"])

tts = CartesiaTTSService(
    api_key=os.environ["CARTESIA_API_KEY"],
    settings=CartesiaTTSService.Settings(voice="your-voice-id"),
)

# Create the context and its user/assistant aggregators
context = LLMContext()
user_aggregator, assistant_aggregator = LLMContextAggregatorPair(
    context,
    user_params=LLMUserAggregatorParams(vad_analyzer=SileroVADAnalyzer()),
)

# Build pipeline
pipeline = Pipeline(
    [
        transport.input(),
        stt,
        user_aggregator,
        llm,
        tts,
        transport.output(),
        assistant_aggregator,
    ]
)

# Create the pipeline worker with tracing enabled
worker = PipelineWorker(
    pipeline,
    params=PipelineParams(
        enable_metrics=True,
        enable_usage_metrics=True,
    ),
    enable_tracing=True,
    enable_turn_tracking=True,
)

# Run the pipeline
runner = WorkerRunner()
await runner.add_workers(worker)
await runner.run()

After running your Pipecat application, open the MLflow UI at http://localhost:5000 and navigate to the Traces tab to see detailed traces of your voice agent conversations, including STT, LLM, and TTS spans with latency and token usage.

Troubleshooting

No traces visible: Verify the MLflow server is running and the OTEL_EXPORTER_OTLP_ENDPOINT points to the correct address
Missing service data: Ensure enable_metrics=True is set in PipelineParams
Connection errors: Check that the MLflow server is accessible from your application and the endpoint URL is correct
Wrong experiment: Set the x-mlflow-experiment-id header to direct traces to the correct experiment

Get Started

Pipecat Server

Client SDKs

Pipecat Flows

Pipecat Cloud

CLI

Pipecat Context Hub

Overview

MLflow Tracing Docs

MLflow Pipecat Integration

MLflow GitHub

MLflow Platform

Installation

Prerequisites

Start MLflow

Key Features

Configuration

Usage

Basic Setup

Troubleshooting

References

​Overview

MLflow Tracing Docs

MLflow Pipecat Integration

MLflow GitHub

MLflow Platform

​Installation

​Prerequisites

​Start MLflow

​Key Features

​Configuration

​Usage

​Basic Setup

​Troubleshooting

​References

Overview

Installation

Prerequisites

Start MLflow

Key Features

Configuration

Usage

Basic Setup

Troubleshooting

References