Datadog with Pipecat Cloud

Ship Datadog logging, traces, and custom metrics from Pipecat Cloud agents for production monitoring and alerting.

This guide explains how to use Datadog with your Pipecat Cloud agents for logging, traces, and custom metrics.

This guide is a work in progress and may have some sharp edges. However, it has been vetted for basic usage.

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Prerequisites

• Have a Datadog account and API key.
• Use at least version 0.0.7 of the Pipecat Cloud base image:

# dailyco/pipecat-base:latest should also work
FROM dailyco/pipecat-base:0.0.7

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Add Datadog API key as a secret

If you’re using keeping your secrets in an .env file as recommended, add your Datadog API key to that file:

DD_API_KEY=<your-datadog-api-key>

Then update your secret set from that file:

pipecat cloud secrets set <your-agent-secrets> --file .env

See Secrets for more details.

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Create datadog.yaml

This file contains the Datadog Agent configuration.

The configuration shown below assumes you want all Datadog features covered in this guide (logging, traces, and custom metrics).

site: <your-datadog-site> # e.g. us5.datadoghq.com
hostname: pipecat.daily.co # (or whatever would be useful for you to see in the Datadog dashboards)

# Include the following line only if you need logging (see "Enabling Logging")
logs_enabled: true

# Uncomment the below lines if you DON'T need traces (see "Enabling Traces")
# apm_config:
#   enabled: false

# Uncomment the below lines if you DON'T need custom metrics (see "Enabling Custom Metrics")
# use_dogstatsd: false

kubelet_tls_verify: false
autoconfig_exclude_features:
  - kubernetes
  - orchestratorexplorer

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Create pre-app.sh

This script runs before the agent launcher runs.

The file shown below assumes you want all Datadog features covered in this guide (logging, traces, and custom metrics).

#!/bin/bash

# Write the Datadog API key to the config file
echo "api_key: $DD_API_KEY" >> /etc/datadog-agent/datadog.yaml

# Start the Datadog Agent service
service datadog-agent start || true

# Include the below line only if you need traces (see "Enabling Traces")
# HACK: here we restart the Datadog Trace Agent specifically, since we know it failed to start above
service datadog-agent-trace restart || true

## DEBUGGING

# Check the status of the Datadog Agent subsystems for logging, custom metrics, and traces
# service datadog-agent status
# service datadog-agent-trace status
# datadog-agent status

# Display the Datadog agent log
# cat /var/log/datadog/agent.log

You may see Starting Datadog Agent: datadog-agent failed! in your logs, triggered by service datadog-agent start. You can safely ignore this! As long as service datadog-agent status and service datadog-agent-trace status indicate that both of those services end up running, then we should have no trouble sending logs, traces, and custom metrics to Datadog.

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Update Dockerfile with the Datadog Agent

The snippet shown below assumes you want all Datadog features covered in this guide (logging, traces, and custom metrics).

# Install the Datadog Agent
# (note that you can literally use "dummy"—or anything else—as the API key)
RUN apt-get -y update; apt-get -y install curl
RUN DD_API_KEY=dummy \
    DD_INSTALL_ONLY=true \
    bash -c "$(curl -L https://install.datadoghq.com/scripts/install_script_agent7.sh)"

# Configure Datadog Agent
COPY ./datadog.yaml /etc/datadog-agent
# Include the below line if you need logging (see "Enabling Logging")
COPY ./python.d /etc/datadog-agent/conf.d/python.d

# Include the below lines only if you need traces or custom metrics (see "Enabling Traces" or "Enabling Custom Metrics")
# Prepare file system for the Datadog Agent to use for Unix Sockets
RUN mkdir -p /var/run/datadog/
RUN chown dd-agent:dd-agent /var/run/datadog/

# Start Datadog Agent (move pre-app script to expected place where it'll be invoked by Pipecat Cloud base image)
COPY --chmod=755 ./pre-app.sh /app/

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Enabling logging

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Enable logging in datadog.yaml

Ensure the following is present in your datadog.yaml:

logs_enabled: true

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Create python.d/conf.yaml

Configure log collection for your agent:

# Use whatever agent name would be useful for you to see in the Datadog dashboard
logs:
  - type: file
    path: /var/log/<my-agent-name>/datadog.log
    service: <my-agent-name>
    source: python
    sourcecategory: sourcecode

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Update Dockerfile for logging

Ensure the following is present in your Dockerfile:

COPY ./python.d /etc/datadog-agent/conf.d/python.d

This configures the Datadog Agent to collect logs from your agent.

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Configure logger in bot.py

Add a Datadog sink to your logger.

This sink will write log lines into a file that your Datadog Agent will read from, in a Datadog-friendly format.

import json

# Format log lines so that:
# - Datadog can parse and display log levels, messages, and timestamps
# - You can search Datadog logs by session_id
def datadog_format(record):
    format_string_safe_message = (
        json.dumps(record["message"]).replace("{", "{{").replace("}", "}}")
    )
    session_id = record["extra"].get("session_id", "")
    return (
        "{{"
        '"timestamp":"{time:YYYY-MM-DD HH:mm:ss.SSS}",'
        '"levelname":"{level}",'
        '"name":"{name}",'
        '"function":"{function}",'
        '"line":{line},'
        f'"message":{format_string_safe_message},'
        f'"session_id": "{session_id}"'
        "}}\n"
    )

async def bot(args: DailySessionArguments):
    # Add Datadog-specific sink
    logger.add(
        "/var/log/<my-agent-name>/datadog.log", # Use same path as in python.d/conf.yaml
        rotation="10 MB",
        retention="7 days",
        format=datadog_format,
    )
    # ...

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Enabling traces

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Enable traces in datadog.yaml

Ensure apm_config.enabled is NOT set to false in your datadog.yaml:

# DELETE or COMMENT OUT the following lines, if they're present
# apm_config:
#  enabled: false

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Start the Datadog Trace Agent in pre-app.sh

Ensure the following is present in your pre-app.sh:

service datadog-agent-trace restart || true

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Prepare file system in Dockerfile

Ensure the following is present in your Dockerfile:

RUN mkdir -p /var/run/datadog/
RUN chown dd-agent:dd-agent /var/run/datadog/

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Add ddtrace Python library

In your requirements.txt:

ddtrace

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Record traces in bot.py

from ddtrace import tracer

@tracer.wrap()
def my_function_call():
    # ...

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Enabling custom metrics

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Enable custom metrics in datadog.yaml

Ensure use_dogstatsd is NOT set to false in your datadog.yaml:

# DELETE or COMMENT OUT the following line, if it's present
# use_dogstatsd: false

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Prepare file system in Dockerfile

Ensure the following is present in your Dockerfile:

RUN mkdir -p /var/run/datadog/
RUN chown dd-agent:dd-agent /var/run/datadog/

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Add datadog Python library

In your requirements.txt:

datadog

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Record custom metrics in bot.py

from datadog import statsd

def my_function_call():
    statsd.increment("myagent.my_function_call")