Datadog Agent Connector

Configure the Datadog Agent connector to receive metrics, logs, and traces from Datadog agents using the native Datadog protocol.

  5 minute read  

Overview

The Datadog Agent connector receives telemetry data from Datadog agents using the native Datadog protocol. It collects metrics, logs, and APM traces from Datadog-instrumented applications and infrastructure, making the data available for AI teammates to query through the Edge Delta MCP connector.

When you add this streaming connector, it appears as a Datadog Agent source in your selected pipeline. AI teammates access this data by querying the Edge Delta backend with the Edge Delta MCP connector.

Add the Datadog Agent Connector

To add the Datadog Agent connector, you configure a network endpoint where Edge Delta will listen for Datadog protocol traffic, then reconfigure your Datadog agents to send data to that endpoint.

Prerequisites

Before configuring the connector, ensure you have:

  • Datadog agents or applications instrumented with Datadog client libraries
  • Network connectivity between Datadog agents and Edge Delta infrastructure
  • Firewall rules allowing inbound traffic on the connector port

Configuration Steps

  1. Navigate to AI Team > Connectors in the Edge Delta application
  2. Find the Datadog Agent connector in Streaming Connectors
  3. Click the connector card
  4. Select the pipeline (environment) to receive this data
  5. Configure network and timeout options (see below)
  6. Click Save

The connector is now ready to receive Datadog protocol traffic.

Datadog Agent connector configuration showing listen address, port, and timeout settings

Reconfiguring Datadog Agents

After deploying the connector, reconfigure your Datadog agents to send data to Edge Delta instead of the Datadog backend.

For Datadog Agents on Servers

Edit /etc/datadog-agent/datadog.yaml:

dd_url: http://edge-delta-host:3421
apm_config:
  apm_dd_url: http://edge-delta-host:3421

Restart the agent: sudo systemctl restart datadog-agent

For DogStatsD Client Libraries

Python:

from datadog import initialize
initialize(statsd_host='edge-delta-host', statsd_port=3421)

Node.js:

const StatsD = require('node-statsd');
const client = new StatsD('edge-delta-host', 3421);

For Datadog APM Libraries

Java: Add JVM arguments:

-Ddd.agent.host=edge-delta-host -Ddd.agent.port=3421

Python:

from ddtrace import tracer
tracer.configure(hostname='edge-delta-host', port=3421)

Go:

tracer.Start(tracer.WithAgentAddr("edge-delta-host:3421"))

Configuration Options

Connector Name

Name to identify this Datadog Agent connector instance.

Listen

Network address where the connector listens for incoming Datadog agent traffic. Use 0.0.0.0 to accept connections on all network interfaces.

Default: 0.0.0.0

Format: Valid IP address (e.g., 0.0.0.0, 192.168.1.100, 127.0.0.1)

Port

TCP port number where the connector listens for Datadog protocol connections.

Default: 3421

Format: Port number between 1 and 65535

Read Timeout

How long the connector waits for data from an established connection before closing it due to inactivity.

Default: 1m (1 minute)

Format: Duration with unit suffix (e.g., 1m, 30s, 2m, 5000ms)

Target Environments

Select the Edge Delta pipeline (environment) where you want to deploy this connector.

Advanced Settings

TLS

Optional TLS/SSL configuration for encrypted communication between Datadog agents and Edge Delta. When enabled, all telemetry data is transmitted over an encrypted connection.

TLS Options:

  • Ignore Certificate Check: Disables SSL/TLS certificate verification (use with caution)
  • CA File: Absolute file path to the CA certificate for SSL/TLS connections
  • CA Path: Absolute path where CA certificate files are located
  • CRT File: Absolute path to the SSL/TLS certificate file
  • Key File: Absolute path to the private key file
  • Key Password: Optional password for the key file
  • Client Auth Type: Client authentication type (default: noclientcert)
  • Minimum Version: Minimum TLS version (default: TLSv1_2)
  • Maximum Version: Maximum TLS version

Metadata Level

This option is used to define which detected resources and attributes to add to each data item as it is ingested by Edge Delta. You can select:

  • Required Only: This option includes the minimum required resources and attributes for Edge Delta to operate.
  • Default: This option includes the required resources and attributes plus those selected by Edge Delta
  • High: This option includes the required resources and attributes along with a larger selection of common optional fields.
  • Custom: With this option selected, you can choose which attributes and resources to include. The required fields are selected by default and can’t be unchecked.

Based on your selection in the GUI, the source_metadata YAML is populated as two dictionaries (resource_attributes and attributes) with Boolean values.

See Choose Data Item Metadata for more information on selecting metadata.

Rate Limit

Rate limit configuration to control maximum data ingestion volume and manage processing capacity. Configure evaluation policies to determine when rate limiting applies based on conditions like data volume or time windows.

How to Use the Datadog Agent Connector

The Datadog Agent connector integrates seamlessly with AI Team, enabling analysis of metrics, logs, and traces from Datadog-instrumented infrastructure. AI teammates automatically leverage the ingested data based on the queries they receive and the context of the conversation.

Use Case: Microservices Performance Analysis

Applications using DogStatsD client libraries emit custom metrics like request counts, error rates, and response times. AI teammates can analyze these metrics to identify performance issues. For example, when investigating slow API responses, teammates can correlate DogStatsD metrics with APM traces to identify database bottlenecks or external service delays.

Use Case: Infrastructure Health Monitoring

Datadog agents running on servers send system-level metrics (CPU, memory, disk, network). AI teammates can analyze resource utilization patterns and predict capacity issues. When combined with alert connectors like PagerDuty, teammates can investigate infrastructure alerts by querying recent metrics from affected hosts.

Use Case: Distributed Trace Analysis

Applications instrumented with Datadog APM libraries send detailed trace data. AI teammates can analyze latency patterns across microservices, identify slow database queries, and detect bottlenecks in external API calls. This is valuable when investigating production incidents—teammates correlate traces with logs and metrics for root cause analysis.

Troubleshooting

No data appearing: Verify Datadog agents are configured to send data to the Edge Delta endpoint (check /etc/datadog-agent/datadog.yaml). Test connectivity with telnet edge-delta-host 3421.

Connection refused errors: Confirm Edge Delta is listening on the configured port (netstat -tuln | grep 3421). Check firewall rules allow inbound traffic on the connector port.

Missing tags or metadata: Verify Datadog agents include tags in metric submissions. Check that DogStatsD metrics use proper tag format: metric_name:value|type|#tag1:value1,tag2:value2.

APM traces not appearing: Confirm tracer libraries are configured with the Edge Delta endpoint (check DD_AGENT_HOST and DD_TRACE_AGENT_PORT environment variables). Increase read timeout to 2m for large trace payloads.

High memory usage: Reduce read timeout to release connections faster. Configure rate limiting in your pipeline if receiving unexpectedly high volumes.

Next Steps

For additional help, visit AI Team Support.