Alerts in LangSmith

Private Beta

This is feature is currently in private beta. If interested, please express interest in the #feature-requests channel in the LangChain Community Slack.

Overview

Effective observability in LLM applications requires proactive detection of failures, performance degradations, and regressions. LangSmith's alerts feature helps identify critical issues such as:

• API rate limit violations from model providers
• Latency increases for your application
• Application changes that affect feedback scores reflecting end-user experience

Alerts in LangSmith are project-scoped, requiring separate configuration for each monitored project.

Configuring an alert

Step 1: Navigate To Create Alert

First navigate to the Tracing project that you would like to configure alerts for. Click + New Alert in the top right hand corner of the page to set up an alert.

Step 2: Select Metric Type

LangSmith offers threshold-based alerting on three core metrics:

Metric Type	Description	Use Case
Errored Runs	Tracks runs with error status	Monitors for failures in application runs.
Feedback Score	Measures average feedback score metrics	Tracks feedback from application users or automated evaluation rules to track regressions in prompts or switching models / providers.
Latency	Measures average run execution time	Identifies performance bottlenecks from switching models / providers or code regressions.

Additionally, for Errored Runs and Run Latency, you can define filters to narrow down the runs that trigger alerts. For example, you might create an error alert filter for all llm runs tagged with support_agent that encounter a RateLimitExceeded error.

Step 2: Define Alert Conditions

Alert conditions consist of several components:

• Aggregation Method: Average, Percentage, or Count
• Comparison Operator: >=, <=, or exceeds threshold
• Threshold Value: Numerical value triggering the alert
• Aggregation Window: Time period for metric calculation (currently choose between 5 or 15 minutes)
• Feedback Key (Feedback Score alerts only): Specific feedback metric to monitor

Example: The configuration shown above would generate an alert when more than 5% of runs within the past 5 minutes result in errors.

You can preview alert behavior over a historical time window to understand how many datapoints—and which ones—would have triggered an alert at a chosen threshold (indicated in red). For example, setting an average latency threshold of 60 seconds for a project lets you visualize potential alerts, as shown in the image below.

Step 3: Configure Notification Channel

LangSmith supports the following notification channels:

1. PagerDuty Integration
2. Webhook Notifications

Select the appropriate channel to ensure notifications reach the responsible team members.

Best Practices

• Adjust sensitivity based on application criticality
• Start with broader thresholds and refine based on observed patterns
• Ensure alert routing reaches appropriate on-call personnel