custom-metrics

✓ Official★ 19

by launchdarkly · part of launchdarkly/ai-tooling

Lets you measure whether an AI feature is doing its job in your own terms, rather than only how fast it answered or how many words it used.

🧩 One of 41 skills in the launchdarkly/ai-tooling package — works on its own, and pairs well with its siblings.

WHEN YOUR AGENT SHOULD USE IT

A QUICK BOUNDARY

USE FOR

Track whether a customer's request actually got resolved, not just whether the model responded.
Measure revenue generated after an AI agent's interaction with a customer.
Record a satisfaction score and flag negative feedback for closer review.
Roll up a whole user session's interaction count and success rate into one event.
Rename an existing metric or update its description without recreating it.

DO NOT USE FOR

Tracking token counts, latency, or cost automatically — that's built-in-metrics, not this.

This is the playbook your agent receives when the skill activates — you don't need to read it to use the skill, but it's here to audit before installing.

Custom Metrics for Configs

Full lifecycle management of custom business metrics: create metric definitions via API, track events via SDK, retrieve metric data, and manage metrics programmatically.

Prerequisites

LaunchDarkly SDK initialized (see sdk)
LaunchDarkly API token with writer role for metric management
Understanding of built-in agent metrics (see built-in-metrics)

API Key Detection

Before prompting the user for an API key, try to detect it automatically:

Check Claude MCP config - Read ~/.claude/config.json and look for mcpServers.launchdarkly.env.LAUNCHDARKLY_API_KEY
Check environment variables - Look for LAUNCHDARKLY_API_KEY, LAUNCHDARKLY_API_TOKEN, or LD_API_KEY
Prompt user - Only if detection fails, ask the user for their API key

import os
import json
from pathlib import Path

def get_launchdarkly_api_key():
    """Auto-detect LaunchDarkly API key from Claude config or environment."""
    # 1. Check Claude MCP config
    claude_config = Path.home() / ".claude" / "config.json"
    if claude_config.exists():
        try:
            config = json.load(open(claude_config))
            api_key = config.get("mcpServers", {}).get("launchdarkly", {}).get("env", {}).get("LAUNCHDARKLY_API_KEY")
            if api_key:
                return api_key
        except (json.JSONDecodeError, IOError):
            pass

    # 2. Check environment variables
    for var in ["LAUNCHDARKLY_API_KEY", "LAUNCHDARKLY_API_TOKEN", "LD_API_KEY"]:
        if os.environ.get(var):
            return os.environ[var]

    return None

Metrics Lifecycle Overview

Step	Method	Purpose
1. Create	API	Define metric in LaunchDarkly
2. Track	SDK	Send events to the metric
3. Get	API	Retrieve metric definition/data
4. Update	API	Modify metric properties
5. Delete	API	Remove metric

1. Create Metric (API)

Required fields for numeric custom metrics:

successCriteria - Must be one of: "HigherThanBaseline", "LowerThanBaseline"
unit - e.g., "count", "percent", "milliseconds"

The API will return 400 Bad Request if these are missing for numeric metrics.

import requests
import os

def create_metric(
    project_key: str,
    metric_key: str,
    name: str,
    kind: str = "custom",
    is_numeric: bool = True,
    unit: str = "count",
    success_criteria: str = "HigherThanBaseline",
    event_key: str = None,
    description: str = None
):
    """Create a new metric definition in LaunchDarkly."""
    API_TOKEN = os.environ.get("LAUNCHDARKLY_API_TOKEN")

    url = f"https://app.launchdarkly.com/api/v2/metrics/{project_key}"

    payload = {
        "key": metric_key,
        "name": name,
        "kind": kind,
        "isNumeric": is_numeric,
        "eventKey": event_key or metric_key
    }

    # Unit and successCriteria are required for numeric custom metrics
    if is_numeric and kind == "custom":
        payload["unit"] = unit
        payload["successCriteria"] = success_criteria

    if description:
        payload["description"] = description

    headers = {
        "Authorization": API_TOKEN,
        "Content-Type": "application/json"
    }

    response = requests.post(url, json=payload, headers=headers)

    if response.status_code == 201:
        print(f"[OK] Created metric: {metric_key}")
        return response.json()
    elif response.status_code == 409:
        print(f"[INFO] Metric already exists: {metric_key}")
        return None
    else:
        print(f"[ERROR] Failed to create metric: {response.status_code}")
        print(f"        {response.text}")
        return None

Metric Kinds:

custom - Track any event (most common for agent metrics)
pageview - Track page views
click - Track click events

Success Criteria (for numeric metrics):

HigherThanBaseline - Higher values are better (e.g., revenue, satisfaction)
LowerThanBaseline - Lower values are better (e.g., errors, latency)

Common Units:

count - Generic count
milliseconds - Time duration
percent - Percentage values
dollars - Currency

2. Track Events (SDK)

Once the metric is created, track events using the SDK:

from ldclient import Context
from ldclient.config import Config
import ldclient

# Initialize (see sdk for details)
ldclient.set_config(Config("your-sdk-key"))
ld_client = ldclient.get()

def track_metric(ld_client, user_id: str, metric_key: str, value: float, data: dict = None):
    """Track an event to a metric."""
    context = Context.builder(user_id).build()

    ld_client.track(
        metric_key,
        context,
        data=data,
        metric_value=value
    )

Common Tracking Patterns

def track_conversion(ld_client, user_id: str, amount: float, config_key: str):
    """Track a conversion event with revenue."""
    context = Context.builder(user_id).build()

    ld_client.track(
        "business.conversion",
        context,
        data={"configKey": config_key, "category": "electronics"},
        metric_value=amount
    )

def track_task_success(ld_client, user_id: str, task_type: str, success: bool):
    """Track task completion success/failure."""
    context = Context.builder(user_id).build()

    ld_client.track(
        "task.success_rate",
        context,
        data={"taskType": task_type},
        metric_value=1.0 if success else 0.0
    )

def track_satisfaction(ld_client, user_id: str, score: float, feedback_type: str):
    """Track user satisfaction (0-100 scale)."""
    context = Context.builder(user_id).build()

    ld_client.track(
        "user.satisfaction",
        context,
        data={"feedbackType": feedback_type},
        metric_value=score
    )

    # Track negative feedback separately for alerts
    if score < 50:
        ld_client.track(
            "user.negative_feedback",
            context,
            metric_value=1.0
        )

def track_revenue(ld_client, user_id: str, revenue: float, source: str):
    """Track revenue generated after agent interaction."""
    context = Context.builder(user_id).set("tier", "premium").build()

    if revenue > 0:
        ld_client.track(
            "revenue.impact",
            context,
            data={"source": source},
            metric_value=revenue
        )

3. Get Metrics (API)

Get Single Metric

def get_metric(project_key: str, metric_key: str):
    """Get a single metric definition."""
    API_TOKEN = os.environ.get("LAUNCHDARKLY_API_TOKEN")

    url = f"https://app.launchdarkly.com/api/v2/metrics/{project_key}/{metric_key}"

    headers = {"Authorization": API_TOKEN}

    response = requests.get(url, headers=headers)

    if response.status_code == 200:
        metric = response.json()
        print(f"[OK] Metric: {metric['key']}")
        print(f"     Name: {metric.get('name', 'N/A')}")
        print(f"     Kind: {metric.get('kind', 'N/A')}")
        print(f"     Numeric: {metric.get('isNumeric', False)}")
        print(f"     Event Key: {metric.get('eventKey', 'N/A')}")
        return metric
    elif response.status_code == 404:
        print(f"[INFO] Metric not found: {metric_key}")
        return None
    else:
        print(f"[ERROR] Failed to get metric: {response.status_code}")
        return None

List All Metrics

def list_metrics(project_key: str, limit: int = 20):
    """List all metrics in a project."""
    API_TOKEN = os.environ.get("LAUNCHDARKLY_API_TOKEN")

    url = f"https://app.launchdarkly.com/api/v2/metrics/{project_key}"

    headers = {"Authorization": API_TOKEN}
    params = {"limit": limit}

    response = requests.get(url, headers=headers, params=params)

    if response.status_code == 200:
        data = response.json()
        metrics = data.get("items", [])
        print(f"[OK] Found {len(metrics)} metrics:")
        for metric in metrics:
            numeric = "numeric" if metric.get("isNumeric") else "non-numeric"
            print(f"     - {metric['key']} ({metric.get('kind', 'custom')}, {numeric})")
        return metrics
    else:
        print(f"[ERROR] Failed to list metrics: {response.status_code}")
        return None

4. Update Metric (API)

def update_metric(project_key: str, metric_key: str, updates: list):
    """
    Update a metric using JSON Patch operations.

    Args:
        updates: List of patch operations, e.g.:
            [{"op": "replace", "path": "/name", "value": "New Name"}]
    """
    API_TOKEN = os.environ.get("LAUNCHDARKLY_API_TOKEN")

    url = f"https://app.launchdarkly.com/api/v2/metrics/{project_key}/{metric_key}"

    headers = {
        "Authorization": API_TOKEN,
        "Content-Type": "application/json"
    }

    response = requests.patch(url, json=updates, headers=headers)

    if response.status_code == 200:
        print(f"[OK] Updated metric: {metric_key}")
        return response.json()
    elif response.status_code == 404:
        print(f"[ERROR] Metric not found: {metric_key}")
        return None
    else:
        print(f"[ERROR] Failed to update metric: {response.status_code}")
        print(f"        {response.text}")
        return None

# Example: Update metric name and description
def rename_metric(project_key: str, metric_key: str, new_name: str, new_description: str = None):
    """Rename a metric and optionally update description."""
    updates = [
        {"op": "replace", "path": "/name", "value": new_name}
    ]
    if new_description:
        updates.append({"op": "replace", "path": "/description", "value": new_description})

    return update_metric(project_key, metric_key, updates)

5. Delete Metric (API)

def delete_metric(project_key: str, metric_key: str):
    """Delete a metric from the project."""
    API_TOKEN = os.environ.get("LAUNCHDARKLY_API_TOKEN")

    url = f"https://app.launchdarkly.com/api/v2/metrics/{project_key}/{metric_key}"

    headers = {"Authorization": API_TOKEN}

    response = requests.delete(url, headers=headers)

    if response.status_code == 204:
        print(f"[OK] Deleted metric: {metric_key}")
        return True
    elif response.status_code == 404:
        print(f"[INFO] Metric not found: {metric_key}")
        return False
    else:
        print(f"[ERROR] Failed to delete metric: {response.status_code}")
        return False

Complete Workflow Example

import os
import requests
from ldclient import Context
from ldclient.config import Config
import ldclient

# Setup
API_TOKEN = os.environ.get("LAUNCHDARKLY_API_TOKEN")
SDK_KEY = os.environ.get("LAUNCHDARKLY_SDK_KEY")
PROJECT_KEY = "support-ai"

ldclient.set_config(Config(SDK_KEY))
ld_client = ldclient.get()

# 1. Create metric
create_metric(
    PROJECT_KEY,
    "ai.task.completion",
    name="Agent Task Completion Rate",
    kind="custom",
    is_numeric=True,
    description="Tracks successful agent task completions"
)

# 2. Track events
context = Context.builder("user-123").build()
ld_client.track("ai.task.completion", context, metric_value=1.0)
ld_client.track("ai.task.completion", context, metric_value=1.0)
ld_client.track("ai.task.completion", context, metric_value=0.0)  # failure
ld_client.flush()

# 3. Get metric definition
metric = get_metric(PROJECT_KEY, "ai.task.completion")

# 4. Update metric name
rename_metric(PROJECT_KEY, "ai.task.completion", "Agent Task Success Rate")

# 5. List all metrics
list_metrics(PROJECT_KEY)

# 6. Delete metric (when no longer needed)
# delete_metric(PROJECT_KEY, "ai.task.completion")

Session Metrics Tracker

import time
from ldclient import Context

class SessionMetricsTracker:
    """Track metrics across an entire user session."""

    def __init__(self, ld_client):
        self.ld_client = ld_client
        self.session_data = {}

    def start_session(self, user_id: str, session_id: str):
        """Initialize session tracking."""
        self.session_data[session_id] = {
            "user_id": user_id,
            "start_time": time.time(),
            "interactions": 0,
            "successful_tasks": 0
        }

    def track_interaction(self, session_id: str, success: bool):
        """Track individual interaction within session."""
        if session_id not in self.session_data:
            return
        session = self.session_data[session_id]
        session["interactions"] += 1
        if success:
            session["successful_tasks"] += 1

    def end_session(self, session_id: str):
        """Finalize and track session metrics."""
        if session_id not in self.session_data:
            return None

        session = self.session_data[session_id]
        duration = time.time() - session["start_time"]

        context = Context.builder(session["user_id"]).build()

        # Track session duration
        self.ld_client.track(
            "session.duration",
            context,
            data={"interactions": session["interactions"]},
            metric_value=duration
        )

        # Track session success rate
        if session["interactions"] > 0:
            success_rate = session["successful_tasks"] / session["interactions"]
            self.ld_client.track(
                "session.success_rate",
                context,
                metric_value=success_rate * 100
            )

        result = dict(session)
        result["duration"] = duration
        del self.session_data[session_id]
        return result

Naming Conventions

# Use dot notation for hierarchy
"quality.accuracy"
"quality.relevance"
"user.satisfaction"
"user.engagement"
"revenue.conversion"
"task.success_rate"
"session.duration"
"ai.task.completion"
"ai.recommendation.conversion"

Best Practices

Create Before Track - Metric must exist before tracking events
Use Numeric Metrics - Set isNumeric=True for aggregation
Consistent Keys - Use same key in create_metric() and ld_client.track()
Always flush before close - Call ld_client.flush() (await in Node) before close(). Trailing events are at risk of being lost otherwise, in short-lived scripts and long-running services alike. This is not a serverless-only rule; it applies to any process that exits.
Rate Limit - Don't track on every keystroke

Viewing Metrics

Custom metrics appear in:

Metrics page in LaunchDarkly UI
Monitoring tab of your config
Via API using get_metric() or list_metrics()

sdk - SDK setup
built-in-metrics - Built-in agent metrics (tokens, duration, cost)
online-evals - Quality metrics via judges

References

Copy & paste — that's it

npx skills add launchdarkly/ai-tooling --skill "custom-metrics" --full-depth

Run this in your project — your agent picks the skill up automatically.

BEFORE IT WILL WORK

1 FOR YOU · 1 FOR THE AGENT

01Get a LaunchDarkly API token with writer access
02Initialize the LaunchDarkly SDK first