App tracking for AI requests

Use Cases

Attributing token costs and latency to specific features or services.
Monitoring which internal tools or products drive the most LLM usage.
Filtering observability dashboards by application for debugging or billing.
Enforcing separate budgets per product line or team.

Quick Start

Name your requests to track usage by application or service.

curl -X POST https://api.orq.ai/v3/router/responses \
  -H "Authorization: Bearer $ORQ_API_KEY" \
  -H "Content-Type: application/json" \
  -d '{
    "model": "openai/gpt-4o-mini",
    "input": "Write a professional email",
    "name": "EmailAssistant-Production"
  }'

import OpenAI from "openai";

const client = new OpenAI({
  apiKey: process.env.ORQ_API_KEY,
  baseURL: "https://api.orq.ai/v3/router",
});

const response = await client.responses.create({
  model: "openai/gpt-4o-mini",
  input: "Write a professional email",
  name: "EmailAssistant-Production",
});

console.log(response.output_text);

from openai import OpenAI
import os

client = OpenAI(
    api_key=os.environ.get("ORQ_API_KEY"),
    base_url="https://api.orq.ai/v3/router",
)

response = client.responses.create(
    model="openai/gpt-4o-mini",
    input="Write a professional email",
    extra_body={"name": "EmailAssistant-Production"},
)

print(response.output_text)

import OpenAI from "openai";

const client = new OpenAI({
  apiKey: process.env.ORQ_API_KEY,
  baseURL: "https://api.orq.ai/v3/router",
});

const response = await client.chat.completions.create({
  model: "openai/gpt-4o-mini",
  messages: [{ role: "user", content: "Write a professional email" }],
  name: "EmailAssistant-Production",
});

from openai import OpenAI
import os

client = OpenAI(
    api_key=os.environ.get("ORQ_API_KEY"),
    base_url="https://api.orq.ai/v3/router",
)

response = client.chat.completions.create(
    model="openai/gpt-4o-mini",
    messages=[{"role": "user", "content": "Write a professional email"}],
    extra_body={"name": "EmailAssistant-Production"},
)

Configuration

Parameter	Type	Required	Description
`name`	string	No	The name to display on the trace. If not specified, the default system name will be used.

For backwards compatibility, orq.name is also supported but deprecated. Use top-level name for new implementations.

Default behavior: If no name provided, system uses default identifier.

Naming Conventions

Recommended patterns

// Service-Environment
"UserAPI-Production";
"ChatBot-Development";

// Team-Service-Feature
"Platform-Auth-OAuth";
"ML-Recommendations-v2";

// Application-Version
"MobileApp-v3.1";
"WebPortal-v2.0";

Best practices

Use consistent patterns across team.
Include environment (dev/staging/prod).
Avoid timestamps or dynamic values.
Keep names under 50 characters.
Use alphanumeric and hyphens only.

Use Cases

Scenario	Naming Strategy	Example
Microservices	Service-based naming	`user-service`, `payment-api`
Multi-tenant	Tenant identification	`tenant-123`, `enterprise-client`
A/B testing	Variant tracking	`experiment-A`, `control-group`
Feature flags	Feature identification	`new-ui-beta`, `legacy-flow`

Code examples

curl -X POST https://api.orq.ai/v3/router/responses \
  -H "Authorization: Bearer $ORQ_API_KEY" \
  -H "Content-Type: application/json" \
  -d '{
    "model": "openai/gpt-4o-mini",
    "input": "Help me write a professional email to follow up on a job interview",
    "name": "ContentGenerator-BlogPosts"
  }'

curl -X POST https://api.orq.ai/v3/router/chat/completions \
  -H "Authorization: Bearer $ORQ_API_KEY" \
  -H "Content-Type: application/json" \
  -d '{
    "model": "openai/gpt-4o-mini",
    "messages": [
      {
        "role": "user",
        "content": "Help me write a professional email to follow up on a job interview"
      }
    ],
    "name": "ContentGenerator-BlogPosts"
  }'

import OpenAI from "openai";

const client = new OpenAI({
  apiKey: process.env.ORQ_API_KEY,
  baseURL: "https://api.orq.ai/v3/router",
});

const response = await client.responses.create({
  model: "openai/gpt-4o-mini",
  input: "Help me write a professional email to follow up on a job interview",
  name: "ContentGenerator-BlogPosts",
});

console.log(response.output_text);

from openai import OpenAI
import os

client = OpenAI(
    api_key=os.environ.get("ORQ_API_KEY"),
    base_url="https://api.orq.ai/v3/router",
)

response = client.responses.create(
    model="openai/gpt-4o-mini",
    input="Help me write a professional email to follow up on a job interview",
    extra_body={"name": "ContentGenerator-BlogPosts"},
)

print(response.output_text)

import OpenAI from "openai";

const client = new OpenAI({
  apiKey: process.env.ORQ_API_KEY,
  baseURL: "https://api.orq.ai/v3/router",
});

const response = await client.chat.completions.create({
  model: "openai/gpt-4o-mini",
  messages: [
    {
      role: "user",
      content: "Help me write a professional email to follow up on a job interview",
    },
  ],
  name: "ContentGenerator-BlogPosts",
});

from openai import OpenAI
import os

client = OpenAI(
    api_key=os.environ.get("ORQ_API_KEY"),
    base_url="https://api.orq.ai/v3/router",
)

response = client.chat.completions.create(
    model="openai/gpt-4o-mini",
    messages=[
        {
            "role": "user",
            "content": "Help me write a professional email to follow up on a job interview",
        }
    ],
    extra_body={"name": "ContentGenerator-BlogPosts"},
)

Environment Management

Configuration by environment

const getTrackingName = (service, environment) => {
  return `${service}-${environment}`;
};

// Usage
const trackingConfig = {
  name: getTrackingName("ChatBot", process.env.NODE_ENV),
  // Results in: "ChatBot-development", "ChatBot-production"
};

Environment-specific examples

// Development
name: "UserAPI-Dev"

// Staging
name: "UserAPI-Staging"

// Production
name: "UserAPI-Prod"

AI Studio Integration

Filtering by application

View requests by specific app/service.
Compare performance across applications.
Track costs per application.
Monitor error rates by service.

Metrics available

The following are available metrics available for App Tracking:

Request volume per application.
Response times by service.
Cost allocation by project.
Error patterns by environment.

Advanced Patterns

Dynamic naming

const generateTrackingName = (userId, feature) => {
  // For multi-tenant scenarios
  return `tenant-${userId}-${feature}`;
};

// Usage
name: generateTrackingName(user.id, "chat-assistant")

Feature flag integration

const getFeatureName = (featureFlags) => {
  const activeFeatures = Object.keys(featureFlags)
    .filter((key) => featureFlags[key])
    .join("-");

  return `app-${activeFeatures}`;
};

Version tracking

// Package.json version
const packageVersion = require("./package.json").version;

name: `MyApp-v${packageVersion}`

Troubleshooting

Names not appearing in dashboard

Check name follows alphanumeric + hyphens pattern.
Verify requests are being sent successfully.
Ensure name is under character limit (50 chars).

Fragmented tracking data

Standardize naming conventions across team.
Use environment variables for consistency.
Implement a centralized naming function.

Too many unique names

Avoid timestamps or random values.
Limit to ~50 unique names per account.
Use hierarchical naming instead of flat structure.

Monitoring

The following metrics are available for monitoring.

const appMetrics = {
  requestsByApp: {}, // Volume per application
  costsByApp: {}, // Spending per application
  latencyByApp: {}, // Performance per application
  errorsByApp: {}, // Error rates per application
  activeApps: new Set(), // Unique applications
};

Analytics queries

The following queries can be answered using the above metrics.

Which applications use AI most?
What’s the cost per application?
Which services have highest error rates?
How does performance vary by application?

Best Practices

Naming standards

Document naming conventions for your team.
Use consistent separators (hyphens recommended).
Include environment in name for clarity.
Avoid special characters or spaces.

Example Implementation

// Centralized tracking configuration
class OrqConfig {
  static getName(service, environment = process.env.NODE_ENV) {
    return `${service}-${environment}`;
  }

  static getConfig(service) {
    return {
      name: this.getName(service),
    };
  }
}

// Usage
const orqConfig = OrqConfig.getConfig("ChatBot");

To make sure teams within an Engineering Organization align on App Tracking principles:

Maintain a list of approved application names.
Use code reviews to enforce naming standards.
Set up monitoring alerts for new/unexpected names.
Regular cleanup of unused tracking names.

Limitations

Name constraints: Alphanumeric characters and hyphens only.
Length limits: Maximum 50 characters per name.
Storage impact: Many unique names increase metadata storage.
Query performance: Large numbers of unique names may slow filtering.
No retroactive changes: Historical traces keep original names.

Integration Examples

With external monitoring systems

const prometheus = {
  histogram: (name: string, value: number, labels: Record<string, string>) => {},
  counter: (name: string, value: number, labels: Record<string, string>) => {},
}; // replace with your Prometheus client, e.g. from prom-client

// Export metrics by application
const exportMetrics = (trackingName: string, responseTime: number, cost: number) => {
  prometheus.histogram("ai_request_duration", responseTime, {
    app: trackingName,
  });
  prometheus.counter("ai_request_cost", cost, { app: trackingName });
};

With logging

logger.info("AI request completed", {
  trackingName: "ChatBot-Prod",
  responseTime: 1250,
  model: "gpt-4o",
  success: true,
});

Get Started

AI Gateway

AI Observability

AI Engineering

AI Governance

AI Chat

Organization

Quick Start

Configuration

Naming Conventions

Recommended patterns

Best practices

Use Cases

Code examples

Environment Management

Configuration by environment

Environment-specific examples

AI Studio Integration

Filtering by application

Metrics available

Advanced Patterns

Dynamic naming

Feature flag integration

Version tracking

Troubleshooting

Monitoring

Analytics queries

Best Practices

Naming standards

Example Implementation

Limitations

Integration Examples

With external monitoring systems

With logging

​Quick Start

​Configuration

​Naming Conventions

​Recommended patterns

​Best practices

​Use Cases

​Code examples

​Environment Management

​Configuration by environment

​Environment-specific examples

​AI Studio Integration

​Filtering by application

​Metrics available

​Advanced Patterns

​Dynamic naming

​Feature flag integration

​Version tracking

​Troubleshooting

​Monitoring

​Analytics queries

​Best Practices

​Naming standards

​Example Implementation

​Limitations

​Integration Examples

​With external monitoring systems

​With logging

Quick Start

Configuration

Naming Conventions

Recommended patterns

Best practices

Use Cases

Code examples

Environment Management

Configuration by environment

Environment-specific examples

AI Studio Integration

Filtering by application

Metrics available

Advanced Patterns

Dynamic naming

Feature flag integration

Version tracking

Troubleshooting

Monitoring

Analytics queries

Best Practices

Naming standards

Example Implementation

Limitations

Integration Examples

With external monitoring systems

With logging