tracekit-apm 1.3.1

TraceKit APM for Python - Zero-config distributed tracing and code monitoring for Flask, FastAPI, and Django

TraceKit APM for Python

Zero-config distributed tracing and performance monitoring for Flask, FastAPI, and Django applications.

Features

• ✅ Zero Configuration - Works out of the box with sensible defaults
• ✅ Automatic Instrumentation - No code changes needed
• ✅ Flask Support - Simple middleware integration
• ✅ FastAPI Support - ASGI middleware-based tracing
• ✅ Django Support - Django middleware integration
• ✅ HTTP Request Tracing - Track every request, route, and handler
• ✅ Client IP Capture - Automatic IP detection for DDoS & traffic analysis
• ✅ Error Tracking - Capture exceptions with full Python stack traces
• ✅ Code Discovery - Automatically index code from exception stack traces
• ✅ Code Monitoring - Live debugging with breakpoints and variable inspection
• ✅ Nested Spans - Parent-child span relationships with OpenTelemetry
• ✅ Low Overhead - < 5% performance impact
• ✅ OpenTelemetry Standard - Built on industry-standard OpenTelemetry

Installation

pip install tracekit-apm

Framework-Specific Installation

# Flask
pip install tracekit-apm[flask]

# FastAPI
pip install tracekit-apm[fastapi]

# Django
pip install tracekit-apm[django]

# All frameworks
pip install tracekit-apm[all]

Quick Start

Flask

from flask import Flask
import tracekit
from tracekit.middleware.flask import init_flask_app

app = Flask(__name__)

# Initialize TraceKit with code monitoring enabled
client = tracekit.init(
    api_key="your-api-key",
    service_name="my-flask-app",
    enable_code_monitoring=True  # Enable live debugging
)

# Add middleware
init_flask_app(app, client)

@app.route('/')
def hello():
    return 'Hello World!'

@app.route('/api/users/<int:user_id>')
def get_user(user_id):
    # Capture snapshot for debugging (synchronous call)
    if client.get_snapshot_client():
        client.capture_snapshot('get-user', {
            'user_id': user_id,
            'request_path': request.path,
            'request_method': request.method
        })

    # Your business logic here
    user = fetch_user_from_db(user_id)
    return jsonify(user)

if __name__ == '__main__':
    app.run()

FastAPI

from fastapi import FastAPI
import tracekit
from tracekit.middleware.fastapi import init_fastapi_app

app = FastAPI()

# Initialize TraceKit
client = tracekit.init(
    api_key="your-api-key",
    service_name="my-fastapi-app"
)

# Add middleware
init_fastapi_app(app, client)

@app.get("/")
async def root():
    return {"message": "Hello World"}

Django

# settings.py

# Add middleware
MIDDLEWARE = [
    'tracekit.middleware.django.TracekitDjangoMiddleware',
    # ... other middleware
]

# Initialize TraceKit in your app's apps.py
# apps.py
from django.apps import AppConfig
import tracekit
import os

class MyAppConfig(AppConfig):
    name = 'myapp'

    def ready(self):
        tracekit.init(
            api_key=os.environ['TRACEKIT_API_KEY'],
            service_name='my-django-app'
        )

Local Development

Debug your Python application locally without creating a cloud account using TraceKit Local UI.

Quick Start

# Install Local UI globally
npm install -g @tracekit/local-ui

# Start it
tracekit-local

The Local UI will start at http://localhost:9999 and automatically open in your browser.

How It Works

When running in development mode (ENV=development or NODE_ENV=development), the SDK automatically:

1. Detects if Local UI is running at http://localhost:9999
2. Sends traces to both Local UI and cloud (if API key is present)
3. Falls back gracefully if Local UI is not available

No code changes needed! Just set the environment variable:

export ENV=development
export TRACEKIT_API_KEY=your-key  # Optional - works without it!
python app.py

You'll see traces appear in real-time at http://localhost:9999.

Features

• Real-time trace viewing in your browser
• Works completely offline
• No cloud account required
• Zero configuration
• Automatic cleanup (1000 traces max, 1 hour retention)

Local-Only Development

To use Local UI without cloud sending:

# Don't set TRACEKIT_API_KEY
export ENV=development
python app.py

Traces will only go to Local UI.

Disabling Local UI

To disable automatic Local UI detection:

export ENV=production
# or don't run Local UI

Learn More

• GitHub: https://github.com/Tracekit-Dev/local-debug-ui
• npm: @tracekit/local-ui

Code Monitoring (Live Debugging)

TraceKit includes production-safe code monitoring for live debugging without redeployment.

Enable Code Monitoring

import tracekit

# Enable code monitoring
client = tracekit.init(
    api_key="your-api-key",
    service_name="my-app",
    enable_code_monitoring=True  # Enable live debugging
)

Add Debug Points

Add checkpoints anywhere in your code to capture variable state and stack traces:

@app.post('/checkout')
async def checkout(request):
    cart = await request.json()
    user_id = cart['user_id']

    # Capture snapshot at this point (synchronous - no await needed)
    client.capture_snapshot('checkout-validation', {
        'user_id': user_id,
        'cart_items': len(cart.get('items', [])),
        'total_amount': cart.get('total', 0),
    })

    # Process payment...
    result = await process_payment(cart)

    # Another checkpoint
    client.capture_snapshot('payment-complete', {
        'user_id': user_id,
        'payment_id': result['payment_id'],
        'success': result['success'],
    })

    return {'status': 'success', 'result': result}

Automatic Breakpoint Management

• Auto-Registration: First call to capture_snapshot() automatically creates breakpoints in TraceKit
• Smart Matching: Breakpoints match by function name + label (stable across code changes)
• Background Sync: SDK polls for active breakpoints every 30 seconds
• Production Safe: No performance impact when breakpoints are inactive
• Synchronous API: capture_snapshot() is synchronous - no await needed (works in both sync and async code)

Important Notes

🔑 Key Points:

• capture_snapshot() is synchronous (no await needed)
• Works in both sync and async functions
• Automatically registers breakpoints on first call
• Captures are rate-limited (1 per second per breakpoint)
• Max 100 captures per breakpoint by default

View Captured Data

Snapshots include:

• Variables: Local variables at capture point
• Stack Trace: Full call stack with file/line numbers
• Request Context: HTTP method, URL, headers, query params
• Execution Time: When the snapshot was captured

Get your API key at https://app.tracekit.dev

PII Scrubbing

TraceKit automatically scans snapshot variables for sensitive data before sending them to the server. This ensures that passwords, API keys, tokens, and other sensitive information never leave your application.

Auto-Detected Patterns

The SDK automatically detects and redacts the following sensitive data types:

• Passwords - Common password field values
• API Keys - API key strings and prefixes
• Tokens - Authentication and session tokens
• Credit Cards - Card numbers (Visa, Mastercard, Amex, etc.)
• Email Addresses - RFC-compliant email patterns
• SSNs - US Social Security Numbers
• JWTs - JSON Web Tokens (eyJ...)
• AWS Keys - AWS access key IDs (AKIA...)
• Stripe Keys - Stripe API keys (sk_live_..., pk_live_...)
• Private Keys - PEM-encoded private key blocks

Sensitive Variable Name Detection

Variables with sensitive names are automatically redacted as [REDACTED:sensitive_name]. The SDK matches the following names: password, passwd, pwd, secret, token, key, credential, api_key, apikey.

The SDK uses letter-based boundaries (not \b) to correctly match names like api_key and user_token, where the underscore would otherwise prevent a word boundary match.

Value Pattern Redaction

When a value matches a known sensitive pattern (e.g., a credit card number or JWT), it is redacted as [REDACTED:type] regardless of the variable name.

Example

# These variables are automatically redacted before sending:
client.capture_snapshot('checkout', {
    'user_id': 123,                          # Sent as-is
    'password': 'hunter2',                   # -> [REDACTED:sensitive_name]
    'api_key': 'sk_live_abc123',             # -> [REDACTED:sensitive_name]
    'user_token': 'eyJhbGci...',             # -> [REDACTED:sensitive_name]
    'card_number': '4111111111111111',        # -> [REDACTED:credit_card]
    'email': '[e-mail]',             # -> [REDACTED:email]
    'note': 'contains eyJhbGci... in text',  # -> [REDACTED:jwt]
})

PII scrubbing is enabled by default when code monitoring is active. No additional configuration is needed.

Kill Switch

TraceKit provides a server-side kill switch to disable code monitoring per service without any code changes.

How It Works

• Enable: Toggle the kill switch from the TraceKit dashboard or API
• Immediate Effect: The SDK stops capturing snapshots as soon as the kill switch is detected
• Reduced Polling: When the kill switch is active, polling frequency drops to every 60 seconds to minimize overhead
• Auto-Resume: When the kill switch is disabled, snapshot captures resume automatically on the next poll cycle

No Code Changes Required

The kill switch is controlled entirely from the server side:

# Your code stays exactly the same
client = tracekit.init(
    api_key="your-api-key",
    service_name="my-app",
    enable_code_monitoring=True
)

# This call is a no-op when kill switch is active
client.capture_snapshot('checkout-validation', {
    'user_id': user_id,
    'cart_total': cart_total,
})

You can toggle the kill switch from the TraceKit dashboard under Services > [Your Service] > Code Monitoring or via the API.

SSE Real-time Updates

The SDK supports Server-Sent Events (SSE) for receiving breakpoint changes and kill switch events in real time, without waiting for the next poll cycle.

How It Works

1. The SDK auto-discovers the SSE endpoint from the poll response
2. Once connected, breakpoint activations/deactivations and kill switch changes are received instantly
3. If the SSE connection fails, the SDK falls back to standard polling seamlessly

No Configuration Needed

SSE is automatically enabled when the server provides an SSE endpoint. The SDK handles connection management, reconnection, and fallback internally.

# SSE is automatic - no code changes needed
client = tracekit.init(
    api_key="your-api-key",
    service_name="my-app",
    enable_code_monitoring=True
)
# SDK will connect to SSE if available, otherwise polls every 30s

Circuit Breaker

The SDK includes a built-in circuit breaker to protect your application if the TraceKit backend becomes unreachable.

How It Works

• Failure Threshold: After 3 consecutive capture failures within a 60-second window, the circuit breaker trips
• Pause: Code monitoring is automatically paused, stopping all snapshot capture attempts
• Cooldown: After a 5-minute cooldown period, the circuit breaker resets and captures resume
• Transparent: No exceptions are raised in your application code; snapshots are silently skipped while the circuit is open

Example Behavior

# Normal operation: snapshots are captured and sent
client.capture_snapshot('label', {'key': 'value'})

# If backend is down:
# - First 3 failures within 60s: retries normally
# - After 3rd failure: circuit breaker trips, captures paused
# - After 5 minutes: circuit breaker resets, captures resume

No configuration is required. The circuit breaker is always active when code monitoring is enabled.

Automatic Service Discovery

TraceKit automatically instruments outgoing HTTP calls to create service dependency graphs. This enables you to see which services talk to each other in your distributed system.

How It Works

When your service makes an HTTP request to another service:

1. ✅ TraceKit creates a CLIENT span for the outgoing request
2. ✅ Trace context is automatically injected into request headers (traceparent)
3. ✅ The receiving service creates a SERVER span linked to your CLIENT span
4. ✅ TraceKit maps the dependency: YourService → TargetService

Supported HTTP Libraries

TraceKit automatically instruments these HTTP libraries:

• ✅ requests (most popular Python HTTP library)
• ✅ urllib (Python standard library)
• ✅ urllib3 (used by requests under the hood)
• ✅ httpx (async HTTP library - works via urllib3)

Zero configuration required! Just make HTTP calls as normal:

import requests
import urllib.request
import httpx

# All of these automatically create CLIENT spans:
requests.get('http://payment-service/charge')
urllib.request.urlopen('http://inventory-service/check')
await httpx.AsyncClient().get('http://user-service/profile/123')

Service Name Detection

TraceKit intelligently extracts service names from URLs:

URL	Extracted Service Name
http://payment-service:3000	payment-service
http://payment.internal	payment
http://payment.svc.cluster.local	payment
https://api.example.com	api.example.com

This works seamlessly with:

• Kubernetes service names
• Internal DNS names
• Docker Compose service names
• External APIs

Custom Service Name Mappings

For local development or when service names can't be inferred from hostnames, use service_name_mappings:

client = tracekit.init(
    api_key="your-api-key",
    service_name="my-service",
    # Map localhost URLs to actual service names
    service_name_mappings={
        'localhost:8082': 'payment-service',
        'localhost:8083': 'user-service',
        'localhost:8084': 'inventory-service',
        'localhost:5001': 'analytics-service',
    }
)

# Now requests to localhost:8082 will show as "payment-service" in the service graph
response = requests.get('http://localhost:8082/charge')
# -> Creates CLIENT span with peer.service = "payment-service"

This is especially useful when:

• Running microservices locally on different ports
• Using Docker Compose with localhost networking
• Testing distributed tracing in development

Example: Multi-Service Application

from flask import Flask
import tracekit
import requests

app = Flask(__name__)

# Initialize TraceKit
client = tracekit.init(
    api_key="your-api-key",
    service_name="checkout-service",
    auto_instrument_http_client=True  # default: True
)

@app.route('/checkout')
def checkout():
    # This HTTP call automatically creates a CLIENT span
    payment_response = requests.post(
        'http://payment-service/charge',
        json={'amount': 99.99}
    )

    # This one too!
    inventory_response = requests.post(
        'http://inventory-service/reserve',
        json={'item_id': 123}
    )

    return {'status': 'success'}

Viewing Service Dependencies

Visit your TraceKit dashboard to see:

• Service Map: Visual graph showing which services call which
• Service List: Table of all services with health metrics
• Service Detail: Deep dive on individual services with upstream/downstream dependencies

Disabling Auto-Instrumentation

If you need to disable automatic HTTP client instrumentation:

client = tracekit.init(
    api_key="your-api-key",
    auto_instrument_http_client=False  # Disable auto-instrumentation
)

Metrics

TraceKit APM provides a lightweight metrics API for tracking application performance and business metrics. Metrics are automatically buffered and exported in OTLP format.

Metric Types

• Counter: Monotonically increasing values (e.g., total requests, errors)
• Gauge: Point-in-time values that can increase or decrease (e.g., active connections, memory usage)
• Histogram: Distribution of values (e.g., request duration, payload sizes)

Flask Usage

from flask import Flask, jsonify
import tracekit
import time

app = Flask(__name__)

# Initialize TraceKit
client = tracekit.init(
    api_key="your-api-key",
    service_name="my-service"
)

# Create metrics with optional tags
request_counter = client.counter("http.requests.total", tags={"service": "api"})
active_requests_gauge = client.gauge("http.requests.active")
request_duration_histogram = client.histogram("http.request.duration", tags={"unit": "ms"})

@app.route('/api/users')
def get_users():
    start_time = time.time()
    active_requests_gauge.inc()

    try:
        request_counter.inc()

        # Your business logic here
        users = fetch_users_from_db()

        return jsonify(users)
    finally:
        duration = (time.time() - start_time) * 1000  # Convert to ms
        request_duration_histogram.record(duration)
        active_requests_gauge.dec()

if __name__ == '__main__':
    app.run()

FastAPI Usage

from fastapi import FastAPI
import tracekit
import time

app = FastAPI()

# Initialize TraceKit
client = tracekit.init(
    api_key="your-api-key",
    service_name="my-service"
)

# Create metrics
request_counter = client.counter("http.requests.total")
request_duration_histogram = client.histogram("http.request.duration", tags={"unit": "ms"})

@app.get("/api/users")
async def get_users():
    start_time = time.time()

    try:
        request_counter.inc()

        # Your business logic here
        users = await fetch_users_from_db()

        return users
    finally:
        duration = (time.time() - start_time) * 1000
        request_duration_histogram.record(duration)

Vanilla Python Usage

import tracekit

# Initialize TraceKit
client = tracekit.init(
    api_key="your-api-key",
    service_name="my-service"
)

# Create metrics
job_counter = client.counter("jobs.processed")
job_duration = client.histogram("job.duration", tags={"unit": "seconds"})

# Use metrics
job_counter.inc()
job_duration.record(3.5)

# Metrics are automatically flushed on shutdown

Metric API Reference

Counter

# Create counter
counter = client.counter("metric.name")
counter_with_tags = client.counter("metric.name", tags={"tag": "value"})

# Increment by 1
counter.inc()

# Add specific value
counter.add(5.0)

Gauge

# Create gauge
gauge = client.gauge("metric.name")
gauge_with_tags = client.gauge("metric.name", tags={"tag": "value"})

# Set to specific value
gauge.set(42.0)

# Increment by 1
gauge.inc()

# Decrement by 1
gauge.dec()

Histogram

# Create histogram
histogram = client.histogram("metric.name")
histogram_with_tags = client.histogram("metric.name", tags={"unit": "ms"})

# Record a value
histogram.record(123.45)

Common Use Cases

HTTP Request Metrics:

http_requests = client.counter("http.requests.total", tags={"method": "GET"})
active_connections = client.gauge("http.connections.active")
request_duration = client.histogram("http.request.duration", tags={"unit": "ms"})

Database Metrics:

queries = client.counter("db.queries.total", tags={"database": "users"})
query_duration = client.histogram("db.query.duration", tags={"unit": "ms"})
connection_pool_size = client.gauge("db.connections.active")

Business Metrics:

orders = client.counter("orders.total")
order_value = client.histogram("order.value", tags={"currency": "usd"})
inventory = client.gauge("inventory.items")

Metric Export Behavior

• Buffering: Metrics are buffered in memory (max 100 metrics or 10 seconds)
• Auto-Flush: Automatically exports when buffer is full or on interval
• Format: Exported in OTLP (OpenTelemetry Protocol) JSON format
• Endpoint: Sent to /v1/metrics endpoint (derived from traces endpoint)
• Shutdown: All pending metrics are flushed on application shutdown

Configuration

Basic Configuration

import tracekit

client = tracekit.init(
    # Required: Your TraceKit API key
    api_key="your-api-key",

    # Optional: Service name (default: 'python-app')
    service_name="my-service",

    # Optional: TraceKit endpoint (default: 'https://app.tracekit.dev/v1/traces')
    endpoint="https://app.tracekit.dev/v1/traces",

    # Optional: Enable/disable tracing (default: True)
    enabled=True,

    # Optional: Sample rate 0.0-1.0 (default: 1.0 = 100%)
    sample_rate=0.5,  # Trace 50% of requests

    # Optional: Enable live code debugging (default: False)
    enable_code_monitoring=True,

    # Optional: Map hostnames to service names for service graph
    service_name_mappings={
        'localhost:8082': 'payment-service',
        'localhost:8083': 'user-service',
    }
)

Environment Variables

Create a .env file or set these environment variables:

TRACEKIT_API_KEY=your_api_key_here
TRACEKIT_ENDPOINT=https://app.tracekit.dev/v1/traces
TRACEKIT_SERVICE_NAME=my-python-app

Then use them in your code:

import os
import tracekit

client = tracekit.init(
    api_key=os.getenv('TRACEKIT_API_KEY'),
    service_name=os.getenv('TRACEKIT_SERVICE_NAME', 'python-app'),
    endpoint=os.getenv('TRACEKIT_ENDPOINT', 'https://app.tracekit.dev/v1/traces')
)

What Gets Traced?

HTTP Requests

Every HTTP request is automatically traced with:

• Route path and HTTP method
• Request URL and query parameters
• HTTP status code
• Request duration
• User agent and client IP
• Controller and handler names

Errors and Exceptions

All exceptions are automatically captured with:

• Exception type and message
• Full stack trace (Python format preserved)
• Request context
• Handler information

Automatic Code Discovery

TraceKit automatically discovers and indexes your Python code from exception stack traces:

Python Stack Trace Format (Native):

File "/path/to/app.py", line 123, in function_name

What Gets Indexed:

• File paths: app.py, handlers.py, etc.
• Function names: get_user, process_payment, etc.
• Line numbers: Exact location in your code
• First/last seen timestamps
• Occurrence counts

Example: When this exception occurs:

File "/app/handlers/users.py", line 42, in get_user
    user = User.objects.get(id=user_id)
DoesNotExist: User matching query does not exist.

TraceKit indexes:

• File: users.py
• Function: get_user
• Line: 42
• Service: Your service name

View discovered code in the TraceKit UI under Code Inventory.

Advanced Usage

Manual Tracing

from opentelemetry import trace

@app.post('/process-order')
async def process_order(request):
    # Get the client
    client = tracekit.get_client()

    # Start a custom span
    span = client.start_span('process-order', {
        'order.id': order_id,
        'customer.id': customer_id
    })

    try:
        # Your business logic
        result = await process_order_logic(order_id)

        # Add attributes
        client.end_span(span, {
            'order.status': result['status'],
            'order.total': result['total']
        })

        return result

    except Exception as error:
        # Record exception
        client.record_exception(span, error)
        client.end_span(span, {}, status='ERROR')
        raise

Custom Spans with Context Manager (Nested Spans)

Use OpenTelemetry's context manager for automatic parent-child span relationships:

from opentelemetry import trace
from flask import Flask, jsonify
import time

@app.route('/api/users/<int:user_id>')
def get_user(user_id):
    # Get the tracer
    tracer = trace.get_tracer(__name__)

    # Parent span is automatically managed by Flask middleware
    # Create child span using context manager
    with tracer.start_as_current_span('db.query.user') as span:
        span.set_attributes({
            'db.system': 'postgresql',
            'db.operation': 'SELECT',
            'db.table': 'users',
            'db.statement': 'SELECT * FROM users WHERE id = ?',
            'user.id': user_id
        })

        time.sleep(0.01)  # Simulate DB query
        user = fetch_user_from_db(user_id)

        span.set_attributes({
            'user.found': user is not None,
            'user.role': user.get('role') if user else None
        })

    return jsonify(user)

This creates a nested trace with parent-child relationships:

GET /api/users/1 (parent span - auto-created by middleware)
  └─ db.query.user (child span - manually created)

Database Query Tracing

@app.get('/users')
async def list_users():
    client = tracekit.get_client()

    # Start a database query span
    span = client.start_span('db.query.users', {
        'db.system': 'postgresql',
        'db.operation': 'SELECT',
        'db.table': 'users'
    })

    try:
        users = await db.query('SELECT * FROM users')

        client.end_span(span, {
            'db.rows_affected': len(users)
        })

        return users

    except Exception as error:
        client.record_exception(span, error)
        client.end_span(span, {}, status='ERROR')
        raise

External API Call Tracing

import httpx

@app.get('/external-data')
async def fetch_external_data():
    client = tracekit.get_client()

    span = client.start_span('http.client.get', {
        'http.url': 'https://api.example.com/data',
        'http.method': 'GET'
    })

    try:
        async with httpx.AsyncClient() as http_client:
            response = await http_client.get('https://api.example.com/data')

        client.end_span(span, {
            'http.status_code': response.status_code,
            'response.size': len(response.content)
        })

        return response.json()

    except Exception as error:
        client.record_exception(span, error)
        client.end_span(span, {}, status='ERROR')
        raise

Environment-Based Configuration

Disable tracing in development

import os

tracekit.init(
    api_key=os.getenv('TRACEKIT_API_KEY'),
    enabled=os.getenv('ENVIRONMENT') == 'production'
)

Sample only 10% of requests

tracekit.init(
    api_key=os.getenv('TRACEKIT_API_KEY'),
    sample_rate=0.1  # Trace 10% of requests
)

Performance

TraceKit APM is designed to have minimal performance impact:

• < 5% overhead on average request time
• Asynchronous trace sending (doesn't block responses)
• Automatic batching and compression
• Configurable sampling for high-traffic apps

Requirements

• Python 3.8 or higher
• Flask 2.0+ (for Flask support)
• FastAPI 0.100+ (for FastAPI support)
• Django 3.2+ (for Django support)

Examples

See the examples/ directory for complete working applications:

• Flask example: examples/flask_example.py
• FastAPI example: examples/fastapi_example.py
• Django example: examples/django_example/

Troubleshooting

Snapshots Not Capturing

If snapshots are registered but not capturing:

1. Check if code monitoring is enabled:

   client = tracekit.init(
       api_key="your-api-key",
       enable_code_monitoring=True  # Must be True
   )
   

2. Verify snapshot client is available:

   if client.get_snapshot_client():
       client.capture_snapshot('label', {'var': value})
   

3. Check backend logs for errors:

   • Look for "Snapshot captured successfully" messages
   • Check for datetime format errors (should use UTC with Z suffix)

Code Discovery Not Working

If Python code isn't being indexed:

1. Trigger an exception - Code discovery works from exception stack traces
2. Check stack trace format - Should be native Python format:

   File "/path/to/file.py", line 123, in function_name
   

3. View backend logs - Look for "Parsed X frames from stack trace"

Context Token Errors

If you see RuntimeError: Token has already been used once:

• This was fixed in the latest version
• Make sure you're using the latest tracekit-apm package
• The error occurs when exceptions are not properly cleaned up

Nested Spans Not Showing

To create nested spans, use OpenTelemetry's context manager:

from opentelemetry import trace

tracer = trace.get_tracer(__name__)

# This creates a child span under the parent request span
with tracer.start_as_current_span('operation-name') as span:
    span.set_attribute('key', 'value')
    # Your code here

Support

• Documentation: https://app.tracekit.dev/docs/languages/python
• Issues: https://github.com/Tracekit-Dev/python-apm/issues
• Email: [e-mail]

License

MIT License. See LICENSE for details.

Credits

Built with ❤️ by the TraceKit team using OpenTelemetry.