Python, PHP, JavaScript, CSS & HTTP. Stop the fix-run-fix-run cycle. Save up to 99% debugging time (avg 66%)
Every developer knows the painful fix-run-fix-run cycle. Here's what it's really costing you.
The expensive way everyone does it
Find everything in one trace
Faster
Fewer Tokens
Saved per Debug
Run to Fix All
Paste your code and see bugs detected in seconds. No signup required.
Supports Python, PHP, JavaScript, CSS, and HTTP
Click "Scan for Bugs" to see instant analysis
Average scan time
Code stays in your browser
Error types detected
We catch bugs before your users do. Monitoring tools catch them after.
Sentry • Datadog • Rollbar • New Relic
Only catches errors when users trigger them
Wait for bugs to happen in production
Misses syntax, logic, and edge case errors
Users experience errors before you know about them
Ongoing monitoring costs
Pre-deployment bug detection
Catch ALL bugs before code ships
Find and fix issues during development
Syntax, logic, security, performance, and more
Users never see bugs in the first place
One-time scan cost, not ongoing monitoring
Feature | Traditional Monitoring | DevTraceTool |
---|---|---|
Detects bugs before deployment | ||
100% error coverage (not just user-triggered) | ||
Multi-language support (Python, PHP, JS, CSS, HTTP) | Single language | |
AI-powered fix suggestions | ||
Works with AI coding assistants | ||
Up to 98% LLM token savings | ||
Average cost per month | $99-$500 | $15 |
They're complementary, but we prevent 99% of issues from ever reaching production
Detailed before/after examples showing how DevTraceTool found bugs that would have taken days to debug manually
Legacy payment processing code needed refactoring for PCI compliance. Team feared breaking production checkout flow.
Code with 7 hidden bugs
# Legacy payment processor (before DevTraceTool)
def process_payment(order_data):
# Multiple hidden bugs in this code
user_id = order_data['user_id'] # KeyError if missing
amount = order_data['amount'] # No type validation
# Unsafe type coercion
discount = int(order_data.get('discount', 0)) # Fails if discount="10%"
# Missing null check
payment_method = order_data['payment_method']
token = payment_method['stripe_token'] # Crashes if None
# Unhandled exception
final_amount = amount - discount # TypeError if amount is string
# Network call without timeout
response = stripe.charge(token, final_amount) # Can hang forever
return response['transaction_id'] # No error handling
All 7 errors found and fixed in ONE trace
# Fixed with DevTraceTool findings (all 7 errors caught in ONE trace)
def process_payment(order_data):
# FIX 1: KeyError prevention
user_id = order_data.get('user_id')
if not user_id:
raise ValueError("user_id is required")
# FIX 2 & 3: Type validation and coercion
try:
amount = float(order_data.get('amount', 0))
discount_raw = order_data.get('discount', 0)
# Handle both numeric and percentage discounts
if isinstance(discount_raw, str) and '%' in discount_raw:
discount = amount * (float(discount_raw.strip('%')) / 100)
else:
discount = float(discount_raw)
except (ValueError, TypeError) as e:
raise ValueError(f"Invalid amount or discount: {e}")
# FIX 4 & 5: Null safety for payment method
payment_method = order_data.get('payment_method')
if not payment_method or not payment_method.get('stripe_token'):
raise ValueError("Valid payment method required")
token = payment_method['stripe_token']
# FIX 6: Safe arithmetic
final_amount = max(0, amount - discount)
# FIX 7: Timeout and error handling
try:
response = stripe.charge(token, final_amount, timeout=30)
if not response or 'transaction_id' not in response:
raise ValueError("Invalid payment response")
return response['transaction_id']
except stripe.StripeError as e:
logger.error(f"Payment failed: {e}")
raise PaymentProcessingError(str(e))
KeyError: 'user_id' may not exist in order_data
KeyError: 'payment_method' may not exist
TypeError: amount could be string, can't subtract from string
ValueError: discount='10%' fails int() coercion
AttributeError: payment_method could be None, can't access ['stripe_token']
NetworkError: stripe.charge has no timeout, can hang indefinitely
KeyError: response['transaction_id'] may not exist if charge fails
Developer time saved
Found in one trace
Total cost savings
These are real examples from actual projects. Your code likely has similar hidden bugs that are costing you time and money.
Try the Interactive DemoComplete transparency into our instrumentation, trace pipeline, and error detection methods. No black boxes.
┌─────────────────┐ │ Your Code │ │ (unmodified) │ └────────┬────────┘ │ ▼ ┌─────────────────┐ │ DevTraceTool │◄──── sys.settrace() / AST instrumentation │ Runtime Hook │ (Python: trace module, JS: V8 hooks) └────────┬────────┘ │ ▼ ┌─────────────────┐ │ Event Stream │◄──── function_call, line_execution, return, │ (in-memory) │ exception, variable_change └────────┬────────┘ │ ▼ ┌─────────────────┐ │ Smart Filters │◄──── Remove stdlib noise (90% reduction) │ & Aggregation │ Group related errors └────────┬────────┘ │ ▼ ┌─────────────────┐ │ Error Detector │◄──── Pattern matching: KeyError, TypeError, │ (35+ patterns) │ AttributeError, IndexError, etc. └────────┬────────┘ │ ▼ ┌─────────────────┐ │ JSON Output │◄──── Structured trace with: │ (local file) │ - All errors found └─────────────────┘ - Stack traces with line numbers - Variable states at error points
Performance Overhead
Only during tracing. Zero overhead in production.
Memory Overhead
For typical 1,000-line script. Scales linearly.
Avg Trace Time
For 500-line script with 7 errors.
sys.settrace()
Built-in Python trace API. Same method used by debuggers like pdb.
10-15% slower execution. Not recommended for production use.
Xdebug / register_tick_function()
PHP trace API with custom error handlers.
12-18% slower. Use only in development/staging.
V8 Inspector Protocol
Chrome DevTools protocol for Node.js debugging.
8-12% slower. Safe for local development.
True Positive Rate
Real bugs correctly identified
False Positive Rate
Noise from intentional patterns
Traces Analyzed
Validation dataset size
Raw execution traces generate thousands of events. We filter out 90% of noise to show only YOUR code issues.
Total trace events
Relevant events (your code only)
{ "session_id": "trace-2024-01-15-14-23-45", "language": "python", "total_events": 1387, "filtered_events": 32, "execution_time": "2.1s", "errors_found": 7, "errors": [ { "type": "KeyError", "severity": "error", "line": 42, "file": "/project/app/payment.py", "function": "process_payment", "message": "Key 'discount' not found in order_data", "stack_trace": [ "process_payment (payment.py:42)", "handle_checkout (views.py:18)", "__call__ (middleware.py:23)" ], "context": { "order_data": {"user_id": 123, "amount": 49.99}, "expected_keys": ["user_id", "amount", "discount"] }, "suggested_fix": "Use order_data.get('discount', 0) to provide default" }, { "type": "TypeError", "severity": "error", "line": 45, "file": "/project/app/payment.py", "function": "process_payment", "message": "Cannot subtract string from float", "context": { "amount": 49.99, "discount": "10%", "operation": "amount - discount" }, "suggested_fix": "Convert discount to float or handle percentage string" } ], "performance": { "memory_overhead": "52MB", "slowdown_factor": 1.12 } }
Try the interactive demo to see real trace output, or follow our step-by-step getting started guide.
Real data backing our claims. All benchmarks are reproducible with our open test suite.
True Positive Rate
Real bugs correctly identified as bugs
False Positive Rate
Noise flagged as bugs (mostly intentional patterns)
Error Types Detected
Across Python, PHP, JavaScript
try/except blocks where exception is expected behavior
getattr() / __getattr__ patterns flagged as AttributeError
Unit tests intentionally triggering errors
Solution: Configure ignore patterns in .devtrace/config.json to suppress known false positives
Lines of Code | Trace Time | Memory Usage | Errors Found | Events (Before Filter) | Events (After Filter) |
---|---|---|---|---|---|
100 | 0.8s | 12MB | 2 | 243 | 18 |
500 | 2.1s | 52MB | 7 | 1,387 | 32 |
1,000 | 4.3s | 98MB | 12 | 2,894 | 67 |
5,000 | 18.7s | 412MB | 34 | 14,231 | 189 |
10,000 | 41.2s | 891MB | 58 | 32,118 | 421 |
Note: Times include full execution + trace generation. Memory overhead shown is peak usage during tracing.
No Tracing (Baseline)
Normal execution speed
DevTraceTool (Avg)
12% slower (10-15% range)
Python Debugger (pdb)
250% slower with breakpoints
Token Reduction
(49,000 → 1,000 tokens)
Saved Per Session
($0.49 → $0.01)
Annual Savings
(100 sessions/month)
Time Reduction
(210s → 2.1s)
Saved Per Debugging Session
Compounds with complexity
Real-World Impact:
All benchmarks are reproducible using our open-source test suite. Run the same tests we use to validate our claims.
The best way to validate our benchmarks is to try DevTraceTool on your own code. Follow our getting started guide to run your first trace in under 2 minutes.
Comprehensive support for the most popular frameworks, libraries, and tools. If you use it, we support it.
Version 3.7+
All versions 2.2+
All versions 1.0+
Async support included
Async support
Async support
Version 7.4+
All versions 8.0+
All versions 5.0+
Laravel micro-framework
Version 14+
Pages & App Router
Connect DevTraceTool to your existing development workflow
Fully Supported: Actively tested and maintained by our team. Guaranteed to work out of the box.
Tested: Verified working in production environments. May require minor configuration.
Community Verified: Successfully used by community members. Contact support for assistance.
We're constantly adding support for new frameworks and libraries. Contact us to request support for your stack or ask about compatibility.
Request Framework SupportFour simple steps to find all bugs in your code. No complex configuration required.
pip install devtracetool
from devtracetool import trace
# Option 1: Trace a specific function
@trace
def my_function():
# Your code here
pass
# Option 2: Trace entire script
if __name__ == "__main__":
with trace.context():
# Your application code
main()
The @trace decorator automatically captures all errors and execution paths
python your_script.py
# Or use CLI for existing scripts (no code changes)
devtrace run your_script.py
# Results saved to: .devtrace/trace-2024-01-15-14-23-45.json
# View in terminal
devtrace show
# Or upload to dashboard for AI analysis
devtrace upload --analyze
AI-powered analysis identifies patterns and suggests fixes automatically
Copy-paste examples for popular frameworks
# settings.py
MIDDLEWARE = [
'devtracetool.django.TraceMiddleware',
# ... other middleware
]
# views.py
from devtracetool import trace
@trace
def my_view(request):
# Automatically traces all errors
return render(request, 'template.html')
// app/Http/Kernel.php
protected $middleware = [
\DevTraceTool\Laravel\TraceMiddleware::class,
];
// routes/web.php
use DevTraceTool\Tracer;
Route::post('/orders', function (Request $request) {
return Tracer::trace(fn() =>
OrderController::create($request->all())
);
});
import express from 'express';
import { trace } from '@devtracetool/node';
const app = express();
// Auto-trace all routes
app.use(trace.middleware());
app.post('/api/orders', async (req, res) => {
// All errors automatically captured
const order = await createOrder(req.body);
res.json(order);
});
Automatically trace every pull request and block merges with critical bugs
# .github/workflows/devtrace.yml
name: DevTraceTool Analysis
on: [push, pull_request]
jobs:
trace-analysis:
runs-on: ubuntu-latest
steps:
- uses: actions/checkout@v3
- name: Set up Python
uses: actions/setup-python@v4
with:
python-version: '3.11'
- name: Install DevTraceTool
run: pip install devtracetool
- name: Run trace analysis
run: |
devtrace run tests/
devtrace upload --analyze
env:
DEVTRACE_API_KEY: ${{ secrets.DEVTRACE_API_KEY }}
- name: Fail on critical bugs
run: devtrace ci --fail-on-critical
Average trace analysis time
In a single run, not 7 debug cycles
On LLM API costs vs traditional debugging
Start with the free tier (10 scans/month) or try Pro free for 14 days. No credit card required.
See exactly how much DevTraceTool will save your team in developer time and LLM token costs
total saved per year
ROI
Net Profit
saved per year
saved per year
Pro plan annually
*Based on $50/hour developer salary and GPT-5-Codex pricing at $0.01 per 1K tokens (October 2025)
Every feature designed to save you time and money while improving code quality
Events reduced to 32
Noise filtered out
User code traced
See how DevTraceTool solves common debugging challenges that waste developer time
Inherited a legacy project with zero documentation?
Run one trace and see ALL problems immediately. No more guessing where bugs might be hiding.
1,387 lines of unfamiliar code → 32 relevant events showing all issues
Worried your refactor broke something?
One trace shows ALL breaking changes across the entire codebase. Fix everything before committing.
Renamed a function → Found 7 places that need updates in one run
Try/except blocks hiding bugs in production?
Capture ALL exceptions, even caught ones. See what errors your code is silently swallowing.
Found 3 caught exceptions that indicated data quality issues
Need to test if/else branches and edge cases?
All branches traced in one execution. See which paths have errors without manual testing.
5 different code paths traced → Found errors in 2 untested branches
No more mental overhead of fix-run-fix cycles
See all issues and create comprehensive fixes
Understand execution flow in unfamiliar projects
Used by developers working with Claude Code, Cursor, Windsurf, and other AI coding assistants
Event reduction
Only user code traced
Events → 32
Proven on complex codebase
Average trace time
vs 3.5min traditional
Token savings
1K vs 49K tokens
Seamless integration with the latest agentic coding assistants (2024/2025)
Anthropic's AI assistant
AI-first code editor with agent mode
First agentic IDE with Cascade
Instant full-stack AI apps
AI web app generation
AI pair programming by GitHub
Traditional debugging required multiple fix-run cycles. Now we see all errors at once, fix them together, and move on. Saves us hours every week.
We were spending $1.47 per debugging session sending 49,000 tokens. DevTraceTool reduced that to $0.03 with just 1,000 tokens. The ROI is incredible.
Inherited a 1,387-event Python project with zero docs. One trace showed us all 32 critical issues. We fixed everything in one afternoon instead of spending weeks exploring.
All plans include secure trace data handling, encryption at rest, and GDPR compliance.
Need help choosing? Contact our team
Everything you need to debug Python applications efficiently
Feature | Free | Pro | Enterprise |
---|---|---|---|
Core Tracing | |||
Traces per month | 100 | Unlimited | Unlimited |
Exception detection | |||
Function call tracking | |||
Variable state capture | |||
Stack trace analysis | |||
Advanced Features | |||
Parallel tracing (multiple scripts)NEW | |||
Advanced filters (regex patterns) | |||
Performance profiling | |||
Memory tracking | |||
Trace history (save & replay) | |||
Team collaboration | |||
Custom integrations (Jira, Slack) | |||
Enterprise | |||
Multi-tenant support | |||
SSO/SAML integration | |||
Audit logging | |||
Priority support | |||
SLA guarantees | |||
On-premise deployment |
Your Pro trial lasts 14 days. After that, you'll be automatically billed unless you cancel. You can cancel anytime from your account settings.
Yes! You can upgrade or downgrade your plan at any time. Changes take effect immediately and we'll prorate any charges.
We accept all major credit cards (Visa, Mastercard, American Express) via Stripe. Enterprise customers can also pay via invoice.
Yes, we offer a 30-day money-back guarantee for annual plans. Monthly plans can be canceled anytime with no refund for the current month.
Join developers saving $8,978/year with DevTraceTool
Time Savings
Token Reduction
ROI
Join developers already saving thousands with DevTraceTool
Stop the fix-run-fix-run cycle. Find ALL bugs in ONE run.