AI-Powered Debugging: Fix Bugs 10x Faster

The Problem with Traditional Debugging

Every developer knows the pain: a cryptic error message, a stack trace that leads nowhere, and hours spent adding print statements. Traditional debugging is slow because it relies on you to form hypotheses about what's wrong. AI debugging flips this — it analyzes your code holistically and identifies the root cause directly.

Vincony's debugging tools go beyond simple error explanation. They analyze the execution flow, identify the root cause, suggest a fix, and even generate a test to prevent regression. With Smart Model Router, the best model for your specific bug type is automatically selected.

Quick Start: Debug Any Code in Seconds

Here's how to use Vincony's debug endpoint to instantly analyze buggy code:

import vincony

client = vincony.Client(api_key="YOUR_KEY")

buggy_code = """
def merge_sorted(a, b):
    result = []
    i = j = 0
    while i < len(a) and j < len(b):
        if a[i] <= b[j]:
            result.append(a[i])
            i += 1
        else:
            result.append(b[j])
            j += 1
    return result  # Bug: remaining elements not appended
"""

result = client.debug(
    code=buggy_code,
    language="python",
    auto_fix=True,
    include_tests=True
)

print("Root Cause:", result.root_cause)
print("Fixed Code:", result.fixed_code)
print("Test:", result.regression_test)

Smart Model Router for Debugging

Different bugs require different expertise. Memory leaks need a model that understands resource management. Concurrency bugs need one that reasons about parallel execution. Vincony's Smart Model Router automatically selects the best model based on:

• • Error type — syntax errors vs. logic bugs vs. performance issues
• • Language — Python-optimized models for Python, TypeScript-optimized for TS
• • Complexity — simple fixes use fast models, complex bugs use advanced reasoning models
• • Context size — large codebases route to models with bigger context windows

JavaScript/TypeScript Debugging

The debugger works across all major languages. Here's an example debugging a React component with a stale closure bug:

import Vincony from 'vincony';

const client = new Vincony({ apiKey: 'YOUR_KEY' });

const buggyComponent = `
function Counter() {
  const [count, setCount] = useState(0);
  
  useEffect(() => {
    const interval = setInterval(() => {
      setCount(count + 1); // Stale closure bug
    }, 1000);
    return () => clearInterval(interval);
  }, []); // Missing dependency
  
  return <span>{count}</span>;
}`;

const result = await client.debug({
  code: buggyComponent,
  language: 'typescript',
  framework: 'react',
  autoFix: true
});

// Output: "Stale closure in useEffect. 
//  Fix: Use functional updater setCount(c => c + 1)
//  or add 'count' to dependency array."

Batch Debugging for Large Codebases

Need to scan an entire project for bugs? Use Batch Generation to process multiple files simultaneously:

import vincony
from pathlib import Path

client = vincony.Client(api_key="YOUR_KEY")

# Scan entire directory
files = list(Path("src/").rglob("*.py"))

results = client.debug.batch(
    files=[{"path": str(f), "code": f.read_text()} for f in files],
    severity_threshold="warning",
    parallel=True  # Process all files simultaneously
)

for r in results:
    if r.issues:
        print(f"\n{r.path}: {len(r.issues)} issues found")
        for issue in r.issues:
            print(f"  [{issue.severity}] {issue.description}")

Getting Started

AI debugging is available on all plans. The free Developer plan includes 100 credits for basic debugging. The Power plan ($54.99/mo) adds Smart Model Router and batch debugging with 5,000 credits. Business ($199/mo) unlocks fine-tuned models trained on your codebase for even more accurate bug detection.