stack-replayer

stack-replayer

Turn cryptic error logs into reproducible bugs, replay scripts, and fix suggestions — with optional AI.

Features

• 🔍 Parse error logs - Extract structured data from stack traces
• 🎯 Generate replay scripts - Create Node.js scripts that attempt to reproduce bugs
• 🤖 Optional AI enhancement - Use OpenAI, Ollama, or any LLM for smarter analysis
• 🔬 Sandboxed execution - Safely run replay scripts in isolated environments
• 💡 Fix suggestions - Get actionable recommendations to resolve issues
• 📝 Patch generation - AI can suggest code patches and tests (when enabled)
• 🚀 Zero config - Works immediately without any setup or API keys
• 📦 CLI & Library - Use as a command-line tool or import into your code

Installation

npm install stack-replayer
# or
pnpm add stack-replayer
# or
yarn add stack-replayer

Quick Start

1. Basic Usage (No AI, Zero Config)

The library works immediately without any configuration or API keys:

import { replayBug } from "stack-replayer";

try {
  // Your code that might throw
  const user = null;
  console.log(user.name); // TypeError!
} catch (err) {
  const errorLog = err instanceof Error ? err.stack ?? String(err) : String(err);
  
  const result = await replayBug(errorLog);
  
  console.log(result.explanation);
  console.log(result.reproductionSteps);
  console.log(result.suggestedFix);
}

Output:

TypeError occurred: "Cannot read properties of null (reading 'name')"

This error was thrown at /home/user/app.js:5 in function "<anonymous>".

The error likely indicates a runtime issue in your code. Review the stack trace and the code at the specified location for potential bugs.

2. Enable AI with OpenAI (2 env vars, no code changes)

Set two environment variables and your analysis gets dramatically smarter:

export AI_BUG_REPLAYER_PROVIDER=openai
export OPENAI_API_KEY=sk-...

Then run the same code as above. The library automatically detects and uses OpenAI for enhanced analysis including:

• Root cause explanation
• Detailed reproduction steps
• Better replay scripts
• Suggested fixes and patches
• Generated test cases

3. Enable AI with Ollama (Local, Free)

Run a local LLM with Ollama (completely free, no API keys):

# Install and start Ollama
ollama pull llama3
ollama serve &

# Configure environment
export AI_BUG_REPLAYER_PROVIDER=ollama
# Optional: export OLLAMA_MODEL=llama3
# Optional: export OLLAMA_BASE_URL=http://localhost:11434

Now your same code uses local AI with no external API calls or costs.

CLI Usage

Install globally:

npm install -g stack-replayer

Read from a file:

stack-replayer --log error.log

Read from stdin:

cat error.log | stack-replayer

Execute the replay script:

stack-replayer --log error.log --run

Specify project root:

stack-replayer --log error.log --root /path/to/project

JSON output:

stack-replayer --log error.log --json > result.json

API Reference

replayBug(errorLog, options?)

Convenience function for one-line bug replay.

Parameters:

• errorLog: string - The error log or stack trace
• options?: object
  • llmClient?: LlmClient - Custom LLM client (overrides auto-detection)
  • dryRun?: boolean - If true, don't execute replay script (default: false)
  • projectRoot?: string - Project root directory
  • metadata?: object - Additional context (nodeVersion, os, etc.)

Returns: Promise<BugReplayResult>

interface BugReplayResult {
  explanation: string;
  reproductionSteps: string[];
  replayScript: string;
  suggestedFix?: string;
  suggestedPatch?: string;
  suggestedTest?: string;
  sandboxResult?: {
    success: boolean;
    reproduced: boolean;
    stdout: string;
    stderr: string;
    exitCode: number | null;
  };
}

AiBugReplayer Class

For more control, use the class directly:

import { AiBugReplayer, OpenAiLlmClient } from "stack-replayer";

const replayer = new AiBugReplayer({
  llmClient: new OpenAiLlmClient({
    apiKey: process.env.OPENAI_API_KEY!,
    model: "gpt-4o-mini"
  }),
  dryRun: false
});

const result = await replayer.replay({
  errorLog: errorStack,
  projectRoot: "/path/to/project",
  metadata: {
    nodeVersion: process.version,
    os: process.platform
  }
});

LLM Providers

Built-in Providers

OpenAI

import { OpenAiLlmClient } from "stack-replayer";

const client = new OpenAiLlmClient({
  apiKey: "sk-...",
  model: "gpt-4o-mini", // optional
  baseURL: "https://api.openai.com/v1" // optional
});

Ollama (Local)

import { OllamaLlmClient } from "stack-replayer";

const client = new OllamaLlmClient({
  baseUrl: "http://localhost:11434",
  model: "llama3"
});

Generic HTTP (OpenAI-compatible)

import { HttpLlmClient } from "stack-replayer";

const client = new HttpLlmClient({
  baseUrl: "https://your-api.com/v1/chat/completions",
  apiKey: "your-key",
  model: "your-model"
});

Custom LLM Client

Implement the LlmClient interface:

import { LlmClient, ParsedErrorLog, BugReplayInput } from "stack-replayer";

class MyCustomLlmClient implements LlmClient {
  async generateReplay(parsed: ParsedErrorLog, input: BugReplayInput) {
    // Your custom logic here
    return {
      explanation: "...",
      reproductionSteps: ["..."],
      replayScript: "...",
      suggestedFix: "..."
    };
  }
}

Environment Variables

Variable	Description	Default
AI_BUG_REPLAYER_PROVIDER	LLM provider: openai or ollama	None (no-AI mode)
OPENAI_API_KEY	OpenAI API key	-
OPENAI_MODEL	OpenAI model to use	gpt-4o-mini
OPENAI_BASE_URL	Custom OpenAI endpoint	https://api.openai.com/v1
OLLAMA_BASE_URL	Ollama server URL	http://localhost:11434
OLLAMA_MODEL	Ollama model to use	llama3

Examples

Catch and analyze in production

import { replayBug } from "stack-replayer";

process.on('uncaughtException', async (err) => {
  console.error('Uncaught exception:', err);
  
  const analysis = await replayBug(err.stack ?? String(err), {
    projectRoot: process.cwd(),
    metadata: {
      nodeVersion: process.version,
      os: process.platform,
      timestamp: new Date().toISOString()
    }
  });
  
  // Send to your logging service
  await sendToLoggingService({
    error: err,
    analysis: analysis.explanation,
    suggestedFix: analysis.suggestedFix
  });
});

Analyze test failures

import { replayBug } from "stack-replayer";

afterEach(async function() {
  if (this.currentTest?.state === 'failed') {
    const err = this.currentTest.err;
    if (err?.stack) {
      const analysis = await replayBug(err.stack);
      console.log('\n🔍 AI Analysis:');
      console.log(analysis.explanation);
      console.log('\n💡 Suggested Fix:');
      console.log(analysis.suggestedFix);
    }
  }
});

Dry run (skip sandbox execution)

const result = await replayBug(errorLog, { dryRun: true });
// Only get analysis and script, don't execute
console.log(result.replayScript);

How It Works

No-AI Mode (Default)

• Parse the error log using regex patterns
• Extract error type, message, and stack frames
• Identify user code vs. node internals
• Generate a basic replay script heuristically
• Execute in sandbox (unless dry-run)
• Provide generic fix suggestions based on error type

AI Mode (Optional)

• Parse the error log (same as above)
• Send to LLM with structured prompt
• Receive enhanced analysis:
  • Root cause explanation
  • Step-by-step reproduction
  • Smart replay script
  • Code patches
  • Test cases
• Execute in sandbox (unless dry-run)

Why stack-replayer?

• ✅ Works immediately - No setup, no config, no API keys required
• ✅ Progressive enhancement - Add AI when you want better results
• ✅ Privacy-friendly - Use Ollama for completely local processing
• ✅ Framework agnostic - Works with any Node.js code
• ✅ Production ready - TypeScript, tests, proper error handling
• ✅ Extensible - Bring your own LLM provider

Requirements

• Node.js 18+
• TypeScript 5+ (for development)

License

MIT

Contributing

Contributions welcome! Please read our contributing guidelines and submit PRs.

Roadmap

• Support for browser error logs
• Python error log parsing
• More LLM providers (Anthropic, Gemini, etc.)
• Better heuristic replay generation
• Automatic fix application
• Integration with issue trackers

Credits

Built with ❤️ by the open source community.