charles5277/analyze-logs

Analyze application logs

Read and analyze structured wide-event logs from the local .evlog/logs/ directory to debug errors, investigate performance issues, and understand application behavior.

When to Use

• User asks to debug an error, investigate a bug, or understand why something failed
• User asks about request patterns, slow endpoints, or error rates
• User asks "what happened" or "what's going on" with their application
• User asks to analyze logs, check recent errors, or review application behavior
• User mentions a specific error message or status code they're seeing

Finding the logs

Logs are written by evlog's file system drain as .jsonl files, organized by date.

Format detection: The drain supports two modes:

• NDJSON (default, pretty: false): One compact JSON object per line. Parse line-by-line.
• Pretty (pretty: true): Multi-line indented JSON per event. Parse by reading the entire file and splitting on top-level objects (e.g. JSON.parse('[' + content.replace(/\}\n\{/g, '},{') + ']')) or use a streaming JSON parser.

Always check the first few bytes of the file to detect the format: if the second character is a newline or ", it's NDJSON; if it's a space or newline followed by spaces, it's pretty-printed.

Search order — check these locations relative to the project root:

1. .evlog/logs/ (default)
2. Any .evlog/logs/ inside app directories (monorepos: apps/*/.evlog/logs/)

Use glob to find log files:

.evlog/logs/*.jsonl
*/.evlog/logs/*.jsonl
apps/*/.evlog/logs/*.jsonl

Files are named by date: 2026-03-14.jsonl. Start with the most recent file.

If no logs are found

The file system drain may not be enabled. Guide the user to set it up:

import { createFsDrain } from 'evlog/fs'

// Nuxt / Nitro: server/plugins/evlog-drain.ts
export default defineNitroPlugin((nitroApp) => {
  nitroApp.hooks.hook('evlog:drain', createFsDrain())
})

// Hono / Express / Elysia: pass in middleware options
app.use(evlog({ drain: createFsDrain() }))

// Fastify: pass in plugin options
await app.register(evlog, { drain: createFsDrain() })

// NestJS: pass in module options
EvlogModule.forRoot({ drain: createFsDrain() })

// Standalone: pass to initLogger
initLogger({ drain: createFsDrain() })

After setup, the user needs to trigger some requests to generate logs, then re-analyze.

Log format

Each line is a self-contained JSON object (wide event). Key fields:

| Field | Type | Description | | ---------------- | -------- | --------------------------------------------------------------- | | timestamp | string | ISO 8601 timestamp | | level | string | info, warn, error, debug | | service | string | Service name | | environment | string | development, production, etc. | | method | string | HTTP method (GET, POST, etc.) | | path | string | Request path (/api/checkout) | | status | number | HTTP response status code | | duration | string | Request duration ("234ms") | | requestId | string | Unique request identifier | | error | object | Error details: name, message, stack, statusCode, data | | error.data.why | string | Human-readable explanation of what went wrong | | error.data.fix | string | Suggested fix for the error | | source | string | client for browser logs, absent for server logs | | userAgent | object | Parsed browser/OS/device info |

All other fields are application-specific context added via log.set() (e.g. user, cart, payment).

How to analyze

Step 1: Read the most recent log file

Read the latest .jsonl file. Each line is one JSON event. Parse each line independently.

Step 2: Identify the relevant events

Filter based on the user's question:

• Errors: look for "level":"error" or status >= 400
• Specific endpoint: match on path
• Slow requests: parse duration (e.g. "706ms") and filter high values
• Specific user/action: match on application-specific fields
• Client-side issues: filter by "source":"client"
• Time range: compare timestamp values

Step 3: Analyze and explain

For each relevant event:

1. What happened: summarize the path, method, status, level
2. Why it failed (errors): read error.message, error.data.why, and the stack trace
3. How to fix: check error.data.fix for suggested remediation
4. Context: examine application-specific fields for business context (user info, payment details, etc.)
5. Patterns: look for recurring errors, degrading performance, or correlated failures

Analysis patterns

Find all errors

Filter: level === "error"
Group by: error.message or path
Look for: recurring patterns, common failure modes

Find slow requests

Filter: parse duration string, compare > threshold (e.g. 1000ms)
Sort by: duration descending
Look for: specific endpoints, time-of-day patterns

Trace a specific request

Filter: requestId === "the-request-id"
Result: single wide event with all context for that request

Error rate by endpoint

Group events by: path
Count: total events vs error events per path
Look for: endpoints with high error ratios

Client vs server errors

Split by: source === "client" vs no source field
Compare: error patterns between client and server
Look for: client errors that don't have corresponding server errors (network issues)

Important notes

• Each line is a complete, self-contained event. Unlike traditional logs, you don't need to correlate multiple lines — one line has all the context for one request.
• The error.data.why and error.data.fix fields are evlog-specific structured error fields. When present, they provide the most actionable information.
• Duration values are strings with units (e.g. "706ms"). Parse the numeric part for comparisons.
• Events with "source":"client" originated from browser-side logging and were sent to the server via the transport endpoint.
• Log files are .gitignore'd automatically — they exist only on the local machine or server where the app runs.