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.