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.

Programmatic reading: instead of hand-parsing, a small script can use the readers shipped with evlog: readFsLogs() and tailFsLogs() from evlog/fs are async generators that handle both formats, date ordering, and filtering. Prefer them when the project already has evlog installed and the analysis needs more than a quick grep.

Memory drain alternative: some apps use the Memory adapter (evlog/memory) instead of (or alongside) the FS drain, exposing recent events through a dev-only HTTP endpoint via readMemoryLogs(). If .evlog/logs/ is empty but the app wires createMemoryDrain(), query that endpoint instead.

If no logs are found

Before wiring a new drain, you can try npx @evlog/cli doctor --json, which checks whether evlog is installed and whether a local .evlog/logs drain already exists (read-only). Optional; skip if the CLI is unavailable.

The file system drain may not be enabled. On Nuxt, Nitro, Next.js, TanStack Start, or Hono, the fastest path is the CLI, which detects the framework and wires the fs drain (its default dev drain) in one pass:

npx @evlog/cli init --dry-run --yes   # preview first
npx @evlog/cli init --yes --drain fs  # apply

Ask before running it. On other frameworks (or if the user declines), guide the manual setup:

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:

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: filter on durationMs (e.g. durationMs > 500)
• 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: durationMs > 1000
Sort by: durationMs 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 is human-formatted with units (e.g. "706ms"). durationMs is the same duration in milliseconds; filter and sort on durationMs.
• Events with "source":"client" originated from browser-side logging and were sent to the server via the HTTP drain endpoint.
• Log files are .gitignore'd automatically. They exist only on the local machine or server where the app runs.