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Log Parser & Highlighter

Parse logs with auto-detection of JSON Lines, Python logging, syslog and nginx access formats, then filter by level and search.

Log Parser & Highlighter

Format: Python
Total: 15 lines
DEBUG: 2
INFO: 8
WARNING: 1
ERROR: 3
CRITICAL: 1
Time span: 2026-02-12 15:30:45,123 → 2026-02-12 15:33:30,890
Levels:

15 of 15 entries shown

Log entries
12026-02-12 15:30:45,123INFO
Starting application server on port 8080
22026-02-12 15:30:45,456INFO
Database connection established
32026-02-12 15:30:47,234DEBUG
Loading configuration from config.yaml
42026-02-12 15:30:47,567INFO
Configuration loaded successfully
52026-02-12 15:31:02,890WARNING
High memory usage detected: 85%
62026-02-12 15:31:15,123INFO
User login: user@example.com
72026-02-12 15:31:45,456ERROR
Failed to process payment: connection timeout
82026-02-12 15:31:45,789ERROR
Retrying payment processing (attempt 1/3)
92026-02-12 15:31:50,012INFO
Payment processed successfully
102026-02-12 15:32:05,345DEBUG
Cache hit for key: user:12345:profile
112026-02-12 15:32:10,678INFO
API request: GET /api/v1/users/12345 - 200 OK
122026-02-12 15:33:00,901CRITICAL
Database connection lost
132026-02-12 15:33:01,234ERROR
Failed to reconnect to database
142026-02-12 15:33:05,567INFO
Database connection restored
152026-02-12 15:33:30,890INFO
Health check passed

What this tool does

Paste a log file to parse, filter, and search it in the browser — nothing is uploaded. The parser auto-detects JSONL, Python logging, syslog, nginx access, and generic timestamped formats, extracts timestamps and levels, and filters by level or regex. Nginx access lines carry no level, so the tool derives one from the HTTP status: 5xx maps to ERROR, 4xx to WARNING.

Updated . Provided as is. Check the output before you rely on it in production.

How to use Log Parser & Highlighter

  1. 1

    Paste your log file

    Paste raw logs: JSONL, Python logging, syslog, nginx access logs or generic timestamped lines.

  2. 2

    Filter by severity

    Show or hide levels such as ERROR, WARN, INFO and DEBUG to focus on what matters.

  3. 3

    Scan the highlighted output

    Timestamps, levels and messages are color-coded, and a format badge confirms which parser was applied.

  4. 4

    Search

    Search with plain text or a regular expression. The stats bar shows total entries, time span and level counts.

  5. 5

    Download the results

    Download the parsed output for further analysis.

Questions and answers

What is Log Parser & Highlighter?
A log parser splits raw log lines into timestamp, level and message so they can be filtered and searched. This tool auto-detects JSON Lines, Python logging, syslog, nginx access or generic timestamped logs, colors lines by level, filters by level, searches text and counts entries per level.
Does Log Parser & Highlighter store or send my data?
No. All processing happens entirely in your browser. Your log data never leaves your device — nothing is sent to any server.
Which log formats are supported?
It auto-detects JSON Lines, Python logging output, syslog and nginx access logs in the combined format. Other logs that start with an ISO-style timestamp, including nginx error logs, are parsed as generic lines with level detection.

Use it as an API

Log Parser & Highlighter is also callable as a free HTTP JSON API at https://aidevhub.io/api/log-parser/ — GET with query parameters or POST with a JSON body, no authentication, CORS enabled, fair use (abusive traffic is throttled at the edge; there is no per-request quota header). Responses return { ok, tool, result, meta }. 

curl -s -X POST https://aidevhub.io/api/log-parser/ \
  -H "Content-Type: application/json" \
  -d '{"logs":"2026-07-12T10:00:00Z ERROR db timeout\n2026-07-12T10:00:05Z INFO retry succeeded"}'

Machine-readable contract: /api/tool/log-parser.json All API endpoints: /agents/ LLM site index: /llms.txt

For AI agents: how to call this tool

Machine-readable contract, endpoints and examples. Humans can ignore this section.

Best Path For Builders

Dedicated API endpoint

Deterministic outputs, machine-safe contracts, and production-ready examples.

Dedicated API

https://aidevhub.io/api/log-parser/

OpenAPI: https://aidevhub.io/api/openapi.yaml

GET /api/log-parser/ GET log-parser
POST /api/log-parser/ POST log-parser

How do I parse and filter a log file online without uploading it?

Paste the log. The parser samples the first non-empty lines, detects the format, and shows a badge with the result; each line is then split into timestamp, level, and message, and the stats bar reports totals per level plus the time span from first to last timestamp. All of it happens in the page — there is no upload step.

Step by step

  1. Paste log content, or load the sample to see the parsed view.
  2. Confirm the detected format badge — JSONL, Python, syslog, nginx, or generic.
  3. Uncheck levels you want hidden; the per-level counts next to each checkbox come from the full file.
  4. Search with plain text or a regular expression — matches highlight inline and the match count updates live.
  5. Copy or download the filtered result. The export contains the raw log lines, so it drops straight into a ticket or a diff.

Formats this parser detects

Format Recognized shape What gets extracted
JSONL Each line is a JSON object Timestamp from timestamp/time/ts/@timestamp; level from level/severity/loglevel; message from message/msg/text
Python logging 2026-02-12 15:30:45,123 INFO message Timestamp, level, message
syslog Feb 12 15:30:45 host proc[pid]: message Timestamp and message; level from keywords in the message
nginx access IP - - [date] "request" status bytes Timestamp and request line; level derived from the HTTP status code
Generic ISO-style timestamp prefix, bracketed or bare Timestamp and message; level from keywords

Detection as implemented in this parser: a format wins when at least half of the sampled lines match its pattern (JSONL requires 70%). Mixed-format files fall back to generic keyword-based level extraction.

How does level filtering work when lines have no level?

Lines without a detectable level always stay visible, so stack traces and continuation lines keep their context when you filter to ERROR. Keyword extraction looks for DEBUG, INFO, WARNING, ERROR, or CRITICAL anywhere in the line, and nginx status codes map to levels — which turns an access log into something you can triage by severity even though the format has no level field.

Can it handle large log files?

Yes, within browser memory — there is no network quota because there is no upload. Above 1,000 lines the viewer switches to virtualized rendering and only draws the visible slice, so scrolling a six-figure-line paste stays responsive.

Do my logs leave the browser?

No. Production logs routinely carry IP addresses, user identifiers, session tokens, and internal hostnames. Parsing and filtering run in the page, nothing is stored, and closing the tab discards the data — the safe default when the log you are debugging came from a live system.