Which Python Logging Library Should I Use in 2026?

Apr 29, 2026

1. The Problem

I was building a Python service that processes thousands of requests per second. My logs were a mess - unstructured text scattered across files, no correlation IDs, impossible to query in our observability stack.

I started with the standard library logging module. It worked fine for a prototype. But when I tried to add structured JSON output and OpenTelemetry trace context, I hit a wall.

2026-04-15 10:23:45 - INFO - Processing request for user 123
2026-04-15 10:23:46 - ERROR - Failed to connect to database
2026-04-15 10:23:47 - INFO - Retrying...

This is useless when you have 50 microservices and need to trace a failed request across them.

So I started researching alternatives. Loguru looked promising - “zero configuration” sounded appealing. structlog kept showing up in performance discussions. And everyone on Reddit had opinions.

2. My Trial-and-Error Process

2.1 First Attempt: stdlib + python-json-logger

I tried to make stdlib work with structured logging by adding python-json-logger:

import logging
from pythonjsonlogger.json import JsonFormatter

logger = logging.getLogger("my_app")
logger.setLevel(logging.INFO)

handler = logging.StreamHandler()
formatter = JsonFormatter()
handler.setFormatter(formatter)
logger.addHandler(handler)

logger.info("User logged in", extra={"user_id": 12345, "ip_address": "192.168.1.1"})

This worked. But every time I needed to add context (worker_id, request_id, trace_id), I had to manually pass it through extra={}. And when a third-party library logged something, its messages came through as plain text, breaking my JSON parsing.

2.2 Second Attempt: Loguru

Loguru promised “just works” setup:

from loguru import logger

logger.info("Application started")
logger.add("logs/app.log", rotation="1 day", retention="7 days")

It was indeed easy. But then I tried to add OpenTelemetry trace context:

from opentelemetry.trace import get_current_span

def add_trace_context(record):
    span = get_current_span()
    ctx = span.get_span_context()
    record["extra"]["otelTraceID"] = format(ctx.trace_id, "032x")
    record["extra"]["otelSpanID"] = format(ctx.span_id, "016x")

logger.configure(patcher=add_trace_context)

This felt hacky. I was manually patching every log record. And when I ran benchmarks, Loguru was noticeably slower.

2.3 Third Attempt: structlog

structlog’s processor pipeline approach was different:

import structlog

structlog.configure(
    processors=[
        structlog.stdlib.filter_by_level,
        structlog.stdlib.add_log_level,
        structlog.processors.TimeStamper(fmt="iso"),
        structlog.processors.StackInfoRenderer(),
        structlog.processors.format_exc_info,
        structlog.processors.JSONRenderer(),
    ],
    logger_factory=structlog.stdlib.LoggerFactory(),
    wrapper_class=structlog.stdlib.BoundLogger,
    cache_logger_on_first_use=True,
)

logger = structlog.get_logger()
log = logger.bind(worker_id=1, target_domain="api.example.com")
log.info("Processing request", retry_count=0)

The bind() method was the key - I could attach context once and it propagated to all subsequent logs. No manual extra={} passing.

3. Performance Benchmarks

I ran benchmarks with 100,000 log iterations to settle the performance question:

Scenario            | stdlib    | loguru    | structlog
--------------------|-----------|-----------|----------
Simple String       | 0.142s    | 0.287s    | 0.203s
                    | baseline  | 2.0x slow | 1.4x slow
Structured (5 keys) | 0.198s    | 0.351s    | 0.264s
                    | baseline  | 1.8x slow | 1.3x slow
Memory (1M msgs)    | 12.3 MB   | 18.7 MB   | 14.1 MB
JSON Serialization  | baseline  | slower    | 25% faster than loguru

The results confirmed what I suspected: structlog’s processor pipeline is optimized for structured output, making it ~25% faster than Loguru for JSON serialization. stdlib remains fastest, but that’s because it does less work by default.

4. Feature Comparison

Feature              | stdlib+json | structlog | Loguru
--------------------|-------------|-----------|--------
Zero Dependencies   | Yes         | No        | No
Structured JSON     | Via format  | Native    | Via serialize
OpenTelemetry       | Via handler | Native    | Manual patcher
Processor Pipeline  | No          | Yes       | No
Auto-rotation       | No          | No        | Yes
Learning Curve      | Steep       | Medium    | Easy

4.1 Why Processor Pipeline Matters

The processor pipeline in structlog lets you transform log data before it hits the output. This is powerful for:

PII masking (strip sensitive fields before logging)
Correlation IDs (inject trace context automatically)
Filtering (drop DEBUG logs in production)

Raw log event → TimeStamper → add_log_level → StackInfoRenderer → JSONRenderer → Output
                    ↓              ↓                ↓                  ↓
               Add timestamp   Add level      Add stack trace      Convert to JSON

Loguru uses bind() for context, but lacks this pipeline architecture. stdlib has no equivalent.

5. The Reddit Reality Check

I found a Reddit thread that echoed my experience:

“stdlib is fine for smaller stuff but once you need structured logging with proper context, structlog just makes everything cleaner”

“pretty much every library already logs through stdlib, so going against that just creates friction”

“OTel only really matters if you’re already doing tracing. if not, I’d just pick whatever feels easiest”

The second comment hit home. When I used Loguru exclusively, I had to bridge third-party library logs manually. structlog can wrap stdlib, solving this compatibility problem.

6. My Final Decision Matrix

Your Situation                    | Recommendation
----------------------------------|----------------
Building a library                | stdlib only
Small project, want simple        | Loguru
High-throughput service           | structlog
Already using OpenTelemetry       | structlog
Need PII masking / processors     | structlog
Want "just works"                 | Loguru
Maximum compatibility             | stdlib + python-json-logger

7. What I Chose

For my high-throughput service with OpenTelemetry already deployed, I went with structlog. The processor pipeline let me inject trace context automatically without manual patching. The ~25% performance advantage over Loguru for JSON output mattered at my scale.

But I kept stdlib compatibility by using structlog.stdlib.LoggerFactory(). Third-party libraries log through stdlib, and structlog captures their output with full context.

8. Summary

In 2026, the Python logging landscape has three clear winners:

stdlib + python-json-logger: The safe, universal choice. Use for libraries and when you want zero surprises.
structlog: The performance and observability champion. Choose for microservices, high-throughput applications, or when you need OpenTelemetry with minimal friction.
Loguru: The developer experience leader. Pick for new projects, prototypes, or when you want the cleanest API.

My journey taught me that the “best” library depends on your constraints. I wasted time trying to make Loguru work with OpenTelemetry when structlog had native support. Don’t do what I did - match your library to your actual needs, not just what sounds easiest.

Final Words + More Resources

My intention with this article was to help others share my knowledge and experience. If you want to contact me, you can contact by email: Email me

Here are also the most important links from this article along with some further resources that will help you in this scope:

Oh, and if you found these resources useful, don’t forget to support me by starring the repo on GitHub!