Add structured logging to Python with structlog. Use when a user asks to implement structured logging, add context to Python logs, configure log processing pipelines, or replace standard logging with typed output.
Scanned 9/6/2026
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---
name: structlog-python
description: >-
Add structured logging to Python with structlog. Use when a user asks to
implement structured logging, add context to Python logs, configure log
processing pipelines, or replace standard logging with typed output.
license: Apache-2.0
compatibility: 'Python 3.8+'
metadata:
author: terminal-skills
version: 1.0.0
category: devops
tags:
- structlog
- logging
- python
- structured
- observability
---
# structlog
## Overview
structlog adds structured, context-rich logging to Python. Instead of format strings, you pass key-value pairs that render as JSON (production) or colorized human-readable output (development). Bound loggers carry context across function calls.
## Instructions
### Step 1: Configuration
```python
# logging_config.py — structlog setup
import structlog
import logging
import sys
def setup_logging(environment: str = "development"):
"""Configure structlog for the application.
Args:
environment: 'development' for pretty output, 'production' for JSON
"""
shared_processors = [
structlog.contextvars.merge_contextvars,
structlog.stdlib.add_logger_name,
structlog.stdlib.add_log_level,
structlog.processors.TimeStamper(fmt="iso"),
structlog.processors.StackInfoRenderer(),
structlog.processors.UnicodeDecoder(),
]
if environment == "production":
renderer = structlog.processors.JSONRenderer()
else:
renderer = structlog.dev.ConsoleRenderer(colors=True)
structlog.configure(
processors=[
*shared_processors,
structlog.stdlib.ProcessorFormatter.wrap_for_formatter,
],
logger_factory=structlog.stdlib.LoggerFactory(),
cache_logger_on_first_use=True,
)
handler = logging.StreamHandler(sys.stdout)
handler.setFormatter(structlog.stdlib.ProcessorFormatter(
processors=[*shared_processors, renderer],
))
root_logger = logging.getLogger()
root_logger.addHandler(handler)
root_logger.setLevel(logging.INFO)
```
### Step 2: Usage
```python
# services/orders.py — Structured logging in business logic
import structlog
log = structlog.get_logger()
async def process_order(order_id: str, user_id: str):
# Bind context that carries through the entire function
log_ctx = log.bind(order_id=order_id, user_id=user_id)
log_ctx.info("Processing order")
items = await fetch_order_items(order_id)
log_ctx.info("Items fetched", item_count=len(items), total=sum(i.price for i in items))
try:
payment = await charge_payment(order_id)
log_ctx.info("Payment charged", payment_id=payment.id, amount=payment.amount)
except PaymentError as e:
log_ctx.error("Payment failed", error=str(e), error_code=e.code)
raise
log_ctx.info("Order completed", status="success")
```
### Step 3: Request Context
```python
# middleware.py — Add request context to all logs
import structlog
from uuid import uuid4
async def logging_middleware(request, call_next):
request_id = request.headers.get("x-request-id", str(uuid4()))
structlog.contextvars.clear_contextvars()
structlog.contextvars.bind_contextvars(
request_id=request_id,
method=request.method,
path=request.url.path,
user_id=getattr(request.state, "user_id", None),
)
log = structlog.get_logger()
log.info("Request started")
response = await call_next(request)
log.info("Request completed", status_code=response.status_code)
response.headers["x-request-id"] = request_id
return response
```
## Guidelines
- Use `contextvars` for request-scoped context — all logs in the request include requestId, userId.
- JSON output in production, pretty console in development — same code, different renderer.
- Bind context early (`log.bind(...)`) and it carries through all subsequent log calls.
- structlog wraps stdlib logging — it works with existing libraries that use `logging`.
- Log events, not strings: `log.info("order_processed", order_id=id)` not `log.info(f"Processed order {id}")`.
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