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structlog

Structured logging for Python applications with context support and powerful processors

231 stars

Installation

Claude Code / Cursor / Codex

$curl -o ~/.claude/skills/structlog/SKILL.md --create-dirs "https://raw.githubusercontent.com/aiskillstore/marketplace/main/skills/bossjones/structlog/SKILL.md"

Manual Installation

  1. Download SKILL.md from GitHub
  2. Place it in .claude/skills/structlog/SKILL.md inside your project
  3. Restart your AI agent — it will auto-discover the skill

How structlog Compares

Feature / AgentstructlogStandard Approach
Platform SupportmultiLimited / Varies
Context Awareness High Baseline
Installation ComplexityUnknownN/A

Frequently Asked Questions

What does this skill do?

Structured logging for Python applications with context support and powerful processors

Which AI agents support this skill?

This skill is compatible with multi.

Where can I find the source code?

You can find the source code on GitHub using the link provided at the top of the page.

SKILL.md Source

# Structlog Skill

## Quick Start

```python
import structlog

# Basic usage
log = structlog.get_logger()
log.info("hello, %s!", "world", key="value", more_than_strings=[1, 2, 3])
```

## Common Patterns

### Basic Configuration

```python
import structlog

structlog.configure(
    processors=[
        structlog.contextvars.merge_contextvars,
        structlog.processors.add_log_level,
        structlog.processors.StackInfoRenderer(),
        structlog.dev.set_exc_info,
        structlog.processors.TimeStamper(fmt="%Y-%m-%d %H:%M:%S", utc=False),
        structlog.dev.ConsoleRenderer()
    ],
    wrapper_class=structlog.make_filtering_bound_logger(logging.NOTSET),
    context_class=dict,
    logger_factory=structlog.PrintLoggerFactory(),
    cache_logger_on_first_use=False
)
```

### JSON Logging

```python
import structlog

# Configure for JSON output
structlog.configure(
    processors=[structlog.processors.JSONRenderer()]
)

log = structlog.get_logger()
log.info("Processing request", request_id="req-123", user_id=456)
# Output: {"event": "Processing request", "request_id": "req-123", "user_id": 456}
```

### Standard Library Integration

```python
import logging
import structlog

# Configure standard logging
logging.basicConfig(
    format="%(message)s",
    stream=sys.stdout,
    level=logging.INFO
)

# Configure structlog to use standard library
structlog.configure(
    processors=[
        structlog.stdlib.filter_by_level,
        structlog.stdlib.add_logger_name,
        structlog.stdlib.add_log_level,
        structlog.stdlib.PositionalArgumentsFormatter(),
        structlog.processors.StackInfoRenderer(),
        structlog.processors.format_exc_info,
        structlog.stdlib.render_to_log_kwargs,
    ],
    context_class=dict,
    logger_factory=structlog.stdlib.LoggerFactory(),
    wrapper_class=structlog.stdlib.BoundLogger,
    cache_logger_on_first_use=True,
)
```

### Context Binding

```python
import structlog

log = structlog.get_logger()

# Bind context that persists across log calls
request_log = log.bind(request_id="req-789", user="alice")
request_log.info("Processing started")
request_log.info("Database query executed", query="SELECT * FROM users")
request_log.info("Processing completed")

# Output includes request_id and user in all log entries
```

### Custom Processors

```python
import time

def add_custom_context(logger, log_method, event_dict):
    """Add custom context to every log entry"""
    event_dict["custom_field"] = "custom_value"
    event_dict["timestamp"] = time.time()
    return event_dict

structlog.configure(
    processors=[
        add_custom_context,
        structlog.processors.JSONRenderer()
    ]
)
```

### Exception Handling

```python
import structlog

structlog.configure(
    processors=[
        structlog.processors.dict_tracebacks,
        structlog.processors.JSONRenderer(),
    ],
)

log = structlog.get_logger()

try:
    1 / 0
except ZeroDivisionError:
    log.exception("Division error occurred")
```

### Testing with Structlog

```python
import pytest
import structlog
from structlog.testing import LogCapture

@pytest.fixture
def log_output():
    return LogCapture()

@pytest.fixture(autouse=True)
def configure_structlog(log_output):
    structlog.configure(
        processors=[log_output]
    )

def test_logging(log_output):
    log = structlog.get_logger()
    log.info("test message", key="value")

    assert len(log_output.entries) == 1
    assert log_output.entries[0]["event"] == "test message"
    assert log_output.entries[0]["key"] == "value"
```

### Performance-Optimized Configuration

```python
import structlog

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

### Advanced Console Output

```python
import logging.config
import structlog

timestamper = structlog.processors.TimeStamper(fmt="%Y-%m-%d %H:%M:%S")
pre_chain = [
    structlog.stdlib.add_log_level,
    structlog.stdlib.ExtraAdder(),
    timestamper,
]

logging.config.dictConfig({
    "version": 1,
    "disable_existing_loggers": False,
    "formatters": {
        "plain": {
            "()": structlog.stdlib.ProcessorFormatter,
            "processors": [
                structlog.stdlib.ProcessorFormatter.remove_processors_meta,
                structlog.dev.ConsoleRenderer(colors=False),
            ],
            "foreign_pre_chain": pre_chain,
        },
        "colored": {
            "()": structlog.stdlib.ProcessorFormatter,
            "processors": [
                structlog.stdlib.ProcessorFormatter.remove_processors_meta,
                structlog.dev.ConsoleRenderer(colors=True),
            ],
            "foreign_pre_chain": pre_chain,
        },
    },
    "handlers": {
        "default": {
            "level": "DEBUG",
            "class": "logging.StreamHandler",
            "formatter": "colored",
        },
        "file": {
            "level": "DEBUG",
            "class": "logging.handlers.WatchedFileHandler",
            "filename": "app.log",
            "formatter": "plain",
        },
    },
    "loggers": {
        "": {
            "handlers": ["default", "file"],
            "level": "DEBUG",
        }
    }
})
```

## Key Features

- **Structured Logging**: Log events as dictionaries with context
- **Multiple Output Formats**: Console, JSON, logfmt, and custom renderers
- **Context Binding**: Persistent context across log calls
- **Standard Library Integration**: Works seamlessly with Python's logging module
- **Performance**: Optimized for high-throughput applications
- **Testing Support**: Built-in testing utilities
- **Exception Handling**: Enhanced exception formatting and rendering
- **Custom Processors**: Flexible pipeline for log processing

## Best Practices

1. **Configure Once**: Set up structlog configuration at application startup
2. **Use Context**: Bind relevant context (request_id, user_id) early in request handling
3. **Choose Right Renderer**: Use ConsoleRenderer for development, JSONRenderer for production
4. **Test Logging**: Use LogCapture for unit testing logging behavior
5. **Performance**: Cache loggers and use efficient processors for production