WrenchCL 5.2.0

Comprehensive library for seamless integration with AWS, OpenAI, and utility tools.

Wrench Code Library

---

ReadTheDocs

---

Description

WrenchCL is a comprehensive library designed to facilitate seamless interactions with AWS services, OpenAI models, and various utility tools. This package aims to streamline the development process by providing robust components for database interactions, cloud storage, and AI-powered functionalities.

PyPI Link: WrenchCL on PyPI

Installation

Basic Installation

To install the core package with minimal dependencies:

pip install WrenchCL

Optional Dependencies

WrenchCL uses optional dependencies to keep the core package lightweight while providing additional functionality when needed:

Color Support (Logger)

pip install WrenchCL[color]
# Adds: colorama for beautiful terminal colors

AWS Services

pip install WrenchCL[aws]
# Adds: boto3, psycopg2-binary, sshtunnel, and AWS service type hints
# Enables: RDS connections, S3 operations, Lambda functions, Secrets Manager

Distributed Tracing

pip install WrenchCL[trace]
# Adds: ddtrace for Datadog APM integration
# Enables: Automatic trace correlation in logs

Development Tools

pip install WrenchCL[dev]
# Adds: pytest, coverage, pydantic for development and testing

Complete Installation

pip install WrenchCL[all]
# Installs all optional dependencies for full functionality

Development

To locally develop the plugin, clone the repository locally and make your changes.

Open the console in your working directory; the building command is

python setup.py sdist bdist_wheel

You can then install the package with

pip install ./dist/WrenchCL-0.0.1.dev0-py3-none-any.whl --force-reinstall

Use the --no-dependencies flag to reinstall quickly if there are no dependency changes

pip install ./dist/WrenchCL-0.0.1.dev0-py3-none-any.whl --force-reinstall --no-dependencies

Logger

WrenchCL ships a structured, colorized logger available as a singleton:

from WrenchCL import logger

Output modes

Mode	When to use
terminal	Local development — colorized, multi-line, syntax-highlighted
json	Deployed services — single-line JSON with Datadog triad fields
compact	Space-constrained output — single-line, no color

logger.configure(mode="json")          # structured JSON
logger.configure(mode="terminal")      # colored terminal output (default)
logger.configure(deployment_mode=True) # deployed terminal — no color, shows context prefix

Logging methods

logger.debug("message")
logger.info("message")
logger.warning("message")
logger.error("message")
logger.critical("message")
logger.exception("message")   # ERROR level + current exception info

logger.data(obj)               # pretty-print dicts, DataFrames, Pydantic models
logger.header("Section title") # formatted section header

Context prefix

In deployed or verbose mode every log line includes a bracket group that identifies the run, request context, and thread:

[A3X7K | api-handler | Thread-2 | WrenchCL] INFO    [12:34:56|file.py:10] -> message

Scoped prefix — the primary way to attach a prefix. Uses contextvars.ContextVar so it is per-context (thread and async-task safe) and cleans up automatically:

# Context manager — prefix active only inside the block
with logger.prefix("api-handler"):
    logger.info("handling request")   # [run_id | api-handler | WrenchCL]

logger.info("idle")                   # [run_id | WrenchCL]

# Decorator — prefix active for the entire function or method call
@logger.prefix("PaymentService")
def process_payment(order_id):
    logger.info("processing %s", order_id)

Global prefix — process-wide fallback, overridden by any scoped prefix:

logger.set_prefix("worker-1")   # set
logger.set_prefix()             # clear

Thread name — opt-in, off by default:

logger.configure(show_thread_name=True)
# [run_id | prefix | Thread-2 | WrenchCL]

Run ID — a 5-character uppercase alphanumeric ID generated at startup, refreshed with:

logger.initiate_new_run()
print(logger.run_id)   # e.g. "A3X7K"

Datadog integration

Install the trace extra and set LOG_DD_TRACE=true:

pip install WrenchCL[trace]
LOG_DD_TRACE=true python app.py

dd.trace_id and dd.span_id are injected automatically into JSON log records for APM correlation. In AWS Lambda or ECS the logger auto-detects the environment and switches to JSON mode.

Configuration reference

logger.configure(
    mode="terminal",          # "terminal" | "json" | "compact"
    level="INFO",             # "DEBUG" | "INFO" | "WARNING" | "ERROR" | "CRITICAL"
    deployment_mode=False,    # strip color, activate context prefix
    show_thread_name=False,   # include thread name in context bracket
    prefix=None,              # global prefix string
    color_enabled=True,
    highlight_syntax=True,
    trace_enabled=False,      # Datadog APM correlation
)

Temporary overrides:

with logger.temporary(level="DEBUG", mode="compact"):
    logger.debug("verbose output for this block only")