Open-Source Observability · alexmojaki

snoop

snoop is a Python debugging library that decorates functions to output detailed execution traces, including line-by-line execution, variable changes, and nested call stacks. It also provides pp() for enhanced print debugging and integrates with Jupyter/IPython for interactive development.

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Source: GitHub — github.com/alexmojaki/snoop

1.5k

GitHub stars

Forks

Python

Primary language

MIT

License (OSI-approved)

Key facts

Objective fields from the source. Values we can't verify are shown as “Unknown” rather than guessed.

Field	Value
Repository	alexmojaki/snoop
Owner	alexmojaki
Primary language	Python
License	MIT — OSI-approved
Stars	1.5k
Forks	43
Open issues	26
Latest release	Unknown
Last updated	2026-07-06
Source	https://github.com/alexmojaki/snoop

What snoop is

snoop instruments Python functions via decorators (@snoop) or context managers (with snoop:) to generate execution logs with variable state tracking, depth-based call tracing, and customizable watch expressions. The pp() function offers source-aware pretty-printing with pp.deep() for subexpression tracing. MIT-licensed, pure Python, supports 2.7 and 3.5+.

Quickstart

Get the snoop source

Clone the repository and explore it locally.

terminalbash

1git clone https://github.com/alexmojaki/snoop.git2cd snoop3# follow the project's README for install & configuration

Need it deployed, integrated, or customized instead? DEV.co ships production installs.

Best use cases

Local development debugging

Developers debugging complex functions without setting up a full IDE debugger. Minimal setup—just add @snoop decorator to see execution flow and variable mutations in real time.

Exploratory data science / notebooks

Data scientists and analysts iterating in Jupyter/IPython, needing quick visibility into intermediate values and function call sequences without verbose print statements.

Production logging and diagnostics

Temporary instrumentation in staging/test environments to diagnose intermittent issues or understand user-reported bugs without deploying a full debugger.

Implementation considerations

Decorator and context manager syntax is straightforward; integrate by adding @snoop or wrapping code in 'with snoop:' blocks. No configuration required for basic use.
Output goes to stderr by default; may require redirection or custom output handlers if integrating with logging infrastructure.
watch_explode and depth parameters control verbosity; start conservatively to avoid output bloat on complex objects or deep call stacks.
pp() source introspection may fail in interactive shells, pytest, or code with runtime transformation; graceful fallback provided but results vary.
Python 2.7 support and PyPy compatibility noted in docs; verify against your target interpreter if using edge-case environments.

When to avoid it — and what to weigh

Performance-critical code paths — snoop's instrumentation overhead (extra logging, variable evaluation, stack introspection) makes it unsuitable for tight inner loops or real-time systems. Use only in development or low-throughput diagnostic contexts.
Permanent production instrumentation — snoop is designed as a development tool, not a logging framework. For persistent observability, use structured logging libraries (e.g., loguru, structlog) instead.
Large distributed systems requiring centralized tracing — snoop outputs to stderr/files on the local machine. For microservices or cloud-native architectures, use dedicated tracing/observability platforms (OpenTelemetry, Datadog, Jaeger).
Projects requiring enterprise support or SLAs — snoop is community-maintained without formal support, SLA, or security response guarantees. High-risk or regulated environments should evaluate organizational liability.

License & commercial use

MIT License: permissive OSI license allowing unrestricted use, modification, distribution, and commercial use with attribution and no warranty.

MIT license permits commercial use without royalties. However, snoop is a development-only tool; using it in production debugging workflows should be transient. Ensure your team assumes operational risk, as the project is community-maintained without enterprise support contracts or security response SLAs.

DEV.co evaluation signals

Editorial assessment — not user reviews. Directional, with an explicit confidence level.

Signal	Assessment
Maintenance	Active
Documentation	Adequate
License clarity	Clear
Deployment complexity	Low
DEV.co fit	Good
Assessment confidence	High

Security considerations

snoop outputs unfiltered variable values to stderr/files, potentially exposing sensitive data (API keys, passwords, PII, tokens). Do not use in code paths handling secrets or personally identifiable information without careful output redirection and sanitization. No built-in filtering or redaction mechanisms. Community-maintained project with unknown security review practices; no formal security policy or advisories noted.

Alternatives to consider

PySnooper

snoop's predecessor and direct inspiration. Lighter-weight, less feature-rich, but simpler. Choose if you need basic line-by-line tracing without watch_explode, pp, or advanced nesting.

icecream

Lightweight print-debugging library offering source-aware printing. Simpler API than snoop and less overhead; choose if you need only enhanced print() without full execution tracing.

pdb / ipdb

Interactive debuggers with breakpoints, watches, and REPL. More powerful but require IDE integration and breakpoint management. Choose for complex stepping and state inspection; avoid if rapid lightweight instrumentation is preferred.

Software development agency

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snoop FAQ

Can I use snoop in production?

Not recommended. snoop is a development debugging tool with significant overhead. Use only transiently in staging/test to diagnose issues. For persistent production logging, use structured logging libraries (loguru, structlog) or APM platforms.

Does snoop work in Jupyter notebooks?

Yes. README states native IPython/Jupyter integration. Import and use @snoop or pp() directly in cells; no special setup required. Output appears inline in notebook.

What Python versions does snoop support?

Per README badge: Python 2.7, 3.5+, including PyPy. Verify compatibility with your target interpreter, especially for edge cases like PyPy or Python 2.7 (increasingly rare).

How do I prevent snoop from logging sensitive data?

snoop has no built-in redaction. Manually avoid instrumenting functions handling secrets, or redirect stderr to a secure log sink. Consider using watch to log only non-sensitive expressions.

Software developers & web developers for hire

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