dspy-guardrails 0.1.1

DSPy-based guardrails for AI content moderation and security

DSPy Guardrails

A comprehensive collection of AI guardrails built with DSPy for content moderation and security.
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Table of Contents

1. About
2. Quick Start
3. Usage
4. Development
5. Contributing
6. License

About

DSPy Guardrails is a comprehensive suite of AI guardrails built with DSPy. Each guardrail is implemented as a self-contained module that can be used to test and validate different types of content moderation and security checks.

• Modular design — Each guardrail type is implemented as a separate, self-contained module
• Programmatic testing — Run guardrails directly in Python for fast iteration
• Comprehensive coverage — Covers major content moderation and security scenarios
• DSPy integration — Leverages DSPy's programmatic prompting for consistent, reliable results
• Type safety — Full type hints and dataclass definitions for robust implementations

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Quick Start

Install

Install dspy-guardrails with uv (recommended)

uv add dspy-guardrails

Install with pip (alternative)

pip install dspy-guardrails

Continue with the usage examples below.

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Usage

Basic Usage

import dspy
from dspy_guardrails import guardrail

# Configure DSPy (required)
lm = dspy.LM("openrouter/google/gemini-2.5-flash-preview-09-2025")
guardrail.configure(lm=lm)

# Create and run guardrails
topic_guardrail = guardrail.Topic(topic_scopes=["AI", "Machine Learning"])
result = guardrail.Run(topic_guardrail, "I want to learn about neural networks")
print(f"Allowed: {result.is_allowed}")  # True

Multiple Guardrails

Assumes guardrail.configure(lm=lm) has already been called.

all_guardrails = [
    guardrail.Topic(topic_scopes=["AI"]),
    guardrail.Nsfw(),
    guardrail.Pii(),
]
result = guardrail.Run(all_guardrails, "Safe AI content")
print(f"All passed: {result.is_allowed}")  # True

For more examples and patterns, see the complete quickstart guide and guardrail types.

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Available Guardrails

Guardrail	Example variables	Example instantiation
Topic	topic_scopes=["AI", "Machine Learning"], blocked_topics=["spam"]	guardrail.Topic(topic_scopes=["AI", "Machine Learning"], blocked_topics=["spam"])
NSFW	sensitivity_level="high"	guardrail.Nsfw(sensitivity_level="high")
PII	allowed_pii_types=["email"]	guardrail.Pii(allowed_pii_types=["email"])
Toxicity	toxicity_threshold=0.8	guardrail.Toxicity(toxicity_threshold=0.8)
Tone	desired_tone="helpful", unwanted_tones=["sarcastic"]	guardrail.Tone(desired_tone="helpful", unwanted_tones=["sarcastic"])
Grounding	grounding_threshold=0.8	guardrail.Grounding(grounding_threshold=0.8)
Language	allowed_languages=["en", "es"]	guardrail.Language(allowed_languages=["en", "es"])
Keywords	blocked_keywords=["password", "secret"], case_sensitive=False	guardrail.Keywords(blocked_keywords=["password", "secret"], case_sensitive=False)
Secret Keys	key_patterns=["sk-", "ghp_"], entropy_threshold=3.5	guardrail.SecretKeys(key_patterns=["sk-", "ghp_"], entropy_threshold=3.5)
Gibberish	prob_threshold=0.7	guardrail.Gibberish(prob_threshold=0.7)
Prompt Injection	injection_patterns=["ignore previous", "system override"]	guardrail.PromptInjection(injection_patterns=["ignore previous", "system override"])
Jailbreak	detection_threshold=0.9	guardrail.Jailbreak(detection_threshold=0.9)

Development

Code Quality

This project uses several tools to maintain code quality:

• Ruff: Linting and formatting
• isort: Import sorting
• pytest: Testing framework

Available commands:

# Run all quality checks
uv run poe clean

# Individual checks
uv run poe lint          # Ruff linting
uv run poe format        # Ruff formatting
uv run poe sort          # Import sorting

Testing

Run tests using pytest:

# Run all tests
uv run pytest

# Run specific test
uv run pytest path/to/test.py::test_name

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Contributing

Quick workflow:

1. Fork and branch: git checkout -b feature/name
2. Make changes
3. Run checks: uv run poe clean-full
4. Commit and push
5. Open a Pull Request

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License

MIT (as declared in pyproject.toml).

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