LiteLLM Guardrails Marketplace

A community collection of custom code guardrails for LiteLLM. Browse, copy, and deploy guardrails to protect your LLM applications.

🌐 Live Site: models.litellm.ai/guardrails

What are Custom Code Guardrails?

Custom code guardrails are Python functions that run before (pre-call) or after (post-call) your LLM requests. They can:

• Block harmful or policy-violating content
• Modify inputs/outputs (redact PII, format responses)
• Allow requests to proceed normally

Learn more: LiteLLM Custom Code Guardrails Documentation

Available Guardrails

Name	Category	Type	Description
pii_detector	🔒 Security	Request	Detects and blocks PII (SSNs, credit cards, phones)
pii_redactor	🔒 Security	Request	Redacts PII with [REDACTED] markers
prompt_injection_detector	🔒 Security	Request	Detects jailbreak and injection attempts
api_key_detector	🔒 Security	Request	Prevents accidental API key exposure
url_validator	🔒 Security	Request	Blocks suspicious URLs and phishing domains
json_schema_validator	✅ Validation	Response	Validates responses against JSON schemas
word_count_limit	✅ Validation	Both	Enforces word count limits
language_detector	✅ Validation	Request	Detects and restricts content languages
toxic_content_filter	🛡️ Moderation	Both	Filters toxic and offensive content
code_detector	📝 Content	Response	Detects code and blocks dangerous languages
rate_limit_by_user	⏱️ Rate Limiting	Request	Per-user rate limiting template
response_formatter	✨ Formatting	Response	Cleans and formats LLM responses

Quick Start

1. Copy a Guardrail

Find a guardrail you need in guardrails.json and copy its code.

2. Add to Your LiteLLM Config

# config.yaml
litellm_settings:
  guardrails:
    - guardrail_name: "pii_detector"
      litellm_params:
        guardrail: custom_code.pii_detector
        mode: "pre_call"  # or "post_call" for response guardrails

3. Save Your Guardrail Code

Save the guardrail function in a file referenced by your config, or add it directly via the LiteLLM UI.

Contributing a Guardrail

We welcome contributions! Here's how to add a new guardrail:

1. Fork this repository

2. Add your guardrail to guardrails.json

{
  "your_guardrail_id": {
    "name": "Your Guardrail Name",
    "description": "Brief description of what it does",
    "category": "security|validation|moderation|rate-limiting|content|formatting",
    "tags": ["relevant", "tags", "for", "search"],
    "author": "Your Name or GitHub handle",
    "version": "1.0.0",
    "input_type": "request|response|both",
    "code": "def apply_guardrail(inputs, request_data, input_type):\n    # Your code here\n    return allow()"
  }
}

3. Create a standalone file in guardrails/

# guardrails/your_guardrail_id.py
def apply_guardrail(inputs, request_data, input_type):
    """
    Description of your guardrail.
    
    Args:
        inputs: Dict with 'texts', 'images', 'tool_calls' lists
        request_data: Full request payload with metadata
        input_type: "request" or "response"
    
    Returns:
        allow(), block(reason), or modify(texts=[], images=[], tool_calls=[])
    """
    # Your implementation
    return allow()

4. Submit a Pull Request

Include:

• What your guardrail does
• Example use cases
• Any configuration options

Guardrail Function Signature

def apply_guardrail(inputs, request_data, input_type):
    """
    inputs: {
        "texts": ["message content", ...],
        "images": [...],
        "tool_calls": [...]
    }
    
    request_data: {
        "model": "gpt-4",
        "messages": [...],
        "metadata": {...},
        ...
    }
    
    input_type: "request" | "response"
    
    Returns one of:
        allow()                              # Let it through
        block("reason")                      # Block with message
        modify(texts=[], images=[], ...)     # Transform content
    """

Available Primitives

Custom code guardrails have access to these built-in functions:

Function	Description
regex_match(pattern, text)	Check if pattern matches
regex_replace(pattern, replacement, text)	Replace matches
regex_find_all(pattern, text)	Find all matches
json_parse(text)	Parse JSON string
json_stringify(obj)	Convert to JSON string
json_schema_valid(data, schema)	Validate against JSON schema
extract_urls(text)	Extract URLs from text
detect_code(text)	Detect code blocks
http_get(url)	Async HTTP GET (sandbox-approved URLs only)
http_post(url, data)	Async HTTP POST (sandbox-approved URLs only)

Development

# Install dependencies
npm install

# Run dev server
npm run dev

# Build for production
npm run build

# Preview production build
npm run preview

Deployment

This site is deployed on Vercel. Any push to main automatically deploys.

License

MIT License - see LICENSE

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Built with ❤️ by the LiteLLM team