Praison AI

Guardrail

Validate agent outputs with LLM-based guardrails

The --guardrail flag enables LLM-based output validation to ensure agent responses meet specific criteria.

Quick Start

bash
praisonai "Write code" --guardrail "Ensure code is secure and follows best practices"

!Guardrail Validates Output Quality

Usage

Basic Guardrail

bash
praisonai "Generate SQL query" --guardrail "No DROP or DELETE statements allowed"

Expected Output:

🛡️ Guardrail enabled: No DROP or DELETE statements allowed

╭─ Agent Info ─────────────────────────────────────────────────────────────────╮
│  👤 Agent: DirectAgent                                                       │
│  Role: Assistant                                                             │
╰──────────────────────────────────────────────────────────────────────────────╯

╭────────────────────────────────── Response ──────────────────────────────────╮
│ SELECT * FROM users WHERE status = 'active';                                 │
╰──────────────────────────────────────────────────────────────────────────────╯

✅ Guardrail passed: Output meets criteria

Combine with Other Flags

bash
# Guardrail with save
praisonai "Write API documentation" --guardrail "Include all endpoints" --save

# Guardrail with metrics
praisonai "Generate report" --guardrail "Must include sources" --metrics

# Guardrail with planning
praisonai "Create security audit" --guardrail "Follow OWASP guidelines" --planning

Common Guardrail Criteria

Security
bash
--guardrail "No sensitive data exposure"
--guardrail "Follow security best practices"
--guardrail "Sanitize all inputs"
 Quality
bash
--guardrail "Include error handling"
--guardrail "Add type hints"
--guardrail "Follow PEP 8 style"
 Content
bash
--guardrail "Professional tone only"
--guardrail "Include citations"
--guardrail "No speculation"
 Format
bash
--guardrail "Output as JSON"
--guardrail "Include headers"
--guardrail "Maximum 500 words"

How It Works

  1. Agent Execution: The agent processes your prompt normally
  2. Validation: The guardrail LLM evaluates the output against your criteria
  3. Result: Pass/fail status is displayed with feedback
Diagram

Pass

Fail

Prompt

Agent

Output

Guardrail

✅ Return Output

⚠️ Warning + Output

Examples

Code Quality Guardrail

bash
praisonai "Write a Python function to parse JSON" \
  --guardrail "Must include docstring, type hints, and error handling"

Expected Output:

python
def parse_json(json_string: str) -> dict:
    """
    Parse a JSON string into a Python dictionary.

    Args:
        json_string: A valid JSON formatted string

    Returns:
        Parsed dictionary from the JSON string

    Raises:
        json.JSONDecodeError: If the string is not valid JSON
    """
    import json
    try:
        return json.loads(json_string)
    except json.JSONDecodeError as e:
        raise json.JSONDecodeError(f"Invalid JSON: {e.msg}", e.doc, e.pos)

Content Guardrail

bash
praisonai "Write a product description" \
  --guardrail "No exaggerated claims, include specifications"

SQL Safety Guardrail

bash
praisonai "Generate database queries for user management" \
  --guardrail "Read-only queries, no modifications allowed"

Best Practices

DoDon’t
“Include error handling for all edge cases”“Make it good”
“No SQL injection vulnerabilities”“Be secure”
“Output must be valid JSON”“Format nicely”
“Maximum 3 paragraphs”“Keep it short”

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