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

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

Usage

Basic Guardrail

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

# 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

--guardrail "No sensitive data exposure"
--guardrail "Follow security best practices"
--guardrail "Sanitize all inputs"

Quality

--guardrail "Include error handling"
--guardrail "Add type hints"
--guardrail "Follow PEP 8 style"

Content

--guardrail "Professional tone only"
--guardrail "Include citations"
--guardrail "No speculation"

Format

--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

Examples

Code Quality Guardrail

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

Expected Output:

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

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

SQL Safety Guardrail

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

Best Practices

Be specific with your guardrail criteria. Vague criteria may lead to inconsistent validation.

Guardrails add an additional LLM call, which increases latency and token usage. Use --metrics to monitor costs.

Do	Don’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”

Related

• Guardrails Concept
• Metrics CLI
• Planning Mode