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Validate task output with guardrails — failed checks retry automatically with feedback about what to fix. 
from praisonaiagents import Agent, Task, AgentTeam

def validate_word_count(output):
    words = len(str(output.raw).split())
    if words >= 100:
        return True, output
    return False, f"Article has {words} words, expected at least 100"

writer = Agent(name="Writer", instructions="Write clear articles")

task = Task(
    description="Write a 500-word article about AI",
    agent=writer,
    guardrails=validate_word_count,
    max_retries=3,
)

AgentTeam(agents=[writer], tasks=[task]).start()
The user asks for output that must meet a guardrail; failed validation sends feedback into the next retry until the check passes or retries are exhausted.

​
How It Works

​
Quick Start

1

Install Package

First, install the PraisonAI Agents package:
pip install praisonaiagents
2

Create Validation with Guardrails

The simplest way to add validation is using guardrails:
from praisonaiagents import AgentFlow, Task, WorkflowContext, StepResult
from typing import Tuple, Any

# Define validation function
def validate_word_count(result: StepResult) -> Tuple[bool, str]:
    word_count = len(result.output.split())
    if word_count >= 100:
        return (True, None)
    else:
        return (False, f"Article has {word_count} words, expected at least 100")

# Step handler
def write_article(ctx: WorkflowContext) -> StepResult:
    feedback = ctx.variables.get("validation_feedback", "")
    # In real usage, call an agent here
    return StepResult(output="AI is transforming industries..." * 20)

# Workflow with guardrail validation
workflow = AgentFlow(
    steps=[
        Task(
            name="write_article",
            handler=write_article,
            guardrails=validate_word_count,  # Validation function
            max_retries=3  # Will retry up to 3 times if validation fails
        )
    ]
)

# Run the workflow
result = workflow.start("Write a 500-word article about AI")
print(result["output"])

​
Validation Methods

PraisonAI offers two primary validation approaches: Guardrails provide inline validation with automatic retry and feedback mechanisms.

​
Function-Based Guardrails

from typing import Tuple, Any

def validate_output(task_output) -> Tuple[bool, Any]:
    """
    Validate task output
    Returns: (is_valid, feedback_or_output)
    """
    content = task_output.raw
    
    # Your validation logic
    if len(content) > 100:
        return True, task_output
    else:
        return False, "Content too short, needs at least 100 characters"

task = Task(
    description="Generate detailed analysis",
    agent=agent,
    guardrails=validate_output,
    max_retries=3
)

​
LLM-Based Guardrails

For complex validation that requires understanding: 
writer = Agent(
    name="Writer",
    role="Content creator",
    goal="Write high-quality content",
    llm="gpt-4"  # Required for LLM guardrails
)

task = Task(
    description="Write a technical blog post about quantum computing",
    expected_output="Technical blog post",
    agent=writer,
    guardrails="Validate that the blog post: 1) Is technically accurate, 2) Contains at least 3 code examples, 3) Has proper introduction and conclusion sections",
    max_retries=2
)

​
2. Decision-Based Validation Workflows

For complex validation flows with multiple validators: 
from praisonaiagents import Agent, Task, AgentTeam

# Create agents
writer = Agent(
    name="Writer",
    role="Content creator",
    goal="Write content"
)

validator = Agent(
    name="Quality Checker",
    role="Content validator",
    goal="Ensure content meets requirements"
)

# Writing task
write_task = Task(
    name="write_article",
    description="Write a 500-word article about AI ethics",
    expected_output="500-word article",
    agent=writer,
    is_start=True,
    next_tasks=["validate_article"]
)

# Validation task (decision type)
validate_task = Task(
    name="validate_article",
    description="Check if article is exactly 500 words and covers key ethical points. Respond with 'valid' if correct, 'retry' with specific feedback if not.",
    expected_output="Validation decision with feedback",
    agent=validator,
    task_type="decision",
    condition={
        "valid": [],  # End workflow if valid
        "retry": ["write_article"]  # Retry writing if invalid
    }
)

# Create workflow with guardrails
from praisonaiagents import AgentFlow, Task

workflow = AgentFlow(
    steps=[
        Task(
            name="write",
            handler=write_handler,
            guardrails=validate_output,
            max_retries=3
        )
    ]
)

result = workflow.start("Write article")

​
How Validation Feedback Works

When validation fails, the system automatically:
  1. Captures the validation feedback including:
    • The validation decision (e.g., “retry”, “invalid”)
    • Detailed feedback about what was wrong
    • The original output that failed
    • Which validator made the decision
  2. Creates typed validation outcome (recommended): 
    # Tasks now expose typed validation outcome
task.validation_outcome = AgentRunOutcome(
    status="invalid_output",  # Typed status
    error="Article has 450 words, needs exactly 500",
    elapsed_s=2.3,
    agent_name="validator",
    context={
        "validator_task": "validate_article",
        "validated_task": "write_article"
    }
)
  3. Passes feedback to retry task via context (legacy): 
    # The retry task also receives legacy dict format
task.validation_feedback = {
    "validation_response": "retry",
    "validation_details": "Article has 450 words, needs exactly 500",
    "rejected_output": "The original article text...",
    "validator_task": "validate_article",
    "validated_task": "write_article"
}
  4. Includes feedback in task context for the next attempt
Decision tasks expose task.validation_feedback (dict) with retry details. Use it in the next attempt’s prompt or handler.
Use the typed outcome for robust error handling: 
from praisonaiagents import Agent, Task, AgentTeam

# After validation task runs
validation_task = workflow.get_task("validate_article")

# Check typed outcome (recommended)
if validation_task.validation_outcome.status == "invalid_output":
    print(f"Validation failed: {validation_task.validation_outcome.error}")
    retry_with_specific_fixes()
elif validation_task.validation_outcome.status == "success":
    proceed_to_next_step()

# Legacy dict access still works
if validation_task.validation_feedback["validation_response"] == "retry":
    handle_legacy_retry()

​
Complete Examples

​
Example 1: Data Validation Pipeline

from praisonaiagents import Agent, Task, AgentTeam
import json

# Validation function for JSON data
def validate_json_schema(task_output) -> Tuple[bool, Any]:
    try:
        data = json.loads(task_output.raw)
        
        # Check required fields
        required_fields = ["name", "email", "age"]
        missing_fields = [f for f in required_fields if f not in data]
        
        if missing_fields:
            return False, f"Missing required fields: {', '.join(missing_fields)}"
        
        # Validate data types
        if not isinstance(data["age"], int) or data["age"] < 0:
            return False, "Age must be a positive integer"
        
        if "@" not in data["email"]:
            return False, "Invalid email format"
        
        return True, task_output
        
    except json.JSONDecodeError:
        return False, "Output is not valid JSON"

# Create data processor agent
processor = Agent(
    name="Data Processor",
    role="JSON data generator",
    goal="Generate valid user data in JSON format"
)

# Task with validation
generate_task = Task(
    description="Generate user data for John Doe, age 30, email john@example.com",
    expected_output="Valid JSON with name, email, and age fields",
    agent=processor,
    guardrails=validate_json_schema,
    max_retries=3
)

# Run pipeline
pipeline = AgentTeam(
    agents=[processor],
    tasks=[generate_task]
)

result = pipeline.start()

​
Example 2: Multi-Stage Validation

from praisonaiagents import Agent, Task, AgentTeam

# Create specialized agents
writer = Agent(
    name="Technical Writer",
    role="Documentation writer",
    goal="Write comprehensive technical documentation"
)

tech_reviewer = Agent(
    name="Technical Reviewer",
    role="Technical accuracy validator",
    goal="Ensure technical accuracy"
)

style_checker = Agent(
    name="Style Checker",
    role="Writing style validator",
    goal="Ensure documentation follows style guide"
)

# Writing task
write_docs = Task(
    name="write_documentation",
    description="Write API documentation for the new authentication endpoint",
    expected_output="Complete API documentation",
    agent=writer,
    is_start=True,
    next_tasks=["technical_review"]
)

# Technical validation
tech_review = Task(
    name="technical_review",
    description="Validate technical accuracy. Check: correct HTTP methods, proper authentication flow, accurate error codes. Respond 'pass' or 'rewrite' with specific issues.",
    expected_output="Technical validation result",
    agent=tech_reviewer,
    task_type="decision",
    condition={
        "pass": ["style_check"],
        "rewrite": ["write_documentation"]
    }
)

# Style validation
style_check = Task(
    name="style_check",
    description="Check documentation style. Verify: consistent formatting, clear examples, proper headings. Respond 'approved' or 'revise' with style issues.",
    expected_output="Style validation result",
    agent=style_checker,
    task_type="decision",
    condition={
        "approved": [],  # Complete
        "revise": ["write_documentation"]
    }
)

# Create validation pipeline with Workflow
from praisonaiagents import AgentFlow, Task

validation_pipeline = AgentFlow(
    steps=[
        Task(name="write", handler=write_docs_handler),
        Task(name="tech", handler=tech_review_handler, guardrails=tech_validator),
        Task(name="style", handler=style_check_handler, guardrails=style_validator)
    ]
)

result = validation_pipeline.start("Write documentation")

​
Example 3: Complex Validation with Context

from praisonaiagents import Agent, Task, AgentTeam

# Validation that checks against requirements
class RequirementsValidator:
    def __init__(self, requirements):
        self.requirements = requirements
    
    def validate(self, task_output) -> Tuple[bool, Any]:
        content = task_output.raw
        missing_requirements = []
        
        for req in self.requirements:
            if req.lower() not in content.lower():
                missing_requirements.append(req)
        
        if missing_requirements:
            feedback = f"Missing requirements: {', '.join(missing_requirements)}"
            return False, feedback
        
        return True, task_output

# Create agent
analyst = Agent(
    name="Business Analyst",
    role="Requirements analyst",
    goal="Create comprehensive requirement documents"
)

# Define requirements
requirements = [
    "user authentication",
    "data encryption",
    "audit logging",
    "error handling",
    "performance metrics"
]

# Create validator instance
validator = RequirementsValidator(requirements)

# Task with custom validator
requirements_task = Task(
    description="Write security requirements for the new banking application",
    expected_output="Comprehensive security requirements document",
    agent=analyst,
    guardrails=validator.validate,
    max_retries=3
)

# Run task
agents = AgentTeam(
    agents=[analyst],
    tasks=[requirements_task]
)

result = agents.start()

​
Validation Feedback in Action

When validation fails, agents receive both typed outcomes and legacy feedback: Typed Outcome (Recommended): 
# Access typed validation outcome
task.validation_outcome = AgentRunOutcome(
    status="invalid_output",    # Typed status for exhaustive matching  
    error="Article word count is 450, expected 500",
    elapsed_s=1.2,
    agent_name="quality_checker",
    context={
        "failed_criteria": ["word_count"],
        "suggestions": "Add 50 more words to meet requirement"
    }
)

# Use typed status and helper methods
if task.validation_outcome.is_retryable():
    schedule_retry(task.validation_outcome.error)
Legacy Dict Format (Backward Compatibility): 
# Example of legacy validation feedback structure (still available)
task.validation_feedback = {
    "validation_response": "retry",
    "validation_details": {
        "reason": "Article word count is 450, expected 500",
        "suggestions": "Add 50 more words to meet requirement", 
        "failed_criteria": ["word_count"]
    },
    "rejected_output": "The original article content...",
    "validator_task": "validate_article",
    "validated_task": "write_article",
    "retry_count": 1
}

# Available from typed outcome's to_dict() method
typed_dict = task.validation_outcome.to_dict()

​
Best Practices

Define specific, measurable criteria and return actionable feedback strings when validation fails. Include examples of valid output in task descriptions.
Use function guardrails for simple checks (length, format, required fields). Reserve LLM guardrails (string prompts) for subjective quality checks.
Use max_retries=3 as a default. Increase only when feedback is precise enough for the agent to self-correct.
Validate JSON schema, required sections, or word counts before expensive downstream tasks run.

​
Advanced Configuration

​
Retry Strategies

# Configure retry behavior
task = Task(
    description="Generate report",
    agent=agent,
    guardrails=validate_report,
    max_retries=3,
    retry_strategy="exponential",  # or "linear"
    retry_delay=2  # seconds between retries
)

​
Custom Feedback Formatting

def validate_with_detailed_feedback(task_output) -> Tuple[bool, Any]:
    # Perform validation
    issues = []
    
    if len(task_output.raw) < 100:
        issues.append("Content too short")
    
    if "conclusion" not in task_output.raw.lower():
        issues.append("Missing conclusion section")
    
    if issues:
        feedback = {
            "status": "failed",
            "issues": issues,
            "suggestions": "Please address the above issues",
            "example": "See template for reference"
        }
        return False, json.dumps(feedback)
    
    return True, task_output

​
Common Validation Patterns

​
Word/Character Count

def validate_length(min_words=100, max_words=500):
    def validator(task_output):
        word_count = len(task_output.raw.split())
        if min_words <= word_count <= max_words:
            return True, task_output
        else:
            return False, f"Word count {word_count} not in range {min_words}-{max_words}"
    return validator

task = Task(
    description="Write article",
    guardrails=validate_length(400, 600)
)

​
Content Requirements

def validate_content_includes(required_sections):
    def validator(task_output):
        content = task_output.raw.lower()
        missing = [s for s in required_sections if s.lower() not in content]
        
        if missing:
            return False, f"Missing sections: {', '.join(missing)}"
        return True, task_output
    return validator

task = Task(
    description="Write report",
    guardrails=validate_content_includes([
        "Executive Summary",
        "Methodology",
        "Results",
        "Conclusion"
    ])
)

​
Troubleshooting

  • Check validation criteria are achievable
  • Verify feedback is clear and actionable
  • Test validation function separately
  • Increase max_retries if needed
  • Ensure using proper validation return format
  • Check workflow connections
  • Verify decision task conditions
  • Enable verbose mode for debugging
  • Set appropriate max_retries
  • Implement retry counters
  • Add fallback conditions
  • Log validation attempts

Agent Run Outcomes

Typed validation outcomes and status handling

Guardrails

Deep dive into the guardrails system

Task Retry Policy

Per-task retry with exponential backoff

Workflows

Complex workflow patterns