Merge branch 'develop' of https://github.com/MervinPraison/PraisonAI into develop

Author: MervinPraison

src/praisonai-agents/CLAUDE.md

@@ -115,7 +115,9 @@ agent = Agent(
     self_reflect=True,  # Enable self-reflection
     min_reflect=1,      # Minimum reflection iterations
     max_reflect=3,      # Maximum reflection iterations
-    tools=[tool1, tool2]  # Optional tools
+    tools=[tool1, tool2],  # Optional tools
+    guardrail=validate_function,  # Agent-level guardrail (function or string)
+    max_guardrail_retries=3  # Retry limit for guardrail failures
 )
 ```
 
@@ -133,6 +135,8 @@ task = Task(
 ```
 
 ### Guardrails Usage
+
+#### Task-Level Guardrails
 ```python
 from typing import Tuple, Any
 
@@ -163,6 +167,35 @@ task = Task(
 )
 ```
 
+#### Agent-Level Guardrails
+```python
+# Agent guardrails apply to ALL outputs from that agent
+def validate_professional_tone(task_output: TaskOutput) -> Tuple[bool, Any]:
+    """Ensure professional tone in all agent responses."""
+    content = task_output.raw.lower()
+    casual_words = ['yo', 'dude', 'awesome', 'cool']
+    for word in casual_words:
+        if word in content:
+            return False, f"Unprofessional language detected: {word}"
+    return True, task_output
+
+# Agent with function-based guardrail
+agent = Agent(
+    name="BusinessWriter",
+    instructions="You are a professional business writer",
+    guardrail=validate_professional_tone,  # Function guardrail
+    max_guardrail_retries=3
+)
+
+# Agent with LLM-based guardrail
+agent = Agent(
+    name="ContentWriter", 
+    instructions="You are a content writer",
+    guardrail="Ensure all responses are professional, accurate, and appropriate for business use",  # String guardrail
+    max_guardrail_retries=2
+)
+```
+
 ### Multi-Agent Workflow
 ```python
 workflow = PraisonAIAgents(

src/praisonai-agents/agent_guardrails_example.py

@@ -0,0 +1,98 @@
+#!/usr/bin/env python3
+"""
+Agent-level guardrails example.
+
+This example demonstrates how to use guardrails at the Agent level,
+which will apply to all tasks executed by that agent.
+"""
+
+from typing import Tuple, Any
+from praisonaiagents import Agent, TaskOutput
+
+def validate_content_length(task_output: TaskOutput) -> Tuple[bool, Any]:
+    """
+    Validate that task output content meets minimum length requirement.
+    
+    Args:
+        task_output: The task output to validate
+        
+    Returns:
+        Tuple of (success, result_or_error_message)
+    """
+    if len(task_output.raw) < 50:
+        return False, "Content too short - must be at least 50 characters"
+    return True, task_output
+
+def validate_professional_tone(task_output: TaskOutput) -> Tuple[bool, Any]:
+    """
+    Validate that the content has a professional tone.
+    
+    Args:
+        task_output: The task output to validate
+        
+    Returns:
+        Tuple of (success, result_or_error_message)
+    """
+    content = task_output.raw.lower()
+    unprofessional_words = ['yo', 'dude', 'awesome', 'cool', 'lol']
+    
+    for word in unprofessional_words:
+        if word in content:
+            return False, f"Content contains unprofessional word: '{word}'"
+    
+    return True, task_output
+
+def main():
+    """Demonstrate Agent-level guardrails with function-based and LLM-based validation."""
+    
+    print("=== Agent Guardrail Examples ===\n")
+    
+    # Example 1: Function-based guardrail
+    print("1. Function-based guardrail (content length validation):")
+    agent1 = Agent(
+        name="ContentWriter",
+        instructions="You are a professional content writer who creates detailed responses",
+        guardrail=validate_content_length,
+        max_guardrail_retries=2
+    )
+    
+    try:
+        result1 = agent1.start("Write a brief welcome message")
+        print(f"Result: {result1}\n")
+    except Exception as e:
+        print(f"Error: {e}\n")
+    
+    # Example 2: LLM-based guardrail (string description)
+    print("2. LLM-based guardrail (professional tone validation):")
+    agent2 = Agent(
+        name="BusinessWriter", 
+        instructions="You are a business communication expert",
+        guardrail="Ensure the content is professional, formal, and suitable for business communication. No casual language or slang.",
+        max_guardrail_retries=3
+    )
+    
+    try:
+        result2 = agent2.start("Write a welcome message for new employees")
+        print(f"Result: {result2}\n")
+    except Exception as e:
+        print(f"Error: {e}\n")
+    
+    # Example 3: Multiple agents with different guardrails
+    print("3. Professional tone function-based guardrail:")
+    agent3 = Agent(
+        name="ProfessionalWriter",
+        instructions="Write professional business content",
+        guardrail=validate_professional_tone,
+        max_guardrail_retries=2
+    )
+    
+    try:
+        result3 = agent3.start("Write a casual greeting message")
+        print(f"Result: {result3}\n")
+    except Exception as e:
+        print(f"Error: {e}\n")
+    
+    print("=== Agent Guardrails Demonstration Complete ===")
+
+if __name__ == "__main__":
+    main()