diff --git a/swarms/schemas/hierarchical_structured_communication_schemas.py b/swarms/schemas/hierarchical_structured_communication_schemas.py
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+++ b/swarms/schemas/hierarchical_structured_communication_schemas.py
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+"""
+Schemas for Talk Structurally, Act Hierarchically Framework
+
+This module defines the Pydantic schemas used in the TalkHierarchical framework
+for structured communication and hierarchical evaluation.
+"""
+
+from typing import Dict, List, Optional, Union
+from pydantic import BaseModel, Field
+from enum import Enum
+
+
+class CommunicationType(str, Enum):
+    """Types of communication in the structured protocol"""
+    MESSAGE = "message"  # M_ij: Specific task instructions
+    BACKGROUND = "background"  # B_ij: Context and problem background
+    INTERMEDIATE_OUTPUT = "intermediate_output"  # I_ij: Intermediate results
+
+
+class AgentRole(str, Enum):
+    """Roles for agents in the hierarchical system"""
+    SUPERVISOR = "supervisor"
+    GENERATOR = "generator"
+    EVALUATOR = "evaluator"
+    REFINER = "refiner"
+    COORDINATOR = "coordinator"
+
+
+class StructuredMessageSchema(BaseModel):
+    """Schema for structured communication messages"""
+    message: str = Field(
+        description="Specific task instructions (M_ij)",
+        min_length=1
+    )
+    background: str = Field(
+        description="Context and problem background (B_ij)",
+        default=""
+    )
+    intermediate_output: str = Field(
+        description="Intermediate results (I_ij)",
+        default=""
+    )
+    sender: str = Field(
+        description="Name of the sending agent",
+        min_length=1
+    )
+    recipient: str = Field(
+        description="Name of the receiving agent",
+        min_length=1
+    )
+    timestamp: Optional[str] = Field(
+        description="Timestamp of the message",
+        default=None
+    )
+    communication_type: CommunicationType = Field(
+        description="Type of communication",
+        default=CommunicationType.MESSAGE
+    )
+
+
+class HierarchicalOrderSchema(BaseModel):
+    """Schema for hierarchical task orders"""
+    agent_name: str = Field(
+        description="Name of the agent to receive the task",
+        min_length=1
+    )
+    task: str = Field(
+        description="Specific task description",
+        min_length=1
+    )
+    communication_type: CommunicationType = Field(
+        description="Type of communication to use",
+        default=CommunicationType.MESSAGE
+    )
+    background_context: str = Field(
+        description="Background context for the task",
+        default=""
+    )
+    intermediate_output: str = Field(
+        description="Intermediate output to pass along",
+        default=""
+    )
+    priority: int = Field(
+        description="Task priority (1-10, higher is more important)",
+        default=5,
+        ge=1,
+        le=10
+    )
+
+
+class EvaluationCriterionSchema(BaseModel):
+    """Schema for evaluation criteria"""
+    name: str = Field(
+        description="Name of the evaluation criterion",
+        min_length=1
+    )
+    description: str = Field(
+        description="Description of what this criterion evaluates",
+        min_length=1
+    )
+    weight: float = Field(
+        description="Weight of this criterion in overall evaluation (0-1)",
+        default=1.0,
+        ge=0.0,
+        le=1.0
+    )
+    scale: str = Field(
+        description="Scale for evaluation (e.g., '0-10', 'A-F', 'Poor-Excellent')",
+        default="0-10"
+    )
+
+
+class EvaluationResultSchema(BaseModel):
+    """Schema for evaluation results"""
+    evaluator_name: str = Field(
+        description="Name of the evaluator",
+        min_length=1
+    )
+    criterion: str = Field(
+        description="Evaluation criterion",
+        min_length=1
+    )
+    score: float = Field(
+        description="Evaluation score",
+        ge=0.0
+    )
+    feedback: str = Field(
+        description="Detailed feedback",
+        min_length=1
+    )
+    confidence: float = Field(
+        description="Confidence in evaluation (0-1)",
+        ge=0.0,
+        le=1.0
+    )
+    reasoning: str = Field(
+        description="Reasoning behind the evaluation",
+        default=""
+    )
+    suggestions: List[str] = Field(
+        description="Suggestions for improvement",
+        default=[]
+    )
+
+
+class EvaluationSummarySchema(BaseModel):
+    """Schema for evaluation summaries"""
+    overall_score: float = Field(
+        description="Overall evaluation score",
+        ge=0.0
+    )
+    criterion_scores: Dict[str, float] = Field(
+        description="Scores for each criterion",
+        default={}
+    )
+    strengths: List[str] = Field(
+        description="Identified strengths",
+        default=[]
+    )
+    weaknesses: List[str] = Field(
+        description="Identified weaknesses",
+        default=[]
+    )
+    recommendations: List[str] = Field(
+        description="Recommendations for improvement",
+        default=[]
+    )
+    confidence: float = Field(
+        description="Overall confidence in evaluation",
+        ge=0.0,
+        le=1.0
+    )
+
+
+class AgentConfigSchema(BaseModel):
+    """Schema for agent configuration"""
+    name: str = Field(
+        description="Name of the agent",
+        min_length=1
+    )
+    role: AgentRole = Field(
+        description="Role of the agent in the system"
+    )
+    model_name: str = Field(
+        description="Model to use for this agent",
+        default="gpt-4o-mini"
+    )
+    system_prompt: str = Field(
+        description="System prompt for the agent",
+        min_length=1
+    )
+    tools: List[str] = Field(
+        description="Tools available to this agent",
+        default=[]
+    )
+    capabilities: List[str] = Field(
+        description="Capabilities of this agent",
+        default=[]
+    )
+    evaluation_criteria: List[str] = Field(
+        description="Evaluation criteria this agent can assess",
+        default=[]
+    )
+
+
+class TalkHierarchicalConfigSchema(BaseModel):
+    """Schema for TalkHierarchical swarm configuration"""
+    name: str = Field(
+        description="Name of the swarm",
+        default="TalkHierarchicalSwarm"
+    )
+    description: str = Field(
+        description="Description of the swarm",
+        default="Talk Structurally, Act Hierarchically Framework"
+    )
+    max_loops: int = Field(
+        description="Maximum number of refinement loops",
+        default=3,
+        ge=1
+    )
+    enable_structured_communication: bool = Field(
+        description="Enable structured communication protocol",
+        default=True
+    )
+    enable_hierarchical_evaluation: bool = Field(
+        description="Enable hierarchical evaluation system",
+        default=True
+    )
+    shared_memory: bool = Field(
+        description="Enable shared memory between agents",
+        default=True
+    )
+    evaluation_criteria: List[EvaluationCriterionSchema] = Field(
+        description="Evaluation criteria for the system",
+        default=[]
+    )
+    agents: List[AgentConfigSchema] = Field(
+        description="Configuration for all agents",
+        default=[]
+    )
+    quality_threshold: float = Field(
+        description="Quality threshold for stopping refinement",
+        default=8.0,
+        ge=0.0,
+        le=10.0
+    )
+
+
+class TalkHierarchicalStateSchema(BaseModel):
+    """Schema for TalkHierarchical swarm state"""
+    conversation_history: List[StructuredMessageSchema] = Field(
+        description="History of structured messages",
+        default=[]
+    )
+    intermediate_outputs: Dict[str, str] = Field(
+        description="Intermediate outputs from agents",
+        default={}
+    )
+    evaluation_results: List[EvaluationResultSchema] = Field(
+        description="Results from evaluations",
+        default=[]
+    )
+    current_loop: int = Field(
+        description="Current refinement loop",
+        default=0
+    )
+    task: str = Field(
+        description="Current task being processed",
+        default=""
+    )
+    final_result: Optional[str] = Field(
+        description="Final result of the workflow",
+        default=None
+    )
+
+
+class TalkHierarchicalResponseSchema(BaseModel):
+    """Schema for TalkHierarchical swarm response"""
+    final_result: str = Field(
+        description="Final result of the workflow"
+    )
+    total_loops: int = Field(
+        description="Total number of loops executed",
+        ge=1
+    )
+    conversation_history: List[StructuredMessageSchema] = Field(
+        description="Complete conversation history"
+    )
+    evaluation_results: List[EvaluationResultSchema] = Field(
+        description="All evaluation results"
+    )
+    intermediate_outputs: Dict[str, str] = Field(
+        description="All intermediate outputs"
+    )
+    evaluation_summary: Optional[EvaluationSummarySchema] = Field(
+        description="Summary of evaluations",
+        default=None
+    )
+    performance_metrics: Dict[str, Union[float, int, str]] = Field(
+        description="Performance metrics",
+        default={}
+    )
+
+
+class GraphNodeSchema(BaseModel):
+    """Schema for graph nodes in agent orchestration"""
+    node_id: str = Field(
+        description="Unique identifier for the node",
+        min_length=1
+    )
+    agent_name: str = Field(
+        description="Name of the agent at this node",
+        min_length=1
+    )
+    role: AgentRole = Field(
+        description="Role of the agent"
+    )
+    capabilities: List[str] = Field(
+        description="Capabilities of this node",
+        default=[]
+    )
+    connections: List[str] = Field(
+        description="IDs of connected nodes",
+        default=[]
+    )
+    conditions: Dict[str, str] = Field(
+        description="Conditions for routing to this node",
+        default={}
+    )
+
+
+class GraphSchema(BaseModel):
+    """Schema for agent orchestration graph"""
+    nodes: List[GraphNodeSchema] = Field(
+        description="Nodes in the graph",
+        default=[]
+    )
+    start_node: str = Field(
+        description="ID of the start node",
+        min_length=1
+    )
+    end_nodes: List[str] = Field(
+        description="IDs of end nodes",
+        default=[]
+    )
+    routing_rules: Dict[str, str] = Field(
+        description="Rules for routing between nodes",
+        default={}
+    )
+    max_depth: int = Field(
+        description="Maximum depth of the graph",
+        default=10,
+        ge=1
+    )
+
+
+# Response schemas for different agent types
+class GeneratorResponseSchema(BaseModel):
+    """Schema for generator agent responses"""
+    content: str = Field(
+        description="Generated content",
+        min_length=1
+    )
+    intermediate_output: str = Field(
+        description="Intermediate output for next agent",
+        default=""
+    )
+    reasoning: str = Field(
+        description="Reasoning behind the generation",
+        default=""
+    )
+    confidence: float = Field(
+        description="Confidence in the generated content",
+        ge=0.0,
+        le=1.0
+    )
+
+
+class EvaluatorResponseSchema(BaseModel):
+    """Schema for evaluator agent responses"""
+    criterion: str = Field(
+        description="Evaluation criterion",
+        min_length=1
+    )
+    score: float = Field(
+        description="Evaluation score",
+        ge=0.0
+    )
+    feedback: str = Field(
+        description="Detailed feedback",
+        min_length=1
+    )
+    confidence: float = Field(
+        description="Confidence in evaluation",
+        ge=0.0,
+        le=1.0
+    )
+    reasoning: str = Field(
+        description="Reasoning behind the evaluation",
+        default=""
+    )
+    suggestions: List[str] = Field(
+        description="Suggestions for improvement",
+        default=[]
+    )
+
+
+class RefinerResponseSchema(BaseModel):
+    """Schema for refiner agent responses"""
+    refined_content: str = Field(
+        description="Refined content",
+        min_length=1
+    )
+    changes_made: List[str] = Field(
+        description="List of changes made",
+        default=[]
+    )
+    reasoning: str = Field(
+        description="Reasoning behind refinements",
+        default=""
+    )
+    confidence: float = Field(
+        description="Confidence in refinements",
+        ge=0.0,
+        le=1.0
+    )
+    feedback_addressed: List[str] = Field(
+        description="Feedback points addressed",
+        default=[]
+    ) 
\ No newline at end of file