diff --git a/swarms/schemas/hierarchical_structured_communication_schemas.py b/swarms/schemas/hierarchical_structured_communication_schemas.py
deleted file mode 100644
index bf18fdff..00000000
--- a/swarms/schemas/hierarchical_structured_communication_schemas.py
+++ /dev/null
@@ -1,430 +0,0 @@
-"""
-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