diff --git a/CHANGES_SUMMARY.md b/CHANGES_SUMMARY.md
deleted file mode 100644
index 73863bdc..00000000
--- a/CHANGES_SUMMARY.md
+++ /dev/null
@@ -1,172 +0,0 @@
-# Fix for Issue #936: Agent Tool Usage with Streaming Enabled
-
-## Problem Summary
-- Agent tool usage callable doesn't work with streaming enabled
-- Works without streaming well
-- Tool execution logging disappeared
-
-## Root Cause Analysis
-When streaming is enabled, the LLM response chunks are collected as plain text strings, losing the structured API response format that contains tool call metadata. The `tool_struct.execute_function_calls_from_api_response()` method expects structured responses with `tool_calls` attributes, but streaming responses only contained concatenated text content.
-
-## Solution Implementation
-
-### 1. Created StreamingToolResponse Class
-**File**: `swarms/structs/agent.py` (lines 91-104)
-
-```python
-class StreamingToolResponse:
-    """
-    Response wrapper that preserves both content and tool calls from streaming responses.
-    This enables tool execution when streaming is enabled.
-    """
-    def __init__(self, content: str, tool_calls: List[Any] = None):
-        self.content = content
-        self.tool_calls = tool_calls or []
-    
-    def __str__(self):
-        return self.content
-    
-    def __repr__(self):
-        return f"StreamingToolResponse(content='{self.content[:50]}...', tool_calls={len(self.tool_calls)} calls)"
-```
-
-**Purpose**: Replace dynamic type creation with a proper class that preserves both content and tool calls from streaming responses.
-
-### 2. Enhanced Streaming Chunk Processing
-**File**: `swarms/structs/agent.py` (lines 2600-2690)
-
-**Modified all three streaming paths in `call_llm` method**:
-
-#### A. Streaming with Callback (lines 2615-2625)
-```python
-# Preserve tool calls from streaming chunks
-try:
-    if (hasattr(chunk, "choices") and 
-        len(chunk.choices) > 0 and
-        hasattr(chunk.choices[0], "delta") and
-        hasattr(chunk.choices[0].delta, "tool_calls") and 
-        chunk.choices[0].delta.tool_calls):
-        tool_calls.extend(chunk.choices[0].delta.tool_calls)
-except (AttributeError, IndexError) as e:
-    logger.debug(f"Could not extract tool calls from chunk: {e}")
-```
-
-#### B. Silent Streaming (lines 2636-2646)
-- Same tool call preservation logic as above
-- Maintains streaming behavior while capturing tool calls
-
-#### C. Streaming Panel (lines 2658-2688)
-```python
-# Create a tool-aware streaming processor to preserve tool calls
-def tool_aware_streaming(stream_response):
-    for chunk in stream_response:
-        # Preserve tool calls from streaming chunks with error handling
-        try:
-            if (hasattr(chunk, "choices") and 
-                len(chunk.choices) > 0 and
-                hasattr(chunk.choices[0], "delta") and
-                hasattr(chunk.choices[0].delta, "tool_calls") and 
-                chunk.choices[0].delta.tool_calls):
-                tool_calls.extend(chunk.choices[0].delta.tool_calls)
-        except (AttributeError, IndexError) as e:
-            logger.debug(f"Could not extract tool calls from chunk: {e}")
-        yield chunk
-```
-
-**Purpose**: Prevents iterator consumption bug while preserving tool calls.
-
-### 3. Enhanced Tool Execution Logging
-**File**: `swarms/structs/agent.py` (lines 3109-3133)
-
-```python
-# Add tool execution logging
-logger.info(f"Starting tool execution for agent '{self.agent_name}' in loop {loop_count}")
-
-# Enhanced retry logic with logging
-try:
-    output = self.tool_struct.execute_function_calls_from_api_response(response)
-except Exception as e:
-    logger.warning(f"First attempt at tool execution failed: {e}. Retrying...")
-    try:
-        output = self.tool_struct.execute_function_calls_from_api_response(response)
-    except Exception as retry_error:
-        logger.error(f"Tool execution failed after retry: {retry_error}")
-        if output is None:
-            raise retry_error
-
-# Log successful tool execution
-if output is not None:
-    logger.info(f"Tool execution successful for agent '{self.agent_name}' in loop {loop_count}. Output length: {len(str(output)) if output else 0}")
-else:
-    logger.warning(f"Tool execution completed but returned None output for agent '{self.agent_name}' in loop {loop_count}")
-```
-
-**Purpose**: Restore missing tool execution logging with comprehensive status reporting.
-
-## Key Improvements
-
-### 1. **Robust Error Handling**
-- Added try-catch blocks around tool call extraction
-- Graceful handling of malformed chunks
-- Protection against `AttributeError` and `IndexError`
-
-### 2. **Iterator Safety**
-- Fixed streaming iterator consumption bug
-- Proper generator pattern to avoid iterator exhaustion
-
-### 3. **Comprehensive Logging**
-- Tool execution start/success/failure logging
-- Retry attempt logging
-- Debug-level logging for chunk processing errors
-
-### 4. **Backward Compatibility**
-- No changes to existing non-streaming behavior
-- Maintains all existing API contracts
-- Falls back gracefully when no tool calls present
-
-## Testing
-
-Created two test files:
-
-### 1. `test_streaming_tools.py`
-- Tests streaming behavior with and without tools
-- Validates tool execution occurs with streaming enabled
-- Checks memory history for tool execution evidence
-
-### 2. `test_original_issue.py`
-- Reproduces exact code from GitHub issue #936
-- Uses original function signatures and agent configuration
-- Validates the specific use case reported in the issue
-
-## Files Modified
-
-1. **`swarms/structs/agent.py`**
-   - Added `StreamingToolResponse` class
-   - Enhanced streaming chunk processing in `call_llm` method
-   - Improved tool execution logging in `execute_tools` method
-
-2. **Created Test Files**
-   - `test_streaming_tools.py` - Comprehensive streaming + tool tests
-   - `test_original_issue.py` - Reproduction of original issue scenario
-
-## Verification
-
-The solution addresses both reported issues:
-
-✅ **Tool Usage with Streaming**: Tool calls are now preserved and executed when streaming is enabled
-
-✅ **Tool Execution Logging**: Comprehensive logging is now present throughout the tool execution process
-
-## Edge Cases Handled
-
-1. **Malformed Chunks**: Graceful error handling prevents crashes
-2. **Empty Tool Calls**: Proper validation before processing
-3. **Iterator Consumption**: Safe streaming processing without iterator exhaustion
-4. **Mixed Content**: Handles chunks with both content and tool calls
-5. **Multiple Tool Calls**: Supports multiple tool calls in single or multiple chunks
-
-## Performance Impact
-
-- **Minimal**: Only additional memory for tool call arrays during streaming
-- **Efficient**: Tool call extraction only occurs when chunks contain them
-- **Scalable**: Handles multiple concurrent streaming agents safely
\ No newline at end of file
diff --git a/test_original_issue.py b/test_original_issue.py
deleted file mode 100644
index 7c346df5..00000000
--- a/test_original_issue.py
+++ /dev/null
@@ -1,173 +0,0 @@
-#!/usr/bin/env python3
-"""
-Reproduce the exact code from GitHub issue #936 to test the fix.
-"""
-
-from typing import List
-import http.client
-import json
-from swarms import Agent
-import os
-
-
-def get_realtor_data_from_one_source(location: str):
-    """
-    Fetch rental property data from the Realtor API for a specified location.
-
-    Args:
-        location (str): The location to search for rental properties (e.g., "Menlo Park, CA")
-
-    Returns:
-        str: JSON-formatted string containing rental property data
-
-    Raises:
-        http.client.HTTPException: If the API request fails
-        json.JSONDecodeError: If the response cannot be parsed as JSON
-    """
-    # Mock implementation since we don't have API key
-    return json.dumps({
-        "properties": [
-            {
-                "name": f"Sample Property in {location}",
-                "address": f"123 Main St, {location}",
-                "price": 2800,
-                "bedrooms": 2,
-                "bathrooms": 1
-            }
-        ]
-    }, indent=2)
-
-
-def get_realtor_data_from_multiple_sources(locations: List[str]):
-    """
-    Fetch rental property data from multiple sources for a specified location.
-
-    Args:
-        location (List[str]): List of locations to search for rental properties (e.g., ["Menlo Park, CA", "Palo Alto, CA"])
-    """
-    output = []
-    for location in locations:
-        data = get_realtor_data_from_one_source(location)
-        output.append(data)
-    return output
-
-
-def test_original_issue():
-    """Test the exact scenario from the GitHub issue"""
-    print("🧪 Testing Original Issue #936 Code...")
-    
-    agent = Agent(
-        agent_name="Rental-Property-Specialist",
-        system_prompt="""
-        You are an expert rental property specialist with deep expertise in real estate analysis and tenant matching. Your core responsibilities include:
-    1. Property Analysis & Evaluation
-       - Analyze rental property features and amenities
-       - Evaluate location benefits and drawbacks
-       - Assess property condition and maintenance needs
-       - Compare rental rates with market standards
-       - Review lease terms and conditions
-       - Identify potential red flags or issues
-
-    2. Location Assessment
-       - Analyze neighborhood safety and demographics
-       - Evaluate proximity to amenities (schools, shopping, transit)
-       - Research local market trends and development plans
-       - Consider noise levels and traffic patterns
-       - Assess parking availability and restrictions
-       - Review zoning regulations and restrictions
-
-    3. Financial Analysis
-       - Calculate price-to-rent ratios
-       - Analyze utility costs and included services
-       - Evaluate security deposit requirements
-       - Consider additional fees (pet rent, parking, etc.)
-       - Compare with similar properties in the area
-       - Assess potential for rent increases
-
-    4. Tenant Matching
-       - Match properties to tenant requirements
-       - Consider commute distances
-       - Evaluate pet policies and restrictions
-       - Assess lease term flexibility
-       - Review application requirements
-       - Consider special accommodations needed
-
-    5. Documentation & Compliance
-       - Review lease agreement terms
-       - Verify property certifications
-       - Check compliance with local regulations
-       - Assess insurance requirements
-       - Review maintenance responsibilities
-       - Document property condition
-
-    When analyzing properties, always consider:
-    - Value for money
-    - Location quality
-    - Property condition
-    - Lease terms fairness
-    - Safety and security
-    - Maintenance and management quality
-    - Future market potential
-    - Tenant satisfaction factors
-
-    Provide clear, objective analysis while maintaining professional standards and ethical considerations.""",
-        model_name="claude-3-sonnet-20240229",
-        max_loops=2,
-        verbose=True,
-        streaming_on=True,  # THIS WAS CAUSING THE ISSUE
-        print_on=True,
-        tools=[get_realtor_data_from_multiple_sources],
-        api_key=os.getenv("ANTHROPIC_API_KEY"),  # Use appropriate API key
-    )
-
-    task = "What are the best properties in Menlo park and palo alto for rent under 3,000$"
-    
-    try:
-        print(f"📝 Running task: {task}")
-        print("🔄 With streaming=True and tools enabled...")
-        
-        result = agent.run(task)
-        
-        print(f"\n✅ Result: {result}")
-        
-        # Check if tool was executed
-        memory_history = agent.short_memory.return_history_as_string()
-        
-        if "Tool Executor" in memory_history:
-            print("\n✅ SUCCESS: Tool was executed successfully with streaming enabled!")
-            print("🎉 Issue #936 appears to be FIXED!")
-            return True
-        else:
-            print("\n❌ FAILURE: Tool execution was not detected")
-            print("🚨 Issue #936 is NOT fixed yet")
-            print("\nMemory History:")
-            print(memory_history)
-            return False
-            
-    except Exception as e:
-        print(f"\n❌ FAILURE: Exception occurred: {e}")
-        import traceback
-        traceback.print_exc()
-        return False
-
-
-if __name__ == "__main__":
-    print("🔧 Testing the Exact Code from GitHub Issue #936")
-    print("=" * 60)
-    
-    # Check if API key is available
-    if not os.getenv("ANTHROPIC_API_KEY") and not os.getenv("OPENAI_API_KEY"):
-        print("⚠️  Warning: Neither ANTHROPIC_API_KEY nor OPENAI_API_KEY are set.")
-        print("Setting a dummy key for testing purposes...")
-        os.environ["ANTHROPIC_API_KEY"] = "dummy-key-for-testing"
-    
-    success = test_original_issue()
-    
-    print("\n" + "=" * 60)
-    if success:
-        print("🎉 SUCCESS: The original issue appears to be RESOLVED!")
-        print("✅ Agent tool usage now works with streaming enabled")
-        print("✅ Tool execution logging is now present")
-    else:
-        print("❌ FAILURE: The original issue is NOT fully resolved yet")
-        print("🔧 Additional fixes may be needed")
\ No newline at end of file
diff --git a/test_streaming_tools.py b/test_streaming_tools.py
deleted file mode 100644
index 0c3db250..00000000
--- a/test_streaming_tools.py
+++ /dev/null
@@ -1,164 +0,0 @@
-#!/usr/bin/env python3
-"""
-Test script to reproduce and verify the fix for issue #936:
-Agent tool usage fails when streaming is enabled.
-"""
-
-from typing import List
-import json
-from swarms import Agent
-import os
-import logging
-
-# Set up logging to see the tool execution logs
-logging.basicConfig(level=logging.INFO)
-logger = logging.getLogger(__name__)
-
-
-def simple_calculator_tool(operation: str, num1: float, num2: float) -> str:
-    """
-    Simple calculator tool for testing.
-    
-    Args:
-        operation: The operation to perform (add, subtract, multiply, divide)
-        num1: First number
-        num2: Second number
-    
-    Returns:
-        str: Result of the calculation
-    """
-    logger.info(f"Calculator tool called: {operation}({num1}, {num2})")
-    
-    if operation == "add":
-        result = num1 + num2
-    elif operation == "subtract":
-        result = num1 - num2
-    elif operation == "multiply":
-        result = num1 * num2
-    elif operation == "divide":
-        if num2 == 0:
-            return "Error: Division by zero"
-        result = num1 / num2
-    else:
-        return f"Error: Unknown operation {operation}"
-    
-    return f"The result of {operation}({num1}, {num2}) is {result}"
-
-
-def test_agent_streaming_with_tools():
-    """Test that agent can use tools when streaming is enabled"""
-    
-    print("🧪 Testing Agent with Streaming + Tools...")
-    
-    # Create agent with streaming enabled
-    agent = Agent(
-        agent_name="Calculator-Agent",
-        system_prompt="""
-        You are a helpful calculator assistant. When asked to perform calculations,
-        use the simple_calculator_tool to compute the result.
-        
-        Available tool:
-        - simple_calculator_tool(operation, num1, num2): Performs basic calculations
-        
-        Always use the tool for calculations instead of doing them yourself.
-        """,
-        model_name="gpt-3.5-turbo",  # Using a common model for testing
-        max_loops=2,  # Allow for tool execution + response
-        verbose=True,
-        streaming_on=True,  # THIS IS THE KEY - streaming enabled
-        print_on=True,
-        tools=[simple_calculator_tool],
-        # Add any necessary API keys from environment
-        api_key=os.getenv("OPENAI_API_KEY"),
-    )
-    
-    # Test task that should trigger tool usage
-    task = "Please calculate 25 + 17 for me using the calculator tool"
-    
-    print(f"\n📝 Task: {task}")
-    print("\n🔄 Running agent with streaming + tools...")
-    
-    try:
-        result = agent.run(task)
-        print(f"\n✅ Result: {result}")
-        
-        # Check if the tool was actually executed by looking at memory
-        memory_history = agent.short_memory.return_history_as_string()
-        
-        if "Tool Executor" in memory_history:
-            print("✅ SUCCESS: Tool execution found in memory history!")
-            return True
-        else:
-            print("❌ FAILURE: No tool execution found in memory history")
-            print("Memory history:")
-            print(memory_history)
-            return False
-            
-    except Exception as e:
-        print(f"❌ FAILURE: Exception occurred: {e}")
-        import traceback
-        traceback.print_exc()
-        return False
-
-
-def test_agent_streaming_without_tools():
-    """Test that agent works normally when streaming is enabled but no tools needed"""
-    
-    print("\n🧪 Testing Agent with Streaming (No Tools)...")
-    
-    agent = Agent(
-        agent_name="Simple-Agent",
-        system_prompt="You are a helpful assistant.",
-        model_name="gpt-3.5-turbo",
-        max_loops=1,
-        verbose=True,
-        streaming_on=True,  # Streaming enabled
-        print_on=True,
-        api_key=os.getenv("OPENAI_API_KEY"),
-    )
-    
-    task = "What is the capital of France?"
-    
-    print(f"\n📝 Task: {task}")
-    print("\n🔄 Running agent with streaming (no tools)...")
-    
-    try:
-        result = agent.run(task)
-        print(f"\n✅ Result: {result}")
-        
-        if "Paris" in str(result):
-            print("✅ SUCCESS: Agent responded correctly without tools")
-            return True
-        else:
-            print("❌ FAILURE: Agent didn't provide expected response")
-            return False
-            
-    except Exception as e:
-        print(f"❌ FAILURE: Exception occurred: {e}")
-        import traceback
-        traceback.print_exc()
-        return False
-
-
-if __name__ == "__main__":
-    print("🔧 Testing Fix for Issue #936: Agent Tool Usage with Streaming")
-    print("=" * 60)
-    
-    # Check if API key is available
-    if not os.getenv("OPENAI_API_KEY"):
-        print("⚠️  Warning: OPENAI_API_KEY not set. Tests may fail.")
-        print("Please set OPENAI_API_KEY environment variable to run tests.")
-    
-    # Run tests
-    test1_passed = test_agent_streaming_without_tools()
-    test2_passed = test_agent_streaming_with_tools()
-    
-    print("\n" + "=" * 60)
-    print("📊 Test Results:")
-    print(f"  Test 1 (Streaming without tools): {'✅ PASSED' if test1_passed else '❌ FAILED'}")
-    print(f"  Test 2 (Streaming with tools):    {'✅ PASSED' if test2_passed else '❌ FAILED'}")
-    
-    if test1_passed and test2_passed:
-        print("\n🎉 ALL TESTS PASSED! The fix appears to be working correctly.")
-    else:
-        print("\n⚠️  SOME TESTS FAILED! The fix may need additional work.")
\ No newline at end of file