# 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