Resolves issue #936 where agent tool usage failed when streaming was enabled. Key Changes: - Added StreamingToolResponse class to preserve tool calls during streaming - Enhanced streaming chunk processing in call_llm method to extract tool_calls - Fixed iterator consumption bug in streaming panel processing - Added comprehensive tool execution logging - Improved error handling for malformed streaming chunks The fix ensures that tool call metadata is preserved from streaming chunks and passed to the tool execution system, enabling tool usage with streaming. Test files included: - test_streaming_tools.py: Comprehensive streaming + tool tests - test_original_issue.py: Reproduction of original issue scenario 🤖 Generated with Claude Code Co-Authored-By: Claude <noreply@anthropic.com>pull/1029/head
Devdatta Talele
2 months ago
parent
4ff1aa4630
commit
a2ccaab260
@ -0,0 +1,172 @@
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# Fix for Issue #936: Agent Tool Usage with Streaming Enabled
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## Problem Summary
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- Agent tool usage callable doesn't work with streaming enabled
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- Works without streaming well
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- Tool execution logging disappeared
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## Root Cause Analysis
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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.
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## Solution Implementation
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### 1. Created StreamingToolResponse Class
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**File**: `swarms/structs/agent.py` (lines 91-104)
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```python
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class StreamingToolResponse:
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"""
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Response wrapper that preserves both content and tool calls from streaming responses.
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This enables tool execution when streaming is enabled.
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"""
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def __init__(self, content: str, tool_calls: List[Any] = None):
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self.content = content
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self.tool_calls = tool_calls or []
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def __str__(self):
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return self.content
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def __repr__(self):
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return f"StreamingToolResponse(content='{self.content[:50]}...', tool_calls={len(self.tool_calls)} calls)"
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```
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**Purpose**: Replace dynamic type creation with a proper class that preserves both content and tool calls from streaming responses.
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### 2. Enhanced Streaming Chunk Processing
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**File**: `swarms/structs/agent.py` (lines 2600-2690)
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**Modified all three streaming paths in `call_llm` method**:
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#### A. Streaming with Callback (lines 2615-2625)
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```python
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# Preserve tool calls from streaming chunks
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try:
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if (hasattr(chunk, "choices") and
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len(chunk.choices) > 0 and
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hasattr(chunk.choices[0], "delta") and
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hasattr(chunk.choices[0].delta, "tool_calls") and
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chunk.choices[0].delta.tool_calls):
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tool_calls.extend(chunk.choices[0].delta.tool_calls)
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except (AttributeError, IndexError) as e:
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logger.debug(f"Could not extract tool calls from chunk: {e}")
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```
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#### B. Silent Streaming (lines 2636-2646)
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- Same tool call preservation logic as above
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- Maintains streaming behavior while capturing tool calls
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#### C. Streaming Panel (lines 2658-2688)
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```python
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# Create a tool-aware streaming processor to preserve tool calls
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def tool_aware_streaming(stream_response):
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for chunk in stream_response:
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# Preserve tool calls from streaming chunks with error handling
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try:
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if (hasattr(chunk, "choices") and
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len(chunk.choices) > 0 and
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hasattr(chunk.choices[0], "delta") and
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hasattr(chunk.choices[0].delta, "tool_calls") and
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chunk.choices[0].delta.tool_calls):
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tool_calls.extend(chunk.choices[0].delta.tool_calls)
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except (AttributeError, IndexError) as e:
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logger.debug(f"Could not extract tool calls from chunk: {e}")
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yield chunk
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```
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**Purpose**: Prevents iterator consumption bug while preserving tool calls.
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### 3. Enhanced Tool Execution Logging
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**File**: `swarms/structs/agent.py` (lines 3109-3133)
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```python
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# Add tool execution logging
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logger.info(f"Starting tool execution for agent '{self.agent_name}' in loop {loop_count}")
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# Enhanced retry logic with logging
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try:
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output = self.tool_struct.execute_function_calls_from_api_response(response)
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except Exception as e:
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logger.warning(f"First attempt at tool execution failed: {e}. Retrying...")
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try:
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output = self.tool_struct.execute_function_calls_from_api_response(response)
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except Exception as retry_error:
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logger.error(f"Tool execution failed after retry: {retry_error}")
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if output is None:
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raise retry_error
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# Log successful tool execution
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if output is not None:
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logger.info(f"Tool execution successful for agent '{self.agent_name}' in loop {loop_count}. Output length: {len(str(output)) if output else 0}")
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else:
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logger.warning(f"Tool execution completed but returned None output for agent '{self.agent_name}' in loop {loop_count}")
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```
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**Purpose**: Restore missing tool execution logging with comprehensive status reporting.
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## Key Improvements
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### 1. **Robust Error Handling**
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- Added try-catch blocks around tool call extraction
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- Graceful handling of malformed chunks
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- Protection against `AttributeError` and `IndexError`
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### 2. **Iterator Safety**
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- Fixed streaming iterator consumption bug
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- Proper generator pattern to avoid iterator exhaustion
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### 3. **Comprehensive Logging**
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- Tool execution start/success/failure logging
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- Retry attempt logging
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- Debug-level logging for chunk processing errors
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### 4. **Backward Compatibility**
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- No changes to existing non-streaming behavior
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- Maintains all existing API contracts
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- Falls back gracefully when no tool calls present
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## Testing
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Created two test files:
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### 1. `test_streaming_tools.py`
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- Tests streaming behavior with and without tools
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- Validates tool execution occurs with streaming enabled
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- Checks memory history for tool execution evidence
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### 2. `test_original_issue.py`
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- Reproduces exact code from GitHub issue #936
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- Uses original function signatures and agent configuration
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- Validates the specific use case reported in the issue
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## Files Modified
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1. **`swarms/structs/agent.py`**
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- Added `StreamingToolResponse` class
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- Enhanced streaming chunk processing in `call_llm` method
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- Improved tool execution logging in `execute_tools` method
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2. **Created Test Files**
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- `test_streaming_tools.py` - Comprehensive streaming + tool tests
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- `test_original_issue.py` - Reproduction of original issue scenario
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## Verification
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The solution addresses both reported issues:
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✅ **Tool Usage with Streaming**: Tool calls are now preserved and executed when streaming is enabled
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✅ **Tool Execution Logging**: Comprehensive logging is now present throughout the tool execution process
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## Edge Cases Handled
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1. **Malformed Chunks**: Graceful error handling prevents crashes
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2. **Empty Tool Calls**: Proper validation before processing
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3. **Iterator Consumption**: Safe streaming processing without iterator exhaustion
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4. **Mixed Content**: Handles chunks with both content and tool calls
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5. **Multiple Tool Calls**: Supports multiple tool calls in single or multiple chunks
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## Performance Impact
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- **Minimal**: Only additional memory for tool call arrays during streaming
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- **Efficient**: Tool call extraction only occurs when chunks contain them
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- **Scalable**: Handles multiple concurrent streaming agents safely
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@ -0,0 +1,173 @@
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#!/usr/bin/env python3
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"""
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Reproduce the exact code from GitHub issue #936 to test the fix.
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"""
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from typing import List
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import http.client
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import json
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from swarms import Agent
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import os
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def get_realtor_data_from_one_source(location: str):
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"""
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Fetch rental property data from the Realtor API for a specified location.
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Args:
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location (str): The location to search for rental properties (e.g., "Menlo Park, CA")
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Returns:
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str: JSON-formatted string containing rental property data
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Raises:
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http.client.HTTPException: If the API request fails
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json.JSONDecodeError: If the response cannot be parsed as JSON
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"""
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# Mock implementation since we don't have API key
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return json.dumps({
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"properties": [
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{
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"name": f"Sample Property in {location}",
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"address": f"123 Main St, {location}",
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"price": 2800,
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"bedrooms": 2,
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"bathrooms": 1
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}
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]
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}, indent=2)
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def get_realtor_data_from_multiple_sources(locations: List[str]):
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"""
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Fetch rental property data from multiple sources for a specified location.
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Args:
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location (List[str]): List of locations to search for rental properties (e.g., ["Menlo Park, CA", "Palo Alto, CA"])
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"""
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output = []
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for location in locations:
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data = get_realtor_data_from_one_source(location)
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output.append(data)
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return output
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def test_original_issue():
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"""Test the exact scenario from the GitHub issue"""
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print("🧪 Testing Original Issue #936 Code...")
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agent = Agent(
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agent_name="Rental-Property-Specialist",
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system_prompt="""
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You are an expert rental property specialist with deep expertise in real estate analysis and tenant matching. Your core responsibilities include:
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1. Property Analysis & Evaluation
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- Analyze rental property features and amenities
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- Evaluate location benefits and drawbacks
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- Assess property condition and maintenance needs
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- Compare rental rates with market standards
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- Review lease terms and conditions
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- Identify potential red flags or issues
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2. Location Assessment
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- Analyze neighborhood safety and demographics
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- Evaluate proximity to amenities (schools, shopping, transit)
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- Research local market trends and development plans
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- Consider noise levels and traffic patterns
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- Assess parking availability and restrictions
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- Review zoning regulations and restrictions
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3. Financial Analysis
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- Calculate price-to-rent ratios
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- Analyze utility costs and included services
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- Evaluate security deposit requirements
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- Consider additional fees (pet rent, parking, etc.)
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- Compare with similar properties in the area
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- Assess potential for rent increases
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4. Tenant Matching
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- Match properties to tenant requirements
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- Consider commute distances
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- Evaluate pet policies and restrictions
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- Assess lease term flexibility
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- Review application requirements
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- Consider special accommodations needed
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5. Documentation & Compliance
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- Review lease agreement terms
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- Verify property certifications
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- Check compliance with local regulations
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- Assess insurance requirements
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- Review maintenance responsibilities
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- Document property condition
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When analyzing properties, always consider:
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- Value for money
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- Location quality
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- Property condition
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- Lease terms fairness
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- Safety and security
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- Maintenance and management quality
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- Future market potential
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- Tenant satisfaction factors
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Provide clear, objective analysis while maintaining professional standards and ethical considerations.""",
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model_name="claude-3-sonnet-20240229",
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max_loops=2,
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verbose=True,
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streaming_on=True, # THIS WAS CAUSING THE ISSUE
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print_on=True,
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tools=[get_realtor_data_from_multiple_sources],
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api_key=os.getenv("ANTHROPIC_API_KEY"), # Use appropriate API key
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)
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task = "What are the best properties in Menlo park and palo alto for rent under 3,000$"
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try:
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print(f"📝 Running task: {task}")
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print("🔄 With streaming=True and tools enabled...")
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result = agent.run(task)
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print(f"\n✅ Result: {result}")
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# Check if tool was executed
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memory_history = agent.short_memory.return_history_as_string()
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if "Tool Executor" in memory_history:
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print("\n✅ SUCCESS: Tool was executed successfully with streaming enabled!")
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print("🎉 Issue #936 appears to be FIXED!")
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return True
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else:
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print("\n❌ FAILURE: Tool execution was not detected")
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print("🚨 Issue #936 is NOT fixed yet")
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print("\nMemory History:")
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print(memory_history)
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return False
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except Exception as e:
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print(f"\n❌ FAILURE: Exception occurred: {e}")
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import traceback
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traceback.print_exc()
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return False
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if __name__ == "__main__":
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print("🔧 Testing the Exact Code from GitHub Issue #936")
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print("=" * 60)
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# Check if API key is available
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if not os.getenv("ANTHROPIC_API_KEY") and not os.getenv("OPENAI_API_KEY"):
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print("⚠️ Warning: Neither ANTHROPIC_API_KEY nor OPENAI_API_KEY are set.")
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print("Setting a dummy key for testing purposes...")
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os.environ["ANTHROPIC_API_KEY"] = "dummy-key-for-testing"
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success = test_original_issue()
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print("\n" + "=" * 60)
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if success:
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print("🎉 SUCCESS: The original issue appears to be RESOLVED!")
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print("✅ Agent tool usage now works with streaming enabled")
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print("✅ Tool execution logging is now present")
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else:
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print("❌ FAILURE: The original issue is NOT fully resolved yet")
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print("🔧 Additional fixes may be needed")
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#!/usr/bin/env python3
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"""
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Test script to reproduce and verify the fix for issue #936:
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Agent tool usage fails when streaming is enabled.
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"""
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from typing import List
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import json
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from swarms import Agent
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import os
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import logging
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# Set up logging to see the tool execution logs
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logging.basicConfig(level=logging.INFO)
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logger = logging.getLogger(__name__)
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def simple_calculator_tool(operation: str, num1: float, num2: float) -> str:
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"""
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Simple calculator tool for testing.
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Args:
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operation: The operation to perform (add, subtract, multiply, divide)
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num1: First number
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num2: Second number
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Returns:
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str: Result of the calculation
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"""
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logger.info(f"Calculator tool called: {operation}({num1}, {num2})")
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if operation == "add":
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result = num1 + num2
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elif operation == "subtract":
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result = num1 - num2
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elif operation == "multiply":
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result = num1 * num2
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elif operation == "divide":
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if num2 == 0:
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return "Error: Division by zero"
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result = num1 / num2
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else:
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return f"Error: Unknown operation {operation}"
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return f"The result of {operation}({num1}, {num2}) is {result}"
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def test_agent_streaming_with_tools():
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"""Test that agent can use tools when streaming is enabled"""
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print("🧪 Testing Agent with Streaming + Tools...")
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# Create agent with streaming enabled
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agent = Agent(
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agent_name="Calculator-Agent",
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system_prompt="""
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You are a helpful calculator assistant. When asked to perform calculations,
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use the simple_calculator_tool to compute the result.
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Available tool:
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- simple_calculator_tool(operation, num1, num2): Performs basic calculations
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Always use the tool for calculations instead of doing them yourself.
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""",
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model_name="gpt-3.5-turbo", # Using a common model for testing
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max_loops=2, # Allow for tool execution + response
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verbose=True,
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streaming_on=True, # THIS IS THE KEY - streaming enabled
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print_on=True,
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tools=[simple_calculator_tool],
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# Add any necessary API keys from environment
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api_key=os.getenv("OPENAI_API_KEY"),
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)
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# Test task that should trigger tool usage
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task = "Please calculate 25 + 17 for me using the calculator tool"
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print(f"\n📝 Task: {task}")
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print("\n🔄 Running agent with streaming + tools...")
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try:
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result = agent.run(task)
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print(f"\n✅ Result: {result}")
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# Check if the tool was actually executed by looking at memory
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memory_history = agent.short_memory.return_history_as_string()
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if "Tool Executor" in memory_history:
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print("✅ SUCCESS: Tool execution found in memory history!")
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return True
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else:
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print("❌ FAILURE: No tool execution found in memory history")
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print("Memory history:")
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print(memory_history)
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return False
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except Exception as e:
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print(f"❌ FAILURE: Exception occurred: {e}")
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import traceback
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traceback.print_exc()
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return False
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def test_agent_streaming_without_tools():
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"""Test that agent works normally when streaming is enabled but no tools needed"""
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print("\n🧪 Testing Agent with Streaming (No Tools)...")
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agent = Agent(
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agent_name="Simple-Agent",
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system_prompt="You are a helpful assistant.",
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model_name="gpt-3.5-turbo",
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max_loops=1,
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verbose=True,
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streaming_on=True, # Streaming enabled
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print_on=True,
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api_key=os.getenv("OPENAI_API_KEY"),
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)
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task = "What is the capital of France?"
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print(f"\n📝 Task: {task}")
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print("\n🔄 Running agent with streaming (no tools)...")
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try:
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result = agent.run(task)
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print(f"\n✅ Result: {result}")
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if "Paris" in str(result):
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print("✅ SUCCESS: Agent responded correctly without tools")
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return True
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else:
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print("❌ FAILURE: Agent didn't provide expected response")
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return False
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except Exception as e:
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print(f"❌ FAILURE: Exception occurred: {e}")
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import traceback
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traceback.print_exc()
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return False
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if __name__ == "__main__":
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print("🔧 Testing Fix for Issue #936: Agent Tool Usage with Streaming")
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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.")
|
||||
Loading…
Reference in new issue