adjust tests

tests/agent_test.py

@@ -18,6 +18,7 @@ from crewai.tools.tool_calling import InstructorToolCalling
 from crewai.tools.tool_usage import ToolUsage
 from crewai.utilities import RPMController
 from crewai.utilities.events import crewai_event_bus
+from crewai.utilities.events.llm_events import LLMStreamChunkEvent
 from crewai.utilities.events.tool_usage_events import ToolUsageFinishedEvent
 
 
@@ -259,9 +260,7 @@ def test_cache_hitting():
     def handle_tool_end(source, event):
         received_events.append(event)
 
-    with (
+    with (patch.object(CacheHandler, "read") as read,):
-        patch.object(CacheHandler, "read") as read,
-    ):
         read.return_value = "0"
         task = Task(
             description="What is 2 times 6? Ignore correctness and just return the result of the multiplication tool, you must use the tool.",
@@ -1798,3 +1797,66 @@ def test_litellm_anthropic_error_handling():
 
     # Verify the LLM call was only made once (no retries)
     mock_llm_call.assert_called_once()
+
+
+@pytest.mark.vcr(filter_headers=["authorization"])
+def test_agent_streaming_with_tool_calling():
+    """Test that streaming works correctly with tool calling."""
+    received_chunks = []
+    tool_called = False
+
+    # Set up event listener to capture streaming chunks
+    with crewai_event_bus.scoped_handlers():
+
+        @crewai_event_bus.on(LLMStreamChunkEvent)
+        def handle_stream_chunk(source, event):
+            received_chunks.append(event.chunk)
+
+        # Define a tool that will be called
+        @tool
+        def calculator(expression: str) -> str:
+            """Calculate the result of a mathematical expression."""
+            nonlocal tool_called
+            tool_called = True
+            try:
+                result = eval(expression)
+                return f"The result of {expression} is {result}"
+            except Exception as e:
+                return f"Error calculating {expression}: {str(e)}"
+
+        # Create an agent with streaming enabled
+        agent = Agent(
+            role="Math Assistant",
+            goal="Help users with mathematical calculations",
+            backstory="I am an AI assistant specialized in mathematics.",
+            verbose=True,
+            llm=LLM(model="gpt-4o", stream=True),  # Enable streaming
+        )
+
+        # Create a task that will require tool calling
+        task = Task(
+            description="Calculate 23 * 45 using the calculator tool.",
+            agent=agent,
+            expected_output="The result of the calculation.",
+        )
+
+        # Execute the task with the calculator tool
+        output = agent.execute_task(task=task, tools=[calculator])
+
+        # Verify that streaming chunks were received
+        assert len(received_chunks) > 0, "No streaming chunks were received"
+
+        # Verify that the tool was called
+        assert tool_called, "Calculator tool was not called"
+
+        # Verify that the output contains the correct result
+        assert "1035" in output, f"Output does not contain the correct result: {output}"
+
+        # Verify that the streaming chunks form a coherent response
+        combined_chunks = "".join(received_chunks)
+        assert len(combined_chunks) > 0, "Combined chunks are empty"
+
+        # Log for debugging
+        print(f"Received {len(received_chunks)} chunks")
+        print(f"First few chunks: {received_chunks[:3]}")
+        print(f"Output: {output}")

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tests/utilities/test_events.py

@@ -630,7 +630,7 @@ def test_llm_emits_stream_chunk_events():
             received_chunks.append(event.chunk)
 
         # Create an LLM with streaming enabled
-        llm = LLM(model="gpt-3.5-turbo", stream=True)
+        llm = LLM(model="gpt-4o", stream=True)
 
         # Call the LLM with a simple message
         response = llm.call("Tell me a short joke")
@@ -654,7 +654,7 @@ def test_llm_no_stream_chunks_when_streaming_disabled():
             received_chunks.append(event.chunk)
 
         # Create an LLM with streaming disabled
-        llm = LLM(model="gpt-3.5-turbo", stream=False)
+        llm = LLM(model="gpt-4o", stream=False)
 
         # Call the LLM with a simple message
         response = llm.call("Tell me a short joke")
@@ -666,89 +666,6 @@ def test_llm_no_stream_chunks_when_streaming_disabled():
         assert response and isinstance(response, str)
 
 
-@pytest.mark.vcr(filter_headers=["authorization"])
-def test_llm_tool_calling_with_streaming():
-    """Test that tool calling works correctly with streaming enabled."""
-    received_chunks = []
-    tool_called = False
-
-    def sample_tool(text: str) -> str:
-        nonlocal tool_called
-        tool_called = True
-        return f"Tool processed: {text}"
-
-    available_functions = {"sample_tool": sample_tool}
-
-    tools = [
-        {
-            "type": "function",
-            "function": {
-                "name": "sample_tool",
-                "description": "A sample tool that processes text",
-                "parameters": {
-                    "type": "object",
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-                        "text": {"type": "string", "description": "The text to process"}
-                    },
-                    "required": ["text"],
-                },
-            },
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-    ]
-
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-
-        @crewai_event_bus.on(LLMStreamChunkEvent)
-        def handle_stream_chunk(source, event):
-            received_chunks.append(event.chunk)
-
-        # Create an LLM with streaming enabled
-        llm = LLM(model="gpt-4", stream=True)
-
-        # Store original methods
-        original_call = llm.call
-        original_handle_tool_call = llm._handle_tool_call
-
-        # Create a mock call method that simulates streaming and tool calling
-        def mock_call(messages, tools=None, callbacks=None, available_functions=None):
-            # Emit some chunks first
-            crewai_event_bus.emit(llm, event=LLMStreamChunkEvent(chunk="I'll process "))
-            crewai_event_bus.emit(llm, event=LLMStreamChunkEvent(chunk="that text "))
-            crewai_event_bus.emit(llm, event=LLMStreamChunkEvent(chunk="for you."))
-
-            # Call the tool
-            if available_functions and "sample_tool" in available_functions:
-                result = available_functions["sample_tool"]("Hello, world!")
-                return result
-
-            return "No tool was called"
-
-        # Replace the methods with our mocks
-        llm.call = mock_call
-
-        try:
-            # Call the LLM with a message that should trigger tool use
-            response = llm.call(
-                "Process this text with the sample tool: 'Hello, world!'",
-                tools=tools,
-                available_functions=available_functions,
-            )
-
-            # Verify that we received chunks
-            assert len(received_chunks) == 3
-            assert "".join(received_chunks) == "I'll process that text for you."
-
-            # Verify that the tool was called
-            assert tool_called
-
-            # Verify the response contains the tool's output
-            assert response == "Tool processed: Hello, world!"
-
-        finally:
-            # Restore the original methods
-            llm.call = original_call
-
-
 @pytest.mark.vcr(filter_headers=["authorization"])
 def test_streaming_fallback_to_non_streaming():
     """Test that streaming falls back to non-streaming when there's an error."""
@@ -762,7 +679,7 @@ def test_streaming_fallback_to_non_streaming():
             received_chunks.append(event.chunk)
 
         # Create an LLM with streaming enabled
-        llm = LLM(model="gpt-3.5-turbo", stream=True)
+        llm = LLM(model="gpt-4o", stream=True)
 
         # Store original methods
         original_call = llm.call