feat: implement execution tracing functionality for CrewAI

- Add ExecutionStep and ExecutionTrace models to track crew execution steps
- Add ExecutionTraceCollector to capture events and build execution traces
- Add trace_execution parameter to Crew class (disabled by default)
- Add execution_trace field to CrewOutput to return trace data
- Integrate trace collection into crew.kickoff() method
- Add comprehensive tests covering execution tracing functionality
- Add example demonstrating how to use execution tracing
- Export new classes in __init__.py

Addresses issue #3268: Users can now track the sequence of steps/actions
that a crew takes to complete a goal, including agent thoughts, tool calls,
and intermediate results, similar to LangGraph's conversation state.

Co-Authored-By: João <joao@crewai.com>

examples/execution_trace_example.py

@@ -0,0 +1,49 @@
+from crewai import Agent, Crew, Task, Process, LLM
+
+researcher = Agent(
+    role="Researcher",
+    goal="Research and analyze information",
+    backstory="You are an expert researcher with years of experience.",
+    llm=LLM(model="gpt-4o-mini")
+)
+
+writer = Agent(
+    role="Writer", 
+    goal="Write compelling content",
+    backstory="You are a skilled writer who creates engaging content.",
+    llm=LLM(model="gpt-4o-mini")
+)
+
+research_task = Task(
+    description="Research the latest trends in AI",
+    expected_output="A comprehensive report on AI trends",
+    agent=researcher
+)
+
+writing_task = Task(
+    description="Write an article based on the research",
+    expected_output="A well-written article about AI trends",
+    agent=writer
+)
+
+crew = Crew(
+    agents=[researcher, writer],
+    tasks=[research_task, writing_task],
+    process=Process.sequential,
+    trace_execution=True
+)
+
+result = crew.kickoff(inputs={"topic": "artificial intelligence"})
+
+if result.execution_trace:
+    print(f"Total execution steps: {result.execution_trace.total_steps}")
+    print(f"Execution duration: {result.execution_trace.end_time - result.execution_trace.start_time}")
+    
+    thoughts = result.execution_trace.get_steps_by_type("agent_thought")
+    print(f"Agent thoughts captured: {len(thoughts)}")
+    
+    tool_calls = result.execution_trace.get_steps_by_type("tool_call_started")
+    print(f"Tool calls made: {len(tool_calls)}")
+    
+    for step in result.execution_trace.steps:
+        print(f"{step.timestamp}: {step.step_type} - {step.agent_role or 'System'}")