Introduce Evaluator Experiment (#3133)

* feat: add exchanged messages in LLMCallCompletedEvent

* feat: add GoalAlignment metric for Agent evaluation

* feat: add SemanticQuality metric for Agent evaluation

* feat: add Tool Metrics for Agent evaluation

* feat: add Reasoning Metrics for Agent evaluation, still in progress

* feat: add AgentEvaluator class

This class will evaluate Agent' results and report to user

* fix: do not evaluate Agent by default

This is a experimental feature we still need refine it further

* test: add Agent eval tests

* fix: render all feedback per iteration

* style: resolve linter issues

* style: fix mypy issues

* fix: allow messages be empty on LLMCallCompletedEvent

* feat: add Experiment evaluation framework with baseline comparison

* fix: reset evaluator for each experiement iteraction

* fix: fix track of new test cases

* chore: split Experimental evaluation classes

* refactor: remove unused method

* refactor: isolate Console print in a dedicated class

* fix: make crew required to run an experiment

* fix: use time-aware to define experiment result

* test: add tests for Evaluator Experiment

* style: fix linter issues

* fix: encode string before hashing

* style: resolve linter issues

* feat: add experimental folder for beta features (#3141)

* test: move tests to experimental folder

tests/experimental/evaluation/metrics/base_evaluation_metrics_test.py

@@ -0,0 +1,28 @@
+import pytest
+from unittest.mock import MagicMock
+from crewai.agent import Agent
+from crewai.task import Task
+
+class BaseEvaluationMetricsTest:
+    @pytest.fixture
+    def mock_agent(self):
+        agent = MagicMock(spec=Agent)
+        agent.id = "test_agent_id"
+        agent.role = "Test Agent"
+        agent.goal = "Test goal"
+        agent.tools = []
+        return agent
+
+    @pytest.fixture
+    def mock_task(self):
+        task = MagicMock(spec=Task)
+        task.description = "Test task description"
+        task.expected_output = "Test expected output"
+        return task
+
+    @pytest.fixture
+    def execution_trace(self):
+        return {
+            "thinking": ["I need to analyze this data carefully"],
+            "actions": ["Gathered information", "Analyzed data"]
+        }