Fix lint issues: remove unused imports

- Remove unused 'os' import from reproduce_issue.py
- Remove unused imports (os, Agent, Crew, Task) from test_mem0_storage.py
- Addresses lint check failure in CI

Co-Authored-By: Jo\u00E3o <joao@crewai.com>

docs/docs.json

@@ -9,7 +9,12 @@
   },
   "favicon": "/images/favicon.svg",
   "contextual": {
-    "options": ["copy", "view", "chatgpt", "claude"]
+    "options": [
+      "copy",
+      "view",
+      "chatgpt",
+      "claude"
+    ]
   },
   "navigation": {
     "languages": [
@@ -50,22 +55,32 @@
             "groups": [
               {
                 "group": "Get Started",
-                "pages": ["en/introduction", "en/installation", "en/quickstart"]
+                "pages": [
+                  "en/introduction",
+                  "en/installation",
+                  "en/quickstart"
+                ]
               },
               {
                 "group": "Guides",
                 "pages": [
                   {
                     "group": "Strategy",
-                    "pages": ["en/guides/concepts/evaluating-use-cases"]
+                    "pages": [
+                      "en/guides/concepts/evaluating-use-cases"
+                    ]
                   },
                   {
                     "group": "Agents",
-                    "pages": ["en/guides/agents/crafting-effective-agents"]
+                    "pages": [
+                      "en/guides/agents/crafting-effective-agents"
+                    ]
                   },
                   {
                     "group": "Crews",
-                    "pages": ["en/guides/crews/first-crew"]
+                    "pages": [
+                      "en/guides/crews/first-crew"
+                    ]
                   },
                   {
                     "group": "Flows",
@@ -79,6 +94,7 @@
                     "pages": [
                       "en/guides/advanced/customizing-prompts",
                       "en/guides/advanced/fingerprinting"
+
                     ]
                   }
                 ]
@@ -225,7 +241,6 @@
                   "en/observability/langtrace",
                   "en/observability/maxim",
                   "en/observability/mlflow",
-                  "en/observability/neatlogs",
                   "en/observability/openlit",
                   "en/observability/opik",
                   "en/observability/patronus-evaluation",
@@ -259,7 +274,9 @@
               },
               {
                 "group": "Telemetry",
-                "pages": ["en/telemetry"]
+                "pages": [
+                  "en/telemetry"
+                ]
               }
             ]
           },
@@ -268,7 +285,9 @@
             "groups": [
               {
                 "group": "Getting Started",
-                "pages": ["en/enterprise/introduction"]
+                "pages": [
+                  "en/enterprise/introduction"
+                ]
               },
               {
                 "group": "Features",
@@ -323,7 +342,9 @@
               },
               {
                 "group": "Resources",
-                "pages": ["en/enterprise/resources/frequently-asked-questions"]
+                "pages": [
+                  "en/enterprise/resources/frequently-asked-questions"
+                ]
               }
             ]
           },
@@ -332,7 +353,9 @@
             "groups": [
               {
                 "group": "Getting Started",
-                "pages": ["en/api-reference/introduction"]
+                "pages": [
+                  "en/api-reference/introduction"
+                ]
               },
               {
                 "group": "Endpoints",
@@ -342,13 +365,16 @@
           },
           {
             "tab": "Examples",
-            "groups": [
+                        "groups": [
               {
                 "group": "Examples",
-                "pages": ["en/examples/example"]
+                "pages": [
+                  "en/examples/example"
+                ]
               }
             ]
           }
+
         ]
       },
       {
@@ -399,15 +425,21 @@
                 "pages": [
                   {
                     "group": "Estratégia",
-                    "pages": ["pt-BR/guides/concepts/evaluating-use-cases"]
+                    "pages": [
+                      "pt-BR/guides/concepts/evaluating-use-cases"
+                    ]
                   },
                   {
                     "group": "Agentes",
-                    "pages": ["pt-BR/guides/agents/crafting-effective-agents"]
+                    "pages": [
+                      "pt-BR/guides/agents/crafting-effective-agents"
+                    ]
                   },
                   {
                     "group": "Crews",
-                    "pages": ["pt-BR/guides/crews/first-crew"]
+                    "pages": [
+                      "pt-BR/guides/crews/first-crew"
+                    ]
                   },
                   {
                     "group": "Flows",
@@ -600,7 +632,9 @@
               },
               {
                 "group": "Telemetria",
-                "pages": ["pt-BR/telemetry"]
+                "pages": [
+                  "pt-BR/telemetry"
+                ]
               }
             ]
           },
@@ -609,7 +643,9 @@
             "groups": [
               {
                 "group": "Começando",
-                "pages": ["pt-BR/enterprise/introduction"]
+                "pages": [
+                  "pt-BR/enterprise/introduction"
+                ]
               },
               {
                 "group": "Funcionalidades",
@@ -674,7 +710,9 @@
             "groups": [
               {
                 "group": "Começando",
-                "pages": ["pt-BR/api-reference/introduction"]
+                "pages": [
+                  "pt-BR/api-reference/introduction"
+                ]
               },
               {
                 "group": "Endpoints",
@@ -684,13 +722,16 @@
           },
           {
             "tab": "Exemplos",
-            "groups": [
+                        "groups": [
               {
                 "group": "Exemplos",
-                "pages": ["pt-BR/examples/example"]
+                "pages": [
+                  "pt-BR/examples/example"
+                ]
               }
             ]
           }
+
         ]
       }
     ]

docs/en/observability/neatlogs.mdx

@@ -1,140 +0,0 @@
----
-title: Neatlogs Integration
-description: Understand, debug, and share your CrewAI agent runs
-icon: magnifying-glass-chart
----
-
-# Introduction
-
-Neatlogs helps you **see what your agent did**, **why**, and **share it**.
-
-It captures every step: thoughts, tool calls, responses, evaluations. No raw logs. Just clear, structured traces. Great for debugging and collaboration.
-
----
-
-## Why use Neatlogs?
-
-CrewAI agents use multiple tools and reasoning steps. When something goes wrong, you need context — not just errors.
-
-Neatlogs lets you:
-
-- Follow the full decision path
-- Add feedback directly on steps
-- Chat with the trace using AI assistant
-- Share runs publicly for feedback
-- Turn insights into tasks
-
-All in one place.
-
-Manage your traces effortlessly
-
-![Traces](/images/neatlogs-1.png)
-![Trace Response](/images/neatlogs-2.png)
-
-The best UX to view a CrewAI trace. Post comments anywhere you want. Use AI to debug.
-
-![Trace Details](/images/neatlogs-3.png)
-![Ai Chat Bot With A Trace](/images/neatlogs-4.png)
-![Comments Drawer](/images/neatlogs-5.png)
-
----
-
-## Core Features
-
-- **Trace Viewer**: Track thoughts, tools, and decisions in sequence
-- **Inline Comments**: Tag teammates on any trace step
-- **Feedback & Evaluation**: Mark outputs as correct or incorrect
-- **Error Highlighting**: Automatic flagging of API/tool failures
-- **Task Conversion**: Convert comments into assigned tasks
-- **Ask the Trace (AI)**: Chat with your trace using Neatlogs AI bot
-- **Public Sharing**: Publish trace links to your community
-
----
-
-## Quick Setup with CrewAI
-
-<Steps>
-  <Step title="Sign Up & Get API Key">
-    Visit [neatlogs.com](https://neatlogs.com/?utm_source=crewAI-docs), create a project, copy the API key.
-  </Step>
-  <Step title="Install SDK">
-    ```bash
-    pip install neatlogs
-    ```
-    (Latest version 0.8.0, Python 3.8+; MIT license) :contentReference[oaicite:1]{index=1}
-  </Step>
-  <Step title="Initialize Neatlogs">
-    Before starting Crew agents, add:
-
-    ```python
-    import neatlogs
-    neatlogs.init("YOUR_PROJECT_API_KEY")
-    ```
-
-    Agents run as usual. Neatlogs captures everything automatically.
-
-  </Step>
-</Steps>
-
----
-
-## Under the Hood
-
-According to GitHub, Neatlogs:
-
-- Captures thoughts, tool calls, responses, errors, and token stats :contentReference[oaicite:2]{index=2}
-- Supports AI-powered task generation and robust evaluation workflows :contentReference[oaicite:3]{index=3}
-
-All with just two lines of code.
-
----
-
-## Watch It Work
-
-### 🔍 Full Demo (4 min)
-
-<iframe
-  width="100%"
-  height="315"
-  src="https://www.youtube.com/embed/8KDme9T2I7Q?si=b8oHteaBwFNs_Duk"
-  title="YouTube video player"
-  frameBorder="0"
-  allow="accelerometer; autoplay; clipboard-write; encrypted-media; gyroscope; picture-in-picture"
-  allowFullScreen
-></iframe>
-
-### ⚙️ CrewAI Integration (30 s)
-
-<iframe
-  className="w-full aspect-video rounded-xl"
-  src="https://www.loom.com/embed/9c78b552af43452bb3e4783cb8d91230?sid=e9d7d370-a91a-49b0-809e-2f375d9e801d"
-  title="Loom video player"
-  frameBorder="0"
-  allowFullScreen
-></iframe>
-
----
-
-## Links & Support
-
-- 📘 [Neatlogs Docs](https://docs.neatlogs.com/)
-- 🔐 [Dashboard & API Key](https://app.neatlogs.com/)
-- 🐦 [Follow on Twitter](https://twitter.com/neatlogs)
-- 📧 Contact: hello@neatlogs.com
-- 🛠 [GitHub SDK](https://github.com/NeatLogs/neatlogs) :contentReference[oaicite:4]{index=4}
-
----
-
-## TL;DR
-
-With just:
-
-```bash
-pip install neatlogs
-
-import neatlogs
-neatlogs.init("YOUR_API_KEY")
-
-You can now capture, understand, share, and act on your CrewAI agent runs in seconds.
-No setup overhead. Full trace transparency. Full team collaboration.
-```

pyproject.toml

@@ -47,7 +47,7 @@ Documentation = "https://docs.crewai.com"
 Repository = "https://github.com/crewAIInc/crewAI"
 
 [project.optional-dependencies]
-tools = ["crewai-tools~=0.55.0"]
+tools = ["crewai-tools~=0.51.0"]
 embeddings = [
     "tiktoken~=0.8.0"
 ]

reproduce_issue.py

@@ -0,0 +1,54 @@
+"""
+Reproduction script for issue #3152 - mem0 external memory format error
+Based on the code provided in the GitHub issue
+"""
+
+from crewai import Agent, Task, Crew
+from crewai.memory.external.external_memory import ExternalMemory
+
+def test_mem0_external_memory():
+    """Test that reproduces the mem0 external memory format error"""
+    
+    embedder_config = {
+        "provider": "mem0",
+        "config": {
+            "user_id": "test_user_123",
+        }
+    }
+    
+    external_memory = ExternalMemory(embedder_config=embedder_config)
+    
+    agent = Agent(
+        role="Test Agent",
+        goal="Test external memory functionality",
+        backstory="A test agent for reproducing the mem0 issue",
+        verbose=True
+    )
+    
+    task = Task(
+        description="Test task for external memory",
+        expected_output="Test output",
+        agent=agent
+    )
+    
+    crew = Crew(
+        agents=[agent],
+        tasks=[task],
+        external_memory=external_memory,
+        verbose=True
+    )
+    
+    print("Testing mem0 external memory integration...")
+    
+    try:
+        result = crew.kickoff()
+        print("SUCCESS: External memory integration worked!")
+        print(f"Result: {result}")
+    except Exception as e:
+        print(f"ERROR: {e}")
+        if "Expected a list of items but got type" in str(e):
+            print("CONFIRMED: This is the mem0 format error from issue #3152")
+        raise
+
+if __name__ == "__main__":
+    test_mem0_external_memory()

src/crewai/__init__.py

@@ -54,7 +54,7 @@ def _track_install_async():
 
 _track_install_async()
 
-__version__ = "0.148.0"
+__version__ = "0.141.0"
 __all__ = [
     "Agent",
     "Crew",

src/crewai/cli/templates/crew/pyproject.toml

@@ -5,7 +5,7 @@ description = "{{name}} using crewAI"
 authors = [{ name = "Your Name", email = "you@example.com" }]
 requires-python = ">=3.10,<3.14"
 dependencies = [
-    "crewai[tools]>=0.148.0,<1.0.0"
+    "crewai[tools]>=0.141.0,<1.0.0"
 ]
 
 [project.scripts]

src/crewai/cli/templates/flow/pyproject.toml

@@ -5,7 +5,7 @@ description = "{{name}} using crewAI"
 authors = [{ name = "Your Name", email = "you@example.com" }]
 requires-python = ">=3.10,<3.14"
 dependencies = [
-    "crewai[tools]>=0.148.0,<1.0.0",
+    "crewai[tools]>=0.141.0,<1.0.0",
 ]
 
 [project.scripts]

src/crewai/cli/templates/tool/pyproject.toml

@@ -5,7 +5,7 @@ description = "Power up your crews with {{folder_name}}"
 readme = "README.md"
 requires-python = ">=3.10,<3.14"
 dependencies = [
-    "crewai[tools]>=0.148.0"
+    "crewai[tools]>=0.141.0"
 ]
 
 [tool.crewai]

src/crewai/crew.py

@@ -1313,6 +1313,7 @@ class Crew(FlowTrackable, BaseModel):
         n_iterations: int,
         eval_llm: Union[str, InstanceOf[BaseLLM]],
         inputs: Optional[Dict[str, Any]] = None,
+        include_agent_eval: Optional[bool] = False
     ) -> None:
         """Test and evaluate the Crew with the given inputs for n iterations concurrently using concurrent.futures."""
         try:
@@ -1332,13 +1333,28 @@ class Crew(FlowTrackable, BaseModel):
             )
             test_crew = self.copy()
 
+            # TODO: Refator to use a single Evaluator Manage class
             evaluator = CrewEvaluator(test_crew, llm_instance)
 
+            if include_agent_eval:
+                from crewai.evaluation import create_default_evaluator
+                agent_evaluator = create_default_evaluator(crew=test_crew)
+
             for i in range(1, n_iterations + 1):
                 evaluator.set_iteration(i)
+
+                if include_agent_eval:
+                    agent_evaluator.set_iteration(i)
+
                 test_crew.kickoff(inputs=inputs)
 
+                # TODO: Refactor to use ListenerEvents instead of trigger each iteration manually
+                if include_agent_eval:
+                    agent_evaluator.evaluate_current_iteration()
+
             evaluator.print_crew_evaluation_result()
+            if include_agent_eval:
+                agent_evaluator.get_agent_evaluation(include_evaluation_feedback=True)
 
             crewai_event_bus.emit(
                 self,

src/crewai/evaluation/__init__.py

@@ -1,35 +1,40 @@
-from crewai.experimental.evaluation.base_evaluator import (
+from crewai.evaluation.base_evaluator import (
     BaseEvaluator,
     EvaluationScore,
     MetricCategory,
     AgentEvaluationResult
 )
 
-from crewai.experimental.evaluation.metrics import (
-    SemanticQualityEvaluator,
-    GoalAlignmentEvaluator,
-    ReasoningEfficiencyEvaluator,
+from crewai.evaluation.metrics.semantic_quality_metrics import (
+    SemanticQualityEvaluator
+)
+
+from crewai.evaluation.metrics.goal_metrics import (
+    GoalAlignmentEvaluator
+)
+
+from crewai.evaluation.metrics.reasoning_metrics import (
+    ReasoningEfficiencyEvaluator
+)
+
+
+from crewai.evaluation.metrics.tools_metrics import (
     ToolSelectionEvaluator,
     ParameterExtractionEvaluator,
     ToolInvocationEvaluator
 )
 
-from crewai.experimental.evaluation.evaluation_listener import (
+from crewai.evaluation.evaluation_listener import (
     EvaluationTraceCallback,
     create_evaluation_callbacks
 )
 
-from crewai.experimental.evaluation.agent_evaluator import (
+
+from crewai.evaluation.agent_evaluator import (
     AgentEvaluator,
     create_default_evaluator
 )
 
-from crewai.experimental.evaluation.experiment import (
-    ExperimentRunner,
-    ExperimentResults,
-    ExperimentResult
-)
-
 __all__ = [
     "BaseEvaluator",
     "EvaluationScore",
@@ -44,8 +49,5 @@ __all__ = [
     "EvaluationTraceCallback",
     "create_evaluation_callbacks",
     "AgentEvaluator",
-    "create_default_evaluator",
-    "ExperimentRunner",
-    "ExperimentResults",
-    "ExperimentResult"
-]
+    "create_default_evaluator"
+]

src/crewai/evaluation/agent_evaluator.py

@@ -0,0 +1,178 @@
+from crewai.evaluation.base_evaluator import AgentEvaluationResult, AggregationStrategy
+from crewai.agent import Agent
+from crewai.task import Task
+from crewai.evaluation.evaluation_display import EvaluationDisplayFormatter
+
+from typing import Any, Dict
+from collections import defaultdict
+from crewai.evaluation import BaseEvaluator, create_evaluation_callbacks
+from collections.abc import Sequence
+from crewai.crew import Crew
+from crewai.utilities.events.crewai_event_bus import crewai_event_bus
+from crewai.utilities.events.utils.console_formatter import ConsoleFormatter
+
+class AgentEvaluator:
+    def __init__(
+        self,
+        evaluators: Sequence[BaseEvaluator] | None = None,
+        crew: Crew | None = None,
+    ):
+        self.crew: Crew | None = crew
+        self.evaluators: Sequence[BaseEvaluator] | None = evaluators
+
+        self.agent_evaluators: dict[str, Sequence[BaseEvaluator] | None] = {}
+        if crew is not None:
+            assert crew and crew.agents is not None
+            for agent in crew.agents:
+                self.agent_evaluators[str(agent.id)] = self.evaluators
+
+        self.callback = create_evaluation_callbacks()
+        self.console_formatter = ConsoleFormatter()
+        self.display_formatter = EvaluationDisplayFormatter()
+
+        self.iteration = 1
+        self.iterations_results: dict[int, dict[str, list[AgentEvaluationResult]]] = {}
+
+    def set_iteration(self, iteration: int) -> None:
+        self.iteration = iteration
+
+    def evaluate_current_iteration(self) -> dict[str, list[AgentEvaluationResult]]:
+        if not self.crew:
+            raise ValueError("Cannot evaluate: no crew was provided to the evaluator.")
+
+        if not self.callback:
+            raise ValueError("Cannot evaluate: no callback was set. Use set_callback() method first.")
+
+        from rich.progress import Progress, SpinnerColumn, TextColumn, BarColumn
+        evaluation_results: defaultdict[str, list[AgentEvaluationResult]] = defaultdict(list)
+
+        total_evals = 0
+        for agent in self.crew.agents:
+            for task in self.crew.tasks:
+                if task.agent and task.agent.id == agent.id and self.agent_evaluators.get(str(agent.id)):
+                    total_evals += 1
+
+        with Progress(
+            SpinnerColumn(),
+            TextColumn("[bold blue]{task.description}[/bold blue]"),
+            BarColumn(),
+            TextColumn("{task.percentage:.0f}% completed"),
+            console=self.console_formatter.console
+        ) as progress:
+            eval_task = progress.add_task(f"Evaluating agents (iteration {self.iteration})...", total=total_evals)
+
+            for agent in self.crew.agents:
+                evaluator = self.agent_evaluators.get(str(agent.id))
+                if not evaluator:
+                    continue
+
+                for task in self.crew.tasks:
+
+                    if task.agent and str(task.agent.id) != str(agent.id):
+                        continue
+
+                    trace = self.callback.get_trace(str(agent.id), str(task.id))
+                    if not trace:
+                        self.console_formatter.print(f"[yellow]Warning: No trace found for agent {agent.role} on task {task.description[:30]}...[/yellow]")
+                        progress.update(eval_task, advance=1)
+                        continue
+
+                    with crewai_event_bus.scoped_handlers():
+                        result = self.evaluate(
+                            agent=agent,
+                            task=task,
+                            execution_trace=trace,
+                            final_output=task.output
+                        )
+                        evaluation_results[agent.role].append(result)
+                        progress.update(eval_task, advance=1)
+
+        self.iterations_results[self.iteration] = evaluation_results
+        return evaluation_results
+
+    def get_evaluation_results(self):
+        if self.iteration in self.iterations_results:
+            return self.iterations_results[self.iteration]
+
+        return self.evaluate_current_iteration()
+
+    def display_results_with_iterations(self):
+        self.display_formatter.display_summary_results(self.iterations_results)
+
+    def get_agent_evaluation(self, strategy: AggregationStrategy = AggregationStrategy.SIMPLE_AVERAGE, include_evaluation_feedback: bool = False):
+        agent_results = {}
+        with crewai_event_bus.scoped_handlers():
+            task_results = self.get_evaluation_results()
+            for agent_role, results in task_results.items():
+                if not results:
+                    continue
+
+                agent_id = results[0].agent_id
+
+                aggregated_result = self.display_formatter._aggregate_agent_results(
+                    agent_id=agent_id,
+                    agent_role=agent_role,
+                    results=results,
+                    strategy=strategy
+                )
+
+                agent_results[agent_role] = aggregated_result
+
+
+            if self.iteration == max(self.iterations_results.keys()):
+                self.display_results_with_iterations()
+
+            if include_evaluation_feedback:
+                self.display_evaluation_with_feedback()
+
+        return agent_results
+
+    def display_evaluation_with_feedback(self):
+        self.display_formatter.display_evaluation_with_feedback(self.iterations_results)
+
+    def evaluate(
+        self,
+        agent: Agent,
+        task: Task,
+        execution_trace: Dict[str, Any],
+        final_output: Any
+    ) -> AgentEvaluationResult:
+        result = AgentEvaluationResult(
+            agent_id=str(agent.id),
+            task_id=str(task.id)
+        )
+        assert self.evaluators is not None
+        for evaluator in self.evaluators:
+            try:
+                score = evaluator.evaluate(
+                    agent=agent,
+                    task=task,
+                    execution_trace=execution_trace,
+                    final_output=final_output
+                )
+                result.metrics[evaluator.metric_category] = score
+            except Exception as e:
+                self.console_formatter.print(f"Error in {evaluator.metric_category.value} evaluator: {str(e)}")
+
+        return result
+
+def create_default_evaluator(crew, llm=None):
+    from crewai.evaluation import (
+        GoalAlignmentEvaluator,
+        SemanticQualityEvaluator,
+        ToolSelectionEvaluator,
+        ParameterExtractionEvaluator,
+        ToolInvocationEvaluator,
+        ReasoningEfficiencyEvaluator
+    )
+
+    evaluators = [
+        GoalAlignmentEvaluator(llm=llm),
+        SemanticQualityEvaluator(llm=llm),
+        ToolSelectionEvaluator(llm=llm),
+        ParameterExtractionEvaluator(llm=llm),
+        ToolInvocationEvaluator(llm=llm),
+        ReasoningEfficiencyEvaluator(llm=llm),
+    ]
+
+    return AgentEvaluator(evaluators=evaluators, crew=crew)

src/crewai/evaluation/base_evaluator.py

@@ -57,9 +57,9 @@ class BaseEvaluator(abc.ABC):
     def evaluate(
         self,
         agent: Agent,
+        task: Task,
         execution_trace: Dict[str, Any],
         final_output: Any,
-        task: Task | None = None,
     ) -> EvaluationScore:
         pass
 

src/crewai/evaluation/evaluation_display.py

@@ -3,8 +3,8 @@ from typing import Dict, Any, List
 from rich.table import Table
 from rich.box import HEAVY_EDGE, ROUNDED
 from collections.abc import Sequence
-from crewai.experimental.evaluation.base_evaluator import AgentAggregatedEvaluationResult, AggregationStrategy, AgentEvaluationResult, MetricCategory
-from crewai.experimental.evaluation import EvaluationScore
+from crewai.evaluation.base_evaluator import AgentAggregatedEvaluationResult, AggregationStrategy, AgentEvaluationResult, MetricCategory
+from crewai.evaluation import EvaluationScore
 from crewai.utilities.events.utils.console_formatter import ConsoleFormatter
 from crewai.utilities.llm_utils import create_llm
 
@@ -17,6 +17,7 @@ class EvaluationDisplayFormatter:
             self.console_formatter.print("[yellow]No evaluation results to display[/yellow]")
             return
 
+        # Get all agent roles across all iterations
         all_agent_roles: set[str] = set()
         for iter_results in iterations_results.values():
             all_agent_roles.update(iter_results.keys())
@@ -24,6 +25,7 @@ class EvaluationDisplayFormatter:
         for agent_role in sorted(all_agent_roles):
             self.console_formatter.print(f"\n[bold cyan]Agent: {agent_role}[/bold cyan]")
 
+            # Process each iteration
             for iter_num, results in sorted(iterations_results.items()):
                 if agent_role not in results or not results[agent_role]:
                     continue
@@ -31,19 +33,23 @@ class EvaluationDisplayFormatter:
                 agent_results = results[agent_role]
                 agent_id = agent_results[0].agent_id
 
+                # Aggregate results for this agent in this iteration
                 aggregated_result = self._aggregate_agent_results(
                     agent_id=agent_id,
                     agent_role=agent_role,
                     results=agent_results,
                 )
 
+                # Display iteration header
                 self.console_formatter.print(f"\n[bold]Iteration {iter_num}[/bold]")
 
+                # Create table for this iteration
                 table = Table(box=ROUNDED)
                 table.add_column("Metric", style="cyan")
                 table.add_column("Score (1-10)", justify="center")
                 table.add_column("Feedback", style="green")
 
+                # Add metrics to table
                 if aggregated_result.metrics:
                     for metric, evaluation_score in aggregated_result.metrics.items():
                         score = evaluation_score.score
@@ -85,6 +91,7 @@ class EvaluationDisplayFormatter:
                         "Overall agent evaluation score"
                     )
 
+                # Print the table for this iteration
                 self.console_formatter.print(table)
 
     def display_summary_results(self, iterations_results: Dict[int, Dict[str, List[AgentAggregatedEvaluationResult]]]):
@@ -241,6 +248,7 @@ class EvaluationDisplayFormatter:
             feedback_summary = None
             if feedbacks:
                 if len(feedbacks) > 1:
+                    # Use the summarization method for multiple feedbacks
                     feedback_summary = self._summarize_feedbacks(
                         agent_role=agent_role,
                         metric=category.title(),
@@ -299,7 +307,7 @@ class EvaluationDisplayFormatter:
                 strategy_guidance = "Focus on the highest-scoring aspects and strengths demonstrated."
             elif strategy == AggregationStrategy.WORST_PERFORMANCE:
                 strategy_guidance = "Focus on areas that need improvement and common issues across tasks."
-            else:
+            else:  # Default/average strategies
                 strategy_guidance = "Provide a balanced analysis of strengths and weaknesses across all tasks."
 
             prompt = [

src/crewai/evaluation/evaluation_listener.py

@@ -9,9 +9,7 @@ from crewai.utilities.events.base_event_listener import BaseEventListener
 from crewai.utilities.events.crewai_event_bus import CrewAIEventsBus
 from crewai.utilities.events.agent_events import (
     AgentExecutionStartedEvent,
-    AgentExecutionCompletedEvent,
-    LiteAgentExecutionStartedEvent,
-    LiteAgentExecutionCompletedEvent
+    AgentExecutionCompletedEvent
 )
 from crewai.utilities.events.tool_usage_events import (
     ToolUsageFinishedEvent,
@@ -54,18 +52,10 @@ class EvaluationTraceCallback(BaseEventListener):
         def on_agent_started(source, event: AgentExecutionStartedEvent):
             self.on_agent_start(event.agent, event.task)
 
-        @event_bus.on(LiteAgentExecutionStartedEvent)
-        def on_lite_agent_started(source, event: LiteAgentExecutionStartedEvent):
-            self.on_lite_agent_start(event.agent_info)
-
         @event_bus.on(AgentExecutionCompletedEvent)
         def on_agent_completed(source, event: AgentExecutionCompletedEvent):
             self.on_agent_finish(event.agent, event.task, event.output)
 
-        @event_bus.on(LiteAgentExecutionCompletedEvent)
-        def on_lite_agent_completed(source, event: LiteAgentExecutionCompletedEvent):
-            self.on_lite_agent_finish(event.output)
-
         @event_bus.on(ToolUsageFinishedEvent)
         def on_tool_completed(source, event: ToolUsageFinishedEvent):
             self.on_tool_use(event.tool_name, event.tool_args, event.output, success=True)
@@ -98,38 +88,19 @@ class EvaluationTraceCallback(BaseEventListener):
         def on_llm_call_completed(source, event: LLMCallCompletedEvent):
             self.on_llm_call_end(event.messages, event.response)
 
-    def on_lite_agent_start(self, agent_info: dict[str, Any]):
-        self.current_agent_id = agent_info['id']
-        self.current_task_id = "lite_task"
-
-        trace_key = f"{self.current_agent_id}_{self.current_task_id}"
-        self._init_trace(
-            trace_key=trace_key,
-            agent_id=self.current_agent_id,
-            task_id=self.current_task_id,
-            tool_uses=[],
-            llm_calls=[],
-            start_time=datetime.now(),
-            final_output=None
-        )
-
-    def _init_trace(self, trace_key: str, **kwargs: Any):
-        self.traces[trace_key] = kwargs
-
     def on_agent_start(self, agent: Agent, task: Task):
         self.current_agent_id = agent.id
         self.current_task_id = task.id
 
         trace_key = f"{agent.id}_{task.id}"
-        self._init_trace(
-            trace_key=trace_key,
-            agent_id=agent.id,
-            task_id=task.id,
-            tool_uses=[],
-            llm_calls=[],
-            start_time=datetime.now(),
-            final_output=None
-        )
+        self.traces[trace_key] = {
+            "agent_id": agent.id,
+            "task_id": task.id,
+            "tool_uses": [],
+            "llm_calls": [],
+            "start_time": datetime.now(),
+            "final_output": None
+        }
 
     def on_agent_finish(self, agent: Agent, task: Task, output: Any):
         trace_key = f"{agent.id}_{task.id}"
@@ -137,20 +108,9 @@ class EvaluationTraceCallback(BaseEventListener):
             self.traces[trace_key]["final_output"] = output
             self.traces[trace_key]["end_time"] = datetime.now()
 
-        self._reset_current()
-
-    def _reset_current(self):
         self.current_agent_id = None
         self.current_task_id = None
 
-    def on_lite_agent_finish(self, output: Any):
-        trace_key = f"{self.current_agent_id}_lite_task"
-        if trace_key in self.traces:
-            self.traces[trace_key]["final_output"] = output
-            self.traces[trace_key]["end_time"] = datetime.now()
-
-        self._reset_current()
-
     def on_tool_use(self, tool_name: str, tool_args: dict[str, Any] | str, result: Any,
                    success: bool = True, error_type: str | None = None):
         if not self.current_agent_id or not self.current_task_id:
@@ -227,8 +187,4 @@ class EvaluationTraceCallback(BaseEventListener):
 
 
 def create_evaluation_callbacks() -> EvaluationTraceCallback:
-    from crewai.utilities.events.crewai_event_bus import crewai_event_bus
-
-    callback = EvaluationTraceCallback()
-    callback.setup_listeners(crewai_event_bus)
-    return callback
+    return EvaluationTraceCallback()

src/crewai/evaluation/metrics/goal_metrics.py

@@ -3,8 +3,8 @@ from typing import Any, Dict
 from crewai.agent import Agent
 from crewai.task import Task
 
-from crewai.experimental.evaluation.base_evaluator import BaseEvaluator, EvaluationScore, MetricCategory
-from crewai.experimental.evaluation.json_parser import extract_json_from_llm_response
+from crewai.evaluation.base_evaluator import BaseEvaluator, EvaluationScore, MetricCategory
+from crewai.evaluation.json_parser import extract_json_from_llm_response
 
 class GoalAlignmentEvaluator(BaseEvaluator):
     @property
@@ -14,14 +14,10 @@ class GoalAlignmentEvaluator(BaseEvaluator):
     def evaluate(
         self,
         agent: Agent,
+        task: Task,
         execution_trace: Dict[str, Any],
         final_output: Any,
-        task: Task | None = None,
     ) -> EvaluationScore:
-        task_context = ""
-        if task is not None:
-            task_context = f"Task description: {task.description}\nExpected output: {task.expected_output}\n"
-
         prompt = [
             {"role": "system", "content": """You are an expert evaluator assessing how well an AI agent's output aligns with its assigned task goal.
 
@@ -41,7 +37,8 @@ Return your evaluation as JSON with fields 'score' (number) and 'feedback' (stri
             {"role": "user", "content": f"""
 Agent role: {agent.role}
 Agent goal: {agent.goal}
-{task_context}
+Task description: {task.description}
+Expected output: {task.expected_output}
 
 Agent's final output:
 {final_output}

src/crewai/evaluation/metrics/reasoning_metrics.py

@@ -16,8 +16,8 @@ from collections.abc import Sequence
 from crewai.agent import Agent
 from crewai.task import Task
 
-from crewai.experimental.evaluation.base_evaluator import BaseEvaluator, EvaluationScore, MetricCategory
-from crewai.experimental.evaluation.json_parser import extract_json_from_llm_response
+from crewai.evaluation.base_evaluator import BaseEvaluator, EvaluationScore, MetricCategory
+from crewai.evaluation.json_parser import extract_json_from_llm_response
 from crewai.tasks.task_output import TaskOutput
 
 class ReasoningPatternType(Enum):
@@ -36,14 +36,10 @@ class ReasoningEfficiencyEvaluator(BaseEvaluator):
     def evaluate(
         self,
         agent: Agent,
+        task: Task,
         execution_trace: Dict[str, Any],
-        final_output: TaskOutput | str,
-        task: Task | None = None,
+        final_output: TaskOutput,
     ) -> EvaluationScore:
-        task_context = ""
-        if task is not None:
-            task_context = f"Task description: {task.description}\nExpected output: {task.expected_output}\n"
-
         llm_calls = execution_trace.get("llm_calls", [])
 
         if not llm_calls or len(llm_calls) < 2:
@@ -87,8 +83,6 @@ class ReasoningEfficiencyEvaluator(BaseEvaluator):
 
         call_samples = self._get_call_samples(llm_calls)
 
-        final_output = final_output.raw if isinstance(final_output, TaskOutput) else final_output
-
         prompt = [
             {"role": "system", "content": """You are an expert evaluator assessing the reasoning efficiency of an AI agent's thought process.
 
@@ -123,7 +117,7 @@ Return your evaluation as JSON with the following structure:
 }"""},
             {"role": "user", "content": f"""
 Agent role: {agent.role}
-{task_context}
+Task description: {task.description}
 
 Reasoning efficiency metrics:
 - Total LLM calls: {efficiency_metrics["total_llm_calls"]}
@@ -136,7 +130,7 @@ Sample of agent reasoning flow (chronological sequence):
 {call_samples}
 
 Agent's final output:
-{final_output[:500]}... (truncated)
+{final_output.raw[:500]}... (truncated)
 
 Evaluate the reasoning efficiency of this agent based on these interaction patterns.
 Identify any inefficient reasoning patterns and provide specific suggestions for optimization.

src/crewai/evaluation/metrics/semantic_quality_metrics.py

@@ -3,8 +3,8 @@ from typing import Any, Dict
 from crewai.agent import Agent
 from crewai.task import Task
 
-from crewai.experimental.evaluation.base_evaluator import BaseEvaluator, EvaluationScore, MetricCategory
-from crewai.experimental.evaluation.json_parser import extract_json_from_llm_response
+from crewai.evaluation.base_evaluator import BaseEvaluator, EvaluationScore, MetricCategory
+from crewai.evaluation.json_parser import extract_json_from_llm_response
 
 class SemanticQualityEvaluator(BaseEvaluator):
     @property
@@ -14,13 +14,10 @@ class SemanticQualityEvaluator(BaseEvaluator):
     def evaluate(
         self,
         agent: Agent,
+        task: Task,
         execution_trace: Dict[str, Any],
         final_output: Any,
-        task: Task | None = None,
     ) -> EvaluationScore:
-        task_context = ""
-        if task is not None:
-            task_context = f"Task description: {task.description}"
         prompt = [
             {"role": "system", "content": """You are an expert evaluator assessing the semantic quality of an AI agent's output.
 
@@ -40,7 +37,7 @@ Return your evaluation as JSON with fields 'score' (number) and 'feedback' (stri
 """},
             {"role": "user", "content": f"""
 Agent role: {agent.role}
-{task_context}
+Task description: {task.description}
 
 Agent's final output:
 {final_output}

src/crewai/evaluation/metrics/tools_metrics.py

@@ -1,8 +1,8 @@
 import json
 from typing import Dict, Any
 
-from crewai.experimental.evaluation.base_evaluator import BaseEvaluator, EvaluationScore, MetricCategory
-from crewai.experimental.evaluation.json_parser import extract_json_from_llm_response
+from crewai.evaluation.base_evaluator import BaseEvaluator, EvaluationScore, MetricCategory
+from crewai.evaluation.json_parser import extract_json_from_llm_response
 from crewai.agent import Agent
 from crewai.task import Task
 
@@ -16,14 +16,10 @@ class ToolSelectionEvaluator(BaseEvaluator):
     def evaluate(
         self,
         agent: Agent,
+        task: Task,
         execution_trace: Dict[str, Any],
         final_output: str,
-        task: Task | None = None,
     ) -> EvaluationScore:
-        task_context = ""
-        if task is not None:
-            task_context = f"Task description: {task.description}"
-
         tool_uses = execution_trace.get("tool_uses", [])
         tool_count = len(tool_uses)
         unique_tool_types = set([tool.get("tool", "Unknown tool") for tool in tool_uses])
@@ -76,7 +72,7 @@ Return your evaluation as JSON with these fields:
 """},
             {"role": "user", "content": f"""
 Agent role: {agent.role}
-{task_context}
+Task description: {task.description}
 
 Available tools for this agent:
 {available_tools_info}
@@ -132,13 +128,10 @@ class ParameterExtractionEvaluator(BaseEvaluator):
     def evaluate(
         self,
         agent: Agent,
+        task: Task,
         execution_trace: Dict[str, Any],
         final_output: str,
-        task: Task | None = None,
     ) -> EvaluationScore:
-        task_context = ""
-        if task is not None:
-            task_context = f"Task description: {task.description}"
         tool_uses = execution_trace.get("tool_uses", [])
         tool_count = len(tool_uses)
 
@@ -219,7 +212,7 @@ Return your evaluation as JSON with these fields:
 """},
             {"role": "user", "content": f"""
 Agent role: {agent.role}
-{task_context}
+Task description: {task.description}
 
 Parameter extraction examples:
 {param_samples_text}
@@ -274,13 +267,10 @@ class ToolInvocationEvaluator(BaseEvaluator):
     def evaluate(
         self,
         agent: Agent,
+        task: Task,
         execution_trace: Dict[str, Any],
         final_output: str,
-        task: Task | None = None,
     ) -> EvaluationScore:
-        task_context = ""
-        if task is not None:
-            task_context = f"Task description: {task.description}"
         tool_uses = execution_trace.get("tool_uses", [])
         tool_errors = []
         tool_count = len(tool_uses)
@@ -362,7 +352,7 @@ Return your evaluation as JSON with these fields:
 """},
             {"role": "user", "content": f"""
 Agent role: {agent.role}
-{task_context}
+Task description: {task.description}
 
 Tool invocation examples:
 {invocation_samples_text}

src/crewai/experimental/__init__.py

@@ -1,40 +0,0 @@
-from crewai.experimental.evaluation import (
-    BaseEvaluator,
-    EvaluationScore,
-    MetricCategory,
-    AgentEvaluationResult,
-    SemanticQualityEvaluator,
-    GoalAlignmentEvaluator,
-    ReasoningEfficiencyEvaluator,
-    ToolSelectionEvaluator,
-    ParameterExtractionEvaluator,
-    ToolInvocationEvaluator,
-    EvaluationTraceCallback,
-    create_evaluation_callbacks,
-    AgentEvaluator,
-    create_default_evaluator,
-    ExperimentRunner,
-    ExperimentResults,
-    ExperimentResult,
-)
-
-
-__all__ = [
-    "BaseEvaluator",
-    "EvaluationScore",
-    "MetricCategory",
-    "AgentEvaluationResult",
-    "SemanticQualityEvaluator",
-    "GoalAlignmentEvaluator",
-    "ReasoningEfficiencyEvaluator",
-    "ToolSelectionEvaluator",
-    "ParameterExtractionEvaluator",
-    "ToolInvocationEvaluator",
-    "EvaluationTraceCallback",
-    "create_evaluation_callbacks",
-    "AgentEvaluator",
-    "create_default_evaluator",
-    "ExperimentRunner",
-    "ExperimentResults",
-    "ExperimentResult"
-]

src/crewai/experimental/evaluation/agent_evaluator.py

@@ -1,245 +0,0 @@
-import threading
-from typing import Any
-
-from crewai.experimental.evaluation.base_evaluator import AgentEvaluationResult, AggregationStrategy
-from crewai.agent import Agent
-from crewai.task import Task
-from crewai.experimental.evaluation.evaluation_display import EvaluationDisplayFormatter
-from crewai.utilities.events.agent_events import AgentEvaluationStartedEvent, AgentEvaluationCompletedEvent, AgentEvaluationFailedEvent
-from crewai.experimental.evaluation import BaseEvaluator, create_evaluation_callbacks
-from collections.abc import Sequence
-from crewai.utilities.events.crewai_event_bus import crewai_event_bus
-from crewai.utilities.events.utils.console_formatter import ConsoleFormatter
-from crewai.utilities.events.task_events import TaskCompletedEvent
-from crewai.utilities.events.agent_events import LiteAgentExecutionCompletedEvent
-from crewai.experimental.evaluation.base_evaluator import AgentAggregatedEvaluationResult, EvaluationScore, MetricCategory
-
-class ExecutionState:
-    def __init__(self):
-        self.traces = {}
-        self.current_agent_id: str | None = None
-        self.current_task_id: str | None = None
-        self.iteration = 1
-        self.iterations_results = {}
-        self.agent_evaluators = {}
-
-class AgentEvaluator:
-    def __init__(
-        self,
-        agents: list[Agent],
-        evaluators: Sequence[BaseEvaluator] | None = None,
-    ):
-        self.agents: list[Agent] = agents
-        self.evaluators: Sequence[BaseEvaluator] | None = evaluators
-
-        self.callback = create_evaluation_callbacks()
-        self.console_formatter = ConsoleFormatter()
-        self.display_formatter = EvaluationDisplayFormatter()
-
-        self._thread_local: threading.local = threading.local()
-
-        for agent in self.agents:
-            self._execution_state.agent_evaluators[str(agent.id)] = self.evaluators
-
-        self._subscribe_to_events()
-
-    @property
-    def _execution_state(self) -> ExecutionState:
-        if not hasattr(self._thread_local, 'execution_state'):
-            self._thread_local.execution_state = ExecutionState()
-        return self._thread_local.execution_state
-
-    def _subscribe_to_events(self) -> None:
-        from typing import cast
-        crewai_event_bus.register_handler(TaskCompletedEvent, cast(Any, self._handle_task_completed))
-        crewai_event_bus.register_handler(LiteAgentExecutionCompletedEvent, cast(Any, self._handle_lite_agent_completed))
-
-    def _handle_task_completed(self, source: Any, event: TaskCompletedEvent) -> None:
-        assert event.task is not None
-        agent = event.task.agent
-        if agent and str(getattr(agent, 'id', 'unknown')) in self._execution_state.agent_evaluators:
-            self.emit_evaluation_started_event(agent_role=agent.role, agent_id=str(agent.id), task_id=str(event.task.id))
-
-            state = ExecutionState()
-            state.current_agent_id = str(agent.id)
-            state.current_task_id = str(event.task.id)
-
-            assert state.current_agent_id is not None and state.current_task_id is not None
-            trace = self.callback.get_trace(state.current_agent_id, state.current_task_id)
-
-            if not trace:
-                return
-
-            result = self.evaluate(
-                agent=agent,
-                task=event.task,
-                execution_trace=trace,
-                final_output=event.output,
-                state=state
-            )
-
-            current_iteration = self._execution_state.iteration
-            if current_iteration not in self._execution_state.iterations_results:
-                self._execution_state.iterations_results[current_iteration] = {}
-
-            if agent.role not in self._execution_state.iterations_results[current_iteration]:
-                self._execution_state.iterations_results[current_iteration][agent.role] = []
-
-            self._execution_state.iterations_results[current_iteration][agent.role].append(result)
-
-    def _handle_lite_agent_completed(self, source: object, event: LiteAgentExecutionCompletedEvent) -> None:
-        agent_info = event.agent_info
-        agent_id = str(agent_info["id"])
-
-        if agent_id in self._execution_state.agent_evaluators:
-            state = ExecutionState()
-            state.current_agent_id = agent_id
-            state.current_task_id = "lite_task"
-
-            target_agent = None
-            for agent in self.agents:
-                if str(agent.id) == agent_id:
-                    target_agent = agent
-                    break
-
-            if not target_agent:
-                return
-
-            assert state.current_agent_id is not None and state.current_task_id is not None
-            trace = self.callback.get_trace(state.current_agent_id, state.current_task_id)
-
-            if not trace:
-                return
-
-            result = self.evaluate(
-                agent=target_agent,
-                execution_trace=trace,
-                final_output=event.output,
-                state=state
-            )
-
-            current_iteration = self._execution_state.iteration
-            if current_iteration not in self._execution_state.iterations_results:
-                self._execution_state.iterations_results[current_iteration] = {}
-
-            agent_role = target_agent.role
-            if agent_role not in self._execution_state.iterations_results[current_iteration]:
-                self._execution_state.iterations_results[current_iteration][agent_role] = []
-
-            self._execution_state.iterations_results[current_iteration][agent_role].append(result)
-
-    def set_iteration(self, iteration: int) -> None:
-        self._execution_state.iteration = iteration
-
-    def reset_iterations_results(self) -> None:
-        self._execution_state.iterations_results = {}
-
-    def get_evaluation_results(self) -> dict[str, list[AgentEvaluationResult]]:
-        if self._execution_state.iterations_results and self._execution_state.iteration in self._execution_state.iterations_results:
-            return self._execution_state.iterations_results[self._execution_state.iteration]
-        return {}
-
-    def display_results_with_iterations(self) -> None:
-        self.display_formatter.display_summary_results(self._execution_state.iterations_results)
-
-    def get_agent_evaluation(self, strategy: AggregationStrategy = AggregationStrategy.SIMPLE_AVERAGE, include_evaluation_feedback: bool = True) -> dict[str, AgentAggregatedEvaluationResult]:
-        agent_results = {}
-        with crewai_event_bus.scoped_handlers():
-            task_results = self.get_evaluation_results()
-            for agent_role, results in task_results.items():
-                if not results:
-                    continue
-
-                agent_id = results[0].agent_id
-
-                aggregated_result = self.display_formatter._aggregate_agent_results(
-                    agent_id=agent_id,
-                    agent_role=agent_role,
-                    results=results,
-                    strategy=strategy
-                )
-
-                agent_results[agent_role] = aggregated_result
-
-
-            if self._execution_state.iterations_results and self._execution_state.iteration == max(self._execution_state.iterations_results.keys(), default=0):
-                self.display_results_with_iterations()
-
-            if include_evaluation_feedback:
-                self.display_evaluation_with_feedback()
-
-        return agent_results
-
-    def display_evaluation_with_feedback(self) -> None:
-        self.display_formatter.display_evaluation_with_feedback(self._execution_state.iterations_results)
-
-    def evaluate(
-        self,
-        agent: Agent,
-        execution_trace: dict[str, Any],
-        final_output: Any,
-        state: ExecutionState,
-        task: Task | None = None,
-    ) -> AgentEvaluationResult:
-        result = AgentEvaluationResult(
-            agent_id=state.current_agent_id or str(agent.id),
-            task_id=state.current_task_id or (str(task.id) if task else "unknown_task")
-        )
-
-        assert self.evaluators is not None
-        task_id = str(task.id) if task else None
-        for evaluator in self.evaluators:
-            try:
-                self.emit_evaluation_started_event(agent_role=agent.role, agent_id=str(agent.id), task_id=task_id)
-                score = evaluator.evaluate(
-                    agent=agent,
-                    task=task,
-                    execution_trace=execution_trace,
-                    final_output=final_output
-                )
-                result.metrics[evaluator.metric_category] = score
-                self.emit_evaluation_completed_event(agent_role=agent.role, agent_id=str(agent.id), task_id=task_id, metric_category=evaluator.metric_category, score=score)
-            except Exception as e:
-                self.emit_evaluation_failed_event(agent_role=agent.role, agent_id=str(agent.id), task_id=task_id, error=str(e))
-                self.console_formatter.print(f"Error in {evaluator.metric_category.value} evaluator: {str(e)}")
-
-        return result
-
-    def emit_evaluation_started_event(self, agent_role: str, agent_id: str, task_id: str | None = None):
-        crewai_event_bus.emit(
-            self,
-            AgentEvaluationStartedEvent(agent_role=agent_role, agent_id=agent_id, task_id=task_id, iteration=self._execution_state.iteration)
-        )
-
-    def emit_evaluation_completed_event(self, agent_role: str, agent_id: str, task_id: str | None = None, metric_category: MetricCategory | None = None, score: EvaluationScore | None = None):
-        crewai_event_bus.emit(
-            self,
-            AgentEvaluationCompletedEvent(agent_role=agent_role, agent_id=agent_id, task_id=task_id, iteration=self._execution_state.iteration, metric_category=metric_category, score=score)
-        )
-
-    def emit_evaluation_failed_event(self, agent_role: str, agent_id: str, error: str, task_id: str | None = None):
-        crewai_event_bus.emit(
-            self,
-            AgentEvaluationFailedEvent(agent_role=agent_role, agent_id=agent_id, task_id=task_id, iteration=self._execution_state.iteration, error=error)
-        )
-
-def create_default_evaluator(agents: list[Agent], llm: None = None):
-    from crewai.experimental.evaluation import (
-        GoalAlignmentEvaluator,
-        SemanticQualityEvaluator,
-        ToolSelectionEvaluator,
-        ParameterExtractionEvaluator,
-        ToolInvocationEvaluator,
-        ReasoningEfficiencyEvaluator
-    )
-
-    evaluators = [
-        GoalAlignmentEvaluator(llm=llm),
-        SemanticQualityEvaluator(llm=llm),
-        ToolSelectionEvaluator(llm=llm),
-        ParameterExtractionEvaluator(llm=llm),
-        ToolInvocationEvaluator(llm=llm),
-        ReasoningEfficiencyEvaluator(llm=llm),
-    ]
-
-    return AgentEvaluator(evaluators=evaluators, agents=agents)

src/crewai/experimental/evaluation/experiment/__init__.py

@@ -1,8 +0,0 @@
-from crewai.experimental.evaluation.experiment.runner import ExperimentRunner
-from crewai.experimental.evaluation.experiment.result import ExperimentResults, ExperimentResult
-
-__all__ = [
-    "ExperimentRunner",
-    "ExperimentResults",
-    "ExperimentResult"
-]

src/crewai/experimental/evaluation/experiment/result.py

@@ -1,122 +0,0 @@
-import json
-import os
-from datetime import datetime, timezone
-from typing import Any
-from pydantic import BaseModel
-
-class ExperimentResult(BaseModel):
-    identifier: str
-    inputs: dict[str, Any]
-    score: int | dict[str, int | float]
-    expected_score: int | dict[str, int | float]
-    passed: bool
-    agent_evaluations: dict[str, Any] | None = None
-
-class ExperimentResults:
-    def __init__(self, results: list[ExperimentResult], metadata: dict[str, Any] | None = None):
-        self.results = results
-        self.metadata = metadata or {}
-        self.timestamp = datetime.now(timezone.utc)
-
-        from crewai.experimental.evaluation.experiment.result_display import ExperimentResultsDisplay
-        self.display = ExperimentResultsDisplay()
-
-    def to_json(self, filepath: str | None = None) -> dict[str, Any]:
-        data = {
-            "timestamp": self.timestamp.isoformat(),
-            "metadata": self.metadata,
-            "results": [r.model_dump(exclude={"agent_evaluations"}) for r in self.results]
-        }
-
-        if filepath:
-            with open(filepath, 'w') as f:
-                json.dump(data, f, indent=2)
-            self.display.console.print(f"[green]Results saved to {filepath}[/green]")
-
-        return data
-
-    def compare_with_baseline(self, baseline_filepath: str, save_current: bool = True, print_summary: bool = False) -> dict[str, Any]:
-        baseline_runs = []
-
-        if os.path.exists(baseline_filepath) and os.path.getsize(baseline_filepath) > 0:
-            try:
-                with open(baseline_filepath, 'r') as f:
-                    baseline_data = json.load(f)
-
-                if isinstance(baseline_data, dict) and "timestamp" in baseline_data:
-                    baseline_runs = [baseline_data]
-                elif isinstance(baseline_data, list):
-                    baseline_runs = baseline_data
-            except (json.JSONDecodeError, FileNotFoundError) as e:
-                self.display.console.print(f"[yellow]Warning: Could not load baseline file: {str(e)}[/yellow]")
-
-        if not baseline_runs:
-            if save_current:
-                current_data = self.to_json()
-                with open(baseline_filepath, 'w') as f:
-                    json.dump([current_data], f, indent=2)
-                self.display.console.print(f"[green]Saved current results as new baseline to {baseline_filepath}[/green]")
-            return {"is_baseline": True, "changes": {}}
-
-        baseline_runs.sort(key=lambda x: x.get("timestamp", ""), reverse=True)
-        latest_run = baseline_runs[0]
-
-        comparison = self._compare_with_run(latest_run)
-
-        if print_summary:
-            self.display.comparison_summary(comparison, latest_run["timestamp"])
-
-        if save_current:
-            current_data = self.to_json()
-            baseline_runs.append(current_data)
-            with open(baseline_filepath, 'w') as f:
-                json.dump(baseline_runs, f, indent=2)
-            self.display.console.print(f"[green]Added current results to baseline file {baseline_filepath}[/green]")
-
-        return comparison
-
-    def _compare_with_run(self, baseline_run: dict[str, Any]) -> dict[str, Any]:
-        baseline_results = baseline_run.get("results", [])
-
-        baseline_lookup = {}
-        for result in baseline_results:
-            test_identifier = result.get("identifier")
-            if test_identifier:
-                baseline_lookup[test_identifier] = result
-
-        improved = []
-        regressed = []
-        unchanged = []
-        new_tests = []
-
-        for result in self.results:
-            test_identifier = result.identifier
-            if not test_identifier or test_identifier not in baseline_lookup:
-                new_tests.append(test_identifier)
-                continue
-
-            baseline_result = baseline_lookup[test_identifier]
-            baseline_passed = baseline_result.get("passed", False)
-            if result.passed and not baseline_passed:
-                improved.append(test_identifier)
-            elif not result.passed and baseline_passed:
-                regressed.append(test_identifier)
-            else:
-                unchanged.append(test_identifier)
-
-        missing_tests = []
-        current_test_identifiers = {result.identifier for result in self.results}
-        for result in baseline_results:
-            test_identifier = result.get("identifier")
-            if test_identifier and test_identifier not in current_test_identifiers:
-                missing_tests.append(test_identifier)
-
-        return {
-            "improved": improved,
-            "regressed": regressed,
-            "unchanged": unchanged,
-            "new_tests": new_tests,
-            "missing_tests": missing_tests,
-            "total_compared": len(improved) + len(regressed) + len(unchanged),
-            "baseline_timestamp": baseline_run.get("timestamp", "unknown")
-        }

src/crewai/experimental/evaluation/experiment/result_display.py

@@ -1,70 +0,0 @@
-from typing import Dict, Any
-from rich.console import Console
-from rich.table import Table
-from rich.panel import Panel
-from crewai.experimental.evaluation.experiment.result import ExperimentResults
-
-class ExperimentResultsDisplay:
-    def __init__(self):
-        self.console = Console()
-
-    def summary(self, experiment_results: ExperimentResults):
-        total = len(experiment_results.results)
-        passed = sum(1 for r in experiment_results.results if r.passed)
-
-        table = Table(title="Experiment Summary")
-        table.add_column("Metric", style="cyan")
-        table.add_column("Value", style="green")
-
-        table.add_row("Total Test Cases", str(total))
-        table.add_row("Passed", str(passed))
-        table.add_row("Failed", str(total - passed))
-        table.add_row("Success Rate", f"{(passed / total * 100):.1f}%" if total > 0 else "N/A")
-
-        self.console.print(table)
-
-    def comparison_summary(self, comparison: Dict[str, Any], baseline_timestamp: str):
-        self.console.print(Panel(f"[bold]Comparison with baseline run from {baseline_timestamp}[/bold]",
-                                 expand=False))
-
-        table = Table(title="Results Comparison")
-        table.add_column("Metric", style="cyan")
-        table.add_column("Count", style="white")
-        table.add_column("Details", style="dim")
-
-        improved = comparison.get("improved", [])
-        if improved:
-            details = ", ".join([f"{test_identifier}" for test_identifier in improved[:3]])
-            if len(improved) > 3:
-                details += f" and {len(improved) - 3} more"
-            table.add_row("✅ Improved", str(len(improved)), details)
-        else:
-            table.add_row("✅ Improved", "0", "")
-
-        regressed = comparison.get("regressed", [])
-        if regressed:
-            details = ", ".join([f"{test_identifier}" for test_identifier in regressed[:3]])
-            if len(regressed) > 3:
-                details += f" and {len(regressed) - 3} more"
-            table.add_row("❌ Regressed", str(len(regressed)), details, style="red")
-        else:
-            table.add_row("❌ Regressed", "0", "")
-
-        unchanged = comparison.get("unchanged", [])
-        table.add_row("⏺ Unchanged", str(len(unchanged)), "")
-
-        new_tests = comparison.get("new_tests", [])
-        if new_tests:
-            details = ", ".join(new_tests[:3])
-            if len(new_tests) > 3:
-                details += f" and {len(new_tests) - 3} more"
-            table.add_row("➕ New Tests", str(len(new_tests)), details)
-
-        missing_tests = comparison.get("missing_tests", [])
-        if missing_tests:
-            details = ", ".join(missing_tests[:3])
-            if len(missing_tests) > 3:
-                details += f" and {len(missing_tests) - 3} more"
-            table.add_row("➖ Missing Tests", str(len(missing_tests)), details)
-
-        self.console.print(table)

src/crewai/experimental/evaluation/experiment/runner.py

@@ -1,125 +0,0 @@
-from collections import defaultdict
-from hashlib import md5
-from typing import Any
-
-from crewai import Crew, Agent
-from crewai.experimental.evaluation import AgentEvaluator, create_default_evaluator
-from crewai.experimental.evaluation.experiment.result_display import ExperimentResultsDisplay
-from crewai.experimental.evaluation.experiment.result import ExperimentResults, ExperimentResult
-from crewai.experimental.evaluation.evaluation_display import AgentAggregatedEvaluationResult
-
-class ExperimentRunner:
-    def __init__(self, dataset: list[dict[str, Any]]):
-        self.dataset = dataset or []
-        self.evaluator: AgentEvaluator | None = None
-        self.display = ExperimentResultsDisplay()
-
-    def run(self, crew: Crew | None = None, agents: list[Agent] | None = None, print_summary: bool = False) -> ExperimentResults:
-        if crew and not agents:
-            agents = crew.agents
-
-        assert agents is not None
-        self.evaluator = create_default_evaluator(agents=agents)
-
-        results = []
-
-        for test_case in self.dataset:
-            self.evaluator.reset_iterations_results()
-            result = self._run_test_case(test_case=test_case, crew=crew, agents=agents)
-            results.append(result)
-
-        experiment_results = ExperimentResults(results)
-
-        if print_summary:
-            self.display.summary(experiment_results)
-
-        return experiment_results
-
-    def _run_test_case(self, test_case: dict[str, Any], agents: list[Agent], crew: Crew | None = None) -> ExperimentResult:
-        inputs = test_case["inputs"]
-        expected_score = test_case["expected_score"]
-        identifier = test_case.get("identifier") or md5(str(test_case).encode(), usedforsecurity=False).hexdigest()
-
-        try:
-            self.display.console.print(f"[dim]Running crew with input: {str(inputs)[:50]}...[/dim]")
-            self.display.console.print("\n")
-            if crew:
-                crew.kickoff(inputs=inputs)
-            else:
-                for agent in agents:
-                    agent.kickoff(**inputs)
-
-            assert self.evaluator is not None
-            agent_evaluations = self.evaluator.get_agent_evaluation()
-
-            actual_score = self._extract_scores(agent_evaluations)
-
-            passed = self._assert_scores(expected_score, actual_score)
-            return ExperimentResult(
-                identifier=identifier,
-                inputs=inputs,
-                score=actual_score,
-                expected_score=expected_score,
-                passed=passed,
-                agent_evaluations=agent_evaluations
-            )
-
-        except Exception as e:
-            self.display.console.print(f"[red]Error running test case: {str(e)}[/red]")
-            return ExperimentResult(
-                identifier=identifier,
-                inputs=inputs,
-                score=0,
-                expected_score=expected_score,
-                passed=False
-            )
-
-    def _extract_scores(self, agent_evaluations: dict[str, AgentAggregatedEvaluationResult]) -> float | dict[str,  float]:
-        all_scores: dict[str, list[float]] = defaultdict(list)
-        for evaluation in agent_evaluations.values():
-            for metric_name, score in evaluation.metrics.items():
-                if score.score is not None:
-                    all_scores[metric_name.value].append(score.score)
-
-        avg_scores = {m: sum(s)/len(s) for m, s in all_scores.items()}
-
-        if len(avg_scores) == 1:
-            return list(avg_scores.values())[0]
-
-        return avg_scores
-
-    def _assert_scores(self, expected: float | dict[str, float],
-                        actual: float | dict[str, float]) -> bool:
-        """
-        Compare expected and actual scores, and return whether the test case passed.
-
-        The rules for comparison are as follows:
-        - If both expected and actual scores are single numbers, the actual score must be >= expected.
-        - If expected is a single number and actual is a dict, compare against the average of actual values.
-        - If expected is a dict and actual is a single number, actual must be >= all expected values.
-        - If both are dicts, actual must have matching keys with values >= expected values.
-        """
-
-        if isinstance(expected, (int, float)) and isinstance(actual, (int, float)):
-            return actual >= expected
-
-        if isinstance(expected, dict) and isinstance(actual, (int, float)):
-            return all(actual >= exp_score for exp_score in expected.values())
-
-        if isinstance(expected, (int, float)) and isinstance(actual, dict):
-            if not actual:
-                return False
-            avg_score = sum(actual.values()) / len(actual)
-            return avg_score >= expected
-
-        if isinstance(expected, dict) and isinstance(actual, dict):
-            if not expected:
-                return True
-            matching_keys = set(expected.keys()) & set(actual.keys())
-            if not matching_keys:
-                return False
-
-            # All matching keys must have actual >= expected
-            return all(actual[key] >= expected[key] for key in matching_keys)
-
-        return False

src/crewai/experimental/evaluation/metrics/__init__.py

@@ -1,26 +0,0 @@
-from crewai.experimental.evaluation.metrics.reasoning_metrics import (
-    ReasoningEfficiencyEvaluator
-)
-
-from crewai.experimental.evaluation.metrics.tools_metrics import (
-    ToolSelectionEvaluator,
-    ParameterExtractionEvaluator,
-    ToolInvocationEvaluator
-)
-
-from crewai.experimental.evaluation.metrics.goal_metrics import (
-    GoalAlignmentEvaluator
-)
-
-from crewai.experimental.evaluation.metrics.semantic_quality_metrics import (
-    SemanticQualityEvaluator
-)
-
-__all__ = [
-    "ReasoningEfficiencyEvaluator",
-    "ToolSelectionEvaluator",
-    "ParameterExtractionEvaluator",
-    "ToolInvocationEvaluator",
-    "GoalAlignmentEvaluator",
-    "SemanticQualityEvaluator"
-]

src/crewai/experimental/evaluation/testing.py

@@ -1,52 +0,0 @@
-import inspect
-
-from typing_extensions import Any
-import warnings
-from crewai.experimental.evaluation.experiment import ExperimentResults, ExperimentRunner
-from crewai import Crew, Agent
-
-def assert_experiment_successfully(experiment_results: ExperimentResults, baseline_filepath: str | None = None) -> None:
-    failed_tests = [result for result in experiment_results.results if not result.passed]
-
-    if failed_tests:
-        detailed_failures: list[str] = []
-
-        for result in failed_tests:
-            expected = result.expected_score
-            actual = result.score
-            detailed_failures.append(f"- {result.identifier}: expected {expected}, got {actual}")
-
-        failure_details = "\n".join(detailed_failures)
-        raise AssertionError(f"The following test cases failed:\n{failure_details}")
-
-    baseline_filepath = baseline_filepath or _get_baseline_filepath_fallback()
-    comparison = experiment_results.compare_with_baseline(baseline_filepath=baseline_filepath)
-    assert_experiment_no_regression(comparison)
-
-def assert_experiment_no_regression(comparison_result: dict[str, list[str]]) -> None:
-    regressed = comparison_result.get("regressed", [])
-    if regressed:
-        raise AssertionError(f"Regression detected! The following tests that previously passed now fail: {regressed}")
-
-    missing_tests = comparison_result.get("missing_tests", [])
-    if missing_tests:
-        warnings.warn(
-            f"Warning: {len(missing_tests)} tests from the baseline are missing in the current run: {missing_tests}",
-            UserWarning
-        )
-
-def run_experiment(dataset: list[dict[str, Any]], crew: Crew | None = None, agents: list[Agent] | None = None, verbose: bool = False) -> ExperimentResults:
-    runner = ExperimentRunner(dataset=dataset)
-
-    return runner.run(agents=agents, crew=crew, print_summary=verbose)
-
-def _get_baseline_filepath_fallback() -> str:
-    test_func_name = "experiment_fallback"
-
-    try:
-        current_frame = inspect.currentframe()
-        if current_frame is not None:
-            test_func_name = current_frame.f_back.f_back.f_code.co_name # type: ignore[union-attr]
-    except Exception:
-        ...
-    return f"{test_func_name}_results.json"

src/crewai/lite_agent.py

@@ -305,7 +305,6 @@ class LiteAgent(FlowTrackable, BaseModel):
         """
         # Create agent info for event emission
         agent_info = {
-            "id": self.id,
             "role": self.role,
             "goal": self.goal,
             "backstory": self.backstory,

src/crewai/memory/storage/mem0_storage.py

@@ -93,7 +93,13 @@ class Mem0Storage(Storage):
         if params:
             if isinstance(self.memory, MemoryClient):
                 params["output_format"] = "v1.1"
-            self.memory.add(value, **params)
+            
+            if isinstance(value, str):
+                messages = [{"role": "assistant", "content": value}]
+            else:
+                messages = value
+            
+            self.memory.add(messages, **params)
 
     def search(
         self,

src/crewai/task.py

@@ -67,7 +67,6 @@ class Task(BaseModel):
         description: Descriptive text detailing task's purpose and execution.
         expected_output: Clear definition of expected task outcome.
         output_file: File path for storing task output.
-        create_directory: Whether to create the directory for output_file if it doesn't exist.
         output_json: Pydantic model for structuring JSON output.
         output_pydantic: Pydantic model for task output.
         security_config: Security configuration including fingerprinting.
@@ -116,10 +115,6 @@ class Task(BaseModel):
         description="A file path to be used to create a file output.",
         default=None,
     )
-    create_directory: Optional[bool] = Field(
-        description="Whether to create the directory for output_file if it doesn't exist.",
-        default=True,
-    )
     output: Optional[TaskOutput] = Field(
         description="Task output, it's final result after being executed", default=None
     )
@@ -758,10 +753,8 @@ Follow these guidelines:
             resolved_path = Path(self.output_file).expanduser().resolve()
             directory = resolved_path.parent
 
-            if self.create_directory and not directory.exists():
+            if not directory.exists():
                 directory.mkdir(parents=True, exist_ok=True)
-            elif not self.create_directory and not directory.exists():
-                raise RuntimeError(f"Directory {directory} does not exist and create_directory is False")
 
             with resolved_path.open("w", encoding="utf-8") as file:
                 if isinstance(result, dict):

src/crewai/utilities/events/__init__.py

@@ -17,9 +17,6 @@ from .agent_events import (
     AgentExecutionStartedEvent,
     AgentExecutionCompletedEvent,
     AgentExecutionErrorEvent,
-    AgentEvaluationStartedEvent,
-    AgentEvaluationCompletedEvent,
-    AgentEvaluationFailedEvent,
 )
 from .task_events import (
     TaskStartedEvent,
@@ -77,9 +74,6 @@ __all__ = [
     "AgentExecutionStartedEvent",
     "AgentExecutionCompletedEvent",
     "AgentExecutionErrorEvent",
-    "AgentEvaluationStartedEvent",
-    "AgentEvaluationCompletedEvent",
-    "AgentEvaluationFailedEvent",
     "TaskStartedEvent",
     "TaskCompletedEvent",
     "TaskFailedEvent",

src/crewai/utilities/events/agent_events.py

@@ -123,28 +123,3 @@ class AgentLogsExecutionEvent(BaseEvent):
     type: str = "agent_logs_execution"
 
     model_config = {"arbitrary_types_allowed": True}
-
-# Agent Eval events
-class AgentEvaluationStartedEvent(BaseEvent):
-    agent_id: str
-    agent_role: str
-    task_id: str | None = None
-    iteration: int
-    type: str = "agent_evaluation_started"
-
-class AgentEvaluationCompletedEvent(BaseEvent):
-    agent_id: str
-    agent_role: str
-    task_id: str | None = None
-    iteration: int
-    metric_category: Any
-    score: Any
-    type: str = "agent_evaluation_completed"
-
-class AgentEvaluationFailedEvent(BaseEvent):
-    agent_id: str
-    agent_role: str
-    task_id: str | None = None
-    iteration: int
-    error: str
-    type: str = "agent_evaluation_failed"

src/crewai/utilities/events/event_types.py

@@ -4,7 +4,6 @@ from .agent_events import (
     AgentExecutionCompletedEvent,
     AgentExecutionErrorEvent,
     AgentExecutionStartedEvent,
-    LiteAgentExecutionCompletedEvent,
 )
 from .crew_events import (
     CrewKickoffCompletedEvent,
@@ -81,7 +80,6 @@ EventTypes = Union[
     CrewTrainFailedEvent,
     AgentExecutionStartedEvent,
     AgentExecutionCompletedEvent,
-    LiteAgentExecutionCompletedEvent,
     TaskStartedEvent,
     TaskCompletedEvent,
     TaskFailedEvent,

tests/cassettes/TestAgentEvaluator.test_eval_lite_agent.yaml

@@ -1,237 +0,0 @@
-interactions:
-- request:
-    body: '{"messages": [{"role": "system", "content": "You are Test Agent. An agent
-      created for testing purposes\nYour personal goal is: Complete test tasks successfully\n\nTo
-      give my best complete final answer to the task respond using the exact following
-      format:\n\nThought: I now can give a great answer\nFinal Answer: Your final
-      answer must be the great and the most complete as possible, it must be outcome
-      described.\n\nI MUST use these formats, my job depends on it!"}, {"role": "user",
-      "content": "Complete this task successfully"}], "model": "gpt-4o-mini", "stop":
-      ["\nObservation:"]}'
-    headers:
-      accept:
-      - application/json
-      accept-encoding:
-      - gzip, deflate, zstd
-      connection:
-      - keep-alive
-      content-length:
-      - '583'
-      content-type:
-      - application/json
-      host:
-      - api.openai.com
-      user-agent:
-      - OpenAI/Python 1.93.0
-      x-stainless-arch:
-      - arm64
-      x-stainless-async:
-      - 'false'
-      x-stainless-lang:
-      - python
-      x-stainless-os:
-      - MacOS
-      x-stainless-package-version:
-      - 1.93.0
-      x-stainless-raw-response:
-      - 'true'
-      x-stainless-read-timeout:
-      - '600.0'
-      x-stainless-retry-count:
-      - '0'
-      x-stainless-runtime:
-      - CPython
-      x-stainless-runtime-version:
-      - 3.11.12
-    method: POST
-    uri: https://api.openai.com/v1/chat/completions
-  response:
-    body:
-      string: !!binary |
-        H4sIAAAAAAAAAwAAAP//jFNNb9swDL3nVxA6J0U+HKTNbd0woMAOw7Bu6LbCUCXa1iqLgkgnzYr8
-        98FKWqdbB+wiQHx81OMj9TgCUM6qNSjTaDFt9JNL+TZ7N/dfrusPN01NyV6vPk3f/mrl5vLrXI17
-        Bt39RCNPrDNDbfQojsIBNgm1YF91tlrOl+fzxXKWgZYs+p5WR5kUNGldcJP5dF5MpqvJ7PzIbsgZ
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tests/evaluation/metrics/test_goal_metrics.py

@@ -1,8 +1,8 @@
 from unittest.mock import patch, MagicMock
-from tests.experimental.evaluation.metrics.base_evaluation_metrics_test import BaseEvaluationMetricsTest
+from tests.evaluation.metrics.base_evaluation_metrics_test import BaseEvaluationMetricsTest
 
-from crewai.experimental.evaluation.base_evaluator import EvaluationScore
-from crewai.experimental.evaluation.metrics.goal_metrics import GoalAlignmentEvaluator
+from crewai.evaluation.base_evaluator import EvaluationScore
+from crewai.evaluation.metrics.goal_metrics import GoalAlignmentEvaluator
 from crewai.utilities.llm_utils import LLM
 
 

tests/evaluation/metrics/test_reasoning_metrics.py

@@ -3,12 +3,12 @@ from unittest.mock import patch, MagicMock
 from typing import List, Dict, Any
 
 from crewai.tasks.task_output import TaskOutput
-from crewai.experimental.evaluation.metrics.reasoning_metrics import (
+from crewai.evaluation.metrics.reasoning_metrics import (
     ReasoningEfficiencyEvaluator,
 )
-from tests.experimental.evaluation.metrics.base_evaluation_metrics_test import BaseEvaluationMetricsTest
+from tests.evaluation.metrics.base_evaluation_metrics_test import BaseEvaluationMetricsTest
 from crewai.utilities.llm_utils import LLM
-from crewai.experimental.evaluation.base_evaluator import EvaluationScore
+from crewai.evaluation.base_evaluator import EvaluationScore
 
 class TestReasoningEfficiencyEvaluator(BaseEvaluationMetricsTest):
     @pytest.fixture

tests/evaluation/metrics/test_semantic_quality_metrics.py

@@ -1,8 +1,8 @@
 from unittest.mock import patch, MagicMock
 
-from crewai.experimental.evaluation.base_evaluator import EvaluationScore
-from crewai.experimental.evaluation.metrics.semantic_quality_metrics import SemanticQualityEvaluator
-from tests.experimental.evaluation.metrics.base_evaluation_metrics_test import BaseEvaluationMetricsTest
+from crewai.evaluation.base_evaluator import EvaluationScore
+from crewai.evaluation.metrics.semantic_quality_metrics import SemanticQualityEvaluator
+from tests.evaluation.metrics.base_evaluation_metrics_test import BaseEvaluationMetricsTest
 from crewai.utilities.llm_utils import LLM
 
 class TestSemanticQualityEvaluator(BaseEvaluationMetricsTest):

tests/evaluation/metrics/test_tools_metrics.py

@@ -1,12 +1,12 @@
 from unittest.mock import patch, MagicMock
 
-from crewai.experimental.evaluation.metrics.tools_metrics import (
+from crewai.evaluation.metrics.tools_metrics import (
     ToolSelectionEvaluator,
     ParameterExtractionEvaluator,
     ToolInvocationEvaluator
 )
 from crewai.utilities.llm_utils import LLM
-from tests.experimental.evaluation.metrics.base_evaluation_metrics_test import BaseEvaluationMetricsTest
+from tests.evaluation.metrics.base_evaluation_metrics_test import BaseEvaluationMetricsTest
 
 class TestToolSelectionEvaluator(BaseEvaluationMetricsTest):
     def test_no_tools_available(self, mock_task, mock_agent):

tests/evaluation/test_agent_evaluator.py

@@ -0,0 +1,95 @@
+import pytest
+
+from crewai.agent import Agent
+from crewai.task import Task
+from crewai.crew import Crew
+from crewai.evaluation.agent_evaluator import AgentEvaluator
+from crewai.evaluation.base_evaluator import AgentEvaluationResult
+from crewai.evaluation import (
+    GoalAlignmentEvaluator,
+    SemanticQualityEvaluator,
+    ToolSelectionEvaluator,
+    ParameterExtractionEvaluator,
+    ToolInvocationEvaluator,
+    ReasoningEfficiencyEvaluator
+)
+
+from crewai.evaluation import create_default_evaluator
+class TestAgentEvaluator:
+    @pytest.fixture
+    def mock_crew(self):
+        agent = Agent(
+            role="Test Agent",
+            goal="Complete test tasks successfully",
+            backstory="An agent created for testing purposes",
+            allow_delegation=False,
+            verbose=False
+        )
+
+        task = Task(
+            description="Test task description",
+            agent=agent,
+            expected_output="Expected test output"
+        )
+
+        crew = Crew(
+            agents=[agent],
+            tasks=[task]
+        )
+        return crew
+
+    def test_set_iteration(self):
+        agent_evaluator = AgentEvaluator()
+
+        agent_evaluator.set_iteration(3)
+        assert agent_evaluator.iteration == 3
+
+    @pytest.mark.vcr(filter_headers=["authorization"])
+    def test_evaluate_current_iteration(self, mock_crew):
+        agent_evaluator = AgentEvaluator(crew=mock_crew, evaluators=[GoalAlignmentEvaluator()])
+
+        mock_crew.kickoff()
+
+        results = agent_evaluator.evaluate_current_iteration()
+
+        assert isinstance(results, dict)
+
+        agent, = mock_crew.agents
+        task, = mock_crew.tasks
+
+        assert len(mock_crew.agents) == 1
+        assert agent.role in results
+        assert len(results[agent.role]) == 1
+
+        result, = results[agent.role]
+        assert isinstance(result, AgentEvaluationResult)
+
+        assert result.agent_id == str(agent.id)
+        assert result.task_id == str(task.id)
+
+        goal_alignment, = result.metrics.values()
+        assert goal_alignment.score == 5.0
+
+        expected_feedback = "The agent's output demonstrates an understanding of the need for a comprehensive document"
+        assert expected_feedback in goal_alignment.feedback
+
+        assert goal_alignment.raw_response is not None
+        assert '"score": 5' in goal_alignment.raw_response
+
+    def test_create_default_evaluator(self, mock_crew):
+        agent_evaluator = create_default_evaluator(crew=mock_crew)
+        assert isinstance(agent_evaluator, AgentEvaluator)
+        assert agent_evaluator.crew == mock_crew
+
+        expected_types = [
+            GoalAlignmentEvaluator,
+            SemanticQualityEvaluator,
+            ToolSelectionEvaluator,
+            ParameterExtractionEvaluator,
+            ToolInvocationEvaluator,
+            ReasoningEfficiencyEvaluator
+        ]
+
+        assert len(agent_evaluator.evaluators) == len(expected_types)
+        for evaluator, expected_type in zip(agent_evaluator.evaluators, expected_types):
+            assert isinstance(evaluator, expected_type)

tests/experimental/evaluation/test_agent_evaluator.py

@@ -1,278 +0,0 @@
-import pytest
-
-from crewai.agent import Agent
-from crewai.task import Task
-from crewai.crew import Crew
-from crewai.experimental.evaluation.agent_evaluator import AgentEvaluator
-from crewai.experimental.evaluation.base_evaluator import AgentEvaluationResult
-from crewai.experimental.evaluation import (
-    GoalAlignmentEvaluator,
-    SemanticQualityEvaluator,
-    ToolSelectionEvaluator,
-    ParameterExtractionEvaluator,
-    ToolInvocationEvaluator,
-    ReasoningEfficiencyEvaluator,
-    MetricCategory,
-    EvaluationScore
-)
-
-from crewai.utilities.events.agent_events import AgentEvaluationStartedEvent, AgentEvaluationCompletedEvent, AgentEvaluationFailedEvent
-from crewai.utilities.events.crewai_event_bus import crewai_event_bus
-from crewai.experimental.evaluation import create_default_evaluator
-
-class TestAgentEvaluator:
-    @pytest.fixture
-    def mock_crew(self):
-        agent = Agent(
-            role="Test Agent",
-            goal="Complete test tasks successfully",
-            backstory="An agent created for testing purposes",
-            allow_delegation=False,
-            verbose=False
-        )
-
-        task = Task(
-            description="Test task description",
-            agent=agent,
-            expected_output="Expected test output"
-        )
-
-        crew = Crew(
-            agents=[agent],
-            tasks=[task]
-        )
-        return crew
-
-    def test_set_iteration(self):
-        agent_evaluator = AgentEvaluator(agents=[])
-
-        agent_evaluator.set_iteration(3)
-        assert agent_evaluator._execution_state.iteration == 3
-
-    @pytest.mark.vcr(filter_headers=["authorization"])
-    def test_evaluate_current_iteration(self, mock_crew):
-        agent_evaluator = AgentEvaluator(agents=mock_crew.agents, evaluators=[GoalAlignmentEvaluator()])
-
-        mock_crew.kickoff()
-
-        results = agent_evaluator.get_evaluation_results()
-
-        assert isinstance(results, dict)
-
-        agent, = mock_crew.agents
-        task, = mock_crew.tasks
-
-        assert len(mock_crew.agents) == 1
-        assert agent.role in results
-        assert len(results[agent.role]) == 1
-
-        result, = results[agent.role]
-        assert isinstance(result, AgentEvaluationResult)
-
-        assert result.agent_id == str(agent.id)
-        assert result.task_id == str(task.id)
-
-        goal_alignment, = result.metrics.values()
-        assert goal_alignment.score == 5.0
-
-        expected_feedback = "The agent's output demonstrates an understanding of the need for a comprehensive document outlining task"
-        assert expected_feedback in goal_alignment.feedback
-
-        assert goal_alignment.raw_response is not None
-        assert '"score": 5' in goal_alignment.raw_response
-
-    def test_create_default_evaluator(self, mock_crew):
-        agent_evaluator = create_default_evaluator(agents=mock_crew.agents)
-        assert isinstance(agent_evaluator, AgentEvaluator)
-        assert agent_evaluator.agents == mock_crew.agents
-
-        expected_types = [
-            GoalAlignmentEvaluator,
-            SemanticQualityEvaluator,
-            ToolSelectionEvaluator,
-            ParameterExtractionEvaluator,
-            ToolInvocationEvaluator,
-            ReasoningEfficiencyEvaluator
-        ]
-
-        assert len(agent_evaluator.evaluators) == len(expected_types)
-        for evaluator, expected_type in zip(agent_evaluator.evaluators, expected_types):
-            assert isinstance(evaluator, expected_type)
-
-    @pytest.mark.vcr(filter_headers=["authorization"])
-    def test_eval_lite_agent(self):
-        agent = Agent(
-            role="Test Agent",
-            goal="Complete test tasks successfully",
-            backstory="An agent created for testing purposes",
-        )
-
-        with crewai_event_bus.scoped_handlers():
-            events = {}
-            @crewai_event_bus.on(AgentEvaluationStartedEvent)
-            def capture_started(source, event):
-                events["started"] = event
-
-            @crewai_event_bus.on(AgentEvaluationCompletedEvent)
-            def capture_completed(source, event):
-                events["completed"] = event
-
-            @crewai_event_bus.on(AgentEvaluationFailedEvent)
-            def capture_failed(source, event):
-                events["failed"] = event
-
-            agent_evaluator = AgentEvaluator(agents=[agent], evaluators=[GoalAlignmentEvaluator()])
-
-            agent.kickoff(messages="Complete this task successfully")
-
-            assert events.keys() == {"started", "completed"}
-            assert events["started"].agent_id == str(agent.id)
-            assert events["started"].agent_role == agent.role
-            assert events["started"].task_id is None
-            assert events["started"].iteration == 1
-
-            assert events["completed"].agent_id == str(agent.id)
-            assert events["completed"].agent_role == agent.role
-            assert events["completed"].task_id is None
-            assert events["completed"].iteration == 1
-            assert events["completed"].metric_category == MetricCategory.GOAL_ALIGNMENT
-            assert isinstance(events["completed"].score, EvaluationScore)
-            assert events["completed"].score.score == 2.0
-
-            results = agent_evaluator.get_evaluation_results()
-
-            assert isinstance(results, dict)
-
-            result, = results[agent.role]
-            assert isinstance(result, AgentEvaluationResult)
-
-            assert result.agent_id == str(agent.id)
-            assert result.task_id == "lite_task"
-
-            goal_alignment, = result.metrics.values()
-            assert goal_alignment.score == 2.0
-
-            expected_feedback = "The agent did not demonstrate a clear understanding of the task goal, which is to complete test tasks successfully"
-            assert expected_feedback in goal_alignment.feedback
-
-            assert goal_alignment.raw_response is not None
-            assert '"score": 2' in goal_alignment.raw_response
-
-    @pytest.mark.vcr(filter_headers=["authorization"])
-    def test_eval_specific_agents_from_crew(self, mock_crew):
-        agent = Agent(
-            role="Test Agent Eval",
-            goal="Complete test tasks successfully",
-            backstory="An agent created for testing purposes",
-        )
-        task = Task(
-            description="Test task description",
-            agent=agent,
-            expected_output="Expected test output"
-        )
-        mock_crew.agents.append(agent)
-        mock_crew.tasks.append(task)
-
-        with crewai_event_bus.scoped_handlers():
-            events = {}
-            @crewai_event_bus.on(AgentEvaluationStartedEvent)
-            def capture_started(source, event):
-                events["started"] = event
-
-            @crewai_event_bus.on(AgentEvaluationCompletedEvent)
-            def capture_completed(source, event):
-                events["completed"] = event
-
-            @crewai_event_bus.on(AgentEvaluationFailedEvent)
-            def capture_failed(source, event):
-                events["failed"] = event
-
-            agent_evaluator = AgentEvaluator(agents=[agent], evaluators=[GoalAlignmentEvaluator()])
-            mock_crew.kickoff()
-
-            assert events.keys() == {"started", "completed"}
-            assert events["started"].agent_id == str(agent.id)
-            assert events["started"].agent_role == agent.role
-            assert events["started"].task_id == str(task.id)
-            assert events["started"].iteration == 1
-
-            assert events["completed"].agent_id == str(agent.id)
-            assert events["completed"].agent_role == agent.role
-            assert events["completed"].task_id == str(task.id)
-            assert events["completed"].iteration == 1
-            assert events["completed"].metric_category == MetricCategory.GOAL_ALIGNMENT
-            assert isinstance(events["completed"].score, EvaluationScore)
-            assert events["completed"].score.score == 5.0
-
-            results = agent_evaluator.get_evaluation_results()
-
-            assert isinstance(results, dict)
-            assert len(results.keys()) == 1
-            result, = results[agent.role]
-            assert isinstance(result, AgentEvaluationResult)
-
-            assert result.agent_id == str(agent.id)
-            assert result.task_id == str(task.id)
-
-            goal_alignment, = result.metrics.values()
-            assert goal_alignment.score == 5.0
-
-            expected_feedback = "The agent provided a thorough guide on how to conduct a test task but failed to produce specific expected output"
-            assert expected_feedback in goal_alignment.feedback
-
-            assert goal_alignment.raw_response is not None
-            assert '"score": 5' in goal_alignment.raw_response
-
-
-    @pytest.mark.vcr(filter_headers=["authorization"])
-    def test_failed_evaluation(self, mock_crew):
-        agent, = mock_crew.agents
-        task, = mock_crew.tasks
-
-        with crewai_event_bus.scoped_handlers():
-            events = {}
-
-            @crewai_event_bus.on(AgentEvaluationStartedEvent)
-            def capture_started(source, event):
-                events["started"] = event
-
-            @crewai_event_bus.on(AgentEvaluationCompletedEvent)
-            def capture_completed(source, event):
-                events["completed"] = event
-
-            @crewai_event_bus.on(AgentEvaluationFailedEvent)
-            def capture_failed(source, event):
-                events["failed"] = event
-
-            # Create a mock evaluator that will raise an exception
-            from crewai.experimental.evaluation.base_evaluator import BaseEvaluator
-            from crewai.experimental.evaluation import MetricCategory
-            class FailingEvaluator(BaseEvaluator):
-                metric_category = MetricCategory.GOAL_ALIGNMENT
-
-                def evaluate(self, agent, task, execution_trace, final_output):
-                    raise ValueError("Forced evaluation failure")
-
-            agent_evaluator = AgentEvaluator(agents=[agent], evaluators=[FailingEvaluator()])
-            mock_crew.kickoff()
-
-            assert events.keys() == {"started", "failed"}
-            assert events["started"].agent_id == str(agent.id)
-            assert events["started"].agent_role == agent.role
-            assert events["started"].task_id == str(task.id)
-            assert events["started"].iteration == 1
-
-            assert events["failed"].agent_id == str(agent.id)
-            assert events["failed"].agent_role == agent.role
-            assert events["failed"].task_id == str(task.id)
-            assert events["failed"].iteration == 1
-            assert events["failed"].error == "Forced evaluation failure"
-
-            results = agent_evaluator.get_evaluation_results()
-            result, = results[agent.role]
-            assert isinstance(result, AgentEvaluationResult)
-
-            assert result.agent_id == str(agent.id)
-            assert result.task_id == str(task.id)
-
-            assert result.metrics == {}

tests/experimental/evaluation/test_experiment_result.py

@@ -1,111 +0,0 @@
-import pytest
-from unittest.mock import MagicMock, patch
-
-from crewai.experimental.evaluation.experiment.result import ExperimentResult, ExperimentResults
-
-
-class TestExperimentResult:
-    @pytest.fixture
-    def mock_results(self):
-        return [
-            ExperimentResult(
-                identifier="test-1",
-                inputs={"query": "What is the capital of France?"},
-                score=10,
-                expected_score=7,
-                passed=True
-            ),
-            ExperimentResult(
-                identifier="test-2",
-                inputs={"query": "Who wrote Hamlet?"},
-                score={"relevance": 9, "factuality": 8},
-                expected_score={"relevance": 7, "factuality": 7},
-                passed=True,
-                agent_evaluations={"agent1": {"metrics": {"goal_alignment": {"score": 9}}}}
-            ),
-            ExperimentResult(
-                identifier="test-3",
-                inputs={"query": "Any query"},
-                score={"relevance": 9, "factuality": 8},
-                expected_score={"relevance": 7, "factuality": 7},
-                passed=False,
-                agent_evaluations={"agent1": {"metrics": {"goal_alignment": {"score": 9}}}}
-            ),
-            ExperimentResult(
-                identifier="test-4",
-                inputs={"query": "Another query"},
-                score={"relevance": 9, "factuality": 8},
-                expected_score={"relevance": 7, "factuality": 7},
-                passed=True,
-                agent_evaluations={"agent1": {"metrics": {"goal_alignment": {"score": 9}}}}
-            ),
-            ExperimentResult(
-                identifier="test-6",
-                inputs={"query": "Yet another query"},
-                score={"relevance": 9, "factuality": 8},
-                expected_score={"relevance": 7, "factuality": 7},
-                passed=True,
-                agent_evaluations={"agent1": {"metrics": {"goal_alignment": {"score": 9}}}}
-            )
-        ]
-
-    @patch('os.path.exists', return_value=True)
-    @patch('os.path.getsize', return_value=1)
-    @patch('json.load')
-    @patch('builtins.open', new_callable=MagicMock)
-    def test_experiment_results_compare_with_baseline(self, mock_open, mock_json_load, mock_path_getsize, mock_path_exists, mock_results):
-        baseline_data = {
-            "timestamp": "2023-01-01T00:00:00+00:00",
-            "results": [
-                {
-                    "identifier": "test-1",
-                    "inputs": {"query": "What is the capital of France?"},
-                    "score": 7,
-                    "expected_score": 7,
-                    "passed": False
-                },
-                {
-                    "identifier": "test-2",
-                    "inputs": {"query": "Who wrote Hamlet?"},
-                    "score": {"relevance": 8, "factuality": 7},
-                    "expected_score": {"relevance": 7, "factuality": 7},
-                    "passed": True
-                },
-                {
-                    "identifier": "test-3",
-                    "inputs": {"query": "Any query"},
-                    "score": {"relevance": 8, "factuality": 7},
-                    "expected_score": {"relevance": 7, "factuality": 7},
-                    "passed": True
-                },
-                {
-                    "identifier": "test-4",
-                    "inputs": {"query": "Another query"},
-                    "score": {"relevance": 8, "factuality": 7},
-                    "expected_score": {"relevance": 7, "factuality": 7},
-                    "passed": True
-                },
-                {
-                    "identifier": "test-5",
-                    "inputs": {"query": "Another query"},
-                    "score": {"relevance": 8, "factuality": 7},
-                    "expected_score": {"relevance": 7, "factuality": 7},
-                    "passed": True
-                }
-            ]
-        }
-
-        mock_json_load.return_value = baseline_data
-
-        results = ExperimentResults(results=mock_results)
-        results.display = MagicMock()
-
-        comparison = results.compare_with_baseline(baseline_filepath="baseline.json")
-
-        assert "baseline_timestamp" in comparison
-        assert comparison["baseline_timestamp"] == "2023-01-01T00:00:00+00:00"
-        assert comparison["improved"] == ["test-1"]
-        assert comparison["regressed"] == ["test-3"]
-        assert comparison["unchanged"] == ["test-2", "test-4"]
-        assert comparison["new_tests"] == ["test-6"]
-        assert comparison["missing_tests"] == ["test-5"]

tests/experimental/evaluation/test_experiment_runner.py

@@ -1,197 +0,0 @@
-import pytest
-from unittest.mock import MagicMock, patch
-
-from crewai.crew import Crew
-from crewai.experimental.evaluation.experiment.runner import ExperimentRunner
-from crewai.experimental.evaluation.experiment.result import ExperimentResults
-from crewai.experimental.evaluation.evaluation_display import AgentAggregatedEvaluationResult
-from crewai.experimental.evaluation.base_evaluator import MetricCategory, EvaluationScore
-
-
-class TestExperimentRunner:
-    @pytest.fixture
-    def mock_crew(self):
-        return MagicMock(llm=Crew)
-
-    @pytest.fixture
-    def mock_evaluator_results(self):
-        agent_evaluation = AgentAggregatedEvaluationResult(
-            agent_id="Test Agent",
-            agent_role="Test Agent Role",
-            metrics={
-                MetricCategory.GOAL_ALIGNMENT: EvaluationScore(
-                    score=9,
-                    feedback="Test feedback for goal alignment",
-                    raw_response="Test raw response for goal alignment"
-                ),
-                MetricCategory.REASONING_EFFICIENCY: EvaluationScore(
-                    score=None,
-                    feedback="Reasoning efficiency not applicable",
-                    raw_response="Reasoning efficiency not applicable"
-                ),
-                MetricCategory.PARAMETER_EXTRACTION: EvaluationScore(
-                    score=7,
-                    feedback="Test parameter extraction explanation",
-                    raw_response="Test raw output"
-                ),
-                MetricCategory.TOOL_SELECTION: EvaluationScore(
-                    score=8,
-                    feedback="Test tool selection explanation",
-                    raw_response="Test raw output"
-                )
-            }
-        )
-
-        return {"Test Agent": agent_evaluation}
-
-    @patch('crewai.experimental.evaluation.experiment.runner.create_default_evaluator')
-    def test_run_success(self, mock_create_evaluator, mock_crew, mock_evaluator_results):
-        dataset = [
-            {
-                "identifier": "test-case-1",
-                "inputs": {"query": "Test query 1"},
-                "expected_score": 8
-            },
-            {
-                "identifier": "test-case-2",
-                "inputs": {"query": "Test query 2"},
-                "expected_score": {"goal_alignment": 7}
-            },
-            {
-                "inputs": {"query": "Test query 3"},
-                "expected_score": {"tool_selection": 9}
-            }
-        ]
-
-        mock_evaluator = MagicMock()
-        mock_evaluator.get_agent_evaluation.return_value = mock_evaluator_results
-        mock_evaluator.reset_iterations_results = MagicMock()
-        mock_create_evaluator.return_value = mock_evaluator
-
-        runner = ExperimentRunner(dataset=dataset)
-
-        results = runner.run(crew=mock_crew)
-
-        assert isinstance(results, ExperimentResults)
-        result_1, result_2, result_3 = results.results
-        assert len(results.results) == 3
-
-        assert result_1.identifier == "test-case-1"
-        assert result_1.inputs == {"query": "Test query 1"}
-        assert result_1.expected_score == 8
-        assert result_1.passed is True
-
-        assert result_2.identifier == "test-case-2"
-        assert result_2.inputs == {"query": "Test query 2"}
-        assert isinstance(result_2.expected_score, dict)
-        assert "goal_alignment" in result_2.expected_score
-        assert result_2.passed is True
-
-        assert result_3.identifier == "c2ed49e63aa9a83af3ca382794134fd5"
-        assert result_3.inputs == {"query": "Test query 3"}
-        assert isinstance(result_3.expected_score, dict)
-        assert "tool_selection" in result_3.expected_score
-        assert result_3.passed is False
-
-        assert mock_crew.kickoff.call_count == 3
-        mock_crew.kickoff.assert_any_call(inputs={"query": "Test query 1"})
-        mock_crew.kickoff.assert_any_call(inputs={"query": "Test query 2"})
-        mock_crew.kickoff.assert_any_call(inputs={"query": "Test query 3"})
-
-        assert mock_evaluator.reset_iterations_results.call_count == 3
-        assert mock_evaluator.get_agent_evaluation.call_count == 3
-
-
-    @patch('crewai.experimental.evaluation.experiment.runner.create_default_evaluator')
-    def test_run_success_with_unknown_metric(self, mock_create_evaluator, mock_crew, mock_evaluator_results):
-        dataset = [
-            {
-                "identifier": "test-case-2",
-                "inputs": {"query": "Test query 2"},
-                "expected_score": {"goal_alignment": 7, "unknown_metric": 8}
-            }
-        ]
-
-        mock_evaluator = MagicMock()
-        mock_evaluator.get_agent_evaluation.return_value = mock_evaluator_results
-        mock_evaluator.reset_iterations_results = MagicMock()
-        mock_create_evaluator.return_value = mock_evaluator
-
-        runner = ExperimentRunner(dataset=dataset)
-
-        results = runner.run(crew=mock_crew)
-
-        result, = results.results
-
-        assert result.identifier == "test-case-2"
-        assert result.inputs == {"query": "Test query 2"}
-        assert isinstance(result.expected_score, dict)
-        assert "goal_alignment" in result.expected_score.keys()
-        assert "unknown_metric" in result.expected_score.keys()
-        assert result.passed is True
-
-    @patch('crewai.experimental.evaluation.experiment.runner.create_default_evaluator')
-    def test_run_success_with_single_metric_evaluator_and_expected_specific_metric(self, mock_create_evaluator, mock_crew, mock_evaluator_results):
-        dataset = [
-            {
-                "identifier": "test-case-2",
-                "inputs": {"query": "Test query 2"},
-                "expected_score": {"goal_alignment": 7}
-            }
-        ]
-
-        mock_evaluator = MagicMock()
-        mock_create_evaluator["Test Agent"].metrics = {
-            MetricCategory.GOAL_ALIGNMENT: EvaluationScore(
-                    score=9,
-                    feedback="Test feedback for goal alignment",
-                    raw_response="Test raw response for goal alignment"
-                )
-        }
-        mock_evaluator.get_agent_evaluation.return_value = mock_evaluator_results
-        mock_evaluator.reset_iterations_results = MagicMock()
-        mock_create_evaluator.return_value = mock_evaluator
-
-        runner = ExperimentRunner(dataset=dataset)
-
-        results = runner.run(crew=mock_crew)
-        result, = results.results
-
-        assert result.identifier == "test-case-2"
-        assert result.inputs == {"query": "Test query 2"}
-        assert isinstance(result.expected_score, dict)
-        assert "goal_alignment" in result.expected_score.keys()
-        assert result.passed is True
-
-    @patch('crewai.experimental.evaluation.experiment.runner.create_default_evaluator')
-    def test_run_success_when_expected_metric_is_not_available(self, mock_create_evaluator, mock_crew, mock_evaluator_results):
-        dataset = [
-            {
-                "identifier": "test-case-2",
-                "inputs": {"query": "Test query 2"},
-                "expected_score": {"unknown_metric": 7}
-            }
-        ]
-
-        mock_evaluator = MagicMock()
-        mock_create_evaluator["Test Agent"].metrics = {
-            MetricCategory.GOAL_ALIGNMENT: EvaluationScore(
-                score=5,
-                feedback="Test feedback for goal alignment",
-                raw_response="Test raw response for goal alignment"
-            )
-        }
-        mock_evaluator.get_agent_evaluation.return_value = mock_evaluator_results
-        mock_evaluator.reset_iterations_results = MagicMock()
-        mock_create_evaluator.return_value = mock_evaluator
-
-        runner = ExperimentRunner(dataset=dataset)
-
-        results = runner.run(crew=mock_crew)
-        result, = results.results
-
-        assert result.identifier == "test-case-2"
-        assert result.inputs == {"query": "Test query 2"}
-        assert isinstance(result.expected_score, dict)
-        assert "unknown_metric" in result.expected_score.keys()
-        assert result.passed is False

tests/memory/external_memory_test.py

@@ -329,3 +329,29 @@ def test_external_memory_save_events(custom_storage, external_memory_with_mocked
         'agent_role': "test_agent",
         'save_time_ms': ANY
     }
+
+
+def test_external_memory_with_mem0_storage_integration():
+    """Test external memory integration with mem0 storage specifically"""
+    from crewai.memory.storage.mem0_storage import Mem0Storage
+    
+    with patch('crewai.memory.external.external_memory.ExternalMemory._configure_mem0') as mock_configure:
+        mock_storage = MagicMock(spec=Mem0Storage)
+        mock_configure.return_value = mock_storage
+        
+        embedder_config = {"provider": "mem0", "config": {"user_id": "test_user"}}
+        external_memory = ExternalMemory(embedder_config=embedder_config)
+        
+        mock_crew = MagicMock()
+        external_memory.set_crew(mock_crew)
+        
+        test_value = "Test external memory content"
+        test_metadata = {"task": "test_task"}
+        test_agent = "test_agent"
+        
+        external_memory.save(value=test_value, metadata=test_metadata, agent=test_agent)
+        
+        mock_storage.save.assert_called_once_with(
+            test_value,
+            {'task': 'test_task', 'agent': 'test_agent'}
+        )

tests/storage/test_mem0_storage.py

@@ -1,14 +1,10 @@
-import os
 from unittest.mock import MagicMock, patch
 
 import pytest
 from mem0.client.main import MemoryClient
 from mem0.memory.main import Memory
 
-from crewai.agent import Agent
-from crewai.crew import Crew
 from crewai.memory.storage.mem0_storage import Mem0Storage
-from crewai.task import Task
 
 
 # Define the class (if not already defined)
@@ -171,8 +167,9 @@ def test_save_method_with_memory_oss(mem0_storage_with_mocked_config):
     
     mem0_storage.save(test_value, test_metadata)
     
+    expected_messages = [{"role": "assistant", "content": test_value}]
     mem0_storage.memory.add.assert_called_once_with(
-        test_value,
+        expected_messages,
         agent_id="Test_Agent",
         infer=False,
         metadata={"type": "short_term", "key": "value"},
@@ -190,8 +187,9 @@ def test_save_method_with_memory_client(mem0_storage_with_memory_client_using_co
     
     mem0_storage.save(test_value, test_metadata)
     
+    expected_messages = [{"role": "assistant", "content": test_value}]
     mem0_storage.memory.add.assert_called_once_with(
-        test_value,
+        expected_messages,
         agent_id="Test_Agent",
         infer=False,
         metadata={"type": "short_term", "key": "value"},
@@ -218,6 +216,67 @@ def test_search_method_with_memory_oss(mem0_storage_with_mocked_config):
     assert results[0]["content"] == "Result 1"
 
 
+def test_save_method_external_memory_type():
+    """Test save method specifically for external memory type"""
+    crew = MockCrew(
+        memory_config={
+            "provider": "mem0",
+            "config": {"user_id": "test_user", "api_key": "test-key"},
+        }
+    )
+    
+    with patch.object(MemoryClient, "__new__") as mock_client:
+        mock_memory_instance = MagicMock(spec=MemoryClient)
+        mock_client.return_value = mock_memory_instance
+        
+        mem0_storage = Mem0Storage(type="external", crew=crew)
+        mem0_storage.memory.add = MagicMock()
+        
+        test_value = "External memory test content"
+        test_metadata = {"task": "test_task", "agent": "test_agent"}
+        
+        mem0_storage.save(test_value, test_metadata)
+        
+        expected_messages = [{"role": "assistant", "content": test_value}]
+        mem0_storage.memory.add.assert_called_once_with(
+            expected_messages,
+            user_id="test_user",
+            agent_id="Test_Agent",
+            metadata={"type": "external", "task": "test_task", "agent": "test_agent"},
+            output_format="v1.1"
+        )
+
+
+def test_save_method_with_non_string_value():
+    """Test save method when value is already in message format"""
+    crew = MockCrew(
+        memory_config={
+            "provider": "mem0",
+            "config": {"user_id": "test_user", "api_key": "test-key"},
+        }
+    )
+    
+    with patch.object(MemoryClient, "__new__") as mock_client:
+        mock_memory_instance = MagicMock(spec=MemoryClient)
+        mock_client.return_value = mock_memory_instance
+        
+        mem0_storage = Mem0Storage(type="external", crew=crew)
+        mem0_storage.memory.add = MagicMock()
+        
+        test_messages = [{"role": "user", "content": "Test message"}]
+        test_metadata = {"task": "test_task"}
+        
+        mem0_storage.save(test_messages, test_metadata)
+        
+        mem0_storage.memory.add.assert_called_once_with(
+            test_messages,
+            user_id="test_user",
+            agent_id="Test_Agent",
+            metadata={"type": "external", "task": "test_task"},
+            output_format="v1.1"
+        )
+
+
 def test_search_method_with_memory_client(mem0_storage_with_memory_client_using_config_from_crew):
     """Test search method for different memory types"""
     mem0_storage = mem0_storage_with_memory_client_using_config_from_crew

tests/task_test.py

@@ -1133,119 +1133,6 @@ def test_output_file_validation():
         )
 
 
-def test_create_directory_true():
-    """Test that directories are created when create_directory=True."""
-    from pathlib import Path
-    
-    output_path = "test_create_dir/output.txt"
-    
-    task = Task(
-        description="Test task",
-        expected_output="Test output",
-        output_file=output_path,
-        create_directory=True,
-    )
-    
-    resolved_path = Path(output_path).expanduser().resolve()
-    resolved_dir = resolved_path.parent
-    
-    if resolved_path.exists():
-        resolved_path.unlink()
-    if resolved_dir.exists():
-        import shutil
-        shutil.rmtree(resolved_dir)
-    
-    assert not resolved_dir.exists()
-    
-    task._save_file("test content")
-    
-    assert resolved_dir.exists()
-    assert resolved_path.exists()
-    
-    if resolved_path.exists():
-        resolved_path.unlink()
-    if resolved_dir.exists():
-        import shutil
-        shutil.rmtree(resolved_dir)
-
-
-def test_create_directory_false():
-    """Test that directories are not created when create_directory=False."""
-    from pathlib import Path
-    
-    output_path = "nonexistent_test_dir/output.txt"
-    
-    task = Task(
-        description="Test task",
-        expected_output="Test output",
-        output_file=output_path,
-        create_directory=False,
-    )
-    
-    resolved_path = Path(output_path).expanduser().resolve()
-    resolved_dir = resolved_path.parent
-    
-    if resolved_dir.exists():
-        import shutil
-        shutil.rmtree(resolved_dir)
-    
-    assert not resolved_dir.exists()
-    
-    with pytest.raises(RuntimeError, match="Directory .* does not exist and create_directory is False"):
-        task._save_file("test content")
-
-
-def test_create_directory_default():
-    """Test that create_directory defaults to True for backward compatibility."""
-    task = Task(
-        description="Test task",
-        expected_output="Test output",
-        output_file="output.txt",
-    )
-    
-    assert task.create_directory is True
-
-
-def test_create_directory_with_existing_directory():
-    """Test that create_directory=False works when directory already exists."""
-    from pathlib import Path
-    
-    output_path = "existing_test_dir/output.txt"
-    
-    resolved_path = Path(output_path).expanduser().resolve()
-    resolved_dir = resolved_path.parent
-    resolved_dir.mkdir(parents=True, exist_ok=True)
-    
-    task = Task(
-        description="Test task",
-        expected_output="Test output",
-        output_file=output_path,
-        create_directory=False,
-    )
-    
-    task._save_file("test content")
-    assert resolved_path.exists()
-    
-    if resolved_path.exists():
-        resolved_path.unlink()
-    if resolved_dir.exists():
-        import shutil
-        shutil.rmtree(resolved_dir)
-
-
-def test_github_issue_3149_reproduction():
-    """Test that reproduces the exact issue from GitHub issue #3149."""
-    task = Task(
-        description="Test task for issue reproduction",
-        expected_output="Test output",
-        output_file="test_output.txt",
-        create_directory=True,
-    )
-    
-    assert task.create_directory is True
-    assert task.output_file == "test_output.txt"
-
-
 @pytest.mark.vcr(filter_headers=["authorization"])
 def test_task_execution_times():
     researcher = Agent(