Update uv.lock after environment fix

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

.github/workflows/tests.yml

@@ -37,25 +37,9 @@ jobs:
       - name: Install the project
         run: uv sync --dev --all-extras
 
-      - name: Install SQLite with FTS5 support
-        run: |
-          # WORKAROUND: GitHub Actions' Ubuntu runner uses SQLite without FTS5 support compiled in.
-          # This is a temporary fix until the runner includes SQLite with FTS5 or Python's sqlite3
-          # module is compiled with FTS5 support by default.
-          # TODO: Remove this workaround once GitHub Actions runners include SQLite FTS5 support
-          
-          # Install pysqlite3-binary which has FTS5 support
-          uv pip install pysqlite3-binary
-          # Create a sitecustomize.py to override sqlite3 with pysqlite3
-          mkdir -p .pytest_sqlite_override
-          echo "import sys; import pysqlite3; sys.modules['sqlite3'] = pysqlite3" > .pytest_sqlite_override/sitecustomize.py
-          # Test FTS5 availability
-          PYTHONPATH=.pytest_sqlite_override uv run python -c "import sqlite3; print(f'SQLite version: {sqlite3.sqlite_version}')"
-          PYTHONPATH=.pytest_sqlite_override uv run python -c "import sqlite3; conn = sqlite3.connect(':memory:'); conn.execute('CREATE VIRTUAL TABLE test USING fts5(content)'); print('FTS5 module available')"
-
       - name: Run tests (group ${{ matrix.group }} of 8)
         run: |
-          PYTHONPATH=.pytest_sqlite_override uv run pytest \
+          uv run pytest \
             --block-network \
             --timeout=30 \
             -vv \

.gitignore

@@ -26,4 +26,5 @@ test_flow.html
 crewairules.mdc
 plan.md
 conceptual_plan.md
-build_image
+build_image
+chromadb-*.lock

docs/docs.json

@@ -9,12 +9,7 @@
   },
   "favicon": "/images/favicon.svg",
   "contextual": {
-    "options": [
-      "copy",
-      "view",
-      "chatgpt",
-      "claude"
-    ]
+    "options": ["copy", "view", "chatgpt", "claude"]
   },
   "navigation": {
     "languages": [
@@ -37,11 +32,6 @@
               "href": "https://chatgpt.com/g/g-qqTuUWsBY-crewai-assistant",
               "icon": "robot"
             },
-            {
-              "anchor": "Get Help",
-              "href": "mailto:support@crewai.com",
-              "icon": "headset"
-            },
             {
               "anchor": "Releases",
               "href": "https://github.com/crewAIInc/crewAI/releases",
@@ -55,32 +45,22 @@
             "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",
@@ -94,7 +74,6 @@
                     "pages": [
                       "en/guides/advanced/customizing-prompts",
                       "en/guides/advanced/fingerprinting"
-
                     ]
                   }
                 ]
@@ -182,7 +161,9 @@
                       "en/tools/search-research/websitesearchtool",
                       "en/tools/search-research/codedocssearchtool",
                       "en/tools/search-research/youtubechannelsearchtool",
-                      "en/tools/search-research/youtubevideosearchtool"
+                      "en/tools/search-research/youtubevideosearchtool",
+                      "en/tools/search-research/tavilysearchtool",
+                      "en/tools/search-research/tavilyextractortool"
                     ]
                   },
                   {
@@ -241,6 +222,7 @@
                   "en/observability/langtrace",
                   "en/observability/maxim",
                   "en/observability/mlflow",
+                  "en/observability/neatlogs",
                   "en/observability/openlit",
                   "en/observability/opik",
                   "en/observability/patronus-evaluation",
@@ -274,9 +256,7 @@
               },
               {
                 "group": "Telemetry",
-                "pages": [
-                  "en/telemetry"
-                ]
+                "pages": ["en/telemetry"]
               }
             ]
           },
@@ -285,9 +265,7 @@
             "groups": [
               {
                 "group": "Getting Started",
-                "pages": [
-                  "en/enterprise/introduction"
-                ]
+                "pages": ["en/enterprise/introduction"]
               },
               {
                 "group": "Features",
@@ -342,9 +320,7 @@
               },
               {
                 "group": "Resources",
-                "pages": [
-                  "en/enterprise/resources/frequently-asked-questions"
-                ]
+                "pages": ["en/enterprise/resources/frequently-asked-questions"]
               }
             ]
           },
@@ -353,9 +329,7 @@
             "groups": [
               {
                 "group": "Getting Started",
-                "pages": [
-                  "en/api-reference/introduction"
-                ]
+                "pages": ["en/api-reference/introduction"]
               },
               {
                 "group": "Endpoints",
@@ -365,16 +339,13 @@
           },
           {
             "tab": "Examples",
-                        "groups": [
+            "groups": [
               {
                 "group": "Examples",
-                "pages": [
-                  "en/examples/example"
-                ]
+                "pages": ["en/examples/example"]
               }
             ]
           }
-
         ]
       },
       {
@@ -396,11 +367,6 @@
               "href": "https://chatgpt.com/g/g-qqTuUWsBY-crewai-assistant",
               "icon": "robot"
             },
-            {
-              "anchor": "Obter Ajuda",
-              "href": "mailto:support@crewai.com",
-              "icon": "headset"
-            },
             {
               "anchor": "Lançamentos",
               "href": "https://github.com/crewAIInc/crewAI/releases",
@@ -425,21 +391,15 @@
                 "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",
@@ -632,9 +592,7 @@
               },
               {
                 "group": "Telemetria",
-                "pages": [
-                  "pt-BR/telemetry"
-                ]
+                "pages": ["pt-BR/telemetry"]
               }
             ]
           },
@@ -643,9 +601,7 @@
             "groups": [
               {
                 "group": "Começando",
-                "pages": [
-                  "pt-BR/enterprise/introduction"
-                ]
+                "pages": ["pt-BR/enterprise/introduction"]
               },
               {
                 "group": "Funcionalidades",
@@ -710,9 +666,7 @@
             "groups": [
               {
                 "group": "Começando",
-                "pages": [
-                  "pt-BR/api-reference/introduction"
-                ]
+                "pages": ["pt-BR/api-reference/introduction"]
               },
               {
                 "group": "Endpoints",
@@ -722,16 +676,13 @@
           },
           {
             "tab": "Exemplos",
-                        "groups": [
+            "groups": [
               {
                 "group": "Exemplos",
-                "pages": [
-                  "pt-BR/examples/example"
-                ]
+                "pages": ["pt-BR/examples/example"]
               }
             ]
           }
-
         ]
       }
     ]

docs/en/concepts/llms.mdx

@@ -270,7 +270,7 @@ In this section, you'll find detailed examples that help you select, configure,
     from crewai import LLM
 
     llm = LLM(
-        model="gemini/gemini-1.5-pro-latest",
+        model="gemini-1.5-pro-latest", # or vertex_ai/gemini-1.5-pro-latest
         temperature=0.7,
         vertex_credentials=vertex_credentials_json
     )

docs/en/concepts/memory.mdx

@@ -623,7 +623,7 @@ for provider in providers_to_test:
 **Model not found errors:**
 ```python
 # Verify model availability
-from crewai.utilities.embedding_configurator import EmbeddingConfigurator
+from crewai.rag.embeddings.configurator import EmbeddingConfigurator
 
 configurator = EmbeddingConfigurator()
 try:
@@ -712,7 +712,7 @@ crew = Crew(
     memory_config={
         "provider": "mem0",
         "config": {"user_id": "john"},
-        "user_memory": {}  # Required - triggers user memory initialization
+        "user_memory": {}  # DEPRECATED: Will be removed in version 0.156.0 or on 2025-08-04, use external_memory instead
     },
     process=Process.sequential,
     verbose=True
@@ -720,7 +720,16 @@ crew = Crew(
 ```
 
 ### Advanced Mem0 Configuration
+When using Mem0 Client, you can customize the memory configuration further, by using parameters like 'includes', 'excludes', 'custom_categories', 'infer' and 'run_id' (this is only for short-term memory).
+You can find more details in the [Mem0 documentation](https://docs.mem0.ai/).
 ```python
+
+new_categories = [
+    {"lifestyle_management_concerns": "Tracks daily routines, habits, hobbies and interests including cooking, time management and work-life balance"},
+    {"seeking_structure": "Documents goals around creating routines, schedules, and organized systems in various life areas"},
+    {"personal_information": "Basic information about the user including name, preferences, and personality traits"}
+]
+
 crew = Crew(
     agents=[...],
     tasks=[...],
@@ -732,6 +741,11 @@ crew = Crew(
             "org_id": "my_org_id",        # Optional
             "project_id": "my_project_id", # Optional
             "api_key": "custom-api-key"    # Optional - overrides env var
+            "run_id": "my_run_id",        # Optional - for short-term memory
+            "includes": "include1",       # Optional 
+            "excludes": "exclude1",       # Optional
+            "infer": True                 # Optional defaults to True
+            "custom_categories": new_categories  # Optional - custom categories for user memory
         },
         "user_memory": {}
     }
@@ -761,7 +775,8 @@ crew = Crew(
                     "provider": "openai",
                     "config": {"api_key": "your-api-key", "model": "text-embedding-3-small"}
                 }
-            }
+            },
+            "infer": True                   # Optional defaults to True
         },
         "user_memory": {}
     }

docs/en/concepts/tasks.mdx

@@ -54,10 +54,11 @@ crew = Crew(
 | **Markdown** _(optional)_        | `markdown`        | `Optional[bool]`              | Whether the task should instruct the agent to return the final answer formatted in Markdown. Defaults to False.      |
 | **Config** _(optional)_          | `config`          | `Optional[Dict[str, Any]]`    | Task-specific configuration parameters.                                                                              |
 | **Output File** _(optional)_     | `output_file`     | `Optional[str]`               | File path for storing the task output.                                                                               |
+| **Create Directory** _(optional)_ | `create_directory` | `Optional[bool]`             | Whether to create the directory for output_file if it doesn't exist. Defaults to True.                               |
 | **Output JSON** _(optional)_     | `output_json`     | `Optional[Type[BaseModel]]`   | A Pydantic model to structure the JSON output.                                                                       |
 | **Output Pydantic** _(optional)_ | `output_pydantic` | `Optional[Type[BaseModel]]`   | A Pydantic model for task output.                                                                                    |
 | **Callback** _(optional)_        | `callback`        | `Optional[Any]`               | Function/object to be executed after task completion.                                                                |
-| **Guardrail** _(optional)_       | `guardrail`       | `Optional[Union[Callable, str]]` | Function or string description to validate task output before proceeding to next task.                            |
+| **Guardrail** _(optional)_       | `guardrail`       | `Optional[Callable]`             | Function to validate task output before proceeding to next task.                                                  |
 
 ## Creating Tasks
 
@@ -87,7 +88,6 @@ research_task:
   expected_output: >
     A list with 10 bullet points of the most relevant information about {topic}
   agent: researcher
-  guardrail: ensure each bullet contains a minimum of 100 words
 
 reporting_task:
   description: >
@@ -334,9 +334,7 @@ Task guardrails provide a way to validate and transform task outputs before they
 are passed to the next task. This feature helps ensure data quality and provides
 feedback to agents when their output doesn't meet specific criteria.
 
-**Guardrails can be defined in two ways:**
-1. **Function-based guardrails**: Python functions that implement custom validation logic
-2. **String-based guardrails**: Natural language descriptions that are automatically converted to LLM-powered validation
+Guardrails are implemented as Python functions that contain custom validation logic, giving you complete control over the validation process and ensuring reliable, deterministic results.
 
 ### Function-Based Guardrails
 
@@ -378,82 +376,7 @@ blog_task = Task(
    - On success: it returns a tuple of `(bool, Any)`. For example: `(True, validated_result)`
    - On Failure: it returns a tuple of `(bool, str)`. For example: `(False, "Error message explain the failure")`
 
-### String-Based Guardrails
 
-String-based guardrails allow you to describe validation criteria in natural language. When you provide a string instead of a function, CrewAI automatically converts it to an `LLMGuardrail` that uses an AI agent to validate the task output.
-
-#### Using String Guardrails in Python
-
-```python Code
-from crewai import Task
-
-# Simple string-based guardrail
-blog_task = Task(
-    description="Write a blog post about AI",
-    expected_output="A blog post under 200 words",
-    agent=blog_agent,
-    guardrail="Ensure the blog post is under 200 words and includes practical examples"
-)
-
-# More complex validation criteria
-research_task = Task(
-    description="Research AI trends for 2025",
-    expected_output="A comprehensive research report",
-    agent=research_agent,
-    guardrail="Ensure each finding includes a credible source and is backed by recent data from 2024-2025"
-)
-```
-
-#### Using String Guardrails in YAML
-
-```yaml
-research_task:
-  description: Research the latest AI developments
-  expected_output: A list of 10 bullet points about AI
-  agent: researcher
-  guardrail: ensure each bullet contains a minimum of 100 words
-
-validation_task:
-  description: Validate the research findings
-  expected_output: A validation report
-  agent: validator
-  guardrail: confirm all sources are from reputable publications and published within the last 2 years
-```
-
-#### How String Guardrails Work
-
-When you provide a string guardrail, CrewAI automatically:
-1. Creates an `LLMGuardrail` instance using the string as validation criteria
-2. Uses the task's agent LLM to power the validation
-3. Creates a temporary validation agent that checks the output against your criteria
-4. Returns detailed feedback if validation fails
-
-This approach is ideal when you want to use natural language to describe validation rules without writing custom validation functions.
-
-### LLMGuardrail Class
-
-The `LLMGuardrail` class is the underlying mechanism that powers string-based guardrails. You can also use it directly for more advanced control:
-
-```python Code
-from crewai import Task
-from crewai.tasks.llm_guardrail import LLMGuardrail
-from crewai.llm import LLM
-
-# Create a custom LLMGuardrail with specific LLM
-custom_guardrail = LLMGuardrail(
-    description="Ensure the response contains exactly 5 bullet points with proper citations",
-    llm=LLM(model="gpt-4o-mini")
-)
-
-task = Task(
-    description="Research AI safety measures",
-    expected_output="A detailed analysis with bullet points",
-    agent=research_agent,
-    guardrail=custom_guardrail
-)
-```
-
-**Note**: When you use a string guardrail, CrewAI automatically creates an `LLMGuardrail` instance using your task's agent LLM. Using `LLMGuardrail` directly gives you more control over the validation process and LLM selection.
 
 ### Error Handling Best Practices
 
@@ -881,21 +804,87 @@ These validations help in maintaining the consistency and reliability of task ex
 
 ## Creating Directories when Saving Files
 
-You can now specify if a task should create directories when saving its output to a file. This is particularly useful for organizing outputs and ensuring that file paths are correctly structured.
+The `create_directory` parameter controls whether CrewAI should automatically create directories when saving task outputs to files. This feature is particularly useful for organizing outputs and ensuring that file paths are correctly structured, especially when working with complex project hierarchies.
+
+### Default Behavior
+
+By default, `create_directory=True`, which means CrewAI will automatically create any missing directories in the output file path:
 
 ```python Code
-# ...
-
-save_output_task = Task(
-    description='Save the summarized AI news to a file',
-    expected_output='File saved successfully',
-    agent=research_agent,
-    tools=[file_save_tool],
-    output_file='outputs/ai_news_summary.txt',
-    create_directory=True
+# Default behavior - directories are created automatically
+report_task = Task(
+    description='Generate a comprehensive market analysis report',
+    expected_output='A detailed market analysis with charts and insights',
+    agent=analyst_agent,
+    output_file='reports/2025/market_analysis.md',  # Creates 'reports/2025/' if it doesn't exist
+    markdown=True
 )
+```
 
-#...
+### Disabling Directory Creation
+
+If you want to prevent automatic directory creation and ensure that the directory already exists, set `create_directory=False`:
+
+```python Code
+# Strict mode - directory must already exist
+strict_output_task = Task(
+    description='Save critical data that requires existing infrastructure',
+    expected_output='Data saved to pre-configured location',
+    agent=data_agent,
+    output_file='secure/vault/critical_data.json',
+    create_directory=False  # Will raise RuntimeError if 'secure/vault/' doesn't exist
+)
+```
+
+### YAML Configuration
+
+You can also configure this behavior in your YAML task definitions:
+
+```yaml tasks.yaml
+analysis_task:
+  description: >
+    Generate quarterly financial analysis
+  expected_output: >
+    A comprehensive financial report with quarterly insights
+  agent: financial_analyst
+  output_file: reports/quarterly/q4_2024_analysis.pdf
+  create_directory: true  # Automatically create 'reports/quarterly/' directory
+
+audit_task:
+  description: >
+    Perform compliance audit and save to existing audit directory
+  expected_output: >
+    A compliance audit report
+  agent: auditor
+  output_file: audit/compliance_report.md
+  create_directory: false  # Directory must already exist
+```
+
+### Use Cases
+
+**Automatic Directory Creation (`create_directory=True`):**
+- Development and prototyping environments
+- Dynamic report generation with date-based folders
+- Automated workflows where directory structure may vary
+- Multi-tenant applications with user-specific folders
+
+**Manual Directory Management (`create_directory=False`):**
+- Production environments with strict file system controls
+- Security-sensitive applications where directories must be pre-configured
+- Systems with specific permission requirements
+- Compliance environments where directory creation is audited
+
+### Error Handling
+
+When `create_directory=False` and the directory doesn't exist, CrewAI will raise a `RuntimeError`:
+
+```python Code
+try:
+    result = crew.kickoff()
+except RuntimeError as e:
+    # Handle missing directory error
+    print(f"Directory creation failed: {e}")
+    # Create directory manually or use fallback location
 ```
 
 Check out the video below to see how to use structured outputs in CrewAI:

docs/en/observability/neatlogs.mdx

@@ -0,0 +1,134 @@
+---
+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)
+  </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
+- Supports AI-powered task generation and robust evaluation workflows
+
+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)
+
+
+
+## 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.
+```

docs/en/tools/search-research/overview.mdx

@@ -44,6 +44,14 @@ These tools enable your agents to search the web, research topics, and find info
   <Card title="YouTube Video Search" icon="play" href="/en/tools/search-research/youtubevideosearchtool">
     Find and analyze YouTube videos by topic, keyword, or criteria.
   </Card>
+
+  <Card title="Tavily Search Tool" icon="magnifying-glass" href="/en/tools/search-research/tavilysearchtool">
+    Comprehensive web search using Tavily's AI-powered search API.
+  </Card>
+
+  <Card title="Tavily Extractor Tool" icon="file-text" href="/en/tools/search-research/tavilyextractortool">
+    Extract structured content from web pages using the Tavily API.
+  </Card>
 </CardGroup>
 
 ## **Common Use Cases**
@@ -55,17 +63,19 @@ These tools enable your agents to search the web, research topics, and find info
 - **Academic Research**: Find scholarly articles and technical papers
 
 ```python
-from crewai_tools import SerperDevTool, GitHubSearchTool, YoutubeVideoSearchTool
+from crewai_tools import SerperDevTool, GitHubSearchTool, YoutubeVideoSearchTool, TavilySearchTool, TavilyExtractorTool
 
 # Create research tools
 web_search = SerperDevTool()
 code_search = GitHubSearchTool()
 video_research = YoutubeVideoSearchTool()
+tavily_search = TavilySearchTool()
+content_extractor = TavilyExtractorTool()
 
 # Add to your agent
 agent = Agent(
     role="Research Analyst",
-    tools=[web_search, code_search, video_research],
+    tools=[web_search, code_search, video_research, tavily_search, content_extractor],
     goal="Gather comprehensive information on any topic"
 )
 ```

docs/en/tools/search-research/serperdevtool.mdx

@@ -6,10 +6,6 @@ icon: google
 
 # `SerperDevTool`
 
-<Note>
-    We are still working on improving tools, so there might be unexpected behavior or changes in the future.
-</Note>
-
 ## Description
 
 This tool is designed to perform a semantic search for a specified query from a text's content across the internet. It utilizes the [serper.dev](https://serper.dev) API 
@@ -17,6 +13,12 @@ to fetch and display the most relevant search results based on the query provide
 
 ## Installation
 
+To effectively use the `SerperDevTool`, follow these steps:
+
+1. **Package Installation**: Confirm that the `crewai[tools]` package is installed in your Python environment.
+2. **API Key Acquisition**: Acquire a `serper.dev` API key by registering for a free account at `serper.dev`.
+3. **Environment Configuration**: Store your obtained API key in an environment variable named `SERPER_API_KEY` to facilitate its use by the tool.
+
 To incorporate this tool into your project, follow the installation instructions below:
 
 ```shell
@@ -34,14 +36,6 @@ from crewai_tools import SerperDevTool
 tool = SerperDevTool()
 ```
 
-## Steps to Get Started
-
-To effectively use the `SerperDevTool`, follow these steps:
-
-1. **Package Installation**: Confirm that the `crewai[tools]` package is installed in your Python environment.
-2. **API Key Acquisition**: Acquire a `serper.dev` API key by registering for a free account at `serper.dev`.
-3. **Environment Configuration**: Store your obtained API key in an environment variable named `SERPER_API_KEY` to facilitate its use by the tool.
-
 ## Parameters
 
 The `SerperDevTool` comes with several parameters that will be passed to the API :

docs/en/tools/search-research/tavilyextractortool.mdx

@@ -0,0 +1,139 @@
+---
+title: "Tavily Extractor Tool"
+description: "Extract structured content from web pages using the Tavily API"
+icon: "file-text"
+---
+
+The `TavilyExtractorTool` allows CrewAI agents to extract structured content from web pages using the Tavily API. It can process single URLs or lists of URLs and provides options for controlling the extraction depth and including images.
+
+## Installation
+
+To use the `TavilyExtractorTool`, you need to install the `tavily-python` library:
+
+```shell
+pip install 'crewai[tools]' tavily-python
+```
+
+You also need to set your Tavily API key as an environment variable:
+
+```bash
+export TAVILY_API_KEY='your-tavily-api-key'
+```
+
+## Example Usage
+
+Here's how to initialize and use the `TavilyExtractorTool` within a CrewAI agent:
+
+```python
+import os
+from crewai import Agent, Task, Crew
+from crewai_tools import TavilyExtractorTool
+
+# Ensure TAVILY_API_KEY is set in your environment
+# os.environ["TAVILY_API_KEY"] = "YOUR_API_KEY"
+
+# Initialize the tool
+tavily_tool = TavilyExtractorTool()
+
+# Create an agent that uses the tool
+extractor_agent = Agent(
+    role='Web Content Extractor',
+    goal='Extract key information from specified web pages',
+    backstory='You are an expert at extracting relevant content from websites using the Tavily API.',
+    tools=[tavily_tool],
+    verbose=True
+)
+
+# Define a task for the agent
+extract_task = Task(
+    description='Extract the main content from the URL https://example.com using basic extraction depth.',
+    expected_output='A JSON string containing the extracted content from the URL.',
+    agent=extractor_agent
+)
+
+# Create and run the crew
+crew = Crew(
+    agents=[extractor_agent],
+    tasks=[extract_task],
+    verbose=2
+)
+
+result = crew.kickoff()
+print(result)
+```
+
+## Configuration Options
+
+The `TavilyExtractorTool` accepts the following arguments:
+
+- `urls` (Union[List[str], str]): **Required**. A single URL string or a list of URL strings to extract data from.
+- `include_images` (Optional[bool]): Whether to include images in the extraction results. Defaults to `False`.
+- `extract_depth` (Literal["basic", "advanced"]): The depth of extraction. Use `"basic"` for faster, surface-level extraction or `"advanced"` for more comprehensive extraction. Defaults to `"basic"`.
+- `timeout` (int): The maximum time in seconds to wait for the extraction request to complete. Defaults to `60`.
+
+## Advanced Usage
+
+### Multiple URLs with Advanced Extraction
+
+```python
+# Example with multiple URLs and advanced extraction
+multi_extract_task = Task(
+    description='Extract content from https://example.com and https://anotherexample.org using advanced extraction.',
+    expected_output='A JSON string containing the extracted content from both URLs.',
+    agent=extractor_agent
+)
+
+# Configure the tool with custom parameters
+custom_extractor = TavilyExtractorTool(
+    extract_depth='advanced',
+    include_images=True,
+    timeout=120
+)
+
+agent_with_custom_tool = Agent(
+    role="Advanced Content Extractor",
+    goal="Extract comprehensive content with images",
+    tools=[custom_extractor]
+)
+```
+
+### Tool Parameters
+
+You can customize the tool's behavior by setting parameters during initialization:
+
+```python
+# Initialize with custom configuration
+extractor_tool = TavilyExtractorTool(
+    extract_depth='advanced',  # More comprehensive extraction
+    include_images=True,       # Include image results
+    timeout=90                 # Custom timeout
+)
+```
+
+## Features
+
+- **Single or Multiple URLs**: Extract content from one URL or process multiple URLs in a single request
+- **Configurable Depth**: Choose between basic (fast) and advanced (comprehensive) extraction modes
+- **Image Support**: Optionally include images in the extraction results
+- **Structured Output**: Returns well-formatted JSON containing the extracted content
+- **Error Handling**: Robust handling of network timeouts and extraction errors
+
+## Response Format
+
+The tool returns a JSON string representing the structured data extracted from the provided URL(s). The exact structure depends on the content of the pages and the `extract_depth` used.
+
+Common response elements include:
+- **Title**: The page title
+- **Content**: Main text content of the page
+- **Images**: Image URLs and metadata (when `include_images=True`)
+- **Metadata**: Additional page information like author, description, etc.
+
+## Use Cases
+
+- **Content Analysis**: Extract and analyze content from competitor websites
+- **Research**: Gather structured data from multiple sources for analysis
+- **Content Migration**: Extract content from existing websites for migration
+- **Monitoring**: Regular extraction of content for change detection
+- **Data Collection**: Systematic extraction of information from web sources
+
+Refer to the [Tavily API documentation](https://docs.tavily.com/docs/tavily-api/python-sdk#extract) for detailed information about the response structure and available options.

docs/en/tools/search-research/tavilysearchtool.mdx

@@ -0,0 +1,122 @@
+---
+title: "Tavily Search Tool"
+description: "Perform comprehensive web searches using the Tavily Search API"
+icon: "magnifying-glass"
+---
+
+The `TavilySearchTool` provides an interface to the Tavily Search API, enabling CrewAI agents to perform comprehensive web searches. It allows for specifying search depth, topics, time ranges, included/excluded domains, and whether to include direct answers, raw content, or images in the results.
+
+## Installation
+
+To use the `TavilySearchTool`, you need to install the `tavily-python` library:
+
+```shell
+pip install 'crewai[tools]' tavily-python
+```
+
+## Environment Variables
+
+Ensure your Tavily API key is set as an environment variable:
+
+```bash
+export TAVILY_API_KEY='your_tavily_api_key'
+```
+
+## Example Usage
+
+Here's how to initialize and use the `TavilySearchTool` within a CrewAI agent:
+
+```python
+import os
+from crewai import Agent, Task, Crew
+from crewai_tools import TavilySearchTool
+
+# Ensure the TAVILY_API_KEY environment variable is set
+# os.environ["TAVILY_API_KEY"] = "YOUR_TAVILY_API_KEY"
+
+# Initialize the tool
+tavily_tool = TavilySearchTool()
+
+# Create an agent that uses the tool
+researcher = Agent(
+    role='Market Researcher',
+    goal='Find information about the latest AI trends',
+    backstory='An expert market researcher specializing in technology.',
+    tools=[tavily_tool],
+    verbose=True
+)
+
+# Create a task for the agent
+research_task = Task(
+    description='Search for the top 3 AI trends in 2024.',
+    expected_output='A JSON report summarizing the top 3 AI trends found.',
+    agent=researcher
+)
+
+# Form the crew and kick it off
+crew = Crew(
+    agents=[researcher],
+    tasks=[research_task],
+    verbose=2
+)
+
+result = crew.kickoff()
+print(result)
+```
+
+## Configuration Options
+
+The `TavilySearchTool` accepts the following arguments during initialization or when calling the `run` method:
+
+- `query` (str): **Required**. The search query string.
+- `search_depth` (Literal["basic", "advanced"], optional): The depth of the search. Defaults to `"basic"`.
+- `topic` (Literal["general", "news", "finance"], optional): The topic to focus the search on. Defaults to `"general"`.
+- `time_range` (Literal["day", "week", "month", "year"], optional): The time range for the search. Defaults to `None`.
+- `days` (int, optional): The number of days to search back. Relevant if `time_range` is not set. Defaults to `7`.
+- `max_results` (int, optional): The maximum number of search results to return. Defaults to `5`.
+- `include_domains` (Sequence[str], optional): A list of domains to prioritize in the search. Defaults to `None`.
+- `exclude_domains` (Sequence[str], optional): A list of domains to exclude from the search. Defaults to `None`.
+- `include_answer` (Union[bool, Literal["basic", "advanced"]], optional): Whether to include a direct answer synthesized from the search results. Defaults to `False`.
+- `include_raw_content` (bool, optional): Whether to include the raw HTML content of the searched pages. Defaults to `False`.
+- `include_images` (bool, optional): Whether to include image results. Defaults to `False`.
+- `timeout` (int, optional): The request timeout in seconds. Defaults to `60`.
+
+## Advanced Usage
+
+You can configure the tool with custom parameters:
+
+```python
+# Example: Initialize with specific parameters
+custom_tavily_tool = TavilySearchTool(
+    search_depth='advanced',
+    max_results=10,
+    include_answer=True
+)
+
+# The agent will use these defaults
+agent_with_custom_tool = Agent(
+    role="Advanced Researcher",
+    goal="Conduct detailed research with comprehensive results",
+    tools=[custom_tavily_tool]
+)
+```
+
+## Features
+
+- **Comprehensive Search**: Access to Tavily's powerful search index
+- **Configurable Depth**: Choose between basic and advanced search modes
+- **Topic Filtering**: Focus searches on general, news, or finance topics
+- **Time Range Control**: Limit results to specific time periods
+- **Domain Control**: Include or exclude specific domains
+- **Direct Answers**: Get synthesized answers from search results
+- **Content Filtering**: Prevent context window issues with automatic content truncation
+
+## Response Format
+
+The tool returns search results as a JSON string containing:
+- Search results with titles, URLs, and content snippets
+- Optional direct answers to queries
+- Optional image results
+- Optional raw HTML content (when enabled)
+
+Content for each result is automatically truncated to prevent context window issues while maintaining the most relevant information.

docs/en/tools/web-scraping/serperscrapewebsitetool.mdx

@@ -0,0 +1,100 @@
+---
+title: Serper Scrape Website
+description: The `SerperScrapeWebsiteTool` is designed to scrape websites and extract clean, readable content using Serper's scraping API.
+icon: globe
+---
+
+# `SerperScrapeWebsiteTool`
+
+## Description
+
+This tool is designed to scrape website content and extract clean, readable text from any website URL. It utilizes the [serper.dev](https://serper.dev) scraping API to fetch and process web pages, optionally including markdown formatting for better structure and readability.
+
+## Installation
+
+To effectively use the `SerperScrapeWebsiteTool`, follow these steps:
+
+1. **Package Installation**: Confirm that the `crewai[tools]` package is installed in your Python environment.
+2. **API Key Acquisition**: Acquire a `serper.dev` API key by registering for an account at `serper.dev`.
+3. **Environment Configuration**: Store your obtained API key in an environment variable named `SERPER_API_KEY` to facilitate its use by the tool.
+
+To incorporate this tool into your project, follow the installation instructions below:
+
+```shell
+pip install 'crewai[tools]'
+```
+
+## Example
+
+The following example demonstrates how to initialize the tool and scrape a website:
+
+```python Code
+from crewai_tools import SerperScrapeWebsiteTool
+
+# Initialize the tool for website scraping capabilities
+tool = SerperScrapeWebsiteTool()
+
+# Scrape a website with markdown formatting
+result = tool.run(url="https://example.com", include_markdown=True)
+```
+
+## Arguments
+
+The `SerperScrapeWebsiteTool` accepts the following arguments:
+
+- **url**: Required. The URL of the website to scrape.
+- **include_markdown**: Optional. Whether to include markdown formatting in the scraped content. Defaults to `True`.
+
+## Example with Parameters
+
+Here is an example demonstrating how to use the tool with different parameters:
+
+```python Code
+from crewai_tools import SerperScrapeWebsiteTool
+
+tool = SerperScrapeWebsiteTool()
+
+# Scrape with markdown formatting (default)
+markdown_result = tool.run(
+    url="https://docs.crewai.com",
+    include_markdown=True
+)
+
+# Scrape without markdown formatting for plain text
+plain_result = tool.run(
+    url="https://docs.crewai.com",
+    include_markdown=False
+)
+
+print("Markdown formatted content:")
+print(markdown_result)
+
+print("\nPlain text content:")
+print(plain_result)
+```
+
+## Use Cases
+
+The `SerperScrapeWebsiteTool` is particularly useful for:
+
+- **Content Analysis**: Extract and analyze website content for research purposes
+- **Data Collection**: Gather structured information from web pages
+- **Documentation Processing**: Convert web-based documentation into readable formats
+- **Competitive Analysis**: Scrape competitor websites for market research
+- **Content Migration**: Extract content from existing websites for migration purposes
+
+## Error Handling
+
+The tool includes comprehensive error handling for:
+
+- **Network Issues**: Handles connection timeouts and network errors gracefully
+- **API Errors**: Provides detailed error messages for API-related issues
+- **Invalid URLs**: Validates and reports issues with malformed URLs
+- **Authentication**: Clear error messages for missing or invalid API keys
+
+## Security Considerations
+
+- Always store your `SERPER_API_KEY` in environment variables, never hardcode it in your source code
+- Be mindful of rate limits imposed by the Serper API
+- Respect robots.txt and website terms of service when scraping content
+- Consider implementing delays between requests for large-scale scraping operations

docs/pt-BR/concepts/cli.mdx

@@ -84,8 +84,8 @@ filename = "seu_modelo.pkl"
 
 try:
     SuaCrew().crew().train(
-      n_iterations=n_iterations, 
-      inputs=inputs, 
+      n_iterations=n_iterations,
+      inputs=inputs,
       filename=filename
     )
 except Exception as e:
@@ -103,7 +103,7 @@ crewai replay [OPTIONS]
 - `-t, --task_id TEXT`: Reexecuta o crew a partir deste task ID, incluindo todas as tarefas subsequentes
 
 Exemplo:
-```shell Terminal    
+```shell Terminal
 crewai replay -t task_123456
 ```
 
@@ -149,7 +149,7 @@ crewai test [OPTIONS]
 - `-m, --model TEXT`: Modelo LLM para executar os testes no Crew (padrão: "gpt-4o-mini")
 
 Exemplo:
-```shell Terminal    
+```shell Terminal
 crewai test -n 5 -m gpt-3.5-turbo
 ```
 
@@ -203,10 +203,7 @@ def crew(self) -> Crew:
 Implemente o crew ou flow no [CrewAI Enterprise](https://app.crewai.com).
 
 - **Autenticação**: Você precisa estar autenticado para implementar no CrewAI Enterprise.
-    ```shell Terminal
-    crewai signup
-    ```
-    Caso já tenha uma conta, você pode fazer login com:
+    Você pode fazer login ou criar uma conta com:
     ```shell Terminal
     crewai login
     ```
@@ -253,7 +250,7 @@ Você deve estar autenticado no CrewAI Enterprise para usar estes comandos de ge
 - **Implantar o Crew**: Depois de autenticado, você pode implantar seu crew ou flow no CrewAI Enterprise.
     ```shell Terminal
     crewai deploy push
-    ``` 
+    ```
     - Inicia o processo de deployment na plataforma CrewAI Enterprise.
     - Após a iniciação bem-sucedida, será exibida a mensagem Deployment created successfully! juntamente com o Nome do Deployment e um Deployment ID (UUID) único.
 
@@ -326,4 +323,4 @@ Ao escolher um provedor, o CLI solicitará que você informe o nome da chave e a
 
 Veja o seguinte link para o nome de chave de cada provedor:
 
-* [LiteLLM Providers](https://docs.litellm.ai/docs/providers)
+* [LiteLLM Providers](https://docs.litellm.ai/docs/providers)

docs/pt-BR/concepts/llms.mdx

@@ -268,7 +268,7 @@ Nesta seção, você encontrará exemplos detalhados que ajudam a selecionar, co
     from crewai import LLM
 
     llm = LLM(
-        model="gemini/gemini-1.5-pro-latest",
+        model="gemini-1.5-pro-latest", # or vertex_ai/gemini-1.5-pro-latest
         temperature=0.7,
         vertex_credentials=vertex_credentials_json
     )

docs/pt-BR/concepts/memory.mdx

@@ -623,7 +623,7 @@ for provider in providers_to_test:
 **Erros de modelo não encontrado:**
 ```python
 # Verifique disponibilidade do modelo
-from crewai.utilities.embedding_configurator import EmbeddingConfigurator
+from crewai.rag.embeddings.configurator import EmbeddingConfigurator
 
 configurator = EmbeddingConfigurator()
 try:

docs/pt-BR/concepts/tasks.mdx

@@ -54,10 +54,11 @@ crew = Crew(
 | **Markdown** _(opcional)_        | `markdown`        | `Optional[bool]`             | Se a tarefa deve instruir o agente a retornar a resposta final formatada em Markdown. O padrão é False.            |
 | **Config** _(opcional)_          | `config`          | `Optional[Dict[str, Any]]`   | Parâmetros de configuração específicos da tarefa.                                                                  |
 | **Arquivo de Saída** _(opcional)_| `output_file`     | `Optional[str]`              | Caminho do arquivo para armazenar a saída da tarefa.                                                               |
+| **Criar Diretório** _(opcional)_ | `create_directory` | `Optional[bool]`            | Se deve criar o diretório para output_file caso não exista. O padrão é True.                                       |
 | **Saída JSON** _(opcional)_      | `output_json`     | `Optional[Type[BaseModel]]`  | Um modelo Pydantic para estruturar a saída em JSON.                                                                |
 | **Output Pydantic** _(opcional)_ | `output_pydantic` | `Optional[Type[BaseModel]]`  | Um modelo Pydantic para a saída da tarefa.                                                                         |
 | **Callback** _(opcional)_        | `callback`        | `Optional[Any]`              | Função/objeto a ser executado após a conclusão da tarefa.                                                          |
-| **Guardrail** _(opcional)_       | `guardrail`       | `Optional[Union[Callable, str]]` | Função ou descrição em string para validar a saída da tarefa antes de prosseguir para a próxima tarefa.        |
+| **Guardrail** _(opcional)_       | `guardrail`       | `Optional[Callable]`             | Função para validar a saída da tarefa antes de prosseguir para a próxima tarefa.                                |
 
 ## Criando Tarefas
 
@@ -87,7 +88,6 @@ research_task:
   expected_output: >
     Uma lista com 10 tópicos em bullet points das informações mais relevantes sobre {topic}
   agent: researcher
-  guardrail: garanta que cada bullet point contenha no mínimo 100 palavras
 
 reporting_task:
   description: >
@@ -332,9 +332,7 @@ analysis_task = Task(
 
 Guardrails (trilhas de proteção) de tarefas fornecem uma maneira de validar e transformar as saídas das tarefas antes que elas sejam passadas para a próxima tarefa. Esse recurso assegura a qualidade dos dados e oferece feedback aos agentes quando sua saída não atende a critérios específicos.
 
-**Guardrails podem ser definidos de duas maneiras:**
-1. **Guardrails baseados em função**: Funções Python que implementam lógica de validação customizada
-2. **Guardrails baseados em string**: Descrições em linguagem natural que são automaticamente convertidas em validação baseada em LLM
+Guardrails são implementados como funções Python que contêm lógica de validação customizada, proporcionando controle total sobre o processo de validação e garantindo resultados confiáveis e determinísticos.
 
 ### Guardrails Baseados em Função
 
@@ -376,82 +374,7 @@ blog_task = Task(
    - Em caso de sucesso: retorna uma tupla `(True, resultado_validado)`
    - Em caso de falha: retorna uma tupla `(False, "mensagem de erro explicando a falha")`
 
-### Guardrails Baseados em String
 
-Guardrails baseados em string permitem que você descreva critérios de validação em linguagem natural. Quando você fornece uma string em vez de uma função, o CrewAI automaticamente a converte em um `LLMGuardrail` que usa um agente de IA para validar a saída da tarefa.
-
-#### Usando Guardrails de String em Python
-
-```python Code
-from crewai import Task
-
-# Guardrail simples baseado em string
-blog_task = Task(
-    description="Escreva um post de blog sobre IA",
-    expected_output="Um post de blog com menos de 200 palavras",
-    agent=blog_agent,
-    guardrail="Garanta que o post do blog tenha menos de 200 palavras e inclua exemplos práticos"
-)
-
-# Critérios de validação mais complexos
-research_task = Task(
-    description="Pesquise tendências de IA para 2025",
-    expected_output="Um relatório abrangente de pesquisa",
-    agent=research_agent,
-    guardrail="Garanta que cada descoberta inclua uma fonte confiável e seja respaldada por dados recentes de 2024-2025"
-)
-```
-
-#### Usando Guardrails de String em YAML
-
-```yaml
-research_task:
-  description: Pesquise os últimos desenvolvimentos em IA
-  expected_output: Uma lista de 10 bullet points sobre IA
-  agent: researcher
-  guardrail: garanta que cada bullet point contenha no mínimo 100 palavras
-
-validation_task:
-  description: Valide os achados da pesquisa
-  expected_output: Um relatório de validação
-  agent: validator
-  guardrail: confirme que todas as fontes são de publicações respeitáveis e publicadas nos últimos 2 anos
-```
-
-#### Como Funcionam os Guardrails de String
-
-Quando você fornece um guardrail de string, o CrewAI automaticamente:
-1. Cria uma instância `LLMGuardrail` usando a string como critério de validação
-2. Usa o LLM do agente da tarefa para alimentar a validação
-3. Cria um agente temporário de validação que verifica a saída contra seus critérios
-4. Retorna feedback detalhado se a validação falhar
-
-Esta abordagem é ideal quando você quer usar linguagem natural para descrever regras de validação sem escrever funções de validação customizadas.
-
-### Classe LLMGuardrail
-
-A classe `LLMGuardrail` é o mecanismo subjacente que alimenta os guardrails baseados em string. Você também pode usá-la diretamente para maior controle avançado:
-
-```python Code
-from crewai import Task
-from crewai.tasks.llm_guardrail import LLMGuardrail
-from crewai.llm import LLM
-
-# Crie um LLMGuardrail customizado com LLM específico
-custom_guardrail = LLMGuardrail(
-    description="Garanta que a resposta contenha exatamente 5 bullet points com citações adequadas",
-    llm=LLM(model="gpt-4o-mini")
-)
-
-task = Task(
-    description="Pesquise medidas de segurança em IA",
-    expected_output="Uma análise detalhada com bullet points",
-    agent=research_agent,
-    guardrail=custom_guardrail
-)
-```
-
-**Nota**: Quando você usa um guardrail de string, o CrewAI automaticamente cria uma instância `LLMGuardrail` usando o LLM do agente da sua tarefa. Usar `LLMGuardrail` diretamente lhe dá mais controle sobre o processo de validação e seleção de LLM.
 
 ### Melhores Práticas de Tratamento de Erros
 
@@ -902,26 +825,7 @@ task = Task(
 )
 ```
 
-#### Use uma abordagem no-code para validação
 
-```python Code
-from crewai import Task
-
-task = Task(
-    description="Gerar dados em JSON",
-    expected_output="Objeto JSON válido",
-    guardrail="Garanta que a resposta é um objeto JSON válido"
-)
-```
-
-#### Usando YAML
-
-```yaml
-research_task:
-  ...
-  guardrail: garanta que cada bullet tenha no mínimo 100 palavras
-  ...
-```
 
 ```python Code
 @CrewBase
@@ -1037,21 +941,87 @@ task = Task(
 
 ## Criando Diretórios ao Salvar Arquivos
 
-Agora é possível especificar se uma tarefa deve criar diretórios ao salvar sua saída em arquivo. Isso é útil para organizar outputs e garantir que os caminhos estejam corretos.
+O parâmetro `create_directory` controla se o CrewAI deve criar automaticamente diretórios ao salvar saídas de tarefas em arquivos. Este recurso é particularmente útil para organizar outputs e garantir que os caminhos de arquivos estejam estruturados corretamente, especialmente ao trabalhar com hierarquias de projetos complexas.
+
+### Comportamento Padrão
+
+Por padrão, `create_directory=True`, o que significa que o CrewAI criará automaticamente qualquer diretório ausente no caminho do arquivo de saída:
 
 ```python Code
-# ...
-
-save_output_task = Task(
-    description='Salve o resumo das notícias de IA em um arquivo',
-    expected_output='Arquivo salvo com sucesso',
-    agent=research_agent,
-    tools=[file_save_tool],
-    output_file='outputs/ai_news_summary.txt',
-    create_directory=True
+# Comportamento padrão - diretórios são criados automaticamente
+report_task = Task(
+    description='Gerar um relatório abrangente de análise de mercado',
+    expected_output='Uma análise detalhada de mercado com gráficos e insights',
+    agent=analyst_agent,
+    output_file='reports/2025/market_analysis.md',  # Cria 'reports/2025/' se não existir
+    markdown=True
 )
+```
 
-#...
+### Desabilitando a Criação de Diretórios
+
+Se você quiser evitar a criação automática de diretórios e garantir que o diretório já exista, defina `create_directory=False`:
+
+```python Code
+# Modo estrito - o diretório já deve existir
+strict_output_task = Task(
+    description='Salvar dados críticos que requerem infraestrutura existente',
+    expected_output='Dados salvos em localização pré-configurada',
+    agent=data_agent,
+    output_file='secure/vault/critical_data.json',
+    create_directory=False  # Gerará RuntimeError se 'secure/vault/' não existir
+)
+```
+
+### Configuração YAML
+
+Você também pode configurar este comportamento em suas definições de tarefas YAML:
+
+```yaml tasks.yaml
+analysis_task:
+  description: >
+    Gerar análise financeira trimestral
+  expected_output: >
+    Um relatório financeiro abrangente com insights trimestrais
+  agent: financial_analyst
+  output_file: reports/quarterly/q4_2024_analysis.pdf
+  create_directory: true  # Criar automaticamente o diretório 'reports/quarterly/'
+
+audit_task:
+  description: >
+    Realizar auditoria de conformidade e salvar no diretório de auditoria existente
+  expected_output: >
+    Um relatório de auditoria de conformidade
+  agent: auditor
+  output_file: audit/compliance_report.md
+  create_directory: false  # O diretório já deve existir
+```
+
+### Casos de Uso
+
+**Criação Automática de Diretórios (`create_directory=True`):**
+- Ambientes de desenvolvimento e prototipagem
+- Geração dinâmica de relatórios com pastas baseadas em datas
+- Fluxos de trabalho automatizados onde a estrutura de diretórios pode variar
+- Aplicações multi-tenant com pastas específicas do usuário
+
+**Gerenciamento Manual de Diretórios (`create_directory=False`):**
+- Ambientes de produção com controles rígidos do sistema de arquivos
+- Aplicações sensíveis à segurança onde diretórios devem ser pré-configurados
+- Sistemas com requisitos específicos de permissão
+- Ambientes de conformidade onde a criação de diretórios é auditada
+
+### Tratamento de Erros
+
+Quando `create_directory=False` e o diretório não existe, o CrewAI gerará um `RuntimeError`:
+
+```python Code
+try:
+    result = crew.kickoff()
+except RuntimeError as e:
+    # Tratar erro de diretório ausente
+    print(f"Falha na criação do diretório: {e}")
+    # Criar diretório manualmente ou usar local alternativo
 ```
 
 Veja o vídeo abaixo para aprender como utilizar saídas estruturadas no CrewAI:

pyproject.toml

@@ -11,7 +11,7 @@ dependencies = [
     # Core Dependencies
     "pydantic>=2.4.2",
     "openai>=1.13.3",
-    "litellm==1.72.6",
+    "litellm==1.74.3",
     "instructor>=1.3.3",
     # Text Processing
     "pdfplumber>=0.11.4",
@@ -39,6 +39,7 @@ dependencies = [
     "tomli>=2.0.2",
     "blinker>=1.9.0",
     "json5>=0.10.0",
+    "portalocker==2.7.0",
 ]
 
 [project.urls]
@@ -47,7 +48,7 @@ Documentation = "https://docs.crewai.com"
 Repository = "https://github.com/crewAIInc/crewAI"
 
 [project.optional-dependencies]
-tools = ["crewai-tools~=0.51.0"]
+tools = ["crewai-tools~=0.58.0"]
 embeddings = [
     "tiktoken~=0.8.0"
 ]

src/crewai/__init__.py

@@ -54,7 +54,7 @@ def _track_install_async():
 
 _track_install_async()
 
-__version__ = "0.141.0"
+__version__ = "0.150.0"
 __all__ = [
     "Agent",
     "Crew",

src/crewai/agents/crew_agent_executor.py

@@ -120,11 +120,8 @@ class CrewAgentExecutor(CrewAgentExecutorMixin):
             raise
         except Exception as e:
             handle_unknown_error(self._printer, e)
-            if e.__class__.__module__.startswith("litellm"):
-                # Do not retry on litellm errors
-                raise e
-            else:
-                raise e
+            raise
+
 
         if self.ask_for_human_input:
             formatted_answer = self._handle_human_feedback(formatted_answer)

src/crewai/cli/command.py

@@ -26,7 +26,7 @@ class PlusAPIMixin:
                 "Please sign up/login to CrewAI+ before using the CLI.",
                 style="bold red",
             )
-            console.print("Run 'crewai signup' to sign up/login.", style="bold green")
+            console.print("Run 'crewai login' to sign up/login.", style="bold green")
             raise SystemExit
 
     def _validate_response(self, response: requests.Response) -> None:

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]>=0.141.0,<1.0.0"
+    "crewai[tools]>=0.150.0,<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]>=0.141.0,<1.0.0",
+    "crewai[tools]>=0.150.0,<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]>=0.141.0"
+    "crewai[tools]>=0.150.0"
 ]
 
 [tool.crewai]

src/crewai/crew.py

@@ -161,7 +161,7 @@ class Crew(FlowTrackable, BaseModel):
     )
     user_memory: Optional[InstanceOf[UserMemory]] = Field(
         default=None,
-        description="An instance of the UserMemory to be used by the Crew to store/fetch memories of a specific user.",
+        description="DEPRECATED: Will be removed in version 0.156.0 or on 2025-08-04, whichever comes first. Use external_memory instead.",
     )
     external_memory: Optional[InstanceOf[ExternalMemory]] = Field(
         default=None,
@@ -327,7 +327,7 @@ class Crew(FlowTrackable, BaseModel):
         self._short_term_memory = self.short_term_memory
         self._entity_memory = self.entity_memory
 
-        # UserMemory is gonna to be deprecated in the future, but we have to initialize a default value for now
+        # UserMemory will be removed in version 0.156.0 or on 2025-08-04, whichever comes first
         self._user_memory = None
 
         if self.memory:
@@ -1255,6 +1255,7 @@ class Crew(FlowTrackable, BaseModel):
         if self.external_memory:
             copied_data["external_memory"] = self.external_memory.model_copy(deep=True)
         if self.user_memory:
+            # DEPRECATED: UserMemory will be removed in version 0.156.0 or on 2025-08-04
             copied_data["user_memory"] = self.user_memory.model_copy(deep=True)
 
         copied_data.pop("agents", None)
@@ -1313,7 +1314,6 @@ 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:
@@ -1333,28 +1333,13 @@ 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/agent_evaluator.py

@@ -1,178 +0,0 @@
-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/experimental/__init__.py

@@ -0,0 +1,40 @@
+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/__init__.py

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

src/crewai/experimental/evaluation/agent_evaluator.py

@@ -0,0 +1,245 @@
+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/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/experimental/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.evaluation.base_evaluator import AgentAggregatedEvaluationResult, AggregationStrategy, AgentEvaluationResult, MetricCategory
-from crewai.evaluation import EvaluationScore
+from crewai.experimental.evaluation.base_evaluator import AgentAggregatedEvaluationResult, AggregationStrategy, AgentEvaluationResult, MetricCategory
+from crewai.experimental.evaluation import EvaluationScore
 from crewai.utilities.events.utils.console_formatter import ConsoleFormatter
 from crewai.utilities.llm_utils import create_llm
 
@@ -17,7 +17,6 @@ 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())
@@ -25,7 +24,6 @@ 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
@@ -33,23 +31,19 @@ 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
@@ -91,7 +85,6 @@ 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]]]):
@@ -248,7 +241,6 @@ 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(),
@@ -307,7 +299,7 @@ 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:  # Default/average strategies
+            else:
                 strategy_guidance = "Provide a balanced analysis of strengths and weaknesses across all tasks."
 
             prompt = [

src/crewai/experimental/evaluation/evaluation_listener.py

@@ -9,7 +9,9 @@ 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
+    AgentExecutionCompletedEvent,
+    LiteAgentExecutionStartedEvent,
+    LiteAgentExecutionCompletedEvent
 )
 from crewai.utilities.events.tool_usage_events import (
     ToolUsageFinishedEvent,
@@ -52,10 +54,18 @@ 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)
@@ -88,19 +98,38 @@ 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.traces[trace_key] = {
-            "agent_id": agent.id,
-            "task_id": task.id,
-            "tool_uses": [],
-            "llm_calls": [],
-            "start_time": datetime.now(),
-            "final_output": None
-        }
+        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
+        )
 
     def on_agent_finish(self, agent: Agent, task: Task, output: Any):
         trace_key = f"{agent.id}_{task.id}"
@@ -108,9 +137,20 @@ 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:
@@ -187,4 +227,8 @@ class EvaluationTraceCallback(BaseEventListener):
 
 
 def create_evaluation_callbacks() -> EvaluationTraceCallback:
-    return EvaluationTraceCallback()
+    from crewai.utilities.events.crewai_event_bus import crewai_event_bus
+
+    callback = EvaluationTraceCallback()
+    callback.setup_listeners(crewai_event_bus)
+    return callback

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

@@ -0,0 +1,8 @@
+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

@@ -0,0 +1,122 @@
+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

@@ -0,0 +1,70 @@
+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

@@ -0,0 +1,125 @@
+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

@@ -0,0 +1,26 @@
+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/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.evaluation.base_evaluator import BaseEvaluator, EvaluationScore, MetricCategory
-from crewai.evaluation.json_parser import extract_json_from_llm_response
+from crewai.experimental.evaluation.base_evaluator import BaseEvaluator, EvaluationScore, MetricCategory
+from crewai.experimental.evaluation.json_parser import extract_json_from_llm_response
 
 class GoalAlignmentEvaluator(BaseEvaluator):
     @property
@@ -14,10 +14,14 @@ 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.
 
@@ -37,8 +41,7 @@ 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 description: {task.description}
-Expected output: {task.expected_output}
+{task_context}
 
 Agent's final output:
 {final_output}

src/crewai/experimental/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.evaluation.base_evaluator import BaseEvaluator, EvaluationScore, MetricCategory
-from crewai.evaluation.json_parser import extract_json_from_llm_response
+from crewai.experimental.evaluation.base_evaluator import BaseEvaluator, EvaluationScore, MetricCategory
+from crewai.experimental.evaluation.json_parser import extract_json_from_llm_response
 from crewai.tasks.task_output import TaskOutput
 
 class ReasoningPatternType(Enum):
@@ -36,10 +36,14 @@ class ReasoningEfficiencyEvaluator(BaseEvaluator):
     def evaluate(
         self,
         agent: Agent,
-        task: Task,
         execution_trace: Dict[str, Any],
-        final_output: TaskOutput,
+        final_output: TaskOutput | str,
+        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"
+
         llm_calls = execution_trace.get("llm_calls", [])
 
         if not llm_calls or len(llm_calls) < 2:
@@ -83,6 +87,8 @@ 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.
 
@@ -117,7 +123,7 @@ Return your evaluation as JSON with the following structure:
 }"""},
             {"role": "user", "content": f"""
 Agent role: {agent.role}
-Task description: {task.description}
+{task_context}
 
 Reasoning efficiency metrics:
 - Total LLM calls: {efficiency_metrics["total_llm_calls"]}
@@ -130,7 +136,7 @@ Sample of agent reasoning flow (chronological sequence):
 {call_samples}
 
 Agent's final output:
-{final_output.raw[:500]}... (truncated)
+{final_output[: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/experimental/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.evaluation.base_evaluator import BaseEvaluator, EvaluationScore, MetricCategory
-from crewai.evaluation.json_parser import extract_json_from_llm_response
+from crewai.experimental.evaluation.base_evaluator import BaseEvaluator, EvaluationScore, MetricCategory
+from crewai.experimental.evaluation.json_parser import extract_json_from_llm_response
 
 class SemanticQualityEvaluator(BaseEvaluator):
     @property
@@ -14,10 +14,13 @@ 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.
 
@@ -37,7 +40,7 @@ Return your evaluation as JSON with fields 'score' (number) and 'feedback' (stri
 """},
             {"role": "user", "content": f"""
 Agent role: {agent.role}
-Task description: {task.description}
+{task_context}
 
 Agent's final output:
 {final_output}

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

@@ -1,8 +1,8 @@
 import json
 from typing import Dict, Any
 
-from crewai.evaluation.base_evaluator import BaseEvaluator, EvaluationScore, MetricCategory
-from crewai.evaluation.json_parser import extract_json_from_llm_response
+from crewai.experimental.evaluation.base_evaluator import BaseEvaluator, EvaluationScore, MetricCategory
+from crewai.experimental.evaluation.json_parser import extract_json_from_llm_response
 from crewai.agent import Agent
 from crewai.task import Task
 
@@ -16,10 +16,14 @@ 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])
@@ -72,7 +76,7 @@ Return your evaluation as JSON with these fields:
 """},
             {"role": "user", "content": f"""
 Agent role: {agent.role}
-Task description: {task.description}
+{task_context}
 
 Available tools for this agent:
 {available_tools_info}
@@ -128,10 +132,13 @@ 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)
 
@@ -212,7 +219,7 @@ Return your evaluation as JSON with these fields:
 """},
             {"role": "user", "content": f"""
 Agent role: {agent.role}
-Task description: {task.description}
+{task_context}
 
 Parameter extraction examples:
 {param_samples_text}
@@ -267,10 +274,13 @@ 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)
@@ -352,7 +362,7 @@ Return your evaluation as JSON with these fields:
 """},
             {"role": "user", "content": f"""
 Agent role: {agent.role}
-Task description: {task.description}
+{task_context}
 
 Tool invocation examples:
 {invocation_samples_text}

src/crewai/experimental/evaluation/testing.py

@@ -0,0 +1,52 @@
+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/flow/flow.py

@@ -436,6 +436,7 @@ class Flow(Generic[T], metaclass=FlowMeta):
     _routers: Set[str] = set()
     _router_paths: Dict[str, List[str]] = {}
     initial_state: Union[Type[T], T, None] = None
+    name: Optional[str] = None
 
     def __class_getitem__(cls: Type["Flow"], item: Type[T]) -> Type["Flow"]:
         class _FlowGeneric(cls):  # type: ignore
@@ -473,7 +474,7 @@ class Flow(Generic[T], metaclass=FlowMeta):
             self,
             FlowCreatedEvent(
                 type="flow_created",
-                flow_name=self.__class__.__name__,
+                flow_name=self.name or self.__class__.__name__,
             ),
         )
 
@@ -769,7 +770,7 @@ class Flow(Generic[T], metaclass=FlowMeta):
             self,
             FlowStartedEvent(
                 type="flow_started",
-                flow_name=self.__class__.__name__,
+                flow_name=self.name or self.__class__.__name__,
                 inputs=inputs,
             ),
         )
@@ -792,7 +793,7 @@ class Flow(Generic[T], metaclass=FlowMeta):
             self,
             FlowFinishedEvent(
                 type="flow_finished",
-                flow_name=self.__class__.__name__,
+                flow_name=self.name or self.__class__.__name__,
                 result=final_output,
             ),
         )
@@ -834,7 +835,7 @@ class Flow(Generic[T], metaclass=FlowMeta):
                 MethodExecutionStartedEvent(
                     type="method_execution_started",
                     method_name=method_name,
-                    flow_name=self.__class__.__name__,
+                    flow_name=self.name or self.__class__.__name__,
                     params=dumped_params,
                     state=self._copy_state(),
                 ),
@@ -856,7 +857,7 @@ class Flow(Generic[T], metaclass=FlowMeta):
                 MethodExecutionFinishedEvent(
                     type="method_execution_finished",
                     method_name=method_name,
-                    flow_name=self.__class__.__name__,
+                    flow_name=self.name or self.__class__.__name__,
                     state=self._copy_state(),
                     result=result,
                 ),
@@ -869,7 +870,7 @@ class Flow(Generic[T], metaclass=FlowMeta):
                 MethodExecutionFailedEvent(
                     type="method_execution_failed",
                     method_name=method_name,
-                    flow_name=self.__class__.__name__,
+                    flow_name=self.name or self.__class__.__name__,
                     error=e,
                 ),
             )
@@ -1076,7 +1077,7 @@ class Flow(Generic[T], metaclass=FlowMeta):
             self,
             FlowPlotEvent(
                 type="flow_plot",
-                flow_name=self.__class__.__name__,
+                flow_name=self.name or self.__class__.__name__,
             ),
         )
         plot_flow(self, filename)

src/crewai/knowledge/embedder/base_embedder.py

@@ -1,55 +0,0 @@
-from abc import ABC, abstractmethod
-from typing import List
-
-import numpy as np
-
-
-class BaseEmbedder(ABC):
-    """
-    Abstract base class for text embedding models
-    """
-
-    @abstractmethod
-    def embed_chunks(self, chunks: List[str]) -> np.ndarray:
-        """
-        Generate embeddings for a list of text chunks
-
-        Args:
-            chunks: List of text chunks to embed
-
-        Returns:
-            Array of embeddings
-        """
-        pass
-
-    @abstractmethod
-    def embed_texts(self, texts: List[str]) -> np.ndarray:
-        """
-        Generate embeddings for a list of texts
-
-        Args:
-            texts: List of texts to embed
-
-        Returns:
-            Array of embeddings
-        """
-        pass
-
-    @abstractmethod
-    def embed_text(self, text: str) -> np.ndarray:
-        """
-        Generate embedding for a single text
-
-        Args:
-            text: Text to embed
-
-        Returns:
-            Embedding array
-        """
-        pass
-
-    @property
-    @abstractmethod
-    def dimension(self) -> int:
-        """Get the dimension of the embeddings"""
-        pass

src/crewai/knowledge/storage/knowledge_storage.py

@@ -13,11 +13,12 @@ from chromadb.api.types import OneOrMany
 from chromadb.config import Settings
 
 from crewai.knowledge.storage.base_knowledge_storage import BaseKnowledgeStorage
-from crewai.utilities import EmbeddingConfigurator
+from crewai.rag.embeddings.configurator import EmbeddingConfigurator
 from crewai.utilities.chromadb import sanitize_collection_name
 from crewai.utilities.constants import KNOWLEDGE_DIRECTORY
 from crewai.utilities.logger import Logger
 from crewai.utilities.paths import db_storage_path
+from crewai.utilities.chromadb import create_persistent_client
 
 
 @contextlib.contextmanager
@@ -84,14 +85,11 @@ class KnowledgeStorage(BaseKnowledgeStorage):
                 raise Exception("Collection not initialized")
 
     def initialize_knowledge_storage(self):
-        base_path = os.path.join(db_storage_path(), "knowledge")
-        chroma_client = chromadb.PersistentClient(
-            path=base_path,
+        self.app = create_persistent_client(
+            path=os.path.join(db_storage_path(), "knowledge"),
             settings=Settings(allow_reset=True),
         )
 
-        self.app = chroma_client
-
         try:
             collection_name = (
                 f"knowledge_{self.collection_name}"
@@ -111,9 +109,8 @@ class KnowledgeStorage(BaseKnowledgeStorage):
     def reset(self):
         base_path = os.path.join(db_storage_path(), KNOWLEDGE_DIRECTORY)
         if not self.app:
-            self.app = chromadb.PersistentClient(
-                path=base_path,
-                settings=Settings(allow_reset=True),
+            self.app = create_persistent_client(
+                path=base_path, settings=Settings(allow_reset=True)
             )
 
         self.app.reset()

src/crewai/lite_agent.py

@@ -305,6 +305,7 @@ 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/llm.py

@@ -59,6 +59,7 @@ from crewai.utilities.exceptions.context_window_exceeding_exception import (
 
 load_dotenv()
 
+litellm.suppress_debug_info = True
 
 class FilteredStream(io.TextIOBase):
     _lock = None
@@ -76,9 +77,7 @@ class FilteredStream(io.TextIOBase):
 
             # Skip common noisy LiteLLM banners and any other lines that contain "litellm"
             if (
-                "give feedback / get help" in lower_s
-                or "litellm.info:" in lower_s
-                or "litellm" in lower_s
+                "litellm.info:" in lower_s
                 or "Consider using a smaller input or implementing a text splitting strategy" in lower_s
             ):
                 return 0
@@ -309,6 +308,7 @@ class LLM(BaseLLM):
         api_version: Optional[str] = None,
         api_key: Optional[str] = None,
         callbacks: List[Any] = [],
+        reasoning: Optional[bool] = None,
         reasoning_effort: Optional[Literal["none", "low", "medium", "high"]] = None,
         stream: bool = False,
         **kwargs,
@@ -333,6 +333,7 @@ class LLM(BaseLLM):
         self.api_key = api_key
         self.callbacks = callbacks
         self.context_window_size = 0
+        self.reasoning = reasoning
         self.reasoning_effort = reasoning_effort
         self.additional_params = kwargs
         self.is_anthropic = self._is_anthropic_model(model)
@@ -407,10 +408,15 @@ class LLM(BaseLLM):
             "api_key": self.api_key,
             "stream": self.stream,
             "tools": tools,
-            "reasoning_effort": self.reasoning_effort,
             **self.additional_params,
         }
 
+        if self.reasoning is False:
+            # When reasoning is explicitly disabled, don't include reasoning_effort
+            pass
+        elif self.reasoning is True or self.reasoning_effort is not None:
+            params["reasoning_effort"] = self.reasoning_effort
+
         # Remove None values from params
         return {k: v for k, v in params.items() if v is not None}
 
@@ -760,7 +766,7 @@ class LLM(BaseLLM):
         available_functions: Optional[Dict[str, Any]] = None,
         from_task: Optional[Any] = None,
         from_agent: Optional[Any] = None,
-    ) -> str:
+    ) -> str | Any:
         """Handle a non-streaming response from the LLM.
 
         Args:
@@ -784,13 +790,11 @@ class LLM(BaseLLM):
             # Convert litellm's context window error to our own exception type
             # for consistent handling in the rest of the codebase
             raise LLMContextLengthExceededException(str(e))
-
         # --- 2) Extract response message and content
         response_message = cast(Choices, cast(ModelResponse, response).choices)[
             0
         ].message
         text_response = response_message.content or ""
-
         # --- 3) Handle callbacks with usage info
         if callbacks and len(callbacks) > 0:
             for callback in callbacks:
@@ -803,21 +807,22 @@ class LLM(BaseLLM):
                             start_time=0,
                             end_time=0,
                         )
-
         # --- 4) Check for tool calls
         tool_calls = getattr(response_message, "tool_calls", [])
 
-        # --- 5) If no tool calls or no available functions, return the text response directly
-        if not tool_calls or not available_functions:
+        # --- 5) If no tool calls or no available functions, return the text response directly as long as there is a text response
+        if (not tool_calls or not available_functions) and text_response:
             self._handle_emit_call_events(response=text_response, call_type=LLMCallType.LLM_CALL, from_task=from_task, from_agent=from_agent, messages=params["messages"])
             return text_response
+        # --- 6) If there is no text response, no available functions, but there are tool calls, return the tool calls
+        elif tool_calls and not available_functions and not text_response:
+            return tool_calls
 
-        # --- 6) Handle tool calls if present
+        # --- 7) Handle tool calls if present
         tool_result = self._handle_tool_call(tool_calls, available_functions)
         if tool_result is not None:
             return tool_result
-
-        # --- 7) If tool call handling didn't return a result, emit completion event and return text response
+        # --- 8) If tool call handling didn't return a result, emit completion event and return text response
         self._handle_emit_call_events(response=text_response, call_type=LLMCallType.LLM_CALL, from_task=from_task, from_agent=from_agent, messages=params["messages"])
         return text_response
 
@@ -952,22 +957,18 @@ class LLM(BaseLLM):
         # --- 3) Convert string messages to proper format if needed
         if isinstance(messages, str):
             messages = [{"role": "user", "content": messages}]
-
         # --- 4) Handle O1 model special case (system messages not supported)
         if "o1" in self.model.lower():
             for message in messages:
                 if message.get("role") == "system":
                     message["role"] = "assistant"
-
         # --- 5) Set up callbacks if provided
         with suppress_warnings():
             if callbacks and len(callbacks) > 0:
                 self.set_callbacks(callbacks)
-
             try:
                 # --- 6) Prepare parameters for the completion call
                 params = self._prepare_completion_params(messages, tools)
-
                 # --- 7) Make the completion call and handle response
                 if self.stream:
                     return self._handle_streaming_response(
@@ -984,12 +985,32 @@ class LLM(BaseLLM):
                 # whether to summarize the content or abort based on the respect_context_window flag
                 raise
             except Exception as e:
+                unsupported_stop = "Unsupported parameter" in str(e) and "'stop'" in str(e)
+
+                if unsupported_stop:
+                    if "additional_drop_params" in self.additional_params and isinstance(self.additional_params["additional_drop_params"], list):
+                        self.additional_params["additional_drop_params"].append("stop")
+                    else:
+                        self.additional_params = {"additional_drop_params": ["stop"]}
+
+                    logging.info(
+                        "Retrying LLM call without the unsupported 'stop'"
+                    )
+
+                    return self.call(
+                        messages,
+                        tools=tools,
+                        callbacks=callbacks,
+                        available_functions=available_functions,
+                        from_task=from_task,
+                        from_agent=from_agent,
+                    )
+
                 assert hasattr(crewai_event_bus, "emit")
                 crewai_event_bus.emit(
                     self,
                     event=LLMCallFailedEvent(error=str(e), from_task=from_task, from_agent=from_agent),
                 )
-                logging.error(f"LiteLLM call failed: {str(e)}")
                 raise
 
     def _handle_emit_call_events(self, response: Any, call_type: LLMCallType, from_task: Optional[Any] = None, from_agent: Optional[Any] = None, messages: str | list[dict[str, Any]] | None = None):
@@ -1058,6 +1079,15 @@ class LLM(BaseLLM):
                 messages.append({"role": "user", "content": "Please continue."})
             return messages
 
+        # TODO: Remove this code after merging PR https://github.com/BerriAI/litellm/pull/10917
+        # Ollama doesn't supports last message to be 'assistant'
+        if "ollama" in self.model.lower() and messages and messages[-1]["role"] == "assistant":
+            messages = messages.copy()
+            messages.append(
+                {"role": "user", "content": ""}
+            )
+            return messages
+
         # Handle Anthropic models
         if not self.is_anthropic:
             return messages

src/crewai/memory/contextual/contextual_memory.py

@@ -108,6 +108,7 @@ class ContextualMemory:
 
     def _fetch_user_context(self, query: str) -> str:
         """
+        DEPRECATED: Will be removed in version 0.156.0 or on 2025-08-04, whichever comes first.
         Fetches and formats relevant user information from User Memory.
         Args:
             query (str): The search query to find relevant user memories.

src/crewai/memory/storage/mem0_storage.py

@@ -4,7 +4,6 @@ from typing import Any, Dict, List
 from mem0 import Memory, MemoryClient
 
 from crewai.memory.storage.interface import Storage
-from crewai.utilities.chromadb import sanitize_collection_name
 
 MAX_AGENT_ID_LENGTH_MEM0 = 255
 
@@ -13,135 +12,150 @@ class Mem0Storage(Storage):
     """
     Extends Storage to handle embedding and searching across entities using Mem0.
     """
-
     def __init__(self, type, crew=None, config=None):
         super().__init__()
-        supported_types = ["user", "short_term", "long_term", "entities", "external"]
-        if type not in supported_types:
-            raise ValueError(
-                f"Invalid type '{type}' for Mem0Storage. Must be one of: "
-                + ", ".join(supported_types)
-            )
 
+        self._validate_type(type)
         self.memory_type = type
         self.crew = crew
-        self.config = config or {}
-        # TODO: Memory config will be removed in the future the config will be passed as a parameter
-        self.memory_config = self.config or getattr(crew, "memory_config", {}) or {}
 
-        # User ID is required for user memory type "user" since it's used as a unique identifier for the user.
-        user_id = self._get_user_id()
-        if type == "user" and not user_id:
+        # TODO: Memory config will be removed in the future the config will be passed as a parameter
+        self.config = config or getattr(crew, "memory_config", {}).get("config", {}) or {}
+
+        self._validate_user_id()
+        self._extract_config_values()
+        self._initialize_memory()
+
+    def _validate_type(self, type):
+        supported_types = {"user", "short_term", "long_term", "entities", "external"}
+        if type not in supported_types:
+            raise ValueError(
+                f"Invalid type '{type}' for Mem0Storage. Must be one of: {', '.join(supported_types)}"
+            )
+
+    def _validate_user_id(self):
+        if self.memory_type == "user" and not self.config.get("user_id", ""):
             raise ValueError("User ID is required for user memory type")
 
-        # API key in memory config overrides the environment variable
-        config = self._get_config()
-        mem0_api_key = config.get("api_key") or os.getenv("MEM0_API_KEY")
-        mem0_org_id = config.get("org_id")
-        mem0_project_id = config.get("project_id")
-        mem0_local_config = config.get("local_mem0_config")
+    def _extract_config_values(self):
+        cfg = self.config
+        self.mem0_run_id = cfg.get("run_id")
+        self.includes = cfg.get("includes")
+        self.excludes = cfg.get("excludes")
+        self.custom_categories = cfg.get("custom_categories")
+        self.infer = cfg.get("infer", True)
 
-        # Initialize MemoryClient or Memory based on the presence of the mem0_api_key
-        if mem0_api_key:
-            if mem0_org_id and mem0_project_id:
-                self.memory = MemoryClient(
-                    api_key=mem0_api_key, org_id=mem0_org_id, project_id=mem0_project_id
-                )
-            else:
-                self.memory = MemoryClient(api_key=mem0_api_key)
+    def _initialize_memory(self):
+        api_key = self.config.get("api_key") or os.getenv("MEM0_API_KEY")
+        org_id = self.config.get("org_id")
+        project_id = self.config.get("project_id")
+        local_config = self.config.get("local_mem0_config")
+
+        if api_key:
+            self.memory = (
+                MemoryClient(api_key=api_key, org_id=org_id, project_id=project_id)
+                if org_id and project_id
+                else MemoryClient(api_key=api_key)
+            )
+            if self.custom_categories:
+                self.memory.update_project(custom_categories=self.custom_categories)
         else:
-            if mem0_local_config and len(mem0_local_config):
-                self.memory = Memory.from_config(mem0_local_config)
-            else:
-                self.memory = Memory()
+            self.memory = (
+                Memory.from_config(local_config)
+                if local_config and len(local_config)
+                else Memory()
+            )
 
-    def _sanitize_role(self, role: str) -> str:
+    def _create_filter_for_search(self):
         """
-        Sanitizes agent roles to ensure valid directory names.
+        Returns:
+            dict: A filter dictionary containing AND conditions for querying data.
+                - Includes user_id if memory_type is 'external'.
+                - Includes run_id if memory_type is 'short_term' and mem0_run_id is present.
         """
-        return role.replace("\n", "").replace(" ", "_").replace("/", "_")
+        filter = {
+            "AND": []
+        }
+
+        # Add user_id condition if the memory type is external
+        if self.memory_type == "external":
+            filter["AND"].append({"user_id": self.config.get("user_id", "")})
+
+        # Add run_id condition if the memory type is short_term and a run ID is set
+        if self.memory_type == "short_term" and self.mem0_run_id:
+            filter["AND"].append({"run_id": self.mem0_run_id})
+
+        return filter
 
     def save(self, value: Any, metadata: Dict[str, Any]) -> None:
-        user_id = self._get_user_id()
-        agent_name = self._get_agent_name()
-        params = None
-        if self.memory_type == "short_term":
-            params = {
-                "agent_id": agent_name,
-                "infer": False,
-                "metadata": {"type": "short_term", **metadata},
-            }
-        elif self.memory_type == "long_term":
-            params = {
-                "agent_id": agent_name,
-                "infer": False,
-                "metadata": {"type": "long_term", **metadata},
-            }
-        elif self.memory_type == "entities":
-            params = {
-                "agent_id": agent_name,
-                "infer": False,
-                "metadata": {"type": "entity", **metadata},
-            }
-        elif self.memory_type == "external":
-            params = {
-                "user_id": user_id,
-                "agent_id": agent_name,
-                "metadata": {"type": "external", **metadata},
-            }
+        user_id = self.config.get("user_id", "")
+        assistant_message = [{"role" : "assistant","content" : value}] 
 
-        if params:
-            if isinstance(self.memory, MemoryClient):
-                params["output_format"] = "v1.1"
-            self.memory.add(value, **params)
+        base_metadata = {
+            "short_term": "short_term",
+            "long_term": "long_term",
+            "entities": "entity",
+            "external": "external"
+        }
 
-    def search(
-        self,
-        query: str,
-        limit: int = 3,
-        score_threshold: float = 0.35,
-    ) -> List[Any]:
-        params = {"query": query, "limit": limit, "output_format": "v1.1"}
-        if user_id := self._get_user_id():
+        # Shared base params
+        params: dict[str, Any] = {
+            "metadata": {"type": base_metadata[self.memory_type], **metadata},
+            "infer": self.infer
+        }
+
+        if self.memory_type == "external":
             params["user_id"] = user_id
 
-        agent_name = self._get_agent_name()
-        if self.memory_type == "short_term":
-            params["agent_id"] = agent_name
-            params["metadata"] = {"type": "short_term"}
-        elif self.memory_type == "long_term":
-            params["agent_id"] = agent_name
-            params["metadata"] = {"type": "long_term"}
-        elif self.memory_type == "entities":
-            params["agent_id"] = agent_name
-            params["metadata"] = {"type": "entity"}
-        elif self.memory_type == "external":
-            params["agent_id"] = agent_name
-            params["metadata"] = {"type": "external"}
+        
+        if params:
+            # MemoryClient-specific overrides
+            if isinstance(self.memory, MemoryClient):
+                params["includes"] = self.includes
+                params["excludes"] = self.excludes
+                params["output_format"] = "v1.1"
+                params["version"]="v2"
+
+                if self.memory_type == "short_term":
+                    params["run_id"] = self.mem0_run_id
+
+            self.memory.add(assistant_message, **params)
+
+    def search(self,query: str,limit: int = 3,score_threshold: float = 0.35) -> List[Any]:
+        params = {
+            "query": query, 
+            "limit": limit, 
+            "version": "v2",
+            "output_format": "v1.1"
+            }
+        
+        if user_id := self.config.get("user_id", ""):
+            params["user_id"] = user_id
+
+        memory_type_map = {
+            "short_term": {"type": "short_term"},
+            "long_term": {"type": "long_term"},
+            "entities": {"type": "entity"},
+            "external": {"type": "external"},
+        }
+        
+        if self.memory_type in memory_type_map:
+            params["metadata"] = memory_type_map[self.memory_type]
+            if self.memory_type == "short_term":
+                params["run_id"] = self.mem0_run_id
 
         # Discard the filters for now since we create the filters
         # automatically when the crew is created.
+
+        params["filters"] = self._create_filter_for_search()
+        params['threshold'] = score_threshold
+
         if isinstance(self.memory, Memory):
-            del params["metadata"], params["output_format"]
-            
+            del params["metadata"], params["version"], params["run_id"], params['output_format']
+
         results = self.memory.search(**params)
-        return [r for r in results["results"] if r["score"] >= score_threshold]
-
-    def _get_user_id(self) -> str:
-        return self._get_config().get("user_id", "")
-
-    def _get_agent_name(self) -> str:
-        if not self.crew:
-            return ""
-
-        agents = self.crew.agents
-        agents = [self._sanitize_role(agent.role) for agent in agents]
-        agents = "_".join(agents)
-        return sanitize_collection_name(name=agents,max_collection_length=MAX_AGENT_ID_LENGTH_MEM0)
-
-    def _get_config(self) -> Dict[str, Any]:
-        return self.config or getattr(self, "memory_config", {}).get("config", {}) or {}
-
+        return [r for r in results["results"]]
+    
     def reset(self):
         if self.memory:
             self.memory.reset()

src/crewai/memory/storage/rag_storage.py

@@ -4,12 +4,12 @@ import logging
 import os
 import shutil
 import uuid
+
 from typing import Any, Dict, List, Optional
-
 from chromadb.api import ClientAPI
-
-from crewai.memory.storage.base_rag_storage import BaseRAGStorage
-from crewai.utilities import EmbeddingConfigurator
+from crewai.rag.storage.base_rag_storage import BaseRAGStorage
+from crewai.rag.embeddings.configurator import EmbeddingConfigurator
+from crewai.utilities.chromadb import create_persistent_client
 from crewai.utilities.constants import MAX_FILE_NAME_LENGTH
 from crewai.utilities.paths import db_storage_path
 
@@ -60,17 +60,15 @@ class RAGStorage(BaseRAGStorage):
         self.embedder_config = configurator.configure_embedder(self.embedder_config)
 
     def _initialize_app(self):
-        import chromadb
         from chromadb.config import Settings
 
         self._set_embedder_config()
-        chroma_client = chromadb.PersistentClient(
+
+        self.app = create_persistent_client(
             path=self.path if self.path else self.storage_file_name,
             settings=Settings(allow_reset=self.allow_reset),
         )
 
-        self.app = chroma_client
-
         self.collection = self.app.get_or_create_collection(
             name=self.type, embedding_function=self.embedder_config
         )

src/crewai/memory/user/user_memory.py

@@ -14,7 +14,8 @@ class UserMemory(Memory):
 
     def __init__(self, crew=None):
         warnings.warn(
-            "UserMemory is deprecated and will be removed in a future version. "
+            "UserMemory is deprecated and will be removed in version 0.156.0 "
+            "or on 2025-08-04, whichever comes first. "
             "Please use ExternalMemory instead.",
             DeprecationWarning,
             stacklevel=2,

src/crewai/memory/user/user_memory_item.py

@@ -1,8 +1,16 @@
+import warnings
 from typing import Any, Dict, Optional
 
 
 class UserMemoryItem:
     def __init__(self, data: Any, user: str, metadata: Optional[Dict[str, Any]] = None):
+        warnings.warn(
+            "UserMemoryItem is deprecated and will be removed in version 0.156.0 "
+            "or on 2025-08-04, whichever comes first. "
+            "Please use ExternalMemory instead.",
+            DeprecationWarning,
+            stacklevel=2,
+        )
         self.data = data
         self.user = user
         self.metadata = metadata if metadata is not None else {}

src/crewai/rag/__init__.py

@@ -0,0 +1 @@
+"""RAG (Retrieval-Augmented Generation) infrastructure for CrewAI."""

src/crewai/rag/embeddings/__init__.py

@@ -0,0 +1 @@
+"""Embedding components for RAG infrastructure."""

src/crewai/rag/embeddings/configurator.py

@@ -38,7 +38,14 @@ class EmbeddingConfigurator:
                 f"Unsupported embedding provider: {provider}, supported providers: {list(self.embedding_functions.keys())}"
             )
 
-        embedding_function = self.embedding_functions[provider]
+        try:
+            embedding_function = self.embedding_functions[provider]
+        except ImportError as e:
+            missing_package = str(e).split()[-1]
+            raise ImportError( 
+                f"{missing_package} is not installed. Please install it with: pip install {missing_package}"
+            )
+
         return (
             embedding_function(config)
             if provider == "custom"

src/crewai/rag/storage/__init__.py

@@ -0,0 +1 @@
+"""Storage components for RAG infrastructure."""

src/crewai/utilities/__init__.py

@@ -10,7 +10,6 @@ from .rpm_controller import RPMController
 from .exceptions.context_window_exceeding_exception import (
     LLMContextLengthExceededException,
 )
-from .embedding_configurator import EmbeddingConfigurator
 
 __all__ = [
     "Converter",
@@ -24,5 +23,4 @@ __all__ = [
     "RPMController",
     "YamlParser",
     "LLMContextLengthExceededException",
-    "EmbeddingConfigurator",
 ]

src/crewai/utilities/agent_utils.py

@@ -157,10 +157,6 @@ def get_llm_response(
             from_agent=from_agent,
         )
     except Exception as e:
-        printer.print(
-            content=f"Error during LLM call: {e}",
-            color="red",
-        )
         raise e
     if not answer:
         printer.print(
@@ -232,12 +228,17 @@ def handle_unknown_error(printer: Any, exception: Exception) -> None:
         printer: Printer instance for output
         exception: The exception that occurred
     """
+    error_message = str(exception)
+
+    if "litellm" in error_message:
+        return
+
     printer.print(
         content="An unknown error occurred. Please check the details below.",
         color="red",
     )
     printer.print(
-        content=f"Error details: {exception}",
+        content=f"Error details: {error_message}",
         color="red",
     )
 

src/crewai/utilities/chromadb.py

@@ -1,6 +1,10 @@
 import re
+import portalocker
+from chromadb import PersistentClient
+from hashlib import md5
 from typing import Optional
 
+
 MIN_COLLECTION_LENGTH = 3
 MAX_COLLECTION_LENGTH = 63
 DEFAULT_COLLECTION = "default_collection"
@@ -60,3 +64,16 @@ def sanitize_collection_name(name: Optional[str], max_collection_length: int = M
             sanitized = sanitized[:-1] + "z"
 
     return sanitized
+
+
+def create_persistent_client(path: str, **kwargs):
+    """
+    Creates a persistent client for ChromaDB with a lock file to prevent
+    concurrent creations. Works for both multi-threads and multi-processes
+    environments.
+    """
+    lockfile = f"chromadb-{md5(path.encode(), usedforsecurity=False).hexdigest()}.lock"
+    with portalocker.Lock(lockfile):
+        client = PersistentClient(path=path, **kwargs)
+
+    return client

src/crewai/utilities/events/__init__.py

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

src/crewai/utilities/events/agent_events.py

@@ -123,3 +123,28 @@ 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/base_events.py

@@ -1,6 +1,5 @@
-from datetime import datetime
+from datetime import datetime, timezone
 from typing import Any, Dict, Optional
-
 from pydantic import BaseModel, Field
 
 from crewai.utilities.serialization import to_serializable
@@ -9,7 +8,7 @@ from crewai.utilities.serialization import to_serializable
 class BaseEvent(BaseModel):
     """Base class for all events"""
 
-    timestamp: datetime = Field(default_factory=datetime.now)
+    timestamp: datetime = Field(default_factory=lambda: datetime.now(timezone.utc))
     type: str
     source_fingerprint: Optional[str] = None  # UUID string of the source entity
     source_type: Optional[str] = None  # "agent", "task", "crew", "memory", "entity_memory", "short_term_memory", "long_term_memory", "external_memory"

src/crewai/utilities/events/event_types.py

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

tests/agent_reasoning_test.py

@@ -2,6 +2,7 @@
 
 import json
 import pytest
+from unittest.mock import patch
 
 from crewai import Agent, Task
 from crewai.llm import LLM
@@ -259,3 +260,31 @@ def test_agent_with_function_calling_fallback():
     assert result == "4"
     assert "Reasoning Plan:" in task.description
     assert "Invalid JSON that will trigger fallback" in task.description
+
+
+def test_agent_with_llm_reasoning_disabled():
+    """Test agent with LLM reasoning disabled."""
+    llm = LLM("gpt-3.5-turbo", reasoning=False)
+    
+    agent = Agent(
+        role="Test Agent",
+        goal="To test the LLM reasoning parameter",
+        backstory="I am a test agent created to verify the LLM reasoning parameter works correctly.",
+        llm=llm,
+        reasoning=False,
+        verbose=True
+    )
+    
+    task = Task(
+        description="Simple math task: What's 3+3?",
+        expected_output="The answer should be a number.",
+        agent=agent
+    )
+    
+    with patch.object(agent.llm, 'call') as mock_call:
+        mock_call.return_value = "6"
+        
+        result = agent.execute_task(task)
+        
+        assert result == "6"
+        assert "Reasoning Plan:" not in task.description

tests/agent_test.py

@@ -2010,7 +2010,6 @@ def test_crew_agent_executor_litellm_auth_error():
     from litellm.exceptions import AuthenticationError
 
     from crewai.agents.tools_handler import ToolsHandler
-    from crewai.utilities import Printer
 
     # Create an agent and executor
     agent = Agent(
@@ -2043,7 +2042,6 @@ def test_crew_agent_executor_litellm_auth_error():
     # Mock the LLM call to raise AuthenticationError
     with (
         patch.object(LLM, "call") as mock_llm_call,
-        patch.object(Printer, "print") as mock_printer,
         pytest.raises(AuthenticationError) as exc_info,
     ):
         mock_llm_call.side_effect = AuthenticationError(
@@ -2057,13 +2055,6 @@ def test_crew_agent_executor_litellm_auth_error():
             }
         )
 
-    # Verify error handling messages
-    error_message = f"Error during LLM call: {str(mock_llm_call.side_effect)}"
-    mock_printer.assert_any_call(
-        content=error_message,
-        color="red",
-    )
-
     # Verify the call was only made once (no retries)
     mock_llm_call.assert_called_once()
 

tests/cassettes/TestAgentEvaluator.test_eval_lite_agent.yaml

@@ -0,0 +1,237 @@
+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
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tests/evaluation/test_agent_evaluator.py

@@ -1,95 +0,0 @@
-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/metrics/test_goal_metrics.py

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

tests/experimental/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.evaluation.metrics.reasoning_metrics import (
+from crewai.experimental.evaluation.metrics.reasoning_metrics import (
     ReasoningEfficiencyEvaluator,
 )
-from tests.evaluation.metrics.base_evaluation_metrics_test import BaseEvaluationMetricsTest
+from tests.experimental.evaluation.metrics.base_evaluation_metrics_test import BaseEvaluationMetricsTest
 from crewai.utilities.llm_utils import LLM
-from crewai.evaluation.base_evaluator import EvaluationScore
+from crewai.experimental.evaluation.base_evaluator import EvaluationScore
 
 class TestReasoningEfficiencyEvaluator(BaseEvaluationMetricsTest):
     @pytest.fixture

tests/experimental/evaluation/metrics/test_semantic_quality_metrics.py

@@ -1,8 +1,8 @@
 from unittest.mock import patch, MagicMock
 
-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.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.utilities.llm_utils import LLM
 
 class TestSemanticQualityEvaluator(BaseEvaluationMetricsTest):

tests/experimental/evaluation/metrics/test_tools_metrics.py

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

tests/experimental/evaluation/test_agent_evaluator.py

@@ -0,0 +1,278 @@
+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

@@ -0,0 +1,111 @@
+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

@@ -0,0 +1,197 @@
+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/flow_test.py

@@ -755,3 +755,15 @@ def test_multiple_routers_from_same_trigger():
     assert execution_order.index("anemia_analysis") > execution_order.index(
         "anemia_router"
     )
+
+
+def test_flow_name():
+    class MyFlow(Flow):
+        name = "MyFlow"
+
+        @start()
+        def start(self):
+            return "Hello, world!"
+
+    flow = MyFlow()
+    assert flow.name == "MyFlow"

tests/llm_test.py

@@ -1,3 +1,4 @@
+import logging
 import os
 from time import sleep
 from unittest.mock import MagicMock, patch
@@ -664,3 +665,145 @@ def test_handle_streaming_tool_calls_no_tools(mock_emit):
         expected_completed_llm_call=1,
         expected_final_chunk_result=response,
     )
+
+
+@pytest.mark.vcr(filter_headers=["authorization"])
+def test_llm_call_when_stop_is_unsupported(caplog):
+    llm = LLM(model="o1-mini", stop=["stop"])
+    with caplog.at_level(logging.INFO):
+        result = llm.call("What is the capital of France?")
+        assert "Retrying LLM call without the unsupported 'stop'" in caplog.text
+    assert isinstance(result, str)
+    assert "Paris" in result
+
+@pytest.mark.vcr(filter_headers=["authorization"])
+def test_llm_call_when_stop_is_unsupported_when_additional_drop_params_is_provided(caplog):
+    llm = LLM(model="o1-mini", stop=["stop"], additional_drop_params=["another_param"])
+    with caplog.at_level(logging.INFO):
+        result = llm.call("What is the capital of France?")
+        assert "Retrying LLM call without the unsupported 'stop'" in caplog.text
+    assert isinstance(result, str)
+    assert "Paris" in result
+
+
+@pytest.fixture
+def ollama_llm():
+    return LLM(model="ollama/llama3.2:3b")
+
+def test_ollama_appends_dummy_user_message_when_last_is_assistant(ollama_llm):
+    original_messages = [
+        {"role": "user", "content": "Hi there"},
+        {"role": "assistant", "content": "Hello!"},
+    ]
+
+    formatted = ollama_llm._format_messages_for_provider(original_messages)
+
+    assert len(formatted) == len(original_messages) + 1
+    assert formatted[-1]["role"] == "user"
+    assert formatted[-1]["content"] == ""
+
+
+def test_ollama_does_not_modify_when_last_is_user(ollama_llm):
+    original_messages = [
+        {"role": "user", "content": "Tell me a joke."},
+    ]
+
+    formatted = ollama_llm._format_messages_for_provider(original_messages)
+
+    assert formatted == original_messages
+
+
+def test_llm_reasoning_parameter_false():
+    """Test that reasoning=False disables reasoning mode."""
+    llm = LLM(model="ollama/qwen", reasoning=False)
+    
+    with patch("litellm.completion") as mock_completion:
+        mock_message = MagicMock()
+        mock_message.content = "Test response"
+        mock_choice = MagicMock()
+        mock_choice.message = mock_message
+        mock_response = MagicMock()
+        mock_response.choices = [mock_choice]
+        mock_response.usage = {"prompt_tokens": 5, "completion_tokens": 5, "total_tokens": 10}
+        mock_completion.return_value = mock_response
+        
+        llm.call("Test message")
+        
+        _, kwargs = mock_completion.call_args
+        assert "reasoning_effort" not in kwargs
+
+
+def test_llm_reasoning_parameter_true():
+    """Test that reasoning=True enables reasoning mode."""
+    llm = LLM(model="ollama/qwen", reasoning=True, reasoning_effort="medium")
+    
+    with patch("litellm.completion") as mock_completion:
+        mock_message = MagicMock()
+        mock_message.content = "Test response"
+        mock_choice = MagicMock()
+        mock_choice.message = mock_message
+        mock_response = MagicMock()
+        mock_response.choices = [mock_choice]
+        mock_response.usage = {"prompt_tokens": 5, "completion_tokens": 5, "total_tokens": 10}
+        mock_completion.return_value = mock_response
+        
+        llm.call("Test message")
+        
+        _, kwargs = mock_completion.call_args
+        assert kwargs["reasoning_effort"] == "medium"
+
+
+def test_llm_reasoning_parameter_none_with_reasoning_effort():
+    """Test that reasoning=None with reasoning_effort still includes reasoning_effort."""
+    llm = LLM(model="ollama/qwen", reasoning=None, reasoning_effort="high")
+    
+    with patch("litellm.completion") as mock_completion:
+        mock_message = MagicMock()
+        mock_message.content = "Test response"
+        mock_choice = MagicMock()
+        mock_choice.message = mock_message
+        mock_response = MagicMock()
+        mock_response.choices = [mock_choice]
+        mock_response.usage = {"prompt_tokens": 5, "completion_tokens": 5, "total_tokens": 10}
+        mock_completion.return_value = mock_response
+        
+        llm.call("Test message")
+        
+        _, kwargs = mock_completion.call_args
+        assert kwargs["reasoning_effort"] == "high"
+
+
+def test_llm_reasoning_false_overrides_reasoning_effort():
+    """Test that reasoning=False overrides reasoning_effort."""
+    llm = LLM(model="ollama/qwen", reasoning=False, reasoning_effort="high")
+    
+    with patch("litellm.completion") as mock_completion:
+        mock_message = MagicMock()
+        mock_message.content = "Test response"
+        mock_choice = MagicMock()
+        mock_choice.message = mock_message
+        mock_response = MagicMock()
+        mock_response.choices = [mock_choice]
+        mock_response.usage = {"prompt_tokens": 5, "completion_tokens": 5, "total_tokens": 10}
+        mock_completion.return_value = mock_response
+        
+        llm.call("Test message")
+        
+        _, kwargs = mock_completion.call_args
+        assert "reasoning_effort" not in kwargs
+
+
+@pytest.mark.vcr(filter_headers=["authorization"])
+def test_ollama_qwen_with_reasoning_disabled():
+    """Test Ollama Qwen model with reasoning disabled."""
+    if not os.getenv("OLLAMA_BASE_URL"):
+        pytest.skip("OLLAMA_BASE_URL not set; skipping test.")
+    
+    llm = LLM(
+        model="ollama/qwen",
+        base_url=os.getenv("OLLAMA_BASE_URL", "http://localhost:11434"),
+        reasoning=False
+    )
+    result = llm.call("What is 2+2?")
+    assert isinstance(result, str)
+    assert len(result.strip()) > 0

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)
@@ -59,10 +55,11 @@ def mem0_storage_with_mocked_config(mock_mem0_memory):
         }
 
         # Instantiate the class with memory_config
+        # Parameters like run_id, includes, and excludes doesn't matter in Memory OSS
         crew = MockCrew(
             memory_config={
                 "provider": "mem0",
-                "config": {"user_id": "test_user", "local_mem0_config": config},
+                "config": {"user_id": "test_user", "local_mem0_config": config, "run_id": "my_run_id", "includes": "include1","excludes": "exclude1", "infer" : True},
             }
         )
 
@@ -99,6 +96,10 @@ def mem0_storage_with_memory_client_using_config_from_crew(mock_mem0_memory_clie
                     "api_key": "ABCDEFGH",
                     "org_id": "my_org_id",
                     "project_id": "my_project_id",
+                    "run_id": "my_run_id",
+                    "includes": "include1",
+                    "excludes": "exclude1",
+                    "infer": True
                 },
             }
         )
@@ -154,11 +155,37 @@ def test_mem0_storage_with_explict_config(
     assert (
         mem0_storage_with_memory_client_using_explictly_config.config == expected_config
     )
-    assert (
-        mem0_storage_with_memory_client_using_explictly_config.memory_config
-        == expected_config
+
+
+def test_mem0_storage_updates_project_with_custom_categories(mock_mem0_memory_client):
+    mock_mem0_memory_client.update_project = MagicMock()
+
+    new_categories = [
+    {"lifestyle_management_concerns": "Tracks daily routines, habits, hobbies and interests including cooking, time management and work-life balance"},
+    ]
+
+    crew = MockCrew(
+        memory_config={
+            "provider": "mem0",
+            "config": {
+                "user_id": "test_user",
+                "api_key": "ABCDEFGH",
+                "org_id": "my_org_id",
+                "project_id": "my_project_id",
+                "custom_categories": new_categories,
+            },
+        }
     )
 
+    with patch.object(MemoryClient, "__new__", return_value=mock_mem0_memory_client):
+        _ = Mem0Storage(type="short_term", crew=crew)
+
+    mock_mem0_memory_client.update_project.assert_called_once_with(
+        custom_categories=new_categories
+    )
+
+
+
 
 def test_save_method_with_memory_oss(mem0_storage_with_mocked_config):
     """Test save method for different memory types"""
@@ -172,9 +199,8 @@ def test_save_method_with_memory_oss(mem0_storage_with_mocked_config):
     mem0_storage.save(test_value, test_metadata)
     
     mem0_storage.memory.add.assert_called_once_with(
-        test_value,
-        agent_id="Test_Agent",
-        infer=False,
+        [{'role': 'assistant' , 'content': test_value}],
+        infer=True,
         metadata={"type": "short_term", "key": "value"},
     )
 
@@ -191,11 +217,14 @@ def test_save_method_with_memory_client(mem0_storage_with_memory_client_using_co
     mem0_storage.save(test_value, test_metadata)
     
     mem0_storage.memory.add.assert_called_once_with(
-        test_value,
-        agent_id="Test_Agent",
-        infer=False,
+        [{'role': 'assistant' , 'content': test_value}],
+        infer=True,
         metadata={"type": "short_term", "key": "value"},
-        output_format="v1.1"
+        version="v2",
+        run_id="my_run_id",
+        includes="include1",
+        excludes="exclude1",
+        output_format='v1.1'
     )
 
 
@@ -210,11 +239,12 @@ def test_search_method_with_memory_oss(mem0_storage_with_mocked_config):
     mem0_storage.memory.search.assert_called_once_with(
         query="test query", 
         limit=5, 
-        agent_id="Test_Agent",
-        user_id="test_user"
+        user_id="test_user",
+        filters={'AND': [{'run_id': 'my_run_id'}]}, 
+        threshold=0.5
     )
 
-    assert len(results) == 1
+    assert len(results) == 2
     assert results[0]["content"] == "Result 1"
 
 
@@ -229,11 +259,31 @@ def test_search_method_with_memory_client(mem0_storage_with_memory_client_using_
     mem0_storage.memory.search.assert_called_once_with(
         query="test query", 
         limit=5, 
-        agent_id="Test_Agent", 
         metadata={"type": "short_term"},
         user_id="test_user",
-        output_format='v1.1'
+        version='v2',
+        run_id="my_run_id",
+        output_format='v1.1',
+        filters={'AND': [{'run_id': 'my_run_id'}]},
+        threshold=0.5
     )
 
-    assert len(results) == 1
+    assert len(results) == 2
     assert results[0]["content"] == "Result 1"
+
+
+def test_mem0_storage_default_infer_value(mock_mem0_memory_client):
+    """Test that Mem0Storage sets infer=True by default for short_term memory."""
+    with patch.object(MemoryClient, "__new__", return_value=mock_mem0_memory_client):
+        crew = MockCrew(
+            memory_config={
+                "provider": "mem0",
+                "config": {
+                    "user_id": "test_user",
+                    "api_key": "ABCDEFGH"
+                },
+            }
+        )
+
+        mem0_storage = Mem0Storage(type="short_term", crew=crew)
+        assert mem0_storage.infer is True

tests/utilities/test_chromadb_utils.py

@@ -1,16 +1,27 @@
+import multiprocessing
+import tempfile
 import unittest
-from typing import Any, Dict, List, Union
 
-import pytest
+from chromadb.config import Settings
+from unittest.mock import patch, MagicMock
 
 from crewai.utilities.chromadb import (
     MAX_COLLECTION_LENGTH,
     MIN_COLLECTION_LENGTH,
     is_ipv4_pattern,
     sanitize_collection_name,
+    create_persistent_client,
 )
 
 
+def persistent_client_worker(path, queue):
+    try:
+        create_persistent_client(path=path)
+        queue.put(None)
+    except Exception as e:
+        queue.put(e)
+
+
 class TestChromadbUtils(unittest.TestCase):
     def test_sanitize_collection_name_long_name(self):
         """Test sanitizing a very long collection name."""
@@ -79,3 +90,34 @@ class TestChromadbUtils(unittest.TestCase):
             self.assertLessEqual(len(sanitized), MAX_COLLECTION_LENGTH)
             self.assertTrue(sanitized[0].isalnum())
             self.assertTrue(sanitized[-1].isalnum())
+
+    def test_create_persistent_client_passes_args(self):
+        with patch(
+            "crewai.utilities.chromadb.PersistentClient"
+        ) as mock_persistent_client, tempfile.TemporaryDirectory() as tmpdir:
+            mock_instance = MagicMock()
+            mock_persistent_client.return_value = mock_instance
+
+            settings = Settings(allow_reset=True)
+            client = create_persistent_client(path=tmpdir, settings=settings)
+
+            mock_persistent_client.assert_called_once_with(
+                path=tmpdir, settings=settings
+            )
+            self.assertIs(client, mock_instance)
+
+    def test_create_persistent_client_process_safe(self):
+        with tempfile.TemporaryDirectory() as tmpdir:
+            queue = multiprocessing.Queue()
+            processes = [
+                multiprocessing.Process(
+                    target=persistent_client_worker, args=(tmpdir, queue)
+                )
+                for _ in range(5)
+            ]
+
+            [p.start() for p in processes]
+            [p.join() for p in processes]
+
+            errors = [queue.get(timeout=5) for _ in processes]
+            self.assertTrue(all(err is None for err in errors))

tests/utilities/test_embedding_configuration.py

@@ -0,0 +1,25 @@
+from unittest.mock import patch
+
+import pytest
+
+from crewai.rag.embeddings.configurator import EmbeddingConfigurator
+
+
+def test_configure_embedder_importerror():
+    configurator = EmbeddingConfigurator()
+    
+    embedder_config = {
+        'provider': 'openai',
+        'config': {
+            'model': 'text-embedding-ada-002',
+        }
+    }
+    
+    with patch('chromadb.utils.embedding_functions.openai_embedding_function.OpenAIEmbeddingFunction') as mock_openai:
+        mock_openai.side_effect = ImportError("Module not found.")
+        
+        with pytest.raises(ImportError) as exc_info:
+            configurator.configure_embedder(embedder_config)
+
+        assert str(exc_info.value) == "Module not found."
+        mock_openai.assert_called_once()

tests/utilities/test_events.py

@@ -64,7 +64,8 @@ def base_agent():
         llm="gpt-4o-mini",
         goal="Just say hi",
         backstory="You are a helpful assistant that just says hi",
-)
+    )
+
 
 @pytest.fixture(scope="module")
 def base_task(base_agent):
@@ -74,6 +75,7 @@ def base_task(base_agent):
         agent=base_agent,
     )
 
+
 event_listener = EventListener()
 
 
@@ -448,6 +450,27 @@ def test_flow_emits_start_event():
     assert received_events[0].type == "flow_started"
 
 
+def test_flow_name_emitted_to_event_bus():
+    received_events = []
+
+    class MyFlowClass(Flow):
+        name = "PRODUCTION_FLOW"
+
+        @start()
+        def start(self):
+            return "Hello, world!"
+
+    @crewai_event_bus.on(FlowStartedEvent)
+    def handle_flow_start(source, event):
+        received_events.append(event)
+
+    flow = MyFlowClass()
+    flow.kickoff()
+
+    assert len(received_events) == 1
+    assert received_events[0].flow_name == "PRODUCTION_FLOW"
+
+
 def test_flow_emits_finish_event():
     received_events = []
 
@@ -756,6 +779,7 @@ def test_streaming_empty_response_handling():
     received_chunks = []
 
     with crewai_event_bus.scoped_handlers():
+
         @crewai_event_bus.on(LLMStreamChunkEvent)
         def handle_stream_chunk(source, event):
             received_chunks.append(event.chunk)
@@ -793,6 +817,7 @@ def test_streaming_empty_response_handling():
             # Restore the original method
             llm.call = original_call
 
+
 @pytest.mark.vcr(filter_headers=["authorization"])
 def test_stream_llm_emits_event_with_task_and_agent_info():
     completed_event = []
@@ -801,6 +826,7 @@ def test_stream_llm_emits_event_with_task_and_agent_info():
     stream_event = []
 
     with crewai_event_bus.scoped_handlers():
+
         @crewai_event_bus.on(LLMCallFailedEvent)
         def handle_llm_failed(source, event):
             failed_event.append(event)
@@ -827,7 +853,7 @@ def test_stream_llm_emits_event_with_task_and_agent_info():
             description="Just say hi",
             expected_output="hi",
             llm=LLM(model="gpt-4o-mini", stream=True),
-            agent=agent
+            agent=agent,
         )
 
         crew = Crew(agents=[agent], tasks=[task])
@@ -855,6 +881,7 @@ def test_stream_llm_emits_event_with_task_and_agent_info():
     assert set(all_task_id) == {task.id}
     assert set(all_task_name) == {task.name}
 
+
 @pytest.mark.vcr(filter_headers=["authorization"])
 def test_llm_emits_event_with_task_and_agent_info(base_agent, base_task):
     completed_event = []
@@ -863,6 +890,7 @@ def test_llm_emits_event_with_task_and_agent_info(base_agent, base_task):
     stream_event = []
 
     with crewai_event_bus.scoped_handlers():
+
         @crewai_event_bus.on(LLMCallFailedEvent)
         def handle_llm_failed(source, event):
             failed_event.append(event)
@@ -904,6 +932,7 @@ def test_llm_emits_event_with_task_and_agent_info(base_agent, base_task):
     assert set(all_task_id) == {base_task.id}
     assert set(all_task_name) == {base_task.name}
 
+
 @pytest.mark.vcr(filter_headers=["authorization"])
 def test_llm_emits_event_with_lite_agent():
     completed_event = []
@@ -912,6 +941,7 @@ def test_llm_emits_event_with_lite_agent():
     stream_event = []
 
     with crewai_event_bus.scoped_handlers():
+
         @crewai_event_bus.on(LLMCallFailedEvent)
         def handle_llm_failed(source, event):
             failed_event.append(event)
@@ -936,7 +966,6 @@ def test_llm_emits_event_with_lite_agent():
         )
         agent.kickoff(messages=[{"role": "user", "content": "say hi!"}])
 
-
     assert len(completed_event) == 2
     assert len(failed_event) == 0
     assert len(started_event) == 2