fix: cast dict values to str in _format_prompt

- Add str() casts for type safety
- These values are always strings when called from invoke

.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": [
@@ -55,32 +50,22 @@
             "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 +79,6 @@
                     "pages": [
                       "en/guides/advanced/customizing-prompts",
                       "en/guides/advanced/fingerprinting"
-
                     ]
                   }
                 ]
@@ -182,7 +166,9 @@
                       "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 +227,7 @@
                   "en/observability/langtrace",
                   "en/observability/maxim",
                   "en/observability/mlflow",
+                  "en/observability/neatlogs",
                   "en/observability/openlit",
                   "en/observability/opik",
                   "en/observability/patronus-evaluation",
@@ -274,9 +261,7 @@
               },
               {
                 "group": "Telemetry",
-                "pages": [
-                  "en/telemetry"
-                ]
+                "pages": ["en/telemetry"]
               }
             ]
           },
@@ -285,9 +270,7 @@
             "groups": [
               {
                 "group": "Getting Started",
-                "pages": [
-                  "en/enterprise/introduction"
-                ]
+                "pages": ["en/enterprise/introduction"]
               },
               {
                 "group": "Features",
@@ -342,9 +325,7 @@
               },
               {
                 "group": "Resources",
-                "pages": [
-                  "en/enterprise/resources/frequently-asked-questions"
-                ]
+                "pages": ["en/enterprise/resources/frequently-asked-questions"]
               }
             ]
           },
@@ -353,9 +334,7 @@
             "groups": [
               {
                 "group": "Getting Started",
-                "pages": [
-                  "en/api-reference/introduction"
-                ]
+                "pages": ["en/api-reference/introduction"]
               },
               {
                 "group": "Endpoints",
@@ -365,16 +344,13 @@
           },
           {
             "tab": "Examples",
-                        "groups": [
+            "groups": [
               {
                 "group": "Examples",
-                "pages": [
-                  "en/examples/example"
-                ]
+                "pages": ["en/examples/example"]
               }
             ]
           }
-
         ]
       },
       {
@@ -425,21 +401,15 @@
                 "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 +602,7 @@
               },
               {
                 "group": "Telemetria",
-                "pages": [
-                  "pt-BR/telemetry"
-                ]
+                "pages": ["pt-BR/telemetry"]
               }
             ]
           },
@@ -643,9 +611,7 @@
             "groups": [
               {
                 "group": "Começando",
-                "pages": [
-                  "pt-BR/enterprise/introduction"
-                ]
+                "pages": ["pt-BR/enterprise/introduction"]
               },
               {
                 "group": "Funcionalidades",
@@ -710,9 +676,7 @@
             "groups": [
               {
                 "group": "Começando",
-                "pages": [
-                  "pt-BR/api-reference/introduction"
-                ]
+                "pages": ["pt-BR/api-reference/introduction"]
               },
               {
                 "group": "Endpoints",
@@ -722,16 +686,13 @@
           },
           {
             "tab": "Exemplos",
-                        "groups": [
+            "groups": [
               {
                 "group": "Exemplos",
-                "pages": [
-                  "pt-BR/examples/example"
-                ]
+                "pages": ["pt-BR/examples/example"]
               }
             ]
           }
-
         ]
       }
     ]

docs/en/concepts/crews.mdx

@@ -32,6 +32,7 @@ A crew in crewAI represents a collaborative group of agents working together to
 | **Prompt File** _(optional)_          | `prompt_file`          | Path to the prompt JSON file to be used for the crew.                                                                                                                                                                                                     |
 | **Planning** *(optional)*             | `planning`             | Adds planning ability to the Crew. When activated before each Crew iteration, all Crew data is sent to an AgentPlanner that will plan the tasks and this plan will be added to each task description.                                                     |
 | **Planning LLM** *(optional)*         | `planning_llm`         | The language model used by the AgentPlanner in a planning process.                                                                                                                                                                                        |
+| **Knowledge Sources** _(optional)_    | `knowledge_sources`    | Knowledge sources available at the crew level, accessible to all the agents.                                                                                                                                                                                    |
 
 <Tip>
 **Crew Max RPM**: The `max_rpm` attribute sets the maximum number of requests per minute the crew can perform to avoid rate limits and will override individual agents' `max_rpm` settings if you set it.

docs/en/concepts/memory.mdx

@@ -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

docs/en/concepts/tasks.mdx

@@ -57,6 +57,7 @@ crew = Crew(
 | **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.                            |
 
 ## Creating Tasks
 
@@ -86,6 +87,7 @@ 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: >
@@ -332,9 +334,13 @@ 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.
 
-### Using Task Guardrails
+**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
 
-To add a guardrail to a task, provide a validation function through the `guardrail` parameter:
+### Function-Based Guardrails
+
+To add a function-based guardrail to a task, provide a validation function through the `guardrail` parameter:
 
 ```python Code
 from typing import Tuple, Union, Dict, Any
@@ -372,9 +378,82 @@ 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")`
 
-### LLMGuardrail
+### String-Based Guardrails
 
-The `LLMGuardrail` class offers a robust mechanism for validating task outputs.
+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
 
@@ -798,166 +877,7 @@ While creating and executing tasks, certain validation mechanisms are in place t
 
 These validations help in maintaining the consistency and reliability of task executions within the crewAI framework.
 
-## Task Guardrails
 
-Task guardrails provide a powerful way to validate, transform, or filter task outputs before they are passed to the next task. Guardrails are optional functions that execute before the next task starts, allowing you to ensure that task outputs meet specific requirements or formats.
-
-### Basic Usage
-
-#### Define your own logic to validate
-
-```python Code
-from typing import Tuple, Union
-from crewai import Task
-
-def validate_json_output(result: str) -> Tuple[bool, Union[dict, str]]:
-    """Validate that the output is valid JSON."""
-    try:
-        json_data = json.loads(result)
-        return (True, json_data)
-    except json.JSONDecodeError:
-        return (False, "Output must be valid JSON")
-
-task = Task(
-    description="Generate JSON data",
-    expected_output="Valid JSON object",
-    guardrail=validate_json_output
-)
-```
-
-#### Leverage a no-code approach for validation
-
-```python Code
-from crewai import Task
-
-task = Task(
-    description="Generate JSON data",
-    expected_output="Valid JSON object",
-    guardrail="Ensure the response is a valid JSON object"
-)
-```
-
-#### Using YAML
-
-```yaml
-research_task:
-  ...
-  guardrail: make sure each bullet contains a minimum of 100 words
-  ...
-```
-
-```python Code
-@CrewBase
-class InternalCrew:
-    agents_config = "config/agents.yaml"
-    tasks_config = "config/tasks.yaml"
-
-    ...
-    @task
-    def research_task(self):
-        return Task(config=self.tasks_config["research_task"])  # type: ignore[index]
-    ...
-```
-
-
-#### Use custom models for code generation
-
-```python Code
-from crewai import Task
-from crewai.llm import LLM
-
-task = Task(
-    description="Generate JSON data",
-    expected_output="Valid JSON object",
-    guardrail=LLMGuardrail(
-        description="Ensure the response is a valid JSON object",
-        llm=LLM(model="gpt-4o-mini"),
-    )
-)
-```
-
-### How Guardrails Work
-
-1. **Optional Attribute**: Guardrails are an optional attribute at the task level, allowing you to add validation only where needed.
-2. **Execution Timing**: The guardrail function is executed before the next task starts, ensuring valid data flow between tasks.
-3. **Return Format**: Guardrails must return a tuple of `(success, data)`:
-   - If `success` is `True`, `data` is the validated/transformed result
-   - If `success` is `False`, `data` is the error message
-4. **Result Routing**:
-   - On success (`True`), the result is automatically passed to the next task
-   - On failure (`False`), the error is sent back to the agent to generate a new answer
-
-### Common Use Cases
-
-#### Data Format Validation
-```python Code
-def validate_email_format(result: str) -> Tuple[bool, Union[str, str]]:
-    """Ensure the output contains a valid email address."""
-    import re
-    email_pattern = r'^[\w\.-]+@[\w\.-]+\.\w+$'
-    if re.match(email_pattern, result.strip()):
-        return (True, result.strip())
-    return (False, "Output must be a valid email address")
-```
-
-#### Content Filtering
-```python Code
-def filter_sensitive_info(result: str) -> Tuple[bool, Union[str, str]]:
-    """Remove or validate sensitive information."""
-    sensitive_patterns = ['SSN:', 'password:', 'secret:']
-    for pattern in sensitive_patterns:
-        if pattern.lower() in result.lower():
-            return (False, f"Output contains sensitive information ({pattern})")
-    return (True, result)
-```
-
-#### Data Transformation
-```python Code
-def normalize_phone_number(result: str) -> Tuple[bool, Union[str, str]]:
-    """Ensure phone numbers are in a consistent format."""
-    import re
-    digits = re.sub(r'\D', '', result)
-    if len(digits) == 10:
-        formatted = f"({digits[:3]}) {digits[3:6]}-{digits[6:]}"
-        return (True, formatted)
-    return (False, "Output must be a 10-digit phone number")
-```
-
-### Advanced Features
-
-#### Chaining Multiple Validations
-```python Code
-def chain_validations(*validators):
-    """Chain multiple validators together."""
-    def combined_validator(result):
-        for validator in validators:
-            success, data = validator(result)
-            if not success:
-                return (False, data)
-            result = data
-        return (True, result)
-    return combined_validator
-
-# Usage
-task = Task(
-    description="Get user contact info",
-    expected_output="Email and phone",
-    guardrail=chain_validations(
-        validate_email_format,
-        filter_sensitive_info
-    )
-)
-```
-
-#### Custom Retry Logic
-```python Code
-task = Task(
-    description="Generate data",
-    expected_output="Valid data",
-    guardrail=validate_data,
-    max_retries=5  # Override default retry limit
-)
-```
 
 ## Creating Directories when Saving Files
 

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/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/pt-BR/concepts/cli.mdx

@@ -76,6 +76,7 @@ Exemplo:
 crewai train -n 10 -f my_training_data.pkl
 ```
 
+```python
 # Exemplo de uso programático do comando train
 n_iterations = 2
 inputs = {"topic": "Treinamento CrewAI"}
@@ -89,6 +90,7 @@ try:
     )
 except Exception as e:
     raise Exception(f"Ocorreu um erro ao treinar a crew: {e}")
+```
 
 ### 4. Replay
 

docs/pt-BR/concepts/tasks.mdx

@@ -57,6 +57,7 @@ crew = Crew(
 | **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.        |
 
 ## Criando Tarefas
 
@@ -86,6 +87,7 @@ 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: >
@@ -330,9 +332,13 @@ 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.
 
-### Usando Guardrails em Tarefas
+**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
 
-Para adicionar um guardrail a uma tarefa, forneça uma função de validação por meio do parâmetro `guardrail`:
+### Guardrails Baseados em Função
+
+Para adicionar um guardrail baseado em função a uma tarefa, forneça uma função de validação por meio do parâmetro `guardrail`:
 
 ```python Code
 from typing import Tuple, Union, Dict, Any
@@ -370,9 +376,82 @@ 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")`
 
-### LLMGuardrail
+### Guardrails Baseados em String
 
-A classe `LLMGuardrail` oferece um mecanismo robusto para validação das saídas das tarefas.
+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
 

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~=0.55.0"]
 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__ = "0.148.0"
 __all__ = [
     "Agent",
     "Crew",

src/crewai/agent.py

@@ -1,7 +1,18 @@
 import shutil
 import subprocess
 import time
-from typing import Any, Callable, Dict, List, Literal, Optional, Sequence, Tuple, Type, Union
+from typing import (
+    Any,
+    Callable,
+    Dict,
+    List,
+    Literal,
+    Optional,
+    Sequence,
+    Tuple,
+    Type,
+    Union,
+)
 
 from pydantic import Field, InstanceOf, PrivateAttr, model_validator
 
@@ -76,6 +87,12 @@ class Agent(BaseAgent):
     """
 
     _times_executed: int = PrivateAttr(default=0)
+    agent_executor: Optional[CrewAgentExecutor] = Field(
+        default=None,
+        init=False,  # Not included in __init__ as it's created dynamically in create_agent_executor()
+        exclude=True,  # Excluded from serialization to avoid circular references
+        description="The agent executor instance for running tasks. Created dynamically when needed.",
+    )
     max_execution_time: Optional[int] = Field(
         default=None,
         description="Maximum execution time for an agent to execute a task",
@@ -162,7 +179,7 @@ class Agent(BaseAgent):
     )
     guardrail: Optional[Union[Callable[[Any], Tuple[bool, Any]], str]] = Field(
         default=None,
-        description="Function or string description of a guardrail to validate agent output"
+        description="Function or string description of a guardrail to validate agent output",
     )
     guardrail_max_retries: int = Field(
         default=3, description="Maximum number of retries when guardrail fails"
@@ -210,7 +227,6 @@ class Agent(BaseAgent):
                         sources=self.knowledge_sources,
                         embedder=self.embedder,
                         collection_name=self.role,
-                        storage=self.knowledge_storage or None,
                     )
                     self.knowledge.add_sources()
         except (TypeError, ValueError) as e:
@@ -341,7 +357,7 @@ class Agent(BaseAgent):
             self.knowledge_config.model_dump() if self.knowledge_config else {}
         )
 
-        if self.knowledge:
+        if self.knowledge or (self.crew and self.crew.knowledge):
             crewai_event_bus.emit(
                 self,
                 event=KnowledgeRetrievalStartedEvent(
@@ -353,25 +369,28 @@ class Agent(BaseAgent):
                     task_prompt
                 )
                 if self.knowledge_search_query:
-                    agent_knowledge_snippets = self.knowledge.query(
-                        [self.knowledge_search_query], **knowledge_config
-                    )
-                    if agent_knowledge_snippets:
-                        self.agent_knowledge_context = extract_knowledge_context(
-                            agent_knowledge_snippets
-                        )
-                        if self.agent_knowledge_context:
-                            task_prompt += self.agent_knowledge_context
-                    if self.crew:
-                        knowledge_snippets = self.crew.query_knowledge(
+                    # Quering agent specific knowledge
+                    if self.knowledge:
+                        agent_knowledge_snippets = self.knowledge.query(
                             [self.knowledge_search_query], **knowledge_config
                         )
-                        if knowledge_snippets:
-                            self.crew_knowledge_context = extract_knowledge_context(
-                                knowledge_snippets
+                        if agent_knowledge_snippets:
+                            self.agent_knowledge_context = extract_knowledge_context(
+                                agent_knowledge_snippets
                             )
-                            if self.crew_knowledge_context:
-                                task_prompt += self.crew_knowledge_context
+                            if self.agent_knowledge_context:
+                                task_prompt += self.agent_knowledge_context
+
+                    # Quering crew specific knowledge
+                    knowledge_snippets = self.crew.query_knowledge(
+                        [self.knowledge_search_query], **knowledge_config
+                    )
+                    if knowledge_snippets:
+                        self.crew_knowledge_context = extract_knowledge_context(
+                            knowledge_snippets
+                        )
+                        if self.crew_knowledge_context:
+                            task_prompt += self.crew_knowledge_context
 
                     crewai_event_bus.emit(
                         self,
@@ -528,6 +547,11 @@ class Agent(BaseAgent):
         Returns:
             The output of the agent.
         """
+        if not self.agent_executor:
+            raise ValueError(
+                "Agent executor not initialized. Call create_agent_executor() first."
+            )
+
         return self.agent_executor.invoke(
             {
                 "input": task_prompt,

src/crewai/agents/crew_agent_executor.py

@@ -96,7 +96,7 @@ class CrewAgentExecutor(CrewAgentExecutorMixin):
             )
         )
 
-    def invoke(self, inputs: Dict[str, str]) -> Dict[str, Any]:
+    def invoke(self, inputs: Dict[str, Union[str, bool, None]]) -> Dict[str, Any]:
         if "system" in self.prompt:
             system_prompt = self._format_prompt(self.prompt.get("system", ""), inputs)
             user_prompt = self._format_prompt(self.prompt.get("user", ""), inputs)
@@ -120,11 +120,7 @@ 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)
@@ -159,7 +155,7 @@ class CrewAgentExecutor(CrewAgentExecutorMixin):
                     messages=self.messages,
                     callbacks=self.callbacks,
                     printer=self._printer,
-                    from_task=self.task
+                    from_task=self.task,
                 )
                 formatted_answer = process_llm_response(answer, self.use_stop_words)
 
@@ -375,10 +371,13 @@ class CrewAgentExecutor(CrewAgentExecutorMixin):
         training_data[agent_id] = agent_training_data
         training_handler.save(training_data)
 
-    def _format_prompt(self, prompt: str, inputs: Dict[str, str]) -> str:
-        prompt = prompt.replace("{input}", inputs["input"])
-        prompt = prompt.replace("{tool_names}", inputs["tool_names"])
-        prompt = prompt.replace("{tools}", inputs["tools"])
+    def _format_prompt(
+        self, prompt: str, inputs: Dict[str, Union[str, bool, None]]
+    ) -> str:
+        # Cast to str to satisfy type checker - these are always strings when called
+        prompt = prompt.replace("{input}", str(inputs["input"]))
+        prompt = prompt.replace("{tool_names}", str(inputs["tool_names"]))
+        prompt = prompt.replace("{tools}", str(inputs["tools"]))
         return prompt
 
     def _handle_human_feedback(self, formatted_answer: AgentFinish) -> AgentFinish:

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]>=0.148.0,<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]>=0.148.0,<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]>=0.148.0"
 ]
 
 [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.experimental.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/experimental/evaluation/agent_evaluator.py

@@ -1,109 +1,148 @@
+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 typing import Any, Dict
-from collections import defaultdict
+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.crew import Crew
 from crewai.utilities.events.crewai_event_bus import crewai_event_bus
 from crewai.utilities.events.utils.console_formatter import ConsoleFormatter
-from crewai.experimental.evaluation.evaluation_display import AgentAggregatedEvaluationResult
+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,
-        crew: Crew | None = None,
     ):
-        self.crew: Crew | None = crew
+        self.agents: list[Agent] = agents
         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]]] = {}
+        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.iteration = iteration
+        self._execution_state.iteration = iteration
 
-    def reset_iterations_results(self):
-        self.iterations_results = {}
+    def reset_iterations_results(self) -> None:
+        self._execution_state.iterations_results = {}
 
-    def evaluate_current_iteration(self) -> dict[str, list[AgentEvaluationResult]]:
-        if not self.crew:
-            raise ValueError("Cannot evaluate: no crew was provided to the evaluator.")
+    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 {}
 
-        if not self.callback:
-            raise ValueError("Cannot evaluate: no callback was set. Use set_callback() method first.")
+    def display_results_with_iterations(self) -> None:
+        self.display_formatter.display_summary_results(self._execution_state.iterations_results)
 
-        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) -> Dict[str, AgentAggregatedEvaluationResult]:
+    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()
@@ -123,7 +162,7 @@ class AgentEvaluator:
                 agent_results[agent_role] = aggregated_result
 
 
-            if self.iteration == max(self.iterations_results.keys()):
+            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:
@@ -131,23 +170,27 @@ class AgentEvaluator:
 
         return agent_results
 
-    def display_evaluation_with_feedback(self):
-        self.display_formatter.display_evaluation_with_feedback(self.iterations_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,
-        task: Task,
-        execution_trace: Dict[str, Any],
-        final_output: Any
+        execution_trace: dict[str, Any],
+        final_output: Any,
+        state: ExecutionState,
+        task: Task | None = None,
     ) -> AgentEvaluationResult:
         result = AgentEvaluationResult(
-            agent_id=str(agent.id),
-            task_id=str(task.id)
+            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,
@@ -155,12 +198,32 @@ class AgentEvaluator:
                     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 create_default_evaluator(crew, llm=None):
+    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,
@@ -179,4 +242,4 @@ def create_default_evaluator(crew, llm=None):
         ReasoningEfficiencyEvaluator(llm=llm),
     ]
 
-    return AgentEvaluator(evaluators=evaluators, crew=crew)
+    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

@@ -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/runner.py

@@ -2,7 +2,7 @@ from collections import defaultdict
 from hashlib import md5
 from typing import Any
 
-from crewai import Crew
+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
@@ -14,14 +14,18 @@ class ExperimentRunner:
         self.evaluator: AgentEvaluator | None = None
         self.display = ExperimentResultsDisplay()
 
-    def run(self, crew: Crew, print_summary: bool = False) -> ExperimentResults:
-        self.evaluator = create_default_evaluator(crew=crew)
+    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, crew)
+            result = self._run_test_case(test_case=test_case, crew=crew, agents=agents)
             results.append(result)
 
         experiment_results = ExperimentResults(results)
@@ -31,7 +35,7 @@ class ExperimentRunner:
 
         return experiment_results
 
-    def _run_test_case(self, test_case: dict[str, Any], crew: Crew) -> ExperimentResult:
+    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()
@@ -39,7 +43,11 @@ class ExperimentRunner:
         try:
             self.display.console.print(f"[dim]Running crew with input: {str(inputs)[:50]}...[/dim]")
             self.display.console.print("\n")
-            crew.kickoff(inputs=inputs)
+            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()

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

@@ -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

@@ -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

@@ -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

@@ -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/knowledge/storage/knowledge_storage.py

@@ -18,6 +18,7 @@ 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

@@ -28,7 +28,7 @@ from pydantic import (
     InstanceOf,
     PrivateAttr,
     model_validator,
-    field_validator,
+    field_validator
 )
 
 from crewai.agents.agent_builder.base_agent import BaseAgent
@@ -40,7 +40,7 @@ from crewai.agents.parser import (
     OutputParserException,
 )
 from crewai.flow.flow_trackable import FlowTrackable
-from crewai.llm import LLM
+from crewai.llm import LLM, BaseLLM
 from crewai.tools.base_tool import BaseTool
 from crewai.tools.structured_tool import CrewStructuredTool
 from crewai.utilities import I18N
@@ -135,7 +135,7 @@ class LiteAgent(FlowTrackable, BaseModel):
     role: str = Field(description="Role of the agent")
     goal: str = Field(description="Goal of the agent")
     backstory: str = Field(description="Backstory of the agent")
-    llm: Optional[Union[str, InstanceOf[LLM], Any]] = Field(
+    llm: Optional[Union[str, InstanceOf[BaseLLM], Any]] = Field(
         default=None, description="Language model that will run the agent"
     )
     tools: List[BaseTool] = Field(
@@ -209,8 +209,8 @@ class LiteAgent(FlowTrackable, BaseModel):
     def setup_llm(self):
         """Set up the LLM and other components after initialization."""
         self.llm = create_llm(self.llm)
-        if not isinstance(self.llm, LLM):
-            raise ValueError("Unable to create LLM instance")
+        if not isinstance(self.llm, BaseLLM):
+            raise ValueError(f"Expected LLM instance of type BaseLLM, got {type(self.llm).__name__}")
 
         # Initialize callbacks
         token_callback = TokenCalcHandler(token_cost_process=self._token_process)
@@ -232,7 +232,8 @@ class LiteAgent(FlowTrackable, BaseModel):
         elif isinstance(self.guardrail, str):
             from crewai.tasks.llm_guardrail import LLMGuardrail
 
-            assert isinstance(self.llm, LLM)
+            if not isinstance(self.llm, BaseLLM):
+                raise TypeError(f"Guardrail requires LLM instance of type BaseLLM, got {type(self.llm).__name__}")
 
             self._guardrail = LLMGuardrail(description=self.guardrail, llm=self.llm)
 
@@ -304,6 +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,
@@ -620,4 +622,4 @@ class LiteAgent(FlowTrackable, BaseModel):
 
     def _append_message(self, text: str, role: str = "assistant") -> None:
         """Append a message to the message list with the given role."""
-        self._messages.append(format_message_for_llm(text, role=role))
+        self._messages.append(format_message_for_llm(text, role=role))

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
@@ -760,7 +759,7 @@ 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 +783,11 @@ 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 +800,22 @@ 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 +950,18 @@ 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 +978,32 @@ 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 +1072,15 @@ 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

@@ -64,6 +64,7 @@ class Mem0Storage(Storage):
     def save(self, value: Any, metadata: Dict[str, Any]) -> None:
         user_id = self._get_user_id()
         agent_name = self._get_agent_name()
+        assistant_message = [{"role" : "assistant","content" : value}] 
         params = None
         if self.memory_type == "short_term":
             params = {
@@ -93,7 +94,8 @@ class Mem0Storage(Storage):
         if params:
             if isinstance(self.memory, MemoryClient):
                 params["output_format"] = "v1.1"
-            self.memory.add(value, **params)
+            
+            self.memory.add(assistant_message, **params)
 
     def search(
         self,

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.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/task.py

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

src/crewai/tasks/llm_guardrail.py

@@ -1,10 +1,9 @@
-from typing import Any, Optional, Tuple
+from typing import Any, Tuple
 
 from pydantic import BaseModel, Field
 
 from crewai.agent import Agent, LiteAgentOutput
-from crewai.llm import LLM
-from crewai.task import Task
+from crewai.llm import BaseLLM
 from crewai.tasks.task_output import TaskOutput
 
 
@@ -32,11 +31,11 @@ class LLMGuardrail:
     def __init__(
         self,
         description: str,
-        llm: LLM,
+        llm: BaseLLM,
     ):
         self.description = description
 
-        self.llm: LLM = llm
+        self.llm: BaseLLM = llm
 
     def _validate_output(self, task_output: TaskOutput) -> LiteAgentOutput:
         agent = Agent(

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/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_test.py

@@ -1896,6 +1896,80 @@ def test_agent_with_knowledge_sources_generate_search_query():
         assert "red" in result.raw.lower()
 
 
+@pytest.mark.vcr(record_mode='none', filter_headers=["authorization"])
+def test_agent_with_knowledge_with_no_crewai_knowledge():
+    mock_knowledge = MagicMock(spec=Knowledge)
+
+    agent = Agent(
+        role="Information Agent",
+        goal="Provide information based on knowledge sources",
+        backstory="You have access to specific knowledge sources.",
+        llm=LLM(model="openrouter/openai/gpt-4o-mini",api_key=os.getenv('OPENROUTER_API_KEY')),
+        knowledge=mock_knowledge
+    )
+
+    # Create a task that requires the agent to use the knowledge
+    task = Task(
+        description="What is Vidit's favorite color?",
+        expected_output="Vidit's favorclearite color.",
+        agent=agent,
+    )
+
+    crew = Crew(agents=[agent], tasks=[task])
+    crew.kickoff()
+    mock_knowledge.query.assert_called_once()
+
+
+@pytest.mark.vcr(record_mode='none', filter_headers=["authorization"])
+def test_agent_with_only_crewai_knowledge():
+    mock_knowledge = MagicMock(spec=Knowledge)
+
+    agent = Agent(
+        role="Information Agent",
+        goal="Provide information based on knowledge sources",
+        backstory="You have access to specific knowledge sources.",
+        llm=LLM(model="openrouter/openai/gpt-4o-mini",api_key=os.getenv('OPENROUTER_API_KEY'))
+    )
+
+    # Create a task that requires the agent to use the knowledge
+    task = Task(
+        description="What is Vidit's favorite color?",
+        expected_output="Vidit's favorclearite color.",
+        agent=agent
+    )
+
+    crew = Crew(agents=[agent], tasks=[task],knowledge=mock_knowledge)
+    crew.kickoff()
+    mock_knowledge.query.assert_called_once()
+
+
+@pytest.mark.vcr(record_mode='none', filter_headers=["authorization"])
+def test_agent_knowledege_with_crewai_knowledge():
+    crew_knowledge = MagicMock(spec=Knowledge)
+    agent_knowledge = MagicMock(spec=Knowledge)
+
+
+    agent = Agent(
+        role="Information Agent",
+        goal="Provide information based on knowledge sources",
+        backstory="You have access to specific knowledge sources.",
+        llm=LLM(model="openrouter/openai/gpt-4o-mini",api_key=os.getenv('OPENROUTER_API_KEY')),
+        knowledge=agent_knowledge
+    )
+
+    # Create a task that requires the agent to use the knowledge
+    task = Task(
+        description="What is Vidit's favorite color?",
+        expected_output="Vidit's favorclearite color.",
+        agent=agent,
+    )
+
+    crew = Crew(agents=[agent],tasks=[task],knowledge=crew_knowledge)
+    crew.kickoff()
+    agent_knowledge.query.assert_called_once()
+    crew_knowledge.query.assert_called_once()
+
+
 @pytest.mark.vcr(filter_headers=["authorization"])
 def test_litellm_auth_error_handling():
     """Test that LiteLLM authentication errors are handled correctly and not retried."""
@@ -1936,7 +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(
@@ -1969,7 +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(
@@ -1983,13 +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

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tests/cassettes/test_agent_with_only_crewai_knowledge.yaml

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+      code: 200
+      message: OK
+version: 1

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.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
-)
-
-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()
-
-        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

@@ -1,5 +1,5 @@
 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.experimental.evaluation.base_evaluator import EvaluationScore
 from crewai.experimental.evaluation.metrics.goal_metrics import GoalAlignmentEvaluator

tests/experimental/evaluation/metrics/test_reasoning_metrics.py

@@ -6,7 +6,7 @@ from crewai.tasks.task_output import TaskOutput
 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.experimental.evaluation.base_evaluator import EvaluationScore
 

tests/experimental/evaluation/metrics/test_semantic_quality_metrics.py

@@ -2,7 +2,7 @@ from unittest.mock import patch, MagicMock
 
 from crewai.experimental.evaluation.base_evaluator import EvaluationScore
 from crewai.experimental.evaluation.metrics.semantic_quality_metrics import SemanticQualityEvaluator
-from tests.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
 
 class TestSemanticQualityEvaluator(BaseEvaluationMetricsTest):

tests/experimental/evaluation/metrics/test_tools_metrics.py

@@ -6,7 +6,7 @@ from crewai.experimental.evaluation.metrics.tools_metrics import (
     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/llm_test.py

@@ -1,3 +1,4 @@
+import logging
 import os
 from time import sleep
 from unittest.mock import MagicMock, patch
@@ -664,3 +665,49 @@ 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

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)
@@ -172,7 +168,7 @@ 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,
+        [{'role': 'assistant' , 'content': test_value}],
         agent_id="Test_Agent",
         infer=False,
         metadata={"type": "short_term", "key": "value"},
@@ -191,7 +187,7 @@ 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,
+        [{'role': 'assistant' , 'content': test_value}],
         agent_id="Test_Agent",
         infer=False,
         metadata={"type": "short_term", "key": "value"},

tests/task_test.py

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

tests/test_lite_agent.py

@@ -12,6 +12,8 @@ from crewai.tools import BaseTool
 from crewai.utilities.events import crewai_event_bus
 from crewai.utilities.events.agent_events import LiteAgentExecutionStartedEvent
 from crewai.utilities.events.tool_usage_events import ToolUsageStartedEvent
+from crewai.llms.base_llm import BaseLLM
+from unittest.mock import patch
 
 
 # A simple test tool
@@ -418,3 +420,76 @@ def test_agent_output_when_guardrail_returns_base_model():
     result = agent.kickoff(messages="Top 10 best players in the world?")
 
     assert result.pydantic == Player(name="Lionel Messi", country="Argentina")
+
+def test_lite_agent_with_custom_llm_and_guardrails():
+    """Test that CustomLLM (inheriting from BaseLLM) works with guardrails."""
+    class CustomLLM(BaseLLM):
+        def __init__(self, response: str = "Custom response"):
+            super().__init__(model="custom-model")
+            self.response = response
+            self.call_count = 0
+
+        def call(self, messages, tools=None, callbacks=None, available_functions=None, from_task=None, from_agent=None) -> str:
+            self.call_count += 1
+
+            if "valid" in str(messages) and "feedback" in str(messages):
+                return '{"valid": true, "feedback": null}'
+
+            if "Thought:" in str(messages):
+                return f"Thought: I will analyze soccer players\nFinal Answer: {self.response}"
+
+            return self.response
+
+        def supports_function_calling(self) -> bool:
+            return False
+
+        def supports_stop_words(self) -> bool:
+            return False
+
+        def get_context_window_size(self) -> int:
+            return 4096
+
+    custom_llm = CustomLLM(response="Brazilian soccer players are the best!")
+
+    agent = LiteAgent(
+        role="Sports Analyst",
+        goal="Analyze soccer players",
+        backstory="You analyze soccer players and their performance.",
+        llm=custom_llm,
+        guardrail="Only include Brazilian players"
+    )
+
+    result = agent.kickoff("Tell me about the best soccer players")
+
+    assert custom_llm.call_count > 0
+    assert "Brazilian" in result.raw
+
+    custom_llm2 = CustomLLM(response="Original response")
+
+    def test_guardrail(output):
+        return (True, "Modified by guardrail")
+
+    agent2 = LiteAgent(
+        role="Test Agent",
+        goal="Test goal",
+        backstory="Test backstory",
+        llm=custom_llm2,
+        guardrail=test_guardrail
+    )
+
+    result2 = agent2.kickoff("Test message")
+    assert result2.raw == "Modified by guardrail"
+
+
+@pytest.mark.vcr(filter_headers=["authorization"])
+def test_lite_agent_with_invalid_llm():
+    """Test that LiteAgent raises proper error when create_llm returns None."""
+    with patch('crewai.lite_agent.create_llm', return_value=None):
+        with pytest.raises(ValueError) as exc_info:
+            LiteAgent(
+                role="Test Agent",
+                goal="Test goal", 
+                backstory="Test backstory",
+                llm="invalid-model"
+            )
+        assert "Expected LLM instance of type BaseLLM" in str(exc_info.value)

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))

uv.lock

@@ -696,6 +696,7 @@ dependencies = [
     { name = "opentelemetry-exporter-otlp-proto-http" },
     { name = "opentelemetry-sdk" },
     { name = "pdfplumber" },
+    { name = "portalocker" },
     { name = "pydantic" },
     { name = "pyjwt" },
     { name = "python-dotenv" },
@@ -762,13 +763,13 @@ requires-dist = [
     { name = "blinker", specifier = ">=1.9.0" },
     { name = "chromadb", specifier = ">=0.5.23" },
     { name = "click", specifier = ">=8.1.7" },
-    { name = "crewai-tools", marker = "extra == 'tools'", specifier = "~=0.51.0" },
+    { name = "crewai-tools", marker = "extra == 'tools'", specifier = "~=0.55.0" },
     { name = "docling", marker = "extra == 'docling'", specifier = ">=2.12.0" },
     { name = "instructor", specifier = ">=1.3.3" },
     { name = "json-repair", specifier = "==0.25.2" },
     { name = "json5", specifier = ">=0.10.0" },
     { name = "jsonref", specifier = ">=1.1.0" },
-    { name = "litellm", specifier = "==1.72.6" },
+    { name = "litellm", specifier = "==1.74.3" },
     { name = "mem0ai", marker = "extra == 'mem0'", specifier = ">=0.1.94" },
     { name = "onnxruntime", specifier = "==1.22.0" },
     { name = "openai", specifier = ">=1.13.3" },
@@ -780,6 +781,7 @@ requires-dist = [
     { name = "pandas", marker = "extra == 'pandas'", specifier = ">=2.2.3" },
     { name = "pdfplumber", specifier = ">=0.11.4" },
     { name = "pdfplumber", marker = "extra == 'pdfplumber'", specifier = ">=0.11.4" },
+    { name = "portalocker", specifier = "==2.7.0" },
     { name = "pydantic", specifier = ">=2.4.2" },
     { name = "pyjwt", specifier = ">=2.9.0" },
     { name = "python-dotenv", specifier = ">=1.0.0" },
@@ -813,7 +815,7 @@ dev = [
 
 [[package]]
 name = "crewai-tools"
-version = "0.51.0"
+version = "0.55.0"
 source = { registry = "https://pypi.org/simple" }
 dependencies = [
     { name = "chromadb" },
@@ -829,9 +831,9 @@ dependencies = [
     { name = "requests" },
     { name = "tiktoken" },
 ]
-sdist = { url = "https://files.pythonhosted.org/packages/a1/ef/3426aebf495a887898466d38d6b78b09317d4c102a89493699d6af5aa823/crewai_tools-0.51.0.tar.gz", hash = "sha256:a5d73f344b740b13ffef8d189d6d210da143227982edf619e4de77896e2fd017", size = 1011735, upload-time = "2025-07-09T16:39:24.179Z" }
+sdist = { url = "https://files.pythonhosted.org/packages/f6/75/d8cae7f84e78a93210f91a4580aec8eb72dc1f33368655a8ad4e381d575b/crewai_tools-0.55.0.tar.gz", hash = "sha256:0961821128b07148197b89b1827b6c0a548424fa8a01674991528a56fd03fe81", size = 1015820, upload-time = "2025-07-16T19:16:36.648Z" }
 wheels = [
-    { url = "https://files.pythonhosted.org/packages/61/ea/9931f130dae5910a1b2e9d1fc6347d991100538e1faf3ece37ec8380ec96/crewai_tools-0.51.0-py3-none-any.whl", hash = "sha256:ba67e6bed6134e374c96fe9038bce6045600ff3b5358f6a6d75ff8f316defd06", size = 633012, upload-time = "2025-07-09T16:39:22.239Z" },
+    { url = "https://files.pythonhosted.org/packages/c3/98/da76dff3b814f5a6c9cbce7dacc09462669174083fd872b21c9373cdd412/crewai_tools-0.55.0-py3-none-any.whl", hash = "sha256:f69967394a9b5c85cab8722dfbae320e0a80d6124a3f36063c5864fe3516ee06", size = 634456, upload-time = "2025-07-16T19:16:35.259Z" },
 ]
 
 [[package]]
@@ -2266,7 +2268,7 @@ wheels = [
 
 [[package]]
 name = "litellm"
-version = "1.72.6"
+version = "1.74.3"
 source = { registry = "https://pypi.org/simple" }
 dependencies = [
     { name = "aiohttp" },
@@ -2281,9 +2283,9 @@ dependencies = [
     { name = "tiktoken" },
     { name = "tokenizers" },
 ]
-sdist = { url = "https://files.pythonhosted.org/packages/8d/15/df75f278fd998f6d6900f692b9de2fba2814b316c123c99072a813668aac/litellm-1.72.6.tar.gz", hash = "sha256:4e5c7e4273b09b765302d2faaec30f77b42255c0055b427b55ea02b8092b8582", size = 8393603, upload-time = "2025-06-14T21:43:11.023Z" }
+sdist = { url = "https://files.pythonhosted.org/packages/cd/e3/3091066f6682016840e9a36111560656b609b95de04b2ec7b19ad2580eaa/litellm-1.74.3.tar.gz", hash = "sha256:a9e87ebe78947ceec67e75f830f1c956cc653b84563574241acea9c84e7e3ca1", size = 9256457, upload-time = "2025-07-12T20:06:06.128Z" }
 wheels = [
-    { url = "https://files.pythonhosted.org/packages/96/c9/4aae0b77632279eef9716dbcb98edd8b36c08a9da070e2470ca9c410c0f8/litellm-1.72.6-py3-none-any.whl", hash = "sha256:e0ae98d25db4910e78b1a0a604f24c0d6875f6cdea02426b264a45d4fbdb8c46", size = 8302810, upload-time = "2025-06-14T21:43:08.628Z" },
+    { url = "https://files.pythonhosted.org/packages/14/6f/07735b5178f32e28daf8a30ed6ad3e2c8c06ac374dc06aecde007110470f/litellm-1.74.3-py3-none-any.whl", hash = "sha256:638ec73633c6f2cf78a7343723d8f3bc13c192558fcbaa29f3ba6bc7802e8663", size = 8618899, upload-time = "2025-07-12T20:06:03.609Z" },
 ]
 
 [[package]]
@@ -3797,14 +3799,14 @@ wheels = [
 
 [[package]]
 name = "portalocker"
-version = "2.10.1"
+version = "2.7.0"
 source = { registry = "https://pypi.org/simple" }
 dependencies = [
     { name = "pywin32", marker = "sys_platform == 'win32'" },
 ]
-sdist = { url = "https://files.pythonhosted.org/packages/ed/d3/c6c64067759e87af98cc668c1cc75171347d0f1577fab7ca3749134e3cd4/portalocker-2.10.1.tar.gz", hash = "sha256:ef1bf844e878ab08aee7e40184156e1151f228f103aa5c6bd0724cc330960f8f", size = 40891, upload-time = "2024-07-13T23:15:34.86Z" }
+sdist = { url = "https://files.pythonhosted.org/packages/1f/f8/969e6f280201b40b31bcb62843c619f343dcc351dff83a5891530c9dd60e/portalocker-2.7.0.tar.gz", hash = "sha256:032e81d534a88ec1736d03f780ba073f047a06c478b06e2937486f334e955c51", size = 20183, upload-time = "2023-01-18T23:36:14.436Z" }
 wheels = [
-    { url = "https://files.pythonhosted.org/packages/9b/fb/a70a4214956182e0d7a9099ab17d50bfcba1056188e9b14f35b9e2b62a0d/portalocker-2.10.1-py3-none-any.whl", hash = "sha256:53a5984ebc86a025552264b459b46a2086e269b21823cb572f8f28ee759e45bf", size = 18423, upload-time = "2024-07-13T23:15:32.602Z" },
+    { url = "https://files.pythonhosted.org/packages/8c/df/d4f711d168524f5aebd7fb30969eaa31e3048cf8979688cde3b08f6e5eb8/portalocker-2.7.0-py2.py3-none-any.whl", hash = "sha256:a07c5b4f3985c3cf4798369631fb7011adb498e2a46d8440efc75a8f29a0f983", size = 15502, upload-time = "2023-01-18T23:36:12.849Z" },
 ]
 
 [[package]]