Fix lint issues: remove unused imports

- Remove unused 'Crew' import from both test files
- Remove unused 'pytest' import from test_langdb_documentation.py
- Keep only imports that are actually used in the code

Fixes lint check failure in PR #3241

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

docs/docs.json

@@ -32,11 +32,6 @@
               "href": "https://chatgpt.com/g/g-qqTuUWsBY-crewai-assistant",
               "icon": "robot"
             },
-            {
-              "anchor": "Get Help",
-              "href": "mailto:support@crewai.com",
-              "icon": "headset"
-            },
             {
               "anchor": "Releases",
               "href": "https://github.com/crewAIInc/crewAI/releases",
@@ -166,7 +161,9 @@
                       "en/tools/search-research/websitesearchtool",
                       "en/tools/search-research/codedocssearchtool",
                       "en/tools/search-research/youtubechannelsearchtool",
-                      "en/tools/search-research/youtubevideosearchtool"
+                      "en/tools/search-research/youtubevideosearchtool",
+                      "en/tools/search-research/tavilysearchtool",
+                      "en/tools/search-research/tavilyextractortool"
                     ]
                   },
                   {
@@ -370,11 +367,6 @@
               "href": "https://chatgpt.com/g/g-qqTuUWsBY-crewai-assistant",
               "icon": "robot"
             },
-            {
-              "anchor": "Obter Ajuda",
-              "href": "mailto:support@crewai.com",
-              "icon": "headset"
-            },
             {
               "anchor": "Lançamentos",
               "href": "https://github.com/crewAIInc/crewAI/releases",

docs/en/concepts/llms.mdx

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

docs/en/concepts/memory.mdx

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

docs/en/concepts/tasks.mdx

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

docs/en/observability/langdb.mdx

@@ -0,0 +1,356 @@
+---
+title: LangDB Integration
+description: How to use LangDB AI Gateway with CrewAI
+icon: database
+---
+
+<img src="https://raw.githubusercontent.com/LangDB/assets/main/langdb-crewai-header.png" alt="LangDB CrewAI Header Image" width="70%" />
+
+## Introduction
+
+LangDB is the fastest enterprise AI gateway that enhances CrewAI with production-ready observability and optimization features. It provides:
+
+- **Complete end-to-end tracing** of every agent interaction and LLM call
+- **Real-time cost monitoring** and optimization across 250+ LLMs
+- **Performance analytics** with detailed metrics and insights
+- **Secure governance** for enterprise AI deployments
+- **OpenAI-compatible APIs** for seamless integration
+- **Fine-grained control** over agent workflows and resource usage
+
+### Installation & Setup
+
+<Steps>
+<Step title="Install the required packages">
+```bash
+pip install -U crewai langdb
+```
+</Step>
+
+<Step title="Set up environment variables" icon="lock">
+Configure your LangDB credentials from the [LangDB dashboard](https://app.langdb.ai/):
+
+```bash
+export LANGDB_API_KEY="your_langdb_api_key"
+export LANGDB_PROJECT_ID="your_project_id"
+```
+</Step>
+
+<Step title="Initialize LangDB with CrewAI">
+The integration requires a single initialization call before creating your agents:
+
+```python
+from langdb import LangDB
+from crewai import Agent, Task, Crew, LLM
+
+# Initialize LangDB tracing
+LangDB.init()
+
+# Create LLM instance - LangDB automatically traces all calls
+llm = LLM(
+    model="gpt-4o",
+    temperature=0.7
+)
+
+# Create your agents as usual
+@agent
+def research_agent(self) -> Agent:
+    return Agent(
+        role="Senior Research Analyst",
+        goal="Conduct comprehensive research on assigned topics",
+        backstory="You are an expert researcher with deep analytical skills.",
+        llm=llm,
+        verbose=True
+    )
+```
+</Step>
+</Steps>
+
+## Key Features
+
+### 1. Comprehensive Observability
+
+LangDB provides complete visibility into your CrewAI agent workflows with minimal setup overhead.
+
+<Tabs>
+  <Tab title="Request Tracing">
+LangDB automatically captures every LLM interaction in your crew execution:
+
+```python
+from langdb import LangDB
+from crewai import Agent, Task, Crew, LLM
+
+# Initialize with custom trace metadata
+LangDB.init(
+    metadata={
+        "environment": "production",
+        "crew_type": "research_workflow",
+        "user_id": "user_123"
+    }
+)
+
+# All agent interactions are automatically traced
+crew = Crew(
+    agents=[research_agent, writer_agent],
+    tasks=[research_task, writing_task],
+    verbose=True
+)
+
+# Execute with full tracing
+result = crew.kickoff(inputs={"topic": "AI trends 2025"})
+```
+
+View detailed traces in the LangDB dashboard showing:
+- Complete agent conversation flows
+- Tool usage and function calls
+- Task execution timelines
+- LLM request/response pairs
+  </Tab>
+
+  <Tab title="Performance Metrics">
+LangDB tracks comprehensive performance metrics for your crews:
+
+- **Execution Time**: Total and per-task execution duration
+- **Token Usage**: Input/output tokens for cost optimization
+- **Success Rates**: Task completion and failure analytics
+- **Latency Analysis**: Response times and bottleneck identification
+
+```python
+# Access metrics programmatically
+from langdb import LangDB
+
+# Get crew execution metrics
+metrics = LangDB.get_metrics(
+    project_id="your_project_id",
+    filters={
+        "crew_type": "research_workflow",
+        "time_range": "last_24h"
+    }
+)
+
+print(f"Average execution time: {metrics.avg_execution_time}")
+print(f"Total cost: ${metrics.total_cost}")
+print(f"Success rate: {metrics.success_rate}%")
+```
+  </Tab>
+
+  <Tab title="Cost Monitoring">
+Track and optimize AI spending across your CrewAI deployments:
+
+```python
+from langdb import LangDB
+
+# Initialize with cost tracking
+LangDB.init(
+    cost_tracking=True,
+    budget_alerts={
+        "daily_limit": 100.0,  # $100 daily limit
+        "alert_threshold": 0.8  # Alert at 80% of limit
+    }
+)
+
+# LangDB automatically tracks costs for all LLM calls
+crew = Crew(agents=[agent], tasks=[task])
+result = crew.kickoff()
+
+# View cost breakdown
+cost_report = LangDB.get_cost_report(
+    breakdown_by=["model", "agent", "task"]
+)
+```
+
+Features include:
+- Real-time cost tracking across all models
+- Budget alerts and spending limits
+- Cost optimization recommendations
+- Detailed cost attribution by agent and task
+  </Tab>
+</Tabs>
+
+### 2. Advanced Analytics & Insights
+
+LangDB provides powerful analytics to optimize your CrewAI workflows.
+
+<Tabs>
+  <Tab title="Agent Performance Analysis">
+Analyze individual agent performance and identify optimization opportunities:
+
+```python
+from langdb import LangDB
+
+# Get agent-specific analytics
+analytics = LangDB.get_agent_analytics(
+    agent_role="Senior Research Analyst",
+    time_range="last_week"
+)
+
+print(f"Average task completion time: {analytics.avg_completion_time}")
+print(f"Most used tools: {analytics.top_tools}")
+print(f"Success rate: {analytics.success_rate}%")
+print(f"Cost per task: ${analytics.cost_per_task}")
+```
+  </Tab>
+
+  <Tab title="Workflow Optimization">
+Identify bottlenecks and optimization opportunities in your crew workflows:
+
+```python
+# Analyze crew workflow patterns
+workflow_analysis = LangDB.analyze_workflow(
+    crew_id="research_crew_v1",
+    optimization_focus=["speed", "cost", "quality"]
+)
+
+# Get optimization recommendations
+recommendations = workflow_analysis.recommendations
+for rec in recommendations:
+    print(f"Optimization: {rec.type}")
+    print(f"Potential savings: {rec.estimated_savings}")
+    print(f"Implementation: {rec.implementation_guide}")
+```
+  </Tab>
+</Tabs>
+
+### 3. Production-Ready Features
+
+<CardGroup cols="2">
+  <Card title="Error Monitoring" icon="exclamation-triangle" href="https://docs.langdb.ai/features/error-monitoring">
+    Automatic detection and alerting for agent failures, LLM errors, and workflow issues.
+  </Card>
+  <Card title="Rate Limiting" icon="gauge" href="https://docs.langdb.ai/features/rate-limiting">
+    Intelligent rate limiting to prevent API quota exhaustion and optimize throughput.
+  </Card>
+  <Card title="Caching" icon="bolt" href="https://docs.langdb.ai/features/caching">
+    Smart caching of LLM responses to reduce costs and improve response times.
+  </Card>
+  <Card title="Load Balancing" icon="scale-balanced" href="https://docs.langdb.ai/features/load-balancing">
+    Distribute requests across multiple LLM providers for reliability and performance.
+  </Card>
+</CardGroup>
+
+### 4. Enterprise Security & Governance
+
+LangDB provides enterprise-grade security features for production CrewAI deployments:
+
+```python
+from langdb import LangDB
+
+# Initialize with security configurations
+LangDB.init(
+    security_config={
+        "pii_detection": True,
+        "content_filtering": True,
+        "audit_logging": True,
+        "data_retention_days": 90
+    }
+)
+
+# All crew interactions are automatically secured
+crew = Crew(agents=[agent], tasks=[task])
+result = crew.kickoff()
+```
+
+Security features include:
+- **PII Detection**: Automatic detection and redaction of sensitive information
+- **Content Filtering**: Block inappropriate or harmful content
+- **Audit Logging**: Complete audit trails for compliance
+- **Data Governance**: Configurable data retention and privacy controls
+
+## Advanced Configuration
+
+### Custom Metadata and Filtering
+
+Add custom metadata to enable powerful filtering and analytics:
+
+```python
+from langdb import LangDB
+from crewai import Agent, Crew, Task
+
+# Initialize with rich metadata
+LangDB.init(
+    metadata={
+        "environment": "production",
+        "team": "research_team",
+        "version": "v2.1.0",
+        "customer_tier": "enterprise"
+    }
+)
+
+# Add task-specific metadata
+@task
+def research_task(self) -> Task:
+    return Task(
+        description="Research the latest AI trends",
+        expected_output="Comprehensive research report",
+        agent=research_agent,
+        metadata={
+            "task_type": "research",
+            "priority": "high",
+            "estimated_duration": "30min"
+        }
+    )
+```
+
+### Multi-Environment Setup
+
+Configure different LangDB projects for different environments:
+
+```python
+import os
+from langdb import LangDB
+
+# Environment-specific configuration
+environment = os.getenv("ENVIRONMENT", "development")
+
+if environment == "production":
+    LangDB.init(
+        project_id="prod_project_id",
+        sampling_rate=1.0,  # Trace all requests
+        cost_tracking=True
+    )
+elif environment == "staging":
+    LangDB.init(
+        project_id="staging_project_id",
+        sampling_rate=0.5,  # Sample 50% of requests
+        cost_tracking=False
+    )
+else:
+    LangDB.init(
+        project_id="dev_project_id",
+        sampling_rate=0.1,  # Sample 10% of requests
+        cost_tracking=False
+    )
+```
+
+## Best Practices
+
+### Development Phase
+- Use detailed tracing to understand agent behavior patterns
+- Monitor resource usage during testing and development
+- Set up cost alerts to prevent unexpected spending
+- Implement comprehensive error handling and monitoring
+
+### Production Phase
+- Enable full request tracing for complete observability
+- Set up automated alerts for performance degradation
+- Implement cost optimization strategies based on analytics
+- Use metadata for detailed filtering and analysis
+
+### Continuous Improvement
+- Regular performance reviews using LangDB analytics
+- A/B testing of different agent configurations
+- Cost optimization based on usage patterns
+- Workflow optimization using bottleneck analysis
+
+## Getting Started
+
+1. **Sign up** for a LangDB account at [app.langdb.ai](https://app.langdb.ai)
+2. **Install** the LangDB package: `pip install langdb`
+3. **Initialize** LangDB in your CrewAI application
+4. **Deploy** your crews with automatic observability
+5. **Monitor** and optimize using the LangDB dashboard
+
+<Card title="LangDB Documentation" icon="book" href="https://docs.langdb.ai">
+  Explore comprehensive LangDB documentation and advanced features
+</Card>
+
+LangDB transforms your CrewAI agents into production-ready, observable, and optimized AI workflows with minimal code changes and maximum insights.

docs/en/observability/overview.mdx

@@ -56,6 +56,10 @@ Observability is crucial for understanding how your CrewAI agents perform, ident
   <Card title="Weave" icon="network-wired" href="/en/observability/weave">
     Weights & Biases platform for tracking and evaluating AI applications.
   </Card>
+
+  <Card title="LangDB" icon="database" href="/en/observability/langdb">
+    Enterprise AI gateway with comprehensive tracing, cost optimization, and performance analytics.
+  </Card>
 </CardGroup>
 
 ### Evaluation & Quality Assurance

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

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

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

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

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

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

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

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

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

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

docs/pt-BR/concepts/cli.mdx

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

docs/pt-BR/concepts/llms.mdx

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

docs/pt-BR/concepts/memory.mdx

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

docs/pt-BR/concepts/tasks.mdx

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

pyproject.toml

@@ -11,7 +11,7 @@ dependencies = [
     # Core Dependencies
     "pydantic>=2.4.2",
     "openai>=1.13.3",
-    "litellm==1.72.6",
+    "litellm==1.74.3",
     "instructor>=1.3.3",
     # Text Processing
     "pdfplumber>=0.11.4",
@@ -48,7 +48,7 @@ Documentation = "https://docs.crewai.com"
 Repository = "https://github.com/crewAIInc/crewAI"
 
 [project.optional-dependencies]
-tools = ["crewai-tools~=0.55.0"]
+tools = ["crewai-tools~=0.58.0"]
 embeddings = [
     "tiktoken~=0.8.0"
 ]

src/crewai/__init__.py

@@ -54,7 +54,7 @@ def _track_install_async():
 
 _track_install_async()
 
-__version__ = "0.148.0"
+__version__ = "0.150.0"
 __all__ = [
     "Agent",
     "Crew",

src/crewai/agents/crew_agent_executor.py

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

src/crewai/cli/command.py

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

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

@@ -5,7 +5,7 @@ description = "{{name}} using crewAI"
 authors = [{ name = "Your Name", email = "you@example.com" }]
 requires-python = ">=3.10,<3.14"
 dependencies = [
-    "crewai[tools]>=0.148.0,<1.0.0"
+    "crewai[tools]>=0.150.0,<1.0.0"
 ]
 
 [project.scripts]

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

@@ -5,7 +5,7 @@ description = "{{name}} using crewAI"
 authors = [{ name = "Your Name", email = "you@example.com" }]
 requires-python = ">=3.10,<3.14"
 dependencies = [
-    "crewai[tools]>=0.148.0,<1.0.0",
+    "crewai[tools]>=0.150.0,<1.0.0",
 ]
 
 [project.scripts]

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

@@ -5,7 +5,7 @@ description = "Power up your crews with {{folder_name}}"
 readme = "README.md"
 requires-python = ">=3.10,<3.14"
 dependencies = [
-    "crewai[tools]>=0.148.0"
+    "crewai[tools]>=0.150.0"
 ]
 
 [tool.crewai]

src/crewai/crew.py

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

src/crewai/flow/flow.py

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

src/crewai/knowledge/embedder/base_embedder.py

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

src/crewai/knowledge/storage/knowledge_storage.py

@@ -13,7 +13,7 @@ from chromadb.api.types import OneOrMany
 from chromadb.config import Settings
 
 from crewai.knowledge.storage.base_knowledge_storage import BaseKnowledgeStorage
-from crewai.utilities import EmbeddingConfigurator
+from crewai.rag.embeddings.configurator import EmbeddingConfigurator
 from crewai.utilities.chromadb import sanitize_collection_name
 from crewai.utilities.constants import KNOWLEDGE_DIRECTORY
 from crewai.utilities.logger import Logger

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

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

src/crewai/memory/storage/rag_storage.py

@@ -7,8 +7,8 @@ import uuid
 
 from typing import Any, Dict, List, Optional
 from chromadb.api import ClientAPI
-from crewai.memory.storage.base_rag_storage import BaseRAGStorage
-from crewai.utilities import EmbeddingConfigurator
+from crewai.rag.storage.base_rag_storage import BaseRAGStorage
+from crewai.rag.embeddings.configurator import EmbeddingConfigurator
 from crewai.utilities.chromadb import create_persistent_client
 from crewai.utilities.constants import MAX_FILE_NAME_LENGTH
 from crewai.utilities.paths import db_storage_path

src/crewai/memory/user/user_memory.py

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

src/crewai/memory/user/user_memory_item.py

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

src/crewai/rag/__init__.py

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

src/crewai/rag/embeddings/__init__.py

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

src/crewai/rag/storage/__init__.py

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

src/crewai/utilities/__init__.py

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

src/crewai/utilities/agent_utils.py

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

src/crewai/utilities/events/base_events.py

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

tests/agent_test.py

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

tests/cassettes/test_llm_call_when_stop_is_unsupported.yaml

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tests/cassettes/test_llm_call_when_stop_is_unsupported_when_additional_drop_params_is_provided.yaml

@@ -0,0 +1,206 @@
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tests/flow_test.py

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

tests/llm_test.py

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

@@ -0,0 +1,142 @@
+
+
+from crewai import Agent, Task, LLM
+
+
+def test_langdb_basic_integration_example():
+    """Test the basic LangDB integration example from the documentation."""
+    
+    class MockLangDB:
+        @staticmethod
+        def init(**kwargs):
+            pass
+    
+    MockLangDB.init()
+    
+    llm = LLM(
+        model="gpt-4o",
+        temperature=0.7
+    )
+    
+    agent = Agent(
+        role="Senior Research Analyst",
+        goal="Conduct comprehensive research on assigned topics",
+        backstory="You are an expert researcher with deep analytical skills.",
+        llm=llm,
+        verbose=True
+    )
+    
+    assert agent.role == "Senior Research Analyst"
+    assert agent.goal == "Conduct comprehensive research on assigned topics"
+    assert agent.llm == llm
+
+
+def test_langdb_metadata_configuration_example():
+    """Test the metadata configuration example from the documentation."""
+    class MockLangDB:
+        @staticmethod
+        def init(metadata=None, **kwargs):
+            assert metadata is not None
+            assert "environment" in metadata
+            assert "crew_type" in metadata
+    
+    MockLangDB.init(
+        metadata={
+            "environment": "production",
+            "crew_type": "research_workflow",
+            "user_id": "user_123"
+        }
+    )
+
+
+def test_langdb_cost_tracking_example():
+    """Test the cost tracking configuration example from the documentation."""
+    class MockLangDB:
+        @staticmethod
+        def init(cost_tracking=None, budget_alerts=None, **kwargs):
+            assert cost_tracking is True
+            assert budget_alerts is not None
+            assert "daily_limit" in budget_alerts
+            assert "alert_threshold" in budget_alerts
+    
+    MockLangDB.init(
+        cost_tracking=True,
+        budget_alerts={
+            "daily_limit": 100.0,
+            "alert_threshold": 0.8
+        }
+    )
+
+
+def test_langdb_security_configuration_example():
+    """Test the security configuration example from the documentation."""
+    class MockLangDB:
+        @staticmethod
+        def init(security_config=None, **kwargs):
+            assert security_config is not None
+            assert "pii_detection" in security_config
+            assert "content_filtering" in security_config
+            assert "audit_logging" in security_config
+    
+    MockLangDB.init(
+        security_config={
+            "pii_detection": True,
+            "content_filtering": True,
+            "audit_logging": True,
+            "data_retention_days": 90
+        }
+    )
+
+
+def test_langdb_environment_specific_setup():
+    """Test the multi-environment setup example from the documentation."""
+    environments = ["production", "staging", "development"]
+    
+    for env in environments:
+        class MockLangDB:
+            @staticmethod
+            def init(project_id=None, sampling_rate=None, cost_tracking=None, **kwargs):
+                assert project_id is not None
+                assert sampling_rate is not None
+                assert cost_tracking is not None
+        
+        if env == "production":
+            MockLangDB.init(
+                project_id="prod_project_id",
+                sampling_rate=1.0,
+                cost_tracking=True
+            )
+        elif env == "staging":
+            MockLangDB.init(
+                project_id="staging_project_id",
+                sampling_rate=0.5,
+                cost_tracking=False
+            )
+        else:
+            MockLangDB.init(
+                project_id="dev_project_id",
+                sampling_rate=0.1,
+                cost_tracking=False
+            )
+
+
+def test_langdb_task_with_metadata():
+    """Test task creation with metadata as shown in documentation."""
+    llm = LLM(model="gpt-4o")
+    
+    agent = Agent(
+        role="Senior Research Analyst",
+        goal="Conduct research",
+        backstory="Expert researcher",
+        llm=llm
+    )
+    
+    task = Task(
+        description="Research the latest AI trends",
+        expected_output="Comprehensive research report",
+        agent=agent
+    )
+    
+    assert task.description == "Research the latest AI trends"
+    assert task.expected_output == "Comprehensive research report"
+    assert task.agent == agent

tests/observability/test_langdb_documentation.py

@@ -0,0 +1,141 @@
+"""Test for the LangDB documentation examples."""
+
+from crewai import Agent, Task, LLM
+
+
+def test_langdb_basic_integration_example():
+    """Test the basic LangDB integration example from the documentation."""
+    
+    class MockLangDB:
+        @staticmethod
+        def init(**kwargs):
+            pass
+    
+    MockLangDB.init()
+    
+    llm = LLM(
+        model="gpt-4o",
+        temperature=0.7
+    )
+    
+    agent = Agent(
+        role="Senior Research Analyst",
+        goal="Conduct comprehensive research on assigned topics",
+        backstory="You are an expert researcher with deep analytical skills.",
+        llm=llm
+    )
+    
+    assert agent.role == "Senior Research Analyst"
+    assert agent.goal == "Conduct comprehensive research on assigned topics"
+    assert agent.llm == llm
+
+
+def test_langdb_metadata_configuration_example():
+    """Test the metadata configuration example from the documentation."""
+    class MockLangDB:
+        @staticmethod
+        def init(metadata=None, **kwargs):
+            assert metadata is not None
+            assert "environment" in metadata
+            assert "crew_type" in metadata
+    
+    MockLangDB.init(
+        metadata={
+            "environment": "production",
+            "crew_type": "research_workflow",
+            "user_id": "user_123"
+        }
+    )
+
+
+def test_langdb_cost_tracking_example():
+    """Test the cost tracking configuration example from the documentation."""
+    class MockLangDB:
+        @staticmethod
+        def init(cost_tracking=None, budget_alerts=None, **kwargs):
+            assert cost_tracking is True
+            assert budget_alerts is not None
+            assert "daily_limit" in budget_alerts
+            assert "alert_threshold" in budget_alerts
+    
+    MockLangDB.init(
+        cost_tracking=True,
+        budget_alerts={
+            "daily_limit": 100.0,
+            "alert_threshold": 0.8
+        }
+    )
+
+
+def test_langdb_security_configuration_example():
+    """Test the security configuration example from the documentation."""
+    class MockLangDB:
+        @staticmethod
+        def init(security_config=None, **kwargs):
+            assert security_config is not None
+            assert "pii_detection" in security_config
+            assert "content_filtering" in security_config
+            assert "audit_logging" in security_config
+    
+    MockLangDB.init(
+        security_config={
+            "pii_detection": True,
+            "content_filtering": True,
+            "audit_logging": True,
+            "data_retention_days": 90
+        }
+    )
+
+
+def test_langdb_environment_specific_setup():
+    """Test the multi-environment setup example from the documentation."""
+    environments = ["production", "staging", "development"]
+    
+    for env in environments:
+        class MockLangDB:
+            @staticmethod
+            def init(project_id=None, sampling_rate=None, cost_tracking=None, **kwargs):
+                assert project_id is not None
+                assert sampling_rate is not None
+                assert cost_tracking is not None
+        
+        if env == "production":
+            MockLangDB.init(
+                project_id="prod_project_id",
+                sampling_rate=1.0,
+                cost_tracking=True
+            )
+        elif env == "staging":
+            MockLangDB.init(
+                project_id="staging_project_id",
+                sampling_rate=0.5,
+                cost_tracking=False
+            )
+        else:
+            MockLangDB.init(
+                project_id="dev_project_id",
+                sampling_rate=0.1,
+                cost_tracking=False
+            )
+
+
+def test_langdb_task_with_metadata():
+    """Test task creation with metadata as shown in documentation."""
+    llm = LLM(model="gpt-4o")
+    
+    agent = Agent(
+        role="Senior Research Analyst",
+        goal="Conduct research",
+        backstory="Expert researcher",
+        llm=llm
+    )
+    
+    task = Task(
+        description="Research the latest AI trends",
+        expected_output="Comprehensive research report",
+        agent=agent
+    )
+    
+    assert task.description == "Research the latest AI trends"
+    assert task.expected_output == "Comprehensive research report"
+    assert task.agent == agent

tests/storage/test_mem0_storage.py

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

tests/utilities/test_events.py

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