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/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:
@@ -720,7 +720,7 @@ crew = Crew(
 ```
 
 ### Advanced Mem0 Configuration
-When using Mem0 Client, you can customize the memory configuration further, by using parameters like 'includes', 'excludes', 'custom_categories' and 'run_id' (this is only for short-term memory).
+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
 
@@ -744,7 +744,7 @@ crew = Crew(
             "run_id": "my_run_id",        # Optional - for short-term memory
             "includes": "include1",       # Optional 
             "excludes": "exclude1",       # Optional
-            "infer": True
+            "infer": True                 # Optional defaults to True
             "custom_categories": new_categories  # Optional - custom categories for user memory
         },
         "user_memory": {}
@@ -776,7 +776,7 @@ crew = Crew(
                     "config": {"api_key": "your-api-key", "model": "text-embedding-3-small"}
                 }
             },
-            "infer": True
+            "infer": True                   # Optional defaults to True
         },
         "user_memory": {}
     }

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/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:

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/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/memory/storage/mem0_storage.py

@@ -43,7 +43,7 @@ class Mem0Storage(Storage):
         self.includes = cfg.get("includes")
         self.excludes = cfg.get("excludes")
         self.custom_categories = cfg.get("custom_categories")
-        self.infer = cfg.get("infer", False)
+        self.infer = cfg.get("infer", True)
 
     def _initialize_memory(self):
         api_key = self.config.get("api_key") or os.getenv("MEM0_API_KEY")

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/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/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/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/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

@@ -270,3 +270,20 @@ def test_search_method_with_memory_client(mem0_storage_with_memory_client_using_
 
     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