Fix CI: Rename metadata column to doc_metadata and fix SQL injection vulnerability

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

docs/concepts/agents.mdx

@@ -58,6 +58,8 @@ The Visual Agent Builder enables:
 | **Embedder** _(optional)_               | `embedder`               | `Optional[Dict[str, Any]]`    | Configuration for the embedder used by the agent.                                                                     |
 | **Knowledge Sources** _(optional)_      | `knowledge_sources`      | `Optional[List[BaseKnowledgeSource]]` | Knowledge sources available to the agent.                                                                     |
 | **Use System Prompt** _(optional)_      | `use_system_prompt`      | `Optional[bool]`              | Whether to use system prompt (for o1 model support). Default is True.                                                 |
+| **Inject Date** _(optional)_           | `inject_date`            | `bool`                        | Whether to automatically inject the current date into tasks. Default is False.                                         |
+| **Date Format** _(optional)_           | `date_format`            | `str`                         | Format string for date when inject_date is enabled. Default is "%Y-%m-%d" (ISO format).                                |
 
 ## Creating Agents
 
@@ -226,6 +228,18 @@ custom_agent = Agent(
 )
 ```
 
+#### Date-Aware Agent
+```python Code
+date_aware_agent = Agent(
+    role="Market Analyst",
+    goal="Track market movements with precise date references",
+    backstory="Expert in time-sensitive financial analysis and reporting",
+    inject_date=True,  # Automatically inject current date into tasks
+    date_format="%B %d, %Y",  # Format as "May 21, 2025"
+    verbose=True
+)
+```
+
 ### Parameter Details
 
 #### Critical Parameters
@@ -332,6 +346,12 @@ When `memory` is enabled, the agent will maintain context across multiple intera
   - Main `llm` for complex reasoning
   - `function_calling_llm` for efficient tool usage
 
+### Date Awareness
+- Use `inject_date: true` to provide agents with current date awareness
+- Customize the date format with `date_format` using standard Python datetime format codes
+- Valid format codes include: %Y (year), %m (month), %d (day), %B (full month name), etc.
+- Invalid date formats will be logged as warnings and will not modify the task description
+
 ### Model Compatibility
 - Set `use_system_prompt: false` for older models that don't support system messages
 - Ensure your chosen `llm` supports the features you need (like function calling)

docs/concepts/knowledge.mdx

@@ -736,6 +736,214 @@ recent_news = SpaceNewsKnowledgeSource(
 )
 ```
 
+## Custom Knowledge Storage with pgvector
+
+CrewAI allows you to use custom knowledge storage backends to store and retrieve knowledge. One powerful option is using PostgreSQL with the pgvector extension, which provides efficient vector similarity search capabilities.
+
+### Prerequisites
+
+Before using pgvector as your knowledge storage backend, you need to:
+
+1. Set up a PostgreSQL database with the pgvector extension installed
+2. Install the required Python packages
+
+#### PostgreSQL Setup
+
+```bash
+# Install PostgreSQL (Ubuntu example)
+sudo apt update
+sudo apt install postgresql postgresql-contrib
+
+# Connect to PostgreSQL
+sudo -u postgres psql
+
+# Create a database
+CREATE DATABASE crewai_knowledge;
+
+# Connect to the database
+\c crewai_knowledge
+
+# Install the pgvector extension
+CREATE EXTENSION vector;
+
+# Create a user (optional)
+CREATE USER crewai WITH PASSWORD 'your_password';
+GRANT ALL PRIVILEGES ON DATABASE crewai_knowledge TO crewai;
+```
+
+#### Python Dependencies
+
+Add these dependencies to your project:
+
+```bash
+# Install required packages
+uv add sqlalchemy pgvector psycopg2-binary
+```
+
+### Using pgvector Knowledge Storage
+
+Here's how to use pgvector as your knowledge storage backend in CrewAI:
+
+```python
+from crewai import Agent, Task, Crew, Process
+from crewai.knowledge.source.string_knowledge_source import StringKnowledgeSource
+from crewai.knowledge.storage.pgvector_knowledge_storage import PGVectorKnowledgeStorage
+
+# Create a connection string for PostgreSQL
+connection_string = "postgresql://username:password@localhost:5432/crewai_knowledge"
+
+# Create a custom knowledge storage
+pgvector_storage = PGVectorKnowledgeStorage(
+    connection_string=connection_string,
+    embedding_dimension=1536,  # Dimension for OpenAI embeddings
+)
+
+# Create a knowledge source
+content = "CrewAI is a framework for orchestrating role-playing autonomous agents."
+string_source = StringKnowledgeSource(
+    content=content,
+    storage=pgvector_storage  # Use pgvector storage
+)
+
+# Create an agent with the knowledge store
+agent = Agent(
+    role="CrewAI Expert",
+    goal="Explain CrewAI concepts accurately.",
+    backstory="You are an expert in the CrewAI framework.",
+    knowledge_sources=[string_source],
+)
+
+# Create a task
+task = Task(
+    description="Answer this question about CrewAI: {question}",
+    expected_output="A detailed answer about CrewAI.",
+    agent=agent,
+)
+
+# Create a crew with the knowledge sources
+crew = Crew(
+    agents=[agent],
+    tasks=[task],
+    verbose=True,
+    process=Process.sequential,
+)
+
+# Run the crew
+result = crew.kickoff(inputs={"question": "What is CrewAI?"})
+```
+
+### Configuration Options
+
+The `PGVectorKnowledgeStorage` class supports the following configuration options:
+
+| Option | Description | Default |
+|--------|-------------|---------|
+| `connection_string` | PostgreSQL connection string | Required |
+| `embedder` | Embedding configuration | OpenAI embeddings |
+| `table_name` | Name of the table to store documents | "documents" |
+| `embedding_dimension` | Dimension of the embedding vectors | 1536 |
+
+#### Connection String Format
+
+The PostgreSQL connection string follows this format:
+```
+postgresql://username:password@hostname:port/database_name
+```
+
+#### Custom Embedding Models
+
+You can configure custom embedding models just like with the default knowledge storage:
+
+```python
+pgvector_storage = PGVectorKnowledgeStorage(
+    connection_string="postgresql://username:password@localhost:5432/crewai_knowledge",
+    embedder={
+        "provider": "openai",
+        "config": {
+            "model": "text-embedding-3-large"
+        }
+    },
+    embedding_dimension=3072,  # Dimension for text-embedding-3-large
+)
+```
+
+### Advanced Usage
+
+#### Custom Table Names
+
+You can specify a custom table name to store your documents:
+
+```python
+pgvector_storage = PGVectorKnowledgeStorage(
+    connection_string="postgresql://username:password@localhost:5432/crewai_knowledge",
+    table_name="my_custom_documents_table"
+)
+```
+
+#### Multiple Knowledge Collections
+
+You can create multiple knowledge collections by using different table names:
+
+```python
+# Create a storage for product knowledge
+product_storage = PGVectorKnowledgeStorage(
+    connection_string="postgresql://username:password@localhost:5432/crewai_knowledge",
+    table_name="product_knowledge"
+)
+
+# Create a storage for customer knowledge
+customer_storage = PGVectorKnowledgeStorage(
+    connection_string="postgresql://username:password@localhost:5432/crewai_knowledge",
+    table_name="customer_knowledge"
+)
+```
+
+### Troubleshooting
+
+#### Common Issues
+
+1. **pgvector Extension Not Found**
+
+   Error: `ERROR: could not load library "/usr/local/lib/postgresql/pgvector.so"`
+   
+   Solution: Make sure the pgvector extension is properly installed in your PostgreSQL instance:
+   ```sql
+   CREATE EXTENSION vector;
+   ```
+
+2. **Dimension Mismatch**
+
+   Error: `ERROR: vector dimensions do not match`
+   
+   Solution: Ensure that the `embedding_dimension` parameter matches the dimension of your embedding model.
+
+3. **Connection Issues**
+
+   Error: `Could not connect to PostgreSQL server`
+   
+   Solution: Check your connection string and make sure the PostgreSQL server is running and accessible.
+
+#### Performance Tips
+
+1. **Create an Index**
+
+   For better performance with large datasets, create an index on the embedding column:
+   
+   ```sql
+   CREATE INDEX ON documents USING hnsw (embedding vector_l2_ops);
+   ```
+
+2. **Batch Processing**
+
+   When saving large numbers of documents, process them in batches to avoid memory issues:
+   
+   ```python
+   batch_size = 100
+   for i in range(0, len(documents), batch_size):
+       batch = documents[i:i+batch_size]
+       pgvector_storage.save(batch)
+   ```
+
 ## Best Practices
 
 <AccordionGroup>

docs/concepts/reasoning.mdx

@@ -116,7 +116,7 @@ Here's an example of what a reasoning plan might look like for a data analysis t
 Task: Analyze the provided sales data and identify key trends.
 
 Reasoning Plan:
-I'll analyze the sales data to identify the top 3 trends. 
+I'll analyze the sales data to identify the top 3 trends.
 
 1. Understanding of the task:
    I need to analyze sales data to identify key trends that would be valuable for business decision-making.

docs/how-to/before-and-after-kickoff-hooks.mdx

@@ -12,7 +12,8 @@ The before kickoff hook is executed before the crew starts its tasks. It receive
 Here's an example of defining a before kickoff function in your `crew.py`:
 
 ```python
-from crewai import CrewBase, before_kickoff
+from crewai import CrewBase
+from crewai.project import before_kickoff
 
 @CrewBase
 class MyCrew:
@@ -34,7 +35,8 @@ The after kickoff hook is executed after the crew has completed its tasks. It re
 Here's how you can define an after kickoff function in your `crew.py`:
 
 ```python
-from crewai import CrewBase, after_kickoff
+from crewai import CrewBase
+from crewai.project import after_kickoff
 
 @CrewBase
 class MyCrew:

pyproject.toml

@@ -1,6 +1,6 @@
 [project]
 name = "crewai"
-version = "0.120.1"
+version = "0.121.0"
 description = "Cutting-edge framework for orchestrating role-playing, autonomous AI agents. By fostering collaborative intelligence, CrewAI empowers agents to work together seamlessly, tackling complex tasks."
 readme = "README.md"
 requires-python = ">=3.10,<3.13"
@@ -67,6 +67,11 @@ docling = [
 aisuite = [
     "aisuite>=0.1.10",
 ]
+pgvector = [
+    "pgvector>=0.2.0",
+    "sqlalchemy>=2.0.0",
+    "psycopg2-binary>=2.9.0",
+]
 
 [tool.uv]
 dev-dependencies = [

src/crewai/__init__.py

@@ -17,7 +17,7 @@ warnings.filterwarnings(
     category=UserWarning,
     module="pydantic.main",
 )
-__version__ = "0.120.1"
+__version__ = "0.121.0"
 __all__ = [
     "Agent",
     "Crew",

src/crewai/agent.py

@@ -115,6 +115,14 @@ class Agent(BaseAgent):
         default=False,
         description="Whether the agent is multimodal.",
     )
+    inject_date: bool = Field(
+        default=False,
+        description="Whether to automatically inject the current date into tasks.",
+    )
+    date_format: str = Field(
+        default="%Y-%m-%d",
+        description="Format string for date when inject_date is enabled.",
+    )
     code_execution_mode: Literal["safe", "unsafe"] = Field(
         default="safe",
         description="Mode for code execution: 'safe' (using Docker) or 'unsafe' (direct execution).",
@@ -248,6 +256,8 @@ class Agent(BaseAgent):
                 else:
                     print(f"Error during reasoning process: {str(e)}")
             
+        self._inject_date_to_task(task)
+            
         if self.tools_handler:
             self.tools_handler.last_used_tool = {}  # type: ignore # Incompatible types in assignment (expression has type "dict[Never, Never]", variable has type "ToolCalling")
 
@@ -608,6 +618,26 @@ class Agent(BaseAgent):
 
         return description
 
+    def _inject_date_to_task(self, task):
+        """Inject the current date into the task description if inject_date is enabled."""
+        if self.inject_date:
+            from datetime import datetime
+            try:
+                valid_format_codes = ['%Y', '%m', '%d', '%H', '%M', '%S', '%B', '%b', '%A', '%a']
+                is_valid = any(code in self.date_format for code in valid_format_codes)
+                
+                if not is_valid:
+                    raise ValueError(f"Invalid date format: {self.date_format}")
+                
+                current_date: str = datetime.now().strftime(self.date_format)
+                task.description += f"\n\nCurrent Date: {current_date}"
+            except Exception as e:
+                if hasattr(self, '_logger'):
+                    self._logger.log("warning", f"Failed to inject date: {str(e)}")
+                else:
+                    print(f"Warning: Failed to inject date: {str(e)}")
+
+
     def _validate_docker_installation(self) -> None:
         """Check if Docker is installed and running."""
         if not shutil.which("docker"):

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,<3.13"
 dependencies = [
-    "crewai[tools]>=0.120.1,<1.0.0"
+    "crewai[tools]>=0.121.0,<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,<3.13"
 dependencies = [
-    "crewai[tools]>=0.120.1,<1.0.0",
+    "crewai[tools]>=0.121.0,<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,<3.13"
 dependencies = [
-    "crewai[tools]>=0.120.1"
+    "crewai[tools]>=0.121.0"
 ]
 
 [tool.crewai]

src/crewai/crew.py

@@ -315,9 +315,7 @@ class Crew(FlowTrackable, BaseModel):
         """Initialize private memory attributes."""
         self._external_memory = (
             # External memory doesn’t support a default value since it was designed to be managed entirely externally
-            self.external_memory.set_crew(self)
-            if self.external_memory
-            else None
+            self.external_memory.set_crew(self) if self.external_memory else None
         )
 
         self._long_term_memory = self.long_term_memory
@@ -1204,7 +1202,6 @@ class Crew(FlowTrackable, BaseModel):
             "_long_term_memory",
             "_entity_memory",
             "_external_memory",
-            "_telemetry",
             "agents",
             "tasks",
             "knowledge_sources",
@@ -1397,10 +1394,10 @@ class Crew(FlowTrackable, BaseModel):
         memory_systems = self._get_memory_systems()
 
         for memory_type, config in memory_systems.items():
-            if (system := config.get('system')) is not None:
-                name = config.get('name')
+            if (system := config.get("system")) is not None:
+                name = config.get("name")
                 try:
-                    reset_fn: Callable = cast(Callable, config.get('reset'))
+                    reset_fn: Callable = cast(Callable, config.get("reset"))
                     reset_fn(system)
                     self._logger.log(
                         "info",
@@ -1422,14 +1419,14 @@ class Crew(FlowTrackable, BaseModel):
         """
         memory_systems = self._get_memory_systems()
         config = memory_systems[memory_type]
-        system = config.get('system')
-        name = config.get('name')
+        system = config.get("system")
+        name = config.get("name")
 
         if system is None:
             raise RuntimeError(f"{name} memory system is not initialized")
-        
+
         try:
-            reset_fn: Callable = cast(Callable, config.get('reset'))
+            reset_fn: Callable = cast(Callable, config.get("reset"))
             reset_fn(system)
             self._logger.log(
                 "info",
@@ -1442,58 +1439,67 @@ class Crew(FlowTrackable, BaseModel):
 
     def _get_memory_systems(self):
         """Get all available memory systems with their configuration.
-        
+
         Returns:
             Dict containing all memory systems with their reset functions and display names.
         """
+
         def default_reset(memory):
             return memory.reset()
+
         def knowledge_reset(memory):
             return self.reset_knowledge(memory)
-        
-        # Get knowledge for agents 
-        agent_knowledges = [getattr(agent, "knowledge", None) for agent in self.agents 
-                                    if getattr(agent, "knowledge", None) is not None]
+
+        # Get knowledge for agents
+        agent_knowledges = [
+            getattr(agent, "knowledge", None)
+            for agent in self.agents
+            if getattr(agent, "knowledge", None) is not None
+        ]
         # Get knowledge for crew and agents
         crew_knowledge = getattr(self, "knowledge", None)
-        crew_and_agent_knowledges = ([crew_knowledge] if crew_knowledge is not None else []) + agent_knowledges
+        crew_and_agent_knowledges = (
+            [crew_knowledge] if crew_knowledge is not None else []
+        ) + agent_knowledges
 
         return {
-            'short': {
-                'system': getattr(self, "_short_term_memory", None),
-                'reset': default_reset,
-                'name': 'Short Term'
+            "short": {
+                "system": getattr(self, "_short_term_memory", None),
+                "reset": default_reset,
+                "name": "Short Term",
             },
-            'entity': {
-                'system': getattr(self, "_entity_memory", None),
-                'reset': default_reset,
-                'name': 'Entity'
+            "entity": {
+                "system": getattr(self, "_entity_memory", None),
+                "reset": default_reset,
+                "name": "Entity",
             },
-            'external': {
-                'system': getattr(self, "_external_memory", None),
-                'reset': default_reset,
-                'name': 'External'
+            "external": {
+                "system": getattr(self, "_external_memory", None),
+                "reset": default_reset,
+                "name": "External",
             },
-            'long': {
-                'system': getattr(self, "_long_term_memory", None),
-                'reset': default_reset,
-                'name': 'Long Term'
+            "long": {
+                "system": getattr(self, "_long_term_memory", None),
+                "reset": default_reset,
+                "name": "Long Term",
             },
-            'kickoff_outputs': {
-                'system': getattr(self, "_task_output_handler", None),
-                'reset': default_reset,
-                'name': 'Task Output'
+            "kickoff_outputs": {
+                "system": getattr(self, "_task_output_handler", None),
+                "reset": default_reset,
+                "name": "Task Output",
             },
-            'knowledge': {
-                'system': crew_and_agent_knowledges if crew_and_agent_knowledges else None,
-                'reset': knowledge_reset,
-                'name': 'Crew Knowledge and Agent Knowledge'
+            "knowledge": {
+                "system": crew_and_agent_knowledges
+                if crew_and_agent_knowledges
+                else None,
+                "reset": knowledge_reset,
+                "name": "Crew Knowledge and Agent Knowledge",
+            },
+            "agent_knowledge": {
+                "system": agent_knowledges if agent_knowledges else None,
+                "reset": knowledge_reset,
+                "name": "Agent Knowledge",
             },
-            'agent_knowledge': {
-                'system': agent_knowledges if agent_knowledges else None,
-                'reset': knowledge_reset,
-                'name': 'Agent Knowledge'
-            }
         }
 
     def reset_knowledge(self, knowledges: List[Knowledge]) -> None:

src/crewai/knowledge/storage/__init__.py

@@ -0,0 +1,5 @@
+try:
+    from crewai.knowledge.storage.pgvector_knowledge_storage import PGVectorKnowledgeStorage
+    __all__ = ["PGVectorKnowledgeStorage"]
+except ImportError:
+    __all__ = []

src/crewai/knowledge/storage/pgvector_knowledge_storage.py

@@ -0,0 +1,220 @@
+from typing import Any, Dict, List, Optional
+import hashlib
+import logging
+import os
+from sqlalchemy import create_engine, Column, String, Text
+from sqlalchemy.ext.declarative import declarative_base
+from sqlalchemy.orm import sessionmaker
+from sqlalchemy.sql import text
+
+from crewai.knowledge.storage.base_knowledge_storage import BaseKnowledgeStorage
+from crewai.utilities import EmbeddingConfigurator
+
+try:
+    from pgvector.sqlalchemy import Vector
+    HAS_PGVECTOR = True
+except ImportError:
+    HAS_PGVECTOR = False
+    class VectorType:
+        def __init__(self, dimensions: int):
+            self.dimensions = dimensions
+    Vector = VectorType  # type: ignore
+
+Base = declarative_base()
+
+class Document(Base):  # type: ignore
+    """SQLAlchemy model for document storage with pgvector."""
+    __tablename__ = "documents"
+    
+    id = Column(String, primary_key=True)
+    content = Column(Text)
+    doc_metadata = Column(Text)  # JSON serialized metadata
+    embedding: Column = Column(Vector(1536))  # Adjust dimension based on embedding model
+
+class PGVectorKnowledgeStorage(BaseKnowledgeStorage):
+    """
+    Knowledge storage implementation using pgvector.
+    
+    This class provides an implementation of BaseKnowledgeStorage using PostgreSQL
+    with the pgvector extension for vector similarity search.
+    """
+    
+    def __init__(
+        self,
+        connection_string: str,
+        embedder: Optional[Dict[str, Any]] = None,
+        table_name: str = "documents",
+        embedding_dimension: int = 1536,
+    ):
+        """
+        Initialize the pgvector knowledge storage.
+        
+        Args:
+            connection_string: PostgreSQL connection string
+            embedder: Configuration dictionary for the embedder
+            table_name: Name of the table to store documents
+            embedding_dimension: Dimension of the embedding vectors
+        """
+        if not HAS_PGVECTOR:
+            raise ImportError(
+                "pgvector is not installed. Please install it with: pip install pgvector"
+            )
+            
+        self.connection_string = connection_string
+        self.table_name = table_name
+        self.embedding_dimension = embedding_dimension
+        
+        self.engine = create_engine(connection_string)
+        self.Session = sessionmaker(bind=self.engine)
+        
+        self._set_embedder_config(embedder)
+        
+        Base.metadata.create_all(self.engine)
+    
+    def _set_embedder_config(self, embedder: Optional[Dict[str, Any]] = None) -> None:
+        """
+        Set the embedding configuration for the knowledge storage.
+        
+        Args:
+            embedder_config: Configuration dictionary for the embedder.
+                If None or empty, defaults to the default embedding function.
+        """
+        self.embedder = (
+            EmbeddingConfigurator().configure_embedder(embedder)
+            if embedder
+            else self._create_default_embedding_function()
+        )
+    
+    def search(
+        self,
+        query: List[str],
+        limit: int = 3,
+        filter: Optional[dict] = None,
+        score_threshold: float = 0.35,
+    ) -> List[Dict[str, Any]]:
+        """
+        Search for documents in the knowledge base.
+        
+        Args:
+            query: List of query strings
+            limit: Maximum number of results to return
+            filter: Optional metadata filter
+            score_threshold: Minimum similarity score threshold
+            
+        Returns:
+            List of search results with id, metadata, context, and score
+        """
+        session = self.Session()
+        
+        try:
+            query_embedding = self.embedder([query[0]])[0]
+            
+            sql_query = text("""
+            SELECT id, content, doc_metadata, 1 - (embedding <=> :query_embedding) as similarity
+            FROM :table_name
+            ORDER BY embedding <=> :query_embedding
+            LIMIT :limit
+            """).bindparams(
+                query_embedding=query_embedding,
+                limit=limit,
+                table_name=self.table_name
+            )
+            
+            results = session.execute(
+                sql_query, 
+                {"query_embedding": query_embedding, "limit": limit}
+            ).fetchall()
+            
+            formatted_results = []
+            for row in results:
+                similarity = float(row[3])
+                if similarity >= score_threshold:
+                    formatted_results.append({
+                        "id": row[0],
+                        "context": row[1],
+                        "metadata": row[2],  # Keep the key as 'metadata' for API compatibility
+                        "score": similarity,
+                    })
+            
+            return formatted_results
+        finally:
+            session.close()
+    
+    def save(
+        self,
+        documents: List[str],
+        metadata: Optional[Dict[str, Any] | List[Dict[str, Any]]] = None,
+    ) -> None:
+        """
+        Save documents to the knowledge base.
+        
+        Args:
+            documents: List of document strings
+            metadata: Optional metadata for the documents
+        """
+        session = self.Session()
+        
+        try:
+            unique_docs = {}
+            
+            for idx, doc in enumerate(documents):
+                doc_id = hashlib.sha256(doc.encode("utf-8")).hexdigest()
+                doc_metadata = None
+                if metadata is not None:
+                    if isinstance(metadata, list):
+                        doc_metadata = metadata[idx]
+                    else:
+                        doc_metadata = metadata
+                unique_docs[doc_id] = (doc, doc_metadata)
+            
+            docs_list = [doc for doc, _ in unique_docs.values()]
+            embeddings = self.embedder(docs_list)
+            
+            for i, (doc_id, (doc, meta)) in enumerate(unique_docs.items()):
+                embedding = embeddings[i]
+                
+                existing = session.query(Document).filter(Document.id == doc_id).first()
+                
+                if existing:
+                    setattr(existing, "content", doc)
+                    setattr(existing, "doc_metadata", str(meta) if meta else None)
+                    setattr(existing, "embedding", embedding)
+                else:
+                    new_doc = Document(
+                        id=doc_id,
+                        content=doc,
+                        doc_metadata=str(meta) if meta else None,
+                        embedding=embedding,
+                    )
+                    session.add(new_doc)
+            
+            session.commit()
+        except Exception as e:
+            session.rollback()
+            logging.error(f"Failed to save documents: {e}")
+            raise
+        finally:
+            session.close()
+    
+    def reset(self) -> None:
+        """Reset the knowledge base by dropping and recreating the table."""
+        session = self.Session()
+        try:
+            session.query(Document).delete()
+            session.commit()
+        except Exception as e:
+            session.rollback()
+            logging.error(f"Failed to reset knowledge base: {e}")
+            raise
+        finally:
+            session.close()
+    
+    def _create_default_embedding_function(self):
+        """Create a default embedding function for the knowledge storage."""
+        from chromadb.utils.embedding_functions.openai_embedding_function import (
+            OpenAIEmbeddingFunction,
+        )
+        
+        return OpenAIEmbeddingFunction(
+            api_key=os.getenv("OPENAI_API_KEY"), model_name="text-embedding-3-small"
+        )

src/crewai/tasks/hallucination_guardrail.py

@@ -0,0 +1,96 @@
+"""Hallucination Guardrail Placeholder for CrewAI.
+
+This is a no-op version of the HallucinationGuardrail for the open-source repository.
+
+Classes:
+    HallucinationGuardrail: Placeholder guardrail that validates task outputs.
+"""
+
+from typing import Any, Optional, Tuple
+
+from crewai.llm import LLM
+from crewai.tasks.task_output import TaskOutput
+from crewai.utilities.logger import Logger
+
+
+class HallucinationGuardrail:
+    """Placeholder for the HallucinationGuardrail feature.
+
+    Attributes:
+        context: The reference context that outputs would be checked against.
+        llm: The language model that would be used for evaluation.
+        threshold: Optional minimum faithfulness score that would be required to pass.
+        tool_response: Optional tool response information that would be used in evaluation.
+
+    Examples:
+        >>> # Basic usage with default verdict logic
+        >>> guardrail = HallucinationGuardrail(
+        ...     context="AI helps with various tasks including analysis and generation.",
+        ...     llm=agent.llm
+        ... )
+
+        >>> # With custom threshold for stricter validation
+        >>> strict_guardrail = HallucinationGuardrail(
+        ...     context="Quantum computing uses qubits in superposition.",
+        ...     llm=agent.llm,
+        ...     threshold=8.0  # Would require score >= 8 to pass in enterprise version
+        ... )
+
+        >>> # With tool response for additional context
+        >>> guardrail_with_tools = HallucinationGuardrail(
+        ...     context="The current weather data",
+        ...     llm=agent.llm,
+        ...     tool_response="Weather API returned: Temperature 22°C, Humidity 65%"
+        ... )
+    """
+
+    def __init__(
+        self,
+        context: str,
+        llm: LLM,
+        threshold: Optional[float] = None,
+        tool_response: str = "",
+    ):
+        """Initialize the HallucinationGuardrail placeholder.
+
+        Args:
+            context: The reference context that outputs would be checked against.
+            llm: The language model that would be used for evaluation.
+            threshold: Optional minimum faithfulness score that would be required to pass.
+            tool_response: Optional tool response information that would be used in evaluation.
+        """
+        self.context = context
+        self.llm: LLM = llm
+        self.threshold = threshold
+        self.tool_response = tool_response
+        self._logger = Logger(verbose=True)
+        self._logger.log(
+            "warning",
+            """Hallucination detection is a no-op in open source, use it for free at https://app.crewai.com\n""",
+            color="red",
+        )
+
+    @property
+    def description(self) -> str:
+        """Generate a description of this guardrail for event logging."""
+        return "HallucinationGuardrail (no-op)"
+
+    def __call__(self, task_output: TaskOutput) -> Tuple[bool, Any]:
+        """Validate a task output against hallucination criteria.
+
+        In the open source, this method always returns that the output is valid.
+
+        Args:
+            task_output: The output to be validated.
+
+        Returns:
+            A tuple containing:
+                - True
+                - The raw task output
+        """
+        self._logger.log(
+            "warning",
+            "Premium hallucination detection skipped (use for free at https://app.crewai.com)\n",
+            color="red",
+        )
+        return True, task_output.raw

src/crewai/telemetry/telemetry.py

@@ -9,6 +9,7 @@ import warnings
 from contextlib import contextmanager
 from importlib.metadata import version
 from typing import TYPE_CHECKING, Any, Optional
+import threading
 
 from opentelemetry import trace
 from opentelemetry.exporter.otlp.proto.http.trace_exporter import (
@@ -64,7 +65,17 @@ class Telemetry:
     attribute in the Crew class.
     """
 
-    def __init__(self):
+    _instance = None
+    _lock = threading.Lock()
+
+    def __new__(cls):
+        if cls._instance is None:
+            with cls._lock:
+                if cls._instance is None:
+                    cls._instance = super(Telemetry, cls).__new__(cls)
+        return cls._instance
+
+    def __init__(self) -> None:
         self.ready: bool = False
         self.trace_set: bool = False
 

src/crewai/utilities/evaluators/crew_evaluator_handler.py

@@ -9,7 +9,8 @@ from crewai.agent import Agent
 from crewai.llm import BaseLLM
 from crewai.task import Task
 from crewai.tasks.task_output import TaskOutput
-from crewai.telemetry import Telemetry
+from crewai.utilities.events import crewai_event_bus
+from crewai.utilities.events.crew_events import CrewTestResultEvent
 
 
 class TaskEvaluationPydanticOutput(BaseModel):
@@ -36,7 +37,6 @@ class CrewEvaluator:
     def __init__(self, crew, eval_llm: InstanceOf[BaseLLM]):
         self.crew = crew
         self.llm = eval_llm
-        self._telemetry = Telemetry()
         self._setup_for_evaluating()
 
     def _setup_for_evaluating(self) -> None:
@@ -178,11 +178,15 @@ class CrewEvaluator:
         evaluation_result = evaluation_task.execute_sync()
 
         if isinstance(evaluation_result.pydantic, TaskEvaluationPydanticOutput):
-            self._test_result_span = self._telemetry.individual_test_result_span(
+            crewai_event_bus.emit(
                 self.crew,
-                evaluation_result.pydantic.quality,
-                current_task.execution_duration,
-                self.llm.model,
+                CrewTestResultEvent(
+                    quality=evaluation_result.pydantic.quality,
+                    execution_duration=current_task.execution_duration,
+                    model=self.llm.model,
+                    crew_name=self.crew.name,
+                    crew=self.crew,
+                ),
             )
             self.tasks_scores[self.iteration].append(evaluation_result.pydantic.quality)
             self.run_execution_times[self.iteration].append(

src/crewai/utilities/events/crew_events.py

@@ -100,3 +100,12 @@ class CrewTestFailedEvent(CrewBaseEvent):
 
     error: str
     type: str = "crew_test_failed"
+
+
+class CrewTestResultEvent(CrewBaseEvent):
+    """Event emitted when a crew test result is available"""
+
+    quality: float
+    execution_duration: float
+    model: str
+    type: str = "crew_test_result"

src/crewai/utilities/events/event_listener.py

@@ -37,6 +37,7 @@ from .crew_events import (
     CrewKickoffStartedEvent,
     CrewTestCompletedEvent,
     CrewTestFailedEvent,
+    CrewTestResultEvent,
     CrewTestStartedEvent,
     CrewTrainCompletedEvent,
     CrewTrainFailedEvent,
@@ -134,6 +135,15 @@ class EventListener(BaseEventListener):
         def on_crew_train_failed(source, event: CrewTrainFailedEvent):
             self.formatter.handle_crew_train_failed(event.crew_name or "Crew")
 
+        @crewai_event_bus.on(CrewTestResultEvent)
+        def on_crew_test_result(source, event: CrewTestResultEvent):
+            self._telemetry.individual_test_result_span(
+                source.crew,
+                event.quality,
+                int(event.execution_duration),
+                event.model,
+            )
+
         # ----------- TASK EVENTS -----------
 
         @crewai_event_bus.on(TaskStartedEvent)

src/crewai/utilities/events/llm_guardrail_events.py

@@ -19,10 +19,13 @@ class LLMGuardrailStartedEvent(BaseEvent):
         from inspect import getsource
 
         from crewai.tasks.llm_guardrail import LLMGuardrail
+        from crewai.tasks.hallucination_guardrail import HallucinationGuardrail
 
         super().__init__(**data)
 
-        if isinstance(self.guardrail, LLMGuardrail):
+        if isinstance(self.guardrail, LLMGuardrail) or isinstance(
+            self.guardrail, HallucinationGuardrail
+        ):
             self.guardrail = self.guardrail.description.strip()
         elif isinstance(self.guardrail, Callable):
             self.guardrail = getsource(self.guardrail).strip()

src/crewai/utilities/events/utils/console_formatter.py

@@ -4,6 +4,7 @@ from rich.console import Console
 from rich.panel import Panel
 from rich.text import Text
 from rich.tree import Tree
+from rich.live import Live
 
 
 class ConsoleFormatter:
@@ -20,6 +21,12 @@ class ConsoleFormatter:
     def __init__(self, verbose: bool = False):
         self.console = Console(width=None)
         self.verbose = verbose
+        # Live instance to dynamically update a Tree renderable (e.g. the Crew tree)
+        # When multiple Tree objects are printed sequentially we reuse this Live
+        # instance so the previous render is replaced instead of writing a new one.
+        # Once any non-Tree renderable is printed we stop the Live session so the
+        # final Tree persists on the terminal.
+        self._live: Optional[Live] = None
 
     def create_panel(self, content: Text, title: str, style: str = "blue") -> Panel:
         """Create a standardized panel with consistent styling."""
@@ -60,7 +67,7 @@ class ConsoleFormatter:
         label.append(f"{prefix} ", style=f"{style} bold")
         label.append(name, style=style)
         if status:
-            label.append("\n    Status: ", style="white")
+            label.append("\nStatus: ", style="white")
             label.append(status, style=f"{style} bold")
         tree.label = label
 
@@ -69,7 +76,46 @@ class ConsoleFormatter:
         return parent.add(Text(text, style=style))
 
     def print(self, *args, **kwargs) -> None:
-        """Print to console with consistent formatting if verbose is enabled."""
+        """Custom print that replaces consecutive Tree renders.
+
+        * If the argument is a single ``Tree`` instance, we either start a
+          ``Live`` session (first tree) or update the existing one (subsequent
+          trees). This results in the tree being rendered in-place instead of
+          being appended repeatedly to the log.
+
+        * A blank call (no positional arguments) is ignored while a Live
+          session is active so it does not prematurely terminate the tree
+          rendering.
+
+        * Any other renderable will terminate the Live session (if one is
+          active) so the last tree stays on screen and the new content is
+          printed normally.
+        """
+
+        # Case 1: updating / starting live Tree rendering
+        if len(args) == 1 and isinstance(args[0], Tree):
+            tree = args[0]
+
+            if not self._live:
+                # Start a new Live session for the first tree
+                self._live = Live(tree, console=self.console, refresh_per_second=4)
+                self._live.start()
+            else:
+                # Update existing Live session
+                self._live.update(tree, refresh=True)
+            return  # Nothing else to do
+
+        # Case 2: blank line while a live session is running – ignore so we
+        # don't break the in-place rendering behaviour
+        if len(args) == 0 and self._live:
+            return
+
+        # Case 3: printing something other than a Tree → terminate live session
+        if self._live:
+            self._live.stop()
+            self._live = None
+
+        # Finally, pass through to the regular Console.print implementation
         self.console.print(*args, **kwargs)
 
     def print_panel(
@@ -157,7 +203,7 @@ class ConsoleFormatter:
 
         task_content = Text()
         task_content.append(f"📋 Task: {task_id}", style="yellow bold")
-        task_content.append("\n   Status: ", style="white")
+        task_content.append("\nStatus: ", style="white")
         task_content.append("Executing Task...", style="yellow dim")
 
         task_branch = None
@@ -197,11 +243,17 @@ class ConsoleFormatter:
         # Update tree label
         for branch in crew_tree.children:
             if str(task_id) in str(branch.label):
+                # Build label without introducing stray blank lines
                 task_content = Text()
+                # First line: Task ID
                 task_content.append(f"📋 Task: {task_id}", style=f"{style} bold")
-                task_content.append("\n   Assigned to: ", style="white")
+
+                # Second line: Assigned to
+                task_content.append("\nAssigned to: ", style="white")
                 task_content.append(agent_role, style=style)
-                task_content.append("\n   Status: ", style="white")
+
+                # Third line: Status
+                task_content.append("Status: ", style="white")
                 task_content.append(status_text, style=f"{style} bold")
                 branch.label = task_content
                 self.print(crew_tree)
@@ -220,18 +272,16 @@ class ConsoleFormatter:
         if not self.verbose or not task_branch or not crew_tree:
             return None
 
-        agent_branch = task_branch.add("")
-        self.update_tree_label(
-            agent_branch, "🤖 Agent:", agent_role, "green", "In Progress"
-        )
+        # Instead of creating a separate Agent node, we treat the task branch
+        # itself as the logical agent branch so that Reasoning/Tool nodes are
+        # nested under the task without an extra visual level.
 
-        self.print(crew_tree)
-        self.print()
+        # Store the task branch as the current_agent_branch for future nesting.
+        self.current_agent_branch = task_branch
 
-        # Set the current_agent_branch attribute directly
-        self.current_agent_branch = agent_branch
-
-        return agent_branch
+        # No additional tree modification needed; return the task branch so
+        # caller logic remains unchanged.
+        return task_branch
 
     def update_agent_status(
         self,
@@ -241,19 +291,10 @@ class ConsoleFormatter:
         status: str = "completed",
     ) -> None:
         """Update agent status in the tree."""
-        if not self.verbose or agent_branch is None or crew_tree is None:
-            return
-
-        self.update_tree_label(
-            agent_branch,
-            "🤖 Agent:",
-            agent_role,
-            "green",
-            "✅ Completed" if status == "completed" else "❌ Failed",
-        )
-
-        self.print(crew_tree)
-        self.print()
+        # We no longer render a separate agent branch, so this method simply
+        # updates the stored branch reference (already the task branch) without
+        # altering the tree. Keeping it a no-op avoids duplicate status lines.
+        return
 
     def create_flow_tree(self, flow_name: str, flow_id: str) -> Optional[Tree]:
         """Create and initialize a flow tree."""
@@ -266,7 +307,7 @@ class ConsoleFormatter:
         flow_label = Text()
         flow_label.append("🌊 Flow: ", style="blue bold")
         flow_label.append(flow_name, style="blue")
-        flow_label.append("\n    ID: ", style="white")
+        flow_label.append("\nID: ", style="white")
         flow_label.append(flow_id, style="blue")
 
         flow_tree = Tree(flow_label)
@@ -281,7 +322,7 @@ class ConsoleFormatter:
         flow_label = Text()
         flow_label.append("🌊 Flow: ", style="blue bold")
         flow_label.append(flow_name, style="blue")
-        flow_label.append("\n    ID: ", style="white")
+        flow_label.append("\nID: ", style="white")
         flow_label.append(flow_id, style="blue")
         flow_tree.label = flow_label
 
@@ -395,9 +436,15 @@ class ConsoleFormatter:
         if not self.verbose:
             return None
 
-        # Use LiteAgent branch if available, otherwise use regular agent branch
-        branch_to_use = self.current_lite_agent_branch or agent_branch
-        tree_to_use = branch_to_use or crew_tree
+        # Parent for tool usage: LiteAgent > Agent > Task
+        branch_to_use = (
+            self.current_lite_agent_branch
+            or agent_branch
+            or self.current_task_branch
+        )
+
+        # Render full crew tree when available for consistent live updates
+        tree_to_use = self.current_crew_tree or crew_tree or branch_to_use
 
         if branch_to_use is None or tree_to_use is None:
             # If we don't have a valid branch, default to crew_tree if provided
@@ -423,10 +470,9 @@ class ConsoleFormatter:
             "yellow",
         )
 
-        # Only print if this is a new tool usage
-        if tool_branch not in branch_to_use.children:
-            self.print(tree_to_use)
-            self.print()
+        # Print updated tree immediately
+        self.print(tree_to_use)
+        self.print()
 
         return tool_branch
 
@@ -440,8 +486,8 @@ class ConsoleFormatter:
         if not self.verbose or tool_branch is None:
             return
 
-        # Use LiteAgent branch if available, otherwise use crew tree
-        tree_to_use = self.current_lite_agent_branch or crew_tree
+        # Decide which tree to render: prefer full crew tree, else parent branch
+        tree_to_use = self.current_crew_tree or crew_tree or self.current_task_branch
         if tree_to_use is None:
             return
 
@@ -472,8 +518,8 @@ class ConsoleFormatter:
         if not self.verbose:
             return
 
-        # Use LiteAgent branch if available, otherwise use crew tree
-        tree_to_use = self.current_lite_agent_branch or crew_tree
+        # Decide which tree to render: prefer full crew tree, else parent branch
+        tree_to_use = self.current_crew_tree or crew_tree or self.current_task_branch
 
         if tool_branch:
             self.update_tree_label(
@@ -501,9 +547,15 @@ class ConsoleFormatter:
         if not self.verbose:
             return None
 
-        # Use LiteAgent branch if available, otherwise use regular agent branch
-        branch_to_use = self.current_lite_agent_branch or agent_branch
-        tree_to_use = branch_to_use or crew_tree
+        # Parent for tool usage: LiteAgent > Agent > Task
+        branch_to_use = (
+            self.current_lite_agent_branch
+            or agent_branch
+            or self.current_task_branch
+        )
+
+        # Render full crew tree when available for consistent live updates
+        tree_to_use = self.current_crew_tree or crew_tree or branch_to_use
 
         if branch_to_use is None or tree_to_use is None:
             # If we don't have a valid branch, default to crew_tree if provided
@@ -532,24 +584,33 @@ class ConsoleFormatter:
         if not self.verbose or tool_branch is None:
             return
 
-        # Use LiteAgent branch if available, otherwise use regular agent branch
-        branch_to_use = self.current_lite_agent_branch or agent_branch
-        tree_to_use = branch_to_use or crew_tree
-
-        if branch_to_use is None or tree_to_use is None:
+        # Decide which tree to render: prefer full crew tree, else parent branch
+        tree_to_use = self.current_crew_tree or crew_tree or self.current_task_branch
+        if tree_to_use is None:
             return
 
-        # Remove the thinking status node when complete, but only if it exists
+        # Remove the thinking status node when complete
         if "Thinking" in str(tool_branch.label):
-            try:
-                # Check if the node is actually in the children list
-                if tool_branch in branch_to_use.children:
-                    branch_to_use.children.remove(tool_branch)
-                    self.print(tree_to_use)
-                    self.print()
-            except Exception:
-                # If any error occurs during removal, just continue without removing
-                pass
+            parents = [
+                self.current_lite_agent_branch,
+                self.current_agent_branch,
+                self.current_task_branch,
+                tree_to_use,
+            ]
+            removed = False
+            for parent in parents:
+                if isinstance(parent, Tree) and tool_branch in parent.children:
+                    parent.children.remove(tool_branch)
+                    removed = True
+                    break
+
+            # Clear pointer if we just removed the current_tool_branch
+            if self.current_tool_branch is tool_branch:
+                self.current_tool_branch = None
+
+            if removed:
+                self.print(tree_to_use)
+                self.print()
 
     def handle_llm_call_failed(
         self, tool_branch: Optional[Tree], error: str, crew_tree: Optional[Tree]
@@ -558,8 +619,8 @@ class ConsoleFormatter:
         if not self.verbose:
             return
 
-        # Use LiteAgent branch if available, otherwise use crew tree
-        tree_to_use = self.current_lite_agent_branch or crew_tree
+        # Decide which tree to render: prefer full crew tree, else parent branch
+        tree_to_use = self.current_crew_tree or crew_tree or self.current_task_branch
 
         # Update tool branch if it exists
         if tool_branch:
@@ -601,7 +662,7 @@ class ConsoleFormatter:
         test_label = Text()
         test_label.append("🧪 Test: ", style="blue bold")
         test_label.append(crew_name or "Crew", style="blue")
-        test_label.append("\n    Status: ", style="white")
+        test_label.append("\nStatus: ", style="white")
         test_label.append("In Progress", style="yellow")
 
         test_tree = Tree(test_label)
@@ -623,7 +684,7 @@ class ConsoleFormatter:
             test_label = Text()
             test_label.append("✅ Test: ", style="green bold")
             test_label.append(crew_name or "Crew", style="green")
-            test_label.append("\n    Status: ", style="white")
+            test_label.append("\nStatus: ", style="white")
             test_label.append("Completed", style="green bold")
             flow_tree.label = test_label
 
@@ -712,7 +773,7 @@ class ConsoleFormatter:
             lite_agent_label = Text()
             lite_agent_label.append("🤖 LiteAgent: ", style="cyan bold")
             lite_agent_label.append(lite_agent_role, style="cyan")
-            lite_agent_label.append("\n    Status: ", style="white")
+            lite_agent_label.append("\nStatus: ", style="white")
             lite_agent_label.append("In Progress", style="yellow")
 
             lite_agent_tree = Tree(lite_agent_label)
@@ -751,7 +812,7 @@ class ConsoleFormatter:
         lite_agent_label = Text()
         lite_agent_label.append(f"{prefix} ", style=f"{style} bold")
         lite_agent_label.append(lite_agent_role, style=style)
-        lite_agent_label.append("\n    Status: ", style="white")
+        lite_agent_label.append("Status: ", style="white")
         lite_agent_label.append(status_text, style=f"{style} bold")
         lite_agent_branch.label = lite_agent_label
 
@@ -1004,16 +1065,20 @@ class ConsoleFormatter:
         if not self.verbose:
             return None
 
-        # Prefer LiteAgent branch if we are within a LiteAgent context
-        branch_to_use = self.current_lite_agent_branch or agent_branch
-        tree_to_use = branch_to_use or crew_tree
+        # Prefer LiteAgent > Agent > Task branch as the parent for reasoning
+        branch_to_use = (
+            self.current_lite_agent_branch
+            or agent_branch
+            or self.current_task_branch
+        )
 
-        if branch_to_use is None or tree_to_use is None:
-            # If we don't have a valid branch, default to crew_tree if provided
-            if crew_tree is not None:
-                branch_to_use = tree_to_use = crew_tree
-            else:
-                return None
+        # We always want to render the full crew tree when possible so the
+        # Live view updates coherently. Fallbacks: crew tree → branch itself.
+        tree_to_use = self.current_crew_tree or crew_tree or branch_to_use
+
+        if branch_to_use is None:
+            # Nothing to attach to, abort
+            return None
 
         # Reuse existing reasoning branch if present
         reasoning_branch = self.current_reasoning_branch
@@ -1044,8 +1109,9 @@ class ConsoleFormatter:
 
         reasoning_branch = self.current_reasoning_branch
         tree_to_use = (
-            self.current_lite_agent_branch
-            or self.current_agent_branch
+            self.current_crew_tree
+            or self.current_lite_agent_branch
+            or self.current_task_branch
             or crew_tree
         )
 
@@ -1084,8 +1150,9 @@ class ConsoleFormatter:
 
         reasoning_branch = self.current_reasoning_branch
         tree_to_use = (
-            self.current_lite_agent_branch
-            or self.current_agent_branch
+            self.current_crew_tree
+            or self.current_lite_agent_branch
+            or self.current_task_branch
             or crew_tree
         )
 

tests/cassettes/test_increment_delegations_for_hierarchical_process.yaml

@@ -497,4 +497,114 @@ interactions:
       - req_b5328d8437f89c577e939689b2dddd69
     http_version: HTTP/1.1
     status_code: 200
+- request:
+    body: null
+    headers: {}
+    method: GET
+    uri: https://pypi.org/pypi/agentops/json
+  response:
+    body:
+      string: '{"info":{"author":null,"author_email":"Alex Reibman <areibman@gmail.com>,
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+      Description: My tool\n\nIMPORTANT: Use the following format in your response:\n\n```\nThought:
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@@ -46,4 +46,6 @@ def setup_test_environment():
 def vcr_config(request) -> dict:
     return {
         "cassette_library_dir": "tests/cassettes",
+        "record_mode": "new_episodes",
+        "filter_headers": [("authorization", "AUTHORIZATION-XXX")],
     }

tests/knowledge/pgvector_knowledge_storage_test.py

@@ -0,0 +1,88 @@
+import pytest
+from unittest.mock import patch, MagicMock
+
+from crewai.knowledge.storage.pgvector_knowledge_storage import PGVectorKnowledgeStorage
+
+class MockSession:
+    def __init__(self):
+        self.queries = []
+        self.commits = 0
+        self.rollbacks = 0
+        self.closes = 0
+    
+    def query(self, *args, **kwargs):
+        return self
+    
+    def filter(self, *args, **kwargs):
+        return self
+    
+    def first(self):
+        return None
+    
+    def add(self, *args, **kwargs):
+        pass
+    
+    def commit(self):
+        self.commits += 1
+    
+    def rollback(self):
+        self.rollbacks += 1
+    
+    def close(self):
+        self.closes += 1
+    
+    def execute(self, *args, **kwargs):
+        return self
+    
+    def fetchall(self):
+        return [
+            ("doc1", "This is a test document", '{"source": "test"}', 0.9),
+            ("doc2", "Another test document", '{"source": "test"}', 0.8),
+            ("doc3", "A third test document", '{"source": "test"}', 0.7),
+        ]
+
+@pytest.fixture
+def mock_embedder():
+    return lambda x: [[0.1] * 1536 for _ in range(len(x))]
+
+@pytest.fixture
+def mock_session():
+    return MockSession()
+
+@pytest.fixture
+def mock_session_maker(mock_session):
+    def session_maker():
+        return mock_session
+    return session_maker
+
+@pytest.fixture
+def mock_engine():
+    return MagicMock()
+
+@pytest.fixture
+def pgvector_storage(mock_embedder, mock_session_maker, mock_engine):
+    with patch("crewai.knowledge.storage.pgvector_knowledge_storage.create_engine", return_value=mock_engine), \
+         patch("crewai.knowledge.storage.pgvector_knowledge_storage.sessionmaker", return_value=mock_session_maker), \
+         patch("crewai.knowledge.storage.pgvector_knowledge_storage.Base.metadata.create_all"):
+        storage = PGVectorKnowledgeStorage(connection_string="postgresql://test:test@localhost:5432/test")
+        storage.embedder = mock_embedder
+        return storage
+
+def test_search(pgvector_storage, mock_session):
+    results = pgvector_storage.search(["test query"], limit=3, score_threshold=0.5)
+    
+    assert len(results) == 3
+    assert results[0]["id"] == "doc1"
+    assert results[0]["context"] == "This is a test document"
+    assert results[0]["score"] == 0.9
+    
+    assert mock_session.closes == 1
+
+def test_save(pgvector_storage, mock_session):
+    documents = ["Document 1", "Document 2"]
+    metadata = [{"source": "test1"}, {"source": "test2"}]
+    
+    pgvector_storage.save(documents, metadata)
+    
+    assert mock_session.commits == 1
+    assert mock_session.closes == 1

tests/task_test.py

@@ -398,6 +398,79 @@ def test_output_json_hierarchical():
     assert result.json == '{"score": 4}'
     assert result.to_dict() == {"score": 4}
 
+@pytest.mark.vcr(filter_headers=["authorization"])
+def test_inject_date():
+    reporter = Agent(
+        role="Reporter",
+        goal="Report the date",
+        backstory="You're an expert reporter, specialized in reporting the date.",
+        allow_delegation=False,
+        inject_date=True,
+    )
+
+    task = Task(
+        description="What is the date today?",
+        expected_output="The date today as you were told, same format as the date you were told.",
+        agent=reporter,
+    )
+
+    crew = Crew(
+        agents=[reporter],
+        tasks=[task],
+        process=Process.sequential,
+    )
+    result = crew.kickoff()
+    assert "2025-05-21" in result.raw
+
+@pytest.mark.vcr(filter_headers=["authorization"])
+def test_inject_date_custom_format():
+    reporter = Agent(
+        role="Reporter",
+        goal="Report the date",
+        backstory="You're an expert reporter, specialized in reporting the date.",
+        allow_delegation=False,
+        inject_date=True,
+        date_format="%B %d, %Y",
+    )
+
+    task = Task(
+        description="What is the date today?",
+        expected_output="The date today.",
+        agent=reporter,
+    )
+
+    crew = Crew(
+        agents=[reporter],
+        tasks=[task],
+        process=Process.sequential,
+    )
+    result = crew.kickoff()
+    assert "May 21, 2025" in result.raw
+
+@pytest.mark.vcr(filter_headers=["authorization"])
+def test_no_inject_date():
+    reporter = Agent(
+        role="Reporter",
+        goal="Report the date",
+        backstory="You're an expert reporter, specialized in reporting the date.",
+        allow_delegation=False,
+        inject_date=False,
+    )
+
+    task = Task(
+        description="What is the date today?",
+        expected_output="The date today.",
+        agent=reporter,
+    )
+
+    crew = Crew(
+        agents=[reporter],
+        tasks=[task],
+        process=Process.sequential,
+    )
+    result = crew.kickoff()
+    assert "2025-05-21" not in result.raw
+
 
 @pytest.mark.vcr(filter_headers=["authorization"])
 def test_json_property_without_output_json():

tests/telemetry/test_telemetry.py

@@ -6,6 +6,8 @@ import pytest
 from crewai import Agent, Crew, Task
 from crewai.telemetry import Telemetry
 
+from opentelemetry import trace
+
 
 @pytest.mark.parametrize(
     "env_var,value,expected_ready",
@@ -34,9 +36,6 @@ def test_telemetry_enabled_by_default():
             assert telemetry.ready is True
 
 
-from opentelemetry import trace
-
-
 @patch("crewai.telemetry.telemetry.logger.error")
 @patch(
     "opentelemetry.exporter.otlp.proto.http.trace_exporter.OTLPSpanExporter.export",
@@ -67,3 +66,32 @@ def test_telemetry_fails_due_connect_timeout(export_mock, logger_mock):
 
     export_mock.assert_called_once()
     logger_mock.assert_called_once_with(error)
+
+
+def test_telemetry_singleton_pattern():
+    """Test that Telemetry uses the singleton pattern correctly."""
+    Telemetry._instance = None
+
+    telemetry1 = Telemetry()
+    telemetry2 = Telemetry()
+
+    assert telemetry1 is telemetry2
+
+    setattr(telemetry1, "test_attribute", "test_value")
+    assert hasattr(telemetry2, "test_attribute")
+    assert getattr(telemetry2, "test_attribute") == "test_value"
+
+    import threading
+
+    instances = []
+
+    def create_instance():
+        instances.append(Telemetry())
+
+    threads = [threading.Thread(target=create_instance) for _ in range(5)]
+    for thread in threads:
+        thread.start()
+    for thread in threads:
+        thread.join()
+
+    assert all(instance is telemetry1 for instance in instances)

tests/test_agent_inject_date.py

@@ -0,0 +1,117 @@
+from datetime import datetime
+from unittest.mock import patch
+
+from crewai.agent import Agent
+from crewai.task import Task
+
+
+def test_agent_inject_date():
+    """Test that the inject_date flag injects the current date into the task.
+    
+    Tests that when inject_date=True, the current date is added to the task description.
+    """
+    with patch('datetime.datetime') as mock_datetime:
+        mock_datetime.now.return_value = datetime(2025, 1, 1)
+        
+        agent = Agent(
+            role="test_agent",
+            goal="test_goal",
+            backstory="test_backstory",
+            inject_date=True,
+        )
+        
+        task = Task(
+            description="Test task",
+            expected_output="Test output",
+            agent=agent,
+        )
+        
+        # Store original description
+        original_description = task.description
+        
+        agent._inject_date_to_task(task)
+        
+        assert "Current Date: 2025-01-01" in task.description
+        assert task.description != original_description
+
+
+def test_agent_without_inject_date():
+    """Test that without inject_date flag, no date is injected.
+    
+    Tests that when inject_date=False (default), no date is added to the task description.
+    """
+    agent = Agent(
+        role="test_agent",
+        goal="test_goal",
+        backstory="test_backstory",
+        # inject_date is False by default
+    )
+    
+    task = Task(
+        description="Test task",
+        expected_output="Test output",
+        agent=agent,
+    )
+    
+    original_description = task.description
+    
+    agent._inject_date_to_task(task)
+    
+    assert task.description == original_description
+
+
+def test_agent_inject_date_custom_format():
+    """Test that the inject_date flag with custom date_format works correctly.
+    
+    Tests that when inject_date=True with a custom date_format, the date is formatted correctly.
+    """
+    with patch('datetime.datetime') as mock_datetime:
+        mock_datetime.now.return_value = datetime(2025, 1, 1)
+        
+        agent = Agent(
+            role="test_agent",
+            goal="test_goal",
+            backstory="test_backstory",
+            inject_date=True,
+            date_format="%d/%m/%Y",
+        )
+        
+        task = Task(
+            description="Test task",
+            expected_output="Test output",
+            agent=agent,
+        )
+        
+        # Store original description
+        original_description = task.description
+        
+        agent._inject_date_to_task(task)
+        
+        assert "Current Date: 01/01/2025" in task.description
+        assert task.description != original_description
+
+
+def test_agent_inject_date_invalid_format():
+    """Test error handling with invalid date format.
+    
+    Tests that when an invalid date_format is provided, the task description remains unchanged.
+    """
+    agent = Agent(
+        role="test_agent",
+        goal="test_goal",
+        backstory="test_backstory",
+        inject_date=True,
+        date_format="invalid",
+    )
+    
+    task = Task(
+        description="Test task",
+        expected_output="Test output",
+        agent=agent,
+    )
+    
+    original_description = task.description
+    
+    agent._inject_date_to_task(task)
+    
+    assert task.description == original_description

tests/test_hallucination_guardrail.py

@@ -0,0 +1,108 @@
+from unittest.mock import Mock
+
+import pytest
+
+from crewai.llm import LLM
+from crewai.tasks.hallucination_guardrail import HallucinationGuardrail
+from crewai.tasks.task_output import TaskOutput
+
+
+def test_hallucination_guardrail_initialization():
+    """Test that the hallucination guardrail initializes correctly with all parameters."""
+    mock_llm = Mock(spec=LLM)
+
+    guardrail = HallucinationGuardrail(context="Test reference context", llm=mock_llm)
+
+    assert guardrail.context == "Test reference context"
+    assert guardrail.llm == mock_llm
+    assert guardrail.threshold is None
+    assert guardrail.tool_response == ""
+
+    guardrail = HallucinationGuardrail(
+        context="Test reference context",
+        llm=mock_llm,
+        threshold=8.5,
+        tool_response="Sample tool response",
+    )
+
+    assert guardrail.context == "Test reference context"
+    assert guardrail.llm == mock_llm
+    assert guardrail.threshold == 8.5
+    assert guardrail.tool_response == "Sample tool response"
+
+
+def test_hallucination_guardrail_no_op_behavior():
+    """Test that the guardrail always returns True in the open-source version."""
+    mock_llm = Mock(spec=LLM)
+    guardrail = HallucinationGuardrail(
+        context="Test reference context",
+        llm=mock_llm,
+        threshold=9.0,
+    )
+
+    task_output = TaskOutput(
+        raw="Sample task output",
+        description="Test task",
+        expected_output="Expected output",
+        agent="Test Agent",
+    )
+
+    result, output = guardrail(task_output)
+
+    assert result is True
+    assert output == "Sample task output"
+
+
+def test_hallucination_guardrail_description():
+    """Test that the guardrail provides the correct description for event logging."""
+    guardrail = HallucinationGuardrail(
+        context="Test reference context", llm=Mock(spec=LLM)
+    )
+
+    assert guardrail.description == "HallucinationGuardrail (no-op)"
+
+
+@pytest.mark.parametrize(
+    "context,task_output_text,threshold,tool_response",
+    [
+        (
+            "Earth orbits the Sun once every 365.25 days.",
+            "It takes Earth approximately one year to go around the Sun.",
+            None,
+            "",
+        ),
+        (
+            "Python was created by Guido van Rossum in 1991.",
+            "Python is a programming language developed by Guido van Rossum.",
+            7.5,
+            "",
+        ),
+        (
+            "The capital of France is Paris.",
+            "Paris is the largest city and capital of France.",
+            9.0,
+            "Geographic API returned: France capital is Paris",
+        ),
+    ],
+)
+def test_hallucination_guardrail_always_passes(
+    context, task_output_text, threshold, tool_response
+):
+    """Test that the guardrail always passes regardless of configuration in open-source version."""
+    mock_llm = Mock(spec=LLM)
+
+    guardrail = HallucinationGuardrail(
+        context=context, llm=mock_llm, threshold=threshold, tool_response=tool_response
+    )
+
+    task_output = TaskOutput(
+        raw=task_output_text,
+        description="Test task",
+        expected_output="Expected output",
+        agent="Test Agent",
+    )
+
+    result, output = guardrail(task_output)
+
+    assert result is True
+    assert output == task_output_text

tests/test_task_guardrails.py

@@ -1,9 +1,10 @@
-from unittest.mock import ANY, Mock, patch
+from unittest.mock import Mock, patch
 
 import pytest
 
 from crewai import Agent, Task
 from crewai.llm import LLM
+from crewai.tasks.hallucination_guardrail import HallucinationGuardrail
 from crewai.tasks.llm_guardrail import LLMGuardrail
 from crewai.tasks.task_output import TaskOutput
 from crewai.utilities.events import (
@@ -267,3 +268,37 @@ def test_guardrail_when_an_error_occurs(sample_agent, task_output):
             max_retries=0,
         )
         task.execute_sync(agent=sample_agent)
+
+
+def test_hallucination_guardrail_integration():
+    """Test that HallucinationGuardrail integrates properly with the task system."""
+    agent = Mock()
+    agent.role = "test_agent"
+    agent.execute_task.return_value = "test result"
+    agent.crew = None
+
+    mock_llm = Mock(spec=LLM)
+    guardrail = HallucinationGuardrail(
+        context="Test reference context for validation", llm=mock_llm, threshold=8.0
+    )
+
+    task = Task(
+        description="Test task with hallucination guardrail",
+        expected_output="Valid output",
+        guardrail=guardrail,
+    )
+
+    result = task.execute_sync(agent=agent)
+    assert isinstance(result, TaskOutput)
+    assert result.raw == "test result"
+
+
+def test_hallucination_guardrail_description_in_events():
+    """Test that HallucinationGuardrail description appears correctly in events."""
+    mock_llm = Mock(spec=LLM)
+    guardrail = HallucinationGuardrail(context="Test context", llm=mock_llm)
+
+    assert guardrail.description == "HallucinationGuardrail (no-op)"
+
+    event = LLMGuardrailStartedEvent(guardrail=guardrail, retry_count=0)
+    assert event.guardrail == "HallucinationGuardrail (no-op)"

tests/utilities/cassettes/test_crew_emits_test_kickoff_type_event.yaml

@@ -1,4 +1,114 @@
 interactions:
+- request:
+    body: null
+    headers: {}
+    method: GET
+    uri: https://pypi.org/pypi/agentops/json
+  response:
+    body:
+      string: '{"info":{"author":null,"author_email":"Alex Reibman <areibman@gmail.com>,
+        Shawn Qiu <siyangqiu@gmail.com>, Braelyn Boynton <bboynton97@gmail.com>, Howard
+        Gil <howardbgil@gmail.com>, Constantin Teodorescu <teocns@gmail.com>, Pratyush
+        Shukla <ps4534@nyu.edu>, Travis Dent <tcdent@gmail.com>, Dwij Patel <dwijpatel1704@gmail.com>","bugtrack_url":null,"classifiers":["License
+        :: OSI Approved :: MIT License","Operating System :: OS Independent","Programming
+        Language :: Python :: 3","Programming Language :: Python :: 3.10","Programming
+        Language :: Python :: 3.11","Programming Language :: Python :: 3.12","Programming
+        Language :: Python :: 3.13","Programming Language :: Python :: 3.9"],"description":"","description_content_type":null,"docs_url":null,"download_url":null,"downloads":{"last_day":-1,"last_month":-1,"last_week":-1},"dynamic":null,"home_page":null,"keywords":null,"license":null,"license_expression":null,"license_files":["LICENSE"],"maintainer":null,"maintainer_email":null,"name":"agentops","package_url":"https://pypi.org/project/agentops/","platform":null,"project_url":"https://pypi.org/project/agentops/","project_urls":{"Homepage":"https://github.com/AgentOps-AI/agentops","Issues":"https://github.com/AgentOps-AI/agentops/issues"},"provides_extra":null,"release_url":"https://pypi.org/project/agentops/0.4.12/","requires_dist":["httpx<0.29.0,>=0.24.0","opentelemetry-api==1.29.0;
+        python_version < \"3.10\"","opentelemetry-api>1.29.0; python_version >= \"3.10\"","opentelemetry-exporter-otlp-proto-http==1.29.0;
+        python_version < \"3.10\"","opentelemetry-exporter-otlp-proto-http>1.29.0;
+        python_version >= \"3.10\"","opentelemetry-instrumentation==0.50b0; python_version
+        < \"3.10\"","opentelemetry-instrumentation>=0.50b0; python_version >= \"3.10\"","opentelemetry-sdk==1.29.0;
+        python_version < \"3.10\"","opentelemetry-sdk>1.29.0; python_version >= \"3.10\"","opentelemetry-semantic-conventions==0.50b0;
+        python_version < \"3.10\"","opentelemetry-semantic-conventions>=0.50b0; python_version
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+        and DevTool Platform for AI 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tests/utilities/test_events.py

@@ -1,4 +1,3 @@
-import os
 from datetime import datetime
 from unittest.mock import Mock, patch
 
@@ -22,6 +21,7 @@ from crewai.utilities.events.crew_events import (
     CrewKickoffFailedEvent,
     CrewKickoffStartedEvent,
     CrewTestCompletedEvent,
+    CrewTestResultEvent,
     CrewTestStartedEvent,
 )
 from crewai.utilities.events.crewai_event_bus import crewai_event_bus
@@ -38,7 +38,6 @@ from crewai.utilities.events.llm_events import (
     LLMCallCompletedEvent,
     LLMCallFailedEvent,
     LLMCallStartedEvent,
-    LLMCallType,
     LLMStreamChunkEvent,
 )
 from crewai.utilities.events.task_events import (
@@ -132,6 +131,10 @@ def test_crew_emits_test_kickoff_type_event():
     def handle_crew_test_end(source, event):
         received_events.append(event)
 
+    @crewai_event_bus.on(CrewTestResultEvent)
+    def handle_crew_test_result(source, event):
+        received_events.append(event)
+
     eval_llm = LLM(model="gpt-4o-mini")
     with (
         patch.object(
@@ -149,13 +152,16 @@ def test_crew_emits_test_kickoff_type_event():
         assert args[2] is None
         assert args[3] == eval_llm
 
-    assert len(received_events) == 2
+    assert len(received_events) == 3
     assert received_events[0].crew_name == "TestCrew"
     assert isinstance(received_events[0].timestamp, datetime)
     assert received_events[0].type == "crew_test_started"
     assert received_events[1].crew_name == "TestCrew"
     assert isinstance(received_events[1].timestamp, datetime)
-    assert received_events[1].type == "crew_test_completed"
+    assert received_events[1].type == "crew_test_result"
+    assert received_events[2].crew_name == "TestCrew"
+    assert isinstance(received_events[2].timestamp, datetime)
+    assert received_events[2].type == "crew_test_completed"
 
 
 @pytest.mark.vcr(filter_headers=["authorization"])
@@ -309,7 +315,7 @@ def test_agent_emits_execution_error_event():
     ) as invoke_mock:
         invoke_mock.side_effect = Exception(error_message)
 
-        with pytest.raises(Exception) as e:
+        with pytest.raises(Exception):
             base_agent.execute_task(
                 task=base_task,
             )

uv.lock

@@ -738,7 +738,7 @@ wheels = [
 
 [[package]]
 name = "crewai"
-version = "0.120.1"
+version = "0.121.0"
 source = { editable = "." }
 dependencies = [
     { name = "appdirs" },