feat: centralize default memory path logic & add path validation tests

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

src/crewai/agent.py

@@ -294,14 +294,7 @@ class Agent(BaseAgent):
             )
 
         if self.crew and self.crew.memory:
-            contextual_memory = ContextualMemory(
-                self.crew.memory_config,
-                self.crew._short_term_memory,
-                self.crew._long_term_memory,
-                self.crew._entity_memory,
-                self.crew._user_memory,
-            )
-            memory = contextual_memory.build_context_for_task(task, context)
+            memory = self.crew.contextual_memory.build_context_for_task(task, context)
             if memory.strip() != "":
                 task_prompt += self.i18n.slice("memory").format(memory=memory)
 

src/crewai/agents/crew_agent_executor.py

@@ -358,9 +358,9 @@ class CrewAgentExecutor(CrewAgentExecutorMixin):
             if self.crew is not None and hasattr(self.crew, "_train_iteration"):
                 train_iteration = self.crew._train_iteration
                 if agent_id in training_data and isinstance(train_iteration, int):
-                    training_data[agent_id][train_iteration][
-                        "improved_output"
-                    ] = result.output
+                    training_data[agent_id][train_iteration]["improved_output"] = (
+                        result.output
+                    )
                     training_handler.save(training_data)
                 else:
                     self._printer.print(

src/crewai/cli/tools/main.py

@@ -153,8 +153,12 @@ class ToolCommand(BaseCommand, PlusAPIMixin):
         login_response_json = login_response.json()
 
         settings = Settings()
-        settings.tool_repository_username = login_response_json["credential"]["username"]
-        settings.tool_repository_password = login_response_json["credential"]["password"]
+        settings.tool_repository_username = login_response_json["credential"][
+            "username"
+        ]
+        settings.tool_repository_password = login_response_json["credential"][
+            "password"
+        ]
         settings.dump()
 
         console.print(
@@ -179,7 +183,7 @@ class ToolCommand(BaseCommand, PlusAPIMixin):
             capture_output=False,
             env=self._build_env_with_credentials(repository_handle),
             text=True,
-            check=True
+            check=True,
         )
 
         if add_package_result.stderr:
@@ -204,7 +208,11 @@ class ToolCommand(BaseCommand, PlusAPIMixin):
         settings = Settings()
 
         env = os.environ.copy()
-        env[f"UV_INDEX_{repository_handle}_USERNAME"] = str(settings.tool_repository_username or "")
-        env[f"UV_INDEX_{repository_handle}_PASSWORD"] = str(settings.tool_repository_password or "")
+        env[f"UV_INDEX_{repository_handle}_USERNAME"] = str(
+            settings.tool_repository_username or ""
+        )
+        env[f"UV_INDEX_{repository_handle}_PASSWORD"] = str(
+            settings.tool_repository_password or ""
+        )
 
         return env

src/crewai/crew.py

@@ -6,7 +6,6 @@ from concurrent.futures import Future
 from hashlib import md5
 from typing import Any, Callable, Dict, List, Optional, Tuple, Union
 
-from langchain_core.language_models.base import BaseLanguageModel
 from pydantic import (
     UUID4,
     BaseModel,
@@ -26,6 +25,7 @@ from crewai.crews.crew_output import CrewOutput
 from crewai.knowledge.knowledge import Knowledge
 from crewai.knowledge.source.base_knowledge_source import BaseKnowledgeSource
 from crewai.llm import LLM
+from crewai.memory.contextual.contextual_memory import ContextualMemory
 from crewai.memory.entity.entity_memory import EntityMemory
 from crewai.memory.long_term.long_term_memory import LongTermMemory
 from crewai.memory.short_term.short_term_memory import ShortTermMemory
@@ -279,6 +279,13 @@ class Crew(BaseModel):
                 )
             else:
                 self._user_memory = None
+            self.contextual_memory = ContextualMemory(
+                memory_config=self.memory_config,
+                stm=self._short_term_memory,
+                ltm=self._long_term_memory,
+                em=self._entity_memory,
+                um=self._user_memory,
+            )
         return self
 
     @model_validator(mode="after")
@@ -1076,39 +1083,19 @@ class Crew(BaseModel):
     def test(
         self,
         n_iterations: int,
-        llm: Optional[Union[str, InstanceOf[LLM], Any]] = None,
-        openai_model_name: Optional[str] = None,  # Kept for backward compatibility
+        openai_model_name: Optional[str] = None,
         inputs: Optional[Dict[str, Any]] = None,
     ) -> None:
-        """Test and evaluate the Crew with the given inputs for n iterations.
-
-        Args:
-            n_iterations (int): Number of test iterations to run
-            llm (Optional[Union[str, LLM, BaseLanguageModel]]): Language model to use for testing
-            openai_model_name (Optional[str]): Legacy parameter for OpenAI models (deprecated)
-            inputs (Optional[Dict[str, Any]]): Test inputs for the crew
-
-        Raises:
-            ValueError: If n_iterations is less than 1 or if llm type is unsupported
-
-        Returns:
-            None
-        """
-        if n_iterations < 1:
-            raise ValueError("n_iterations must be greater than 0")
-        if llm is not None and not isinstance(llm, (str, LLM, BaseLanguageModel)):
-            raise ValueError(f"Unsupported LLM type: {type(llm)}")
-
+        """Test and evaluate the Crew with the given inputs for n iterations concurrently using concurrent.futures."""
         test_crew = self.copy()
-        test_llm = llm if llm is not None else openai_model_name
 
         self._test_execution_span = test_crew._telemetry.test_execution_span(
             test_crew,
             n_iterations,
             inputs,
-            test_llm,  # type: ignore[arg-type]
+            openai_model_name,  # type: ignore[arg-type]
         )  # type: ignore[arg-type]
-        evaluator = CrewEvaluator(test_crew, test_llm)  # type: ignore[arg-type]
+        evaluator = CrewEvaluator(test_crew, openai_model_name)  # type: ignore[arg-type]
 
         for i in range(1, n_iterations + 1):
             evaluator.set_iteration(i)

src/crewai/knowledge/knowledge.py

@@ -14,13 +14,13 @@ class Knowledge(BaseModel):
     Knowledge is a collection of sources and setup for the vector store to save and query relevant context.
     Args:
         sources: List[BaseKnowledgeSource] = Field(default_factory=list)
-        storage: KnowledgeStorage = Field(default_factory=KnowledgeStorage)
+        storage: Optional[KnowledgeStorage] = Field(default=None)
         embedder_config: Optional[Dict[str, Any]] = None
     """
 
     sources: List[BaseKnowledgeSource] = Field(default_factory=list)
     model_config = ConfigDict(arbitrary_types_allowed=True)
-    storage: KnowledgeStorage = Field(default_factory=KnowledgeStorage)
+    storage: Optional[KnowledgeStorage] = Field(default=None)
     embedder_config: Optional[Dict[str, Any]] = None
     collection_name: Optional[str] = None
 
@@ -49,8 +49,13 @@ class Knowledge(BaseModel):
         """
         Query across all knowledge sources to find the most relevant information.
         Returns the top_k most relevant chunks.
+        
+        Raises:
+            ValueError: If storage is not initialized.
         """
-
+        if self.storage is None:
+            raise ValueError("Storage is not initialized.")
+            
         results = self.storage.search(
             query,
             limit,

src/crewai/knowledge/source/base_file_knowledge_source.py

@@ -22,7 +22,7 @@ class BaseFileKnowledgeSource(BaseKnowledgeSource, ABC):
         default_factory=list, description="The path to the file"
     )
     content: Dict[Path, str] = Field(init=False, default_factory=dict)
-    storage: KnowledgeStorage = Field(default_factory=KnowledgeStorage)
+    storage: Optional[KnowledgeStorage] = Field(default=None)
     safe_file_paths: List[Path] = Field(default_factory=list)
 
     @field_validator("file_path", "file_paths", mode="before")
@@ -62,7 +62,10 @@ class BaseFileKnowledgeSource(BaseKnowledgeSource, ABC):
 
     def _save_documents(self):
         """Save the documents to the storage."""
-        self.storage.save(self.chunks)
+        if self.storage:
+            self.storage.save(self.chunks)
+        else:
+            raise ValueError("No storage found to save documents.")
 
     def convert_to_path(self, path: Union[Path, str]) -> Path:
         """Convert a path to a Path object."""

src/crewai/knowledge/source/base_knowledge_source.py

@@ -16,7 +16,7 @@ class BaseKnowledgeSource(BaseModel, ABC):
     chunk_embeddings: List[np.ndarray] = Field(default_factory=list)
 
     model_config = ConfigDict(arbitrary_types_allowed=True)
-    storage: KnowledgeStorage = Field(default_factory=KnowledgeStorage)
+    storage: Optional[KnowledgeStorage] = Field(default=None)
     metadata: Dict[str, Any] = Field(default_factory=dict)  # Currently unused
     collection_name: Optional[str] = Field(default=None)
 
@@ -46,4 +46,7 @@ class BaseKnowledgeSource(BaseModel, ABC):
         Save the documents to the storage.
         This method should be called after the chunks and embeddings are generated.
         """
-        self.storage.save(self.chunks)
+        if self.storage:
+            self.storage.save(self.chunks)
+        else:
+            raise ValueError("No storage found to save documents.")

src/crewai/memory/contextual/contextual_memory.py

@@ -1,4 +1,5 @@
 from typing import Any, Dict, Optional
+from crewai.task import Task
 
 from crewai.memory import EntityMemory, LongTermMemory, ShortTermMemory, UserMemory
 
@@ -10,7 +11,7 @@ class ContextualMemory:
         stm: ShortTermMemory,
         ltm: LongTermMemory,
         em: EntityMemory,
-        um: UserMemory,
+        um: Optional[UserMemory],
     ):
         if memory_config is not None:
             self.memory_provider = memory_config.get("provider")
@@ -21,7 +22,7 @@ class ContextualMemory:
         self.em = em
         self.um = um
 
-    def build_context_for_task(self, task, context) -> str:
+    def build_context_for_task(self, task: Task, context: str) -> str:
         """
         Automatically builds a minimal, highly relevant set of contextual information
         for a given task.
@@ -39,7 +40,7 @@ class ContextualMemory:
             context.append(self._fetch_user_context(query))
         return "\n".join(filter(None, context))
 
-    def _fetch_stm_context(self, query) -> str:
+    def _fetch_stm_context(self, query: str) -> str:
         """
         Fetches recent relevant insights from STM related to the task's description and expected_output,
         formatted as bullet points.
@@ -53,7 +54,7 @@ class ContextualMemory:
         )
         return f"Recent Insights:\n{formatted_results}" if stm_results else ""
 
-    def _fetch_ltm_context(self, task) -> Optional[str]:
+    def _fetch_ltm_context(self, task: str) -> Optional[str]:
         """
         Fetches historical data or insights from LTM that are relevant to the task's description and expected_output,
         formatted as bullet points.
@@ -72,7 +73,7 @@ class ContextualMemory:
 
         return f"Historical Data:\n{formatted_results}" if ltm_results else ""
 
-    def _fetch_entity_context(self, query) -> str:
+    def _fetch_entity_context(self, query: str) -> str:
         """
         Fetches relevant entity information from Entity Memory related to the task's description and expected_output,
         formatted as bullet points.
@@ -94,6 +95,8 @@ class ContextualMemory:
         Returns:
             str: Formatted user memories as bullet points, or an empty string if none found.
         """
+        if not self.um:
+            return ""
         user_memories = self.um.search(query)
         if not user_memories:
             return ""

src/crewai/memory/entity/entity_memory.py

@@ -11,7 +11,7 @@ class EntityMemory(Memory):
     """
 
     def __init__(self, crew=None, embedder_config=None, storage=None, path=None):
-        if hasattr(crew, "memory_config") and crew.memory_config is not None:
+        if crew and hasattr(crew, "memory_config") and crew.memory_config is not None:
             self.memory_provider = crew.memory_config.get("provider")
         else:
             self.memory_provider = None

src/crewai/memory/long_term/long_term_memory.py

@@ -15,8 +15,17 @@ class LongTermMemory(Memory):
     """
 
     def __init__(self, storage=None, path=None):
+        """Initialize long term memory.
+
+        Args:
+            storage: Optional custom storage instance
+            path: Optional custom path for storage location
+
+        Note:
+            If both storage and path are provided, storage takes precedence
+        """
         if not storage:
-            storage = LTMSQLiteStorage(db_path=path) if path else LTMSQLiteStorage()
+            storage = LTMSQLiteStorage(storage_path=path) if path else LTMSQLiteStorage()
         super().__init__(storage)
 
     def save(self, item: LongTermMemoryItem) -> None:  # type: ignore # BUG?: Signature of "save" incompatible with supertype "Memory"

src/crewai/memory/short_term/short_term_memory.py

@@ -15,7 +15,7 @@ class ShortTermMemory(Memory):
     """
 
     def __init__(self, crew=None, embedder_config=None, storage=None, path=None):
-        if hasattr(crew, "memory_config") and crew.memory_config is not None:
+        if crew and hasattr(crew, "memory_config") and crew.memory_config is not None:
             self.memory_provider = crew.memory_config.get("provider")
         else:
             self.memory_provider = None

src/crewai/memory/storage/base_rag_storage.py

@@ -1,5 +1,11 @@
 from abc import ABC, abstractmethod
-from typing import Any, Dict, List, Optional
+from pathlib import Path
+import os
+from typing import Any, Dict, List, Optional, TypeVar
+from abc import ABC, abstractmethod
+from pathlib import Path
+
+from crewai.utilities.paths import get_default_storage_path
 
 
 class BaseRAGStorage(ABC):
@@ -12,17 +18,46 @@ class BaseRAGStorage(ABC):
     def __init__(
         self,
         type: str,
+        storage_path: Optional[Path] = None,
         allow_reset: bool = True,
         embedder_config: Optional[Any] = None,
         crew: Any = None,
-    ):
+    ) -> None:
+        """Initialize the BaseRAGStorage.
+
+        Args:
+            type: Type of storage being used
+            storage_path: Optional custom path for storage location
+            allow_reset: Whether storage can be reset
+            embedder_config: Optional configuration for the embedder
+            crew: Optional crew instance this storage belongs to
+        
+        Raises:
+            PermissionError: If storage path is not writable
+            OSError: If storage path cannot be created
+        """
         self.type = type
+        self.storage_path = storage_path if storage_path else get_default_storage_path('rag')
+        
+        # Validate storage path
+        try:
+            self.storage_path.parent.mkdir(parents=True, exist_ok=True)
+            if not os.access(self.storage_path.parent, os.W_OK):
+                raise PermissionError(f"No write permission for storage path: {self.storage_path}")
+        except OSError as e:
+            raise OSError(f"Failed to initialize storage path: {str(e)}")
+            
         self.allow_reset = allow_reset
         self.embedder_config = embedder_config
         self.crew = crew
         self.agents = self._initialize_agents()
 
     def _initialize_agents(self) -> str:
+        """Initialize agent identifiers for storage.
+        
+        Returns:
+            str: Underscore-joined string of sanitized agent role names
+        """
         if self.crew:
             return "_".join(
                 [self._sanitize_role(agent.role) for agent in self.crew.agents]
@@ -31,12 +66,27 @@ class BaseRAGStorage(ABC):
 
     @abstractmethod
     def _sanitize_role(self, role: str) -> str:
-        """Sanitizes agent roles to ensure valid directory names."""
+        """Sanitizes agent roles to ensure valid directory names.
+        
+        Args:
+            role: The agent role name to sanitize
+            
+        Returns:
+            str: Sanitized role name safe for use in paths
+        """
         pass
 
     @abstractmethod
     def save(self, value: Any, metadata: Dict[str, Any]) -> None:
-        """Save a value with metadata to the storage."""
+        """Save a value with metadata to the storage.
+        
+        Args:
+            value: The value to store
+            metadata: Additional metadata to store with the value
+            
+        Raises:
+            OSError: If there is an error writing to storage
+        """
         pass
 
     @abstractmethod
@@ -46,25 +96,55 @@ class BaseRAGStorage(ABC):
         limit: int = 3,
         filter: Optional[dict] = None,
         score_threshold: float = 0.35,
-    ) -> List[Any]:
-        """Search for entries in the storage."""
+    ) -> List[Dict[str, Any]]:
+        """Search for entries in the storage.
+        
+        Args:
+            query: The search query string
+            limit: Maximum number of results to return
+            filter: Optional filter criteria
+            score_threshold: Minimum similarity score threshold
+            
+        Returns:
+            List[Dict[str, Any]]: List of matching entries with their metadata
+        """
         pass
 
     @abstractmethod
     def reset(self) -> None:
-        """Reset the storage."""
+        """Reset the storage.
+        
+        Raises:
+            OSError: If there is an error clearing storage
+            PermissionError: If reset is not allowed
+        """
         pass
 
     @abstractmethod
     def _generate_embedding(
         self, text: str, metadata: Optional[Dict[str, Any]] = None
-    ) -> Any:
-        """Generate an embedding for the given text and metadata."""
+    ) -> List[float]:
+        """Generate an embedding for the given text and metadata.
+        
+        Args:
+            text: Text to generate embedding for
+            metadata: Optional metadata to include in embedding
+            
+        Returns:
+            List[float]: Vector embedding of the text
+            
+        Raises:
+            ValueError: If text is empty or invalid
+        """
         pass
 
     @abstractmethod
-    def _initialize_app(self):
-        """Initialize the vector db."""
+    def _initialize_app(self) -> None:
+        """Initialize the vector db.
+        
+        Raises:
+            OSError: If vector db initialization fails
+        """
         pass
 
     def setup_config(self, config: Dict[str, Any]):

src/crewai/memory/storage/kickoff_task_outputs_storage.py

@@ -1,11 +1,13 @@
 import json
+import os
 import sqlite3
+from pathlib import Path
 from typing import Any, Dict, List, Optional
 
 from crewai.task import Task
 from crewai.utilities import Printer
 from crewai.utilities.crew_json_encoder import CrewJSONEncoder
-from crewai.utilities.paths import db_storage_path
+from crewai.utilities.paths import get_default_storage_path
 
 
 class KickoffTaskOutputsSQLiteStorage:
@@ -13,10 +15,26 @@ class KickoffTaskOutputsSQLiteStorage:
     An updated SQLite storage class for kickoff task outputs storage.
     """
 
-    def __init__(
-        self, db_path: str = f"{db_storage_path()}/latest_kickoff_task_outputs.db"
-    ) -> None:
-        self.db_path = db_path
+    def __init__(self, storage_path: Optional[Path] = None) -> None:
+        """Initialize kickoff task outputs storage.
+        
+        Args:
+            storage_path: Optional custom path for storage location
+            
+        Raises:
+            PermissionError: If storage path is not writable
+            OSError: If storage path cannot be created
+        """
+        self.storage_path = storage_path if storage_path else get_default_storage_path('kickoff')
+        
+        # Validate storage path
+        try:
+            self.storage_path.parent.mkdir(parents=True, exist_ok=True)
+            if not os.access(self.storage_path.parent, os.W_OK):
+                raise PermissionError(f"No write permission for storage path: {self.storage_path}")
+        except OSError as e:
+            raise OSError(f"Failed to initialize storage path: {str(e)}")
+            
         self._printer: Printer = Printer()
         self._initialize_db()
 
@@ -25,7 +43,7 @@ class KickoffTaskOutputsSQLiteStorage:
         Initializes the SQLite database and creates LTM table
         """
         try:
-            with sqlite3.connect(self.db_path) as conn:
+            with sqlite3.connect(str(self.storage_path)) as conn:
                 cursor = conn.cursor()
                 cursor.execute(
                     """
@@ -55,9 +73,21 @@ class KickoffTaskOutputsSQLiteStorage:
         task_index: int,
         was_replayed: bool = False,
         inputs: Dict[str, Any] = {},
-    ):
+    ) -> None:
+        """Add a task output to storage.
+        
+        Args:
+            task: The task whose output is being stored
+            output: The output data from the task
+            task_index: Index of this task in the sequence
+            was_replayed: Whether this was from a replay
+            inputs: Optional input data that led to this output
+            
+        Raises:
+            sqlite3.Error: If there is an error saving to database
+        """
         try:
-            with sqlite3.connect(self.db_path) as conn:
+            with sqlite3.connect(str(self.storage_path)) as conn:
                 cursor = conn.cursor()
                 cursor.execute(
                     """
@@ -90,7 +120,7 @@ class KickoffTaskOutputsSQLiteStorage:
         Updates an existing row in the latest_kickoff_task_outputs table based on task_index.
         """
         try:
-            with sqlite3.connect(self.db_path) as conn:
+            with sqlite3.connect(str(self.storage_path)) as conn:
                 cursor = conn.cursor()
 
                 fields = []
@@ -119,7 +149,7 @@ class KickoffTaskOutputsSQLiteStorage:
 
     def load(self) -> Optional[List[Dict[str, Any]]]:
         try:
-            with sqlite3.connect(self.db_path) as conn:
+            with sqlite3.connect(str(self.storage_path)) as conn:
                 cursor = conn.cursor()
                 cursor.execute("""
                 SELECT *
@@ -155,7 +185,7 @@ class KickoffTaskOutputsSQLiteStorage:
         Deletes all rows from the latest_kickoff_task_outputs table.
         """
         try:
-            with sqlite3.connect(self.db_path) as conn:
+            with sqlite3.connect(str(self.storage_path)) as conn:
                 cursor = conn.cursor()
                 cursor.execute("DELETE FROM latest_kickoff_task_outputs")
                 conn.commit()

src/crewai/memory/storage/ltm_sqlite_storage.py

@@ -1,9 +1,11 @@
 import json
+import os
 import sqlite3
+from pathlib import Path
 from typing import Any, Dict, List, Optional, Union
 
 from crewai.utilities import Printer
-from crewai.utilities.paths import db_storage_path
+from crewai.utilities.paths import get_default_storage_path
 
 
 class LTMSQLiteStorage:
@@ -11,10 +13,26 @@ class LTMSQLiteStorage:
     An updated SQLite storage class for LTM data storage.
     """
 
-    def __init__(
-        self, db_path: str = f"{db_storage_path()}/long_term_memory_storage.db"
-    ) -> None:
-        self.db_path = db_path
+    def __init__(self, storage_path: Optional[Path] = None) -> None:
+        """Initialize LTM SQLite storage.
+        
+        Args:
+            storage_path: Optional custom path for storage location
+            
+        Raises:
+            PermissionError: If storage path is not writable
+            OSError: If storage path cannot be created
+        """
+        self.storage_path = storage_path if storage_path else get_default_storage_path('ltm')
+        
+        # Validate storage path
+        try:
+            self.storage_path.parent.mkdir(parents=True, exist_ok=True)
+            if not os.access(self.storage_path.parent, os.W_OK):
+                raise PermissionError(f"No write permission for storage path: {self.storage_path}")
+        except OSError as e:
+            raise OSError(f"Failed to initialize storage path: {str(e)}")
+            
         self._printer: Printer = Printer()
         self._initialize_db()
 
@@ -23,7 +41,7 @@ class LTMSQLiteStorage:
         Initializes the SQLite database and creates LTM table
         """
         try:
-            with sqlite3.connect(self.db_path) as conn:
+            with sqlite3.connect(str(self.storage_path)) as conn:
                 cursor = conn.cursor()
                 cursor.execute(
                     """
@@ -51,9 +69,20 @@ class LTMSQLiteStorage:
         datetime: str,
         score: Union[int, float],
     ) -> None:
+        """Save a memory entry to long-term memory.
+        
+        Args:
+            task_description: Description of the task this memory relates to
+            metadata: Additional data to store with the memory
+            datetime: Timestamp for when this memory was created
+            score: Relevance score for this memory (higher is more relevant)
+            
+        Raises:
+            sqlite3.Error: If there is an error saving to the database
+        """
         """Saves data to the LTM table with error handling."""
         try:
-            with sqlite3.connect(self.db_path) as conn:
+            with sqlite3.connect(str(self.storage_path)) as conn:
                 cursor = conn.cursor()
                 cursor.execute(
                     """
@@ -74,7 +103,7 @@ class LTMSQLiteStorage:
     ) -> Optional[List[Dict[str, Any]]]:
         """Queries the LTM table by task description with error handling."""
         try:
-            with sqlite3.connect(self.db_path) as conn:
+            with sqlite3.connect(str(self.storage_path)) as conn:
                 cursor = conn.cursor()
                 cursor.execute(
                     f"""
@@ -109,7 +138,7 @@ class LTMSQLiteStorage:
     ) -> None:
         """Resets the LTM table with error handling."""
         try:
-            with sqlite3.connect(self.db_path) as conn:
+            with sqlite3.connect(str(self.storage_path)) as conn:
                 cursor = conn.cursor()
                 cursor.execute("DELETE FROM long_term_memories")
                 conn.commit()

src/crewai/memory/storage/mem0_storage.py

@@ -19,7 +19,7 @@ class Mem0Storage(Storage):
 
         self.memory_type = type
         self.crew = crew
-        self.memory_config = crew.memory_config
+        self.memory_config = crew.memory_config if crew else None
 
         # User ID is required for user memory type "user" since it's used as a unique identifier for the user.
         user_id = self._get_user_id()
@@ -27,9 +27,10 @@ class Mem0Storage(Storage):
             raise ValueError("User ID is required for user memory type")
 
         # API key in memory config overrides the environment variable
-        mem0_api_key = self.memory_config.get("config", {}).get("api_key") or os.getenv(
-            "MEM0_API_KEY"
-        )
+        if self.memory_config and self.memory_config.get("config"):
+            mem0_api_key = self.memory_config.get("config").get("api_key")
+        else:
+            mem0_api_key = os.getenv("MEM0_API_KEY")
         self.memory = MemoryClient(api_key=mem0_api_key)
 
     def _sanitize_role(self, role: str) -> str:

src/crewai/memory/storage/rag_storage.py

@@ -11,7 +11,6 @@ from chromadb.api import ClientAPI
 from crewai.memory.storage.base_rag_storage import BaseRAGStorage
 from crewai.utilities import EmbeddingConfigurator
 from crewai.utilities.constants import MAX_FILE_NAME_LENGTH
-from crewai.utilities.paths import db_storage_path
 
 
 @contextlib.contextmanager
@@ -40,9 +39,15 @@ class RAGStorage(BaseRAGStorage):
     app: ClientAPI | None = None
 
     def __init__(
-        self, type, allow_reset=True, embedder_config=None, crew=None, path=None
+        self,
+        type,
+        storage_path=None,
+        allow_reset=True,
+        embedder_config=None,
+        crew=None,
+        path=None,
     ):
-        super().__init__(type, allow_reset, embedder_config, crew)
+        super().__init__(type, storage_path, allow_reset, embedder_config, crew)
         agents = crew.agents if crew else []
         agents = [self._sanitize_role(agent.role) for agent in agents]
         agents = "_".join(agents)
@@ -90,7 +95,7 @@ class RAGStorage(BaseRAGStorage):
         """
         Ensures file name does not exceed max allowed by OS
         """
-        base_path = f"{db_storage_path()}/{type}"
+        base_path = f"{self.storage_path}/{type}"
 
         if len(file_name) > MAX_FILE_NAME_LENGTH:
             logging.warning(
@@ -152,7 +157,7 @@ class RAGStorage(BaseRAGStorage):
         try:
             if self.app:
                 self.app.reset()
-                shutil.rmtree(f"{db_storage_path()}/{self.type}")
+                shutil.rmtree(f"{self.storage_path}/{self.type}")
                 self.app = None
                 self.collection = None
         except Exception as e:

src/crewai/project/annotations.py

@@ -66,7 +66,6 @@ def cache_handler(func):
 
 
 def crew(func) -> Callable[..., Crew]:
-
     @wraps(func)
     def wrapper(self, *args, **kwargs) -> Crew:
         instantiated_tasks = []

src/crewai/project/crew_base.py

@@ -216,5 +216,5 @@ def CrewBase(cls: T) -> T:
     # Include base class (qual)name in the wrapper class (qual)name.
     WrappedClass.__name__ = CrewBase.__name__ + "(" + cls.__name__ + ")"
     WrappedClass.__qualname__ = CrewBase.__qualname__ + "(" + cls.__name__ + ")"
-  
+
     return cast(T, WrappedClass)

src/crewai/task.py

@@ -373,7 +373,9 @@ class Task(BaseModel):
             content = (
                 json_output
                 if json_output
-                else pydantic_output.model_dump_json() if pydantic_output else result
+                else pydantic_output.model_dump_json()
+                if pydantic_output
+                else result
             )
             self._save_file(content)
 

src/crewai/utilities/embedding_configurator.py

@@ -27,7 +27,7 @@ class EmbeddingConfigurator:
         if embedder_config is None:
             return self._create_default_embedding_function()
 
-        provider = embedder_config.get("provider")
+        provider = embedder_config.get("provider", "")
         config = embedder_config.get("config", {})
         model_name = config.get("model")
 
@@ -38,12 +38,13 @@ class EmbeddingConfigurator:
             except Exception as e:
                 raise ValueError(f"Invalid custom embedding function: {str(e)}")
 
-        if provider not in self.embedding_functions:
+        embedding_function = self.embedding_functions.get(provider, None)
+        if not embedding_function:
             raise Exception(
                 f"Unsupported embedding provider: {provider}, supported providers: {list(self.embedding_functions.keys())}"
             )
 
-        return self.embedding_functions[provider](config, model_name)
+        return embedding_function(config, model_name)
 
     @staticmethod
     def _create_default_embedding_function():

src/crewai/utilities/evaluators/crew_evaluator_handler.py

@@ -1,19 +1,14 @@
-import os
 from collections import defaultdict
-from typing import Any, Dict, List, Optional, Union
 
-from langchain_core.language_models.base import BaseLanguageModel
-from pydantic import BaseModel, Field, InstanceOf
+from pydantic import BaseModel, Field
 from rich.box import HEAVY_EDGE
 from rich.console import Console
 from rich.table import Table
 
 from crewai.agent import Agent
-from crewai.llm import LLM
 from crewai.task import Task
 from crewai.tasks.task_output import TaskOutput
 from crewai.telemetry import Telemetry
-from crewai.utilities.logger import Logger
 
 
 class TaskEvaluationPydanticOutput(BaseModel):
@@ -27,62 +22,22 @@ class CrewEvaluator:
     A class to evaluate the performance of the agents in the crew based on the tasks they have performed.
 
     Attributes:
-        crew (Crew): The crew of agents to evaluate
-        llm (Union[str, LLM, BaseLanguageModel]): Language model to use for evaluation
-        tasks_scores (defaultdict): Dictionary to store the scores of the agents for each task
-        iteration (int): Current iteration of the evaluation
-        run_execution_times (defaultdict): Dictionary to store execution times for each run
+        crew (Crew): The crew of agents to evaluate.
+        openai_model_name (str): The model to use for evaluating the performance of the agents (for now ONLY OpenAI accepted).
+        tasks_scores (defaultdict): A dictionary to store the scores of the agents for each task.
+        iteration (int): The current iteration of the evaluation.
     """
 
     tasks_scores: defaultdict = defaultdict(list)
     run_execution_times: defaultdict = defaultdict(list)
     iteration: int = 0
 
-    def __init__(self, crew, llm: Union[str, InstanceOf[LLM], BaseLanguageModel]):
-        """Initialize the CrewEvaluator.
-        
-        Args:
-            crew (Crew): The crew to evaluate
-            llm (Union[str, LLM, BaseLanguageModel]): Language model to use for evaluation
-        
-        Raises:
-            ValueError: If llm is of an unsupported type
-        """
-        if not isinstance(llm, (str, LLM, BaseLanguageModel, type(None))):
-            raise ValueError(f"Unsupported LLM type: {type(llm)}")
-
+    def __init__(self, crew, openai_model_name: str):
         self.crew = crew
-        self.llm = llm
+        self.openai_model_name = openai_model_name
         self._telemetry = Telemetry()
-        self._logger = Logger()
-        self._setup_llm()
         self._setup_for_evaluating()
 
-    def _setup_llm(self):
-        """Set up the LLM following the Agent class pattern.
-        
-        This method initializes the language model based on the provided llm parameter:
-        - If string: creates new LLM instance with model name
-        - If LLM instance: uses as-is
-        - If None: uses default model from environment or "gpt-4"
-        - Otherwise: attempts to extract model name from object attributes
-        """
-        if isinstance(self.llm, str):
-            self.llm = LLM(model=self.llm)
-        elif isinstance(self.llm, LLM):
-            pass
-        elif self.llm is None:
-            model_name = os.environ.get("OPENAI_MODEL_NAME") or "gpt-4"
-            self.llm = LLM(model=model_name)
-        else:
-            llm_params = {
-                "model": getattr(self.llm, "model_name", None)
-                or getattr(self.llm, "deployment_name", None)
-                or str(self.llm),
-            }
-            self.llm = LLM(**llm_params)
-
-
     def _setup_for_evaluating(self) -> None:
         """Sets up the crew for evaluating."""
         for task in self.crew.tasks:
@@ -96,7 +51,7 @@ class CrewEvaluator:
             ),
             backstory="Evaluator agent for crew evaluation with precise capabilities to evaluate the performance of the agents in the crew based on the tasks they have performed",
             verbose=False,
-            llm=self.llm,
+            llm=self.openai_model_name,
         )
 
     def _evaluation_task(
@@ -202,48 +157,35 @@ class CrewEvaluator:
         console.print(table)
 
     def evaluate(self, task_output: TaskOutput):
-        """Evaluates the performance of the agents in the crew based on the tasks they have performed.
-        
-        Args:
-            task_output (TaskOutput): The output from the task to evaluate
+        """Evaluates the performance of the agents in the crew based on the tasks they have performed."""
+        current_task = None
+        for task in self.crew.tasks:
+            if task.description == task_output.description:
+                current_task = task
+                break
 
-        Raises:
-            ValueError: If task to evaluate or task output is missing, or if evaluation result is invalid
-        """
-        try:
-            current_task = None
-            for task in self.crew.tasks:
-                if task.description == task_output.description:
-                    current_task = task
-                    break
-
-            if not current_task or not task_output:
-                raise ValueError(
-                    "Task to evaluate and task output are required for evaluation"
-                )
-
-            self._logger.log("info", f"Starting evaluation for task: {task_output.description}")
-            evaluator_agent = self._evaluator_agent()
-            evaluation_task = self._evaluation_task(
-                evaluator_agent, current_task, task_output.raw
+        if not current_task or not task_output:
+            raise ValueError(
+                "Task to evaluate and task output are required for evaluation"
             )
 
-            evaluation_result = evaluation_task.execute_sync()
+        evaluator_agent = self._evaluator_agent()
+        evaluation_task = self._evaluation_task(
+            evaluator_agent, current_task, task_output.raw
+        )
 
-            if isinstance(evaluation_result.pydantic, TaskEvaluationPydanticOutput):
-                self._test_result_span = self._telemetry.individual_test_result_span(
-                    self.crew,
-                    evaluation_result.pydantic.quality,
-                    current_task._execution_time,
-                    self.llm,
-                )
-                self.tasks_scores[self.iteration].append(evaluation_result.pydantic.quality)
-                self.run_execution_times[self.iteration].append(
-                    current_task._execution_time
-                )
-                self._logger.log("info", f"Evaluation completed with score: {evaluation_result.pydantic.quality}")
-            else:
-                raise ValueError("Evaluation result is not in the expected format")
-        except Exception as e:
-            self._logger.log("error", f"Evaluation failed: {str(e)}")
-            raise
+        evaluation_result = evaluation_task.execute_sync()
+
+        if isinstance(evaluation_result.pydantic, TaskEvaluationPydanticOutput):
+            self._test_result_span = self._telemetry.individual_test_result_span(
+                self.crew,
+                evaluation_result.pydantic.quality,
+                current_task._execution_time,
+                self.openai_model_name,
+            )
+            self.tasks_scores[self.iteration].append(evaluation_result.pydantic.quality)
+            self.run_execution_times[self.iteration].append(
+                current_task._execution_time
+            )
+        else:
+            raise ValueError("Evaluation result is not in the expected format")

src/crewai/utilities/paths.py

@@ -22,3 +22,26 @@ def get_project_directory_name():
         cwd = Path.cwd()
         project_directory_name = cwd.name
         return project_directory_name
+
+def get_default_storage_path(storage_type: str) -> Path:
+    """Returns the default storage path for a given storage type.
+    
+    Args:
+        storage_type: Type of storage ('ltm', 'kickoff', 'rag')
+        
+    Returns:
+        Path: Default storage path for the specified type
+        
+    Raises:
+        ValueError: If storage_type is not recognized
+    """
+    base_path = db_storage_path()
+    
+    if storage_type == 'ltm':
+        return base_path / 'latest_long_term_memories.db'
+    elif storage_type == 'kickoff':
+        return base_path / 'latest_kickoff_task_outputs.db'
+    elif storage_type == 'rag':
+        return base_path
+    else:
+        raise ValueError(f"Unknown storage type: {storage_type}")

tests/cli/tools/test_main.py

@@ -28,9 +28,10 @@ def test_create_success(mock_subprocess):
     with in_temp_dir():
         tool_command = ToolCommand()
 
-        with patch.object(tool_command, "login") as mock_login, patch(
-            "sys.stdout", new=StringIO()
-        ) as fake_out:
+        with (
+            patch.object(tool_command, "login") as mock_login,
+            patch("sys.stdout", new=StringIO()) as fake_out,
+        ):
             tool_command.create("test-tool")
             output = fake_out.getvalue()
 
@@ -82,7 +83,7 @@ def test_install_success(mock_get, mock_subprocess_run):
         capture_output=False,
         text=True,
         check=True,
-        env=unittest.mock.ANY
+        env=unittest.mock.ANY,
     )
 
     assert "Successfully installed sample-tool" in output

tests/crew_test.py

@@ -24,36 +24,6 @@ from crewai.types.usage_metrics import UsageMetrics
 from crewai.utilities import Logger
 from crewai.utilities.rpm_controller import RPMController
 from crewai.utilities.task_output_storage_handler import TaskOutputStorageHandler
-from crewai.llm import LLM
-
-class MockLLM(LLM):
-    """Mock LLM for testing."""
-    def __init__(self):
-        super().__init__(model="gpt-4")  # Use a known model name
-        
-    def chat_completion(self, messages, tools=None, tool_choice=None, **kwargs):
-        # Mock a proper response that matches the expected format
-        if tools and any('output' in tool.get('function', {}).get('name', '') for tool in tools):
-            return {
-                "choices": [{
-                    "message": {
-                        "content": None,
-                        "role": "assistant",
-                        "function_call": {
-                            "name": "output",
-                            "arguments": '{"quality": 8.5}'
-                        }
-                    }
-                }]
-            }
-        return {
-            "choices": [{
-                "message": {
-                    "content": "Mock LLM Response",
-                    "role": "assistant"
-                }
-            }]
-        }
 
 ceo = Agent(
     role="CEO",
@@ -77,60 +47,6 @@ writer = Agent(
 )
 
 
-def test_crew_test_with_custom_llm():
-    """Test that Crew.test() works with a custom LLM implementation."""
-    task = Task(
-        description="Test task",
-        expected_output="Test output",
-        agent=researcher,
-    )
-    crew = Crew(agents=[researcher], tasks=[task])
-    
-    # Test with custom LLM
-    custom_llm = MockLLM()
-    crew.test(n_iterations=1, llm=custom_llm)
-    # No assertion needed as we just verify it runs without errors
-
-def test_crew_test_backward_compatibility():
-    """Test that Crew.test() maintains backward compatibility with openai_model_name."""
-    task = Task(
-        description="Test task",
-        expected_output="Test output",
-        agent=researcher,
-    )
-    crew = Crew(agents=[researcher], tasks=[task])
-    
-    # Test with openai_model_name
-    crew.test(n_iterations=1, openai_model_name="gpt-4")
-    # No assertion needed as we just verify it runs without errors
-
-def test_crew_test_with_invalid_llm():
-    """Test that Crew.test() properly handles invalid LLM inputs."""
-    task = Task(
-        description="Test task",
-        expected_output="Test output",
-        agent=researcher,
-    )
-    crew = Crew(agents=[researcher], tasks=[task])
-    
-    # Test with invalid LLM type
-    with pytest.raises(ValueError, match="Unsupported LLM type"):
-        crew.test(n_iterations=1, llm=123)  # type: ignore
-
-def test_crew_test_with_invalid_iterations():
-    """Test that Crew.test() validates n_iterations."""
-    task = Task(
-        description="Test task",
-        expected_output="Test output",
-        agent=researcher,
-    )
-    crew = Crew(agents=[researcher], tasks=[task])
-    
-    # Test with invalid n_iterations
-    with pytest.raises(ValueError, match="n_iterations must be greater than 0"):
-        crew.test(n_iterations=0, llm=MockLLM())
-
-
 def test_crew_config_conditional_requirement():
     with pytest.raises(ValueError):
         Crew(process=Process.sequential)
@@ -1207,7 +1123,7 @@ def test_kickoff_for_each_empty_input():
     assert results == []
 
 
-@pytest.mark.vcr(filter_headeruvs=["authorization"])
+@pytest.mark.vcr(filter_headers=["authorization"])
 def test_kickoff_for_each_invalid_input():
     """Tests if kickoff_for_each raises TypeError for invalid input types."""
 
@@ -3209,4 +3125,4 @@ def test_multimodal_agent_live_image_analysis():
     # Verify we got a meaningful response
     assert isinstance(result.raw, str)
     assert len(result.raw) > 100  # Expecting a detailed analysis
-    assert "error" not in result.raw.lower()  # No error messages in response
+    assert "error" not in result.raw.lower()  # No error messages in response

tests/memory/test_storage_paths.py

@@ -0,0 +1,83 @@
+import os
+import tempfile
+from pathlib import Path
+import pytest
+from unittest.mock import patch
+
+from crewai.memory.storage.ltm_sqlite_storage import LTMSQLiteStorage
+from crewai.memory.storage.kickoff_task_outputs_storage import KickoffTaskOutputsSQLiteStorage
+from crewai.memory.storage.base_rag_storage import BaseRAGStorage
+from crewai.utilities.paths import get_default_storage_path
+
+class MockRAGStorage(BaseRAGStorage):
+    """Mock implementation of BaseRAGStorage for testing."""
+    def _sanitize_role(self, role: str) -> str:
+        return role.lower()
+    
+    def save(self, value, metadata):
+        pass
+    
+    def search(self, query, limit=3, filter=None, score_threshold=0.35):
+        return []
+    
+    def reset(self):
+        pass
+    
+    def _generate_embedding(self, text, metadata=None):
+        return []
+    
+    def _initialize_app(self):
+        pass
+
+def test_default_storage_paths():
+    """Test that default storage paths are created correctly."""
+    ltm_path = get_default_storage_path('ltm')
+    kickoff_path = get_default_storage_path('kickoff')
+    rag_path = get_default_storage_path('rag')
+    
+    assert str(ltm_path).endswith('latest_long_term_memories.db')
+    assert str(kickoff_path).endswith('latest_kickoff_task_outputs.db')
+    assert isinstance(rag_path, Path)
+
+def test_custom_storage_paths():
+    """Test that custom storage paths are respected."""
+    with tempfile.TemporaryDirectory() as temp_dir:
+        custom_path = Path(temp_dir) / 'custom.db'
+        
+        ltm = LTMSQLiteStorage(storage_path=custom_path)
+        assert ltm.storage_path == custom_path
+        
+        kickoff = KickoffTaskOutputsSQLiteStorage(storage_path=custom_path)
+        assert kickoff.storage_path == custom_path
+        
+        rag = MockRAGStorage('test', storage_path=custom_path)
+        assert rag.storage_path == custom_path
+
+def test_directory_creation():
+    """Test that storage directories are created automatically."""
+    with tempfile.TemporaryDirectory() as temp_dir:
+        test_dir = Path(temp_dir) / 'test_storage'
+        storage_path = test_dir / 'test.db'
+        
+        assert not test_dir.exists()
+        LTMSQLiteStorage(storage_path=storage_path)
+        assert test_dir.exists()
+
+def test_permission_error():
+    """Test that permission errors are handled correctly."""
+    with tempfile.TemporaryDirectory() as temp_dir:
+        test_dir = Path(temp_dir) / 'readonly'
+        test_dir.mkdir()
+        os.chmod(test_dir, 0o444)  # Read-only
+        
+        storage_path = test_dir / 'test.db'
+        with pytest.raises((PermissionError, OSError)) as exc_info:
+            LTMSQLiteStorage(storage_path=storage_path)
+        # Verify that the error message mentions permission
+        assert "permission" in str(exc_info.value).lower()
+
+def test_invalid_path():
+    """Test that invalid paths raise appropriate errors."""
+    with pytest.raises(OSError):
+        # Try to create storage in a non-existent root directory
+        LTMSQLiteStorage(storage_path=Path('/nonexistent/dir/test.db'))

tests/utilities/evaluators/test_crew_evaluator_handler.py

@@ -23,7 +23,7 @@ class TestCrewEvaluator:
         )
         crew = Crew(agents=[agent], tasks=[task])
 
-        return CrewEvaluator(crew, llm="openai/gpt-4o-mini")
+        return CrewEvaluator(crew, openai_model_name="gpt-4o-mini")
 
     def test_setup_for_evaluating(self, crew_planner):
         crew_planner._setup_for_evaluating()
@@ -45,7 +45,7 @@ class TestCrewEvaluator:
             == "Evaluator agent for crew evaluation with precise capabilities to evaluate the performance of the agents in the crew based on the tasks they have performed"
         )
         assert agent.verbose is False
-        assert agent.llm.model == "openai/gpt-4o-mini"
+        assert agent.llm.model == "gpt-4o-mini"
 
     def test_evaluation_task(self, crew_planner):
         evaluator_agent = Agent(

uv.lock

@@ -1,10 +1,18 @@
 version = 1
 requires-python = ">=3.10, <3.13"
 resolution-markers = [
-    "python_full_version < '3.11'",
-    "python_full_version == '3.11.*'",
-    "python_full_version >= '3.12' and python_full_version < '3.12.4'",
-    "python_full_version >= '3.12.4'",
+    "python_full_version < '3.11' and sys_platform == 'darwin'",
+    "python_full_version < '3.11' and platform_machine == 'aarch64' and sys_platform == 'linux'",
+    "(python_full_version < '3.11' and platform_machine != 'aarch64' and sys_platform == 'linux') or (python_full_version < '3.11' and sys_platform != 'darwin' and sys_platform != 'linux')",
+    "python_full_version == '3.11.*' and sys_platform == 'darwin'",
+    "python_full_version == '3.11.*' and platform_machine == 'aarch64' and sys_platform == 'linux'",
+    "(python_full_version == '3.11.*' and platform_machine != 'aarch64' and sys_platform == 'linux') or (python_full_version == '3.11.*' and sys_platform != 'darwin' and sys_platform != 'linux')",
+    "python_full_version >= '3.12' and python_full_version < '3.12.4' and sys_platform == 'darwin'",
+    "python_full_version >= '3.12' and python_full_version < '3.12.4' and platform_machine == 'aarch64' and sys_platform == 'linux'",
+    "(python_full_version >= '3.12' and python_full_version < '3.12.4' and platform_machine != 'aarch64' and sys_platform == 'linux') or (python_full_version >= '3.12' and python_full_version < '3.12.4' and sys_platform != 'darwin' and sys_platform != 'linux')",
+    "python_full_version >= '3.12.4' and sys_platform == 'darwin'",
+    "python_full_version >= '3.12.4' and platform_machine == 'aarch64' and sys_platform == 'linux'",
+    "(python_full_version >= '3.12.4' and platform_machine != 'aarch64' and sys_platform == 'linux') or (python_full_version >= '3.12.4' and sys_platform != 'darwin' and sys_platform != 'linux')",
 ]
 
 [[package]]
@@ -300,7 +308,7 @@ name = "build"
 version = "1.2.2.post1"
 source = { registry = "https://pypi.org/simple" }
 dependencies = [
-    { name = "colorama", marker = "os_name == 'nt'" },
+    { name = "colorama", marker = "(os_name == 'nt' and platform_machine != 'aarch64' and sys_platform == 'linux') or (os_name == 'nt' and sys_platform != 'darwin' and sys_platform != 'linux')" },
     { name = "importlib-metadata", marker = "python_full_version < '3.10.2'" },
     { name = "packaging" },
     { name = "pyproject-hooks" },
@@ -535,7 +543,7 @@ name = "click"
 version = "8.1.7"
 source = { registry = "https://pypi.org/simple" }
 dependencies = [
-    { name = "colorama", marker = "platform_system == 'Windows'" },
+    { name = "colorama", marker = "sys_platform == 'win32'" },
 ]
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-    { name = "crewai-tools" },
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     { name = "mkdocs-material" },
     { name = "mkdocs-material-extensions" },
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     { name = "markdown" },
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 source = { registry = "https://pypi.org/simple" }
 dependencies = [
     { name = "pygments" },
-    { name = "pywin32", marker = "platform_system == 'Windows'" },
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-    { name = "nvidia-nvjitlink-cu12", marker = "(platform_machine != 'aarch64' and platform_system != 'Darwin') or (platform_system != 'Darwin' and platform_system != 'Linux')" },
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 version = "2.10.1"
 source = { registry = "https://pypi.org/simple" }
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-    { name = "nvidia-nvtx-cu12", marker = "platform_machine == 'x86_64' and platform_system == 'Linux'" },
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 dependencies = [
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 dependencies = [
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