Merge branch 'main' into fix-knowledgestorage-default-instantiation

src/crewai/agents/crew_agent_executor.py

@@ -108,42 +108,14 @@ class CrewAgentExecutor(CrewAgentExecutorMixin):
         self._create_long_term_memory(formatted_answer)
         return {"output": formatted_answer.output}
 
-    def _has_reached_max_iterations(self) -> bool:
-        """Check if the maximum number of iterations has been reached."""
-        return self.iterations >= self.max_iter
-
-    def _handle_max_iterations_exceeded(self, formatted_answer) -> AgentFinish:
-        """Handle the case when maximum iterations have been exceeded."""
-        error_msg = f"Agent has exceeded maximum iterations ({self.max_iter}). This could be due to repeated failures or infinite loops."
-        self._printer.print(content=error_msg, color="red")
-        return AgentFinish(
-            thought="Maximum iterations exceeded",
-            output=error_msg,
-            text=formatted_answer.text if formatted_answer else error_msg
-        )
-
     def _invoke_loop(self, formatted_answer=None):
         try:
             while not isinstance(formatted_answer, AgentFinish):
                 if not self.request_within_rpm_limit or self.request_within_rpm_limit():
-                    # Increment iteration counter before LLM call to prevent hanging
-                    self.iterations += 1
-                    
-                    try:
-                        answer = self.llm.call(
-                            self.messages,
-                            callbacks=self.callbacks,
-                        )
-                    except Exception as llm_error:
-                        self._printer.print(
-                            content=f"LLM call failed: {str(llm_error)}",
-                            color="red",
-                        )
-                        # Check if we've reached max iterations to avoid infinite loops
-                        if self._has_reached_max_iterations():
-                            return self._handle_max_iterations_exceeded(formatted_answer)
-                        # Re-raise authentication and other critical errors
-                        raise
+                    answer = self.llm.call(
+                        self.messages,
+                        callbacks=self.callbacks,
+                    )
 
                     if answer is None or answer == "":
                         self._printer.print(
@@ -164,6 +136,7 @@ class CrewAgentExecutor(CrewAgentExecutorMixin):
                             ):
                                 answer = answer.split("Observation:")[0].strip()
 
+                    self.iterations += 1
                     formatted_answer = self._format_answer(answer)
 
                     if isinstance(formatted_answer, AgentAction):

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
 

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.")