more fixes

src/crewai/agent.py

@@ -3,6 +3,7 @@ import shutil
 import subprocess
 from typing import Any, Dict, List, Literal, Optional, Union
 
+from litellm import AuthenticationError as LiteLLMAuthenticationError
 from pydantic import Field, InstanceOf, PrivateAttr, model_validator
 
 from crewai.agents import CacheHandler
@@ -261,6 +262,9 @@ class Agent(BaseAgent):
                 }
             )["output"]
         except Exception as e:
+            if isinstance(e, LiteLLMAuthenticationError):
+                # Do not retry on authentication errors
+                raise e
             self._times_executed += 1
             if self._times_executed > self.max_retry_limit:
                 raise e

src/crewai/agents/crew_agent_executor.py

@@ -3,6 +3,8 @@ import re
 from dataclasses import dataclass
 from typing import Any, Callable, Dict, List, Optional, Union
 
+from litellm.exceptions import AuthenticationError as LiteLLMAuthenticationError
+
 from crewai.agents.agent_builder.base_agent import BaseAgent
 from crewai.agents.agent_builder.base_agent_executor_mixin import CrewAgentExecutorMixin
 from crewai.agents.parser import (
@@ -13,6 +15,7 @@ from crewai.agents.parser import (
     OutputParserException,
 )
 from crewai.agents.tools_handler import ToolsHandler
+from crewai.llm import LLM
 from crewai.tools.base_tool import BaseTool
 from crewai.tools.tool_usage import ToolUsage, ToolUsageErrorException
 from crewai.utilities import I18N, Printer
@@ -54,7 +57,7 @@ class CrewAgentExecutor(CrewAgentExecutorMixin):
         callbacks: List[Any] = [],
     ):
         self._i18n: I18N = I18N()
-        self.llm = llm
+        self.llm: LLM = llm
         self.task = task
         self.agent = agent
         self.crew = crew
@@ -80,10 +83,8 @@ class CrewAgentExecutor(CrewAgentExecutorMixin):
         self.tool_name_to_tool_map: Dict[str, BaseTool] = {
             tool.name: tool for tool in self.tools
         }
-        if self.llm.stop:
-            self.llm.stop = list(set(self.llm.stop + self.stop))
-        else:
-            self.llm.stop = self.stop
+        self.stop = stop_words
+        self.llm.stop = list(set(self.llm.stop + self.stop))
 
     def invoke(self, inputs: Dict[str, str]) -> Dict[str, Any]:
         if "system" in self.prompt:
@@ -98,7 +99,11 @@ class CrewAgentExecutor(CrewAgentExecutorMixin):
         self._show_start_logs()
 
         self.ask_for_human_input = bool(inputs.get("ask_for_human_input", False))
-        formatted_answer = self._invoke_loop()
+
+        try:
+            formatted_answer = self._invoke_loop()
+        except Exception as e:
+            raise e
 
         if self.ask_for_human_input:
             formatted_answer = self._handle_human_feedback(formatted_answer)
@@ -124,7 +129,6 @@ class CrewAgentExecutor(CrewAgentExecutorMixin):
                 self._enforce_rpm_limit()
 
                 answer = self._get_llm_response()
-
                 formatted_answer = self._process_llm_response(answer)
 
                 if isinstance(formatted_answer, AgentAction):
@@ -145,10 +149,40 @@ class CrewAgentExecutor(CrewAgentExecutorMixin):
                 if self._is_context_length_exceeded(e):
                     self._handle_context_length()
                     continue
+                elif self._is_litellm_authentication_error(e):
+                    self._handle_litellm_auth_error(e)
+                    raise e
+                else:
+                    self._printer.print(
+                        content=f"Unhandled exception: {e}",
+                        color="red",
+                    )
+            finally:
+                self.iterations += 1
 
         self._show_logs(formatted_answer)
         return formatted_answer
 
+    def _is_litellm_authentication_error(self, exception: Exception) -> bool:
+        """Check if the exception is a litellm authentication error."""
+        if LiteLLMAuthenticationError and isinstance(
+            exception, LiteLLMAuthenticationError
+        ):
+            return True
+
+        return False
+
+    def _handle_litellm_auth_error(self, exception: Exception) -> None:
+        """Handle litellm authentication error by informing the user and exiting."""
+        self._printer.print(
+            content="Authentication error with litellm occurred. Please check your API key and configuration.",
+            color="red",
+        )
+        self._printer.print(
+            content=f"Error details: {exception}",
+            color="red",
+        )
+
     def _has_reached_max_iterations(self) -> bool:
         """Check if the maximum number of iterations has been reached."""
         return self.iterations >= self.max_iter
@@ -160,10 +194,17 @@ class CrewAgentExecutor(CrewAgentExecutorMixin):
 
     def _get_llm_response(self) -> str:
         """Call the LLM and return the response, handling any invalid responses."""
-        answer = self.llm.call(
-            self.messages,
-            callbacks=self.callbacks,
-        )
+        try:
+            answer = self.llm.call(
+                self.messages,
+                callbacks=self.callbacks,
+            )
+        except Exception as e:
+            self._printer.print(
+                content=f"Error during LLM call: {e}",
+                color="red",
+            )
+            raise e
 
         if not answer:
             self._printer.print(
@@ -184,7 +225,6 @@ class CrewAgentExecutor(CrewAgentExecutorMixin):
                 if FINAL_ANSWER_AND_PARSABLE_ACTION_ERROR_MESSAGE in e.error:
                     answer = answer.split("Observation:")[0].strip()
 
-        self.iterations += 1
         return self._format_answer(answer)
 
     def _handle_agent_action(

src/crewai/llm.py

@@ -142,7 +142,6 @@ class LLM:
         self.temperature = temperature
         self.top_p = top_p
         self.n = n
-        self.stop = stop
         self.max_completion_tokens = max_completion_tokens
         self.max_tokens = max_tokens
         self.presence_penalty = presence_penalty
@@ -160,6 +159,14 @@ class LLM:
 
         litellm.drop_params = True
 
+        # Normalize self.stop to always be a List[str]
+        if stop is None:
+            self.stop: List[str] = []
+        elif isinstance(stop, str):
+            self.stop = [stop]
+        else:
+            self.stop = stop
+
         self.set_callbacks(callbacks)
         self.set_env_callbacks()
 
@@ -222,7 +229,7 @@ class LLM:
                 ].message
                 text_response = response_message.content or ""
                 tool_calls = getattr(response_message, "tool_calls", [])
-                
+
                 # Ensure callbacks get the full response object with usage info
                 if callbacks and len(callbacks) > 0:
                     for callback in callbacks:

src/crewai/utilities/llm_utils.py

@@ -24,12 +24,10 @@ def create_llm(
 
     # 1) If llm_value is already an LLM object, return it directly
     if isinstance(llm_value, LLM):
-        print("LLM value is already an LLM object")
         return llm_value
 
     # 2) If llm_value is a string (model name)
     if isinstance(llm_value, str):
-        print("LLM value is a string")
         try:
             created_llm = LLM(model=llm_value)
             return created_llm
@@ -39,12 +37,10 @@ def create_llm(
 
     # 3) If llm_value is None, parse environment variables or use default
     if llm_value is None:
-        print("LLM value is None")
         return _llm_via_environment_or_fallback()
 
     # 4) Otherwise, attempt to extract relevant attributes from an unknown object
     try:
-        print("LLM value is an unknown object")
         # Extract attributes with explicit types
         model = (
             getattr(llm_value, "model_name", None)

tests/agent_test.py

@@ -16,7 +16,7 @@ from crewai.tools import tool
 from crewai.tools.tool_calling import InstructorToolCalling
 from crewai.tools.tool_usage import ToolUsage
 from crewai.tools.tool_usage_events import ToolUsageFinished
-from crewai.utilities import RPMController
+from crewai.utilities import Printer, RPMController
 from crewai.utilities.events import Emitter
 
 
@@ -1600,3 +1600,103 @@ def test_agent_with_knowledge_sources():
 
         # Assert that the agent provides the correct information
         assert "red" in result.raw.lower()
+
+
+@pytest.mark.vcr(filter_headers=["authorization"])
+def test_litellm_auth_error_handling():
+    """Test that LiteLLM authentication errors are handled correctly and not retried."""
+    from litellm import AuthenticationError as LiteLLMAuthenticationError
+
+    # Create an agent with a mocked LLM and max_retry_limit=0
+    agent = Agent(
+        role="test role",
+        goal="test goal",
+        backstory="test backstory",
+        llm=LLM(model="gpt-4"),
+        max_retry_limit=0,  # Disable retries for authentication errors
+    )
+
+    # Create a task
+    task = Task(
+        description="Test task",
+        expected_output="Test output",
+        agent=agent,
+    )
+
+    # Mock the LLM call to raise LiteLLMAuthenticationError
+    with (
+        patch.object(LLM, "call") as mock_llm_call,
+        pytest.raises(LiteLLMAuthenticationError, match="Invalid API key"),
+    ):
+        mock_llm_call.side_effect = LiteLLMAuthenticationError(
+            message="Invalid API key", llm_provider="openai", model="gpt-4"
+        )
+        agent.execute_task(task)
+
+    # Verify the call was only made once (no retries)
+    mock_llm_call.assert_called_once()
+
+
+def test_crew_agent_executor_litellm_auth_error():
+    """Test that CrewAgentExecutor properly identifies and handles LiteLLM authentication errors."""
+    from litellm import AuthenticationError as LiteLLMAuthenticationError
+
+    from crewai.agents.tools_handler import ToolsHandler
+    from crewai.utilities import Printer
+
+    # Create an agent and executor with max_retry_limit=0
+    agent = Agent(
+        role="test role",
+        goal="test goal",
+        backstory="test backstory",
+        llm=LLM(model="gpt-4", api_key="invalid_api_key"),
+    )
+    task = Task(
+        description="Test task",
+        expected_output="Test output",
+        agent=agent,
+    )
+
+    # Create executor with all required parameters
+    executor = CrewAgentExecutor(
+        agent=agent,
+        task=task,
+        llm=agent.llm,
+        crew=None,
+        prompt={"system": "You are a test agent", "user": "Execute the task: {input}"},
+        max_iter=5,
+        tools=[],
+        tools_names="",
+        stop_words=[],
+        tools_description="",
+        tools_handler=ToolsHandler(),
+    )
+
+    # Mock the LLM call to raise LiteLLMAuthenticationError
+    with (
+        patch.object(LLM, "call") as mock_llm_call,
+        patch.object(Printer, "print") as mock_printer,
+        pytest.raises(LiteLLMAuthenticationError, match="Invalid API key"),
+    ):
+        mock_llm_call.side_effect = LiteLLMAuthenticationError(
+            message="Invalid API key", llm_provider="openai", model="gpt-4"
+        )
+        executor.invoke(
+            {
+                "input": "test input",
+                "tool_names": "",
+                "tools": "",
+            }
+        )
+
+    # Verify error handling
+    mock_printer.assert_any_call(
+        content="Authentication error with litellm occurred. Please check your API key and configuration.",
+        color="red",
+    )
+    mock_printer.assert_any_call(
+        content="Error details: litellm.AuthenticationError: Invalid API key",
+        color="red",
+    )
+    # Verify the call was only made once (no retries)
+    mock_llm_call.assert_called_once()

tests/cassettes/test_agent_error_on_parsing_tool.yaml

@@ -2,21 +2,21 @@ interactions:
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+      should NEVER make up tools that are not listed here:\n\nTool Name: get_final_answer\nTool
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+      I have to use the available tool to get the final answer. Let''s proceed with
+      executing it.\nAction: get_final_answer\nAction Input: {}\nObservation: I encountered
+      an error: Error on parsing tool.\nMoving on then. I MUST either use a tool (use
+      one at time) OR give my best final answer not both at the same time. When responding,
+      I must use the following format:\n\n```\nThought: you should always think about
+      what to do\nAction: the action to take, should be one of [get_final_answer]\nAction
+      Input: the input to the action, dictionary enclosed in curly braces\nObservation:
+      the result of the action\n```\nThis Thought/Action/Action Input/Result can repeat
+      N times. Once I know the final answer, I must return the following format:\n\n```\nThought:
+      I now can give a great answer\nFinal Answer: Your final answer must be the great
+      and the most complete as possible, it must be outcome described\n\n```"}, {"role":
+      "assistant", "content": "```\nThought: I have to use the available tool to get
+      the final answer. Let''s proceed with executing it.\nAction: get_final_answer\nAction
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+      not both at the same time. When responding, I must use the following format:\n\n```\nThought:
+      you should always think about what to do\nAction: the action to take, should
+      be one of [get_final_answer]\nAction Input: the input to the action, dictionary
+      enclosed in curly braces\nObservation: the result of the action\n```\nThis Thought/Action/Action
+      Input/Result can repeat N times. Once I know the final answer, I must return
+      the following format:\n\n```\nThought: I now can give a great answer\nFinal
       Answer: Your final answer must be the great and the most complete as possible,
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