fix: update default LLM model and improve error logging in LLM utilities (#3785)

* fix: update default LLM model and improve error logging in LLM utilities

* Updated the default LLM model from "gpt-4o-mini" to "gpt-4.1-mini" for better performance.
* Enhanced error logging in the LLM utilities to use logger.error instead of logger.debug, ensuring that errors are properly reported and raised.
* Added tests to verify behavior when OpenAI API key is missing and when Anthropic dependency is not available, improving robustness and error handling in LLM creation.

* fix: update test for default LLM model usage

* Refactored the test_create_llm_with_none_uses_default_model to use the imported DEFAULT_LLM_MODEL constant instead of a hardcoded string.
* Ensured that the test correctly asserts the model used is the current default, improving maintainability and consistency across tests.

* change default model to gpt-4.1-mini

* change default model use defualt

lib/crewai/src/crewai/cli/constants.py

@@ -322,7 +322,7 @@ MODELS = {
     ],
 }
 
-DEFAULT_LLM_MODEL = "gpt-4o-mini"
+DEFAULT_LLM_MODEL = "gpt-4.1-mini"
 
 JSON_URL = "https://raw.githubusercontent.com/BerriAI/litellm/main/model_prices_and_context_window.json"
 

lib/crewai/src/crewai/utilities/llm_utils.py

@@ -29,8 +29,8 @@ def create_llm(
         try:
             return LLM(model=llm_value)
         except Exception as e:
-            logger.debug(f"Failed to instantiate LLM with model='{llm_value}': {e}")
+            logger.error(f"Error instantiating LLM from string: {e}")
-            return None
+            raise e
 
     if llm_value is None:
         return _llm_via_environment_or_fallback()
@@ -62,8 +62,8 @@ def create_llm(
         )
 
     except Exception as e:
-        logger.debug(f"Error instantiating LLM from unknown object type: {e}")
+        logger.error(f"Error instantiating LLM from unknown object type: {e}")
-        return None
+        raise e
 
 
 UNACCEPTED_ATTRIBUTES: Final[list[str]] = [
@@ -176,10 +176,10 @@ def _llm_via_environment_or_fallback() -> LLM | None:
     try:
         return LLM(**llm_params)
     except Exception as e:
-        logger.debug(
+        logger.error(
             f"Error instantiating LLM from environment/fallback: {type(e).__name__}: {e}"
         )
-        return None
+        raise e
 
 
 def _normalize_key_name(key_name: str) -> str:

lib/crewai/tests/agents/test_agent.py

@@ -6,6 +6,7 @@ from unittest import mock
 from unittest.mock import MagicMock, patch
 
 from crewai.agents.crew_agent_executor import AgentFinish, CrewAgentExecutor
+from crewai.cli.constants import DEFAULT_LLM_MODEL
 from crewai.events.event_bus import crewai_event_bus
 from crewai.events.types.tool_usage_events import ToolUsageFinishedEvent
 from crewai.knowledge.knowledge import Knowledge
@@ -135,7 +136,7 @@ def test_agent_with_missing_response_template():
 
 def test_agent_default_values():
     agent = Agent(role="test role", goal="test goal", backstory="test backstory")
-    assert agent.llm.model == "gpt-4o-mini"
+    assert agent.llm.model == DEFAULT_LLM_MODEL
     assert agent.allow_delegation is False
 
 
@@ -225,7 +226,7 @@ def test_logging_tool_usage():
         verbose=True,
     )
 
-    assert agent.llm.model == "gpt-4o-mini"
+    assert agent.llm.model == DEFAULT_LLM_MODEL
     assert agent.tools_handler.last_used_tool is None
     task = Task(
         description="What is 3 times 4?",

lib/crewai/tests/utilities/test_llm_utils.py

@@ -1,77 +1,79 @@
 import os
+from typing import Any
 from unittest.mock import patch
 
+from crewai.cli.constants import DEFAULT_LLM_MODEL
 from crewai.llm import LLM
 from crewai.llms.base_llm import BaseLLM
 from crewai.utilities.llm_utils import create_llm
 import pytest
 
 
-try:
+def test_create_llm_with_llm_instance() -> None:
-    from litellm.exceptions import BadRequestError
-except ImportError:
-    BadRequestError = Exception
-
-
-def test_create_llm_with_llm_instance():
-    existing_llm = LLM(model="gpt-4o")
-    llm = create_llm(llm_value=existing_llm)
-    assert llm is existing_llm
-
-
-def test_create_llm_with_valid_model_string():
-    llm = create_llm(llm_value="gpt-4o")
-    assert isinstance(llm, BaseLLM)
-    assert llm.model == "gpt-4o"
-
-
-def test_create_llm_with_invalid_model_string():
-    # For invalid model strings, create_llm succeeds but call() fails with API error
-    llm = create_llm(llm_value="invalid-model")
-    assert llm is not None
-    assert isinstance(llm, BaseLLM)
-
-    # The error should occur when making the actual API call
-    # We expect some kind of API error (NotFoundError, etc.)
-    with pytest.raises(Exception):  # noqa: B017
-        llm.call(messages=[{"role": "user", "content": "Hello, world!"}])
-
-
-def test_create_llm_with_unknown_object_missing_attributes():
-    class UnknownObject:
-        pass
-
-    unknown_obj = UnknownObject()
-    llm = create_llm(llm_value=unknown_obj)
-
-    # Should succeed because str(unknown_obj) provides a model name
-    assert llm is not None
-    assert isinstance(llm, BaseLLM)
-
-
-def test_create_llm_with_none_uses_default_model():
     with patch.dict(os.environ, {"OPENAI_API_KEY": "fake-key"}, clear=True):
-        with patch("crewai.utilities.llm_utils.DEFAULT_LLM_MODEL", "gpt-4o-mini"):
+        existing_llm = LLM(model="gpt-4o")
+        llm = create_llm(llm_value=existing_llm)
+        assert llm is existing_llm
+
+
+def test_create_llm_with_valid_model_string() -> None:
+    with patch.dict(os.environ, {"OPENAI_API_KEY": "fake-key"}, clear=True):
+        llm = create_llm(llm_value="gpt-4o")
+        assert isinstance(llm, BaseLLM)
+        assert llm.model == "gpt-4o"
+
+
+def test_create_llm_with_invalid_model_string() -> None:
+    with patch.dict(os.environ, {"OPENAI_API_KEY": "fake-key"}, clear=True):
+        # For invalid model strings, create_llm succeeds but call() fails with API error
+        llm = create_llm(llm_value="invalid-model")
+        assert llm is not None
+        assert isinstance(llm, BaseLLM)
+
+        # The error should occur when making the actual API call
+        # We expect some kind of API error (NotFoundError, etc.)
+        with pytest.raises(Exception):  # noqa: B017
+            llm.call(messages=[{"role": "user", "content": "Hello, world!"}])
+
+
+def test_create_llm_with_unknown_object_missing_attributes() -> None:
+    with patch.dict(os.environ, {"OPENAI_API_KEY": "fake-key"}, clear=True):
+        class UnknownObject:
+            pass
+
+        unknown_obj = UnknownObject()
+        llm = create_llm(llm_value=unknown_obj)
+
+        # Should succeed because str(unknown_obj) provides a model name
+        assert llm is not None
+        assert isinstance(llm, BaseLLM)
+
+
+def test_create_llm_with_none_uses_default_model() -> None:
+    with patch.dict(os.environ, {"OPENAI_API_KEY": "fake-key"}, clear=True):
+        with patch("crewai.utilities.llm_utils.DEFAULT_LLM_MODEL", DEFAULT_LLM_MODEL):
             llm = create_llm(llm_value=None)
             assert isinstance(llm, BaseLLM)
-            assert llm.model == "gpt-4o-mini"
+            assert llm.model == DEFAULT_LLM_MODEL
 
 
-def test_create_llm_with_unknown_object():
+def test_create_llm_with_unknown_object() -> None:
-    class UnknownObject:
+    with patch.dict(os.environ, {"OPENAI_API_KEY": "fake-key"}, clear=True):
-        model_name = "gpt-4o"
+        class UnknownObject:
-        temperature = 0.7
+            model_name = "gpt-4o"
-        max_tokens = 1500
+            temperature = 0.7
+            max_tokens = 1500
 
-    unknown_obj = UnknownObject()
+        unknown_obj = UnknownObject()
-    llm = create_llm(llm_value=unknown_obj)
+        llm = create_llm(llm_value=unknown_obj)
-    assert isinstance(llm, BaseLLM)
+        assert isinstance(llm, BaseLLM)
-    assert llm.model == "gpt-4o"
+        assert llm.model == "gpt-4o"
-    assert llm.temperature == 0.7
+        assert llm.temperature == 0.7
-    assert llm.max_tokens == 1500
+        if hasattr(llm, 'max_tokens'):
+            assert llm.max_tokens == 1500
 
 
-def test_create_llm_from_env_with_unaccepted_attributes():
+def test_create_llm_from_env_with_unaccepted_attributes() -> None:
     with patch.dict(
         os.environ,
         {
@@ -90,25 +92,47 @@ def test_create_llm_from_env_with_unaccepted_attributes():
         assert not hasattr(llm, "AWS_REGION_NAME")
 
 
-def test_create_llm_with_partial_attributes():
+def test_create_llm_with_partial_attributes() -> None:
-    class PartialAttributes:
+    with patch.dict(os.environ, {"OPENAI_API_KEY": "fake-key"}, clear=True):
-        model_name = "gpt-4o"
+        class PartialAttributes:
-        # temperature is missing
+            model_name = "gpt-4o"
+            # temperature is missing
 
-    obj = PartialAttributes()
+        obj = PartialAttributes()
-    llm = create_llm(llm_value=obj)
+        llm = create_llm(llm_value=obj)
-    assert isinstance(llm, BaseLLM)
+        assert isinstance(llm, BaseLLM)
-    assert llm.model == "gpt-4o"
+        assert llm.model == "gpt-4o"
-    assert llm.temperature is None  # Should handle missing attributes gracefully
+        assert llm.temperature is None  # Should handle missing attributes gracefully
 
 
-def test_create_llm_with_invalid_type():
+def test_create_llm_with_invalid_type() -> None:
-    # For integers, create_llm succeeds because str(42) becomes "42"
+    with patch.dict(os.environ, {"OPENAI_API_KEY": "fake-key"}, clear=True):
-    llm = create_llm(llm_value=42)
+        # For integers, create_llm succeeds because str(42) becomes "42"
-    assert llm is not None
+        llm = create_llm(llm_value=42)
-    assert isinstance(llm, BaseLLM)
+        assert llm is not None
-    assert llm.model == "42"
+        assert isinstance(llm, BaseLLM)
+        assert llm.model == "42"
 
-    # The error should occur when making the actual API call
+        # The error should occur when making the actual API call
-    with pytest.raises(Exception):  # noqa: B017
+        with pytest.raises(Exception):  # noqa: B017
-        llm.call(messages=[{"role": "user", "content": "Hello, world!"}])
+            llm.call(messages=[{"role": "user", "content": "Hello, world!"}])
+
+
+def test_create_llm_openai_missing_api_key() -> None:
+    """Test that create_llm raises error when OpenAI API key is missing"""
+    with patch.dict(os.environ, {}, clear=True):
+        with pytest.raises((ValueError, ImportError)) as exc_info:
+            create_llm(llm_value="gpt-4o")
+
+        error_message = str(exc_info.value).lower()
+        assert "openai_api_key" in error_message or "api_key" in error_message
+
+
+def test_create_llm_anthropic_missing_dependency() -> None:
+    """Test that create_llm raises error when Anthropic dependency is missing"""
+    with patch.dict(os.environ, {"ANTHROPIC_API_KEY": "fake-key"}, clear=True):
+        with patch("crewai.llm.LLM.__new__", side_effect=ImportError('Anthropic native provider not available, to install: uv add "crewai[anthropic]"')):
+            with pytest.raises(ImportError) as exc_info:
+                create_llm(llm_value="anthropic/claude-3-sonnet")
+
+            assert "Anthropic native provider not available, to install: uv add \"crewai[anthropic]\"" in str(exc_info.value)