fix: route llm model syntax to litellm

* fix: route llm model syntax to litellm

* wip: add list of supported models

lib/crewai/tests/llms/anthropic/test_anthropic.py

@@ -36,7 +36,7 @@ def test_anthropic_completion_is_used_when_claude_provider():
 
     from crewai.llms.providers.anthropic.completion import AnthropicCompletion
     assert isinstance(llm, AnthropicCompletion)
-    assert llm.provider == "claude"
+    assert llm.provider == "anthropic"
     assert llm.model == "claude-3-5-sonnet-20241022"
 
 

lib/crewai/tests/llms/azure/test_azure.py

@@ -39,7 +39,7 @@ def test_azure_completion_is_used_when_azure_openai_provider():
 
     from crewai.llms.providers.azure.completion import AzureCompletion
     assert isinstance(llm, AzureCompletion)
-    assert llm.provider == "azure_openai"
+    assert llm.provider == "azure"
     assert llm.model == "gpt-4"
 
 

lib/crewai/tests/llms/google/test_google.py

@@ -24,7 +24,7 @@ def test_gemini_completion_is_used_when_google_provider():
     llm = LLM(model="google/gemini-2.0-flash-001")
 
     assert llm.__class__.__name__ == "GeminiCompletion"
-    assert llm.provider == "google"
+    assert llm.provider == "gemini"
     assert llm.model == "gemini-2.0-flash-001"
 
 

lib/crewai/tests/llms/hooks/test_unsupported_providers.py

@@ -154,7 +154,7 @@ class TestGeminiProviderInterceptor:
         # Gemini provider should raise NotImplementedError
         with pytest.raises(NotImplementedError) as exc_info:
             LLM(
-                model="gemini/gemini-pro",
+                model="gemini/gemini-2.5-pro",
                 interceptor=interceptor,
                 api_key="test-gemini-key",
             )
@@ -169,7 +169,7 @@ class TestGeminiProviderInterceptor:
 
         with pytest.raises(NotImplementedError) as exc_info:
             LLM(
-                model="gemini/gemini-pro",
+                model="gemini/gemini-2.5-pro",
                 interceptor=interceptor,
                 api_key="test-gemini-key",
             )
@@ -181,7 +181,7 @@ class TestGeminiProviderInterceptor:
     def test_gemini_without_interceptor_works(self) -> None:
         """Test that Gemini LLM works without interceptor."""
         llm = LLM(
-            model="gemini/gemini-pro",
+            model="gemini/gemini-2.5-pro",
             api_key="test-gemini-key",
         )
 
@@ -231,7 +231,7 @@ class TestUnsupportedProviderMessages:
 
         with pytest.raises(NotImplementedError) as exc_info:
             LLM(
-                model="gemini/gemini-pro",
+                model="gemini/gemini-2.5-pro",
                 interceptor=interceptor,
                 api_key="test-gemini-key",
             )
@@ -282,7 +282,7 @@ class TestProviderSupportMatrix:
         # Gemini - NOT SUPPORTED
         with pytest.raises(NotImplementedError):
             LLM(
-                model="gemini/gemini-pro",
+                model="gemini/gemini-2.5-pro",
                 interceptor=interceptor,
                 api_key="test",
             )
@@ -315,5 +315,5 @@ class TestProviderSupportMatrix:
         assert not hasattr(bedrock_llm, 'interceptor') or bedrock_llm.interceptor is None
 
         # Gemini - doesn't have interceptor attribute
-        gemini_llm = LLM(model="gemini/gemini-pro", api_key="test")
-        assert not hasattr(gemini_llm, 'interceptor') or gemini_llm.interceptor is None
+        gemini_llm = LLM(model="gemini/gemini-2.5-pro", api_key="test")
+        assert not hasattr(gemini_llm, 'interceptor') or gemini_llm.interceptor is None

lib/crewai/tests/llms/openai/test_openai.py

@@ -16,7 +16,7 @@ def test_openai_completion_is_used_when_openai_provider():
     """
     Test that OpenAICompletion from completion.py is used when LLM uses provider 'openai'
     """
-    llm = LLM(model="openai/gpt-4o")
+    llm = LLM(model="gpt-4o")
 
     assert llm.__class__.__name__ == "OpenAICompletion"
     assert llm.provider == "openai"
@@ -70,7 +70,7 @@ def test_openai_completion_module_is_imported():
         del sys.modules[module_name]
 
     # Create LLM instance - this should trigger the import
-    LLM(model="openai/gpt-4o")
+    LLM(model="gpt-4o")
 
     # Verify the module was imported
     assert module_name in sys.modules
@@ -97,7 +97,7 @@ def test_native_openai_raises_error_when_initialization_fails():
 
         # This should raise ImportError, not fall back to LiteLLM
         with pytest.raises(ImportError) as excinfo:
-            LLM(model="openai/gpt-4o")
+            LLM(model="gpt-4o")
 
         assert "Error importing native provider" in str(excinfo.value)
         assert "Native SDK failed" in str(excinfo.value)
@@ -108,7 +108,7 @@ def test_openai_completion_initialization_parameters():
     Test that OpenAICompletion is initialized with correct parameters
     """
     llm = LLM(
-        model="openai/gpt-4o",
+        model="gpt-4o",
         temperature=0.7,
         max_tokens=1000,
         api_key="test-key"
@@ -311,7 +311,7 @@ def test_openai_completion_call_returns_usage_metrics():
         role="Research Assistant",
         goal="Find information about the population of Tokyo",
         backstory="You are a helpful research assistant.",
-        llm=LLM(model="openai/gpt-4o"),
+        llm=LLM(model="gpt-4o"),
         verbose=True,
     )
 
@@ -331,6 +331,7 @@ def test_openai_completion_call_returns_usage_metrics():
     assert result.token_usage.cached_prompt_tokens == 0
 
 
+@pytest.mark.skip(reason="Allow for litellm")
 def test_openai_raises_error_when_model_not_supported():
     """Test that OpenAICompletion raises ValueError when model not supported"""
 
@@ -354,7 +355,7 @@ def test_openai_client_setup_with_extra_arguments():
     Test that OpenAICompletion is initialized with correct parameters
     """
     llm = LLM(
-        model="openai/gpt-4o",
+        model="gpt-4o",
         temperature=0.7,
         max_tokens=1000,
         top_p=0.5,
@@ -391,7 +392,7 @@ def test_extra_arguments_are_passed_to_openai_completion():
     """
     Test that extra arguments are passed to OpenAICompletion
     """
-    llm = LLM(model="openai/gpt-4o", temperature=0.7, max_tokens=1000, top_p=0.5, max_retries=3)
+    llm = LLM(model="gpt-4o", temperature=0.7, max_tokens=1000, top_p=0.5, max_retries=3)
 
     with patch.object(llm.client.chat.completions, 'create') as mock_create:
         mock_create.return_value = MagicMock(