Suppressed userWarnings from litellm pydantic issues (#1833)

* Suppressed userWarnings from litellm pydantic issues

* change litellm version

* Fix failling ollama tasks

pyproject.toml

@@ -11,7 +11,7 @@ dependencies = [
     # Core Dependencies
     "pydantic>=2.4.2",
     "openai>=1.13.3",
-    "litellm>=1.44.22",
+    "litellm>=1.56.4",
     "instructor>=1.3.3",
 
     # Text Processing

src/crewai/crew.py

@@ -726,11 +726,7 @@ def _execute_tasks(
 
             # Determine which tools to use - task tools take precedence over agent tools
             tools_for_task = task.tools or agent_to_use.tools or []
-            tools_for_task = self._prepare_tools(
-                agent_to_use,
-                task,
-                tools_for_task
-            )
+            tools_for_task = self._prepare_tools(agent_to_use, task, tools_for_task)
 
             self._log_task_start(task, agent_to_use.role)
 
@@ -797,14 +793,18 @@ def _handle_conditional_task(
             return skipped_task_output
         return None
 
-    def _prepare_tools(self, agent: BaseAgent, task: Task, tools: List[Tool]) -> List[Tool]:
+    def _prepare_tools(
+        self, agent: BaseAgent, task: Task, tools: List[Tool]
+    ) -> List[Tool]:
         # Add delegation tools if agent allows delegation
         if agent.allow_delegation:
             if self.process == Process.hierarchical:
                 if self.manager_agent:
                     tools = self._update_manager_tools(task, tools)
                 else:
-                    raise ValueError("Manager agent is required for hierarchical process.")
+                    raise ValueError(
+                        "Manager agent is required for hierarchical process."
+                    )
 
             elif agent and agent.allow_delegation:
                 tools = self._add_delegation_tools(task, tools)
@@ -823,7 +823,9 @@ def _get_agent_to_use(self, task: Task) -> Optional[BaseAgent]:
             return self.manager_agent
         return task.agent
 
-    def _merge_tools(self, existing_tools: List[Tool], new_tools: List[Tool]) -> List[Tool]:
+    def _merge_tools(
+        self, existing_tools: List[Tool], new_tools: List[Tool]
+    ) -> List[Tool]:
         """Merge new tools into existing tools list, avoiding duplicates by tool name."""
         if not new_tools:
             return existing_tools
@@ -839,7 +841,9 @@ def _merge_tools(self, existing_tools: List[Tool], new_tools: List[Tool]) -> Lis
 
         return tools
 
-    def _inject_delegation_tools(self, tools: List[Tool], task_agent: BaseAgent, agents: List[BaseAgent]):
+    def _inject_delegation_tools(
+        self, tools: List[Tool], task_agent: BaseAgent, agents: List[BaseAgent]
+    ):
         delegation_tools = task_agent.get_delegation_tools(agents)
         return self._merge_tools(tools, delegation_tools)
 
@@ -856,7 +860,9 @@ def _add_delegation_tools(self, task: Task, tools: List[Tool]):
         if len(self.agents) > 1 and len(agents_for_delegation) > 0 and task.agent:
             if not tools:
                 tools = []
-            tools = self._inject_delegation_tools(tools, task.agent, agents_for_delegation)
+            tools = self._inject_delegation_tools(
+                tools, task.agent, agents_for_delegation
+            )
         return tools
 
     def _log_task_start(self, task: Task, role: str = "None"):
@@ -870,7 +876,9 @@ def _update_manager_tools(self, task: Task, tools: List[Tool]):
             if task.agent:
                 tools = self._inject_delegation_tools(tools, task.agent, [task.agent])
             else:
-                tools = self._inject_delegation_tools(tools, self.manager_agent, self.agents)
+                tools = self._inject_delegation_tools(
+                    tools, self.manager_agent, self.agents
+                )
         return tools
 
     def _get_context(self, task: Task, task_outputs: List[TaskOutput]):

src/crewai/llm.py

@@ -6,8 +6,10 @@
 from contextlib import contextmanager
 from typing import Any, Dict, List, Optional, Union
 
-import litellm
-from litellm import get_supported_openai_params
+with warnings.catch_warnings():
+    warnings.simplefilter("ignore", UserWarning)
+    import litellm
+    from litellm import get_supported_openai_params
 
 from crewai.utilities.exceptions.context_window_exceeding_exception import (
     LLMContextLengthExceededException,
@@ -138,7 +140,7 @@ def __init__(
         self.kwargs = kwargs
 
         litellm.drop_params = True
-        litellm.set_verbose = False
+
         self.set_callbacks(callbacks)
         self.set_env_callbacks()
 

src/crewai/utilities/internal_instructor.py

@@ -1,3 +1,4 @@
+import warnings
 from typing import Any, Optional, Type
 
 
@@ -25,9 +26,10 @@ def set_instructor(self):
         if self.agent and not self.llm:
             self.llm = self.agent.function_calling_llm or self.agent.llm
 
-        # Lazy import
-        import instructor
-        from litellm import completion
+        with warnings.catch_warnings():
+            warnings.simplefilter("ignore", UserWarning)
+            import instructor
+            from litellm import completion
 
         self._client = instructor.from_litellm(
             completion,

src/crewai/utilities/token_counter_callback.py

@@ -1,3 +1,5 @@
+import warnings
+
 from litellm.integrations.custom_logger import CustomLogger
 from litellm.types.utils import Usage
 
@@ -12,11 +14,13 @@ def log_success_event(self, kwargs, response_obj, start_time, end_time):
         if self.token_cost_process is None:
             return
 
-        usage: Usage = response_obj["usage"]
-        self.token_cost_process.sum_successful_requests(1)
-        self.token_cost_process.sum_prompt_tokens(usage.prompt_tokens)
-        self.token_cost_process.sum_completion_tokens(usage.completion_tokens)
-        if usage.prompt_tokens_details:
-            self.token_cost_process.sum_cached_prompt_tokens(
-                usage.prompt_tokens_details.cached_tokens
-            )
+        with warnings.catch_warnings():
+            warnings.simplefilter("ignore", UserWarning)
+            usage: Usage = response_obj["usage"]
+            self.token_cost_process.sum_successful_requests(1)
+            self.token_cost_process.sum_prompt_tokens(usage.prompt_tokens)
+            self.token_cost_process.sum_completion_tokens(usage.completion_tokens)
+            if usage.prompt_tokens_details:
+                self.token_cost_process.sum_cached_prompt_tokens(
+                    usage.prompt_tokens_details.cached_tokens
+                )

tests/agent_test.py

@@ -1445,34 +1445,31 @@ def test_llm_call_with_all_attributes():
 
 
 @pytest.mark.vcr(filter_headers=["authorization"])
-def test_agent_with_ollama_gemma():
+def test_agent_with_ollama_llama3():
     agent = Agent(
         role="test role",
         goal="test goal",
         backstory="test backstory",
-        llm=LLM(
-            model="ollama/gemma2:latest",
-            base_url="http://localhost:8080",
-        ),
+        llm=LLM(model="ollama/llama3.2:3b", base_url="http://localhost:11434"),
     )
 
     assert isinstance(agent.llm, LLM)
-    assert agent.llm.model == "ollama/gemma2:latest"
-    assert agent.llm.base_url == "http://localhost:8080"
+    assert agent.llm.model == "ollama/llama3.2:3b"
+    assert agent.llm.base_url == "http://localhost:11434"
 
     task = "Respond in 20 words. Who are you?"
     response = agent.llm.call([{"role": "user", "content": task}])
 
     assert response
     assert len(response.split()) <= 25  # Allow a little flexibility in word count
-    assert "Gemma" in response or "AI" in response or "language model" in response
+    assert "Llama3" in response or "AI" in response or "language model" in response
 
 
 @pytest.mark.vcr(filter_headers=["authorization"])
-def test_llm_call_with_ollama_gemma():
+def test_llm_call_with_ollama_llama3():
     llm = LLM(
-        model="ollama/gemma2:latest",
-        base_url="http://localhost:8080",
+        model="ollama/llama3.2:3b",
+        base_url="http://localhost:11434",
         temperature=0.7,
         max_tokens=30,
     )
@@ -1482,7 +1479,7 @@ def test_llm_call_with_ollama_gemma():
 
     assert response
     assert len(response.split()) <= 25  # Allow a little flexibility in word count
-    assert "Gemma" in response or "AI" in response or "language model" in response
+    assert "Llama3" in response or "AI" in response or "language model" in response
 
 
 @pytest.mark.vcr(filter_headers=["authorization"])
@@ -1578,7 +1575,7 @@ def test_agent_execute_task_with_ollama():
         role="test role",
         goal="test goal",
         backstory="test backstory",
-        llm=LLM(model="ollama/gemma2:latest", base_url="http://localhost:8080"),
+        llm=LLM(model="ollama/llama3.2:3b", base_url="http://localhost:11434"),
     )
 
     task = Task(