[BUG] When supports_function_calling() is True, only the output_pydantic model is sent to LiteLLM (the custom agent tools are discard)

[Killian-fal]

Description

When supports_function_calling() returns True (either natively or via override) and a output_pydantic is set, the custom agent tools are skip and only the output_pydantic model is send as a tool.

The result: the LLM only sees the output_pydantic Pydantic model as a native tool, not the agent's actual tools. The agent is forced to return structured output immediately without calling any of its tools.

Note: I believe CrewAI 1.10.0 introduced a major regression on Ollama + LiteLLM that renders CrewAI unusable. Perhaps more tests should be written for this specific feature to prevent this from happening in the future.

Steps to Reproduce

from crewai import LLM, Agent, Task, Crew
from crewai.tools import BaseTool
from pydantic import BaseModel

class MyOutput(BaseModel):
    name: str
    value: str

class MySearchTool(BaseTool):
    name: str = "my_search_tool"
    description: str = "Search for data"
    def _run(self, query: str) -> str:
        return '{"name": "Alice", "value": "42"}'

llm = LLM(model="ollama_chat/mistral-small3.2:24b", api_base="https://my-remote-ollama.example.com")

agent = Agent(role="Researcher", goal="Find data", backstory="...", llm=llm, tools=[MySearchTool()])
task = Task(
    description="Find the name and value",
    expected_output="Structured result",
    agent=agent,
    output_pydantic=MyOutput,
)
crew = Crew(agents=[agent], tasks=[task])
crew.kickoff() # Or with async

1. Set supports_function_calling() to True.
2. Add a tool to the agent and set output_pydantic on the task.
3. Run the crew.
4. Observe that LiteLLM only receives the output_pydantic model as a tool — the agent's my_search_tool is absent.

Expected behavior

• When supports_function_calling() is True and the agent has tools, all agent tools should be forwarded to LiteLLM.
• The agent should be able to call its tools (e.g. my_search_tool) before returning the final structured output.

Screenshots/Code snippets

X

Operating System

Ubuntu 20.04

Python Version

3.10

crewAI Version

1.10.0

crewAI Tools Version

1.10.0

Virtual Environment

Venv

Evidence

I've nothing to show sorry (with mlflow we can see that the tools are not send to litellm)

Possible Solution

I tried to debug this issue and found that InternalInstructor intercepts even when params["tools"] is populated. And InternalInstructor create his own instance of litellm to call the provider. Maybe the bug is here ?

Additional context

X