fix(llm): preserve Field() constraints on function tool arguments

livekit-agents/livekit/agents/llm/utils.py

@@ -347,7 +347,13 @@ def function_arguments_to_pydantic_model(func: Callable[..., Any]) -> type[BaseM
             )
             if annotated_field and hasattr(annotated_field, "asdict"):
                 # `asdict` is available after pydantic 2.12
-                field_attrs = annotated_field.asdict()["attributes"]
+                field_dict = annotated_field.asdict()
+                field_attrs = field_dict["attributes"]
+                # Constraints (ge/le/gt/lt/multiple_of/min_length/pattern/...) live
+                # in `metadata`, not `attributes`. Re-attach them to the annotation
+                # so `Field(...)` constraints on a tool argument are preserved.
+                if field_dict["metadata"]:
+                    type_hint = Annotated[(type_hint, *field_dict["metadata"])]
             elif annotated_field:
                 field_attrs["default"] = annotated_field.default
                 field_attrs["description"] = annotated_field.description

tests/test_tools.py

@@ -3,7 +3,7 @@
 from typing import Annotated, Any, Literal
 
 import pytest
-from pydantic import BaseModel, Field
+from pydantic import BaseModel, Field, ValidationError
 
 from livekit.agents import Agent
 from livekit.agents.llm import ProviderTool, Tool, ToolContext, ToolError, Toolset, function_tool
@@ -338,6 +338,26 @@ def test_function_arguments_to_pydantic_model(self):
             "type": "object",
         }
 
+    def test_field_constraints_preserved(self):
+        # Field(...) constraints (ge/le/pattern/...) live in FieldInfo.metadata,
+        # not its attributes; they must survive the signature -> pydantic model
+        # conversion so the model both advertises and enforces them.
+        @function_tool
+        async def book(count: Annotated[int, Field(ge=1, le=10, description="how many")]) -> str:
+            """Book a thing."""
+            return "ok"
+
+        model = function_arguments_to_pydantic_model(book)
+        prop = model.model_json_schema()["properties"]["count"]
+        assert prop["minimum"] == 1
+        assert prop["maximum"] == 10
+        assert prop["description"] == "how many"
+
+        model(count=5)  # within bounds
+        for bad in (0, 11):
+            with pytest.raises(ValidationError):
+                model(count=bad)
+
     async def test_tool_execution(self):
         args, kwargs = prepare_function_arguments(
             fnc=mock_tool_1, json_arguments='{"arg1": "test", "opt_arg2": "test2"}'