Add typed output schemas for tools

Tools can now declare an `output_schema`, set explicitly or inferred
from a Pydantic return annotation. `format_output_for_agent` validates
the raw result against it and serializes to JSON for the agent, while
`run` keeps returning the raw value. Falls back to `str(raw_result)`
with a warning when validation or serialization fails.

lib/crewai/src/crewai/tools/base_tool.py

@@ -33,6 +33,8 @@ from typing_extensions import TypeIs
 from crewai.tools.structured_tool import (
     CrewStructuredTool,
     _deserialize_schema,
+    _format_tool_output_for_agent,
+    _infer_output_schema_from_callable,
     _serialize_schema,
     build_schema_hint,
 )
@@ -149,6 +151,11 @@ class BaseTool(BaseModel, ABC):
         validate_default=True,
         description="The schema for the arguments that the tool accepts.",
     )
+    output_schema: type[PydanticBaseModel] | None = Field(
+        default=None,
+        validate_default=True,
+        description="The schema for the output that the tool returns.",
+    )
 
     @field_serializer("args_schema", when_used="json")
     def _serialize_args_schema(
@@ -156,6 +163,12 @@ class BaseTool(BaseModel, ABC):
     ) -> dict[str, Any] | None:
         return _serialize_schema(schema)
 
+    @field_serializer("output_schema", when_used="json")
+    def _serialize_output_schema(
+        self, schema: type[PydanticBaseModel] | None
+    ) -> dict[str, Any] | None:
+        return _serialize_schema(schema)
+
     description_updated: bool = Field(
         default=False, description="Flag to check if the description has been updated."
     )
@@ -233,6 +246,17 @@ class BaseTool(BaseModel, ABC):
 
         return create_model(f"{cls.__name__}Schema", **fields)
 
+    @field_validator("output_schema", mode="before")
+    @classmethod
+    def _default_output_schema(
+        cls, v: type[PydanticBaseModel] | dict[str, Any] | None
+    ) -> type[PydanticBaseModel] | None:
+        if isinstance(v, dict):
+            return _deserialize_schema(v)
+        if v is not None:
+            return v
+        return _infer_output_schema_from_callable(cls._run)
+
     @field_validator("max_usage_count", mode="before")
     @classmethod
     def validate_max_usage_count(cls, v: int | None) -> int | None:
@@ -340,6 +364,10 @@ class BaseTool(BaseModel, ABC):
             "Override _arun for async support or use run() for sync execution."
         )
 
+    def format_output_for_agent(self, raw_result: Any) -> str:
+        """Format a raw tool result into the string representation sent to an agent."""
+        return _format_tool_output_for_agent(self, raw_result)
+
     def reset_usage_count(self) -> None:
         """Reset the current usage count to zero."""
         self.current_usage_count = 0
@@ -369,6 +397,7 @@ class BaseTool(BaseModel, ABC):
             name=self.name,
             description=self.description,
             args_schema=self.args_schema,
+            output_schema=self.output_schema,
             func=self._run,
             result_as_answer=self.result_as_answer,
             max_usage_count=self.max_usage_count,
@@ -390,6 +419,9 @@ class BaseTool(BaseModel, ABC):
             raise ValueError("The provided tool must have a callable 'func' attribute.")
 
         args_schema = getattr(tool, "args_schema", None)
+        output_schema = getattr(tool, "output_schema", None)
+        if output_schema is None:
+            output_schema = _infer_output_schema_from_callable(tool.func)
 
         if args_schema is None:
             func_signature = signature(tool.func)
@@ -420,6 +452,7 @@ class BaseTool(BaseModel, ABC):
             description=getattr(tool, "description", ""),
             func=tool.func,
             args_schema=args_schema,
+            output_schema=output_schema,
         )
 
     def _set_args_schema(self) -> None:
@@ -568,6 +601,9 @@ class Tool(BaseTool, Generic[P, R]):
             raise ValueError("The provided tool must have a callable 'func' attribute.")
 
         args_schema = getattr(tool, "args_schema", None)
+        output_schema = getattr(tool, "output_schema", None)
+        if output_schema is None:
+            output_schema = _infer_output_schema_from_callable(tool.func)
 
         if args_schema is None:
             func_signature = signature(tool.func)
@@ -598,6 +634,7 @@ class Tool(BaseTool, Generic[P, R]):
             description=getattr(tool, "description", ""),
             func=tool.func,
             args_schema=args_schema,
+            output_schema=output_schema,
         )
 
 
@@ -621,6 +658,7 @@ def tool(
     name: str,
     /,
     *,
+    output_schema: type[BaseModel] | None = ...,
     result_as_answer: bool = ...,
     max_usage_count: int | None = ...,
 ) -> Callable[[Callable[P2, R2]], Tool[P2, R2]]: ...
@@ -629,6 +667,7 @@ def tool(
 @overload
 def tool(
     *,
+    output_schema: type[BaseModel] | None = ...,
     result_as_answer: bool = ...,
     max_usage_count: int | None = ...,
 ) -> Callable[[Callable[P2, R2]], Tool[P2, R2]]: ...
@@ -636,6 +675,7 @@ def tool(
 
 def tool(
     *args: Callable[P2, R2] | str,
+    output_schema: type[BaseModel] | None = None,
     result_as_answer: bool = False,
     max_usage_count: int | None = None,
 ) -> Tool[P2, R2] | Callable[[Callable[P2, R2]], Tool[P2, R2]]:
@@ -649,6 +689,7 @@ def tool(
     Args:
         *args: Either the function to decorate or a custom tool name.
         result_as_answer: If True, the tool result becomes the final agent answer.
+        output_schema: Optional schema for the output that the tool returns.
         max_usage_count: Maximum times this tool can be used. None means unlimited.
 
     Returns:
@@ -690,12 +731,16 @@ def tool(
 
             class_name = "".join(tool_name.split()).title()
             args_schema = create_model(class_name, **fields)
+            resolved_output_schema = (
+                output_schema or _infer_output_schema_from_callable(f)
+            )
 
             return Tool(
                 name=tool_name,
                 description=f.__doc__,
                 func=f,
                 args_schema=args_schema,
+                output_schema=resolved_output_schema,
                 result_as_answer=result_as_answer,
                 max_usage_count=max_usage_count,
                 current_usage_count=0,

lib/crewai/src/crewai/tools/structured_tool.py

@@ -5,7 +5,8 @@ from collections.abc import Callable
 import inspect
 import json
 import textwrap
-from typing import TYPE_CHECKING, Annotated, Any, get_type_hints
+from typing import TYPE_CHECKING, Annotated, Any, cast, get_type_hints
+import warnings
 
 from pydantic import (
     BaseModel,
@@ -36,6 +37,47 @@ def _deserialize_schema(v: Any) -> type[BaseModel] | None:
     return None
 
 
+def _infer_output_schema_from_callable(
+    func: Callable[..., Any],
+) -> type[BaseModel] | None:
+    try:
+        return_annotation = get_type_hints(func).get("return", inspect.Signature.empty)
+    except Exception:
+        return_annotation = inspect.signature(func).return_annotation
+
+    if isinstance(return_annotation, type) and issubclass(return_annotation, BaseModel):
+        return return_annotation
+
+    return None
+
+
+def _format_tool_output_for_agent(tool: Any, raw_result: Any) -> str:
+    output_schema = getattr(tool, "output_schema", None)
+    if output_schema is None:
+        return str(raw_result)
+
+    try:
+        validation_input = raw_result
+        if isinstance(raw_result, BaseModel) and not isinstance(
+            raw_result, output_schema
+        ):
+            validation_input = raw_result.model_dump()
+
+        validated = output_schema.model_validate(validation_input)
+        return cast(str, validated.model_dump_json())
+    except Exception as exc:
+        warnings.warn(
+            (
+                f"Failed to validate or serialize output from tool "
+                f"'{getattr(tool, 'name', '<unknown>')}' using output_schema "
+                f"'{output_schema.__name__}': {exc}. Falling back to str(raw_result)."
+            ),
+            RuntimeWarning,
+            stacklevel=2,
+        )
+        return str(raw_result)
+
+
 if TYPE_CHECKING:
     pass
 
@@ -81,6 +123,11 @@ class CrewStructuredTool(BaseModel):
         BeforeValidator(_deserialize_schema),
         PlainSerializer(_serialize_schema),
     ] = Field(default=None)
+    output_schema: Annotated[
+        type[BaseModel] | None,
+        BeforeValidator(_deserialize_schema),
+        PlainSerializer(_serialize_schema),
+    ] = Field(default=None)
     func: Any = Field(default=None, exclude=True)
     result_as_answer: bool = Field(default=False)
     max_usage_count: int | None = Field(default=None)
@@ -103,6 +150,7 @@ class CrewStructuredTool(BaseModel):
         description: str | None = None,
         return_direct: bool = False,
         args_schema: type[BaseModel] | None = None,
+        output_schema: type[BaseModel] | None = None,
         infer_schema: bool = True,
         **kwargs: Any,
     ) -> CrewStructuredTool:
@@ -114,6 +162,7 @@ class CrewStructuredTool(BaseModel):
             description: The description of the tool. Defaults to the function docstring
             return_direct: Whether to return the output directly
             args_schema: Optional schema for the function arguments
+            output_schema: Optional schema for the function output
             infer_schema: Whether to infer the schema from the function signature
             **kwargs: Additional arguments to pass to the tool
 
@@ -149,10 +198,16 @@ class CrewStructuredTool(BaseModel):
             name=name,
             description=description,
             args_schema=schema,
+            output_schema=output_schema or _infer_output_schema_from_callable(func),
             func=func,
             result_as_answer=return_direct,
+            **kwargs,
         )
 
+    def format_output_for_agent(self, raw_result: Any) -> str:
+        """Format a raw tool result into the string representation sent to an agent."""
+        return _format_tool_output_for_agent(self, raw_result)
+
     @staticmethod
     def _create_schema_from_function(
         name: str,

lib/crewai/tests/tools/test_base_tool.py

@@ -1,12 +1,13 @@
 import asyncio
 from collections.abc import Callable
+import json
 from unittest.mock import patch
 
 from crewai.agent import Agent
 from crewai.crew import Crew
 from crewai.task import Task
 from crewai.tools import BaseTool, tool
-from pydantic import BaseModel, Field
+from pydantic import BaseModel, Field, RootModel
 import pytest
 
 
@@ -351,6 +352,240 @@ class TestToolDecoratorRunValidation:
         assert result == "Hello, World!"
 
 
+class SearchOutput(BaseModel):
+    query: str
+    score: float
+
+
+class SearchResults(RootModel[list[SearchOutput]]):
+    pass
+
+
+class ExplicitSearchTool(BaseTool):
+    name: str = "search"
+    description: str = "Search for a query"
+    output_schema: type[BaseModel] = SearchOutput
+
+    def _run(self, query: str) -> dict[str, object]:
+        return {"query": query, "score": 0.8}
+
+
+class InferredSearchTool(BaseTool):
+    name: str = "search"
+    description: str = "Search for a query"
+
+    def _run(self, query: str) -> SearchOutput:
+        return SearchOutput(query=query, score=0.7)
+
+
+class RootSearchTool(BaseTool):
+    name: str = "search"
+    description: str = "Search for a query"
+
+    def _run(self, query: str) -> SearchResults:
+        return SearchResults([SearchOutput(query=query, score=1.0)])
+
+
+class DictAnnotatedSearchTool(BaseTool):
+    name: str = "search"
+    description: str = "Search for a query"
+
+    def _run(self, query: str) -> dict[str, object]:
+        return {"query": query, "score": 0.5}
+
+
+def _make_explicit_decorator_tool() -> BaseTool:
+    @tool("search", output_schema=SearchOutput)
+    def search(query: str) -> dict[str, object]:
+        """Search for a query."""
+        return {"query": query, "score": 0.8}
+
+    return search
+
+
+def _make_inferred_decorator_tool() -> BaseTool:
+    @tool("search")
+    def search(query: str) -> SearchOutput:
+        """Search for a query."""
+        return SearchOutput(query=query, score=0.6)
+
+    return search
+
+
+def _make_root_decorator_tool() -> BaseTool:
+    @tool("search")
+    def search(query: str) -> SearchResults:
+        """Search for a query."""
+        return SearchResults([SearchOutput(query=query, score=1.0)])
+
+    return search
+
+
+class TestToolOutputSchema:
+    """Tests for typed tool output behavior."""
+
+    @pytest.mark.parametrize(
+        ("tool_cls", "expected_raw", "expected_agent_payload"),
+        [
+            pytest.param(
+                ExplicitSearchTool,
+                {"query": "crew", "score": 0.8},
+                {"query": "crew", "score": 0.8},
+                id="explicit-schema",
+            ),
+            pytest.param(
+                InferredSearchTool,
+                SearchOutput(query="crew", score=0.7),
+                {"query": "crew", "score": 0.7},
+                id="inferred-base-model",
+            ),
+            pytest.param(
+                RootSearchTool,
+                SearchResults([SearchOutput(query="crew", score=1.0)]),
+                [{"query": "crew", "score": 1.0}],
+                id="inferred-root-model",
+            ),
+        ],
+    )
+    def test_base_tools_return_raw_result_and_json_agent_text(
+        self,
+        tool_cls: type[BaseTool],
+        expected_raw: object,
+        expected_agent_payload: object,
+    ) -> None:
+        t = tool_cls()
+
+        raw_result = t.run(query="crew")
+
+        assert raw_result == expected_raw
+        assert json.loads(t.format_output_for_agent(raw_result)) == (
+            expected_agent_payload
+        )
+
+    def test_base_tool_does_not_infer_non_pydantic_return_annotation(self) -> None:
+        t = DictAnnotatedSearchTool()
+
+        raw_result = t.run(query="crew")
+
+        assert raw_result == {"query": "crew", "score": 0.5}
+        assert t.format_output_for_agent(raw_result) == str(raw_result)
+
+    @pytest.mark.parametrize(
+        ("make_tool", "expected_raw", "expected_agent_payload"),
+        [
+            pytest.param(
+                _make_explicit_decorator_tool,
+                {"query": "crew", "score": 0.8},
+                {"query": "crew", "score": 0.8},
+                id="explicit-schema",
+            ),
+            pytest.param(
+                _make_inferred_decorator_tool,
+                SearchOutput(query="crew", score=0.6),
+                {"query": "crew", "score": 0.6},
+                id="inferred-base-model",
+            ),
+            pytest.param(
+                _make_root_decorator_tool,
+                SearchResults([SearchOutput(query="crew", score=1.0)]),
+                [{"query": "crew", "score": 1.0}],
+                id="inferred-root-model",
+            ),
+        ],
+    )
+    def test_decorator_tools_return_raw_result_and_json_agent_text(
+        self,
+        make_tool: Callable[[], BaseTool],
+        expected_raw: object,
+        expected_agent_payload: object,
+    ) -> None:
+        search = make_tool()
+
+        raw_result = search.run(query="crew")
+
+        assert raw_result == expected_raw
+        assert json.loads(search.format_output_for_agent(raw_result)) == (
+            expected_agent_payload
+        )
+
+    def test_decorator_tool_does_not_infer_non_pydantic_return_annotation(
+        self,
+    ) -> None:
+        @tool("search")
+        def search(query: str) -> dict[str, object]:
+            """Search for a query."""
+            return {"query": query, "score": 0.5}
+
+        raw_result = search.run(query="crew")
+
+        assert raw_result == {"query": "crew", "score": 0.5}
+        assert search.format_output_for_agent(raw_result) == str(raw_result)
+
+    def test_explicit_output_schema_wins_over_return_annotation(self) -> None:
+        class AlternateOutput(BaseModel):
+            value: str
+
+        @tool("search", output_schema=AlternateOutput)
+        def search(query: str) -> SearchOutput:
+            """Search for a query."""
+            return SearchOutput(query=query, score=0.6)
+
+        raw_result = search.run(query="crew")
+
+        with pytest.warns(RuntimeWarning, match="AlternateOutput"):
+            agent_text = search.format_output_for_agent(raw_result)
+
+        assert raw_result == SearchOutput(query="crew", score=0.6)
+        assert agent_text == str(raw_result)
+
+    def test_invalid_typed_output_warns_and_uses_string_agent_text(
+        self,
+    ) -> None:
+        @tool("search", output_schema=SearchOutput)
+        def search(query: str) -> dict[str, object]:
+            """Search for a query."""
+            return {"query": query, "score": "not-a-float"}
+
+        raw_result = search.run(query="crew")
+
+        with pytest.warns(RuntimeWarning, match="Failed to validate or serialize"):
+            agent_text = search.format_output_for_agent(raw_result)
+
+        assert raw_result == {"query": "crew", "score": "not-a-float"}
+        assert agent_text == str(raw_result)
+
+    def test_unserializable_typed_output_warns_and_uses_string_agent_text(
+        self,
+    ) -> None:
+        class OpaqueOutput(BaseModel):
+            value: object
+
+        raw_result = OpaqueOutput(value=object())
+
+        @tool("opaque", output_schema=OpaqueOutput)
+        def opaque() -> OpaqueOutput:
+            """Return an opaque object."""
+            return raw_result
+
+        result = opaque.run()
+
+        with pytest.warns(RuntimeWarning, match="Failed to validate or serialize"):
+            agent_text = opaque.format_output_for_agent(result)
+
+        assert result is raw_result
+        assert agent_text == str(raw_result)
+
+    def test_output_schema_behavior_carries_over_to_structured_tool(self) -> None:
+        structured = ExplicitSearchTool().to_structured_tool()
+
+        raw_result = structured.invoke({"query": "crew"})
+
+        assert raw_result == {"query": "crew", "score": 0.8}
+        assert json.loads(structured.format_output_for_agent(raw_result)) == {
+            "query": "crew",
+            "score": 0.8,
+        }
+
 # Async arun() Schema Validation Tests
 
 

lib/crewai/tests/tools/test_structured_tool.py

@@ -1,5 +1,7 @@
+import json
+
 from crewai.tools.structured_tool import CrewStructuredTool
-from pydantic import BaseModel, Field
+from pydantic import BaseModel, Field, RootModel
 import pytest
 
 
@@ -86,6 +88,116 @@ def test_from_function(basic_function):
     assert isinstance(tool.args_schema, type(BaseModel))
 
 
+class StructuredOutput(BaseModel):
+    value: str
+    count: int
+
+
+class StructuredOutputList(RootModel[list[StructuredOutput]]):
+    pass
+
+
+def _build_explicit_structured_value(value: str) -> dict[str, object]:
+    """Build a value."""
+    return {"value": value, "count": 1}
+
+
+def _build_inferred_structured_value(value: str) -> StructuredOutput:
+    """Build a value."""
+    return StructuredOutput(value=value, count=1)
+
+
+def _build_structured_values(value: str) -> StructuredOutputList:
+    """Build values."""
+    return StructuredOutputList([StructuredOutput(value=value, count=1)])
+
+
+def _build_plain_structured_value(value: str) -> dict[str, object]:
+    """Build a value."""
+    return {"value": value, "count": 1}
+
+
+@pytest.mark.parametrize(
+    ("func", "output_schema", "expected_raw", "expected_agent_payload"),
+    [
+        pytest.param(
+            _build_explicit_structured_value,
+            StructuredOutput,
+            {"value": "crew", "count": 1},
+            {"value": "crew", "count": 1},
+            id="explicit-schema",
+        ),
+        pytest.param(
+            _build_inferred_structured_value,
+            None,
+            StructuredOutput(value="crew", count=1),
+            {"value": "crew", "count": 1},
+            id="inferred-base-model",
+        ),
+        pytest.param(
+            _build_structured_values,
+            None,
+            StructuredOutputList([StructuredOutput(value="crew", count=1)]),
+            [{"value": "crew", "count": 1}],
+            id="inferred-root-model",
+        ),
+    ],
+)
+def test_from_function_returns_raw_result_and_json_agent_text(
+    func,
+    output_schema,
+    expected_raw,
+    expected_agent_payload,
+):
+    """Typed structured tools return raw values and format JSON for the agent."""
+    kwargs = {"output_schema": output_schema} if output_schema is not None else {}
+    tool = CrewStructuredTool.from_function(
+        func=func,
+        name="build_value",
+        **kwargs,
+    )
+
+    raw_result = tool.invoke({"value": "crew"})
+
+    assert raw_result == expected_raw
+    assert json.loads(tool.format_output_for_agent(raw_result)) == (
+        expected_agent_payload
+    )
+
+
+def test_from_function_does_not_infer_non_pydantic_output_schema():
+    """Non-Pydantic return annotations use the plain string formatter."""
+    tool = CrewStructuredTool.from_function(
+        func=_build_plain_structured_value,
+        name="build_value",
+    )
+
+    raw_result = tool.invoke({"value": "crew"})
+
+    assert raw_result == {"value": "crew", "count": 1}
+    assert tool.format_output_for_agent(raw_result) == str(raw_result)
+
+
+def test_invalid_typed_output_warns_and_uses_string_agent_text():
+    """Invalid structured output leaves the raw result unchanged."""
+    def build_value(value: str) -> dict[str, object]:
+        """Build a value."""
+        return {"value": value, "count": "wrong"}
+
+    tool = CrewStructuredTool.from_function(
+        func=build_value,
+        name="build_value",
+        output_schema=StructuredOutput,
+    )
+    raw_result = tool.invoke({"value": "crew"})
+
+    with pytest.warns(RuntimeWarning, match="Failed to validate or serialize"):
+        agent_text = tool.format_output_for_agent(raw_result)
+
+    assert raw_result == {"value": "crew", "count": "wrong"}
+    assert agent_text == str(raw_result)
+
+
 def test_validate_function_signature(basic_function, schema_class):
     """Test function signature validation"""
     tool = CrewStructuredTool(