Add typed output schemas for CrewAI tools (#6236)

Currently, tools have a strong input contract through `args_schema`, but no
output contract. This means that anything a tool outputs is converted to
string.

Not only the contract is weak, but the "invisible" conversion to string can
have unexpected effects when the tool returns complex objects like dicts and
arrays.

With this PR, a tool can _optionally_ define an output contract with
`output_schema`. CrewAI validates the raw result and sends the agent JSON.

```python
class ProductResult(BaseModel):
    sku: str
    name: str
    in_stock: bool

class ProductLookupTool(BaseTool):
    name: str = "Product Lookup"
    description: str = "Look up product availability by SKU."

    def _run(self, sku: str) -> ProductResult:
        return ProductResult(sku=sku, name="USB-C dock", in_stock=True)
```

If the result does not match the schema, CrewAI warns and falls back to
`str(raw_result)` instead of failing the run:

```python
@tool("Product Lookup", output_schema=ProductResult)
def product_lookup(sku: str) -> dict[str, object]:
    return {"sku": sku, "name": "USB-C dock", "in_stock": True}

#=> RuntimeWarning: Failed to validate or serialize output from tool 'Bad Product Lookup' using output_schema 'ProductResult'... Falling back to str(raw_result).
```

This is additive and non-breaking. Existing tools do not need to change. Tools
without `output_schema` keep the old string behavior. Invalid typed outputs
warn and fall back to the old formatting path.

lib/crewai/src/crewai/agents/crew_agent_executor.py

@@ -57,6 +57,7 @@ from crewai.utilities.agent_utils import (
     convert_tools_to_openai_schema,
     enforce_rpm_limit,
     format_message_for_llm,
+    format_native_tool_output_for_agent,
     get_llm_response,
     handle_agent_action_core,
     handle_context_length,
@@ -907,19 +908,31 @@ class CrewAgentExecutor(BaseAgentExecutor):
         ):
             max_usage_reached = True
 
+        structured_tool: CrewStructuredTool | None = None
+        if original_tool is not None:
+            for structured in self.tools or []:
+                if getattr(structured, "_original_tool", None) is original_tool:
+                    structured_tool = structured
+                    break
+        if structured_tool is None:
+            for structured in self.tools or []:
+                if sanitize_tool_name(structured.name) == func_name:
+                    structured_tool = structured
+                    break
+
+        output_tool = original_tool or structured_tool
+
         from_cache = False
         result: str = "Tool not found"
+        raw_tool_result: Any = result
         input_str = json.dumps(args_dict) if args_dict else ""
-        if self.tools_handler and self.tools_handler.cache:
+        if self.tools_handler and self.tools_handler.cache and output_tool is not None:
             cached_result = self.tools_handler.cache.read(
                 tool=func_name, input=input_str
             )
             if cached_result is not None:
-                result = (
-                    str(cached_result)
-                    if not isinstance(cached_result, str)
-                    else cached_result
-                )
+                raw_tool_result = cached_result
+                result = format_native_tool_output_for_agent(output_tool, cached_result)
                 from_cache = True
 
         agent_key = getattr(self.agent, "key", "unknown") if self.agent else "unknown"
@@ -938,18 +951,6 @@ class CrewAgentExecutor(BaseAgentExecutor):
 
         track_delegation_if_needed(func_name, args_dict or {}, self.task)
 
-        structured_tool: CrewStructuredTool | None = None
-        if original_tool is not None:
-            for structured in self.tools or []:
-                if getattr(structured, "_original_tool", None) is original_tool:
-                    structured_tool = structured
-                    break
-        if structured_tool is None:
-            for structured in self.tools or []:
-                if sanitize_tool_name(structured.name) == func_name:
-                    structured_tool = structured
-                    break
-
         hook_blocked = False
         before_hook_context = ToolCallHookContext(
             tool_name=func_name,
@@ -975,11 +976,18 @@ class CrewAgentExecutor(BaseAgentExecutor):
 
         if hook_blocked:
             result = f"Tool execution blocked by hook. Tool: {func_name}"
+            raw_tool_result = result
         elif max_usage_reached and original_tool:
             result = f"Tool '{func_name}' has reached its usage limit of {original_tool.max_usage_count} times and cannot be used anymore."
-        elif not from_cache and func_name in available_functions:
+            raw_tool_result = result
+        elif (
+            not from_cache
+            and func_name in available_functions
+            and output_tool is not None
+        ):
             try:
                 raw_result = available_functions[func_name](**(args_dict or {}))
+                raw_tool_result = raw_result
 
                 if self.tools_handler and self.tools_handler.cache:
                     should_cache = True
@@ -996,11 +1004,10 @@ class CrewAgentExecutor(BaseAgentExecutor):
                             tool=func_name, input=input_str, output=raw_result
                         )
 
-                result = (
-                    str(raw_result) if not isinstance(raw_result, str) else raw_result
-                )
+                result = format_native_tool_output_for_agent(output_tool, raw_result)
             except Exception as e:
                 result = f"Error executing tool: {e}"
+                raw_tool_result = result
                 if self.task:
                     self.task.increment_tools_errors()
                 crewai_event_bus.emit(
@@ -1024,6 +1031,7 @@ class CrewAgentExecutor(BaseAgentExecutor):
             task=self.task,
             crew=self.crew,
             tool_result=result,
+            raw_tool_result=raw_tool_result,
         )
         after_hooks = get_after_tool_call_hooks()
         try:

lib/crewai/src/crewai/agents/tools_handler.py

@@ -3,6 +3,7 @@
 from __future__ import annotations
 
 import json
+from typing import Any
 
 from pydantic import BaseModel, Field
 
@@ -25,14 +26,14 @@ class ToolsHandler(BaseModel):
     def on_tool_use(
         self,
         calling: ToolCalling | InstructorToolCalling,
-        output: str,
+        output: Any,
         should_cache: bool = True,
     ) -> None:
         """Run when tool ends running.
 
         Args:
             calling: The tool calling instance.
-            output: The output from the tool execution.
+            output: The raw output from the tool execution.
             should_cache: Whether to cache the tool output.
         """
         self.last_used_tool = calling

lib/crewai/src/crewai/experimental/agent_executor.py

@@ -80,6 +80,7 @@ from crewai.utilities.agent_utils import (
     enforce_rpm_limit,
     extract_tool_call_info,
     format_message_for_llm,
+    format_native_tool_output_for_agent,
     get_llm_response,
     handle_agent_action_core,
     handle_context_length,
@@ -1905,19 +1906,32 @@ class AgentExecutor(Flow[AgentExecutorState], BaseAgentExecutor):
         ):
             max_usage_reached = True
 
+        structured_tool: CrewStructuredTool | None = None
+        if original_tool is not None:
+            for structured in self.tools or []:
+                if getattr(structured, "_original_tool", None) is original_tool:
+                    structured_tool = structured
+                    break
+        if structured_tool is None:
+            for structured in self.tools or []:
+                if sanitize_tool_name(structured.name) == func_name:
+                    structured_tool = structured
+                    break
+
+        output_tool = original_tool or structured_tool
+
         # Check cache before executing
         from_cache = False
+        result = "Tool not found"
+        raw_tool_result: Any = result
         input_str = json.dumps(args_dict) if args_dict else ""
-        if self.tools_handler and self.tools_handler.cache:
+        if self.tools_handler and self.tools_handler.cache and output_tool is not None:
             cached_result = self.tools_handler.cache.read(
                 tool=func_name, input=input_str
             )
             if cached_result is not None:
-                result = (
-                    str(cached_result)
-                    if not isinstance(cached_result, str)
-                    else cached_result
-                )
+                raw_tool_result = cached_result
+                result = format_native_tool_output_for_agent(output_tool, cached_result)
                 from_cache = True
 
         # Emit tool usage started event
@@ -1936,18 +1950,6 @@ class AgentExecutor(Flow[AgentExecutorState], BaseAgentExecutor):
 
         track_delegation_if_needed(func_name, args_dict, self.task)
 
-        structured_tool: CrewStructuredTool | None = None
-        if original_tool is not None:
-            for structured in self.tools or []:
-                if getattr(structured, "_original_tool", None) is original_tool:
-                    structured_tool = structured
-                    break
-        if structured_tool is None:
-            for structured in self.tools or []:
-                if sanitize_tool_name(structured.name) == func_name:
-                    structured_tool = structured
-                    break
-
         hook_blocked = False
         before_hook_context = ToolCallHookContext(
             tool_name=func_name,
@@ -1973,12 +1975,13 @@ class AgentExecutor(Flow[AgentExecutorState], BaseAgentExecutor):
 
         if hook_blocked:
             result = f"Tool execution blocked by hook. Tool: {func_name}"
-        elif not from_cache and not max_usage_reached:
-            result = "Tool not found"
+            raw_tool_result = result
+        elif not from_cache and not max_usage_reached and output_tool is not None:
             if func_name in self._available_functions:
                 try:
                     tool_func = self._available_functions[func_name]
                     raw_result = tool_func(**args_dict)
+                    raw_tool_result = raw_result
 
                     # Add to cache after successful execution (before string conversion)
                     if self.tools_handler and self.tools_handler.cache:
@@ -1992,14 +1995,12 @@ class AgentExecutor(Flow[AgentExecutorState], BaseAgentExecutor):
                                 tool=func_name, input=input_str, output=raw_result
                             )
 
-                    # Convert to string for message
-                    result = (
-                        str(raw_result)
-                        if not isinstance(raw_result, str)
-                        else raw_result
+                    result = format_native_tool_output_for_agent(
+                        output_tool, raw_result
                     )
                 except Exception as e:
                     result = f"Error executing tool: {e}"
+                    raw_tool_result = result
                     if self.task:
                         self.task.increment_tools_errors()
                     # Emit tool usage error event
@@ -2021,6 +2022,7 @@ class AgentExecutor(Flow[AgentExecutorState], BaseAgentExecutor):
                 result = f"Tool '{func_name}' has reached its usage limit of {original_tool.max_usage_count} times and cannot be used anymore."
             else:
                 result = f"Tool '{func_name}' has reached its maximum usage limit and cannot be used anymore."
+            raw_tool_result = result
 
         # Execute after_tool_call hooks (even if blocked, to allow logging/monitoring)
         after_hook_context = ToolCallHookContext(
@@ -2031,6 +2033,7 @@ class AgentExecutor(Flow[AgentExecutorState], BaseAgentExecutor):
             task=self.task,
             crew=self.crew,
             tool_result=result,
+            raw_tool_result=raw_tool_result,
         )
         after_hooks = get_after_tool_call_hooks()
         try:

lib/crewai/src/crewai/hooks/tool_hooks.py

@@ -40,6 +40,8 @@ class ToolCallHookContext:
         crew: Crew instance (may be None)
         tool_result: Tool execution result (only set for after_tool_call hooks).
             Can be modified by returning a new string from after_tool_call hook.
+        raw_tool_result: Raw Python tool execution result (only set for
+            after_tool_call hooks). This is not modified by after hooks.
     """
 
     def __init__(
@@ -51,6 +53,7 @@ class ToolCallHookContext:
         task: Task | None = None,
         crew: Crew | None = None,
         tool_result: str | None = None,
+        raw_tool_result: Any | None = None,
     ) -> None:
         """Initialize tool call hook context.
 
@@ -62,6 +65,7 @@ class ToolCallHookContext:
             task: Optional current task
             crew: Optional crew instance
             tool_result: Optional tool result (for after hooks)
+            raw_tool_result: Optional raw tool result (for after hooks)
         """
         self.tool_name = tool_name
         self.tool_input = tool_input
@@ -70,6 +74,7 @@ class ToolCallHookContext:
         self.task = task
         self.crew = crew
         self.tool_result = tool_result
+        self.raw_tool_result = raw_tool_result
 
     def request_human_input(
         self,

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_result_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.",
     )
+    result_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("result_schema", when_used="json")
+    def _serialize_result_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("result_schema", mode="before")
+    @classmethod
+    def _default_result_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_result_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,
+            result_schema=self.result_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)
+        result_schema = getattr(tool, "result_schema", None)
+        if result_schema is None:
+            result_schema = _infer_result_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,
+            result_schema=result_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)
+        result_schema = getattr(tool, "result_schema", None)
+        if result_schema is None:
+            result_schema = _infer_result_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,
+            result_schema=result_schema,
         )
 
 
@@ -621,6 +658,7 @@ def tool(
     name: str,
     /,
     *,
+    result_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(
     *,
+    result_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,
+    result_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.
+        result_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_result_schema = (
+                result_schema or _infer_result_schema_from_callable(f)
+            )
 
             return Tool(
                 name=tool_name,
                 description=f.__doc__,
                 func=f,
                 args_schema=args_schema,
+                result_schema=resolved_result_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 +37,52 @@ def _deserialize_schema(v: Any) -> type[BaseModel] | None:
     return None
 
 
+def _infer_result_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:
+    original_tool = getattr(tool, "_original_tool", None)
+    if original_tool is not None:
+        return cast(str, original_tool.format_output_for_agent(raw_result))
+
+    result_schema = getattr(tool, "result_schema", None)
+    if not (isinstance(result_schema, type) and issubclass(result_schema, BaseModel)):
+        return str(raw_result)
+
+    try:
+        validation_input = raw_result
+        if isinstance(raw_result, BaseModel) and not isinstance(
+            raw_result, result_schema
+        ):
+            validation_input = raw_result.model_dump()
+
+        validated = result_schema.model_validate(validation_input)
+        return 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 result_schema "
+                f"'{result_schema.__name__}': {exc.__class__.__name__}. "
+                "Falling back to str(raw_result)."
+            ),
+            RuntimeWarning,
+            stacklevel=2,
+        )
+        return str(raw_result)
+
+
 if TYPE_CHECKING:
     pass
 
@@ -81,6 +128,11 @@ class CrewStructuredTool(BaseModel):
         BeforeValidator(_deserialize_schema),
         PlainSerializer(_serialize_schema),
     ] = Field(default=None)
+    result_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 +155,7 @@ class CrewStructuredTool(BaseModel):
         description: str | None = None,
         return_direct: bool = False,
         args_schema: type[BaseModel] | None = None,
+        result_schema: type[BaseModel] | None = None,
         infer_schema: bool = True,
         **kwargs: Any,
     ) -> CrewStructuredTool:
@@ -114,6 +167,7 @@ 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
+            result_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 +203,16 @@ class CrewStructuredTool(BaseModel):
             name=name,
             description=description,
             args_schema=schema,
+            result_schema=result_schema or _infer_result_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/src/crewai/tools/tool_usage.py

@@ -62,6 +62,9 @@ OPENAI_BIGGER_MODELS: list[
 ]
 
 
+_RAW_RESULT_UNSET = object()
+
+
 class ToolUsageError(Exception):
     """Exception raised for errors in the tool usage."""
 
@@ -106,6 +109,7 @@ class ToolUsage:
         self.action = action
         self.function_calling_llm = function_calling_llm
         self.fingerprint_context = fingerprint_context or {}
+        self.last_raw_result: Any = _RAW_RESULT_UNSET
 
         if (
             self.function_calling_llm
@@ -120,6 +124,11 @@ class ToolUsage:
         """Parse the tool string and return the tool calling."""
         return self._tool_calling(tool_string)
 
+    def get_last_raw_result(self, fallback: Any) -> Any:
+        if self.last_raw_result is _RAW_RESULT_UNSET:
+            return fallback
+        return self.last_raw_result
+
     def use(
         self, calling: ToolCalling | InstructorToolCalling, tool_string: str
     ) -> str:
@@ -231,6 +240,7 @@ class ToolUsage:
                 result = I18N_DEFAULT.errors("task_repeated_usage").format(
                     tool_names=self.tools_names
                 )
+                self.last_raw_result = result
                 self._telemetry.tool_repeated_usage(
                     llm=self.function_calling_llm,
                     tool_name=sanitize_tool_name(tool.name),
@@ -298,6 +308,7 @@ class ToolUsage:
             )
             if usage_limit_error:
                 result = usage_limit_error
+                self.last_raw_result = result
                 self._telemetry.tool_usage_error(llm=self.function_calling_llm)
                 result = self._format_result(result=result)
             elif result is None:
@@ -359,7 +370,10 @@ class ToolUsage:
                         tool_name=sanitize_tool_name(tool.name),
                         attempts=self._run_attempts,
                     )
-                    result = self._format_result(result=result)
+                    self.last_raw_result = result
+                    result = self._format_result(
+                        result=tool.format_output_for_agent(result)
+                    )
                     data = {
                         "result": result,
                         "tool_name": sanitize_tool_name(tool.name),
@@ -421,6 +435,7 @@ class ToolUsage:
                         result = ToolUsageError(
                             f"\n{error_message}.\nMoving on then. {I18N_DEFAULT.slice('format').format(tool_names=self.tools_names)}"
                         ).message
+                        self.last_raw_result = result
                         if self.task:
                             self.task.increment_tools_errors()
                         if self.agent and self.agent.verbose:
@@ -430,7 +445,10 @@ class ToolUsage:
                             self.task.increment_tools_errors()
                         should_retry = True
             else:
-                result = self._format_result(result=result)
+                self.last_raw_result = result
+                result = self._format_result(
+                    result=tool.format_output_for_agent(result)
+                )
 
         finally:
             if started_event_emitted and not error_event_emitted:
@@ -460,6 +478,7 @@ class ToolUsage:
                 result = I18N_DEFAULT.errors("task_repeated_usage").format(
                     tool_names=self.tools_names
                 )
+                self.last_raw_result = result
                 self._telemetry.tool_repeated_usage(
                     llm=self.function_calling_llm,
                     tool_name=sanitize_tool_name(tool.name),
@@ -529,6 +548,7 @@ class ToolUsage:
             )
             if usage_limit_error:
                 result = usage_limit_error
+                self.last_raw_result = result
                 self._telemetry.tool_usage_error(llm=self.function_calling_llm)
                 result = self._format_result(result=result)
             elif result is None:
@@ -590,7 +610,10 @@ class ToolUsage:
                         tool_name=sanitize_tool_name(tool.name),
                         attempts=self._run_attempts,
                     )
-                    result = self._format_result(result=result)
+                    self.last_raw_result = result
+                    result = self._format_result(
+                        result=tool.format_output_for_agent(result)
+                    )
                     data = {
                         "result": result,
                         "tool_name": sanitize_tool_name(tool.name),
@@ -652,6 +675,7 @@ class ToolUsage:
                         result = ToolUsageError(
                             f"\n{error_message}.\nMoving on then. {I18N_DEFAULT.slice('format').format(tool_names=self.tools_names)}"
                         ).message
+                        self.last_raw_result = result
                         if self.task:
                             self.task.increment_tools_errors()
                         if self.agent and self.agent.verbose:
@@ -661,7 +685,10 @@ class ToolUsage:
                             self.task.increment_tools_errors()
                         should_retry = True
             else:
-                result = self._format_result(result=result)
+                self.last_raw_result = result
+                result = self._format_result(
+                    result=tool.format_output_for_agent(result)
+                )
 
         finally:
             if started_event_emitted and not error_event_emitted:

lib/crewai/src/crewai/utilities/agent_utils.py

@@ -1383,6 +1383,19 @@ class NativeToolCallResult:
     tool_message: LLMMessage = field(default_factory=dict)  # type: ignore[assignment]
 
 
+def format_native_tool_output_for_agent(tool: Any, raw_result: Any) -> str:
+    """Format native tool output when a tool explicitly defines a formatter."""
+    formatter = inspect.getattr_static(tool, "format_output_for_agent", None)
+    if formatter is None:
+        return str(raw_result)
+
+    runtime_formatter = getattr(tool, "format_output_for_agent", None)
+    if not callable(runtime_formatter):
+        return str(raw_result)
+
+    return str(runtime_formatter(raw_result))
+
+
 def execute_single_native_tool_call(
     tool_call: Any,
     *,
@@ -1456,18 +1469,24 @@ def execute_single_native_tool_call(
             original_tool = tool
             break
 
+    structured_tool: CrewStructuredTool | None = None
+    for structured in structured_tools or []:
+        if sanitize_tool_name(structured.name) == func_name:
+            structured_tool = structured
+            break
+
+    output_tool = original_tool or structured_tool
+
     from_cache = False
     input_str = json.dumps(args_dict) if args_dict else ""
     result = "Tool not found"
+    raw_tool_result: Any = result
 
-    if tools_handler and tools_handler.cache:
+    if tools_handler and tools_handler.cache and output_tool is not None:
         cached_result = tools_handler.cache.read(tool=func_name, input=input_str)
         if cached_result is not None:
-            result = (
-                str(cached_result)
-                if not isinstance(cached_result, str)
-                else cached_result
-            )
+            raw_tool_result = cached_result
+            result = format_native_tool_output_for_agent(output_tool, cached_result)
             from_cache = True
 
     started_at = datetime.now()
@@ -1486,12 +1505,6 @@ def execute_single_native_tool_call(
 
     track_delegation_if_needed(func_name, args_dict, task)
 
-    structured_tool: CrewStructuredTool | None = None
-    for structured in structured_tools or []:
-        if sanitize_tool_name(structured.name) == func_name:
-            structured_tool = structured
-            break
-
     hook_blocked = False
     before_hook_context = ToolCallHookContext(
         tool_name=func_name,
@@ -1512,11 +1525,13 @@ def execute_single_native_tool_call(
     error_event_emitted = False
     if hook_blocked:
         result = f"Tool execution blocked by hook. Tool: {func_name}"
+        raw_tool_result = result
     elif not from_cache:
-        if func_name in available_functions:
+        if func_name in available_functions and output_tool is not None:
             try:
                 tool_func = available_functions[func_name]
                 raw_result = tool_func(**args_dict)
+                raw_tool_result = raw_result
 
                 if tools_handler and tools_handler.cache:
                     should_cache = True
@@ -1529,11 +1544,10 @@ def execute_single_native_tool_call(
                             tool=func_name, input=input_str, output=raw_result
                         )
 
-                result = (
-                    str(raw_result) if not isinstance(raw_result, str) else raw_result
-                )
+                result = format_native_tool_output_for_agent(output_tool, raw_result)
             except Exception as e:
                 result = f"Error executing tool: {e}"
+                raw_tool_result = result
                 if task:
                     task.increment_tools_errors()
                 crewai_event_bus.emit(
@@ -1559,6 +1573,7 @@ def execute_single_native_tool_call(
         task=task,
         crew=crew,
         tool_result=result,
+        raw_tool_result=raw_tool_result,
     )
     try:
         for after_hook in get_after_tool_call_hooks():

lib/crewai/src/crewai/utilities/tool_utils.py

@@ -116,6 +116,7 @@ async def aexecute_tool_and_check_finality(
             logger.log("error", f"Error in before_tool_call hook: {e}")
 
         tool_result = await tool_usage.ause(tool_calling, agent_action.text)
+        raw_tool_result = tool_usage.get_last_raw_result(tool_result)
 
         after_hook_context = ToolCallHookContext(
             tool_name=sanitized_tool_name,
@@ -125,6 +126,7 @@ async def aexecute_tool_and_check_finality(
             task=task,
             crew=crew,
             tool_result=tool_result,
+            raw_tool_result=raw_tool_result,
         )
 
         after_hooks = get_after_tool_call_hooks()
@@ -234,6 +236,7 @@ def execute_tool_and_check_finality(
             logger.log("error", f"Error in before_tool_call hook: {e}")
 
         tool_result = tool_usage.use(tool_calling, agent_action.text)
+        raw_tool_result = tool_usage.get_last_raw_result(tool_result)
 
         after_hook_context = ToolCallHookContext(
             tool_name=sanitized_tool_name,
@@ -243,6 +246,7 @@ def execute_tool_and_check_finality(
             task=task,
             crew=crew,
             tool_result=tool_result,
+            raw_tool_result=raw_tool_result,
         )
 
         after_hooks = get_after_tool_call_hooks()

lib/crewai/tests/agents/test_native_tool_calling.py

@@ -7,6 +7,7 @@ when the LLM supports it, across multiple providers.
 from __future__ import annotations
 
 from collections.abc import Generator
+import json
 import os
 import threading
 import time
@@ -20,7 +21,7 @@ from crewai import Agent, Crew, Task
 from crewai.agents.parser import AgentFinish
 from crewai.events import crewai_event_bus
 from crewai.hooks import register_after_tool_call_hook, register_before_tool_call_hook
-from crewai.hooks.tool_hooks import ToolCallHookContext
+from crewai.hooks.tool_hooks import ToolCallHookContext, clear_after_tool_call_hooks
 from crewai.llm import LLM
 from crewai.tools.base_tool import BaseTool
 
@@ -1197,6 +1198,76 @@ class TestNativeToolCallingJsonParseError:
 
         assert result["result"] == "ran: print(1)"
 
+    def test_typed_output_is_json_agent_text(self) -> None:
+        class SearchOutput(BaseModel):
+            query: str
+            score: float
+
+        class TypedSearchTool(BaseTool):
+            name: str = "typed_search"
+            description: str = "Search for information"
+            result_schema: type[BaseModel] = SearchOutput
+
+            def _run(self, query: str) -> SearchOutput:
+                return SearchOutput(query=query, score=0.8)
+
+        tool = TypedSearchTool()
+        executor = self._make_executor([tool])
+
+        from crewai.utilities.agent_utils import convert_tools_to_openai_schema
+
+        _, available_functions, _ = convert_tools_to_openai_schema([tool])
+
+        result = executor._execute_single_native_tool_call(
+            call_id="call_typed",
+            func_name="typed_search",
+            func_args='{"query": "crew"}',
+            available_functions=available_functions,
+        )
+
+        assert json.loads(result["result"]) == {"query": "crew", "score": 0.8}
+
+    def test_typed_output_after_hook_includes_raw_tool_result(self) -> None:
+        from crewai.utilities.agent_utils import convert_tools_to_openai_schema
+
+        class SearchOutput(BaseModel):
+            query: str
+            score: float
+
+        class TypedSearchTool(BaseTool):
+            name: str = "typed_search"
+            description: str = "Search for information"
+            result_schema: type[BaseModel] = SearchOutput
+
+            def _run(self, query: str) -> SearchOutput:
+                return SearchOutput(query=query, score=0.8)
+
+        seen_results: list[tuple[str | None, object]] = []
+
+        def after_hook(context: ToolCallHookContext) -> None:
+            seen_results.append((context.tool_result, context.raw_tool_result))
+
+        tool = TypedSearchTool()
+        executor = self._make_executor([tool])
+        _, available_functions, _ = convert_tools_to_openai_schema([tool])
+
+        clear_after_tool_call_hooks()
+        register_after_tool_call_hook(after_hook)
+        try:
+            result = executor._execute_single_native_tool_call(
+                call_id="call_typed",
+                func_name="typed_search",
+                func_args='{"query": "crew"}',
+                available_functions=available_functions,
+            )
+        finally:
+            clear_after_tool_call_hooks()
+
+        assert json.loads(result["result"]) == {"query": "crew", "score": 0.8}
+        assert seen_results == [
+            ('{"query":"crew","score":0.8}', SearchOutput(query="crew", score=0.8))
+        ]
+
     def test_native_tool_loop_falls_back_when_provider_rejects_tools(self) -> None:
         """Unsupported native tools errors should continue through ReAct."""
 

lib/crewai/tests/hooks/test_tool_hooks.py

@@ -91,20 +91,24 @@ class TestToolCallHookContext:
         assert context.task == mock_task
         assert context.crew == mock_crew
         assert context.tool_result is None
+        assert context.raw_tool_result is None
 
     def test_context_with_result(self, mock_tool):
         """Test that context includes result when provided."""
         tool_input = {"arg1": "value1"}
         tool_result = "Test tool result"
+        raw_tool_result = {"value": 42}
 
         context = ToolCallHookContext(
             tool_name="test_tool",
             tool_input=tool_input,
             tool=mock_tool,
             tool_result=tool_result,
+            raw_tool_result=raw_tool_result,
         )
 
         assert context.tool_result == tool_result
+        assert context.raw_tool_result == raw_tool_result
 
     def test_tool_input_is_mutable_reference(self, mock_tool):
         """Test that modifying context.tool_input modifies the original dict."""

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 +352,262 @@ 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"
+    result_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", result_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:
+    @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_result_schema_wins_over_return_annotation(self) -> None:
+        class AlternateOutput(BaseModel):
+            value: str
+
+        @tool("search", result_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", result_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", result_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_result_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,
+        }
+
+    def test_custom_agent_output_formatter_carries_over_to_structured_tool(
+        self,
+    ) -> None:
+        class MarkdownSearchTool(BaseTool):
+            name: str = "markdown_search"
+            description: str = "Search for information"
+            result_schema: type[BaseModel] = SearchOutput
+
+            def _run(self, query: str) -> SearchOutput:
+                return SearchOutput(query=query, score=0.8)
+
+            def format_output_for_agent(self, raw_result: object) -> str:
+                result = self.result_schema.model_validate(raw_result)
+                return f"### Search result\n\n- Query: `{result.query}`\n- Score: {result.score}"
+
+        structured = MarkdownSearchTool().to_structured_tool()
+
+        raw_result = structured.invoke({"query": "crew"})
+
+        assert raw_result == SearchOutput(query="crew", score=0.8)
+        assert structured.format_output_for_agent(raw_result) == (
+            "### Search result\n\n- Query: `crew`\n- 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 +88,118 @@ 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", "result_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,
+    result_schema,
+    expected_raw,
+    expected_agent_payload,
+):
+    kwargs = {"result_schema": result_schema} if result_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_result_schema():
+    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():
+    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",
+        result_schema=StructuredOutput,
+    )
+    raw_result = tool.invoke({"value": "crew"})
+
+    with pytest.warns(
+        RuntimeWarning, match="Failed to validate or serialize"
+    ) as warnings:
+        agent_text = tool.format_output_for_agent(raw_result)
+
+    assert raw_result == {"value": "crew", "count": "wrong"}
+    assert agent_text == str(raw_result)
+    warning_message = str(warnings[0].message)
+    assert "ValidationError" in warning_message
+    assert "wrong" not in warning_message
+
+
 def test_validate_function_signature(basic_function, schema_class):
     """Test function signature validation"""
     tool = CrewStructuredTool(

lib/crewai/tests/tools/test_tool_usage.py

@@ -1,4 +1,5 @@
 import datetime
+from collections.abc import Callable
 import json
 import random
 import threading
@@ -6,6 +7,9 @@ import time
 from unittest.mock import MagicMock, patch
 
 from crewai import Agent, Task
+from crewai.agents.cache.cache_handler import CacheHandler
+from crewai.agents.parser import AgentAction
+from crewai.agents.tools_handler import ToolsHandler
 from crewai.events.event_bus import crewai_event_bus
 from crewai.events.types.tool_usage_events import (
     ToolSelectionErrorEvent,
@@ -14,8 +18,15 @@ from crewai.events.types.tool_usage_events import (
     ToolUsageStartedEvent,
     ToolValidateInputErrorEvent,
 )
+from crewai.hooks.tool_hooks import (
+    ToolCallHookContext,
+    clear_after_tool_call_hooks,
+    register_after_tool_call_hook,
+)
 from crewai.tools import BaseTool
+from crewai.tools.tool_calling import ToolCalling
 from crewai.tools.tool_usage import ToolUsage
+from crewai.utilities.tool_utils import execute_tool_and_check_finality
 from pydantic import BaseModel, Field
 import pytest
 
@@ -38,6 +49,19 @@ class RandomNumberTool(BaseTool):
         return random.randint(min_value, max_value)  # noqa: S311
 
 
+class SearchOutput(BaseModel):
+    query: str
+    score: float
+
+
+class TypedSearchTool(BaseTool):
+    name: str = "typed_search"
+    description: str = "Search for a query"
+
+    def _run(self, query: str) -> SearchOutput:
+        return SearchOutput(query=query, score=0.7)
+
+
 # Example agent and task
 example_agent = Agent(
     role="Number Generator",
@@ -117,6 +141,126 @@ def test_tool_usage_render():
     assert '"description": "The maximum value of the range (inclusive)"' in rendered
 
 
+def test_tool_usage_returns_json_agent_text_for_typed_output():
+    tool = TypedSearchTool().to_structured_tool()
+    tool_usage = ToolUsage(
+        tools_handler=None,
+        tools=[tool],
+        task=None,
+        function_calling_llm=MagicMock(),
+        agent=None,
+        action=MagicMock(),
+    )
+
+    result = tool_usage.use(
+        calling=ToolCalling(
+            tool_name="typed_search",
+            arguments={"query": "crew"},
+        ),
+        tool_string='Action: typed_search\nAction Input: {"query": "crew"}',
+    )
+
+    assert json.loads(result) == {"query": "crew", "score": 0.7}
+
+
+def test_tool_usage_cache_callback_receives_raw_typed_output():
+    raw_results: list[object] = []
+
+    def cache_result(_args: object, result: object) -> bool:
+        raw_results.append(result)
+        return True
+
+    class CacheAwareTypedSearchTool(TypedSearchTool):
+        cache_function: Callable = cache_result
+
+    tools_handler = MagicMock()
+    tools_handler.cache = None
+    tools_handler.last_used_tool = None
+    tool = CacheAwareTypedSearchTool().to_structured_tool()
+    tool_usage = ToolUsage(
+        tools_handler=tools_handler,
+        tools=[tool],
+        task=None,
+        function_calling_llm=MagicMock(),
+        agent=None,
+        action=MagicMock(),
+    )
+
+    result = tool_usage.use(
+        calling=ToolCalling(
+            tool_name="typed_search",
+            arguments={"query": "crew"},
+        ),
+        tool_string='Action: typed_search\nAction Input: {"query": "crew"}',
+    )
+
+    assert json.loads(result) == {"query": "crew", "score": 0.7}
+    assert raw_results == [SearchOutput(query="crew", score=0.7)]
+    tools_handler.on_tool_use.assert_called_once()
+    assert tools_handler.on_tool_use.call_args.kwargs["output"] == SearchOutput(
+        query="crew",
+        score=0.7,
+    )
+
+
+def test_react_tool_hooks_receive_agent_text_and_raw_cached_typed_output():
+    structured_tool = TypedSearchTool().to_structured_tool()
+    tools_handler = ToolsHandler(cache=CacheHandler())
+    seen_results: list[tuple[str | None, object]] = []
+
+    def after_hook(context: ToolCallHookContext) -> None:
+        seen_results.append((context.tool_result, context.raw_tool_result))
+
+    clear_after_tool_call_hooks()
+    register_after_tool_call_hook(after_hook)
+
+    action = AgentAction(
+        thought="",
+        tool="typed_search",
+        tool_input='{"query": "crew"}',
+        text='Action: typed_search\nAction Input: {"query": "crew"}',
+    )
+
+    try:
+        first = execute_tool_and_check_finality(
+            agent_action=action,
+            tools=[structured_tool],
+            tools_handler=tools_handler,
+        )
+        tools_handler.last_used_tool = None
+        second = execute_tool_and_check_finality(
+            agent_action=action,
+            tools=[structured_tool],
+            tools_handler=tools_handler,
+        )
+    finally:
+        clear_after_tool_call_hooks()
+
+    assert json.loads(first.result) == {"query": "crew", "score": 0.7}
+    assert json.loads(second.result) == {"query": "crew", "score": 0.7}
+    assert seen_results == [
+        ('{"query":"crew","score":0.7}', SearchOutput(query="crew", score=0.7)),
+        ('{"query":"crew","score":0.7}', SearchOutput(query="crew", score=0.7)),
+    ]
+
+
+def test_last_raw_result_falls_back_only_until_recorded():
+    tool_usage = ToolUsage(
+        tools_handler=None,
+        tools=[],
+        task=None,
+        function_calling_llm=MagicMock(),
+        agent=None,
+        action=MagicMock(),
+    )
+
+    assert tool_usage.get_last_raw_result("formatted result") == "formatted result"
+
+    tool_usage.last_raw_result = None
+
+    assert tool_usage.get_last_raw_result("formatted result") is None
+
+
 def test_validate_tool_input_booleans_and_none():
     tool_usage = ToolUsage(
         tools_handler=MagicMock(),

lib/crewai/tests/utilities/test_agent_utils.py

@@ -3,12 +3,19 @@
 from __future__ import annotations
 
 import asyncio
+import json
 from typing import Any, Literal, Optional
 from unittest.mock import AsyncMock, MagicMock, patch
 
 import pytest
 from pydantic import BaseModel, Field
 
+from crewai.hooks.tool_hooks import (
+    ToolCallHookContext,
+    clear_after_tool_call_hooks,
+    clear_before_tool_call_hooks,
+    register_after_tool_call_hook,
+)
 from crewai.tools.base_tool import BaseTool
 from crewai.utilities.agent_utils import (
     _asummarize_chunks,
@@ -1030,6 +1037,142 @@ class TestParseToolCallArgs:
 class TestExecuteSingleNativeToolCall:
     """Tests for execute_single_native_tool_call."""
 
+    def test_typed_tool_output_is_json_agent_text(self) -> None:
+        clear_before_tool_call_hooks()
+        clear_after_tool_call_hooks()
+
+        class SearchOutput(BaseModel):
+            query: str
+            score: float
+
+        class TypedSearchTool(BaseTool):
+            name: str = "typed_search"
+            description: str = "Search for a query"
+            result_schema: type[BaseModel] = SearchOutput
+
+            def _run(self, query: str) -> SearchOutput:
+                return SearchOutput(query=query, score=0.9)
+
+        tool = TypedSearchTool()
+        tool_call = MagicMock()
+        tool_call.id = "call_1"
+        tool_call.function.name = "typed_search"
+        tool_call.function.arguments = '{"query": "crew"}'
+
+        result = execute_single_native_tool_call(
+            tool_call,
+            available_functions={"typed_search": tool._run},
+            original_tools=[tool],
+            structured_tools=[tool.to_structured_tool()],
+            tools_handler=None,
+            agent=None,
+            task=None,
+            crew=None,
+            event_source=MagicMock(),
+            printer=None,
+            verbose=False,
+        )
+
+        assert json.loads(result.result) == {"query": "crew", "score": 0.9}
+        assert json.loads(result.tool_message["content"]) == {
+            "query": "crew",
+            "score": 0.9,
+        }
+
+    def test_custom_agent_output_formatter_is_used_from_structured_tool(
+        self,
+    ) -> None:
+        clear_before_tool_call_hooks()
+        clear_after_tool_call_hooks()
+
+        class SearchOutput(BaseModel):
+            query: str
+            score: float
+
+        class MarkdownSearchTool(BaseTool):
+            name: str = "markdown_search"
+            description: str = "Search for a query"
+            result_schema: type[BaseModel] = SearchOutput
+
+            def _run(self, query: str) -> SearchOutput:
+                return SearchOutput(query=query, score=0.9)
+
+            def format_output_for_agent(self, raw_result: Any) -> str:
+                result = self.result_schema.model_validate(raw_result)
+                return f"### {result.query}\n\nScore: **{result.score}**"
+
+        tool = MarkdownSearchTool()
+        tool_call = MagicMock()
+        tool_call.id = "call_1"
+        tool_call.function.name = "markdown_search"
+        tool_call.function.arguments = '{"query": "crew"}'
+
+        result = execute_single_native_tool_call(
+            tool_call,
+            available_functions={"markdown_search": tool._run},
+            original_tools=[],
+            structured_tools=[tool.to_structured_tool()],
+            tools_handler=None,
+            agent=None,
+            task=None,
+            crew=None,
+            event_source=MagicMock(),
+            printer=None,
+            verbose=False,
+        )
+
+        assert result.result == "### crew\n\nScore: **0.9**"
+        assert result.tool_message["content"] == "### crew\n\nScore: **0.9**"
+
+    def test_after_hook_includes_raw_tool_result_for_typed_output(self) -> None:
+        clear_after_tool_call_hooks()
+
+        class SearchOutput(BaseModel):
+            query: str
+            score: float
+
+        class TypedSearchTool(BaseTool):
+            name: str = "typed_search"
+            description: str = "Search for a query"
+            result_schema: type[BaseModel] = SearchOutput
+
+            def _run(self, query: str) -> SearchOutput:
+                return SearchOutput(query=query, score=0.9)
+
+        seen_results: list[tuple[str | None, object]] = []
+
+        def after_hook(context: ToolCallHookContext) -> None:
+            seen_results.append((context.tool_result, context.raw_tool_result))
+
+        tool = TypedSearchTool()
+        tool_call = MagicMock()
+        tool_call.id = "call_1"
+        tool_call.function.name = "typed_search"
+        tool_call.function.arguments = '{"query": "crew"}'
+
+        register_after_tool_call_hook(after_hook)
+        try:
+            result = execute_single_native_tool_call(
+                tool_call,
+                available_functions={"typed_search": tool._run},
+                original_tools=[tool],
+                structured_tools=[tool.to_structured_tool()],
+                tools_handler=None,
+                agent=None,
+                task=None,
+                crew=None,
+                event_source=MagicMock(),
+                printer=None,
+                verbose=False,
+            )
+        finally:
+            clear_after_tool_call_hooks()
+
+        assert json.loads(result.result) == {"query": "crew", "score": 0.9}
+        assert seen_results == [
+            ('{"query":"crew","score":0.9}', SearchOutput(query="crew", score=0.9))
+        ]
+
     def test_result_as_answer_false_on_tool_error(self) -> None:
         """When a tool with result_as_answer=True raises, result_as_answer must be False.