Add typed output schemas for CrewAI tools

docs/edge/en/concepts/tools.mdx

@@ -39,6 +39,7 @@ The Enterprise Tools Repository includes:
 - **Error Handling**: Incorporates robust error handling mechanisms to ensure smooth operation.
 - **Caching Mechanism**: Features intelligent caching to optimize performance and reduce redundant operations.
 - **Asynchronous Support**: Handles both synchronous and asynchronous tools, enabling non-blocking operations.
+- **Typed Outputs**: Uses optional Pydantic models to give agents clear JSON fields while direct Python calls still receive the tool's normal return value.
 
 ## Using CrewAI Tools
 
@@ -184,6 +185,55 @@ class MyCustomTool(BaseTool):
         return "Tool's result"
 ```
 
+### Typed Tool Outputs
+
+When a tool returns structured data, define a Pydantic output model. This gives the agent field names it can trust, such as `sku`, `quantity`, or `needs_reorder`.
+
+Direct Python calls still receive the value your tool returns. When an agent uses the tool, CrewAI sends the agent a JSON string based on the output model.
+
+```python Code
+from crewai.tools import BaseTool
+from pydantic import BaseModel
+
+class InventoryResult(BaseModel):
+    sku: str
+    quantity: int
+    needs_reorder: bool
+
+class InventoryTool(BaseTool):
+    name: str = "Inventory Check"
+    description: str = "Checks current stock for a product SKU."
+
+    def _run(self, sku: str) -> InventoryResult:
+        quantity = {"SKU-123": 14, "SKU-456": 0}.get(sku, 0)
+        return InventoryResult(sku=sku, quantity=quantity, needs_reorder=quantity < 5)
+
+tool = InventoryTool()
+
+# Direct calls receive the raw Pydantic object.
+result = tool.run(sku="SKU-123")
+print(result.quantity)
+```
+
+To send Markdown or another short text format to the agent, override `format_output_for_agent`. Direct calls to `tool.run(...)` still return the normal Python value.
+
+```python Code
+class InventoryTool(BaseTool):
+    name: str = "Inventory Check"
+    description: str = "Checks current stock for a product SKU."
+
+    def _run(self, sku: str) -> InventoryResult:
+        quantity = {"SKU-123": 14, "SKU-456": 0}.get(sku, 0)
+        return InventoryResult(sku=sku, quantity=quantity, needs_reorder=quantity < 5)
+
+    def format_output_for_agent(self, raw_result: object) -> str:
+        result = InventoryResult.model_validate(raw_result)
+        status = "reorder needed" if result.needs_reorder else "stock is healthy"
+        return f"{result.sku}: {result.quantity} units. {status}."
+```
+
+If you do not override `format_output_for_agent`, typed outputs are sent to the agent as JSON. Plain string results work as before.
+
 ## Asynchronous Tool Support
 
 CrewAI supports asynchronous tools, allowing you to implement tools that perform non-blocking operations like network requests, file I/O, or other async operations without blocking the main execution thread.

docs/edge/en/guides/tools/publish-custom-tools.mdx

@@ -65,7 +65,7 @@ Regardless of which approach you use, your tool must:
 - Have a **`description`** — tells the agent when and how to use the tool. This directly affects how well agents use your tool, so be clear and specific.
 - Implement **`_run`** (BaseTool) or provide a **function body** (@tool) — the synchronous execution logic.
 - Use **type annotations** on all parameters and return values.
-- Return a **string** result (or something that can be meaningfully converted to one).
+- Return a **string** result, or define an optional Pydantic output schema for structured results.
 
 ### Optional: Async Support
 
@@ -104,6 +104,67 @@ class TranslateInput(BaseModel):
 
 Explicit schemas are recommended for published tools — they produce better agent behavior and clearer documentation for your users.
 
+### Optional: Typed Outputs with `result_schema`
+
+If your tool returns structured data, define a Pydantic output model. This is a good default for published tools because users and agents can rely on named fields.
+
+Direct Python calls still receive the value your tool returns. When an agent uses the tool, CrewAI sends the agent JSON based on the output model.
+
+CrewAI can infer the output schema from a Pydantic return annotation:
+
+```python
+from crewai.tools import BaseTool
+from pydantic import BaseModel, Field
+
+
+class GeolocateResult(BaseModel):
+    latitude: float = Field(..., description="Latitude in decimal degrees.")
+    longitude: float = Field(..., description="Longitude in decimal degrees.")
+
+
+class GeolocateTool(BaseTool):
+    name: str = "Geolocate"
+    description: str = "Converts a street address into latitude/longitude coordinates."
+
+    def _run(self, address: str) -> GeolocateResult:
+        if "1600 Pennsylvania" in address:
+            return GeolocateResult(latitude=38.8977, longitude=-77.0365)
+        return GeolocateResult(latitude=40.7128, longitude=-74.0060)
+```
+
+Set `result_schema` explicitly when your tool returns a dictionary:
+
+```python
+class GeolocateTool(BaseTool):
+    name: str = "Geolocate"
+    description: str = "Converts a street address into latitude/longitude coordinates."
+    result_schema: type[BaseModel] = GeolocateResult
+
+    def _run(self, address: str) -> dict[str, float]:
+        if "1600 Pennsylvania" in address:
+            return {"latitude": 38.8977, "longitude": -77.0365}
+        return {"latitude": 40.7128, "longitude": -74.0060}
+```
+
+If agents should receive a short text summary instead of JSON, override `format_output_for_agent` on your `BaseTool` subclass.
+
+```python
+class GeolocateTool(BaseTool):
+    name: str = "Geolocate"
+    description: str = "Converts a street address into latitude/longitude coordinates."
+
+    def _run(self, address: str) -> GeolocateResult:
+        if "1600 Pennsylvania" in address:
+            return GeolocateResult(latitude=38.8977, longitude=-77.0365)
+        return GeolocateResult(latitude=40.7128, longitude=-74.0060)
+
+    def format_output_for_agent(self, raw_result: object) -> str:
+        result = GeolocateResult.model_validate(raw_result)
+        return f"Latitude {result.latitude}, longitude {result.longitude}"
+```
+
+The override only changes what the agent sees. Direct users of your package still receive the normal value from `tool.run(...)`.
+
 ### Optional: Environment Variables
 
 If your tool requires API keys or other configuration, declare them with `env_vars` so users know what to set:
@@ -241,4 +302,4 @@ agent = Agent(
     tools=[GeolocateTool()],
     # ...
 )
-```
+```

docs/edge/en/learn/create-custom-tools.mdx

@@ -53,6 +53,111 @@ def my_simple_tool(question: str) -> str:
     return "Tool output"
 ```
 
+### Best Practice: Define Typed Outputs
+
+When a tool returns structured data, define a Pydantic output model. This helps the agent read the result as clear fields instead of guessing from plain text.
+
+Typed outputs are useful for results with stable fields, such as IDs, status values, scores, prices, or lists. Plain strings are still fine for short prose results.
+
+Direct Python calls still receive the value your tool returns. When an agent uses a typed tool, CrewAI sends the agent JSON based on the output model.
+
+#### Return a Pydantic Model
+
+CrewAI infers the output schema when your `BaseTool` has a Pydantic return annotation.
+
+```python Code
+from crewai.tools import BaseTool
+from pydantic import BaseModel, Field
+
+class InventoryResult(BaseModel):
+    sku: str = Field(description="The product SKU.")
+    quantity: int = Field(description="Units available.")
+    needs_reorder: bool = Field(description="Whether the item should be reordered.")
+
+class InventoryTool(BaseTool):
+    name: str = "Inventory Check"
+    description: str = "Check current stock for a product SKU."
+
+    def _run(self, sku: str) -> InventoryResult:
+        quantity = {"SKU-123": 14, "SKU-456": 0}.get(sku, 0)
+        return InventoryResult(sku=sku, quantity=quantity, needs_reorder=quantity < 5)
+
+tool = InventoryTool()
+result = tool.run(sku="SKU-123")
+
+# Direct Python calls receive the raw Pydantic object.
+print(result.quantity)
+```
+
+When an agent calls `InventoryTool`, it receives JSON like this:
+
+```json
+{"sku":"SKU-123","quantity":14,"needs_reorder":false}
+```
+
+#### Use `result_schema` with Dictionary Results
+
+If your tool returns a dictionary, set `result_schema` explicitly. You can do this on a `BaseTool` subclass or with the `@tool` decorator:
+
+```python Code
+from crewai.tools import tool
+from pydantic import BaseModel, Field
+
+class ProductResult(BaseModel):
+    sku: str = Field(description="The product SKU.")
+    name: str = Field(description="The product name.")
+    in_stock: bool = Field(description="Whether the product is available.")
+
+@tool("Product Lookup", result_schema=ProductResult)
+def product_lookup(sku: str) -> dict[str, object]:
+    """Look up product availability by SKU."""
+    catalog = {
+        "SKU-123": ("Noise-canceling headset", True),
+        "SKU-456": ("USB-C dock", False),
+    }
+    name, in_stock = catalog.get(sku, ("Unknown product", False))
+    return {
+        "sku": sku,
+        "name": name,
+        "in_stock": in_stock,
+    }
+```
+
+#### Customize the Text Sent to the Agent
+
+By default, typed tool outputs are sent to the agent as JSON. If the agent should receive a short summary instead, subclass `BaseTool` and override `format_output_for_agent`.
+
+```python Code
+from crewai.tools import BaseTool
+from pydantic import BaseModel, Field
+
+class InventoryResult(BaseModel):
+    sku: str = Field(description="The product SKU.")
+    quantity: int = Field(description="Units available.")
+    needs_reorder: bool = Field(description="Whether the item should be reordered.")
+
+class InventoryTool(BaseTool):
+    name: str = "Inventory Check"
+    description: str = "Check current stock for a product SKU."
+
+    def _run(self, sku: str) -> InventoryResult:
+        quantity = {"SKU-123": 14, "SKU-456": 0}.get(sku, 0)
+        return InventoryResult(sku=sku, quantity=quantity, needs_reorder=quantity < 5)
+
+    def format_output_for_agent(self, raw_result: object) -> str:
+        result = InventoryResult.model_validate(raw_result)
+        status = "reorder needed" if result.needs_reorder else "stock is healthy"
+        return f"{result.sku}: {result.quantity} units. {status}."
+
+tool = InventoryTool()
+result = tool.run(sku="SKU-123")
+
+# Direct Python calls receive the raw Pydantic object.
+print(result.quantity)
+```
+
+The override only changes what the agent sees. Direct calls to `tool.run(...)` still return the normal Python value.
+
 ### Defining a Cache Function for the Tool
 
 To optimize tool performance with caching, define custom caching strategies using the `cache_function` attribute.

docs/edge/en/learn/execution-hooks.mdx

@@ -195,9 +195,12 @@ class ToolCallHookContext:
     agent: Agent | None          # Agent executing
     task: Task | None            # Current task
     crew: Crew | None            # Crew instance
-    tool_result: str | None      # Tool result (after hooks)
+    tool_result: str | None      # Agent-facing result string (after hooks)
+    raw_tool_result: Any | None  # Raw Python result (after hooks)
 ```
 
+For typed tool outputs, `tool_result` is the string the agent sees. By default, this is JSON. If the tool uses custom formatting, it can be Markdown or another string. `raw_tool_result` is the original Python value returned by the tool.
+
 ## Common Patterns
 
 ### Safety and Validation

docs/edge/en/learn/tool-hooks.mdx

@@ -60,9 +60,12 @@ class ToolCallHookContext:
     agent: Agent | BaseAgent | None   # Agent executing the tool
     task: Task | None                 # Current task
     crew: Crew | None                 # Crew instance
-    tool_result: str | None           # Tool result (after hooks only)
+    tool_result: str | None           # Agent-facing result string (after hooks only)
+    raw_tool_result: Any | None       # Raw Python result (after hooks only)
 ```
 
+For typed tool outputs, `tool_result` is the string the agent sees. By default, this is JSON. If the tool uses custom formatting, it can be Markdown or another string. Use `raw_tool_result` when your hook needs the typed object or dictionary.
+
 ### Modifying Tool Inputs
 
 **Important:** Always modify tool inputs in-place:

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

@@ -57,6 +57,7 @@
     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 @@ def _execute_single_native_tool_call(
         ):
             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 @@ def _execute_single_native_tool_call(
 
         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 @@ def _execute_single_native_tool_call(
 
         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 @@ 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 self.task:
                     self.task.increment_tools_errors()
                 crewai_event_bus.emit(
@@ -1024,6 +1031,7 @@ def _execute_single_native_tool_call(
             task=self.task,
             crew=self.crew,
             tool_result=result,
+            raw_tool_result=raw_tool_result,
         )
         after_hooks = get_after_tool_call_hooks()
         try: