Repository: openai/openai-agents-python

Issue:
Use tool output as agent output
Is there some equivalent to AutoGen's `reflect_on_tool_use=False`? That is: Some way for an agent to use the output of one of its tools as its own output, without needing another roundtrip to the LLM?

Worked example:
```python
# Untested code, just illustrative

class LoremModel(BaseModel):
    paragraph: str

@function_tool
def generate_lorem(n: int) -> List[LoremModel]:
    '''Generate `n` paragraphs of lorem ipsum'''
    # omitted

agent = Agent(
    name='Lorem Agent',
    tools=[generate_lorem],
    output_type=List[LoremModel],
)

await Runner.run(agent, 'Generate 1000 paragraphs of lorem ipsum')
```

I believe the way this works is:
 1. Prompt gets sent up to the LLM
 2. LLM responds requesting invocation of `generate_lorem(1000)`
 3. We execute that function and now have 1000 paragraphs of lorem ipsum in memory
 4. We send these 1000 paragraphs back up to the LLM
 5. The LLM generates a final response to the user prompt, which is just those same paragraphs of lorem ipsum again.

In practice we already have our answer after step 3, but then have this extra roundtrip in steps 4&5, which can get expensive in terms of both tokens and time if the output from our tool is large.

**Is there functionality or design pattern to work around this? I think it would really help push the composability of agents even further than you already have with this SDK!**

_Real world usecase_: I have an agent whose goal it is to retrieve a chunky piece of content. It has ~5 tools at its disposal. The "main" tool actually retrieves the data, the other tools help the agent determine the correct parameters with which to invoke the main tool. I would like to then use this "data retrieval agent" inside other agents which can then use this dataset to do interesting things.

I can work around this by making my "data retrieval agent" only return the correct parameters with which to call the main tool, and leave the _actual_ retrieval to other agents, but then it feels like my agent isn't encapsulating a nice isolated chunk of work.

Love the library so far, thanks for your work!

Hints from issue comments:
@TimoVink: Apologies if there's mistakes in my understanding of how AutoGen works, or how this library works. New to the space (as many of us are, I imagine!)@rm-openai: That's a cool feature. Currently don't have support for it, but we'll talk about it and see if we can add it!@LeonG7: Sometimes I query a lot of web data through an agent, but the agent summarizes the web information, resulting in missing information. I hope to receive the complete information obtained through web search. Perhaps using an intermediate variable can achieve this task, but this solution is cool@rm-openai: @TimoVink - implemented in #203. Chime in there if you have any feedback!@TimoVink: Wasn't expecting anything so quickly. Thanks!

Had a play, and this definitely solves my usecase. Left 2 small comments on the PR but even if it was merged exactly as-is I'd be a happy user.@SeeknnDestroy: Hey @TimoVink @rm-openai 

Thanks so much for raising and implementing this feature — it’s super helpful! 🙌

I’d love to try this out in my own project. Could you share any details or code samples on how to use the new tool_use_behavior feature with an agent? A quick example or some guidance on how to set it up would be really appreciated!

Thanks again for the quick turnaround and the awesome work!

Solution patch:
diff --git a/docs/agents.md b/docs/agents.md
index 17589b3d..1c314739 100644
--- a/docs/agents.md
+++ b/docs/agents.md
@@ -130,3 +130,16 @@ robot_agent = pirate_agent.clone(
     instructions="Write like a robot",
 )
 ```
+
+## Forcing tool use
+
+Supplying a list of tools doesn't always mean the LLM will use a tool. You can force tool use by setting [`ModelSettings.tool_choice`][agents.model_settings.ModelSettings.tool_choice]. Valid values are:
+
+1. `auto`, which allows the LLM to decide whether or not to use a tool.
+2. `required`, which requires the LLM to use a tool (but it can intelligently decide which tool).
+3. `none`, which requires the LLM to _not_ use a tool.
+4. Setting a specific string e.g. `my_tool`, which requires the LLM to use that specific tool.
+
+!!! note
+
+    If requiring tool use, you should consider setting [`Agent.tool_use_behavior`] to stop the Agent from running when a tool output is produced. Otherwise, the Agent might run in an infinite loop, where the LLM produces a tool call , and the tool result is sent to the LLM, and this infinite loops because the LLM is always forced to use a tool.
diff --git a/docs/guardrails.md b/docs/guardrails.md
index caf32775..70d9649d 100644
--- a/docs/guardrails.md
+++ b/docs/guardrails.md
@@ -111,8 +111,8 @@ class MessageOutput(BaseModel): # (1)!
     response: str
 
 class MathOutput(BaseModel): # (2)!
-    is_math: bool
     reasoning: str
+    is_math: bool
 
 guardrail_agent = Agent(
     name="Guardrail check",
diff --git a/examples/agent_patterns/forcing_tool_use.py b/examples/agent_patterns/forcing_tool_use.py
new file mode 100644
index 00000000..3f4e35ae
--- /dev/null
+++ b/examples/agent_patterns/forcing_tool_use.py
@@ -0,0 +1,99 @@
+from __future__ import annotations
+
+import asyncio
+from typing import Any, Literal
+
+from pydantic import BaseModel
+
+from agents import (
+    Agent,
+    FunctionToolResult,
+    ModelSettings,
+    RunContextWrapper,
+    Runner,
+    ToolsToFinalOutputFunction,
+    ToolsToFinalOutputResult,
+    function_tool,
+)
+
+"""
+This example shows how to force the agent to use a tool. It uses `ModelSettings(tool_choice="required")`
+to force the agent to use any tool.
+
+You can run it with 3 options:
+1. `default`: The default behavior, which is to send the tool output to the LLM. In this case,
+    `tool_choice` is not set, because otherwise it would result in an infinite loop - the LLM would
+    call the tool, the tool would run and send the results to the LLM, and that would repeat
+    (because the model is forced to use a tool every time.)
+2. `first_tool_result`: The first tool result is used as the final output.
+3. `custom`: A custom tool use behavior function is used. The custom function receives all the tool
+    results, and chooses to use the first tool result to generate the final output.
+
+Usage:
+python examples/agent_patterns/forcing_tool_use.py -t default
+python examples/agent_patterns/forcing_tool_use.py -t first_tool
+python examples/agent_patterns/forcing_tool_use.py -t custom
+"""
+
+
+class Weather(BaseModel):
+    city: str
+    temperature_range: str
+    conditions: str
+
+
+@function_tool
+def get_weather(city: str) -> Weather:
+    print("[debug] get_weather called")
+    return Weather(city=city, temperature_range="14-20C", conditions="Sunny with wind")
+
+
+async def custom_tool_use_behavior(
+    context: RunContextWrapper[Any], results: list[FunctionToolResult]
+) -> ToolsToFinalOutputResult:
+    weather: Weather = results[0].output
+    return ToolsToFinalOutputResult(
+        is_final_output=True, final_output=f"{weather.city} is {weather.conditions}."
+    )
+
+
+async def main(tool_use_behavior: Literal["default", "first_tool", "custom"] = "default"):
+    if tool_use_behavior == "default":
+        behavior: Literal["run_llm_again", "stop_on_first_tool"] | ToolsToFinalOutputFunction = (
+            "run_llm_again"
+        )
+    elif tool_use_behavior == "first_tool":
+        behavior = "stop_on_first_tool"
+    elif tool_use_behavior == "custom":
+        behavior = custom_tool_use_behavior
+
+    agent = Agent(
+        name="Weather agent",
+        instructions="You are a helpful agent.",
+        tools=[get_weather],
+        tool_use_behavior=behavior,
+        model_settings=ModelSettings(
+            tool_choice="required" if tool_use_behavior != "default" else None
+        ),
+    )
+
+    result = await Runner.run(agent, input="What's the weather in Tokyo?")
+    print(result.final_output)
+
+
+if __name__ == "__main__":
+    import argparse
+
+    parser = argparse.ArgumentParser()
+    parser.add_argument(
+        "-t",
+        "--tool-use-behavior",
+        type=str,
+        required=True,
+        choices=["default", "first_tool", "custom"],
+        help="The behavior to use for tool use. Default will cause tool outputs to be sent to the model. "
+        "first_tool_result will cause the first tool result to be used as the final output. "
+        "custom will use a custom tool use behavior function.",
+    )
+    args = parser.parse_args()
+    asyncio.run(main(args.tool_use_behavior))
diff --git a/examples/basic/tools.py b/examples/basic/tools.py
new file mode 100644
index 00000000..8936065a
--- /dev/null
+++ b/examples/basic/tools.py
@@ -0,0 +1,34 @@
+import asyncio
+
+from pydantic import BaseModel
+
+from agents import Agent, Runner, function_tool
+
+
+class Weather(BaseModel):
+    city: str
+    temperature_range: str
+    conditions: str
+
+
+@function_tool
+def get_weather(city: str) -> Weather:
+    print("[debug] get_weather called")
+    return Weather(city=city, temperature_range="14-20C", conditions="Sunny with wind.")
+
+
+agent = Agent(
+    name="Hello world",
+    instructions="You are a helpful agent.",
+    tools=[get_weather],
+)
+
+
+async def main():
+    result = await Runner.run(agent, input="What's the weather in Tokyo?")
+    print(result.final_output)
+    # The weather in Tokyo is sunny.
+
+
+if __name__ == "__main__":
+    asyncio.run(main())
diff --git a/src/agents/__init__.py b/src/agents/__init__.py
index 21a2f2a6..a7a1272c 100644
--- a/src/agents/__init__.py
+++ b/src/agents/__init__.py
@@ -5,7 +5,7 @@
 from openai import AsyncOpenAI
 
 from . import _config
-from .agent import Agent
+from .agent import Agent, ToolsToFinalOutputFunction, ToolsToFinalOutputResult
 from .agent_output import AgentOutputSchema
 from .computer import AsyncComputer, Button, Computer, Environment
 from .exceptions import (
@@ -57,6 +57,7 @@
     ComputerTool,
     FileSearchTool,
     FunctionTool,
+    FunctionToolResult,
     Tool,
     WebSearchTool,
     default_tool_error_function,
@@ -137,6 +138,8 @@ def enable_verbose_stdout_logging():
 
 __all__ = [
     "Agent",
+    "ToolsToFinalOutputFunction",
+    "ToolsToFinalOutputResult",
     "Runner",
     "Model",
     "ModelProvider",
@@ -190,6 +193,7 @@ def enable_verbose_stdout_logging():
     "AgentUpdatedStreamEvent",
     "StreamEvent",
     "FunctionTool",
+    "FunctionToolResult",
     "ComputerTool",
     "FileSearchTool",
     "Tool",
diff --git a/src/agents/_run_impl.py b/src/agents/_run_impl.py
index c0c0ebd0..2849538d 100644
--- a/src/agents/_run_impl.py
+++ b/src/agents/_run_impl.py
@@ -1,8 +1,10 @@
 from __future__ import annotations
 
 import asyncio
+import inspect
+from collections.abc import Awaitable
 from dataclasses import dataclass
-from typing import TYPE_CHECKING, Any
+from typing import TYPE_CHECKING, Any, cast
 
 from openai.types.responses import (
     ResponseComputerToolCall,
@@ -25,7 +27,7 @@
 from openai.types.responses.response_input_param import ComputerCallOutput
 from openai.types.responses.response_reasoning_item import ResponseReasoningItem
 
-from .agent import Agent
+from .agent import Agent, ToolsToFinalOutputResult
 from .agent_output import AgentOutputSchema
 from .computer import AsyncComputer, Computer
 from .exceptions import AgentsException, ModelBehaviorError, UserError
@@ -48,7 +50,7 @@
 from .models.interface import ModelTracing
 from .run_context import RunContextWrapper, TContext
 from .stream_events import RunItemStreamEvent, StreamEvent
-from .tool import ComputerTool, FunctionTool
+from .tool import ComputerTool, FunctionTool, FunctionToolResult
 from .tracing import (
     SpanError,
     Trace,
@@ -70,6 +72,8 @@ class QueueCompleteSentinel:
 
 QUEUE_COMPLETE_SENTINEL = QueueCompleteSentinel()
 
+_NOT_FINAL_OUTPUT = ToolsToFinalOutputResult(is_final_output=False, final_output=None)
+
 
 @dataclass
 class ToolRunHandoff:
@@ -199,7 +203,7 @@ async def execute_tools_and_side_effects(
                 config=run_config,
             ),
         )
-        new_step_items.extend(function_results)
+        new_step_items.extend([result.run_item for result in function_results])
         new_step_items.extend(computer_results)
 
         # Second, check if there are any handoffs
@@ -216,6 +220,36 @@ async def execute_tools_and_side_effects(
                 run_config=run_config,
             )
 
+        # Third, we'll check if the tool use should result in a final output
+        check_tool_use = await cls._check_for_final_output_from_tools(
+            agent=agent,
+            tool_results=function_results,
+            context_wrapper=context_wrapper,
+            config=run_config,
+        )
+
+        if check_tool_use.is_final_output:
+            # If the output type is str, then let's just stringify it
+            if not agent.output_type or agent.output_type is str:
+                check_tool_use.final_output = str(check_tool_use.final_output)
+
+            if check_tool_use.final_output is None:
+                logger.error(
+                    "Model returned a final output of None. Not raising an error because we assume"
+                    "you know what you're doing."
+                )
+
+            return await cls.execute_final_output(
+                agent=agent,
+                original_input=original_input,
+                new_response=new_response,
+                pre_step_items=pre_step_items,
+                new_step_items=new_step_items,
+                final_output=check_tool_use.final_output,
+                hooks=hooks,
+                context_wrapper=context_wrapper,
+            )
+
         # Now we can check if the model also produced a final output
         message_items = [item for item in new_step_items if isinstance(item, MessageOutputItem)]
 
@@ -355,10 +389,10 @@ async def execute_function_tool_calls(
         hooks: RunHooks[TContext],
         context_wrapper: RunContextWrapper[TContext],
         config: RunConfig,
-    ) -> list[RunItem]:
+    ) -> list[FunctionToolResult]:
         async def run_single_tool(
             func_tool: FunctionTool, tool_call: ResponseFunctionToolCall
-        ) -> str:
+        ) -> Any:
             with function_span(func_tool.name) as span_fn:
                 if config.trace_include_sensitive_data:
                     span_fn.span_data.input = tool_call.arguments
@@ -404,10 +438,14 @@ async def run_single_tool(
         results = await asyncio.gather(*tasks)
 
         return [
-            ToolCallOutputItem(
-                output=str(result),
-                raw_item=ItemHelpers.tool_call_output_item(tool_run.tool_call, str(result)),
-                agent=agent,
+            FunctionToolResult(
+                tool=tool_run.function_tool,
+                output=result,
+                run_item=ToolCallOutputItem(
+                    output=result,
+                    raw_item=ItemHelpers.tool_call_output_item(tool_run.tool_call, str(result)),
+                    agent=agent,
+                ),
             )
             for tool_run, result in zip(tool_runs, results)
         ]
@@ -646,6 +684,47 @@ def stream_step_result_to_queue(
             if event:
                 queue.put_nowait(event)
 
+    @classmethod
+    async def _check_for_final_output_from_tools(
+        cls,
+        *,
+        agent: Agent[TContext],
+        tool_results: list[FunctionToolResult],
+        context_wrapper: RunContextWrapper[TContext],
+        config: RunConfig,
+    ) -> ToolsToFinalOutputResult:
+        """Returns (i, final_output)."""
+        if not tool_results:
+            return _NOT_FINAL_OUTPUT
+
+        if agent.tool_use_behavior == "run_llm_again":
+            return _NOT_FINAL_OUTPUT
+        elif agent.tool_use_behavior == "stop_on_first_tool":
+            return ToolsToFinalOutputResult(
+                is_final_output=True, final_output=tool_results[0].output
+            )
+        elif isinstance(agent.tool_use_behavior, dict):
+            names = agent.tool_use_behavior.get("stop_at_tool_names", [])
+            for tool_result in tool_results:
+                if tool_result.tool.name in names:
+                    return ToolsToFinalOutputResult(
+                        is_final_output=True, final_output=tool_result.output
+                    )
+            return ToolsToFinalOutputResult(is_final_output=False, final_output=None)
+        elif callable(agent.tool_use_behavior):
+            if inspect.iscoroutinefunction(agent.tool_use_behavior):
+                return await cast(
+                    Awaitable[ToolsToFinalOutputResult],
+                    agent.tool_use_behavior(context_wrapper, tool_results),
+                )
+            else:
+                return cast(
+                    ToolsToFinalOutputResult, agent.tool_use_behavior(context_wrapper, tool_results)
+                )
+
+        logger.error(f"Invalid tool_use_behavior: {agent.tool_use_behavior}")
+        raise UserError(f"Invalid tool_use_behavior: {agent.tool_use_behavior}")
+
 
 class TraceCtxManager:
     """Creates a trace only if there is no current trace, and manages the trace lifecycle."""
diff --git a/src/agents/agent.py b/src/agents/agent.py
index 3c4588e6..2723e678 100644
--- a/src/agents/agent.py
+++ b/src/agents/agent.py
@@ -4,7 +4,9 @@
 import inspect
 from collections.abc import Awaitable
 from dataclasses import dataclass, field
-from typing import TYPE_CHECKING, Any, Callable, Generic, cast
+from typing import TYPE_CHECKING, Any, Callable, Generic, Literal, cast
+
+from typing_extensions import TypeAlias, TypedDict
 
 from .guardrail import InputGuardrail, OutputGuardrail
 from .handoffs import Handoff
@@ -13,7 +15,7 @@
 from .model_settings import ModelSettings
 from .models.interface import Model
 from .run_context import RunContextWrapper, TContext
-from .tool import Tool, function_tool
+from .tool import FunctionToolResult, Tool, function_tool
 from .util import _transforms
 from .util._types import MaybeAwaitable
 
@@ -22,6 +24,33 @@
     from .result import RunResult
 
 
+@dataclass
+class ToolsToFinalOutputResult:
+    is_final_output: bool
+    """Whether this is the final output. If False, the LLM will run again and receive the tool call
+    output.
+    """
+
+    final_output: Any | None = None
+    """The final output. Can be None if `is_final_output` is False, otherwise must match the
+    `output_type` of the agent.
+    """
+
+
+ToolsToFinalOutputFunction: TypeAlias = Callable[
+    [RunContextWrapper[TContext], list[FunctionToolResult]],
+    MaybeAwaitable[ToolsToFinalOutputResult],
+]
+"""A function that takes a run context and a list of tool results, and returns a
+`ToolToFinalOutputResult`.
+"""
+
+
+class StopAtTools(TypedDict):
+    stop_at_tool_names: list[str]
+    """A list of tool names, any of which will stop the agent from running further."""
+
+
 @dataclass
 class Agent(Generic[TContext]):
     """An agent is an AI model configured with instructions, tools, guardrails, handoffs and more.
@@ -95,6 +124,25 @@ class Agent(Generic[TContext]):
     """A class that receives callbacks on various lifecycle events for this agent.
     """
 
+    tool_use_behavior: (
+        Literal["run_llm_again", "stop_on_first_tool"] | StopAtTools | ToolsToFinalOutputFunction
+    ) = "run_llm_again"
+    """This lets you configure how tool use is handled.
+    - "run_llm_again": The default behavior. Tools are run, and then the LLM receives the results
+        and gets to respond.
+    - "stop_on_first_tool": The output of the first tool call is used as the final output. This
+        means that the LLM does not process the result of the tool call.
+    - A list of tool names: The agent will stop running if any of the tools in the list are called.
+        The final output will be the output of the first matching tool call. The LLM does not
+        process the result of the tool call.
+    - A function: If you pass a function, it will be called with the run context and the list of
+      tool results. It must return a `ToolToFinalOutputResult`, which determines whether the tool
+      calls result in a final output.
+
+      NOTE: This configuration is specific to FunctionTools. Hosted tools, such as file search,
+      web search, etc are always processed by the LLM.
+    """
+
     def clone(self, **kwargs: Any) -> Agent[TContext]:
         """Make a copy of the agent, with the given arguments changed. For example, you could do:
         ```
diff --git a/src/agents/items.py b/src/agents/items.py
index ffbeba02..c2af0dfc 100644
--- a/src/agents/items.py
+++ b/src/agents/items.py
@@ -129,8 +129,10 @@ class ToolCallOutputItem(RunItemBase[Union[FunctionCallOutput, ComputerCallOutpu
     raw_item: FunctionCallOutput | ComputerCallOutput
     """The raw item from the model."""
 
-    output: str
-    """The output of the tool call."""
+    output: Any
+    """The output of the tool call. This is whatever the tool call returned; the `raw_item`
+    contains a string representation of the output.
+    """
 
     type: Literal["tool_call_output_item"] = "tool_call_output_item"
 
diff --git a/src/agents/tool.py b/src/agents/tool.py
index 3c309217..c1c16242 100644
--- a/src/agents/tool.py
+++ b/src/agents/tool.py
@@ -15,6 +15,7 @@
 from .computer import AsyncComputer, Computer
 from .exceptions import ModelBehaviorError
 from .function_schema import DocstringStyle, function_schema
+from .items import RunItem
 from .logger import logger
 from .run_context import RunContextWrapper
 from .tracing import SpanError
@@ -29,6 +30,18 @@
 ToolFunction = Union[ToolFunctionWithoutContext[ToolParams], ToolFunctionWithContext[ToolParams]]
 
 
+@dataclass
+class FunctionToolResult:
+    tool: FunctionTool
+    """The tool that was run."""
+
+    output: Any
+    """The output of the tool."""
+
+    run_item: RunItem
+    """The run item that was produced as a result of the tool call."""
+
+
 @dataclass
 class FunctionTool:
     """A tool that wraps a function. In most cases, you should use  the `function_tool` helpers to
@@ -44,15 +57,15 @@ class FunctionTool:
     params_json_schema: dict[str, Any]
     """The JSON schema for the tool's parameters."""
 
-    on_invoke_tool: Callable[[RunContextWrapper[Any], str], Awaitable[str]]
+    on_invoke_tool: Callable[[RunContextWrapper[Any], str], Awaitable[Any]]
     """A function that invokes the tool with the given context and parameters. The params passed
     are:
     1. The tool run context.
     2. The arguments from the LLM, as a JSON string.
 
-    You must return a string representation of the tool output. In case of errors, you can either
-    raise an Exception (which will cause the run to fail) or return a string error message (which
-    will be sent back to the LLM).
+    You must return a string representation of the tool output, or something we can call `str()` on.
+    In case of errors, you can either raise an Exception (which will cause the run to fail) or
+    return a string error message (which will be sent back to the LLM).
     """
 
     strict_json_schema: bool = True
@@ -207,7 +220,7 @@ def _create_function_tool(the_func: ToolFunction[...]) -> FunctionTool:
             strict_json_schema=strict_mode,
         )
 
-        async def _on_invoke_tool_impl(ctx: RunContextWrapper[Any], input: str) -> str:
+        async def _on_invoke_tool_impl(ctx: RunContextWrapper[Any], input: str) -> Any:
             try:
                 json_data: dict[str, Any] = json.loads(input) if input else {}
             except Exception as e:
@@ -254,9 +267,9 @@ async def _on_invoke_tool_impl(ctx: RunContextWrapper[Any], input: str) -> str:
             else:
                 logger.debug(f"Tool {schema.name} returned {result}")
 
-            return str(result)
+            return result
 
-        async def _on_invoke_tool(ctx: RunContextWrapper[Any], input: str) -> str:
+        async def _on_invoke_tool(ctx: RunContextWrapper[Any], input: str) -> Any:
             try:
                 return await _on_invoke_tool_impl(ctx, input)
             except Exception as e:
diff --git a/src/agents/tracing/span_data.py b/src/agents/tracing/span_data.py
index 5e5d38cb..1a49d8e6 100644
--- a/src/agents/tracing/span_data.py
+++ b/src/agents/tracing/span_data.py
@@ -51,7 +51,7 @@ def export(self) -> dict[str, Any]:
 class FunctionSpanData(SpanData):
     __slots__ = ("name", "input", "output")
 
-    def __init__(self, name: str, input: str | None, output: str | None):
+    def __init__(self, name: str, input: str | None, output: Any | None):
         self.name = name
         self.input = input
         self.output = output
@@ -65,7 +65,7 @@ def export(self) -> dict[str, Any]:
             "type": self.type,
             "name": self.name,
             "input": self.input,
-            "output": self.output,
+            "output": str(self.output) if self.output else None,
         }
 
 


Test patch:
diff --git a/tests/test_agent_runner.py b/tests/test_agent_runner.py
index c124915a..e8e060fd 100644
--- a/tests/test_agent_runner.py
+++ b/tests/test_agent_runner.py
@@ -21,6 +21,8 @@
     UserError,
     handoff,
 )
+from agents.agent import ToolsToFinalOutputResult
+from agents.tool import FunctionToolResult, function_tool
 
 from .fake_model import FakeModel
 from .test_responses import (
@@ -552,3 +554,83 @@ def guardrail_function(
 
     with pytest.raises(OutputGuardrailTripwireTriggered):
         await Runner.run(agent, input="user_message")
+
+
+@function_tool
+def test_tool_one():
+    return Foo(bar="tool_one_result")
+
+
+@function_tool
+def test_tool_two():
+    return "tool_two_result"
+
+
+@pytest.mark.asyncio
+async def test_tool_use_behavior_first_output():
+    model = FakeModel()
+    agent = Agent(
+        name="test",
+        model=model,
+        tools=[get_function_tool("foo", "tool_result"), test_tool_one, test_tool_two],
+        tool_use_behavior="stop_on_first_tool",
+        output_type=Foo,
+    )
+
+    model.add_multiple_turn_outputs(
+        [
+            # First turn: a message and tool call
+            [
+                get_text_message("a_message"),
+                get_function_tool_call("test_tool_one", None),
+                get_function_tool_call("test_tool_two", None),
+            ],
+        ]
+    )
+
+    result = await Runner.run(agent, input="user_message")
+
+    assert result.final_output == Foo(bar="tool_one_result"), (
+        "should have used the first tool result"
+    )
+
+
+def custom_tool_use_behavior(
+    context: RunContextWrapper[Any], results: list[FunctionToolResult]
+) -> ToolsToFinalOutputResult:
+    if "test_tool_one" in [result.tool.name for result in results]:
+        return ToolsToFinalOutputResult(is_final_output=True, final_output="the_final_output")
+    else:
+        return ToolsToFinalOutputResult(is_final_output=False, final_output=None)
+
+
+@pytest.mark.asyncio
+async def test_tool_use_behavior_custom_function():
+    model = FakeModel()
+    agent = Agent(
+        name="test",
+        model=model,
+        tools=[get_function_tool("foo", "tool_result"), test_tool_one, test_tool_two],
+        tool_use_behavior=custom_tool_use_behavior,
+    )
+
+    model.add_multiple_turn_outputs(
+        [
+            # First turn: a message and tool call
+            [
+                get_text_message("a_message"),
+                get_function_tool_call("test_tool_two", None),
+            ],
+            # Second turn: a message and tool call
+            [
+                get_text_message("a_message"),
+                get_function_tool_call("test_tool_one", None),
+                get_function_tool_call("test_tool_two", None),
+            ],
+        ]
+    )
+
+    result = await Runner.run(agent, input="user_message")
+
+    assert len(result.raw_responses) == 2, "should have two model responses"
+    assert result.final_output == "the_final_output", "should have used the custom function"
diff --git a/tests/test_function_tool.py b/tests/test_function_tool.py
index 6a78309b..0a57aea8 100644
--- a/tests/test_function_tool.py
+++ b/tests/test_function_tool.py
@@ -49,10 +49,10 @@ async def test_simple_function():
     assert tool.name == "simple_function"
 
     result = await tool.on_invoke_tool(RunContextWrapper(None), '{"a": 1}')
-    assert result == "6"
+    assert result == 6
 
     result = await tool.on_invoke_tool(RunContextWrapper(None), '{"a": 1, "b": 2}')
-    assert result == "3"
+    assert result == 3
 
     # Missing required argument should raise an error
     with pytest.raises(ModelBehaviorError):
diff --git a/tests/test_tool_use_behavior.py b/tests/test_tool_use_behavior.py
new file mode 100644
index 00000000..6a673b7a
--- /dev/null
+++ b/tests/test_tool_use_behavior.py
@@ -0,0 +1,194 @@
+# Copyright
+
+from __future__ import annotations
+
+from typing import cast
+
+import pytest
+from openai.types.responses.response_input_item_param import FunctionCallOutput
+
+from agents import (
+    Agent,
+    FunctionToolResult,
+    RunConfig,
+    RunContextWrapper,
+    ToolCallOutputItem,
+    ToolsToFinalOutputResult,
+    UserError,
+)
+from agents._run_impl import RunImpl
+
+from .test_responses import get_function_tool
+
+
+def _make_function_tool_result(
+    agent: Agent, output: str, tool_name: str | None = None
+) -> FunctionToolResult:
+    # Construct a FunctionToolResult with the given output using a simple function tool.
+    tool = get_function_tool(tool_name or "dummy", return_value=output)
+    raw_item: FunctionCallOutput = cast(
+        FunctionCallOutput,
+        {
+            "call_id": "1",
+            "output": output,
+            "type": "function_call_output",
+        },
+    )
+    # For this test we don't care about the specific RunItem subclass, only the output field
+    run_item = ToolCallOutputItem(agent=agent, raw_item=raw_item, output=output)
+    return FunctionToolResult(tool=tool, output=output, run_item=run_item)
+
+
+@pytest.mark.asyncio
+async def test_no_tool_results_returns_not_final_output() -> None:
+    # If there are no tool results at all, tool_use_behavior should not produce a final output.
+    agent = Agent(name="test")
+    result = await RunImpl._check_for_final_output_from_tools(
+        agent=agent,
+        tool_results=[],
+        context_wrapper=RunContextWrapper(context=None),
+        config=RunConfig(),
+    )
+    assert result.is_final_output is False
+    assert result.final_output is None
+
+
+@pytest.mark.asyncio
+async def test_run_llm_again_behavior() -> None:
+    # With the default run_llm_again behavior, even with tools we still expect to keep running.
+    agent = Agent(name="test", tool_use_behavior="run_llm_again")
+    tool_results = [_make_function_tool_result(agent, "ignored")]
+    result = await RunImpl._check_for_final_output_from_tools(
+        agent=agent,
+        tool_results=tool_results,
+        context_wrapper=RunContextWrapper(context=None),
+        config=RunConfig(),
+    )
+    assert result.is_final_output is False
+    assert result.final_output is None
+
+
+@pytest.mark.asyncio
+async def test_stop_on_first_tool_behavior() -> None:
+    # When tool_use_behavior is stop_on_first_tool, we should surface first tool output as final.
+    agent = Agent(name="test", tool_use_behavior="stop_on_first_tool")
+    tool_results = [
+        _make_function_tool_result(agent, "first_tool_output"),
+        _make_function_tool_result(agent, "ignored"),
+    ]
+    result = await RunImpl._check_for_final_output_from_tools(
+        agent=agent,
+        tool_results=tool_results,
+        context_wrapper=RunContextWrapper(context=None),
+        config=RunConfig(),
+    )
+    assert result.is_final_output is True
+    assert result.final_output == "first_tool_output"
+
+
+@pytest.mark.asyncio
+async def test_custom_tool_use_behavior_sync() -> None:
+    """If tool_use_behavior is a sync function, we should call it and propagate its return."""
+
+    def behavior(
+        context: RunContextWrapper, results: list[FunctionToolResult]
+    ) -> ToolsToFinalOutputResult:
+        assert len(results) == 3
+        return ToolsToFinalOutputResult(is_final_output=True, final_output="custom")
+
+    agent = Agent(name="test", tool_use_behavior=behavior)
+    tool_results = [
+        _make_function_tool_result(agent, "ignored1"),
+        _make_function_tool_result(agent, "ignored2"),
+        _make_function_tool_result(agent, "ignored3"),
+    ]
+    result = await RunImpl._check_for_final_output_from_tools(
+        agent=agent,
+        tool_results=tool_results,
+        context_wrapper=RunContextWrapper(context=None),
+        config=RunConfig(),
+    )
+    assert result.is_final_output is True
+    assert result.final_output == "custom"
+
+
+@pytest.mark.asyncio
+async def test_custom_tool_use_behavior_async() -> None:
+    """If tool_use_behavior is an async function, we should await it and propagate its return."""
+
+    async def behavior(
+        context: RunContextWrapper, results: list[FunctionToolResult]
+    ) -> ToolsToFinalOutputResult:
+        assert len(results) == 3
+        return ToolsToFinalOutputResult(is_final_output=True, final_output="async_custom")
+
+    agent = Agent(name="test", tool_use_behavior=behavior)
+    tool_results = [
+        _make_function_tool_result(agent, "ignored1"),
+        _make_function_tool_result(agent, "ignored2"),
+        _make_function_tool_result(agent, "ignored3"),
+    ]
+    result = await RunImpl._check_for_final_output_from_tools(
+        agent=agent,
+        tool_results=tool_results,
+        context_wrapper=RunContextWrapper(context=None),
+        config=RunConfig(),
+    )
+    assert result.is_final_output is True
+    assert result.final_output == "async_custom"
+
+
+@pytest.mark.asyncio
+async def test_invalid_tool_use_behavior_raises() -> None:
+    """If tool_use_behavior is invalid, we should raise a UserError."""
+    agent = Agent(name="test")
+    # Force an invalid value; mypy will complain, so ignore the type here.
+    agent.tool_use_behavior = "bad_value"  # type: ignore[assignment]
+    tool_results = [_make_function_tool_result(agent, "ignored")]
+    with pytest.raises(UserError):
+        await RunImpl._check_for_final_output_from_tools(
+            agent=agent,
+            tool_results=tool_results,
+            context_wrapper=RunContextWrapper(context=None),
+            config=RunConfig(),
+        )
+
+
+@pytest.mark.asyncio
+async def test_tool_names_to_stop_at_behavior() -> None:
+    agent = Agent(
+        name="test",
+        tools=[
+            get_function_tool("tool1", return_value="tool1_output"),
+            get_function_tool("tool2", return_value="tool2_output"),
+            get_function_tool("tool3", return_value="tool3_output"),
+        ],
+        tool_use_behavior={"stop_at_tool_names": ["tool1"]},
+    )
+
+    tool_results = [
+        _make_function_tool_result(agent, "ignored1", "tool2"),
+        _make_function_tool_result(agent, "ignored3", "tool3"),
+    ]
+    result = await RunImpl._check_for_final_output_from_tools(
+        agent=agent,
+        tool_results=tool_results,
+        context_wrapper=RunContextWrapper(context=None),
+        config=RunConfig(),
+    )
+    assert result.is_final_output is False, "We should not have stopped at tool1"
+
+    # Now test with a tool that matches the list
+    tool_results = [
+        _make_function_tool_result(agent, "output1", "tool1"),
+        _make_function_tool_result(agent, "ignored2", "tool2"),
+        _make_function_tool_result(agent, "ignored3", "tool3"),
+    ]
+    result = await RunImpl._check_for_final_output_from_tools(
+        agent=agent,
+        tool_results=tool_results,
+        context_wrapper=RunContextWrapper(context=None),
+        config=RunConfig(),
+    )
+    assert result.is_final_output is True, "We should have stopped at tool1"
+    assert result.final_output == "output1"