Feature Request: Support all AgentHooks when Agent.as_tool()

[hayescode]

When running the Manager pattern the sub-agents I'm running as tools do not trigger the on_tool_start or on_tool_end methods of the AgentHooks. The documentation does not mention this limitation so i'm not sure if this is a bug or feature request. Maybe related to #1057 .

TestAgentHooks

####### AgentHooks ########

from typing import Any, Generic, Optional
from agents import AgentHooks
from typing_extensions import TypeVar

from agents.agent import Agent, AgentBase
from agents.items import ModelResponse, TResponseInputItem
from agents.run_context import RunContextWrapper, TContext
from agents.tool import Tool

class TestAgentHooks(AgentHooks):

 async def on_start(self, context: RunContextWrapper, agent: Agent) -> None:
 """Called before the agent is invoked. Called each time the running agent is changed to this
 agent."""
 print(f"### AgentHook: on_start ({agent.name}) Agent {agent.name} starting...")

 async def on_end(
 self,
 context: RunContextWrapper,
 agent: Agent,
 output: Any,
 ) -> None:
 """Called when the agent produces a final output."""
 print(f"### AgentHook: on_end Agent ({agent.name}) finished with output: {output}")

 async def on_handoff(
 self,
 context: RunContextWrapper,
 agent: Agent,
 source: Agent,
 ) -> None:
 """Called when the agent is being handed off to. The `source` is the agent that is handing
 off to this agent."""
 print(f"### AgentHook: on_handoff Agent({agent.name}) is being handed off to from {source.name}...")

 async def on_tool_start(
 self,
 context: RunContextWrapper,
 agent: Agent,
 tool: Tool,
 ) -> None:
 """Called concurrently with tool invocation."""
 print(f"### AgentHook: on_tool_start Agent {agent.name} is invoking tool {tool.name}... Tool Context: {context.context}")

 async def on_tool_end(
 self,
 context: RunContextWrapper,
 agent: Agent,
 tool: Tool,
 result: str,
 ) -> None:
 """Called after a tool is invoked."""
 print(f"### AgentHook: on_tool_end Agent ({agent.name}) finished invoking tool {tool.name} with result: {result}")

 async def on_llm_start(
 self,
 context: RunContextWrapper,
 agent: Agent,
 system_prompt: Optional[str],
 input_items: list[TResponseInputItem],
 ) -> None:
 """Called immediately before the agent issues an LLM call."""
 print(f"### AgentHook: on_llm_start Agent ({agent.name}) is about to call LLM with system prompt: {system_prompt} and input items: {input_items}")

 async def on_llm_end(
 self,
 context: RunContextWrapper,
 agent: Agent,
 response: ModelResponse,
 ) -> None:
 """Called immediately after the agent receives the LLM response."""
 print(f"### AgentHook: on_llm_end Agent ({agent.name}) received LLM response: {response}")

coding_agent = Agent(
 name="Coding Agent",
 instructions="You love doing math.",
 model=OpenAIResponsesModel(model="gpt-4.1", openai_client=openai_client),
 tools=[CodeInterpreterTool(tool_config={"type": "code_interpreter", "container":{"type": "auto"}})],
 hooks=TestAgentHooks(),
)

devops_mcp_agent = Agent(
 name="DevOps MCP Agent",
 instructions="You are responsible for managing DevOps pipelines and MCP configurations.",
 tools=[HostedMCPTool(tool_config=AzureDevOps.tool_definition())],
 model=OpenAIResponsesModel(model="gpt-4.1", openai_client=openai_client),
 hooks=TestAgentHooks(),
)

orchestrator = Agent(
 name="Orchestrator",
 instructions="Call the relevant tools when specialized expertise is needed.",
 model=OpenAIResponsesModel(model="gpt-4.1", openai_client=openai_client),
 tools=[
 coding_agent.as_tool(
 tool_name="code_interpreter_agent",
 tool_description="Perform calculations and data analysis using code.",
 ),
 devops_mcp_agent.as_tool(
 tool_name="devops_mcp_agent",
 tool_description="Manage DevOps pipelines and MCP configurations.",
 ),
 ],
 hooks=TestAgentHooks(),
)


stream_result = Runner.run_streamed(
 orchestrator, 
 "Write and run some python to give me the result of 589725*27363/7.23474.",
 context=agent_context,
 run_config=RunConfig(trace_include_sensitive_data=True, tracing_disabled=False),
)

async for event in stream_result.stream_events():
 pass

This is the result. I can see on_llm_* and on_agent_* methods fire, but not the on_tool_*.

### AgentHook: on_start (Orchestrator) Agent Orchestrator starting...
### AgentHook: on_llm_start Agent (Orchestrator) is about to call LLM with system prompt: Call the relevant tools when specialized expertise is needed. and input items: [{'content': 'Write and run some python to give me the result of 589725*27363/7.23474.', 'role': 'user'}]
### AgentHook: on_llm_end Agent (Orchestrator) received LLM response: ModelResponse(output=[ResponseFunctionToolCall(arguments='{"input":"result = 589725 * 27363 / 7.23474\\nresult"}', call_id='call_moMTYk41AvtNZ1eXaa3lQxVV', name='code_interpreter_agent', type='function_call', id='fc_0d3f5cda170b02ae00690927651ba0819684ae7baebe84bd7a', status='completed')], usage=Usage(requests=1, input_tokens=167, input_tokens_details=InputTokensDetails(cached_tokens=0), output_tokens=33, output_tokens_details=OutputTokensDetails(reasoning_tokens=0), total_tokens=200), response_id='resp_0d3f5cda170b02ae0069092764d6d8819698ce15cfa6a2876f')
### AgentHook: on_tool_start Agent (Orchestrator) is invoking tool code_interpreter_agent...
### AgentHook: on_start (Coding Agent) Agent Coding Agent starting...
### AgentHook: on_llm_start Agent (Coding Agent) is about to call LLM with system prompt: You love doing math. 

### THIS IS WHERE I'M EXPECTING on_tool_start for Agent (Coding Agent) 
### THIS IS WHERE I'M EXPECTING on_tool_end for Agent (Coding Agent) 

### AgentHook: on_llm_end Agent (Coding Agent) received LLM response: ModelResponse(output=[ResponseCodeInterpreterToolCall(id='ci_07f8ca8d3798d8ed0069092767686c819381ca4407d3c64f44', code='# Calculate the result of the given expression\nresult = 589725 * 27363 / 7.23474\nresult', container_id='cntr_690927660b0c819097e29c2794c956f603aaf67281e4765a', outputs=None, status='completed', type='code_interpreter_call'), ResponseOutputMessage(id='msg_07f8ca8d3798d8ed00690927687cc88193a6eda7fa80d34b07', content=[ResponseOutputText(annotations=[], text='The result of the expression \\( 589725 \\times 27363 \\div 7.23474 \\) is approximately **2,230,438,851.29**.', type='output_text', logprobs=[])], role='assistant', status='completed', type='message')], usage=Usage(requests=1, input_tokens=426, input_tokens_details=InputTokensDetails(cached_tokens=0), output_tokens=71, output_tokens_details=OutputTokensDetails(reasoning_tokens=0), total_tokens=497), response_id='resp_07f8ca8d3798d8ed00690927656e4c8193a0f25882da6ce3fc')
### AgentHook: on_end Agent (Coding Agent) finished with output: The result of the expression \( 589725 \times 27363 \div 7.23474 \) is approximately **2,230,438,851.29**.
### AgentHook: on_tool_end Agent (Orchestrator) finished invoking tool code_interpreter_agent with result: The result of the expression \( 589725 \times 27363 \div 7.23474 \) is approximately **2,230,438,851.29**.
### AgentHook: on_llm_start Agent (Orchestrator) is about to call LLM with system prompt: Call the relevant tools when specialized expertise is needed. and input items: [{'content': 'Write and run some python to give me the result of 589725*27363/7.23474.', 'role': 'user'},{'arguments': '{"input":"result = 589725 * 27363 / 7.23474\\nresult"}', 'call_id': 'call_moMTYk41AvtNZ1eXaa3lQxVV', 'name': 'code_interpreter_agent', 'type': 'function_call', 'id': 'fc_0d3f5cda170b02ae00690927651ba0819684ae7baebe84bd7a', 'status': 'completed'},{'call_id': 'call_moMTYk41AvtNZ1eXaa3lQxVV', 'output': 'The result of the expression \\( 589725 \\times 27363 \\div 7.23474 \\) is approximately **2,230,438,851.29**.', 'type': 'function_call_output'}]
### AgentHook: on_llm_end Agent (Orchestrator) received LLM response: ModelResponse(output=[ResponseOutputMessage(id='msg_0d3f5cda170b02ae0069092769a9288196a608df25aeedeef4', content=[ResponseOutputText(annotations=[], text='The result of \\( 589725 \\times 27363 \\div 7.23474 \\) is approximately **2,230,438,851.29**.', type='output_text', logprobs=[])], role='assistant', status='completed', type='message')], usage=Usage(requests=1, input_tokens=247, input_tokens_details=InputTokensDetails(cached_tokens=0), output_tokens=37, output_tokens_details=OutputTokensDetails(reasoning_tokens=0), total_tokens=284), response_id='resp_0d3f5cda170b02ae006909276959d0819684d08ffba1f7ab9a')
### AgentHook: on_end Agent (Orchestrator) finished with output: The result of \( 589725 \times 27363 \div 7.23474 \) is approximately **2,230,438,851.29**.

[ihower]

@hayescode This isn’t a bug. AgentHooks are working correctly when using Agent.as_tool().

The reason you don’t see on_tool_start or on_tool_end for your sub-agents is that the tools you’re calling (CodeInterpreterTool and HostedMCPTool) are not local tools. Their execution happens on the OpenAI server side, so local hooks won’t be triggered in those cases.

See also this related comment for clarification: #778 (comment) on_tool_start only applies to local tools.

[hayescode]

@ihower woah you're right, only local functions hit on_tool_* methods. I wonder why that is. For "single tool" hosted tools like code_interpreter or web_search i guess I could just wrap them in a local function, but for MCP this is a much bigger problem since there could be dozens of tools within a single hosted MCP server and we have no insight into what tools are called/arguments/outputs. Updated the title to Feature Request based on your comment (thanks for sharing this!) and hopefully OpenAI can give add the ability to see hosted tools (Responses API does so i don't see why not) 🙏.

[ihower]

@hayescode The main reason is that hosted tools run entirely server-side, so by the time we know a hosted tool was invoked, the LLM call has already completed.

If you want to detect whether any hosted tools were called, you can instead use the on_llm_end hook and inspect the response field, and you’ll see the tool calls there. (Note that only streaming mode can get them in time.
For non-streaming runs, you’ll see them only after everything is done.)

Alternatively, even without hooks, you can observe hosted tool calls from the streaming events or the final result.new_item output.

[hayescode]

Alternatively, even without hooks, you can observe hosted tool calls from the streaming events or the final result.new_item output.

This can work mixing AgentHooks and the RawResponsesEvent, however this only works in the handoff pattern. Using the manager agent.as_tool() there seems to be no way except for the on_llm_end you mentioned.

[ihower]

@hayescode yep! you’re right. Once an agent is wrapped with Agent.as_tool(), we currently can’t access the inner agent’s streaming events. For now, on_llm_end hook works as a temporary workaround.

That said, since @seratch mentioned in #2037 that this would be a good feature addition on the SDK side, I’ll dig a bit deeper to see how we might make it possible.

[hayescode]

Thanks @ihower !

[hayescode]

#2037