feat(agents): Add on_llm_start and on_llm_end Lifecycle Hooks

src/agents/lifecycle.py

@@ -1,6 +1,7 @@
-from typing import Any, Generic
+from typing import Any, Generic, Optional
 
 from .agent import Agent
+from .items import ModelResponse, TResponseInputItem
 from .run_context import RunContextWrapper, TContext
 from .tool import Tool
 
@@ -10,6 +11,25 @@ class RunHooks(Generic[TContext]):
     override the methods you need.
     """
 
+    # Two new hook methods added to the RunHooks class to handle LLM start and end events.
+    # These methods allow you to perform actions just before and after the LLM call for an agent.
+    # This is useful for logging, monitoring, or modifying the context before and after the LLM call
+    async def on_llm_start(
+        self,
+        context: RunContextWrapper[TContext],
+        agent: Agent[TContext],
+        system_prompt: Optional[str],
+        input_items: list[TResponseInputItem],
+    ) -> None:
+        """Called just before invoking the LLM for this agent."""
+        pass
+
+    async def on_llm_end(
+        self, context: RunContextWrapper[TContext], agent: Agent[TContext], response: ModelResponse
+    ) -> None:
+        """Called immediately after the LLM call returns for this agent."""
+        pass
+
     async def on_agent_start(
         self, context: RunContextWrapper[TContext], agent: Agent[TContext]
     ) -> None:
@@ -103,3 +123,22 @@ async def on_tool_end(
     ) -> None:
         """Called after a tool is invoked."""
         pass
+
+    async def on_llm_start(
+        self,
+        context: RunContextWrapper[TContext],
+        agent: Agent[TContext],
+        system_prompt: Optional[str],
+        input_items: list[TResponseInputItem],
+    ) -> None:
+        """Called immediately before the agent issues an LLM call."""
+        pass
+
+    async def on_llm_end(
+        self,
+        context: RunContextWrapper[TContext],
+        agent: Agent[TContext],
+        response: ModelResponse,
+    ) -> None:
+        """Called immediately after the agent receives the LLM response."""
+        pass

src/agents/run.py

@@ -1067,6 +1067,9 @@ async def _get_new_response(
         model = cls._get_model(agent, run_config)
         model_settings = agent.model_settings.resolve(run_config.model_settings)
         model_settings = RunImpl.maybe_reset_tool_choice(agent, tool_use_tracker, model_settings)
+        # If the agent has hooks, we need to call them before and after the LLM call
+        if agent.hooks:
+            await agent.hooks.on_llm_start(context_wrapper, agent, system_prompt, input)
 
         new_response = await model.get_response(
             system_instructions=system_prompt,
@@ -1081,6 +1084,9 @@ async def _get_new_response(
             previous_response_id=previous_response_id,
             prompt=prompt_config,
         )
+        # If the agent has hooks, we need to call them after the LLM call
+        if agent.hooks:
+            await agent.hooks.on_llm_end(context_wrapper, agent, new_response)
 
         context_wrapper.usage.add(new_response.usage)
 

tests/test_agent_llm_hooks.py

@@ -0,0 +1,85 @@
+from collections import defaultdict
+from typing import Any, Optional
+
+import pytest
+
+from agents.agent import Agent
+from agents.items import ModelResponse, TResponseInputItem
+from agents.lifecycle import AgentHooks
+from agents.run import Runner
+from agents.run_context import RunContextWrapper, TContext
+from agents.tool import Tool
+
+from .fake_model import FakeModel
+from .test_responses import (
+    get_function_tool,
+    get_text_message,
+)
+
+
+class AgentHooksForTests(AgentHooks):
+    def __init__(self):
+        self.events: dict[str, int] = defaultdict(int)
+
+    def reset(self):
+        self.events.clear()
+
+    async def on_start(self, context: RunContextWrapper[TContext], agent: Agent[TContext]) -> None:
+        self.events["on_start"] += 1
+
+    async def on_end(
+        self, context: RunContextWrapper[TContext], agent: Agent[TContext], output: Any
+    ) -> None:
+        self.events["on_end"] += 1
+
+    async def on_handoff(
+        self, context: RunContextWrapper[TContext], agent: Agent[TContext], source: Agent[TContext]
+    ) -> None:
+        self.events["on_handoff"] += 1
+
+    async def on_tool_start(
+        self, context: RunContextWrapper[TContext], agent: Agent[TContext], tool: Tool
+    ) -> None:
+        self.events["on_tool_start"] += 1
+
+    async def on_tool_end(
+        self,
+        context: RunContextWrapper[TContext],
+        agent: Agent[TContext],
+        tool: Tool,
+        result: str,
+    ) -> None:
+        self.events["on_tool_end"] += 1
+
+    # NEW: LLM hooks
+    async def on_llm_start(
+        self,
+        context: RunContextWrapper[TContext],
+        agent: Agent[TContext],
+        system_prompt: Optional[str],
+        input_items: list[TResponseInputItem],
+    ) -> None:
+        self.events["on_llm_start"] += 1
+
+    async def on_llm_end(
+        self,
+        ccontext: RunContextWrapper[TContext],
+        agent: Agent[TContext],
+        response: ModelResponse,
+    ) -> None:
+        self.events["on_llm_end"] += 1
+
+
+# Example test using the above hooks:
+@pytest.mark.asyncio
+async def test_non_streamed_agent_hooks_with_llm():
+    hooks = AgentHooksForTests()
+    model = FakeModel()
+    agent = Agent(
+        name="A", model=model, tools=[get_function_tool("f", "res")], handoffs=[], hooks=hooks
+    )
+    # Simulate a single LLM call producing an output:
+    model.set_next_output([get_text_message("hello")])
+    await Runner.run(agent, input="hello")
+    # Expect one on_start, one on_llm_start, one on_llm_end, and one on_end
+    assert hooks.events == {"on_start": 1, "on_llm_start": 1, "on_llm_end": 1, "on_end": 1}