Add AgentRun.{all,new}_messages

This allows convenient access to these even if the run didn't end, at
which point they would not be available via `AgentRun.result`.

Author: tibbe

pydantic_ai_slim/pydantic_ai/run.py

@@ -135,6 +135,20 @@ def result(self) -> AgentRunResult[OutputDataT] | None:
             self._traceparent(required=False),
         )
 
+    def all_messages(self) -> list[_messages.ModelMessage]:
+        """Return all messages.
+
+        Messages from older runs are included.
+        """
+        return self.ctx.state.message_history
+
+    def new_messages(self) -> list[_messages.ModelMessage]:
+        """Return new messages associated with this run.
+
+        Messages from older runs are excluded.
+        """
+        return self.ctx.state.message_history[self.ctx.deps.new_message_index :]
+
     def __aiter__(
         self,
     ) -> AsyncIterator[_agent_graph.AgentNode[AgentDepsT, OutputDataT] | End[FinalResult[OutputDataT]]]:

tests/test_agent.py

@@ -5807,3 +5807,47 @@ def test_agent_builtin_tools_runtime_vs_agent_level():
             MCPServerTool(id='example', url='https://mcp.example.com/mcp'),
         ]
     )
+
+
+async def test_message_history():
+    def llm(messages: list[ModelMessage], _info: AgentInfo) -> ModelResponse:
+        return ModelResponse(parts=[TextPart('ok here is text')])
+
+    agent = Agent(FunctionModel(llm))
+
+    async with agent.iter(
+        'Hello',
+        message_history=[
+            ModelRequest(parts=[UserPromptPart(content='Hello')]),
+        ],
+    ) as run:
+        async for _ in run:
+            pass
+        assert run.new_messages() == snapshot(
+            [
+                ModelResponse(
+                    parts=[TextPart(content='ok here is text')],
+                    usage=RequestUsage(input_tokens=21, output_tokens=6),
+                    model_name='function:llm:',
+                    timestamp=IsDatetime(),
+                ),
+            ]
+        )
+        assert run.all_messages() == snapshot(
+            [
+                ModelRequest(
+                    parts=[
+                        UserPromptPart(
+                            content='Hello',
+                            timestamp=IsDatetime(),
+                        )
+                    ]
+                ),
+                ModelResponse(
+                    parts=[TextPart(content='ok here is text')],
+                    usage=RequestUsage(input_tokens=21, output_tokens=6),
+                    model_name='function:llm:',
+                    timestamp=IsDatetime(),
+                ),
+            ]
+        )