Add REPL run_demo_loop helper

docs/ja/repl.md

@@ -0,0 +1,22 @@
+---
+search:
+  exclude: true
+---
+# REPL ユーティリティ
+
+`run_demo_loop` を使うと、ターミナルから手軽にエージェントを試せます。
+
+```python
+import asyncio
+from agents import Agent, run_demo_loop
+
+async def main() -> None:
+    agent = Agent(name="Assistant", instructions="あなたは親切なアシスタントです")
+    await run_demo_loop(agent)
+
+if __name__ == "__main__":
+    asyncio.run(main())
+```
+
+`run_demo_loop` は入力を繰り返し受け取り、会話履歴を保持したままエージェントを実行します。既定ではストリーミング出力を表示します。
+`quit` または `exit` と入力するか `Ctrl-D` を押すと終了します。

docs/ref/repl.md

@@ -0,0 +1,6 @@
+# `repl`
+
+::: agents.repl
+    options:
+        members:
+            - run_demo_loop

docs/repl.md

@@ -0,0 +1,19 @@
+# REPL utility
+
+The SDK provides `run_demo_loop` for quick interactive testing.
+
+```python
+import asyncio
+from agents import Agent, run_demo_loop
+
+async def main() -> None:
+    agent = Agent(name="Assistant", instructions="You are a helpful assistant.")
+    await run_demo_loop(agent)
+
+if __name__ == "__main__":
+    asyncio.run(main())
+```
+
+`run_demo_loop` prompts for user input in a loop, keeping the conversation
+history between turns. By default it streams model output as it is produced.
+Type `quit` or `exit` (or press `Ctrl-D`) to leave the loop.

mkdocs.yml

@@ -59,6 +59,7 @@ plugins:
                 - running_agents.md
                 - results.md
                 - streaming.md
+                - repl.md
                 - tools.md
                 - mcp.md
                 - handoffs.md
@@ -80,6 +81,7 @@ plugins:
                     - ref/index.md
                     - ref/agent.md
                     - ref/run.md
+                    - ref/repl.md
                     - ref/tool.md
                     - ref/result.md
                     - ref/stream_events.md
@@ -139,6 +141,7 @@ plugins:
                 - running_agents.md
                 - results.md
                 - streaming.md
+                - repl.md
                 - tools.md
                 - mcp.md
                 - handoffs.md

src/agents/__init__.py

@@ -45,6 +45,7 @@
 from .models.openai_chatcompletions import OpenAIChatCompletionsModel
 from .models.openai_provider import OpenAIProvider
 from .models.openai_responses import OpenAIResponsesModel
+from .repl import run_demo_loop
 from .result import RunResult, RunResultStreaming
 from .run import RunConfig, Runner
 from .run_context import RunContextWrapper, TContext
@@ -160,6 +161,7 @@ def enable_verbose_stdout_logging():
     "ToolsToFinalOutputFunction",
     "ToolsToFinalOutputResult",
     "Runner",
+    "run_demo_loop",
     "Model",
     "ModelProvider",
     "ModelTracing",

src/agents/repl.py

@@ -0,0 +1,65 @@
+from __future__ import annotations
+
+from typing import Any
+
+from openai.types.responses.response_text_delta_event import ResponseTextDeltaEvent
+
+from .agent import Agent
+from .items import ItemHelpers, TResponseInputItem
+from .result import RunResultBase
+from .run import Runner
+from .stream_events import AgentUpdatedStreamEvent, RawResponsesStreamEvent, RunItemStreamEvent
+
+
+async def run_demo_loop(agent: Agent[Any], *, stream: bool = True) -> None:
+    """Run a simple REPL loop with the given agent.
+
+    This utility allows quick manual testing and debugging of an agent from the
+    command line. Conversation state is preserved across turns. Enter ``exit``
+    or ``quit`` to stop the loop.
+
+    Args:
+        agent: The starting agent to run.
+        stream: Whether to stream the agent output.
+    """
+
+    current_agent = agent
+    input_items: list[TResponseInputItem] = []
+    while True:
+        try:
+            user_input = input(" > ")
+        except (EOFError, KeyboardInterrupt):
+            print()
+            break
+        if user_input.strip().lower() in {"exit", "quit"}:
+            break
+        if not user_input:
+            continue
+
+        input_items.append({"role": "user", "content": user_input})
+
+        result: RunResultBase
+        if stream:
+            result = Runner.run_streamed(current_agent, input=input_items)
+            async for event in result.stream_events():
+                if isinstance(event, RawResponsesStreamEvent):
+                    if isinstance(event.data, ResponseTextDeltaEvent):
+                        print(event.data.delta, end="", flush=True)
+                elif isinstance(event, RunItemStreamEvent):
+                    if event.item.type == "tool_call_item":
+                        print("\n[tool called]", flush=True)
+                    elif event.item.type == "tool_call_output_item":
+                        print(f"\n[tool output: {event.item.output}]", flush=True)
+                    elif event.item.type == "message_output_item":
+                        message = ItemHelpers.text_message_output(event.item)
+                        print(message, end="", flush=True)
+                elif isinstance(event, AgentUpdatedStreamEvent):
+                    print(f"\n[Agent updated: {event.new_agent.name}]", flush=True)
+            print()
+        else:
+            result = await Runner.run(current_agent, input_items)
+            if result.final_output is not None:
+                print(result.final_output)
+
+        current_agent = result.last_agent
+        input_items = result.to_input_list()

tests/test_repl.py

@@ -0,0 +1,28 @@
+import pytest
+
+from agents import Agent, run_demo_loop
+
+from .fake_model import FakeModel
+from .test_responses import get_text_input_item, get_text_message
+
+
+@pytest.mark.asyncio
+async def test_run_demo_loop_conversation(monkeypatch, capsys):
+    model = FakeModel()
+    model.add_multiple_turn_outputs([[get_text_message("hello")], [get_text_message("good")]])
+
+    agent = Agent(name="test", model=model)
+
+    inputs = iter(["Hi", "How are you?", "quit"])
+    monkeypatch.setattr("builtins.input", lambda _=" > ": next(inputs))
+
+    await run_demo_loop(agent, stream=False)
+
+    output = capsys.readouterr().out
+    assert "hello" in output
+    assert "good" in output
+    assert model.last_turn_args["input"] == [
+        get_text_input_item("Hi"),
+        get_text_message("hello").model_dump(exclude_unset=True),
+        get_text_input_item("How are you?"),
+    ]