yingqi-z20-Agent-libOS

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#1	from __future__ import annotations
#2
#3	import asyncio
#4	import tempfile
#5	import unittest
#6
#7	from agent_libos import Runtime
#8	from agent_libos.llm.client import LLMCompletion
#9	from scripts.human_llm_chat import EchoResponder, ModelResponder, run_chat
#10
#11
#12	class HumanLLMChatScriptTests(unittest.TestCase):
#13	def test_chat_uses_human_question_and_output_tools(self) -> None:
#14	report = asyncio.run(
#15	run_chat(
#16	responder=EchoResponder(),
#17	max_turns=5,
#18	auto_messages=["hello", "/exit"],
#19	echo=False,
#20	)
#21	)
#22
#23	self.assertEqual(report["process_status"], "exited")
#24	self.assertEqual(report["turns"], 1)
#25	self.assertEqual(report["history"], [{"role": "user", "content": "hello"}, {"role": "assistant", "content": "Echo: hello"}])
#26	self.assertIn("Assistant: Echo: hello", report["outputs"])
#27	self.assertIn("Assistant: goodbye.", report["outputs"])
#28	self.assertEqual(
#29	report["actions"],
#30	[None, "ask_human", "human_output", None, "ask_human", "human_output", "process_exit"],
#31	)
#32
#33	def test_model_responder_persists_nested_text_llm_call(self) -> None:
#34	responder = ModelResponder.__new__(ModelResponder)
#35	responder.system_prompt = "System prompt"
#36	responder.client = FakeTextLLMClient()
#37	responder._runtime = None
#38	responder._pid = None
#39
#40	with tempfile.TemporaryDirectory() as temp_dir:
#41	db = f"{temp_dir}/runtime.sqlite"
#42	report = asyncio.run(
#43	run_chat(
#44	db=db,
#45	responder=responder,
#46	max_turns=5,
#47	auto_messages=["hello", "/exit"],
#48	echo=False,
#49	)
#50	)
#51
#52	runtime = Runtime.open(db)
#53	try:
#54	calls = [call for call in runtime.store.list_llm_calls(report["pid"]) if call.purpose == "script_human_chat_reply"]
#55
#56	self.assertEqual(len(calls), 1)
#57	self.assertEqual(calls[0].response_content, "model reply")
#58	self.assertEqual(calls[0].usage["total_tokens"], 6)
#59	self.assertEqual(calls[0].reasoning, {"summary": "fake text response"})
#60	self.assertEqual(calls[0].messages[-1]["content"], "hello")
#61	finally:
#62	runtime.close()
#63
#64
#65	class FakeTextLLMClient:
#66	model = "fake-text-model"
#67
#68	def complete_with_metadata(self, messages, *, json_mode: bool) -> LLMCompletion:
#69	return LLMCompletion(
#70	content="model reply",
#71	tool_calls=[],
#72	raw={"id": "fake_raw"},
#73	api="chat",
#74	response_id="fake_resp",
#75	request_id="fake_req",
#76	model=self.model,
#77	usage={"prompt_tokens": 4, "completion_tokens": 2, "total_tokens": 6},
#78	reasoning={"summary": "fake text response"},
#79	)
#80
#81
#82	if __name__ == "__main__":
#83	unittest.main()
#84

z6MkqRzA/yingqi-z20-Agent-libOS