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Python

# -*- coding: utf-8 -*-
"""Example of Moonshot model calls with MoonshotMultiAgentFormatter.
The multi-agent formatter wraps prior conversation history in
<history></history> tags and preserves reasoning_content for Moonshot's
Preserved Thinking feature in multi-turn conversations.
"""
import asyncio
import os
from _utils import stream_and_collect
from agentscope.formatter import MoonshotMultiAgentFormatter
from agentscope.message import Msg, TextBlock
from agentscope.model import MoonshotChatModel
from agentscope.credential import MoonshotCredential
async def example_multiagent() -> None:
"""Simulate a multi-agent conversation and let kimi-k2.6 summarize it.
Alice and Bob discuss the weather, then a moderator (the model) is asked
to summarize the conversation.
"""
formatter = MoonshotMultiAgentFormatter()
model = MoonshotChatModel(
credential=MoonshotCredential(
api_key=os.environ["MOONSHOT_API_KEY"],
),
model="kimi-k2.6",
stream=True,
context_size=262_144,
parameters=MoonshotChatModel.Parameters(thinking_enable=True),
formatter=formatter,
)
# Multi-agent conversation history between Alice and Bob
msgs = [
Msg(
name="system",
content=[
TextBlock(
text="You are a helpful moderator. Summarize the "
"conversation.",
),
],
role="system",
),
Msg(
name="alice",
content=[
TextBlock(
text="Hi Bob! What do you think about the weather today?",
),
],
role="user",
),
Msg(
name="bob",
content=[
TextBlock(
text="It's quite sunny and warm, Alice. Perfect for a "
"walk!",
),
],
role="assistant",
),
Msg(
name="alice",
content=[
TextBlock(text="Agreed! I might head to the park later."),
],
role="user",
),
Msg(
name="bob",
content=[
TextBlock(
text="Great idea. I'll join you if I finish work early.",
),
],
role="assistant",
),
Msg(
name="moderator",
content=[
TextBlock(
text="Please summarize the conversation above in one "
"sentence.",
),
],
role="user",
),
]
print("=== Multi-Agent Formatter Call ===")
await stream_and_collect(await model(msgs))
if __name__ == "__main__":
asyncio.run(example_multiagent())