init

conversational_agents/multiagent_debate/README.md

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+# MultiAgent Debate
+
+Debate workflow simulates a multi-turn discussion between different agents, mostly several solvers and an aggregator.
+Typically, the solvers generate and exchange their answers, while the aggregator collects and summarizes the answers.
+
+We implement the examples in [EMNLP 2024](https://aclanthology.org/2024.emnlp-main.992/), where two debater agents
+will discuss a topic in a fixed order, and express their arguments based on the previous debate history.
+At each round a moderator agent will decide whether the correct answer can be obtained in the current iteration.
+
+## Setup
+
+The example is built upon DashScope LLM API in [main.py](https://github.com/agentscope-ai/agentscope/blob/main/examples/workflows/multiagent_debate/main.py).
+You can also change to the other LLMs by modifying the ``model`` and ``formatter`` parameters in the code.
+
+To run the example, first install the latest version of AgentScope, then run:
+
+```bash
+python examples/workflows/multiagent_debate/main.py
+```
+
+
+> Note: The example is built with DashScope chat model. If you want to change the model in this example, don't forget
+> to change the formatter at the same time! The corresponding relationship between built-in models and formatters are
+> list in [our tutorial](https://doc.agentscope.io/tutorial/task_prompt.html#id1)

conversational_agents/multiagent_debate/main.py

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+# -*- coding: utf-8 -*-
+"""The multi-agent debate workflow example in AgentScope."""
+import asyncio
+import os
+
+from agentscope.agent import ReActAgent
+from agentscope.formatter import (
+    DashScopeChatFormatter,
+    DashScopeMultiAgentFormatter,
+)
+from agentscope.message import Msg
+from agentscope.model import DashScopeChatModel
+from agentscope.pipeline import MsgHub
+from pydantic import BaseModel, Field
+
+topic = (
+    "The two circles are externally tangent and there is no relative sliding. "
+    "The radius of circle A is 1/3 the radius of circle B. Circle A rolls "
+    "around circle B one trip back to its starting point. How many times will "
+    "circle A revolve in total?"
+)
+
+
+# Create two debater agents, Alice and Bob, who will discuss the topic.
+def create_solver_agent(name: str) -> ReActAgent:
+    """Get a solver agent."""
+    return ReActAgent(
+        name=name,
+        sys_prompt=f"You're a debater named {name}. Hello and welcome to the "
+        "debate competition. It's not necessary to fully agree "
+        "with each other's perspectives, as our objective is to "
+        "find the correct answer. The debate topic is stated as "
+        f"follows: {topic}. Use Chinese to answer the question",
+        model=DashScopeChatModel(
+            model_name="qwen-max",
+            api_key=os.environ["DASHSCOPE_API_KEY"],
+            stream=True,
+        ),
+        formatter=DashScopeChatFormatter(),
+    )
+
+
+alice, bob = [create_solver_agent(name) for name in ["Alice", "Bob"]]
+
+# Create a moderator agent
+moderator = ReActAgent(
+    name="Aggregator",
+    sys_prompt=(
+        "You're a moderator. There will be two debaters involved in a debate "
+        "competition. They will present their answer and discuss their "
+        "perspectives on the topic:\n"
+        "```\n"
+        "{topic}\n"
+        "```\n"
+        "At the end of each round, you will evaluate both sides' answers "
+        "and decide which one is correct."
+    ),
+    model=DashScopeChatModel(
+        model_name="qwen-max",
+        api_key=os.environ["DASHSCOPE_API_KEY"],
+        stream=True,
+    ),
+    formatter=DashScopeMultiAgentFormatter(),
+)
+
+
+# A structured output model for the moderator
+class JudgeModel(BaseModel):
+    """The structured output model for the moderator."""
+
+    finished: bool = Field(
+        description="Whether the debate is finished.",
+    )
+    correct_answer: str | None = Field(
+        description="The correct answer to the debate topic, only if the "
+        "debate is finished. Otherwise, leave it as None.",
+        default=None,
+    )
+
+
+async def run_multiagent_debate() -> None:
+    """Run the multi-agent debate workflow."""
+    while True:
+        # The reply messages in MsgHub from the participants will be
+        # broadcasted to all participants.
+        async with MsgHub(participants=[alice, bob, moderator]):
+            await alice(
+                Msg(
+                    "user",
+                    "You are affirmative side, Please express your "
+                    "viewpoints.",
+                    "user",
+                ),
+            )
+            await bob(
+                Msg(
+                    "user",
+                    "You are negative side. You disagree with the "
+                    "affirmative side. Provide your reason and answer.",
+                    "user",
+                ),
+            )
+
+        # Alice and Bob doesn't need to know the moderator's message,
+        # so moderator is called outside the MsgHub.
+        msg_judge = await moderator(
+            Msg(
+                "user",
+                "Now you have heard the answers from the others, have "
+                "the debate finished, and can you get the correct answer?",
+                "user",
+            ),
+            structured_model=JudgeModel,
+        )
+
+        print("【STRUCTURED_OUTPUT】: ", msg_judge.metadata)
+
+        if msg_judge.metadata.get("finished"):
+            print(
+                "The debate is finished, and the correct answer is: ",
+                msg_judge.metadata.get("correct_answer"),
+            )
+            break
+
+
+asyncio.run(run_multiagent_debate())

conversational_agents/multiagent_debate/requirements.txt

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+agentscope[full]>=1.0.5