init

conversational_agents/multiagent_conversation/README.md

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+# MultiAgent Conversation
+
+This example demonstrates how to build a multi-agent conversation workflow using ``MsgHub`` in AgentScope,
+where multiple agents broadcast messages to each other in a shared conversation space.
+
+## Setup
+
+The example is built upon the DashScope LLM API in [main.py](https://github.com/agentscope-ai/agentscope/blob/main/examples/workflows/multiagent_conversation/main.py). You can switch to 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_conversation/main.py
+```
+
+## Main Workflow
+
+- Create multiple participant agents with different attributes (e.g., Alice, Bob, Charlie).
+- Agents introduce themselves and interact in the message hub.
+- Supports dynamic addition and removal of agents, as well as broadcasting messages.
+
+> 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_conversation/main.py

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+# -*- coding: utf-8 -*-
+"""The example of how to construct multi-agent conversation with MsgHub and
+pipeline in AgentScope."""
+import asyncio
+import os
+
+from agentscope.agent import ReActAgent
+from agentscope.formatter import DashScopeMultiAgentFormatter
+from agentscope.message import Msg
+from agentscope.model import DashScopeChatModel
+from agentscope.pipeline import MsgHub, sequential_pipeline
+
+
+def create_participant_agent(
+    name: str,
+    age: int,
+    career: str,
+    character: str,
+) -> ReActAgent:
+    """Create a participant agent with a specific name, age, and character."""
+    return ReActAgent(
+        name=name,
+        sys_prompt=(
+            f"You're a {age}-year-old {career} named {name} and you're "
+            f"a {character} person."
+        ),
+        model=DashScopeChatModel(
+            model_name="qwen-max",
+            api_key=os.environ["DASHSCOPE_API_KEY"],
+            stream=True,
+        ),
+        # Use multiagent formatter because the multiple entities will
+        # occur in the prompt of the LLM API call
+        formatter=DashScopeMultiAgentFormatter(),
+    )
+
+
+async def main() -> None:
+    """Run a multi-agent conversation workflow."""
+
+    # Create multiple participant agents with different characteristics
+    alice = create_participant_agent("Alice", 30, "teacher", "friendly")
+    bob = create_participant_agent("Bob", 14, "student", "rebellious")
+    charlie = create_participant_agent("Charlie", 28, "doctor", "thoughtful")
+
+    # Create a conversation where participants introduce themselves within
+    # a message hub
+    async with MsgHub(
+        participants=[alice, bob, charlie],
+        # The greeting message will be sent to all participants at the start
+        announcement=Msg(
+            "system",
+            "Now you meet each other with a brief self-introduction.",
+            "system",
+        ),
+    ) as hub:
+        # Quick construct a pipeline to run the conversation
+        await sequential_pipeline([alice, bob, charlie])
+        # Or by the following way:
+        # await alice()
+        # await bob()
+        # await charlie()
+
+        # Delete a participant agent from the hub and fake a broadcast message
+        print("##### We fake Bob's departure #####")
+        hub.delete(bob)
+        await hub.broadcast(
+            Msg(
+                "bob",
+                "I have to start my homework now, see you later!",
+                "assistant",
+            ),
+        )
+        await alice()
+        await charlie()
+
+        # ...
+
+
+asyncio.run(main())

conversational_agents/multiagent_conversation/requirements.txt

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+agentscope[full]>=1.0.5