init

functionality/stream_printing_messages/README.md

@@ -0,0 +1,28 @@
+# Stream Printing Messages
+
+The AgentScope agent is designed to communicate with the user and the other agents by passing messages explicitly.
+However, we notice the requirements that obtain the printing messages from the agent in a streaming manner.
+Therefore, in example we demonstrate how to gather and yield the printing messages from a single agent and
+multi-agent systems in a streaming manner.
+
+
+## Quick Start
+
+Run the following command to see the streaming printing messages from the agent.
+Note the messages with the same ID are the chunks of the same message in accumulated manner.
+
+- For single-agent:
+
+```bash
+python single_agent.py
+```
+
+- For multi-agent:
+
+```bash
+python multi_agent.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)

functionality/stream_printing_messages/multi_agent.py

@@ -0,0 +1,62 @@
+# -*- coding: utf-8 -*-
+"""Example for gather the printing messages from multiple agents."""
+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, stream_printing_messages
+
+
+def create_agent(name: str) -> ReActAgent:
+    """Create an agent with the given name."""
+    return ReActAgent(
+        name=name,
+        sys_prompt=f"You are a student named {name}.",
+        model=DashScopeChatModel(
+            api_key=os.environ["DASHSCOPE_API_KEY"],
+            model_name="qwen-max",
+            stream=False,  # close streaming for simplicity
+        ),
+        formatter=DashScopeMultiAgentFormatter(),
+    )
+
+
+async def workflow(
+    alice: ReActAgent,
+    bob: ReActAgent,
+    charlie: ReActAgent,
+) -> None:
+    """The example workflow for multiple agents."""
+    async with MsgHub(
+        participants=[alice, bob, charlie],
+        announcement=Msg(
+            "user",
+            "Alice, Bob and Charlie, welcome to the meeting! Let's "
+            "meet each other first.",
+            "user",
+        ),
+    ):
+        # agent speaks in turn
+        await alice()
+        await bob()
+        await charlie()
+
+
+async def main() -> None:
+    """The main entry for the example."""
+    # Create agents
+    alice, bob, charlie = [
+        create_agent(_) for _ in ["Alice", "Bob", "Charlie"]
+    ]
+
+    async for msg, last in stream_printing_messages(
+        agents=[alice, bob, charlie],
+        coroutine_task=workflow(alice, bob, charlie),
+    ):
+        print(msg, last)
+
+
+asyncio.run(main())

functionality/stream_printing_messages/requirements.txt

@@ -0,0 +1 @@
+agentscope[full]>=1.0.5

functionality/stream_printing_messages/single_agent.py

@@ -0,0 +1,62 @@
+# -*- coding: utf-8 -*-
+"""The example demonstrating how to obtain the messages from the agent in a
+streaming way."""
+import asyncio
+import os
+
+from agentscope.agent import ReActAgent
+from agentscope.formatter import DashScopeChatFormatter
+from agentscope.memory import InMemoryMemory
+from agentscope.message import Msg
+from agentscope.model import DashScopeChatModel
+from agentscope.pipeline import stream_printing_messages
+from agentscope.tool import (
+    Toolkit,
+    execute_python_code,
+    execute_shell_command,
+    view_text_file,
+)
+
+
+async def main() -> None:
+    """The main function."""
+    toolkit = Toolkit()
+    toolkit.register_tool_function(execute_shell_command)
+    toolkit.register_tool_function(execute_python_code)
+    toolkit.register_tool_function(view_text_file)
+
+    agent = ReActAgent(
+        name="Friday",
+        sys_prompt="You are a helpful assistant named Friday.",
+        # Change the model and formatter together if you want to try other
+        # models
+        model=DashScopeChatModel(
+            api_key=os.environ.get("DASHSCOPE_API_KEY"),
+            model_name="qwen-max",
+            enable_thinking=False,
+            stream=True,
+        ),
+        formatter=DashScopeChatFormatter(),
+        toolkit=toolkit,
+        memory=InMemoryMemory(),
+    )
+
+    # Prepare a user message
+    user_msg = Msg(
+        "user",
+        "Hi! Who are you?",
+        "user",
+    )
+
+    # We disable the terminal printing to avoid messy outputs
+    agent.set_console_output_enabled(False)
+
+    # obtain the printing messages from the agent in a streaming way
+    async for msg, last in stream_printing_messages(
+        agents=[agent],
+        coroutine_task=agent(user_msg),
+    ):
+        print(msg, last)
+
+
+asyncio.run(main())