Agent

In this tutorial, we first focus on introducing the ReAct agent in AgentScope, then we briefly introduce how to customize your own agent from scratch.

ReAct Agent

In AgentScope, the ReActAgent class integrates various features into a final implementation, including

Features of ReActAgent

Feature	Reference
Support realtime steering
Support parallel tool calls
Support structured output
Support fine-grained MCP control	MCP
Support agent-controlled tools management (Meta tool)	Tool
Support self-controlled long-term memory	Long-Term Memory
Support automatic state management	State/Session Management

Due to limited space, in this tutorial we only demonstrate the first three features of ReActAgent class, leaving the others to the corresponding sections listed above.

import asyncio
import json
import os
from datetime import datetime
import time

from pydantic import BaseModel, Field

from agentscope.agent import ReActAgent
from agentscope.formatter import DashScopeChatFormatter
from agentscope.memory import InMemoryMemory
from agentscope.message import TextBlock, Msg
from agentscope.model import DashScopeChatModel
from agentscope.tool import Toolkit, ToolResponse

Realtime Steering

The realtime steering allows user to interrupt the agent’s reply at any time, which is implemented based on the asyncio cancellation mechanism.

Specifically, when calling the interrupt method of the agent, it will cancel the current reply task, and execute the handle_interrupt method for postprocessing.

Hint

With the feature of supporting streaming tool results in Tool, users can interrupt the tool execution if it takes too long or deviates from user expectations by Ctrl+C in the terminal or calling the interrupt method of the agent in your code.

The interruption logic has been implemented in the AgentBase class as a basic feature, leaving a handle_interrupt method for users to customize the post-processing of interruption as follows:

# code snippet of AgentBase
class AgentBase:
    ...
    async def __call__(self, *args: Any, **kwargs: Any) -> Msg:
        ...
        reply_msg: Msg | None = None
        try:
            self._reply_task = asyncio.current_task()
            reply_msg = await self.reply(*args, **kwargs)

        except asyncio.CancelledError:
            # Catch the interruption and handle it by the handle_interrupt method
            reply_msg = await self.handle_interrupt(*args, **kwargs)

        ...

    @abstractmethod
    async def handle_interrupt(self, *args: Any, **kwargs: Any) -> Msg:
        pass

In ReActAgent class, we return a fixed message “I noticed that you have interrupted me. What can I do for you?” as follows:

Example of interruption

You can override it with your own implementation, for example, calling the LLM to generate a simple response to the interruption.

Parallel Tool Calls

ReActAgent supports parallel tool calls by providing a parallel_tool_calls argument in its constructor. When multiple tool calls are generated, and parallel_tool_calls is set to True, they will be executed in parallel by the asyncio.gather function.

# prepare a tool function
def example_tool_function(tag: str) -> ToolResponse:
    """A sample example tool function"""
    start_time = datetime.now().strftime("%H:%M:%S.%f")

    # Sleep for 3 seconds to simulate a long-running task
    time.sleep(3)

    end_time = datetime.now().strftime("%H:%M:%S.%f")
    return ToolResponse(
        content=[
            TextBlock(
                type="text",
                text=f"Tag {tag} started at {start_time} and ended at {end_time}. ",
            ),
        ],
    )


toolkit = Toolkit()
toolkit.register_tool_function(example_tool_function)

# Create an ReAct agent
agent = ReActAgent(
    name="Jarvis",
    sys_prompt="You're a helpful assistant named Jarvis.",
    model=DashScopeChatModel(
        model_name="qwen-max",
        api_key=os.environ["DASHSCOPE_API_KEY"],
    ),
    memory=InMemoryMemory(),
    formatter=DashScopeChatFormatter(),
    toolkit=toolkit,
    parallel_tool_calls=True,
)


async def example_parallel_tool_calls() -> None:
    """Example of parallel tool calls"""
    # prompt the agent to generate two tool calls at once
    await agent(
        Msg(
            "user",
            "Generate two tool calls of the 'example_tool_function' function with tag as 'tag1' and 'tag2' AT ONCE so that they can execute in parallel.",
            "user",
        ),
    )


asyncio.run(example_parallel_tool_calls())

Jarvis: {
    "type": "tool_use",
    "id": "call_62c5acb20db34d1686ee7f",
    "name": "example_tool_function",
    "input": {
        "tag": "tag1"
    }
}
system: {
    "type": "tool_result",
    "id": "call_62c5acb20db34d1686ee7f",
    "name": "example_tool_function",
    "output": [
        {
            "type": "text",
            "text": "Tag tag1 started at 09:50:27.181639 and ended at 09:50:30.184701. "
        }
    ]
}
Jarvis: {
    "type": "tool_use",
    "id": "call_cbe813bd39044af4aeb0b2",
    "name": "example_tool_function",
    "input": {
        "tag": "tag2"
    }
}
system: {
    "type": "tool_result",
    "id": "call_cbe813bd39044af4aeb0b2",
    "name": "example_tool_function",
    "output": [
        {
            "type": "text",
            "text": "Tag tag2 started at 09:50:33.106928 and ended at 09:50:36.109987. "
        }
    ]
}
Jarvis: The 'example_tool_function' has been called with two different tags, 'tag1' and 'tag2', in a way that they can execute in parallel. Here are the results:

- For 'tag1': It started at 09:50:27.181639 and ended at 09:50:30.184701.
- For 'tag2': It started at 09:50:33.106928 and ended at 09:50:36.109987.

Please note that the actual execution in parallel may depend on the environment and system capabilities where these functions are being run. If you need further assistance or have any other requests, feel free to let me know.

Structured Output

To generate a structured output, the ReActAgent instance receives a child class of the pydantic.BaseModel as the structured_model argument in its __call__ function. Then we can get the structured output from the metadata field of the returned message.

Taking introducing Einstein as an example:

# Create an ReAct agent
agent = ReActAgent(
    name="Jarvis",
    sys_prompt="You're a helpful assistant named Jarvis.",
    model=DashScopeChatModel(
        model_name="qwen-max",
        api_key=os.environ["DASHSCOPE_API_KEY"],
        # Preset the generation kwargs to enable parallel tool calls
        generate_kwargs={
            "parallel_tool_calls": True,
        },
    ),
    memory=InMemoryMemory(),
    formatter=DashScopeChatFormatter(),
    toolkit=Toolkit(),
    parallel_tool_calls=True,
)


# The structured model
class Model(BaseModel):
    name: str = Field(description="The name of the person")
    description: str = Field(
        description="A one-sentence description of the person",
    )
    age: int = Field(description="The age")
    honor: list[str] = Field(description="A list of honors of the person")


async def example_structured_output() -> None:
    """The example structured output"""
    res = await agent(
        Msg(
            "user",
            "Introduce Einstein",
            "user",
        ),
        structured_model=Model,
    )
    print("\nThe structured output:")
    print(json.dumps(res.metadata, indent=4))


asyncio.run(example_structured_output())

Jarvis: Albert Einstein was a theoretical physicist who developed the theory of relativity, one of the two pillars of modern physics. He is best known for his mass–energy equivalence formula E = mc2.

The structured output:
{
    "name": "Albert Einstein",
    "description": "Theoretical physicist who developed the theory of relativity",
    "age": 76,
    "honor": [
        "Nobel Prize in Physics (1921)",
        "Copley Medal (1925)",
        "Max Planck Medal (1929)"
    ]
}

Customizing Agent

AgentScope provides two base classes, AgentBase and ReActAgentBase, which differ in the abstract methods they define and the hooks they support. Specifically, the ReActAgentBase extends AgentBase with additional _reasoning and _acting abstract methods, as well as their pre- and post- hooks.

Developers can choose to inherit from either of these base classes based on their needs. We summarize the agent under agentscope.agent module as follows:

Agent classes in AgentScope

Class	Abstract Method	Support Hooks	Description
AgentBase	reply observe print handle_interrupt	pre_/post_reply pre_/post_observe pre_/post_print	The base class for all agents, providing the basic interface and hooks.
ReActAgentBase	reply observe print handle_interrupt _reasoning _acting	pre_/post_reply pre_/post_observe pre_/post_print pre_/post_reasoning pre_/post_acting	The abstract class for ReAct agent, extending AgentBase with reasoning and acting abstract methods and their hooks.
ReActAgent	-	pre_/post_reply pre_/post_observe pre_/post_print pre_/post_reasoning pre_/post_acting	An implementation of ReActAgentBase
UserAgent			A special agent that represents the user, used to interact with the agent

Further Reading

• Tool

• Agent Hooks