model = DashScopeChatModel(model_name="qwen-max", stream=False)  # ✈️ change here
agent_instance = ReActAgent(
   name=f"Friday",
   sys_prompt="You are a helpful assistant",
   model=model,
   formatter=DashScopeChatFormatter(),
)

model = tuner.as_agentscope_model() # ✈️ change here
agent_instance = ReActAgent(
   name=f"Friday",
   sys_prompt="You are a helpful assistant",
   model=model,
   formatter=DashScopeChatFormatter(),
)

url_and_apikey = tuner.as_oai_baseurl_apikey()
base_url = url_and_apikey.base_url
api_key = url_and_apikey.api_key    # the api key contain information, do not discard it
model = OpenAIChatModel(
    model_name="whatever",
    client_args={"base_url": base_url},
    api_key=api_key,
    stream=False,
)
self.agent = ReActAgent(
    name="math_react_agent", sys_prompt=system_prompt,
    model=model,  # ✨✨ compared with a normal agentscope agent, here is the difference!
    formatter=OpenAIChatFormatter(),
    toolkit=self.toolkit,
    memory=InMemoryMemory(), max_iters=2,
)

import re
from loguru import logger
from agentscope.message import Msg
from agentscope.agent import ReActAgent
from agentscope.formatter import DashScopeChatFormatter
from agentscope.memory import InMemoryMemory
from agentscope.tool import Toolkit, execute_python_code
from ajet import AjetTuner, Workflow, WorkflowOutput, WorkflowTask


def extract_final_answer(result) -> str:
    """Extract the final answer from the agent's response."""
    try:
        if (
            hasattr(result, "metadata")
            and isinstance(result.metadata, dict)
            and "result" in result.metadata
        ):
            return result.metadata["result"]
        if hasattr(result, "content"):
            if isinstance(result.content, dict) and "result" in result.content:
                return result.content["result"]
            return str(result.content)
        return str(result)
    except Exception as e:
        logger.warning(f"Extract final answer error: {e}. Raw: {result}")
        return str(result)


system_prompt = """
You are an agent specialized in solving math problems with tools.
Please solve the math problem given to you.
You can write and execute Python code to perform calculation or verify your answer.
You should return your final answer within \\boxed{{}}.
"""


class MathToolWorkflow(Workflow): # ✨✨ inherit `Workflow` class
    name: str = "math_agent_workflow"

    async def execute(self, workflow_task: WorkflowTask, tuner: AjetTuner) -> WorkflowOutput:
        # run agentscope
        query = workflow_task.task.main_query
        self.toolkit = Toolkit()
        self.toolkit.register_tool_function(execute_python_code)
        self.agent = ReActAgent(
            name="math_react_agent", sys_prompt=system_prompt,
            model=tuner.as_agentscope_model(),  # ✨✨ compared with a normal agentscope agent, here is the difference!
            formatter=DashScopeChatFormatter(),
            toolkit=self.toolkit,
            memory=InMemoryMemory(), max_iters=2,
        )
        self.agent.set_console_output_enabled(False)
        msg = Msg("user", query, role="user")
        result = await self.agent.reply(msg)
        final_answer = extract_final_answer(result)

        # compute reward
        reference_answer = workflow_task.task.metadata["answer"].split("####")[-1].strip()
        match = re.search(r"\\boxed\{([^}]*)\}", final_answer)
        if match: is_success = (match.group(1) == reference_answer)
        else:     is_success = False
        return WorkflowOutput(reward=(1.0 if is_success else 0.0), metadata={"final_answer": final_answer})

import re
from loguru import logger
from agentscope.message import Msg
from agentscope.agent import ReActAgent
from agentscope.formatter import OpenAIChatFormatter
from agentscope.model import OpenAIChatModel
from agentscope.memory import InMemoryMemory
from agentscope.tool import Toolkit, execute_python_code
from ajet import AjetTuner, Workflow, WorkflowOutput, WorkflowTask


def extract_final_answer(result) -> str:
    """Extract the final answer from the agent's response."""
    try:
        if (
            hasattr(result, "metadata")
            and isinstance(result.metadata, dict)
            and "result" in result.metadata
        ):
            return result.metadata["result"]
        if hasattr(result, "content"):
            if isinstance(result.content, dict) and "result" in result.content:
                return result.content["result"]
            return str(result.content)
        return str(result)
    except Exception as e:
        logger.warning(f"Extract final answer error: {e}. Raw: {result}")
        return str(result)


system_prompt = """
You are an agent specialized in solving math problems with tools.
Please solve the math problem given to you.
You can write and execute Python code to perform calculation or verify your answer.
You should return your final answer within \\boxed{{}}.
"""


class MathToolWorkflow(Workflow): # ✨✨ inherit `Workflow` class
    name: str = "math_agent_workflow"

    async def execute(self, workflow_task: WorkflowTask, tuner: AjetTuner) -> WorkflowOutput:
        # run agentscope
        query = workflow_task.task.main_query
        self.toolkit = Toolkit()
        self.toolkit.register_tool_function(execute_python_code)

        url_and_apikey = tuner.as_oai_baseurl_apikey()
        base_url = url_and_apikey.base_url
        api_key = url_and_apikey.api_key    # the api key contain information, do not discard it
        model = OpenAIChatModel(
            model_name="whatever",
            client_args={"base_url": base_url},
            api_key=api_key,
            stream=False,
        )
        self.agent = ReActAgent(
            name="math_react_agent", sys_prompt=system_prompt,
            model=model,  # ✨✨ compared with a normal agentscope agent, here is the difference!
            formatter=OpenAIChatFormatter(),
            toolkit=self.toolkit,
            memory=InMemoryMemory(), max_iters=2,
        )
        self.agent.set_console_output_enabled(False)
        msg = Msg("user", query, role="user")
        result = await self.agent.reply(msg)
        final_answer = extract_final_answer(result)

        # compute reward
        reference_answer = workflow_task.task.metadata["answer"].split("####")[-1].strip()
        match = re.search(r"\\boxed\{([^}]*)\}", final_answer)
        if match: is_success = (match.group(1) == reference_answer)
        else:     is_success = False
        return WorkflowOutput(reward=(1.0 if is_success else 0.0), metadata={"final_answer": final_answer})