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* Add example for router agent
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| # Conversation with Router Agent | ||
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| This example will show | ||
| - How to build a router agent to route questions to agents with different abilities. | ||
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| The router agent is expected to route questions to the corresponding agents according to the question type in the following response | ||
| ```text | ||
| <thought>{The thought of router agent}</thought> | ||
| <agent>{agent name}</agent> | ||
| ``` | ||
| If the router agent decides to answer the question itself, the response should be | ||
| ```text | ||
| <thought>{The thought of router agent}</thought> | ||
| <response>{The answer}</response> | ||
| ``` | ||
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| ## Note | ||
| This example is only for demonstration purposes. We simply use two agents who are good at math and history respectively. | ||
| You can replace them with any other agents according to your needs. | ||
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| Besides, the memory management of the involved agents is not considered in this example. | ||
| For example, does the router agent need to know the answer from the sub-agents? | ||
| Improvements are encouraged by developers according to their own needs. | ||
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| ## Tested Models | ||
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| These models are tested in this example. For other models, some modifications may be needed. | ||
| - gpt-4o | ||
| - qwen-max | ||
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| ## Prerequisites | ||
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| 1. Fill your model configuration correctly in `main.py`. | ||
| 2. Install the latest version of Agentscope from GitHub. | ||
| ```bash | ||
| git clone https://github.com/modelscope/agentscope.git | ||
| cd agentscope | ||
| pip install -e . | ||
| ``` | ||
| 3. Run the example and input your questions. | ||
| ```bash | ||
| python main.py | ||
| ``` |
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| # -*- coding: utf-8 -*- | ||
| """The main script for the example of conversation with router agent.""" | ||
| from router_agent import RouterAgent | ||
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| import agentscope | ||
| from agentscope.agents import DialogAgent, UserAgent | ||
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| # ================== Prepare model configuration ============================= | ||
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| YOUR_MODEL_CONFIGURATION_NAME = "{YOUR_MODEL_CONFIGURATION_NAME}" | ||
| YOUR_MODEL_CONFIGURATION = { | ||
| "config_name": YOUR_MODEL_CONFIGURATION_NAME, | ||
| # ... | ||
| } | ||
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| # ============================================================================ | ||
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| agentscope.init( | ||
| model_configs=YOUR_MODEL_CONFIGURATION, | ||
| project="Conversation with router agent", | ||
| ) | ||
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| # Let's build some working agents with different capabilities. For simplicity, | ||
| # we just use the same agent. You can replace them with your own agents. | ||
| agent_math = DialogAgent( | ||
| name="Math", | ||
| sys_prompt="You are a math assistant to help solve math problems.", | ||
| model_config_name=YOUR_MODEL_CONFIGURATION_NAME, | ||
| ) | ||
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| agent_history = DialogAgent( | ||
| name="History", | ||
| sys_prompt="You are an assistant who is good at history.", | ||
| model_config_name=YOUR_MODEL_CONFIGURATION_NAME, | ||
| ) | ||
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| # Init a router agent | ||
| SYS_PROMPT_ROUTER = """You're a router assistant named {name}. | ||
| ## YOUR TARGET | ||
| 1. Given agents with different capabilities, your target is to assign questions to the corresponding agents according to the user requirement. | ||
| 2. You should make full use of the different abilities of the given agents. | ||
| 3. If no agent is suitable to answer user's question, then respond directly. | ||
| ## Agents You Can Use | ||
| The agents are listed in the format of "{index}. {agent_name}: {agent_description}" | ||
| 1. math: An agent who is good at math. | ||
| 2. history: An agent who is good at history. | ||
| """ # noqa | ||
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| router_agent = RouterAgent( | ||
| sys_prompt=SYS_PROMPT_ROUTER, | ||
| model_config_name=YOUR_MODEL_CONFIGURATION_NAME, | ||
| ) | ||
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| # Init a user agent | ||
| user = UserAgent(name="user") | ||
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| # Start the conversation | ||
| msg = None | ||
| while True: | ||
| user_msg = user(msg) | ||
| if user_msg.content == "exit": | ||
| break | ||
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| # Replied by router agent | ||
| router_msg = router_agent(user_msg) | ||
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| # Route the question to the corresponding agents | ||
| if router_msg.metadata == "math": | ||
| msg = agent_math(user_msg) | ||
| elif router_msg.metadata == "history": | ||
| msg = agent_history(user_msg) | ||
| else: | ||
| # Answer the question by router agent directly | ||
| msg = router_msg |
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| # -*- coding: utf-8 -*- | ||
| """The router agent which routes the questions to the corresponding agents.""" | ||
| from typing import Optional, Union, Sequence | ||
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| from agentscope.agents import AgentBase | ||
| from agentscope.message import Msg | ||
| from agentscope.parsers import RegexTaggedContentParser | ||
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| # Init a router agent | ||
| class RouterAgent(AgentBase): | ||
| """ | ||
| The router agent who routes the questions to the corresponding agents. | ||
| """ | ||
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| def __init__( | ||
| self, | ||
| sys_prompt: str, | ||
| model_config_name: str, | ||
| ) -> None: | ||
| """Init a router agent.""" | ||
| self.name = "Router" | ||
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| super().__init__( | ||
| name=self.name, | ||
| model_config_name=model_config_name, | ||
| ) | ||
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| self.sys_prompt = sys_prompt.format_map({"name": self.name}) | ||
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| self.memory.add(Msg(self.name, self.sys_prompt, "system")) | ||
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| self.parser = RegexTaggedContentParser( | ||
| format_instruction="""Respond with specific tags as outlined below: | ||
| - When routing questions to agents: | ||
| <thought>what you thought</thought> | ||
| <agent>the agent name</agent> | ||
| - When answering questions directly: | ||
| <thought>what you thought</thought> | ||
| <response>what you respond</response> | ||
| """, | ||
| required_keys=["thought"], | ||
| ) | ||
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| def reply(self, x: Optional[Union[Msg, Sequence[Msg]]] = None) -> Msg: | ||
| """The reply function.""" | ||
| self.memory.add(x) | ||
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| prompt = self.model.format( | ||
| self.memory.get_memory(), | ||
| Msg("system", self.parser.format_instruction, "system"), | ||
| ) | ||
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| response = self.model(prompt) | ||
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| # To be compatible with streaming mode | ||
| self.speak(response.stream or response.text) | ||
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| # Parse the response by predefined parser | ||
| parsed_dict = self.parser.parse(response).parsed | ||
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| msg = Msg(self.name, response.text, "assistant") | ||
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| # Assign the question to the corresponding agent in the metadata field | ||
| if "agent" in parsed_dict: | ||
| msg.metadata = parsed_dict["agent"] | ||
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| self.memory.add(msg) | ||
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| return msg |