零基础快速构建你的LLM Agent框架，掌握AI的未来！

def router(messages):    if not any(        isinstance(message, dict) and message.get("role") == "system" for message in messages    ):        system_prompt = {"role": "system", "content": SYSTEM_PROMPT}        messages.append(system_prompt)
    response = client.chat.completions.create(        model="gpt-4o",        messages=messages,        tools=skill_map.get_combined_function_description_for_openai(),    )
    messages.append(response.choices[0].message)    tool_calls = response.choices[0].message.tool_calls    if tool_calls:        handle_tool_calls(tool_calls, messages)        return router(messages)    else:        return response.choices[0].message.content

class SkillMap:    def __init__(self):        skills = [AnalyzeData(), GenerateSQLQuery()]
        self.skill_map = {}        for skill in skills:            self.skill_map[skill.get_function_name()] = (                skill.get_function_dict(),                skill.get_function_callable(),            )
    def get_function_callable_by_name(self, skill_name) -> Callable:        return self.skill_map[skill_name][1]
    def get_combined_function_description_for_openai(self):        combined_dict = []        for _, (function_dict, _) in self.skill_map.items():            combined_dict.append(function_dict)        return combined_dict
    def get_function_list(self):        return list(self.skill_map.keys())
    def get_list_of_function_callables(self):        return [skill[1] for skill in self.skill_map.values()]
    def get_function_description_by_name(self, skill_name):        return str(self.skill_map[skill_name][0]["function"])

tools = [generate_and_run_sql_query, data_analyzer]model = ChatOpenAI(model="gpt-4o", temperature=0).bind_tools(tools)
def create_agent_graph():    workflow = StateGraph(MessagesState)
    tool_node = ToolNode(tools)    workflow.add_node("agent", call_model)    workflow.add_node("tools", tool_node)
    workflow.add_edge(START, "agent")    workflow.add_conditional_edges(        "agent",        should_continue,    )    workflow.add_edge("tools", "agent")
    checkpointer = MemorySaver()    app = workflow.compile(checkpointer=checkpointer)    return app

def should_continue(state: MessagesState):    messages = state["messages"]    last_message = messages[-1]    if last_message.tool_calls:        return "tools"    return END
def call_model(state: MessagesState):    messages = state["messages"]    response = model.invoke(messages)    return {"messages": [response]}

class AgentFlow(Workflow):    def __init__(self, llm, timeout=300):        super().__init__(timeout=timeout)        self.llm = llm        self.memory = ChatMemoryBuffer(token_limit=1000).from_defaults(llm=llm)        self.tools = []        for func in skill_map.get_function_list():            self.tools.append(                FunctionTool(                    skill_map.get_function_callable_by_name(func),                    metadata=ToolMetadata(                        name=func, description=skill_map.get_function_description_by_name(func)                    ),                )            )
    @step    async def prepare_agent(self, ev: StartEvent) -> RouterInputEvent:        user_input = ev.input        user_msg = ChatMessage(role="user", content=user_input)        self.memory.put(user_msg)
        chat_history = self.memory.get()        return RouterInputEvent(input=chat_history)

@stepasync def router(self, ev: RouterInputEvent) -> ToolCallEvent | StopEvent:    messages = ev.input
    if not any(        isinstance(message, dict) and message.get("role") == "system" for message in messages    ):        system_prompt = ChatMessage(role="system", content=SYSTEM_PROMPT)        messages.insert(0, system_prompt)
    with using_prompt_template(template=SYSTEM_PROMPT, version="v0.1"):        response = await self.llm.achat_with_tools(            model="gpt-4o",            messages=messages,            tools=self.tools,        )
    self.memory.put(response.message)
    tool_calls = self.llm.get_tool_calls_from_response(response, error_on_no_tool_call=False)    if tool_calls:        return ToolCallEvent(tool_calls=tool_calls)    else:        return StopEvent(result=response.message.content)

@stepasync def tool_call_handler(self, ev: ToolCallEvent) -> RouterInputEvent:    tool_calls = ev.tool_calls
    for tool_call in tool_calls:        function_name = tool_call.tool_name        arguments = tool_call.tool_kwargs        if "input" in arguments:            arguments["prompt"] = arguments.pop("input")
        try:            function_callable = skill_map.get_function_callable_by_name(function_name)        except KeyError:            function_result = "Error: Unknown function call"
        function_result = function_callable(arguments)        message = ChatMessage(            role="tool",            content=function_result,            additional_kwargs={"tool_call_id": tool_call.tool_id},)
        self.memory.put(message)
    return RouterInputEvent(input=self.memory.get())