1. 定义 Agent 状态
首先，我们需要定义 Agent 的状态，这包括 Agent 所持有的消息。
from typing import (Annotated,Sequence,TypedDict,
)
from langchain_core.messages import BaseMessage
from langgraph.graph.message import add_messagesclass AgentState(TypedDict):messages: Annotated[Sequence[BaseMessage], add_messages]
2. 初始化模型和工具
接下来，我们初始化一个 ChatOpenAI 模型，并定义一个工具 get_weather。
from langchain_openai import ChatOpenAI
from langchain_core.tools import toolmodel = ChatOpenAI(temperature=0,model="glm-4-plus",openai_api_key="your_api_key",openai_api_base="https://open.bigmodel.cn/api/paas/v4/"
)@tool
def get_weather(location: str):"""Call to get the weather from a specific location."""# This is a placeholder for the actual implementation# Don't let the LLM know this though 😊if any([city in location.lower() for city in ["sf", "san francisco"]]):return "It's sunny in San Francisco, but you better look out if you're a Gemini 😈."else:return f"I am not sure what the weather is in {location}"tools = [get_weather]model = model.bind_tools(tools)
3. 定义工具节点和模型调用节点
我们需要定义工具节点和模型调用节点，以便在 Agent 工作流中使用。
import json
from langchain_core.messages import ToolMessage, SystemMessage
from langchain_core.runnables import RunnableConfigtools_by_name = {tool.name: tool for tool in tools}def tool_node(state: AgentState):outputs = []for tool_call in state["messages"][-1].tool_calls:tool_result = tools_by_name[tool_call["name"]].invoke(tool_call["args"])outputs.append(ToolMessage(content=json.dumps(tool_result),name=tool_call["name"],tool_call_id=tool_call["id"],))return {"messages": outputs}def call_model(state: AgentState,config: RunnableConfig,
):system_prompt = SystemMessage("You are a helpful AI assistant, please respond to the users query to the best of your ability!")response = model.invoke([system_prompt] + state["messages"], config)return {"messages": [response]}def should_continue(state: AgentState):messages = state["messages"]last_message = messages[-1]# If there is no function call, then we finishif not last_message.tool_calls:return "end"# Otherwise if there is, we continueelse:return "continue"
4. 构建工作流
使用 StateGraph 构建工作流，定义节点和边。
from langgraph.graph import StateGraph, ENDworkflow = StateGraph(AgentState)workflow.add_node("agent", call_model)
workflow.add_node("tools", tool_node)workflow.set_entry_point("agent")workflow.add_conditional_edges("agent",should_continue,{"continue": "tools","end": END,},
)workflow.add_edge("tools", "agent")graph = workflow.compile()from IPython.display import Image, displaytry:display(Image(graph.get_graph().draw_mermaid_png()))
except Exception:pass
5. 运行工作流
最后，我们定义一个辅助函数来格式化输出，并运行工作流。
# Helper function for formatting the stream nicely
def print_stream(stream):for s in stream:message = s["messages"][-1]if isinstance(message, tuple):print(message)else:message.pretty_print()inputs = {"messages": [("user", "what is the weather in sf")]}
print_stream(graph.stream(inputs, stream_mode="values"))
输出结果如下：
================================[1m Human Message [0m=================================
what is the weather in sf
================================[1m Ai Message [0m==================================
Tool Calls:get_weather (call_9208187575599553774)Call ID: call_9208187575599553774Args:location: San Francisco
================================[1m Tool Message [0m=================================
Name: get_weather"It's sunny in San Francisco, but you better look out if you're a Gemini 😈."
================================[1m Ai Message [0m==================================It's sunny in San Francisco, but you better look out if you're a Gemini 😈.
参考链接：https://langchain-ai.github.io/langgraph/how-tos/react-agent-from-scratch/
