手把手教你实现自己的第一个agent 不用看复杂的机器学习不用看各种算法，没有长篇大论只有满满的干货，那就是直接上手实现一个agent，一边又两种方式 1.借助Dify平台快速实现，这个还是有点浪费时间需要我们看懂这个平台如何使用。 2.看最金典的www.langchain.com/ 快速实现，完全不用管什么操作和模型直接上手。
Build a real-world agent
Next, build a practical weather forecasting agent that demonstrates key production concepts:
- Detailed system prompts for better agent behavior
- Create tools that integrate with external data
- Model configuration for consistent responses
- Structured output for predictable results
- Conversational memory for chat-like interactions
- Create and run the agent to test the fully functional agent 官网给的完整的实现步骤。
直接给大家上例子就知道实现一个agent简单到随便一个人都能做到，真是一个伟大的创造，将传统接口和大模型结合起来，天才的想法。 from dataclasses import dataclass from langchain.agents import create_agent from langchain.chat_models import init_chat_model from langchain.tools import tool, ToolRuntime from langgraph.checkpoint.memory import InMemorySaver from langchain.agents.structured_output import ToolStrategy
Define system prompt
SYSTEM_PROMPT = """You are an expert weather forecaster, who speaks in puns.
You have access to two tools:
- get_weather_for_location: use this to get the weather for a specific location
- get_user_location: use this to get the user's location
If a user asks you for the weather, make sure you know the location. If you can tell from the question that they mean wherever they are, use the get_user_location tool to find their location."""
Define context schema
@dataclass class Context: """Custom runtime context schema.""" user_id: str
Define tools
@tool def get_weather_for_location(city: str) -> str: """Get weather for a given city.""" return f"It's always sunny in {city}!"
@tool def get_user_location(runtime: ToolRuntime[Context]) -> str: """Retrieve user information based on user ID.""" user_id = runtime.context.user_id return "Florida" if user_id == "1" else "SF"
Configure model
model = init_chat_model( "claude-sonnet-4-6", temperature=0 )
Define response format
@dataclass class ResponseFormat: """Response schema for the agent.""" # A punny response (always required) punny_response: str # Any interesting information about the weather if available weather_conditions: str | None = None
Set up memory
checkpointer = InMemorySaver()
Create agent
agent = create_agent( model=model, system_prompt=SYSTEM_PROMPT, tools=[get_user_location, get_weather_for_location], context_schema=Context, response_format=ToolStrategy(ResponseFormat), checkpointer=checkpointer )
Run agent
thread_id is a unique identifier for a given conversation.
config = {"configurable": {"thread_id": "1"}}
response = agent.invoke( {"messages": [{"role": "user", "content": "what is the weather outside?"}]}, config=config, context=Context(user_id="1") )
print(response['structured_response'])
ResponseFormat(
punny_response="Florida is still having a 'sun-derful' day! The sunshine is playing 'ray-dio' hits all day long! I'd say it's the perfect weather for some 'solar-bration'! If you were hoping for rain, I'm afraid that idea is all 'washed up' - the forecast remains 'clear-ly' brilliant!",
weather_conditions="It's always sunny in Florida!"
)
Note that we can continue the conversation using the same thread_id.
response = agent.invoke( {"messages": [{"role": "user", "content": "thank you!"}]}, config=config, context=Context(user_id="1") )
print(response['structured_response'])
ResponseFormat(
punny_response="You're 'thund-erfully' welcome! It's always a 'breeze' to help you stay 'current' with the weather. I'm just 'cloud'-ing around waiting to 'shower' you with more forecasts whenever you need them. Have a 'sun-sational' day in the Florida sunshine!",
weather_conditions=None
)
运行效果大家自己跑下，简直完美。这个例子调用传统接口的核心其实就是 ‘提示词’，大模型是根据提示词找到对应工具然后在调用工具最后返回。
这下发现我们依然没有脱离自然语言，agent相当一个能听懂人话并且能分析前后逻辑的一个执行人，提问“今天天气怎么样？” agent会根据tools种工具的提示词找到对应的工具先调用获取用户地点的接口，然后调用根据地点查询天气的接口。
补充一下使用的大模型你可以使用你自己的，ChatOpenAI 这个调用自己的大模型实现这个功能。
