Agent skill

langchain-use

LangChain 1.0 使用指南。提供 Agent、Tool、Memory、Middleware 等核心概念的快速参考。当用户需要创建 AI Agent、集成 LangChain、或解决 LangChain 相关问题时激活。

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Install this agent skill to your Project

npx add-skill https://github.com/NanmiCoder/claude-code-skills/tree/main/plugins/langchain-use/skills/langchain-use-skill

SKILL.md

LangChain Use Skill

LangChain 是构建 LLM 驱动的智能体和应用程序的开源框架。

安装

使用 uv 安装 LangChain（推荐，需要 Python 3.10+）：

bash
# 安装核心包
uv add langchain

# 安装模型提供商集成
uv add langchain-anthropic  # Anthropic/Claude
uv add langchain-openai     # OpenAI

快速参考

核心工作流程

用户查询 -> create_agent() -> ReAct 循环 -> Tool 调用 -> 返回结果

创建 Agent

详见 Agent 基础

python
from langchain.agents import create_agent

agent = create_agent(
    model="claude-sonnet-4-5-20250929",
    tools=[get_weather],
    system_prompt="You are a helpful assistant",
)

# 运行 agent
result = agent.invoke(
    {"messages": [{"role": "user", "content": "what is the weather in sf"}]}
)

定义 Tool

详见 Tool 基础

python
from langchain.tools import tool

@tool
def get_weather(city: str) -> str:
    """Get weather for a given city."""
    return f"It's always sunny in {city}!"

访问 Runtime Context

使用 ToolRuntime 访问 state、context、store：

python
from langchain.tools import tool, ToolRuntime
from dataclasses import dataclass

@dataclass
class Context:
    user_id: str

@tool
def get_user_location(runtime: ToolRuntime[Context]) -> str:
    """Retrieve user location based on user ID."""
    user_id = runtime.context.user_id
    return "Florida" if user_id == "1" else "SF"

管理 Memory

详见 短期记忆 详见 长期记忆

python
from langgraph.checkpoint.memory import InMemorySaver

agent = create_agent(
    model,
    tools,
    checkpointer=InMemorySaver(),  # 短期记忆
)

# 使用 thread_id 维护会话
config = {"configurable": {"thread_id": "1"}}
agent.invoke({"messages": [...]}, config)

添加 Middleware

详见 中间件概述

python
from langchain.agents.middleware import before_model, after_model

@before_model
def trim_messages(state, runtime):
    # 消息修剪逻辑
    return None

进阶主题

主题 文档 说明
Streaming Streaming 实时输出流式更新
Structured Output 结构化输出 Pydantic/dataclass 输出格式
Runtime Runtime ToolRuntime 和上下文访问
Guardrails 安全护栏 PII 检测、内容过滤
MCP MCP Model Context Protocol 集成

集成主题

主题 文档 说明
Models 模型 多提供商模型初始化
Messages 消息 消息类型和内容块
Retrieval 检索 RAG 和知识库构建

关键概念

Agent (智能体)

LangChain 1.0 的核心抽象，基于 LangGraph 构建。使用 create_agent() 创建。

Tool (工具)

使用 @tool 装饰器定义。可选 ToolRuntime 访问状态、上下文和存储。

Memory (记忆)

  • Checkpointer: 短期会话记忆 (InMemorySaver, PostgresSaver)
  • Store: 长期持久化存储 (InMemoryStore)

Middleware (中间件)

装饰器风格的扩展机制：

  • @before_model - 模型调用前处理
  • @after_model - 模型调用后处理
  • @wrap_tool_call - 工具调用包装
  • @dynamic_prompt - 动态系统提示

常见模式

基础 Agent

python
from langchain.agents import create_agent

agent = create_agent(
    model="claude-sonnet-4-5-20250929",
    tools=[my_tool],
    system_prompt="You are helpful.",
)

result = agent.invoke(
    {"messages": [{"role": "user", "content": "hello"}]}
)

带记忆的 Agent（会话持久化）

python
from langgraph.checkpoint.memory import InMemorySaver

agent = create_agent(
    model="claude-sonnet-4-5-20250929",
    tools=[my_tool],
    checkpointer=InMemorySaver(),
)

# 使用 thread_id 标识会话
config = {"configurable": {"thread_id": "1"}}
agent.invoke(
    {"messages": [{"role": "user", "content": "My name is Bob"}]},
    config
)
agent.invoke(
    {"messages": [{"role": "user", "content": "What's my name?"}]},
    config
)

生产环境 Memory（使用数据库）

python
from langgraph.checkpoint.postgres import PostgresSaver

DB_URI = "postgresql://postgres:postgres@localhost:5432/postgres"
with PostgresSaver.from_conn_string(DB_URI) as checkpointer:
    checkpointer.setup()  # 自动创建表
    agent = create_agent(
        model="claude-sonnet-4-5-20250929",
        tools=[my_tool],
        checkpointer=checkpointer,
    )

带上下文的 Agent（Runtime Context）

python
from dataclasses import dataclass
from langchain.tools import tool, ToolRuntime

@dataclass
class Context:
    user_id: str

@tool
def get_user_info(runtime: ToolRuntime[Context]) -> str:
    """Get user information."""
    user_id = runtime.context.user_id
    return f"User ID: {user_id}"

agent = create_agent(
    model="claude-sonnet-4-5-20250929",
    tools=[get_user_info],
    context_schema=Context,
)

result = agent.invoke(
    {"messages": [{"role": "user", "content": "get my info"}]},
    context=Context(user_id="user_123")
)

带结构化输出的 Agent

python
from dataclasses import dataclass
from langchain.agents.structured_output import ToolStrategy

@dataclass
class Response:
    answer: str
    confidence: float

agent = create_agent(
    model="claude-sonnet-4-5-20250929",
    tools=[my_tool],
    response_format=ToolStrategy(Response),
)

result = agent.invoke(
    {"messages": [{"role": "user", "content": "what is 2+2?"}]}
)
print(result['structured_response'])
# Response(answer="4", confidence=1.0)

完整示例（生产级 Agent）

python
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

@dataclass
class Context:
    user_id: str

@dataclass
class ResponseFormat:
    answer: str
    confidence: float | None = None

@tool
def get_weather(city: str) -> str:
    """Get weather for a city."""
    return f"Sunny in {city}"

@tool
def get_user_location(runtime: ToolRuntime[Context]) -> str:
    """Get user's location."""
    return "San Francisco" if runtime.context.user_id == "1" else "Unknown"

# 配置模型
model = init_chat_model(
    "claude-sonnet-4-5-20250929",
    temperature=0.5,
    max_tokens=1000
)

# 创建 agent
agent = create_agent(
    model=model,
    system_prompt="You are a weather assistant.",
    tools=[get_weather, get_user_location],
    context_schema=Context,
    response_format=ToolStrategy(ResponseFormat),
    checkpointer=InMemorySaver(),
)

# 运行 agent
config = {"configurable": {"thread_id": "1"}}
result = agent.invoke(
    {"messages": [{"role": "user", "content": "What's the weather?"}]},
    config=config,
    context=Context(user_id="1")
)

资源链接

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