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。
/plugin marketplace add NanmiCoder/claude-code-skills/plugin install langchain-use@claude-code-skillsThis skill inherits all available tools. When active, it can use any tool Claude has access to.
references/advanced/guardrails.mdreferences/advanced/mcp.mdreferences/advanced/runtime.mdreferences/advanced/streaming.mdreferences/advanced/structured-output.mdreferences/agents/agent-basics.mdreferences/core-concepts/overview.mdreferences/core-concepts/quickstart.mdreferences/integration/messages.mdreferences/integration/models.mdreferences/integration/retrieval.mdreferences/memory/long-term-memory.mdreferences/memory/short-term-memory.mdreferences/middleware/middleware-overview.mdreferences/tools/tool-basics.mdLangChain 是构建 LLM 驱动的智能体和应用程序的开源框架。
使用 uv 安装 LangChain（推荐，需要 Python 3.10+）：
# 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
用户查询 -> create_agent() -> ReAct 循环 -> Tool 调用 -> 返回结果
详见 Agent 基础
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"}]}
)
详见 Tool 基础
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}!"
使用 ToolRuntime 访问 state、context、store：
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"
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)
详见 中间件概述
from langchain.agents.middleware import before_model, after_model
@before_model
def trim_messages(state, runtime):
# 消息修剪逻辑
return None
| 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 |
| 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 |
LangChain 1.0 的核心抽象，基于 LangGraph 构建。使用 create_agent() 创建。
使用 @tool 装饰器定义。可选 ToolRuntime 访问状态、上下文和存储。
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"}]}
)
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
)
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,
)
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")
)
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)
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")
)