langchain-fundamentals（LangChain fundamentals 搜索）

<create_agent>

Creating Agents with create_agent

create_agent() is the recommended way to build agents. It handles the agent loop, tool execution, and state management.

Agent Configuration Options

@tool def get_weather(location: str) -> str: """Get current weather for a location.

Args:
    location: City name
"""
return f"Weather in {location}: Sunny, 72F"

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

result = agent.invoke({"messages": [{"role": "user", "content": "What's the weather in Paris?"}]}) print(result["messages"][-1].content)

</python>
<typescript>

import { createAgent } from "langchain"; import { tool } from "@langchain/core/tools"; import { z } from "zod";

const getWeather = tool( async ({ location }) => Weather in ${location}: Sunny, 72F, { name: "get_weather", description: "Get current weather for a location.", schema: z.object({ location: z.string().describe("City name") }), } );

const agent = createAgent({ model: "anthropic:claude-sonnet-4-5", tools: [getWeather], systemPrompt: "You are a helpful assistant.", });

const result = await agent.invoke({ messages: [{ role: "user", content: "What's the weather in Paris?" }], }); console.log(result.messages[result.messages.length - 1].content);

checkpointer = MemorySaver()

agent = create_agent(model="anthropic:claude-sonnet-4-5", tools=[search], checkpointer=checkpointer,)

config = {"configurable": {"thread_id": "user-123"}} agent.invoke({"messages": [{"role": "user", "content": "My name is Alice"}]}, config=config) result = agent.invoke({"messages": [{"role": "user", "content": "What's my name?"}]}, config=config)

# Agent remembers: "Your name is Alice"

</python> <typescript> Add MemorySaver checkpointer to maintain conversation state across invocations.

import { createAgent } from "langchain";
import { MemorySaver } from "@langchain/langgraph";

const checkpointer = new MemorySaver();

const agent = createAgent({
  model: "anthropic:claude-sonnet-4-5",
  tools: [search],
  checkpointer,
});

const config = { configurable: { thread_id: "user-123" } };
await agent.invoke({ messages: [{ role: "user", content: "My name is Alice" }] }, config);
const result = await agent.invoke({ messages: [{ role: "user", content: "What's my name?" }] }, config);
// Agent remembers: "Your name is Alice"

Tools are functions that agents can call. Use the @tool decorator (Python) or tool() function (TypeScript).

@tool def add(a: float, b: float) -> float: """Add two numbers.

Args:
    a: First number
    b: Second number
"""
return a + b

</python>
<typescript>

import { tool } from "@langchain/core/tools"; import { z } from "zod";

const add = tool( async ({ a, b }) => a + b, { name: "add", description: "Add two numbers.", schema: z.object({ a: z.number().describe("First number"), b: z.number().describe("Second number"), }), } );

Middleware intercepts the agent loop to add human approval, error handling, logging, and more. A deep understanding of middleware is essential for production agents — use `HumanInTheLoopMiddleware` (Python) / `humanInTheLoopMiddleware` (TypeScript) for approval workflows, and `@wrap_tool_call` (Python) / `createMiddleware` (TypeScript) for custom hooks.

Key imports:

from langchain.agents.middleware import HumanInTheLoopMiddleware, wrap_tool_call

import { humanInTheLoopMiddleware, createMiddleware } from "langchain";

Key patterns:

- **HITL**: `middleware=[HumanInTheLoopMiddleware(interrupt_on={"dangerous_tool": True})]` — requires `checkpointer` + `thread_id`
- **Resume after interrupt**: `agent.invoke(Command(resume={"decisions": [{"type": "approve"}]}), config=config)`
- **Custom middleware**: `@wrap_tool_call` decorator (Python) or `createMiddleware({wrapToolCall:...})` (TypeScript)

<structured_output>

## Structured Output

Get typed, validated responses from agents using `response_format` or `with_structured_output()`.

class ContactInfo(BaseModel): name: str email: str phone: str = Field(description="Phone number with area code")

# Option 1: Agent with structured output

agent = create_agent(model="gpt-4.1", tools=[search], response_format=ContactInfo) result = agent.invoke({"messages": [{"role": "user", "content": "Find contact for John"}]}) print(result["structured_response"]) # ContactInfo(name='John',...)

# Option 2: Model-level structured output (no agent needed)

from langchain_openai import ChatOpenAI model = ChatOpenAI(model="gpt-4.1") structured_model = model.with_structured_output(ContactInfo) response = structured_model.invoke("Extract: John, [john@example.com](https://github.com/langchain-ai/langchain-skills/blob/HEAD/config/skills/langchain-fundamentals/mailto:john@example.com), 555-1234")

# ContactInfo(name='John', email='[john@example.com](https://github.com/langchain-ai/langchain-skills/blob/HEAD/config/skills/langchain-fundamentals/mailto:john@example.com)', phone='555-1234')

</python> <typescript>

import { ChatOpenAI } from "@langchain/openai";
import { z } from "zod";

const ContactInfo = z.object({
  name: z.string(),
  email: z.string().email(),
  phone: z.string().describe("Phone number with area code"),
});

// Model-level structured output
const model = new ChatOpenAI({ model: "gpt-4.1" });
const structuredModel = model.withStructuredOutput(ContactInfo);
const response = await structuredModel.invoke("Extract: John, john@example.com, 555-1234");
// { name: 'John', email: 'john@example.com', phone: '555-1234' }

<model_config>

Model Configuration

create_agent accepts model strings ("anthropic:claude-sonnet-4-5", "openai:gpt-4.1") or model instances for custom settings:

from langchain_anthropic import ChatAnthropic
agent = create_agent(model=ChatAnthropic(model="claude-sonnet-4-5", temperature=0), tools=[...])

</model_config>

CORRECT: Clear, specific description with Args

@tool def search(query: str) -> str: """Search the web for current information about a topic.

Use this when you need recent data or facts.

Args:
    query: The search query (2-10 words recommended)
"""
return web_search(query)

</python>
<typescript>
Clear descriptions help the agent know when to use each tool.

// WRONG: Vague description const badTool = tool(async ({ input }) => "result", { name: "bad_tool", description: "Does stuff.", // Too vague! schema: z.object({ input: z.string() }), });

// CORRECT: Clear, specific description const search = tool(async ({ query }) => webSearch(query), { name: "search", description: "Search the web for current information about a topic. Use this when you need recent data or facts.", schema: z.object({ query: z.string().describe("The search query (2-10 words recommended)"), }), });

# CORRECT: Add checkpointer and thread_id

from langgraph.checkpoint.memory import MemorySaver

agent = create_agent(model="anthropic:claude-sonnet-4-5", tools=[search], checkpointer=MemorySaver(),) config = {"configurable": {"thread_id": "session-1"}} agent.invoke({"messages": [{"role": "user", "content": "I'm Bob"}]}, config=config) agent.invoke({"messages": [{"role": "user", "content": "What's my name?"}]}, config=config)

# Agent remembers: "Your name is Bob"

</python> <typescript> Add checkpointer and thread_id for conversation memory across invocations.

// WRONG: No persistence
const agent = createAgent({ model: "anthropic:claude-sonnet-4-5", tools: [search] });
await agent.invoke({ messages: [{ role: "user", content: "I'm Bob" }] });
await agent.invoke({ messages: [{ role: "user", content: "What's my name?" }] });
// Agent doesn't remember!

// CORRECT: Add checkpointer and thread_id
import { MemorySaver } from "@langchain/langgraph";

const agent = createAgent({
  model: "anthropic:claude-sonnet-4-5",
  tools: [search],
  checkpointer: new MemorySaver(),
});
const config = { configurable: { thread_id: "session-1" } };
await agent.invoke({ messages: [{ role: "user", content: "I'm Bob" }] }, config);
await agent.invoke({ messages: [{ role: "user", content: "What's my name?" }] }, config);
// Agent remembers: "Your name is Bob"

CORRECT: Set recursion_limit in config

result = agent.invoke({"messages": [("user", "Do research")]}, config={"recursion_limit": 10}, # Stop after 10 steps)

</python>
<typescript>
Set recursionLimit in the invoke config to prevent runaway agent loops.

// WRONG: No iteration limit const result = await agent.invoke({ messages: [["user", "Do research"]] });

// CORRECT: Set recursionLimit in config const result = await agent.invoke( { messages: [["user", "Do research"]] }, { recursionLimit: 10 }, // Stop after 10 steps );

# CORRECT: Access messages from result dict

result = agent.invoke({"messages": [{"role": "user", "content": "Hello"}]}) print(result["messages"][-1].content) # Last message content

</python> <typescript> Access the messages array from the result, not result.content directly.

// WRONG: Trying to access result.content directly
const result = await agent.invoke({ messages: [{ role: "user", content: "Hello" }] });
console.log(result.content); // undefined!

// CORRECT: Access messages from result object
const result = await agent.invoke({ messages: [{ role: "user", content: "Hello" }] });
console.log(result.messages[result.messages.length - 1].content); // Last message content