Langchain Fundamentals

<oneliner> Build production agents using create_agent(), middleware patterns, and the @tool decorator / tool() function. When creating LangChain agents, you MUST use create_agent(), with middleware for custom flows. All other alternatives are outdated. </oneliner>

<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

</create_agent>

<ex-basic-agent> <python>

from langchain.agents import create_agent
from langchain_core.tools import tool

@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);

</typescript> </ex-basic-agent>

<ex-agent-with-persistence> <python> Add MemorySaver checkpointer to maintain conversation state across invocations.

from langchain.agents import create_agent
from langgraph.checkpoint.memory import MemorySaver

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"

</typescript> </ex-agent-with-persistence>

<tools>

Defining Tools

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

<ex-basic-tool> <python>

from langchain_core.tools import tool

@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"),
    }),
  }
);

</typescript> </ex-basic-tool>

<middleware>

Middleware for Agent Control

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)

</middleware>

<structured_output>

Structured Output

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

<python>

from langchain.agents import create_agent
from pydantic import BaseModel, Field

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, 555-1234")
# ContactInfo(name='John', email='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' }

</typescript> </structured_output>

<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>

<fix-missing-tool-description> <python> Clear descriptions help the agent know when to use each tool.

# WRONG: Vague or missing description
@tool
def bad_tool(input: str) -> str:
    """Does stuff."""
    return "result"

# 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>