/langchain-fundamentals

name: langchain-fundamentals
description: Create LangChain agents with create_agent, define tools, and use middleware for human-in-the-loop and error handling.

<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 All other alternatives are outdated.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

| Parameter | Purpose | Example | |-----------|---------|---------| | `model` | LLM to use | `"anthropic:claude-sonnet-4-5"` or model instance | | `tools` | List of tools | `[search, calculator]` | | `system_prompt` / `systemPrompt` | Agent instructions | `"You are a helpful assistant"` | | `checkpointer` | State persistence | `MemorySaver()` | | `middleware` | Processing hooks | `[HumanInTheLoopMiddleware]` (Python) / `[humanInTheLoopMiddleware({...})]` (TypeScript) | </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: