langchain-fundamentals

★ 108

by langchain-ai · part of langchain-ai/skills-benchmarks

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

🔥🔥🔥🔥✓ VerifiedFreeQuick setup

🧩 One of 7 skills in the langchain-ai/skills-benchmarks package — works on its own, and pairs well with its siblings.

This is the playbook your agent receives when the skill activates — you don't need to read it to use the skill, but it's here to audit before installing.

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 All other alternatives are outdated.

<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>

```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>
```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);

Add MemorySaver checkpointer to maintain conversation state across invocations. ```python 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.
```typescript
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"

Defining Tools

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

```python from langchain_core.tools import tool

@tool def calculate(expression: str) -> str: """Evaluate a mathematical expression.

Args:
    expression: Math expression like "2 + 2" or "10 * 5"
"""
return str(eval(expression))

</python>
<typescript>
```typescript
import { tool } from "@langchain/core/tools";
import { z } from "zod";

const calculate = tool(
  async ({ expression }) => String(eval(expression)),
  {
    name: "calculate",
    description: "Evaluate a mathematical expression.",
    schema: z.object({
      expression: z.string().describe("Math expression like '2 + 2' or '10 * 5'"),
    }),
  }
);

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)

<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>
```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>

Copy & paste — that's it

npx skills add langchain-ai/skills-benchmarks --skill "langchain-fundamentals" --full-depth

Run this in your project — your agent picks the skill up automatically.

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>

Clear descriptions help the agent know when to use each tool. ```python # 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>
Clear descriptions help the agent know when to use each tool.
```typescript
// 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)"),
  }),
});

Add checkpointer and thread_id for conversation memory across invocations. ```python # WRONG: No persistence - agent forgets between calls agent = create_agent(model="anthropic:claude-sonnet-4-5", tools=[search]) agent.invoke({"messages": [{"role": "user", "content": "I'm Bob"}]}) agent.invoke({"messages": [{"role": "user", "content": "What's my name?"}]}) # Agent doesn't remember!

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.
```typescript
// 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"

Set recursion_limit in the invoke config to prevent runaway agent loops. ```python # WRONG: No iteration limit - could loop forever result = agent.invoke({"messages": [("user", "Do research")]})

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.
```typescript
// 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
);

Access the messages array from the result, not result.content directly. ```python # WRONG: Trying to access result.content directly result = agent.invoke({"messages": [{"role": "user", "content": "Hello"}]}) print(result.content) # AttributeError!

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.
```typescript
// 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