langchain-ai/skills-benchmarks-langchain-fundamentals
Create LangChain agents with create_agent, define tools, and use middleware for human-in-the-loop and error handling.
This is a copy. The original lives at comeonoliver/langchain-fundamentals.
npx skills add https://github.com/langchain-ai/skills-benchmarks --skill 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 All other alternatives are outdated.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>
create_agent() is the recommended way to build agents. It handles the agent loop, tool execution, and state management.
| 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>
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 calculate(expression: str) -> str:
"""Evaluate a mathematical expression.
Args:
expression: Math expression like "2 + 2" or "10 * 5"
"""
return str(eval(expression))
</python>
<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'"),
}),
}
);
</typescript>
</ex-basic-tool>
<middleware>
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:
middleware=[HumanInTheLoopMiddleware(interrupt_on={"dangerous_tool": True})] — requires checkpointer + thread_idagent.invoke(Command(resume={"decisions": [{"type": "approve"}]}), config=config)@wrap_tool_call decorator (Python) or createMiddleware({ wrapToolCall: ... }) (TypeScript)</middleware>
<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, [email protected], 555-1234")
# ContactInfo(name='John', email='[email protected]', 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, [email protected], 555-1234");
// { name: 'John', email: '[email protected]', phone: '555-1234' }
</typescript>
</structured_output>
<model_config>
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>
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)"),
}),
});
</typescript>
</fix-missing-tool-description>
<fix-no-checkpointer>
<python>
Add checkpointer and thread_id for conversation memory across invocations.
# 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.
// 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"
</typescript>
</fix-no-checkpointer>
<fix-infinite-loop>
<python>
Set recursion_limit in the invoke config to prevent runaway agent loops.
# 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.
// 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
);
</typescript>
</fix-infinite-loop>
<fix-accessing-result-wrong>
<python>
Access the messages array from the result, not result.content directly.
# 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.
// 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
</typescript>
</fix-accessing-result-wrong>
Take langchain-ai/skills-benchmarks-langchain-fundamentals from the repository into ~/.claude/skills for personal
use, or into .claude/skills inside a project.
The agent identifies a skill by the name field in its header. Two skills with the
same name cannot sit side by side — one of them will be ignored.