AI Agents with LangChain & Autonomous Workflows · 강의

사용자 지정 LangChain 도구 만들기

사전 구축된 옵션을 넘어 에이전트의 기능을 확장할 수 있도록 전문 도구를 직접 개발하고 통합합니다.

사용자 지정 LangChain 도구 만들기은(는) CoddyKit의 무료 AI Agents with LangChain & Autonomous Workflows 강의입니다. 이것은 4개 중 1번째 강의입니다. 아래에서 전체 강의를 무료로 읽을 수 있으며, 내장 코드 에디터와 24/7 AI 튜터와 함께 브라우저에서 직접 실습할 수 있습니다. 이 강의는 AI Agents with LangChain & Autonomous Workflows 학습 경로의 일부이며, 진행 상황이 웹과 CoddyKit 앱에 동기화됩니다. AI Agents with LangChain & Autonomous Workflows 강의에는 총 4개의 강의가 포함되어 있습니다.

이 강의의 일부는 아직 번역되지 않았으며 영어로 표시됩니다.

Extend Agent Capabilities

AI agents are powerful, but sometimes they need to perform very specific actions that aren't covered by standard tools. This is where custom tools come in!

Custom tools allow your agent to interact with unique APIs, proprietary databases, or perform highly specialized calculations tailored to your application.

Tools: Agent's Action Kit

In LangChain, a Tool is essentially a function that an agent can call to perform a specific action. Think of it as an item in the agent's utility belt.

It takes a single string input (e.g., a query, a number).
It returns a single string output (e.g., a result, an error message).
The agent uses the tool's description to decide when and how to use it.

Anatomy of a Custom Tool

To build a custom tool, you primarily need two things:

A standard Python function that contains the logic your tool will execute.
A way to describe this function (its name, what it does, and what kind of input it expects) to the Large Language Model (LLM).

LangChain provides an easy way to define and expose these functions to your agents.

Crafting the Python Function

First, let's create a simple Python function. This function will be the core logic of our custom tool. Remember, it should ideally take a string and return a string.

Here's an example of a function that calculates the square of a number:

def calculate_square(number_str: str) -> str:
  """Calculates the square of a number."""
  try:
    number = int(number_str)
    return str(number * number)
  except ValueError:
    return "Error: Input must be a valid integer."

Making Tools Agent-Ready

LangChain provides the @tool decorator to easily turn a regular Python function into an agent-callable tool. This decorator automatically infers the tool's schema (inputs, description) for the LLM.

The docstring of your function becomes the tool's description, which is crucial for the LLM to understand its purpose.

from langchain_core.tools import tool

@tool
def calculate_square(number_str: str) -> str:
  """Calculates the square of a number.
  Input should be a string representing an integer."""
  try:
    number = int(number_str)
    return str(number * number)
  except ValueError:
    return "Error: Input must be a valid integer."

# You can test it directly:
# print(calculate_square("7"))
# print(calculate_square("hello"))

Equipping Your Agent

Once your custom tool function is defined with the @tool decorator, you need to provide it to your agent. Agents are typically initialized with a list of tools they have access to.

The LLM will then "see" these tools and their descriptions, allowing it to decide when to call them based on the user's prompt.

Agent Setup for Tool Use

Before running our agent with the custom tool, we need a few more pieces:

An LLM (like OpenAI's GPT models).
A Prompt Template to guide the LLM's responses.
The create_tool_calling_agent function to combine the LLM, tools, and prompt.
An Agent Executor to run the agent.

Agent in Action: Custom Square Tool

Let's put it all together! This runnable example shows an agent using our calculate_square custom tool to answer a user's request. Observe the agent's thought process in the output.

from langchain_openai import ChatOpenAI
from langchain_core.prompts import ChatPromptTemplate
from langchain_core.tools import tool
from langchain.agents import AgentExecutor, create_tool_calling_agent

# IMPORTANT: Set your OpenAI API key in your environment variables
# e.g., export OPENAI_API_KEY='your_key_here'

@tool
def calculate_square(number_str: str) -> str:
  """Calculates the square of a number.
  Input should be a string representing an integer."""
  try:
    number = int(number_str)
    return str(number * number)
  except ValueError:
    return "Error: Input must be a valid integer."

# Define the LLM (replace with your setup if not using OpenAI)
llm = ChatOpenAI(model="gpt-3.5-turbo", temperature=0)

# Define the tools the agent can use
tools = [calculate_square]

# Create the prompt template
prompt = ChatPromptTemplate.from_messages([
  ("system", "You are a helpful assistant. Use the tools provided to answer questions."),
  ("human", "{input}"),
  ("placeholder", "{agent_scratchpad}")
])

# Create the agent
agent = create_tool_calling_agent(llm, tools, prompt)

# Create an agent executor with verbosity to see the steps
agent_executor = AgentExecutor(agent=agent, tools=tools, verbose=True)

# Invoke the agent with a question it needs the tool for
result = agent_executor.invoke({"input": "What is the square of 15?"})
print(f"\nAgent's final answer: {result['output']}")

Tips for Effective Tools

When designing your custom tools, consider these best practices:

Clear Descriptions: The tool's docstring is vital. Make it precise so the LLM knows when to use it.
Robust Error Handling: Build try-except blocks into your tool functions to handle unexpected inputs or external service failures.
Single Responsibility: Each tool should ideally do one thing well. Avoid making overly complex tools.
String I/O: Remember, tools typically expect string inputs and return string outputs. Convert types as needed.

Custom Tool Check

Which of the following is the primary purpose of the @tool decorator in LangChain when creating a custom tool?

Custom Tool Power-Up

You've learned how to create and integrate your own custom tools into LangChain agents!

We saw why custom tools are important for extending agent capabilities.
We understood the core components: a Python function and its description.
We used the @tool decorator to make functions agent-ready.
Finally, we built a full agent that successfully used our custom tool.

This skill is fundamental for building truly versatile and domain-specific AI agents.

자주 묻는 질문

“사용자 지정 LangChain 도구 만들기” 강의는 무료인가요?

네 — “사용자 지정 LangChain 도구 만들기” 전체 내용을 이 웹사이트에서 무료로 읽을 수 있습니다. 인터랙티브하게 실습하려면(내장 코드 에디터와 24/7 AI 튜터), CoddyKit PRO로 업그레이드하면 AI Agents with LangChain & Autonomous Workflows 강의 전체를 잠금 해제할 수 있습니다. AI Agents with LangChain & Autonomous Workflows 강의에는 총 4개의 강의가 포함되어 있습니다.

“사용자 지정 LangChain 도구 만들기”에서 뭘 배우나요?

사전 구축된 옵션을 넘어 에이전트의 기능을 확장할 수 있도록 전문 도구를 직접 개발하고 통합합니다. 브라우저에서 직접 실행하는 실습 코드로 AI Agents with LangChain & Autonomous Workflows을(를) 배우며, 24/7 AI 튜터가 강의를 진행하면서 질문에 답변해줍니다.

AI Agents with LangChain & Autonomous Workflows을(를) 시작하는 데 경험이 필요한가요?

사전 경험은 필요하지 않습니다. CoddyKit의 AI Agents with LangChain & Autonomous Workflows은(는) 초급자부터 고급 학습자까지를 위해 구성되어 있으므로, 여기서 시작하거나 처음부터 시작할 수 있으며 자신의 속도대로 진행할 수 있습니다. 이것은 4개 중 1번째 강의입니다.

“사용자 지정 LangChain 도구 만들기” 강의는 얼마나 걸리나요?

대부분의 CoddyKit 강의는 약 5~10분이 소요됩니다. 각 강의는 간결하고 인터랙티브하여 꾸준한 진행이 가능하며, 웹과 앱에서 중단한 부분부터 바로 시작할 수 있습니다.

이 AI Agents with LangChain & Autonomous Workflows 강의에서 코드를 작성하고 실행할 수 있나요?

네. 모든 AI Agents with LangChain & Autonomous Workflows 강의에는 내장 코드 에디터가 포함되어 있으므로, 브라우저에서 바로 실제 코드를 작성하고 실행한 후 즉시 AI 피드백을 받을 수 있습니다 — 로컬 설정이 필요 없습니다.

이 강의의 모든 강의

← AI Agents with LangChain & Autonomous Workflows(으)로 돌아가기