AI Agents with LangChain & Autonomous Workflows · レッスン

カスタムLangChainツールの作成

独自の専門的なツールを開発・統合し、構築済みの選択肢を超えてエージェントの機能を拡張します。

レッスン 1/411 ステップ

「カスタムLangChainツールの作成」はCoddyKit上の無料AI Agents with LangChain & Autonomous Workflowsレッスンです。これはレッスン1/4です。下記で完全なレッスンを無料で読むことができます。その後、ブラウザ内の組み込みコードエディタと24時間対応のAIチューターでハンズオン演習できます。これはAI Agents with LangChain & Autonomous Workflows学習パスの一部であり、ウェブとCoddyKitアプリ全体で進捗が同期されます。 AI Agents with LangChain & Autonomous Workflowsコースには全4レッスンが含まれています。

このレッスンの一部はまだ翻訳されておらず、英語で表示されています。

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?

対話的なレッスンで答える

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.

無料で開始

AI チューターと学ぶ AI Agents with LangChain & Autonomous Workflows — 無料

ブラウザでリアルコードを書いて実行し、24/7 の AI チューターから瞬時にサポートを受け、ウェブまたはアプリで続きから学習できます。

コース: 12
レッスン: 50

無料で学習を始めるコース全体を表示

よくある質問

「カスタムLangChainツールの作成」レッスンは無料ですか？

はい。「カスタムLangChainツールの作成」の完全なテキストはこのウェブで無料で読めます。インタラクティブに演習し（組み込みコードエディタと24時間対応のAIチューター）、AI Agents with LangChain & Autonomous Workflowsコースの残りをアンロックするには、CoddyKit PROにアップグレードしてください。 AI Agents with LangChain & Autonomous Workflowsコースには全4レッスンが含まれています。

「カスタムLangChainツールの作成」で何を学びますか？

独自の専門的なツールを開発・統合し、構築済みの選択肢を超えてエージェントの機能を拡張します。ブラウザで直接実行するハンズオンコードでAI Agents with LangChain & Autonomous Workflowsを演習し、24時間対応のAIチューターがレッスンを進める中での質問に答えます。

AI Agents with LangChain & Autonomous Workflowsを始めるのに経験は必要ですか？

事前経験は必要ありません。CoddyKitのAI Agents with LangChain & Autonomous Workflowsは初級者から上級者向けに構成されているため、ここから始めるか最初から始めて、自分のペースで進むことができます。これはレッスン1/4です。

「カスタムLangChainツールの作成」レッスンにはどのくらい時間がかかりますか？

ほとんどのCoddyKitレッスンは約5～10分かかります。各レッスンはコンパクトでインタラクティブなので、着実に進歩し、ウェブとアプリ全体で正確に前回の場所から再開できます。

このAI Agents with LangChain & Autonomous Workflowsレッスンでコードを書いて実行できますか？

はい。すべてのAI Agents with LangChain & Autonomous Workflowsレッスンに組み込みコードエディタが含まれているため、ブラウザでリアルコードを書いて実行し、即座のAIフィードバックを取得できます。ローカル設定は不要です。

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