AI Agents with LangChain & Autonomous Workflows · 课时

创建自定义 LangChain 工具

开发并集成您自己的专用工具，将智能体的功能扩展到预构建选项之外

创建自定义 LangChain 工具 是 CoddyKit 上的免费 AI Agents with LangChain & Autonomous Workflows 课时。这是第 1 节课，共 4 节。你可以在下方免费阅读本课时的完整内容 — 然后在浏览器中使用内置代码编辑器和全天候 AI 导师进行实践。这是 AI Agents with LangChain & Autonomous Workflows 学习路径的一部分，你的进度在网页和 CoddyKit 应用中同步。 AI Agents with LangChain & Autonomous Workflows 课程共包含 4 节课。

本课时的部分内容尚未翻译，以英文显示。

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.

常见问题解答

「创建自定义 LangChain 工具」课时是免费的吗？

是的 — 「创建自定义 LangChain 工具」的完整文本可在网页上免费阅读。要进行交互式练习（内置代码编辑器和全天候 AI 导师）并解锁 AI Agents with LangChain & Autonomous Workflows 课程的其余内容，请升级到 CoddyKit PRO。 AI Agents with LangChain & Autonomous Workflows 课程共包含 4 节课。

「创建自定义 LangChain 工具」这节课中我会学到什么？

开发并集成您自己的专用工具，将智能体的功能扩展到预构建选项之外你通过在浏览器中直接运行的动手代码来练习 AI Agents with LangChain & Autonomous Workflows，全天候 AI 导师会在你学习这节课的过程中回答你的问题。

学习 AI Agents with LangChain & Autonomous Workflows 需要有经验吗？

无需任何先前经验。CoddyKit 上的 AI Agents with LangChain & Autonomous Workflows 课程适合初学者到高级学习者，你可以从这里开始或从头开始，按照自己的节奏学习。这是第 1 节课，共 4 节。

「创建自定义 LangChain 工具」课时需要多长时间？

大多数 CoddyKit 课程大约需要 5–10 分钟。每节课都很精短且互动，所以你能稳步进步，并在网页和应用中从离开的地方继续。

我能在这节 AI Agents with LangChain & Autonomous Workflows 课中编写并运行代码吗？

能。每节 AI Agents with LangChain & Autonomous Workflows 课都包含内置代码编辑器，你可以在浏览器中直接编写并运行真实代码，并获得即时 AI 反馈 — 无需本地设置。

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