---
title: Using existing tools | Developer Documentation
---

Now that you’ve built a capable agent, we hope you’re excited about all it can do. The core of expanding agent capabilities is the tools available, and we have good news: [LlamaHub](https://llamahub.ai) from LlamaIndex has hundreds of integrations, including [dozens of existing agent tools](https://llamahub.ai/?tab=tools) that you can use right away. We’ll show you how to use one of the existing tools, and also how to build and contribute your own.

## Using an existing tool from LlamaHub

For our example, we’re going to use the [Yahoo Finance tool](https://llamahub.ai/l/tools/llama-index-tools-yahoo-finance?from=tools) from LlamaHub. It provides a set of six agent tools that look up a variety of information about stock ticker symbols.

First we need to install the tool:

Terminal window

```
pip install llama-index-tools-yahoo-finance
```

Our dependencies are the same as our previous example, we just need to add the Yahoo Finance tools:

```
from llama_index.tools.yahoo_finance import YahooFinanceToolSpec
```

To show how you can combine custom tools with LlamaHub tools, we’re going to leave the `add` and `multiply` functions in place even though we don’t need them here. We’ll bring in our tools:

```
finance_tools = YahooFinanceToolSpec().to_tool_list()
```

A tool list is just an array, so we can use Python’s `extend` method to add our own tools to the mix:

```
finance_tools.extend([multiply, add])
```

And we’ll ask a different question than last time, necessitating the use of the new tools:

```
workflow = FunctionAgent(
    name="Agent",
    description="Useful for performing financial operations.",
    llm=OpenAI(model="gpt-4o-mini"),
    tools=finance_tools,
    system_prompt="You are a helpful assistant.",
)




async def main():
    response = await workflow.run(
        user_msg="What's the current stock price of NVIDIA?"
    )
    print(response)
```

We get this response:

```
The current stock price of NVIDIA Corporation (NVDA) is $128.41.
```

(This is cheating a little bit, because our model already knew the ticker symbol for NVIDIA. If it were a less well-known corporation you would need to add a search tool like [Tavily](https://llamahub.ai/l/tools/llama-index-tools-tavily-research) to find the ticker symbol.)

And that’s it! You can now use any of the tools in LlamaHub in your agents.

As always, you can check [the repo](https://github.com/run-llama/python-agents-tutorial/blob/main/2_tools.py) to see this code all in one place.

## Building and contributing your own tools

We love open source contributions of new tools! You can see an example of [what the code of the Yahoo finance tool looks like](https://github.com/run-llama/llama_index/blob/main/llama-index-integrations/tools/llama-index-tools-yahoo-finance/llama_index/tools/yahoo_finance/base.py):

- A class that extends `BaseToolSpec`
- A set of arbitrary Python functions
- A `spec_functions` list that maps the functions to the tool’s API

Once you’ve got a tool working, follow our [contributing guide](https://github.com/run-llama/llama_index/blob/main/CONTRIBUTING.md#2--contribute-a-pack-reader-tool-or-dataset-formerly-from-llama-hub) for instructions on correctly setting metadata and submitting a pull request.

Next we’ll look at [how to maintain state](/python/framework/understanding/agent/state/index.md) in your agents.