langchain_md_files/concepts/tool_calling.mdx

# Tool calling

:::info[Prerequisites]
* [Tools](/docs/concepts/tools)
* [Chat Models](/docs/concepts/chat_models)
:::


## Overview 

Many AI applications interact directly with humans. In these cases, it is appropriate for models to respond in natural language.
But what about cases where we want a model to also interact *directly* with systems, such as databases or an API?
These systems often have a particular input schema; for example, APIs frequently have a required payload structure.
This need motivates the concept of *tool calling*. You can use [tool calling](https://platform.openai.com/docs/guides/function-calling/example-use-cases) to request model responses that match a particular schema.

:::info
You will sometimes hear the term `function calling`. We use this term interchangeably with `tool calling`. 
:::

![Conceptual overview of tool calling](/img/tool_calling_concept.png)

## Key concepts 

**(1) Tool Creation:** Use the [@tool](https://python.langchain.com/api_reference/core/tools/langchain_core.tools.convert.tool.html) decorator to create a [tool](/docs/concepts/tools). A tool is an association between a function and its schema.
**(2) Tool Binding:** The tool needs to be connected to a model that supports tool calling. This gives the model awareness of the tool and the associated input schema required by the tool.
**(3) Tool Calling:** When appropriate, the model can decide to call a tool and ensure its response conforms to the tool's input schema.
**(4) Tool Execution:** The tool can be executed using the arguments provided by the model.

![Conceptual parts of tool calling](/img/tool_calling_components.png)

## Recommended usage

This pseudo-code illustrates the recommended workflow for using tool calling. 
Created tools are passed to `.bind_tools()` method as a list.
This model can be called, as usual. If a tool call is made, model's response will contain the tool call arguments.
The tool call arguments can be passed directly to the tool.

```python
# Tool creation
tools = [my_tool]
# Tool binding
model_with_tools = model.bind_tools(tools)
# Tool calling 
response = model_with_tools.invoke(user_input)
```

## Tool creation

The recommended way to create a tool is using the `@tool` decorator.

```python
from langchain_core.tools import tool

@tool
def multiply(a: int, b: int) -> int:
    """Multiply a and b."""
    return a * b
```

:::info[Further reading]

* See our conceptual guide on [tools](/docs/concepts/tools/) for more details.
* See our [model integrations](/docs/integrations/chat/) that support tool calling.
* See our [how-to guide](/docs/how_to/tool_calling/) on tool calling.

:::

## Tool binding 

[Many](https://platform.openai.com/docs/guides/function-calling) [model providers](https://platform.openai.com/docs/guides/function-calling) support tool calling. 

:::tip
See our [model integration page](/docs/integrations/chat/) for a list of providers that support tool calling.
:::

The central concept to understand is that LangChain provides a standardized interface for connecting tools to models. 
The `.bind_tools()` method can be used to specify which tools are available for a model to call. 

```python
model_with_tools = model.bind_tools(tools_list)
```

As a specific example, let's take a function `multiply` and bind it as a tool to a model that supports tool calling.

```python
def multiply(a: int, b: int) -> int:
    """Multiply a and b.

    Args:
        a: first int
        b: second int
    """
    return a * b

llm_with_tools = tool_calling_model.bind_tools([multiply])
```

## Tool calling

![Diagram of a tool call by a model](/img/tool_call_example.png)

A key principle of tool calling is that the model decides when to use a tool based on the input's relevance. The model doesn't always need to call a tool.
For example, given an unrelated input, the model would not call the tool:

```python
result = llm_with_tools.invoke("Hello world!")
```

The result would be an `AIMessage` containing the model's response in natural language (e.g., "Hello!").
However, if we pass an input *relevant to the tool*, the model should choose to call it:

```python
result = llm_with_tools.invoke("What is 2 multiplied by 3?")
```

As before, the output `result` will be an `AIMessage`. 
But, if the tool was called, `result` will have a `tool_calls` attribute.
This attribute includes everything needed to execute the tool, including the tool name and input arguments:

```
result.tool_calls
{'name': 'multiply', 'args': {'a': 2, 'b': 3}, 'id': 'xxx', 'type': 'tool_call'}
```

For more details on usage, see our [how-to guides](/docs/how_to/#tools)!

## Tool execution

[Tools](/docs/concepts/tools/) implement the [Runnable](/docs/concepts/runnables/) interface, which means that they can be invoked (e.g., `tool.invoke(args)`) directly.

[LangGraph](https://langchain-ai.github.io/langgraph/) offers pre-built components (e.g., [`ToolNode`](https://langchain-ai.github.io/langgraph/reference/prebuilt/#langgraph.prebuilt.tool_node.ToolNode)) that will often invoke the tool in behalf of the user.

:::info[Further reading]

* See our [how-to guide](/docs/how_to/tool_calling/) on tool calling.
* See the [LangGraph documentation on using ToolNode](https://langchain-ai.github.io/langgraph/how-tos/tool-calling/).

:::

## Best practices

When designing [tools](/docs/concepts/tools/) to be used by a model, it is important to keep in mind that:

* Models that have explicit [tool-calling APIs](/docs/concepts/tool_calling) will be better at tool calling than non-fine-tuned models.
* Models will perform better if the tools have well-chosen names and descriptions.
* Simple, narrowly scoped tools are easier for models to use than complex tools.
* Asking the model to select from a large list of tools poses challenges for the model.