How to use a model to call tools

Prerequisites

This guide assumes familiarity with the following concepts:

• Chat models
• LangChain Tools
• Output parsers

Tool calling vs function calling

We use the term tool calling interchangeably with function calling. Although function calling is sometimes meant to refer to invocations of a single function, we treat all models as though they can return multiple tool or function calls in each message.

Supported models

You can find a list of all models that support tool calling.

Tool calling allows a chat model to respond to a given prompt by "calling a tool". While the name implies that the model is performing some action, this is actually not the case! The model generates the arguments to a tool, and actually running the tool (or not) is up to the user. For example, if you want to extract output matching some schema from unstructured text, you could give the model an "extraction" tool that takes parameters matching the desired schema, then treat the generated output as your final result.

note

If you only need formatted values, try the .with_structured_output() chat model method as a simpler entrypoint.

However, tool calling goes beyond structured output since you can pass responses from called tools back to the model to create longer interactions. For instance, given a search engine tool, an LLM might handle a query by first issuing a call to the search engine with arguments. The system calling the LLM can receive the tool call, execute it, and return the output to the LLM to inform its response. LangChain includes a suite of built-in tools and supports several methods for defining your own custom tools.

Tool calling is not universal, but many popular LLM providers, including Anthropic, Cohere, Google, Mistral, OpenAI, and others, support variants of a tool calling feature.

LangChain implements standard interfaces for defining tools, passing them to LLMs, and representing tool calls. This guide and the other How-to pages in the Tool section will show you how to use tools with LangChain.

Passing tools to chat models

Chat models that support tool calling features implement a .bind_tools method, which receives a list of LangChain tool objects and binds them to the chat model in its expected format. Subsequent invocations of the chat model will include tool schemas in its calls to the LLM.

For example, we can define the schema for custom tools using the @tool decorator on Python functions:

from langchain_core.tools import tool


@tool
def add(a: int, b: int) -> int:
    """Adds a and b."""
    return a + b


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


tools = [add, multiply]

API Reference:tool

Or below, we define the schema using Pydantic:

from langchain_core.pydantic_v1 import BaseModel, Field


# Note that the docstrings here are crucial, as they will be passed along
# to the model along with the class name.
class Add(BaseModel):
    """Add two integers together."""

    a: int = Field(..., description="First integer")
    b: int = Field(..., description="Second integer")


class Multiply(BaseModel):
    """Multiply two integers together."""

    a: int = Field(..., description="First integer")
    b: int = Field(..., description="Second integer")


tools = [Add, Multiply]

We can bind them to chat models as follows:

OpenAI

pip install -qU langchain-openai

import getpass
import os

os.environ["OPENAI_API_KEY"] = getpass.getpass()

from langchain_openai import ChatOpenAI

llm = ChatOpenAI(model="gpt-3.5-turbo-0125")

Anthropic

pip install -qU langchain-anthropic

import getpass
import os

os.environ["ANTHROPIC_API_KEY"] = getpass.getpass()

from langchain_anthropic import ChatAnthropic

llm = ChatAnthropic(model="claude-3-sonnet-20240229")

Azure

pip install -qU langchain-openai

import getpass
import os

os.environ["AZURE_OPENAI_API_KEY"] = getpass.getpass()

from langchain_openai import AzureChatOpenAI

llm = AzureChatOpenAI(
    azure_endpoint=os.environ["AZURE_OPENAI_ENDPOINT"],
    azure_deployment=os.environ["AZURE_OPENAI_DEPLOYMENT_NAME"],
    openai_api_version=os.environ["AZURE_OPENAI_API_VERSION"],
)

Google

pip install -qU langchain-google-vertexai

import getpass
import os

os.environ["GOOGLE_API_KEY"] = getpass.getpass()

from langchain_google_vertexai import ChatVertexAI

llm = ChatVertexAI(model="gemini-pro")

Cohere

pip install -qU langchain-cohere

import getpass
import os

os.environ["COHERE_API_KEY"] = getpass.getpass()

from langchain_cohere import ChatCohere

llm = ChatCohere(model="command-r")

FireworksAI

pip install -qU langchain-fireworks

import getpass
import os

os.environ["FIREWORKS_API_KEY"] = getpass.getpass()

from langchain_fireworks import ChatFireworks

llm = ChatFireworks(model="accounts/fireworks/models/firefunction-v1", temperature=0)

Groq

pip install -qU langchain-groq

import getpass
import os

os.environ["GROQ_API_KEY"] = getpass.getpass()

from langchain_groq import ChatGroq

llm = ChatGroq(model="llama3-8b-8192")

MistralAI

pip install -qU langchain-mistralai

import getpass
import os

os.environ["MISTRAL_API_KEY"] = getpass.getpass()

from langchain_mistralai import ChatMistralAI

llm = ChatMistralAI(model="mistral-large-latest")

TogetherAI

pip install -qU langchain-openai

import getpass
import os

os.environ["TOGETHER_API_KEY"] = getpass.getpass()

from langchain_openai import ChatOpenAI

llm = ChatOpenAI(
    base_url="https://api.together.xyz/v1",
    api_key=os.environ["TOGETHER_API_KEY"],
    model="mistralai/Mixtral-8x7B-Instruct-v0.1",
)

We'll use the .bind_tools() method to handle converting Multiply to the proper format for the model, then and bind it (i.e., passing it in each time the model is invoked).

llm_with_tools = llm.bind_tools(tools)

query = "What is 3 * 12?"

llm_with_tools.invoke(query)

AIMessage(content='', additional_kwargs={'tool_calls': [{'id': 'call_g4RuAijtDcSeM96jXyCuiLSN', 'function': {'arguments': '{"a":3,"b":12}', 'name': 'Multiply'}, 'type': 'function'}]}, response_metadata={'token_usage': {'completion_tokens': 18, 'prompt_tokens': 95, 'total_tokens': 113}, 'model_name': 'gpt-3.5-turbo-0125', 'system_fingerprint': None, 'finish_reason': 'tool_calls', 'logprobs': None}, id='run-5157d15a-7e0e-4ab1-af48-3d98010cd152-0', tool_calls=[{'name': 'Multiply', 'args': {'a': 3, 'b': 12}, 'id': 'call_g4RuAijtDcSeM96jXyCuiLSN'}], usage_metadata={'input_tokens': 95, 'output_tokens': 18, 'total_tokens': 113})

As we can see, even though the prompt didn't really suggest a tool call, our LLM made one since it was forced to do so. You can look at the docs for bind_tools() to learn about all the ways to customize how your LLM selects tools.

Tool calls

If tool calls are included in a LLM response, they are attached to the corresponding message or message chunk as a list of tool call objects in the .tool_calls attribute.

Note that chat models can call multiple tools at once.

A ToolCall is a typed dict that includes a tool name, dict of argument values, and (optionally) an identifier. Messages with no tool calls default to an empty list for this attribute.

query = "What is 3 * 12? Also, what is 11 + 49?"

llm_with_tools.invoke(query).tool_calls

[{'name': 'Multiply',
  'args': {'a': 3, 'b': 12},
  'id': 'call_TnadLbWJu9HwDULRb51RNSMw'},
 {'name': 'Add',
  'args': {'a': 11, 'b': 49},
  'id': 'call_Q9vt1up05sOQScXvUYWzSpCg'}]

The .tool_calls attribute should contain valid tool calls. Note that on occasion, model providers may output malformed tool calls (e.g., arguments that are not valid JSON). When parsing fails in these cases, instances of InvalidToolCall are populated in the .invalid_tool_calls attribute. An InvalidToolCall can have a name, string arguments, identifier, and error message.

If desired, output parsers can further process the output. For example, we can convert existing values populated on the .tool_calls attribute back to the original Pydantic class using the PydanticToolsParser:

from langchain_core.output_parsers import PydanticToolsParser

chain = llm_with_tools | PydanticToolsParser(tools=[Multiply, Add])
chain.invoke(query)

API Reference:PydanticToolsParser

[Multiply(a=3, b=12), Add(a=11, b=49)]

Next steps

Now you've learned how to bind tool schemas to a chat model and to call those tools. Next, you can learn more about how to use tools:

• Few shot promting with tools
• Stream tool calls
• Bind model-specific tools
• Pass runtime values to tools
• Pass tool results back to model

You can also check out some more specific uses of tool calling:

• Building tool-using chains and agents
• Getting structured outputs from models