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How to use a chat model to call tools

info

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.

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.

However, tool calling goes beyond structured output since you can pass responses to caled 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 will show you how to use them.

Prerequisites

This guide assumes familiarity with the following concepts:

Passing tools to LLMs​

Chat models that support tool calling features implement a .bindTools() 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.

Let’s walk through a few examples:

Pick your chat model:

Install dependencies

tip

See this section for general instructions on installing integration packages.

yarn add @langchain/anthropic @langchain/core

Add environment variables

ANTHROPIC_API_KEY=your-api-key

Instantiate the model

import { ChatAnthropic } from "@langchain/anthropic";

const llm = new ChatAnthropic({
  model: "claude-3-sonnet-20240229",
  temperature: 0
});

A number of models implement helper methods that will take care of formatting and binding different function-like objects to the model. Let’s take a look at how we might take the following Zod function schema and get different models to invoke it:

import { z } from "zod";

/**
 * Note that the descriptions here are crucial, as they will be passed along
 * to the model along with the class name.
 */
const calculatorSchema = z.object({
  operation: z
    .enum(["add", "subtract", "multiply", "divide"])
    .describe("The type of operation to execute."),
  number1: z.number().describe("The first number to operate on."),
  number2: z.number().describe("The second number to operate on."),
});

We can use the .bindTools() method to handle the conversion from LangChain tool to our model provider’s specific format and bind it to the model (i.e., passing it in each time the model is invoked). Let’s create a DynamicStructuredTool implementing a tool based on the above schema, then bind it to the model:

import { ChatOpenAI } from "@langchain/openai";
import { DynamicStructuredTool } from "@langchain/core/tools";

const calculatorTool = new DynamicStructuredTool({
  name: "calculator",
  description: "Can perform mathematical operations.",
  schema: calculatorSchema,
  func: async ({ operation, number1, number2 }) => {
    // Functions must return strings
    if (operation === "add") {
      return `${number1 + number2}`;
    } else if (operation === "subtract") {
      return `${number1 - number2}`;
    } else if (operation === "multiply") {
      return `${number1 * number2}`;
    } else if (operation === "divide") {
      return `${number1 / number2}`;
    } else {
      throw new Error("Invalid operation.");
    }
  },
});

const llmWithTools = llm.bindTools([calculatorTool]);

Now, let’s invoke it! We expect the model to use the calculator to answer the question:

const res = await llmWithTools.invoke("What is 3 * 12");

console.log(res.tool_calls);
[
  {
    name: "calculator",
    args: { operation: "multiply", number1: 3, number2: 12 },
    id: "call_Ri9s27J17B224FEHrFGkLdxH"
  }
]
tip

See a LangSmith trace for the above here.

We can see that the response message contains a tool_calls field when the model decides to call the tool. This will be in LangChain’s standardized format.

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, the message will contain instances of of InvalidToolCall objects in the .invalid_tool_calls attribute. An InvalidToolCall can have a name, string arguments, identifier, and error message.

Streaming​

When tools are called in a streaming context, message chunks will be populated with tool call chunk objects in a list via the .tool_call_chunks attribute. A ToolCallChunk includes optional string fields for the tool name, args, and id, and includes an optional integer field index that can be used to join chunks together. Fields are optional because portions of a tool call may be streamed across different chunks (e.g., a chunk that includes a substring of the arguments may have null values for the tool name and id).

Because message chunks inherit from their parent message class, an AIMessageChunk with tool call chunks will also include .tool_calls and .invalid_tool_calls fields. These fields are parsed best-effort from the message’s tool call chunks.

Note that not all providers currently support streaming for tool calls. If this is the case for your specific provider, the model will yield a single chunk with the entire call when you call .stream().

const stream = await llmWithTools.stream("What is 308 / 29");

for await (const chunk of stream) {
  console.log(chunk.tool_call_chunks);
}
[
  {
    name: "calculator",
    args: "",
    id: "call_rGqPR1ivppYUeBb0iSAF8HGP",
    index: 0
  }
]
[ { name: undefined, args: '{"', id: undefined, index: 0 } ]
[ { name: undefined, args: "operation", id: undefined, index: 0 } ]
[ { name: undefined, args: '":"', id: undefined, index: 0 } ]
[ { name: undefined, args: "divide", id: undefined, index: 0 } ]
[ { name: undefined, args: '","', id: undefined, index: 0 } ]
[ { name: undefined, args: "number", id: undefined, index: 0 } ]
[ { name: undefined, args: "1", id: undefined, index: 0 } ]
[ { name: undefined, args: '":', id: undefined, index: 0 } ]
[ { name: undefined, args: "308", id: undefined, index: 0 } ]
[ { name: undefined, args: ',"', id: undefined, index: 0 } ]
[ { name: undefined, args: "number", id: undefined, index: 0 } ]
[ { name: undefined, args: "2", id: undefined, index: 0 } ]
[ { name: undefined, args: '":', id: undefined, index: 0 } ]
[ { name: undefined, args: "29", id: undefined, index: 0 } ]
[ { name: undefined, args: "}", id: undefined, index: 0 } ]
[]

Note that using the concat method on message chunks will merge their corresponding tool call chunks. This is the principle by which LangChain’s various tool output parsers support streaming.

For example, below we accumulate tool call chunks:

const streamWithAccumulation = await llmWithTools.stream(
  "What is 32993 - 2339"
);

let final;
for await (const chunk of streamWithAccumulation) {
  if (!final) {
    final = chunk;
  } else {
    final = final.concat(chunk);
  }
}

console.log(final.tool_calls);
[
  {
    name: "calculator",
    args: { operation: "subtract", number1: 32993, number2: 2339 },
    id: "call_WMhL5X0fMBBZPNeyUZY53Xuw"
  }
]

Few shotting with tools​

You can give the model examples of how you would like tools to be called in order to guide generation by inputting manufactured tool call turns. For example, given the above calculator tool, we could define a new operator, 🦜. Let’s see what happens when we use it naively:

const res = await llmWithTools.invoke("What is 3 🦜 12");

console.log(res.content);
console.log(res.tool_calls);
It seems like you've used an emoji (🦜) in your expression, which I'm not familiar with in a mathematical context. Could you clarify what operation you meant by using the parrot emoji? For example, did you mean addition, subtraction, multiplication, or division?
[]

It doesn’t quite know how to interpret 🦜 as an operation. Now, let’s try giving it an example in the form of a manufactured messages to steer it towards divide:

import { HumanMessage, AIMessage, ToolMessage } from "@langchain/core/messages";

const res = await llmWithTools.invoke([
  new HumanMessage("What is 333382 🦜 1932?"),
  new AIMessage({
    content: "",
    tool_calls: [
      {
        id: "12345",
        name: "calulator",
        args: {
          number1: 333382,
          number2: 1932,
          operation: "divide",
        },
      },
    ],
  }),
  new ToolMessage({
    tool_call_id: "12345",
    content: "The answer is 172.558.",
  }),
  new AIMessage("The answer is 172.558."),
  new HumanMessage("What is 3 🦜 12"),
]);
console.log(res.tool_calls);
[
  {
    name: "calculator",
    args: { operation: "divide", number1: 3, number2: 12 },
    id: "call_BDuJv8QkDZ7N7Wsd6v5VDeVa"
  }
]

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