How to stream tool calls

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. Before we start let's define our tools and our model.

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

import os
from getpass import getpass

from langchain_openai import ChatOpenAI

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

llm = ChatOpenAI(model="gpt-3.5-turbo-0125", temperature=0)
llm_with_tools = llm.bind_tools(tools)

API Reference:ChatOpenAI

Now let's define our query and stream our output:

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

async for chunk in llm_with_tools.astream(query):
    print(chunk.tool_call_chunks)

[]
[{'name': 'Multiply', 'args': '', 'id': 'call_3aQwTP9CYlFxwOvQZPHDu6wL', 'index': 0}]
[{'name': None, 'args': '{"a"', 'id': None, 'index': 0}]
[{'name': None, 'args': ': 3, ', 'id': None, 'index': 0}]
[{'name': None, 'args': '"b": 1', 'id': None, 'index': 0}]
[{'name': None, 'args': '2}', 'id': None, 'index': 0}]
[{'name': 'Add', 'args': '', 'id': 'call_SQUoSsJz2p9Kx2x73GOgN1ja', 'index': 1}]
[{'name': None, 'args': '{"a"', 'id': None, 'index': 1}]
[{'name': None, 'args': ': 11,', 'id': None, 'index': 1}]
[{'name': None, 'args': ' "b": ', 'id': None, 'index': 1}]
[{'name': None, 'args': '49}', 'id': None, 'index': 1}]
[]

Note that adding 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:

first = True
async for chunk in llm_with_tools.astream(query):
    if first:
        gathered = chunk
        first = False
    else:
        gathered = gathered + chunk

    print(gathered.tool_call_chunks)

[]
[{'name': 'Multiply', 'args': '', 'id': 'call_AkL3dVeCjjiqvjv8ckLxL3gP', 'index': 0}]
[{'name': 'Multiply', 'args': '{"a"', 'id': 'call_AkL3dVeCjjiqvjv8ckLxL3gP', 'index': 0}]
[{'name': 'Multiply', 'args': '{"a": 3, ', 'id': 'call_AkL3dVeCjjiqvjv8ckLxL3gP', 'index': 0}]
[{'name': 'Multiply', 'args': '{"a": 3, "b": 1', 'id': 'call_AkL3dVeCjjiqvjv8ckLxL3gP', 'index': 0}]
[{'name': 'Multiply', 'args': '{"a": 3, "b": 12}', 'id': 'call_AkL3dVeCjjiqvjv8ckLxL3gP', 'index': 0}]
[{'name': 'Multiply', 'args': '{"a": 3, "b": 12}', 'id': 'call_AkL3dVeCjjiqvjv8ckLxL3gP', 'index': 0}, {'name': 'Add', 'args': '', 'id': 'call_b4iMiB3chGNGqbt5SjqqD2Wh', 'index': 1}]
[{'name': 'Multiply', 'args': '{"a": 3, "b": 12}', 'id': 'call_AkL3dVeCjjiqvjv8ckLxL3gP', 'index': 0}, {'name': 'Add', 'args': '{"a"', 'id': 'call_b4iMiB3chGNGqbt5SjqqD2Wh', 'index': 1}]
[{'name': 'Multiply', 'args': '{"a": 3, "b": 12}', 'id': 'call_AkL3dVeCjjiqvjv8ckLxL3gP', 'index': 0}, {'name': 'Add', 'args': '{"a": 11,', 'id': 'call_b4iMiB3chGNGqbt5SjqqD2Wh', 'index': 1}]
[{'name': 'Multiply', 'args': '{"a": 3, "b": 12}', 'id': 'call_AkL3dVeCjjiqvjv8ckLxL3gP', 'index': 0}, {'name': 'Add', 'args': '{"a": 11, "b": ', 'id': 'call_b4iMiB3chGNGqbt5SjqqD2Wh', 'index': 1}]
[{'name': 'Multiply', 'args': '{"a": 3, "b": 12}', 'id': 'call_AkL3dVeCjjiqvjv8ckLxL3gP', 'index': 0}, {'name': 'Add', 'args': '{"a": 11, "b": 49}', 'id': 'call_b4iMiB3chGNGqbt5SjqqD2Wh', 'index': 1}]
[{'name': 'Multiply', 'args': '{"a": 3, "b": 12}', 'id': 'call_AkL3dVeCjjiqvjv8ckLxL3gP', 'index': 0}, {'name': 'Add', 'args': '{"a": 11, "b": 49}', 'id': 'call_b4iMiB3chGNGqbt5SjqqD2Wh', 'index': 1}]

print(type(gathered.tool_call_chunks[0]["args"]))

<class 'str'>

And below we accumulate tool calls to demonstrate partial parsing:

first = True
async for chunk in llm_with_tools.astream(query):
    if first:
        gathered = chunk
        first = False
    else:
        gathered = gathered + chunk

    print(gathered.tool_calls)

[]
[]
[{'name': 'Multiply', 'args': {}, 'id': 'call_4p0D4tHVXSiae9Mu0e8jlI1m'}]
[{'name': 'Multiply', 'args': {'a': 3}, 'id': 'call_4p0D4tHVXSiae9Mu0e8jlI1m'}]
[{'name': 'Multiply', 'args': {'a': 3, 'b': 1}, 'id': 'call_4p0D4tHVXSiae9Mu0e8jlI1m'}]
[{'name': 'Multiply', 'args': {'a': 3, 'b': 12}, 'id': 'call_4p0D4tHVXSiae9Mu0e8jlI1m'}]
[{'name': 'Multiply', 'args': {'a': 3, 'b': 12}, 'id': 'call_4p0D4tHVXSiae9Mu0e8jlI1m'}]
[{'name': 'Multiply', 'args': {'a': 3, 'b': 12}, 'id': 'call_4p0D4tHVXSiae9Mu0e8jlI1m'}, {'name': 'Add', 'args': {}, 'id': 'call_54Hx3DGjZitFlEjgMe1DYonh'}]
[{'name': 'Multiply', 'args': {'a': 3, 'b': 12}, 'id': 'call_4p0D4tHVXSiae9Mu0e8jlI1m'}, {'name': 'Add', 'args': {'a': 11}, 'id': 'call_54Hx3DGjZitFlEjgMe1DYonh'}]
[{'name': 'Multiply', 'args': {'a': 3, 'b': 12}, 'id': 'call_4p0D4tHVXSiae9Mu0e8jlI1m'}, {'name': 'Add', 'args': {'a': 11}, 'id': 'call_54Hx3DGjZitFlEjgMe1DYonh'}]
[{'name': 'Multiply', 'args': {'a': 3, 'b': 12}, 'id': 'call_4p0D4tHVXSiae9Mu0e8jlI1m'}, {'name': 'Add', 'args': {'a': 11, 'b': 49}, 'id': 'call_54Hx3DGjZitFlEjgMe1DYonh'}]
[{'name': 'Multiply', 'args': {'a': 3, 'b': 12}, 'id': 'call_4p0D4tHVXSiae9Mu0e8jlI1m'}, {'name': 'Add', 'args': {'a': 11, 'b': 49}, 'id': 'call_54Hx3DGjZitFlEjgMe1DYonh'}]

print(type(gathered.tool_calls[0]["args"]))

<class 'dict'>