## See Also

- [from_provider Guide](https://python.useinstructor.com/concepts/from_provider/#async-clients) - Async client setup
- [Batch Processing](https://python.useinstructor.com/examples/batch_job_oai/index.md) - Process multiple requests efficiently
- [Iterable](https://python.useinstructor.com/concepts/iterable/index.md) - Extract multiple objects
- [Lists](https://python.useinstructor.com/concepts/lists/index.md) - Working with collections

# Parallel Tools

Parallel Tool Calling is a feature that allows you to call multiple functions in a single request.

Experimental Feature

Parallel Tool Calling is supported by Google, OpenAI, and Anthropic. Make sure to use the equivalent parallel tool `mode` for your client.

## Understanding Parallel Tool Calling

Parallel Function Calling helps you to significantly reduce the latency of your application without having to build a parent schema as a wrapper around these tool calls.

```python
from __future__ import annotations

import instructor

from typing import Iterable, Literal
from pydantic import BaseModel


class Weather(BaseModel):
    location: str
    units: Literal["imperial", "metric"]


class GoogleSearch(BaseModel):
    query: str


client = instructor.from_provider(
    "openai/gpt-4.1-mini",
    mode=instructor.Mode.PARALLEL_TOOLS,
)
function_calls = client.create(
    messages=[
        {"role": "system", "content": "You must always use tools"},
        {
            "role": "user",
            "content": "What is the weather in toronto and dallas and who won the super bowl?",
        },
    ],
    response_model=Iterable[Weather | GoogleSearch],
)

for fc in function_calls:
    print(fc)
    #> location='Toronto' units='metric'
    #> location='Dallas' units='metric'
    #> query='who won the super bowl 2023'
```

```python
from typing import Iterable, Literal

import instructor
from pydantic import BaseModel

try:
    import vertexai
    import vertexai.generative_models as gm
    from instructor import from_vertexai
except ImportError:
    vertexai = None
    gm = None
    from_vertexai = None


class Weather(BaseModel):
    location: str
    units: Literal["imperial", "metric"]


class GoogleSearch(BaseModel):
    query: str


if from_vertexai is not None and vertexai is not None and gm is not None:
    vertexai.init(project="your-project-id", location="us-central1")
    client = from_vertexai(
        gm.GenerativeModel("gemini-2.5-flash"),
        mode=instructor.Mode.PARALLEL_TOOLS,
    )
    function_calls = client.create(
        messages=[
            {
                "role": "user",
                "content": "What is the weather in toronto and dallas and who won the super bowl?",
            },
        ],
        response_model=Iterable[Weather | GoogleSearch],
    )

    for fc in function_calls:
        print(fc)
        #> location='Toronto' units='metric'
        #> location='Dallas' units='imperial'
        #> query='who won the super bowl'
```

```python
import instructor
from typing import Iterable, Literal
from pydantic import BaseModel


class Weather(BaseModel):
    location: str
    units: Literal["imperial", "metric"]


class GoogleSearch(BaseModel):
    query: str


client = instructor.from_provider(
    "anthropic/claude-3-7-sonnet-latest",
    mode=instructor.Mode.PARALLEL_TOOLS,
)
function_calls = client.create(
    messages=[
        {"role": "system", "content": "You must always use tools"},
        {
            "role": "user",
            "content": "What is the weather in toronto and dallas and who won the super bowl?",
        },
    ],
    response_model=Iterable[Weather | GoogleSearch],
)

for fc in function_calls:
    print(fc)
    #> location='Toronto' units='metric'
```

We need to set the response model to `Iterable[Weather | GoogleSearch]` to indicate that the response will be a list of `Weather` and `GoogleSearch` objects.

This is necessary because the response will be a list of objects, and we need to specify the types of the objects in the list. This returns an iterable which you can then iterate over