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Function calling for open models Stay organized with collections Save and categorize content based on your preferences.

Function calling lets you define custom functions and provide LLMs with the capability to call them to retrieve real-time information or interact with external systems like SQL databases or customer service tools.

For more conceptual information about function calling, see Introduction to function calling.

Use function calling

The following samples show how to use function calling.

Python

Before trying this sample, follow the Python setup instructions in the Agent Platform quickstart using client libraries.

To authenticate to Agent Platform, set up Application Default Credentials. For more information, see Set up authentication for a local development environment.

Before running this sample, make sure to set the OPENAI_BASE_URL environment variable. For more information, see Authentication and credentials.

from openai import OpenAI
client = OpenAI()

response = client.chat.completions.create(
  model="MODEL",
  messages=[
    {"role": "user", "content": "CONTENT"}
  ],
  tools=[
    {
      "type": "function",
      "function": {
        "name": "FUNCTION_NAME",
        "description": "FUNCTION_DESCRIPTION",
        "parameters": PARAMETERS_OBJECT,
      }
    }
  ],
  tool_choice="auto",
)

REST

Before using any of the request data, make the following replacements:

HTTP method and URL:

POST https://LOCATION-aiplatform.googleapis.com/v1/projects/PROJECT_ID/locations/LOCATION/endpoints/openapi/chat/completions

Request JSON body:

{
  "model": "MODEL",
  "messages": [
    {
      "role": "user",
      "content": "CONTENT"
    }
  ],
  "tools": [
    {
      "type": "function",
      "function": {
        "name": "FUNCTION_NAME",
        "description": "FUNCTION_DESCRIPTION",
        "parameters": PARAMETERS_OBJECT
      }
    }
  ],
  "tool_choice": "auto"
}

To send your request, choose one of these options:

curl

Note: The following command assumes that you have logged in to the gcloud CLI with your user account by running gcloud init or gcloud auth login , or by using Cloud Shell, which automatically logs you into the gcloud CLI . You can check the currently active account by running gcloud auth list.

Save the request body in a file named request.json, and execute the following command:

curl -X POST \
-H "Authorization: Bearer $(gcloud auth print-access-token)" \
-H "Content-Type: application/json; charset=utf-8" \
-d @request.json \
"https://LOCATION-aiplatform.googleapis.com/v1/projects/PROJECT_ID/locations/LOCATION/endpoints/openapi/chat/completions"

PowerShell

Note: The following command assumes that you have logged in to the gcloud CLI with your user account by running gcloud init or gcloud auth login . You can check the currently active account by running gcloud auth list.

Save the request body in a file named request.json, and execute the following command:

$cred = gcloud auth print-access-token
$headers = @{ "Authorization" = "Bearer $cred" }

Invoke-WebRequest `
-Method POST `
-Headers $headers `
-ContentType: "application/json; charset=utf-8" `
-InFile request.json `
-Uri "https://LOCATION-aiplatform.googleapis.com/v1/projects/PROJECT_ID/locations/LOCATION/endpoints/openapi/chat/completions" | Select-Object -Expand Content

You should receive a successful status code (2xx) and an empty response.

Example

The following is the complete output you could expect after using the get_current_weather function to fetch meteorological information.

Python

from openai import OpenAI
client = OpenAI()

response = client.chat.completions.create(
  model="qwen/qwen3-next-80b-a3b-instruct-maas",
  messages=[
    {
      "role": "user",
      "content": "Which city has a higher temperature, Boston or new Delhi and by how much in F?"
    },
    {
      "role": "assistant",
      "content": "I'll check the current temperatures for Boston and New Delhi in Fahrenheit and compare them. I'll call the weather function for both cities.",
      "tool_calls": [{"function":{"arguments":"{\"location\":\"Boston, MA\",\"unit\":\"fahrenheit\"}","name":"get_current_weather"},"id":"get_current_weather","type":"function"},{"function":{"arguments":"{\"location\":\"New Delhi, India\",\"unit\":\"fahrenheit\"}","name":"get_current_weather"},"id":"get_current_weather","type":"function"}]
    },
    {
      "role": "tool",
      "content": "The temperature in Boston is 75 degrees Fahrenheit.",
      "tool_call_id": "get_current_weather"
    },
    {
      "role": "tool",
      "content": "The temperature in New Delhi is 50 degrees Fahrenheit.",
      "tool_call_id": "get_current_weather"
    }
  ],
  tools=[
    {
      "type": "function",
      "function": {
        "name": "get_current_weather",
        "description": "Get the current weather in a given location",
        "parameters": {
          "type": "object",
          "properties": {
            "location": {
              "type": "string",
              "description": "The city and state, e.g. San Francisco, CA"
            },
            "unit": {
              "type": "string",
              "enum": ["celsius", "fahrenheit"]
            }
          },
          "required": ["location"]
        }
      }
    }
  ],
  tool_choice="auto"
)

curl

curl -X POST \
-H "Authorization: Bearer $(gcloud auth print-access-token)" \
-H "Content-Type: application/json" \
https://us-central1-aiplatform.googleapis.com/v1/projects/sample-project/locations/us-central1/endpoints/openapi/chat/completions -d \
'{
  "model": "qwen/qwen3-next-80b-a3b-instruct-maas",
  "messages": [
    {
      "role": "user",
      "content": "Which city has a higher temperature, Boston or new Delhi and by how much in F?"
    },
    {
      "role": "assistant",
      "content": "I'll check the current temperatures for Boston and New Delhi in Fahrenheit and compare them. I'll call the weather function for both cities.",
      "tool_calls": [{"function":{"arguments":"{\"location\":\"Boston, MA\",\"unit\":\"fahrenheit\"}","name":"get_current_weather"},"id":"get_current_weather","type":"function"},{"function":{"arguments":"{\"location\":\"New Delhi, India\",\"unit\":\"fahrenheit\"}","name":"get_current_weather"},"id":"get_current_weather","type":"function"}]
    },
    {
      "role": "tool",
      "content": "The temperature in Boston is 75 degrees Fahrenheit.",
      "tool_call_id": "get_current_weather"
    },
    {
      "role": "tool",
      "content": "The temperature in New Delhi is 50 degrees Fahrenheit.",
      "tool_call_id": "get_current_weather"
    }
  ],
  "tools": [
    {
      "type": "function",
      "function": {
        "name": "get_current_weather",
        "description": "Get the current weather in a given location",
        "parameters": {
          "type": "object",
          "properties": {
            "location": {
              "type": "string",
              "description": "The city and state, e.g. San Francisco, CA"
            },
            "unit": {
              "type": "string",
              "enum": ["celsius", "fahrenheit"]
            }
          },
          "required": ["location"]
        }
      }
    }
  ],
  "tool_choice": "auto"
}'
After receiving the information retrieved through the calling the external `get_current_weather` function, the model can synthesize the information from the two `tool` responses and answer the user's question. The following is an example of what that model output could look like:
{
 "choices": [
  {
   "finish_reason": "stop",
   "index": 0,
   "logprobs": null,
   "message": {
    "content": "Based on the current weather data:\n\n- **Boston, MA**: 75F
    \n- **New Delhi, India**: 50F  \n\n**Comparison**:
    \nBoston is **25F warmer** than New Delhi.  \n\n**Answer**:
    \nBoston has a higher temperature than New Delhi by 25 degrees Fahrenheit.",
    "role": "assistant"
   }
  }
 ],
 "created": 1750450289,
 "id": "2025-06-20|13:11:29.240295-07|6.230.75.101|-987540014",
 "model": "qwen/qwen3-next-80b-a3b-instruct-maas",
 "object": "chat.completion",
 "system_fingerprint": "",
 "usage": {
  "completion_tokens": 66,
  "prompt_tokens": 217,
  "total_tokens": 283
 }
}

Model-specific guidance

The following sections provide model-specific guidance for function calling.

DeepSeek

DeepSeek models don't perform as well on function calling if you use a system prompt. For optimal performance, omit the system prompt when using function calling with DeepSeek models.

Llama

meta/llama3-405b-instruct-maas doesn't support tool_choice = 'required'.

OpenAI

When using openai/gpt-oss-120b-instruct-maas and openai/gpt-oss-20b-instruct-maas, place your tool definitions in the system prompt for optimal performance. For example:

{"messages": [
    {"role": "system", "content": "You are a helpful assistant with access to the following functions. Use them if required:\n..."},
    {"role": "user", "content": "What's the weather like in Boston?"},
    ...
]}

These models don't support tool_choice = 'required' or named tool calling.

Qwen

Qwen models perform best when tool_choice is explicitly set to auto or none. If tool_choice is unset, the model might not perform as well.

What's next

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Except as otherwise noted, the content of this page is licensed under the Creative Commons Attribution 4.0 License, and code samples are licensed under the Apache 2.0 License. For details, see the Google Developers Site Policies. Java is a registered trademark of Oracle and/or its affiliates.

Last updated 2026-08-19 UTC.

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