Examples of Consecutive Tool Calls

Cicada MultiModalModel Implementation

This example shows how to use ToolRegistry with the Cicada MultiModalModel

One nice thing here is that it handles consecutive tool calls automatically.

import os
from cicada.core.model import MultiModalModel
from cicada.core.utils import cprint
from toolregistry import ToolRegistry

# Initialize Cicada model
model_name = os.getenv("MODEL", "deepseek-v3")
stream = os.getenv("STREAM", "True").lower() == "true"

llm = MultiModalModel(
    api_key="your-api-key",
    api_base_url="https://api.deepseek.com/",
    model_name=model_name,
    stream=stream,
)

# Initialize ToolRegistry
tool_registry = ToolRegistry()

# Register tools
@tool_registry.register
def get_weather(location: str):
    return f"Weather in {location}: Sunny, 25°C"

@tool_registry.register
def c_to_f(celsius: float) -> float:
    fahrenheit = (celsius * 1.8) + 32
    return f"{celsius} celsius degree == {fahrenheit} fahrenheit degree"

# Query the model with tools
response = llm.query(
    "What's the temperature of Shanghai, reply using Fahrenheit?",
    tools=tool_registry,
    stream=llm.stream,
)
print(response["content"])
cprint(json.dumps(response,indent=2))

response reads

The current temperature in Shanghai is 77°F.

{
  "content": "The current temperature in Shanghai is 77\u00b0F.",
  "formatted_response": "[Response]: The current temperature in Shanghai is 77\u00b0F.",
  "tool_chain": [
    {
      "content": "",
      "tool_responses": {
        "call_mOnZUGqQhhmvj0lIKEUcncAn": "Weather in Shanghai: Sunny, 25\u00b0C"
      }
    },
    {
      "content": "",
      "tool_responses": {
        "call_lDQ0Nq0HRXHnmhoX6PJzmBHo": "25.0 celsius degree == 77.0 fahrenheit degree"
      }
    }
  ]
}

OpenAI Client Implementation

This example demonstrates integration with OpenAI’s API. As we mentioned in previous article, we shall handle consecutive tool calls manually.

import os

from dotenv import load_dotenv
from openai import OpenAI

# Load environment variables from .env file
load_dotenv()
from toolregistry import ToolRegistry

# Initialize ToolRegistry
tool_registry = ToolRegistry()


# Register tools using decorator
@tool_registry.register
def get_weather(location: str):
    """Get the weather for a specific location"""
    return f"Weather in {location}: Sunny, 25°C"


@tool_registry.register
def c_to_f(celsius: float) -> float:
    """Convert Celsius to Fahrenheit"""
    fahrenheit = (celsius * 1.8) + 32
    return f"{celsius} celsius degree == {fahrenheit} fahrenheit degree"


# Set up OpenAI client
client = OpenAI(
    api_key=os.getenv("API_KEY", "your-api-key"),
    base_url=os.getenv("BASE_URL", "https://api.deepseek.com/"),
)


messages = [
    {
        "role": "user",
        "content": "What's the temperature of Shanghai, reply using Fahrenheit?",
    }
]
# Make the chat completion request
response = client.chat.completions.create(
    model="deepseek-v3",
    messages=messages,
    tools=tool_registry.get_tools_json(),
    tool_choice="auto",
)


def handle_tool_calls(response, messages):
    """Handle tool calls in a loop until no more tool calls are needed"""
    while response.choices[0].message.tool_calls:
        tool_calls = response.choices[0].message.tool_calls
        print("Tool calls:", tool_calls)

        # Execute tool calls
        tool_responses = tool_registry.execute_tool_calls(tool_calls)

        # Construct assistant messages with results
        assistant_tool_messages = tool_registry.recover_tool_call_assistant_message(
            tool_calls, tool_responses
        )

        messages.extend(assistant_tool_messages)

        # Send the results back to the model
        response = client.chat.completions.create(
            model="deepseek-v3",
            messages=messages,
            tools=tool_registry.get_tools_json(),
            tool_choice="auto",
        )
    return response


# Handle tool calls using the new function (without iteration limit)
response = handle_tool_calls(response, messages)

# Print final response
if response.choices[0].message.content:
    print(response.choices[0].message.content)

response reads

Tool calls: [ChatCompletionMessageToolCall(id='call_qcb10odnylzts5qhae9jvt7v', function=Function(arguments='{"location":"Shanghai"}', name='get_weather'), type='function', index=0)]
location='Shanghai'
Tool calls: [ChatCompletionMessageToolCall(id='call_ew7cm09z893u57102aeny2zp', function=Function(arguments='{"celsius":25}', name='c_to_f'), type='function', index=0)]
celsius=25.0
The temperature in Shanghai is 77°F.