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Understanding Function Calling

Function calling (also known as tool calling) allows LLMs to request information from external services and APIs. This enables your bot to access real-time data and perform actions that aren’t part of its training data. For example, you could give your bot the ability to:
  • Check current weather conditions
  • Look up stock prices
  • Query a database
  • Control smart home devices
  • Schedule appointments
Here’s how it works:
  1. You define functions the LLM can use and register them to the LLM service used in your pipeline
  2. When needed, the LLM requests a function call
  3. Your application executes any corresponding functions
  4. The result is sent back to the LLM
  5. The LLM uses this information in its response

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Implementation

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1. Define Functions

Pipecat provides a standardized FunctionSchema that works across all supported LLM providers. This makes it easy to define functions once and use them with any provider. As a shorthand, you could also bypass specifying a function configuration at all and instead use “direct” functions. Under the hood, these are converted to FunctionSchemas. 
from pipecat.adapters.schemas.function_schema import FunctionSchema
from pipecat.adapters.schemas.tools_schema import ToolsSchema

# Define a function using the standard schema
weather_function = FunctionSchema(
    name="get_current_weather",
    description="Get the current weather in a location",
    properties={
        "location": {
            "type": "string",
            "description": "The city and state, e.g. San Francisco, CA",
        },
        "format": {
            "type": "string",
            "enum": ["celsius", "fahrenheit"],
            "description": "The temperature unit to use.",
        },
    },
    required=["location", "format"]
)

# Create a tools schema with your functions
tools = ToolsSchema(standard_tools=[weather_function])

# Pass this to your LLM context
context = OpenAILLMContext(
    messages=[{"role": "system", "content": "You are a helpful assistant."}],
    tools=tools
)
The ToolsSchema will be automatically converted to the correct format for your LLM provider through adapters.

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Using Direct Functions (Shorthand)

You can bypass specifying a function configuration (as a FunctionSchema or in a provider-specific format) and instead pass the function directly to your ToolsSchema. Pipecat will auto-configure the function, gathering relevant metadata from its signature and docstring. Metadata includes:
  • name
  • description
  • properties (including individual property descriptions)
  • list of required properties
Note that the function signature is a bit different when using direct functions. The first parameter is FunctionCallParams, followed by any others necessary for the function. 
from pipecat.adapters.schemas.tools_schema import ToolsSchema
from pipecat.services.llm_service import FunctionCallParams

# Define a direct function
async def get_current_weather(params: FunctionCallParams, location: str, format: str):
    """Get the current weather.

    Args:
        location: The city and state, e.g. "San Francisco, CA".
        format: The temperature unit to use. Must be either "celsius" or "fahrenheit".
    """
    weather_data = {"conditions": "sunny", "temperature": "75"}
    await params.result_callback(weather_data)

# Create a tools schema, passing your function directly to it
tools = ToolsSchema(standard_tools=[get_current_weather])

# Pass this to your LLM context
context = OpenAILLMContext(
    messages=[{"role": "system", "content": "You are a helpful assistant."}],
    tools=tools
)

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Using Provider-Specific Formats (Alternative)

You can also define functions in the provider-specific format if needed: 
from openai.types.chat import ChatCompletionToolParam

# OpenAI native format
tools = [
    ChatCompletionToolParam(
        type="function",
        function={
            "name": "get_current_weather",
            "description": "Get the current weather",
            "parameters": {
                "type": "object",
                "properties": {
                    "location": {
                        "type": "string",
                        "description": "The city and state, e.g. San Francisco, CA",
                    },
                    "format": {
                        "type": "string",
                        "enum": ["celsius", "fahrenheit"],
                        "description": "The temperature unit to use.",
                    },
                },
                "required": ["location", "format"],
            },
        },
    )
]

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Provider-Specific Custom Tools

Some providers support unique tools that don’t fit the standard function schema. For these cases, you can add custom tools: 
from pipecat.adapters.schemas.tools_schema import AdapterType, ToolsSchema

# Standard functions
weather_function = FunctionSchema(
    name="get_current_weather",
    description="Get the current weather",
    properties={"location": {"type": "string"}},
    required=["location"]
)

# Custom Gemini search tool
gemini_search_tool = {
    "web_search": {
        "description": "Search the web for information"
    }
}

# Create a tools schema with both standard and custom tools
tools = ToolsSchema(
    standard_tools=[weather_function],
    custom_tools={
        AdapterType.GEMINI: [gemini_search_tool]
    }
)
See the provider-specific documentation for details on custom tools and their formats.

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2. Register Function Handlers

Register handlers for your functions using one of these LLM service methods:
  • register_function
  • register_direct_function
Which one you use depends on whether your function is a “direct” function. 
from pipecat.services.llm_service import FunctionCallParams

llm = OpenAILLMService(api_key="your-api-key")

# Main function handler - called to execute the function
async def fetch_weather_from_api(params: FunctionCallParams):
    # Fetch weather data from your API
    weather_data = {"conditions": "sunny", "temperature": "75"}
    await params.result_callback(weather_data)

# Register the function
llm.register_function(
    "get_current_weather",
    fetch_weather_from_api,
)

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3. Create the Pipeline

Include your LLM service in your pipeline with the registered functions: 
# Initialize the LLM context with your function schemas
context = OpenAILLMContext(
    messages=[{"role": "system", "content": "You are a helpful assistant."}],
    tools=tools
)

# Create the context aggregator to collect the user and assistant context
context_aggregator = llm.create_context_aggregator(context)

# Create the pipeline
pipeline = Pipeline([
    transport.input(),               # Input from the transport
    stt,                             # STT processing
    context_aggregator.user(),       # User context aggregation
    llm,                             # LLM processing
    tts,                             # TTS processing
    transport.output(),              # Output to the transport
    context_aggregator.assistant(),  # Assistant context aggregation
])

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Function Handler Details

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FunctionCallParams

The FunctionCallParams object contains all the information needed for handling function calls:
  • params: FunctionCallParams
    • function_name: Name of the called function
    • arguments: Arguments passed by the LLM
    • tool_call_id: Unique identifier for the function call
    • llm: Reference to the LLM service
    • context: Current conversation context
    • result_callback: Async function to return results
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function_name
str
Name of the function being called
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tool_call_id
str
Unique identifier for the function call
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arguments
Mapping[str, Any]
Arguments passed by the LLM to the function
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llm
LLMService
Reference to the LLM service that initiated the call
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context
OpenAILLMContext
Current conversation context
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result_callback
FunctionCallResultCallback
Async callback function to return results

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Handler Structure

Your function handler should:
  1. Receive necessary arguments, either:
    • From params.arguments
    • Directly From function arguments, if using direct functions
  2. Process data or call external services
  3. Return results via params.result_callback(result)
async def fetch_weather_from_api(params: FunctionCallParams):
    try:
        # Extract arguments
        location = params.arguments.get("location")
        format_type = params.arguments.get("format", "celsius")

        # Call external API
        api_result = await weather_api.get_weather(location, format_type)

        # Return formatted result
        await params.result_callback({
            "location": location,
            "temperature": api_result["temp"],
            "conditions": api_result["conditions"],
            "unit": format_type
        })
    except Exception as e:
        # Handle errors
        await params.result_callback({
            "error": f"Failed to get weather: {str(e)}"
        })

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Controlling Function Call Behavior (Advanced)

When returning results from a function handler, you can control how the LLM processes those results using a FunctionCallResultProperties object passed to the result callback.
It can be handy to skip a completion when you have back-to-back function calls. Note, if you skip a completion, you must manually trigger one from the context.

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Properties

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run_llm
Optional[bool]
Controls whether the LLM should generate a response after the function call:
  • True: Run LLM after function call (default if no other function calls in progress)
  • False: Don’t run LLM after function call
  • None: Use default behavior
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on_context_updated
Optional[Callable[[], Awaitable[None]]]
Optional callback that runs after the function result is added to the context

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Example Usage

from pipecat.frames.frames import FunctionCallResultProperties
from pipecat.services.llm_service import FunctionCallParams

async def fetch_weather_from_api(params: FunctionCallParams):
    # Fetch weather data
    weather_data = {"conditions": "sunny", "temperature": "75"}

    # Don't run LLM after this function call
    properties = FunctionCallResultProperties(run_llm=False)

    await params.result_callback(weather_data, properties=properties)

async def query_database(params: FunctionCallParams):
    # Query database
    results = await db.query(params.arguments["query"])

    async def on_update():
        await notify_system("Database query complete")

    # Run LLM after function call and notify when context is updated
    properties = FunctionCallResultProperties(
        run_llm=True,
        on_context_updated=on_update
    )

    await params.result_callback(results, properties=properties)

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Next steps

  • Check out the function calling examples to see a complete example for specific LLM providers.
  • Refer to your LLM provider’s documentation to learn more about their function calling capabilities.