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Overview

Function calling (also known as tool use) allows AI models to:
  • Call external APIs - Fetch real-time data
  • Execute functions - Perform calculations or operations
  • Access databases - Query and retrieve information
  • Interact with systems - Control external services
  • Chain operations - Build complex multi-step workflows
Parallel Function Calling: MegaLLM supports executing multiple functions simultaneously for improved performance.

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How It Works

1

Define Tools

Specify available functions with JSON Schema descriptions
2

AI Decides

Model determines when and how to use tools based on context
3

Execute Functions

Your application executes the requested functions
4

Return Results

Send function results back to the AI
5

AI Responds

Model incorporates results into final response

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Tool Definition Format

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OpenAI Format

{
  "type": "function",
  "function": {
    "name": "get_weather",
    "description": "Get the current weather in a given location",
    "parameters": {
      "type": "object",
      "properties": {
        "location": {
          "type": "string",
          "description": "City and state, e.g. San Francisco, CA"
        },
        "unit": {
          "type": "string",
          "enum": ["celsius", "fahrenheit"],
          "description": "Temperature unit"
        }
      },
      "required": ["location"]
    }
  }
}

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Anthropic Format

{
  "name": "get_weather",
  "description": "Get the current weather in a given location",
  "input_schema": {
    "type": "object",
    "properties": {
      "location": {
        "type": "string",
        "description": "City and state, e.g. San Francisco, CA"
      }
    },
    "required": ["location"]
  }
}

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Request Parameters

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OpenAI Format

ParameterTypeDescription
toolsarrayArray of tool definitions
tool_choicestring/objectControls tool usage: auto, required, none, or specific function

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Tool Choice Options

# Let AI decide (default)
tool_choice="auto"

# Force AI to call a function
tool_choice="required"

# Prevent function calling
tool_choice="none"

# Force specific function
tool_choice={
    "type": "function",
    "function": {"name": "get_weather"}
}

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Examples

  • Python
  • JavaScript
  • TypeScript
from openai import OpenAI
import json

client = OpenAI(
    base_url="https://ai.megallm.io/v1",
    api_key="your-api-key"
)

# Define your function
def get_weather(location: str, unit: str = "celsius"):
    """Get current weather (simulated)"""
    return {
        "location": location,
        "temperature": 22,
        "unit": unit,
        "condition": "sunny"
    }

# Define tools for AI
tools = [
    {
        "type": "function",
        "function": {
            "name": "get_weather",
            "description": "Get current weather in a location",
            "parameters": {
                "type": "object",
                "properties": {
                    "location": {
                        "type": "string",
                        "description": "City and state, e.g. San Francisco, CA"
                    },
                    "unit": {
                        "type": "string",
                        "enum": ["celsius", "fahrenheit"]
                    }
                },
                "required": ["location"]
            }
        }
    }
]

# Initial request
response = client.chat.completions.create(
    model="gpt-4",
    messages=[
        {"role": "user", "content": "What's the weather in London?"}
    ],
    tools=tools,
    tool_choice="auto"
)

message = response.choices[0].message

# Check if AI wants to call a function
if message.tool_calls:
    # Execute the function
    for tool_call in message.tool_calls:
        function_name = tool_call.function.name
        function_args = json.loads(tool_call.function.arguments)

        if function_name == "get_weather":
            result = get_weather(**function_args)

            # Send result back to AI
            follow_up = client.chat.completions.create(
                model="gpt-4",
                messages=[
                    {"role": "user", "content": "What's the weather in London?"},
                    message,
                    {
                        "role": "tool",
                        "tool_call_id": tool_call.id,
                        "content": json.dumps(result)
                    }
                ]
            )

            print(follow_up.choices[0].message.content)

​
Parallel Function Calling

Execute multiple functions simultaneously: 
# AI can call multiple functions at once
response = client.chat.completions.create(
    model="gpt-4",
    messages=[
        {"role": "user", "content": "Compare weather in London, Paris, and Tokyo"}
    ],
    tools=tools
)

# Process multiple tool calls in parallel
if response.choices[0].message.tool_calls:
    import asyncio

    async def execute_tool(tool_call):
        function_name = tool_call.function.name
        function_args = json.loads(tool_call.function.arguments)

        result = await async_function_registry[function_name](**function_args)

        return {
            "role": "tool",
            "tool_call_id": tool_call.id,
            "content": json.dumps(result)
        }

    # Execute all functions in parallel
    tool_results = await asyncio.gather(
        *[execute_tool(tc) for tc in response.choices[0].message.tool_calls]
    )

​
Advanced Patterns

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Function Chaining

Build complex workflows: 
tools = [
    {
        "type": "function",
        "function": {
            "name": "search_products",
            "description": "Search for products",
            "parameters": {
                "type": "object",
                "properties": {
                    "query": {"type": "string"}
                },
                "required": ["query"]
            }
        }
    },
    {
        "type": "function",
        "function": {
            "name": "check_inventory",
            "description": "Check product inventory",
            "parameters": {
                "type": "object",
                "properties": {
                    "product_id": {"type": "string"}
                },
                "required": ["product_id"]
            }
        }
    },
    {
        "type": "function",
        "function": {
            "name": "place_order",
            "description": "Place an order",
            "parameters": {
                "type": "object",
                "properties": {
                    "product_id": {"type": "string"},
                    "quantity": {"type": "integer"}
                },
                "required": ["product_id", "quantity"]
            }
        }
    }
]

# AI will chain functions to complete complex tasks
# Example: "Find blue t-shirts and order 2 if in stock"
# 1. search_products(query="blue t-shirt")
# 2. check_inventory(product_id="...")
# 3. place_order(product_id="...", quantity=2)

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Error Handling

def safe_function_call(function_name, arguments):
    try:
        result = function_registry[function_name](**arguments)
        return {"success": True, "result": result}
    except Exception as e:
        return {
            "success": False,
            "error": str(e),
            "error_type": type(e).__name__
        }

# In tool response
for tool_call in message.tool_calls:
    function_name = tool_call.function.name
    function_args = json.loads(tool_call.function.arguments)

    result = safe_function_call(function_name, function_args)

    messages.append({
        "role": "tool",
        "tool_call_id": tool_call.id,
        "content": json.dumps(result)
    })

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Streaming with Functions

stream = client.chat.completions.create(
    model="gpt-4",
    messages=messages,
    tools=tools,
    stream=True
)

function_call = None

for chunk in stream:
    delta = chunk.choices[0].delta

    if delta.tool_calls:
        if function_call is None:
            function_call = {"id": "", "name": "", "arguments": ""}

        tool_call = delta.tool_calls[0]
        if tool_call.id:
            function_call["id"] = tool_call.id
        if tool_call.function.name:
            function_call["name"] = tool_call.function.name
        if tool_call.function.arguments:
            function_call["arguments"] += tool_call.function.arguments

    if chunk.choices[0].finish_reason == "tool_calls":
        # Execute function
        result = execute_function(function_call)

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Real-World Use Cases

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Database Query Assistant

{
  "type": "function",
  "function": {
    "name": "execute_sql",
    "description": "Execute a SQL query",
    "parameters": {
      "type": "object",
      "properties": {
        "query": {"type": "string"},
        "database": {
          "type": "string",
          "enum": ["users", "products", "orders"]
        }
      },
      "required": ["query", "database"]
    }
  }
}

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API Integration Agent

{
  "type": "function",
  "function": {
    "name": "call_api",
    "description": "Make an HTTP API call",
    "parameters": {
      "type": "object",
      "properties": {
        "method": {
          "type": "string",
          "enum": ["GET", "POST", "PUT", "DELETE"]
        },
        "url": {"type": "string"},
        "headers": {"type": "object"},
        "body": {"type": "object"}
      },
      "required": ["method", "url"]
    }
  }
}

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File System Operations

{
  "type": "function",
  "function": {
    "name": "read_file",
    "description": "Read file contents",
    "parameters": {
      "type": "object",
      "properties": {
        "path": {"type": "string"}
      },
      "required": ["path"]
    }
  }
}

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Best Practices

Write clear, detailed function descriptions. The AI uses these to decide when and how to call functions.
  1. Clear descriptions - Be specific about what each function does
  2. Validate inputs - Always validate function arguments before execution
  3. Handle errors gracefully - Return error information to the AI
  4. Use type hints - Leverage TypeScript/Python types for safety
  5. Rate limit - Implement rate limiting for external API calls
  6. Security - Validate and sanitize all function inputs
  7. Async execution - Use async/await for better performance

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Response Format

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Tool Call Object (OpenAI)

{
  "id": "call_abc123",
  "type": "function",
  "function": {
    "name": "get_weather",
    "arguments": "{\"location\": \"London\", \"unit\": \"celsius\"}"
  }
}

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Tool Response Format

{
  "role": "tool",
  "tool_call_id": "call_abc123",
  "content": "{\"temperature\": 22, \"condition\": \"sunny\"}"
}

​
Model Support

Function calling is supported by these models:
  • OpenAI: gpt-4, gpt-4-turbo, gpt-3.5-turbo
  • Anthropic: claude-3.5-sonnet, claude-opus-4, claude-sonnet-4
  • Others: Check Models page for full list