Tool Calling with Model APIs

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Goals

• Use tool calling to build your own AI agent with Friendli Model APIs
• Check out the examples below to see how you can interact with state-of-the-art language models while letting them search the web, run Python code, etc.
• Feel free to make your own custom tools!

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Getting started

1. Head to Friendli Suite, and create an account.
2. Grab a Personal API Key to use Friendli Model APIs within an agent.

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Step 1. Playground UI

1. On your left sidebar, click the ‘Model APIs’ option to access the playground page.
2. You will see models that can be used as Model APIs. Select the one you want and click the endpoint.
3. Click ‘Tools’ button, select Search tool, and enter a query to see the response. 😀

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Step 2. Tool calling

1. Add your input as a user role message.
2. Add the web:search tool to the tools option.

# pip install friendli

import os
from friendli import SyncFriendli

with SyncFriendli(
    token=os.getenv("API_KEY", ""),
) as friendli:
    res = friendli.serverless.tool_assisted_chat.complete(
        model="zai-org/GLM-5.2",
        messages=[
            {
                "role": "user",
                "content": "Find information on the popular movies currently showing in theaters and provide their ratings.",
            },
        ],
        tools=[{"type": "web:search"}],
        max_tokens=200,
    )

    print(res)

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Step 3. Multiple tool calling

• math:calculator (tool for calculating arithmetic operations)
• web:search (tool for retrieving data through the web search)
• code:python-interpreter (tool for writing and executing python code)

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Example answer sheet

Prompt: My annual salary is $ 100k. How long it will take to buy a house in San Francisco Bay Area? (`web:search` & `math:calculator` used)

Answer: Based on the web search results, the median price of an existing single-family home in the Bay Area is around $1.25 million.
Using a calculator to calculate how long it would take to buy a house in the San Francisco Bay Area with an annual salary of $100,000, we get:
$1,200,000 (house price) / $100,000 (annual salary) = 12 years
So, it would take approximately 12 years to buy a house in the San Francisco Bay Area with an annual salary of $100,000,
assuming you save your entire salary each year and don't consider other factors like interest rates, taxes, and living expenses.

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Step 4. Build a custom tool

1. Define a function for using as a custom tool

def get_temperature(location: str) -> int:
    """Mock function that returns the city temperature"""
    if "new york" in location.lower():
        return 45
    if "san francisco" in location.lower():
        return 72
    return 30

2. Send a function calling inference request
   1. Add your input as a user role message.
   2. The information about the custom function (e.g., get_temperature) goes into the tools option. JSON schema describes the function’s parameters.
   3. The response includes the arguments field, which are values extracted from the user’s input that can be used as parameters of the custom function.
   # pip install friendli
   
   import os
   from friendli import SyncFriendli
   
   token = os.environ.get("API_KEY") or "YOUR_API_KEY"
   client= SyncFriendli(token=token)
   user_prompt = "I live in New York. What should I wear for today's weather?"
   
   messages = [
       {
           "role": "user",
           "content": user_prompt,
       },
   ]
   
   tools=[
       {
           "type": "function",
           "function": {
               "name": "get_temperature",
               "description": "Get the temperature information in a given location.",
               "parameters": {
                   "type": "object",
                   "properties": {
                       "location": {
                           "type": "string",
                           "description": "The name of current location e.g., New York",
                       },
                   },
               },
           },
       },
   ]
   
   chat = client.serverless.chat.complete(
       model="zai-org/GLM-5.2",
       messages=messages,
       tools=tools,
       temperature=0,
       frequency_penalty=1,
   )
   
   print(chat)
   
3. Generate the final response using the tool calling results
   1. Add the tool_calls response as an assistant role message.
   2. Add the result obtained by calling the get_temperature function as a tool message to the Chat API again.
   import json
   
   func_kwargs = json.loads(chat.choices[0].message.tool_calls[0].function.arguments)
   temperature_info = get_temperature(**func_kwargs)
   
   messages.append(
       {
           "role": "assistant",
           "tool_calls": [
               tool_call.model_dump()
               for tool_call in chat.choices[0].message.tool_calls
           ]
       }
   )
   messages.append(
       {
           "role": "tool",
           "content": str(temperature_info),
           "tool_call_id": chat.choices[0].message.tool_calls[0].id
       }
   )
   
   chat_w_info = client.serverless.chat.complete(
       model="zai-org/GLM-5.2",
       tools=tools,
       messages=messages,
   )
   
   for choice in chat_w_info.choices:
       print(choice.message.content)
   

• Complete Code Snippet
  # pip install friendli
  
  import json
  import os
  from friendli import SyncFriendli
  
  token = os.environ.get("API_KEY") or "YOUR_API_KEY"
  client = SyncFriendli(token=token)
  user_prompt = "I live in New York. What should I wear for today's weather?"
  
  messages = [
      {
          "role": "user",
          "content": user_prompt,
      },
  ]
  
  tools=[
      {
          "type": "function",
          "function": {
              "name": "get_temperature",
              "description": "Get the temperature information in a given location.",
              "parameters": {
                  "type": "object",
                  "properties": {
                      "location": {
                          "type": "string",
                          "description": "The name of current location e.g., New York",
                      },
                  },
              },
          },
      },
  ]
  
  chat = client.serverless.chat.complete(
      model="zai-org/GLM-5.2",
      messages=messages,
      tools=tools,
      temperature=0,
      frequency_penalty=1,
  )
  
  def get_temperature(location: str) -> int:
      """Mock function that returns the city temperature"""
      if "new york" in location.lower():
          return 45
      if "san francisco" in location.lower():
          return 72
      return 30
  
  func_kwargs = json.loads(chat.choices[0].message.tool_calls[0].function.arguments)
  temperature_info = get_temperature(**func_kwargs)
  
  messages.append(
      {
          "role": "assistant",
          "tool_calls": [
              tool_call.model_dump()
              for tool_call in chat.choices[0].message.tool_calls
          ]
      }
  )
  messages.append(
      {
          "role": "tool",
          "content": str(temperature_info),
          "tool_call_id": chat.choices[0].message.tool_calls[0].id
      }
  )
  
  chat_w_info = client.serverless.chat.complete(
      model="zai-org/GLM-5.2",
      tools=tools,
      messages=messages,
  )
  
  for choice in chat_w_info.choices:
      print(choice.message.content)
  

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Congratulations

• Building an AI Agent for Google Calendar
• Building AI Agents Using Function Calling with LLMs
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