Tool Calling with Model APIs

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Goals

• Use tool calling to build your own AI agent with Friendli Model APIs
• Feel free to make your own custom tools!

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Getting Started

1. Go 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 the models available as Model APIs. Select the one you want and click the endpoint.
3. Click the Tools button, select the Search tool, and enter a query to see the response. 😀

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Step 2. 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 contains 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-5.3",
       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-5.3",
       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-5.3",
      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-5.3",
      tools=tools,
      messages=messages,
  )
  
  for choice in chat_w_info.choices:
      print(choice.message.content)
  

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Congratulations

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