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Ollama supports tool calling (also known as function calling) which allows a model to invoke tools and incorporate their results into its replies.

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Calling a single tool

Invoke a single tool and include its response in a follow-up request. Also known as “single-shot” tool calling.
  • cURL
  • Python
  • JavaScript
curl -s http://localhost:11434/api/chat -H "Content-Type: application/json" -d '{
  "model": "qwen3",
  "messages": [{"role": "user", "content": "What's the temperature in New York?"}],
  "stream": false,
  "tools": [
    {
      "type": "function",
      "function": {
        "name": "get_temperature",
        "description": "Get the current temperature for a city",
        "parameters": {
          "type": "object",
          "required": ["city"],
          "properties": {
            "city": {"type": "string", "description": "The name of the city"}
          }
        }
      }
    }
  ]
}'
Generate a response with a single tool result
curl -s http://localhost:11434/api/chat -H "Content-Type: application/json" -d '{
  "model": "qwen3",
  "messages": [
    {"role": "user", "content": "What's the temperature in New York?"},
    {
      "role": "assistant",
      "tool_calls": [
        {
          "type": "function",
          "function": {
            "index": 0,
            "name": "get_temperature",
            "arguments": {"city": "New York"}
          }
        }
      ]
    },
    {"role": "tool", "tool_name": "get_temperature", "content": "22°C"}
  ],
  "stream": false
}'

​
Parallel tool calling

  • cURL
  • Python
  • JavaScript
Request multiple tool calls in parallel, then send all tool responses back to the model.
curl -s http://localhost:11434/api/chat -H "Content-Type: application/json" -d '{
  "model": "qwen3",
  "messages": [{"role": "user", "content": "What are the current weather conditions and temperature in New York and London?"}],
  "stream": false,
  "tools": [
    {
      "type": "function",
      "function": {
        "name": "get_temperature",
        "description": "Get the current temperature for a city",
        "parameters": {
          "type": "object",
          "required": ["city"],
          "properties": {
            "city": {"type": "string", "description": "The name of the city"}
          }
        }
      }
    },
    {
      "type": "function",
      "function": {
        "name": "get_conditions",
        "description": "Get the current weather conditions for a city",
        "parameters": {
          "type": "object",
          "required": ["city"],
          "properties": {
            "city": {"type": "string", "description": "The name of the city"}
          }
        }
      }
    }
  ]
}'
Generate a response with multiple tool results
curl -s http://localhost:11434/api/chat -H "Content-Type: application/json" -d '{
  "model": "qwen3",
  "messages": [
    {"role": "user", "content": "What are the current weather conditions and temperature in New York and London?"},
    {
      "role": "assistant",
      "tool_calls": [
        {
          "type": "function",
          "function": {
            "index": 0,
            "name": "get_temperature",
            "arguments": {"city": "New York"}
          }
        },
        {
          "type": "function",
          "function": {
            "index": 1,
            "name": "get_conditions",
            "arguments": {"city": "New York"}
          }
        },
        {
          "type": "function",
          "function": {
            "index": 2,
            "name": "get_temperature",
            "arguments": {"city": "London"}
          }
        },
        {
          "type": "function",
          "function": {
            "index": 3,
            "name": "get_conditions",
            "arguments": {"city": "London"}
          }
        }
      ]
    },
    {"role": "tool", "tool_name": "get_temperature", "content": "22°C"},
    {"role": "tool", "tool_name": "get_conditions", "content": "Partly cloudy"},
    {"role": "tool", "tool_name": "get_temperature", "content": "15°C"},
    {"role": "tool", "tool_name": "get_conditions", "content": "Rainy"}
  ],
  "stream": false
}'

​
Multi-turn tool calling (Agent loop)

An agent loop allows the model to decide when to invoke tools and incorporate their results into its replies. It also might help to tell the model that it is in a loop and can make multiple tool calls.
  • Python
  • JavaScript
from ollama import chat, ChatResponse


def add(a: int, b: int) -> int:
  """Add two numbers"""
  """
  Args:
    a: The first number
    b: The second number

  Returns:
    The sum of the two numbers
  """
  return a + b


def multiply(a: int, b: int) -> int:
  """Multiply two numbers"""
  """
  Args:
    a: The first number
    b: The second number

  Returns:
    The product of the two numbers
  """
  return a * b


available_functions = {
  'add': add,
  'multiply': multiply,
}

messages = [{'role': 'user', 'content': 'What is (11434+12341)*412?'}]
while True:
    response: ChatResponse = chat(
        model='qwen3',
        messages=messages,
        tools=[add, multiply],
        think=True,
    )
    messages.append(response.message)
    print("Thinking: ", response.message.thinking)
    print("Content: ", response.message.content)
    if response.message.tool_calls:
        for tc in response.message.tool_calls:
            if tc.function.name in available_functions:
                print(f"Calling {tc.function.name} with arguments {tc.function.arguments}")
                result = available_functions[tc.function.name](**tc.function.arguments)
                print(f"Result: {result}")
                # add the tool result to the messages
                messages.append({'role': 'tool', 'tool_name': tc.function.name, 'content': str(result)})
    else:
        # end the loop when there are no more tool calls
        break
  # continue the loop with the updated messages

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Tool calling with streaming

When streaming, gather every chunk of thinking, content, and tool_calls, then return those fields together with any tool results in the follow-up request.
  • Python
  • JavaScript
from ollama import chat 


def get_temperature(city: str) -> str:
  """Get the current temperature for a city
  
  Args:
    city: The name of the city

  Returns:
    The current temperature for the city
  """
  temperatures = {
    'New York': '22°C',
    'London': '15°C',
  }
  return temperatures.get(city, 'Unknown')


messages = [{'role': 'user', 'content': "What's the temperature in New York?"}]

while True:
  stream = chat(
    model='qwen3',
    messages=messages,
    tools=[get_temperature],
    stream=True,
    think=True,
  )

  thinking = ''
  content = ''
  tool_calls = []

  done_thinking = False
  # accumulate the partial fields
  for chunk in stream:
    if chunk.message.thinking:
      thinking += chunk.message.thinking
      print(chunk.message.thinking, end='', flush=True)
    if chunk.message.content:
      if not done_thinking:
        done_thinking = True
        print('\n')
      content += chunk.message.content
      print(chunk.message.content, end='', flush=True)
    if chunk.message.tool_calls:
      tool_calls.extend(chunk.message.tool_calls)
      print(chunk.message.tool_calls)

  # append accumulated fields to the messages
  if thinking or content or tool_calls:
    messages.append({'role': 'assistant', 'thinking': thinking, 'content': content, 'tool_calls': tool_calls})

  if not tool_calls:
    break

  for call in tool_calls:
    if call.function.name == 'get_temperature':
      result = get_temperature(**call.function.arguments)
    else:
      result = 'Unknown tool'
    messages.append({'role': 'tool', 'tool_name': call.function.name, 'content': result})
This loop streams the assistant response, accumulates partial fields, passes them back together, and appends the tool results so the model can complete its answer.

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Using functions as tools with Ollama Python SDK

The Python SDK automatically parses functions as a tool schema so we can pass them directly. Schemas can still be passed if needed. 
from ollama import chat

def get_temperature(city: str) -> str:
  """Get the current temperature for a city
  
  Args:
    city: The name of the city

  Returns:
    The current temperature for the city
  """
  temperatures = {
    'New York': '22°C',
    'London': '15°C',
  }
  return temperatures.get(city, 'Unknown')

available_functions = {
  'get_temperature': get_temperature,
}
# directly pass the function as part of the tools list
response = chat(model='qwen3', messages=messages, tools=available_functions.values(), think=True)
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