ollama37/docs/capabilities/tool-calling.mdx

---
title: Tool calling 
---

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

## Calling a single tool
Invoke a single tool and include its response in a follow-up request. 

Also known as "single-shot" tool calling.

<Tabs>
  <Tab title="cURL">

    ```shell
    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**
    ```shell
    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
    }'
    ```
  </Tab>
  <Tab title="Python">
    Install the Ollama Python SDK:
    ```bash
    # with pip
    pip install ollama -U

    # with uv
    uv add ollama    
    ```

    ```python
    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",
        "Tokyo": "18°C",
      }
      return temperatures.get(city, "Unknown")

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

    # pass functions directly as tools in the tools list or as a JSON schema
    response = chat(model="qwen3", messages=messages, tools=[get_temperature], think=True)

    messages.append(response.message)
    if response.message.tool_calls:
      # only recommended for models which only return a single tool call
      call = response.message.tool_calls[0]
      result = get_temperature(**call.function.arguments)
      # add the tool result to the messages
      messages.append({"role": "tool", "tool_name": call.function.name, "content": str(result)})

      final_response = chat(model="qwen3", messages=messages, tools=[get_temperature], think=True)
      print(final_response.message.content)
    ```
  </Tab>
  <Tab title="JavaScript">
    Install the Ollama JavaScript library:
    ```bash
    # with npm
    npm i ollama

    # with bun
    bun i ollama
    ```

    ```typescript
    import ollama from 'ollama'

    function getTemperature(city: string): string {
      const temperatures: Record<string, string> = {
        'New York': '22°C',
        'London': '15°C',
        'Tokyo': '18°C',
      }
      return temperatures[city] ?? 'Unknown'
    }

    const 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' },
            },
          },
        },
      },
    ]

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

    const response = await ollama.chat({
      model: 'qwen3',
      messages,
      tools,
      think: true,
    })

    messages.push(response.message)
    if (response.message.tool_calls?.length) {
      // only recommended for models which only return a single tool call
      const call = response.message.tool_calls[0]
      const args = call.function.arguments as { city: string }
      const result = getTemperature(args.city)
      // add the tool result to the messages
      messages.push({ role: 'tool', tool_name: call.function.name, content: result })

      // generate the final response
      const finalResponse = await ollama.chat({ model: 'qwen3', messages, tools, think: true })
      console.log(finalResponse.message.content)
    }
    ```
  </Tab>
</Tabs>

## Parallel tool calling

<Tabs>
  <Tab title="cURL">
    Request multiple tool calls in parallel, then send all tool responses back to the model.

    ```shell
    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**
    ```shell
    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
    }'
    ```
  </Tab>
  <Tab title="Python">
    ```python
    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",
        "Tokyo": "18°C"
      }
      return temperatures.get(city, "Unknown")

    def get_conditions(city: str) -> str:
      """Get the current weather conditions for a city
      
      Args:
        city: The name of the city

      Returns:
        The current weather conditions for the city
      """
      conditions = {
        "New York": "Partly cloudy",
        "London": "Rainy",
        "Tokyo": "Sunny"
      }
      return conditions.get(city, "Unknown")


    messages = [{'role': 'user', 'content': 'What are the current weather conditions and temperature in New York and London?'}]

    # The python client automatically parses functions as a tool schema so we can pass them directly
    # Schemas can be passed directly in the tools list as well 
    response = chat(model='qwen3', messages=messages, tools=[get_temperature, get_conditions], think=True)

    # add the assistant message to the messages
    messages.append(response.message)
    if response.message.tool_calls:
      # process each tool call 
      for call in response.message.tool_calls:
        # execute the appropriate tool
        if call.function.name == 'get_temperature':
          result = get_temperature(**call.function.arguments)
        elif call.function.name == 'get_conditions':
          result = get_conditions(**call.function.arguments)
        else:
          result = 'Unknown tool'
        # add the tool result to the messages
        messages.append({'role': 'tool',  'tool_name': call.function.name, 'content': str(result)})

      # generate the final response
      final_response = chat(model='qwen3', messages=messages, tools=[get_temperature, get_conditions], think=True)
      print(final_response.message.content)
    ```
  </Tab>
  <Tab title="JavaScript">
    ```typescript
    import ollama from 'ollama'

    function getTemperature(city: string): string {
      const temperatures: { [key: string]: string } = {
        "New York": "22°C",
        "London": "15°C",
        "Tokyo": "18°C"
      }
      return temperatures[city] || "Unknown"
    }

    function getConditions(city: string): string {
      const conditions: { [key: string]: string } = {
        "New York": "Partly cloudy",
        "London": "Rainy",
        "Tokyo": "Sunny"
      }
      return conditions[city] || "Unknown"
    }

    const 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' },
            },
          },
        },
      }
    ]

    const messages = [{ role: 'user', content: 'What are the current weather conditions and temperature in New York and London?' }]

    const response = await ollama.chat({
      model: 'qwen3',
      messages,
      tools,
      think: true
    })

    // add the assistant message to the messages
    messages.push(response.message)
    if (response.message.tool_calls) {
      // process each tool call 
      for (const call of response.message.tool_calls) {
        // execute the appropriate tool
        let result: string
        if (call.function.name === 'get_temperature') {
          const args = call.function.arguments as { city: string }
          result = getTemperature(args.city)
        } else if (call.function.name === 'get_conditions') {
          const args = call.function.arguments as { city: string }
          result = getConditions(args.city)
        } else {
          result = 'Unknown tool'
        }
        // add the tool result to the messages
        messages.push({ role: 'tool', tool_name: call.function.name, content: result })
      }

      // generate the final response
      const finalResponse = await ollama.chat({ model: 'qwen3', messages, tools, think: true })
      console.log(finalResponse.message.content)
    }
    ```
  </Tab>
</Tabs>


## 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. 

<Tabs>
  <Tab title="Python">
    ```python
    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
    ```
  </Tab>
  <Tab title="JavaScript">
    ```typescript
    import ollama from 'ollama'

    type ToolName = 'add' | 'multiply'

    function add(a: number, b: number): number {
      return a + b
    }

    function multiply(a: number, b: number): number {
      return a * b
    }

    const availableFunctions: Record<ToolName, (a: number, b: number) => number> = {
      add,
      multiply,
    }

    const tools = [
      {
        type: 'function',
        function: {
          name: 'add',
          description: 'Add two numbers',
          parameters: {
            type: 'object',
            required: ['a', 'b'],
            properties: {
              a: { type: 'integer', description: 'The first number' },
              b: { type: 'integer', description: 'The second number' },
            },
          },
        },
      },
      {
        type: 'function',
        function: {
          name: 'multiply',
          description: 'Multiply two numbers',
          parameters: {
            type: 'object',
            required: ['a', 'b'],
            properties: {
              a: { type: 'integer', description: 'The first number' },
              b: { type: 'integer', description: 'The second number' },
            },
          },
        },
      },
    ]

    async function agentLoop() {
      const messages = [{ role: 'user', content: 'What is (11434+12341)*412?' }]

      while (true) {
        const response = await ollama.chat({
          model: 'qwen3',
          messages,
          tools,
          think: true,
        })

        messages.push(response.message)
        console.log('Thinking:', response.message.thinking)
        console.log('Content:', response.message.content)

        const toolCalls = response.message.tool_calls ?? []
        if (toolCalls.length) {
          for (const call of toolCalls) {
            const fn = availableFunctions[call.function.name as ToolName]
            if (!fn) {
              continue
            }

            const args = call.function.arguments as { a: number; b: number }
            console.log(`Calling ${call.function.name} with arguments`, args)
            const result = fn(args.a, args.b)
            console.log(`Result: ${result}`)
            messages.push({ role: 'tool', tool_name: call.function.name, content: String(result) })
          }
        } else {
          break
        }
      }
    }

    agentLoop().catch(console.error)
    ```
  </Tab>
</Tabs>


## 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.

<Tabs>
  <Tab title="Python">
```python
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})
```

  </Tab>
  <Tab title="JavaScript">
```typescript
import ollama from 'ollama'

function getTemperature(city: string): string {
  const temperatures: Record<string, string> = {
    'New York': '22°C',
    'London': '15°C',
  }
  return temperatures[city] ?? 'Unknown'
}

const getTemperatureTool = {
  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' },
      },
    },
  },
}

async function agentLoop() {
  const messages = [{ role: 'user', content: "What's the temperature in New York?" }]

  while (true) {
    const stream = await ollama.chat({
      model: 'qwen3',
      messages,
      tools: [getTemperatureTool],
      stream: true,
      think: true,
    })

    let thinking = ''
    let content = ''
    const toolCalls: any[] = []
    let doneThinking = false

    for await (const chunk of stream) {
      if (chunk.message.thinking) {
        thinking += chunk.message.thinking
        process.stdout.write(chunk.message.thinking)
      }
      if (chunk.message.content) {
        if (!doneThinking) {
          doneThinking = true
          process.stdout.write('\n')
        }
        content += chunk.message.content
        process.stdout.write(chunk.message.content)
      }
      if (chunk.message.tool_calls?.length) {
        toolCalls.push(...chunk.message.tool_calls)
        console.log(chunk.message.tool_calls)
      }
    }

    if (thinking || content || toolCalls.length) {
      messages.push({ role: 'assistant', thinking, content, tool_calls: toolCalls } as any)
    }

    if (!toolCalls.length) {
      break
    }

    for (const call of toolCalls) {
      if (call.function.name === 'get_temperature') {
        const args = call.function.arguments as { city: string }
        const result = getTemperature(args.city)
        messages.push({ role: 'tool', tool_name: call.function.name, content: result } )
      } else {
        messages.push({ role: 'tool', tool_name: call.function.name, content: 'Unknown tool' } )
      }
    }
  }
}

agentLoop().catch(console.error)
    ```
  </Tab>
</Tabs>

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.


## 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.

```python
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)
```