Tool loop

from IPython.display import display, Markdown, clear_output
from pprint import pprint

' '.join(models)

'gpt-5 gpt-5-mini gpt-5-nano o1-preview o1-mini gpt-4o gpt-4o-mini gpt-4-turbo gpt-4 gpt-4-32k gpt-3.5-turbo gpt-3.5-turbo-instruct o1 o3-mini chatgpt-4o-latest o1-pro o3 o4-mini gpt-4.1 gpt-4.1-mini gpt-4.1-nano'

model = models[1]
model

'gpt-5-mini'

Sample Data

def _get_orders_customers():
    orders = {
        "O1": dict(id="O1", product="Widget A", quantity=2, price=19.99, status="Shipped"),
        "O2": dict(id="O2", product="Gadget B", quantity=1, price=49.99, status="Processing"),
        "O3": dict(id="O3", product="Gadget B", quantity=2, price=49.99, status="Shipped")}

    customers = {
        "C1": dict(name="John Doe", email="john@example.com", phone="123-456-7890",
                   orders=[orders['O1'], orders['O2']]),
        "C2": dict(name="Jane Smith", email="jane@example.com", phone="987-654-3210",
                   orders=[orders['O3']])
    }
    return orders, customers

orders, customers = _get_orders_customers()

def get_customer_info(
    customer_id:str # ID of the customer
): # Customer's name, email, phone number, and list of orders
    "Retrieves a customer's information and their orders based on the customer ID"
    print(f'- Retrieving customer {customer_id}')
    return customers.get(customer_id, "Customer not found")

def get_order_details(
    order_id:str # ID of the order
): # Order's ID, product name, quantity, price, and order status
    "Retrieves the details of a specific order based on the order ID"
    print(f'- Retrieving order {order_id}')
    return orders.get(order_id, "Order not found")

def cancel_order(
    order_id:str # ID of the order to cancel
)->bool: # True if the cancellation is successful
    "Cancels an order based on the provided order ID"
    print(f'- Cancelling order {order_id}')
    if order_id not in orders: return False
    orders[order_id]['status'] = 'Cancelled'
    return True

chatkw = dict(
    text={ "verbosity": "low" },
    reasoning={ "effort": "minimal" }
)

tools = [get_customer_info, get_order_details, cancel_order]
chat = Chat(model, tools=tools, **chatkw)

r = chat('Hi.')
r

Hello! How can I help you today?

id: resp_6897e0de4c348190bf1946e354518b8b0c0dd261ca702a77
created_at: 1754783966.0
error: None
incomplete_details: None
instructions: None
metadata: {}
model: gpt-5-mini-2025-08-07
object: response
output: [ResponseReasoningItem(id=‘rs_6897e0def4188190ac0593dbdac886be0c0dd261ca702a77’, summary=[], type=‘reasoning’, content=None, encrypted_content=None, status=None), ResponseOutputMessage(id=‘msg_6897e0df0d088190954040dcc2c7f18c0c0dd261ca702a77’, content=[ResponseOutputText(annotations=[], text=‘Hello! How can I help you today?’, type=‘output_text’, logprobs=[])], role=‘assistant’, status=‘completed’, type=‘message’)]
parallel_tool_calls: True
temperature: 1.0
tool_choice: auto
tools: [FunctionTool(name=‘get_customer_info’, parameters={‘type’: ‘object’, ‘properties’: {‘customer_id’: {‘type’: ‘string’, ‘description’: ‘ID of the customer’}}, ‘required’: [‘customer_id’], ‘additionalProperties’: False}, strict=True, type=‘function’, description=“Retrieves a customer’s information and their orders based on the customer ID”), FunctionTool(name=‘get_order_details’, parameters={‘type’: ‘object’, ‘properties’: {‘order_id’: {‘type’: ‘string’, ‘description’: ‘ID of the order’}}, ‘required’: [‘order_id’], ‘additionalProperties’: False}, strict=True, type=‘function’, description=‘Retrieves the details of a specific order based on the order ID’), FunctionTool(name=‘cancel_order’, parameters={‘type’: ‘object’, ‘properties’: {‘order_id’: {‘type’: ‘string’, ‘description’: ‘ID of the order to cancel’}}, ‘required’: [‘order_id’], ‘additionalProperties’: False}, strict=True, type=‘function’, description=‘Cancels an order based on the provided order ID:- type: boolean’)]
top_p: 1.0
background: False
max_output_tokens: 4096
max_tool_calls: None
previous_response_id: None
prompt: None
prompt_cache_key: None
reasoning: Reasoning(effort=‘minimal’, generate_summary=None, summary=None)
safety_identifier: None
service_tier: default
status: completed
text: ResponseTextConfig(format=ResponseFormatText(type=‘text’), verbosity=‘low’)
top_logprobs: 0
truncation: disabled
usage: ResponseUsage(input_tokens=136, input_tokens_details=InputTokensDetails(cached_tokens=0), output_tokens=15, output_tokens_details=OutputTokensDetails(reasoning_tokens=0), total_tokens=151)
user: None
store: True

r = chat('Can you tell me the email address for customer C2?')
r.output

- Retrieving customer C2

[ResponseReasoningItem(id='rs_6897e0f0949c8190af24da7373614a300c0dd261ca702a77', summary=[], type='reasoning', content=None, encrypted_content=None, status=None),
 ResponseFunctionToolCall(arguments='{"customer_id":"C2"}', call_id='call_7qPUHLapCRWKcPqdoFkRYChc', name='get_customer_info', type='function_call', id='fc_6897e0f0c5248190b17036339c4be62a0c0dd261ca702a77', status='completed')]

r = chat()
r.output

[ResponseOutputMessage(id='msg_6897e0f5a8b48190b648cb12f43898240c0dd261ca702a77', content=[ResponseOutputText(annotations=[], text='The email for customer C2 is jane@example.com.', type='output_text', logprobs=[])], role='assistant', status='completed', type='message')]

chat = Chat(model, tools=tools)
r = chat('Please cancel all orders for customer C1 for me.')
r.output

- Retrieving customer C1

[ResponseReasoningItem(id='rs_6897e0f99ddc8191b8f2fafa74dc06d40913d9b020597909', summary=[], type='reasoning', content=None, encrypted_content=None, status=None),
 ResponseFunctionToolCall(arguments='{"customer_id":"C1"}', call_id='call_LwrqtbAyvJshnZo2a6MIG8GK', name='get_customer_info', type='function_call', id='fc_6897e0fb1f648191b15ad2d73156e6260913d9b020597909', status='completed')]

r = chat()
r.output

- Cancelling order O1
- Cancelling order O2

[ResponseReasoningItem(id='rs_6897e0fd41e0819189fc599d98d476280913d9b020597909', summary=[], type='reasoning', content=None, encrypted_content=None, status=None),
 ResponseFunctionToolCall(arguments='{"order_id":"O1"}', call_id='call_q8Xfu230VPo7EtebReDFkXQW', name='cancel_order', type='function_call', id='fc_6897e0fec5fc8191989ee1c6f5456bf90913d9b020597909', status='completed'),
 ResponseFunctionToolCall(arguments='{"order_id":"O2"}', call_id='call_eyVUvcqHfbLPdRluohRibIC1', name='cancel_order', type='function_call', id='fc_6897e0fee2c48191be1350e29034816a0913d9b020597909', status='completed')]

`toolloop` implementation

source

Chat.toolloop

 Chat.toolloop (pr, max_steps=10, cont_func:<built-
                infunctioncallable>=<function noop>, final_prompt='You
                have no more tool uses. Please summarize your findings. If
                you did not complete your goal please tell the user what
                further work needs to be done so they can choose how best
                to proceed.', stream:bool=False, tools=None,
                tool_choice=None,
                background:Optional[bool]|NotGiven=NOT_GIVEN, include:Opti
                onal[List[ResponseIncludable]]|NotGiven=NOT_GIVEN,
                input:Union[str,ResponseInputParam]|NotGiven=NOT_GIVEN,
                instructions:Optional[str]|NotGiven=NOT_GIVEN,
                max_output_tokens:Optional[int]|NotGiven=NOT_GIVEN,
                max_tool_calls:Optional[int]|NotGiven=NOT_GIVEN,
                metadata:Optional[Metadata]|NotGiven=NOT_GIVEN,
                model:ResponsesModel|NotGiven=NOT_GIVEN,
                parallel_tool_calls:Optional[bool]|NotGiven=NOT_GIVEN,
                previous_response_id:Optional[str]|NotGiven=NOT_GIVEN,
                prompt:Optional[ResponsePromptParam]|NotGiven=NOT_GIVEN,
                prompt_cache_key:str|NotGiven=NOT_GIVEN,
                reasoning:Optional[Reasoning]|NotGiven=NOT_GIVEN,
                safety_identifier:str|NotGiven=NOT_GIVEN, service_tier:"Op
                tional[Literal['auto','default','flex','scale','priority']
                ]|NotGiven"=NOT_GIVEN,
                store:Optional[bool]|NotGiven=NOT_GIVEN, stream_options:Op
                tional[response_create_params.StreamOptions]|NotGiven=NOT_
                GIVEN, temperature:Optional[float]|NotGiven=NOT_GIVEN,
                text:ResponseTextConfigParam|NotGiven=NOT_GIVEN,
                top_logprobs:Optional[int]|NotGiven=NOT_GIVEN,
                top_p:Optional[float]|NotGiven=NOT_GIVEN, truncation:"Opti
                onal[Literal['auto','disabled']]|NotGiven"=NOT_GIVEN,
                user:str|NotGiven=NOT_GIVEN,
                extra_headers:Headers|None=None,
                extra_query:Query|None=None, extra_body:Body|None=None,
                timeout:float|httpx.Timeout|None|NotGiven=NOT_GIVEN)

Add prompt pr to dialog and get a response from Claude, automatically following up with tool_use messages

	Type	Default	Details
pr			Prompt to pass to Claude
max_steps	int	10	Maximum number of tool requests to loop through
cont_func	callable	noop	Function that stops loop if returns False
final_prompt	str	You have no more tool uses. Please summarize your findings. If you did not complete your goal please tell the user what further work needs to be done so they can choose how best to proceed.	Prompt to add if last message is a tool call
stream	bool	False	Stream response?
tools	NoneType	None	Tools to use
tool_choice	NoneType	None	Required tools to use
background	Optional[bool] \| NotGiven	NOT_GIVEN
include	Optional[List[ResponseIncludable]] \| NotGiven	NOT_GIVEN
input	Union[str, ResponseInputParam] \| NotGiven	NOT_GIVEN
instructions	Optional[str] \| NotGiven	NOT_GIVEN
max_output_tokens	Optional[int] \| NotGiven	NOT_GIVEN
max_tool_calls	Optional[int] \| NotGiven	NOT_GIVEN
metadata	Optional[Metadata] \| NotGiven	NOT_GIVEN
model	ResponsesModel \| NotGiven	NOT_GIVEN
parallel_tool_calls	Optional[bool] \| NotGiven	NOT_GIVEN
previous_response_id	Optional[str] \| NotGiven	NOT_GIVEN
prompt	Optional[ResponsePromptParam] \| NotGiven	NOT_GIVEN
prompt_cache_key	str \| NotGiven	NOT_GIVEN
reasoning	Optional[Reasoning] \| NotGiven	NOT_GIVEN
safety_identifier	str \| NotGiven	NOT_GIVEN
service_tier	Optional[Literal[‘auto’, ‘default’, ‘flex’, ‘scale’, ‘priority’]] \| NotGiven	NOT_GIVEN
store	Optional[bool] \| NotGiven	NOT_GIVEN
stream_options	Optional[response_create_params.StreamOptions] \| NotGiven	NOT_GIVEN
temperature	Optional[float] \| NotGiven	NOT_GIVEN
text	ResponseTextConfigParam \| NotGiven	NOT_GIVEN
top_logprobs	Optional[int] \| NotGiven	NOT_GIVEN
top_p	Optional[float] \| NotGiven	NOT_GIVEN
truncation	Optional[Literal[‘auto’, ‘disabled’]] \| NotGiven	NOT_GIVEN
user	str \| NotGiven	NOT_GIVEN
extra_headers	Optional	None	Use the following arguments if you need to pass additional parameters to the API that aren’t available via kwargs. The extra values given here take precedence over values defined on the client or passed to this method.
extra_query	Query \| None	None
extra_body	Body \| None	None
timeout	float \| httpx.Timeout \| None \| NotGiven	NOT_GIVEN

Exported source

_final_prompt = "You have no more tool uses. Please summarize your findings. If you did not complete your goal please tell the user what further work needs to be done so they can choose how best to proceed."

Exported source

@patch
@delegates(Chat.__call__)
def toolloop(self:Chat,
             pr, # Prompt to pass to Claude
             max_steps=10, # Maximum number of tool requests to loop through
             cont_func:callable=noop, # Function that stops loop if returns False
             final_prompt=_final_prompt, # Prompt to add if last message is a tool call
             **kwargs):
    "Add prompt `pr` to dialog and get a response from Claude, automatically following up with `tool_use` messages"
    @save_iter
    def _f(o):
        init_n = len(self.h)
        r = self(pr, **kwargs)
        yield r
        if len(self.last)>1: yield from self.last[1:]
        for i in range(max_steps-1):
            x = self.h[-1]
            if not (isinstance(x, dict) and x['type']=='function_call_output'): break
            r = self(final_prompt if i==max_steps-2 else None, **kwargs)
            yield r
            if len(self.last)>1: yield from self.last[1:]
            if not cont_func(*self.h[-3:]): break
        o.value = self.h[init_n+1:]
    return _f()

Test Customer Dataset

def show(x):
    if getattr(x, 'output_text', None): r = x
    else: r = getattr(x,'output',x)
    display(r)

chat = Chat(model, tools=tools)
pr = 'Can you tell me the email address for customer C1?'
r = chat.toolloop(pr)
res = list(r)
for o in r: show(o)

- Retrieving customer C1

The email for customer C1 is john@example.com. Need anything else about this customer?

id: resp_6897e128b4448193bf249ab92f1de8780e7bd7a6ca08f04c
created_at: 1754784041.0
error: None
incomplete_details: None
instructions: None
metadata: {}
model: gpt-5-mini-2025-08-07
object: response
output: [ResponseReasoningItem(id=‘rs_6897e12998dc8193b861f1ead58b702e0e7bd7a6ca08f04c’, summary=[], type=‘reasoning’, content=None, encrypted_content=None, status=None), ResponseOutputMessage(id=‘msg_6897e12a73f4819387e6321b9d3acbd90e7bd7a6ca08f04c’, content=[ResponseOutputText(annotations=[], text=‘The email for customer C1 is john@example.com. Need anything else about this customer?’, type=‘output_text’, logprobs=[])], role=‘assistant’, status=‘completed’, type=‘message’)]
parallel_tool_calls: True
temperature: 1.0
tool_choice: auto
tools: [FunctionTool(name=‘get_customer_info’, parameters={‘type’: ‘object’, ‘properties’: {‘customer_id’: {‘type’: ‘string’, ‘description’: ‘ID of the customer’}}, ‘required’: [‘customer_id’], ‘additionalProperties’: False}, strict=True, type=‘function’, description=“Retrieves a customer’s information and their orders based on the customer ID”), FunctionTool(name=‘get_order_details’, parameters={‘type’: ‘object’, ‘properties’: {‘order_id’: {‘type’: ‘string’, ‘description’: ‘ID of the order’}}, ‘required’: [‘order_id’], ‘additionalProperties’: False}, strict=True, type=‘function’, description=‘Retrieves the details of a specific order based on the order ID’), FunctionTool(name=‘cancel_order’, parameters={‘type’: ‘object’, ‘properties’: {‘order_id’: {‘type’: ‘string’, ‘description’: ‘ID of the order to cancel’}}, ‘required’: [‘order_id’], ‘additionalProperties’: False}, strict=True, type=‘function’, description=‘Cancels an order based on the provided order ID:- type: boolean’)]
top_p: 1.0
background: False
max_output_tokens: 4096
max_tool_calls: None
previous_response_id: None
prompt: None
prompt_cache_key: None
reasoning: Reasoning(effort=‘medium’, generate_summary=None, summary=None)
safety_identifier: None
service_tier: default
status: completed
text: ResponseTextConfig(format=ResponseFormatText(type=‘text’), verbosity=‘medium’)
top_logprobs: 0
truncation: disabled
usage: ResponseUsage(input_tokens=327, input_tokens_details=InputTokensDetails(cached_tokens=0), output_tokens=24, output_tokens_details=OutputTokensDetails(reasoning_tokens=0), total_tokens=351)
user: None
store: True

ResponseOutputMessage(id='msg_6897e12a73f4819387e6321b9d3acbd90e7bd7a6ca08f04c', content=[ResponseOutputText(annotations=[], text='The email for customer C1 is john@example.com. Need anything else about this customer?', type='output_text', logprobs=[])], role='assistant', status='completed', type='message')

source

loop_outputs

 loop_outputs (res)

Exported source

def loop_outputs(res):
    return [dict(p) for o in res for p in ([o] if isinstance(o,dict) else getattr(o,'output',[]))]

cl = loop_outputs(res)
cl

[{'id': 'rs_6897e1248f908193a6cf91c7b9d24f180e7bd7a6ca08f04c',
  'summary': [],
  'type': 'reasoning',
  'content': None,
  'encrypted_content': None,
  'status': None},
 {'arguments': '{"customer_id":"C1"}',
  'call_id': 'call_iSsBtRfj1j7BrbBfilN81sWt',
  'name': 'get_customer_info',
  'type': 'function_call',
  'id': 'fc_6897e12505408193898876b3915318c40e7bd7a6ca08f04c',
  'status': 'completed'},
 {'type': 'function_call_output',
  'call_id': 'call_iSsBtRfj1j7BrbBfilN81sWt',
  'output': "{'name': 'John Doe', 'email': 'john@example.com', 'phone': '123-456-7890', 'orders': [{'id': 'O1', 'product': 'Widget A', 'quantity': 2, 'price': 19.99, 'status': 'Cancelled'}, {'id': 'O2', 'product': 'Gadget B', 'quantity': 1, 'price': 49.99, 'status': 'Cancelled'}]}"},
 {'id': 'rs_6897e1268cb48193aeec6139bf6891d20e7bd7a6ca08f04c',
  'summary': [],
  'type': 'reasoning',
  'content': None,
  'encrypted_content': None,
  'status': None},
 {'id': 'msg_6897e1275b4c8193b2b2e8505b757b470e7bd7a6ca08f04c',
  'content': [ResponseOutputText(annotations=[], text='The email address for customer C1 is john@example.com. Would you like any other details (phone number, orders, etc.)?', type='output_text', logprobs=[])],
  'role': 'assistant',
  'status': 'completed',
  'type': 'message'}]

def disp_tc(x):
    if x['type']=='function_call': return f"- `{x['name']}({x['arguments']})`\n"
    elif x['type']=='function_call_output': return f"  - `{x['output']}`\n\n"
    else: return ''.join(o.text for o in x['content'])

# Markdown(''.join(map(disp_tc, cl)))

pprint(r.value)

[ResponseReasoningItem(id='rs_6897e12998dc8193b861f1ead58b702e0e7bd7a6ca08f04c', summary=[], type='reasoning', content=None, encrypted_content=None, status=None),
 ResponseOutputMessage(id='msg_6897e12a73f4819387e6321b9d3acbd90e7bd7a6ca08f04c', content=[ResponseOutputText(annotations=[], text='The email for customer C1 is john@example.com. Need anything else about this customer?', type='output_text', logprobs=[])], role='assistant', status='completed', type='message')]

orders, customers = _get_orders_customers()

chat = Chat(model, tools=tools)
r = chat.toolloop('What is the status of order O2?')
for o in r: display(getattr(o,'output',o))

- Retrieving order O2

[ResponseReasoningItem(id='rs_6897e152296c81938b18183a7b3a3f2b070371bcef68a1b8', summary=[], type='reasoning', content=None, encrypted_content=None, status=None),
 ResponseFunctionToolCall(arguments='{"order_id":"O2"}', call_id='call_nO3ZOyxkiOtKQTY6UMwDsp9d', name='get_order_details', type='function_call', id='fc_6897e152a24881938ecb7209c4408013070371bcef68a1b8', status='completed')]

ResponseFunctionToolCall(arguments='{"order_id":"O2"}', call_id='call_nO3ZOyxkiOtKQTY6UMwDsp9d', name='get_order_details', type='function_call', id='fc_6897e152a24881938ecb7209c4408013070371bcef68a1b8', status='completed')

{'type': 'function_call_output',
 'call_id': 'call_nO3ZOyxkiOtKQTY6UMwDsp9d',
 'output': "{'id': 'O2', 'product': 'Gadget B', 'quantity': 1, 'price': 49.99, 'status': 'Processing'}"}

[ResponseOutputMessage(id='msg_6897e155231c8193946aeae2f3c7bf85070371bcef68a1b8', content=[ResponseOutputText(annotations=[], text='Order O2 (Gadget B, qty 1) is currently: Processing.', type='output_text', logprobs=[])], role='assistant', status='completed', type='message')]

r = chat.toolloop('Please cancel all orders for customer C1 for me.')
res = list(r)
for o in res: display(getattr(o,'output',o))

- Retrieving customer C1
- Cancelling order O1
- Cancelling order O2

[ResponseReasoningItem(id='rs_6897e15728f4819396207358d0b5ff31070371bcef68a1b8', summary=[], type='reasoning', content=None, encrypted_content=None, status=None),
 ResponseFunctionToolCall(arguments='{"customer_id":"C1"}', call_id='call_pfwlNfsFTJxcwhO5MiDnKFCc', name='get_customer_info', type='function_call', id='fc_6897e15a33c481938b60a80c6c9c6e75070371bcef68a1b8', status='completed')]

ResponseFunctionToolCall(arguments='{"customer_id":"C1"}', call_id='call_pfwlNfsFTJxcwhO5MiDnKFCc', name='get_customer_info', type='function_call', id='fc_6897e15a33c481938b60a80c6c9c6e75070371bcef68a1b8', status='completed')

{'type': 'function_call_output',
 'call_id': 'call_pfwlNfsFTJxcwhO5MiDnKFCc',
 'output': "{'name': 'John Doe', 'email': 'john@example.com', 'phone': '123-456-7890', 'orders': [{'id': 'O1', 'product': 'Widget A', 'quantity': 2, 'price': 19.99, 'status': 'Shipped'}, {'id': 'O2', 'product': 'Gadget B', 'quantity': 1, 'price': 49.99, 'status': 'Processing'}]}"}

[ResponseReasoningItem(id='rs_6897e15c06048193a117eba237950a60070371bcef68a1b8', summary=[], type='reasoning', content=None, encrypted_content=None, status=None),
 ResponseFunctionToolCall(arguments='{"order_id":"O1"}', call_id='call_pGZ7LfguiTY7pTpE8IPPZDW3', name='cancel_order', type='function_call', id='fc_6897e15e276c8193a8bb5668615cb96d070371bcef68a1b8', status='completed'),
 ResponseFunctionToolCall(arguments='{"order_id":"O2"}', call_id='call_TqGkkG2TRTXlXvoiBTgwJlZx', name='cancel_order', type='function_call', id='fc_6897e15e5ef88193b8678bd20f6d0104070371bcef68a1b8', status='completed')]

ResponseFunctionToolCall(arguments='{"order_id":"O1"}', call_id='call_pGZ7LfguiTY7pTpE8IPPZDW3', name='cancel_order', type='function_call', id='fc_6897e15e276c8193a8bb5668615cb96d070371bcef68a1b8', status='completed')

ResponseFunctionToolCall(arguments='{"order_id":"O2"}', call_id='call_TqGkkG2TRTXlXvoiBTgwJlZx', name='cancel_order', type='function_call', id='fc_6897e15e5ef88193b8678bd20f6d0104070371bcef68a1b8', status='completed')

{'type': 'function_call_output',
 'call_id': 'call_pGZ7LfguiTY7pTpE8IPPZDW3',
 'output': 'True'}

{'type': 'function_call_output',
 'call_id': 'call_TqGkkG2TRTXlXvoiBTgwJlZx',
 'output': 'True'}

[ResponseReasoningItem(id='rs_6897e15fd9388193acca7aca2463bb9d070371bcef68a1b8', summary=[], type='reasoning', content=None, encrypted_content=None, status=None),
 ResponseOutputMessage(id='msg_6897e16302e48193ad31367b6139e859070371bcef68a1b8', content=[ResponseOutputText(annotations=[], text='I cancelled all orders for customer C1 (John Doe, john@example.com).\n\nResults:\n- Order O1 (Widget A, qty 2): Cancelled successfully.\n- Order O2 (Gadget B, qty 1): Cancelled successfully.\n\nWould you like me to start refunds, send a cancellation confirmation email, or do anything else for John Doe?', type='output_text', logprobs=[])], role='assistant', status='completed', type='message')]

ResponseOutputMessage(id='msg_6897e16302e48193ad31367b6139e859070371bcef68a1b8', content=[ResponseOutputText(annotations=[], text='I cancelled all orders for customer C1 (John Doe, john@example.com).\n\nResults:\n- Order O1 (Widget A, qty 2): Cancelled successfully.\n- Order O2 (Gadget B, qty 1): Cancelled successfully.\n\nWould you like me to start refunds, send a cancellation confirmation email, or do anything else for John Doe?', type='output_text', logprobs=[])], role='assistant', status='completed', type='message')

# cl = loop_outputs(res)
# Markdown('\n'.join(map(disp_tc, cl)))

for o in chat.toolloop('What is the status of order O2?'): display(o)

- Retrieving order O2

id: resp_6897e16824308193863428bf4240a87c070371bcef68a1b8
created_at: 1754784104.0
error: None
incomplete_details: None
instructions: None
metadata: {}
model: gpt-5-mini-2025-08-07
object: response
output: [ResponseReasoningItem(id=‘rs_6897e168e56c8193a92dbe9d9ac28d8c070371bcef68a1b8’, summary=[], type=‘reasoning’, content=None, encrypted_content=None, status=None), ResponseFunctionToolCall(arguments=‘{“order_id”:“O2”}’, call_id=‘call_s79z5383Uxatc6sNpm33VEl1’, name=‘get_order_details’, type=‘function_call’, id=‘fc_6897e16a30708193a4f46fa44c3bb203070371bcef68a1b8’, status=‘completed’)]
parallel_tool_calls: True
temperature: 1.0
tool_choice: auto
tools: [FunctionTool(name=‘get_customer_info’, parameters={‘type’: ‘object’, ‘properties’: {‘customer_id’: {‘type’: ‘string’, ‘description’: ‘ID of the customer’}}, ‘required’: [‘customer_id’], ‘additionalProperties’: False}, strict=True, type=‘function’, description=“Retrieves a customer’s information and their orders based on the customer ID”), FunctionTool(name=‘get_order_details’, parameters={‘type’: ‘object’, ‘properties’: {‘order_id’: {‘type’: ‘string’, ‘description’: ‘ID of the order’}}, ‘required’: [‘order_id’], ‘additionalProperties’: False}, strict=True, type=‘function’, description=‘Retrieves the details of a specific order based on the order ID’), FunctionTool(name=‘cancel_order’, parameters={‘type’: ‘object’, ‘properties’: {‘order_id’: {‘type’: ‘string’, ‘description’: ‘ID of the order to cancel’}}, ‘required’: [‘order_id’], ‘additionalProperties’: False}, strict=True, type=‘function’, description=‘Cancels an order based on the provided order ID:- type: boolean’)]
top_p: 1.0
background: False
max_output_tokens: 4096
max_tool_calls: None
previous_response_id: None
prompt: None
prompt_cache_key: None
reasoning: Reasoning(effort=‘medium’, generate_summary=None, summary=None)
safety_identifier: None
service_tier: default
status: completed
text: ResponseTextConfig(format=ResponseFormatText(type=‘text’), verbosity=‘medium’)
top_logprobs: 0
truncation: disabled
usage: ResponseUsage(input_tokens=532, input_tokens_details=InputTokensDetails(cached_tokens=0), output_tokens=87, output_tokens_details=OutputTokensDetails(reasoning_tokens=64), total_tokens=619)
user: None
store: True

ResponseFunctionToolCall(arguments='{"order_id":"O2"}', call_id='call_s79z5383Uxatc6sNpm33VEl1', name='get_order_details', type='function_call', id='fc_6897e16a30708193a4f46fa44c3bb203070371bcef68a1b8', status='completed')

{'type': 'function_call_output',
 'call_id': 'call_s79z5383Uxatc6sNpm33VEl1',
 'output': "{'id': 'O2', 'product': 'Gadget B', 'quantity': 1, 'price': 49.99, 'status': 'Cancelled'}"}

Order O2 (Gadget B, qty 1) is currently: Cancelled.

id: resp_6897e16b5fa88193b00c69f98e9dd053070371bcef68a1b8
created_at: 1754784107.0
error: None
incomplete_details: None
instructions: None
metadata: {}
model: gpt-5-mini-2025-08-07
object: response
output: [ResponseOutputMessage(id=‘msg_6897e16be6248193aa6c8c7968793ee4070371bcef68a1b8’, content=[ResponseOutputText(annotations=[], text=‘Order O2 (Gadget B, qty 1) is currently: Cancelled.’, type=‘output_text’, logprobs=[])], role=‘assistant’, status=‘completed’, type=‘message’)]
parallel_tool_calls: True
temperature: 1.0
tool_choice: auto
tools: [FunctionTool(name=‘get_customer_info’, parameters={‘type’: ‘object’, ‘properties’: {‘customer_id’: {‘type’: ‘string’, ‘description’: ‘ID of the customer’}}, ‘required’: [‘customer_id’], ‘additionalProperties’: False}, strict=True, type=‘function’, description=“Retrieves a customer’s information and their orders based on the customer ID”), FunctionTool(name=‘get_order_details’, parameters={‘type’: ‘object’, ‘properties’: {‘order_id’: {‘type’: ‘string’, ‘description’: ‘ID of the order’}}, ‘required’: [‘order_id’], ‘additionalProperties’: False}, strict=True, type=‘function’, description=‘Retrieves the details of a specific order based on the order ID’), FunctionTool(name=‘cancel_order’, parameters={‘type’: ‘object’, ‘properties’: {‘order_id’: {‘type’: ‘string’, ‘description’: ‘ID of the order to cancel’}}, ‘required’: [‘order_id’], ‘additionalProperties’: False}, strict=True, type=‘function’, description=‘Cancels an order based on the provided order ID:- type: boolean’)]
top_p: 1.0
background: False
max_output_tokens: 4096
max_tool_calls: None
previous_response_id: None
prompt: None
prompt_cache_key: None
reasoning: Reasoning(effort=‘medium’, generate_summary=None, summary=None)
safety_identifier: None
service_tier: default
status: completed
text: ResponseTextConfig(format=ResponseFormatText(type=‘text’), verbosity=‘medium’)
top_logprobs: 0
truncation: disabled
usage: ResponseUsage(input_tokens=685, input_tokens_details=InputTokensDetails(cached_tokens=535), output_tokens=22, output_tokens_details=OutputTokensDetails(reasoning_tokens=0), total_tokens=707)
user: None
store: True

Test Math Example

def add(x: int, y: int) -> int:
    "adds x and y."
    return x + y

def mul(x: int, y: int) -> int:
    "multiplies x and y."
    return x * y

chat = Chat(model, tools=[add, mul], **chatkw)
pr = 'Can you add 1258585825128 to 34959234595, multiply by 93, and then add (-12439149)?'
r = chat.toolloop(pr)
for o in r: show(o)

[ResponseReasoningItem(id='rs_6897e18c5388819190a54e0ce1441bdb015feb53bd0dd55d', summary=[], type='reasoning', content=None, encrypted_content=None, status=None),
 ResponseFunctionToolCall(arguments='{"x":1258585825128,"y":34959234595}', call_id='call_ueQGOLD99pG6r2sGdgsVKlw6', name='add', type='function_call', id='fc_6897e18e8f88819191645a755cda603e015feb53bd0dd55d', status='completed')]

ResponseFunctionToolCall(arguments='{"x":1258585825128,"y":34959234595}', call_id='call_ueQGOLD99pG6r2sGdgsVKlw6', name='add', type='function_call', id='fc_6897e18e8f88819191645a755cda603e015feb53bd0dd55d', status='completed')

{'type': 'function_call_output',
 'call_id': 'call_ueQGOLD99pG6r2sGdgsVKlw6',
 'output': '1293545059723'}

[ResponseReasoningItem(id='rs_6897e19014fc81919b186f766cb9b0b7015feb53bd0dd55d', summary=[], type='reasoning', content=None, encrypted_content=None, status=None),
 ResponseFunctionToolCall(arguments='{"x":1293545059723,"y":93}', call_id='call_3bMfuqmgxtFi73cSXwFpypFP', name='mul', type='function_call', id='fc_6897e190e7b88191a01ac2531eb232a4015feb53bd0dd55d', status='completed')]

ResponseFunctionToolCall(arguments='{"x":1293545059723,"y":93}', call_id='call_3bMfuqmgxtFi73cSXwFpypFP', name='mul', type='function_call', id='fc_6897e190e7b88191a01ac2531eb232a4015feb53bd0dd55d', status='completed')

{'type': 'function_call_output',
 'call_id': 'call_3bMfuqmgxtFi73cSXwFpypFP',
 'output': '120299690554239'}

[ResponseReasoningItem(id='rs_6897e192bbbc8191bf48ca20d003dfc1015feb53bd0dd55d', summary=[], type='reasoning', content=None, encrypted_content=None, status=None),
 ResponseFunctionToolCall(arguments='{"x":120299690554239,"y":-12439149}', call_id='call_ildkWrO6EHnC0jtBFXAq93P5', name='add', type='function_call', id='fc_6897e1935f7481918b1d79751e99aede015feb53bd0dd55d', status='completed')]

ResponseFunctionToolCall(arguments='{"x":120299690554239,"y":-12439149}', call_id='call_ildkWrO6EHnC0jtBFXAq93P5', name='add', type='function_call', id='fc_6897e1935f7481918b1d79751e99aede015feb53bd0dd55d', status='completed')

{'type': 'function_call_output',
 'call_id': 'call_ildkWrO6EHnC0jtBFXAq93P5',
 'output': '120299678115090'}

120,299,678,115,090

id: resp_6897e19438d081919b9c36459cc8cd50015feb53bd0dd55d
created_at: 1754784148.0
error: None
incomplete_details: None
instructions: None
metadata: {}
model: gpt-5-mini-2025-08-07
object: response
output: [ResponseReasoningItem(id=‘rs_6897e194c62481918b6ac433e9648d01015feb53bd0dd55d’, summary=[], type=‘reasoning’, content=None, encrypted_content=None, status=None), ResponseOutputMessage(id=‘msg_6897e195ccbc8191806147fc7db8fe8e015feb53bd0dd55d’, content=[ResponseOutputText(annotations=[], text=‘120,299,678,115,090’, type=‘output_text’, logprobs=[])], role=‘assistant’, status=‘completed’, type=‘message’)]
parallel_tool_calls: True
temperature: 1.0
tool_choice: auto
tools: [FunctionTool(name=‘add’, parameters={‘type’: ‘object’, ‘properties’: {‘x’: {‘type’: ‘integer’, ‘description’: ’‘}, ’y’: {‘type’: ‘integer’, ‘description’: ’‘}}, ’required’: [‘x’, ‘y’], ‘additionalProperties’: False}, strict=True, type=‘function’, description=‘adds x and y.:- type: integer’), FunctionTool(name=‘mul’, parameters={‘type’: ‘object’, ‘properties’: {‘x’: {‘type’: ‘integer’, ‘description’: ’‘}, ’y’: {‘type’: ‘integer’, ‘description’: ’‘}}, ’required’: [‘x’, ‘y’], ‘additionalProperties’: False}, strict=True, type=‘function’, description=‘multiplies x and y.:- type: integer’)]
top_p: 1.0
background: False
max_output_tokens: 4096
max_tool_calls: None
previous_response_id: None
prompt: None
prompt_cache_key: None
reasoning: Reasoning(effort=‘medium’, generate_summary=None, summary=None)
safety_identifier: None
service_tier: default
status: completed
text: ResponseTextConfig(format=ResponseFormatText(type=‘text’), verbosity=‘medium’)
top_logprobs: 0
truncation: disabled
usage: ResponseUsage(input_tokens=496, input_tokens_details=InputTokensDetails(cached_tokens=0), output_tokens=79, output_tokens_details=OutputTokensDetails(reasoning_tokens=64), total_tokens=575)
user: None
store: True

ResponseOutputMessage(id='msg_6897e195ccbc8191806147fc7db8fe8e015feb53bd0dd55d', content=[ResponseOutputText(annotations=[], text='120,299,678,115,090', type='output_text', logprobs=[])], role='assistant', status='completed', type='message')

(1258585825128 + 34959234595) * 93 - 12439149

120299678115090

chat = Chat(model, tools=[add, mul], **chatkw)
r = chat.toolloop(pr, stream=True)
for o in r:
    if isinstance(o, dict): print('- ', o)
    else:
        for p in o: print(p, end='')
        if hasattr(o, 'value'): show(o.value)

[ResponseReasoningItem(id='rs_6897e1c5d79081a3ae88e03578f661a90c8460ccb833b112', summary=[], type='reasoning', content=None, encrypted_content=None, status=None),
 ResponseFunctionToolCall(arguments='{"x":1258585825128,"y":34959234595}', call_id='call_SdIaAHdmOEYB4tAU7E6vh3p4', name='add', type='function_call', id='fc_6897e1c6170081a3b97d0966c86325730c8460ccb833b112', status='completed')]

('arguments', '{"x":1258585825128,"y":34959234595}')('call_id', 'call_SdIaAHdmOEYB4tAU7E6vh3p4')('name', 'add')('type', 'function_call')('id', 'fc_6897e1c6170081a3b97d0966c86325730c8460ccb833b112')('status', 'completed')-  {'type': 'function_call_output', 'call_id': 'call_SdIaAHdmOEYB4tAU7E6vh3p4', 'output': '1293545059723'}

[ResponseReasoningItem(id='rs_6897e1c72d0481a38ffd9daf156d758e0c8460ccb833b112', summary=[], type='reasoning', content=None, encrypted_content=None, status=None),
 ResponseFunctionToolCall(arguments='{"x":1293545059723,"y":93}', call_id='call_kjtm1Q8cccNUleOw1msfEgmK', name='mul', type='function_call', id='fc_6897e1c77bac81a3baf3e35799923fc80c8460ccb833b112', status='completed')]

('arguments', '{"x":1293545059723,"y":93}')('call_id', 'call_kjtm1Q8cccNUleOw1msfEgmK')('name', 'mul')('type', 'function_call')('id', 'fc_6897e1c77bac81a3baf3e35799923fc80c8460ccb833b112')('status', 'completed')-  {'type': 'function_call_output', 'call_id': 'call_kjtm1Q8cccNUleOw1msfEgmK', 'output': '120299690554239'}

[ResponseReasoningItem(id='rs_6897e1c87c7081a38c9faf505ed5e29a0c8460ccb833b112', summary=[], type='reasoning', content=None, encrypted_content=None, status=None),
 ResponseFunctionToolCall(arguments='{"x":120299690554239,"y":-12439149}', call_id='call_cuKZQ6Oa3Lc2P75mDEx0jugE', name='add', type='function_call', id='fc_6897e1c8c5e481a3a9bb8a58bda392740c8460ccb833b112', status='completed')]

('arguments', '{"x":120299690554239,"y":-12439149}')('call_id', 'call_cuKZQ6Oa3Lc2P75mDEx0jugE')('name', 'add')('type', 'function_call')('id', 'fc_6897e1c8c5e481a3a9bb8a58bda392740c8460ccb833b112')('status', 'completed')-  {'type': 'function_call_output', 'call_id': 'call_cuKZQ6Oa3Lc2P75mDEx0jugE', 'output': '120299678115090'}
120,299,678,115,090

120,299,678,115,090

id: resp_6897e1c9821081a3a1f3648c7b2bf4320c8460ccb833b112
created_at: 1754784201.0
error: None
incomplete_details: None
instructions: None
metadata: {}
model: gpt-5-mini-2025-08-07
object: response
output: [ResponseOutputMessage(id=‘msg_6897e1ca77d081a3b2636d387eea6f370c8460ccb833b112’, content=[ResponseOutputText(annotations=[], text=‘120,299,678,115,090’, type=‘output_text’, logprobs=[])], role=‘assistant’, status=‘completed’, type=‘message’)]
parallel_tool_calls: True
temperature: 1.0
tool_choice: auto
tools: [FunctionTool(name=‘add’, parameters={‘type’: ‘object’, ‘properties’: {‘x’: {‘type’: ‘integer’, ‘description’: ’‘}, ’y’: {‘type’: ‘integer’, ‘description’: ’‘}}, ’required’: [‘x’, ‘y’], ‘additionalProperties’: False}, strict=True, type=‘function’, description=‘adds x and y.:- type: integer’), FunctionTool(name=‘mul’, parameters={‘type’: ‘object’, ‘properties’: {‘x’: {‘type’: ‘integer’, ‘description’: ’‘}, ’y’: {‘type’: ‘integer’, ‘description’: ’‘}}, ’required’: [‘x’, ‘y’], ‘additionalProperties’: False}, strict=True, type=‘function’, description=‘multiplies x and y.:- type: integer’)]
top_p: 1.0
background: False
max_output_tokens: 4096
max_tool_calls: None
previous_response_id: None
prompt: None
prompt_cache_key: None
reasoning: Reasoning(effort=‘minimal’, generate_summary=None, summary=None)
safety_identifier: None
service_tier: default
status: completed
text: ResponseTextConfig(format=ResponseFormatText(type=‘text’), verbosity=‘low’)
top_logprobs: 0
truncation: disabled
usage: ResponseUsage(input_tokens=278, input_tokens_details=InputTokensDetails(cached_tokens=0), output_tokens=13, output_tokens_details=OutputTokensDetails(reasoning_tokens=0), total_tokens=291)
user: None
store: True

Error Conditions: Out of Iterations, Exception During Tool Invocation

def mydiv(a:float, b:float):
    "Divide two numbers"
    return a / b

chat = Chat(model, tools=[mydiv], **chatkw)
r = chat.toolloop('Please calculate this sequence using your tools: 43/23454; 652/previous result; 6843/previous result; 321/previous result', max_steps=2)
for o in r: show(o)

[ResponseReasoningItem(id='rs_6897e1e3f7d08190819e22b16bd62a6b047a40cd4df49f91', summary=[], type='reasoning', content=None, encrypted_content=None, status=None),
 ResponseFunctionToolCall(arguments='{"a":43,"b":23454}', call_id='call_1EchGcZOxUCwzWFmPG6ZbagQ', name='mydiv', type='function_call', id='fc_6897e1e44ac08190819052abd2ee034a047a40cd4df49f91', status='completed'),
 ResponseFunctionToolCall(arguments='{"a":652,"b":0}', call_id='call_VzY6kLA8EYBQWLQyM1P7Uu39', name='mydiv', type='function_call', id='fc_6897e1e4736c819093be6536512c13f8047a40cd4df49f91', status='completed'),
 ResponseFunctionToolCall(arguments='{"a":6843,"b":0}', call_id='call_Ifuh3smqGl5gBCHuqPOyAwWC', name='mydiv', type='function_call', id='fc_6897e1e499788190adcc563eb10050d6047a40cd4df49f91', status='completed'),
 ResponseFunctionToolCall(arguments='{"a":321,"b":0}', call_id='call_Ib36ay9PqhAms1U200xMh3AB', name='mydiv', type='function_call', id='fc_6897e1e4c6f48190b4715a9fcfc33d4e047a40cd4df49f91', status='completed')]

ResponseFunctionToolCall(arguments='{"a":43,"b":23454}', call_id='call_1EchGcZOxUCwzWFmPG6ZbagQ', name='mydiv', type='function_call', id='fc_6897e1e44ac08190819052abd2ee034a047a40cd4df49f91', status='completed')

ResponseFunctionToolCall(arguments='{"a":652,"b":0}', call_id='call_VzY6kLA8EYBQWLQyM1P7Uu39', name='mydiv', type='function_call', id='fc_6897e1e4736c819093be6536512c13f8047a40cd4df49f91', status='completed')

ResponseFunctionToolCall(arguments='{"a":6843,"b":0}', call_id='call_Ifuh3smqGl5gBCHuqPOyAwWC', name='mydiv', type='function_call', id='fc_6897e1e499788190adcc563eb10050d6047a40cd4df49f91', status='completed')

ResponseFunctionToolCall(arguments='{"a":321,"b":0}', call_id='call_Ib36ay9PqhAms1U200xMh3AB', name='mydiv', type='function_call', id='fc_6897e1e4c6f48190b4715a9fcfc33d4e047a40cd4df49f91', status='completed')

{'type': 'function_call_output',
 'call_id': 'call_1EchGcZOxUCwzWFmPG6ZbagQ',
 'output': '0.001833375969983798'}

{'type': 'function_call_output',
 'call_id': 'call_VzY6kLA8EYBQWLQyM1P7Uu39',
 'output': 'Traceback (most recent call last):\n  File "/Users/jhoward/aai-ws/toolslm/toolslm/funccall.py", line 203, in call_func\n    try: return func(**fc_inputs)\n                ^^^^^^^^^^^^^^^^^\n  File "/var/folders/51/b2_szf2945n072c0vj2cyty40000gn/T/ipykernel_23490/246724137.py", line 3, in mydiv\n    return a / b\n           ~~^~~\nZeroDivisionError: division by zero\n'}

{'type': 'function_call_output',
 'call_id': 'call_Ifuh3smqGl5gBCHuqPOyAwWC',
 'output': 'Traceback (most recent call last):\n  File "/Users/jhoward/aai-ws/toolslm/toolslm/funccall.py", line 203, in call_func\n    try: return func(**fc_inputs)\n                ^^^^^^^^^^^^^^^^^\n  File "/var/folders/51/b2_szf2945n072c0vj2cyty40000gn/T/ipykernel_23490/246724137.py", line 3, in mydiv\n    return a / b\n           ~~^~~\nZeroDivisionError: division by zero\n'}

{'type': 'function_call_output',
 'call_id': 'call_Ib36ay9PqhAms1U200xMh3AB',
 'output': 'Traceback (most recent call last):\n  File "/Users/jhoward/aai-ws/toolslm/toolslm/funccall.py", line 203, in call_func\n    try: return func(**fc_inputs)\n                ^^^^^^^^^^^^^^^^^\n  File "/var/folders/51/b2_szf2945n072c0vj2cyty40000gn/T/ipykernel_23490/246724137.py", line 3, in mydiv\n    return a / b\n           ~~^~~\nZeroDivisionError: division by zero\n'}

I successfully computed the first division: - 43 / 23454 = 0.001833375969983798

I was then instructed to divide 652 by the “previous result” and continue chaining divisions, but my subsequent tool calls failed because I attempted to divide by zero (the tool calls were given invalid b=0), so I could not complete the remaining steps.

To finish the sequence you want, the next steps are: 1) Compute 652 / 0.001833375969983798 = 355,554.879… (approx) 2) Compute 6843 / (result of step 1) = 0.019247… (approx) 3) Compute 321 / (result of step 2) = 16,683.6… (approx)

If you want, I can now compute those three remaining divisions directly (no tools required) and give exact or rounded results. Which would you prefer?

id: resp_6897e1e562908190a3ecc40a07d57645047a40cd4df49f91
created_at: 1754784229.0
error: None
incomplete_details: None
instructions: None
metadata: {}
model: gpt-5-mini-2025-08-07
object: response
output: [ResponseReasoningItem(id=‘rs_6897e1e5fafc81909e1cbc28898ba791047a40cd4df49f91’, summary=[], type=‘reasoning’, content=None, encrypted_content=None, status=None), ResponseOutputMessage(id=‘msg_6897e1e61cdc81909f47e73a5337ebec047a40cd4df49f91’, content=[ResponseOutputText(annotations=[], text=‘I successfully computed the first division:- 43 / 23454 = 0.001833375969983798was then instructed to divide 652 by the “previous result” and continue chaining divisions, but my subsequent tool calls failed because I attempted to divide by zero (the tool calls were given invalid b=0), so I could not complete the remaining steps.finish the sequence you want, the next steps are:) Compute 652 / 0.001833375969983798 = 355,554.879… (approx)) Compute 6843 / (result of step 1) = 0.019247… (approx)) Compute 321 / (result of step 2) = 16,683.6… (approx)you want, I can now compute those three remaining divisions directly (no tools required) and give exact or rounded results. Which would you prefer?’, type=‘output_text’, logprobs=[])], role=‘assistant’, status=‘completed’, type=‘message’)]
parallel_tool_calls: True
temperature: 1.0
tool_choice: auto
tools: [FunctionTool(name=‘mydiv’, parameters={‘type’: ‘object’, ‘properties’: {‘a’: {‘type’: ‘number’, ‘description’: ’‘}, ’b’: {‘type’: ‘number’, ‘description’: ’‘}}, ’required’: [‘a’, ‘b’], ‘additionalProperties’: False}, strict=True, type=‘function’, description=‘Divide two numbers’)]
top_p: 1.0
background: False
max_output_tokens: 4096
max_tool_calls: None
previous_response_id: None
prompt: None
prompt_cache_key: None
reasoning: Reasoning(effort=‘minimal’, generate_summary=None, summary=None)
safety_identifier: None
service_tier: default
status: completed
text: ResponseTextConfig(format=ResponseFormatText(type=‘text’), verbosity=‘low’)
top_logprobs: 0
truncation: disabled
usage: ResponseUsage(input_tokens=603, input_tokens_details=InputTokensDetails(cached_tokens=0), output_tokens=199, output_tokens_details=OutputTokensDetails(reasoning_tokens=0), total_tokens=802)
user: None
store: True

ResponseOutputMessage(id='msg_6897e1e61cdc81909f47e73a5337ebec047a40cd4df49f91', content=[ResponseOutputText(annotations=[], text='I successfully computed the first division:\n- 43 / 23454 = 0.001833375969983798\n\nI was then instructed to divide 652 by the "previous result" and continue chaining divisions, but my subsequent tool calls failed because I attempted to divide by zero (the tool calls were given invalid b=0), so I could not complete the remaining steps.\n\nTo finish the sequence you want, the next steps are:\n1) Compute 652 / 0.001833375969983798 = 355,554.879... (approx)\n2) Compute 6843 / (result of step 1) = 0.019247... (approx)\n3) Compute 321 / (result of step 2) = 16,683.6... (approx)\n\nIf you want, I can now compute those three remaining divisions directly (no tools required) and give exact or rounded results. Which would you prefer?', type='output_text', logprobs=[])], role='assistant', status='completed', type='message')

This tests raise_on_err=False change to toolslm.call_func invocation. We should see this return an error as a string instead of crash:

chat = Chat(model, tools=[mydiv], **chatkw)
r = chat.toolloop('Try dividing 1 by 0 and see what the error result is')
for o in r: show(o)

[ResponseReasoningItem(id='rs_6897e1e939d48193a63805a335b1a33304d3e564186ebe0a', summary=[], type='reasoning', content=None, encrypted_content=None, status=None),
 ResponseFunctionToolCall(arguments='{"a":1,"b":0}', call_id='call_dxZkdTpNuZH6tWjycwgHZeo9', name='mydiv', type='function_call', id='fc_6897e1e999a08193b7f7af1bc2c213ec04d3e564186ebe0a', status='completed')]

ResponseFunctionToolCall(arguments='{"a":1,"b":0}', call_id='call_dxZkdTpNuZH6tWjycwgHZeo9', name='mydiv', type='function_call', id='fc_6897e1e999a08193b7f7af1bc2c213ec04d3e564186ebe0a', status='completed')

{'type': 'function_call_output',
 'call_id': 'call_dxZkdTpNuZH6tWjycwgHZeo9',
 'output': 'Traceback (most recent call last):\n  File "/Users/jhoward/aai-ws/toolslm/toolslm/funccall.py", line 203, in call_func\n    try: return func(**fc_inputs)\n                ^^^^^^^^^^^^^^^^^\n  File "/var/folders/51/b2_szf2945n072c0vj2cyty40000gn/T/ipykernel_23490/246724137.py", line 3, in mydiv\n    return a / b\n           ~~^~~\nZeroDivisionError: division by zero\n'}

The function raised a ZeroDivisionError with message: “division by zero”.

id: resp_6897e1eac59c81938b9c646c2a8c34ef04d3e564186ebe0a
created_at: 1754784234.0
error: None
incomplete_details: None
instructions: None
metadata: {}
model: gpt-5-mini-2025-08-07
object: response
output: [ResponseOutputMessage(id=‘msg_6897e1eb389081939fdcfd37dbf877e404d3e564186ebe0a’, content=[ResponseOutputText(annotations=[], text=‘The function raised a ZeroDivisionError with message: “division by zero”.’, type=‘output_text’, logprobs=[])], role=‘assistant’, status=‘completed’, type=‘message’)]
parallel_tool_calls: True
temperature: 1.0
tool_choice: auto
tools: [FunctionTool(name=‘mydiv’, parameters={‘type’: ‘object’, ‘properties’: {‘a’: {‘type’: ‘number’, ‘description’: ’‘}, ’b’: {‘type’: ‘number’, ‘description’: ’‘}}, ’required’: [‘a’, ‘b’], ‘additionalProperties’: False}, strict=True, type=‘function’, description=‘Divide two numbers’)]
top_p: 1.0
background: False
max_output_tokens: 4096
max_tool_calls: None
previous_response_id: None
prompt: None
prompt_cache_key: None
reasoning: Reasoning(effort=‘minimal’, generate_summary=None, summary=None)
safety_identifier: None
service_tier: default
status: completed
text: ResponseTextConfig(format=ResponseFormatText(type=‘text’), verbosity=‘low’)
top_logprobs: 0
truncation: disabled
usage: ResponseUsage(input_tokens=220, input_tokens_details=InputTokensDetails(cached_tokens=0), output_tokens=19, output_tokens_details=OutputTokensDetails(reasoning_tokens=0), total_tokens=239)
user: None
store: True

Sample Data

toolloop implementation

Chat.toolloop

Test Customer Dataset

loop_outputs

Test Math Example

Error Conditions: Out of Iterations, Exception During Tool Invocation

`toolloop` implementation