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 = first(m for m in models if 'mini' in m)
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_0610e51711a17c8b006943fce162a0819396584fc43c687fba
created_at: 1766063329.0
error: None
incomplete_details: None
instructions: None
metadata: {}
model: gpt-5-mini-2025-08-07
object: response
output: [ResponseReasoningItem(id=‘rs_0610e51711a17c8b006943fce1b7e08193a742dd433b62cbcb’, summary=[], type=‘reasoning’, content=None, encrypted_content=None, status=None), ResponseOutputMessage(id=‘msg_0610e51711a17c8b006943fce1dfc081939b2f726e067bd88c’, 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
conversation: None
max_output_tokens: 4096
max_tool_calls: None
previous_response_id: None
prompt: None
prompt_cache_key: None
prompt_cache_retention: 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
billing: {‘payer’: ‘openai’}
store: True

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

- Retrieving customer C2

[ResponseReasoningItem(id='rs_0610e51711a17c8b006943fce2fb408193a0298d1762a4b19e', summary=[], type='reasoning', content=None, encrypted_content=None, status=None),
 ResponseFunctionToolCall(arguments='{"customer_id":"C2"}', call_id='call_1iQyH2m7zBT6AxtxpVfgOARS', name='get_customer_info', type='function_call', id='fc_0610e51711a17c8b006943fce34518819385d6cbeb29b1f63f', status='completed')]

r = chat()
r.output

[ResponseOutputMessage(id='msg_0610e51711a17c8b006943fce4a59c8193b7914069038e07b0', content=[ResponseOutputText(annotations=[], text='The email address 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_067a83d17b75c4ea006943fce594008194904f7f4b1710ab11', summary=[], type='reasoning', content=None, encrypted_content=None, status=None),
 ResponseFunctionToolCall(arguments='{"customer_id":"C1"}', call_id='call_5MXLQEl4LdzFyRHIR3DynD9I', name='get_customer_info', type='function_call', id='fc_067a83d17b75c4ea006943fce6c9848194b442cb73ffe02035', status='completed')]

r = chat()
r.output

- Cancelling order O1
- Cancelling order O2

[ResponseReasoningItem(id='rs_067a83d17b75c4ea006943fce7b6648194aa8afa477dc81c42', summary=[], type='reasoning', content=None, encrypted_content=None, status=None),
 ResponseFunctionToolCall(arguments='{"order_id":"O1"}', call_id='call_y3LWRVEn8X80nCQf50EOVOu5', name='cancel_order', type='function_call', id='fc_067a83d17b75c4ea006943fce992f88194b8de77d411f78e44', status='completed'),
 ResponseFunctionToolCall(arguments='{"order_id":"O2"}', call_id='call_oPiTCOxwXjo8uac0mzXK3rEv', name='cancel_order', type='function_call', id='fc_067a83d17b75c4ea006943fce9f498819495f077de035b7454', 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]|Omit=<openai.Omit object at
                0x7f67d1e4c850>, conversation:Optional[response_create_par
                ams.Conversation]|Omit=<openai.Omit object at
                0x7f67d1e4c850>, include:Optional[List[ResponseIncludable]
                ]|Omit=<openai.Omit object at 0x7f67d1e4c850>,
                input:Union[str,ResponseInputParam]|Omit=<openai.Omit
                object at 0x7f67d1e4c850>,
                instructions:Optional[str]|Omit=<openai.Omit object at
                0x7f67d1e4c850>,
                max_output_tokens:Optional[int]|Omit=<openai.Omit object
                at 0x7f67d1e4c850>,
                max_tool_calls:Optional[int]|Omit=<openai.Omit object at
                0x7f67d1e4c850>,
                metadata:Optional[Metadata]|Omit=<openai.Omit object at
                0x7f67d1e4c850>, model:ResponsesModel|Omit=<openai.Omit
                object at 0x7f67d1e4c850>,
                parallel_tool_calls:Optional[bool]|Omit=<openai.Omit
                object at 0x7f67d1e4c850>,
                previous_response_id:Optional[str]|Omit=<openai.Omit
                object at 0x7f67d1e4c850>,
                prompt:Optional[ResponsePromptParam]|Omit=<openai.Omit
                object at 0x7f67d1e4c850>,
                prompt_cache_key:str|Omit=<openai.Omit object at
                0x7f67d1e4c850>,
                prompt_cache_retention:"Optional[Literal['in-
                memory','24h']]|Omit"=<openai.Omit object at
                0x7f67d1e4c850>,
                reasoning:Optional[Reasoning]|Omit=<openai.Omit object at
                0x7f67d1e4c850>, safety_identifier:str|Omit=<openai.Omit
                object at 0x7f67d1e4c850>, service_tier:"Optional[Literal[
                'auto','default','flex','scale','priority']]|Omit"=<openai
                .Omit object at 0x7f67d1e4c850>,
                store:Optional[bool]|Omit=<openai.Omit object at
                0x7f67d1e4c850>, stream_options:Optional[response_create_p
                arams.StreamOptions]|Omit=<openai.Omit object at
                0x7f67d1e4c850>,
                temperature:Optional[float]|Omit=<openai.Omit object at
                0x7f67d1e4c850>,
                text:ResponseTextConfigParam|Omit=<openai.Omit object at
                0x7f67d1e4c850>,
                top_logprobs:Optional[int]|Omit=<openai.Omit object at
                0x7f67d1e4c850>, top_p:Optional[float]|Omit=<openai.Omit
                object at 0x7f67d1e4c850>, truncation:"Optional[Literal['a
                uto','disabled']]|Omit"=<openai.Omit object at
                0x7f67d1e4c850>, user:str|Omit=<openai.Omit object at
                0x7f67d1e4c850>, 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] \| Omit	<openai.Omit object at 0x7f67d1e4c850>
conversation	Optional[response_create_params.Conversation] \| Omit	<openai.Omit object at 0x7f67d1e4c850>
include	Optional[List[ResponseIncludable]] \| Omit	<openai.Omit object at 0x7f67d1e4c850>
input	Union[str, ResponseInputParam] \| Omit	<openai.Omit object at 0x7f67d1e4c850>
instructions	Optional[str] \| Omit	<openai.Omit object at 0x7f67d1e4c850>
max_output_tokens	Optional[int] \| Omit	<openai.Omit object at 0x7f67d1e4c850>
max_tool_calls	Optional[int] \| Omit	<openai.Omit object at 0x7f67d1e4c850>
metadata	Optional[Metadata] \| Omit	<openai.Omit object at 0x7f67d1e4c850>
model	ResponsesModel \| Omit	<openai.Omit object at 0x7f67d1e4c850>
parallel_tool_calls	Optional[bool] \| Omit	<openai.Omit object at 0x7f67d1e4c850>
previous_response_id	Optional[str] \| Omit	<openai.Omit object at 0x7f67d1e4c850>
prompt	Optional[ResponsePromptParam] \| Omit	<openai.Omit object at 0x7f67d1e4c850>
prompt_cache_key	str \| Omit	<openai.Omit object at 0x7f67d1e4c850>
prompt_cache_retention	Optional[Literal[‘in-memory’, ‘24h’]] \| Omit	<openai.Omit object at 0x7f67d1e4c850>
reasoning	Optional[Reasoning] \| Omit	<openai.Omit object at 0x7f67d1e4c850>
safety_identifier	str \| Omit	<openai.Omit object at 0x7f67d1e4c850>
service_tier	Optional[Literal[‘auto’, ‘default’, ‘flex’, ‘scale’, ‘priority’]] \| Omit	<openai.Omit object at 0x7f67d1e4c850>
store	Optional[bool] \| Omit	<openai.Omit object at 0x7f67d1e4c850>
stream_options	Optional[response_create_params.StreamOptions] \| Omit	<openai.Omit object at 0x7f67d1e4c850>
temperature	Optional[float] \| Omit	<openai.Omit object at 0x7f67d1e4c850>
text	ResponseTextConfigParam \| Omit	<openai.Omit object at 0x7f67d1e4c850>
top_logprobs	Optional[int] \| Omit	<openai.Omit object at 0x7f67d1e4c850>
top_p	Optional[float] \| Omit	<openai.Omit object at 0x7f67d1e4c850>
truncation	Optional[Literal[‘auto’, ‘disabled’]] \| Omit	<openai.Omit object at 0x7f67d1e4c850>
user	str \| Omit	<openai.Omit object at 0x7f67d1e4c850>
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 address for customer C1 (John Doe) is john@example.com.

id: resp_0787bac936d9204f006943fcee2ba48195ad60e3c1ec52d1d7
created_at: 1766063342.0
error: None
incomplete_details: None
instructions: None
metadata: {}
model: gpt-5-mini-2025-08-07
object: response
output: [ResponseReasoningItem(id=‘rs_0787bac936d9204f006943fcee7cd881959b82edf88db5e70d’, summary=[], type=‘reasoning’, content=None, encrypted_content=None, status=None), ResponseOutputMessage(id=‘msg_0787bac936d9204f006943fcef3ee88195832b056ec436b34f’, content=[ResponseOutputText(annotations=[], text=‘The email address for customer C1 (John Doe) is john@example.com.’, 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
conversation: None
max_output_tokens: 4096
max_tool_calls: None
previous_response_id: None
prompt: None
prompt_cache_key: None
prompt_cache_retention: 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=316, input_tokens_details=InputTokensDetails(cached_tokens=0), output_tokens=86, output_tokens_details=OutputTokensDetails(reasoning_tokens=64), total_tokens=402)
user: None
billing: {‘payer’: ‘openai’}
store: True

ResponseOutputMessage(id='msg_0787bac936d9204f006943fcef3ee88195832b056ec436b34f', content=[ResponseOutputText(annotations=[], text='The email address for customer C1 (John Doe) is john@example.com.', 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_0787bac936d9204f006943fceb6f088195a44ade2155966909',
  'summary': [],
  'type': 'reasoning',
  'content': None,
  'encrypted_content': None,
  'status': None},
 {'arguments': '{"customer_id":"C1"}',
  'call_id': 'call_wqbYqGHnvgMg8lSY9JMUrwzU',
  'name': 'get_customer_info',
  'type': 'function_call',
  'id': 'fc_0787bac936d9204f006943fcebe6d08195b9cd3ca97a01b7d2',
  'status': 'completed'},
 {'type': 'function_call_output',
  'call_id': 'call_wqbYqGHnvgMg8lSY9JMUrwzU',
  '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_0787bac936d9204f006943fcec9c108195b41ad9735c482bc4',
  'summary': [],
  'type': 'reasoning',
  'content': None,
  'encrypted_content': None,
  'status': None},
 {'id': 'msg_0787bac936d9204f006943fced6e3c8195b5b4c9710ecb6485',
  'content': [ResponseOutputText(annotations=[], text='The email address for customer C1 (John Doe) is john@example.com.', 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_0787bac936d9204f006943fcee7cd881959b82edf88db5e70d', summary=[], type='reasoning', content=None, encrypted_content=None, status=None),
 ResponseOutputMessage(id='msg_0787bac936d9204f006943fcef3ee88195832b056ec436b34f', content=[ResponseOutputText(annotations=[], text='The email address for customer C1 (John Doe) is john@example.com.', 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_080cc194cfa17c94006943fcf092688194adde78fdb74e90d3', summary=[], type='reasoning', content=None, encrypted_content=None, status=None),
 ResponseFunctionToolCall(arguments='{"order_id":"O2"}', call_id='call_ARnBd6xSrlcAfn3wBQGdLtiu', name='get_order_details', type='function_call', id='fc_080cc194cfa17c94006943fcf1ed8c8194adc4f0474afe98cc', status='completed')]

ResponseFunctionToolCall(arguments='{"order_id":"O2"}', call_id='call_ARnBd6xSrlcAfn3wBQGdLtiu', name='get_order_details', type='function_call', id='fc_080cc194cfa17c94006943fcf1ed8c8194adc4f0474afe98cc', status='completed')

{'type': 'function_call_output',
 'call_id': 'call_ARnBd6xSrlcAfn3wBQGdLtiu',
 'output': "{'id': 'O2', 'product': 'Gadget B', 'quantity': 1, 'price': 49.99, 'status': 'Processing'}"}

[ResponseOutputMessage(id='msg_080cc194cfa17c94006943fcf3101081948ac3faafe2dcac65', content=[ResponseOutputText(annotations=[], text='Order O2 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_080cc194cfa17c94006943fcf412208194a4c7b87ce81c4736', summary=[], type='reasoning', content=None, encrypted_content=None, status=None),
 ResponseFunctionToolCall(arguments='{"customer_id":"C1"}', call_id='call_Btl5F6UrkBoJJaDQzke1VvVN', name='get_customer_info', type='function_call', id='fc_080cc194cfa17c94006943fcf82ba08194a65b676e302e9aa6', status='completed')]

ResponseFunctionToolCall(arguments='{"customer_id":"C1"}', call_id='call_Btl5F6UrkBoJJaDQzke1VvVN', name='get_customer_info', type='function_call', id='fc_080cc194cfa17c94006943fcf82ba08194a65b676e302e9aa6', status='completed')

{'type': 'function_call_output',
 'call_id': 'call_Btl5F6UrkBoJJaDQzke1VvVN',
 '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_080cc194cfa17c94006943fcf913188194ab017b95ba08a13e', summary=[], type='reasoning', content=None, encrypted_content=None, status=None),
 ResponseFunctionToolCall(arguments='{"order_id":"O1"}', call_id='call_4yaVagsUWe86YMk25kyCU9x2', name='cancel_order', type='function_call', id='fc_080cc194cfa17c94006943fcfa8bd081949731f7da8c5adc86', status='completed'),
 ResponseFunctionToolCall(arguments='{"order_id":"O2"}', call_id='call_Vy0mCP8ocOYR31qk4sbVgRw5', name='cancel_order', type='function_call', id='fc_080cc194cfa17c94006943fcfab9688194a97ce8bb7ff46706', status='completed')]

ResponseFunctionToolCall(arguments='{"order_id":"O1"}', call_id='call_4yaVagsUWe86YMk25kyCU9x2', name='cancel_order', type='function_call', id='fc_080cc194cfa17c94006943fcfa8bd081949731f7da8c5adc86', status='completed')

ResponseFunctionToolCall(arguments='{"order_id":"O2"}', call_id='call_Vy0mCP8ocOYR31qk4sbVgRw5', name='cancel_order', type='function_call', id='fc_080cc194cfa17c94006943fcfab9688194a97ce8bb7ff46706', status='completed')

{'type': 'function_call_output',
 'call_id': 'call_4yaVagsUWe86YMk25kyCU9x2',
 'output': 'True'}

{'type': 'function_call_output',
 'call_id': 'call_Vy0mCP8ocOYR31qk4sbVgRw5',
 'output': 'True'}

[ResponseReasoningItem(id='rs_080cc194cfa17c94006943fcfbd120819490c3ad1d75350865', summary=[], type='reasoning', content=None, encrypted_content=None, status=None),
 ResponseOutputMessage(id='msg_080cc194cfa17c94006943fd043f948194ad1bb58d51d2de71', content=[ResponseOutputText(annotations=[], text='Done — I canceled all orders for customer C1 (John Doe).\n\nSummary:\n- O1 — Widget A — previous status: Shipped — cancellation: Success\n- O2 — Gadget B — previous status: Processing — cancellation: Success\n\nWould you like me to check refund status, send a confirmation to john@example.com, or do anything else?', type='output_text', logprobs=[])], role='assistant', status='completed', type='message')]

ResponseOutputMessage(id='msg_080cc194cfa17c94006943fd043f948194ad1bb58d51d2de71', content=[ResponseOutputText(annotations=[], text='Done — I canceled all orders for customer C1 (John Doe).\n\nSummary:\n- O1 — Widget A — previous status: Shipped — cancellation: Success\n- O2 — Gadget B — previous status: Processing — cancellation: Success\n\nWould you like me to check refund status, send a confirmation to john@example.com, or do anything else?', 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_080cc194cfa17c94006943fd06606c8194acbf56e9291c70ca
created_at: 1766063366.0
error: None
incomplete_details: None
instructions: None
metadata: {}
model: gpt-5-mini-2025-08-07
object: response
output: [ResponseReasoningItem(id=‘rs_080cc194cfa17c94006943fd06bbd4819499041c0e2a896d43’, summary=[], type=‘reasoning’, content=None, encrypted_content=None, status=None), ResponseFunctionToolCall(arguments=‘{“order_id”:“O2”}’, call_id=‘call_47iqPnLAFKxbJ4JJ9EQwWw71’, name=‘get_order_details’, type=‘function_call’, id=‘fc_080cc194cfa17c94006943fd07c3308194a643843cb1720a9e’, 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
conversation: None
max_output_tokens: 4096
max_tool_calls: None
previous_response_id: None
prompt: None
prompt_cache_key: None
prompt_cache_retention: 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=521, input_tokens_details=InputTokensDetails(cached_tokens=0), output_tokens=87, output_tokens_details=OutputTokensDetails(reasoning_tokens=64), total_tokens=608)
user: None
billing: {‘payer’: ‘openai’}
store: True

ResponseFunctionToolCall(arguments='{"order_id":"O2"}', call_id='call_47iqPnLAFKxbJ4JJ9EQwWw71', name='get_order_details', type='function_call', id='fc_080cc194cfa17c94006943fd07c3308194a643843cb1720a9e', status='completed')

{'type': 'function_call_output',
 'call_id': 'call_47iqPnLAFKxbJ4JJ9EQwWw71',
 'output': "{'id': 'O2', 'product': 'Gadget B', 'quantity': 1, 'price': 49.99, 'status': 'Cancelled'}"}

Order O2 is now: Cancelled.

id: resp_080cc194cfa17c94006943fd0841e88194b12aa8c8f30b4e64
created_at: 1766063368.0
error: None
incomplete_details: None
instructions: None
metadata: {}
model: gpt-5-mini-2025-08-07
object: response
output: [ResponseOutputMessage(id=‘msg_080cc194cfa17c94006943fd08ad488194a093653ee36021db’, content=[ResponseOutputText(annotations=[], text=‘Order O2 is now: 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
conversation: None
max_output_tokens: 4096
max_tool_calls: None
previous_response_id: None
prompt: None
prompt_cache_key: None
prompt_cache_retention: 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=676, input_tokens_details=InputTokensDetails(cached_tokens=0), output_tokens=13, output_tokens_details=OutputTokensDetails(reasoning_tokens=0), total_tokens=689)
user: None
billing: {‘payer’: ‘openai’}
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_02621606c3b513dc006943fd09ad40819781fb7b32d5e5996a', summary=[], type='reasoning', content=None, encrypted_content=None, status=None),
 ResponseFunctionToolCall(arguments='{"x":1258585825128,"y":34959234595}', call_id='call_Fg3v2kfgWIogSl1IEB0w1Y0K', name='add', type='function_call', id='fc_02621606c3b513dc006943fd09fdf881979ba71de45af8d588', status='completed')]

ResponseFunctionToolCall(arguments='{"x":1258585825128,"y":34959234595}', call_id='call_Fg3v2kfgWIogSl1IEB0w1Y0K', name='add', type='function_call', id='fc_02621606c3b513dc006943fd09fdf881979ba71de45af8d588', status='completed')

{'type': 'function_call_output',
 'call_id': 'call_Fg3v2kfgWIogSl1IEB0w1Y0K',
 'output': '1293545059723'}

[ResponseFunctionToolCall(arguments='{"x":1293545059723,"y":93}', call_id='call_9rimpAnaVInF4ssbHosAfTi8', name='mul', type='function_call', id='fc_02621606c3b513dc006943fd0bae9c81978026c7bfaae4a759', status='completed')]

{'type': 'function_call_output',
 'call_id': 'call_9rimpAnaVInF4ssbHosAfTi8',
 'output': '120299690554239'}

[ResponseFunctionToolCall(arguments='{"x":120299690554239,"y":-12439149}', call_id='call_OS43dlWKEYtYAocZpHJ94RXM', name='add', type='function_call', id='fc_02621606c3b513dc006943fd0c9f5c8197a11bc188b0174d6d', status='completed')]

{'type': 'function_call_output',
 'call_id': 'call_OS43dlWKEYtYAocZpHJ94RXM',
 'output': '120299678115090'}

120299678115090

id: resp_02621606c3b513dc006943fd0d30f48197a911787dca9e5d00
created_at: 1766063373.0
error: None
incomplete_details: None
instructions: None
metadata: {}
model: gpt-5-mini-2025-08-07
object: response
output: [ResponseOutputMessage(id=‘msg_02621606c3b513dc006943fd0d80f88197b58e845951e295e1’, content=[ResponseOutputText(annotations=[], text=‘120299678115090’, 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
conversation: None
max_output_tokens: 4096
max_tool_calls: None
previous_response_id: None
prompt: None
prompt_cache_key: None
prompt_cache_retention: 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=250, input_tokens_details=InputTokensDetails(cached_tokens=0), output_tokens=9, output_tokens_details=OutputTokensDetails(reasoning_tokens=0), total_tokens=259)
user: None
billing: {‘payer’: ‘openai’}
store: True

(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_0944024f48105103006943fd116be8819483df3a9f25e3d328', summary=[], type='reasoning', content=None, encrypted_content=None, status=None),
 ResponseFunctionToolCall(arguments='{"x":1258585825128,"y":34959234595}', call_id='call_B6xGJDpQJm5GrS2hnngybWTe', name='add', type='function_call', id='fc_0944024f48105103006943fd11abcc81949200a4eabf12e97b', status='completed')]

('arguments', '{"x":1258585825128,"y":34959234595}')('call_id', 'call_B6xGJDpQJm5GrS2hnngybWTe')('name', 'add')('type', 'function_call')('id', 'fc_0944024f48105103006943fd11abcc81949200a4eabf12e97b')('status', 'completed')-  {'type': 'function_call_output', 'call_id': 'call_B6xGJDpQJm5GrS2hnngybWTe', 'output': '1293545059723'}

[ResponseFunctionToolCall(arguments='{"x":1293545059723,"y":93}', call_id='call_Vb3v1awZxvoZuUjbq9RRZrfM', name='mul', type='function_call', id='fc_0944024f48105103006943fd1402188194a2adccf222c76591', status='completed')]

-  {'type': 'function_call_output', 'call_id': 'call_Vb3v1awZxvoZuUjbq9RRZrfM', 'output': '120299690554239'}

[ResponseFunctionToolCall(arguments='{"x":120299690554239,"y":-12439149}', call_id='call_Dd1Uzt8rMfWJCH0irbMa5OVV', name='add', type='function_call', id='fc_0944024f48105103006943fd14e54481948906bd955b405418', status='completed')]

-  {'type': 'function_call_output', 'call_id': 'call_Dd1Uzt8rMfWJCH0irbMa5OVV', 'output': '120299678115090'}
120299678115090

120299678115090

id: resp_0944024f48105103006943fd1583ec8194863012f06a95bb9a
created_at: 1766063381.0
error: None
incomplete_details: None
instructions: None
metadata: {}
model: gpt-5-mini-2025-08-07
object: response
output: [ResponseOutputMessage(id=‘msg_0944024f48105103006943fd16463c81949df34dd0158af4c1’, content=[ResponseOutputText(annotations=[], text=‘120299678115090’, 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
conversation: None
max_output_tokens: 4096
max_tool_calls: None
previous_response_id: None
prompt: None
prompt_cache_key: None
prompt_cache_retention: 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=250, input_tokens_details=InputTokensDetails(cached_tokens=0), output_tokens=9, output_tokens_details=OutputTokensDetails(reasoning_tokens=0), total_tokens=259)
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_02f9e0725e9fb6a5006943fd16f3dc81979a3aa89dfcb98aca', summary=[], type='reasoning', content=None, encrypted_content=None, status=None),
 ResponseFunctionToolCall(arguments='{"a":43,"b":23454}', call_id='call_Fnsc5iNP8rMuj0rqeBLzXVsO', name='mydiv', type='function_call', id='fc_02f9e0725e9fb6a5006943fd177c6c8197a5ad34f31755933a', status='completed'),
 ResponseFunctionToolCall(arguments='{"a":652,"b":0}', call_id='call_k7APDmfK4dy57LeiHYhBCNq6', name='mydiv', type='function_call', id='fc_02f9e0725e9fb6a5006943fd17b4d08197ada7efed9792c679', status='completed'),
 ResponseFunctionToolCall(arguments='{"a":6843,"b":0}', call_id='call_ZpzLBH2zM3Wcy9aoFCbnZ0mN', name='mydiv', type='function_call', id='fc_02f9e0725e9fb6a5006943fd17e5bc8197b3e6f2903db6ce9d', status='completed'),
 ResponseFunctionToolCall(arguments='{"a":321,"b":0}', call_id='call_JGS2KeOU7ku3n56kqbSrvEGy', name='mydiv', type='function_call', id='fc_02f9e0725e9fb6a5006943fd181dd88197bd6492c3465327ea', status='completed')]

ResponseFunctionToolCall(arguments='{"a":43,"b":23454}', call_id='call_Fnsc5iNP8rMuj0rqeBLzXVsO', name='mydiv', type='function_call', id='fc_02f9e0725e9fb6a5006943fd177c6c8197a5ad34f31755933a', status='completed')

ResponseFunctionToolCall(arguments='{"a":652,"b":0}', call_id='call_k7APDmfK4dy57LeiHYhBCNq6', name='mydiv', type='function_call', id='fc_02f9e0725e9fb6a5006943fd17b4d08197ada7efed9792c679', status='completed')

ResponseFunctionToolCall(arguments='{"a":6843,"b":0}', call_id='call_ZpzLBH2zM3Wcy9aoFCbnZ0mN', name='mydiv', type='function_call', id='fc_02f9e0725e9fb6a5006943fd17e5bc8197b3e6f2903db6ce9d', status='completed')

ResponseFunctionToolCall(arguments='{"a":321,"b":0}', call_id='call_JGS2KeOU7ku3n56kqbSrvEGy', name='mydiv', type='function_call', id='fc_02f9e0725e9fb6a5006943fd181dd88197bd6492c3465327ea', status='completed')

{'type': 'function_call_output',
 'call_id': 'call_Fnsc5iNP8rMuj0rqeBLzXVsO',
 'output': '0.001833375969983798'}

{'type': 'function_call_output',
 'call_id': 'call_k7APDmfK4dy57LeiHYhBCNq6',
 'output': 'Traceback (most recent call last):\n  File "/usr/local/lib/python3.12/site-packages/toolslm/funccall.py", line 215, in call_func\n    try: return func(**inps)\n                ^^^^^^^^^^^^\n  File "/tmp/ipykernel_5385/246724137.py", line 3, in mydiv\n    return a / b\n           ~~^~~\nZeroDivisionError: division by zero\n'}

{'type': 'function_call_output',
 'call_id': 'call_ZpzLBH2zM3Wcy9aoFCbnZ0mN',
 'output': 'Traceback (most recent call last):\n  File "/usr/local/lib/python3.12/site-packages/toolslm/funccall.py", line 215, in call_func\n    try: return func(**inps)\n                ^^^^^^^^^^^^\n  File "/tmp/ipykernel_5385/246724137.py", line 3, in mydiv\n    return a / b\n           ~~^~~\nZeroDivisionError: division by zero\n'}

{'type': 'function_call_output',
 'call_id': 'call_JGS2KeOU7ku3n56kqbSrvEGy',
 'output': 'Traceback (most recent call last):\n  File "/usr/local/lib/python3.12/site-packages/toolslm/funccall.py", line 215, in call_func\n    try: return func(**inps)\n                ^^^^^^^^^^^^\n  File "/tmp/ipykernel_5385/246724137.py", line 3, in mydiv\n    return a / b\n           ~~^~~\nZeroDivisionError: division by zero\n'}

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

I attempted the next steps but they failed because I tried to divide by zero (I passed 0 as the “previous result” for subsequent operations), causing errors. To complete the sequence you want, I need to perform these successive calculations using the preceding result each time: 1. 652 / (43/23454) 2. 6843 / (result of step 2) 3. 321 / (result of step 3)

If you want, I can now: - Recompute the chain without tool limits and give all four results, or - Compute them step-by-step here directly (no tools needed). Which do you prefer?

id: resp_02f9e0725e9fb6a5006943fd18bff081979e13bb5930ee32b7
created_at: 1766063384.0
error: None
incomplete_details: None
instructions: None
metadata: {}
model: gpt-5-mini-2025-08-07
object: response
output: [ResponseReasoningItem(id=‘rs_02f9e0725e9fb6a5006943fd19334c8197b4b20beb1ae09493’, summary=[], type=‘reasoning’, content=None, encrypted_content=None, status=None), ResponseOutputMessage(id=‘msg_02f9e0725e9fb6a5006943fd195a9c81978fb9bcca07f80c87’, content=[ResponseOutputText(annotations=[], text=‘I computed the first division successfully:- 43 / 23454 = 0.001833375969983798attempted the next steps but they failed because I tried to divide by zero (I passed 0 as the “previous result” for subsequent operations), causing errors. To complete the sequence you want, I need to perform these successive calculations using the preceding result each time:. 652 / (43/23454). 6843 / (result of step 2). 321 / (result of step 3)you want, I can now:- Recompute the chain without tool limits and give all four results, or- Compute them step-by-step here directly (no tools needed). Which do 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
conversation: None
max_output_tokens: 4096
max_tool_calls: None
previous_response_id: None
prompt: None
prompt_cache_key: None
prompt_cache_retention: 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=537, input_tokens_details=InputTokensDetails(cached_tokens=0), output_tokens=163, output_tokens_details=OutputTokensDetails(reasoning_tokens=0), total_tokens=700)
user: None
billing: {‘payer’: ‘openai’}
store: True

ResponseOutputMessage(id='msg_02f9e0725e9fb6a5006943fd195a9c81978fb9bcca07f80c87', content=[ResponseOutputText(annotations=[], text='I computed the first division successfully:\n- 43 / 23454 = 0.001833375969983798\n\nI attempted the next steps but they failed because I tried to divide by zero (I passed 0 as the “previous result” for subsequent operations), causing errors. To complete the sequence you want, I need to perform these successive calculations using the preceding result each time:\n1. 652 / (43/23454)\n2. 6843 / (result of step 2)\n3. 321 / (result of step 3)\n\nIf you want, I can now:\n- Recompute the chain without tool limits and give all four results, or\n- Compute them step-by-step here directly (no tools needed). Which do 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_0af57bb05fb6f746006943fd1bd07c81958a319ad4cb70eae8', summary=[], type='reasoning', content=None, encrypted_content=None, status=None),
 ResponseFunctionToolCall(arguments='{"a":1,"b":0}', call_id='call_0FGtqnXi4rLuPC0ivZcaRw24', name='mydiv', type='function_call', id='fc_0af57bb05fb6f746006943fd1c29a08195989bf0c4f670baca', status='completed')]

ResponseFunctionToolCall(arguments='{"a":1,"b":0}', call_id='call_0FGtqnXi4rLuPC0ivZcaRw24', name='mydiv', type='function_call', id='fc_0af57bb05fb6f746006943fd1c29a08195989bf0c4f670baca', status='completed')

{'type': 'function_call_output',
 'call_id': 'call_0FGtqnXi4rLuPC0ivZcaRw24',
 'output': 'Traceback (most recent call last):\n  File "/usr/local/lib/python3.12/site-packages/toolslm/funccall.py", line 215, in call_func\n    try: return func(**inps)\n                ^^^^^^^^^^^^\n  File "/tmp/ipykernel_5385/246724137.py", line 3, in mydiv\n    return a / b\n           ~~^~~\nZeroDivisionError: division by zero\n'}

ZeroDivisionError: division by zero

id: resp_0af57bb05fb6f746006943fd1cbdac8195b5d480c66c146b06
created_at: 1766063388.0
error: None
incomplete_details: None
instructions: None
metadata: {}
model: gpt-5-mini-2025-08-07
object: response
output: [ResponseOutputMessage(id=‘msg_0af57bb05fb6f746006943fd1d335881958929318eb6ad6c40’, content=[ResponseOutputText(annotations=[], text=‘ZeroDivisionError: 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
conversation: None
max_output_tokens: 4096
max_tool_calls: None
previous_response_id: None
prompt: None
prompt_cache_key: None
prompt_cache_retention: 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=198, input_tokens_details=InputTokensDetails(cached_tokens=0), output_tokens=11, output_tokens_details=OutputTokensDetails(reasoning_tokens=0), total_tokens=209)
user: None
billing: {‘payer’: ‘openai’}
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