Currently, when we set `parallel_tool_calls=False` in the
`model_settings`, the responses API is called with `parallel_tool_calls
== NotGiven`, which defaults to true on the Response API side
(https://platform.openai.com/docs/api-reference/responses/create#responses-create-parallel_tool_calls).
This PR attempts to fix that.
```
agent = Agent(
...,
model_settings=ModelSettings(
parallel_tool_calls=False,
),
)
```
# Fix potential infinite tool call loop by resetting tool_choice after
tool execution
## Summary
This PR fixes an issue where setting `tool_choice` to "required" or a
specific function name could cause models to get stuck in an infinite
tool call loop.
When `tool_choice` is set to force tool usage, this setting persists
across model invocations. This PR automatically resets `tool_choice` to
"auto" after tool execution, allowing the model to decide whether to
make additional tool calls in subsequent turns.
Unlike using `tool_use_behavior="stop_on_first_tool"`, this approach
lets the model continue processing tool results while preventing forced
repeated tool calls.
## Test plan
- Added tests to verify tool_choice reset behavior for both agent and
run_config settings
- Added integration test to verify the solution prevents infinite loops
- All tests pass
## Checks
- [x] I've added new tests for the fix
- [x] I've updated the relevant documentation (added comment in code)
- [x] I've run `make lint` and `make format`
- [x] I've made sure tests pass
We don't really need mypy on 3.9 (unit tests would catch any real
issues), and it causes issues with the rest of this stack.
---
[//]: # (BEGIN SAPLING FOOTER)
* #324
* #322
* #321
* #320
* __->__ #319
This update moves the tool_choice reset logic to a more appropriate location within the RunImpl class, ensuring that the original agent's model_settings remains unmodified during the reset process. The logic now checks for problematic scenarios before creating a modified copy of the agent's settings, maintaining expected behavior across sequential runs. This change enhances clarity and efficiency in handling tool choices.
Addresses previous feedback regarding the modification of the agent instance and improves the overall structure of the reset logic.
This fixes the issue where the original agent's model_settings was being directly modified during the tool choice reset process. The original implementation caused the agent's tool_choice to unintentionally reset to "auto" for subsequent runs, which could be unexpected behavior.
The fix creates new copies of the agent and model settings objects using dataclasses.replace() instead of modifying the original objects. This ensures that the tool choice reset is limited to the current run only, maintaining the expected behavior for sequential runs with the same agent.
Addresses feedback from @baderalfahad about the agent instance being modified when it should maintain its original state between runs.
- Refactor tool_choice reset to target only problematic edge cases
- Replace manual ModelSettings recreation with dataclasses.replace
- Fix line length and error handling lint issues in tests
Fix circular dependency in voice streamed example by renaming agents.py
to my_workflow.py
Fix circular dependency in voice streamed example by renaming agents
#291
Martín Bravo
Rohan Mehta
apeccaud
madroid
xianghuijin
Raduan77
Aviral Garg
Scott Condron
Han Hwang Lim
Dmitry Pimenov
James Hills
Alex Hall
Richie Caputo
Jannik Maierhöfer