[Bugfix] Clear response format constraints when tool_choice is auto

[pablopupo]

FIX #39929

Purpose

When a chat request has tools with tool_choice: "auto" (or unset/null, which default to auto) plus a response_format, the format becomes a structured output constraint that boxes decoding into plain JSON, so tool-call tokens can never be emitted and tool_calls comes back empty. #32006 fixed this combination for tool_choice: "required"; this extends the same handling to auto.

ToolParser.adjust_request now clears response_format on ChatCompletionRequest, and the text.format analog on ResponsesRequest, when tool_choice is auto or null. tool_choice: "none" keeps the caller's format since tools are disabled anyway. The condition matches what @sfeng33 suggested in the #39969 review.

Two earlier attempts exist. #39969 implemented the ChatCompletion half, got an approach LGTM, stalled on @chaunceyjiang's request for an E2E test, and was closed by its author as stale. #47394 handles tool_choice == "auto" on ChatCompletionRequest only. This PR differs from both in three ways. It covers an explicit null tool_choice, which request validation leaves in place and which the #39969 review asked to include. It clears the equivalent text.format constraint on the Responses API path, where the same suppression happens through to_sampling_params. And it adds the E2E regression test that was requested on #39969, mirroring the existing test_response_format_with_tool_choice_required. The new E2E test is excluded from the Rust frontend Buildkite job the same way that variant already is.

AI assistance was used on this PR. I reviewed every line and ran the tests below.

Test Plan

Unit tests, no GPU needed

pytest tests/tool_use/test_tool_parser_adjust_request.py -v

E2E, needs the CI GPU runners

pytest tests/tool_use/test_chat_completions.py -k test_response_format_with_tool_choice_auto

Test Result

All 8 unit tests pass locally on a CPU build. The auto, unset, and null cases plus the Responses case fail without the fix, verified by stashing the change. The E2E test follows the shape of the existing required-variant regression test for #32006.

[claude]

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[harche]

Adding downstream context that raises the priority of this fix.

The OpenAI Agents SDK sends the agent's output_type schema as response_format on every model call, including turns where the model is supposed to call tools (openai_responses.py#L781). Since vLLM turns response_format into a decoding constraint from the first token (chat_completion/protocol.py#L632-L648), every agent that combines tools with structured output hits #39929 and never calls its tools. This was reported upstream in openai/openai-agents-python#3709 and the maintainer closed it as a vLLM limitation, declining an SDK workaround: comment. Quoting: "the reported failure occurs because vLLM currently cannot reliably compose those two features and lets its guided-decoding constraint suppress tool calls." So this has to be fixed here.

One note on scope. OpenAI's behavior is composition rather than removal: tool calls are allowed and the schema still constrains the final answer when the model does not call a tool. Clearing response_format restores tool calling but leaves the final turn unconstrained, so client side validation of output_type can still fail. That seems fine as the near term fix, consistent with what #32006 established for required, but full parity needs a grammar of the form "tool call or schema JSON", along the lines of RFC #19097 and the structural tag technique #35904 uses for reasoning. Maybe worth tracking that as follow up in #39929 so this PR can land as is.

[harche]

@pablopupo thanks for picking this up, third time's the charm hopefully. Curious whether you would be open to expanding the scope here, or in a follow up, to fully close the gap with OpenAI's behavior instead of dropping the constraint.

The composed version would keep response_format in the request and build a grammar that permits either a tool call or JSON matching the schema, so the model can still call tools on intermediate turns while the final answer stays schema enforced. xgrammar's structural tags look like the right primitive: #35904 already uses TriggeredTagsFormat to scope a schema to the GPT OSS final channel, and the same idea should extend to tool calls, with the tool call start tokens as triggers. The tricky part is that those trigger tokens are model specific and live in the tool parsers, so the grammar construction would need input from ToolParser rather than just to_sampling_params.

If that feels too big for this PR, totally reasonable to land this as is and track the composed grammar separately in #39929. Happy to help test either way, I have an agents SDK reproduction from openai/openai-agents-python#3709 handy.

[pablopupo]

Thanks for the downstream context, that openai-agents thread is a good pin for why this matters. Agreed on landing this as the near term fix, clearing response_format restores tool calling and matches what #32006 did for required, with the known tradeoff that the final turn is then unconstrained and client side output_type validation can still fail.

The composed grammar (tool call or schema JSON) is the right full fix, and the structural tag approach in #35904 does look like the primitive for it. The wrinkle you called out is real, the trigger tokens are model specific and live in the tool parsers, so building that grammar needs input from ToolParser rather than just to_sampling_params. I would rather not fold that into this PR, so I will open a follow up on #39929 to track the composed version and keep this one focused on unblocking the tool calls. I will take you up on the agents SDK reproduction when I get to that follow up.