vllm-reasoning-parsers

name	vllm-reasoning-parsers
description	vLLM reasoning-parser operator + developer reference. `--reasoning-parser` CLI wiring, `ReasoningParser` contract (non-streaming `extract_reasoning` + per-delta `extract_reasoning_streaming`), `is_reasoning_end` xgrammar gating, `--structured-outputs-config.enable_in_reasoning` bypass, 22 built-in parsers with per-model quirks, 15 production pitfalls, authoring custom parsers via `@ReasoningParserManager.register_module` or plugin.
when_to_use	Trigger on `--reasoning-parser`, `reasoning_content`, `reasoning_parser_plugin`, `<think>`/`</think>` handling, `extract_reasoning`, `extract_reasoning_streaming`, `is_reasoning_end`, `ReasoningParser`, `ReasoningParserManager`, harmony channels (`<|channel|>analysis`/`final`), Qwen3 prompt-side `<think>`, DeepSeek-R1 missing start tag, Kimi K2 tool-section implicit end, Hunyuan state machine, Granite phrase markers, `enable_thinking=False`, `chat_template_kwargs={enable_thinking,thinking}`, reasoning-vs-content split, reasoning leaked into content, content empty while reasoning filled, structured output breaking with thinking disabled, tool calls not parsed when reasoning is on, adding support for a new reasoning model. Symptoms — "why is `reasoning_content` null on DeepSeek-R1", "Qwen3 JSON gibberish with `enable_thinking=False`". Applies even without "parser" in prompt. Also implicit — "audit reasoning config", "deploy-memo reasoning", "thinking split wrong". NOT for prompt-side Jinja (→ `vllm-chat-templates`) or tool-call JSON extraction (→ `vllm-tool-parsers`).

vLLM reasoning parsers

Target: operators wiring up --reasoning-parser NAME on a chat-completion endpoint, or developers authoring a parser for a new thinking model. Source of truth: vllm/reasoning/ on main.

What a reasoning parser actually does

When a reasoning-trained model emits a single token stream like

<think>user asked X, let me check Y...</think>The answer is 42.

vLLM splits this into two fields on the chat-completion response: reasoning (the CoT) and content (the final answer). --reasoning-parser NAME selects the class that does the split. Without it, the whole stream lands in content.

Field-name note. On current main the field is reasoning (see ChatCompletionResponseMessage.reasoning / DeltaMessage.reasoning in vllm/entrypoints/openai/chat_completion/protocol.py). Pre-v0.19 code and many third-party docs / clients call it reasoning_content. If a client is reading reasoning_content against a current-main server it will see null every time even when the parser ran correctly.

The parser is also the gating authority for xgrammar / structured output: by default, grammar enforcement is held off until is_reasoning_end(input_ids) flips true, so the model thinks freely before being constrained to JSON. Flip that default with --structured-outputs-config.enable_in_reasoning=true — then the grammar applies from token 0 regardless of reasoning state (useful for structured CoT).

The contract (ReasoningParser ABC)

vllm/reasoning/abs_reasoning_parsers.py. Every parser implements:

Method	Called by	Purpose
is_reasoning_end(input_ids)	xgrammar, non-streaming serving, tool-call gate	Has </think>-equivalent been emitted yet?
is_reasoning_end_streaming(input_ids, delta_ids)	xgrammar per decode step	Same, but cheap — checks only the delta
extract_content_ids(input_ids) -> list[int]	structured output	Token IDs of post-reasoning content
extract_reasoning(model_output, request) -> (reasoning, content)	non-streaming chat completion	Full-string split; either field may be None
extract_reasoning_streaming(previous_text, current_text, delta_text, previous_token_ids, current_token_ids, delta_token_ids) -> DeltaMessage | None	streaming chat completion	Per-delta split; returns DeltaMessage(reasoning=..., content=...) or None to swallow

Optional:

• count_reasoning_tokens(token_ids) — for usage accounting. Default returns 0. BaseThinkingReasoningParser implements it via depth counter so nested <think>…<think>…</think>…</think> are handled.
• adjust_request(request) — mutate request (rare).
• prepare_structured_tag(original, tool_server) — emit a structural_tag JSON (GPT-OSS uses this for harmony).
• reasoning_start_str / reasoning_end_str — expose delimiter strings so ReasoningConfig.initialize_token_ids can derive the multi-token ID sequences automatically.

Streaming vs. non-streaming are two independent code paths. A parser that's correct on extract_reasoning can be buggy on extract_reasoning_streaming and vice versa. Every parser must be tested on both.

The CLI path (what --reasoning-parser qwen3 actually triggers)

vllm/engine/arg_utils.py:552 declares reasoning_parser: str = StructuredOutputsConfig.reasoning_parser. api_server.py validates it against ReasoningParserManager.list_registered() at startup (invalid name = fast-fail with the list of registered names).

On request, OpenAIServingChat instantiates a fresh parser per request via self.reasoning_parser_cls(tokenizer, chat_template_kwargs=chat_template_kwargs) (vllm/entrypoints/openai/chat_completion/serving.py:240). "Fresh per request" is load-bearing for stateful parsers — see Hunyuan in the matrix.

Registry: vllm/reasoning/__init__.py has a _REASONING_PARSERS_TO_REGISTER dict of name → (filename, ClassName) that feeds ReasoningParserManager.register_lazy_module. Lazy import means a broken parser file won't crash vLLM startup until somebody selects it.

Plugin path: --reasoning-parser-plugin /path/to/my_parser.py calls ReasoningParserManager.import_reasoning_parser(path), which importlib-loads the file. The file registers itself via @ReasoningParserManager.register_module(["my-name"]) at import time. Then --reasoning-parser my-name selects it.

The 15 things that go wrong

See references/pitfalls.md for each with repros and fixes. Quick index:

1. reasoning_content is null on DeepSeek-R1 — chat template injected <think> into the prompt, so the model never emitted a start token. Parser must tolerate missing start (base BaseThinkingReasoningParser does, via the .partition(start_token) pattern).

2. content is empty, CoT in reasoning_content with enable_thinking=False — parser didn't branch on chat_template_kwargs. Qwen3 / DeepSeek-V3 / Kimi K2 route to IdentityReasoningParser (or internal flag) when thinking is off. DeepSeek-V3 has two names: deepseek_v3 (thinking-default-off) and glm45/holo2 = DeepSeekV3ReasoningWithThinkingParser (thinking-default-on).

3. Gibberish JSON when guided_json + enable_thinking=False — the reasoning parser's is_reasoning_end on the prompt must return True so xgrammar enforces from token 0. Serving layer caches this result as prompt_is_reasoning_end_arr[i]. If the parser only checks for </think> in input_ids and the thinking-disabled chat template emits <think>\n\n</think>\n\n in the prompt, this works; if it does something else, xgrammar silently stays gated.

4. Truncated output = wrong field — when the model hits max_tokens mid-reasoning, is the whole output "reasoning" or "content"? Qwen3 with enable_thinking=True (default) → (model_output, None) = all reasoning. Qwen3 with enable_thinking=False → (None, model_output) = all content. DeepSeek-R1 base → (model_output, None). Know the parser's policy before shipping.

5. Nested <think> tags — base parser's partition stops at the first </think>; if a CoT contains a literal </think> substring (sometimes seen in distillation artifacts), content gets split wrong. count_reasoning_tokens uses a depth counter so counts are right, but the split is not.

6. Tool calls break with reasoning enabled — tool parser only sees the half of 's return. If the reasoning parser returns , tool parser sees nothing. Kimi K2 handles this by treating as an implicit reasoning-end marker.

The per-model matrix

references/parser-matrix.md — one row per registered name (deepseek_r1, deepseek_v3, ernie45, gemma4, glm45, openai_gptoss, granite, holo2, hunyuan_a13b, hy_v3, kimi_k2, mimo, minimax_m2, minimax_m2_append_think, mistral, nemotron_v3, olmo3, qwen3, seed_oss, step3, step3p5) with: delimiter style, start-token-in-prompt-or-output, thinking-disable mechanism, truncation policy, structured-output gating peculiarities.

Router:

• deepseek_r1, qwen3, mimo, ernie45, minimax_m2, olmo3, seed_oss, step3, step3p5, gemma4 → <think>/</think> family, inherit BaseThinkingReasoningParser.
• deepseek_v3, glm45, holo2 → delegating wrapper; picks R1 or Identity by chat_template_kwargs.
• kimi_k2 → <think>/</think> + implicit end at tool-section.
• hunyuan_a13b → <think>\n … \n</think>\n<answer>\n … \n</answer>, token-ID state machine.
• hy_v3 → <think>/</think> (BaseThinking) + _identity_parser delegate when chat_template_kwargs.reasoning_effort == "no_think" (the default when unset). Hunyuan V3; distinct from hunyuan_a13b.
• granite → "Here is my thought process:" / "Here is my response:" regex on text.
• openai_gptoss → harmony channels; extract_reasoning is a raise NotImplementedError.
• mistral → [THINK]/[/THINK] via SpecialTokens, requires MistralTokenizer.
• nemotron_v3 → R1 base + swap fields on enable_thinking=False or force_nonempty_content=True.
• minimax_m2_append_think → identity-ish; always prepends <think> to content, never treats anything as reasoning.

Writing a custom parser

references/writing-custom-parser.md for the step-by-step. Shape:

from vllm.reasoning import ReasoningParser, ReasoningParserManager
from vllm.entrypoints.openai.engine.protocol import DeltaMessage

@ReasoningParserManager.register_module(["my_model"])
class MyReasoningParser(ReasoningParser):
    def __init__(self, tokenizer, *args, **kwargs):
        super().__init__(tokenizer, *args, **kwargs)
        # resolve delimiter token IDs via self.vocab or tokenizer.encode(...)

    def is_reasoning_end(self, input_ids): ...
    def extract_content_ids(self, input_ids): ...
    def extract_reasoning(self, model_output, request): ...
    def extract_reasoning_streaming(self, prev_text, cur_text, delta_text,
                                    prev_ids, cur_ids, delta_ids): ...

Register decorator → lazy entry in ReasoningParserManager.lazy_parsers. Pass the file to vllm serve ... --reasoning-parser-plugin /path/to/my_parser.py --reasoning-parser my_model.

Shortcuts when delimiter is a single vocab token: inherit BaseThinkingReasoningParser (vllm/reasoning/basic_parsers.py), override start_token / end_token properties only. That covers 90% of the surface — see deepseek_r1_reasoning_parser.py (32 lines) for the minimal concrete subclass.

Testing a parser

Unit tests live in tests/reasoning/ (DeepSeek R1, Qwen3, Granite, Hunyuan, GPT-OSS, Kimi K2 all have coverage). Run a single file with:

.venv/bin/python -m pytest tests/reasoning/test_qwen3_reasoning_parser.py -v

Every new parser needs tests for:

• Non-streaming: start-in-prompt, start-in-output, truncated (no end), nested, empty.
• Streaming: delta that is only the start token, only the end token, end token spanning two deltas, content-before-end-token in same delta, content-after-end-token in same delta.
• is_reasoning_end on both pre-reasoning, mid-reasoning, and post-reasoning token ID sequences.
• Thinking-disabled path if applicable.

Companion skills

• vllm-chat-templates — how the chat template injects <think> into the prompt (or not); why enable_thinking=False can change what the parser sees.
• vllm-tool-parsers — tool-call extraction runs on the post-reasoning content only. A wrong reasoning split silently breaks tool calling.
• vllm-configuration — chat_template_kwargs flows through ChatCompletionRequest.chat_template_kwargs into the parser __init__.
• vllm-performance-tuning — structured output tuning; reasoning parser choice affects xgrammar gating latency.
• vllm-input-modalities — reasoning parsers don't apply to embedding / rerank / ASR endpoints.

Upstream issue/PR anchors for each pitfall → references/sources.md.