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+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# Structured Outputs For Reasoning Models\n",
+    "\n",
+    "When working with reasoning models that use special tokens like `<think>...</think>` to denote reasoning sections, you might want to allow free-form text within these sections while still enforcing grammar constraints on the rest of the output.\n",
+    "\n",
+    "SGLang provides a feature to disable grammar restrictions within reasoning sections. This is particularly useful for models that need to perform complex reasoning steps before providing a structured output.\n",
+    "\n",
+    "To enable this feature, use the `--reasoning-parser` flag which decide the think_end_token, such as `</think>`, when launching the server. You can also specify the reasoning parser using the `--reasoning-parser` flag.\n",
+    "\n",
+    "## Supported Models\n",
+    "\n",
+    "Currently, SGLang supports the following reasoning models:\n",
+    "- [DeepSeek R1 series](https://huggingface.co/collections/deepseek-ai/deepseek-r1-678e1e131c0169c0bc89728d): The reasoning content is wrapped with `<think>` and `</think>` tags.\n",
+    "- [QwQ](https://huggingface.co/Qwen/QwQ-32B): The reasoning content is wrapped with `<think>` and `</think>` tags.\n",
+    "\n",
+    "\n",
+    "## Usage\n",
+    "\n",
+    "## OpenAI Compatible API"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Specify the `--grammar-backend`, `--reasoning-parser` option."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import openai\n",
+    "import os\n",
+    "from sglang.test.test_utils import is_in_ci\n",
+    "\n",
+    "if is_in_ci():\n",
+    "    from patch import launch_server_cmd\n",
+    "else:\n",
+    "    from sglang.utils import launch_server_cmd\n",
+    "\n",
+    "from sglang.utils import wait_for_server, print_highlight, terminate_process\n",
+    "\n",
+    "os.environ[\"TOKENIZERS_PARALLELISM\"] = \"false\"\n",
+    "\n",
+    "\n",
+    "server_process, port = launch_server_cmd(\n",
+    "    \"python -m sglang.launch_server --model-path deepseek-ai/DeepSeek-R1-Distill-Qwen-7B --host 0.0.0.0 --reasoning-parser deepseek-r1\"\n",
+    ")\n",
+    "\n",
+    "wait_for_server(f\"http://localhost:{port}\")\n",
+    "client = openai.Client(base_url=f\"http://127.0.0.1:{port}/v1\", api_key=\"None\")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### JSON\n",
+    "\n",
+    "you can directly define a JSON schema or use [Pydantic](https://docs.pydantic.dev/latest/) to define and validate the response."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "**Using Pydantic**"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from pydantic import BaseModel, Field\n",
+    "\n",
+    "\n",
+    "# Define the schema using Pydantic\n",
+    "class CapitalInfo(BaseModel):\n",
+    "    name: str = Field(..., pattern=r\"^\\w+$\", description=\"Name of the capital city\")\n",
+    "    population: int = Field(..., description=\"Population of the capital city\")\n",
+    "\n",
+    "\n",
+    "response = client.chat.completions.create(\n",
+    "    model=\"deepseek-ai/DeepSeek-R1-Distill-Qwen-7B\",\n",
+    "    messages=[\n",
+    "        {\n",
+    "            \"role\": \"user\",\n",
+    "            \"content\": \"Please generate the information of the capital of France in the JSON format.\",\n",
+    "        },\n",
+    "    ],\n",
+    "    temperature=0,\n",
+    "    max_tokens=2048,\n",
+    "    response_format={\n",
+    "        \"type\": \"json_schema\",\n",
+    "        \"json_schema\": {\n",
+    "            \"name\": \"foo\",\n",
+    "            # convert the pydantic model to json schema\n",
+    "            \"schema\": CapitalInfo.model_json_schema(),\n",
+    "        },\n",
+    "    },\n",
+    ")\n",
+    "\n",
+    "print_highlight(\n",
+    "    f\"reasoing_content: {response.choices[0].message.reasoning_content}\\n\\ncontent: {response.choices[0].message.content}\"\n",
+    ")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "**JSON Schema Directly**\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import json\n",
+    "\n",
+    "json_schema = json.dumps(\n",
+    "    {\n",
+    "        \"type\": \"object\",\n",
+    "        \"properties\": {\n",
+    "            \"name\": {\"type\": \"string\", \"pattern\": \"^[\\\\w]+$\"},\n",
+    "            \"population\": {\"type\": \"integer\"},\n",
+    "        },\n",
+    "        \"required\": [\"name\", \"population\"],\n",
+    "    }\n",
+    ")\n",
+    "\n",
+    "response = client.chat.completions.create(\n",
+    "    model=\"deepseek-ai/DeepSeek-R1-Distill-Qwen-7B\",\n",
+    "    messages=[\n",
+    "        {\n",
+    "            \"role\": \"user\",\n",
+    "            \"content\": \"Give me the information of the capital of France in the JSON format.\",\n",
+    "        },\n",
+    "    ],\n",
+    "    temperature=0,\n",
+    "    max_tokens=2048,\n",
+    "    response_format={\n",
+    "        \"type\": \"json_schema\",\n",
+    "        \"json_schema\": {\"name\": \"foo\", \"schema\": json.loads(json_schema)},\n",
+    "    },\n",
+    ")\n",
+    "\n",
+    "print_highlight(\n",
+    "    f\"reasoing_content: {response.choices[0].message.reasoning_content}\\n\\ncontent: {response.choices[0].message.content}\"\n",
+    ")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### EBNF"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "ebnf_grammar = \"\"\"\n",
+    "root ::= city | description\n",
+    "city ::= \"London\" | \"Paris\" | \"Berlin\" | \"Rome\"\n",
+    "description ::= city \" is \" status\n",
+    "status ::= \"the capital of \" country\n",
+    "country ::= \"England\" | \"France\" | \"Germany\" | \"Italy\"\n",
+    "\"\"\"\n",
+    "\n",
+    "response = client.chat.completions.create(\n",
+    "    model=\"deepseek-ai/DeepSeek-R1-Distill-Qwen-7B\",\n",
+    "    messages=[\n",
+    "        {\"role\": \"system\", \"content\": \"You are a helpful geography bot.\"},\n",
+    "        {\n",
+    "            \"role\": \"user\",\n",
+    "            \"content\": \"Give me the information of the capital of France.\",\n",
+    "        },\n",
+    "    ],\n",
+    "    temperature=0,\n",
+    "    max_tokens=2048,\n",
+    "    extra_body={\"ebnf\": ebnf_grammar},\n",
+    ")\n",
+    "\n",
+    "print_highlight(\n",
+    "    f\"reasoing_content: {response.choices[0].message.reasoning_content}\\n\\ncontent: {response.choices[0].message.content}\"\n",
+    ")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Regular expression"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "response = client.chat.completions.create(\n",
+    "    model=\"deepseek-ai/DeepSeek-R1-Distill-Qwen-7B\",\n",
+    "    messages=[\n",
+    "        {\"role\": \"user\", \"content\": \"What is the capital of France?\"},\n",
+    "    ],\n",
+    "    temperature=0,\n",
+    "    max_tokens=2048,\n",
+    "    extra_body={\"regex\": \"(Paris|London)\"},\n",
+    ")\n",
+    "\n",
+    "print_highlight(\n",
+    "    f\"reasoing_content: {response.choices[0].message.reasoning_content}\\n\\ncontent: {response.choices[0].message.content}\"\n",
+    ")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Structural Tag"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "tool_get_current_weather = {\n",
+    "    \"type\": \"function\",\n",
+    "    \"function\": {\n",
+    "        \"name\": \"get_current_weather\",\n",
+    "        \"description\": \"Get the current weather in a given location\",\n",
+    "        \"parameters\": {\n",
+    "            \"type\": \"object\",\n",
+    "            \"properties\": {\n",
+    "                \"city\": {\n",
+    "                    \"type\": \"string\",\n",
+    "                    \"description\": \"The city to find the weather for, e.g. 'San Francisco'\",\n",
+    "                },\n",
+    "                \"state\": {\n",
+    "                    \"type\": \"string\",\n",
+    "                    \"description\": \"the two-letter abbreviation for the state that the city is\"\n",
+    "                    \" in, e.g. 'CA' which would mean 'California'\",\n",
+    "                },\n",
+    "                \"unit\": {\n",
+    "                    \"type\": \"string\",\n",
+    "                    \"description\": \"The unit to fetch the temperature in\",\n",
+    "                    \"enum\": [\"celsius\", \"fahrenheit\"],\n",
+    "                },\n",
+    "            },\n",
+    "            \"required\": [\"city\", \"state\", \"unit\"],\n",
+    "        },\n",
+    "    },\n",
+    "}\n",
+    "\n",
+    "tool_get_current_date = {\n",
+    "    \"type\": \"function\",\n",
+    "    \"function\": {\n",
+    "        \"name\": \"get_current_date\",\n",
+    "        \"description\": \"Get the current date and time for a given timezone\",\n",
+    "        \"parameters\": {\n",
+    "            \"type\": \"object\",\n",
+    "            \"properties\": {\n",
+    "                \"timezone\": {\n",
+    "                    \"type\": \"string\",\n",
+    "                    \"description\": \"The timezone to fetch the current date and time for, e.g. 'America/New_York'\",\n",
+    "                }\n",
+    "            },\n",
+    "            \"required\": [\"timezone\"],\n",
+    "        },\n",
+    "    },\n",
+    "}\n",
+    "\n",
+    "schema_get_current_weather = tool_get_current_weather[\"function\"][\"parameters\"]\n",
+    "schema_get_current_date = tool_get_current_date[\"function\"][\"parameters\"]\n",
+    "\n",
+    "\n",
+    "def get_messages():\n",
+    "    return [\n",
+    "        {\n",
+    "            \"role\": \"system\",\n",
+    "            \"content\": f\"\"\"\n",
+    "# Tool Instructions\n",
+    "- Always execute python code in messages that you share.\n",
+    "- When looking for real time information use relevant functions if available else fallback to brave_search\n",
+    "You have access to the following functions:\n",
+    "Use the function 'get_current_weather' to: Get the current weather in a given location\n",
+    "{tool_get_current_weather[\"function\"]}\n",
+    "Use the function 'get_current_date' to: Get the current date and time for a given timezone\n",
+    "{tool_get_current_date[\"function\"]}\n",
+    "If a you choose to call a function ONLY reply in the following format:\n",
+    "<{{start_tag}}={{function_name}}>{{parameters}}{{end_tag}}\n",
+    "where\n",
+    "start_tag => `<function`\n",
+    "parameters => a JSON dict with the function argument name as key and function argument value as value.\n",
+    "end_tag => `</function>`\n",
+    "Here is an example,\n",
+    "<function=example_function_name>{{\"example_name\": \"example_value\"}}</function>\n",
+    "Reminder:\n",
+    "- Function calls MUST follow the specified format\n",
+    "- Required parameters MUST be specified\n",
+    "- Only call one function at a time\n",
+    "- Put the entire function call reply on one line\n",
+    "- Always add your sources when using search results to answer the user query\n",
+    "You are a helpful assistant.\"\"\",\n",
+    "        },\n",
+    "        {\n",
+    "            \"role\": \"user\",\n",
+    "            \"content\": \"You are in New York. Please get the current date and time, and the weather.\",\n",
+    "        },\n",
+    "    ]\n",
+    "\n",
+    "\n",
+    "messages = get_messages()\n",
+    "\n",
+    "response = client.chat.completions.create(\n",
+    "    model=\"deepseek-ai/DeepSeek-R1-Distill-Qwen-7B\",\n",
+    "    messages=messages,\n",
+    "    response_format={\n",
+    "        \"type\": \"structural_tag\",\n",
+    "        \"max_new_tokens\": 2048,\n",
+    "        \"structures\": [\n",
+    "            {\n",
+    "                \"begin\": \"<function=get_current_weather>\",\n",
+    "                \"schema\": schema_get_current_weather,\n",
+    "                \"end\": \"</function>\",\n",
+    "            },\n",
+    "            {\n",
+    "                \"begin\": \"<function=get_current_date>\",\n",
+    "                \"schema\": schema_get_current_date,\n",
+    "                \"end\": \"</function>\",\n",
+    "            },\n",
+    "        ],\n",
+    "        \"triggers\": [\"<function=\"],\n",
+    "    },\n",
+    ")\n",
+    "\n",
+    "print_highlight(\n",
+    "    f\"reasoing_content: {response.choices[0].message.reasoning_content}\\n\\ncontent: {response.choices[0].message.content}\"\n",
+    ")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Native API and SGLang Runtime (SRT)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### JSON"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "**Using Pydantic**"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import requests\n",
+    "from pydantic import BaseModel, Field\n",
+    "from transformers import AutoTokenizer\n",
+    "\n",
+    "tokenizer = AutoTokenizer.from_pretrained(\"deepseek-ai/DeepSeek-R1-Distill-Qwen-7B\")\n",
+    "\n",
+    "\n",
+    "# Define the schema using Pydantic\n",
+    "class CapitalInfo(BaseModel):\n",
+    "    name: str = Field(..., pattern=r\"^\\w+$\", description=\"Name of the capital city\")\n",
+    "    population: int = Field(..., description=\"Population of the capital city\")\n",
+    "\n",
+    "\n",
+    "messages = [\n",
+    "    {\n",
+    "        \"role\": \"user\",\n",
+    "        \"content\": \"Here is the information of the capital of France in the JSON format.\\n\",\n",
+    "    }\n",
+    "]\n",
+    "text = tokenizer.apply_chat_template(\n",
+    "    messages, tokenize=False, add_generation_prompt=True\n",
+    ")\n",
+    "# Make API request\n",
+    "response = requests.post(\n",
+    "    f\"http://localhost:{port}/generate\",\n",
+    "    json={\n",
+    "        \"text\": text,\n",
+    "        \"sampling_params\": {\n",
+    "            \"temperature\": 0,\n",
+    "            \"max_new_tokens\": 2048,\n",
+    "            \"json_schema\": json.dumps(CapitalInfo.model_json_schema()),\n",
+    "        },\n",
+    "    },\n",
+    ")\n",
+    "print(response.json())\n",
+    "\n",
+    "\n",
+    "reasoing_content = response.json()[\"text\"].split(\"</think>\")[0]\n",
+    "content = json.loads(response.json()[\"text\"].split(\"</think>\")[1])\n",
+    "print_highlight(f\"reasoing_content: {reasoing_content}\\n\\ncontent: {content}\")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "**JSON Schema Directly**"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "json_schema = json.dumps(\n",
+    "    {\n",
+    "        \"type\": \"object\",\n",
+    "        \"properties\": {\n",
+    "            \"name\": {\"type\": \"string\", \"pattern\": \"^[\\\\w]+$\"},\n",
+    "            \"population\": {\"type\": \"integer\"},\n",
+    "        },\n",
+    "        \"required\": [\"name\", \"population\"],\n",
+    "    }\n",
+    ")\n",
+    "\n",
+    "# JSON\n",
+    "text = tokenizer.apply_chat_template(text, tokenize=False, add_generation_prompt=True)\n",
+    "response = requests.post(\n",
+    "    f\"http://localhost:{port}/generate\",\n",
+    "    json={\n",
+    "        \"text\": text,\n",
+    "        \"sampling_params\": {\n",
+    "            \"temperature\": 0,\n",
+    "            \"max_new_tokens\": 2048,\n",
+    "            \"json_schema\": json_schema,\n",
+    "        },\n",
+    "    },\n",
+    ")\n",
+    "\n",
+    "print_highlight(response.json())"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### EBNF"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "response = requests.post(\n",
+    "    f\"http://localhost:{port}/generate\",\n",
+    "    json={\n",
+    "        \"text\": \"Give me the information of the capital of France.\",\n",
+    "        \"sampling_params\": {\n",
+    "            \"max_new_tokens\": 2048,\n",
+    "            \"temperature\": 0,\n",
+    "            \"n\": 3,\n",
+    "            \"ebnf\": (\n",
+    "                \"root ::= city | description\\n\"\n",
+    "                'city ::= \"London\" | \"Paris\" | \"Berlin\" | \"Rome\"\\n'\n",
+    "                'description ::= city \" is \" status\\n'\n",
+    "                'status ::= \"the capital of \" country\\n'\n",
+    "                'country ::= \"England\" | \"France\" | \"Germany\" | \"Italy\"'\n",
+    "            ),\n",
+    "        },\n",
+    "        \"stream\": False,\n",
+    "        \"return_logprob\": False,\n",
+    "    },\n",
+    ")\n",
+    "\n",
+    "print(response.json())"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Regular expression"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "response = requests.post(\n",
+    "    f\"http://localhost:{port}/generate\",\n",
+    "    json={\n",
+    "        \"text\": \"Paris is the capital of\",\n",
+    "        \"sampling_params\": {\n",
+    "            \"temperature\": 0,\n",
+    "            \"max_new_tokens\": 2048,\n",
+    "            \"regex\": \"(France|England)\",\n",
+    "        },\n",
+    "    },\n",
+    ")\n",
+    "print(response.json())"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Structural Tag"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "text = tokenizer.apply_chat_template(\n",
+    "    messages, tokenize=False, add_generation_prompt=True\n",
+    ")\n",
+    "payload = {\n",
+    "    \"text\": text,\n",
+    "    \"sampling_params\": {\n",
+    "        \"max_new_tokens\": 2048,\n",
+    "        \"structural_tag\": json.dumps(\n",
+    "            {\n",
+    "                \"type\": \"structural_tag\",\n",
+    "                \"structures\": [\n",
+    "                    {\n",
+    "                        \"begin\": \"<function=get_current_weather>\",\n",
+    "                        \"schema\": schema_get_current_weather,\n",
+    "                        \"end\": \"</function>\",\n",
+    "                    },\n",
+    "                    {\n",
+    "                        \"begin\": \"<function=get_current_date>\",\n",
+    "                        \"schema\": schema_get_current_date,\n",
+    "                        \"end\": \"</function>\",\n",
+    "                    },\n",
+    "                ],\n",
+    "                \"triggers\": [\"<function=\"],\n",
+    "            }\n",
+    "        ),\n",
+    "    },\n",
+    "}\n",
+    "\n",
+    "\n",
+    "# Send POST request to the API endpoint\n",
+    "response = requests.post(f\"http://localhost:{port}/generate\", json=payload)\n",
+    "print_highlight(response.json())"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "terminate_process(server_process)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Offline Engine API"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import sglang as sgl\n",
+    "\n",
+    "llm = sgl.Engine(\n",
+    "    model_path=\"deepseek-ai/DeepSeek-R1-Distill-Qwen-7B\",\n",
+    "    reasoning_parser=\"deepseek-r1\",\n",
+    "    grammar_backend=\"xgrammar\",\n",
+    ")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### JSON"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "**Using Pydantic**"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import json\n",
+    "from pydantic import BaseModel, Field\n",
+    "\n",
+    "\n",
+    "prompts = [\n",
+    "    \"Give me the information of the capital of China in the JSON format.\",\n",
+    "    \"Give me the information of the capital of France in the JSON format.\",\n",
+    "    \"Give me the information of the capital of Ireland in the JSON format.\",\n",
+    "]\n",
+    "\n",
+    "\n",
+    "# Define the schema using Pydantic\n",
+    "class CapitalInfo(BaseModel):\n",
+    "    name: str = Field(..., pattern=r\"^\\w+$\", description=\"Name of the capital city\")\n",
+    "    population: int = Field(..., description=\"Population of the capital city\")\n",
+    "\n",
+    "\n",
+    "sampling_params = {\n",
+    "    \"temperature\": 0,\n",
+    "    \"top_p\": 0.95,\n",
+    "    \"max_new_tokens\": 2048,\n",
+    "    \"json_schema\": json.dumps(CapitalInfo.model_json_schema()),\n",
+    "}\n",
+    "\n",
+    "outputs = llm.generate(prompts, sampling_params)\n",
+    "for prompt, output in zip(prompts, outputs):\n",
+    "    print(\"===============================\")\n",
+    "    print(f\"Prompt: {prompt}\\nGenerated text: {output['text']}\")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "**JSON Schema Directly**"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "prompts = [\n",
+    "    \"Give me the information of the capital of China in the JSON format.\",\n",
+    "    \"Give me the information of the capital of France in the JSON format.\",\n",
+    "    \"Give me the information of the capital of Ireland in the JSON format.\",\n",
+    "]\n",
+    "\n",
+    "json_schema = json.dumps(\n",
+    "    {\n",
+    "        \"type\": \"object\",\n",
+    "        \"properties\": {\n",
+    "            \"name\": {\"type\": \"string\", \"pattern\": \"^[\\\\w]+$\"},\n",
+    "            \"population\": {\"type\": \"integer\"},\n",
+    "        },\n",
+    "        \"required\": [\"name\", \"population\"],\n",
+    "    }\n",
+    ")\n",
+    "\n",
+    "sampling_params = {\"temperature\": 0, \"max_new_tokens\": 2048, \"json_schema\": json_schema}\n",
+    "\n",
+    "outputs = llm.generate(prompts, sampling_params)\n",
+    "for prompt, output in zip(prompts, outputs):\n",
+    "    print(\"===============================\")\n",
+    "    print(f\"Prompt: {prompt}\\nGenerated text: {output['text']}\")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### EBNF\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "prompts = [\n",
+    "    \"Give me the information of the capital of France.\",\n",
+    "    \"Give me the information of the capital of Germany.\",\n",
+    "    \"Give me the information of the capital of Italy.\",\n",
+    "]\n",
+    "\n",
+    "sampling_params = {\n",
+    "    \"temperature\": 0.8,\n",
+    "    \"top_p\": 0.95,\n",
+    "    \"ebnf\": (\n",
+    "        \"root ::= city | description\\n\"\n",
+    "        'city ::= \"London\" | \"Paris\" | \"Berlin\" | \"Rome\"\\n'\n",
+    "        'description ::= city \" is \" status\\n'\n",
+    "        'status ::= \"the capital of \" country\\n'\n",
+    "        'country ::= \"England\" | \"France\" | \"Germany\" | \"Italy\"'\n",
+    "    ),\n",
+    "}\n",
+    "\n",
+    "outputs = llm.generate(prompts, sampling_params)\n",
+    "for prompt, output in zip(prompts, outputs):\n",
+    "    print(\"===============================\")\n",
+    "    print(f\"Prompt: {prompt}\\nGenerated text: {output['text']}\")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Regular expression"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "prompts = [\n",
+    "    \"Please provide information about London as a major global city:\",\n",
+    "    \"Please provide information about Paris as a major global city:\",\n",
+    "]\n",
+    "\n",
+    "sampling_params = {\"temperature\": 0.8, \"top_p\": 0.95, \"regex\": \"(France|England)\"}\n",
+    "\n",
+    "outputs = llm.generate(prompts, sampling_params)\n",
+    "for prompt, output in zip(prompts, outputs):\n",
+    "    print(\"===============================\")\n",
+    "    print(f\"Prompt: {prompt}\\nGenerated text: {output['text']}\")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "text = tokenizer.apply_chat_template(\n",
+    "    messages, tokenize=False, add_generation_prompt=True\n",
+    ")\n",
+    "prompts = [text]\n",
+    "\n",
+    "\n",
+    "sampling_params = {\n",
+    "    \"temperature\": 0.8,\n",
+    "    \"top_p\": 0.95,\n",
+    "    \"max_new_tokens\": 2048,\n",
+    "    \"structural_tag\": json.dumps(\n",
+    "        {\n",
+    "            \"type\": \"structural_tag\",\n",
+    "            \"structures\": [\n",
+    "                {\n",
+    "                    \"begin\": \"<function=get_current_weather>\",\n",
+    "                    \"schema\": schema_get_current_weather,\n",
+    "                    \"end\": \"</function>\",\n",
+    "                },\n",
+    "                {\n",
+    "                    \"begin\": \"<function=get_current_date>\",\n",
+    "                    \"schema\": schema_get_current_date,\n",
+    "                    \"end\": \"</function>\",\n",
+    "                },\n",
+    "            ],\n",
+    "            \"triggers\": [\"<function=\"],\n",
+    "        }\n",
+    "    ),\n",
+    "}\n",
+    "\n",
+    "\n",
+    "# Send POST request to the API endpoint\n",
+    "outputs = llm.generate(prompts, sampling_params)\n",
+    "for prompt, output in zip(prompts, outputs):\n",
+    "    print(\"===============================\")\n",
+    "    print(f\"Prompt: {prompt}\\nGenerated text: {output['text']}\")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "llm.shutdown()"
+   ]
+  }
+ ],
+ "metadata": {
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 2
+}
diff --git a/docs/index.rst b/docs/index.rst
index 99fea71ea..788ba99da 100644
--- a/docs/index.rst
+++ b/docs/index.rst
@@ -31,6 +31,7 @@ The core features include:
    backend/server_arguments.md
    backend/sampling_params.md
    backend/hyperparameter_tuning.md
+   backend/structured_outputs_for_reasoning_models.ipynb
 
 .. toctree::
    :maxdepth: 1
diff --git a/python/sglang/srt/constrained/base_grammar_backend.py b/python/sglang/srt/constrained/base_grammar_backend.py
index 181f0d213..58ee5ccb2 100644
--- a/python/sglang/srt/constrained/base_grammar_backend.py
+++ b/python/sglang/srt/constrained/base_grammar_backend.py
@@ -28,6 +28,18 @@ logger = logging.getLogger(__name__)
 
 
 class BaseGrammarObject(ABC):
+
+    def __init__(self):
+        self._finished = False
+
+    @property
+    def finished(self):
+        return self._finished
+
+    @finished.setter
+    def finished(self, finished):
+        self._finished = finished
+
     @abstractmethod
     def try_jump_forward(self, tokenizer) -> Optional[Tuple[List[int], str]]:
         """
@@ -59,6 +71,13 @@ class BaseGrammarObject(ABC):
         """
         raise NotImplementedError
 
+    @abstractmethod
+    def accept_token(self, token: int) -> None:
+        """
+        Accept a token in the grammar.
+        """
+        raise NotImplementedError
+
     @abstractmethod
     def allocate_vocab_mask(
         self, vocab_size: int, batch_size: int, device
@@ -90,7 +109,7 @@ class CacheEntry:
     event: Event
 
 
-class BaseGrammarBackend(ABC):
+class BaseGrammarBackend:
     def __init__(self):
         self.executor = ThreadPoolExecutor()
         self.cache: Dict[Tuple[str, str], CacheEntry] = {}
@@ -107,19 +126,15 @@ class BaseGrammarBackend(ABC):
         """
         raise ValueError(f"Invalid key_type: {key_type}={key_string}")
 
-    @abstractmethod
     def dispatch_json(self, key_string: str) -> Optional[BaseGrammarObject]:
         return self._not_supported("json", key_string)
 
-    @abstractmethod
     def dispatch_regex(self, key_string: str) -> Optional[BaseGrammarObject]:
         return self._not_supported("regex", key_string)
 
-    @abstractmethod
     def dispatch_ebnf(self, key_string: str) -> Optional[BaseGrammarObject]:
         return self._not_supported("ebnf", key_string)
 
-    @abstractmethod
     def dispatch_structural_tag(self, key_string: str) -> Optional[BaseGrammarObject]:
         return self._not_supported("structural_tag", key_string)
 
@@ -195,4 +210,10 @@ def create_grammar_backend(
     else:
         raise ValueError(f"Invalid grammar backend: {server_args.grammar_backend}")
 
+    if server_args.reasoning_parser and hasattr(tokenizer, "think_end_id"):
+        from .reasoner_grammar_backend import ReasonerGrammarBackend
+
+        grammar_backend = ReasonerGrammarBackend(
+            grammar_backend, tokenizer.think_end_id
+        )
     return grammar_backend
diff --git a/python/sglang/srt/constrained/llguidance_backend.py b/python/sglang/srt/constrained/llguidance_backend.py
index 24893a49d..6926e1c30 100644
--- a/python/sglang/srt/constrained/llguidance_backend.py
+++ b/python/sglang/srt/constrained/llguidance_backend.py
@@ -33,6 +33,7 @@ class GuidanceGrammar(BaseGrammarObject):
     def __init__(
         self, llguidance_tokenizer: llguidance.LLTokenizer, serialized_grammar: str
     ):
+        super().__init__()
         self.llguidance_tokenizer = llguidance_tokenizer
         self.serialized_grammar = serialized_grammar
 
diff --git a/python/sglang/srt/constrained/outlines_backend.py b/python/sglang/srt/constrained/outlines_backend.py
index 7a05bb2c5..41128108a 100644
--- a/python/sglang/srt/constrained/outlines_backend.py
+++ b/python/sglang/srt/constrained/outlines_backend.py
@@ -44,6 +44,7 @@ class OutlinesGrammar(BaseGrammarObject):
         guide: RegexGuide,
         jump_forward_map: Union[OutlinesJumpForwardMap, None],
     ) -> None:
+        super().__init__()
         self.guide = guide
         self.jump_forward_map = jump_forward_map
         self.state = 0
diff --git a/python/sglang/srt/constrained/reasoner_grammar_backend.py b/python/sglang/srt/constrained/reasoner_grammar_backend.py
new file mode 100644
index 000000000..3f6f59e5b
--- /dev/null
+++ b/python/sglang/srt/constrained/reasoner_grammar_backend.py
@@ -0,0 +1,101 @@
+# Copyright 2023-2024 SGLang Team
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#     http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+# ==============================================================================
+"""The baseclass of a backend for reasoner grammar-guided constrained decoding."""
+
+from concurrent.futures import Future
+from typing import List, Optional, Tuple
+
+import torch
+
+from .base_grammar_backend import BaseGrammarBackend, BaseGrammarObject
+
+
+class ReasonerGrammarObject(BaseGrammarObject):
+    def __init__(self, grammar: BaseGrammarObject, think_end_id):
+        super().__init__()
+        self.grammar = grammar
+        self.think_end_id = think_end_id
+        self.is_in_reasoning = True
+
+    @property
+    def finished(self):
+        return self.grammar.finished
+
+    @finished.setter
+    def finished(self, finished):
+        self.grammar.finished = finished
+
+    def allocate_vocab_mask(
+        self, vocab_size: int, batch_size: int, device
+    ) -> torch.Tensor:
+        return self.grammar.allocate_vocab_mask(vocab_size, batch_size, device)
+
+    def fill_vocab_mask(self, vocab_mask: torch.Tensor, idx: int) -> None:
+        if not self.is_in_reasoning:
+            self.grammar.fill_vocab_mask(vocab_mask, idx)
+
+    def move_vocab_mask(self, vocab_mask: torch.Tensor, device) -> torch.Tensor:
+        return self.grammar.move_vocab_mask(vocab_mask, device)
+
+    @property
+    def apply_vocab_mask(self):
+        return self.grammar.apply_vocab_mask
+
+    def accept_token(self, token: int):
+        if token == self.think_end_id:
+            self.is_in_reasoning = False
+
+        if not self.is_in_reasoning and token != self.think_end_id:
+            self.grammar.accept_token(token)
+
+    def try_jump_forward(self, tokenizer):
+        return self.grammar.try_jump_forward(tokenizer)
+
+    def jump_forward_str_state(self, helper):
+        return self.grammar.jump_forward_str_state(helper)
+
+    def jump_and_retokenize(
+        self, old_output_ids: List[int], new_output_ids: List[int], next_state: int
+    ):
+        return self.grammar.jump_and_retokenize(
+            old_output_ids, new_output_ids, next_state
+        )
+
+    def copy(self) -> BaseGrammarObject:
+        return ReasonerGrammarObject(self.grammar.copy(), self.think_end_id)
+
+
+class ReasonerGrammarBackend(BaseGrammarBackend):
+    def __init__(self, grammar_backend: BaseGrammarBackend, think_end_id):
+        self.grammar_backend = grammar_backend
+        self.think_end_id = think_end_id
+
+    def get_cached_value(self, key: Tuple[str, str]) -> Optional[ReasonerGrammarObject]:
+        grammar = self.grammar_backend.get_cached_value(key)
+        return ReasonerGrammarObject(grammar, self.think_end_id) if grammar else None
+
+    def get_future_value(self, key: Tuple[str, str]) -> Future:
+        grammar = Future()
+
+        def callback(f: Future):
+            if result := f.result():
+                grammar.set_result(ReasonerGrammarObject(result, self.think_end_id))
+            else:
+                grammar.set_result(None)
+
+        self.grammar_backend.get_future_value(key).add_done_callback(callback)
+        return grammar
+
+    def reset(self):
+        self.grammar_backend.reset()
diff --git a/python/sglang/srt/constrained/xgrammar_backend.py b/python/sglang/srt/constrained/xgrammar_backend.py
index 4df3ae286..5aef05f9b 100644
--- a/python/sglang/srt/constrained/xgrammar_backend.py
+++ b/python/sglang/srt/constrained/xgrammar_backend.py
@@ -48,6 +48,7 @@ class XGrammarGrammar(BaseGrammarObject):
         ctx: CompiledGrammar,
         override_stop_tokens: Optional[Union[List[int], int]],
     ) -> None:
+        super().__init__()
         self.matcher = matcher
         self.vocab_size = vocab_size
         self.ctx = ctx
diff --git a/python/sglang/srt/managers/scheduler.py b/python/sglang/srt/managers/scheduler.py
index e917f42b1..533db7d87 100644
--- a/python/sglang/srt/managers/scheduler.py
+++ b/python/sglang/srt/managers/scheduler.py
@@ -113,6 +113,7 @@ from sglang.srt.mem_cache.hiradix_cache import HiRadixCache
 from sglang.srt.mem_cache.radix_cache import RadixCache
 from sglang.srt.metrics.collector import SchedulerMetricsCollector, SchedulerStats
 from sglang.srt.model_executor.forward_batch_info import ForwardMode
+from sglang.srt.reasoning_parser import ReasoningParser
 from sglang.srt.server_args import PortArgs, ServerArgs
 from sglang.srt.speculative.spec_info import SpeculativeAlgorithm
 from sglang.srt.torch_memory_saver_adapter import TorchMemorySaverAdapter
@@ -232,6 +233,15 @@ class Scheduler(
         # Init tokenizer
         self.init_tokenizer()
 
+        # Set reasoning_parser and think_end_id if --reasoning_parser is enabled
+        if self.server_args.reasoning_parser and self.tokenizer:
+            reasoning_parser = ReasoningParser(
+                model_type=self.server_args.reasoning_parser, stream_reasoning=False
+            )
+            self.tokenizer.think_end_id = self.tokenizer.encode(
+                reasoning_parser.detector.think_end_token, add_special_tokens=False
+            )[0]
+
         # Check whether overlap can be enabled
         if not self.is_generation:
             self.enable_overlap = False