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SGLang enables you to constrain model outputs to follow specific formats using JSON schemas, regular expressions, or EBNF grammars. The model output is guaranteed to follow the specified constraints.

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Grammar Backends

SGLang supports three grammar backends for constrained generation:

XGrammar

Default backend - Best performance and utility. Supports JSON schema, regex, and EBNF.

Outlines

Supports JSON schema and regex constraints.

Llguidance

Supports JSON schema, regex, and EBNF constraints.
To select a backend, use --grammar-backend when launching the server: 
# Use default XGrammar backend
python -m sglang.launch_server --model-path meta-llama/Meta-Llama-3.1-8B-Instruct

# Use Outlines backend
python -m sglang.launch_server --model-path meta-llama/Meta-Llama-3.1-8B-Instruct --grammar-backend outlines

# Use Llguidance backend
python -m sglang.launch_server --model-path meta-llama/Meta-Llama-3.1-8B-Instruct --grammar-backend llguidance
For better output quality, explicitly include instructions in your prompt to guide the model. For example: “Please generate the output in the following JSON format: …”

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JSON Schema Constraints

Constrain outputs to valid JSON following a specific schema. This is useful for extracting structured data from model responses.

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Using Pydantic Models

from pydantic import BaseModel, Field
import openai

class CapitalInfo(BaseModel):
    name: str = Field(..., pattern=r"^\w+$", description="Name of the capital city")
    population: int = Field(..., description="Population of the capital city")

client = openai.Client(base_url="http://127.0.0.1:30000/v1", api_key="None")

response = client.chat.completions.create(
    model="meta-llama/Meta-Llama-3.1-8B-Instruct",
    messages=[
        {
            "role": "user",
            "content": "Please generate the information of the capital of France in JSON format.",
        },
    ],
    temperature=0,
    max_tokens=128,
    response_format={
        "type": "json_schema",
        "json_schema": {
            "name": "capital_info",
            "schema": CapitalInfo.model_json_schema(),
        },
    },
)

# Validate the response
capital_info = CapitalInfo.model_validate_json(response.choices[0].message.content)
print(capital_info.model_dump_json())

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Using Direct JSON Schema

import json

json_schema = json.dumps({
    "type": "object",
    "properties": {
        "name": {"type": "string", "pattern": "^[\\w]+$"},
        "population": {"type": "integer"},
    },
    "required": ["name", "population"],
})

response = client.chat.completions.create(
    model="meta-llama/Meta-Llama-3.1-8B-Instruct",
    messages=[
        {
            "role": "user",
            "content": "Give me the information of the capital of France in JSON format.",
        },
    ],
    temperature=0,
    max_tokens=128,
    response_format={
        "type": "json_schema",
        "json_schema": {"name": "capital_info", "schema": json.loads(json_schema)},
    },
)

print(response.choices[0].message.content)

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EBNF Grammars

Define custom grammars using Extended Backus-Naur Form (EBNF) notation. XGrammar uses the GGML BNF format. 
ebnf_grammar = """
root ::= city | description
city ::= "London" | "Paris" | "Berlin" | "Rome"
description ::= city " is " status
status ::= "the capital of " country
country ::= "England" | "France" | "Germany" | "Italy"
"""

response = client.chat.completions.create(
    model="meta-llama/Meta-Llama-3.1-8B-Instruct",
    messages=[
        {"role": "system", "content": "You are a helpful geography bot."},
        {
            "role": "user",
            "content": "Give me the information of the capital of France.",
        },
    ],
    temperature=0,
    max_tokens=32,
    extra_body={"ebnf": ebnf_grammar},
)

print(response.choices[0].message.content)
# Output: "Paris is the capital of France"

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Regular Expression Constraints

Constrain outputs to match a specific regex pattern. 
response = client.chat.completions.create(
    model="meta-llama/Meta-Llama-3.1-8B-Instruct",
    messages=[
        {"role": "user", "content": "What is the capital of France?"},
    ],
    temperature=0,
    max_tokens=128,
    extra_body={"regex": "(Paris|London)"},
)

print(response.choices[0].message.content)
# Output: "Paris"

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Structural Tags

Combine multiple schemas with trigger patterns for complex structured outputs, such as function calling. 
tool_weather = {
    "type": "object",
    "properties": {
        "city": {"type": "string"},
        "state": {"type": "string"},
        "unit": {"type": "string", "enum": ["celsius", "fahrenheit"]},
    },
    "required": ["city", "state", "unit"],
}

response = client.chat.completions.create(
    model="meta-llama/Meta-Llama-3.1-8B-Instruct",
    messages=messages,
    response_format={
        "type": "structural_tag",
        "format": {
            "type": "triggered_tags",
            "triggers": ["<function="],
            "tags": [
                {
                    "begin": "<function=get_current_weather>",
                    "content": {
                        "type": "json_schema",
                        "json_schema": tool_weather,
                    },
                    "end": "</function>",
                },
            ],
        },
    },
)

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Native API Usage

You can also use structured outputs with the native SGLang API: 
import requests

response = requests.post(
    "http://localhost:30000/generate",
    json={
        "text": "What is the capital of France?",
        "sampling_params": {
            "temperature": 0,
            "max_new_tokens": 128,
            "regex": "(Paris|London)",
        },
    },
)

print(response.json()["text"])

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Implementation Details

SGLang’s constrained generation is implemented through the GrammarManager which:
  1. Compiles grammars - Converts JSON schemas, regex, or EBNF into efficient grammar objects
  2. Caches compiled grammars - Reuses compiled grammars across requests for better performance
  3. Applies constraints during generation - Modifies logits to ensure only valid tokens are sampled
  4. Supports jump-forward optimization - Skips ahead when only one valid continuation exists
The grammar compilation happens asynchronously to avoid blocking request processing. Requests wait in a grammar queue until their grammar objects are ready. Source: python/sglang/srt/constrained/grammar_manager.py:24

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Performance Considerations

The first request with a new schema incurs compilation overhead. Subsequent requests with the same schema benefit from caching.
Applying grammar constraints adds per-token overhead. The impact varies by grammar complexity.
When the grammar has only one valid continuation, SGLang can skip token-by-token generation and jump forward, significantly improving throughput.

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Configuration Options

ParameterDescriptionDefault
--grammar-backendBackend to use: xgrammar, outlines, or llguidancexgrammar
--skip-tokenizer-initSkip tokenizer initialization (disables grammar support)False
For XGrammar technical details and performance characteristics, see the XGrammar technical overview.