sglang

SGLang

High-performance serving framework for LLMs and VLMs with RadixAttention for automatic prefix caching.

When to use SGLang

Use SGLang when:

• Need structured outputs (JSON, regex, grammar)
• Building agents with repeated prefixes (system prompts, tools)
• Agentic workflows with function calling
• Multi-turn conversations with shared context
• Need faster JSON decoding (3× vs standard)

Use vLLM instead when:

• Simple text generation without structure
• Don't need prefix caching
• Want mature, widely-tested production system

Use TensorRT-LLM instead when:

• Maximum single-request latency (no batching needed)
• NVIDIA-only deployment
• Need FP8/INT4 quantization on H100

Quick start

Installation

# pip install (recommended)
pip install "sglang[all]"

# With FlashInfer (faster, CUDA 11.8/12.1)
pip install sglang[all] flashinfer -i https://flashinfer.ai/whl/cu121/torch2.4/

# From source
git clone https://github.com/sgl-project/sglang.git
cd sglang
pip install -e "python[all]"

Launch server

# Basic server (Llama 3-8B)
python -m sglang.launch_server \
    --model-path meta-llama/Meta-Llama-3-8B-Instruct \
    --port 30000

# With RadixAttention (automatic prefix caching)
python -m sglang.launch_server \
    --model-path meta-llama/Meta-Llama-3-8B-Instruct \
    --port 30000 \
    --enable-radix-cache  # Default: enabled

# Multi-GPU (tensor parallelism)
python -m sglang.launch_server \
    --model-path meta-llama/Meta-Llama-3-70B-Instruct \
    --tp 4 \
    --port 30000

Basic inference

import sglang as sgl

# Set backend
sgl.set_default_backend(sgl.OpenAI("http://localhost:30000/v1"))

# Simple generation
@sgl.function
def simple_gen(s, question):
    s += "Q: " + question + "\n"
    s += "A:" + sgl.gen("answer", max_tokens=100)

# Run
state = simple_gen.run(question="What is the capital of France?")
print(state["answer"])
# Output: "The capital of France is Paris."

Structured JSON output

import sglang as sgl

@sgl.function
def extract_person(s, text):
    s += f"Extract person information from: {text}\n"
    s += "Output JSON:\n"

    # Constrained JSON generation
    s += sgl.gen(
        "json_output",
        max_tokens=200,
        regex=r'\{"name": "[^"]+", "age": \d+, "occupation": "[^"]+"\}'
    )

# Run
state = extract_person.run(
    text="John Smith is a 35-year-old software engineer."
)
print(state["json_output"])
# Output: {"name": "John Smith", "age": 35, "occupation": "software engineer"}

RadixAttention (Key Innovation)

What it does: Automatically caches and reuses common prefixes across requests.

Performance:

• 5× faster for agentic workloads with shared system prompts
• 10× faster for few-shot prompting with repeated examples
• Zero configuration - works automatically

How it works:

1. Builds radix tree of all processed tokens
2. Automatically detects shared prefixes
3. Reuses KV cache for matching prefixes
4. Only computes new tokens

Example (Agent with system prompt):

Request 1: [SYSTEM_PROMPT] + "What's the weather?"
→ Computes full prompt (1000 tokens)

Request 2: [SAME_SYSTEM_PROMPT] + "Book a flight"
→ Reuses system prompt KV cache (998 tokens)
→ Only computes 2 new tokens
→ 5× faster!

Structured generation patterns

JSON with schema

@sgl.function
def structured_extraction(s, article):
    s += f"Article: {article}\n\n"
    s += "Extract key information as JSON:\n"

    # JSON schema constraint
    schema = {
        "type": "object",
        "properties": {
            "title": {"type": "string"},
            "author": {"type": "string"},
            "summary": {"type": "string"},
            "sentiment": {"type": "string", "enum": ["positive", "negative", "neutral"]}
        },
        "required": ["title", "author", "summary", "sentiment"]
    }

    s += sgl.gen("info", max_tokens=300, json_schema=schema)

state = structured_extraction.run(article="...")
print(state["info"])
# Output: Valid JSON matching schema

Regex-constrained generation

@sgl.function
def extract_email(s, text):
    s += f"Extract email from: {text}\n"
    s += "Email: "

    # Email regex pattern
    s += sgl.gen(
        "email",
        max_tokens=50,
        regex=r'[a-zA-Z0-9._%+-]+@[a-zA-Z0-9.-]+\.[a-zA-Z]{2,}'
    )

state = extract_email.run(text="Contact [email protected] for details")
print(state["email"])
# Output: "[email protected]"

Grammar-based generation

@sgl.function
def generate_code(s, description):
    s += f"Generate Python code for: {description}\n"
    s += "```python\n"

    # EBNF grammar for Python
    python_grammar = """
    ?start: function_def
    function_def: "def" NAME "(" [parameters] "):" suite
    parameters: parameter ("," parameter)*
    parameter: NAME
    suite: simple_stmt | NEWLINE INDENT stmt+ DEDENT
    """

    s += sgl.gen("code", max_tokens=200, grammar=python_grammar)
    s += "\n```"

Agent workflows with function calling

import sglang as sgl

# Define tools
tools = [
    {
        "name": "get_weather",
        "description": "Get weather for a location",
        "parameters": {
            "type": "object",
            "properties": {
                "location": {"type": "string"}
            }
        }
    },
    {
        "name": "book_flight",
        "description": "Book a flight",
        "parameters": {
            "type": "object",
            "properties": {
                "from": {"type": "string"},
                "to": {"type": "string"},
                "date": {"type": "string"}
            }
        }
    }
]

@sgl.function
def agent_workflow(s, user_query, tools):
    # System prompt (cached with RadixAttention)
    s += "You are a helpful assistant with access to tools.\n"
    s += f"Available tools: {tools}\n\n"

    # User query
    s += f"User: {user_query}\n"
    s += "Assistant: "

    # Generate with function calling
    s += sgl.gen(
        "response",
        max_tokens=200,
        tools=tools,  # SGLang handles tool call format
        stop=["User:", "\n\n"]
    )

# Multiple queries reuse system prompt
state1 = agent_workflow.run(
    user_query="What's the weather in NYC?",
    tools=tools
)
# First call: Computes full system prompt

state2 = agent_workflow.run(
    user_query="Book a flight to LA",
    tools=tools
)
# Second call: Reuses system prompt (5× faster)

Performance benchmarks

RadixAttention speedup

Few-shot prompting (10 examples in prompt):

• vLLM: 2.5 sec/request
• SGLang: 0.25 sec/request (10× faster)
• Throughput: 4× higher

Agent workflows (1000-token system prompt):

• vLLM: 1.8 sec/request
• SGLang: 0.35 sec/request (5× faster)

JSON decoding:

• Standard: 45 tok/s
• SGLang: 135 tok/s (3× faster)

Throughput (Llama 3-8B, A100)

Workload	vLLM	SGLang	Speedup
Simple generation	2500 tok/s	2800 tok/s	1.12×
Few-shot (10 examples)	500 tok/s	5000 tok/s	10×
Agent (tool calls)	800 tok/s	4000 tok/s	5×
JSON output	600 tok/s	2400 tok/s	4×

Multi-turn conversations

@sgl.function
def multi_turn_chat(s, history, new_message):
    # System prompt (always cached)
    s += "You are a helpful AI assistant.\n\n"

    # Conversation history (cached as it grows)
    for msg in history:
        s += f"{msg['role']}: {msg['content']}\n"

    # New user message (only new part)
    s += f"User: {new_message}\n"
    s += "Assistant: "
    s += sgl.gen("response", max_tokens=200)

# Turn 1
history = []
state = multi_turn_chat.run(history=history, new_message="Hi there!")
history.append({"role": "User", "content": "Hi there!"})
history.append({"role": "Assistant", "content": state["response"]})

# Turn 2 (reuses Turn 1 KV cache)
state = multi_turn_chat.run(history=history, new_message="What's 2+2?")
# Only computes new message (much faster!)

# Turn 3 (reuses Turn 1 + Turn 2 KV cache)
state = multi_turn_chat.run(history=history, new_message="Tell me a joke")
# Progressively faster as history grows

Advanced features

Speculative decoding

# Launch with draft model (2-3× faster)
python -m sglang.launch_server \
    --model-path meta-llama/Meta-Llama-3-70B-Instruct \
    --speculative-model meta-llama/Meta-Llama-3-8B-Instruct \
    --speculative-num-steps 5

Multi-modal (vision models)

@sgl.function
def describe_image(s, image_path):
    s += sgl.image(image_path)
    s += "Describe this image in detail: "
    s += sgl.gen("description", max_tokens=200)

state = describe_image.run(image_path="photo.jpg")
print(state["description"])

Batching and parallel requests

# Automatic batching (continuous batching)
states = sgl.run_batch(
    [
        simple_gen.bind(question="What is AI?"),
        simple_gen.bind(question="What is ML?"),
        simple_gen.bind(question="What is DL?"),
    ]
)

# All 3 processed in single batch (efficient)

OpenAI-compatible API

# Start server with OpenAI API
python -m sglang.launch_server \
    --model-path meta-llama/Meta-Llama-3-8B-Instruct \
    --port 30000

# Use with OpenAI client
curl http://localhost:30000/v1/chat/completions \
  -H "Content-Type: application/json" \
  -d '{
    "model": "default",
    "messages": [
      {"role": "system", "content": "You are helpful"},
      {"role": "user", "content": "Hello"}
    ],
    "temperature": 0.7,
    "max_tokens": 100
  }'

# Works with OpenAI Python SDK
from openai import OpenAI
client = OpenAI(base_url="http://localhost:30000/v1", api_key="EMPTY")

response = client.chat.completions.create(
    model="default",
    messages=[{"role": "user", "content": "Hello"}]
)

Supported models

Text models:

• Llama 2, Llama 3, Llama 3.1, Llama 3.2
• Mistral, Mixtral
• Qwen, Qwen2, QwQ
• DeepSeek-V2, DeepSeek-V3
• Gemma, Phi-3

Vision models:

• LLaVA, LLaVA-OneVision
• Phi-3-Vision
• Qwen2-VL

100+ models from HuggingFace

Hardware support

NVIDIA: A100, H100, L4, T4 (CUDA 11.8+) AMD: MI300, MI250 (ROCm 6.0+) Intel: Xeon with GPU (coming soon) Apple: M1/M2/M3 via MPS (experimental)

References

• Structured Generation Guide - JSON schemas, regex, grammars, validation
• RadixAttention Deep Dive - How it works, optimization, benchmarks
• Production Deployment - Multi-GPU, monitoring, autoscaling

Resources

• GitHub: https://github.com/sgl-project/sglang
• Docs: https://sgl-project.github.io/
• Paper: RadixAttention (arXiv:2312.07104)
• Discord: https://discord.gg/sglang

Agent operating procedure

1. Check the environment. Confirm hardware, framework and server versions, model format, and expected load.
2. Pin down the inputs. Confirm formats, identifiers and parameters from the data or the user. Ask rather than guess any value that changes the result.
3. Run a small version first. Serve or log a single request or run end to end before scaling.
4. Execute the full task using the instructions and references above.
5. Validate the result. Measure latency, throughput and output correctness against a reference; check resource usage and costs.
6. Report. State what was run (versions, commands, parameters), what was checked, and what is still uncertain.

If this happens	Do this
The server fails to start or OOMs	Check model size versus memory, quantization and parallelism settings.
A function, flag or endpoint in these instructions is missing in the installed version	Check the installed version's own documentation (help(), --help, official docs), adapt, and tell the user. Never invent an API.
A required input, identifier or parameter is ambiguous	Ask the user, or state the assumption explicitly before running.

Integrity rules

• Never fabricate results, parameters, identifiers, citations or statistics. If something cannot be run or verified, say so plainly.
• Do not expose services or credentials publicly; confirm cloud costs before provisioning.
• Treat version-specific details here as possibly outdated: confirm them against the official documentation for the installed version.
• Ask before actions that cost money, consume shared GPUs or cloud quota, touch personal or patient data, or cannot be undone.

Related skills

• tensorrt-llm: Optimizes LLM inference with NVIDIA TensorRT for maximum throughput and lowest latency.
• outlines: Generates guaranteed-valid structured output from LLMs with Outlines (dottxt.ai), constraining token sampling via finite state machines for…
• llama-cpp: Runs LLM inference on CPU, Apple Silicon, and consumer GPUs without NVIDIA hardware.