Fast structured generation and serving for LLMs with RadixAttention prefix caching. Use for JSON/regex outputs, constrained decoding, agentic workflows with tool calls, or when you need 5× faster inference than vLLM with prefix sharing. Powers 300,000+ GPUs at xAI, AMD, NVIDIA, and LinkedIn.
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Fast structured generation and serving for LLMs with RadixAttention prefix caching. Use for JSON/regex outputs, constrained decoding, agentic workflows with tool calls, or when you need 5× faster inference than vLLM with prefix sharing. Powers 300,000+ GPUs at xAI, AMD, NVIDIA, and LinkedIn.
@sgl.functiondefextract_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 john.doe@example.com for details")
print(state["email"])
# Output: "john.doe@example.com"
Grammar-based generation
@sgl.functiondefgenerate_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.functiondefagent_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.functiondefmulti_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.functiondefdescribe_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"}]
)
Do not install alongside vllm in the same environment. Both bind to full GPU memory and cannot coexist in the same process. Use separate environments or containers.
