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.
Installer avec Codex ou Claude Copiez ce prompt, collez-le dans Codex, Claude ou un autre assistant, puis laissez-le vérifier la page du skill et l'installer pour vous.
Une commande directe contourne le prompt de vérification. Examinez la source avant de l'exécuter.
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.
