Unlock the power of structured text generation for LLM applications. Generate JSON, XML, and more with precision and control.
Discover what makes SGLang the go-to solution for structured generation
Generate exactly what you need with type-safe structured outputs. No more parsing errors or malformed data.
Optimized token usage and efficient generation algorithms for faster results and lower costs.
Seamlessly integrate with popular LLM providers and frameworks. Minimal setup required.
Support for JSON, XML, YAML, CSV, and custom schemas. Generate any structured format you need.
Define schemas and ensure generated content matches your exact requirements. Catch errors early.
Use templates to define generation patterns and reuse them across your applications.
Get up and running with SGLang in minutes
Install SGLang using Python's package manager
pip install sglang
Start using SGLang in your Python code
from sglang import SGLang # Initialize with your API key sg = SGLang(api_key="your-api-key")
Create your first structured generation
# Generate JSON output schema = { "type": "object", "properties": { "name": {"type": "string"}, "age": {"type": "number"} } } result = sg.generate( prompt="Create a user profile", schema=schema )
Learn by doing with real-world examples
Generate structured JSON data with type safety
schema = {
"type": "object",
"properties": {
"product": {"type": "string"},
"price": {"type": "number"},
"in_stock": {"type": "boolean"}
}
}
result = sg.generate(
prompt="Product info",
schema=schema,
format="json"
)
Create well-formed XML documents
schema = {
"root": "catalog",
"children": [
{"name": "book", "type": "string"},
{"name": "author", "type": "string"}
]
}
result = sg.generate(
prompt="Book catalog",
schema=schema,
format="xml"
)
Define your own complex schemas
schema = {
"type": "array",
"items": {
"type": "object",
"properties": {
"id": {"type": "number"},
"data": {"type": "string"}
}
}
}
result = sg.generate(
prompt="Generate list",
schema=schema
)
Level up your SGLang skills with expert advice
Define detailed schemas with exact types, required fields, and constraints to get more accurate outputs.
Create reusable templates for common patterns to save time and ensure consistency across generations.
Use concise prompts and leverage schema constraints to reduce unnecessary token consumption.
Always validate generated outputs against your schema, especially for critical applications.
Generate multiple structured outputs in a single request for better performance and cost efficiency.
Implement proper error handling and retry logic for robust production applications.
Test your schemas with various inputs to ensure they handle edge cases correctly.
Keep clear documentation of your schemas and templates for easier maintenance and collaboration.
See how SGLang powers real-world applications
Generate structured API responses that match your OpenAPI specifications automatically.
Extract structured data from unstructured text with high accuracy and consistency.
Generate configuration files in YAML, JSON, or XML formats for DevOps automation.
Create structured content like product descriptions, metadata, and SEO data at scale.
Deepen your understanding with these helpful resources
Comprehensive guides and API reference
Source code, issues, and contributions
Step-by-step video guides and demos
Connect with other SGLang users
Real-world projects using SGLang
Complete API documentation