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SGLang: Structured Prompt Programming for Language Models

vijay k

What is SGLang?

SGLang is an innovative structured programming language and execution framework designed for controlling Large Language Models (LLMs) more reliably and modularly. Developed by Skywork AI, SGLang provides a flexible yet deterministic interface for creating LLM-powered applications. It acts as a hybrid between a traditional programming language and a high-level prompt templating system, optimized specifically for LLM orchestration.

Unlike raw prompt engineering or black-box APIs, SGLang lets developers define structured flows, functions, and execution contexts for LLMs — enabling consistency, modularity, and logic branching within prompt executions. It is built on top of Python and integrates seamlessly with modern LLMs like OpenAI GPT, Claude, LLaMA, or any vLLM-compatible backend.

SGLang significantly reduces prompt sprawl, improves reproducibility of results, and provides better tooling around chaining, post-processing, and debugging LLM calls.

What are the Major Use Cases of SGLang?

SGLang is crafted for teams and individuals building advanced LLM-based systems. It is particularly useful when dealing with multi-step logic, structured outputs, and prompt reuse. Key use cases include:

1. Chatbot Orchestration

  • Define complex dialogue flows with multiple steps, memory, and conditional logic.

2. Agent Frameworks

  • Create LLM-powered agents that reason over tools, databases, and user inputs in structured ways.

3. Text-to-Structured Output

  • Convert natural language inputs into JSON, SQL, XML, or other structured formats reliably.

4. Data Extraction Pipelines

  • Parse documents, conversations, or reports using reusable SGLang functions to extract entities or tabular data.

5. LLM Tool Integration

  • Enable LLMs to call tools like search engines, calculators, or APIs with precise, parameterized prompts.

6. Educational Use and Debugging

  • Great for teaching structured thinking with LLMs or debugging language model inconsistencies through declarative flows.

How SGLang Works (with Architecture)

SGLang sits on top of Python and uses a DSL (domain-specific language) syntax to create LLM functions. These functions can be composed, reused, chained, and executed deterministically. Behind the scenes, SGLang compiles your structured logic into prompt templates, dispatches them to the LLM backend, and parses the results based on the defined output schema.

SGLang Architecture Overview:

[sglang Program]
  ├── Function Definitions
  ├── Prompt Blocks
  ├── Logic/Conditions
  ↓
[SGLang Runtime]
  ├── Prompt Compiler
  ├── LLM API (vLLM, OpenAI, Claude)
  ├── Output Parser
  └── Execution Context
  ↓
[Final Output or Chained Action]
  • Prompt Compiler: Transforms structured logic into optimized prompts.
  • LLM API Layer: Connects with local or remote LLMs using the vLLM or OpenAI-compatible interface.
  • Execution Context: Maintains memory and flow between calls.
  • Output Parser: Supports type annotations (e.g., int, List[str], Dict) for parsing model outputs reliably.

This architecture enables precise execution, version control, and modular design of LLM-based programs.

What is the Basic Workflow of SGLang?

  1. Define Functions
    • Write LLM functions using Python decorators like @sg.function.
  2. Add Prompt Blocks
    • Inside the function, define your input, instruction, and expected output structure.
  3. Execute the Program
    • Call the function with parameters. SGLang compiles the prompt and executes it via the LLM backend.
  4. Chain or Use Output
    • Use the result for further computation, logic branching, or UI rendering.
  5. Debug and Iterate
    • Adjust prompt blocks or add post-processors as needed to ensure correctness.

Step-by-Step Getting Started Guide for SGLang

Step 1: Install SGLang

pip install sglang

Or clone the repo:

git clone https://github.com/skywork-ai/sglang.git
cd sglang
pip install -e .

Step 2: Set Up an LLM Backend

You can use:

  • OpenAI API (requires API key)
  • vLLM Local Server
  • Skywork Inference Server

Set the LLM endpoint and API key in your environment:

export SGLANG_BACKEND=openai
export OPENAI_API_KEY=sk-xxxxx

Step 3: Write Your First Program

import sglang as sgl

@sgl.function
def summarize_article(context: sgl.Context, text: str) -> str:
    context.prompt("Summarize the following article in one paragraph:")
    context.prompt(text)
    return context.output()

Step 4: Run the Function

summarized = summarize_article("ChatGPT is a powerful language model...")
print(summarized)

SGLang will generate the prompt, call the LLM, and return the output.

Step 5: Add Structured Output

@sgl.function
def extract_data(context: sgl.Context, text: str) -> dict:
    context.prompt("Extract the name, date, and location from the following text:")
    context.prompt(text)
    return context.output({
        "name": str,
        "date": str,
        "location": str
    })

This ensures the model returns a structured JSON object.

Step 6: Experiment with Tool Use and Chaining

You can call multiple functions, use if conditions, or even wrap LLM responses with further logic.

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