SGLang
sgl-project/sglang
Fast serving runtime for LLMs — backend speed for agent workloads. GitHub: sgl-project/sglang (~32k stars).
GitHub repository
Overview
SGLang (sgl-project/sglang) is listed in LimeDock Directories as an AI agent resource. Fast serving runtime for LLMs — backend speed for agent workloads. Group: memory-infra. Community size: ~32k GitHub stars. Repo (copy/paste): https://github.com/sgl-project/sglang This page is a plain-English guide so searches for the GitHub project can land on how teams actually use it.
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Link
Installation guide
Open the GitHub repository, then follow the README for your stack.
Official GitHub repository: https://github.com/sgl-project/sglang GitHub path: sgl-project/sglang
How to open it: 1. Copy: https://github.com/sgl-project/sglang 2. Clone or follow the README for your OS/stack. 3. Start with the smallest example before production use.
How to use it
**Simple example** Agent latency/cost is dominated by serving; you need a fast runtime.
1. Deploy SGLang with a supported model. 2. Point agents at the OpenAI-compatible endpoint. 3. Load-test realistic agent prompt shapes.
Example prompts
- “Serve a 7B model with SGLang for our agents.”
- “SGLang vs vLLM?”
- “Caching tips for agent workloads.”
Use cases and examples
- Evaluate sgl-project/sglang as an AI agent building block
- Pilot SGLang on one staging workflow
- Compare against agents you already pay for
- Document a team playbook after the pilot
Prerequisites
- GPU server
- Model license
Tips
- Match serving stack to traffic pattern.
- Auth in front.