SGLang

Open Source

LLM Inference Updated Feb 15, 2026

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Compared with 16 other LLM Inference agents Open source Self-hostable Updated Feb 2026

💰 Open Source 🔄 Updated Feb 2026 🖥️ Self-hostable

💰 Pricing

Open Source

Pricing not applicable (open-source).

🎯 Use Cases

Real-time text generation for chatbots Efficient deployment of vision-language models for multimodal tasks Scalable inference for large-scale NLP applications Low-latency model serving in research environments

⚖️ Pros & Cons

✅ Pros

High-performance inference optimization
Open-source and self-hostable
Supports both LLMs and VLMs

❌ Cons

Requires technical expertise to deploy and optimize
Limited to inference (no training capabilities)
Community support may be smaller than commercial alternatives

Overview

SGLang is an open-source framework designed for efficient serving of large language models (LLMs) and vision language models (VLMs), focusing on high-performance inference with optimized throughput and latency. It provides tools and APIs to streamline deployment and scaling of these models in production environments.

Problem It Solves

It reduces the computational overhead and latency typically associated with serving large-scale language and vision models, making them more practical for real-time applications.

Target Audience: Developers and teams working with llm inference automation.

Inputs

• User configuration
• API credentials (if required)
• Task parameters

Outputs

• Automated task results
• Status reports
• Generated content or actions

Example Workflow

1 User configures the agent with required parameters
2 Agent receives input data or trigger
3 Agent processes the request using its core logic
4 Agent interacts with external services if needed
5 Results are returned to the user

Sample System Prompt


              You are SGLang, an AI assistant. Help the user accomplish their task efficiently.

Tools & Technologies

LLM APIs Python

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FAQs

Is this agent open-source?: Yes
Can this agent be self-hosted?: Yes
What skill level is required?: Intermediate

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