{"data":{"slug":"sgl-project-sglang","name":"sglang","tagline":"High-performance serving framework for large language and multimodal models","github_url":"https://github.com/sgl-project/sglang","owner":"sgl-project","repo":"sglang","owner_avatar_url":"https://avatars.githubusercontent.com/u/147780389?v=4","primary_language":"Python","stars":31454,"forks":7720,"topics":["attention","blackwell","cuda","deepseek","diffusion","glm","gpt-oss","inference","llama","llm","minimax","moe","qwen","qwen-image","reinforcement-learning","transformer","vlm","wan"],"archived":false,"github_pushed_at":"2026-08-07T06:00:20+00:00","maintenance_label":"Very active","stars_delta_30d":1409,"url":"https://www.graphcanon.com/tools/sgl-project-sglang","markdown_url":"https://www.graphcanon.com/tools/sgl-project-sglang.md","api_url":"https://www.graphcanon.com/api/graphcanon/tools/sgl-project-sglang","graph_url":"https://www.graphcanon.com/api/graphcanon/graph?tool=sgl-project-sglang","description":"SGLang is a high-performance serving framework for large language models and multimodal models.","homepage_url":"https://sglang.io","license":"Apache-2.0","open_issues":5080,"watchers":170,"ai_summary":"SGLang provides a comprehensive environment to serve both large language and multimodal models with high performance, supporting various model types like diffusion models, reinforcement learning, and transformers.","readme_excerpt":"## Getting Started\n- [Install SGLang](https://docs.sglang.io/get_started/install.html)\n- [Quick Start](https://docs.sglang.io/basic_usage/send_request.html)\n- [Backend Tutorial](https://docs.sglang.io/basic_usage/openai_api_completions.html)\n- [Frontend Tutorial](https://docs.sglang.io/references/frontend/frontend_tutorial.html)\n- [Contribution Guide](https://docs.sglang.io/developer_guide/contribution_guide.html)","github_created_at":"2024-01-08T04:15:52+00:00","created_at":"2026-07-07T17:32:07.726861+00:00","updated_at":"2026-08-07T06:01:05.5162+00:00","categories":[{"slug":"inference-serving","name":"Inference & Serving","url":"https://www.graphcanon.com/categories/inference-serving","markdown_url":"https://www.graphcanon.com/categories/inference-serving.md","api_url":"https://www.graphcanon.com/api/graphcanon/categories/inference-serving"}],"tags":[{"slug":"attention","name":"attention"},{"slug":"cuda","name":"cuda"},{"slug":"diffusion","name":"diffusion"},{"slug":"inference","name":"inference"},{"slug":"llm","name":"llm"},{"slug":"moe","name":"moe"},{"slug":"reinforcement-learning","name":"reinforcement-learning"},{"slug":"transformer","name":"transformer"}],"trust":{"provenance":{"is_fork":false,"github_id":740303686,"owner_type":"Organization","methodology":"github_public_v1","parent_repo":null,"near_duplicate_slugs":[]},"computed_at":"2026-08-07T06:01:04.727Z","maintenance":{"label":"Very active","score":96,"methodology":"github_public_v1","releases_90d":7,"days_since_push":0,"last_release_at":"2026-07-25T00:13:18Z","stars_delta_30d":1409,"open_issues_delta_30d":1050},"security_summary":{"status":"no_lockfile","scanner":null,"low_count":0,"high_count":0,"last_scan_at":"2026-07-11T10:36:47.517Z","medium_count":0,"scan_profile":"none","critical_count":0}},"capability_facts":{"scan":{"source":"repo_scan","observed_at":"2026-08-07T06:01:05.197Z"},"languages":{"value":["python"],"source":"github.language","observed_at":"2026-08-07T06:01:05.197Z"},"license_spdx":{"value":"Apache-2.0","source":"github.license","observed_at":"2026-08-07T06:01:05.197Z"}},"decision_facts":{"hosting":null,"pricing":null,"requirements":null,"constraints":null,"when_to_use":["- When you need to deploy large language or multimodal models efficiently across various types including transformers and diffusion models.","- If your project requires high-performance serving capabilities in Python, especially if it involves complex model architectures like reinforcement learning.","- SGLang is ideal when looking for an ecosystem that supports a variety of use cases from text generation (LLMs) to image processing (multimodal)."],"when_not_to_use":["- Avoid using SGLang if your project or infrastructure already heavily relies on specific serving solutions that do not integrate easily with Python deployments.","- If real-time performance is less critical than maintaining a lightweight and easy-to-deploy framework, another more specialized tool might be preferable.","- For projects where the model types are limited to those beyond large language models (LLMs) or multimodal models, such as strictly CNNs or RNNs without a need for transformer support, SGLang may not"],"source":"enrich:decision_facts","observed_at":"2026-07-11T13:04:47.295Z"},"constraint_facets":null,"decision_summary":[{"label":"Adopt for","value":"SGLang is a high-performance serving framework designed for deploying large language and multimodal models, with notable support for diffusion models and reinforcement learning."}]}}