by Kenza-AI · Agent Tool · ★ 442
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LLMs and Machine Learning done easily. sagify Sagify provides a simplified interface to manage machine learning workflows on AWS SageMaker, helping you focus on building ML models rather than infrastructure. Its modular architecture includes an LLM Gateway module to provide a unified interface for leveraging both open source and proprietary large language models. The LLM Gateway gives access to various LLMs through a simple API, letting you easily incorporate them into your workflows. For detailed reference to Sagify please go to: Read the Docs Installation Prerequisites sagify requires the following: Python (3.7, 3.8, 3.9, 3.10, 3.11) Docker installed and running Configured awscli Install sagify At the command line: pip install sagify Getting started - LLM Deployment with no code Make sure to configure your AWS account by following the instructions at section Configure AWS Account Finally, run the following command: sh sagify cloud foundation-model-deploy --model-id model-t
| Stars | 442 |
| Forks | 68 |
| Language | Python |
| Category | Agent Tool |
| License | MIT |
| Quality Score | 32.75/100 |
| Open Issues | 18 |
| Last Updated | 2026-02-11 |
| Created | 2018-03-03 |
| Platforms | python |
| Est. Tokens | ~2456k |
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sagify is LLMs and Machine Learning done easily. It is categorized as a Agent Tool with 442 GitHub stars.
sagify is primarily written in Python. It covers topics such as ai-gateway, anthropic, cohere.
You can find installation instructions and usage details in the sagify GitHub repository at github.com/Kenza-AI/sagify. The project has 442 stars and 68 forks, indicating an active community.
sagify is released under the MIT license, making it free to use and modify according to the license terms.
The top alternatives to sagify on Agent Skills Hub include autollm, GPTRouter, spacy-llm. Each offers a different approach to the same problem space — compare them side-by-side by stars, quality score, and community activity.