{"data":{"slug":"kenza-ai-sagify","name":"sagify","tagline":"LLMs and Machine Learning done easily","github_url":"https://github.com/Kenza-AI/sagify","owner":"Kenza-AI","repo":"sagify","owner_avatar_url":"https://avatars.githubusercontent.com/u/33576995?v=4","primary_language":"Python","stars":442,"forks":68,"topics":["ai-gateway","anthropic","cohere","generative-ai","langchain","langchain-python","large-language-model","large-language-models","llm","llm-inference","llmops","open-source-llm","openai","sagemaker"],"archived":false,"github_pushed_at":"2026-02-11T10:56:37+00:00","maintenance_label":"Slowing","stars_delta_30d":0,"url":"https://www.graphcanon.com/tools/kenza-ai-sagify","markdown_url":"https://www.graphcanon.com/tools/kenza-ai-sagify.md","api_url":"https://www.graphcanon.com/api/graphcanon/tools/kenza-ai-sagify","graph_url":"https://www.graphcanon.com/api/graphcanon/graph?tool=kenza-ai-sagify","description":"LLMs and Machine Learning done easily","homepage_url":"https://kenza-ai.github.io/sagify/","license":"MIT","open_issues":18,"watchers":16,"ai_summary":"A repository providing resources for managing large language models and machine learning tasks in an accessible manner.","readme_excerpt":"### Install sagify\n\nAt the command line:\n\n    pip install sagify\n\n---\n\n## Getting started -  LLM Deployment with no code\n                \n1. Make sure to configure your AWS account by following the instructions at section [Configure AWS Account](#configure-aws-account)\n  \n2. Finally, run the following command:\n\n```sh\nsagify cloud foundation-model-deploy --model-id model-txt2img-stabilityai-stable-diffusion-v2-1-base --model-version 1.* -n 1 -e ml.p3.2xlarge --aws-region us-east-1 --aws-profile sagemaker-dev\n```\n        \nYou can change the values for ec2 type (-e), aws region and aws profile with your preferred ones.\n\nOnce the Stable Diffusion model is deployed, you can use the generated code snippet to query it. Enjoy!\n\n---\n\n## Deploy FastAPI LLM Gateway - Docker\n\nOnce you have set up your backend platform, you can deploy the FastAPI LLM Gateway locally. \n\nIn case of using the AWS Sagemaker platform, you need to define the following env variables before you start the LLM Gateway server:\n\n- `AWS_ACCESS_KEY_ID`: It can be the same one you use locally for Sagify. It should have access to Sagemaker and S3. Example: `export AWS_ACCESS_KEY_ID=...`.\n- `AWS_SECRET_ACCESS_KEY`:  It can be the same one you use locally for Sagify. It should have access to Sagemaker and S3. Example: `export AWS_ACCESS_KEY_ID=...`.\n- `AWS_REGION_NAME`: AWS region where the LLM backend services (Sagemaker endpoints) are deployed.\n- `S3_BUCKET_NAME`: S3 bucket name where the created images by the image creation backend service are stored.\n- `IMAGE_URL_TTL_IN_SECONDS`: TTL in seconds of the temporary url to the created images. Default value: 3600.\n- `SM_CHAT_COMPLETIONS_MODEL`: The Sagemaker endpoint name where the chat completions model is deployed.\n- `SM_EMBEDDINGS_MODEL`: The Sagemaker endpoint name where the embeddings model is deployed.\n- `SM_IMAGE_CREATION_MODEL`: The Sagemaker endpoint name where the image creation model is deployed.\n\nIn case of using the OpenAI platform, you need to define the following env variables before you start the LLM Gateway server:\n\n- `OPENAI_API_KEY`: Your OpenAI API key. Example: `export OPENAI_API_KEY=...`.\n- `OPENAI_CHAT_COMPLETIONS_MODEL`: It should have one of values [here](https://platform.openai.com/docs/models/gpt-3-5-turbo) or [here](https://platform.openai.com/docs/models/gpt-4-and-gpt-4-turbo).\n- `OPENAI_EMBEDDINGS_MODEL`: It should have one of values [here](https://platform.openai.com/docs/models/embeddings).\n- `OPENAI_IMAGE_CREATION_MODEL`: It should have one of values [here](https://platform.openai.com/docs/models/dall-e).\n\nNow, you can run the command `sagify llm gateway --image sagify-llm-gateway:v0.1.0 --start-local` to start the LLM Gateway locally. You can change the name of the image via the `--image` argument.\n\nThis command will output the Docker container id. You can stop the container by executing `docker stop <CONTAINER_ID>`.\n\n**Examples**\n\n(*Remember to export first all the environment variables you need*)\n\nIn the case you want to create a docker image and then run it\n```{bash}\nsagify llm gateway --image sagify-llm-gateway:v0.1.0 --start-local\n ```\n\n If you want to use just build the image\n ```{bash}\n sagify llm gateway --image sagify-llm-gateway:v0.1.0\n ```\n\nIf you want to support both platforms (OpenAI and AWS Sagemaker), then pass all the env variables for both platforms.","github_created_at":"2018-03-03T13:12:14+00:00","created_at":"2026-07-11T11:46:21.454698+00:00","updated_at":"2026-09-20T06:36:24.004586+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"},{"slug":"llm-frameworks","name":"LLM Frameworks","url":"https://www.graphcanon.com/categories/llm-frameworks","markdown_url":"https://www.graphcanon.com/categories/llm-frameworks.md","api_url":"https://www.graphcanon.com/api/graphcanon/categories/llm-frameworks"},{"slug":"model-training","name":"Model Training","url":"https://www.graphcanon.com/categories/model-training","markdown_url":"https://www.graphcanon.com/categories/model-training.md","api_url":"https://www.graphcanon.com/api/graphcanon/categories/model-training"}],"tags":[{"slug":"ai-gateway","name":"ai-gateway"},{"slug":"anthropic","name":"anthropic"},{"slug":"cohere","name":"cohere"},{"slug":"generative-ai","name":"generative-ai"},{"slug":"langchain","name":"langchain"},{"slug":"large-language-model","name":"large-language-model"},{"slug":"llm","name":"llm"},{"slug":"llm-inference","name":"llm-inference"}],"trust":{"provenance":{"is_fork":false,"github_id":123693096,"owner_type":"Organization","methodology":"github_public_v1","parent_repo":null,"near_duplicate_slugs":[]},"computed_at":"2026-09-20T03:46:04.000Z","maintenance":{"label":"Slowing","score":36,"methodology":"github_public_v1","releases_90d":0,"days_since_push":220,"last_release_at":"2024-03-10T12:32:19Z","stars_delta_30d":0,"open_issues_delta_30d":0},"security_summary":{"status":"no_lockfile","scanner":null,"low_count":0,"high_count":0,"last_scan_at":"2026-07-11T11:46:22.669Z","medium_count":0,"scan_profile":"none","critical_count":0}},"capability_facts":{"scan":{"source":"repo_scan","observed_at":"2026-09-20T03:46:05.037Z"},"languages":{"value":["python"],"source":"github.language","observed_at":"2026-09-20T03:46:05.037Z"},"license_spdx":{"value":"MIT","source":"github.license","observed_at":"2026-09-20T03:46:05.037Z"}},"decision_facts":{"hosting":null,"pricing":null,"requirements":{"notes":["- Requires Docker to manage environments consistently across different platforms."],"requires_docker":true},"constraints":{"requires_docker":true},"when_to_use":["- When you need an integrated solution for various aspects of working with LLMs and ML tasks that is easy to understand and use, without deep technical expertise.","- If your project involves several LLM-related operations like model training, inference serving, and operationalization, and you want a comprehensive toolset."],"when_not_to_use":["- When your focus is exclusively on advanced fine-tuning or customization of machine learning models which require deep configuration options tailored to specific needs.","- If you prioritize working within a highly specialized ML ecosystem that has its own set of tools and workflows, as Sagify might not integrate seamlessly with every specialized tool."],"source":"enrich:decision_facts","observed_at":"2026-07-14T20:46:28.376Z"},"constraint_facets":{"requires_docker":true},"decision_summary":[{"label":"Requirements","value":"Requires Docker; - Requires Docker to manage environments consistently across different platforms."},{"label":"Adopt for","value":"An accessible tool for managing large language models and other machine learning tasks in Python."},{"label":"License detail","value":"Offered under the MIT license, allowing broad use for both commercial and non-commercial purposes with few restrictions."}]}}