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Decision brief
An accessible tool for managing large language models and other machine learning tasks in Python.
Good fit when
- - 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.
Avoid when
- - 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.
- Requirements:
- Requires Docker; - Requires Docker to manage environments consistently across different platforms.
Observed Jul 14, 2026 · Source: enrich:decision_facts
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Maintenance and security
Full trust report- Maintenance
- Slowing (195d since push)
- As of today
- Provenance
- Not a fork · Organization account
- As of today
- Security (OSV)
- No lockfile
- As of 1mo
Public GitHub metadata and optional OSV scans. Signals, not a guarantee. Trust methodology.
Install
pip install sagifySimilar tools
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Evidence and technical details
Sourced facts, taxonomy, compatibility claims, README excerpt, and machine-readable endpoints.
Overview
A repository providing resources for managing large language models and machine learning tasks in an accessible manner.
Capability facts
- Languages
- python
Source: github.language · Aug 25, 2026
Categories
Compatibility
Sourced claims from the README excerpt - not unsourced marketing copy.
Source: README excerpt (regex_v1, Aug 25, 2026)
- `OPENAI_API_KEY`: Your OpenAI API key. Example: `export OPENAI_API_KEY=...`.Source link
Tags
README
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:
sagify 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
You can change the values for ec2 type (-e), aws region and aws profile with your preferred ones.
Once the Stable Diffusion model is deployed, you can use the generated code snippet to query it. Enjoy!
Deploy FastAPI LLM Gateway - Docker
Once you have set up your backend platform, you can deploy the FastAPI LLM Gateway locally.
In case of using the AWS Sagemaker platform, you need to define the following env variables before you start the LLM Gateway server:
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=....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=....AWS_REGION_NAME: AWS region where the LLM backend services (Sagemaker endpoints) are deployed.S3_BUCKET_NAME: S3 bucket name where the created images by the image creation backend service are stored.IMAGE_URL_TTL_IN_SECONDS: TTL in seconds of the temporary url to the created images. Default value: 3600.SM_CHAT_COMPLETIONS_MODEL: The Sagemaker endpoint name where the chat completions model is deployed.SM_EMBEDDINGS_MODEL: The Sagemaker endpoint name where the embeddings model is deployed.SM_IMAGE_CREATION_MODEL: The Sagemaker endpoint name where the image creation model is deployed.
In case of using the OpenAI platform, you need to define the following env variables before you start the LLM Gateway server:
OPENAI_API_KEY: Your OpenAI API key. Example:export OPENAI_API_KEY=....OPENAI_CHAT_COMPLETIONS_MODEL: It should have one of values here or here.OPENAI_EMBEDDINGS_MODEL: It should have one of values here.OPENAI_IMAGE_CREATION_MODEL: It should have one of values here.
Now, 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.
This command will output the Docker container id. You can stop the container by executing docker stop <CONTAINER_ID>.
Examples
(Remember to export first all the environment variables you need)
In the case you want to create a docker image and then run it
sagify llm gateway --image sagify-llm-gateway:v0.1.0 --start-local
If you want to use just build the image
sagify llm gateway --image sagify-llm-gateway:v0.1.0
If you want to support both platforms (OpenAI and AWS Sagemaker), then pass all the env variables for both platforms.
For agents
This page has a .md twin and JSON over the API.