OpenLLM

OpenLLM is an open-source framework (machine learning frameworks) for running, serving, and managing open-source large language models in production environments.

• Framework for serving and operating open-source large language models (machine learning frameworks)
• Command-Line Interface (CLI) and Software Development Kit (SDK) for building, deploying, and managing Large Language Model (LLM) services (developer tooling)
• Integration with BentoML for packaging and shipping LLM applications (MLOps / model serving)
• Support for running models on local or cloud infrastructure (infrastructure orchestration)
• APIs for inference and application integration around LLM endpoints (application integration)

More About OpenLLM

OpenLLM is an open-source framework (machine learning frameworks) developed under the BentoML ecosystem for running, serving, and managing open-source large language models in production. It targets scenarios where organizations want to operate their own LLMs instead of relying solely on third-party hosted APIs, while reusing common tooling for deployment, observability, and lifecycle management. The project sits in the broader Machine Learning Operations (MLOps) and model-serving category and is designed to work with the BentoML platform (MLOps / model serving).

The project focuses on providing a unified interface and operational layer for various open-source large language models (LLM serving). It exposes tools to start LLM servers, configure runtime parameters, and provide standardized Hypertext Transfer Protocol (HTTP) or gRPC endpoints for inference (application integration). Through its CLI and Python SDK (developer tooling), engineers can build and manage LLM-driven services, integrate them into microservices architectures, and automate workflows such as containerization and deployment.

OpenLLM integrates with BentoML (MLOps / model serving), which provides model packaging, container image builds, and deployment targets such as Kubernetes clusters or other container platforms (container orchestration). Using this integration, teams can turn LLMs into deployable services, bundle dependencies, and apply consistent DevOps practices like Continuous Integration and Continuous Deployment (CI/CD) pipelines, logging, and monitoring. The framework is positioned to help standardize how LLMs are exposed as networked services inside larger application stacks.

From an enterprise usage perspective, OpenLLM supports running LLM workloads on local infrastructure, in private clouds, or in public cloud environments (infrastructure orchestration). This deployment flexibility enables use in regulated or data-sensitive settings where models and inference data must remain within controlled environments. Organizations can host LLM endpoints behind internal gateways or service meshes and integrate them with existing security, observability, and Application Programming Interface (API) management tooling.

Technically, OpenLLM aligns with Python-based Machine Learning (ML) ecosystems and works in concert with BentoML’s packaging and service definitions (machine learning frameworks). It offers configuration options around model selection and runtime parameters so that multiple LLMs can be operated with standardized tooling. In an architectural taxonomy, OpenLLM can be classified as an LLM-serving and orchestration layer that complements broader MLOps platforms, focusing on the run-time management of open-source large language models as production services.