Awesome LLMOps is a curated list of tools and resources for developers working with large language models and machine learning operations.
The project addresses the challenge of navigating the rapidly expanding ecosystem of LLMOps tools by organizing them into a structured, categorized reference. It works by collecting and curating links to tools across the full spectrum of LLM development workflows, from model selection and serving through training, data management, observability, security, and deployment. The list spans foundation models for language, vision, audio, and robotics; serving frameworks; security and observability solutions; vector search systems; code AI tools; training infrastructure; experiment tracking; data management platforms; and optimization techniques.
Developers should use this list when building systems around large language models and need to understand what tools exist in specific categories. It suits teams evaluating their LLMOps stack, researchers exploring available frameworks, and engineers looking for solutions to particular problems in the LLM development lifecycle. The list is organized by functional area rather than by recommendation or ranking, making it useful for discovering options within a category rather than identifying a single best choice.
The project receives issues almost exclusively from outside users rather than the core team, reflecting a substantial base of adopters relying on it as a reference. Work in the issue tracker is dominated by enhancement requests, indicating that the community actively drives the list's growth and refinement.
