# tensorchord/Awesome-LLMOps

An awesome & curated list of best LLMOps tools for developers

Repository: https://github.com/tensorchord/Awesome-LLMOps
Canonical: https://ross.abutalabs.com/products/awesome-llmops
Language: Shell
License: CC0-1.0
License Family: permissive
Topics: awesome-list, mlops, ai-development-tools, llmops
Last push: 2026-05-21T09:12:50+00:00

## Health v2 (maintenance only)
Score: 70/100 (v2, computed 2026-09-03T02:20:16.233290+00:00)
- activity 83, release rhythm 35, longevity 100
- inputs: {"age_days": 1602, "days_push": 104, "days_rel": null, "gap_med": null, "n_releases_24m": 0}
- flags: no_releases
- formula: round(0.45*activity + 0.35*rhythm + 0.20*longevity); archived -> min(score, 10)

## Adoption (not part of the score)
Stars 5921, forks 998 (observed 2026-08-28T04:09:32.338702+00:00)

## What it is
A curated awesome-list cataloging the best LLMOps tools for developers, covering models, serving, security, training, data management, deployment, and performance. It is a reference resource of links and descriptions rather than runnable software.

## Use cases
- find tools for serving large language models
- discover LLM fine-tuning and training frameworks
- compare vector search and RAG tooling options
- find LLM security and observability tools
- explore ML platforms for large-scale model deployment
- find experiment tracking and data management tools for ML

## When to choose
- you want a curated overview of the LLMOps ecosystem before picking tools
- you are researching options for model serving, training, or deployment
- you need a starting point to discover MLOps and LLM tooling

## When to avoid
- you need runnable software rather than a list of links
- you need in-depth tutorials or documentation for a specific tool
- you need guaranteed up-to-date or benchmarked comparisons

## Facets
- artifact type: learning-resource
- maturity: active
- function: developer-tools, machine-learning, llm-inference, llm-training, monitoring, vector-database, rag
- domain: large-language-models, machine-learning, developer-tools, awesome-lists
- platform: cross-platform
- tags: awesome-list, llmops, mlops, curated-list, ai-tools

## Member repositories
- tensorchord/Awesome-LLMOps (main) score 70

## Provenance
- Observed fields: from GitHub, fetched 2026-08-28T04:09:32.338702+00:00.
- Health v2: computed from the inputs above; adoption is never an input.
- Inferred fields (summary, facets, guidance): AI-extracted, prompt v1, taxonomy v1, on 2026-08-29T17:50:50.734441+00:00, confidence not recorded.
  - readme: https://github.com/tensorchord/Awesome-LLMOps (fetched 2026-08-28T04:09:32.338702+00:00, sha cb9adbdb4cff)
- Data as of 2026-08-30T08:39:29.467469+00:00.