---
title: "Awesome-LLMOps"
type: "tool"
slug: "tensorchord-awesome-llmops"
canonical_url: "https://www.graphcanon.com/tools/tensorchord-awesome-llmops"
github_url: "https://github.com/tensorchord/Awesome-LLMOps"
homepage_url: null
stars: 5875
forks: 890
primary_language: "Shell"
license: "CC0-1.0"
categories: ["vector-databases", "model-training", "inference-serving", "evaluation-observability", "developer-tools", "ai-agents", "llm-frameworks", "speech-audio", "computer-vision", "data-retrieval"]
tags: ["llmops", "ml-ops", "awesome-list", "ai-development-tools"]
updated_at: "2026-07-07T18:39:04.051854+00:00"
---
# Awesome-LLMOps
> An awesome & curated list of the best LLMOps tools for developers
The repository contains a comprehensive collection of resources and tools tailored to developers interested in large language models (LLMs) and their operational management, including frameworks, serving solutions, security measures, training utilities, data solutions, deployment strategies, optimizations, performance tuning, federated learning techniques, and automation approaches.
## Facts
- Repository: https://github.com/tensorchord/Awesome-LLMOps
- Stars: 5,875 · Forks: 890 · Open issues: 147 · Watchers: 82
- Primary language: Shell
- License: CC0-1.0
- Last pushed: 2026-05-21T09:12:50+00:00
## Categories
- [Vector Databases](/categories/vector-databases.md)
- [Model Training](/categories/model-training.md)
- [Inference & Serving](/categories/inference-serving.md)
- [Evaluation & Observability](/categories/evaluation-observability.md)
- [Developer Tools](/categories/developer-tools.md)
- [AI Agents](/categories/ai-agents.md)
- [LLM Frameworks](/categories/llm-frameworks.md)
- [Speech & Audio](/categories/speech-audio.md)
- [Computer Vision](/categories/computer-vision.md)
- [Data & Retrieval](/categories/data-retrieval.md)
## Tags
llmops, ml-ops, awesome-list, ai-development-tools
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## README (excerpt)
```text
# Awesome LLMOps
An awesome & curated list of the best LLMOps tools for developers.
> [!NOTE]
> Contributions are most welcome, please adhere to the [contribution guidelines](contributing.md).
## Table of Contents
- [Awesome LLMOps](#awesome-llmops)
- [Table of Contents](#table-of-contents)
- [Model](#model)
- [Large Language Model](#large-language-model)
- [CV Foundation Model](#cv-foundation-model)
- [Audio Foundation Model](#audio-foundation-model)
- [Robotics Foundation Model](#robotics-foundation-model)
- [Serving](#serving)
- [Large Model Serving](#large-model-serving)
- [Frameworks/Servers for Serving](#frameworksservers-for-serving)
- [Security](#security)
- [Frameworks for LLM security](#frameworks-for-llm-security)
- [Observability](#observability)
- [LLMOps](#llmops)
- [Search](#search)
- [Vector search](#vector-search)
- [Code AI](#code-ai)
- [Training](#training)
- [IDEs and Workspaces](#ides-and-workspaces)
- [Foundation Model Fine Tuning](#foundation-model-fine-tuning)
- [Frameworks for Training](#frameworks-for-training)
- [Experiment Tracking](#experiment-tracking)
- [Visualization](#visualization)
- [Model Editing](#model-editing)
- [Data](#data)
- [Data Management](#data-management)
- [Data Storage](#data-storage)
- [Data Tracking](#data-tracking)
- [Feature Engineering](#feature-engineering)
- [Data/Feature enrichment](#datafeature-enrichment)
- [Large Scale Deployment](#large-scale-deployment)
- [ML Platforms](#ml-platforms)
- [Workflow](#workflow)
- [Scheduling](#scheduling)
- [Model Management](#model-management)
- [Performance](#performance)
- [ML Compiler](#ml-compiler)
- [Profiling](#profiling)
- [AutoML](#automl)
- [Optimizations](#optimizations)
- [Federated ML](#federated-ml)
- [Awesome Lists](#awesome-lists)
## Model
### Large Language Model
| Project | Details | Repository |
| ----------------------------------------------------------------------- | ------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------ | --------------------------------------------------------------------------------------------------------- |
| [Alpaca](https://github.com/tatsu-lab/stanford_alpaca) | Code and documentation to train Stanford's Alpaca models, and generate the data. | |
| [BELLE](https://github.com/LianjiaTech/BELLE) | A 7B Large Language Model fine-tune by 34B Chinese Character Corpus, based on LLaMA and Alpaca. | |
| [Bloom](https://github.com/bigscience-workshop/model_card) | BigScience Large Open-science Open-access Multilingual Language Model | |
| [dolly](https://github.com/databrickslabs/dolly) | Databricks’ Dolly, a large language model trained on the Databricks Machine Learning Platform
```
---
**Machine-readable endpoints**
- JSON: [`/api/graphcanon/tools/tensorchord-awesome-llmops`](/api/graphcanon/tools/tensorchord-awesome-llmops)
- LLM index: [/llms.txt](/llms.txt)
- Full corpus: [/llms-full.txt](/llms-full.txt)
_GraphCanon - The knowledge graph for AI development. https://www.graphcanon.com/_