Awesome-LLMOps
An awesome & curated list of best LLMOps tools for developers
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Decision brief
Awesome-LLMOps is a curated list tailored for developers working with Large Language Models (LLMs), providing resources for model training, serving, evaluation, deployment, and more.
Good fit when
- - When you need a comprehensive directory of tools specifically focused on LLM development, training, fine-tuning, and management.
- - If your projects involve multiple stages of the LLMOps lifecycle and you require categorized resources to streamline different phases.
Avoid when
- - When you are looking for a hands-on platform or framework for developing and deploying models rather than just a resource list.
- - If your focus is on general artificial intelligence development that includes areas beyond LLMOps like image processing, robotics, or federated learning without the need for LLM-specific resources.
Observed Jul 11, 2026 · Source: enrich:decision_facts
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Maintenance and security
Full trust report- Maintenance
- Slowing (91d since push)
- As of 1d
- Provenance
- Not a fork · Organization account
- As of 1d
- Security (OSV)
- No lockfile
- As of 1mo
Public GitHub metadata and optional OSV scans. Signals, not a guarantee. Trust methodology.
Backing
Company context for TensorChord. Display-only - separate from trust and ranking.
- Company
- TensorChord·GitHub org profile·1mo
- Commercial model
- Pure OSS·GitHub org profile (public repos)·1mo
Install
git clone https://github.com/tensorchord/Awesome-LLMOpsHow it fits your stack(31)
Typed graph edges - alternatives, integrations, successors, and dependencies. Ranked by relationship type, not raw GitHub stars.
Alternative
Integrates
Depends on
Relationship graph
Optional deeper exploration of typed edges and category neighbours.
Similar tools
Same-category neighbours not already linked as typed edges.
Evidence and technical details
Sourced facts, taxonomy, compatibility claims, README excerpt, and machine-readable endpoints.
Overview
A comprehensive curated list that covers a wide range of categories within the LLM and MLOps ecosystem, including frameworks, serving methods, security aspects, training processes, data handling, large scale deployment strategies, performance optimization techniques, AutoML solutions, optimizations in model management, federated learning approaches, and even specific models for various domains like CV, Audio, Robotics.
Capability facts
- Languages
- shell
Source: github.language · Aug 20, 2026
Categories
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README
Awesome LLMOps
An awesome & curated list of the best LLMOps tools for developers.
[!NOTE] Contributions are most welcome, please adhere to the contribution guidelines.
Table of Contents
- Awesome LLMOps
- Table of Contents
- Model
- Large Language Model
- CV Foundation Model
- Audio Foundation Model
- Robotics Foundation Model
- Serving
- Large Model Serving
- Frameworks/Servers for Serving
- Security
- Frameworks for LLM security
- Observability
- LLMOps
- Search
- Vector search
- Code AI
- Training
- IDEs and Workspaces
- Foundation Model Fine Tuning
- Frameworks for Training
- Experiment Tracking
- Visualization
- Model Editing
- Data
- Data Management
- Data Storage
- Data Tracking
- Feature Engineering
- Data/Feature enrichment
- Large Scale Deployment
- ML Platforms
- Workflow
- Scheduling
- Model Management
- Performance
- ML Compiler
- Profiling
- AutoML
- Optimizations
- Federated ML
- Awesome Lists
Model
Large Language Model
| Project | Details | Repository |
|---|---|---|
| Alpaca | Code and documentation to train Stanford's Alpaca models, and generate the data. | |
| BELLE | A 7B Large Language Model fine-tune by 34B Chinese Character Corpus, based on LLaMA and Alpaca. | |
| Bloom | BigScience Large Open-science Open-access Multilingual Language Model | |
| dolly | Databricks’ Dolly, a large language model trained on the Databricks Machine Learning Platform |
For agents
This page has a .md twin and JSON over the API.