Comparison
onyx vs Awesome-LLMOps
Verdict
Pick onyx if onyx is an open-source AI platform that supports AI chat functionalities with advanced features, suitable for deployment in various environments including Docker, Kubernetes, and cloud providers. It offers both Lite and,; pick Awesome-LLMOps if awesome-LLMOps is a curated list tailored for developers working with Large Language Models (LLMs), providing resources for model training, serving, evaluation, deployment, and.
Markdown twin · onyx alternatives · Awesome-LLMOps alternatives
GraphCanon updated Sep 20, 2026
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Trust & integrity
| Signal | onyx | Awesome-LLMOps |
|---|---|---|
| Maintenance | Very active (0d since push) As of Sep 18, 2026 · github_public_v1 | Slowing (121d since push) As of Sep 20, 2026 · github_public_v1 |
| Provenance | Not a fork · Organization account As of Sep 18, 2026 · github_public_v1 | Not a fork · Organization account As of Sep 20, 2026 · github_public_v1 |
| OSV dependency advisories | No lockfile (source not queried) As of Sep 20, 2026 · osv@v1 | No lockfile (source not queried) As of Jul 11, 2026 · osv@v1 |
| deps.dev advisories | Not queried deps.dev@v1 | Not queried deps.dev@v1 |
| OpenSSF Scorecard | Not queried openssf-scorecard@v1 | Not queried openssf-scorecard@v1 |
Tagline
- onyx
- Open Source AI Platform - AI Chat with advanced features that works with every LLM
- Awesome-LLMOps
- An awesome & curated list of best LLMOps tools for developers
Stars
- onyx
- 32k
- Awesome-LLMOps
- 5.9k
Forks
- onyx
- 4.5k
- Awesome-LLMOps
- 1.1k
Open issues
- onyx
- 470
- Awesome-LLMOps
- 317
Language
- onyx
- Python
- Awesome-LLMOps
- Shell
Adopt for
- onyx
- Onyx is an open-source AI platform that supports AI chat functionalities with advanced features, suitable for deployment in various environments including Docker, Kubernetes, and cloud providers. It offers both Lite and,
- Awesome-LLMOps
- 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.
Persona
- onyx
- -
- Awesome-LLMOps
- -
Runtime
- onyx
- -
- Awesome-LLMOps
- -
License
- onyx
- Onyx is available in two editions: Community Edition (CE) under the MIT license, which covers core features, and Enterprise Edition (EE) with additional features for larger organizations.
- Awesome-LLMOps
- CC0-1.0
Last pushed
- onyx
- Sep 18, 2026
- Awesome-LLMOps
- May 21, 2026
Categories
- onyx
- AI Agents, Data & Retrieval, Developer Tools, Inference & Serving, Model Training
- Awesome-LLMOps
- Computer Vision, Data & Retrieval, Evaluation & Observability, Inference & Serving, LLM Frameworks, Model Training, Speech & Audio
Trust and health
Maintenance
- onyx
- Very active (96%)
- Awesome-LLMOps
- Slowing (36%)
Days since push
- onyx
- 0d
- Awesome-LLMOps
- 121d
Open issues (now)
- onyx
- 470
- Awesome-LLMOps
- 317
Stars delta
- onyx
- +536 (30d)
- Awesome-LLMOps
- +26 (30d)
Open issues delta
- onyx
- +69 (30d)
- Awesome-LLMOps
- +70 (30d)
Full report
- onyx
- Trust report
- Awesome-LLMOps
- Trust report
Choose onyx if…
- onyx is primarily Python; Awesome-LLMOps is Shell.
- License: onyx is Other, Awesome-LLMOps is CC0-1.0.
- Pricing: Onyx Community Edition is free under the MIT license, while the Enterprise Edition includes extra features for larger organizations and requires a paid subscription..
- Requirements: Min 1 GB RAM; Lite mode requires under 1GB of memory, while Standard mode requires more resources for its additional features..
- Tags unique to onyx: ai-chat, chatui, enterprise-search, gen-ai.
- Also covers AI Agents, Developer Tools.
- use Onyx when you need a platform that supports both lightweight and full-featured deployments, including vector and keyword indexing, and performance optimizations for large-scale use.
When NOT to use onyx
- avoid Onyx if you are looking for a platform that requires minimal setup and resource usage beyond what the Lite mode offers.
- do not use Onyx if your project does not benefit from vector and keyword indexing, AI model inference servers, or performance optimizations, as these features are only available in the Standard mode.
Choose Awesome-LLMOps if…
- Awesome-LLMOps is primarily Shell; onyx is Python.
- License: Awesome-LLMOps is CC0-1.0, onyx is Other.
- Tags unique to Awesome-LLMOps: ai development tools, awesome-list, llmops, mlops.
- Also covers Computer Vision, Evaluation & Observability, LLM Frameworks, Speech & Audio.
- - When you need a comprehensive directory of tools specifically focused on LLM development, training, fine-tuning, and management.
When NOT to use Awesome-LLMOps
- - 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.
Explore
onyx trust report →Awesome-LLMOps trust report →AI Agents category →Data & Retrieval category →Developer Tools category →Inference & Serving category →Model Training category →Computer Vision category →Evaluation & Observability category →LLM Frameworks category →Speech & Audio category →All comparisonsStack workflowsTrending tools
Sources
Every stat on this page traces to a dated GitHub sync, license file, enrichment field, or trust scan.
- GitHub stars (onyx-dot-app/onyx) · observed Sep 20, 2026
- GitHub forks (onyx-dot-app/onyx) · observed Sep 20, 2026
- Last push (onyx-dot-app/onyx) · observed Sep 18, 2026
- License file (Other) · observed Sep 20, 2026
- Decision facts (enrichment) · observed Sep 18, 2026
- Trust scan (lockfile / OSV) · observed Sep 20, 2026
- GitHub stars (tensorchord/Awesome-LLMOps) · observed Sep 20, 2026
- GitHub forks (tensorchord/Awesome-LLMOps) · observed Sep 20, 2026
- Last push (tensorchord/Awesome-LLMOps) · observed May 21, 2026
- License file (CC0-1.0) · observed Sep 20, 2026
- Decision facts (enrichment) · observed Jul 11, 2026
- Trust scan (lockfile / OSV) · observed Jul 11, 2026
GitHub stars on cards: onyx 32k · Awesome-LLMOps 5.9k (synced Sep 20, 2026).
Common questions
- What is the difference between onyx and Awesome-LLMOps?
- onyx: Open Source AI Platform - AI Chat with advanced features that works with every LLM. Awesome-LLMOps: An awesome & curated list of best LLMOps tools for developers. See the comparison table for live GitHub stats and shared categories.
- When should I choose onyx over Awesome-LLMOps?
- Choose onyx over Awesome-LLMOps when onyx is primarily Python; Awesome-LLMOps is Shell; License: onyx is Other, Awesome-LLMOps is CC0-1.0; Pricing: Onyx Community Edition is free under the MIT license, while the Enterprise Edition includes extra features for larger organizations and requires a paid subscription.; Requirements: Min 1 GB RAM; Lite mode requires under 1GB of memory, while Standard mode requires more resources for its additional features.; Tags unique to onyx: ai-chat, chatui, enterprise-search, gen-ai; Also covers AI Agents, Developer Tools; use Onyx when you need a platform that supports both lightweight and full-featured deployments, including vector and keyword indexing, and performance optimizations for large-scale use.
- When should I choose Awesome-LLMOps over onyx?
- Choose Awesome-LLMOps over onyx when Awesome-LLMOps is primarily Shell; onyx is Python; License: Awesome-LLMOps is CC0-1.0, onyx is Other; Tags unique to Awesome-LLMOps: ai development tools, awesome-list, llmops, mlops; Also covers Computer Vision, Evaluation & Observability, LLM Frameworks, Speech & Audio; - When you need a comprehensive directory of tools specifically focused on LLM development, training, fine-tuning, and management.
- When should I avoid onyx?
- avoid Onyx if you are looking for a platform that requires minimal setup and resource usage beyond what the Lite mode offers. do not use Onyx if your project does not benefit from vector and keyword indexing, AI model inference servers, or performance optimizations, as these features are only available in the Standard mode.
- When should I avoid Awesome-LLMOps?
- - 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.
- Is onyx or Awesome-LLMOps more popular on GitHub?
- onyx has more GitHub stars (32,153 vs 5,941). Stars measure visibility, not whether either tool fits your constraints.
- Are onyx and Awesome-LLMOps open source?
- Yes - both are open-source projects on GitHub (onyx: Other, Awesome-LLMOps: CC0-1.0).
- Where can I find alternatives to onyx or Awesome-LLMOps?
- GraphCanon lists graph-backed alternatives at onyx alternatives and Awesome-LLMOps alternatives (onyx markdown twin, Awesome-LLMOps markdown twin), ranked by typed relationship edges rather than popularity votes.
- Is there a machine-readable version of this comparison?
- Yes. The markdown twin at this comparison mirrors this page for agents and LLM crawlers, with the same stats table and FAQ answers.
- Which is better maintained, onyx or Awesome-LLMOps?
- onyx: Very active. Awesome-LLMOps: Slowing. Compare maintenance labels, days since push, and release cadence in the trust section below - stars alone do not measure maintenance.
- Where are the full trust reports for onyx and Awesome-LLMOps?
- GraphCanon publishes per-repo trust reports with dated maintenance, provenance, and scan summaries: onyx trust report; Awesome-LLMOps trust report.