Comparison
hamilton vs Awesome-LLMOps
Verdict
Pick hamilton if apache Hamilton aids Python data environments by enabling modular, self-documenting dataflows with lineage/tracing and metadata support; 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 more.
Markdown twin · hamilton alternatives · Awesome-LLMOps alternatives
GraphCanon updated Sep 29, 2026
8views this month
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Trust & integrity
| Signal | hamilton | Awesome-LLMOps |
|---|---|---|
| Maintenance | Very active (0d since push) As of Sep 29, 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 29, 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 Jul 11, 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
- hamilton
- Modular dataflow definition for Python environments
- Awesome-LLMOps
- An awesome & curated list of best LLMOps tools for developers
Stars
- hamilton
- 2.6k
- Awesome-LLMOps
- 5.9k
Forks
- hamilton
- 215
- Awesome-LLMOps
- 1.1k
Open issues
- hamilton
- 153
- Awesome-LLMOps
- 317
Language
- hamilton
- Jupyter Notebook
- Awesome-LLMOps
- Shell
Adopt for
- hamilton
- Apache Hamilton aids Python data environments by enabling modular, self-documenting dataflows with lineage/tracing and metadata support.
- 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
- hamilton
- -
- Awesome-LLMOps
- -
Runtime
- hamilton
- -
- Awesome-LLMOps
- -
License
- hamilton
- Apache-2.0
- Awesome-LLMOps
- CC0-1.0
Last pushed
- hamilton
- Sep 29, 2026
- Awesome-LLMOps
- May 21, 2026
Categories
- hamilton
- Data & Retrieval, Developer Tools
- Awesome-LLMOps
- Computer Vision, Data & Retrieval, Evaluation & Observability, Inference & Serving, LLM Frameworks, Model Training, Speech & Audio
Trust and health
Maintenance
- hamilton
- Very active (96%)
- Awesome-LLMOps
- Slowing (36%)
Days since push
- hamilton
- 0d
- Awesome-LLMOps
- 121d
Open issues (now)
- hamilton
- 153
- Awesome-LLMOps
- 317
Stars delta
- hamilton
- +44 (30d)
- Awesome-LLMOps
- +26 (30d)
Open issues delta
- hamilton
- +5 (30d)
- Awesome-LLMOps
- +70 (30d)
Full report
- hamilton
- Trust report
- Awesome-LLMOps
- Trust report
Choose hamilton if…
- hamilton is primarily Jupyter Notebook; Awesome-LLMOps is Shell.
- License: hamilton is Apache-2.0, Awesome-LLMOps is CC0-1.0.
- Tags unique to hamilton: dag, data-analysis, data-engineering, data-science.
- Also covers Developer Tools.
- Requires detailed lineage and tracing in Python-based ETL processes
When NOT to use hamilton
- Working exclusively within a non-Python environment where broader platform support is needed
- Projects that do not require extensive metadata capture or lineage tracking capabilities
Choose Awesome-LLMOps if…
- Awesome-LLMOps is primarily Shell; hamilton is Jupyter Notebook.
- License: Awesome-LLMOps is CC0-1.0, hamilton is Apache-2.0.
- Tags unique to Awesome-LLMOps: ai development tools, awesome-list.
- Also covers Computer Vision, Evaluation & Observability, Inference & Serving, LLM Frameworks, Model Training, 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
hamilton trust report →Awesome-LLMOps trust report →Data & Retrieval category →Developer Tools category →Computer Vision category →Evaluation & Observability category →Inference & Serving category →LLM Frameworks category →Model Training 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 (apache/hamilton) · observed Sep 29, 2026
- GitHub forks (apache/hamilton) · observed Sep 29, 2026
- Last push (apache/hamilton) · observed Sep 29, 2026
- License file (Apache-2.0) · observed Sep 29, 2026
- Decision facts (enrichment) · observed Jul 17, 2026
- Trust scan (lockfile / OSV) · observed Jul 11, 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: hamilton 2.6k · Awesome-LLMOps 5.9k (synced Sep 29, 2026).
Common questions
- What is the difference between hamilton and Awesome-LLMOps?
- hamilton: Modular dataflow definition for Python environments. 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 hamilton over Awesome-LLMOps?
- Choose hamilton over Awesome-LLMOps when hamilton is primarily Jupyter Notebook; Awesome-LLMOps is Shell; License: hamilton is Apache-2.0, Awesome-LLMOps is CC0-1.0; Tags unique to hamilton: dag, data-analysis, data-engineering, data-science; Also covers Developer Tools; Requires detailed lineage and tracing in Python-based ETL processes.
- When should I choose Awesome-LLMOps over hamilton?
- Choose Awesome-LLMOps over hamilton when Awesome-LLMOps is primarily Shell; hamilton is Jupyter Notebook; License: Awesome-LLMOps is CC0-1.0, hamilton is Apache-2.0; Tags unique to Awesome-LLMOps: ai development tools, awesome-list; Also covers Computer Vision, Evaluation & Observability, Inference & Serving, LLM Frameworks, Model Training, Speech & Audio; - When you need a comprehensive directory of tools specifically focused on LLM development, training, fine-tuning, and management.
- When should I avoid hamilton?
- Working exclusively within a non-Python environment where broader platform support is needed Projects that do not require extensive metadata capture or lineage tracking capabilities
- 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 hamilton or Awesome-LLMOps more popular on GitHub?
- Awesome-LLMOps has more GitHub stars (5,941 vs 2,601). Stars measure visibility, not whether either tool fits your constraints.
- Are hamilton and Awesome-LLMOps open source?
- Yes - both are open-source projects on GitHub (hamilton: Apache-2.0, Awesome-LLMOps: CC0-1.0).
- Where can I find alternatives to hamilton or Awesome-LLMOps?
- GraphCanon lists graph-backed alternatives at hamilton alternatives and Awesome-LLMOps alternatives (hamilton 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, hamilton or Awesome-LLMOps?
- hamilton: 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 hamilton and Awesome-LLMOps?
- GraphCanon publishes per-repo trust reports with dated maintenance, provenance, and scan summaries: hamilton trust report; Awesome-LLMOps trust report.