Comparison
langchain-tutorials vs Awesome-LLMOps
Verdict
Pick langchain-tutorials if langchain-tutorials offers educational material to aid in understanding and applying the LangChain library via Jupyter Notebooks; 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 · langchain-tutorials alternatives · Awesome-LLMOps alternatives
GraphCanon updated 2d
vs
Trust & integrity
| Signal | langchain-tutorials | Awesome-LLMOps |
|---|---|---|
| Maintenance | Dormant (740d since push) As of 1w · github_public_v1 | Slowing (91d since push) As of 2d · github_public_v1 |
| Provenance | Not a fork · Personal account As of 1w · github_public_v1 | Not a fork · Organization account As of 2d · github_public_v1 |
| OSV dependency advisories | Published findings As of 1mo · osv@v1 | No lockfile (source not queried) As of 1mo · 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
- langchain-tutorials
- Overview and tutorial of the LangChain Library
- Awesome-LLMOps
- An awesome & curated list of best LLMOps tools for developers
Stars
- langchain-tutorials
- 7.5k
- Awesome-LLMOps
- 5.9k
Forks
- langchain-tutorials
- 2.0k
- Awesome-LLMOps
- 993
Open issues
- langchain-tutorials
- 15
- Awesome-LLMOps
- 247
Language
- langchain-tutorials
- Jupyter Notebook
- Awesome-LLMOps
- Shell
Adopt for
- langchain-tutorials
- langchain-tutorials offers educational material to aid in understanding and applying the LangChain library via Jupyter Notebooks.
- 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
- langchain-tutorials
- -
- Awesome-LLMOps
- -
Runtime
- langchain-tutorials
- -
- Awesome-LLMOps
- -
License
- langchain-tutorials
- The license details for this tool are unknown.
- Awesome-LLMOps
- CC0-1.0
Last pushed
- langchain-tutorials
- Aug 5, 2024
- Awesome-LLMOps
- May 21, 2026
Categories
- langchain-tutorials
- Developer Tools, Model Training
- Awesome-LLMOps
- Computer Vision, Data & Retrieval, Evaluation & Observability, Inference & Serving, LLM Frameworks, Model Training, Speech & Audio
Trust and health
Maintenance
- langchain-tutorials
- Dormant (18%)
- Awesome-LLMOps
- Slowing (36%)
Days since push
- langchain-tutorials
- 740d
- Awesome-LLMOps
- 91d
Open issues (now)
- langchain-tutorials
- 15
- Awesome-LLMOps
- 247
Stars delta
- langchain-tutorials
- +10 (30d)
- Awesome-LLMOps
- +28 (30d)
Open issues delta
- langchain-tutorials
- 0 (30d)
- Awesome-LLMOps
- +66 (30d)
Owner type
- langchain-tutorials
- User
- Awesome-LLMOps
- Organization
OSV dependency advisories
- langchain-tutorials
- Published findings
- Awesome-LLMOps
- No lockfile (source not queried)
Full report
- langchain-tutorials
- Trust report
- Awesome-LLMOps
- Trust report
Choose langchain-tutorials if…
- langchain-tutorials is primarily Jupyter Notebook; Awesome-LLMOps is Shell.
- Pricing: The repository is freely accessible with no stated fees; however, specific services or advanced features (if any) may require payment and aren't detailed in the provided data..
- Tags unique to langchain-tutorials: jupyter-notebook, langchain, prompt-engineering, tutorials.
- Also covers Developer Tools.
- - When you're interested in hands-on learning through Jupyter Notebooks and want a structured approach to mastering LangChain with guided examples.
When NOT to use langchain-tutorials
- - When you prefer video tutorials or written articles over interactive notebooks; although the repository links to supplementary videos and online resources, its primary medium is Jupyter Notebooks.
- - If your goal is immediate application without foundational knowledge, since langchain-tutorials emphasizes a learning path from basics up, which may add time before practical applications.
Choose Awesome-LLMOps if…
- Awesome-LLMOps is primarily Shell; langchain-tutorials is Jupyter Notebook.
- Tags unique to Awesome-LLMOps: ai-development-tools, awesome-list, llmops, mlops.
- Also covers Computer Vision, Data & Retrieval, Evaluation & Observability, Inference & Serving, 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
langchain-tutorials trust report →Awesome-LLMOps trust report →Developer Tools category →Model Training category →Computer Vision category →Data & Retrieval category →Evaluation & Observability category →Inference & Serving 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 (gkamradt/langchain-tutorials) · observed Aug 15, 2026
- GitHub forks (gkamradt/langchain-tutorials) · observed Aug 15, 2026
- Last push (gkamradt/langchain-tutorials) · observed Aug 5, 2024
- License file (unknown) · observed Aug 15, 2026
- Decision facts (enrichment) · observed Jul 12, 2026
- Trust scan (lockfile / OSV) · observed Jul 11, 2026
- GitHub stars (tensorchord/Awesome-LLMOps) · observed Aug 20, 2026
- GitHub forks (tensorchord/Awesome-LLMOps) · observed Aug 20, 2026
- Last push (tensorchord/Awesome-LLMOps) · observed May 21, 2026
- License file (CC0-1.0) · observed Aug 20, 2026
- Decision facts (enrichment) · observed Jul 11, 2026
- Trust scan (lockfile / OSV) · observed Jul 11, 2026
GitHub stars on cards: langchain-tutorials 7.5k · Awesome-LLMOps 5.9k (synced Aug 15, 2026).
Common questions
- What is the difference between langchain-tutorials and Awesome-LLMOps?
- langchain-tutorials: Overview and tutorial of the LangChain Library. 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 langchain-tutorials over Awesome-LLMOps?
- Choose langchain-tutorials over Awesome-LLMOps when langchain-tutorials is primarily Jupyter Notebook; Awesome-LLMOps is Shell; Pricing: The repository is freely accessible with no stated fees; however, specific services or advanced features (if any) may require payment and aren't detailed in the provided data.; Tags unique to langchain-tutorials: jupyter-notebook, langchain, prompt-engineering, tutorials; Also covers Developer Tools; - When you're interested in hands-on learning through Jupyter Notebooks and want a structured approach to mastering LangChain with guided examples.
- When should I choose Awesome-LLMOps over langchain-tutorials?
- Choose Awesome-LLMOps over langchain-tutorials when Awesome-LLMOps is primarily Shell; langchain-tutorials is Jupyter Notebook; Tags unique to Awesome-LLMOps: ai-development-tools, awesome-list, llmops, mlops; Also covers Computer Vision, Data & Retrieval, Evaluation & Observability, Inference & Serving, 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 langchain-tutorials?
- - When you prefer video tutorials or written articles over interactive notebooks; although the repository links to supplementary videos and online resources, its primary medium is Jupyter Notebooks. - If your goal is immediate application without foundational knowledge, since langchain-tutorials emphasizes a learning path from basics up, which may add time before practical applications.
- 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 langchain-tutorials or Awesome-LLMOps more popular on GitHub?
- langchain-tutorials has more GitHub stars (7,480 vs 5,915). Stars measure visibility, not whether either tool fits your constraints.
- Are langchain-tutorials and Awesome-LLMOps open source?
- Yes - both are open-source projects on GitHub.
- Where can I find alternatives to langchain-tutorials or Awesome-LLMOps?
- GraphCanon lists graph-backed alternatives at langchain-tutorials alternatives and Awesome-LLMOps alternatives (langchain-tutorials 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, langchain-tutorials or Awesome-LLMOps?
- langchain-tutorials: Dormant. 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 langchain-tutorials and Awesome-LLMOps?
- GraphCanon publishes per-repo trust reports with dated maintenance, provenance, and scan summaries: langchain-tutorials trust report; Awesome-LLMOps trust report.