--- title: "langchain-tutorials vs Awesome-LLMOps" type: "comparison" canonical_url: "https://www.graphcanon.com/compare/gkamradt-langchain-tutorials-vs-tensorchord-awesome-llmops" tools: ["gkamradt-langchain-tutorials", "tensorchord-awesome-llmops"] --- # langchain-tutorials vs Awesome-LLMOps *GraphCanon updated Aug 20, 2026* ## 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. [langchain-tutorials](https://github.com/gkamradt/langchain-tutorials) reports 7.5k GitHub stars, 2.0k forks, and 15 open issues, last pushed Aug 5, 2024. [Awesome-LLMOps](https://github.com/tensorchord/Awesome-LLMOps) has 5.9k stars, 993 forks, and 247 open issues, last pushed May 21, 2026. Figures are from public GitHub metadata via [langchain-tutorials's repository](https://github.com/gkamradt/langchain-tutorials) and [Awesome-LLMOps's repository](https://github.com/tensorchord/Awesome-LLMOps). | | [langchain-tutorials](/tools/gkamradt-langchain-tutorials.md) | [Awesome-LLMOps](/tools/tensorchord-awesome-llmops.md) | | --- | --- | --- | | Tagline | Overview and tutorial of the LangChain Library | An awesome & curated list of best LLMOps tools for developers | | Stars | 7,480 | 5,915 | | Forks | 2,013 | 993 | | Open issues | 15 | 247 | | Language | Jupyter Notebook | Shell | | Adopt for | langchain-tutorials offers educational material to aid in understanding and applying the LangChain library via Jupyter Notebooks. | 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 | - | - | | Runtime | - | - | | License | The license details for this tool are unknown. | CC0-1.0 | | Categories | Developer Tools, Model Training | Computer Vision, Data & Retrieval, Evaluation & Observability, Inference & Serving, LLM Frameworks, Model Training, Speech & Audio | ## Trust and health _Sourced signals - not a safety guarantee. No winner column._ | | [langchain-tutorials](/tools/gkamradt-langchain-tutorials.md) | [Awesome-LLMOps](/tools/tensorchord-awesome-llmops.md) | | --- | --- | --- | | Maintenance | Dormant (18%) | Slowing (36%) | | Days since push | 740d | 91d | | Open issues (now) | 15 | 247 | | Stars delta | +10 (30d) | +28 (30d) | | Open issues delta | 0 (30d) | +66 (30d) | | Owner type | User | Organization | | Full report | [trust report](/tools/gkamradt-langchain-tutorials/trust.md) | [trust report](/tools/tensorchord-awesome-llmops/trust.md) | ## Decision facts: langchain-tutorials - **Pricing:** freemium - 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. - **Adopt for:** langchain-tutorials offers educational material to aid in understanding and applying the LangChain library via Jupyter Notebooks. - **License detail:** The license details for this tool are unknown. ## Decision facts: Awesome-LLMOps - **Adopt for:** 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. ## Choose when ### 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. ### 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 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 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. ## 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](/tools/gkamradt-langchain-tutorials/alternatives) and [Awesome-LLMOps alternatives](/tools/tensorchord-awesome-llmops/alternatives) ([langchain-tutorials markdown twin](/tools/gkamradt-langchain-tutorials/alternatives.md), [Awesome-LLMOps markdown twin](/tools/tensorchord-awesome-llmops/alternatives.md)), 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](/compare/gkamradt-langchain-tutorials-vs-tensorchord-awesome-llmops.md) 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](/tools/gkamradt-langchain-tutorials/trust); [Awesome-LLMOps trust report](/tools/tensorchord-awesome-llmops/trust). --- **Machine-readable endpoints** - JSON: [`/api/graphcanon/graph?tool=gkamradt-langchain-tutorials`](/api/graphcanon/graph?tool=gkamradt-langchain-tutorials) - 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/_