--- title: "Awesome-LLMOps vs Awesome-LLM-in-Social-Science" type: "comparison" canonical_url: "https://www.graphcanon.com/compare/tensorchord-awesome-llmops-vs-valuebyte-ai-awesome-llm-in-social-science" tools: ["tensorchord-awesome-llmops", "valuebyte-ai-awesome-llm-in-social-science"] --- # Awesome-LLMOps vs Awesome-LLM-in-Social-Science *GraphCanon updated Aug 20, 2026* ## Verdict 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; pick Awesome-LLM-in-Social-Science if curate research papers on LLM applications in social science, covering topics like alignment, economics, policy, psychology, and more. [Awesome-LLMOps](https://github.com/tensorchord/Awesome-LLMOps) reports 5.9k GitHub stars, 993 forks, and 247 open issues, last pushed May 21, 2026. [Awesome-LLM-in-Social-Science](https://github.com/ValueByte-AI/Awesome-LLM-in-Social-Science) has 639 stars, 48 forks, and 0 open issues, last pushed Jun 8, 2026. Figures are from public GitHub metadata via [Awesome-LLMOps's repository](https://github.com/tensorchord/Awesome-LLMOps) and [Awesome-LLM-in-Social-Science's repository](https://github.com/ValueByte-AI/Awesome-LLM-in-Social-Science). | | [Awesome-LLMOps](/tools/tensorchord-awesome-llmops.md) | [Awesome-LLM-in-Social-Science](/tools/valuebyte-ai-awesome-llm-in-social-science.md) | | --- | --- | --- | | Tagline | An awesome & curated list of best LLMOps tools for developers | Awesome papers involving LLMs in Social Science | | Stars | 5,915 | 639 | | Forks | 993 | 48 | | Open issues | 247 | 0 | | Language | Shell | - | | 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. | Curate research papers on LLM applications in social science, covering topics like alignment, economics, policy, psychology, and more. | | Persona | - | - | | Runtime | - | - | | License | CC0-1.0 | MIT | | Categories | Computer Vision, Data & Retrieval, Evaluation & Observability, Inference & Serving, LLM Frameworks, Model Training, Speech & Audio | Evaluation & Observability, Model Training | ## Trust and health _Sourced signals - not a safety guarantee. No winner column._ | | [Awesome-LLMOps](/tools/tensorchord-awesome-llmops.md) | [Awesome-LLM-in-Social-Science](/tools/valuebyte-ai-awesome-llm-in-social-science.md) | | --- | --- | --- | | Maintenance | Slowing (36%) | Steady (60%) | | Days since push | 91d | 49d | | Open issues (now) | 247 | 0 | | Stars delta | +28 (30d) | Unknown | | Open issues delta | +66 (30d) | Unknown | | Full report | [trust report](/tools/tensorchord-awesome-llmops/trust.md) | [trust report](/tools/valuebyte-ai-awesome-llm-in-social-science/trust.md) | ## 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. ## Decision facts: Awesome-LLM-in-Social-Science - **Adopt for:** Curate research papers on LLM applications in social science, covering topics like alignment, economics, policy, psychology, and more. ## Choose when ### Choose Awesome-LLMOps if… - License: Awesome-LLMOps is CC0-1.0, Awesome-LLM-in-Social-Science is MIT. - Tags unique to Awesome-LLMOps: ai-development-tools, awesome-list, llmops, mlops. - Also covers Computer Vision, Data & Retrieval, Inference & Serving, LLM Frameworks, Speech & Audio. - - When you need a comprehensive directory of tools specifically focused on LLM development, training, fine-tuning, and management. ### Choose Awesome-LLM-in-Social-Science if… - License: Awesome-LLM-in-Social-Science is MIT, Awesome-LLMOps is CC0-1.0. - Tags unique to Awesome-LLM-in-Social-Science: alignment, economics, large language models, llm-agent. - Need to explore academic insights into LLM impacts on specific social areas ## 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. ## When NOT to use Awesome-LLM-in-Social-Science - Looking for a hands-on coding or practical implementation guide of LLMs - In need of real-time data analysis tools for immediate social science research outcomes ## Common questions ### What is the difference between Awesome-LLMOps and Awesome-LLM-in-Social-Science? Awesome-LLMOps: An awesome & curated list of best LLMOps tools for developers. Awesome-LLM-in-Social-Science: Awesome papers involving LLMs in Social Science. See the comparison table for live GitHub stats and shared categories. ### When should I choose Awesome-LLMOps over Awesome-LLM-in-Social-Science? Choose Awesome-LLMOps over Awesome-LLM-in-Social-Science when License: Awesome-LLMOps is CC0-1.0, Awesome-LLM-in-Social-Science is MIT; Tags unique to Awesome-LLMOps: ai-development-tools, awesome-list, llmops, mlops; Also covers Computer Vision, Data & Retrieval, 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 choose Awesome-LLM-in-Social-Science over Awesome-LLMOps? Choose Awesome-LLM-in-Social-Science over Awesome-LLMOps when License: Awesome-LLM-in-Social-Science is MIT, Awesome-LLMOps is CC0-1.0; Tags unique to Awesome-LLM-in-Social-Science: alignment, economics, large language models, llm-agent; Need to explore academic insights into LLM impacts on specific social areas. ### 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. ### When should I avoid Awesome-LLM-in-Social-Science? Looking for a hands-on coding or practical implementation guide of LLMs In need of real-time data analysis tools for immediate social science research outcomes ### Is Awesome-LLMOps or Awesome-LLM-in-Social-Science more popular on GitHub? Awesome-LLMOps has more GitHub stars (5,915 vs 639). Stars measure visibility, not whether either tool fits your constraints. ### Are Awesome-LLMOps and Awesome-LLM-in-Social-Science open source? Yes - both are open-source projects on GitHub (Awesome-LLMOps: CC0-1.0, Awesome-LLM-in-Social-Science: MIT). ### Where can I find alternatives to Awesome-LLMOps or Awesome-LLM-in-Social-Science? GraphCanon lists graph-backed alternatives at [Awesome-LLMOps alternatives](/tools/tensorchord-awesome-llmops/alternatives) and [Awesome-LLM-in-Social-Science alternatives](/tools/valuebyte-ai-awesome-llm-in-social-science/alternatives) ([Awesome-LLMOps markdown twin](/tools/tensorchord-awesome-llmops/alternatives.md), [Awesome-LLM-in-Social-Science markdown twin](/tools/valuebyte-ai-awesome-llm-in-social-science/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/tensorchord-awesome-llmops-vs-valuebyte-ai-awesome-llm-in-social-science.md) mirrors this page for agents and LLM crawlers, with the same stats table and FAQ answers. ### Which is better maintained, Awesome-LLMOps or Awesome-LLM-in-Social-Science? Awesome-LLMOps: Slowing. Awesome-LLM-in-Social-Science: Steady. 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 Awesome-LLMOps and Awesome-LLM-in-Social-Science? GraphCanon publishes per-repo trust reports with dated maintenance, provenance, and scan summaries: [Awesome-LLMOps trust report](/tools/tensorchord-awesome-llmops/trust); [Awesome-LLM-in-Social-Science trust report](/tools/valuebyte-ai-awesome-llm-in-social-science/trust). --- **Machine-readable endpoints** - JSON: [`/api/graphcanon/graph?tool=tensorchord-awesome-llmops`](/api/graphcanon/graph?tool=tensorchord-awesome-llmops) - 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/_