--- title: "rellm vs LLMSurvey" type: "comparison" canonical_url: "https://www.graphcanon.com/compare/r2d4-rellm-vs-rucaibox-llmsurvey" tools: ["r2d4-rellm", "rucaibox-llmsurvey"] --- # rellm vs LLMSurvey *GraphCanon updated Sep 20, 2026* ## Verdict Pick rellm if rellm is a Python tool that guarantees structured outputs from language model completions by leveraging the Hugging Face Transformers library; pick LLMSurvey if lLMSurvey is a repository that provides a comprehensive survey paper and resources on Large Language Models, including a beginner-friendly Chinese book and detailed content on long chain-of-thought reasoning. [rellm](https://github.com/r2d4/rellm) reports 511 GitHub stars, 24 forks, and 5 open issues, last pushed Aug 10, 2023. [LLMSurvey](https://arxiv.org/abs/2303.18223) has 12k stars, 930 forks, and 30 open issues, last pushed Mar 11, 2025. Figures are from public GitHub metadata via [rellm's repository](https://github.com/r2d4/rellm) and [LLMSurvey's repository](https://github.com/RUCAIBox/LLMSurvey). | | [rellm](/tools/r2d4-rellm.md) | [LLMSurvey](/tools/rucaibox-llmsurvey.md) | | --- | --- | --- | | Tagline | Exact structure out of any language model completion | A collection of papers and resources related to Large Language Models | | Stars | 511 | 12,216 | | Forks | 24 | 930 | | Open issues | 5 | 30 | | Language | Python | Python | | Adopt for | rellm is a Python tool that guarantees structured outputs from language model completions by leveraging the Hugging Face Transformers library. | LLMSurvey is a repository that provides a comprehensive survey paper and resources on Large Language Models, including a beginner-friendly Chinese book and detailed content on long chain-of-thought reasoning. | | Persona | - | - | | Runtime | - | - | | License | MIT | The license information for LLMSurvey is unknown. | | Categories | LLM Frameworks, Model Training | LLM Frameworks, Model Training | ## Trust and health _Sourced signals - not a safety guarantee. No winner column._ | | [rellm](/tools/r2d4-rellm.md) | [LLMSurvey](/tools/rucaibox-llmsurvey.md) | | --- | --- | --- | | Days since push | 1100d | 555d | | Open issues (now) | 5 | 30 | | Stars delta | -2 (30d) | +11 (30d) | | Owner type | User | Organization | | Full report | [trust report](/tools/r2d4-rellm/trust.md) | [trust report](/tools/rucaibox-llmsurvey/trust.md) | ## Decision facts: rellm - **Adopt for:** rellm is a Python tool that guarantees structured outputs from language model completions by leveraging the Hugging Face Transformers library. ## Decision facts: LLMSurvey - **Pricing:** freemium - The repository is free to access, but the Chinese book is available for purchase. - **Requirements:** Min 4 GB RAM; A basic understanding of deep learning is recommended for the Chinese book. - **Adopt for:** LLMSurvey is a repository that provides a comprehensive survey paper and resources on Large Language Models, including a beginner-friendly Chinese book and detailed content on long chain-of-thought reasoning. - **License detail:** The license information for LLMSurvey is unknown. ## Choose when ### Choose rellm if… - Tags unique to rellm: huggingface-transformers, transformers. - - When you require precise and exact structure in output data generated from any language model, utilizing rellm can ensure consistency. - Leaner open-issue backlog (5). ### Choose LLMSurvey if… - Pricing: The repository is free to access, but the Chinese book is available for purchase.. - Requirements: Min 4 GB RAM; A basic understanding of deep learning is recommended for the Chinese book.. - Tags unique to LLMSurvey: chain-of-thought, chatgpt, in-context-learning, instruction-tuning. - Use LLMSurvey when you need a detailed survey paper and resources on Large Language Models, especially if you are interested in long chain-of-thought reasoning. ## When NOT to use rellm - - Avoid using rellm if you are not working with the Hugging Face Transformers library or do not need structured output formats. - - If your project can tolerate some level of unstructured or less rigidly formatted outputs from language models, other solutions might be more appropriate. ## When NOT to use LLMSurvey - Avoid LLMSurvey if you are looking for a tool that provides hands-on training or implementation of LLMs, as it focuses more on theoretical resources and surveys. - Do not use LLMSurvey if you require real-time updates or interactive tools for LLM development, as it primarily offers static content and resources. ## Common questions ### What is the difference between rellm and LLMSurvey? rellm: Exact structure out of any language model completion. LLMSurvey: A collection of papers and resources related to Large Language Models. See the comparison table for live GitHub stats and shared categories. ### When should I choose rellm over LLMSurvey? Choose rellm over LLMSurvey when Tags unique to rellm: huggingface-transformers, transformers; - When you require precise and exact structure in output data generated from any language model, utilizing rellm can ensure consistency; Leaner open-issue backlog (5). ### When should I choose LLMSurvey over rellm? Choose LLMSurvey over rellm when Pricing: The repository is free to access, but the Chinese book is available for purchase.; Requirements: Min 4 GB RAM; A basic understanding of deep learning is recommended for the Chinese book.; Tags unique to LLMSurvey: chain-of-thought, chatgpt, in-context-learning, instruction-tuning; Use LLMSurvey when you need a detailed survey paper and resources on Large Language Models, especially if you are interested in long chain-of-thought reasoning. ### When should I avoid rellm? - Avoid using rellm if you are not working with the Hugging Face Transformers library or do not need structured output formats. - If your project can tolerate some level of unstructured or less rigidly formatted outputs from language models, other solutions might be more appropriate. ### When should I avoid LLMSurvey? Avoid LLMSurvey if you are looking for a tool that provides hands-on training or implementation of LLMs, as it focuses more on theoretical resources and surveys. Do not use LLMSurvey if you require real-time updates or interactive tools for LLM development, as it primarily offers static content and resources. ### Is rellm or LLMSurvey more popular on GitHub? LLMSurvey has more GitHub stars (12,216 vs 511). Stars measure visibility, not whether either tool fits your constraints. ### Are rellm and LLMSurvey open source? Yes - both are open-source projects on GitHub. ### Where can I find alternatives to rellm or LLMSurvey? GraphCanon lists graph-backed alternatives at [rellm alternatives](/tools/r2d4-rellm/alternatives) and [LLMSurvey alternatives](/tools/rucaibox-llmsurvey/alternatives) ([rellm markdown twin](/tools/r2d4-rellm/alternatives.md), [LLMSurvey markdown twin](/tools/rucaibox-llmsurvey/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/r2d4-rellm-vs-rucaibox-llmsurvey.md) mirrors this page for agents and LLM crawlers, with the same stats table and FAQ answers. ### Which is better maintained, rellm or LLMSurvey? rellm: Dormant. LLMSurvey: Dormant. 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 rellm and LLMSurvey? GraphCanon publishes per-repo trust reports with dated maintenance, provenance, and scan summaries: [rellm trust report](/tools/r2d4-rellm/trust); [LLMSurvey trust report](/tools/rucaibox-llmsurvey/trust). --- **Machine-readable endpoints** - JSON: [`/api/graphcanon/graph?tool=r2d4-rellm`](/api/graphcanon/graph?tool=r2d4-rellm) - 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/_