Comparison
rellm vs LLMSurvey
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.
Markdown twin · rellm alternatives · LLMSurvey alternatives
GraphCanon updated Sep 20, 2026
8views this month
Trust & integrity
| Signal | rellm | LLMSurvey |
|---|---|---|
| Maintenance | Dormant (1100d since push) As of Aug 15, 2026 · github_public_v1 | Dormant (555d since push) As of Sep 18, 2026 · github_public_v1 |
| Provenance | Not a fork · Personal account As of Aug 15, 2026 · github_public_v1 | Not a fork · Organization account As of Sep 18, 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 Sep 18, 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
- rellm
- Exact structure out of any language model completion
- LLMSurvey
- A collection of papers and resources related to Large Language Models
Stars
- rellm
- 511
- LLMSurvey
- 12k
Forks
- rellm
- 24
- LLMSurvey
- 930
Open issues
- rellm
- 5
- LLMSurvey
- 30
Language
- rellm
- Python
- LLMSurvey
- Python
Adopt for
- rellm
- rellm is a Python tool that guarantees structured outputs from language model completions by leveraging the Hugging Face Transformers library.
- LLMSurvey
- 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
- rellm
- -
- LLMSurvey
- -
Runtime
- rellm
- -
- LLMSurvey
- -
License
- rellm
- MIT
- LLMSurvey
- The license information for LLMSurvey is unknown.
Last pushed
- rellm
- Aug 10, 2023
- LLMSurvey
- Mar 11, 2025
Categories
- rellm
- LLM Frameworks, Model Training
- LLMSurvey
- LLM Frameworks, Model Training
Trust and health
Days since push
- rellm
- 1100d
- LLMSurvey
- 555d
Open issues (now)
- rellm
- 5
- LLMSurvey
- 30
Stars delta
- rellm
- -2 (30d)
- LLMSurvey
- +11 (30d)
Owner type
- rellm
- User
- LLMSurvey
- Organization
Full report
- rellm
- Trust report
- LLMSurvey
- Trust report
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).
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.
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 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.
Explore
Sources
Every stat on this page traces to a dated GitHub sync, license file, enrichment field, or trust scan.
- GitHub stars (r2d4/rellm) · observed Sep 20, 2026
- GitHub forks (r2d4/rellm) · observed Sep 20, 2026
- Last push (r2d4/rellm) · observed Aug 10, 2023
- License file (MIT) · observed Sep 20, 2026
- Decision facts (enrichment) · observed Jul 12, 2026
- Trust scan (lockfile / OSV) · observed Jul 11, 2026
- GitHub stars (RUCAIBox/LLMSurvey) · observed Sep 20, 2026
- GitHub forks (RUCAIBox/LLMSurvey) · observed Sep 20, 2026
- Last push (RUCAIBox/LLMSurvey) · observed Mar 11, 2025
- License file (unknown) · observed Sep 20, 2026
- Decision facts (enrichment) · observed Sep 18, 2026
- Trust scan (lockfile / OSV) · observed Sep 18, 2026
GitHub stars on cards: rellm 511 · LLMSurvey 12k (synced Sep 20, 2026).
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 and LLMSurvey alternatives (rellm markdown twin, LLMSurvey 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, 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; LLMSurvey trust report.