Comparison
lmql vs awesome-LLM-resources
Verdict
Pick lmql if facilitates LLM programming with constraints for efficiency, Python-based; pick awesome-LLM-resources if awesome-LLM-resources is a curated list of resources related to large language models, covering a wide range of topics from multimodal generation to model training and inference.
Markdown twin · lmql alternatives · awesome-LLM-resources alternatives
GraphCanon updated Sep 20, 2026
12views this month
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Trust & integrity
| Signal | lmql | awesome-LLM-resources |
|---|---|---|
| Maintenance | Dormant (450d since push) As of Aug 16, 2026 · github_public_v1 | Very active (3d since push) As of Sep 18, 2026 · github_public_v1 |
| Provenance | Not a fork · Organization account As of Aug 16, 2026 · github_public_v1 | Not a fork · Personal 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
- lmql
- A language for constraint-guided and efficient LLM programming.
- awesome-LLM-resources
- Summary of the world's best LLM resources.
Stars
- lmql
- 4.2k
- awesome-LLM-resources
- 9.0k
Forks
- lmql
- 221
- awesome-LLM-resources
- 993
Open issues
- lmql
- 120
- awesome-LLM-resources
- 40
Language
- lmql
- Python
- awesome-LLM-resources
- -
Adopt for
- lmql
- Facilitates LLM programming with constraints for efficiency, Python-based.
- awesome-LLM-resources
- awesome-LLM-resources is a curated list of resources related to large language models, covering a wide range of topics from multimodal generation to model training and inference.
Persona
- lmql
- -
- awesome-LLM-resources
- -
Runtime
- lmql
- -
- awesome-LLM-resources
- -
License
- lmql
- Apache-2.0
- awesome-LLM-resources
- The repository is licensed under Apache-2.0, allowing for free use, modification, and distribution.
Last pushed
- lmql
- May 22, 2025
- awesome-LLM-resources
- Sep 14, 2026
Categories
- lmql
- LLM Frameworks
- awesome-LLM-resources
- AI Agents, Computer Vision, Data & Retrieval, Developer Tools, Evaluation & Observability, Inference & Serving, LLM Frameworks, Model Training
Trust and health
Maintenance
- lmql
- Dormant (18%)
- awesome-LLM-resources
- Very active (96%)
Days since push
- lmql
- 450d
- awesome-LLM-resources
- 3d
Open issues (now)
- lmql
- 120
- awesome-LLM-resources
- 40
Stars delta
- lmql
- +1 (30d)
- awesome-LLM-resources
- +123 (30d)
Open issues delta
- lmql
- 0 (30d)
- awesome-LLM-resources
- +17 (30d)
Owner type
- lmql
- Organization
- awesome-LLM-resources
- User
Full report
- lmql
- Trust report
- awesome-LLM-resources
- Trust report
Choose lmql if…
- Tags unique to lmql: chatgpt, huggingface, language-model, programming-language.
- When needing precise control over language model output through programmable constraints
When NOT to use lmql
- For general-purpose coding without leveraging specific LLM functionalities
- If the project does not benefit from constraint-guided interactions with language models
Choose awesome-LLM-resources if…
- Pricing: The repository itself is free to use, but some linked resources may require payment or have associated costs..
- Requirements: The repository does not specify any technical requirements for accessing its content..
- Tags unique to awesome-LLM-resources: awesome-list, book, course, large-language-models.
- Also covers AI Agents, Computer Vision, Data & Retrieval, Developer Tools, Evaluation & Observability, Inference & Serving, Model Training.
- When you need a comprehensive list of resources for large language models, including multimodal generation, agents, programming assistance, and more.
When NOT to use awesome-LLM-resources
- If you are looking for a tool that provides direct access to LLM APIs or services, as this repository is a list of resources rather than a service provider.
- When you need real-time support or a community forum for troubleshooting LLM-related issues, as this repository is a static list of resources without interactive support.
Explore
lmql trust report →awesome-LLM-resources trust report →LLM Frameworks category →AI Agents category →Computer Vision category →Data & Retrieval category →Developer Tools category →Evaluation & Observability category →Inference & Serving category →Model Training 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 (eth-sri/lmql) · observed Sep 20, 2026
- GitHub forks (eth-sri/lmql) · observed Sep 20, 2026
- Last push (eth-sri/lmql) · observed May 22, 2025
- License file (Apache-2.0) · observed Sep 20, 2026
- Decision facts (enrichment) · observed Jul 14, 2026
- Trust scan (lockfile / OSV) · observed Jul 11, 2026
- GitHub stars (WangRongsheng/awesome-LLM-resources) · observed Sep 20, 2026
- GitHub forks (WangRongsheng/awesome-LLM-resources) · observed Sep 20, 2026
- Last push (WangRongsheng/awesome-LLM-resources) · observed Sep 14, 2026
- License file (Apache-2.0) · observed Sep 20, 2026
- Decision facts (enrichment) · observed Sep 18, 2026
- Trust scan (lockfile / OSV) · observed Sep 18, 2026
GitHub stars on cards: lmql 4.2k · awesome-LLM-resources 9.0k (synced Sep 20, 2026).
Common questions
- What is the difference between lmql and awesome-LLM-resources?
- lmql: A language for constraint-guided and efficient LLM programming.. awesome-LLM-resources: Summary of the world's best LLM resources.. See the comparison table for live GitHub stats and shared categories.
- When should I choose lmql over awesome-LLM-resources?
- Choose lmql over awesome-LLM-resources when Tags unique to lmql: chatgpt, huggingface, language-model, programming-language; When needing precise control over language model output through programmable constraints.
- When should I choose awesome-LLM-resources over lmql?
- Choose awesome-LLM-resources over lmql when Pricing: The repository itself is free to use, but some linked resources may require payment or have associated costs.; Requirements: The repository does not specify any technical requirements for accessing its content.; Tags unique to awesome-LLM-resources: awesome-list, book, course, large-language-models; Also covers AI Agents, Computer Vision, Data & Retrieval, Developer Tools, Evaluation & Observability, Inference & Serving, Model Training; When you need a comprehensive list of resources for large language models, including multimodal generation, agents, programming assistance, and more.
- When should I avoid lmql?
- For general-purpose coding without leveraging specific LLM functionalities If the project does not benefit from constraint-guided interactions with language models
- When should I avoid awesome-LLM-resources?
- If you are looking for a tool that provides direct access to LLM APIs or services, as this repository is a list of resources rather than a service provider. When you need real-time support or a community forum for troubleshooting LLM-related issues, as this repository is a static list of resources without interactive support.
- Is lmql or awesome-LLM-resources more popular on GitHub?
- awesome-LLM-resources has more GitHub stars (8,968 vs 4,203). Stars measure visibility, not whether either tool fits your constraints.
- Are lmql and awesome-LLM-resources open source?
- Yes - both are open-source projects on GitHub (lmql: Apache-2.0, awesome-LLM-resources: Apache-2.0).
- Where can I find alternatives to lmql or awesome-LLM-resources?
- GraphCanon lists graph-backed alternatives at lmql alternatives and awesome-LLM-resources alternatives (lmql markdown twin, awesome-LLM-resources 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, lmql or awesome-LLM-resources?
- lmql: Dormant. awesome-LLM-resources: Very active. 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 lmql and awesome-LLM-resources?
- GraphCanon publishes per-repo trust reports with dated maintenance, provenance, and scan summaries: lmql trust report; awesome-LLM-resources trust report.