Comparison
lmql vs DeepInception
Verdict
Pick lmql if facilitates LLM programming with constraints for efficiency, Python-based; pick DeepInception if deepInception is an exploration framework for modifying large language model responses to understand their behavior and influence their outputs.
Markdown twin · lmql alternatives · DeepInception alternatives
GraphCanon updated 1w
vs
Trust & integrity
| Signal | lmql | DeepInception |
|---|---|---|
| Maintenance | Dormant (450d since push) As of 1w · github_public_v1 | Dormant (896d since push) As of 2w · github_public_v1 |
| Provenance | Not a fork · Organization account As of 1w · github_public_v1 | Not a fork · Organization account As of 2w · github_public_v1 |
| OSV dependency advisories | No lockfile (source not queried) As of 1mo · osv@v1 | Published findings As of 1mo · 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.
- DeepInception
- Develops techniques to influence large language model behavior
Stars
- lmql
- 4.2k
- DeepInception
- 177
Forks
- lmql
- 221
- DeepInception
- 19
Open issues
- lmql
- 120
- DeepInception
- 0
Language
- lmql
- Python
- DeepInception
- Python
Adopt for
- lmql
- Facilitates LLM programming with constraints for efficiency, Python-based.
- DeepInception
- DeepInception is an exploration framework for modifying large language model responses to understand their behavior and influence their outputs.
Persona
- lmql
- -
- DeepInception
- -
Runtime
- lmql
- -
- DeepInception
- -
License
- lmql
- Apache-2.0
- DeepInception
- MIT
Last pushed
- lmql
- May 22, 2025
- DeepInception
- Feb 20, 2024
Categories
- lmql
- LLM Frameworks
- DeepInception
- LLM Frameworks
Trust and health
Days since push
- lmql
- 450d
- DeepInception
- 896d
Open issues (now)
- lmql
- 120
- DeepInception
- 0
Stars delta
- lmql
- +1 (30d)
- DeepInception
- Unknown
Open issues delta
- lmql
- 0 (30d)
- DeepInception
- Unknown
OSV dependency advisories
- lmql
- No lockfile (source not queried)
- DeepInception
- Published findings
Full report
- lmql
- Trust report
- DeepInception
- Trust report
Shared compatibility
- Python · lmql: Python runtime · DeepInception: Python runtime
Choose lmql if…
- License: lmql is Apache-2.0, DeepInception is MIT.
- 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 DeepInception if…
- License: DeepInception is MIT, lmql is Apache-2.0.
- Pricing: The tool is free under the MIT license. However, using it may incur costs from third-party services like OpenAI API keys for accessing closed-source models.
- Requirements: Requires PyTorch ≥1.10 with GPU support; Environment modification needed to include path configurations for Vicuna, Llama-2, and Falcon.
- Tags unique to DeepInception: deep, gpt, inception, jailbreak.
- When you need to research the effects of specific modifications on the safety and trustworthiness of GPT-3, GPT-4, Vicuna, Llama-2, or Falcon models
When NOT to use DeepInception
- For deployment in production environments where strict adherence to ethical and regulatory guidelines is mandatory, due to the experimental nature of DeepInception
- When there's a need for direct application without exploring modification effects, as DeepInception requires setting up an environment that supports specific models and modifications
Explore
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 Aug 16, 2026
- GitHub forks (eth-sri/lmql) · observed Aug 16, 2026
- Last push (eth-sri/lmql) · observed May 22, 2025
- License file (Apache-2.0) · observed Aug 16, 2026
- Decision facts (enrichment) · observed Jul 14, 2026
- Trust scan (lockfile / OSV) · observed Jul 11, 2026
- GitHub stars (tmlr-group/DeepInception) · observed Aug 5, 2026
- GitHub forks (tmlr-group/DeepInception) · observed Aug 5, 2026
- Last push (tmlr-group/DeepInception) · observed Feb 20, 2024
- License file (MIT) · observed Aug 5, 2026
- Decision facts (enrichment) · observed Jul 16, 2026
- Trust scan (lockfile / OSV) · observed Jul 11, 2026
GitHub stars on cards: lmql 4.2k · DeepInception 177 (synced Aug 16, 2026).
Common questions
- What is the difference between lmql and DeepInception?
- lmql: A language for constraint-guided and efficient LLM programming.. DeepInception: Develops techniques to influence large language model behavior. See the comparison table for live GitHub stats and shared categories.
- When should I choose lmql over DeepInception?
- Choose lmql over DeepInception when License: lmql is Apache-2.0, DeepInception is MIT; 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 DeepInception over lmql?
- Choose DeepInception over lmql when License: DeepInception is MIT, lmql is Apache-2.0; Pricing: The tool is free under the MIT license. However, using it may incur costs from third-party services like OpenAI API keys for accessing closed-source models; Requirements: Requires PyTorch ≥1.10 with GPU support; Environment modification needed to include path configurations for Vicuna, Llama-2, and Falcon; Tags unique to DeepInception: deep, gpt, inception, jailbreak; When you need to research the effects of specific modifications on the safety and trustworthiness of GPT-3, GPT-4, Vicuna, Llama-2, or Falcon models.
- 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 DeepInception?
- For deployment in production environments where strict adherence to ethical and regulatory guidelines is mandatory, due to the experimental nature of DeepInception When there's a need for direct application without exploring modification effects, as DeepInception requires setting up an environment that supports specific models and modifications
- Is lmql or DeepInception more popular on GitHub?
- lmql has more GitHub stars (4,203 vs 177). Stars measure visibility, not whether either tool fits your constraints.
- Are lmql and DeepInception open source?
- Yes - both are open-source projects on GitHub (lmql: Apache-2.0, DeepInception: MIT).
- Where can I find alternatives to lmql or DeepInception?
- GraphCanon lists graph-backed alternatives at lmql alternatives and DeepInception alternatives (lmql markdown twin, DeepInception 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 DeepInception?
- lmql: Dormant. DeepInception: 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 lmql and DeepInception?
- GraphCanon publishes per-repo trust reports with dated maintenance, provenance, and scan summaries: lmql trust report; DeepInception trust report.