--- title: "lmql vs DeepInception" type: "comparison" canonical_url: "https://www.graphcanon.com/compare/eth-sri-lmql-vs-tmlr-group-deepinception" tools: ["eth-sri-lmql", "tmlr-group-deepinception"] --- # lmql vs DeepInception *GraphCanon updated Aug 16, 2026* ## 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. [lmql](https://lmql.ai) reports 4.2k GitHub stars, 221 forks, and 120 open issues, last pushed May 22, 2025. [DeepInception](https://arxiv.org/pdf/2311.03191.pdf) has 177 stars, 19 forks, and 0 open issues, last pushed Feb 20, 2024. Figures are from public GitHub metadata via [lmql's repository](https://github.com/eth-sri/lmql) and [DeepInception's repository](https://github.com/tmlr-group/DeepInception). | | [lmql](/tools/eth-sri-lmql.md) | [DeepInception](/tools/tmlr-group-deepinception.md) | | --- | --- | --- | | Tagline | A language for constraint-guided and efficient LLM programming. | Develops techniques to influence large language model behavior | | Stars | 4,203 | 177 | | Forks | 221 | 19 | | Open issues | 120 | 0 | | Language | Python | Python | | Adopt for | Facilitates LLM programming with constraints for efficiency, Python-based. | DeepInception is an exploration framework for modifying large language model responses to understand their behavior and influence their outputs. | | Persona | - | - | | Runtime | - | - | | License | Apache-2.0 | MIT | | Categories | LLM Frameworks | LLM Frameworks | ## Trust and health _Sourced signals - not a safety guarantee. No winner column._ | | [lmql](/tools/eth-sri-lmql.md) | [DeepInception](/tools/tmlr-group-deepinception.md) | | --- | --- | --- | | Days since push | 450d | 896d | | Open issues (now) | 120 | 0 | | Stars delta | +1 (30d) | Unknown | | Open issues delta | 0 (30d) | Unknown | | Full report | [trust report](/tools/eth-sri-lmql/trust.md) | [trust report](/tools/tmlr-group-deepinception/trust.md) | ## Shared compatibility - **Python**: [lmql](/tools/eth-sri-lmql.md) - Python runtime; [DeepInception](/tools/tmlr-group-deepinception.md) - Python runtime ## Decision facts: lmql - **Adopt for:** Facilitates LLM programming with constraints for efficiency, Python-based. ## Decision facts: DeepInception - **Pricing:** freemium - 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 - **Adopt for:** DeepInception is an exploration framework for modifying large language model responses to understand their behavior and influence their outputs. ## Choose when ### 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 ### 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 lmql - For general-purpose coding without leveraging specific LLM functionalities - If the project does not benefit from constraint-guided interactions with language 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 ## 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](/tools/eth-sri-lmql/alternatives) and [DeepInception alternatives](/tools/tmlr-group-deepinception/alternatives) ([lmql markdown twin](/tools/eth-sri-lmql/alternatives.md), [DeepInception markdown twin](/tools/tmlr-group-deepinception/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/eth-sri-lmql-vs-tmlr-group-deepinception.md) 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](/tools/eth-sri-lmql/trust); [DeepInception trust report](/tools/tmlr-group-deepinception/trust). --- **Machine-readable endpoints** - JSON: [`/api/graphcanon/graph?tool=eth-sri-lmql`](/api/graphcanon/graph?tool=eth-sri-lmql) - 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/_