--- title: "forge vs lmql" type: "comparison" canonical_url: "https://www.graphcanon.com/compare/antoinezambelli-forge-vs-eth-sri-lmql" tools: ["antoinezambelli-forge", "eth-sri-lmql"] --- # forge vs lmql *GraphCanon updated Aug 16, 2026* ## Verdict Pick forge if developers working on self-hosted LLM tooling who need flexibility in backend setup and seamless integration of function calling in multi-step workflows might benefit from Forge; pick lmql if facilitates LLM programming with constraints for efficiency, Python-based. [forge](https://github.com/antoinezambelli/forge) reports 2.2k GitHub stars, 173 forks, and 4 open issues, last pushed Aug 13, 2026. [lmql](https://lmql.ai) has 4.2k stars, 221 forks, and 120 open issues, last pushed May 22, 2025. Figures are from public GitHub metadata via [forge's repository](https://github.com/antoinezambelli/forge) and [lmql's repository](https://github.com/eth-sri/lmql). | | [forge](/tools/antoinezambelli-forge.md) | [lmql](/tools/eth-sri-lmql.md) | | --- | --- | --- | | Tagline | A Python framework for self-hosted LLM tool-calling and multi-step agentic workflows | A language for constraint-guided and efficient LLM programming. | | Stars | 2,217 | 4,203 | | Forks | 173 | 221 | | Open issues | 4 | 120 | | Language | Python | Python | | Adopt for | Developers working on self-hosted LLM tooling who need flexibility in backend setup and seamless integration of function calling in multi-step workflows might benefit from Forge. | Facilitates LLM programming with constraints for efficiency, Python-based. | | Persona | - | - | | Runtime | - | - | | License | MIT | Apache-2.0 | | Categories | AI Agents, LLM Frameworks | LLM Frameworks | ## Trust and health _Sourced signals - not a safety guarantee. No winner column._ | | [forge](/tools/antoinezambelli-forge.md) | [lmql](/tools/eth-sri-lmql.md) | | --- | --- | --- | | Maintenance | Very active (96%) | Dormant (18%) | | Days since push | 0d | 450d | | Open issues (now) | 4 | 120 | | Stars delta | Unknown | +1 (30d) | | Open issues delta | Unknown | 0 (30d) | | Owner type | User | Organization | | Full report | [trust report](/tools/antoinezambelli-forge/trust.md) | [trust report](/tools/eth-sri-lmql/trust.md) | ## Shared compatibility - **Python**: [forge](/tools/antoinezambelli-forge.md) - Python runtime; [lmql](/tools/eth-sri-lmql.md) - Python runtime ## Decision facts: forge - **Requirements:** Min 4 GB RAM; Requires Docker; Requires Python 3.12+ and a running LLM backend.; Can be set up with local backends (e.g., llama.cpp) or Anthropic via its API, requiring an API key for the latter case. - **Adopt for:** Developers working on self-hosted LLM tooling who need flexibility in backend setup and seamless integration of function calling in multi-step workflows might benefit from Forge. ## Decision facts: lmql - **Adopt for:** Facilitates LLM programming with constraints for efficiency, Python-based. ## Choose when ### Choose forge if… - License: forge is MIT, lmql is Apache-2.0. - Requirements: Min 4 GB RAM; Requires Docker; Requires Python 3.12+ and a running LLM backend.; Can be set up with local backends (e.g., llama.cpp) or Anthropic via its API, requiring an API key for the latter case.. - Tags unique to forge: agentic-ai, function-calling, multi-step-workflows, python-framework. - Also covers AI Agents. - forge ships Docker support for self-hosted deployment. - - You require an agnostic backend setup, such as local LLM backends like llama.cpp or cloud-based services with Anthropic. ### Choose lmql if… - License: lmql is Apache-2.0, forge 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 forge - - If your application does not require flexibility in backend selection, and you prefer a single cloud provider like Anthropic without local setup. - - For scenarios where simplicity of setup outweighs the need for customization in function calling and workflow management. - - When working within environments strictly regulated against self-hosted infrastructure or requiring fully managed services. ## 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 ## Common questions ### What is the difference between forge and lmql? forge: A Python framework for self-hosted LLM tool-calling and multi-step agentic workflows. lmql: A language for constraint-guided and efficient LLM programming.. See the comparison table for live GitHub stats and shared categories. ### When should I choose forge over lmql? Choose forge over lmql when License: forge is MIT, lmql is Apache-2.0; Requirements: Min 4 GB RAM; Requires Docker; Requires Python 3.12+ and a running LLM backend.; Can be set up with local backends (e.g., llama.cpp) or Anthropic via its API, requiring an API key for the latter case.; Tags unique to forge: agentic-ai, function-calling, multi-step-workflows, python-framework; Also covers AI Agents; forge ships Docker support for self-hosted deployment; - You require an agnostic backend setup, such as local LLM backends like llama.cpp or cloud-based services with Anthropic. ### When should I choose lmql over forge? Choose lmql over forge when License: lmql is Apache-2.0, forge 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 avoid forge? - If your application does not require flexibility in backend selection, and you prefer a single cloud provider like Anthropic without local setup. - For scenarios where simplicity of setup outweighs the need for customization in function calling and workflow management. - When working within environments strictly regulated against self-hosted infrastructure or requiring fully managed services. ### 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 ### Is forge or lmql more popular on GitHub? lmql has more GitHub stars (4,203 vs 2,217). Stars measure visibility, not whether either tool fits your constraints. ### Are forge and lmql open source? Yes - both are open-source projects on GitHub (forge: MIT, lmql: Apache-2.0). ### Where can I find alternatives to forge or lmql? GraphCanon lists graph-backed alternatives at [forge alternatives](/tools/antoinezambelli-forge/alternatives) and [lmql alternatives](/tools/eth-sri-lmql/alternatives) ([forge markdown twin](/tools/antoinezambelli-forge/alternatives.md), [lmql markdown twin](/tools/eth-sri-lmql/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/antoinezambelli-forge-vs-eth-sri-lmql.md) mirrors this page for agents and LLM crawlers, with the same stats table and FAQ answers. ### Which is better maintained, forge or lmql? forge: Very active. lmql: 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 forge and lmql? GraphCanon publishes per-repo trust reports with dated maintenance, provenance, and scan summaries: [forge trust report](/tools/antoinezambelli-forge/trust); [lmql trust report](/tools/eth-sri-lmql/trust). --- **Machine-readable endpoints** - JSON: [`/api/graphcanon/graph?tool=antoinezambelli-forge`](/api/graphcanon/graph?tool=antoinezambelli-forge) - 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/_