--- title: "openlm vs llm" type: "comparison" canonical_url: "https://www.graphcanon.com/compare/r2d4-openlm-vs-simonw-llm" tools: ["r2d4-openlm", "simonw-llm"] --- # openlm vs llm *GraphCanon updated Sep 20, 2026* ## Verdict Pick openlm if openLM is a flexible Python library designed to interface with various Language Model frameworks similarly to the OpenAI API; pick llm if decision-critical facts for 'llm'. [openlm](https://github.com/r2d4/openlm) reports 368 GitHub stars, 22 forks, and 1 open issues, last pushed May 19, 2023. [llm](https://llm.datasette.io) has 12k stars, 978 forks, and 689 open issues, last pushed Sep 2, 2026. Figures are from public GitHub metadata via [openlm's repository](https://github.com/r2d4/openlm) and [llm's repository](https://github.com/simonw/llm). | | [openlm](/tools/r2d4-openlm.md) | [llm](/tools/simonw-llm.md) | | --- | --- | --- | | Tagline | OpenAI-compatible Python client for calling any LLM | Access large language models from the command-line | | Stars | 368 | 12,473 | | Forks | 22 | 978 | | Open issues | 1 | 689 | | Language | Python | Python | | Adopt for | OpenLM is a flexible Python library designed to interface with various Language Model frameworks similarly to the OpenAI API. | Decision-critical facts for 'llm' | | Persona | - | - | | Runtime | - | - | | License | MIT License | Apache-2.0 | | Categories | LLM Frameworks | Inference & Serving, LLM Frameworks | ## Trust and health _Sourced signals - not a safety guarantee. No winner column._ | | [openlm](/tools/r2d4-openlm.md) | [llm](/tools/simonw-llm.md) | | --- | --- | --- | | Maintenance | Dormant (18%) | Very active (96%) | | Days since push | 1219d | 4d | | Open issues (now) | 1 | 689 | | Stars delta | 0 (30d) | +149 (30d) | | Open issues delta | 0 (30d) | +25 (30d) | | Full report | [trust report](/tools/r2d4-openlm/trust.md) | [trust report](/tools/simonw-llm/trust.md) | ## Shared compatibility - **Python**: [openlm](/tools/r2d4-openlm.md) - Python runtime; [llm](/tools/simonw-llm.md) - Python runtime ## Decision facts: openlm - **Requirements:** Installation is as simple as using pip to install the openlm package. - **Adopt for:** OpenLM is a flexible Python library designed to interface with various Language Model frameworks similarly to the OpenAI API. - **License detail:** MIT License ## Decision facts: llm - **Requirements:** - Installation supports multiple methods including `pip`, Homebrew (with caveats noted), `pipx`, and `uv`.; - Requires an OpenAI API key for certain functionalities. - **Adopt for:** Decision-critical facts for 'llm' - **License detail:** Apache-2.0 ## Choose when ### Choose openlm if… - License: openlm is MIT, llm is Apache-2.0. - Requirements: Installation is as simple as using pip to install the openlm package.. - Tags unique to openlm: cohere, huggingface, llm. - Use when you want to seamlessly integrate different LLM frameworks, like Hugging Face or Cohere, under a uniform interface similar to OpenAI’s. ### Choose llm if… - License: llm is Apache-2.0, openlm is MIT. - Requirements: - Installation supports multiple methods including `pip`, Homebrew (with caveats noted), `pipx`, and `uv`.; - Requires an OpenAI API key for certain functionalities.. - Tags unique to llm: ai, llms. - Also covers Inference & Serving. - - You prioritize command-line interaction over graphical interfaces, as llm is designed to provide a seamless CLI experience with multiple installation methods. ## When NOT to use openlm - Avoid when you require maximum performance optimization specific to one LLM framework, as using an intermediary library like OpenLM might introduce additional overhead. - Not suitable if you are strictly working with models that do not have Python support or don't fit within the frameworks supported by OpenLM. ## When NOT to use llm - - If you require real-time visual feedback or a graphical interface for interacting with language models, as llm is strictly command-line-based. - - If your primary focus is on model training rather than inference or serving, since llm is aimed at accessing and using pre-trained models. ## Common questions ### What is the difference between openlm and llm? openlm: OpenAI-compatible Python client for calling any LLM. llm: Access large language models from the command-line. See the comparison table for live GitHub stats and shared categories. ### When should I choose openlm over llm? Choose openlm over llm when License: openlm is MIT, llm is Apache-2.0; Requirements: Installation is as simple as using pip to install the openlm package.; Tags unique to openlm: cohere, huggingface, llm; Use when you want to seamlessly integrate different LLM frameworks, like Hugging Face or Cohere, under a uniform interface similar to OpenAI’s. ### When should I choose llm over openlm? Choose llm over openlm when License: llm is Apache-2.0, openlm is MIT; Requirements: - Installation supports multiple methods including `pip`, Homebrew (with caveats noted), `pipx`, and `uv`.; - Requires an OpenAI API key for certain functionalities.; Tags unique to llm: ai, llms; Also covers Inference & Serving; - You prioritize command-line interaction over graphical interfaces, as llm is designed to provide a seamless CLI experience with multiple installation methods. ### When should I avoid openlm? Avoid when you require maximum performance optimization specific to one LLM framework, as using an intermediary library like OpenLM might introduce additional overhead. Not suitable if you are strictly working with models that do not have Python support or don't fit within the frameworks supported by OpenLM. ### When should I avoid llm? - If you require real-time visual feedback or a graphical interface for interacting with language models, as llm is strictly command-line-based. - If your primary focus is on model training rather than inference or serving, since llm is aimed at accessing and using pre-trained models. ### Is openlm or llm more popular on GitHub? llm has more GitHub stars (12,473 vs 368). Stars measure visibility, not whether either tool fits your constraints. ### Are openlm and llm open source? Yes - both are open-source projects on GitHub (openlm: MIT, llm: Apache-2.0). ### Where can I find alternatives to openlm or llm? GraphCanon lists graph-backed alternatives at [openlm alternatives](/tools/r2d4-openlm/alternatives) and [llm alternatives](/tools/simonw-llm/alternatives) ([openlm markdown twin](/tools/r2d4-openlm/alternatives.md), [llm markdown twin](/tools/simonw-llm/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/r2d4-openlm-vs-simonw-llm.md) mirrors this page for agents and LLM crawlers, with the same stats table and FAQ answers. ### Which is better maintained, openlm or llm? openlm: Dormant. llm: 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 openlm and llm? GraphCanon publishes per-repo trust reports with dated maintenance, provenance, and scan summaries: [openlm trust report](/tools/r2d4-openlm/trust); [llm trust report](/tools/simonw-llm/trust). --- **Machine-readable endpoints** - JSON: [`/api/graphcanon/graph?tool=r2d4-openlm`](/api/graphcanon/graph?tool=r2d4-openlm) - 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/_