--- title: "ai-getting-started vs llm-ui" type: "comparison" canonical_url: "https://www.graphcanon.com/compare/a16z-infra-ai-getting-started-vs-richardgill-llm-ui" tools: ["a16z-infra-ai-getting-started", "richardgill-llm-ui"] --- # ai-getting-started vs llm-ui *GraphCanon updated Aug 15, 2026* ## Verdict Pick ai-getting-started if ai-getting-started is a TypeScript-based JavaScript AI tool tailored for weekend projects, offering everything from model implementation to deployment configurations; pick llm-ui if llm-ui is a React library developed in TypeScript that focuses on creating user interfaces for large language models, it includes features such as throttling of streamed output and support for custom component rendering. [ai-getting-started](https://ai-getting-started.com/) reports 4.1k GitHub stars, 660 forks, and 16 open issues, last pushed Aug 21, 2024. [llm-ui](https://llm-ui.com) has 1.7k stars, 89 forks, and 16 open issues, last pushed Jul 2, 2025. Figures are from public GitHub metadata via [ai-getting-started's repository](https://github.com/a16z-infra/ai-getting-started) and [llm-ui's repository](https://github.com/richardgill/llm-ui). | | [ai-getting-started](/tools/a16z-infra-ai-getting-started.md) | [llm-ui](/tools/richardgill-llm-ui.md) | | --- | --- | --- | | Tagline | A Javascript AI getting started stack for weekend projects | The React library for LLMs | | Stars | 4,141 | 1,749 | | Forks | 660 | 89 | | Open issues | 16 | 16 | | Language | TypeScript | TypeScript | | Adopt for | ai-getting-started is a TypeScript-based JavaScript AI tool tailored for weekend projects, offering everything from model implementation to deployment configurations. | llm-ui is a React library developed in TypeScript that focuses on creating user interfaces for large language models, it includes features such as throttling of streamed output and support for custom component rendering. | | Persona | - | - | | Runtime | - | - | | License | MIT | llm-ui is released under the MIT license, allowing for free use and modification in both open source and commercial projects. | | Categories | Developer Tools, Model Training, Vector Databases | Developer Tools, LLM Frameworks | ## Trust and health _Sourced signals - not a safety guarantee. No winner column._ | | [ai-getting-started](/tools/a16z-infra-ai-getting-started.md) | [llm-ui](/tools/richardgill-llm-ui.md) | | --- | --- | --- | | Days since push | 723d | 401d | | Stars delta | 0 (30d) | Unknown | | Open issues delta | 0 (30d) | Unknown | | Owner type | Organization | User | | Full report | [trust report](/tools/a16z-infra-ai-getting-started/trust.md) | [trust report](/tools/richardgill-llm-ui/trust.md) | ## Decision facts: ai-getting-started - **Adopt for:** ai-getting-started is a TypeScript-based JavaScript AI tool tailored for weekend projects, offering everything from model implementation to deployment configurations. ## Decision facts: llm-ui - **Pricing:** freemium - The core library of llm-ui is available free-of-charge with no license fee. Premium support or additional services may be offered by its sponsor Stream. - **Requirements:** Developers must have familiarity with React and TypeScript to leverage llm-ui effectively.; The library includes integration support for Shiki, so installations or configurations of related dependencies may be required. - **Adopt for:** llm-ui is a React library developed in TypeScript that focuses on creating user interfaces for large language models, it includes features such as throttling of streamed output and support for custom component rendering. - **License detail:** llm-ui is released under the MIT license, allowing for free use and modification in both open source and commercial projects. ## Choose when ### Choose ai-getting-started if… - Tags unique to ai-getting-started: deployment, image models, javascript, text models. - Also covers Model Training, Vector Databases. - ai-getting-started ships Docker support for self-hosted deployment. - * Use this tool if you are already familiar with or prefer working in TypeScript and want an easy entry point into AI project development. ### Choose llm-ui if… - Pricing: The core library of llm-ui is available free-of-charge with no license fee. Premium support or additional services may be offered by its sponsor Stream.. - Requirements: Developers must have familiarity with React and TypeScript to leverage llm-ui effectively.; The library includes integration support for Shiki, so installations or configurations of related dependencies may be required.. - Tags unique to llm-ui: chatgpt, claude, component-library, generative-ai. - Also covers LLM Frameworks. - Use llm-ui if you are specifically working with React applications and require an optimized UI experience for integrating with LLMs through smooth streaming and custom components. ## When NOT to use ai-getting-started - * If your focus is on developing large-scale, production-level applications, this tool may not offer the necessary scalability features. - * Not suitable if you require highly customized vector stores or specific AI model training environments beyond what the package provides as it focuses more on a general setup. ## When NOT to use llm-ui - Avoid llm-ui if your development environment is not based on React or TypeScript, as this tool is not suitable for other frontend frameworks. - Do not use llm-ui if seamless integration with external styling libraries is not a priority, since its headless approach requires manual styling efforts. ## Common questions ### What is the difference between ai-getting-started and llm-ui? ai-getting-started: A Javascript AI getting started stack for weekend projects. llm-ui: The React library for LLMs. See the comparison table for live GitHub stats and shared categories. ### When should I choose ai-getting-started over llm-ui? Choose ai-getting-started over llm-ui when Tags unique to ai-getting-started: deployment, image models, javascript, text models; Also covers Model Training, Vector Databases; ai-getting-started ships Docker support for self-hosted deployment; * Use this tool if you are already familiar with or prefer working in TypeScript and want an easy entry point into AI project development. ### When should I choose llm-ui over ai-getting-started? Choose llm-ui over ai-getting-started when Pricing: The core library of llm-ui is available free-of-charge with no license fee. Premium support or additional services may be offered by its sponsor Stream.; Requirements: Developers must have familiarity with React and TypeScript to leverage llm-ui effectively.; The library includes integration support for Shiki, so installations or configurations of related dependencies may be required.; Tags unique to llm-ui: chatgpt, claude, component-library, generative-ai; Also covers LLM Frameworks; Use llm-ui if you are specifically working with React applications and require an optimized UI experience for integrating with LLMs through smooth streaming and custom components. ### When should I avoid ai-getting-started? * If your focus is on developing large-scale, production-level applications, this tool may not offer the necessary scalability features. * Not suitable if you require highly customized vector stores or specific AI model training environments beyond what the package provides as it focuses more on a general setup. ### When should I avoid llm-ui? Avoid llm-ui if your development environment is not based on React or TypeScript, as this tool is not suitable for other frontend frameworks. Do not use llm-ui if seamless integration with external styling libraries is not a priority, since its headless approach requires manual styling efforts. ### Is ai-getting-started or llm-ui more popular on GitHub? ai-getting-started has more GitHub stars (4,141 vs 1,749). Stars measure visibility, not whether either tool fits your constraints. ### Are ai-getting-started and llm-ui open source? Yes - both are open-source projects on GitHub (ai-getting-started: MIT, llm-ui: MIT). ### Where can I find alternatives to ai-getting-started or llm-ui? GraphCanon lists graph-backed alternatives at [ai-getting-started alternatives](/tools/a16z-infra-ai-getting-started/alternatives) and [llm-ui alternatives](/tools/richardgill-llm-ui/alternatives) ([ai-getting-started markdown twin](/tools/a16z-infra-ai-getting-started/alternatives.md), [llm-ui markdown twin](/tools/richardgill-llm-ui/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/a16z-infra-ai-getting-started-vs-richardgill-llm-ui.md) mirrors this page for agents and LLM crawlers, with the same stats table and FAQ answers. ### Which is better maintained, ai-getting-started or llm-ui? ai-getting-started: Dormant. llm-ui: 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 ai-getting-started and llm-ui? GraphCanon publishes per-repo trust reports with dated maintenance, provenance, and scan summaries: [ai-getting-started trust report](/tools/a16z-infra-ai-getting-started/trust); [llm-ui trust report](/tools/richardgill-llm-ui/trust). --- **Machine-readable endpoints** - JSON: [`/api/graphcanon/graph?tool=a16z-infra-ai-getting-started`](/api/graphcanon/graph?tool=a16z-infra-ai-getting-started) - 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/_