--- title: "llama-hub vs upgini" type: "comparison" canonical_url: "https://www.graphcanon.com/compare/run-llama-llama-hub-vs-upgini-upgini" tools: ["run-llama-llama-hub", "upgini-upgini"] --- # llama-hub vs upgini *GraphCanon updated Aug 8, 2026* ## Verdict Pick llama-hub if community-driven data loaders for LlamaIndex/LangChain; pick upgini if automate feature engineering by integrating vast external datasets into ML workflows. [llama-hub](https://llamahub.ai/) reports 3.5k GitHub stars, 721 forks, and 96 open issues, last pushed Mar 1, 2024. [upgini](https://upgini.com) has 355 stars, 26 forks, and 1 open issues, last pushed Jul 30, 2026. Figures are from public GitHub metadata via [llama-hub's repository](https://github.com/run-llama/llama-hub) and [upgini's repository](https://github.com/upgini/upgini). | | [llama-hub](/tools/run-llama-llama-hub.md) | [upgini](/tools/upgini-upgini.md) | | --- | --- | --- | | Tagline | A library of data loaders for LLMs made by the community | Data search & enrichment library for Machine Learning | | Stars | 3,469 | 355 | | Forks | 721 | 26 | | Open issues | 96 | 1 | | Language | Jupyter Notebook | Python | | Adopt for | community-driven data loaders for LlamaIndex/LangChain | Automate feature engineering by integrating vast external datasets into ML workflows. | | Persona | - | - | | Runtime | - | - | | License | MIT | BSD-3-Clause | | Categories | Data & Retrieval, Model Training | Data & Retrieval, Model Training | ## Trust and health _Sourced signals - not a safety guarantee. No winner column._ | | [llama-hub](/tools/run-llama-llama-hub.md) | [upgini](/tools/upgini-upgini.md) | | --- | --- | --- | | Maintenance | Archived (8%) | Very active (96%) | | Days since push | 889d | 4d | | Archived on GitHub | Yes | No | | Open issues (now) | 96 | 1 | | Full report | [trust report](/tools/run-llama-llama-hub/trust.md) | [trust report](/tools/upgini-upgini/trust.md) | ## Shared compatibility - **Python**: [llama-hub](/tools/run-llama-llama-hub.md) - Python runtime; [upgini](/tools/upgini-upgini.md) - Python runtime ## Decision facts: llama-hub - **Adopt for:** community-driven data loaders for LlamaIndex/LangChain ## Decision facts: upgini - **Adopt for:** Automate feature engineering by integrating vast external datasets into ML workflows. ## Choose when ### Choose llama-hub if… - llama-hub is primarily Jupyter Notebook; upgini is Python. - License: llama-hub is MIT, upgini is BSD-3-Clause. - Tags unique to llama-hub: community-driven, jupyter-notebook, langchain, llamaindex. - Community-specific features require engagement with community ### Choose upgini if… - upgini is primarily Python; llama-hub is Jupyter Notebook. - License: upgini is BSD-3-Clause, llama-hub is MIT. - Tags unique to upgini: automated-feature-engineering, automl, chatgpt, data-enrichment. - upgini ships Docker support for self-hosted deployment. - Need rapid access to diverse external data for model enrichment ## When NOT to use llama-hub - Limited support if the community lacks activity - Not suitable without familiarity with Poetry for dependency management ## When NOT to use upgini - Seeking full control over the source code of all components integrated into ML pipelines - Working with proprietary data that cannot be sourced or merged via external services - Aiming for a solution without reliance on internet-accessible datasets ## Common questions ### What is the difference between llama-hub and upgini? llama-hub: A library of data loaders for LLMs made by the community. upgini: Data search & enrichment library for Machine Learning. See the comparison table for live GitHub stats and shared categories. ### When should I choose llama-hub over upgini? Choose llama-hub over upgini when llama-hub is primarily Jupyter Notebook; upgini is Python; License: llama-hub is MIT, upgini is BSD-3-Clause; Tags unique to llama-hub: community-driven, jupyter-notebook, langchain, llamaindex; Community-specific features require engagement with community. ### When should I choose upgini over llama-hub? Choose upgini over llama-hub when upgini is primarily Python; llama-hub is Jupyter Notebook; License: upgini is BSD-3-Clause, llama-hub is MIT; Tags unique to upgini: automated-feature-engineering, automl, chatgpt, data-enrichment; upgini ships Docker support for self-hosted deployment; Need rapid access to diverse external data for model enrichment. ### When should I avoid llama-hub? Limited support if the community lacks activity Not suitable without familiarity with Poetry for dependency management ### When should I avoid upgini? Seeking full control over the source code of all components integrated into ML pipelines Working with proprietary data that cannot be sourced or merged via external services Aiming for a solution without reliance on internet-accessible datasets ### Is llama-hub or upgini more popular on GitHub? llama-hub has more GitHub stars (3,469 vs 355). Stars measure visibility, not whether either tool fits your constraints. ### Are llama-hub and upgini open source? Yes - both are open-source projects on GitHub (llama-hub: MIT, upgini: BSD-3-Clause). ### Where can I find alternatives to llama-hub or upgini? GraphCanon lists graph-backed alternatives at [llama-hub alternatives](/tools/run-llama-llama-hub/alternatives) and [upgini alternatives](/tools/upgini-upgini/alternatives) ([llama-hub markdown twin](/tools/run-llama-llama-hub/alternatives.md), [upgini markdown twin](/tools/upgini-upgini/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/run-llama-llama-hub-vs-upgini-upgini.md) mirrors this page for agents and LLM crawlers, with the same stats table and FAQ answers. ### Which is better maintained, llama-hub or upgini? llama-hub: Archived. upgini: 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 llama-hub and upgini? GraphCanon publishes per-repo trust reports with dated maintenance, provenance, and scan summaries: [llama-hub trust report](/tools/run-llama-llama-hub/trust); [upgini trust report](/tools/upgini-upgini/trust). --- **Machine-readable endpoints** - JSON: [`/api/graphcanon/graph?tool=run-llama-llama-hub`](/api/graphcanon/graph?tool=run-llama-llama-hub) - 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/_