--- title: "unstructured vs upgini" type: "comparison" canonical_url: "https://www.graphcanon.com/compare/unstructured-io-unstructured-vs-upgini-upgini" tools: ["unstructured-io-unstructured", "upgini-upgini"] --- # unstructured vs upgini *GraphCanon updated Aug 3, 2026* ## Verdict Pick unstructured if unstructured is an open-source ETL solution designed specifically to convert complex documents into structured data suitable for language models; pick upgini if automate feature engineering by integrating vast external datasets into ML workflows. [unstructured](https://www.unstructured.io/) reports 15k GitHub stars, 1.3k forks, and 277 open issues, last pushed Jul 31, 2026. [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 [unstructured's repository](https://github.com/Unstructured-IO/unstructured) and [upgini's repository](https://github.com/upgini/upgini). | | [unstructured](/tools/unstructured-io-unstructured.md) | [upgini](/tools/upgini-upgini.md) | | --- | --- | --- | | Tagline | Convert documents to structured data effortlessly | Data search & enrichment library for Machine Learning | | Stars | 15,238 | 355 | | Forks | 1,284 | 26 | | Open issues | 277 | 1 | | Language | HTML | Python | | Adopt for | Unstructured is an open-source ETL solution designed specifically to convert complex documents into structured data suitable for language models. | Automate feature engineering by integrating vast external datasets into ML workflows. | | Persona | - | - | | Runtime | - | - | | License | The tool is distributed under the Apache-2.0 license, which allows for free use, modification, and distribution as long as compatibility with the license terms is maintained. | BSD-3-Clause | | Categories | Data & Retrieval, Model Training | Data & Retrieval, Model Training | ## Trust and health _Sourced signals - not a safety guarantee. No winner column._ | | [unstructured](/tools/unstructured-io-unstructured.md) | [upgini](/tools/upgini-upgini.md) | | --- | --- | --- | | Days since push | 0d | 4d | | Open issues (now) | 277 | 1 | | Full report | [trust report](/tools/unstructured-io-unstructured/trust.md) | [trust report](/tools/upgini-upgini/trust.md) | ## Shared compatibility - **Python**: [unstructured](/tools/unstructured-io-unstructured.md) - Python runtime; [upgini](/tools/upgini-upgini.md) - Python runtime ## Decision facts: unstructured - **Requirements:** Requires Docker; Building Docker images independently may require customizing the `Dockerfile` to include only necessary packages/requirements based on specific data parsing use; The base image `wolfi-base`, a regularly updated image, might cause build failures due to upstream changes but can be managed by specifying requirements. - **Adopt for:** Unstructured is an open-source ETL solution designed specifically to convert complex documents into structured data suitable for language models. - **License detail:** The tool is distributed under the Apache-2.0 license, which allows for free use, modification, and distribution as long as compatibility with the license terms is maintained. ## Decision facts: upgini - **Adopt for:** Automate feature engineering by integrating vast external datasets into ML workflows. ## Choose when ### Choose unstructured if… - unstructured is primarily HTML; upgini is Python. - License: unstructured is Apache-2.0, upgini is BSD-3-Clause. - Requirements: Requires Docker; Building Docker images independently may require customizing the `Dockerfile` to include only necessary packages/requirements based on specific data parsing use; The base image `wolfi-base`, a regularly updated image, might cause build failures due to upstream changes but can be managed by specifying requirements.. - Tags unique to unstructured: data-pipelines, deep-learning, document-parser, document-processing. - When you need to transform various types of unprocessed, complex documents (including PDFs, images with text) into formats that are easily usable by machine learning pipelines. ### Choose upgini if… - upgini is primarily Python; unstructured is HTML. - License: upgini is BSD-3-Clause, unstructured is Apache-2.0. - Tags unique to upgini: automated-feature-engineering, automl, chatgpt, data-enrichment. - Need rapid access to diverse external data for model enrichment ## When NOT to use unstructured - When your workflow is limited to only one type of data, as Unstructured might introduce unnecessary complexity due to its broad support for multiple data types. - If you prefer a proprietary solution with dedicated enterprise-level support and features that are beyond the scope of an open-source project like Unstructured. ## 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 unstructured and upgini? unstructured: Convert documents to structured data effortlessly. upgini: Data search & enrichment library for Machine Learning. See the comparison table for live GitHub stats and shared categories. ### When should I choose unstructured over upgini? Choose unstructured over upgini when unstructured is primarily HTML; upgini is Python; License: unstructured is Apache-2.0, upgini is BSD-3-Clause; Requirements: Requires Docker; Building Docker images independently may require customizing the `Dockerfile` to include only necessary packages/requirements based on specific data parsing use; The base image `wolfi-base`, a regularly updated image, might cause build failures due to upstream changes but can be managed by specifying requirements.; Tags unique to unstructured: data-pipelines, deep-learning, document-parser, document-processing; When you need to transform various types of unprocessed, complex documents (including PDFs, images with text) into formats that are easily usable by machine learning pipelines. ### When should I choose upgini over unstructured? Choose upgini over unstructured when upgini is primarily Python; unstructured is HTML; License: upgini is BSD-3-Clause, unstructured is Apache-2.0; Tags unique to upgini: automated-feature-engineering, automl, chatgpt, data-enrichment; Need rapid access to diverse external data for model enrichment. ### When should I avoid unstructured? When your workflow is limited to only one type of data, as Unstructured might introduce unnecessary complexity due to its broad support for multiple data types. If you prefer a proprietary solution with dedicated enterprise-level support and features that are beyond the scope of an open-source project like Unstructured. ### 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 unstructured or upgini more popular on GitHub? unstructured has more GitHub stars (15,238 vs 355). Stars measure visibility, not whether either tool fits your constraints. ### Are unstructured and upgini open source? Yes - both are open-source projects on GitHub (unstructured: Apache-2.0, upgini: BSD-3-Clause). ### Where can I find alternatives to unstructured or upgini? GraphCanon lists graph-backed alternatives at [unstructured alternatives](/tools/unstructured-io-unstructured/alternatives) and [upgini alternatives](/tools/upgini-upgini/alternatives) ([unstructured markdown twin](/tools/unstructured-io-unstructured/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/unstructured-io-unstructured-vs-upgini-upgini.md) mirrors this page for agents and LLM crawlers, with the same stats table and FAQ answers. ### Which is better maintained, unstructured or upgini? unstructured: Very active. 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 unstructured and upgini? GraphCanon publishes per-repo trust reports with dated maintenance, provenance, and scan summaries: [unstructured trust report](/tools/unstructured-io-unstructured/trust); [upgini trust report](/tools/upgini-upgini/trust). --- **Machine-readable endpoints** - JSON: [`/api/graphcanon/graph?tool=unstructured-io-unstructured`](/api/graphcanon/graph?tool=unstructured-io-unstructured) - 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/_