--- title: "knowledge-gpt vs unstract" type: "comparison" canonical_url: "https://www.graphcanon.com/compare/geeks-of-data-knowledge-gpt-vs-zipstack-unstract" tools: ["geeks-of-data-knowledge-gpt", "zipstack-unstract"] --- # knowledge-gpt vs unstract *GraphCanon updated Aug 23, 2026* ## Verdict Pick knowledge-gpt if knowledge-gpt: Python toolkit for indexing and Q&A sessions with info sources using GPT & transformers; pick unstract if unstract is a Python-driven tool for transforming unstructured data into structured formats using OCR, PDF extraction, and other techniques to integrate with APIs and ETL workflows under AGPL-3.0 license. [knowledge-gpt](https://pypi.org/project/knowledgegpt/) reports 291 GitHub stars, 52 forks, and 8 open issues, last pushed Apr 25, 2023. [unstract](https://unstract.com) has 6.9k stars, 663 forks, and 88 open issues, last pushed Jul 27, 2026. Figures are from public GitHub metadata via [knowledge-gpt's repository](https://github.com/geeks-of-data/knowledge-gpt) and [unstract's repository](https://github.com/Zipstack/unstract). | | [knowledge-gpt](/tools/geeks-of-data-knowledge-gpt.md) | [unstract](/tools/zipstack-unstract.md) | | --- | --- | --- | | Tagline | Extract knowledge from all information sources using GPT and other language models. Index and conduct Q&A sessions with information sources. | LLM-Driven Extraction of Unstructured Data for API Deployments and ETL Pipeline Workflows | | Stars | 291 | 6,932 | | Forks | 52 | 663 | | Open issues | 8 | 88 | | Language | Python | Python | | Adopt for | knowledge-gpt: Python toolkit for indexing and Q&A sessions with info sources using GPT & transformers. | Unstract is a Python-driven tool for transforming unstructured data into structured formats using OCR, PDF extraction, and other techniques to integrate with APIs and ETL workflows under AGPL-3.0 license. | | Persona | - | - | | Runtime | - | - | | License | MIT | AGPL-3.0 | | Categories | Data & Retrieval, Evaluation & Observability, LLM Frameworks, Model Training | Data & Retrieval, LLM Frameworks | ## Trust and health _Sourced signals - not a safety guarantee. No winner column._ | | [knowledge-gpt](/tools/geeks-of-data-knowledge-gpt.md) | [unstract](/tools/zipstack-unstract.md) | | --- | --- | --- | | Maintenance | Dormant (18%) | Very active (96%) | | Days since push | 1216d | 0d | | Open issues (now) | 8 | 88 | | Stars delta | 0 (30d) | Unknown | | Open issues delta | 0 (30d) | Unknown | | Full report | [trust report](/tools/geeks-of-data-knowledge-gpt/trust.md) | [trust report](/tools/zipstack-unstract/trust.md) | ## Decision facts: knowledge-gpt - **Adopt for:** knowledge-gpt: Python toolkit for indexing and Q&A sessions with info sources using GPT & transformers. ## Decision facts: unstract - **Adopt for:** Unstract is a Python-driven tool for transforming unstructured data into structured formats using OCR, PDF extraction, and other techniques to integrate with APIs and ETL workflows under AGPL-3.0 license. ## Choose when ### Choose knowledge-gpt if… - License: knowledge-gpt is MIT, unstract is AGPL-3.0. - Tags unique to knowledge-gpt: context, embedding-vectors, gpt, huggingface-transformers. - Also covers Evaluation & Observability, Model Training. - knowledge-gpt ships Docker support for self-hosted deployment. - When you need a flexible, model-agnostic approach for Q&A over diverse data sources ### Choose unstract if… - License: unstract is AGPL-3.0, knowledge-gpt is MIT. - Tags unique to unstract: ai-agents, data-engineering, document-ai, generative-ai. - You prioritize open-source contributions and require the flexibility of the AGPL-3.0 license. ## When NOT to use knowledge-gpt - Avoid if strictly needing real-time response performance without indexing capabilities - Not recommended if focusing solely on visual or multimedia content extraction ## When NOT to use unstract - Your workflow strictly adheres to closed-source software management policies and requires proprietary control. - Projects needing direct integration with commercial data processing services incompatible with AGPL-3.0 licensing. - Cases where real-time performance is critical, as the LLM-driven extraction may introduce latency. ## Common questions ### What is the difference between knowledge-gpt and unstract? knowledge-gpt: Extract knowledge from all information sources using GPT and other language models. Index and conduct Q&A sessions with information sources.. unstract: LLM-Driven Extraction of Unstructured Data for API Deployments and ETL Pipeline Workflows. See the comparison table for live GitHub stats and shared categories. ### When should I choose knowledge-gpt over unstract? Choose knowledge-gpt over unstract when License: knowledge-gpt is MIT, unstract is AGPL-3.0; Tags unique to knowledge-gpt: context, embedding-vectors, gpt, huggingface-transformers; Also covers Evaluation & Observability, Model Training; knowledge-gpt ships Docker support for self-hosted deployment; When you need a flexible, model-agnostic approach for Q&A over diverse data sources. ### When should I choose unstract over knowledge-gpt? Choose unstract over knowledge-gpt when License: unstract is AGPL-3.0, knowledge-gpt is MIT; Tags unique to unstract: ai-agents, data-engineering, document-ai, generative-ai; You prioritize open-source contributions and require the flexibility of the AGPL-3.0 license. ### When should I avoid knowledge-gpt? Avoid if strictly needing real-time response performance without indexing capabilities Not recommended if focusing solely on visual or multimedia content extraction ### When should I avoid unstract? Your workflow strictly adheres to closed-source software management policies and requires proprietary control. Projects needing direct integration with commercial data processing services incompatible with AGPL-3.0 licensing. Cases where real-time performance is critical, as the LLM-driven extraction may introduce latency. ### Is knowledge-gpt or unstract more popular on GitHub? unstract has more GitHub stars (6,932 vs 291). Stars measure visibility, not whether either tool fits your constraints. ### Are knowledge-gpt and unstract open source? Yes - both are open-source projects on GitHub (knowledge-gpt: MIT, unstract: AGPL-3.0). ### Where can I find alternatives to knowledge-gpt or unstract? GraphCanon lists graph-backed alternatives at [knowledge-gpt alternatives](/tools/geeks-of-data-knowledge-gpt/alternatives) and [unstract alternatives](/tools/zipstack-unstract/alternatives) ([knowledge-gpt markdown twin](/tools/geeks-of-data-knowledge-gpt/alternatives.md), [unstract markdown twin](/tools/zipstack-unstract/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/geeks-of-data-knowledge-gpt-vs-zipstack-unstract.md) mirrors this page for agents and LLM crawlers, with the same stats table and FAQ answers. ### Which is better maintained, knowledge-gpt or unstract? knowledge-gpt: Dormant. unstract: 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 knowledge-gpt and unstract? GraphCanon publishes per-repo trust reports with dated maintenance, provenance, and scan summaries: [knowledge-gpt trust report](/tools/geeks-of-data-knowledge-gpt/trust); [unstract trust report](/tools/zipstack-unstract/trust). --- **Machine-readable endpoints** - JSON: [`/api/graphcanon/graph?tool=geeks-of-data-knowledge-gpt`](/api/graphcanon/graph?tool=geeks-of-data-knowledge-gpt) - 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/_