--- title: "llm-app vs unstract" type: "comparison" canonical_url: "https://www.graphcanon.com/compare/pathwaycom-llm-app-vs-zipstack-unstract" tools: ["pathwaycom-llm-app", "zipstack-unstract"] --- # llm-app vs unstract *GraphCanon updated Aug 16, 2026* ## Verdict Pick llm-app if llm-app offers pre-configured cloud deployment templates designed specifically for creating AI-driven applications such as chatbots and machine learning projects leveraging Hugging Face models. It supports direct integrz; 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. [llm-app](https://pathway.com/developers/templates/) reports 59k GitHub stars, 1.5k forks, and 8 open issues, last pushed Jul 5, 2026. [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 [llm-app's repository](https://github.com/pathwaycom/llm-app) and [unstract's repository](https://github.com/Zipstack/unstract). | | [llm-app](/tools/pathwaycom-llm-app.md) | [unstract](/tools/zipstack-unstract.md) | | --- | --- | --- | | Tagline | Ready-to-run cloud templates for RAG, AI pipelines, and enterprise search with live data. | LLM-Driven Extraction of Unstructured Data for API Deployments and ETL Pipeline Workflows | | Stars | 59,037 | 6,932 | | Forks | 1,466 | 663 | | Open issues | 8 | 88 | | Language | Jupyter Notebook | Python | | Adopt for | llm-app offers pre-configured cloud deployment templates designed specifically for creating AI-driven applications such as chatbots and machine learning projects leveraging Hugging Face models. It supports direct integrz | 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, LLM Frameworks, Vector Databases | Data & Retrieval, LLM Frameworks | ## Trust and health _Sourced signals - not a safety guarantee. No winner column._ | | [llm-app](/tools/pathwaycom-llm-app.md) | [unstract](/tools/zipstack-unstract.md) | | --- | --- | --- | | Maintenance | Steady (60%) | Very active (96%) | | Days since push | 41d | 0d | | Open issues (now) | 8 | 88 | | Stars delta | +11 (30d) | Unknown | | Open issues delta | -2 (30d) | Unknown | | Full report | [trust report](/tools/pathwaycom-llm-app/trust.md) | [trust report](/tools/zipstack-unstract/trust.md) | ## Decision facts: llm-app - **Requirements:** Requires Docker; The tool is Docker-friendly and designed to ensure synchronization with cloud-based storage solutions among others. - **Adopt for:** llm-app offers pre-configured cloud deployment templates designed specifically for creating AI-driven applications such as chatbots and machine learning projects leveraging Hugging Face models. It supports direct integrz ## 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 llm-app if… - llm-app is primarily Jupyter Notebook; unstract is Python. - License: llm-app is MIT, unstract is AGPL-3.0. - Requirements: Requires Docker; The tool is Docker-friendly and designed to ensure synchronization with cloud-based storage solutions among others.. - Tags unique to llm-app: chatbot, hugging-face, retrieval-augmented-generation, vector-database. - Also covers Vector Databases. - - You need a ready-to-run solution that directly integrates with various data sources like Sharepoint, Google Drive, S3, Kafka, PostgreSQL, and live APIs. ### Choose unstract if… - unstract is primarily Python; llm-app is Jupyter Notebook. - License: unstract is AGPL-3.0, llm-app 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 llm-app - - You require custom deployment configurations that extend beyond the pre-set cloud templates available through llm-app. - - There’s a need for tightly integrated support with data sources or APIs not explicitly mentioned, such as specialized CRM systems (Salesforce), which may lack direct template support in llm-app. ## 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 llm-app and unstract? llm-app: Ready-to-run cloud templates for RAG, AI pipelines, and enterprise search with live data.. 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 llm-app over unstract? Choose llm-app over unstract when llm-app is primarily Jupyter Notebook; unstract is Python; License: llm-app is MIT, unstract is AGPL-3.0; Requirements: Requires Docker; The tool is Docker-friendly and designed to ensure synchronization with cloud-based storage solutions among others.; Tags unique to llm-app: chatbot, hugging-face, retrieval-augmented-generation, vector-database; Also covers Vector Databases; - You need a ready-to-run solution that directly integrates with various data sources like Sharepoint, Google Drive, S3, Kafka, PostgreSQL, and live APIs. ### When should I choose unstract over llm-app? Choose unstract over llm-app when unstract is primarily Python; llm-app is Jupyter Notebook; License: unstract is AGPL-3.0, llm-app 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 llm-app? - You require custom deployment configurations that extend beyond the pre-set cloud templates available through llm-app. - There’s a need for tightly integrated support with data sources or APIs not explicitly mentioned, such as specialized CRM systems (Salesforce), which may lack direct template support in llm-app. ### 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 llm-app or unstract more popular on GitHub? llm-app has more GitHub stars (59,037 vs 6,932). Stars measure visibility, not whether either tool fits your constraints. ### Are llm-app and unstract open source? Yes - both are open-source projects on GitHub (llm-app: MIT, unstract: AGPL-3.0). ### Where can I find alternatives to llm-app or unstract? GraphCanon lists graph-backed alternatives at [llm-app alternatives](/tools/pathwaycom-llm-app/alternatives) and [unstract alternatives](/tools/zipstack-unstract/alternatives) ([llm-app markdown twin](/tools/pathwaycom-llm-app/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/pathwaycom-llm-app-vs-zipstack-unstract.md) mirrors this page for agents and LLM crawlers, with the same stats table and FAQ answers. ### Which is better maintained, llm-app or unstract? llm-app: Steady. 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 llm-app and unstract? GraphCanon publishes per-repo trust reports with dated maintenance, provenance, and scan summaries: [llm-app trust report](/tools/pathwaycom-llm-app/trust); [unstract trust report](/tools/zipstack-unstract/trust). --- **Machine-readable endpoints** - JSON: [`/api/graphcanon/graph?tool=pathwaycom-llm-app`](/api/graphcanon/graph?tool=pathwaycom-llm-app) - 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/_