--- title: "prompttools vs llm-app" type: "comparison" canonical_url: "https://www.graphcanon.com/compare/hegelai-prompttools-vs-pathwaycom-llm-app" tools: ["hegelai-prompttools", "pathwaycom-llm-app"] --- # prompttools vs llm-app *GraphCanon updated Jul 12, 2026* ## Verdict Pick prompttools if prompttools aims to support developers in the testing and experimentation of prompts for language models as well as integrating vector databases through Python utilities; 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. [prompttools](http://prompttools.readthedocs.io) reports 3.0k GitHub stars, 255 forks, and 41 open issues, last pushed Feb 11, 2026. [llm-app](https://pathway.com/developers/templates/) has 59k stars, 1.4k forks, and 10 open issues, last pushed Jul 5, 2026. Figures are from public GitHub metadata via [prompttools's repository](https://github.com/hegelai/prompttools) and [llm-app's repository](https://github.com/pathwaycom/llm-app). | | [prompttools](/tools/hegelai-prompttools.md) | [llm-app](/tools/pathwaycom-llm-app.md) | | --- | --- | --- | | Tagline | Open-source tools for prompt testing and experimentation | Ready-to-run cloud templates for RAG, AI pipelines, and enterprise search with live data. | | Stars | 3,041 | 59,068 | | Forks | 255 | 1,432 | | Open issues | 41 | 10 | | Language | Python | Jupyter Notebook | | Adopt for | Prompttools aims to support developers in the testing and experimentation of prompts for language models as well as integrating vector databases through Python utilities. | 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 | | Persona | - | - | | Runtime | - | - | | License | Apache-2.0 | MIT | | Categories | Vector Databases, LLM Frameworks, Developer Tools | LLM Frameworks, Vector Databases, Data & Retrieval | ## Trust and health _Sourced signals - not a safety guarantee. No winner column._ | | [prompttools](/tools/hegelai-prompttools.md) | [llm-app](/tools/pathwaycom-llm-app.md) | | --- | --- | --- | | Maintenance | Slowing (36%) | Very active (96%) | | Days since push | 150d | 5d | | Open issues (now) | 41 | 10 | | Security scan | 2 low (2 low) | No lockfile | | Full report | [trust report](/tools/hegelai-prompttools/trust.md) | [trust report](/tools/pathwaycom-llm-app/trust.md) | ## Decision facts: prompttools - **Hosting:** self hosted - **Pricing:** freemium - PromptsTools is open-source under the Apache-2.0 license, making it free to use but with no official support available. - **Adopt for:** Prompttools aims to support developers in the testing and experimentation of prompts for language models as well as integrating vector databases through Python utilities. ## 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 ## Choose when ### Choose prompttools if… - prompttools is primarily Python; llm-app is Jupyter Notebook. - License: prompttools is Apache-2.0, llm-app is MIT. - Pricing: PromptsTools is open-source under the Apache-2.0 license, making it free to use but with no official support available.. - Tags unique to prompttools: llms, embeddings, deep-learning, machine-learning. - Also covers Developer Tools. - Prompttools aims to support developers in the testing and experimentation of prompts for language models as well as integrating vector databases through Python utilities. ### Choose llm-app if… - llm-app is primarily Jupyter Notebook; prompttools is Python. - License: llm-app is MIT, prompttools is Apache-2.0. - 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: vector-database, llm, hugging-face, retrieval-augmented-generation. - Also covers Data & Retrieval. - - 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 NOT to use prompttools - Last GitHub push was 151 days ago (slowing maintenance, Feb 11, 2026). Validate activity before betting a new project on prompttools. - Vector Databases: Don't reach for a dedicated vector DB under ~100k vectors; pgvector on your existing Postgres is simpler to operate. - LLM Frameworks: Avoid a framework for a single prompt-and-retrieve call; the abstraction can cost more than it saves. - Developer Tools: A gateway is overkill when you're pinned to a single provider and model. ## 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. ## Common questions ### What is the difference between prompttools and llm-app? prompttools: Open-source tools for prompt testing and experimentation. llm-app: Ready-to-run cloud templates for RAG, AI pipelines, and enterprise search with live data.. See the comparison table for live GitHub stats and shared categories. ### When should I choose prompttools over llm-app? Choose prompttools over llm-app when prompttools is primarily Python; llm-app is Jupyter Notebook; License: prompttools is Apache-2.0, llm-app is MIT; Pricing: PromptsTools is open-source under the Apache-2.0 license, making it free to use but with no official support available.; Tags unique to prompttools: llms, embeddings, deep-learning, machine-learning; Also covers Developer Tools; Prompttools aims to support developers in the testing and experimentation of prompts for language models as well as integrating vector databases through Python utilities. ### When should I choose llm-app over prompttools? Choose llm-app over prompttools when llm-app is primarily Jupyter Notebook; prompttools is Python; License: llm-app is MIT, prompttools is Apache-2.0; 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: vector-database, llm, hugging-face, retrieval-augmented-generation; Also covers Data & Retrieval; - 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 avoid prompttools? Last GitHub push was 151 days ago (slowing maintenance, Feb 11, 2026). Validate activity before betting a new project on prompttools. Vector Databases: Don't reach for a dedicated vector DB under ~100k vectors; pgvector on your existing Postgres is simpler to operate. LLM Frameworks: Avoid a framework for a single prompt-and-retrieve call; the abstraction can cost more than it saves. Developer Tools: A gateway is overkill when you're pinned to a single provider and model. ### 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. ### Is prompttools or llm-app more popular on GitHub? llm-app has more GitHub stars (59,068 vs 3,041). Stars measure visibility, not whether either tool fits your constraints. ### Are prompttools and llm-app open source? Yes - both are open-source projects on GitHub (prompttools: Apache-2.0, llm-app: MIT). ### Where can I find alternatives to prompttools or llm-app? GraphCanon lists graph-backed alternatives at [prompttools alternatives](/tools/hegelai-prompttools/alternatives) and [llm-app alternatives](/tools/pathwaycom-llm-app/alternatives) ([prompttools markdown twin](/tools/hegelai-prompttools/alternatives.md), [llm-app markdown twin](/tools/pathwaycom-llm-app/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/hegelai-prompttools-vs-pathwaycom-llm-app.md) mirrors this page for agents and LLM crawlers, with the same stats table and FAQ answers. ### Which is better maintained, prompttools or llm-app? prompttools: Slowing. llm-app: 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 prompttools and llm-app? GraphCanon publishes per-repo trust reports with dated maintenance, provenance, and scan summaries: [prompttools trust report](/tools/hegelai-prompttools/trust); [llm-app trust report](/tools/pathwaycom-llm-app/trust). --- **Machine-readable endpoints** - JSON: [`/api/graphcanon/graph?tool=hegelai-prompttools`](/api/graphcanon/graph?tool=hegelai-prompttools) - 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/_