--- title: "oasysdb vs llm-app" type: "comparison" canonical_url: "https://www.graphcanon.com/compare/edwinkys-oasysdb-vs-pathwaycom-llm-app" tools: ["edwinkys-oasysdb", "pathwaycom-llm-app"] --- # oasysdb vs llm-app *GraphCanon updated Jul 11, 2026* ## Verdict Pick oasysdb when oasysdb is primarily Rust; llm-app is Jupyter Notebook; pick llm-app when llm-app is primarily Jupyter Notebook; oasysdb is Rust. [oasysdb](https://github.com/edwinkys/oasysdb) reports 375 GitHub stars, 14 forks, and 0 open issues, last pushed Nov 29, 2024. [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 [oasysdb's repository](https://github.com/edwinkys/oasysdb) and [llm-app's repository](https://github.com/pathwaycom/llm-app). | | [oasysdb](/tools/edwinkys-oasysdb.md) | [llm-app](/tools/pathwaycom-llm-app.md) | | --- | --- | --- | | Tagline | In-memory vector store with efficient read and write performance for semantic caching and retrieval system. Redis for Semantic Caching. | Ready-to-run cloud templates for RAG, AI pipelines, and enterprise search with live data. | | Stars | 375 | 59,068 | | Forks | 14 | 1,432 | | Open issues | 0 | 10 | | Language | Rust | Jupyter Notebook | | 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 | | Persona | - | - | | Runtime | - | - | | License | Apache-2.0 | MIT | | Categories | Vector Databases, Data & Retrieval, Evaluation & Observability | LLM Frameworks, Vector Databases, Data & Retrieval | ## Trust and health _Sourced signals - not a safety guarantee. No winner column._ | | [oasysdb](/tools/edwinkys-oasysdb.md) | [llm-app](/tools/pathwaycom-llm-app.md) | | --- | --- | --- | | Maintenance | Dormant (18%) | Very active (96%) | | Days since push | 589d | 5d | | Open issues (now) | 0 | 10 | | Owner type | User | Organization | | Full report | [trust report](/tools/edwinkys-oasysdb/trust.md) | [trust report](/tools/pathwaycom-llm-app/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 ## Choose when ### Choose oasysdb if… - oasysdb is primarily Rust; llm-app is Jupyter Notebook. - License: oasysdb is Apache-2.0, llm-app is MIT. - Tags unique to oasysdb: postgresql, similarity-search, ivfpq, rust. - Also covers Evaluation & Observability. ### Choose llm-app if… - llm-app is primarily Jupyter Notebook; oasysdb is Rust. - License: llm-app is MIT, oasysdb 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 LLM Frameworks. - - 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 oasysdb - Last GitHub push was 590 days ago (dormant maintenance, Nov 29, 2024). Validate activity before betting a new project on oasysdb. - Vector Databases: Don't reach for a dedicated vector DB under ~100k vectors; pgvector on your existing Postgres is simpler to operate. - Data & Retrieval: Skip a heavy ingestion framework when your corpus is small and static; a script plus the embedding API is enough. - Evaluation & Observability: Defer heavyweight eval infra only until you have real traffic - never skip it once users depend on answers. ## 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 oasysdb and llm-app? oasysdb: In-memory vector store with efficient read and write performance for semantic caching and retrieval system. Redis for Semantic Caching.. 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 oasysdb over llm-app? Choose oasysdb over llm-app when oasysdb is primarily Rust; llm-app is Jupyter Notebook; License: oasysdb is Apache-2.0, llm-app is MIT; Tags unique to oasysdb: postgresql, similarity-search, ivfpq, rust; Also covers Evaluation & Observability. ### When should I choose llm-app over oasysdb? Choose llm-app over oasysdb when llm-app is primarily Jupyter Notebook; oasysdb is Rust; License: llm-app is MIT, oasysdb 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 LLM Frameworks; - 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 oasysdb? Last GitHub push was 590 days ago (dormant maintenance, Nov 29, 2024). Validate activity before betting a new project on oasysdb. Vector Databases: Don't reach for a dedicated vector DB under ~100k vectors; pgvector on your existing Postgres is simpler to operate. Data & Retrieval: Skip a heavy ingestion framework when your corpus is small and static; a script plus the embedding API is enough. Evaluation & Observability: Defer heavyweight eval infra only until you have real traffic - never skip it once users depend on answers. ### 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 oasysdb or llm-app more popular on GitHub? llm-app has more GitHub stars (59,068 vs 375). Stars measure visibility, not whether either tool fits your constraints. ### Are oasysdb and llm-app open source? Yes - both are open-source projects on GitHub (oasysdb: Apache-2.0, llm-app: MIT). ### Where can I find alternatives to oasysdb or llm-app? GraphCanon lists graph-backed alternatives at [oasysdb alternatives](/tools/edwinkys-oasysdb/alternatives) and [llm-app alternatives](/tools/pathwaycom-llm-app/alternatives) ([oasysdb markdown twin](/tools/edwinkys-oasysdb/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/edwinkys-oasysdb-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, oasysdb or llm-app? oasysdb: Dormant. 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 oasysdb and llm-app? GraphCanon publishes per-repo trust reports with dated maintenance, provenance, and scan summaries: [oasysdb trust report](/tools/edwinkys-oasysdb/trust); [llm-app trust report](/tools/pathwaycom-llm-app/trust). --- **Machine-readable endpoints** - JSON: [`/api/graphcanon/graph?tool=edwinkys-oasysdb`](/api/graphcanon/graph?tool=edwinkys-oasysdb) - 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/_