llm-app is by pathwaycom — not by us.
We indexed this project so people can find it. We are not selling it, we host no copy of the code, and we are not affiliated with or endorsed by its authors — get it from them.
Indexed Oct 5, 2026 · 58,842 stars at index time. Maintainers: claiming verifies your identity and unlocks a higher assurance tier. Removal requests are honored.
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llm-app
unclaimed listingactively maintainedFreeMIT
Ready-to-run cloud templates for RAG, AI pipelines, and enterprise search with live data. 🐳Docker-friendly.⚡Always in s
by Open Source Community · New publisher
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pathwaycom/llm-app is an open-source project by pathwaycom: Ready-to-run cloud templates for RAG, AI pipelines, and enterprise search with live data. 🐳Docker-friendly.⚡Always in sync with Sharepoint, Google Drive, S3, Kafka, PostgreSQL, real-time data APIs, and more.. Indexed here so it can be found — not resold.
It is free. Get it from the upstream repository: https://github.com/pathwaycom/llm-app
From the project's own README (excerpt, reproduced for discovery under its MIT license):
Pathway Live Data Framework AI Pipelines
The Pathway Live Data Framework's AI Pipelines allow you to quickly put in production AI applications that offer high-accuracy RAG and AI enterprise search at scale using the most up-to-date knowledge available in your data sources. It provides you ready-to-deploy LLM (Large Language Model) App Templates. You can test them on your own machine and deploy on-cloud (GCP, AWS, Azure, Render,...) or on-premises.
The apps connect and sync (all new data additions, deletions, updates) with data sources on your file system, Google Drive, Sharepoint, S3, Kafka, PostgreSQL, real-time data APIs. They come with no infrastructure dependencies that would need a separate setup. They include built-in data indexing enabling vector search, hybrid search, and full-text search - all done in-memory, with cache.
Application Templates
The application templates provided in this repo scale up to millions of pages of documents. Some of them are optimized for simplicity, some are optimized for amazing accuracy. Pick the one that suits you best. You can use it out of the box, or change some steps of the pipeline - for example, if you would like to add a new data source, or change a Vector Index into a Hybrid Index, it's just a one-line change.
Application (template) Description --------------------------------------------------------------------------------- ------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------- Question-Answering RAG…
Preview
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01Capabilities
Does
- No capabilities recorded
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- No exclusions declared
02Requirements & stack
Depends on
No declared dependencies
Credentials needed
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Stack
03Community
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Issues 0
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04Trust Passport
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05Versions
Full history →| Version | Channel | Released | Notes |
|---|---|---|---|
| 0.0.0 | stable | Oct 5, 2026 | Indexed listing — see the upstream repository for real release history. |