llm-app
Ready-to-run cloud templates for RAG, AI pipelines, and enterprise search with live data.
GraphCanon updated 4d · GitHub synced 4d · 28 views this month
Decision brief
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
Good fit when
- - You need a ready-to-run solution that directly integrates with various data sources like Sharepoint, Google Drive, S3, Kafka, PostgreSQL, and live APIs.
- - Your project requires a seamless setup of RAG (Retrieval-Augmented Generation) capabilities directly out of the box without extensive configuration.
Avoid when
- - 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.
- Requirements:
- Requires Docker; The tool is Docker-friendly and designed to ensure synchronization with cloud-based storage solutions among others.
Observed Jul 11, 2026 · Source: enrich:decision_facts
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Maintenance and security
Full trust report- Maintenance
- Steady (41d since push)
- As of 4d
- Provenance
- Not a fork · Organization account
- As of 4d
- Security (OSV)
- No lockfile
- As of 1mo
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Install
git clone https://github.com/pathwaycom/llm-appHow it fits your stack(36)
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Overview
Provides pre-configured cloud deployment templates with capabilities to integrate with various data sources like Sharepoint, Google Drive, S3, Kafka, PostgreSQL, real-time APIs. Supports creation of chatbots, machine learning projects leveraging Hugging Face models, and integration with RAG (Retrieval-Augmented Generation) and Vector Databases.
Capability facts
- Languages
- jupyter notebook, python
Source: github.language+pyproject.toml · Aug 16, 2026
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README
Getting started
Each of the App templates in this repo contains a README.md with instructions on how to run it.
You can also find more ready-to-run code templates on the Pathway website.
For agents
This page has a .md twin and JSON over the API.