llm-app open source analysis

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.

Project overview

⭐ 46830 · Jupyter Notebook · Last activity on GitHub: 2025-10-23

GitHub: https://github.com/pathwaycom/llm-app

Why it matters for engineering teams

llm-app addresses the challenge of integrating large language models with live data sources in a reliable and scalable way. It offers ready-to-run cloud templates for retrieval-augmented generation (RAG), AI pipelines, and enterprise search, making it a practical open source tool for engineering teams focused on machine learning and AI. Its compatibility with popular data platforms like Sharepoint, Google Drive, S3, Kafka, and PostgreSQL ensures seamless data syncing in production environments. This project is mature and Docker-friendly, supporting real-time data APIs which enhances its reliability for production use. However, it may not be the right choice for teams seeking lightweight or purely experimental LLM applications, as its focus is on robust, production ready solutions with complex data integrations.

When to use this project

Choose llm-app when your team needs a self hosted option for integrating LLMs with multiple live data sources and requires a production ready solution for enterprise search or AI pipelines. Consider alternatives if your use case involves simple chatbot prototypes or does not require real-time data syncing.

Team fit and typical use cases

Machine learning and AI engineering teams benefit most from llm-app, using it to build and maintain LLM-driven applications that rely on up-to-date data from various sources. It is commonly used in products involving retrieval-augmented generation, real-time analytics, and enterprise search solutions. Its design supports teams looking for a self hosted option for managing complex data workflows alongside large language models.

Best suited for

Topics and ecosystem

chatbot hugging-face llm llm-local llm-prompting llm-security llmops machine-learning open-ai pathway rag real-time retrieval-augmented-generation vector-database vector-index

Activity and freshness

Latest commit on GitHub: 2025-10-23. Activity data is based on repeated RepoPi snapshots of the GitHub repository. It gives a quick, factual view of how alive the project is.