RAG & Knowledge
llmware
Unified framework for enterprise RAG pipelines with small, specialized models - local, private and secure.
★ 14.8k starsApache-2.0
View guide →Ready-to-run templates for RAG, AI pipelines and enterprise search that stay in sync with live data
llm-app is an open-source rag & knowledge project by pathwaycom (pathwaycom/llm-app on GitHub). It has 58,820 GitHub stars and 1,504 forks, is released under the MIT license, and was last updated on GitHub on 5 July 2026.
Decide what you want from llm-app and check the README and requirements to confirm it fits your setup and Jupyter Notebook environment.
Clone the repository from GitHub (github.com/pathwaycom/llm-app) or use the documented install channel if you do not need the source.
Follow the README quickstart to install dependencies and set any required configuration or credentials.
Work through the README's first example end to end, then adapt it to your own project or data.
Ready-to-run templates for RAG, AI pipelines and enterprise search.
Always in sync with SharePoint, Google Drive, S3, Kafka, PostgreSQL and real-time APIs.
Templates are Docker-friendly for local or cloud deployment.
Built on Pathway's streaming data engine for real-time updates.
Maintains a vector index over your connected sources for retrieval.
Templates address LLM prompting and security concerns for enterprise data.
Start by getting the source and entering the project directory.
git clone https://github.com/pathwaycom/llm-app.git
cd llm-appClone the repo and choose a template folder for your data source and use case.
# see README for the exact command — Getting started sectionStart the template with Docker and connect your data source credentials.
docker compose up # see README for the exact command per templateIt is maintained by pathwaycom and contributors on GitHub, where the repository github.com/pathwaycom/llm-app is the canonical home for code, issues and releases.
Yes — it is open source under the MIT license. Read the license in the repository for what is permitted, including any conditions on redistribution or commercial use.
Yes — templates call an LLM provider, so you need your own API key (for example OpenAI), plus source-system access.
Compared with LangChain with a static index: llm-app templates keep the index continuously in sync with live sources via Pathway, instead of one-off ingestion.
Clone github.com/pathwaycom/llm-app, follow the README quickstart to install it, and run the documented first example before adapting it to your own work.
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