kotaemon vs rags

Common questions

What is the difference between kotaemon and rags?

kotaemon: An open-source RAG-based tool for chatting with your documents.. rags: Build ChatGPT over your data using natural language with RAGs. See the comparison table for live GitHub stats and shared categories.

When should I choose kotaemon over rags?

Choose kotaemon over rags when License: kotaemon is Apache-2.0, rags is MIT; Pricing: Offered under Apache-2.0 license, completely free for use and modification.; Requirements: Min 4 GB RAM; Both kotaemon and rags are RAG-based tools used for chatting with documents, differing in implementation but addressing similar use cases; Tags unique to kotaemon: llms, open-source; Also covers Developer Tools; kotaemon ships Docker support for self-hosted deployment; - **Customizable Interface**: When you need a highly customizable user interface built on Gradio that can be tailored specifically for your document interactions.

When should I choose rags over kotaemon?

Choose rags over kotaemon when License: rags is MIT, kotaemon is Apache-2.0; Pricing: RAGs is open-source under MIT license. Costs arise from any third-party API usage such as OpenAI and are not covered by RAGs itself.; Requirements: Min 4 GB RAM; RAGs requires an internet connection to interact with external APIs like OpenAI.; Ensure you configure your environment with the necessary API keys and secrets as per the installation guide.; Both kotaemon and rags are RAG-based tools used for chatting with documents, differing in implementation but addressing similar use cases; Tags unique to rags: llm, streamlit, agent; Also covers AI Agents; Use RAGs if you want an interactive way to configure and query your data with simple textual instructions through an intuitive UI in a Streamlit app.

When should I avoid kotaemon?

- **Non-GUI Projects**: For projects that do not benefit from graphical user interfaces but require more raw API access. - **Small-Scale Operations**: If you are working on a small-scale operation where the setup of an entire RAG UI is overkill since kotaemon requires more comprehensive setup compared to simpler QA chat

When should I avoid rags?

Avoid RAGs if you need full customization of the backend logic and don't want the constraints imposed by the Streamlit interface. Not recommended for environments with strict security policies that forbid the use of external APIs like OpenAI, unless you have the capability to replace those services.

Is kotaemon or rags more popular on GitHub?

kotaemon has more GitHub stars (25,527 vs 6,546). Stars measure visibility, not whether either tool fits your constraints.

Are kotaemon and rags open source?

Yes - both are open-source projects on GitHub (kotaemon: Apache-2.0, rags: MIT).

Where can I find alternatives to kotaemon or rags?

GraphCanon lists graph-backed alternatives at /tools/cinnamon-kotaemon/alternatives and /tools/run-llama-rags/alternatives (/tools/cinnamon-kotaemon/alternatives.md, /tools/run-llama-rags/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 /compare/cinnamon-kotaemon-vs-run-llama-rags.md mirrors this page for agents and LLM crawlers, with the same stats table and FAQ answers.

Which is better maintained, kotaemon or rags?

kotaemon: Active. rags: Dormant. 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 kotaemon and rags?

GraphCanon publishes per-repo trust reports with dated maintenance, provenance, and scan summaries: kotaemon: /tools/cinnamon-kotaemon/trust; rags: /tools/run-llama-rags/trust.