RagLeapShort answer: run RagLeap Core, load your course catalogue, admission requirements, timetables and FAQ, and use the built-in admissions_agent and tutoring_agent roles. It answers from the documents you load. The steps are in the general setup guide.
Which roles apply?
Among the 46 built-in roles: admissions_agent, tutoring_agent, it_helpdesk, secretary and support. Names are the identifiers in defaults.py at v0.11.0.
What would you upload?
Course catalogues, admission requirements and deadlines, timetables, the fees you publish, FAQs, and study material you have the right to use. Use the Documents tab or a public web page by address.
How do people reach it?
Through WhatsApp, Telegram, Discord or a voice line (Twilio). A website chat widget is planned, not built. Language detection runs across every channel; short messages in closely related languages can be misdetected.
What should you check first?
- Students and children. If your learners are minors, check the rules for children's data in your country before using any AI provider. No age-specific or compliance claim is made.
- Accuracy. Answers come only from the documents you load; no learning-outcome claim is made.
- Autonomy is
offby default;semiasks before acting;fullis opt-in. Read real conversations before going live. - Your documents and each conversation go to the AI provider you configure. A local Ollama model keeps those steps on your machine, but local models on a CPU are slower and less accurate than Gemini.
- Single-tenant: one deployment, one business, one document set. Named users and roles arrived in v0.12.0: four roles, personal keys and an audit log of who made each state-changing request (updated 2026-10-29: this page said “No named users or roles yet”). No single sign-on or multi-factor sign-in, and no independent security review yet.
- No results for this field are claimed, and no compliance certification. Test with your own documents and questions.
Sources
The v0.11.0 README, defaults.py and the AI employees documentation.