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Top AI Repos tracks AI repositories on GitHub and answers two different questions about each one: is it moving right now, and would you bet a product on it.
Top AI Repos tracks AI repositories on GitHub and answers two different questions about each one: is it moving right now, and would you bet a product on it.
💬 RasaGPT is the first headless LLM chatbot platform built on top of Rasa and Langchain. Built w/ Rasa, FastAPI, Langchain, LlamaIndex, SQLModel, pgvector, ngrok, telegram
| Date | Stars |
|---|---|
| 2026-07-31 | 2464 |
| 2026-08-01 | 2464 |
| 2026-08-06 | 2464 |
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 <br/><br/> # 🏠 Overview 💬 RasaGPT is the first headless LLM chatbot platform built on top of [Rasa](https://github.com/RasaHQ/rasa) and [Langchain](https://github.com/hwchase17/langchain). It is boilerplate and a reference implementation of Rasa and Telegram utilizing an LLM library like Langchain for indexing, retrieval and context injection. <br/> - 📚 Resources: [https://rasagpt.dev](https://rasagpt.dev) - 🧑💻 Github: [https://github.com/paulpierre/RasaGPT](https://github.com/paulpierre/RasaGPT) - 🧙 Author: [@paulpierre](https://twitter.com/paulpierre) <br/><br/> [](https://youtu.be/GAPnQ0qf1-E) <br/><br/> # 💬 What is Rasa? In their own words: >💬 Rasa is an open source (Python) machine learning framework to automate text- and voice-based conversations: NLU, dialogue management, connect to Slack, Facebook, and more - Create chatbots and voice assistants <br/> In my words: <br/> [Rasa](https://rasa.com/) is a very popular (dare I say de facto?) and easy-enough to use chatbot framework with built in NLU ML pipelines that are obsolete and a conceptual starting point for a reimagined chatbot framework in a world of LLMs. <br/><br/> # 💁♀️ Why RasaGPT? RasaGPT works out of the box. A lot of the implementing headaches were sorted out so you don’t have to, including: - Creating your own proprietary bot end-point using FastAPI, document upload and “training” 'pipeline included - How to integrate Langchain/LlamaIndex and Rasa - Library conflicts with LLM libraries and passing metadata - Dockerized [support on MacOS](https://github.com/khalo-sa/rasa-apple-silicon) for running Rasa - Reverse proxy with chatbots [via ngrok](https://ngrok.com/docs/ngrok-agent/) - Implementing pgvector with your own custom schema instead of using Langchain’s highly opinionated [PGVector class](https://python.langchain.com/en/latest/modules/indexes/vectorstores/examples/pgvector.html) - Adding multi-tenancy (Rasa [doesn't natively support this](https://forum.rasa.com/t/multi-tenancy-in-rasa-core/2382)), sessions and metadata between Rasa and your own backend / application The backstory is familiar. A friend came to me with a problem. I scoured Google and Github for a decent reference implementation of LLM’s integrated with Rasa but came up empty-handed. I figured this to be a great opportunity to satiate my curiosity and 2 days later I had a proof of concept, and a week later this is what I came up with. <br/> > ⚠️ **Caveat emptor:** This is far from production code and rife with prompt injection and general security vulnerabilities. I just hope someone finds this useful 😊 <br/><br/> # **✨** Quick start Getting started is easy, just make sure you meet the dependencies below. <br/> > ⚠️⚠️⚠️ ** ATTENTION NON-MACOS USERS: ** If you are using Linux or Windows, you will need to change the image name from `khalosa/rasa-aarch64:3.5.2` to `rasa/rasa:latest` in [docker-compose.yml on line #64](https://github.com/paulpierre/RasaGPT/blob/0463274ee3174580f2099501e0f8c58238987f9b/docker-compose.yml#L64) and in [the actions Dockerfile on line #1 here](https://github.com/paulpierre/RasaGPT/blob/0463274ee3174580f2099501e0f8c58238987f9b/app/rasa/actions/Dockerfile#L1) <br/> ```bash # Get the code git clone https://github.com/paulpierre/RasaGPT.git cd RasaGPT ## Setup the .env file cp .env-example .env # Edit your .env file and add all the necessary credentials make install # Type "make" to see more options make ``` <br/><br/> # 🔥 Features ## Full Application and API - LLM “learns” on an arbitrary corpus of data using Langchain - Upload documents and “train” all via [FastAPI](https://fastapi.tiangolo.com/) - Document versioning and automatic “re-training” implemented on upload - Customize your own async e
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Read on GitHubWould you bet a product on this? Bounded 0–100 and slow moving.
matched fp:09daa2a7c0f4e08f, topic:chatbot, topic:chatgpt, desc:chatbot
