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RasaGPT

paulpierre/RasaGPT

First headless LLM chatbot platform built on top of Rasa and Langchain

GraphCanon updated 1w · GitHub synced 1w

2.5k stars250 forksLast push 9mo Python MIT

Decision brief

RasaGPT is a pioneering headless chatbot platform that merges Rasa with technologies like FastAPI, Langchain, and LlamaIndex.

Good fit when

  • When you need a robust framework for developing conversational AI solutions and are already familiar with Rasa's ecosystem.
  • If integration with Telegram bots and APIs is required along with advanced NLU capabilities.

Avoid when

  • For users preferring platforms that require less technical setup, particularly without needing Docker or specific Python versions.
  • Avoid if development is focused on non-headless chatbot applications that don't leverage the Rasa framework's architecture.

Observed Jul 12, 2026 · Source: enrich:decision_facts

Verify the decision

Maintenance and security

Full trust report
Maintenance
Slowing (268d since push)
As of 1w
Provenance
Not a fork · Personal account
As of 1w
Security (OSV)
No lockfile
As of 1mo

Public GitHub metadata and optional OSV scans. Signals, not a guarantee. Trust methodology.

Install

pip install RasaGPT
PyPI

Similar tools

Same-category neighbours. No typed graph edges are catalogued for this tool yet.

Evidence and technical details

Sourced facts, taxonomy, compatibility claims, README excerpt, and machine-readable endpoints.

Overview

RasaGPT integrates Rasa for conversational AI with various frameworks such as FastAPI, Langchain, and LlamaIndex to create robust chatbot solutions.

Capability facts

Deploy
Self-host

Source: dockerfile:docker-compose.yml · Aug 8, 2026

Docker
Dockerfile present

Source: dockerfile:docker-compose.yml · Aug 8, 2026

Languages
python

Source: github.language · Aug 8, 2026

Categories

Compatibility

Sourced claims from the README excerpt - not unsourced marketing copy.

Python runtimePython

Source: README excerpt (regex_v1, Aug 8, 2026)

- Python 3.9
Source link

Tags

README

 Quick start

Getting started is easy, just make sure you meet the dependencies below.


⚠️⚠️⚠️ ** 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 and in the actions Dockerfile on line #1 here



---

## Requirements

- Python 3.9
- Docker & Docker compose ([Docker desktop MacOS](https://www.docker.com/products/docker-desktop/))
- Open AI [API key](https://platform.openai.com/account/api-keys)
- Telegram [bot credentials](https://core.telegram.org/bots#how-do-i-create-a-bot)
- Ngrok [auth token](https://dashboard.ngrok.com/tunnels/authtokens)
- Make ([MacOS](https://formulae.brew.sh/formula/make)/[Windows](https://stackoverflow.com/questions/32127524/how-to-install-and-use-make-in-windows))
- SQLModel

<br/>

---

## Docker-compose

The easiest way to get started is using the `Makefile` in the root directory. It will install and run all the services for RasaGPT in the correct order.

```bash
make install

---

# This will automatically install and run RasaGPT

---

# After installation, to run again you can simply run

make run


After installation, to run again you can simply run

make run

<br/>

Similarly, enter `make` to see a full list of commands



<br/>

---

## Installation process

Installation should be automated should look like this:



👉 Full installation log: [https://app.warp.dev/block/vflua6Eue29EPk8EVvW8Kd](https://app.warp.dev/block/vflua6Eue29EPk8EVvW8Kd)

<br/>

The installation process for Docker takes the following steps at a high level

1. Check to make sure you have `.env` available
2. Database is initialized with [`pgvector`](https://github.com/pgvector/pgvector)
3. Database models create the database schema
4. Trains the Rasa model so it is ready to run
5. Sets up ngrok with Rasa so Telegram has a webhook back to your API server
6. Sets up the Rasa actions server so Rasa can talk to the RasaGPT API
7. Database is populated with dummy data via `seed.py`

<br/><br/>

---

# 📜 Open source license

Copyright (c) 2023 Paul Pierre. Permission is hereby granted, free of charge, to any person obtaining a copy of this software and associated documentation files (the “Software”), to deal in the Software without restriction, including without limitation the rights to use, copy, modify, merge, publish, distribute, sublicense, and/or sell copies of the Software, and to permit persons to whom the Software is furnished to do so, subject to the following conditions: The above copyright notice and this permission notice shall be included in all copies or substantial portions of the Software. THE SOFTWARE IS PROVIDED “AS IS”, WITHOUT WARRANTY OF ANY KIND, EXPRESS OR IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY, FITNESS FOR A PARTICULAR PURPOSE AND NON-INFRINGEMENT. IN NO EVENT SHALL THE AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM, OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE SOFTWARE.

For agents

This page has a .md twin and JSON over the API.

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