Rasa
Open-source framework for building contextual AI assistants and chatbots.
- AI & LLM Tools
- Python
- Apache-2.0
- Docker
- Actively maintained
GitHub stats
- GitHub stars
- 21,344
- Forks
- 4,917
- Open issues
- 9
- Commits, last 12 months
- 2
- Last commit
- Jul 24, 2026
- Latest release
- 3.6.21Jan 14, 2025
- Created
- Oct 14, 2016
Data from the GitHub API, refreshed daily.
About Rasa
Open-source framework for building contextual AI assistants and chatbots. Rasa is open source under the Apache-2.0 license and written mainly in Python, so you can read the code, run it on your own hardware and keep full control of your data.
The project has 21,344 stars and 4,917 forks on GitHub, with 2 commits over the last year. It ships a Docker image, which makes it a good fit for a small VPS or a home server running Docker Compose.
Topics
- nlp
- machine-learning
- machine-learning-library
- bot
- bots
- botkit
- rasa
- wit
- nlu
- conversational-bots
Repository preview
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What is Rasa?
Rasa is a free, open-source app you can host on your own server, listed in our AI & LLM Tools category. Open-source framework for building contextual AI assistants and chatbots. The source code lives at github.com/RasaHQ/rasa, where it has 21,344 stars.
Is Rasa free and open source?
Yes. Rasa is open-source software released under the Apache-2.0 license, so you can self-host it at no cost. You only pay for the server it runs on, and some projects also offer an optional paid cloud plan.
Can I run Rasa with Docker?
Yes. Rasa provides a Docker image, so you can run it on any Linux server with Docker and Docker Compose installed. The project README lists the image name and the environment variables to set.
What are the best alternatives to Rasa?
Popular open-source alternatives to Rasa include Ollama, Stable Diffusion WebUI and Langflow.
Is Rasa actively maintained?
The most recent commit to Rasa was on Jul 24, 2026 with 2 commits in the past twelve months. The latest release is 3.6.21, published Jan 14, 2025. Check the open issues on GitHub to gauge how quickly maintainers respond.