Learn how Botpress and Parlant differ in their key features, development activity, technology stack and community adoption, so you can decide which of these ai agent platforms is best for you.
Last 30 days
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Repository age
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Self-hosted
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Last 30 days
Last commit
Repository age
Version
License
Repository

Botpress appears to have several advantages over Parlant, particularly in activity, maturity, licensing and features. Consider your specific needs regarding popularity, growth, activity, technology, maturity, licensing and features when making your decision.
Both tools have similar popularity levels, with Botpress having 14,872 stars and Parlant having 18,253 stars on GitHub. In terms of developer contributions, Botpress has 2,295 forks, indicating strong developer engagement.
Botpress and Parlant are moving at much the same rate, adding 64 stars and adding 72 stars respectively over the last 30 days (+0.4% vs +0.4%). Neither is pulling away from the other on momentum alone.
Botpress shows more recent development activity with its last commit 1 day ago, while Parlant was last updated 1 month ago. This suggests Botpress is being more actively maintained.
Both tools share common technology foundations, being built with JavaScript, Bash, Typescript, JSX. However, they differ in their additional technology choices: Parlant leverages CSS, Python, SCSS.
Botpress has been in development longer, starting 10 years ago, compared to Parlant which began 3 years ago. This 7.4-year head start suggests Botpress may have more mature features and established processes.
Botpress uses the MIT license, which is more permissive than Parlant's Apache-2.0 license, potentially offering greater flexibility for commercial use and integration.
Both tools serve similar use cases in AI Agent Platforms, Chatbot Platforms. However, they also have distinct specializations: Parlant extends into AI Chat Interfaces.
Botpress provides self-hosting options for complete data control and customization, while Parlant may be primarily cloud-based or require different deployment approaches.