Learn how AgentOS and Rivet 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
Last commit
7 hours agoRepository age
1 yearVersion
v0.15.27License
Apache-2.0Repository
framersai/agentos
Last 30 days
Last commit
1 day agoRepository age
3 yearsVersion
v2.3.30License
Apache-2.0Self-hosted
YesRepository
rivet-dev/rivet
Rivet appears to have several advantages over AgentOS, particularly in popularity, growth, maturity and features. Consider your specific needs regarding popularity, growth, activity, technology, maturity, licensing and features when making your decision.
Rivet significantly outpaces AgentOS in community adoption with 6,371 stars compared to 683 stars on GitHub. This 9.3x difference suggests Rivet has a much larger and more active community. In terms of developer contributions, Rivet has 284 forks, indicating moderate developer engagement.
Rivet is growing faster, adding 178 stars in the last 30 days (+2.9%) against adding 10 stars for AgentOS (+1.5%). Rivet is both larger and pulling further ahead.
Both projects show recent activity, with AgentOS last updated 7 hours ago and Rivet 1 day ago.
Both tools share common technology foundations, being built with JavaScript, Typescript. However, they differ in their additional technology choices: Rivet leverages CSS, Bash, JSX, Python, Next.js, Golang, Rust, Lua.
Rivet has been in development longer, starting 3 years ago, compared to AgentOS which began 1 year ago. This 2.4-year head start suggests Rivet may have more mature features and established processes.
Both projects use the Apache-2.0 license, providing identical terms for usage and distribution.
Both tools serve similar use cases in AI Agent Platforms. However, they also have distinct specializations: Rivet extends into PaaS & Deployment Tools.
Rivet provides self-hosting options for complete data control and customization, while AgentOS may be primarily cloud-based or require different deployment approaches.