Built for change.
Designed for stability.

The principles behind RocketRide.

Model Agnostic

Use the best model for every task without changing your application.

Provider Agnostic

Move between OpenAI, Anthropic, Google or any future provider with minimal friction.

Tool Agnostic

Integrate the tools, frameworks and databases that fit your workflow.

Built for Production

From prototype to deployment, every workflow is designed for real-world applications.

Full Observability

Understand what’s happening across every pipeline, model call and deployment.

Open by Design

No vendor lock-in. No hidden dependencies. Full control over your stack.

Maintained by engineers, grown by the community.

There is no product team behind a paywall shaping what gets built. The roadmap is public, the issues are public, and the people who use RocketRide most are the ones who steer it.

Infrastructure should be boring.

The interesting problem is what you are building, not the connectors, queues, and stage wiring underneath. We make the boring parts boring so you can focus on the interesting parts.

Scalability is not a phase two concern.

Single-threaded code that works in a demo and dies under real load wastes everyone’s time. Concurrency and backpressure belong in the foundation, not in a future refactor.

Open roadmap, open discourse.

Features should be shaped by developers who actually use the tool in production, not by a product team with a quarterly release schedule. That only works in public.

Vendor lock-in is a design flaw.

You should be able to swap models, switch storage layers, and move to a different cloud without rewriting your pipeline. That is why everything here is modular by default.

Where the project
actually lives.

The RocketRide community is developers who are building real applications and agentic workflows in VSCode and Cursor.

Most conversations happen on Discord, most decisions happen in GitHub issues, and most patterns get documented through conference talks and community write-ups.
We present at open source conferences and developer meetups — not to market, but to get feedback from the people who push these patterns hardest.

If you are building at that level, we want to hear how it goes.

01

File real issues

Use it on a real pipeline, find what breaks, and write it up with a reproduction. That is the most useful contribution there is.

02

Contribute a connector or pattern

If you built a custom integration for a model or data source, open a PR. Every pre-built connector is one less thing agents will rewrite.

03

Share what you shipped

Real production architectures and lessons learned are the best documentation the project has. Discord is where those conversations happen.

Join the Community

Join developers who are already deploying production AI solutions

Join our Discord

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