RocketRide is the open source AIDE: the AI Development Environment.

Build, deploy and harness production-ready AI solutions at light speed, all within your IDE or using the CLI in your terminal.

Your code editor just became the AIDE. RocketRide turns the classic IDE you already use into a full AI Development Environment: one place to compose, debug, observe, and deploy AI runtimes using any model, any tool, any framework, with zero vendor lock-in. Equipped with deep observability and backed by a battle-tested, high-throughput C++ engine, what you build is production-ready the moment it runs. It's the harness for everything behind your AI applications, not just the agents, but the whole stack beneath them.

Under the hood, RocketRide is an open source data pipeline builder and runtime built for AI and ML workloads. With 100+ pipeline nodes spanning 15+ LLM providers, 9 vector databases, OCR, NER, and more, pipelines are defined as portable JSON, built visually in VS Code, and executed by a multithreaded C++ runtime. From real-time data processing to multimodal AI search, RocketRide runs entirely on your own infrastructure.

Design, test, and ship complex AI workflows from a visual canvas, right where you write code.

Drop pipelines into any Python or TypeScript app with a few lines of code, no infrastructure glue required.

Where to start

Open source, MIT. The whole engine is MIT-licensed and OSI-compliant. No enterprise edition, nothing behind a paywall.

Features

Quick Start

1. Install the extension for your IDE. Search for RocketRide in the extension marketplace.
2. Click the RocketRide extension in your IDE.
3. Deploy a server - you'll be prompted on how you want to run the server. Choose the option that fits your setup:
   • Local (Recommended) - This pulls the server directly into your IDE without any additional setup.
   • On-Premises - Run the server on your own hardware for full control and data residency. Pull the image and deploy to Docker or clone this repo and build from source.

Your first AI feature

Whether you build web apps, integrate APIs, or run backend services, you already have the mental model.

Three steps, no API keys:

1. Run it locally. Follow the Quick Start and pick Local when asked. That's the whole install.
2. Open a working pipeline. Open examples/document-processor.pipe in your IDE. Give it a PDF or an image and it pulls out the text, finds names, addresses and other personal data, and returns a cleaned copy.
3. Call it from your code. The pipeline is now a function your service can call.

   pip install rocketride

   import asyncio
   from rocketride import RocketRideClient
   
   async def main():
       async with RocketRideClient() as client:
           run = await client.use(filepath='examples/document-processor.pipe')
           out = await client.send(
               run['token'], 'Alice Smith, 12 Elm St, Springfield.', objinfo={'name': 'note.txt'}, mimetype='text/plain'
           )
           print(out)
           await client.terminate(run['token'])
   
   asyncio.run(main())

Observability

Selecting running pipelines allows for in-depth analytics. Trace call trees, token usage, memory consumption, and more to optimize your pipelines before scaling and deploying.

Contributing

Good places to start

• Claim an issue with one comment. Comment /assign on any open issue and it's yours.
• Add a node. Every node is a small Python package. A new provider, tool, or store makes a good first contribution.
• Fix what you just read. Docs PRs are welcome, including this README.

How it works

• One build tool for the whole monorepo: ./builder manages the C++ toolchain, Python environments, and Java/Tika for you. Clone, build, run.