Your IDE is the New
‍AI Command Center

Build scalable, production-ready AI pipelines 10x faster with a native
open-source developer platform

Install Extension

Docs

Open Source from
‍day one

AI agents keep reinventing infrastructure. RocketRide gives you proven, scalable patterns built for petabyte workloads—right in VSCode.

@rocketride

Visual
Pipeline Builder

Drag an drop nodes to build complex workflows.
Connect, configure, and visualize your data flow in real-time.

Docker
Deployment

Build an image for your workflow, then generate a run script. Copy the output to your server and start it with one command.


# Build (in repo dir on your server)
docker build -f docker/Dockerfile.engine -t rocketride-engine .
# Run on your server
docker run -p 8080:8080 rocketride-engine

Everything
in Your IDE

Build, test, and deploy your pipelines with tools that do the work for you.

One Click
Cloud Deploy

Go from local runs to cloud deployment in one click, with the same settings.

Local Dev
Localhost: 3000

Coming Soon

RocketRide Cloud
api.rocketride.io

One-click deploy

Live

Multi Agent
Workflow

Framework independent builder configurable to any tool via MCP or Phython nodes.

A Simple SDK for
‍Real AI Solutions

Integrate AI pipelines you build and deploy into applications, workflows, and automations with Python or TypeScript.

TypeScript SDK


import { RocketRideClient } from 'rocketride';
const client = new RocketRideClient({
  uri: 'http://localhost:5565'
});
await client.connect();
const { token } = await client.use({
  filepath: './pipeline.json'
});
const result = await client.send(
  token,
  'Hello, pipeline!',
  { name: 'input.txt' },
  'text/plain'
);
console.log(result);
await client.terminate(token);
await client.disconnect();

Python SDK


import asyncio
from rocketride import RocketRideClient
async def main():
    async with RocketRideClient(uri="http://localhost:5565") as client:
        # Run a pipeline
        result = await client.use(filepath="pipeline.json")
        token = result["token"]
        # Send data into the pipeline
        response = await client.send(token, "Hello, RocketRide!")
        # Check pipeline status
        status = await client.get_task_status(token)
        print(f"State: {status['state']}")
asyncio.run(main())