rocketride-org/rocketride-server: High-performance AI pipeline engine with a C++ core and 50+ Python-extensible nodes. Build, debug, and scale LLM workflows with 13+ model providers, 8+ vector databases, and agent orchestration, all from your IDE. In

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.

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_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

| Feature | Description | | :-------------------------------- | :----------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------- | | Visual Pipeline Builder | Drag, connect, and configure nodes in VS Code, no boilerplate. Real-time observability tracks token usage, LLM calls, latency, and execution. Pipelines are portable JSON, version-controllable, shareable, and runnable anywhere. | | High-Performance C++ Runtime | Native multithreading purpose-built for the throughput demands of AI and data workloads. No bottlenecks, no compromises for production scale. | | 100+ Pipeline Nodes | 15+ LLM providers, 9 vector databases, OCR, NER, PII anonymization, chunking strategies, embedding models, and more. All nodes are Python-extensible, build and publish your own. | | Multi-Agent Workflows | Built-in CrewAI and LangChain support. Chain agents, share memory across pipeline runs, and manage multi-step reasoning at scale. | | Coding Agent Ready | RocketRide auto-detects your coding agent: Claude, Cursor, and more. Build, modify, and deploy pipelines through natural language. | | TypeScript, Python & MCP SDKs | Integrate pipelines into native apps, expose them as callable tools for AI assistants, or build programmatic workflows into your existing codebase. | | Zero Dependency Headaches | Python environments, C++ toolchains, Java/Tika, and all node dependencies managed automatically. Clone, build, run, no manual setup. | | One-Click Deploy | Run on Docker, on-prem, or RocketRide Cloud. Production-ready architecture from day one, not retrofitted from a demo. |

Quick Start

1. Install the extension for your IDE. Search for RocketRide in the extension marketplace:

_{Not seeing your IDE? Open an issue · Download directly}

1. Click the RocketRide extension in your IDE

1. Deploy a server - you'll be prompted on how you want to run the server. Choose the option that fits your setup:

Your first AI feature

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

| You already know | Same idea in RocketRide | | :-- | :-- | | A route that receives a request | A source node: webhook, chat, or dropper (file drop) | | Middleware chained in order | Nodes, wired together on a canvas. Most are Python you can open and read | | The response you return | A response node | | Config in git, like a Dockerfile | The .pipe file: plain JSON, diffable, reviewable | | Calling a service from your app | The Python or TypeScript SDK: one call in, one result out |

Three steps, no API keys:

1. Run it locally. Follow the Quick Start and pick Local when asked. That's the whole install.

1. 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 (including text inside images), finds names, addresses and other personal data, and returns a cleaned copy. It runs on local models, so nothing leaves your machine. Press ▶ on the source node to start it. Its source is a webhook, so it waits for input — step 3 is how you send some.

1. Call it from your code. The pipeline is now a function your service can call. Run it from the folder you opened in your IDE: the extension writes the engine's connection details into a .env there, and the SDK reads them automatically. Run it from anywhere else and the client falls back to RocketRide Cloud instead.

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())

TypeScript works the same way: npm install rocketride · SDK docs

Next steps, one at a time:

• Add a model. Drop an llm_* node between the source and the response and add one API key. Same pipeline, now with an LLM in the loop.
• Search by meaning. examples/rag-pipeline.pipe is the standard "ask questions about my documents" pattern. Needs one LLM key and a vector database (Qdrant can run locally).
• Give it tools. An agent node is an LLM allowed to call other nodes in a loop until it's done. examples/agent-workflow.pipe shows the shape.

Building Your First Pipe

1. All pipelines are recognized with the *.pipe format. Each pipeline and its configuration are JSON objects - but the extension in your IDE will render within our visual builder canvas.

1. All pipelines begin with a source node: _webhook_, _chat_, or _dropper_. For specific usage, examples, and inspiration on how to build pipelines, check out our guides and documentation.

1. Connect input lanes and output lanes by type to properly wire your pipeline. Some nodes like agents or LLMs can be invoked as tools for use by a parent node as shown below:

1. You can run a pipeline from the canvas by pressing the ▶ button on the source node or from the Connection Manager directly.

1. Deploy your pipelines - pick the path that fits:

docker pull ghcr.io/rocketride-org/rocketride-engine:latest
     docker create --name rocketride-engine -p 5565:5565 ghcr.io/rocketride-org/rocketride-engine:latest

- Local Deployment - Download your preferred runtime as a standalone process from the Deploy page in the Connection Manager.

- RocketRide Cloud - Skip the setup and ship straight to managed hosting. Same portable pipeline JSON, zero infrastructure to run, from prototype to production. Get started

1. Run your pipelines as standalone processes or integrate them into your existing Python and TypeScript/JS applications utilizing our SDK.

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. Find the models, agents, and tools best fit for your task.

Contributing

Good places to start

• Claim an issue with one comment. Comment /assign on any open issue and it's yours. No permissions or membership needed.
• Good first issues are labelled good first issue; help wanted marks bigger ones we'd like a hand with.
• Add a node. Every node is a small Python package under nodes/src/nodes/. A new provider, tool, or store makes a good first contribution. Guide: Adding a New Node.
• Fix what you just read. Docs PRs are welcome, including this README.

• Fork, branch as /RR--, open a PR against develop, link the issue. Full process, style guides, and test commands: CONTRIBUTING.md.
• One build tool for the whole monorepo: ./builder manages the C++ toolchain, Python environments, and Java/Tika for you. Clone, build, run.
• Roles and decisions: GOVERNANCE.md · Releases: RELEASE.md · Security reports: SECURITY.md · Questions: Discussions, Discord, SUPPORT.md

RocketRide Cloud  NOW LIVE

Connecting takes two lines. Same portable pipeline JSON, now hosted for you:

ROCKETRIDE_URI=https://api.rocketride.ai
ROCKETRIDE_AUTH=your-api-token

Get Started   ·   Read the Cloud docs

On-Prem  FREE & MIT

Point a client at your local engine in one line:

ROCKETRIDE_URI=ws://localhost:5565

Quick Start   ·   Read the On-Prem docs

Ready to ship? Deploy your pipeline on RocketRide Cloud, or run on-prem, free.

Contributors

RocketRide is built by a growing community of contributors. Whether you've fixed a bug, added a node, improved docs, or helped someone on Discord, thank you. New contributions are always welcome - check out our contributing guide to get started.

Made with ♥ in SF & EU