RocketRide

What is RocketRide about?

RocketRide provides a visual pipeline builder and a multithreaded C++ runtime for creating, testing, and deploying AI/ML workflows. Pipelines are defined in portable JSON (*.pipe files) and can incorporate LLMs, vector stores, OCR/NER tools, and custom Python nodes, enabling end‑to‑end AI solutions without vendor lock‑in.

How to use RocketRide?

1. Install the VS Code extension (or the Open VSX version) from the marketplace.
2. Deploy a server – choose one of the following:
   • Local: the extension downloads and runs the C++ engine automatically.
   • Docker: ```bash docker pull ghcr.io/rocketride-org/rocketride-engine:latest docker create --name rocketride-engine -p 5565:5565 ghcr.io/rocketride-org/rocketride-engine:latest

   • RocketRide Cloud: set ROCKETRIDE_URI and ROCKETRIDE_AUTH to the cloud endpoint.
3. Create a pipeline using the visual canvas or edit a *.pipe JSON file directly.
4. Run the pipeline from the canvas (▶ on a source node) or programmatically via the SDKs:
   • Python: pip install rocketride then use RocketRideClient.
   • TypeScript: npm install rocketride then import the client.
5. Iterate and observe real‑time token usage, latency, and call traces inside the IDE.

Key Features

• Visual pipeline builder integrated into VS Code.
• Native multithreaded C++ runtime for high throughput.
• 100+ built‑in nodes covering 15+ LLM providers, 9 vector DBs, OCR, NER, PII masking, chunking, etc.
• Python‑extensible nodes; developers can add custom steps as small Python packages.
• Multi‑agent orchestration with CrewAI and LangChain support.
• Automatic dependency handling for Python, Java/Tika, and C++ toolchains.
• One‑click Docker deployment and optional managed Cloud service.
• Observability panel showing token counts, latency, and execution graph.
• MIT‑licensed, no hidden enterprise edition.

Use Cases

• Document processing: extract text from PDFs/images, run OCR/NER, and sanitize PII.
• RAG (retrieval‑augmented generation): combine a vector store (e.g., Qdrant) with LLMs to answer questions over private corpora.
• Multi‑agent assistants: chain LLM agents that call tools, enabling complex reasoning and tool use.
• Real‑time data pipelines: ingest streams via webhook nodes, transform with custom Python logic, and output to databases or APIs.
• Prototype‑to‑production: design a pipeline locally, then switch to RocketRide Cloud or on‑prem Docker without code changes.

FAQ

Q: Is RocketRide free to use? A: Yes. The entire engine, VS Code extension, and SDKs are released under the MIT license.

Q: Do I need an API key for LLM providers? A: Only when you add a provider node that requires authentication (e.g., OpenAI, Anthropic). Nodes can be added without keys for local models.

Q: Can I run the engine on air‑gapped infrastructure? A: Absolutely. Use the Docker image or build the C++ binary locally; no external services are required unless you configure remote model providers.

Q: How do I add my own node? A: Create a Python package under nodes/src/nodes/, follow the "Adding a New Node" guide, and the extension will automatically surface it.

Q: What languages can I call pipelines from? A: The official SDKs support Python and TypeScript/JavaScript. Any language that can make HTTP/WebSocket calls can also interact with the MCP server.

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

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.

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.

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 (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.

3. 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.

Term	What it means here
Pipeline	A request handler built from steps, saved as a .pipe JSON file
Node	One step. Providers (OpenAI, Anthropic…), tools (GitHub, Slack…), stores, parsers
Embedding	Turning text into a list of numbers so "similar meaning" becomes "nearby numbers"
Vector database	A store that finds records by meaning instead of exact match
RAG	Retrieve the relevant documents first, then ask the model with them in context
Agent	An LLM that can call tools and nodes repeatedly until the task is done
OCR / NER / PII	Read text out of images / find names, dates, organisations / detect personal data
Chunking	Splitting long documents into pieces small enough to embed and retrieve

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.

2. 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.

3. 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:

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

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

   • Docker - Download the RocketRide server image and create a container. Requires Docker to be installed.

     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

6. 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.

Documentation

Everything on docs.rocketride.org is built from the docs/ tree in this repo, split by audience. docs/README.md is the full map; the short version:

Looking for	Start at
Using RocketRide: quickstart, concepts, guides, cloud, self-hosting	docs/public/product/ (the site, page for page)
SDK guides	TypeScript · Python · MCP
Building your own client on the engine protocol	docs/public/product/connect/websocket/
Contributing: setup, builder, engine internals, node authoring	docs/development/
Node catalog	each node's README.md under nodes/src/nodes/
Apps: VS Code extension and the shell UIs	each app's own folder under apps/; how they fit the monorepo in docs/development/apps/
Pointing an AI assistant at RocketRide	docs/agents/

Repo-wide contributor rules live in AGENTS.md.

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.

How it works

• Fork, branch as <type>/RR-<issue>-<short-description>, 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

Two ways to run RocketRide

Let us handle the infrastructure, or own every layer.

RocketRide Cloud  NOW LIVE

Run pipelines, not infrastructure.

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

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

On-Prem  FREE & MIT

Own every layer.

Point a client at your local engine in one line:

ROCKETRIDE_URI=ws://localhost:5565

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.

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