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Rocketride Server

OrganizationPopular
rocketride-org

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. Includes VS Code extension, TypeScript/Python SDKs, and Docker deployment.

Publisherrocketride-org
Repositoryrocketride-server
LanguagePython
Forks
7.8K
Stars
18K
Available tools
0
Transport typestdio
LicenseMIT
Links
  • Connect tools to AI workflows

    Rocketride Server exposes MCP capabilities that can be used by compatible AI clients and agents.

  • 0 available tools

    Browse the callable actions below, including names and descriptions when provided by the server.

  • Ready-to-copy setup

    Use the installation snippets to configure this server in your preferred MCP client.

  • Open source signals

    18K stars and 7.8K forks from the linked repository.

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

FeatureDescription
Visual Pipeline BuilderDrag, 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++ RuntimeNative multithreading purpose-built for the throughput demands of AI and data workloads. No bottlenecks, no compromises for production scale.
100+ Pipeline Nodes15+ 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 WorkflowsBuilt-in CrewAI and LangChain support. Chain agents, share memory across pipeline runs, and manage multi-step reasoning at scale.
Coding Agent ReadyRocketRide auto-detects your coding agent: Claude, Cursor, and more. Build, modify, and deploy pipelines through natural language.
TypeScript, Python & MCP SDKsIntegrate pipelines into native apps, expose them as callable tools for AI assistants, or build programmatic workflows into your existing codebase.
Zero Dependency HeadachesPython environments, C++ toolchains, Java/Tika, and all node dependencies managed automatically. Clone, build, run, no manual setup.
One-Click DeployRun 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 knowSame idea in RocketRide
A route that receives a requestA source node: webhook, chat, or dropper (file drop)
Middleware chained in orderNodes, wired together on a canvas. Most are Python you can open and read
The response you returnA response node
Config in git, like a DockerfileThe .pipe file: plain JSON, diffable, reviewable
Calling a service from your appThe 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.

    bash
    pip install rocketride
    python
    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.
TermWhat it means here
PipelineA request handler built from steps, saved as a .pipe JSON file
NodeOne step. Providers (OpenAI, Anthropic…), tools (GitHub, Slack…), stores, parsers
EmbeddingTurning text into a list of numbers so "similar meaning" becomes "nearby numbers"
Vector databaseA store that finds records by meaning instead of exact match
RAGRetrieve the relevant documents first, then ask the model with them in context
AgentAn LLM that can call tools and nodes repeatedly until the task is done
OCR / NER / PIIRead text out of images / find names, dates, organisations / detect personal data
ChunkingSplitting 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.

      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
    • 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 forStart at
Using RocketRide: quickstart, concepts, guides, cloud, self-hostingdocs/public/product/ (the site, page for page)
SDK guidesTypeScript · Python · MCP
Building your own client on the engine protocoldocs/public/product/connect/websocket/
Contributing: setup, builder, engine internals, node authoringdocs/development/
Node catalogeach node's README.md under nodes/src/nodes/
Apps: VS Code extension and the shell UIseach app's own folder under apps/; how they fit the monorepo in docs/development/apps/
Pointing an AI assistant at RocketRidedocs/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:

bash
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:

bash
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.


Use Rocketride Server MCP with multiple AI models

TypingMind connects MCP tools at the workspace level, so once Rocketride Server is connected, you can use it with different AI models in TypingMind instead of setting it up separately for each model. This MCP runs locally through the TypingMind MCP connector on your device.

Setup guide to use the local connector

Use this when the MCP server needs access to local files, apps, or private resources on your computer.

1

Open the MCP settings

In TypingMind, go to Settings, Advanced Settings, then Model Context Protocol and choose Setup Connector.

  1. Open TypingMind in your browser.
  2. Click the Settings icon.
  3. Go to Advanced Settings.
  4. Open the Model Context Protocol section.
  5. Click Setup Connector and choose This Device.
TypingMind MCP connector setup screen with This Device selected
2

Run the connector command

Choose This Device, copy the command from TypingMind, and run it in Terminal. Keep the process running while you use MCP.

  1. Copy the setup command shown by TypingMind.
  2. Open Terminal on macOS or Windows Terminal on Windows.
  3. Paste and run the command.
  4. Approve the package install if Terminal asks you to proceed.
  5. Keep the Terminal window running while using MCP tools.
3

Add Rocketride Server as a server

When the connector status is Ready, click Edit Servers and paste the MCP server configuration.

  1. Wait until the connector status shows Ready.
  2. Click Edit Servers.
  3. Paste the Rocketride Server MCP server configuration.
  4. Save the server list.
  5. Refresh if you want to confirm the connector is still ready.
TypingMind MCP settings showing active server and Edit Servers button
{
  "mcpServers": {
    "rocketride-server": {
      "command": "npx",
      "args": [
        "-y",
        "<mcp-server-package>"
      ]
    }
  }
}
4

Use it across models

Save the server list, open Plugins, enable the Rocketride Server MCP tools, then select any supported AI model in TypingMind and use the tools in chat or assign them to an AI agent.

  1. Open the Plugins page in TypingMind.
  2. Enable the Rocketride Server MCP tools.
  3. Start a chat and choose the AI model you want to use.
  4. Use the MCP tools in chat or assign them to an AI agent.
  5. Switch to another AI model whenever needed without reconnecting MCP.
TypingMind chat using enabled MCP tools with a selected AI model
Can you use Rocketride Server to help me with this task?
Rocketride Server
Sure. I read it.
Here is what I found using Rocketride Server.

Frequently asked questions

What is the Rocketride Server MCP server used for?

Rocketride Server is an MCP server that lets compatible AI clients connect to external tools and context. In TypingMind, you can add this MCP server once and make its tools available in your AI workspace.

Can I use Rocketride Server MCP with multiple AI models in TypingMind?

Yes. TypingMind connects MCP tools at the workspace level, so you can use Rocketride Server with different AI models such as Claude, ChatGPT, Gemini, or other models you have configured in TypingMind without setting up the MCP server separately for each model.

Why use Rocketride Server MCP with TypingMind?

TypingMind is one of the best frontends for LLM chat because it brings multiple AI models, prompts, plugins, AI agents, API keys, and MCP tools into one workspace. With Rocketride Server connected, you can use its MCP tools across your preferred models while keeping your chat workflow organized in TypingMind.

How do I connect Rocketride Server MCP to TypingMind?

Rocketride Server runs through the TypingMind local MCP connector. This is best when the MCP server needs access to local files, desktop apps, command-line tools, or private resources on your computer.

What tools does Rocketride Server MCP provide in TypingMind?

Rocketride Server exposes MCP capabilities that can be enabled from the TypingMind Plugins page and used in chat or assigned to AI agents.

Do I need to share my API keys with TypingMind to use Rocketride Server MCP?

No. TypingMind is local-first and lets you keep your model providers, API keys, prompts, and MCP configuration under your control. If Rocketride Server requires authentication, add the required headers, OAuth settings, or local configuration for that MCP server when you create the connection.

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