RocketRide
RocketRide is an AI pipeline orchestration platform. Here's what you can do with this server:
Process Documents: Submit documents via filepath to RocketRide's AI pipeline engine, which can include OCR, NER, PII anonymization, chunking, and embedding
Build & Deploy AI Pipelines: Visually design complex AI/ML workflows using a VS Code canvas, defined as portable JSON and executed by a high-performance multithreaded C++ runtime
Integrate Diverse AI Components: Access 50+ pre-built pipeline nodes supporting 13 LLM providers, 8 vector databases, OCR, NER, embedding models, and more
Orchestrate Multi-Agent Workflows: Build complex agent pipelines with built-in support for CrewAI and LangChain
Automate with Coding Agents: Enable AI agents (e.g., Claude, Cursor) to build, modify, and deploy pipelines via natural language
Monitor & Optimize: Observe running pipelines with tracing, token usage, memory consumption, and execution analytics
Deploy Flexibly: Run locally, on-premises (Docker), or integrate via Python or TypeScript/JS SDKs
Extend Functionality: Develop and publish custom pipeline nodes using Python
Provides built-in support for orchestrating and scaling multi-agent workflows within data processing pipelines.
Enables the orchestration and scaling of AI agents and workflows within data processing pipelines using LangChain.
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.
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. |
50+ Pipeline Nodes | 13 LLM providers, 8 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 (coming soon). Production-ready architecture from day one — not retrofitted from a demo. |
Quick Start
Install the extension for your IDE. Search for RocketRide in the extension marketplace:
Click the RocketRide extension in your IDE
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.
Building Your First Pipe
All pipelines are recognized with the
*.pipeformat. Each pipeline and configuration is a JSON object - but the extension in your IDE will render within our visual builder canvas.All pipelines begin with source node: webhook, chat, or dropper. For specific usage, examples, and inspiration on how to build pipelines, check out our guides and documentation.
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:
You can run a pipeline from the canvas by pressing the ▶ button on the source node or from the
Connection Managerdirectly.Deploy your pipelines on your own infrastructure.
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:latestLocal deployment - Download the runtime of your choice as a standalone process in the 'Deploy' page of the
Connection Manager
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.
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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MCP directory API
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curl -X GET 'https://glama.ai/api/mcp/v1/servers/rocketride-org/rocketride-server'
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