https://github.com/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. Includes VS Code extension, TypeScript/Python SDKs, and Docker deployment.
https://github.com/rocketride-org/rocketride-server

ai cpp data-pipeline data-processing machine-learning mcp python sdk typescript vscode-extension

Last synced: 1 day ago
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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.

• Host: GitHub
• URL: https://github.com/rocketride-org/rocketride-server
• Owner: rocketride-org
• License: mit
• Created: 2026-02-11T17:02:53.000Z (2 months ago)
• Default Branch: develop
• Last Pushed: 2026-04-16T05:55:35.000Z (7 days ago)
• Last Synced: 2026-04-16T07:37:45.699Z (7 days ago)
• Topics: ai, cpp, data-pipeline, data-processing, machine-learning, mcp, python, sdk, typescript, vscode-extension
• Language: C++
• Homepage:
• Size: 92.7 MB
• Stars: 1,719
• Watchers: 1
• Forks: 39
• Open Issues: 98
• Metadata Files:
  • Readme: README.md
  • Changelog: CHANGELOG.md
  • Contributing: CONTRIBUTING.md
  • Funding: .github/FUNDING.yml
  • License: LICENSE
  • Code of conduct: CODE_OF_CONDUCT.md
  • Codeowners: .github/CODEOWNERS
  • Security: SECURITY.md
  • Support: .github/SUPPORT.md
  • Governance: GOVERNANCE.md
  • Notice: NOTICE

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README

          




  





  Open-source, developer-native AI pipeline tool.


  Build, debug, and deploy production AI workflows - without leaving your IDE.





  

  







 RocketRide is an open-source data pipeline builder and runtime built for AI and ML workloads. With 50+ pipeline nodes spanning 13 LLM providers, 8 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.





  Home |

  Documentation |

  Python SDK |

  TypeScript SDK |

  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._



## 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

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

   


     

   


   [Not seeing your IDE? Open an issue](https://github.com/rocketride-org/rocketride-server/issues/new) · [Download directly](https://open-vsx.org/extension/RocketRide/rocketride)

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](CONTRIBUTING.md#getting-started).

## Building Your First Pipe

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

2. 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](https://docs.rocketride.org/).

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 on your own infrastructure.

   - **Docker** - Download the RocketRide server image and create a container. Requires [Docker](https://docs.docker.com/get-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 the runtime of your choice as a standalone process in the 'Deploy' page of the `Connection Manager`

6. Run your pipelines as standalone processes or integrate them into your existing [Python](https://docs.rocketride.org/sdk/python-sdk) and [TypeScript/JS](https://docs.rocketride.org/sdk/node-sdk) 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](CONTRIBUTING.md) to get started.



  



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