<div align="center">

<a href="https://rocketride.org">
  <img src="https://raw.githubusercontent.com/rocketride-org/rocketride-server/develop/images/banner-root.png" alt="RocketRide" width="100%">
</a>

<p>
  Open-source, developer-native AI pipeline tool.<br/>
  Build, debug, and deploy production AI workflows - without leaving your IDE.
</p>

<p>
<img src="https://raw.githubusercontent.com/rocketride-org/rocketride-server/develop/images/icon-cpp.png" alt="C++" />&nbsp;&nbsp;
<img src="https://raw.githubusercontent.com/rocketride-org/rocketride-server/develop/images/icon-python.png" alt="Python" />&nbsp;&nbsp;
<img src="https://raw.githubusercontent.com/rocketride-org/rocketride-server/develop/images/icon-typescript.png" alt="TypeScript" />
</p>

<p>
 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.
</p>

<p>
  <a href="https://rocketride.org">Home</a> |
  <a href="https://docs.rocketride.org/">Documentation</a> |
  <a href="https://pypi.org/project/rocketride/">Python SDK</a> |
  <a href="https://www.npmjs.com/package/rocketride">TypeScript SDK</a> |
  <a href="https://pypi.org/project/rocketride-mcp/">MCP Server</a>
</p>

<p>
  <a href="https://github.com/rocketride-org/rocketride-server/actions/workflows/ci.yml"><img src="https://github.com/rocketride-org/rocketride-server/actions/workflows/ci.yml/badge.svg" alt="CI"></a>
  <a href="https://github.com/rocketride-org/rocketride-server/releases/tag/server-v3.1.0"><img src="https://img.shields.io/badge/Runtime-v3.1.0-5f2167?logo=data:image/svg%2bxml;base64, " alt="Engine v3.1.0"></a>
  <a href="https://discord.gg/9hr3tdZmEG"><img src="https://img.shields.io/badge/Discord-Join-370b7a?logo=discord&logoColor=white" alt="Discord"></a>
  <a href="https://github.com/rocketride-org/rocketride-server/blob/develop/LICENSE"><img src="https://img.shields.io/badge/License-MIT-41b6e6" alt="MIT License"></a>
</p>

<img src="https://raw.githubusercontent.com/rocketride-org/rocketride-server/develop/images/screenshot-ide.png" alt="Build and run AI pipelines inside your IDE" width="100%">

_Design, test, and ship complex AI workflows from a visual canvas, right where you write code._

<img src="https://raw.githubusercontent.com/rocketride-org/rocketride-server/develop/images/screenshot-sdk.png" alt="Integrate real AI solutions using a simple SDK" width="100%">

_Drop pipelines into any Python or TypeScript app with a few lines of code, no infrastructure glue required._

</div>

## 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:

   <p align="center">
     <img src="https://raw.githubusercontent.com/rocketride-org/rocketride-server/develop/images/install.png" alt="Install RocketRide extension">
   </p>

   <sub>[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)</sub>

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 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](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:

<p align="center">
  <img src="https://raw.githubusercontent.com/rocketride-org/rocketride-server/develop/images/first_pipe.gif" alt="Pipeline canvas example" width="100%">
</p>

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 your preferred runtime as a standalone process from the **Deploy** page in 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.

<p align="center">
  <img src="https://raw.githubusercontent.com/rocketride-org/rocketride-server/develop/images/tracing.gif" alt="Pipeline observability and tracing">
</p>

## 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.

<a href="https://github.com/rocketride-org/rocketride-server/graphs/contributors">
  <img alt="contributors" src="https://contrib.rocks/image?repo=rocketride-org/rocketride-server&columns=16" width="100%" />
</a>

---

<p align="center">Made with ♥ in SF &amp; EU</p>
