https://github.com/rocketride-org/rocketride-server
https://github.com/rocketride-org/rocketride-server
ai cpp data-pipeline data-processing machine-learning mcp python sdk typescript vscode-extension
Last synced: about 14 hours ago
JSON representation
- Host: GitHub
- URL: https://github.com/rocketride-org/rocketride-server
- Owner: rocketride-org
- License: mit
- Created: 2026-02-11T17:02:53.000Z (about 2 months ago)
- Default Branch: develop
- Last Pushed: 2026-03-06T09:19:48.000Z (27 days ago)
- Last Synced: 2026-03-06T09:43:58.477Z (27 days ago)
- Topics: ai, cpp, data-pipeline, data-processing, machine-learning, mcp, python, sdk, typescript, vscode-extension
- Language: C++
- Size: 28 MB
- Stars: 10
- Watchers: 1
- Forks: 2
- Open Issues: 6
-
Metadata Files:
- Readme: README.md
- Changelog: CHANGELOG.md
- Contributing: CONTRIBUTING.md
- Funding: .github/FUNDING.yml
- License: LICENSE
- Codeowners: .github/CODEOWNERS
- Security: SECURITY.md
- Support: .github/SUPPORT.md
- Governance: GOVERNANCE.md
- Notice: NOTICE
Awesome Lists containing this project
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
- **VS Code Extension** — Build, visualize, and monitor pipelines directly in your editor. The visual pipeline builder lets you drag, connect, and configure nodes without writing boilerplate. Real-time observability tracks token usage, LLM calls, latency, and execution — all without leaving VS Code. Pipelines are defined as portable JSON, meaning they're version-controllable, shareable, and runnable anywhere.
- **High-performance C++ runtime** — RocketRide's runtime is built in C++ with native multithreading, purpose-built for the throughput demands of AI and data workloads. No bottlenecks, no compromises for production scale.
- **50+ pipeline nodes** — A comprehensive library of pre-built nodes covering 13 LLM providers, 8 vector databases, OCR, NER, PII anonymization, chunking strategies, embedding models, and more. All nodes are Python-extensible, so you can build and publish your own.
- **Multi-agent workflows** — Orchestrate and scale complex agent pipelines with built-in support for CrewAI and LangChain. Chain agents, share memory across pipeline runs, and manage multi-step reasoning workflows at scale. Switch between agentic frameworks with a few clicks for your task.
- **Coding agent ready** — Install the VS Code extension and RocketRide automatically detects and configures your coding agent — Claude, Cursor, and more. Your agent can build, modify, and deploy pipelines through natural language.
- **TypeScript, Python & MCP SDKs** — Integrate pipelines into native applications, expose them as callable tools for AI assistants, or build programmatic pipeline workflows into your existing codebase.
- **Zero dependency headaches** — RocketRide manages Python environments, C++ toolchains, Java/Tika, and all node dependencies automatically. Clone, build, run — no manual setup, no version conflicts, no glue scripts.
- **One-click deploy** — Run on Docker, on-prem, or RocketRide Cloud (coming soon). RocketRide's architecture is designed for production 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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