RocketRide Python SDK

Build, run, and manage AI pipelines from Python.

Full documentation: docs.rocketride.org/clients/python — guides, the complete API reference, and worked examples.

Quick Start

pip install rocketride

import asyncio
from rocketride import RocketRideClient


async def main():
    async with RocketRideClient(uri='https://api.rocketride.ai', auth='my-key') as client:
        result = await client.use(filepath='pipeline.pipe')
        token = result['token']
        out = await client.send(token, 'Hello, pipeline!', objinfo={'name': 'input.txt'}, mimetype='text/plain')
        print(out)
        await client.terminate(token)


asyncio.run(main())

send() / send_files() are for pipelines whose source is webhook or dropper; if your pipeline source is chat, use client.chat() instead. Don't have a pipeline yet? Build one visually with the RocketRide IDE extension.

The SDK is async-first (built on asyncio and websockets), includes the rocketride CLI, and covers the full engine surface: pipeline execution, streaming data, chat, deployments with cron schedules, server-side file storage, and run-log replay.

What is RocketRide?

RocketRide is an open-source, developer-native AI pipeline platform: build, debug, and deploy production AI workflows without leaving your IDE, on a visual canvas or code-first. 140+ ready-to-use nodes (15+ LLM providers, 10+ vector stores, OCR, NER, PII anonymization) run on a high-performance C++ engine, deployable anywhere, MIT licensed. You build your .pipe — and run it against the fastest AI runtime available.

Configuration

All constructor options, timeouts, and reconnection behavior: Configuration.

Documentation

• Overview & quickstart
• Running pipelines · Sending data · Chat
• Deployments · File storage · Run logs
• Error handling · API reference · Examples

Links

• Documentation
• GitHub
• Discord
• Release notes

License

MIT - see LICENSE.