openorch
Build AI products faster. A language-agnostic microservices platform for building AI applications.
Stars: 186
OpenOrch is a daemon that transforms servers into a powerful development environment, running AI models, containers, and microservices. It serves as a blend of Kubernetes and a language-agnostic backend framework for building applications on fixed-resource setups. Users can deploy AI models and build microservices, managing applications while retaining control over infrastructure and data.
README:
OpenOrch was initially created to solve the challenge of running AI models on private servers, handling many concurrent prompts from both users and services. The goal was to build a ChatGPT-like interface for humans and a network-accessible API for machines.
As the system grew, the authors—despite 10+ years of building both closed and open-source microservices platforms—realized there was still no backend framework that met their needs. So, OpenOrch evolved into the flexible, scalable microservices platform they had been searching for.
- On-premise ChatGPT alternative – Run your AI models locally through a UI, CLI or API.
- A "microservices-first" web framework – Think of it like Angular for the backend, built for large, scalable enterprise codebases.
- Out-of-the-box services – Includes built-in file uploads, downloads, user management, and more.
- Infrastructure simplification – Acts as a container orchestrator, reverse proxy, and more.
- Multi-database support – Comes with its own built-in ORM.
- AI integration – Works with LlamaCpp, StableDiffusion, and other AI platforms.
Easiest way to run OpenOrch is with Docker. Install Docker if you don't have it. Step into repo root and:
docker compose upto run the platform in foreground. It stops running if you Ctrl+C it. If you want to run it in the background:
docker compose up -dNow that the OpenOrch is running you have a few options to interact with it.
You can go to http://127.0.0.1:3901 and log in with username openorch and password changeme and start using it just like you would use ChatGPT.
Click on the big "AI" button and download a model first. Don't worry, this model will be persisted across restarts (see volumes in the docker-compose.yaml).
For brevity the below example assumes you went to the UI and downloaded a model already. (That could also be done in code with the clients but then the code snippet would be longer).
Let's do a sync prompting in JS. In your project run
npm init -y && jq '. + { "type": "module" }' package.json > temp.json && mv temp.json package.json
npm i -s @openorch/clientMake sure your package.json contains "type": "module", put the following snippet into index.js
import { UserSvcApi, PromptSvcApi, Configuration } from "@openorch/client";
async function testDrive() {
let userService = new UserSvcApi();
let loginResponse = await userService.login({
body: {
slug: "openorch",
password: "changeme",
},
});
const promptSvc = new PromptSvcApi(
new Configuration({
apiKey: loginResponse.token?.token,
})
);
// Make sure there is a model downloaded and active at this point,
// either through the UI or programmatically .
let promptRsp = await promptSvc.prompt({
body: {
sync: true,
prompt: "Is a cat an animal? Just answer with yes or no please.",
},
});
console.log(promptRsp);
}
testDrive();and run
$ node index.js
{
prompt: {
createdAt: '2025-02-03T16:53:09.883792389Z',
id: 'prom_emaAv7SlM2',
prompt: 'Is a cat an animal? Just answer with yes or no please.',
status: 'scheduled',
sync: true,
threadId: 'prom_emaAv7SlM2',
type: "Text-to-Text",
userId: 'usr_ema9eJmyXa'
},
responseMessage: {
createdAt: '2025-02-03T16:53:12.128062235Z',
id: 'msg_emaAzDnLtq',
text: '\n' +
'I think the question is asking about dogs, so we should use "Dogs are animals". But what about cats?',
threadId: 'prom_emaAv7SlM2'
}
}Depending on your system it might take a while for the AI to respond. In case it takes long check the backend logs if it's processing, you should see something like this:
openorch-backend-1 | {"time":"2024-11-27T17:27:14.602762664Z","level":"DEBUG","msg":"LLM is streaming","promptId":"prom_e3SA9bJV5u","responsesPerSecond":1,"totalResponses":1}
openorch-backend-1 | {"time":"2024-11-27T17:27:15.602328634Z","level":"DEBUG","msg":"LLM is streaming","promptId":"prom_e3SA9bJV5u","responsesPerSecond":4,"totalResponses":9}Install oo to get started (at the moment you need Go to install it):
go install github.com/openorch/openorch/cli/oo@latest$ oo env add local http://127.0.0.1:58231
$ oo env ls
ENV NAME SELECTED URL DESCRIPTION
local * http://127.0.0.1:58231$ oo login openorch changeme
$ oo whoami
slug: openorch
id: usr_e9WSQYiJc9
roles:
- user-svc:admin$ oo post /prompt-svc/prompt --sync=true --prompt="Is a cat an animal? Just answer with yes or no please."
# see example response above...OpenOrch is a microservices-based AI platform, the seeds of which began taking shape in 2013 while I was at Hailo, an Uber competitor. The idea stuck with me and kept evolving over the years – including during my time at Micro, a microservices platform company. I assumed someone else would eventually build it, but with the AI boom and the wave of AI apps we’re rolling out, I’ve realized it’s time to build it myself.
See the Running the daemon page to help you get started.
For articles about the built-in services see the Built-in services page. For comprehensive API docs see the OpenOrch API page.
We have temporarily discontinued the distribution of the desktop version. Please refer to this page for alternative methods to run the software.
OpenOrch is licensed under AGPL-3.0.
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