Text (body): TL;DR: OmniFlow Beta is a lightweight, Azure-backed testbed for multi-user AI agents with per-user namespacing, audit logging, and a Streamlit demo. I’m looking for people to test it — especially for concurrency, isolation, cost-control, and UX feedback.
What it is
A developer-focused playground to prototype multi-user agent workflows.
Backend: Python + Azure Blob storage (Azurite for local dev).
Frontend: Streamlit demo for interactive testing.
LLM-ready: supports Azure OpenAI or OpenAI with server-side proxying, token accounting, and light audit metadata.
Why this exists
To explore safe, low-cost patterns for multi-user LLM apps (per-user data isolation, auditability, and demo-friendly setups).
To give testers an easy local dev flow (dockerized Azurite + simple quickstart) and repeatable CI with Azurite.
Key features
Per-user blob namespaces (users/{user_id}/...) to limit accidental data leakage
Audit metadata for LLM calls (tokens, model/deployment, duration)
Streamlit demo with chat-style UX and optional document context
CI workflow that runs pytest against Azurite
CONTRIBUTING.md, CODE_OF_CONDUCT.md, and CITATION.cff included for community use
Try it (5 minutes) git clone https://github.com/dokuczacz/OmniFlowBeta.git cd OmniFlowBeta docker run -d --name azurite -p 10000:10000 mcr.microsoft.com/azure-storage/azurite python -m pip install --upgrade pip pip install -r requirements.txt streamlit run frontend/app.py
Live demo: (paste demo URL here if available)
Demo GIF: (paste a 10–15s GIF link; strongly recommended)
What I’d love testers to try and report
Isolation: try to read or access another user’s blobs or cross-namespace leaks
Concurrency: multiple users sending requests at once; report race conditions or failures
Cost controls: try edge cases (large contexts) and report token counts or cost surprises
UX feedback: Streamlit flows, helpful error messages, clarity of quickstart
Security: anything that feels unsafe (PII handling, secrets leakage, logs)
Safety notes (please read)
The demo is set up for testing, not production. If you publish a public demo, use conservative defaults: OMNIFLOW_LLM_MAX_TOKENS=256, per-user rate limits, and no user-supplied API keys.
Do NOT commit secrets. Use platform secrets for deployments (Azure Key Vault / GitHub Secrets).
Logs store metadata; we avoid storing full raw prompts with PII. If you find anything sensitive in logs, flag it in an issue.
How to report issues / help
Open an issue or a PR on GitHub: https://github.com/dokuczacz/OmniFlowBeta
Label issues with bug, enhancement, or good-first-issue if you want to contribute
If you want to test privately or share sensitive findings, mention it in the issue and I’ll provide a private contact path
Extras
CITATION.cff included for academic/archival use (Zenodo-ready after a release)
CONTRIBUTING.md and CODE_OF_CONDUCT.md are present — contributions welcome
If you try it, please post a short note here with:
What you tried (isolation/concurrency/UX)
One thing that failed or surprised you
One small improvement you’d like
I’ll be monitoring comments and the repo — happy to answer technical questions and iterate quickly on feedback. Thanks!
dokuczacz•1mo ago
What it is
Why this exists Key features Try it (5 minutes) git clone https://github.com/dokuczacz/OmniFlowBeta.git cd OmniFlowBeta docker run -d --name azurite -p 10000:10000 mcr.microsoft.com/azure-storage/azurite python -m pip install --upgrade pip pip install -r requirements.txt streamlit run frontend/app.py(If running tests) export AZURE_STORAGE_CONNECTION_STRING="DefaultEndpointsProtocol=http;AccountName=devstoreaccount1;AccountKey=Eby8vd...;BlobEndpoint=http://127.0.0.1:10000/devstoreaccount1" pytest -q
Demo & media
What I’d love testers to try and report Safety notes (please read) How to report issues / help Extras If you try it, please post a short note here with: I’ll be monitoring comments and the repo — happy to answer technical questions and iterate quickly on feedback. Thanks!