Mini-Runbot – Ephemeral Odoo Enviroments Using Python and Docker

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I build a Mini-Runbot, an open source PoC for creating ephemeral enviroments for Odoo from a Git commit. This is a mini implementation of the real workflow used by Odoo in Odoo-Runbot and the workflow using in the platform Odoo.sh This workdflow take a testing branch and prepare a database, install modules, start Odoo instance, export a port, check that everything works and cleaning everything up afterward. A build can: - resolve a Git branch or tag - prepare an isolated checkout - install selected Odoo modules - run Odoo test - start Odoo instance - test an HTTP healtcheck - expose a temporary preview - destoy the environment when it is no longer needed The project is written mainly in Python, using FastAPI, Typer and Codex. It is intentionally a small and single-host PoC, thats not a replacement for Odoo official runbot, I wanted to explore a lightweight preview environment platform. The applications is available on PyPI: pipx install mini-runbot Docs: https://rafnixg.github.io/odoo-platform-ephemeral-environmen... Source: https://github.com/rafnixg/odoo-platform-ephemeral-environme... PyPI: https://pypi.org/project/mini-runbot/ The next areas I am exploring are GitHub Pull Request integration, workers and job scheduling, observability, concurrency, and eventually distributed execution. I would especially appreciate feedback from people working with Python, Odoo, CI/CD, or Platform Engineering on the architecture and isolation model.

Visit product View on Hacker NewsSep 20, 2026

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Product HuntOn track for Day 1 leaderboard · Strong signals: model, codex, dock · Missing: mac, agents, macos

86%86% predicted probability of success on Product Hunt, based on ML models trained on real launch data.

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Indie HackersFits the IH revenue-focused audience · Missing: supports, reddit linkedin, podcasting

67%67% predicted probability of success on Indie Hackers, based on ML models trained on real launch data.

Hacker NewsStrong engagement from HN community · Strong signals: open source, io · Missing: https docs, excited, just released

57%57% predicted probability of success on Hacker News, based on ML models trained on real launch data.

nativeThis product was originally launched on this platform.

TrustMRRLess likely to generate early MRR · Missing: mobile apps, ios, personal

43%43% predicted probability of success on TrustMRR, based on ML models trained on real launch data.

AppSumoMay struggle as an AppSumo deal · Strong signals: platform, host · Missing: plus, intuitive, reviews

41%41% predicted probability of success on AppSumo, based on ML models trained on real launch data.

Acquire.comPre-revenue stage for this audience · Missing: arr, mrr, revenue

15%15% predicted probability of success on Acquire.com, based on ML models trained on real launch data.

BetaListMay not resonate with beta-testers · Missing: web3, chat, crypto

0%0% predicted probability of success on BetaList, based on ML models trained on real launch data.

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