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We’re a small team, and our main company supplies voice data. But we kept running into the same problem with coding agents. We’d have a feature request, a refactor, a bug, and some internal tooling work all happening at once, and managing that through local agent sessions meant a lot of context switching, worktree juggling, and laptops left open just so tasks could keep running.
So we built Broccoli. Each task gets its own cloud sandbox to be executed end to end independently. Broccoli checks out the repo, uses the context in the ticket, works through an implementation, runs tests and review loops, and opens a PR for someone on the team to inspect.
Over the last four weeks, 100% of the PRs from non-developers are shipped via Broccoli, which is a safer and more efficient route. For developers on the team, this share is around 60%. More complicated features require more back and forth design with Codex / Claude Code and get shipped manually using the same set of skills locally.
Our implementation uses:
1. Webhook deployment: GCP 2. Sandbox: GCP or Blaxel 3. Project management: Linear 4. Code hosting & CI/CD: Github
Repo: https://github.com/besimple-oss/broccoli
We believe that if you should invest in your own coding harness if coding is an essential part of your business. That’s why we decided to open-source it as an alternative to all the cloud coding agents out there. Would love to hear your feedback on this!
Discussion (26 Comments)Read Original on HackerNews
However I feel it will be an uphill battle competing with OpenAI and Anthropic, I doubt your harness can be better since they see so much traffic through theirs.
So this is for those who care about the harness running on their own infra? Not sure why anyone would since the LLM call means you are sending your code to the lab anyway.
Sorry I don’t want to sound negative, I am just trying to understand the market for this.
Good luck!
Teams would use Anthropic and OpenAI, but they shouldn't just use Anthropic or OpenAI. We see much better results from calling the models independently and do adversarial review and response.
This doesn't replace your need for the models, but you certainly don't need to rely on any of the cloud agent solutions out there that call these models underneath the hood.
On a separate note, READMEs written by AI are unpleasant to read. It would be great if they were written by a human for humans.
As for Jira, would love it if you contribute that integration to us! Someone asked for it in this thread :D
It worked great but time to first token was slow and multi repo PRs took very long to create (30+ mins)
Now im working on my standalone implementation for cloud native agents
Also agree that teams should invest in their own harness (or maybe pedantically, build a system on top of harness likes Claude Code, Codex, Pi, or OpenCode)