Running an issue-fixing agent on a $6 Fly Machine: a queue, three budgets, and an honest test report

Samuel Diniz Casimiro — Mon, 02 Nov 2026 14:21:48 +0000

Most "AI fixes your issues" demos are a session: you run a CLI, it edits files, you look at the diff. I wanted the opposite: a standing service that sits next to a repository, picks up issues labeled bug, docs or chore, and opens a pull request — and that I can leave running without watching my LLM bill.

That's fly-swe, built on mini-swe-agent. Here's how it's put together, and what it cost on a real TypeScript monorepo.

One Machine, one process pair, one SQLite file

The whole thing is a FastAPI webhook receiver and a worker, run together by honcho on a single shared-cpu-1x Fly Machine with a 1 GB volume. Webhooks are verified (HMAC, timing-safe, fail-closed on an empty secret), de-duplicated by delivery ID, and written to a SQLite queue in WAL mode on the volume. The worker polls it.

No Postgres, no Redis, no broker. If the Machine restarts mid-task, orphan recovery picks the task back up: nothing lost, nothing run twice. The infra costs around $6/month.

The LLM never touches git

This is the invariant I care most about. The coding agent edits files and leaves them in the working tree. Then it emits a completion signal, and everything after that belongs to the harness: git add, commit, push and PR. The agent's shell gets a tokenless remote, no push credential, no gh, and an env allowlist with no secrets.

A side effect: when the target repo's AGENTS.md says "commit and open a PR", the agent follows it for style and tests but not for git, so nothing goes sideways.

Three budgets, enforced in code

max_iterations: a hard cap on agent steps
max_cost_per_task_usd: per task
max_daily_cost_usd: per repo per day

cost_tracking: strict goes one step further: if a step can't be priced, it isn't run.

When a run hits a limit, it doesn't just die. It force-pushes its partial work to a WIP branch, and the next trigger resumes from there, along with a summary of what was already tried. That summary costs zero LLM tokens.

"How I tested" is what the harness saw, not what the model said

After the agent's loop ends, the harness re-runs the test command the agent says it used, in the same environment. The PR's "How I tested" checkbox reflects that run. Anything the agent couldn't run (say, a suite that needs a database the sandbox lacks) is listed separately, labeled as the agent's report, and never checked off.

If verification fails, the harness says so on the issue and retries automatically. That happened on 3 of the 10 issues below.

Numbers from a real repo

10 consecutive issues (bugs and docs) on a TypeScript monorepo with an SDK and a docs site, using GLM-5.3:

median cost per issue: ~$0.31; worst case $1.85 (225 iterations, two verification retries)
typical run: ~80 iterations
all 10 PRs were reviewed by a human before merge. fly-swe never merges.

What it isn't (yet)

Execution isn't sandboxed: the agent's commands run as a subprocess in the same container, mitigated by the env allowlist and the absence of credentials (SECURITY.md). A per-task sandbox is next on the roadmap. Ephemeral Fly Machines / Sprites are an obvious candidate.

GitHub is the only forge today, but it sits behind a provider plugin with a conformance suite. GitLab or Gitea would be a good first contribution.

Deploy in ~10 minutes: Quickstart · AGPL-3.0